Research ArticleTechnoeconomic Distribution Network Planning Using SmartGrid Techniques with Evolutionary Self-Healing Network States

Jesus Nieto-Martin 1 Timoleon Kipouros12 Mark Savill1 Jennifer Woodruff3

and Jevgenijs Butans4

1Power and Propulsion Sciences Group Cranfield University Cranfield MK43 0AL UK2Engineering Design Centre Cambridge University Trumpington Street Cambridge CB2 1PZ UK3Western Power Distribution Innovation Pegasus Business Park Derby DE74 2TU UK4Complex System Research Centre Cranfield School of Management Cranfield MK43 0AL UK

Correspondence should be addressed to Jesus Nieto-Martin jnietomartincranfieldacuk

Received 26 January 2018 Revised 4 July 2018 Accepted 25 July 2018 Published 10 October 2018

Academic Editor Fernando Lezama

Copyright copy 2018 Jesus Nieto-Martin et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

The transition to a secure low-carbon system is raising a set of uncertainties when planning the path to a reliable decarbonisedsupply The electricity sector is committing large investments in the transmission and distribution sector upon 2050 in order toensure grid resilience The cost and limited flexibility of traditional approaches to 11 kV network reinforcement threaten toconstrain the uptake of low-carbon technologies This paper investigates the suitability and cost-effectiveness of smart gridtechniques along with traditional reinforcements for the 11 kV electricity distribution network in order to analyse expectedinvestments up to 2050 under different DECC demand scenarios The evaluation of asset planning is based on an area of studyin Milton Keynes (East Midlands United Kingdom) being composed of six 11 kV primaries To undertake this the analysisused a revolutionary new model tool for electricity distribution network planning called scenario investment model (SIM)Comprehensive comparisons of short- and long-term evolutionary investment planning strategies are presented The work helpselectricity network operators to visualise and design operational planning investments providing bottom-up decision support

1 Introduction

Distribution networks are a key enabler for a low-carbonfuture In the UK distribution network operators (DNOs)are entering a period of significant changes due to UK energytargets up to 2050 [1] By 2020 it is expected that 15 ofits total demand will be from renewable energy sourceswith a 20 reduction in greenhouse gas emissions andmoreover 80 reduction in greenhouse gas emissions by2050 The challenges presented by the transition to alow-carbon economy will directly impact the electricitydistribution network

As for an increasing connection of distributed generatorsand electrification of heat and transport new approaches todesign construct and operate networks will be required[2] A more active management of local distribution

networks interconnections storage or flexibility servicesare some of the strategic propositions that will maximisethe full potential of the digital network revolution In thatsense the UK National Infrastructure Commission the UKregulator OFGEM and the Department of Business Energyand Industrial Strategy (BEIS) are contributing to the smartflexible energy system debate [3]

In 2010 OFGEM introduced RIIO namely settingrevenue using incentives to deliver innovation and outputs[4] This new performance-based pricing framework soughtto make network operators more consumer-centric encour-aging longer-term thinking greater innovation and moreefficient delivery

The RIIO framework has been applied to both gasand electricity transmission and distribution networks Thecurrent price control (called ldquoRIIO-1rdquo) is the first generation

HindawiComplexityVolume 2018 Article ID 1543179 18 pageshttpsdoiorg10115520181543179

(2015ndash2023) of controls under this new framework As welook forward towards the discussion of defining the pricecontrol for RIIO-2 (2023ndash2031) we have to take account ofthe dramatic changes and the increased complexity that areunderway in the energy sector as well as the experienceand lessons learned from RIIO-ED1 by DNOs

In response to these challenges DNOs are evaluatingthe performance of novel intervention techniques alongwith traditional reinforcements for future network planning[5ndash7] Furthermore the deployment of these novel tech-niques is expected to improve the quality of service [8]The rising number of stakeholders in the electricity valuechain increases the complexity for asset planning Besidesthe number of decision makers the energy sector is facinga data revolution and therefore utilities of the future mustinclude in their planning capabilities the implementation ofinformation and communication technologies (ICTs)

Most network modelling tools such as IPSA PowerETAP or DINIS [9ndash11] perform power flow analysis andlook after overloads and stress points of the network Theirapproach can be considered static in the sense that theyevaluate an instantaneous view of the network at a certaingiven time However dynamic modelling like the ones imple-mented within the SIM [12] extends those static approachesmaking a series of evaluation runs adjusting future networkstates (configuration of the network) to previous fixed stateswhere the grid needed an intervention across its topology

The novel techniques under study in this researchare classified as engineering techniques automatic loadtransfer (ALT) dynamic asset rating (DAR) for cables andtransformers meshed networks and energy storage andcommercial techniques distributed generation (DG) anddemand side management (DSM) Traditional reinforce-ments (TRAD) are modelled as follows transformer replace-ment or addition cable or overhead line (OHL) replacementtransfer load to adjacent feeder and installation of new feeder[13] In addition the assets considered to be fixed duringthis assessment are the cables and transformers of thelocal 11 kV network

This study is populated with the data from the FALCON(Flexible Approaches to Low Carbon Optimised Networks)project trials using a section of Western Power Distribution(WPD) in Milton Keynes area composed of six 11 kV pri-maries In contrast with the parametric top-down represen-tation embedded in the transform model [14] the SIMaims at creating long-term strategic investment plans Thisstudy will deliver insights and scalability of these novelinterventions for asset planning of the UK distributionpower networks

There are a number of previous notable projects thataddress the uncertainty around the integration of low carbonand low-carbon technologies into the distribution grid[15ndash18] The smart distribution network operation for max-imising the integration of renewable generation project [19]performs the optimisation of network operation modes andreinforcement planning in the presence of renewable genera-tion The OFGEM smart grid forum work stream 3 whichlater became the EA technology transform model [14] isa parametric representation of the electricity distribution

network that aimed at creating long-term strategic invest-ment plans [20] It is important to note that there are certainlimitations in transform that are characteristic to all paramet-ric models The operating characteristics of devices and theirrelationship to other technologies require extensive calibra-tion to produce a qualified answer To some extent thelimitations of transform were addressed by smart grid forumwork stream 7 [21] which took four of transformrsquos paramet-ric representations of typical distribution networks andconverted them into nodal network models Other examplesinclude the energy system catapult energy path model whichtargets local energy systems [22] and the Comillas Universityreference network model (RNM) [23] which is a large-scale distribution network planning tools that can createoptimal networks

Despite the differences in their respective approaches theaforementioned models and software tools share some com-mon limitations [24] They have limited ability to captureemerging behaviour arising from the simultaneous applica-tion of multiple low-carbon and smart technologies to theelectricity distribution network Likewise it is complex toadd new technologies into the mix due to either the lack ofautomatic application of smart techniques or as in the casewith transform the parametric approach which needs infor-mation about the way different technologies compete witheach other which is difficult to obtain And finally no deci-sion support for a particular piece of distribution networkcan be provided because of either lack of automation or theparametric nature of the model The following sectionsintroduce and describe the smart techniques and the noveltechnoeconomic approach for performing dynamic networkmodelling in distribution networks and analysis in thepresence of multiple smart grid techniques It uses nodalnetwork modelling to capture the emerging behaviour andcreate localised network development plans

2 Overview of Techniques

21 Technique 1 Dynamic Asset Rating The heating effect ofcurrent passing through a metal restricts the capacity of alltransformers overhead conductors and cables on a distribu-tion network represented in Figure 1 This restriction is basedon the maximum temperature on a critical componentwithin the asset Therefore each asset will have a finitecurrent-carrying capacity rating based on assumed values ofexternal conditions which affect thermal buildup ie windspeed ambient temperature soil and humidity As the assetsin general do not have temperature monitoring the assumedvalues of the external conditions used in these calculationshave as a basis a statistically low level of the risk of the assetexceeding its critical temperature By more accurately mon-itoring metrological conditions and modelling asset ratingsin real time the capacity of the asset can be increased whilekeeping the risk of exceeding the critical temperature to aminimum Further models and algorithms will be developedas part of this second implementation to cater for theincreased information available

In addition many assets have a thermal capacity suchthat it takes time for the asset to raise its temperature

2 Complexity

(ie an increase in the current passing through the asset willnot cause a step change in the temperature of the asset) Suchassets typically have short-term current ratings which are sig-nificantly greater than their continuous current rating Theseshort-term ratings are based on specific current-carryingcurves By being able to forecast the actual current-carryingcurves the asset ratings can be further refined such that aneven greater short-term current can be supported Trans-formers and underground cables have significant thermalcapacity that can utilise this method whereas overhead linecircuits do not have significant thermal capacity

22 Technique 2 Automated Load Transfer Consumers ofelectricity on the network use energy at different rates at

different times of the day and by actively managing thenetwork connectivity the loads across connected feederscan be evenly balanced Rather than the position of normalswitching open points being determined for average networkconditions the positions can be changed automatically bythe network management system to a more optimum loca-tion based on a number of factors such as security voltagedrop capacity utilisation and load forecasts as displayedin Figure 2

23 Technique 3 Meshed Networks This technique repre-sents the process by which circuit breakers on the networkare switched in order to feed loads from multiple locationsThis approach fundamentally allows the load on each feeder

RTU

Metrologicalsensors

Alstom P341

PowerOn fusion

Communicationnetwork Asset

current ampvoltagesensors

Asset temperature(underground cables

transformers)

Asset data and metrological data

Asset temperature and sag data

Ampacity

Asset temperatureamp sag sensors

(overhead lines)

Demandforecast

DARcalculation

Figure 1 DAR trial schematic

Communication network

RTU

Substation 1

RTU

Substation 2

RTU

Substation 3

RTU

Substation 4

PowerOn fusion

Demandforecast

ALT function

Automated switch

11 kV network (which may include additional substations)

Figure 2 ALT trial schematic

3Complexity

in a meshed circuit to deviate according to the routine varia-tions in the connected load without the need for pre-existinganalysis and changes to switch states

However simply closing normal open points (NOPs)exposes more connected customers to supply interrup-tion following a network fault Therefore any plannedclosure of open points for long-term operation is routinelyaccompanied by the installation of along-the-feeder faultsensing and interruption equipment (protection relaysand circuit breakers) The installation of along-the-feederprotection devices restores and potentially reduces theprobability of customer interruption under fault conditionswith mesh operations

The aim of trialing this technique was to operate thedesignated 11 kV networks with parallel feeding arrange-ments protective device-driven autosectioning zones whileexploring potential impacts both benefits and trade-offsthat could be derived from parallel feeder configurationsand potential impact both benefits and constraints of opera-tion with autosectioning zones balanced against timeeffortand cost

24 Technique 4 Storage Energy demand in an 11 kV feedertends to occur in peaks and troughs throughout a 24-hourcycle The current supplying capacity of a feeder is limitedto the current-carrying capability of the smallest cable or

conductor in the circuit and these usually decrease incross-sectional area size further away from the primary theyare located This is acceptable when the load is spread evenlyacross a circuit but when the load occurs unevenly then theutilisation factor of the assets will also be uneven By intro-ducing energy storage devices on the network (Figure 3) theycan feed out onto the system at peak demands and rechargeduring times of low demand thus deferring the need toreplace existing assets

25 Technique 5 Distributed Generation Control A numberof industrial and commercial customers have their ownon-site generation and this number is likely to increase withthe transition to a low-carbon economy In some cases thismay be uncontrollable renewable generation (wind or solar)but the majority is in the form of either standby generatorsor controllable plants such as biomass refuse incineratorsor combined heat and power (CHP) plants If customers withcontrollable distributed generation can be incentivised toaccept instruction from a DNO to increase or decreasegeneration this can be used to reduce or increase sitedemand andor provide or remove supply from the grid asa means of rectifying network problems

26 Technique 6 Demand Side Management Similar todistributed generation DSM involves putting in place

Communication network

RTU

Substation

PowerOn fusion

Demandforecast

Energystorage

controller

Energystorage

unitLV fe

eder

sto

custo

mer

s

Substation

Energystorage

unitLV fe

eder

sto

custo

mer

s

Datacontrol connectionDistribution network connection

Volta

gec

urre

ntm

easu

rem

ents

Energystorage

unitcontroller

Operation mode-local control

Communication network

RTU

Alar

ms

indi

catio

ns

PowerOn fusion

Operation mode-remote control

Figure 3 Storage trial representation

4 Complexity

commercial agreements between the DNO and industrialand commercial customers who have the ability to controlappreciable amounts of load in a relatively short period oftime We expect demand side response to be in two formsusing the representation in Figure 4

(i) To reduce the impact of predicted peak loads

(ii) To respond to an unplanned event such as a fault

Demand side response actions can be used by the DNOto enable a change in behaviour by a customer site inresponse to an explicit signal triggering a preagreed actionThe action should be the interruption of a customerrsquosinternal electricity-consuming processes either to avoid ormore likely to defer these to a later time Its metricscapacitydelta reduction durationfrequency and OPEXare detailed in [13]

3 Methodology

To undertake the analysis of the aforementioned techniquesa revolutionary new software tool for electricity distributionnetwork planning called the scenario investment model(SIM) is used Results from evaluations using the SIM areobtained through a series of experiments that modelled thenetwork evolution under different demand scenarios pre-sented in Table 1 at short- (2015ndash2023) and long-termlookahead (2015ndash2050) to assist decision-makers in futurepower network planning

Currently electricity distribution networks have beenplanned with typically linear load growths of up to 1 perannum The expected increase in low-carbon technologieswill have a significant effect on the electricity demands onthe network which may have significant rapid sporadicincreases in the electricity demand on the 11 kV networks

[25] In addition the daily electricity load shapes may be alsoaltered significantly The networks will need to be upgradedand systems are being able to evolve and cope with newdemand profiles

There had been identified two main streams of work toconsider the use of the innovative techniques namely strate-gic and tactical planning and Design Build and OperationThe strategic and tactical planning stream will consider thenetwork planning roles while the design and operationstream will consider design build and operation roles InFigure 5 the smart grid planning framework diagram pre-sents the key elements of each stream which are displayedwith their main interactions

In order to leverage the capability of existing networkanalysis tools which are already extensively used by elec-tricity network operators the SIM is separated into twomain packages a network modelling tool which primarilyperforms the technical assessment of the application ofthe techniques and the SIM harness which manages theoverall process and perform the economic assessment andreporting functions

Demand data modelling has been based on a bottom-upapproach The methods used provide an estimate of demandfor each half-hour at each secondary substation for 18different season-day types [12]

Communication network

DGDSM

Customer site

PowerOn fusion

Demandforecast

Networkanalyser

Networkevent

DGDSMscheduler

DSMDG manager

Performancemonitor

Paymentscalculator

RTUsmartmeter

Figure 4 Commercial trials representation DGDSM

Table 1 Demand scenarios

Demand scenariosFuel

efficiencyLow-carbon

heatWall

insulation

DECC1 Medium High High

DECC2 High Medium High

DECC3 High High Low

DECC4 Low Low Medium

5Complexity

The research objective is settled to prove the suitability ofthe six novel smart interventions presented in Section 2and along with the traditional reinforcements provide anevolutionary planning insight for future power networksTo undertake this study specific experiments were selectedfor a certain power trial network under different demandscenarios and evaluation periods assessing smart tech-niques along with traditional reinforcements The approachinvolved running a set of experiments using the SIM for thesix 11 kV primaries in the FALCON trial area

31 SIM Support Algorithms The essence of the SIMapproach is its ability to take a network configuration andcorresponding load profiles in a particular year (termed asinitial network state) perform power flow and reliabilityanalysis and create derivative network states in a processknown as ldquonetwork state expansionrdquo The expansion happenseither by transitioning to the following year for networkstates without any failures or by applying intervention

techniques to resolve network issues With each new net-work state created the SIM therefore is faced with adecision as to which network state from the executionhistory to expand next The expansion can be guided bysimple depth first or breadth first algorithms which areimplemented in the SIM for verification purposes Thedepth-first algorithm always selects the newest ie themost recently created network state that is not fullyexpanded for expansion while the breadth-first algorithmalways selects the oldest network state However thosesimple heuristics are inadequate for any practical usebeyond simple test cases due to the size of the searchspace obtained by permuting all possible interventionsover a number of years To perform intelligent explorationof the search space the SIM uses a heuristic approach thatis based on an Alowast algorithm [26 27]

The baseline Alowast algorithm aims at finding the least-costpath through the search space As Alowast traverses the searchspace it builds a tree of partial paths The leaf nodes of this

Learning transfer

Key component

Systempackage conatinga set of key components

Learninganalysis of results for theimprovement of models and development ofpolicies

Smart grid planning framework

Substationmonitoring

Dynamicasset ratings

data

Automatedload transfer

dataMeshed

network data

Communication network trial

Monitoringdata collection

system

Trials data distributionmanagement system

Scenario investment model

Network modelling tool(technical Assessment)

Dynamic assetratings

techniquemodel

Automatedload transfer

techniquemodel

Batterystorage

techniquemodel

Meshednetwork

techniquemodel

Traditional reinforcement

technique model

Networktopology amp

loads

Distributedgenerationtechnique

model

Demand sidemanagement

techniquemodel

Multi-yearscheduler

Economicinvestmentassesment

Networkperformance

analysisVisualisationand reporting

SIM harness(economic assesment and

reporting)

Performance analysisamp learning

Energy modelminusmeasured data

comparison

Demand datamodelling and

scenario development

Impr

ove e

nerg

ym

odel

Design build andoperation

Strategic andtactical planning

Strategic planning supportconnections and

infrastructure planningplanning policy development

Impr

ove t

echn

ique

and

netw

ork m

odels

Batterystorage data

Distributedgeneration

trial

Demand sidemanagement

data

Design amp build learning

Impr

ove i

mpl

emen

tatio

n co

st m

odels

Figure 5 Smart grid planning framework diagram

6 Complexity

tree (failed network states) are stored in a priority queue thatis ordered using a cost function

f x = g x + h x 1

where h x is a heuristic estimate of the path cost toreach the goal and g x is the distance travelled fromthe initial node

The SIM selects network states from the priority queue toapply intervention techniques one application at a timeDeployment of a technique produces a new network statefor which a power flow analysis is performed in intact andall n minus 1 (contingency) network operation modes If allthe failures are resolved a reliability analysis comprisingcustomer minutes lost (CML) customer interruptions (CI)losses and fault level studies is performed A new networkstate is subsequently created in the next year of evaluationor if it is already the last year of evaluation the costsof interventions are calculated and the network statetogether with all its expansion history is saved to theresult store as a new result The evaluation is terminated

(Figure 6) when criteria such as the number of resultsnumber of network state evaluations or run time arereached As noted previously Alowast uses a combination ofdistance travelled so far and a heuristic estimate of thedistance to reach the endpoint

In SIM case this corresponds to g x being the totalexpenditures (TOTEX) incurred so far and h x being aheuristic estimate of TOTEX to reach the end year ofthe experiment TOTEX also referred to as the totalexpenditure comprises implementation costs (CAPEX)that occur only once when an intervention is applied tothe network operation costs (OPEX) that refer to anongoing expense of operating an asset or a scheme alongwith asset life degradation and metric costs that includeincentive payments for losses fault levels and networkreliability Referring to (2) the g x for a network statexi in year i is defined as

g xi = ci + oi + jminus1

j=1cj + oj +mj 2

Initial network state

Create new networkstate in the next year Analyse power flow

Generate and applynext intervention

technique application

Save network state tofailed network state

store

Update path costestimates

Reorder priority queue

Select top mostnetwork state from the

priority queueTermination

criteria reached

Failure present

Analyse reliabilityEnd yearreached

minus

+

minus

+

minus

+

Update solution costs

Save result

End

Figure 6 SIM evaluation flowchart

7Complexity

where ci is CAPEX in the current year oi is OPEX in thecurrent year and cj oj and mj are CAPEX OPEX andmetrics costs respectively of the ancestor network statewith no issues in year j The heuristic estimate h x ofthe cost to reach the end year is given by

h xi = cREMi +mi + n

k=i+1ck + ok +mk 3

where cREMi is the average remaining CAPEX in the cur-rent year i mi is the average metric cost in this year ckok and mk are the average CAPEX OPEX and metriccosts respectively of the descendant network state in yeark with no issues and n is the end year of evaluation Pre-seeded to a constant value c0 the average CAPEX isupdated each time a network state is expanded in a partic-ular year thus the estimated costs of fixing all issues in aparticular year progressively approach true average Settingc0 to a value greater than 0 speeds up the expansion pro-cess ie all interventions that cost less than c0 will resultin new network states that are at the top of the priorityqueue The business rationale is that DNOs are not thatinterested in optimizing interventions costing less than acertain threshold The learned averages are propagatedback to network states in the priority queue thus updatingtheir estimated effort and leading to the reordering of thequeue unlike the average CAPEX average OPEX andmetrics costs which are assumed to be 0 for years withno network states The average OPEX value for a year isobtained using

o = jToc jT jminus1 4

where j is a column vector of ones and oc is an OPEXvector of compliant network states in that year Likewisethe average metric cost is obtained according to

m = jTmc jT jminus1 5

where j is a column vector of ones and mc is a vector ofthe metric costs of compliant network states in that year

32 Conceptualising Bottom-Up Evolutionary PlanningModel-driven engineering (MDE) uses analysis constructionand development of frameworks to formulate metamodelsThose models are usually characterised using domain-specific modelling approaches [28] containing appropriatedetail abstraction of particular domain through a specificmetamodel The use of metamodels requires therefore inputsfrom domain experts which can be used to generate aggre-gated or disaggregated models Top-down and bottom-upare the conceptual definition of aggregated and disaggregatedmodels [29] These two modelling paradigms are frequentlyused to epitomise domain interactions among the operationof the energy system the econometrics related and thetechnical performance indicators [30]

From a bottom-up modelling approach [31 32] thetop-down perspective is a simplistic characterisation ofhow electrical power networks combine locational eventsand individual asset performance with high-level objectiveslike improving the CML of a certain congested area [33 34]From an engineering point of view both are still validsince outputs and strategic forecast are produced in bothHow those outcomes are calculated validated and trans-formed to strategy is presented in Figure 7 (top-down)and Figure 8 (bottom-up)

Interventiontechnique costs

Relative usage ofinterventiontechnique

Interventiontechnique installation

details

Interventiontechnique models

Validationphysical trials

Interventiontechnique operation

details

Purchase andoperation cost data

Cost model

Network data

Representativenetworks

Model network through years

Calculate intervention technique usage

Strategic forecasts

Validationsampled nodal

networks

Demand scenarios

Figure 7 Top-down conceptualisation of evolutionary power networks planning

8 Complexity

The ability of bottom-up to capture discrete locationalimpacts of technologies on the system and their disaggre-gated costs is triggering the following subsections Trade-offmethodologies are needed for planning evolutionary powersystems where observing disaggregated result strategic fore-casts are to be produced These methodologies need to beinteractive in the sense that starting from an initial stateand after a testing or learning phase the network is able toaccommodate techniques that have improved the systemproviding an exploratory set of solutions that can beexpanded or discarded as the model evolves through time

33 Experiment Characterisation To illustrate the processingof the methodology adopted we consider a typical networkscenario tree created by the tool that consists of failed (red)and compliant (green) network states in different years ofevaluation The SIM starts with a single network state inthe first year of evaluation Every year the network isevaluated for compliance in intact and n minus 1 contingencyoperation modes If failures are detected the SIM appliesintervention techniques described in Section 2 to create net-work patches that resolve the failures Each application of anintervention technique creates a new network state The toolhas to resolve all issues in the network before transitioninginto the next year DNO planners usually run power flowstudies with a single fixed load pattern In contrast the SIMchecks each network state for compliance under 18 charac-teristic day load scenarios each comprising of 48 half-hoursettlement periods All studies are performed under intactand n minus 1 contingency network operation modes The SIM

calculates patch costs using cost drivers returned by the net-work modelling tool The cost driver describes a networkintervention and consists of two parts namely the patchkey and the scaling The patch key identifies the nature ofthe modification of the network performed (removal or addi-tion of an asset and the type of the asset) Scaling data is rel-evant only to patches that can be installed in multiples of onesuch as cable upgrades and additional transformer installa-tion Scaling data structure provides a list of multipliers tothe base cost data available in the SIM database In case ofcable upgrade or replacement it enables the SIM to correctlyestimate full installation costs from per unit of length valuesFinally for postprocessing analysis the SIM also returns a listof failures by asset It was identified by DNO planners asindispensable features to help validate the system and corre-late the expansion trees to the actual assets on the networkdiagram The failure detail table contains asset ID anddescription alongside information about the number of fail-ures in intact and n minus 1 operating modes as well as absoluteand per unit thermal and voltage failure magnitude

34 Case Characterisation Two set of experiments wereperformed for this study one for comparing short-termevaluation period for RIIO-ED1 (2015ndash2023) where theRIIO-ED1 investment planning has been stylised and alonger planning period for RIIO-ED1 to RIIO-ED4 from2015 up to 2047 The other set aimed at evaluating differ-ent DECC demand scenarios Experiments evaluated twodemand scenarios DECC2 and DECC4 DECC4 representsas displayed in Table 1 the slow-progression scenario and

Purchase andoperation cost data

Cost model Nodal network model

Model network through years

Apply intervention technique to keep the network compilant

Find optimal network development options

Solutions for individual networks

Aggregate results for larger network areas

Strategic forecasts

Network data Demand scenarios

Interventiontechnique costs

Interventiontechnique installation

details

Nodal interventiontechnique models

Validation withphysical trials

Relative usage ofinterventiontechniques

Validation of powerflow and reliability

models

Interventiontechnique operation

details

Load profiles

Figure 8 Bottom-up conceptualisation of evolutionary power networks planning

9Complexity

DECC2 is with DECC3 the most challenging scenario interms of electrification and low-carbon technology integra-tion The procedure to run the experiments in the SIM isshown in Figure 6 The inputs to be sent to the SIM aredetailed in Figure 5 the selected network techniques evalu-ation period demand scenario and cost model whereas theoutputs of the SIM we obtain are techniques used failuressolved assets fixed and electrical performance indicatorsThe SIM address mentioned outputs for long-term plan-ning reinforcements to optimise investment asset planningresolving network constraints The performance criteria eval-uated were capital expenditures (CAPEX) operating expen-ditures (OPEX) utilisation of assets CMLs CIs and lossesThese parameter values are delivered by the SIM after eachsimulation In order to look for the optimal solution amongthe feasible solution pathways the SIM allows a granularitystudy of each network state

4 Results

This section presents the assessment of the six smart gridinterventions along with traditional reinforcements in thetrial area compounded by six 11 kV primaries in MiltonKeynes UK for two different evaluation periods DECC2and DECC4 Subsection 41 introduces the results for the2015ndash2023 period as short-term planning and Subsection42 presents the results for a long-term evaluation period2015ndash2047

41 Short-Term Planning This section presents the assess-ment of the six smart grid interventions along with tradi-tional reinforcements in the trial area compounded by six11 kV primaries in Milton Keynes UK for two differentdemand scenarios DECC2 and DECC4

411 DECC4 2015ndash2023 Figure 9 shows the trends ofCAPEX and OPEX Note that in the year 2015 there is aCAPEXrsquos peak due to the application of more techniquesand OPEX increments over time from 2017 to 2023

Despite the investment the increase is compensated bya benefit in the electricity distribution network with areduction in CML and CI as shown in Figure 10

The summary of the proportion of installations requiredduring this evaluation period and by capital expenditures pertechnique is presented in Table 2 for both DECC scenarios

The average yearly price of each technique implementedto fix a network state is key for future decision-makingconsiderations

Figure 11 displays the average price of each techniquedisaggregated by CAPEX and OPEX

Performance indicators are key parameters for futuredecision-making within electricity distribution planning asquantifiers due to their influence on the quality of service

Therefore as shown in Figure 12 the only smart tech-nique used in combination with traditional reinforcementsin this scenario and evaluation period that slightly influenceslightly CML and CI is meshed networks

412 DECC2 2015ndash2023 Figure 13 plots the trend ofCAPEX and OPEX over the evaluation period It is important

to highlight that there is a CAPEX peak in 2015 due to anincrease of techniques applied in this period

This CAPEX increment produces a benefit in terms ofCML and CI as observed in Figure 14

The distribution of techniques applied and capital expen-ditures per technique are shown in Table 2 The average priceof each technique implementation in this demand scenariofor this evaluation period is shown in Figure 15

As displayed in Figure 12 for DECC4rsquos simulation theonly smart grid technique that improves the quality of serviceis the meshed network In Figure 16 the contribution ofsmart techniques on CML and CI for DECC2 demandevaluation is captured

413 Comparison between DECC2 and DECC4 for 2015ndash2023 The results presented may facilitate improvementsin electricity distribution network operations and planningresulting in better-informed decision-making when upgrad-ing current electricity distribution networks The assess-ment of the six smart grid techniques discovered thatonly three of them were selected as part of optimal

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

Mill

ions

pound p

er an

num

CAPEXOPEX

442444648

55254

Figure 9 CAPEX and OPEX DECC4 2015ndash2023

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

pu

Figure 10 CML and CI DECC4 2015ndash2023

10 Complexity

solutions for DECC4 and DECC2 as described in Table 2These three techniques applied are DAR for cables andtransformers and meshed networks

Comparing the cost trends of the two assessed scenariosit is notable that in both demand scenarios DECC4 and

DECC2 the CAPEX peak occurs in 2015 (Figures 9 and13) reflecting the more difficult nature of network statesto be feasible in a demanding scenario whereas DECC2shows a higher improvement of CI and CML performanceindicators as can be seen in Figures 10 and 14

Table 2 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2023

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 15 2 14 2

DAR-transformer 7 1 5 1

ALT 0 0 0 0

Mesh 3 1 4 1

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 8 3 7 2

TRAD-cable 60 89 65 91

TRAD-transfer load 6 2 4 1

TRAD-new feeder 1 2 1 2

DAR - cable DAR -transformer

Meshednetworks

TRAD -transformer

TRAD - cable

CAPEXOPEX

0

20000

40000

60000

80000

100000

pound

Figure 11 Average cost disaggregation per technique DECC4 2015ndash2023

Figure 12 Average CML and CI improvement per technique DECC4 2015ndash2023

11Complexity

The techniques applied are the key consideration to beassessed It is necessary to analyse the number of interven-tions applied by type the effect of these techniques in theelectric grid solving failures and fixing assets Figures 11and 15 show average prices of techniques used in eachdemand scenario Smart techniques have a lower TOTEXthan traditional reinforcements These novel techniquesabove are not frequently able to fix failures and thereforeproduce feasible network states In terms of CML and CIthe only technique able to moderately contribute to theirimprovement is meshed networks as shown in Figures 12and 16 Results attribute the majority of fixes to traditionalreinforcements with a comparatively smaller number offailures being solved by smart techniques (Table 2) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention forDECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

For DECC4 the three mentioned techniques along withtraditional cable and transformer replacement are applied(Table 2) whereas for DECC2 two of them are appliedDAR for cables and meshed networks (Table 2) Comparingthe cost trends of the two assessed scenarios it is notable thatin DECC4 the CAPEX peak occurs in 2015 (Figure 7)whereas in DECC2 besides the peak in 2015 there is alsoone in 2019 (Figure 11) reflecting the more difficult natureof network states to be feasible in a demanding scenarioHowever DECC2 shows a higher improvement of electricalperformance indicators as can be seen in Figure 12

In addition traditional cable replacement DAR forcables and meshed networks are able to fix the cable whereastraditional transformer replacement and DAR for trans-formers are able to fix transformer issues (Figure 16) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention for

DECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

42 Long-Term Planning This section evaluates two experi-ments namely characterising the DECC2 and DECC4 sce-narios for 2015ndash2047 evaluation period For each demandscenario expected investments disaggregating CAPEX andOPEX are presented the evolution of electrical performanceindicators and the number of techniques applied

421 DECC4 2015ndash2047 In this experiment the mostsignificant results to analyse the suitability of each techniqueare presented

Figure 17 shows the trend of CAPEX and OPEX from2015 to 2047

There are CAPEX peaks in the year 2018 and during thebeginning of RIIO-ED2 in the years 2025 to 2026 due to theapplication of more techniques to fulfill low-carbon targets

Figure 18 indicates that upgrades on the network aredirectly related to technical performance indicators CMLand CI

In Table 3 the techniques applied and their contributionto CAPEX during the evaluation period from 2015 to 2047 bya demand scenario are shown

422 DECC2 2015ndash2047 Within this experiment the mostrelevant results to analyse the evolutionary network statesare presented

In Figure 19 the trend of CAPEX and OPEX from 2015to 2047 is shown There is a significant increase of CAPEXin the years 2024 to 2026 at the beginning of RIIO-ED2due to the necessary implementation of new techniques toreach low-carbon targets The evolution of CML and CI ispresented in Figure 20 linking larger investment years whenmajor reductions are found The contribution of each solutiontechnique is presented in Table 3

423 Comparison between DECC2 and DECC4 for 2015ndash2047 Figure 21 presents a multidimensional parallel coor-dinate representation of the feasible network statersquoscombinations that produced a 2015ndash2047 investment path-way for the evaluated area It bundles economic indicatorsie CAPEX and OPEX with technical performance indica-tors providing valuable insights on the number of traditionalreinforcements utilised to heal falling network states Resultsare also clustered by the two demand scenarios assessedduring the experiments

Solutions of DECC2 (represented in orange and green)and DECC4 (in black and blue) are clustered by the per-centage of traditional reinforcements utilised as well asthe utilisation factor

Solutions in green and in black represent those wheresmart grids were more used whereas orange and blue repre-sent the cheapest (CAPEX and OPEX) less utilised and theworst responding to the decrease of CML CI and lossesand the ones using more traditional reinforcement to fixnetwork states

Besides the fact that DECC2 smart solutions are between16 and 28 more expensive than DECC4 traditional

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

CAPEXOPEX

442444648

552

Mill

ions

pound p

er an

num

54

Figure 13 CAPEX and OPEX DECC2 2015ndash2023

12 Complexity

reinforcement solutions (green solutions versus blue solu-tions in Figure 21) DECC2 traditional solutions are in therange of costs of smart solutions of DECC4 (orange andblack solutions in Figure 21) It can also be concluded thatinvestment pathways using less smart techniques provide a

cheaper response to technical performance evaluators suchas utilisation CML CI and losses

43 Summary Experiment evaluation runs from the2015ndash2047 period present lower investment rates for the

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

PU

Figure 14 CML and CI DECC2 2015ndash2023

CAPEXOPEX

DAR -transformer

Meshednetworks

Traditional -transformer

Traditional -cable

0

20000

40000

60000

80000

100000

120000

140000

pound

Figure 15 Average cost disaggregation per technique DECC2 2015ndash2023

Figure 16 Average CML and CI improvement per technique DECC2 2015ndash2023

13Complexity

2015ndash2023 period than if the evaluation is just performedwith a 2015ndash2023 time frame This occurs as a result of thechallenging low-carbon targets up to 2047 and a myopicplanning with short-term lookahead For an evaluationperiod four times larger the CAPEX and TOTEX increasedjust 18 compared to 2015ndash2023 concluding that betweenpound52M and pound68M the evaluation area regardless of thetime horizon for the investment or the demand scenarioconsidered is required

Furthermore it can be inferred comparing Tables 2 and 3that for less smart interventions the short-term planningis used compared to the long-term horizon planningDAR-cable and DAR-transformer experienced a significantimplementation variation between the two evaluationperiods for both demand scenarios as well as CAPEX allo-cated in cable upgrades falling from 89ndash91 (2015ndash2023)to 29ndash66 in the long term 2015ndash2047 run Both technicalperformance evaluators CML and CI respond to theirrespective CAPEX curve shape Due to a more incentivisedsmart grid technique during RIIO-ED2 and ED3 the per-centages of smart techniques implemented varied notablyFor DECC4 in 2023rsquos outlook the share of techniques is

25 for smart grid interventions whereas for 2050rsquos out-look the share is increased up to 58 In the same wayfor DECC2 in 2023rsquos outlook the share of smart interven-tions is 23 whereas in 2050rsquos outlook the share increasesup to 69

Feasible solutions characterising the solution space(Figure 21) differ in the degree of investment required andtechnical performance evaluators Each of the solution pathrepresented in the multidimensional solution space inFigure 21 represents the intersection of that dimension witheach axis Parallel coordinates [35] are low complexityworking for any N-dimension In the case of Figure 21it has 8 dimensions treating every variable uniformlyEach intersection of one solution path with each axis isthe value of that solution for that axis As for axis DECC2and DECC4 it represents the feasible solutions for eachdemand scenario Making a query by the percentage oftraditional reinforcement used in that solution we have thepartition between orange and green for DECC2 and blueand black being the first solutions that use more traditionalreinforcement compared with the ones that use more smarttechniques represented in green (DECC2) and black(DECC4) From Figure 21 we can argue that solutions thatuse more smart techniques (green and blacks) are moreexpensive in terms of CAPEX and OPEX than their peers(orange and blues) when compared by a demand scenarioIt can also be stated that solutions with a higher use of tradi-tional reinforcements are the ones that decrease the mostCML CI and losses

Disaggregating results by network state of the six 11 kVprimaries and by the feeder if necessary for further granulardebugging can discern locational capacity Under bothdemand scenarios Secklow Gate was seen to be the one withgreater capacity being necessary to apply fewer techniquesover the evaluation period 2015ndash2047 On the other handNewport Pagnell exceeds the capacity as soon as 2015requiring a high number of network state evaluation to befixed that happens for each subsequent year hence the largenumber of network states is presented in Table 4

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

pound p

er an

num

Figure 17 CAPEX and OPEX DECC4 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

pu

Figure 18 CML and CI DECC4 2015ndash2047

14 Complexity

5 Conclusions

Power flow analysis using a nodal network model is essentialwhen determining the benefits of trailed smart interventions

because the interactions and implications are highly specificto a particular location and scenario

This suggests that while the bottom-up approach isonerous in terms of data handling and manipulation this isworthwhile for strategic planning and policy evaluationComparing both investment strategies investment strategyadjustments will be necessary in future regulation periodsif an over-invested network behaves as displayed in theshort-term section of this study

Traditional reinforcement will continue to be the mainmethod by which network issues are mostly resolvedfollowed by dynamic asset rating and meshed networks

The assessment of the six novel smart interventions in theFALCON 11kV primary test area in Milton Keynes hasproved the suitability of three techniques able to fix failuresimproving the quality of service and their readiness to bedeployed in the near future These techniques are DAR forcables and transformers and meshed networks Meshednetworks have been repeatedly selected as a feasible tech-nique because using it will reduce CML CI and power losses

Table 3 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2047

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 18 5 31 6

DAR-transformer 32 16 32 6

ALT 0 0 0 0

Mesh 8 3 6 3

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 25 44 9 16

TRAD-cable 15 29 20 66

TRAD-transfer load 1 1 1 1

TRAD-new feeder 1 2 1 2

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

s pound p

er an

num

Figure 19 CAPEX and OPEX DECC2 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

PU

Figure 20 CML and CI DECC2 2015ndash2047

15Complexity

improving the quality of service and the efficiency whilebeing a cost-effective solution

The initial capacity at primary substations differedsignificantly and this affected the number and complexityof interventions required by the SIM Due to the loadscenarios showing significant peak load increases DAR wasoften a temporary measure that would delay but not removethe eventual need for traditional reinforcement

Implementing DAR for cables and transformers themonitoring of assets when their peak capacity is increasedwas analysed On the other hand it was observed thattraditional reinforcements still play a key role in keepingthe electricity distribution networks free of constraintsTRAD techniques such as transformer and cable replace-ments are able to fix the majority of failures and will beessential also in the future

The comparison between traditional reinforcements andnovel smart techniques has provided a new knowledge aboutthe suitability of each technique to be applied in termsof costs electrical performance failures fixed and assetreplaced Cable replacement is the most costly techniquehowever its use is unavoidable in a number of cases Further-more the applicability of each technique regarding costsinvolved improvements on power quality and efficiency

and failures solved lead to new questions to be analysed suchas the lack of these techniques to provide flexible capacitywithin the trailed area

To sum up this study has performed a comparativeanalysis of novel smart intervention techniques providinginsights for future investments in electricity distributionplanning Further work can focus on scaling up the analysisto include a larger section of the network or a constrainedarea to evaluate national applicability of the current findings

Data Availability

Some of the data relevant to this submission is proprietaryfrom Western Power Distribution It can be accessed forresearch purposes under a signed collaboration agreement

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors would like to thank the rest of the WesternPower Distribution Innovation team particularly the other

2400

2200

2000

350

360

370

380

390

400

1400

1300

1200

1100

1000DECC 4

50

55

60

12

11

10

9

8

4400

4200

4000

3800

3600

2400

CAPEX (in kpound) OPEX (in kpound) Utilisation Traditional reinforcements () DECC 2 998796 CML 998796 CI 998796 Losses

2200

2000

1800

Figure 21 Parallel coordinate visualisation of technoeconomic performance indicators under DECC4 and DECC2

Table 4 Summary of network states and results for demand scenario DECC4 and DECC2

Primary 11 kV substation Feeders Year TechniquesResultsDECC4

NSDECC4

ResultsDECC2

NSDECC2

Fox Milne 13 2047 Smart and traditional 54 276 47 259

Newport Pagnell 9 2047 Smart and traditional 48 317 31 318

Secklow Gate 9 2047 Smart and traditional 27 29 15 16

Bletchley 19 2047 Smart and traditional 32 40 21 48

Marlborough Street 11 2047 Smart and traditional 31 52 19 37

Childs Way 17 2047 Smart and traditional 67 398 54 181

16 Complexity

members of the FALCON project as well as Paul CharletonStuart Fowler Ilja Orlovs Dan Taylor and Steve IngramAlso the authors want to acknowledge OFGEM and theLowCarbon Network Fund for funding this piece of research

References

[1] European Commission ldquoCommunication from the Commis-sion to the European Parliament the Council the EuropeanEconomic and Social Committee and the Committee of theRegionsrdquo 2011 December 2016 httpseur-lexeuropaeulegal-contentENTXTqid=1537473508968ampuri=CELEX52016PC076721

[2] F G N Li and E Trutnevyte ldquoInvestment appraisal ofcost-optimal and near-optimal pathways for the UK elec-tricity sector transition to 2050rdquo Applied Energy vol 189pp 89ndash109 2017

[3] G Strbac I Konstantelos M Pollitt and R Green DeliveringFuture-Proof Energy Infrastructure National InfrastructureCommission 2016

[4] Ofgem ldquoGB Network regulation - the RIIO modelrdquo 2010December 2014 httpswwwofgemgovuknetwork-regulation-riio-model

[5] R Poudineh and T Jamasb ldquoDistributed generation storagedemand response and energy efficiency as alternatives togrid capacity enhancementrdquo Energy Policy vol 67 pp 222ndash231 2014

[6] J Yang X Bai D Strickland L Jenkins and A M CrossldquoDynamic network rating for low carbon distribution networkoperationmdashA UK applicationrdquo IEEE Transactions on SmartGrid vol 6 no 2 pp 988ndash998 2015

[7] M Behnke W Erdman S Horgan et al ldquoSecondary net-work distribution systems background and issues relatedto the interconnection of distributed resourcesrdquo NationalRenewable Energy Laboratory (NREL) Tech Rep NRELTP-560-38079 2005

[8] J Crispim J Braz R Castro and J Esteves ldquoSmart grids in theEU with smart regulation experiences from the UK Italy andPortugalrdquo Utilities Policy vol 31 pp 85ndash93 2014

[9] Tnei ldquoIPSA Power software for power systemsrdquo March 2016httpwwwipsa-powercom

[10] ETAP ldquoETAP PSrdquo 2018 April 2016 httpsetapcomsoftwareproduct-overview-main

[11] DINIS ldquoDINIS distribution network information sys-temsrdquo 2002 March 2016 httpsetapcomsoftwareproduct-overview-main

[12] Western Power Distribution ldquoSIM final reportrdquo 2015November 2016 httpwwwwesternpowerinnovationcoukProjectsFalconaspx

[13] Western Power Distribution ldquoProject FALCONrdquo 2015November 2017 httpwwwdiniscom

[14] EA Technology ldquoTransform modelrdquo March 2016 httpwwweatechnologycomproducts-and-servicescreate-smarter-gridstransform-model

[15] L Sigrist E Lobato L Rouco M Gazzino and M CantuldquoEconomic assessment of smart grid initiatives for islandpower systemsrdquo Applied Energy vol 189 pp 403ndash415 2017

[16] J A Momoh ldquoSmart grid design for efficient and flexiblepower networks operation and controlrdquo in 2009 IEEEPESPower Systems Conference and Exposition pp 1ndash8 SeattleWA USA 2009

[17] N Good K A Ellis and P Mancarella ldquoReview and classifica-tion of barriers and enablers of demand response in the smartgridrdquo Renewable and Sustainable Energy Reviews vol 72pp 57ndash72 2017

[18] Z Yang J Xiang and Y Li ldquoDistributed consensus basedsupplyndashdemand balance algorithm for economic dispatchproblem in a smart grid with switching graphrdquo IEEE Trans-actions on Industrial Electronics vol 64 no 2 pp 1600ndash1610 2017

[19] SuSTAINABLE ldquoSmart distribution network operation formaximising the integration of renewable generationrdquo 2015March 2016 httpwwwsustainableprojecteuHomeaspx

[20] S Mohtashami D Pudjianto and G Strbac ldquoStrategic distri-bution network planning with smart grid technologiesrdquo IEEETransactions on Smart Grid vol 8 no 6 pp 2656ndash2664 2017

[21] Smart Grid Forum ldquoSmart grid forum work stream 7rdquo2015 March 2016 httpwwwsmarternetworksorgprojectnia_nget015425

[22] ETI ldquoEnergyPath networksrdquo March 2016 httpwwweticoukprojectenergypath

[23] C Mateo Domingo T Gomez San Roman Aacute Sanchez-Miralles J P Peco Gonzalez and A Candela Martinez ldquoAreference network model for large-scale distribution planningwith automatic street map generationrdquo IEEE Transactions onPower Systems vol 26 no 1 pp 190ndash197 2011

[24] R Bolton and T J Foxon ldquoInfrastructure transformation as asocio-technical process mdash implications for the governanceof energy distribution networks in the UKrdquo TechnologicalForecasting and Social Change vol 90 pp 538ndash550 2015

[25] UK Power Networks ldquoDNO guide to future Smart man-agement of distribution networks Summary reportrdquo 2014httpsinnovationukpowernetworkscoukinnovationenProjectstier-2-projectsLow-Carbon-London-(LCL)Project-DocumentsLCL20Learning20Report20-20SR20-20Summary20Report20-20DNO20Guide20to20Future20Smart20Management20of20Distribution20Networkspdf

[26] A Botea J Rintanen and D Banerjee ldquoOptimal reconfigura-tion for supply restoration with informed AlowastSearchrdquo IEEETransactions on Smart Grid vol 3 no 2 pp 583ndash593 2012

[27] T Logenthiran D Srinivasan and T Z Shun ldquoDemand sidemanagement in smart grid using heuristic optimizationrdquoIEEE Transactions on Smart Grid vol 3 no 3 pp 1244ndash1252 2012

[28] J Saacutenchez-Cuadrado J de Lara and E Guerra ldquoBottom-upmeta-modelling an interactive approachrdquo in Model DrivenEngineering Languages and Systems pp 3ndash19 Springer2012

[29] Y H Kim F J Sting and C H Loch ldquoTop-down bottom-upor both Toward an integrative perspective on operationsstrategy formationrdquo Journal of Operations Managementvol 32 no 7-8 pp 462ndash474 2014

[30] C Bohringer ldquoThe synthesis of bottom-up and top-down inenergy policy modelingrdquo Energy Economics vol 20 no 3pp 233ndash248 1998

[31] T G R Bower ldquoRepetition in human developmentrdquo Merrill-Palmer Quarterly of Behavior and Development vol 20no 4 pp 303ndash318 1974

[32] H Mintzberg and J A Waters ldquoOf strategies deliberate andemergentrdquo Strategic Management Journal vol 6 no 3pp 257ndash272 1985

17Complexity

(ie an increase in the current passing through the asset willnot cause a step change in the temperature of the asset) Suchassets typically have short-term current ratings which are sig-nificantly greater than their continuous current rating Theseshort-term ratings are based on specific current-carryingcurves By being able to forecast the actual current-carryingcurves the asset ratings can be further refined such that aneven greater short-term current can be supported Trans-formers and underground cables have significant thermalcapacity that can utilise this method whereas overhead linecircuits do not have significant thermal capacity

22 Technique 2 Automated Load Transfer Consumers ofelectricity on the network use energy at different rates at

different times of the day and by actively managing thenetwork connectivity the loads across connected feederscan be evenly balanced Rather than the position of normalswitching open points being determined for average networkconditions the positions can be changed automatically bythe network management system to a more optimum loca-tion based on a number of factors such as security voltagedrop capacity utilisation and load forecasts as displayedin Figure 2

23 Technique 3 Meshed Networks This technique repre-sents the process by which circuit breakers on the networkare switched in order to feed loads from multiple locationsThis approach fundamentally allows the load on each feeder

RTU

Metrologicalsensors

Alstom P341

PowerOn fusion

Communicationnetwork Asset

current ampvoltagesensors

Asset temperature(underground cables

transformers)

Asset data and metrological data

Asset temperature and sag data

Ampacity

Asset temperatureamp sag sensors

(overhead lines)

Demandforecast

DARcalculation

Figure 1 DAR trial schematic

Communication network

RTU

Substation 1

RTU

Substation 2

RTU

Substation 3

RTU

Substation 4

PowerOn fusion

Demandforecast

ALT function

Automated switch

11 kV network (which may include additional substations)

Figure 2 ALT trial schematic

3Complexity

in a meshed circuit to deviate according to the routine varia-tions in the connected load without the need for pre-existinganalysis and changes to switch states

However simply closing normal open points (NOPs)exposes more connected customers to supply interrup-tion following a network fault Therefore any plannedclosure of open points for long-term operation is routinelyaccompanied by the installation of along-the-feeder faultsensing and interruption equipment (protection relaysand circuit breakers) The installation of along-the-feederprotection devices restores and potentially reduces theprobability of customer interruption under fault conditionswith mesh operations

The aim of trialing this technique was to operate thedesignated 11 kV networks with parallel feeding arrange-ments protective device-driven autosectioning zones whileexploring potential impacts both benefits and trade-offsthat could be derived from parallel feeder configurationsand potential impact both benefits and constraints of opera-tion with autosectioning zones balanced against timeeffortand cost

24 Technique 4 Storage Energy demand in an 11 kV feedertends to occur in peaks and troughs throughout a 24-hourcycle The current supplying capacity of a feeder is limitedto the current-carrying capability of the smallest cable or

conductor in the circuit and these usually decrease incross-sectional area size further away from the primary theyare located This is acceptable when the load is spread evenlyacross a circuit but when the load occurs unevenly then theutilisation factor of the assets will also be uneven By intro-ducing energy storage devices on the network (Figure 3) theycan feed out onto the system at peak demands and rechargeduring times of low demand thus deferring the need toreplace existing assets

25 Technique 5 Distributed Generation Control A numberof industrial and commercial customers have their ownon-site generation and this number is likely to increase withthe transition to a low-carbon economy In some cases thismay be uncontrollable renewable generation (wind or solar)but the majority is in the form of either standby generatorsor controllable plants such as biomass refuse incineratorsor combined heat and power (CHP) plants If customers withcontrollable distributed generation can be incentivised toaccept instruction from a DNO to increase or decreasegeneration this can be used to reduce or increase sitedemand andor provide or remove supply from the grid asa means of rectifying network problems

26 Technique 6 Demand Side Management Similar todistributed generation DSM involves putting in place

Communication network

RTU

Substation

PowerOn fusion

Demandforecast

Energystorage

controller

Energystorage

unitLV fe

eder

sto

custo

mer

s

Substation

Energystorage

unitLV fe

eder

sto

custo

mer

s

Datacontrol connectionDistribution network connection

Volta

gec

urre

ntm

easu

rem

ents

Energystorage

unitcontroller

Operation mode-local control

Communication network

RTU

Alar

ms

indi

catio

ns

PowerOn fusion

Operation mode-remote control

Figure 3 Storage trial representation

4 Complexity

commercial agreements between the DNO and industrialand commercial customers who have the ability to controlappreciable amounts of load in a relatively short period oftime We expect demand side response to be in two formsusing the representation in Figure 4

(i) To reduce the impact of predicted peak loads

(ii) To respond to an unplanned event such as a fault

Demand side response actions can be used by the DNOto enable a change in behaviour by a customer site inresponse to an explicit signal triggering a preagreed actionThe action should be the interruption of a customerrsquosinternal electricity-consuming processes either to avoid ormore likely to defer these to a later time Its metricscapacitydelta reduction durationfrequency and OPEXare detailed in [13]

3 Methodology

To undertake the analysis of the aforementioned techniquesa revolutionary new software tool for electricity distributionnetwork planning called the scenario investment model(SIM) is used Results from evaluations using the SIM areobtained through a series of experiments that modelled thenetwork evolution under different demand scenarios pre-sented in Table 1 at short- (2015ndash2023) and long-termlookahead (2015ndash2050) to assist decision-makers in futurepower network planning

Currently electricity distribution networks have beenplanned with typically linear load growths of up to 1 perannum The expected increase in low-carbon technologieswill have a significant effect on the electricity demands onthe network which may have significant rapid sporadicincreases in the electricity demand on the 11 kV networks

[25] In addition the daily electricity load shapes may be alsoaltered significantly The networks will need to be upgradedand systems are being able to evolve and cope with newdemand profiles

There had been identified two main streams of work toconsider the use of the innovative techniques namely strate-gic and tactical planning and Design Build and OperationThe strategic and tactical planning stream will consider thenetwork planning roles while the design and operationstream will consider design build and operation roles InFigure 5 the smart grid planning framework diagram pre-sents the key elements of each stream which are displayedwith their main interactions

In order to leverage the capability of existing networkanalysis tools which are already extensively used by elec-tricity network operators the SIM is separated into twomain packages a network modelling tool which primarilyperforms the technical assessment of the application ofthe techniques and the SIM harness which manages theoverall process and perform the economic assessment andreporting functions

Demand data modelling has been based on a bottom-upapproach The methods used provide an estimate of demandfor each half-hour at each secondary substation for 18different season-day types [12]

Communication network

DGDSM

Customer site

PowerOn fusion

Demandforecast

Networkanalyser

Networkevent

DGDSMscheduler

DSMDG manager

Performancemonitor

Paymentscalculator

RTUsmartmeter

Figure 4 Commercial trials representation DGDSM

Table 1 Demand scenarios

Demand scenariosFuel

efficiencyLow-carbon

heatWall

insulation

DECC1 Medium High High

DECC2 High Medium High

DECC3 High High Low

DECC4 Low Low Medium

5Complexity

The research objective is settled to prove the suitability ofthe six novel smart interventions presented in Section 2and along with the traditional reinforcements provide anevolutionary planning insight for future power networksTo undertake this study specific experiments were selectedfor a certain power trial network under different demandscenarios and evaluation periods assessing smart tech-niques along with traditional reinforcements The approachinvolved running a set of experiments using the SIM for thesix 11 kV primaries in the FALCON trial area

31 SIM Support Algorithms The essence of the SIMapproach is its ability to take a network configuration andcorresponding load profiles in a particular year (termed asinitial network state) perform power flow and reliabilityanalysis and create derivative network states in a processknown as ldquonetwork state expansionrdquo The expansion happenseither by transitioning to the following year for networkstates without any failures or by applying intervention

techniques to resolve network issues With each new net-work state created the SIM therefore is faced with adecision as to which network state from the executionhistory to expand next The expansion can be guided bysimple depth first or breadth first algorithms which areimplemented in the SIM for verification purposes Thedepth-first algorithm always selects the newest ie themost recently created network state that is not fullyexpanded for expansion while the breadth-first algorithmalways selects the oldest network state However thosesimple heuristics are inadequate for any practical usebeyond simple test cases due to the size of the searchspace obtained by permuting all possible interventionsover a number of years To perform intelligent explorationof the search space the SIM uses a heuristic approach thatis based on an Alowast algorithm [26 27]

The baseline Alowast algorithm aims at finding the least-costpath through the search space As Alowast traverses the searchspace it builds a tree of partial paths The leaf nodes of this

Learning transfer

Key component

Systempackage conatinga set of key components

Learninganalysis of results for theimprovement of models and development ofpolicies

Smart grid planning framework

Substationmonitoring

Dynamicasset ratings

data

Automatedload transfer

dataMeshed

network data

Communication network trial

Monitoringdata collection

system

Trials data distributionmanagement system

Scenario investment model

Network modelling tool(technical Assessment)

Dynamic assetratings

techniquemodel

Automatedload transfer

techniquemodel

Batterystorage

techniquemodel

Meshednetwork

techniquemodel

Traditional reinforcement

technique model

Networktopology amp

loads

Distributedgenerationtechnique

model

Demand sidemanagement

techniquemodel

Multi-yearscheduler

Economicinvestmentassesment

Networkperformance

analysisVisualisationand reporting

SIM harness(economic assesment and

reporting)

Performance analysisamp learning

Energy modelminusmeasured data

comparison

Demand datamodelling and

scenario development

Impr

ove e

nerg

ym

odel

Design build andoperation

Strategic andtactical planning

Strategic planning supportconnections and

infrastructure planningplanning policy development

Impr

ove t

echn

ique

and

netw

ork m

odels

Batterystorage data

Distributedgeneration

trial

Demand sidemanagement

data

Design amp build learning

Impr

ove i

mpl

emen

tatio

n co

st m

odels

Figure 5 Smart grid planning framework diagram

6 Complexity

tree (failed network states) are stored in a priority queue thatis ordered using a cost function

f x = g x + h x 1

where h x is a heuristic estimate of the path cost toreach the goal and g x is the distance travelled fromthe initial node

The SIM selects network states from the priority queue toapply intervention techniques one application at a timeDeployment of a technique produces a new network statefor which a power flow analysis is performed in intact andall n minus 1 (contingency) network operation modes If allthe failures are resolved a reliability analysis comprisingcustomer minutes lost (CML) customer interruptions (CI)losses and fault level studies is performed A new networkstate is subsequently created in the next year of evaluationor if it is already the last year of evaluation the costsof interventions are calculated and the network statetogether with all its expansion history is saved to theresult store as a new result The evaluation is terminated

(Figure 6) when criteria such as the number of resultsnumber of network state evaluations or run time arereached As noted previously Alowast uses a combination ofdistance travelled so far and a heuristic estimate of thedistance to reach the endpoint

In SIM case this corresponds to g x being the totalexpenditures (TOTEX) incurred so far and h x being aheuristic estimate of TOTEX to reach the end year ofthe experiment TOTEX also referred to as the totalexpenditure comprises implementation costs (CAPEX)that occur only once when an intervention is applied tothe network operation costs (OPEX) that refer to anongoing expense of operating an asset or a scheme alongwith asset life degradation and metric costs that includeincentive payments for losses fault levels and networkreliability Referring to (2) the g x for a network statexi in year i is defined as

g xi = ci + oi + jminus1

j=1cj + oj +mj 2

Initial network state

Create new networkstate in the next year Analyse power flow

Generate and applynext intervention

technique application

Save network state tofailed network state

store

Update path costestimates

Reorder priority queue

Select top mostnetwork state from the

priority queueTermination

criteria reached

Failure present

Analyse reliabilityEnd yearreached

minus

+

minus

+

minus

+

Update solution costs

Save result

End

Figure 6 SIM evaluation flowchart

7Complexity

where ci is CAPEX in the current year oi is OPEX in thecurrent year and cj oj and mj are CAPEX OPEX andmetrics costs respectively of the ancestor network statewith no issues in year j The heuristic estimate h x ofthe cost to reach the end year is given by

h xi = cREMi +mi + n

k=i+1ck + ok +mk 3

where cREMi is the average remaining CAPEX in the cur-rent year i mi is the average metric cost in this year ckok and mk are the average CAPEX OPEX and metriccosts respectively of the descendant network state in yeark with no issues and n is the end year of evaluation Pre-seeded to a constant value c0 the average CAPEX isupdated each time a network state is expanded in a partic-ular year thus the estimated costs of fixing all issues in aparticular year progressively approach true average Settingc0 to a value greater than 0 speeds up the expansion pro-cess ie all interventions that cost less than c0 will resultin new network states that are at the top of the priorityqueue The business rationale is that DNOs are not thatinterested in optimizing interventions costing less than acertain threshold The learned averages are propagatedback to network states in the priority queue thus updatingtheir estimated effort and leading to the reordering of thequeue unlike the average CAPEX average OPEX andmetrics costs which are assumed to be 0 for years withno network states The average OPEX value for a year isobtained using

o = jToc jT jminus1 4

where j is a column vector of ones and oc is an OPEXvector of compliant network states in that year Likewisethe average metric cost is obtained according to

m = jTmc jT jminus1 5

where j is a column vector of ones and mc is a vector ofthe metric costs of compliant network states in that year

32 Conceptualising Bottom-Up Evolutionary PlanningModel-driven engineering (MDE) uses analysis constructionand development of frameworks to formulate metamodelsThose models are usually characterised using domain-specific modelling approaches [28] containing appropriatedetail abstraction of particular domain through a specificmetamodel The use of metamodels requires therefore inputsfrom domain experts which can be used to generate aggre-gated or disaggregated models Top-down and bottom-upare the conceptual definition of aggregated and disaggregatedmodels [29] These two modelling paradigms are frequentlyused to epitomise domain interactions among the operationof the energy system the econometrics related and thetechnical performance indicators [30]

From a bottom-up modelling approach [31 32] thetop-down perspective is a simplistic characterisation ofhow electrical power networks combine locational eventsand individual asset performance with high-level objectiveslike improving the CML of a certain congested area [33 34]From an engineering point of view both are still validsince outputs and strategic forecast are produced in bothHow those outcomes are calculated validated and trans-formed to strategy is presented in Figure 7 (top-down)and Figure 8 (bottom-up)

Interventiontechnique costs

Relative usage ofinterventiontechnique

Interventiontechnique installation

details

Interventiontechnique models

Validationphysical trials

Interventiontechnique operation

details

Purchase andoperation cost data

Cost model

Network data

Representativenetworks

Model network through years

Calculate intervention technique usage

Strategic forecasts

Validationsampled nodal

networks

Demand scenarios

Figure 7 Top-down conceptualisation of evolutionary power networks planning

8 Complexity

The ability of bottom-up to capture discrete locationalimpacts of technologies on the system and their disaggre-gated costs is triggering the following subsections Trade-offmethodologies are needed for planning evolutionary powersystems where observing disaggregated result strategic fore-casts are to be produced These methodologies need to beinteractive in the sense that starting from an initial stateand after a testing or learning phase the network is able toaccommodate techniques that have improved the systemproviding an exploratory set of solutions that can beexpanded or discarded as the model evolves through time

33 Experiment Characterisation To illustrate the processingof the methodology adopted we consider a typical networkscenario tree created by the tool that consists of failed (red)and compliant (green) network states in different years ofevaluation The SIM starts with a single network state inthe first year of evaluation Every year the network isevaluated for compliance in intact and n minus 1 contingencyoperation modes If failures are detected the SIM appliesintervention techniques described in Section 2 to create net-work patches that resolve the failures Each application of anintervention technique creates a new network state The toolhas to resolve all issues in the network before transitioninginto the next year DNO planners usually run power flowstudies with a single fixed load pattern In contrast the SIMchecks each network state for compliance under 18 charac-teristic day load scenarios each comprising of 48 half-hoursettlement periods All studies are performed under intactand n minus 1 contingency network operation modes The SIM

calculates patch costs using cost drivers returned by the net-work modelling tool The cost driver describes a networkintervention and consists of two parts namely the patchkey and the scaling The patch key identifies the nature ofthe modification of the network performed (removal or addi-tion of an asset and the type of the asset) Scaling data is rel-evant only to patches that can be installed in multiples of onesuch as cable upgrades and additional transformer installa-tion Scaling data structure provides a list of multipliers tothe base cost data available in the SIM database In case ofcable upgrade or replacement it enables the SIM to correctlyestimate full installation costs from per unit of length valuesFinally for postprocessing analysis the SIM also returns a listof failures by asset It was identified by DNO planners asindispensable features to help validate the system and corre-late the expansion trees to the actual assets on the networkdiagram The failure detail table contains asset ID anddescription alongside information about the number of fail-ures in intact and n minus 1 operating modes as well as absoluteand per unit thermal and voltage failure magnitude

34 Case Characterisation Two set of experiments wereperformed for this study one for comparing short-termevaluation period for RIIO-ED1 (2015ndash2023) where theRIIO-ED1 investment planning has been stylised and alonger planning period for RIIO-ED1 to RIIO-ED4 from2015 up to 2047 The other set aimed at evaluating differ-ent DECC demand scenarios Experiments evaluated twodemand scenarios DECC2 and DECC4 DECC4 representsas displayed in Table 1 the slow-progression scenario and

Purchase andoperation cost data

Cost model Nodal network model

Model network through years

Apply intervention technique to keep the network compilant

Find optimal network development options

Solutions for individual networks

Aggregate results for larger network areas

Strategic forecasts

Network data Demand scenarios

Interventiontechnique costs

Interventiontechnique installation

details

Nodal interventiontechnique models

Validation withphysical trials

Relative usage ofinterventiontechniques

Validation of powerflow and reliability

models

Interventiontechnique operation

details

Load profiles

Figure 8 Bottom-up conceptualisation of evolutionary power networks planning

9Complexity

DECC2 is with DECC3 the most challenging scenario interms of electrification and low-carbon technology integra-tion The procedure to run the experiments in the SIM isshown in Figure 6 The inputs to be sent to the SIM aredetailed in Figure 5 the selected network techniques evalu-ation period demand scenario and cost model whereas theoutputs of the SIM we obtain are techniques used failuressolved assets fixed and electrical performance indicatorsThe SIM address mentioned outputs for long-term plan-ning reinforcements to optimise investment asset planningresolving network constraints The performance criteria eval-uated were capital expenditures (CAPEX) operating expen-ditures (OPEX) utilisation of assets CMLs CIs and lossesThese parameter values are delivered by the SIM after eachsimulation In order to look for the optimal solution amongthe feasible solution pathways the SIM allows a granularitystudy of each network state

4 Results

This section presents the assessment of the six smart gridinterventions along with traditional reinforcements in thetrial area compounded by six 11 kV primaries in MiltonKeynes UK for two different evaluation periods DECC2and DECC4 Subsection 41 introduces the results for the2015ndash2023 period as short-term planning and Subsection42 presents the results for a long-term evaluation period2015ndash2047

41 Short-Term Planning This section presents the assess-ment of the six smart grid interventions along with tradi-tional reinforcements in the trial area compounded by six11 kV primaries in Milton Keynes UK for two differentdemand scenarios DECC2 and DECC4

411 DECC4 2015ndash2023 Figure 9 shows the trends ofCAPEX and OPEX Note that in the year 2015 there is aCAPEXrsquos peak due to the application of more techniquesand OPEX increments over time from 2017 to 2023

Despite the investment the increase is compensated bya benefit in the electricity distribution network with areduction in CML and CI as shown in Figure 10

The summary of the proportion of installations requiredduring this evaluation period and by capital expenditures pertechnique is presented in Table 2 for both DECC scenarios

The average yearly price of each technique implementedto fix a network state is key for future decision-makingconsiderations

Figure 11 displays the average price of each techniquedisaggregated by CAPEX and OPEX

Performance indicators are key parameters for futuredecision-making within electricity distribution planning asquantifiers due to their influence on the quality of service

Therefore as shown in Figure 12 the only smart tech-nique used in combination with traditional reinforcementsin this scenario and evaluation period that slightly influenceslightly CML and CI is meshed networks

412 DECC2 2015ndash2023 Figure 13 plots the trend ofCAPEX and OPEX over the evaluation period It is important

to highlight that there is a CAPEX peak in 2015 due to anincrease of techniques applied in this period

This CAPEX increment produces a benefit in terms ofCML and CI as observed in Figure 14

The distribution of techniques applied and capital expen-ditures per technique are shown in Table 2 The average priceof each technique implementation in this demand scenariofor this evaluation period is shown in Figure 15

As displayed in Figure 12 for DECC4rsquos simulation theonly smart grid technique that improves the quality of serviceis the meshed network In Figure 16 the contribution ofsmart techniques on CML and CI for DECC2 demandevaluation is captured

413 Comparison between DECC2 and DECC4 for 2015ndash2023 The results presented may facilitate improvementsin electricity distribution network operations and planningresulting in better-informed decision-making when upgrad-ing current electricity distribution networks The assess-ment of the six smart grid techniques discovered thatonly three of them were selected as part of optimal

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

Mill

ions

pound p

er an

num

CAPEXOPEX

442444648

55254

Figure 9 CAPEX and OPEX DECC4 2015ndash2023

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

pu

Figure 10 CML and CI DECC4 2015ndash2023

10 Complexity

solutions for DECC4 and DECC2 as described in Table 2These three techniques applied are DAR for cables andtransformers and meshed networks

Comparing the cost trends of the two assessed scenariosit is notable that in both demand scenarios DECC4 and

DECC2 the CAPEX peak occurs in 2015 (Figures 9 and13) reflecting the more difficult nature of network statesto be feasible in a demanding scenario whereas DECC2shows a higher improvement of CI and CML performanceindicators as can be seen in Figures 10 and 14

Table 2 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2023

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 15 2 14 2

DAR-transformer 7 1 5 1

ALT 0 0 0 0

Mesh 3 1 4 1

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 8 3 7 2

TRAD-cable 60 89 65 91

TRAD-transfer load 6 2 4 1

TRAD-new feeder 1 2 1 2

DAR - cable DAR -transformer

Meshednetworks

TRAD -transformer

TRAD - cable

CAPEXOPEX

0

20000

40000

60000

80000

100000

pound

Figure 11 Average cost disaggregation per technique DECC4 2015ndash2023

Figure 12 Average CML and CI improvement per technique DECC4 2015ndash2023

11Complexity

The techniques applied are the key consideration to beassessed It is necessary to analyse the number of interven-tions applied by type the effect of these techniques in theelectric grid solving failures and fixing assets Figures 11and 15 show average prices of techniques used in eachdemand scenario Smart techniques have a lower TOTEXthan traditional reinforcements These novel techniquesabove are not frequently able to fix failures and thereforeproduce feasible network states In terms of CML and CIthe only technique able to moderately contribute to theirimprovement is meshed networks as shown in Figures 12and 16 Results attribute the majority of fixes to traditionalreinforcements with a comparatively smaller number offailures being solved by smart techniques (Table 2) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention forDECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

For DECC4 the three mentioned techniques along withtraditional cable and transformer replacement are applied(Table 2) whereas for DECC2 two of them are appliedDAR for cables and meshed networks (Table 2) Comparingthe cost trends of the two assessed scenarios it is notable thatin DECC4 the CAPEX peak occurs in 2015 (Figure 7)whereas in DECC2 besides the peak in 2015 there is alsoone in 2019 (Figure 11) reflecting the more difficult natureof network states to be feasible in a demanding scenarioHowever DECC2 shows a higher improvement of electricalperformance indicators as can be seen in Figure 12

In addition traditional cable replacement DAR forcables and meshed networks are able to fix the cable whereastraditional transformer replacement and DAR for trans-formers are able to fix transformer issues (Figure 16) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention for

DECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

42 Long-Term Planning This section evaluates two experi-ments namely characterising the DECC2 and DECC4 sce-narios for 2015ndash2047 evaluation period For each demandscenario expected investments disaggregating CAPEX andOPEX are presented the evolution of electrical performanceindicators and the number of techniques applied

421 DECC4 2015ndash2047 In this experiment the mostsignificant results to analyse the suitability of each techniqueare presented

Figure 17 shows the trend of CAPEX and OPEX from2015 to 2047

There are CAPEX peaks in the year 2018 and during thebeginning of RIIO-ED2 in the years 2025 to 2026 due to theapplication of more techniques to fulfill low-carbon targets

Figure 18 indicates that upgrades on the network aredirectly related to technical performance indicators CMLand CI

In Table 3 the techniques applied and their contributionto CAPEX during the evaluation period from 2015 to 2047 bya demand scenario are shown

422 DECC2 2015ndash2047 Within this experiment the mostrelevant results to analyse the evolutionary network statesare presented

In Figure 19 the trend of CAPEX and OPEX from 2015to 2047 is shown There is a significant increase of CAPEXin the years 2024 to 2026 at the beginning of RIIO-ED2due to the necessary implementation of new techniques toreach low-carbon targets The evolution of CML and CI ispresented in Figure 20 linking larger investment years whenmajor reductions are found The contribution of each solutiontechnique is presented in Table 3

423 Comparison between DECC2 and DECC4 for 2015ndash2047 Figure 21 presents a multidimensional parallel coor-dinate representation of the feasible network statersquoscombinations that produced a 2015ndash2047 investment path-way for the evaluated area It bundles economic indicatorsie CAPEX and OPEX with technical performance indica-tors providing valuable insights on the number of traditionalreinforcements utilised to heal falling network states Resultsare also clustered by the two demand scenarios assessedduring the experiments

Solutions of DECC2 (represented in orange and green)and DECC4 (in black and blue) are clustered by the per-centage of traditional reinforcements utilised as well asthe utilisation factor

Solutions in green and in black represent those wheresmart grids were more used whereas orange and blue repre-sent the cheapest (CAPEX and OPEX) less utilised and theworst responding to the decrease of CML CI and lossesand the ones using more traditional reinforcement to fixnetwork states

Besides the fact that DECC2 smart solutions are between16 and 28 more expensive than DECC4 traditional

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

CAPEXOPEX

442444648

552

Mill

ions

pound p

er an

num

54

Figure 13 CAPEX and OPEX DECC2 2015ndash2023

12 Complexity

reinforcement solutions (green solutions versus blue solu-tions in Figure 21) DECC2 traditional solutions are in therange of costs of smart solutions of DECC4 (orange andblack solutions in Figure 21) It can also be concluded thatinvestment pathways using less smart techniques provide a

cheaper response to technical performance evaluators suchas utilisation CML CI and losses

43 Summary Experiment evaluation runs from the2015ndash2047 period present lower investment rates for the

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

PU

Figure 14 CML and CI DECC2 2015ndash2023

CAPEXOPEX

DAR -transformer

Meshednetworks

Traditional -transformer

Traditional -cable

0

20000

40000

60000

80000

100000

120000

140000

pound

Figure 15 Average cost disaggregation per technique DECC2 2015ndash2023

Figure 16 Average CML and CI improvement per technique DECC2 2015ndash2023

13Complexity

2015ndash2023 period than if the evaluation is just performedwith a 2015ndash2023 time frame This occurs as a result of thechallenging low-carbon targets up to 2047 and a myopicplanning with short-term lookahead For an evaluationperiod four times larger the CAPEX and TOTEX increasedjust 18 compared to 2015ndash2023 concluding that betweenpound52M and pound68M the evaluation area regardless of thetime horizon for the investment or the demand scenarioconsidered is required

Furthermore it can be inferred comparing Tables 2 and 3that for less smart interventions the short-term planningis used compared to the long-term horizon planningDAR-cable and DAR-transformer experienced a significantimplementation variation between the two evaluationperiods for both demand scenarios as well as CAPEX allo-cated in cable upgrades falling from 89ndash91 (2015ndash2023)to 29ndash66 in the long term 2015ndash2047 run Both technicalperformance evaluators CML and CI respond to theirrespective CAPEX curve shape Due to a more incentivisedsmart grid technique during RIIO-ED2 and ED3 the per-centages of smart techniques implemented varied notablyFor DECC4 in 2023rsquos outlook the share of techniques is

25 for smart grid interventions whereas for 2050rsquos out-look the share is increased up to 58 In the same wayfor DECC2 in 2023rsquos outlook the share of smart interven-tions is 23 whereas in 2050rsquos outlook the share increasesup to 69

Feasible solutions characterising the solution space(Figure 21) differ in the degree of investment required andtechnical performance evaluators Each of the solution pathrepresented in the multidimensional solution space inFigure 21 represents the intersection of that dimension witheach axis Parallel coordinates [35] are low complexityworking for any N-dimension In the case of Figure 21it has 8 dimensions treating every variable uniformlyEach intersection of one solution path with each axis isthe value of that solution for that axis As for axis DECC2and DECC4 it represents the feasible solutions for eachdemand scenario Making a query by the percentage oftraditional reinforcement used in that solution we have thepartition between orange and green for DECC2 and blueand black being the first solutions that use more traditionalreinforcement compared with the ones that use more smarttechniques represented in green (DECC2) and black(DECC4) From Figure 21 we can argue that solutions thatuse more smart techniques (green and blacks) are moreexpensive in terms of CAPEX and OPEX than their peers(orange and blues) when compared by a demand scenarioIt can also be stated that solutions with a higher use of tradi-tional reinforcements are the ones that decrease the mostCML CI and losses

Disaggregating results by network state of the six 11 kVprimaries and by the feeder if necessary for further granulardebugging can discern locational capacity Under bothdemand scenarios Secklow Gate was seen to be the one withgreater capacity being necessary to apply fewer techniquesover the evaluation period 2015ndash2047 On the other handNewport Pagnell exceeds the capacity as soon as 2015requiring a high number of network state evaluation to befixed that happens for each subsequent year hence the largenumber of network states is presented in Table 4

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

pound p

er an

num

Figure 17 CAPEX and OPEX DECC4 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

pu

Figure 18 CML and CI DECC4 2015ndash2047

14 Complexity

5 Conclusions

Power flow analysis using a nodal network model is essentialwhen determining the benefits of trailed smart interventions

because the interactions and implications are highly specificto a particular location and scenario

This suggests that while the bottom-up approach isonerous in terms of data handling and manipulation this isworthwhile for strategic planning and policy evaluationComparing both investment strategies investment strategyadjustments will be necessary in future regulation periodsif an over-invested network behaves as displayed in theshort-term section of this study

Traditional reinforcement will continue to be the mainmethod by which network issues are mostly resolvedfollowed by dynamic asset rating and meshed networks

The assessment of the six novel smart interventions in theFALCON 11kV primary test area in Milton Keynes hasproved the suitability of three techniques able to fix failuresimproving the quality of service and their readiness to bedeployed in the near future These techniques are DAR forcables and transformers and meshed networks Meshednetworks have been repeatedly selected as a feasible tech-nique because using it will reduce CML CI and power losses

Table 3 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2047

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 18 5 31 6

DAR-transformer 32 16 32 6

ALT 0 0 0 0

Mesh 8 3 6 3

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 25 44 9 16

TRAD-cable 15 29 20 66

TRAD-transfer load 1 1 1 1

TRAD-new feeder 1 2 1 2

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

s pound p

er an

num

Figure 19 CAPEX and OPEX DECC2 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

PU

Figure 20 CML and CI DECC2 2015ndash2047

15Complexity

improving the quality of service and the efficiency whilebeing a cost-effective solution

The initial capacity at primary substations differedsignificantly and this affected the number and complexityof interventions required by the SIM Due to the loadscenarios showing significant peak load increases DAR wasoften a temporary measure that would delay but not removethe eventual need for traditional reinforcement

Implementing DAR for cables and transformers themonitoring of assets when their peak capacity is increasedwas analysed On the other hand it was observed thattraditional reinforcements still play a key role in keepingthe electricity distribution networks free of constraintsTRAD techniques such as transformer and cable replace-ments are able to fix the majority of failures and will beessential also in the future

The comparison between traditional reinforcements andnovel smart techniques has provided a new knowledge aboutthe suitability of each technique to be applied in termsof costs electrical performance failures fixed and assetreplaced Cable replacement is the most costly techniquehowever its use is unavoidable in a number of cases Further-more the applicability of each technique regarding costsinvolved improvements on power quality and efficiency

and failures solved lead to new questions to be analysed suchas the lack of these techniques to provide flexible capacitywithin the trailed area

To sum up this study has performed a comparativeanalysis of novel smart intervention techniques providinginsights for future investments in electricity distributionplanning Further work can focus on scaling up the analysisto include a larger section of the network or a constrainedarea to evaluate national applicability of the current findings

Data Availability

Some of the data relevant to this submission is proprietaryfrom Western Power Distribution It can be accessed forresearch purposes under a signed collaboration agreement

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors would like to thank the rest of the WesternPower Distribution Innovation team particularly the other

2400

2200

2000

350

360

370

380

390

400

1400

1300

1200

1100

1000DECC 4

50

55

60

12

11

10

9

8

4400

4200

4000

3800

3600

2400

CAPEX (in kpound) OPEX (in kpound) Utilisation Traditional reinforcements () DECC 2 998796 CML 998796 CI 998796 Losses

2200

2000

1800

Figure 21 Parallel coordinate visualisation of technoeconomic performance indicators under DECC4 and DECC2

Table 4 Summary of network states and results for demand scenario DECC4 and DECC2

Primary 11 kV substation Feeders Year TechniquesResultsDECC4

NSDECC4

ResultsDECC2

NSDECC2

Fox Milne 13 2047 Smart and traditional 54 276 47 259

Newport Pagnell 9 2047 Smart and traditional 48 317 31 318

Secklow Gate 9 2047 Smart and traditional 27 29 15 16

Bletchley 19 2047 Smart and traditional 32 40 21 48

Marlborough Street 11 2047 Smart and traditional 31 52 19 37

Childs Way 17 2047 Smart and traditional 67 398 54 181

16 Complexity

members of the FALCON project as well as Paul CharletonStuart Fowler Ilja Orlovs Dan Taylor and Steve IngramAlso the authors want to acknowledge OFGEM and theLowCarbon Network Fund for funding this piece of research

References

[1] European Commission ldquoCommunication from the Commis-sion to the European Parliament the Council the EuropeanEconomic and Social Committee and the Committee of theRegionsrdquo 2011 December 2016 httpseur-lexeuropaeulegal-contentENTXTqid=1537473508968ampuri=CELEX52016PC076721

[2] F G N Li and E Trutnevyte ldquoInvestment appraisal ofcost-optimal and near-optimal pathways for the UK elec-tricity sector transition to 2050rdquo Applied Energy vol 189pp 89ndash109 2017

[3] G Strbac I Konstantelos M Pollitt and R Green DeliveringFuture-Proof Energy Infrastructure National InfrastructureCommission 2016

[4] Ofgem ldquoGB Network regulation - the RIIO modelrdquo 2010December 2014 httpswwwofgemgovuknetwork-regulation-riio-model

[5] R Poudineh and T Jamasb ldquoDistributed generation storagedemand response and energy efficiency as alternatives togrid capacity enhancementrdquo Energy Policy vol 67 pp 222ndash231 2014

[6] J Yang X Bai D Strickland L Jenkins and A M CrossldquoDynamic network rating for low carbon distribution networkoperationmdashA UK applicationrdquo IEEE Transactions on SmartGrid vol 6 no 2 pp 988ndash998 2015

[7] M Behnke W Erdman S Horgan et al ldquoSecondary net-work distribution systems background and issues relatedto the interconnection of distributed resourcesrdquo NationalRenewable Energy Laboratory (NREL) Tech Rep NRELTP-560-38079 2005

[8] J Crispim J Braz R Castro and J Esteves ldquoSmart grids in theEU with smart regulation experiences from the UK Italy andPortugalrdquo Utilities Policy vol 31 pp 85ndash93 2014

[9] Tnei ldquoIPSA Power software for power systemsrdquo March 2016httpwwwipsa-powercom

[10] ETAP ldquoETAP PSrdquo 2018 April 2016 httpsetapcomsoftwareproduct-overview-main

[11] DINIS ldquoDINIS distribution network information sys-temsrdquo 2002 March 2016 httpsetapcomsoftwareproduct-overview-main

[12] Western Power Distribution ldquoSIM final reportrdquo 2015November 2016 httpwwwwesternpowerinnovationcoukProjectsFalconaspx

[13] Western Power Distribution ldquoProject FALCONrdquo 2015November 2017 httpwwwdiniscom

[14] EA Technology ldquoTransform modelrdquo March 2016 httpwwweatechnologycomproducts-and-servicescreate-smarter-gridstransform-model

[15] L Sigrist E Lobato L Rouco M Gazzino and M CantuldquoEconomic assessment of smart grid initiatives for islandpower systemsrdquo Applied Energy vol 189 pp 403ndash415 2017

[16] J A Momoh ldquoSmart grid design for efficient and flexiblepower networks operation and controlrdquo in 2009 IEEEPESPower Systems Conference and Exposition pp 1ndash8 SeattleWA USA 2009

[17] N Good K A Ellis and P Mancarella ldquoReview and classifica-tion of barriers and enablers of demand response in the smartgridrdquo Renewable and Sustainable Energy Reviews vol 72pp 57ndash72 2017

[18] Z Yang J Xiang and Y Li ldquoDistributed consensus basedsupplyndashdemand balance algorithm for economic dispatchproblem in a smart grid with switching graphrdquo IEEE Trans-actions on Industrial Electronics vol 64 no 2 pp 1600ndash1610 2017

[19] SuSTAINABLE ldquoSmart distribution network operation formaximising the integration of renewable generationrdquo 2015March 2016 httpwwwsustainableprojecteuHomeaspx

[20] S Mohtashami D Pudjianto and G Strbac ldquoStrategic distri-bution network planning with smart grid technologiesrdquo IEEETransactions on Smart Grid vol 8 no 6 pp 2656ndash2664 2017

[21] Smart Grid Forum ldquoSmart grid forum work stream 7rdquo2015 March 2016 httpwwwsmarternetworksorgprojectnia_nget015425

[22] ETI ldquoEnergyPath networksrdquo March 2016 httpwwweticoukprojectenergypath

[23] C Mateo Domingo T Gomez San Roman Aacute Sanchez-Miralles J P Peco Gonzalez and A Candela Martinez ldquoAreference network model for large-scale distribution planningwith automatic street map generationrdquo IEEE Transactions onPower Systems vol 26 no 1 pp 190ndash197 2011

[24] R Bolton and T J Foxon ldquoInfrastructure transformation as asocio-technical process mdash implications for the governanceof energy distribution networks in the UKrdquo TechnologicalForecasting and Social Change vol 90 pp 538ndash550 2015

[25] UK Power Networks ldquoDNO guide to future Smart man-agement of distribution networks Summary reportrdquo 2014httpsinnovationukpowernetworkscoukinnovationenProjectstier-2-projectsLow-Carbon-London-(LCL)Project-DocumentsLCL20Learning20Report20-20SR20-20Summary20Report20-20DNO20Guide20to20Future20Smart20Management20of20Distribution20Networkspdf

[26] A Botea J Rintanen and D Banerjee ldquoOptimal reconfigura-tion for supply restoration with informed AlowastSearchrdquo IEEETransactions on Smart Grid vol 3 no 2 pp 583ndash593 2012

[27] T Logenthiran D Srinivasan and T Z Shun ldquoDemand sidemanagement in smart grid using heuristic optimizationrdquoIEEE Transactions on Smart Grid vol 3 no 3 pp 1244ndash1252 2012

[28] J Saacutenchez-Cuadrado J de Lara and E Guerra ldquoBottom-upmeta-modelling an interactive approachrdquo in Model DrivenEngineering Languages and Systems pp 3ndash19 Springer2012

[29] Y H Kim F J Sting and C H Loch ldquoTop-down bottom-upor both Toward an integrative perspective on operationsstrategy formationrdquo Journal of Operations Managementvol 32 no 7-8 pp 462ndash474 2014

[30] C Bohringer ldquoThe synthesis of bottom-up and top-down inenergy policy modelingrdquo Energy Economics vol 20 no 3pp 233ndash248 1998

[31] T G R Bower ldquoRepetition in human developmentrdquo Merrill-Palmer Quarterly of Behavior and Development vol 20no 4 pp 303ndash318 1974

[32] H Mintzberg and J A Waters ldquoOf strategies deliberate andemergentrdquo Strategic Management Journal vol 6 no 3pp 257ndash272 1985

17Complexity

in a meshed circuit to deviate according to the routine varia-tions in the connected load without the need for pre-existinganalysis and changes to switch states

However simply closing normal open points (NOPs)exposes more connected customers to supply interrup-tion following a network fault Therefore any plannedclosure of open points for long-term operation is routinelyaccompanied by the installation of along-the-feeder faultsensing and interruption equipment (protection relaysand circuit breakers) The installation of along-the-feederprotection devices restores and potentially reduces theprobability of customer interruption under fault conditionswith mesh operations

The aim of trialing this technique was to operate thedesignated 11 kV networks with parallel feeding arrange-ments protective device-driven autosectioning zones whileexploring potential impacts both benefits and trade-offsthat could be derived from parallel feeder configurationsand potential impact both benefits and constraints of opera-tion with autosectioning zones balanced against timeeffortand cost

24 Technique 4 Storage Energy demand in an 11 kV feedertends to occur in peaks and troughs throughout a 24-hourcycle The current supplying capacity of a feeder is limitedto the current-carrying capability of the smallest cable or

conductor in the circuit and these usually decrease incross-sectional area size further away from the primary theyare located This is acceptable when the load is spread evenlyacross a circuit but when the load occurs unevenly then theutilisation factor of the assets will also be uneven By intro-ducing energy storage devices on the network (Figure 3) theycan feed out onto the system at peak demands and rechargeduring times of low demand thus deferring the need toreplace existing assets

25 Technique 5 Distributed Generation Control A numberof industrial and commercial customers have their ownon-site generation and this number is likely to increase withthe transition to a low-carbon economy In some cases thismay be uncontrollable renewable generation (wind or solar)but the majority is in the form of either standby generatorsor controllable plants such as biomass refuse incineratorsor combined heat and power (CHP) plants If customers withcontrollable distributed generation can be incentivised toaccept instruction from a DNO to increase or decreasegeneration this can be used to reduce or increase sitedemand andor provide or remove supply from the grid asa means of rectifying network problems

26 Technique 6 Demand Side Management Similar todistributed generation DSM involves putting in place

Communication network

RTU

Substation

PowerOn fusion

Demandforecast

Energystorage

controller

Energystorage

unitLV fe

eder

sto

custo

mer

s

Substation

Energystorage

unitLV fe

eder

sto

custo

mer

s

Datacontrol connectionDistribution network connection

Volta

gec

urre

ntm

easu

rem

ents

Energystorage

unitcontroller

Operation mode-local control

Communication network

RTU

Alar

ms

indi

catio

ns

PowerOn fusion

Operation mode-remote control

Figure 3 Storage trial representation

4 Complexity

commercial agreements between the DNO and industrialand commercial customers who have the ability to controlappreciable amounts of load in a relatively short period oftime We expect demand side response to be in two formsusing the representation in Figure 4

(i) To reduce the impact of predicted peak loads

(ii) To respond to an unplanned event such as a fault

Demand side response actions can be used by the DNOto enable a change in behaviour by a customer site inresponse to an explicit signal triggering a preagreed actionThe action should be the interruption of a customerrsquosinternal electricity-consuming processes either to avoid ormore likely to defer these to a later time Its metricscapacitydelta reduction durationfrequency and OPEXare detailed in [13]

3 Methodology

To undertake the analysis of the aforementioned techniquesa revolutionary new software tool for electricity distributionnetwork planning called the scenario investment model(SIM) is used Results from evaluations using the SIM areobtained through a series of experiments that modelled thenetwork evolution under different demand scenarios pre-sented in Table 1 at short- (2015ndash2023) and long-termlookahead (2015ndash2050) to assist decision-makers in futurepower network planning

Currently electricity distribution networks have beenplanned with typically linear load growths of up to 1 perannum The expected increase in low-carbon technologieswill have a significant effect on the electricity demands onthe network which may have significant rapid sporadicincreases in the electricity demand on the 11 kV networks

[25] In addition the daily electricity load shapes may be alsoaltered significantly The networks will need to be upgradedand systems are being able to evolve and cope with newdemand profiles

There had been identified two main streams of work toconsider the use of the innovative techniques namely strate-gic and tactical planning and Design Build and OperationThe strategic and tactical planning stream will consider thenetwork planning roles while the design and operationstream will consider design build and operation roles InFigure 5 the smart grid planning framework diagram pre-sents the key elements of each stream which are displayedwith their main interactions

In order to leverage the capability of existing networkanalysis tools which are already extensively used by elec-tricity network operators the SIM is separated into twomain packages a network modelling tool which primarilyperforms the technical assessment of the application ofthe techniques and the SIM harness which manages theoverall process and perform the economic assessment andreporting functions

Demand data modelling has been based on a bottom-upapproach The methods used provide an estimate of demandfor each half-hour at each secondary substation for 18different season-day types [12]

Communication network

DGDSM

Customer site

PowerOn fusion

Demandforecast

Networkanalyser

Networkevent

DGDSMscheduler

DSMDG manager

Performancemonitor

Paymentscalculator

RTUsmartmeter

Figure 4 Commercial trials representation DGDSM

Table 1 Demand scenarios

Demand scenariosFuel

efficiencyLow-carbon

heatWall

insulation

DECC1 Medium High High

DECC2 High Medium High

DECC3 High High Low

DECC4 Low Low Medium

5Complexity

The research objective is settled to prove the suitability ofthe six novel smart interventions presented in Section 2and along with the traditional reinforcements provide anevolutionary planning insight for future power networksTo undertake this study specific experiments were selectedfor a certain power trial network under different demandscenarios and evaluation periods assessing smart tech-niques along with traditional reinforcements The approachinvolved running a set of experiments using the SIM for thesix 11 kV primaries in the FALCON trial area

31 SIM Support Algorithms The essence of the SIMapproach is its ability to take a network configuration andcorresponding load profiles in a particular year (termed asinitial network state) perform power flow and reliabilityanalysis and create derivative network states in a processknown as ldquonetwork state expansionrdquo The expansion happenseither by transitioning to the following year for networkstates without any failures or by applying intervention

techniques to resolve network issues With each new net-work state created the SIM therefore is faced with adecision as to which network state from the executionhistory to expand next The expansion can be guided bysimple depth first or breadth first algorithms which areimplemented in the SIM for verification purposes Thedepth-first algorithm always selects the newest ie themost recently created network state that is not fullyexpanded for expansion while the breadth-first algorithmalways selects the oldest network state However thosesimple heuristics are inadequate for any practical usebeyond simple test cases due to the size of the searchspace obtained by permuting all possible interventionsover a number of years To perform intelligent explorationof the search space the SIM uses a heuristic approach thatis based on an Alowast algorithm [26 27]

The baseline Alowast algorithm aims at finding the least-costpath through the search space As Alowast traverses the searchspace it builds a tree of partial paths The leaf nodes of this

Learning transfer

Key component

Systempackage conatinga set of key components

Learninganalysis of results for theimprovement of models and development ofpolicies

Smart grid planning framework

Substationmonitoring

Dynamicasset ratings

data

Automatedload transfer

dataMeshed

network data

Communication network trial

Monitoringdata collection

system

Trials data distributionmanagement system

Scenario investment model

Network modelling tool(technical Assessment)

Dynamic assetratings

techniquemodel

Automatedload transfer

techniquemodel

Batterystorage

techniquemodel

Meshednetwork

techniquemodel

Traditional reinforcement

technique model

Networktopology amp

loads

Distributedgenerationtechnique

model

Demand sidemanagement

techniquemodel

Multi-yearscheduler

Economicinvestmentassesment

Networkperformance

analysisVisualisationand reporting

SIM harness(economic assesment and

reporting)

Performance analysisamp learning

Energy modelminusmeasured data

comparison

Demand datamodelling and

scenario development

Impr

ove e

nerg

ym

odel

Design build andoperation

Strategic andtactical planning

Strategic planning supportconnections and

infrastructure planningplanning policy development

Impr

ove t

echn

ique

and

netw

ork m

odels

Batterystorage data

Distributedgeneration

trial

Demand sidemanagement

data

Design amp build learning

Impr

ove i

mpl

emen

tatio

n co

st m

odels

Figure 5 Smart grid planning framework diagram

6 Complexity

tree (failed network states) are stored in a priority queue thatis ordered using a cost function

f x = g x + h x 1

where h x is a heuristic estimate of the path cost toreach the goal and g x is the distance travelled fromthe initial node

The SIM selects network states from the priority queue toapply intervention techniques one application at a timeDeployment of a technique produces a new network statefor which a power flow analysis is performed in intact andall n minus 1 (contingency) network operation modes If allthe failures are resolved a reliability analysis comprisingcustomer minutes lost (CML) customer interruptions (CI)losses and fault level studies is performed A new networkstate is subsequently created in the next year of evaluationor if it is already the last year of evaluation the costsof interventions are calculated and the network statetogether with all its expansion history is saved to theresult store as a new result The evaluation is terminated

(Figure 6) when criteria such as the number of resultsnumber of network state evaluations or run time arereached As noted previously Alowast uses a combination ofdistance travelled so far and a heuristic estimate of thedistance to reach the endpoint

In SIM case this corresponds to g x being the totalexpenditures (TOTEX) incurred so far and h x being aheuristic estimate of TOTEX to reach the end year ofthe experiment TOTEX also referred to as the totalexpenditure comprises implementation costs (CAPEX)that occur only once when an intervention is applied tothe network operation costs (OPEX) that refer to anongoing expense of operating an asset or a scheme alongwith asset life degradation and metric costs that includeincentive payments for losses fault levels and networkreliability Referring to (2) the g x for a network statexi in year i is defined as

g xi = ci + oi + jminus1

j=1cj + oj +mj 2

Initial network state

Create new networkstate in the next year Analyse power flow

Generate and applynext intervention

technique application

Save network state tofailed network state

store

Update path costestimates

Reorder priority queue

Select top mostnetwork state from the

priority queueTermination

criteria reached

Failure present

Analyse reliabilityEnd yearreached

minus

+

minus

+

minus

+

Update solution costs

Save result

End

Figure 6 SIM evaluation flowchart

7Complexity

where ci is CAPEX in the current year oi is OPEX in thecurrent year and cj oj and mj are CAPEX OPEX andmetrics costs respectively of the ancestor network statewith no issues in year j The heuristic estimate h x ofthe cost to reach the end year is given by

h xi = cREMi +mi + n

k=i+1ck + ok +mk 3

where cREMi is the average remaining CAPEX in the cur-rent year i mi is the average metric cost in this year ckok and mk are the average CAPEX OPEX and metriccosts respectively of the descendant network state in yeark with no issues and n is the end year of evaluation Pre-seeded to a constant value c0 the average CAPEX isupdated each time a network state is expanded in a partic-ular year thus the estimated costs of fixing all issues in aparticular year progressively approach true average Settingc0 to a value greater than 0 speeds up the expansion pro-cess ie all interventions that cost less than c0 will resultin new network states that are at the top of the priorityqueue The business rationale is that DNOs are not thatinterested in optimizing interventions costing less than acertain threshold The learned averages are propagatedback to network states in the priority queue thus updatingtheir estimated effort and leading to the reordering of thequeue unlike the average CAPEX average OPEX andmetrics costs which are assumed to be 0 for years withno network states The average OPEX value for a year isobtained using

o = jToc jT jminus1 4

where j is a column vector of ones and oc is an OPEXvector of compliant network states in that year Likewisethe average metric cost is obtained according to

m = jTmc jT jminus1 5

where j is a column vector of ones and mc is a vector ofthe metric costs of compliant network states in that year

32 Conceptualising Bottom-Up Evolutionary PlanningModel-driven engineering (MDE) uses analysis constructionand development of frameworks to formulate metamodelsThose models are usually characterised using domain-specific modelling approaches [28] containing appropriatedetail abstraction of particular domain through a specificmetamodel The use of metamodels requires therefore inputsfrom domain experts which can be used to generate aggre-gated or disaggregated models Top-down and bottom-upare the conceptual definition of aggregated and disaggregatedmodels [29] These two modelling paradigms are frequentlyused to epitomise domain interactions among the operationof the energy system the econometrics related and thetechnical performance indicators [30]

From a bottom-up modelling approach [31 32] thetop-down perspective is a simplistic characterisation ofhow electrical power networks combine locational eventsand individual asset performance with high-level objectiveslike improving the CML of a certain congested area [33 34]From an engineering point of view both are still validsince outputs and strategic forecast are produced in bothHow those outcomes are calculated validated and trans-formed to strategy is presented in Figure 7 (top-down)and Figure 8 (bottom-up)

Interventiontechnique costs

Relative usage ofinterventiontechnique

Interventiontechnique installation

details

Interventiontechnique models

Validationphysical trials

Interventiontechnique operation

details

Purchase andoperation cost data

Cost model

Network data

Representativenetworks

Model network through years

Calculate intervention technique usage

Strategic forecasts

Validationsampled nodal

networks

Demand scenarios

Figure 7 Top-down conceptualisation of evolutionary power networks planning

8 Complexity

The ability of bottom-up to capture discrete locationalimpacts of technologies on the system and their disaggre-gated costs is triggering the following subsections Trade-offmethodologies are needed for planning evolutionary powersystems where observing disaggregated result strategic fore-casts are to be produced These methodologies need to beinteractive in the sense that starting from an initial stateand after a testing or learning phase the network is able toaccommodate techniques that have improved the systemproviding an exploratory set of solutions that can beexpanded or discarded as the model evolves through time

33 Experiment Characterisation To illustrate the processingof the methodology adopted we consider a typical networkscenario tree created by the tool that consists of failed (red)and compliant (green) network states in different years ofevaluation The SIM starts with a single network state inthe first year of evaluation Every year the network isevaluated for compliance in intact and n minus 1 contingencyoperation modes If failures are detected the SIM appliesintervention techniques described in Section 2 to create net-work patches that resolve the failures Each application of anintervention technique creates a new network state The toolhas to resolve all issues in the network before transitioninginto the next year DNO planners usually run power flowstudies with a single fixed load pattern In contrast the SIMchecks each network state for compliance under 18 charac-teristic day load scenarios each comprising of 48 half-hoursettlement periods All studies are performed under intactand n minus 1 contingency network operation modes The SIM

calculates patch costs using cost drivers returned by the net-work modelling tool The cost driver describes a networkintervention and consists of two parts namely the patchkey and the scaling The patch key identifies the nature ofthe modification of the network performed (removal or addi-tion of an asset and the type of the asset) Scaling data is rel-evant only to patches that can be installed in multiples of onesuch as cable upgrades and additional transformer installa-tion Scaling data structure provides a list of multipliers tothe base cost data available in the SIM database In case ofcable upgrade or replacement it enables the SIM to correctlyestimate full installation costs from per unit of length valuesFinally for postprocessing analysis the SIM also returns a listof failures by asset It was identified by DNO planners asindispensable features to help validate the system and corre-late the expansion trees to the actual assets on the networkdiagram The failure detail table contains asset ID anddescription alongside information about the number of fail-ures in intact and n minus 1 operating modes as well as absoluteand per unit thermal and voltage failure magnitude

34 Case Characterisation Two set of experiments wereperformed for this study one for comparing short-termevaluation period for RIIO-ED1 (2015ndash2023) where theRIIO-ED1 investment planning has been stylised and alonger planning period for RIIO-ED1 to RIIO-ED4 from2015 up to 2047 The other set aimed at evaluating differ-ent DECC demand scenarios Experiments evaluated twodemand scenarios DECC2 and DECC4 DECC4 representsas displayed in Table 1 the slow-progression scenario and

Purchase andoperation cost data

Cost model Nodal network model

Model network through years

Apply intervention technique to keep the network compilant

Find optimal network development options

Solutions for individual networks

Aggregate results for larger network areas

Strategic forecasts

Network data Demand scenarios

Interventiontechnique costs

Interventiontechnique installation

details

Nodal interventiontechnique models

Validation withphysical trials

Relative usage ofinterventiontechniques

Validation of powerflow and reliability

models

Interventiontechnique operation

details

Load profiles

Figure 8 Bottom-up conceptualisation of evolutionary power networks planning

9Complexity

DECC2 is with DECC3 the most challenging scenario interms of electrification and low-carbon technology integra-tion The procedure to run the experiments in the SIM isshown in Figure 6 The inputs to be sent to the SIM aredetailed in Figure 5 the selected network techniques evalu-ation period demand scenario and cost model whereas theoutputs of the SIM we obtain are techniques used failuressolved assets fixed and electrical performance indicatorsThe SIM address mentioned outputs for long-term plan-ning reinforcements to optimise investment asset planningresolving network constraints The performance criteria eval-uated were capital expenditures (CAPEX) operating expen-ditures (OPEX) utilisation of assets CMLs CIs and lossesThese parameter values are delivered by the SIM after eachsimulation In order to look for the optimal solution amongthe feasible solution pathways the SIM allows a granularitystudy of each network state

4 Results

This section presents the assessment of the six smart gridinterventions along with traditional reinforcements in thetrial area compounded by six 11 kV primaries in MiltonKeynes UK for two different evaluation periods DECC2and DECC4 Subsection 41 introduces the results for the2015ndash2023 period as short-term planning and Subsection42 presents the results for a long-term evaluation period2015ndash2047

41 Short-Term Planning This section presents the assess-ment of the six smart grid interventions along with tradi-tional reinforcements in the trial area compounded by six11 kV primaries in Milton Keynes UK for two differentdemand scenarios DECC2 and DECC4

411 DECC4 2015ndash2023 Figure 9 shows the trends ofCAPEX and OPEX Note that in the year 2015 there is aCAPEXrsquos peak due to the application of more techniquesand OPEX increments over time from 2017 to 2023

Despite the investment the increase is compensated bya benefit in the electricity distribution network with areduction in CML and CI as shown in Figure 10

The summary of the proportion of installations requiredduring this evaluation period and by capital expenditures pertechnique is presented in Table 2 for both DECC scenarios

The average yearly price of each technique implementedto fix a network state is key for future decision-makingconsiderations

Figure 11 displays the average price of each techniquedisaggregated by CAPEX and OPEX

Performance indicators are key parameters for futuredecision-making within electricity distribution planning asquantifiers due to their influence on the quality of service

Therefore as shown in Figure 12 the only smart tech-nique used in combination with traditional reinforcementsin this scenario and evaluation period that slightly influenceslightly CML and CI is meshed networks

412 DECC2 2015ndash2023 Figure 13 plots the trend ofCAPEX and OPEX over the evaluation period It is important

to highlight that there is a CAPEX peak in 2015 due to anincrease of techniques applied in this period

This CAPEX increment produces a benefit in terms ofCML and CI as observed in Figure 14

The distribution of techniques applied and capital expen-ditures per technique are shown in Table 2 The average priceof each technique implementation in this demand scenariofor this evaluation period is shown in Figure 15

As displayed in Figure 12 for DECC4rsquos simulation theonly smart grid technique that improves the quality of serviceis the meshed network In Figure 16 the contribution ofsmart techniques on CML and CI for DECC2 demandevaluation is captured

413 Comparison between DECC2 and DECC4 for 2015ndash2023 The results presented may facilitate improvementsin electricity distribution network operations and planningresulting in better-informed decision-making when upgrad-ing current electricity distribution networks The assess-ment of the six smart grid techniques discovered thatonly three of them were selected as part of optimal

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

Mill

ions

pound p

er an

num

CAPEXOPEX

442444648

55254

Figure 9 CAPEX and OPEX DECC4 2015ndash2023

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

pu

Figure 10 CML and CI DECC4 2015ndash2023

10 Complexity

solutions for DECC4 and DECC2 as described in Table 2These three techniques applied are DAR for cables andtransformers and meshed networks

Comparing the cost trends of the two assessed scenariosit is notable that in both demand scenarios DECC4 and

DECC2 the CAPEX peak occurs in 2015 (Figures 9 and13) reflecting the more difficult nature of network statesto be feasible in a demanding scenario whereas DECC2shows a higher improvement of CI and CML performanceindicators as can be seen in Figures 10 and 14

Table 2 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2023

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 15 2 14 2

DAR-transformer 7 1 5 1

ALT 0 0 0 0

Mesh 3 1 4 1

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 8 3 7 2

TRAD-cable 60 89 65 91

TRAD-transfer load 6 2 4 1

TRAD-new feeder 1 2 1 2

DAR - cable DAR -transformer

Meshednetworks

TRAD -transformer

TRAD - cable

CAPEXOPEX

0

20000

40000

60000

80000

100000

pound

Figure 11 Average cost disaggregation per technique DECC4 2015ndash2023

Figure 12 Average CML and CI improvement per technique DECC4 2015ndash2023

11Complexity

The techniques applied are the key consideration to beassessed It is necessary to analyse the number of interven-tions applied by type the effect of these techniques in theelectric grid solving failures and fixing assets Figures 11and 15 show average prices of techniques used in eachdemand scenario Smart techniques have a lower TOTEXthan traditional reinforcements These novel techniquesabove are not frequently able to fix failures and thereforeproduce feasible network states In terms of CML and CIthe only technique able to moderately contribute to theirimprovement is meshed networks as shown in Figures 12and 16 Results attribute the majority of fixes to traditionalreinforcements with a comparatively smaller number offailures being solved by smart techniques (Table 2) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention forDECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

For DECC4 the three mentioned techniques along withtraditional cable and transformer replacement are applied(Table 2) whereas for DECC2 two of them are appliedDAR for cables and meshed networks (Table 2) Comparingthe cost trends of the two assessed scenarios it is notable thatin DECC4 the CAPEX peak occurs in 2015 (Figure 7)whereas in DECC2 besides the peak in 2015 there is alsoone in 2019 (Figure 11) reflecting the more difficult natureof network states to be feasible in a demanding scenarioHowever DECC2 shows a higher improvement of electricalperformance indicators as can be seen in Figure 12

In addition traditional cable replacement DAR forcables and meshed networks are able to fix the cable whereastraditional transformer replacement and DAR for trans-formers are able to fix transformer issues (Figure 16) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention for

DECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

42 Long-Term Planning This section evaluates two experi-ments namely characterising the DECC2 and DECC4 sce-narios for 2015ndash2047 evaluation period For each demandscenario expected investments disaggregating CAPEX andOPEX are presented the evolution of electrical performanceindicators and the number of techniques applied

421 DECC4 2015ndash2047 In this experiment the mostsignificant results to analyse the suitability of each techniqueare presented

Figure 17 shows the trend of CAPEX and OPEX from2015 to 2047

There are CAPEX peaks in the year 2018 and during thebeginning of RIIO-ED2 in the years 2025 to 2026 due to theapplication of more techniques to fulfill low-carbon targets

Figure 18 indicates that upgrades on the network aredirectly related to technical performance indicators CMLand CI

In Table 3 the techniques applied and their contributionto CAPEX during the evaluation period from 2015 to 2047 bya demand scenario are shown

422 DECC2 2015ndash2047 Within this experiment the mostrelevant results to analyse the evolutionary network statesare presented

In Figure 19 the trend of CAPEX and OPEX from 2015to 2047 is shown There is a significant increase of CAPEXin the years 2024 to 2026 at the beginning of RIIO-ED2due to the necessary implementation of new techniques toreach low-carbon targets The evolution of CML and CI ispresented in Figure 20 linking larger investment years whenmajor reductions are found The contribution of each solutiontechnique is presented in Table 3

423 Comparison between DECC2 and DECC4 for 2015ndash2047 Figure 21 presents a multidimensional parallel coor-dinate representation of the feasible network statersquoscombinations that produced a 2015ndash2047 investment path-way for the evaluated area It bundles economic indicatorsie CAPEX and OPEX with technical performance indica-tors providing valuable insights on the number of traditionalreinforcements utilised to heal falling network states Resultsare also clustered by the two demand scenarios assessedduring the experiments

Solutions of DECC2 (represented in orange and green)and DECC4 (in black and blue) are clustered by the per-centage of traditional reinforcements utilised as well asthe utilisation factor

Solutions in green and in black represent those wheresmart grids were more used whereas orange and blue repre-sent the cheapest (CAPEX and OPEX) less utilised and theworst responding to the decrease of CML CI and lossesand the ones using more traditional reinforcement to fixnetwork states

Besides the fact that DECC2 smart solutions are between16 and 28 more expensive than DECC4 traditional

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

CAPEXOPEX

442444648

552

Mill

ions

pound p

er an

num

54

Figure 13 CAPEX and OPEX DECC2 2015ndash2023

12 Complexity

reinforcement solutions (green solutions versus blue solu-tions in Figure 21) DECC2 traditional solutions are in therange of costs of smart solutions of DECC4 (orange andblack solutions in Figure 21) It can also be concluded thatinvestment pathways using less smart techniques provide a

cheaper response to technical performance evaluators suchas utilisation CML CI and losses

43 Summary Experiment evaluation runs from the2015ndash2047 period present lower investment rates for the

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

PU

Figure 14 CML and CI DECC2 2015ndash2023

CAPEXOPEX

DAR -transformer

Meshednetworks

Traditional -transformer

Traditional -cable

0

20000

40000

60000

80000

100000

120000

140000

pound

Figure 15 Average cost disaggregation per technique DECC2 2015ndash2023

Figure 16 Average CML and CI improvement per technique DECC2 2015ndash2023

13Complexity

2015ndash2023 period than if the evaluation is just performedwith a 2015ndash2023 time frame This occurs as a result of thechallenging low-carbon targets up to 2047 and a myopicplanning with short-term lookahead For an evaluationperiod four times larger the CAPEX and TOTEX increasedjust 18 compared to 2015ndash2023 concluding that betweenpound52M and pound68M the evaluation area regardless of thetime horizon for the investment or the demand scenarioconsidered is required

Furthermore it can be inferred comparing Tables 2 and 3that for less smart interventions the short-term planningis used compared to the long-term horizon planningDAR-cable and DAR-transformer experienced a significantimplementation variation between the two evaluationperiods for both demand scenarios as well as CAPEX allo-cated in cable upgrades falling from 89ndash91 (2015ndash2023)to 29ndash66 in the long term 2015ndash2047 run Both technicalperformance evaluators CML and CI respond to theirrespective CAPEX curve shape Due to a more incentivisedsmart grid technique during RIIO-ED2 and ED3 the per-centages of smart techniques implemented varied notablyFor DECC4 in 2023rsquos outlook the share of techniques is

25 for smart grid interventions whereas for 2050rsquos out-look the share is increased up to 58 In the same wayfor DECC2 in 2023rsquos outlook the share of smart interven-tions is 23 whereas in 2050rsquos outlook the share increasesup to 69

Feasible solutions characterising the solution space(Figure 21) differ in the degree of investment required andtechnical performance evaluators Each of the solution pathrepresented in the multidimensional solution space inFigure 21 represents the intersection of that dimension witheach axis Parallel coordinates [35] are low complexityworking for any N-dimension In the case of Figure 21it has 8 dimensions treating every variable uniformlyEach intersection of one solution path with each axis isthe value of that solution for that axis As for axis DECC2and DECC4 it represents the feasible solutions for eachdemand scenario Making a query by the percentage oftraditional reinforcement used in that solution we have thepartition between orange and green for DECC2 and blueand black being the first solutions that use more traditionalreinforcement compared with the ones that use more smarttechniques represented in green (DECC2) and black(DECC4) From Figure 21 we can argue that solutions thatuse more smart techniques (green and blacks) are moreexpensive in terms of CAPEX and OPEX than their peers(orange and blues) when compared by a demand scenarioIt can also be stated that solutions with a higher use of tradi-tional reinforcements are the ones that decrease the mostCML CI and losses

Disaggregating results by network state of the six 11 kVprimaries and by the feeder if necessary for further granulardebugging can discern locational capacity Under bothdemand scenarios Secklow Gate was seen to be the one withgreater capacity being necessary to apply fewer techniquesover the evaluation period 2015ndash2047 On the other handNewport Pagnell exceeds the capacity as soon as 2015requiring a high number of network state evaluation to befixed that happens for each subsequent year hence the largenumber of network states is presented in Table 4

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

pound p

er an

num

Figure 17 CAPEX and OPEX DECC4 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

pu

Figure 18 CML and CI DECC4 2015ndash2047

14 Complexity

5 Conclusions

Power flow analysis using a nodal network model is essentialwhen determining the benefits of trailed smart interventions

because the interactions and implications are highly specificto a particular location and scenario

This suggests that while the bottom-up approach isonerous in terms of data handling and manipulation this isworthwhile for strategic planning and policy evaluationComparing both investment strategies investment strategyadjustments will be necessary in future regulation periodsif an over-invested network behaves as displayed in theshort-term section of this study

Traditional reinforcement will continue to be the mainmethod by which network issues are mostly resolvedfollowed by dynamic asset rating and meshed networks

The assessment of the six novel smart interventions in theFALCON 11kV primary test area in Milton Keynes hasproved the suitability of three techniques able to fix failuresimproving the quality of service and their readiness to bedeployed in the near future These techniques are DAR forcables and transformers and meshed networks Meshednetworks have been repeatedly selected as a feasible tech-nique because using it will reduce CML CI and power losses

Table 3 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2047

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 18 5 31 6

DAR-transformer 32 16 32 6

ALT 0 0 0 0

Mesh 8 3 6 3

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 25 44 9 16

TRAD-cable 15 29 20 66

TRAD-transfer load 1 1 1 1

TRAD-new feeder 1 2 1 2

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

s pound p

er an

num

Figure 19 CAPEX and OPEX DECC2 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

PU

Figure 20 CML and CI DECC2 2015ndash2047

15Complexity

improving the quality of service and the efficiency whilebeing a cost-effective solution

The initial capacity at primary substations differedsignificantly and this affected the number and complexityof interventions required by the SIM Due to the loadscenarios showing significant peak load increases DAR wasoften a temporary measure that would delay but not removethe eventual need for traditional reinforcement

Implementing DAR for cables and transformers themonitoring of assets when their peak capacity is increasedwas analysed On the other hand it was observed thattraditional reinforcements still play a key role in keepingthe electricity distribution networks free of constraintsTRAD techniques such as transformer and cable replace-ments are able to fix the majority of failures and will beessential also in the future

The comparison between traditional reinforcements andnovel smart techniques has provided a new knowledge aboutthe suitability of each technique to be applied in termsof costs electrical performance failures fixed and assetreplaced Cable replacement is the most costly techniquehowever its use is unavoidable in a number of cases Further-more the applicability of each technique regarding costsinvolved improvements on power quality and efficiency

and failures solved lead to new questions to be analysed suchas the lack of these techniques to provide flexible capacitywithin the trailed area

To sum up this study has performed a comparativeanalysis of novel smart intervention techniques providinginsights for future investments in electricity distributionplanning Further work can focus on scaling up the analysisto include a larger section of the network or a constrainedarea to evaluate national applicability of the current findings

Data Availability

Some of the data relevant to this submission is proprietaryfrom Western Power Distribution It can be accessed forresearch purposes under a signed collaboration agreement

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors would like to thank the rest of the WesternPower Distribution Innovation team particularly the other

2400

2200

2000

350

360

370

380

390

400

1400

1300

1200

1100

1000DECC 4

50

55

60

12

11

10

9

8

4400

4200

4000

3800

3600

2400

CAPEX (in kpound) OPEX (in kpound) Utilisation Traditional reinforcements () DECC 2 998796 CML 998796 CI 998796 Losses

2200

2000

1800

Figure 21 Parallel coordinate visualisation of technoeconomic performance indicators under DECC4 and DECC2

Table 4 Summary of network states and results for demand scenario DECC4 and DECC2

Primary 11 kV substation Feeders Year TechniquesResultsDECC4

NSDECC4

ResultsDECC2

NSDECC2

Fox Milne 13 2047 Smart and traditional 54 276 47 259

Newport Pagnell 9 2047 Smart and traditional 48 317 31 318

Secklow Gate 9 2047 Smart and traditional 27 29 15 16

Bletchley 19 2047 Smart and traditional 32 40 21 48

Marlborough Street 11 2047 Smart and traditional 31 52 19 37

Childs Way 17 2047 Smart and traditional 67 398 54 181

16 Complexity

members of the FALCON project as well as Paul CharletonStuart Fowler Ilja Orlovs Dan Taylor and Steve IngramAlso the authors want to acknowledge OFGEM and theLowCarbon Network Fund for funding this piece of research

References

[1] European Commission ldquoCommunication from the Commis-sion to the European Parliament the Council the EuropeanEconomic and Social Committee and the Committee of theRegionsrdquo 2011 December 2016 httpseur-lexeuropaeulegal-contentENTXTqid=1537473508968ampuri=CELEX52016PC076721

[2] F G N Li and E Trutnevyte ldquoInvestment appraisal ofcost-optimal and near-optimal pathways for the UK elec-tricity sector transition to 2050rdquo Applied Energy vol 189pp 89ndash109 2017

[3] G Strbac I Konstantelos M Pollitt and R Green DeliveringFuture-Proof Energy Infrastructure National InfrastructureCommission 2016

[4] Ofgem ldquoGB Network regulation - the RIIO modelrdquo 2010December 2014 httpswwwofgemgovuknetwork-regulation-riio-model

[5] R Poudineh and T Jamasb ldquoDistributed generation storagedemand response and energy efficiency as alternatives togrid capacity enhancementrdquo Energy Policy vol 67 pp 222ndash231 2014

[6] J Yang X Bai D Strickland L Jenkins and A M CrossldquoDynamic network rating for low carbon distribution networkoperationmdashA UK applicationrdquo IEEE Transactions on SmartGrid vol 6 no 2 pp 988ndash998 2015

[7] M Behnke W Erdman S Horgan et al ldquoSecondary net-work distribution systems background and issues relatedto the interconnection of distributed resourcesrdquo NationalRenewable Energy Laboratory (NREL) Tech Rep NRELTP-560-38079 2005

[8] J Crispim J Braz R Castro and J Esteves ldquoSmart grids in theEU with smart regulation experiences from the UK Italy andPortugalrdquo Utilities Policy vol 31 pp 85ndash93 2014

[9] Tnei ldquoIPSA Power software for power systemsrdquo March 2016httpwwwipsa-powercom

[10] ETAP ldquoETAP PSrdquo 2018 April 2016 httpsetapcomsoftwareproduct-overview-main

[11] DINIS ldquoDINIS distribution network information sys-temsrdquo 2002 March 2016 httpsetapcomsoftwareproduct-overview-main

[12] Western Power Distribution ldquoSIM final reportrdquo 2015November 2016 httpwwwwesternpowerinnovationcoukProjectsFalconaspx

[13] Western Power Distribution ldquoProject FALCONrdquo 2015November 2017 httpwwwdiniscom

[14] EA Technology ldquoTransform modelrdquo March 2016 httpwwweatechnologycomproducts-and-servicescreate-smarter-gridstransform-model

[15] L Sigrist E Lobato L Rouco M Gazzino and M CantuldquoEconomic assessment of smart grid initiatives for islandpower systemsrdquo Applied Energy vol 189 pp 403ndash415 2017

[16] J A Momoh ldquoSmart grid design for efficient and flexiblepower networks operation and controlrdquo in 2009 IEEEPESPower Systems Conference and Exposition pp 1ndash8 SeattleWA USA 2009

[17] N Good K A Ellis and P Mancarella ldquoReview and classifica-tion of barriers and enablers of demand response in the smartgridrdquo Renewable and Sustainable Energy Reviews vol 72pp 57ndash72 2017

[18] Z Yang J Xiang and Y Li ldquoDistributed consensus basedsupplyndashdemand balance algorithm for economic dispatchproblem in a smart grid with switching graphrdquo IEEE Trans-actions on Industrial Electronics vol 64 no 2 pp 1600ndash1610 2017

[19] SuSTAINABLE ldquoSmart distribution network operation formaximising the integration of renewable generationrdquo 2015March 2016 httpwwwsustainableprojecteuHomeaspx

[20] S Mohtashami D Pudjianto and G Strbac ldquoStrategic distri-bution network planning with smart grid technologiesrdquo IEEETransactions on Smart Grid vol 8 no 6 pp 2656ndash2664 2017

[21] Smart Grid Forum ldquoSmart grid forum work stream 7rdquo2015 March 2016 httpwwwsmarternetworksorgprojectnia_nget015425

[22] ETI ldquoEnergyPath networksrdquo March 2016 httpwwweticoukprojectenergypath

[23] C Mateo Domingo T Gomez San Roman Aacute Sanchez-Miralles J P Peco Gonzalez and A Candela Martinez ldquoAreference network model for large-scale distribution planningwith automatic street map generationrdquo IEEE Transactions onPower Systems vol 26 no 1 pp 190ndash197 2011

[24] R Bolton and T J Foxon ldquoInfrastructure transformation as asocio-technical process mdash implications for the governanceof energy distribution networks in the UKrdquo TechnologicalForecasting and Social Change vol 90 pp 538ndash550 2015

[25] UK Power Networks ldquoDNO guide to future Smart man-agement of distribution networks Summary reportrdquo 2014httpsinnovationukpowernetworkscoukinnovationenProjectstier-2-projectsLow-Carbon-London-(LCL)Project-DocumentsLCL20Learning20Report20-20SR20-20Summary20Report20-20DNO20Guide20to20Future20Smart20Management20of20Distribution20Networkspdf

[26] A Botea J Rintanen and D Banerjee ldquoOptimal reconfigura-tion for supply restoration with informed AlowastSearchrdquo IEEETransactions on Smart Grid vol 3 no 2 pp 583ndash593 2012

[27] T Logenthiran D Srinivasan and T Z Shun ldquoDemand sidemanagement in smart grid using heuristic optimizationrdquoIEEE Transactions on Smart Grid vol 3 no 3 pp 1244ndash1252 2012

[28] J Saacutenchez-Cuadrado J de Lara and E Guerra ldquoBottom-upmeta-modelling an interactive approachrdquo in Model DrivenEngineering Languages and Systems pp 3ndash19 Springer2012

[29] Y H Kim F J Sting and C H Loch ldquoTop-down bottom-upor both Toward an integrative perspective on operationsstrategy formationrdquo Journal of Operations Managementvol 32 no 7-8 pp 462ndash474 2014

[30] C Bohringer ldquoThe synthesis of bottom-up and top-down inenergy policy modelingrdquo Energy Economics vol 20 no 3pp 233ndash248 1998

[31] T G R Bower ldquoRepetition in human developmentrdquo Merrill-Palmer Quarterly of Behavior and Development vol 20no 4 pp 303ndash318 1974

[32] H Mintzberg and J A Waters ldquoOf strategies deliberate andemergentrdquo Strategic Management Journal vol 6 no 3pp 257ndash272 1985

17Complexity

commercial agreements between the DNO and industrialand commercial customers who have the ability to controlappreciable amounts of load in a relatively short period oftime We expect demand side response to be in two formsusing the representation in Figure 4

(i) To reduce the impact of predicted peak loads

(ii) To respond to an unplanned event such as a fault

Demand side response actions can be used by the DNOto enable a change in behaviour by a customer site inresponse to an explicit signal triggering a preagreed actionThe action should be the interruption of a customerrsquosinternal electricity-consuming processes either to avoid ormore likely to defer these to a later time Its metricscapacitydelta reduction durationfrequency and OPEXare detailed in [13]

3 Methodology

To undertake the analysis of the aforementioned techniquesa revolutionary new software tool for electricity distributionnetwork planning called the scenario investment model(SIM) is used Results from evaluations using the SIM areobtained through a series of experiments that modelled thenetwork evolution under different demand scenarios pre-sented in Table 1 at short- (2015ndash2023) and long-termlookahead (2015ndash2050) to assist decision-makers in futurepower network planning

Currently electricity distribution networks have beenplanned with typically linear load growths of up to 1 perannum The expected increase in low-carbon technologieswill have a significant effect on the electricity demands onthe network which may have significant rapid sporadicincreases in the electricity demand on the 11 kV networks

[25] In addition the daily electricity load shapes may be alsoaltered significantly The networks will need to be upgradedand systems are being able to evolve and cope with newdemand profiles

There had been identified two main streams of work toconsider the use of the innovative techniques namely strate-gic and tactical planning and Design Build and OperationThe strategic and tactical planning stream will consider thenetwork planning roles while the design and operationstream will consider design build and operation roles InFigure 5 the smart grid planning framework diagram pre-sents the key elements of each stream which are displayedwith their main interactions

In order to leverage the capability of existing networkanalysis tools which are already extensively used by elec-tricity network operators the SIM is separated into twomain packages a network modelling tool which primarilyperforms the technical assessment of the application ofthe techniques and the SIM harness which manages theoverall process and perform the economic assessment andreporting functions

Demand data modelling has been based on a bottom-upapproach The methods used provide an estimate of demandfor each half-hour at each secondary substation for 18different season-day types [12]

Communication network

DGDSM

Customer site

PowerOn fusion

Demandforecast

Networkanalyser

Networkevent

DGDSMscheduler

DSMDG manager

Performancemonitor

Paymentscalculator

RTUsmartmeter

Figure 4 Commercial trials representation DGDSM

Table 1 Demand scenarios

Demand scenariosFuel

efficiencyLow-carbon

heatWall

insulation

DECC1 Medium High High

DECC2 High Medium High

DECC3 High High Low

DECC4 Low Low Medium

5Complexity

The research objective is settled to prove the suitability ofthe six novel smart interventions presented in Section 2and along with the traditional reinforcements provide anevolutionary planning insight for future power networksTo undertake this study specific experiments were selectedfor a certain power trial network under different demandscenarios and evaluation periods assessing smart tech-niques along with traditional reinforcements The approachinvolved running a set of experiments using the SIM for thesix 11 kV primaries in the FALCON trial area

31 SIM Support Algorithms The essence of the SIMapproach is its ability to take a network configuration andcorresponding load profiles in a particular year (termed asinitial network state) perform power flow and reliabilityanalysis and create derivative network states in a processknown as ldquonetwork state expansionrdquo The expansion happenseither by transitioning to the following year for networkstates without any failures or by applying intervention

techniques to resolve network issues With each new net-work state created the SIM therefore is faced with adecision as to which network state from the executionhistory to expand next The expansion can be guided bysimple depth first or breadth first algorithms which areimplemented in the SIM for verification purposes Thedepth-first algorithm always selects the newest ie themost recently created network state that is not fullyexpanded for expansion while the breadth-first algorithmalways selects the oldest network state However thosesimple heuristics are inadequate for any practical usebeyond simple test cases due to the size of the searchspace obtained by permuting all possible interventionsover a number of years To perform intelligent explorationof the search space the SIM uses a heuristic approach thatis based on an Alowast algorithm [26 27]

The baseline Alowast algorithm aims at finding the least-costpath through the search space As Alowast traverses the searchspace it builds a tree of partial paths The leaf nodes of this

Learning transfer

Key component

Systempackage conatinga set of key components

Learninganalysis of results for theimprovement of models and development ofpolicies

Smart grid planning framework

Substationmonitoring

Dynamicasset ratings

data

Automatedload transfer

dataMeshed

network data

Communication network trial

Monitoringdata collection

system

Trials data distributionmanagement system

Scenario investment model

Network modelling tool(technical Assessment)

Dynamic assetratings

techniquemodel

Automatedload transfer

techniquemodel

Batterystorage

techniquemodel

Meshednetwork

techniquemodel

Traditional reinforcement

technique model

Networktopology amp

loads

Distributedgenerationtechnique

model

Demand sidemanagement

techniquemodel

Multi-yearscheduler

Economicinvestmentassesment

Networkperformance

analysisVisualisationand reporting

SIM harness(economic assesment and

reporting)

Performance analysisamp learning

Energy modelminusmeasured data

comparison

Demand datamodelling and

scenario development

Impr

ove e

nerg

ym

odel

Design build andoperation

Strategic andtactical planning

Strategic planning supportconnections and

infrastructure planningplanning policy development

Impr

ove t

echn

ique

and

netw

ork m

odels

Batterystorage data

Distributedgeneration

trial

Demand sidemanagement

data

Design amp build learning

Impr

ove i

mpl

emen

tatio

n co

st m

odels

Figure 5 Smart grid planning framework diagram

6 Complexity

tree (failed network states) are stored in a priority queue thatis ordered using a cost function

f x = g x + h x 1

where h x is a heuristic estimate of the path cost toreach the goal and g x is the distance travelled fromthe initial node

The SIM selects network states from the priority queue toapply intervention techniques one application at a timeDeployment of a technique produces a new network statefor which a power flow analysis is performed in intact andall n minus 1 (contingency) network operation modes If allthe failures are resolved a reliability analysis comprisingcustomer minutes lost (CML) customer interruptions (CI)losses and fault level studies is performed A new networkstate is subsequently created in the next year of evaluationor if it is already the last year of evaluation the costsof interventions are calculated and the network statetogether with all its expansion history is saved to theresult store as a new result The evaluation is terminated

(Figure 6) when criteria such as the number of resultsnumber of network state evaluations or run time arereached As noted previously Alowast uses a combination ofdistance travelled so far and a heuristic estimate of thedistance to reach the endpoint

In SIM case this corresponds to g x being the totalexpenditures (TOTEX) incurred so far and h x being aheuristic estimate of TOTEX to reach the end year ofthe experiment TOTEX also referred to as the totalexpenditure comprises implementation costs (CAPEX)that occur only once when an intervention is applied tothe network operation costs (OPEX) that refer to anongoing expense of operating an asset or a scheme alongwith asset life degradation and metric costs that includeincentive payments for losses fault levels and networkreliability Referring to (2) the g x for a network statexi in year i is defined as

g xi = ci + oi + jminus1

j=1cj + oj +mj 2

Initial network state

Create new networkstate in the next year Analyse power flow

Generate and applynext intervention

technique application

Save network state tofailed network state

store

Update path costestimates

Reorder priority queue

Select top mostnetwork state from the

priority queueTermination

criteria reached

Failure present

Analyse reliabilityEnd yearreached

minus

+

minus

+

minus

+

Update solution costs

Save result

End

Figure 6 SIM evaluation flowchart

7Complexity

where ci is CAPEX in the current year oi is OPEX in thecurrent year and cj oj and mj are CAPEX OPEX andmetrics costs respectively of the ancestor network statewith no issues in year j The heuristic estimate h x ofthe cost to reach the end year is given by

h xi = cREMi +mi + n

k=i+1ck + ok +mk 3

where cREMi is the average remaining CAPEX in the cur-rent year i mi is the average metric cost in this year ckok and mk are the average CAPEX OPEX and metriccosts respectively of the descendant network state in yeark with no issues and n is the end year of evaluation Pre-seeded to a constant value c0 the average CAPEX isupdated each time a network state is expanded in a partic-ular year thus the estimated costs of fixing all issues in aparticular year progressively approach true average Settingc0 to a value greater than 0 speeds up the expansion pro-cess ie all interventions that cost less than c0 will resultin new network states that are at the top of the priorityqueue The business rationale is that DNOs are not thatinterested in optimizing interventions costing less than acertain threshold The learned averages are propagatedback to network states in the priority queue thus updatingtheir estimated effort and leading to the reordering of thequeue unlike the average CAPEX average OPEX andmetrics costs which are assumed to be 0 for years withno network states The average OPEX value for a year isobtained using

o = jToc jT jminus1 4

where j is a column vector of ones and oc is an OPEXvector of compliant network states in that year Likewisethe average metric cost is obtained according to

m = jTmc jT jminus1 5

where j is a column vector of ones and mc is a vector ofthe metric costs of compliant network states in that year

32 Conceptualising Bottom-Up Evolutionary PlanningModel-driven engineering (MDE) uses analysis constructionand development of frameworks to formulate metamodelsThose models are usually characterised using domain-specific modelling approaches [28] containing appropriatedetail abstraction of particular domain through a specificmetamodel The use of metamodels requires therefore inputsfrom domain experts which can be used to generate aggre-gated or disaggregated models Top-down and bottom-upare the conceptual definition of aggregated and disaggregatedmodels [29] These two modelling paradigms are frequentlyused to epitomise domain interactions among the operationof the energy system the econometrics related and thetechnical performance indicators [30]

From a bottom-up modelling approach [31 32] thetop-down perspective is a simplistic characterisation ofhow electrical power networks combine locational eventsand individual asset performance with high-level objectiveslike improving the CML of a certain congested area [33 34]From an engineering point of view both are still validsince outputs and strategic forecast are produced in bothHow those outcomes are calculated validated and trans-formed to strategy is presented in Figure 7 (top-down)and Figure 8 (bottom-up)

Interventiontechnique costs

Relative usage ofinterventiontechnique

Interventiontechnique installation

details

Interventiontechnique models

Validationphysical trials

Interventiontechnique operation

details

Purchase andoperation cost data

Cost model

Network data

Representativenetworks

Model network through years

Calculate intervention technique usage

Strategic forecasts

Validationsampled nodal

networks

Demand scenarios

Figure 7 Top-down conceptualisation of evolutionary power networks planning

8 Complexity

The ability of bottom-up to capture discrete locationalimpacts of technologies on the system and their disaggre-gated costs is triggering the following subsections Trade-offmethodologies are needed for planning evolutionary powersystems where observing disaggregated result strategic fore-casts are to be produced These methodologies need to beinteractive in the sense that starting from an initial stateand after a testing or learning phase the network is able toaccommodate techniques that have improved the systemproviding an exploratory set of solutions that can beexpanded or discarded as the model evolves through time

33 Experiment Characterisation To illustrate the processingof the methodology adopted we consider a typical networkscenario tree created by the tool that consists of failed (red)and compliant (green) network states in different years ofevaluation The SIM starts with a single network state inthe first year of evaluation Every year the network isevaluated for compliance in intact and n minus 1 contingencyoperation modes If failures are detected the SIM appliesintervention techniques described in Section 2 to create net-work patches that resolve the failures Each application of anintervention technique creates a new network state The toolhas to resolve all issues in the network before transitioninginto the next year DNO planners usually run power flowstudies with a single fixed load pattern In contrast the SIMchecks each network state for compliance under 18 charac-teristic day load scenarios each comprising of 48 half-hoursettlement periods All studies are performed under intactand n minus 1 contingency network operation modes The SIM

calculates patch costs using cost drivers returned by the net-work modelling tool The cost driver describes a networkintervention and consists of two parts namely the patchkey and the scaling The patch key identifies the nature ofthe modification of the network performed (removal or addi-tion of an asset and the type of the asset) Scaling data is rel-evant only to patches that can be installed in multiples of onesuch as cable upgrades and additional transformer installa-tion Scaling data structure provides a list of multipliers tothe base cost data available in the SIM database In case ofcable upgrade or replacement it enables the SIM to correctlyestimate full installation costs from per unit of length valuesFinally for postprocessing analysis the SIM also returns a listof failures by asset It was identified by DNO planners asindispensable features to help validate the system and corre-late the expansion trees to the actual assets on the networkdiagram The failure detail table contains asset ID anddescription alongside information about the number of fail-ures in intact and n minus 1 operating modes as well as absoluteand per unit thermal and voltage failure magnitude

34 Case Characterisation Two set of experiments wereperformed for this study one for comparing short-termevaluation period for RIIO-ED1 (2015ndash2023) where theRIIO-ED1 investment planning has been stylised and alonger planning period for RIIO-ED1 to RIIO-ED4 from2015 up to 2047 The other set aimed at evaluating differ-ent DECC demand scenarios Experiments evaluated twodemand scenarios DECC2 and DECC4 DECC4 representsas displayed in Table 1 the slow-progression scenario and

Purchase andoperation cost data

Cost model Nodal network model

Model network through years

Apply intervention technique to keep the network compilant

Find optimal network development options

Solutions for individual networks

Aggregate results for larger network areas

Strategic forecasts

Network data Demand scenarios

Interventiontechnique costs

Interventiontechnique installation

details

Nodal interventiontechnique models

Validation withphysical trials

Relative usage ofinterventiontechniques

Validation of powerflow and reliability

models

Interventiontechnique operation

details

Load profiles

Figure 8 Bottom-up conceptualisation of evolutionary power networks planning

9Complexity

DECC2 is with DECC3 the most challenging scenario interms of electrification and low-carbon technology integra-tion The procedure to run the experiments in the SIM isshown in Figure 6 The inputs to be sent to the SIM aredetailed in Figure 5 the selected network techniques evalu-ation period demand scenario and cost model whereas theoutputs of the SIM we obtain are techniques used failuressolved assets fixed and electrical performance indicatorsThe SIM address mentioned outputs for long-term plan-ning reinforcements to optimise investment asset planningresolving network constraints The performance criteria eval-uated were capital expenditures (CAPEX) operating expen-ditures (OPEX) utilisation of assets CMLs CIs and lossesThese parameter values are delivered by the SIM after eachsimulation In order to look for the optimal solution amongthe feasible solution pathways the SIM allows a granularitystudy of each network state

4 Results

This section presents the assessment of the six smart gridinterventions along with traditional reinforcements in thetrial area compounded by six 11 kV primaries in MiltonKeynes UK for two different evaluation periods DECC2and DECC4 Subsection 41 introduces the results for the2015ndash2023 period as short-term planning and Subsection42 presents the results for a long-term evaluation period2015ndash2047

41 Short-Term Planning This section presents the assess-ment of the six smart grid interventions along with tradi-tional reinforcements in the trial area compounded by six11 kV primaries in Milton Keynes UK for two differentdemand scenarios DECC2 and DECC4

411 DECC4 2015ndash2023 Figure 9 shows the trends ofCAPEX and OPEX Note that in the year 2015 there is aCAPEXrsquos peak due to the application of more techniquesand OPEX increments over time from 2017 to 2023

Despite the investment the increase is compensated bya benefit in the electricity distribution network with areduction in CML and CI as shown in Figure 10

The summary of the proportion of installations requiredduring this evaluation period and by capital expenditures pertechnique is presented in Table 2 for both DECC scenarios

The average yearly price of each technique implementedto fix a network state is key for future decision-makingconsiderations

Figure 11 displays the average price of each techniquedisaggregated by CAPEX and OPEX

Performance indicators are key parameters for futuredecision-making within electricity distribution planning asquantifiers due to their influence on the quality of service

Therefore as shown in Figure 12 the only smart tech-nique used in combination with traditional reinforcementsin this scenario and evaluation period that slightly influenceslightly CML and CI is meshed networks

412 DECC2 2015ndash2023 Figure 13 plots the trend ofCAPEX and OPEX over the evaluation period It is important

to highlight that there is a CAPEX peak in 2015 due to anincrease of techniques applied in this period

This CAPEX increment produces a benefit in terms ofCML and CI as observed in Figure 14

The distribution of techniques applied and capital expen-ditures per technique are shown in Table 2 The average priceof each technique implementation in this demand scenariofor this evaluation period is shown in Figure 15

As displayed in Figure 12 for DECC4rsquos simulation theonly smart grid technique that improves the quality of serviceis the meshed network In Figure 16 the contribution ofsmart techniques on CML and CI for DECC2 demandevaluation is captured

413 Comparison between DECC2 and DECC4 for 2015ndash2023 The results presented may facilitate improvementsin electricity distribution network operations and planningresulting in better-informed decision-making when upgrad-ing current electricity distribution networks The assess-ment of the six smart grid techniques discovered thatonly three of them were selected as part of optimal

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

Mill

ions

pound p

er an

num

CAPEXOPEX

442444648

55254

Figure 9 CAPEX and OPEX DECC4 2015ndash2023

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

pu

Figure 10 CML and CI DECC4 2015ndash2023

10 Complexity

solutions for DECC4 and DECC2 as described in Table 2These three techniques applied are DAR for cables andtransformers and meshed networks

Comparing the cost trends of the two assessed scenariosit is notable that in both demand scenarios DECC4 and

DECC2 the CAPEX peak occurs in 2015 (Figures 9 and13) reflecting the more difficult nature of network statesto be feasible in a demanding scenario whereas DECC2shows a higher improvement of CI and CML performanceindicators as can be seen in Figures 10 and 14

Table 2 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2023

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 15 2 14 2

DAR-transformer 7 1 5 1

ALT 0 0 0 0

Mesh 3 1 4 1

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 8 3 7 2

TRAD-cable 60 89 65 91

TRAD-transfer load 6 2 4 1

TRAD-new feeder 1 2 1 2

DAR - cable DAR -transformer

Meshednetworks

TRAD -transformer

TRAD - cable

CAPEXOPEX

0

20000

40000

60000

80000

100000

pound

Figure 11 Average cost disaggregation per technique DECC4 2015ndash2023

Figure 12 Average CML and CI improvement per technique DECC4 2015ndash2023

11Complexity

The techniques applied are the key consideration to beassessed It is necessary to analyse the number of interven-tions applied by type the effect of these techniques in theelectric grid solving failures and fixing assets Figures 11and 15 show average prices of techniques used in eachdemand scenario Smart techniques have a lower TOTEXthan traditional reinforcements These novel techniquesabove are not frequently able to fix failures and thereforeproduce feasible network states In terms of CML and CIthe only technique able to moderately contribute to theirimprovement is meshed networks as shown in Figures 12and 16 Results attribute the majority of fixes to traditionalreinforcements with a comparatively smaller number offailures being solved by smart techniques (Table 2) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention forDECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

For DECC4 the three mentioned techniques along withtraditional cable and transformer replacement are applied(Table 2) whereas for DECC2 two of them are appliedDAR for cables and meshed networks (Table 2) Comparingthe cost trends of the two assessed scenarios it is notable thatin DECC4 the CAPEX peak occurs in 2015 (Figure 7)whereas in DECC2 besides the peak in 2015 there is alsoone in 2019 (Figure 11) reflecting the more difficult natureof network states to be feasible in a demanding scenarioHowever DECC2 shows a higher improvement of electricalperformance indicators as can be seen in Figure 12

In addition traditional cable replacement DAR forcables and meshed networks are able to fix the cable whereastraditional transformer replacement and DAR for trans-formers are able to fix transformer issues (Figure 16) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention for

DECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

42 Long-Term Planning This section evaluates two experi-ments namely characterising the DECC2 and DECC4 sce-narios for 2015ndash2047 evaluation period For each demandscenario expected investments disaggregating CAPEX andOPEX are presented the evolution of electrical performanceindicators and the number of techniques applied

421 DECC4 2015ndash2047 In this experiment the mostsignificant results to analyse the suitability of each techniqueare presented

Figure 17 shows the trend of CAPEX and OPEX from2015 to 2047

There are CAPEX peaks in the year 2018 and during thebeginning of RIIO-ED2 in the years 2025 to 2026 due to theapplication of more techniques to fulfill low-carbon targets

Figure 18 indicates that upgrades on the network aredirectly related to technical performance indicators CMLand CI

In Table 3 the techniques applied and their contributionto CAPEX during the evaluation period from 2015 to 2047 bya demand scenario are shown

422 DECC2 2015ndash2047 Within this experiment the mostrelevant results to analyse the evolutionary network statesare presented

In Figure 19 the trend of CAPEX and OPEX from 2015to 2047 is shown There is a significant increase of CAPEXin the years 2024 to 2026 at the beginning of RIIO-ED2due to the necessary implementation of new techniques toreach low-carbon targets The evolution of CML and CI ispresented in Figure 20 linking larger investment years whenmajor reductions are found The contribution of each solutiontechnique is presented in Table 3

423 Comparison between DECC2 and DECC4 for 2015ndash2047 Figure 21 presents a multidimensional parallel coor-dinate representation of the feasible network statersquoscombinations that produced a 2015ndash2047 investment path-way for the evaluated area It bundles economic indicatorsie CAPEX and OPEX with technical performance indica-tors providing valuable insights on the number of traditionalreinforcements utilised to heal falling network states Resultsare also clustered by the two demand scenarios assessedduring the experiments

Solutions of DECC2 (represented in orange and green)and DECC4 (in black and blue) are clustered by the per-centage of traditional reinforcements utilised as well asthe utilisation factor

Solutions in green and in black represent those wheresmart grids were more used whereas orange and blue repre-sent the cheapest (CAPEX and OPEX) less utilised and theworst responding to the decrease of CML CI and lossesand the ones using more traditional reinforcement to fixnetwork states

Besides the fact that DECC2 smart solutions are between16 and 28 more expensive than DECC4 traditional

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

CAPEXOPEX

442444648

552

Mill

ions

pound p

er an

num

54

Figure 13 CAPEX and OPEX DECC2 2015ndash2023

12 Complexity

reinforcement solutions (green solutions versus blue solu-tions in Figure 21) DECC2 traditional solutions are in therange of costs of smart solutions of DECC4 (orange andblack solutions in Figure 21) It can also be concluded thatinvestment pathways using less smart techniques provide a

cheaper response to technical performance evaluators suchas utilisation CML CI and losses

43 Summary Experiment evaluation runs from the2015ndash2047 period present lower investment rates for the

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

PU

Figure 14 CML and CI DECC2 2015ndash2023

CAPEXOPEX

DAR -transformer

Meshednetworks

Traditional -transformer

Traditional -cable

0

20000

40000

60000

80000

100000

120000

140000

pound

Figure 15 Average cost disaggregation per technique DECC2 2015ndash2023

Figure 16 Average CML and CI improvement per technique DECC2 2015ndash2023

13Complexity

2015ndash2023 period than if the evaluation is just performedwith a 2015ndash2023 time frame This occurs as a result of thechallenging low-carbon targets up to 2047 and a myopicplanning with short-term lookahead For an evaluationperiod four times larger the CAPEX and TOTEX increasedjust 18 compared to 2015ndash2023 concluding that betweenpound52M and pound68M the evaluation area regardless of thetime horizon for the investment or the demand scenarioconsidered is required

Furthermore it can be inferred comparing Tables 2 and 3that for less smart interventions the short-term planningis used compared to the long-term horizon planningDAR-cable and DAR-transformer experienced a significantimplementation variation between the two evaluationperiods for both demand scenarios as well as CAPEX allo-cated in cable upgrades falling from 89ndash91 (2015ndash2023)to 29ndash66 in the long term 2015ndash2047 run Both technicalperformance evaluators CML and CI respond to theirrespective CAPEX curve shape Due to a more incentivisedsmart grid technique during RIIO-ED2 and ED3 the per-centages of smart techniques implemented varied notablyFor DECC4 in 2023rsquos outlook the share of techniques is

25 for smart grid interventions whereas for 2050rsquos out-look the share is increased up to 58 In the same wayfor DECC2 in 2023rsquos outlook the share of smart interven-tions is 23 whereas in 2050rsquos outlook the share increasesup to 69

Feasible solutions characterising the solution space(Figure 21) differ in the degree of investment required andtechnical performance evaluators Each of the solution pathrepresented in the multidimensional solution space inFigure 21 represents the intersection of that dimension witheach axis Parallel coordinates [35] are low complexityworking for any N-dimension In the case of Figure 21it has 8 dimensions treating every variable uniformlyEach intersection of one solution path with each axis isthe value of that solution for that axis As for axis DECC2and DECC4 it represents the feasible solutions for eachdemand scenario Making a query by the percentage oftraditional reinforcement used in that solution we have thepartition between orange and green for DECC2 and blueand black being the first solutions that use more traditionalreinforcement compared with the ones that use more smarttechniques represented in green (DECC2) and black(DECC4) From Figure 21 we can argue that solutions thatuse more smart techniques (green and blacks) are moreexpensive in terms of CAPEX and OPEX than their peers(orange and blues) when compared by a demand scenarioIt can also be stated that solutions with a higher use of tradi-tional reinforcements are the ones that decrease the mostCML CI and losses

Disaggregating results by network state of the six 11 kVprimaries and by the feeder if necessary for further granulardebugging can discern locational capacity Under bothdemand scenarios Secklow Gate was seen to be the one withgreater capacity being necessary to apply fewer techniquesover the evaluation period 2015ndash2047 On the other handNewport Pagnell exceeds the capacity as soon as 2015requiring a high number of network state evaluation to befixed that happens for each subsequent year hence the largenumber of network states is presented in Table 4

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

pound p

er an

num

Figure 17 CAPEX and OPEX DECC4 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

pu

Figure 18 CML and CI DECC4 2015ndash2047

14 Complexity

5 Conclusions

Power flow analysis using a nodal network model is essentialwhen determining the benefits of trailed smart interventions

because the interactions and implications are highly specificto a particular location and scenario

This suggests that while the bottom-up approach isonerous in terms of data handling and manipulation this isworthwhile for strategic planning and policy evaluationComparing both investment strategies investment strategyadjustments will be necessary in future regulation periodsif an over-invested network behaves as displayed in theshort-term section of this study

Traditional reinforcement will continue to be the mainmethod by which network issues are mostly resolvedfollowed by dynamic asset rating and meshed networks

The assessment of the six novel smart interventions in theFALCON 11kV primary test area in Milton Keynes hasproved the suitability of three techniques able to fix failuresimproving the quality of service and their readiness to bedeployed in the near future These techniques are DAR forcables and transformers and meshed networks Meshednetworks have been repeatedly selected as a feasible tech-nique because using it will reduce CML CI and power losses

Table 3 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2047

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 18 5 31 6

DAR-transformer 32 16 32 6

ALT 0 0 0 0

Mesh 8 3 6 3

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 25 44 9 16

TRAD-cable 15 29 20 66

TRAD-transfer load 1 1 1 1

TRAD-new feeder 1 2 1 2

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

s pound p

er an

num

Figure 19 CAPEX and OPEX DECC2 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

PU

Figure 20 CML and CI DECC2 2015ndash2047

15Complexity

improving the quality of service and the efficiency whilebeing a cost-effective solution

The initial capacity at primary substations differedsignificantly and this affected the number and complexityof interventions required by the SIM Due to the loadscenarios showing significant peak load increases DAR wasoften a temporary measure that would delay but not removethe eventual need for traditional reinforcement

Implementing DAR for cables and transformers themonitoring of assets when their peak capacity is increasedwas analysed On the other hand it was observed thattraditional reinforcements still play a key role in keepingthe electricity distribution networks free of constraintsTRAD techniques such as transformer and cable replace-ments are able to fix the majority of failures and will beessential also in the future

The comparison between traditional reinforcements andnovel smart techniques has provided a new knowledge aboutthe suitability of each technique to be applied in termsof costs electrical performance failures fixed and assetreplaced Cable replacement is the most costly techniquehowever its use is unavoidable in a number of cases Further-more the applicability of each technique regarding costsinvolved improvements on power quality and efficiency

and failures solved lead to new questions to be analysed suchas the lack of these techniques to provide flexible capacitywithin the trailed area

To sum up this study has performed a comparativeanalysis of novel smart intervention techniques providinginsights for future investments in electricity distributionplanning Further work can focus on scaling up the analysisto include a larger section of the network or a constrainedarea to evaluate national applicability of the current findings

Data Availability

Some of the data relevant to this submission is proprietaryfrom Western Power Distribution It can be accessed forresearch purposes under a signed collaboration agreement

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors would like to thank the rest of the WesternPower Distribution Innovation team particularly the other

2400

2200

2000

350

360

370

380

390

400

1400

1300

1200

1100

1000DECC 4

50

55

60

12

11

10

9

8

4400

4200

4000

3800

3600

2400

CAPEX (in kpound) OPEX (in kpound) Utilisation Traditional reinforcements () DECC 2 998796 CML 998796 CI 998796 Losses

2200

2000

1800

Figure 21 Parallel coordinate visualisation of technoeconomic performance indicators under DECC4 and DECC2

Table 4 Summary of network states and results for demand scenario DECC4 and DECC2

Primary 11 kV substation Feeders Year TechniquesResultsDECC4

NSDECC4

ResultsDECC2

NSDECC2

Fox Milne 13 2047 Smart and traditional 54 276 47 259

Newport Pagnell 9 2047 Smart and traditional 48 317 31 318

Secklow Gate 9 2047 Smart and traditional 27 29 15 16

Bletchley 19 2047 Smart and traditional 32 40 21 48

Marlborough Street 11 2047 Smart and traditional 31 52 19 37

Childs Way 17 2047 Smart and traditional 67 398 54 181

16 Complexity

members of the FALCON project as well as Paul CharletonStuart Fowler Ilja Orlovs Dan Taylor and Steve IngramAlso the authors want to acknowledge OFGEM and theLowCarbon Network Fund for funding this piece of research

References

[1] European Commission ldquoCommunication from the Commis-sion to the European Parliament the Council the EuropeanEconomic and Social Committee and the Committee of theRegionsrdquo 2011 December 2016 httpseur-lexeuropaeulegal-contentENTXTqid=1537473508968ampuri=CELEX52016PC076721

[2] F G N Li and E Trutnevyte ldquoInvestment appraisal ofcost-optimal and near-optimal pathways for the UK elec-tricity sector transition to 2050rdquo Applied Energy vol 189pp 89ndash109 2017

[3] G Strbac I Konstantelos M Pollitt and R Green DeliveringFuture-Proof Energy Infrastructure National InfrastructureCommission 2016

[4] Ofgem ldquoGB Network regulation - the RIIO modelrdquo 2010December 2014 httpswwwofgemgovuknetwork-regulation-riio-model

[5] R Poudineh and T Jamasb ldquoDistributed generation storagedemand response and energy efficiency as alternatives togrid capacity enhancementrdquo Energy Policy vol 67 pp 222ndash231 2014

[6] J Yang X Bai D Strickland L Jenkins and A M CrossldquoDynamic network rating for low carbon distribution networkoperationmdashA UK applicationrdquo IEEE Transactions on SmartGrid vol 6 no 2 pp 988ndash998 2015

[7] M Behnke W Erdman S Horgan et al ldquoSecondary net-work distribution systems background and issues relatedto the interconnection of distributed resourcesrdquo NationalRenewable Energy Laboratory (NREL) Tech Rep NRELTP-560-38079 2005

[8] J Crispim J Braz R Castro and J Esteves ldquoSmart grids in theEU with smart regulation experiences from the UK Italy andPortugalrdquo Utilities Policy vol 31 pp 85ndash93 2014

[9] Tnei ldquoIPSA Power software for power systemsrdquo March 2016httpwwwipsa-powercom

[10] ETAP ldquoETAP PSrdquo 2018 April 2016 httpsetapcomsoftwareproduct-overview-main

[11] DINIS ldquoDINIS distribution network information sys-temsrdquo 2002 March 2016 httpsetapcomsoftwareproduct-overview-main

[12] Western Power Distribution ldquoSIM final reportrdquo 2015November 2016 httpwwwwesternpowerinnovationcoukProjectsFalconaspx

[13] Western Power Distribution ldquoProject FALCONrdquo 2015November 2017 httpwwwdiniscom

[14] EA Technology ldquoTransform modelrdquo March 2016 httpwwweatechnologycomproducts-and-servicescreate-smarter-gridstransform-model

[15] L Sigrist E Lobato L Rouco M Gazzino and M CantuldquoEconomic assessment of smart grid initiatives for islandpower systemsrdquo Applied Energy vol 189 pp 403ndash415 2017

[16] J A Momoh ldquoSmart grid design for efficient and flexiblepower networks operation and controlrdquo in 2009 IEEEPESPower Systems Conference and Exposition pp 1ndash8 SeattleWA USA 2009

[17] N Good K A Ellis and P Mancarella ldquoReview and classifica-tion of barriers and enablers of demand response in the smartgridrdquo Renewable and Sustainable Energy Reviews vol 72pp 57ndash72 2017

[18] Z Yang J Xiang and Y Li ldquoDistributed consensus basedsupplyndashdemand balance algorithm for economic dispatchproblem in a smart grid with switching graphrdquo IEEE Trans-actions on Industrial Electronics vol 64 no 2 pp 1600ndash1610 2017

[19] SuSTAINABLE ldquoSmart distribution network operation formaximising the integration of renewable generationrdquo 2015March 2016 httpwwwsustainableprojecteuHomeaspx

[20] S Mohtashami D Pudjianto and G Strbac ldquoStrategic distri-bution network planning with smart grid technologiesrdquo IEEETransactions on Smart Grid vol 8 no 6 pp 2656ndash2664 2017

[21] Smart Grid Forum ldquoSmart grid forum work stream 7rdquo2015 March 2016 httpwwwsmarternetworksorgprojectnia_nget015425

[22] ETI ldquoEnergyPath networksrdquo March 2016 httpwwweticoukprojectenergypath

[23] C Mateo Domingo T Gomez San Roman Aacute Sanchez-Miralles J P Peco Gonzalez and A Candela Martinez ldquoAreference network model for large-scale distribution planningwith automatic street map generationrdquo IEEE Transactions onPower Systems vol 26 no 1 pp 190ndash197 2011

[24] R Bolton and T J Foxon ldquoInfrastructure transformation as asocio-technical process mdash implications for the governanceof energy distribution networks in the UKrdquo TechnologicalForecasting and Social Change vol 90 pp 538ndash550 2015

[25] UK Power Networks ldquoDNO guide to future Smart man-agement of distribution networks Summary reportrdquo 2014httpsinnovationukpowernetworkscoukinnovationenProjectstier-2-projectsLow-Carbon-London-(LCL)Project-DocumentsLCL20Learning20Report20-20SR20-20Summary20Report20-20DNO20Guide20to20Future20Smart20Management20of20Distribution20Networkspdf

[26] A Botea J Rintanen and D Banerjee ldquoOptimal reconfigura-tion for supply restoration with informed AlowastSearchrdquo IEEETransactions on Smart Grid vol 3 no 2 pp 583ndash593 2012

[27] T Logenthiran D Srinivasan and T Z Shun ldquoDemand sidemanagement in smart grid using heuristic optimizationrdquoIEEE Transactions on Smart Grid vol 3 no 3 pp 1244ndash1252 2012

[28] J Saacutenchez-Cuadrado J de Lara and E Guerra ldquoBottom-upmeta-modelling an interactive approachrdquo in Model DrivenEngineering Languages and Systems pp 3ndash19 Springer2012

[29] Y H Kim F J Sting and C H Loch ldquoTop-down bottom-upor both Toward an integrative perspective on operationsstrategy formationrdquo Journal of Operations Managementvol 32 no 7-8 pp 462ndash474 2014

[30] C Bohringer ldquoThe synthesis of bottom-up and top-down inenergy policy modelingrdquo Energy Economics vol 20 no 3pp 233ndash248 1998

[31] T G R Bower ldquoRepetition in human developmentrdquo Merrill-Palmer Quarterly of Behavior and Development vol 20no 4 pp 303ndash318 1974

[32] H Mintzberg and J A Waters ldquoOf strategies deliberate andemergentrdquo Strategic Management Journal vol 6 no 3pp 257ndash272 1985

17Complexity

The research objective is settled to prove the suitability ofthe six novel smart interventions presented in Section 2and along with the traditional reinforcements provide anevolutionary planning insight for future power networksTo undertake this study specific experiments were selectedfor a certain power trial network under different demandscenarios and evaluation periods assessing smart tech-niques along with traditional reinforcements The approachinvolved running a set of experiments using the SIM for thesix 11 kV primaries in the FALCON trial area

31 SIM Support Algorithms The essence of the SIMapproach is its ability to take a network configuration andcorresponding load profiles in a particular year (termed asinitial network state) perform power flow and reliabilityanalysis and create derivative network states in a processknown as ldquonetwork state expansionrdquo The expansion happenseither by transitioning to the following year for networkstates without any failures or by applying intervention

techniques to resolve network issues With each new net-work state created the SIM therefore is faced with adecision as to which network state from the executionhistory to expand next The expansion can be guided bysimple depth first or breadth first algorithms which areimplemented in the SIM for verification purposes Thedepth-first algorithm always selects the newest ie themost recently created network state that is not fullyexpanded for expansion while the breadth-first algorithmalways selects the oldest network state However thosesimple heuristics are inadequate for any practical usebeyond simple test cases due to the size of the searchspace obtained by permuting all possible interventionsover a number of years To perform intelligent explorationof the search space the SIM uses a heuristic approach thatis based on an Alowast algorithm [26 27]

The baseline Alowast algorithm aims at finding the least-costpath through the search space As Alowast traverses the searchspace it builds a tree of partial paths The leaf nodes of this

Learning transfer

Key component

Systempackage conatinga set of key components

Learninganalysis of results for theimprovement of models and development ofpolicies

Smart grid planning framework

Substationmonitoring

Dynamicasset ratings

data

Automatedload transfer

dataMeshed

network data

Communication network trial

Monitoringdata collection

system

Trials data distributionmanagement system

Scenario investment model

Network modelling tool(technical Assessment)

Dynamic assetratings

techniquemodel

Automatedload transfer

techniquemodel

Batterystorage

techniquemodel

Meshednetwork

techniquemodel

Traditional reinforcement

technique model

Networktopology amp

loads

Distributedgenerationtechnique

model

Demand sidemanagement

techniquemodel

Multi-yearscheduler

Economicinvestmentassesment

Networkperformance

analysisVisualisationand reporting

SIM harness(economic assesment and

reporting)

Performance analysisamp learning

Energy modelminusmeasured data

comparison

Demand datamodelling and

scenario development

Impr

ove e

nerg

ym

odel

Design build andoperation

Strategic andtactical planning

Strategic planning supportconnections and

infrastructure planningplanning policy development

Impr

ove t

echn

ique

and

netw

ork m

odels

Batterystorage data

Distributedgeneration

trial

Demand sidemanagement

data

Design amp build learning

Impr

ove i

mpl

emen

tatio

n co

st m

odels

Figure 5 Smart grid planning framework diagram

6 Complexity

tree (failed network states) are stored in a priority queue thatis ordered using a cost function

f x = g x + h x 1

where h x is a heuristic estimate of the path cost toreach the goal and g x is the distance travelled fromthe initial node

The SIM selects network states from the priority queue toapply intervention techniques one application at a timeDeployment of a technique produces a new network statefor which a power flow analysis is performed in intact andall n minus 1 (contingency) network operation modes If allthe failures are resolved a reliability analysis comprisingcustomer minutes lost (CML) customer interruptions (CI)losses and fault level studies is performed A new networkstate is subsequently created in the next year of evaluationor if it is already the last year of evaluation the costsof interventions are calculated and the network statetogether with all its expansion history is saved to theresult store as a new result The evaluation is terminated

(Figure 6) when criteria such as the number of resultsnumber of network state evaluations or run time arereached As noted previously Alowast uses a combination ofdistance travelled so far and a heuristic estimate of thedistance to reach the endpoint

In SIM case this corresponds to g x being the totalexpenditures (TOTEX) incurred so far and h x being aheuristic estimate of TOTEX to reach the end year ofthe experiment TOTEX also referred to as the totalexpenditure comprises implementation costs (CAPEX)that occur only once when an intervention is applied tothe network operation costs (OPEX) that refer to anongoing expense of operating an asset or a scheme alongwith asset life degradation and metric costs that includeincentive payments for losses fault levels and networkreliability Referring to (2) the g x for a network statexi in year i is defined as

g xi = ci + oi + jminus1

j=1cj + oj +mj 2

Initial network state

Create new networkstate in the next year Analyse power flow

Generate and applynext intervention

technique application

Save network state tofailed network state

store

Update path costestimates

Reorder priority queue

Select top mostnetwork state from the

priority queueTermination

criteria reached

Failure present

Analyse reliabilityEnd yearreached

minus

+

minus

+

minus

+

Update solution costs

Save result

End

Figure 6 SIM evaluation flowchart

7Complexity

where ci is CAPEX in the current year oi is OPEX in thecurrent year and cj oj and mj are CAPEX OPEX andmetrics costs respectively of the ancestor network statewith no issues in year j The heuristic estimate h x ofthe cost to reach the end year is given by

h xi = cREMi +mi + n

k=i+1ck + ok +mk 3

where cREMi is the average remaining CAPEX in the cur-rent year i mi is the average metric cost in this year ckok and mk are the average CAPEX OPEX and metriccosts respectively of the descendant network state in yeark with no issues and n is the end year of evaluation Pre-seeded to a constant value c0 the average CAPEX isupdated each time a network state is expanded in a partic-ular year thus the estimated costs of fixing all issues in aparticular year progressively approach true average Settingc0 to a value greater than 0 speeds up the expansion pro-cess ie all interventions that cost less than c0 will resultin new network states that are at the top of the priorityqueue The business rationale is that DNOs are not thatinterested in optimizing interventions costing less than acertain threshold The learned averages are propagatedback to network states in the priority queue thus updatingtheir estimated effort and leading to the reordering of thequeue unlike the average CAPEX average OPEX andmetrics costs which are assumed to be 0 for years withno network states The average OPEX value for a year isobtained using

o = jToc jT jminus1 4

where j is a column vector of ones and oc is an OPEXvector of compliant network states in that year Likewisethe average metric cost is obtained according to

m = jTmc jT jminus1 5

where j is a column vector of ones and mc is a vector ofthe metric costs of compliant network states in that year

32 Conceptualising Bottom-Up Evolutionary PlanningModel-driven engineering (MDE) uses analysis constructionand development of frameworks to formulate metamodelsThose models are usually characterised using domain-specific modelling approaches [28] containing appropriatedetail abstraction of particular domain through a specificmetamodel The use of metamodels requires therefore inputsfrom domain experts which can be used to generate aggre-gated or disaggregated models Top-down and bottom-upare the conceptual definition of aggregated and disaggregatedmodels [29] These two modelling paradigms are frequentlyused to epitomise domain interactions among the operationof the energy system the econometrics related and thetechnical performance indicators [30]

From a bottom-up modelling approach [31 32] thetop-down perspective is a simplistic characterisation ofhow electrical power networks combine locational eventsand individual asset performance with high-level objectiveslike improving the CML of a certain congested area [33 34]From an engineering point of view both are still validsince outputs and strategic forecast are produced in bothHow those outcomes are calculated validated and trans-formed to strategy is presented in Figure 7 (top-down)and Figure 8 (bottom-up)

Interventiontechnique costs

Relative usage ofinterventiontechnique

Interventiontechnique installation

details

Interventiontechnique models

Validationphysical trials

Interventiontechnique operation

details

Purchase andoperation cost data

Cost model

Network data

Representativenetworks

Model network through years

Calculate intervention technique usage

Strategic forecasts

Validationsampled nodal

networks

Demand scenarios

Figure 7 Top-down conceptualisation of evolutionary power networks planning

8 Complexity

The ability of bottom-up to capture discrete locationalimpacts of technologies on the system and their disaggre-gated costs is triggering the following subsections Trade-offmethodologies are needed for planning evolutionary powersystems where observing disaggregated result strategic fore-casts are to be produced These methodologies need to beinteractive in the sense that starting from an initial stateand after a testing or learning phase the network is able toaccommodate techniques that have improved the systemproviding an exploratory set of solutions that can beexpanded or discarded as the model evolves through time

33 Experiment Characterisation To illustrate the processingof the methodology adopted we consider a typical networkscenario tree created by the tool that consists of failed (red)and compliant (green) network states in different years ofevaluation The SIM starts with a single network state inthe first year of evaluation Every year the network isevaluated for compliance in intact and n minus 1 contingencyoperation modes If failures are detected the SIM appliesintervention techniques described in Section 2 to create net-work patches that resolve the failures Each application of anintervention technique creates a new network state The toolhas to resolve all issues in the network before transitioninginto the next year DNO planners usually run power flowstudies with a single fixed load pattern In contrast the SIMchecks each network state for compliance under 18 charac-teristic day load scenarios each comprising of 48 half-hoursettlement periods All studies are performed under intactand n minus 1 contingency network operation modes The SIM

calculates patch costs using cost drivers returned by the net-work modelling tool The cost driver describes a networkintervention and consists of two parts namely the patchkey and the scaling The patch key identifies the nature ofthe modification of the network performed (removal or addi-tion of an asset and the type of the asset) Scaling data is rel-evant only to patches that can be installed in multiples of onesuch as cable upgrades and additional transformer installa-tion Scaling data structure provides a list of multipliers tothe base cost data available in the SIM database In case ofcable upgrade or replacement it enables the SIM to correctlyestimate full installation costs from per unit of length valuesFinally for postprocessing analysis the SIM also returns a listof failures by asset It was identified by DNO planners asindispensable features to help validate the system and corre-late the expansion trees to the actual assets on the networkdiagram The failure detail table contains asset ID anddescription alongside information about the number of fail-ures in intact and n minus 1 operating modes as well as absoluteand per unit thermal and voltage failure magnitude

34 Case Characterisation Two set of experiments wereperformed for this study one for comparing short-termevaluation period for RIIO-ED1 (2015ndash2023) where theRIIO-ED1 investment planning has been stylised and alonger planning period for RIIO-ED1 to RIIO-ED4 from2015 up to 2047 The other set aimed at evaluating differ-ent DECC demand scenarios Experiments evaluated twodemand scenarios DECC2 and DECC4 DECC4 representsas displayed in Table 1 the slow-progression scenario and

Purchase andoperation cost data

Cost model Nodal network model

Model network through years

Apply intervention technique to keep the network compilant

Find optimal network development options

Solutions for individual networks

Aggregate results for larger network areas

Strategic forecasts

Network data Demand scenarios

Interventiontechnique costs

Interventiontechnique installation

details

Nodal interventiontechnique models

Validation withphysical trials

Relative usage ofinterventiontechniques

Validation of powerflow and reliability

models

Interventiontechnique operation

details

Load profiles

Figure 8 Bottom-up conceptualisation of evolutionary power networks planning

9Complexity

DECC2 is with DECC3 the most challenging scenario interms of electrification and low-carbon technology integra-tion The procedure to run the experiments in the SIM isshown in Figure 6 The inputs to be sent to the SIM aredetailed in Figure 5 the selected network techniques evalu-ation period demand scenario and cost model whereas theoutputs of the SIM we obtain are techniques used failuressolved assets fixed and electrical performance indicatorsThe SIM address mentioned outputs for long-term plan-ning reinforcements to optimise investment asset planningresolving network constraints The performance criteria eval-uated were capital expenditures (CAPEX) operating expen-ditures (OPEX) utilisation of assets CMLs CIs and lossesThese parameter values are delivered by the SIM after eachsimulation In order to look for the optimal solution amongthe feasible solution pathways the SIM allows a granularitystudy of each network state

4 Results

This section presents the assessment of the six smart gridinterventions along with traditional reinforcements in thetrial area compounded by six 11 kV primaries in MiltonKeynes UK for two different evaluation periods DECC2and DECC4 Subsection 41 introduces the results for the2015ndash2023 period as short-term planning and Subsection42 presents the results for a long-term evaluation period2015ndash2047

41 Short-Term Planning This section presents the assess-ment of the six smart grid interventions along with tradi-tional reinforcements in the trial area compounded by six11 kV primaries in Milton Keynes UK for two differentdemand scenarios DECC2 and DECC4

411 DECC4 2015ndash2023 Figure 9 shows the trends ofCAPEX and OPEX Note that in the year 2015 there is aCAPEXrsquos peak due to the application of more techniquesand OPEX increments over time from 2017 to 2023

Despite the investment the increase is compensated bya benefit in the electricity distribution network with areduction in CML and CI as shown in Figure 10

The summary of the proportion of installations requiredduring this evaluation period and by capital expenditures pertechnique is presented in Table 2 for both DECC scenarios

The average yearly price of each technique implementedto fix a network state is key for future decision-makingconsiderations

Figure 11 displays the average price of each techniquedisaggregated by CAPEX and OPEX

Performance indicators are key parameters for futuredecision-making within electricity distribution planning asquantifiers due to their influence on the quality of service

Therefore as shown in Figure 12 the only smart tech-nique used in combination with traditional reinforcementsin this scenario and evaluation period that slightly influenceslightly CML and CI is meshed networks

412 DECC2 2015ndash2023 Figure 13 plots the trend ofCAPEX and OPEX over the evaluation period It is important

to highlight that there is a CAPEX peak in 2015 due to anincrease of techniques applied in this period

This CAPEX increment produces a benefit in terms ofCML and CI as observed in Figure 14

The distribution of techniques applied and capital expen-ditures per technique are shown in Table 2 The average priceof each technique implementation in this demand scenariofor this evaluation period is shown in Figure 15

As displayed in Figure 12 for DECC4rsquos simulation theonly smart grid technique that improves the quality of serviceis the meshed network In Figure 16 the contribution ofsmart techniques on CML and CI for DECC2 demandevaluation is captured

413 Comparison between DECC2 and DECC4 for 2015ndash2023 The results presented may facilitate improvementsin electricity distribution network operations and planningresulting in better-informed decision-making when upgrad-ing current electricity distribution networks The assess-ment of the six smart grid techniques discovered thatonly three of them were selected as part of optimal

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

Mill

ions

pound p

er an

num

CAPEXOPEX

442444648

55254

Figure 9 CAPEX and OPEX DECC4 2015ndash2023

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

pu

Figure 10 CML and CI DECC4 2015ndash2023

10 Complexity

solutions for DECC4 and DECC2 as described in Table 2These three techniques applied are DAR for cables andtransformers and meshed networks

Comparing the cost trends of the two assessed scenariosit is notable that in both demand scenarios DECC4 and

DECC2 the CAPEX peak occurs in 2015 (Figures 9 and13) reflecting the more difficult nature of network statesto be feasible in a demanding scenario whereas DECC2shows a higher improvement of CI and CML performanceindicators as can be seen in Figures 10 and 14

Table 2 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2023

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 15 2 14 2

DAR-transformer 7 1 5 1

ALT 0 0 0 0

Mesh 3 1 4 1

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 8 3 7 2

TRAD-cable 60 89 65 91

TRAD-transfer load 6 2 4 1

TRAD-new feeder 1 2 1 2

DAR - cable DAR -transformer

Meshednetworks

TRAD -transformer

TRAD - cable

CAPEXOPEX

0

20000

40000

60000

80000

100000

pound

Figure 11 Average cost disaggregation per technique DECC4 2015ndash2023

Figure 12 Average CML and CI improvement per technique DECC4 2015ndash2023

11Complexity

The techniques applied are the key consideration to beassessed It is necessary to analyse the number of interven-tions applied by type the effect of these techniques in theelectric grid solving failures and fixing assets Figures 11and 15 show average prices of techniques used in eachdemand scenario Smart techniques have a lower TOTEXthan traditional reinforcements These novel techniquesabove are not frequently able to fix failures and thereforeproduce feasible network states In terms of CML and CIthe only technique able to moderately contribute to theirimprovement is meshed networks as shown in Figures 12and 16 Results attribute the majority of fixes to traditionalreinforcements with a comparatively smaller number offailures being solved by smart techniques (Table 2) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention forDECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

For DECC4 the three mentioned techniques along withtraditional cable and transformer replacement are applied(Table 2) whereas for DECC2 two of them are appliedDAR for cables and meshed networks (Table 2) Comparingthe cost trends of the two assessed scenarios it is notable thatin DECC4 the CAPEX peak occurs in 2015 (Figure 7)whereas in DECC2 besides the peak in 2015 there is alsoone in 2019 (Figure 11) reflecting the more difficult natureof network states to be feasible in a demanding scenarioHowever DECC2 shows a higher improvement of electricalperformance indicators as can be seen in Figure 12

In addition traditional cable replacement DAR forcables and meshed networks are able to fix the cable whereastraditional transformer replacement and DAR for trans-formers are able to fix transformer issues (Figure 16) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention for

DECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

42 Long-Term Planning This section evaluates two experi-ments namely characterising the DECC2 and DECC4 sce-narios for 2015ndash2047 evaluation period For each demandscenario expected investments disaggregating CAPEX andOPEX are presented the evolution of electrical performanceindicators and the number of techniques applied

421 DECC4 2015ndash2047 In this experiment the mostsignificant results to analyse the suitability of each techniqueare presented

Figure 17 shows the trend of CAPEX and OPEX from2015 to 2047

There are CAPEX peaks in the year 2018 and during thebeginning of RIIO-ED2 in the years 2025 to 2026 due to theapplication of more techniques to fulfill low-carbon targets

Figure 18 indicates that upgrades on the network aredirectly related to technical performance indicators CMLand CI

In Table 3 the techniques applied and their contributionto CAPEX during the evaluation period from 2015 to 2047 bya demand scenario are shown

422 DECC2 2015ndash2047 Within this experiment the mostrelevant results to analyse the evolutionary network statesare presented

In Figure 19 the trend of CAPEX and OPEX from 2015to 2047 is shown There is a significant increase of CAPEXin the years 2024 to 2026 at the beginning of RIIO-ED2due to the necessary implementation of new techniques toreach low-carbon targets The evolution of CML and CI ispresented in Figure 20 linking larger investment years whenmajor reductions are found The contribution of each solutiontechnique is presented in Table 3

423 Comparison between DECC2 and DECC4 for 2015ndash2047 Figure 21 presents a multidimensional parallel coor-dinate representation of the feasible network statersquoscombinations that produced a 2015ndash2047 investment path-way for the evaluated area It bundles economic indicatorsie CAPEX and OPEX with technical performance indica-tors providing valuable insights on the number of traditionalreinforcements utilised to heal falling network states Resultsare also clustered by the two demand scenarios assessedduring the experiments

Solutions of DECC2 (represented in orange and green)and DECC4 (in black and blue) are clustered by the per-centage of traditional reinforcements utilised as well asthe utilisation factor

Solutions in green and in black represent those wheresmart grids were more used whereas orange and blue repre-sent the cheapest (CAPEX and OPEX) less utilised and theworst responding to the decrease of CML CI and lossesand the ones using more traditional reinforcement to fixnetwork states

Besides the fact that DECC2 smart solutions are between16 and 28 more expensive than DECC4 traditional

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

CAPEXOPEX

442444648

552

Mill

ions

pound p

er an

num

54

Figure 13 CAPEX and OPEX DECC2 2015ndash2023

12 Complexity

reinforcement solutions (green solutions versus blue solu-tions in Figure 21) DECC2 traditional solutions are in therange of costs of smart solutions of DECC4 (orange andblack solutions in Figure 21) It can also be concluded thatinvestment pathways using less smart techniques provide a

cheaper response to technical performance evaluators suchas utilisation CML CI and losses

43 Summary Experiment evaluation runs from the2015ndash2047 period present lower investment rates for the

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

PU

Figure 14 CML and CI DECC2 2015ndash2023

CAPEXOPEX

DAR -transformer

Meshednetworks

Traditional -transformer

Traditional -cable

0

20000

40000

60000

80000

100000

120000

140000

pound

Figure 15 Average cost disaggregation per technique DECC2 2015ndash2023

Figure 16 Average CML and CI improvement per technique DECC2 2015ndash2023

13Complexity

2015ndash2023 period than if the evaluation is just performedwith a 2015ndash2023 time frame This occurs as a result of thechallenging low-carbon targets up to 2047 and a myopicplanning with short-term lookahead For an evaluationperiod four times larger the CAPEX and TOTEX increasedjust 18 compared to 2015ndash2023 concluding that betweenpound52M and pound68M the evaluation area regardless of thetime horizon for the investment or the demand scenarioconsidered is required

Furthermore it can be inferred comparing Tables 2 and 3that for less smart interventions the short-term planningis used compared to the long-term horizon planningDAR-cable and DAR-transformer experienced a significantimplementation variation between the two evaluationperiods for both demand scenarios as well as CAPEX allo-cated in cable upgrades falling from 89ndash91 (2015ndash2023)to 29ndash66 in the long term 2015ndash2047 run Both technicalperformance evaluators CML and CI respond to theirrespective CAPEX curve shape Due to a more incentivisedsmart grid technique during RIIO-ED2 and ED3 the per-centages of smart techniques implemented varied notablyFor DECC4 in 2023rsquos outlook the share of techniques is

25 for smart grid interventions whereas for 2050rsquos out-look the share is increased up to 58 In the same wayfor DECC2 in 2023rsquos outlook the share of smart interven-tions is 23 whereas in 2050rsquos outlook the share increasesup to 69

Feasible solutions characterising the solution space(Figure 21) differ in the degree of investment required andtechnical performance evaluators Each of the solution pathrepresented in the multidimensional solution space inFigure 21 represents the intersection of that dimension witheach axis Parallel coordinates [35] are low complexityworking for any N-dimension In the case of Figure 21it has 8 dimensions treating every variable uniformlyEach intersection of one solution path with each axis isthe value of that solution for that axis As for axis DECC2and DECC4 it represents the feasible solutions for eachdemand scenario Making a query by the percentage oftraditional reinforcement used in that solution we have thepartition between orange and green for DECC2 and blueand black being the first solutions that use more traditionalreinforcement compared with the ones that use more smarttechniques represented in green (DECC2) and black(DECC4) From Figure 21 we can argue that solutions thatuse more smart techniques (green and blacks) are moreexpensive in terms of CAPEX and OPEX than their peers(orange and blues) when compared by a demand scenarioIt can also be stated that solutions with a higher use of tradi-tional reinforcements are the ones that decrease the mostCML CI and losses

Disaggregating results by network state of the six 11 kVprimaries and by the feeder if necessary for further granulardebugging can discern locational capacity Under bothdemand scenarios Secklow Gate was seen to be the one withgreater capacity being necessary to apply fewer techniquesover the evaluation period 2015ndash2047 On the other handNewport Pagnell exceeds the capacity as soon as 2015requiring a high number of network state evaluation to befixed that happens for each subsequent year hence the largenumber of network states is presented in Table 4

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

pound p

er an

num

Figure 17 CAPEX and OPEX DECC4 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

pu

Figure 18 CML and CI DECC4 2015ndash2047

14 Complexity

5 Conclusions

Power flow analysis using a nodal network model is essentialwhen determining the benefits of trailed smart interventions

because the interactions and implications are highly specificto a particular location and scenario

This suggests that while the bottom-up approach isonerous in terms of data handling and manipulation this isworthwhile for strategic planning and policy evaluationComparing both investment strategies investment strategyadjustments will be necessary in future regulation periodsif an over-invested network behaves as displayed in theshort-term section of this study

Traditional reinforcement will continue to be the mainmethod by which network issues are mostly resolvedfollowed by dynamic asset rating and meshed networks

The assessment of the six novel smart interventions in theFALCON 11kV primary test area in Milton Keynes hasproved the suitability of three techniques able to fix failuresimproving the quality of service and their readiness to bedeployed in the near future These techniques are DAR forcables and transformers and meshed networks Meshednetworks have been repeatedly selected as a feasible tech-nique because using it will reduce CML CI and power losses

Table 3 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2047

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 18 5 31 6

DAR-transformer 32 16 32 6

ALT 0 0 0 0

Mesh 8 3 6 3

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 25 44 9 16

TRAD-cable 15 29 20 66

TRAD-transfer load 1 1 1 1

TRAD-new feeder 1 2 1 2

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

s pound p

er an

num

Figure 19 CAPEX and OPEX DECC2 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

PU

Figure 20 CML and CI DECC2 2015ndash2047

15Complexity

improving the quality of service and the efficiency whilebeing a cost-effective solution

The initial capacity at primary substations differedsignificantly and this affected the number and complexityof interventions required by the SIM Due to the loadscenarios showing significant peak load increases DAR wasoften a temporary measure that would delay but not removethe eventual need for traditional reinforcement

Implementing DAR for cables and transformers themonitoring of assets when their peak capacity is increasedwas analysed On the other hand it was observed thattraditional reinforcements still play a key role in keepingthe electricity distribution networks free of constraintsTRAD techniques such as transformer and cable replace-ments are able to fix the majority of failures and will beessential also in the future

The comparison between traditional reinforcements andnovel smart techniques has provided a new knowledge aboutthe suitability of each technique to be applied in termsof costs electrical performance failures fixed and assetreplaced Cable replacement is the most costly techniquehowever its use is unavoidable in a number of cases Further-more the applicability of each technique regarding costsinvolved improvements on power quality and efficiency

and failures solved lead to new questions to be analysed suchas the lack of these techniques to provide flexible capacitywithin the trailed area

To sum up this study has performed a comparativeanalysis of novel smart intervention techniques providinginsights for future investments in electricity distributionplanning Further work can focus on scaling up the analysisto include a larger section of the network or a constrainedarea to evaluate national applicability of the current findings

Data Availability

Some of the data relevant to this submission is proprietaryfrom Western Power Distribution It can be accessed forresearch purposes under a signed collaboration agreement

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors would like to thank the rest of the WesternPower Distribution Innovation team particularly the other

2400

2200

2000

350

360

370

380

390

400

1400

1300

1200

1100

1000DECC 4

50

55

60

12

11

10

9

8

4400

4200

4000

3800

3600

2400

CAPEX (in kpound) OPEX (in kpound) Utilisation Traditional reinforcements () DECC 2 998796 CML 998796 CI 998796 Losses

2200

2000

1800

Figure 21 Parallel coordinate visualisation of technoeconomic performance indicators under DECC4 and DECC2

Table 4 Summary of network states and results for demand scenario DECC4 and DECC2

Primary 11 kV substation Feeders Year TechniquesResultsDECC4

NSDECC4

ResultsDECC2

NSDECC2

Fox Milne 13 2047 Smart and traditional 54 276 47 259

Newport Pagnell 9 2047 Smart and traditional 48 317 31 318

Secklow Gate 9 2047 Smart and traditional 27 29 15 16

Bletchley 19 2047 Smart and traditional 32 40 21 48

Marlborough Street 11 2047 Smart and traditional 31 52 19 37

Childs Way 17 2047 Smart and traditional 67 398 54 181

16 Complexity

members of the FALCON project as well as Paul CharletonStuart Fowler Ilja Orlovs Dan Taylor and Steve IngramAlso the authors want to acknowledge OFGEM and theLowCarbon Network Fund for funding this piece of research

References

[1] European Commission ldquoCommunication from the Commis-sion to the European Parliament the Council the EuropeanEconomic and Social Committee and the Committee of theRegionsrdquo 2011 December 2016 httpseur-lexeuropaeulegal-contentENTXTqid=1537473508968ampuri=CELEX52016PC076721

[2] F G N Li and E Trutnevyte ldquoInvestment appraisal ofcost-optimal and near-optimal pathways for the UK elec-tricity sector transition to 2050rdquo Applied Energy vol 189pp 89ndash109 2017

[3] G Strbac I Konstantelos M Pollitt and R Green DeliveringFuture-Proof Energy Infrastructure National InfrastructureCommission 2016

[4] Ofgem ldquoGB Network regulation - the RIIO modelrdquo 2010December 2014 httpswwwofgemgovuknetwork-regulation-riio-model

[5] R Poudineh and T Jamasb ldquoDistributed generation storagedemand response and energy efficiency as alternatives togrid capacity enhancementrdquo Energy Policy vol 67 pp 222ndash231 2014

[6] J Yang X Bai D Strickland L Jenkins and A M CrossldquoDynamic network rating for low carbon distribution networkoperationmdashA UK applicationrdquo IEEE Transactions on SmartGrid vol 6 no 2 pp 988ndash998 2015

[7] M Behnke W Erdman S Horgan et al ldquoSecondary net-work distribution systems background and issues relatedto the interconnection of distributed resourcesrdquo NationalRenewable Energy Laboratory (NREL) Tech Rep NRELTP-560-38079 2005

[8] J Crispim J Braz R Castro and J Esteves ldquoSmart grids in theEU with smart regulation experiences from the UK Italy andPortugalrdquo Utilities Policy vol 31 pp 85ndash93 2014

[9] Tnei ldquoIPSA Power software for power systemsrdquo March 2016httpwwwipsa-powercom

[10] ETAP ldquoETAP PSrdquo 2018 April 2016 httpsetapcomsoftwareproduct-overview-main

[11] DINIS ldquoDINIS distribution network information sys-temsrdquo 2002 March 2016 httpsetapcomsoftwareproduct-overview-main

[12] Western Power Distribution ldquoSIM final reportrdquo 2015November 2016 httpwwwwesternpowerinnovationcoukProjectsFalconaspx

[13] Western Power Distribution ldquoProject FALCONrdquo 2015November 2017 httpwwwdiniscom

[14] EA Technology ldquoTransform modelrdquo March 2016 httpwwweatechnologycomproducts-and-servicescreate-smarter-gridstransform-model

[15] L Sigrist E Lobato L Rouco M Gazzino and M CantuldquoEconomic assessment of smart grid initiatives for islandpower systemsrdquo Applied Energy vol 189 pp 403ndash415 2017

[16] J A Momoh ldquoSmart grid design for efficient and flexiblepower networks operation and controlrdquo in 2009 IEEEPESPower Systems Conference and Exposition pp 1ndash8 SeattleWA USA 2009

[17] N Good K A Ellis and P Mancarella ldquoReview and classifica-tion of barriers and enablers of demand response in the smartgridrdquo Renewable and Sustainable Energy Reviews vol 72pp 57ndash72 2017

[18] Z Yang J Xiang and Y Li ldquoDistributed consensus basedsupplyndashdemand balance algorithm for economic dispatchproblem in a smart grid with switching graphrdquo IEEE Trans-actions on Industrial Electronics vol 64 no 2 pp 1600ndash1610 2017

[19] SuSTAINABLE ldquoSmart distribution network operation formaximising the integration of renewable generationrdquo 2015March 2016 httpwwwsustainableprojecteuHomeaspx

[20] S Mohtashami D Pudjianto and G Strbac ldquoStrategic distri-bution network planning with smart grid technologiesrdquo IEEETransactions on Smart Grid vol 8 no 6 pp 2656ndash2664 2017

[21] Smart Grid Forum ldquoSmart grid forum work stream 7rdquo2015 March 2016 httpwwwsmarternetworksorgprojectnia_nget015425

[22] ETI ldquoEnergyPath networksrdquo March 2016 httpwwweticoukprojectenergypath

[23] C Mateo Domingo T Gomez San Roman Aacute Sanchez-Miralles J P Peco Gonzalez and A Candela Martinez ldquoAreference network model for large-scale distribution planningwith automatic street map generationrdquo IEEE Transactions onPower Systems vol 26 no 1 pp 190ndash197 2011

[24] R Bolton and T J Foxon ldquoInfrastructure transformation as asocio-technical process mdash implications for the governanceof energy distribution networks in the UKrdquo TechnologicalForecasting and Social Change vol 90 pp 538ndash550 2015

[25] UK Power Networks ldquoDNO guide to future Smart man-agement of distribution networks Summary reportrdquo 2014httpsinnovationukpowernetworkscoukinnovationenProjectstier-2-projectsLow-Carbon-London-(LCL)Project-DocumentsLCL20Learning20Report20-20SR20-20Summary20Report20-20DNO20Guide20to20Future20Smart20Management20of20Distribution20Networkspdf

[26] A Botea J Rintanen and D Banerjee ldquoOptimal reconfigura-tion for supply restoration with informed AlowastSearchrdquo IEEETransactions on Smart Grid vol 3 no 2 pp 583ndash593 2012

[27] T Logenthiran D Srinivasan and T Z Shun ldquoDemand sidemanagement in smart grid using heuristic optimizationrdquoIEEE Transactions on Smart Grid vol 3 no 3 pp 1244ndash1252 2012

[28] J Saacutenchez-Cuadrado J de Lara and E Guerra ldquoBottom-upmeta-modelling an interactive approachrdquo in Model DrivenEngineering Languages and Systems pp 3ndash19 Springer2012

[29] Y H Kim F J Sting and C H Loch ldquoTop-down bottom-upor both Toward an integrative perspective on operationsstrategy formationrdquo Journal of Operations Managementvol 32 no 7-8 pp 462ndash474 2014

[30] C Bohringer ldquoThe synthesis of bottom-up and top-down inenergy policy modelingrdquo Energy Economics vol 20 no 3pp 233ndash248 1998

[31] T G R Bower ldquoRepetition in human developmentrdquo Merrill-Palmer Quarterly of Behavior and Development vol 20no 4 pp 303ndash318 1974

[32] H Mintzberg and J A Waters ldquoOf strategies deliberate andemergentrdquo Strategic Management Journal vol 6 no 3pp 257ndash272 1985

17Complexity

tree (failed network states) are stored in a priority queue thatis ordered using a cost function

f x = g x + h x 1

where h x is a heuristic estimate of the path cost toreach the goal and g x is the distance travelled fromthe initial node

The SIM selects network states from the priority queue toapply intervention techniques one application at a timeDeployment of a technique produces a new network statefor which a power flow analysis is performed in intact andall n minus 1 (contingency) network operation modes If allthe failures are resolved a reliability analysis comprisingcustomer minutes lost (CML) customer interruptions (CI)losses and fault level studies is performed A new networkstate is subsequently created in the next year of evaluationor if it is already the last year of evaluation the costsof interventions are calculated and the network statetogether with all its expansion history is saved to theresult store as a new result The evaluation is terminated

(Figure 6) when criteria such as the number of resultsnumber of network state evaluations or run time arereached As noted previously Alowast uses a combination ofdistance travelled so far and a heuristic estimate of thedistance to reach the endpoint

In SIM case this corresponds to g x being the totalexpenditures (TOTEX) incurred so far and h x being aheuristic estimate of TOTEX to reach the end year ofthe experiment TOTEX also referred to as the totalexpenditure comprises implementation costs (CAPEX)that occur only once when an intervention is applied tothe network operation costs (OPEX) that refer to anongoing expense of operating an asset or a scheme alongwith asset life degradation and metric costs that includeincentive payments for losses fault levels and networkreliability Referring to (2) the g x for a network statexi in year i is defined as

g xi = ci + oi + jminus1

j=1cj + oj +mj 2

Initial network state

Create new networkstate in the next year Analyse power flow

Generate and applynext intervention

technique application

Save network state tofailed network state

store

Update path costestimates

Reorder priority queue

Select top mostnetwork state from the

priority queueTermination

criteria reached

Failure present

Analyse reliabilityEnd yearreached

minus

+

minus

+

minus

+

Update solution costs

Save result

End

Figure 6 SIM evaluation flowchart

7Complexity

where ci is CAPEX in the current year oi is OPEX in thecurrent year and cj oj and mj are CAPEX OPEX andmetrics costs respectively of the ancestor network statewith no issues in year j The heuristic estimate h x ofthe cost to reach the end year is given by

h xi = cREMi +mi + n

k=i+1ck + ok +mk 3

where cREMi is the average remaining CAPEX in the cur-rent year i mi is the average metric cost in this year ckok and mk are the average CAPEX OPEX and metriccosts respectively of the descendant network state in yeark with no issues and n is the end year of evaluation Pre-seeded to a constant value c0 the average CAPEX isupdated each time a network state is expanded in a partic-ular year thus the estimated costs of fixing all issues in aparticular year progressively approach true average Settingc0 to a value greater than 0 speeds up the expansion pro-cess ie all interventions that cost less than c0 will resultin new network states that are at the top of the priorityqueue The business rationale is that DNOs are not thatinterested in optimizing interventions costing less than acertain threshold The learned averages are propagatedback to network states in the priority queue thus updatingtheir estimated effort and leading to the reordering of thequeue unlike the average CAPEX average OPEX andmetrics costs which are assumed to be 0 for years withno network states The average OPEX value for a year isobtained using

o = jToc jT jminus1 4

where j is a column vector of ones and oc is an OPEXvector of compliant network states in that year Likewisethe average metric cost is obtained according to

m = jTmc jT jminus1 5

where j is a column vector of ones and mc is a vector ofthe metric costs of compliant network states in that year

32 Conceptualising Bottom-Up Evolutionary PlanningModel-driven engineering (MDE) uses analysis constructionand development of frameworks to formulate metamodelsThose models are usually characterised using domain-specific modelling approaches [28] containing appropriatedetail abstraction of particular domain through a specificmetamodel The use of metamodels requires therefore inputsfrom domain experts which can be used to generate aggre-gated or disaggregated models Top-down and bottom-upare the conceptual definition of aggregated and disaggregatedmodels [29] These two modelling paradigms are frequentlyused to epitomise domain interactions among the operationof the energy system the econometrics related and thetechnical performance indicators [30]

From a bottom-up modelling approach [31 32] thetop-down perspective is a simplistic characterisation ofhow electrical power networks combine locational eventsand individual asset performance with high-level objectiveslike improving the CML of a certain congested area [33 34]From an engineering point of view both are still validsince outputs and strategic forecast are produced in bothHow those outcomes are calculated validated and trans-formed to strategy is presented in Figure 7 (top-down)and Figure 8 (bottom-up)

Interventiontechnique costs

Relative usage ofinterventiontechnique

Interventiontechnique installation

details

Interventiontechnique models

Validationphysical trials

Interventiontechnique operation

details

Purchase andoperation cost data

Cost model

Network data

Representativenetworks

Model network through years

Calculate intervention technique usage

Strategic forecasts

Validationsampled nodal

networks

Demand scenarios

Figure 7 Top-down conceptualisation of evolutionary power networks planning

8 Complexity

The ability of bottom-up to capture discrete locationalimpacts of technologies on the system and their disaggre-gated costs is triggering the following subsections Trade-offmethodologies are needed for planning evolutionary powersystems where observing disaggregated result strategic fore-casts are to be produced These methodologies need to beinteractive in the sense that starting from an initial stateand after a testing or learning phase the network is able toaccommodate techniques that have improved the systemproviding an exploratory set of solutions that can beexpanded or discarded as the model evolves through time

33 Experiment Characterisation To illustrate the processingof the methodology adopted we consider a typical networkscenario tree created by the tool that consists of failed (red)and compliant (green) network states in different years ofevaluation The SIM starts with a single network state inthe first year of evaluation Every year the network isevaluated for compliance in intact and n minus 1 contingencyoperation modes If failures are detected the SIM appliesintervention techniques described in Section 2 to create net-work patches that resolve the failures Each application of anintervention technique creates a new network state The toolhas to resolve all issues in the network before transitioninginto the next year DNO planners usually run power flowstudies with a single fixed load pattern In contrast the SIMchecks each network state for compliance under 18 charac-teristic day load scenarios each comprising of 48 half-hoursettlement periods All studies are performed under intactand n minus 1 contingency network operation modes The SIM

calculates patch costs using cost drivers returned by the net-work modelling tool The cost driver describes a networkintervention and consists of two parts namely the patchkey and the scaling The patch key identifies the nature ofthe modification of the network performed (removal or addi-tion of an asset and the type of the asset) Scaling data is rel-evant only to patches that can be installed in multiples of onesuch as cable upgrades and additional transformer installa-tion Scaling data structure provides a list of multipliers tothe base cost data available in the SIM database In case ofcable upgrade or replacement it enables the SIM to correctlyestimate full installation costs from per unit of length valuesFinally for postprocessing analysis the SIM also returns a listof failures by asset It was identified by DNO planners asindispensable features to help validate the system and corre-late the expansion trees to the actual assets on the networkdiagram The failure detail table contains asset ID anddescription alongside information about the number of fail-ures in intact and n minus 1 operating modes as well as absoluteand per unit thermal and voltage failure magnitude

34 Case Characterisation Two set of experiments wereperformed for this study one for comparing short-termevaluation period for RIIO-ED1 (2015ndash2023) where theRIIO-ED1 investment planning has been stylised and alonger planning period for RIIO-ED1 to RIIO-ED4 from2015 up to 2047 The other set aimed at evaluating differ-ent DECC demand scenarios Experiments evaluated twodemand scenarios DECC2 and DECC4 DECC4 representsas displayed in Table 1 the slow-progression scenario and

Purchase andoperation cost data

Cost model Nodal network model

Model network through years

Apply intervention technique to keep the network compilant

Find optimal network development options

Solutions for individual networks

Aggregate results for larger network areas

Strategic forecasts

Network data Demand scenarios

Interventiontechnique costs

Interventiontechnique installation

details

Nodal interventiontechnique models

Validation withphysical trials

Relative usage ofinterventiontechniques

Validation of powerflow and reliability

models

Interventiontechnique operation

details

Load profiles

Figure 8 Bottom-up conceptualisation of evolutionary power networks planning

9Complexity

DECC2 is with DECC3 the most challenging scenario interms of electrification and low-carbon technology integra-tion The procedure to run the experiments in the SIM isshown in Figure 6 The inputs to be sent to the SIM aredetailed in Figure 5 the selected network techniques evalu-ation period demand scenario and cost model whereas theoutputs of the SIM we obtain are techniques used failuressolved assets fixed and electrical performance indicatorsThe SIM address mentioned outputs for long-term plan-ning reinforcements to optimise investment asset planningresolving network constraints The performance criteria eval-uated were capital expenditures (CAPEX) operating expen-ditures (OPEX) utilisation of assets CMLs CIs and lossesThese parameter values are delivered by the SIM after eachsimulation In order to look for the optimal solution amongthe feasible solution pathways the SIM allows a granularitystudy of each network state

4 Results

This section presents the assessment of the six smart gridinterventions along with traditional reinforcements in thetrial area compounded by six 11 kV primaries in MiltonKeynes UK for two different evaluation periods DECC2and DECC4 Subsection 41 introduces the results for the2015ndash2023 period as short-term planning and Subsection42 presents the results for a long-term evaluation period2015ndash2047

41 Short-Term Planning This section presents the assess-ment of the six smart grid interventions along with tradi-tional reinforcements in the trial area compounded by six11 kV primaries in Milton Keynes UK for two differentdemand scenarios DECC2 and DECC4

411 DECC4 2015ndash2023 Figure 9 shows the trends ofCAPEX and OPEX Note that in the year 2015 there is aCAPEXrsquos peak due to the application of more techniquesand OPEX increments over time from 2017 to 2023

Despite the investment the increase is compensated bya benefit in the electricity distribution network with areduction in CML and CI as shown in Figure 10

The summary of the proportion of installations requiredduring this evaluation period and by capital expenditures pertechnique is presented in Table 2 for both DECC scenarios

The average yearly price of each technique implementedto fix a network state is key for future decision-makingconsiderations

Figure 11 displays the average price of each techniquedisaggregated by CAPEX and OPEX

Performance indicators are key parameters for futuredecision-making within electricity distribution planning asquantifiers due to their influence on the quality of service

Therefore as shown in Figure 12 the only smart tech-nique used in combination with traditional reinforcementsin this scenario and evaluation period that slightly influenceslightly CML and CI is meshed networks

412 DECC2 2015ndash2023 Figure 13 plots the trend ofCAPEX and OPEX over the evaluation period It is important

to highlight that there is a CAPEX peak in 2015 due to anincrease of techniques applied in this period

This CAPEX increment produces a benefit in terms ofCML and CI as observed in Figure 14

The distribution of techniques applied and capital expen-ditures per technique are shown in Table 2 The average priceof each technique implementation in this demand scenariofor this evaluation period is shown in Figure 15

As displayed in Figure 12 for DECC4rsquos simulation theonly smart grid technique that improves the quality of serviceis the meshed network In Figure 16 the contribution ofsmart techniques on CML and CI for DECC2 demandevaluation is captured

413 Comparison between DECC2 and DECC4 for 2015ndash2023 The results presented may facilitate improvementsin electricity distribution network operations and planningresulting in better-informed decision-making when upgrad-ing current electricity distribution networks The assess-ment of the six smart grid techniques discovered thatonly three of them were selected as part of optimal

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

Mill

ions

pound p

er an

num

CAPEXOPEX

442444648

55254

Figure 9 CAPEX and OPEX DECC4 2015ndash2023

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

pu

Figure 10 CML and CI DECC4 2015ndash2023

10 Complexity

solutions for DECC4 and DECC2 as described in Table 2These three techniques applied are DAR for cables andtransformers and meshed networks

Comparing the cost trends of the two assessed scenariosit is notable that in both demand scenarios DECC4 and

DECC2 the CAPEX peak occurs in 2015 (Figures 9 and13) reflecting the more difficult nature of network statesto be feasible in a demanding scenario whereas DECC2shows a higher improvement of CI and CML performanceindicators as can be seen in Figures 10 and 14

Table 2 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2023

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 15 2 14 2

DAR-transformer 7 1 5 1

ALT 0 0 0 0

Mesh 3 1 4 1

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 8 3 7 2

TRAD-cable 60 89 65 91

TRAD-transfer load 6 2 4 1

TRAD-new feeder 1 2 1 2

DAR - cable DAR -transformer

Meshednetworks

TRAD -transformer

TRAD - cable

CAPEXOPEX

0

20000

40000

60000

80000

100000

pound

Figure 11 Average cost disaggregation per technique DECC4 2015ndash2023

Figure 12 Average CML and CI improvement per technique DECC4 2015ndash2023

11Complexity

The techniques applied are the key consideration to beassessed It is necessary to analyse the number of interven-tions applied by type the effect of these techniques in theelectric grid solving failures and fixing assets Figures 11and 15 show average prices of techniques used in eachdemand scenario Smart techniques have a lower TOTEXthan traditional reinforcements These novel techniquesabove are not frequently able to fix failures and thereforeproduce feasible network states In terms of CML and CIthe only technique able to moderately contribute to theirimprovement is meshed networks as shown in Figures 12and 16 Results attribute the majority of fixes to traditionalreinforcements with a comparatively smaller number offailures being solved by smart techniques (Table 2) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention forDECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

For DECC4 the three mentioned techniques along withtraditional cable and transformer replacement are applied(Table 2) whereas for DECC2 two of them are appliedDAR for cables and meshed networks (Table 2) Comparingthe cost trends of the two assessed scenarios it is notable thatin DECC4 the CAPEX peak occurs in 2015 (Figure 7)whereas in DECC2 besides the peak in 2015 there is alsoone in 2019 (Figure 11) reflecting the more difficult natureof network states to be feasible in a demanding scenarioHowever DECC2 shows a higher improvement of electricalperformance indicators as can be seen in Figure 12

In addition traditional cable replacement DAR forcables and meshed networks are able to fix the cable whereastraditional transformer replacement and DAR for trans-formers are able to fix transformer issues (Figure 16) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention for

DECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

42 Long-Term Planning This section evaluates two experi-ments namely characterising the DECC2 and DECC4 sce-narios for 2015ndash2047 evaluation period For each demandscenario expected investments disaggregating CAPEX andOPEX are presented the evolution of electrical performanceindicators and the number of techniques applied

421 DECC4 2015ndash2047 In this experiment the mostsignificant results to analyse the suitability of each techniqueare presented

Figure 17 shows the trend of CAPEX and OPEX from2015 to 2047

There are CAPEX peaks in the year 2018 and during thebeginning of RIIO-ED2 in the years 2025 to 2026 due to theapplication of more techniques to fulfill low-carbon targets

Figure 18 indicates that upgrades on the network aredirectly related to technical performance indicators CMLand CI

In Table 3 the techniques applied and their contributionto CAPEX during the evaluation period from 2015 to 2047 bya demand scenario are shown

422 DECC2 2015ndash2047 Within this experiment the mostrelevant results to analyse the evolutionary network statesare presented

In Figure 19 the trend of CAPEX and OPEX from 2015to 2047 is shown There is a significant increase of CAPEXin the years 2024 to 2026 at the beginning of RIIO-ED2due to the necessary implementation of new techniques toreach low-carbon targets The evolution of CML and CI ispresented in Figure 20 linking larger investment years whenmajor reductions are found The contribution of each solutiontechnique is presented in Table 3

423 Comparison between DECC2 and DECC4 for 2015ndash2047 Figure 21 presents a multidimensional parallel coor-dinate representation of the feasible network statersquoscombinations that produced a 2015ndash2047 investment path-way for the evaluated area It bundles economic indicatorsie CAPEX and OPEX with technical performance indica-tors providing valuable insights on the number of traditionalreinforcements utilised to heal falling network states Resultsare also clustered by the two demand scenarios assessedduring the experiments

Solutions of DECC2 (represented in orange and green)and DECC4 (in black and blue) are clustered by the per-centage of traditional reinforcements utilised as well asthe utilisation factor

Solutions in green and in black represent those wheresmart grids were more used whereas orange and blue repre-sent the cheapest (CAPEX and OPEX) less utilised and theworst responding to the decrease of CML CI and lossesand the ones using more traditional reinforcement to fixnetwork states

Besides the fact that DECC2 smart solutions are between16 and 28 more expensive than DECC4 traditional

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

CAPEXOPEX

442444648

552

Mill

ions

pound p

er an

num

54

Figure 13 CAPEX and OPEX DECC2 2015ndash2023

12 Complexity

reinforcement solutions (green solutions versus blue solu-tions in Figure 21) DECC2 traditional solutions are in therange of costs of smart solutions of DECC4 (orange andblack solutions in Figure 21) It can also be concluded thatinvestment pathways using less smart techniques provide a

cheaper response to technical performance evaluators suchas utilisation CML CI and losses

43 Summary Experiment evaluation runs from the2015ndash2047 period present lower investment rates for the

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

PU

Figure 14 CML and CI DECC2 2015ndash2023

CAPEXOPEX

DAR -transformer

Meshednetworks

Traditional -transformer

Traditional -cable

0

20000

40000

60000

80000

100000

120000

140000

pound

Figure 15 Average cost disaggregation per technique DECC2 2015ndash2023

Figure 16 Average CML and CI improvement per technique DECC2 2015ndash2023

13Complexity

2015ndash2023 period than if the evaluation is just performedwith a 2015ndash2023 time frame This occurs as a result of thechallenging low-carbon targets up to 2047 and a myopicplanning with short-term lookahead For an evaluationperiod four times larger the CAPEX and TOTEX increasedjust 18 compared to 2015ndash2023 concluding that betweenpound52M and pound68M the evaluation area regardless of thetime horizon for the investment or the demand scenarioconsidered is required

Furthermore it can be inferred comparing Tables 2 and 3that for less smart interventions the short-term planningis used compared to the long-term horizon planningDAR-cable and DAR-transformer experienced a significantimplementation variation between the two evaluationperiods for both demand scenarios as well as CAPEX allo-cated in cable upgrades falling from 89ndash91 (2015ndash2023)to 29ndash66 in the long term 2015ndash2047 run Both technicalperformance evaluators CML and CI respond to theirrespective CAPEX curve shape Due to a more incentivisedsmart grid technique during RIIO-ED2 and ED3 the per-centages of smart techniques implemented varied notablyFor DECC4 in 2023rsquos outlook the share of techniques is

25 for smart grid interventions whereas for 2050rsquos out-look the share is increased up to 58 In the same wayfor DECC2 in 2023rsquos outlook the share of smart interven-tions is 23 whereas in 2050rsquos outlook the share increasesup to 69

Feasible solutions characterising the solution space(Figure 21) differ in the degree of investment required andtechnical performance evaluators Each of the solution pathrepresented in the multidimensional solution space inFigure 21 represents the intersection of that dimension witheach axis Parallel coordinates [35] are low complexityworking for any N-dimension In the case of Figure 21it has 8 dimensions treating every variable uniformlyEach intersection of one solution path with each axis isthe value of that solution for that axis As for axis DECC2and DECC4 it represents the feasible solutions for eachdemand scenario Making a query by the percentage oftraditional reinforcement used in that solution we have thepartition between orange and green for DECC2 and blueand black being the first solutions that use more traditionalreinforcement compared with the ones that use more smarttechniques represented in green (DECC2) and black(DECC4) From Figure 21 we can argue that solutions thatuse more smart techniques (green and blacks) are moreexpensive in terms of CAPEX and OPEX than their peers(orange and blues) when compared by a demand scenarioIt can also be stated that solutions with a higher use of tradi-tional reinforcements are the ones that decrease the mostCML CI and losses

Disaggregating results by network state of the six 11 kVprimaries and by the feeder if necessary for further granulardebugging can discern locational capacity Under bothdemand scenarios Secklow Gate was seen to be the one withgreater capacity being necessary to apply fewer techniquesover the evaluation period 2015ndash2047 On the other handNewport Pagnell exceeds the capacity as soon as 2015requiring a high number of network state evaluation to befixed that happens for each subsequent year hence the largenumber of network states is presented in Table 4

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

pound p

er an

num

Figure 17 CAPEX and OPEX DECC4 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

pu

Figure 18 CML and CI DECC4 2015ndash2047

14 Complexity

5 Conclusions

Power flow analysis using a nodal network model is essentialwhen determining the benefits of trailed smart interventions

because the interactions and implications are highly specificto a particular location and scenario

This suggests that while the bottom-up approach isonerous in terms of data handling and manipulation this isworthwhile for strategic planning and policy evaluationComparing both investment strategies investment strategyadjustments will be necessary in future regulation periodsif an over-invested network behaves as displayed in theshort-term section of this study

Traditional reinforcement will continue to be the mainmethod by which network issues are mostly resolvedfollowed by dynamic asset rating and meshed networks

The assessment of the six novel smart interventions in theFALCON 11kV primary test area in Milton Keynes hasproved the suitability of three techniques able to fix failuresimproving the quality of service and their readiness to bedeployed in the near future These techniques are DAR forcables and transformers and meshed networks Meshednetworks have been repeatedly selected as a feasible tech-nique because using it will reduce CML CI and power losses

Table 3 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2047

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 18 5 31 6

DAR-transformer 32 16 32 6

ALT 0 0 0 0

Mesh 8 3 6 3

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 25 44 9 16

TRAD-cable 15 29 20 66

TRAD-transfer load 1 1 1 1

TRAD-new feeder 1 2 1 2

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

s pound p

er an

num

Figure 19 CAPEX and OPEX DECC2 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

PU

Figure 20 CML and CI DECC2 2015ndash2047

15Complexity

improving the quality of service and the efficiency whilebeing a cost-effective solution

The initial capacity at primary substations differedsignificantly and this affected the number and complexityof interventions required by the SIM Due to the loadscenarios showing significant peak load increases DAR wasoften a temporary measure that would delay but not removethe eventual need for traditional reinforcement

Implementing DAR for cables and transformers themonitoring of assets when their peak capacity is increasedwas analysed On the other hand it was observed thattraditional reinforcements still play a key role in keepingthe electricity distribution networks free of constraintsTRAD techniques such as transformer and cable replace-ments are able to fix the majority of failures and will beessential also in the future

The comparison between traditional reinforcements andnovel smart techniques has provided a new knowledge aboutthe suitability of each technique to be applied in termsof costs electrical performance failures fixed and assetreplaced Cable replacement is the most costly techniquehowever its use is unavoidable in a number of cases Further-more the applicability of each technique regarding costsinvolved improvements on power quality and efficiency

and failures solved lead to new questions to be analysed suchas the lack of these techniques to provide flexible capacitywithin the trailed area

To sum up this study has performed a comparativeanalysis of novel smart intervention techniques providinginsights for future investments in electricity distributionplanning Further work can focus on scaling up the analysisto include a larger section of the network or a constrainedarea to evaluate national applicability of the current findings

Data Availability

Some of the data relevant to this submission is proprietaryfrom Western Power Distribution It can be accessed forresearch purposes under a signed collaboration agreement

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors would like to thank the rest of the WesternPower Distribution Innovation team particularly the other

2400

2200

2000

350

360

370

380

390

400

1400

1300

1200

1100

1000DECC 4

50

55

60

12

11

10

9

8

4400

4200

4000

3800

3600

2400

CAPEX (in kpound) OPEX (in kpound) Utilisation Traditional reinforcements () DECC 2 998796 CML 998796 CI 998796 Losses

2200

2000

1800

Figure 21 Parallel coordinate visualisation of technoeconomic performance indicators under DECC4 and DECC2

Table 4 Summary of network states and results for demand scenario DECC4 and DECC2

Primary 11 kV substation Feeders Year TechniquesResultsDECC4

NSDECC4

ResultsDECC2

NSDECC2

Fox Milne 13 2047 Smart and traditional 54 276 47 259

Newport Pagnell 9 2047 Smart and traditional 48 317 31 318

Secklow Gate 9 2047 Smart and traditional 27 29 15 16

Bletchley 19 2047 Smart and traditional 32 40 21 48

Marlborough Street 11 2047 Smart and traditional 31 52 19 37

Childs Way 17 2047 Smart and traditional 67 398 54 181

16 Complexity

members of the FALCON project as well as Paul CharletonStuart Fowler Ilja Orlovs Dan Taylor and Steve IngramAlso the authors want to acknowledge OFGEM and theLowCarbon Network Fund for funding this piece of research

References

[1] European Commission ldquoCommunication from the Commis-sion to the European Parliament the Council the EuropeanEconomic and Social Committee and the Committee of theRegionsrdquo 2011 December 2016 httpseur-lexeuropaeulegal-contentENTXTqid=1537473508968ampuri=CELEX52016PC076721

[2] F G N Li and E Trutnevyte ldquoInvestment appraisal ofcost-optimal and near-optimal pathways for the UK elec-tricity sector transition to 2050rdquo Applied Energy vol 189pp 89ndash109 2017

[3] G Strbac I Konstantelos M Pollitt and R Green DeliveringFuture-Proof Energy Infrastructure National InfrastructureCommission 2016

[4] Ofgem ldquoGB Network regulation - the RIIO modelrdquo 2010December 2014 httpswwwofgemgovuknetwork-regulation-riio-model

[5] R Poudineh and T Jamasb ldquoDistributed generation storagedemand response and energy efficiency as alternatives togrid capacity enhancementrdquo Energy Policy vol 67 pp 222ndash231 2014

[6] J Yang X Bai D Strickland L Jenkins and A M CrossldquoDynamic network rating for low carbon distribution networkoperationmdashA UK applicationrdquo IEEE Transactions on SmartGrid vol 6 no 2 pp 988ndash998 2015

[7] M Behnke W Erdman S Horgan et al ldquoSecondary net-work distribution systems background and issues relatedto the interconnection of distributed resourcesrdquo NationalRenewable Energy Laboratory (NREL) Tech Rep NRELTP-560-38079 2005

[8] J Crispim J Braz R Castro and J Esteves ldquoSmart grids in theEU with smart regulation experiences from the UK Italy andPortugalrdquo Utilities Policy vol 31 pp 85ndash93 2014

[9] Tnei ldquoIPSA Power software for power systemsrdquo March 2016httpwwwipsa-powercom

[10] ETAP ldquoETAP PSrdquo 2018 April 2016 httpsetapcomsoftwareproduct-overview-main

[11] DINIS ldquoDINIS distribution network information sys-temsrdquo 2002 March 2016 httpsetapcomsoftwareproduct-overview-main

[12] Western Power Distribution ldquoSIM final reportrdquo 2015November 2016 httpwwwwesternpowerinnovationcoukProjectsFalconaspx

[13] Western Power Distribution ldquoProject FALCONrdquo 2015November 2017 httpwwwdiniscom

[14] EA Technology ldquoTransform modelrdquo March 2016 httpwwweatechnologycomproducts-and-servicescreate-smarter-gridstransform-model

[15] L Sigrist E Lobato L Rouco M Gazzino and M CantuldquoEconomic assessment of smart grid initiatives for islandpower systemsrdquo Applied Energy vol 189 pp 403ndash415 2017

[16] J A Momoh ldquoSmart grid design for efficient and flexiblepower networks operation and controlrdquo in 2009 IEEEPESPower Systems Conference and Exposition pp 1ndash8 SeattleWA USA 2009

[17] N Good K A Ellis and P Mancarella ldquoReview and classifica-tion of barriers and enablers of demand response in the smartgridrdquo Renewable and Sustainable Energy Reviews vol 72pp 57ndash72 2017

[18] Z Yang J Xiang and Y Li ldquoDistributed consensus basedsupplyndashdemand balance algorithm for economic dispatchproblem in a smart grid with switching graphrdquo IEEE Trans-actions on Industrial Electronics vol 64 no 2 pp 1600ndash1610 2017

[19] SuSTAINABLE ldquoSmart distribution network operation formaximising the integration of renewable generationrdquo 2015March 2016 httpwwwsustainableprojecteuHomeaspx

[20] S Mohtashami D Pudjianto and G Strbac ldquoStrategic distri-bution network planning with smart grid technologiesrdquo IEEETransactions on Smart Grid vol 8 no 6 pp 2656ndash2664 2017

[21] Smart Grid Forum ldquoSmart grid forum work stream 7rdquo2015 March 2016 httpwwwsmarternetworksorgprojectnia_nget015425

[22] ETI ldquoEnergyPath networksrdquo March 2016 httpwwweticoukprojectenergypath

[23] C Mateo Domingo T Gomez San Roman Aacute Sanchez-Miralles J P Peco Gonzalez and A Candela Martinez ldquoAreference network model for large-scale distribution planningwith automatic street map generationrdquo IEEE Transactions onPower Systems vol 26 no 1 pp 190ndash197 2011

[24] R Bolton and T J Foxon ldquoInfrastructure transformation as asocio-technical process mdash implications for the governanceof energy distribution networks in the UKrdquo TechnologicalForecasting and Social Change vol 90 pp 538ndash550 2015

[25] UK Power Networks ldquoDNO guide to future Smart man-agement of distribution networks Summary reportrdquo 2014httpsinnovationukpowernetworkscoukinnovationenProjectstier-2-projectsLow-Carbon-London-(LCL)Project-DocumentsLCL20Learning20Report20-20SR20-20Summary20Report20-20DNO20Guide20to20Future20Smart20Management20of20Distribution20Networkspdf

[26] A Botea J Rintanen and D Banerjee ldquoOptimal reconfigura-tion for supply restoration with informed AlowastSearchrdquo IEEETransactions on Smart Grid vol 3 no 2 pp 583ndash593 2012

[27] T Logenthiran D Srinivasan and T Z Shun ldquoDemand sidemanagement in smart grid using heuristic optimizationrdquoIEEE Transactions on Smart Grid vol 3 no 3 pp 1244ndash1252 2012

[28] J Saacutenchez-Cuadrado J de Lara and E Guerra ldquoBottom-upmeta-modelling an interactive approachrdquo in Model DrivenEngineering Languages and Systems pp 3ndash19 Springer2012

[29] Y H Kim F J Sting and C H Loch ldquoTop-down bottom-upor both Toward an integrative perspective on operationsstrategy formationrdquo Journal of Operations Managementvol 32 no 7-8 pp 462ndash474 2014

[30] C Bohringer ldquoThe synthesis of bottom-up and top-down inenergy policy modelingrdquo Energy Economics vol 20 no 3pp 233ndash248 1998

[31] T G R Bower ldquoRepetition in human developmentrdquo Merrill-Palmer Quarterly of Behavior and Development vol 20no 4 pp 303ndash318 1974

[32] H Mintzberg and J A Waters ldquoOf strategies deliberate andemergentrdquo Strategic Management Journal vol 6 no 3pp 257ndash272 1985

17Complexity

where ci is CAPEX in the current year oi is OPEX in thecurrent year and cj oj and mj are CAPEX OPEX andmetrics costs respectively of the ancestor network statewith no issues in year j The heuristic estimate h x ofthe cost to reach the end year is given by

h xi = cREMi +mi + n

k=i+1ck + ok +mk 3

where cREMi is the average remaining CAPEX in the cur-rent year i mi is the average metric cost in this year ckok and mk are the average CAPEX OPEX and metriccosts respectively of the descendant network state in yeark with no issues and n is the end year of evaluation Pre-seeded to a constant value c0 the average CAPEX isupdated each time a network state is expanded in a partic-ular year thus the estimated costs of fixing all issues in aparticular year progressively approach true average Settingc0 to a value greater than 0 speeds up the expansion pro-cess ie all interventions that cost less than c0 will resultin new network states that are at the top of the priorityqueue The business rationale is that DNOs are not thatinterested in optimizing interventions costing less than acertain threshold The learned averages are propagatedback to network states in the priority queue thus updatingtheir estimated effort and leading to the reordering of thequeue unlike the average CAPEX average OPEX andmetrics costs which are assumed to be 0 for years withno network states The average OPEX value for a year isobtained using

o = jToc jT jminus1 4

where j is a column vector of ones and oc is an OPEXvector of compliant network states in that year Likewisethe average metric cost is obtained according to

m = jTmc jT jminus1 5

where j is a column vector of ones and mc is a vector ofthe metric costs of compliant network states in that year

32 Conceptualising Bottom-Up Evolutionary PlanningModel-driven engineering (MDE) uses analysis constructionand development of frameworks to formulate metamodelsThose models are usually characterised using domain-specific modelling approaches [28] containing appropriatedetail abstraction of particular domain through a specificmetamodel The use of metamodels requires therefore inputsfrom domain experts which can be used to generate aggre-gated or disaggregated models Top-down and bottom-upare the conceptual definition of aggregated and disaggregatedmodels [29] These two modelling paradigms are frequentlyused to epitomise domain interactions among the operationof the energy system the econometrics related and thetechnical performance indicators [30]

From a bottom-up modelling approach [31 32] thetop-down perspective is a simplistic characterisation ofhow electrical power networks combine locational eventsand individual asset performance with high-level objectiveslike improving the CML of a certain congested area [33 34]From an engineering point of view both are still validsince outputs and strategic forecast are produced in bothHow those outcomes are calculated validated and trans-formed to strategy is presented in Figure 7 (top-down)and Figure 8 (bottom-up)

Interventiontechnique costs

Relative usage ofinterventiontechnique

Interventiontechnique installation

details

Interventiontechnique models

Validationphysical trials

Interventiontechnique operation

details

Purchase andoperation cost data

Cost model

Network data

Representativenetworks

Model network through years

Calculate intervention technique usage

Strategic forecasts

Validationsampled nodal

networks

Demand scenarios

Figure 7 Top-down conceptualisation of evolutionary power networks planning

8 Complexity

The ability of bottom-up to capture discrete locationalimpacts of technologies on the system and their disaggre-gated costs is triggering the following subsections Trade-offmethodologies are needed for planning evolutionary powersystems where observing disaggregated result strategic fore-casts are to be produced These methodologies need to beinteractive in the sense that starting from an initial stateand after a testing or learning phase the network is able toaccommodate techniques that have improved the systemproviding an exploratory set of solutions that can beexpanded or discarded as the model evolves through time

33 Experiment Characterisation To illustrate the processingof the methodology adopted we consider a typical networkscenario tree created by the tool that consists of failed (red)and compliant (green) network states in different years ofevaluation The SIM starts with a single network state inthe first year of evaluation Every year the network isevaluated for compliance in intact and n minus 1 contingencyoperation modes If failures are detected the SIM appliesintervention techniques described in Section 2 to create net-work patches that resolve the failures Each application of anintervention technique creates a new network state The toolhas to resolve all issues in the network before transitioninginto the next year DNO planners usually run power flowstudies with a single fixed load pattern In contrast the SIMchecks each network state for compliance under 18 charac-teristic day load scenarios each comprising of 48 half-hoursettlement periods All studies are performed under intactand n minus 1 contingency network operation modes The SIM

calculates patch costs using cost drivers returned by the net-work modelling tool The cost driver describes a networkintervention and consists of two parts namely the patchkey and the scaling The patch key identifies the nature ofthe modification of the network performed (removal or addi-tion of an asset and the type of the asset) Scaling data is rel-evant only to patches that can be installed in multiples of onesuch as cable upgrades and additional transformer installa-tion Scaling data structure provides a list of multipliers tothe base cost data available in the SIM database In case ofcable upgrade or replacement it enables the SIM to correctlyestimate full installation costs from per unit of length valuesFinally for postprocessing analysis the SIM also returns a listof failures by asset It was identified by DNO planners asindispensable features to help validate the system and corre-late the expansion trees to the actual assets on the networkdiagram The failure detail table contains asset ID anddescription alongside information about the number of fail-ures in intact and n minus 1 operating modes as well as absoluteand per unit thermal and voltage failure magnitude

34 Case Characterisation Two set of experiments wereperformed for this study one for comparing short-termevaluation period for RIIO-ED1 (2015ndash2023) where theRIIO-ED1 investment planning has been stylised and alonger planning period for RIIO-ED1 to RIIO-ED4 from2015 up to 2047 The other set aimed at evaluating differ-ent DECC demand scenarios Experiments evaluated twodemand scenarios DECC2 and DECC4 DECC4 representsas displayed in Table 1 the slow-progression scenario and

Purchase andoperation cost data

Cost model Nodal network model

Model network through years

Apply intervention technique to keep the network compilant

Find optimal network development options

Solutions for individual networks

Aggregate results for larger network areas

Strategic forecasts

Network data Demand scenarios

Interventiontechnique costs

Interventiontechnique installation

details

Nodal interventiontechnique models

Validation withphysical trials

Relative usage ofinterventiontechniques

Validation of powerflow and reliability

models

Interventiontechnique operation

details

Load profiles

Figure 8 Bottom-up conceptualisation of evolutionary power networks planning

9Complexity

DECC2 is with DECC3 the most challenging scenario interms of electrification and low-carbon technology integra-tion The procedure to run the experiments in the SIM isshown in Figure 6 The inputs to be sent to the SIM aredetailed in Figure 5 the selected network techniques evalu-ation period demand scenario and cost model whereas theoutputs of the SIM we obtain are techniques used failuressolved assets fixed and electrical performance indicatorsThe SIM address mentioned outputs for long-term plan-ning reinforcements to optimise investment asset planningresolving network constraints The performance criteria eval-uated were capital expenditures (CAPEX) operating expen-ditures (OPEX) utilisation of assets CMLs CIs and lossesThese parameter values are delivered by the SIM after eachsimulation In order to look for the optimal solution amongthe feasible solution pathways the SIM allows a granularitystudy of each network state

4 Results

This section presents the assessment of the six smart gridinterventions along with traditional reinforcements in thetrial area compounded by six 11 kV primaries in MiltonKeynes UK for two different evaluation periods DECC2and DECC4 Subsection 41 introduces the results for the2015ndash2023 period as short-term planning and Subsection42 presents the results for a long-term evaluation period2015ndash2047

41 Short-Term Planning This section presents the assess-ment of the six smart grid interventions along with tradi-tional reinforcements in the trial area compounded by six11 kV primaries in Milton Keynes UK for two differentdemand scenarios DECC2 and DECC4

411 DECC4 2015ndash2023 Figure 9 shows the trends ofCAPEX and OPEX Note that in the year 2015 there is aCAPEXrsquos peak due to the application of more techniquesand OPEX increments over time from 2017 to 2023

Despite the investment the increase is compensated bya benefit in the electricity distribution network with areduction in CML and CI as shown in Figure 10

The summary of the proportion of installations requiredduring this evaluation period and by capital expenditures pertechnique is presented in Table 2 for both DECC scenarios

The average yearly price of each technique implementedto fix a network state is key for future decision-makingconsiderations

Figure 11 displays the average price of each techniquedisaggregated by CAPEX and OPEX

Performance indicators are key parameters for futuredecision-making within electricity distribution planning asquantifiers due to their influence on the quality of service

Therefore as shown in Figure 12 the only smart tech-nique used in combination with traditional reinforcementsin this scenario and evaluation period that slightly influenceslightly CML and CI is meshed networks

412 DECC2 2015ndash2023 Figure 13 plots the trend ofCAPEX and OPEX over the evaluation period It is important

to highlight that there is a CAPEX peak in 2015 due to anincrease of techniques applied in this period

This CAPEX increment produces a benefit in terms ofCML and CI as observed in Figure 14

The distribution of techniques applied and capital expen-ditures per technique are shown in Table 2 The average priceof each technique implementation in this demand scenariofor this evaluation period is shown in Figure 15

As displayed in Figure 12 for DECC4rsquos simulation theonly smart grid technique that improves the quality of serviceis the meshed network In Figure 16 the contribution ofsmart techniques on CML and CI for DECC2 demandevaluation is captured

413 Comparison between DECC2 and DECC4 for 2015ndash2023 The results presented may facilitate improvementsin electricity distribution network operations and planningresulting in better-informed decision-making when upgrad-ing current electricity distribution networks The assess-ment of the six smart grid techniques discovered thatonly three of them were selected as part of optimal

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

Mill

ions

pound p

er an

num

CAPEXOPEX

442444648

55254

Figure 9 CAPEX and OPEX DECC4 2015ndash2023

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

pu

Figure 10 CML and CI DECC4 2015ndash2023

10 Complexity

solutions for DECC4 and DECC2 as described in Table 2These three techniques applied are DAR for cables andtransformers and meshed networks

Comparing the cost trends of the two assessed scenariosit is notable that in both demand scenarios DECC4 and

DECC2 the CAPEX peak occurs in 2015 (Figures 9 and13) reflecting the more difficult nature of network statesto be feasible in a demanding scenario whereas DECC2shows a higher improvement of CI and CML performanceindicators as can be seen in Figures 10 and 14

Table 2 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2023

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 15 2 14 2

DAR-transformer 7 1 5 1

ALT 0 0 0 0

Mesh 3 1 4 1

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 8 3 7 2

TRAD-cable 60 89 65 91

TRAD-transfer load 6 2 4 1

TRAD-new feeder 1 2 1 2

DAR - cable DAR -transformer

Meshednetworks

TRAD -transformer

TRAD - cable

CAPEXOPEX

0

20000

40000

60000

80000

100000

pound

Figure 11 Average cost disaggregation per technique DECC4 2015ndash2023

Figure 12 Average CML and CI improvement per technique DECC4 2015ndash2023

11Complexity

The techniques applied are the key consideration to beassessed It is necessary to analyse the number of interven-tions applied by type the effect of these techniques in theelectric grid solving failures and fixing assets Figures 11and 15 show average prices of techniques used in eachdemand scenario Smart techniques have a lower TOTEXthan traditional reinforcements These novel techniquesabove are not frequently able to fix failures and thereforeproduce feasible network states In terms of CML and CIthe only technique able to moderately contribute to theirimprovement is meshed networks as shown in Figures 12and 16 Results attribute the majority of fixes to traditionalreinforcements with a comparatively smaller number offailures being solved by smart techniques (Table 2) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention forDECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

For DECC4 the three mentioned techniques along withtraditional cable and transformer replacement are applied(Table 2) whereas for DECC2 two of them are appliedDAR for cables and meshed networks (Table 2) Comparingthe cost trends of the two assessed scenarios it is notable thatin DECC4 the CAPEX peak occurs in 2015 (Figure 7)whereas in DECC2 besides the peak in 2015 there is alsoone in 2019 (Figure 11) reflecting the more difficult natureof network states to be feasible in a demanding scenarioHowever DECC2 shows a higher improvement of electricalperformance indicators as can be seen in Figure 12

In addition traditional cable replacement DAR forcables and meshed networks are able to fix the cable whereastraditional transformer replacement and DAR for trans-formers are able to fix transformer issues (Figure 16) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention for

DECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

42 Long-Term Planning This section evaluates two experi-ments namely characterising the DECC2 and DECC4 sce-narios for 2015ndash2047 evaluation period For each demandscenario expected investments disaggregating CAPEX andOPEX are presented the evolution of electrical performanceindicators and the number of techniques applied

421 DECC4 2015ndash2047 In this experiment the mostsignificant results to analyse the suitability of each techniqueare presented

Figure 17 shows the trend of CAPEX and OPEX from2015 to 2047

There are CAPEX peaks in the year 2018 and during thebeginning of RIIO-ED2 in the years 2025 to 2026 due to theapplication of more techniques to fulfill low-carbon targets

Figure 18 indicates that upgrades on the network aredirectly related to technical performance indicators CMLand CI

In Table 3 the techniques applied and their contributionto CAPEX during the evaluation period from 2015 to 2047 bya demand scenario are shown

422 DECC2 2015ndash2047 Within this experiment the mostrelevant results to analyse the evolutionary network statesare presented

In Figure 19 the trend of CAPEX and OPEX from 2015to 2047 is shown There is a significant increase of CAPEXin the years 2024 to 2026 at the beginning of RIIO-ED2due to the necessary implementation of new techniques toreach low-carbon targets The evolution of CML and CI ispresented in Figure 20 linking larger investment years whenmajor reductions are found The contribution of each solutiontechnique is presented in Table 3

423 Comparison between DECC2 and DECC4 for 2015ndash2047 Figure 21 presents a multidimensional parallel coor-dinate representation of the feasible network statersquoscombinations that produced a 2015ndash2047 investment path-way for the evaluated area It bundles economic indicatorsie CAPEX and OPEX with technical performance indica-tors providing valuable insights on the number of traditionalreinforcements utilised to heal falling network states Resultsare also clustered by the two demand scenarios assessedduring the experiments

Solutions of DECC2 (represented in orange and green)and DECC4 (in black and blue) are clustered by the per-centage of traditional reinforcements utilised as well asthe utilisation factor

Solutions in green and in black represent those wheresmart grids were more used whereas orange and blue repre-sent the cheapest (CAPEX and OPEX) less utilised and theworst responding to the decrease of CML CI and lossesand the ones using more traditional reinforcement to fixnetwork states

Besides the fact that DECC2 smart solutions are between16 and 28 more expensive than DECC4 traditional

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

CAPEXOPEX

442444648

552

Mill

ions

pound p

er an

num

54

Figure 13 CAPEX and OPEX DECC2 2015ndash2023

12 Complexity

reinforcement solutions (green solutions versus blue solu-tions in Figure 21) DECC2 traditional solutions are in therange of costs of smart solutions of DECC4 (orange andblack solutions in Figure 21) It can also be concluded thatinvestment pathways using less smart techniques provide a

cheaper response to technical performance evaluators suchas utilisation CML CI and losses

43 Summary Experiment evaluation runs from the2015ndash2047 period present lower investment rates for the

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

PU

Figure 14 CML and CI DECC2 2015ndash2023

CAPEXOPEX

DAR -transformer

Meshednetworks

Traditional -transformer

Traditional -cable

0

20000

40000

60000

80000

100000

120000

140000

pound

Figure 15 Average cost disaggregation per technique DECC2 2015ndash2023

Figure 16 Average CML and CI improvement per technique DECC2 2015ndash2023

13Complexity

2015ndash2023 period than if the evaluation is just performedwith a 2015ndash2023 time frame This occurs as a result of thechallenging low-carbon targets up to 2047 and a myopicplanning with short-term lookahead For an evaluationperiod four times larger the CAPEX and TOTEX increasedjust 18 compared to 2015ndash2023 concluding that betweenpound52M and pound68M the evaluation area regardless of thetime horizon for the investment or the demand scenarioconsidered is required

Furthermore it can be inferred comparing Tables 2 and 3that for less smart interventions the short-term planningis used compared to the long-term horizon planningDAR-cable and DAR-transformer experienced a significantimplementation variation between the two evaluationperiods for both demand scenarios as well as CAPEX allo-cated in cable upgrades falling from 89ndash91 (2015ndash2023)to 29ndash66 in the long term 2015ndash2047 run Both technicalperformance evaluators CML and CI respond to theirrespective CAPEX curve shape Due to a more incentivisedsmart grid technique during RIIO-ED2 and ED3 the per-centages of smart techniques implemented varied notablyFor DECC4 in 2023rsquos outlook the share of techniques is

25 for smart grid interventions whereas for 2050rsquos out-look the share is increased up to 58 In the same wayfor DECC2 in 2023rsquos outlook the share of smart interven-tions is 23 whereas in 2050rsquos outlook the share increasesup to 69

Feasible solutions characterising the solution space(Figure 21) differ in the degree of investment required andtechnical performance evaluators Each of the solution pathrepresented in the multidimensional solution space inFigure 21 represents the intersection of that dimension witheach axis Parallel coordinates [35] are low complexityworking for any N-dimension In the case of Figure 21it has 8 dimensions treating every variable uniformlyEach intersection of one solution path with each axis isthe value of that solution for that axis As for axis DECC2and DECC4 it represents the feasible solutions for eachdemand scenario Making a query by the percentage oftraditional reinforcement used in that solution we have thepartition between orange and green for DECC2 and blueand black being the first solutions that use more traditionalreinforcement compared with the ones that use more smarttechniques represented in green (DECC2) and black(DECC4) From Figure 21 we can argue that solutions thatuse more smart techniques (green and blacks) are moreexpensive in terms of CAPEX and OPEX than their peers(orange and blues) when compared by a demand scenarioIt can also be stated that solutions with a higher use of tradi-tional reinforcements are the ones that decrease the mostCML CI and losses

Disaggregating results by network state of the six 11 kVprimaries and by the feeder if necessary for further granulardebugging can discern locational capacity Under bothdemand scenarios Secklow Gate was seen to be the one withgreater capacity being necessary to apply fewer techniquesover the evaluation period 2015ndash2047 On the other handNewport Pagnell exceeds the capacity as soon as 2015requiring a high number of network state evaluation to befixed that happens for each subsequent year hence the largenumber of network states is presented in Table 4

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

pound p

er an

num

Figure 17 CAPEX and OPEX DECC4 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

pu

Figure 18 CML and CI DECC4 2015ndash2047

14 Complexity

5 Conclusions

Power flow analysis using a nodal network model is essentialwhen determining the benefits of trailed smart interventions

because the interactions and implications are highly specificto a particular location and scenario

This suggests that while the bottom-up approach isonerous in terms of data handling and manipulation this isworthwhile for strategic planning and policy evaluationComparing both investment strategies investment strategyadjustments will be necessary in future regulation periodsif an over-invested network behaves as displayed in theshort-term section of this study

Traditional reinforcement will continue to be the mainmethod by which network issues are mostly resolvedfollowed by dynamic asset rating and meshed networks

The assessment of the six novel smart interventions in theFALCON 11kV primary test area in Milton Keynes hasproved the suitability of three techniques able to fix failuresimproving the quality of service and their readiness to bedeployed in the near future These techniques are DAR forcables and transformers and meshed networks Meshednetworks have been repeatedly selected as a feasible tech-nique because using it will reduce CML CI and power losses

Table 3 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2047

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 18 5 31 6

DAR-transformer 32 16 32 6

ALT 0 0 0 0

Mesh 8 3 6 3

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 25 44 9 16

TRAD-cable 15 29 20 66

TRAD-transfer load 1 1 1 1

TRAD-new feeder 1 2 1 2

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

s pound p

er an

num

Figure 19 CAPEX and OPEX DECC2 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

PU

Figure 20 CML and CI DECC2 2015ndash2047

15Complexity

improving the quality of service and the efficiency whilebeing a cost-effective solution

The initial capacity at primary substations differedsignificantly and this affected the number and complexityof interventions required by the SIM Due to the loadscenarios showing significant peak load increases DAR wasoften a temporary measure that would delay but not removethe eventual need for traditional reinforcement

Implementing DAR for cables and transformers themonitoring of assets when their peak capacity is increasedwas analysed On the other hand it was observed thattraditional reinforcements still play a key role in keepingthe electricity distribution networks free of constraintsTRAD techniques such as transformer and cable replace-ments are able to fix the majority of failures and will beessential also in the future

The comparison between traditional reinforcements andnovel smart techniques has provided a new knowledge aboutthe suitability of each technique to be applied in termsof costs electrical performance failures fixed and assetreplaced Cable replacement is the most costly techniquehowever its use is unavoidable in a number of cases Further-more the applicability of each technique regarding costsinvolved improvements on power quality and efficiency

and failures solved lead to new questions to be analysed suchas the lack of these techniques to provide flexible capacitywithin the trailed area

To sum up this study has performed a comparativeanalysis of novel smart intervention techniques providinginsights for future investments in electricity distributionplanning Further work can focus on scaling up the analysisto include a larger section of the network or a constrainedarea to evaluate national applicability of the current findings

Data Availability

Some of the data relevant to this submission is proprietaryfrom Western Power Distribution It can be accessed forresearch purposes under a signed collaboration agreement

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors would like to thank the rest of the WesternPower Distribution Innovation team particularly the other

2400

2200

2000

350

360

370

380

390

400

1400

1300

1200

1100

1000DECC 4

50

55

60

12

11

10

9

8

4400

4200

4000

3800

3600

2400

CAPEX (in kpound) OPEX (in kpound) Utilisation Traditional reinforcements () DECC 2 998796 CML 998796 CI 998796 Losses

2200

2000

1800

Figure 21 Parallel coordinate visualisation of technoeconomic performance indicators under DECC4 and DECC2

Table 4 Summary of network states and results for demand scenario DECC4 and DECC2

Primary 11 kV substation Feeders Year TechniquesResultsDECC4

NSDECC4

ResultsDECC2

NSDECC2

Fox Milne 13 2047 Smart and traditional 54 276 47 259

Newport Pagnell 9 2047 Smart and traditional 48 317 31 318

Secklow Gate 9 2047 Smart and traditional 27 29 15 16

Bletchley 19 2047 Smart and traditional 32 40 21 48

Marlborough Street 11 2047 Smart and traditional 31 52 19 37

Childs Way 17 2047 Smart and traditional 67 398 54 181

16 Complexity

members of the FALCON project as well as Paul CharletonStuart Fowler Ilja Orlovs Dan Taylor and Steve IngramAlso the authors want to acknowledge OFGEM and theLowCarbon Network Fund for funding this piece of research

References

[1] European Commission ldquoCommunication from the Commis-sion to the European Parliament the Council the EuropeanEconomic and Social Committee and the Committee of theRegionsrdquo 2011 December 2016 httpseur-lexeuropaeulegal-contentENTXTqid=1537473508968ampuri=CELEX52016PC076721

[2] F G N Li and E Trutnevyte ldquoInvestment appraisal ofcost-optimal and near-optimal pathways for the UK elec-tricity sector transition to 2050rdquo Applied Energy vol 189pp 89ndash109 2017

[3] G Strbac I Konstantelos M Pollitt and R Green DeliveringFuture-Proof Energy Infrastructure National InfrastructureCommission 2016

[4] Ofgem ldquoGB Network regulation - the RIIO modelrdquo 2010December 2014 httpswwwofgemgovuknetwork-regulation-riio-model

[5] R Poudineh and T Jamasb ldquoDistributed generation storagedemand response and energy efficiency as alternatives togrid capacity enhancementrdquo Energy Policy vol 67 pp 222ndash231 2014

[6] J Yang X Bai D Strickland L Jenkins and A M CrossldquoDynamic network rating for low carbon distribution networkoperationmdashA UK applicationrdquo IEEE Transactions on SmartGrid vol 6 no 2 pp 988ndash998 2015

[7] M Behnke W Erdman S Horgan et al ldquoSecondary net-work distribution systems background and issues relatedto the interconnection of distributed resourcesrdquo NationalRenewable Energy Laboratory (NREL) Tech Rep NRELTP-560-38079 2005

[8] J Crispim J Braz R Castro and J Esteves ldquoSmart grids in theEU with smart regulation experiences from the UK Italy andPortugalrdquo Utilities Policy vol 31 pp 85ndash93 2014

[9] Tnei ldquoIPSA Power software for power systemsrdquo March 2016httpwwwipsa-powercom

[10] ETAP ldquoETAP PSrdquo 2018 April 2016 httpsetapcomsoftwareproduct-overview-main

[11] DINIS ldquoDINIS distribution network information sys-temsrdquo 2002 March 2016 httpsetapcomsoftwareproduct-overview-main

[12] Western Power Distribution ldquoSIM final reportrdquo 2015November 2016 httpwwwwesternpowerinnovationcoukProjectsFalconaspx

[13] Western Power Distribution ldquoProject FALCONrdquo 2015November 2017 httpwwwdiniscom

[14] EA Technology ldquoTransform modelrdquo March 2016 httpwwweatechnologycomproducts-and-servicescreate-smarter-gridstransform-model

[15] L Sigrist E Lobato L Rouco M Gazzino and M CantuldquoEconomic assessment of smart grid initiatives for islandpower systemsrdquo Applied Energy vol 189 pp 403ndash415 2017

[16] J A Momoh ldquoSmart grid design for efficient and flexiblepower networks operation and controlrdquo in 2009 IEEEPESPower Systems Conference and Exposition pp 1ndash8 SeattleWA USA 2009

[17] N Good K A Ellis and P Mancarella ldquoReview and classifica-tion of barriers and enablers of demand response in the smartgridrdquo Renewable and Sustainable Energy Reviews vol 72pp 57ndash72 2017

[18] Z Yang J Xiang and Y Li ldquoDistributed consensus basedsupplyndashdemand balance algorithm for economic dispatchproblem in a smart grid with switching graphrdquo IEEE Trans-actions on Industrial Electronics vol 64 no 2 pp 1600ndash1610 2017

[19] SuSTAINABLE ldquoSmart distribution network operation formaximising the integration of renewable generationrdquo 2015March 2016 httpwwwsustainableprojecteuHomeaspx

[20] S Mohtashami D Pudjianto and G Strbac ldquoStrategic distri-bution network planning with smart grid technologiesrdquo IEEETransactions on Smart Grid vol 8 no 6 pp 2656ndash2664 2017

[21] Smart Grid Forum ldquoSmart grid forum work stream 7rdquo2015 March 2016 httpwwwsmarternetworksorgprojectnia_nget015425

[22] ETI ldquoEnergyPath networksrdquo March 2016 httpwwweticoukprojectenergypath

[23] C Mateo Domingo T Gomez San Roman Aacute Sanchez-Miralles J P Peco Gonzalez and A Candela Martinez ldquoAreference network model for large-scale distribution planningwith automatic street map generationrdquo IEEE Transactions onPower Systems vol 26 no 1 pp 190ndash197 2011

[24] R Bolton and T J Foxon ldquoInfrastructure transformation as asocio-technical process mdash implications for the governanceof energy distribution networks in the UKrdquo TechnologicalForecasting and Social Change vol 90 pp 538ndash550 2015

[25] UK Power Networks ldquoDNO guide to future Smart man-agement of distribution networks Summary reportrdquo 2014httpsinnovationukpowernetworkscoukinnovationenProjectstier-2-projectsLow-Carbon-London-(LCL)Project-DocumentsLCL20Learning20Report20-20SR20-20Summary20Report20-20DNO20Guide20to20Future20Smart20Management20of20Distribution20Networkspdf

[26] A Botea J Rintanen and D Banerjee ldquoOptimal reconfigura-tion for supply restoration with informed AlowastSearchrdquo IEEETransactions on Smart Grid vol 3 no 2 pp 583ndash593 2012

[27] T Logenthiran D Srinivasan and T Z Shun ldquoDemand sidemanagement in smart grid using heuristic optimizationrdquoIEEE Transactions on Smart Grid vol 3 no 3 pp 1244ndash1252 2012

[28] J Saacutenchez-Cuadrado J de Lara and E Guerra ldquoBottom-upmeta-modelling an interactive approachrdquo in Model DrivenEngineering Languages and Systems pp 3ndash19 Springer2012

[29] Y H Kim F J Sting and C H Loch ldquoTop-down bottom-upor both Toward an integrative perspective on operationsstrategy formationrdquo Journal of Operations Managementvol 32 no 7-8 pp 462ndash474 2014

[30] C Bohringer ldquoThe synthesis of bottom-up and top-down inenergy policy modelingrdquo Energy Economics vol 20 no 3pp 233ndash248 1998

[31] T G R Bower ldquoRepetition in human developmentrdquo Merrill-Palmer Quarterly of Behavior and Development vol 20no 4 pp 303ndash318 1974

[32] H Mintzberg and J A Waters ldquoOf strategies deliberate andemergentrdquo Strategic Management Journal vol 6 no 3pp 257ndash272 1985

17Complexity

The ability of bottom-up to capture discrete locationalimpacts of technologies on the system and their disaggre-gated costs is triggering the following subsections Trade-offmethodologies are needed for planning evolutionary powersystems where observing disaggregated result strategic fore-casts are to be produced These methodologies need to beinteractive in the sense that starting from an initial stateand after a testing or learning phase the network is able toaccommodate techniques that have improved the systemproviding an exploratory set of solutions that can beexpanded or discarded as the model evolves through time

33 Experiment Characterisation To illustrate the processingof the methodology adopted we consider a typical networkscenario tree created by the tool that consists of failed (red)and compliant (green) network states in different years ofevaluation The SIM starts with a single network state inthe first year of evaluation Every year the network isevaluated for compliance in intact and n minus 1 contingencyoperation modes If failures are detected the SIM appliesintervention techniques described in Section 2 to create net-work patches that resolve the failures Each application of anintervention technique creates a new network state The toolhas to resolve all issues in the network before transitioninginto the next year DNO planners usually run power flowstudies with a single fixed load pattern In contrast the SIMchecks each network state for compliance under 18 charac-teristic day load scenarios each comprising of 48 half-hoursettlement periods All studies are performed under intactand n minus 1 contingency network operation modes The SIM

calculates patch costs using cost drivers returned by the net-work modelling tool The cost driver describes a networkintervention and consists of two parts namely the patchkey and the scaling The patch key identifies the nature ofthe modification of the network performed (removal or addi-tion of an asset and the type of the asset) Scaling data is rel-evant only to patches that can be installed in multiples of onesuch as cable upgrades and additional transformer installa-tion Scaling data structure provides a list of multipliers tothe base cost data available in the SIM database In case ofcable upgrade or replacement it enables the SIM to correctlyestimate full installation costs from per unit of length valuesFinally for postprocessing analysis the SIM also returns a listof failures by asset It was identified by DNO planners asindispensable features to help validate the system and corre-late the expansion trees to the actual assets on the networkdiagram The failure detail table contains asset ID anddescription alongside information about the number of fail-ures in intact and n minus 1 operating modes as well as absoluteand per unit thermal and voltage failure magnitude

34 Case Characterisation Two set of experiments wereperformed for this study one for comparing short-termevaluation period for RIIO-ED1 (2015ndash2023) where theRIIO-ED1 investment planning has been stylised and alonger planning period for RIIO-ED1 to RIIO-ED4 from2015 up to 2047 The other set aimed at evaluating differ-ent DECC demand scenarios Experiments evaluated twodemand scenarios DECC2 and DECC4 DECC4 representsas displayed in Table 1 the slow-progression scenario and

Purchase andoperation cost data

Cost model Nodal network model

Model network through years

Apply intervention technique to keep the network compilant

Find optimal network development options

Solutions for individual networks

Aggregate results for larger network areas

Strategic forecasts

Network data Demand scenarios

Interventiontechnique costs

Interventiontechnique installation

details

Nodal interventiontechnique models

Validation withphysical trials

Relative usage ofinterventiontechniques

Validation of powerflow and reliability

models

Interventiontechnique operation

details

Load profiles

Figure 8 Bottom-up conceptualisation of evolutionary power networks planning

9Complexity

DECC2 is with DECC3 the most challenging scenario interms of electrification and low-carbon technology integra-tion The procedure to run the experiments in the SIM isshown in Figure 6 The inputs to be sent to the SIM aredetailed in Figure 5 the selected network techniques evalu-ation period demand scenario and cost model whereas theoutputs of the SIM we obtain are techniques used failuressolved assets fixed and electrical performance indicatorsThe SIM address mentioned outputs for long-term plan-ning reinforcements to optimise investment asset planningresolving network constraints The performance criteria eval-uated were capital expenditures (CAPEX) operating expen-ditures (OPEX) utilisation of assets CMLs CIs and lossesThese parameter values are delivered by the SIM after eachsimulation In order to look for the optimal solution amongthe feasible solution pathways the SIM allows a granularitystudy of each network state

4 Results

This section presents the assessment of the six smart gridinterventions along with traditional reinforcements in thetrial area compounded by six 11 kV primaries in MiltonKeynes UK for two different evaluation periods DECC2and DECC4 Subsection 41 introduces the results for the2015ndash2023 period as short-term planning and Subsection42 presents the results for a long-term evaluation period2015ndash2047

41 Short-Term Planning This section presents the assess-ment of the six smart grid interventions along with tradi-tional reinforcements in the trial area compounded by six11 kV primaries in Milton Keynes UK for two differentdemand scenarios DECC2 and DECC4

411 DECC4 2015ndash2023 Figure 9 shows the trends ofCAPEX and OPEX Note that in the year 2015 there is aCAPEXrsquos peak due to the application of more techniquesand OPEX increments over time from 2017 to 2023

Despite the investment the increase is compensated bya benefit in the electricity distribution network with areduction in CML and CI as shown in Figure 10

The summary of the proportion of installations requiredduring this evaluation period and by capital expenditures pertechnique is presented in Table 2 for both DECC scenarios

The average yearly price of each technique implementedto fix a network state is key for future decision-makingconsiderations

Figure 11 displays the average price of each techniquedisaggregated by CAPEX and OPEX

Performance indicators are key parameters for futuredecision-making within electricity distribution planning asquantifiers due to their influence on the quality of service

Therefore as shown in Figure 12 the only smart tech-nique used in combination with traditional reinforcementsin this scenario and evaluation period that slightly influenceslightly CML and CI is meshed networks

412 DECC2 2015ndash2023 Figure 13 plots the trend ofCAPEX and OPEX over the evaluation period It is important

to highlight that there is a CAPEX peak in 2015 due to anincrease of techniques applied in this period

This CAPEX increment produces a benefit in terms ofCML and CI as observed in Figure 14

The distribution of techniques applied and capital expen-ditures per technique are shown in Table 2 The average priceof each technique implementation in this demand scenariofor this evaluation period is shown in Figure 15

As displayed in Figure 12 for DECC4rsquos simulation theonly smart grid technique that improves the quality of serviceis the meshed network In Figure 16 the contribution ofsmart techniques on CML and CI for DECC2 demandevaluation is captured

413 Comparison between DECC2 and DECC4 for 2015ndash2023 The results presented may facilitate improvementsin electricity distribution network operations and planningresulting in better-informed decision-making when upgrad-ing current electricity distribution networks The assess-ment of the six smart grid techniques discovered thatonly three of them were selected as part of optimal

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

Mill

ions

pound p

er an

num

CAPEXOPEX

442444648

55254

Figure 9 CAPEX and OPEX DECC4 2015ndash2023

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

pu

Figure 10 CML and CI DECC4 2015ndash2023

10 Complexity

solutions for DECC4 and DECC2 as described in Table 2These three techniques applied are DAR for cables andtransformers and meshed networks

Comparing the cost trends of the two assessed scenariosit is notable that in both demand scenarios DECC4 and

DECC2 the CAPEX peak occurs in 2015 (Figures 9 and13) reflecting the more difficult nature of network statesto be feasible in a demanding scenario whereas DECC2shows a higher improvement of CI and CML performanceindicators as can be seen in Figures 10 and 14

Table 2 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2023

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 15 2 14 2

DAR-transformer 7 1 5 1

ALT 0 0 0 0

Mesh 3 1 4 1

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 8 3 7 2

TRAD-cable 60 89 65 91

TRAD-transfer load 6 2 4 1

TRAD-new feeder 1 2 1 2

DAR - cable DAR -transformer

Meshednetworks

TRAD -transformer

TRAD - cable

CAPEXOPEX

0

20000

40000

60000

80000

100000

pound

Figure 11 Average cost disaggregation per technique DECC4 2015ndash2023

Figure 12 Average CML and CI improvement per technique DECC4 2015ndash2023

11Complexity

The techniques applied are the key consideration to beassessed It is necessary to analyse the number of interven-tions applied by type the effect of these techniques in theelectric grid solving failures and fixing assets Figures 11and 15 show average prices of techniques used in eachdemand scenario Smart techniques have a lower TOTEXthan traditional reinforcements These novel techniquesabove are not frequently able to fix failures and thereforeproduce feasible network states In terms of CML and CIthe only technique able to moderately contribute to theirimprovement is meshed networks as shown in Figures 12and 16 Results attribute the majority of fixes to traditionalreinforcements with a comparatively smaller number offailures being solved by smart techniques (Table 2) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention forDECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

For DECC4 the three mentioned techniques along withtraditional cable and transformer replacement are applied(Table 2) whereas for DECC2 two of them are appliedDAR for cables and meshed networks (Table 2) Comparingthe cost trends of the two assessed scenarios it is notable thatin DECC4 the CAPEX peak occurs in 2015 (Figure 7)whereas in DECC2 besides the peak in 2015 there is alsoone in 2019 (Figure 11) reflecting the more difficult natureof network states to be feasible in a demanding scenarioHowever DECC2 shows a higher improvement of electricalperformance indicators as can be seen in Figure 12

In addition traditional cable replacement DAR forcables and meshed networks are able to fix the cable whereastraditional transformer replacement and DAR for trans-formers are able to fix transformer issues (Figure 16) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention for

DECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

42 Long-Term Planning This section evaluates two experi-ments namely characterising the DECC2 and DECC4 sce-narios for 2015ndash2047 evaluation period For each demandscenario expected investments disaggregating CAPEX andOPEX are presented the evolution of electrical performanceindicators and the number of techniques applied

421 DECC4 2015ndash2047 In this experiment the mostsignificant results to analyse the suitability of each techniqueare presented

Figure 17 shows the trend of CAPEX and OPEX from2015 to 2047

There are CAPEX peaks in the year 2018 and during thebeginning of RIIO-ED2 in the years 2025 to 2026 due to theapplication of more techniques to fulfill low-carbon targets

Figure 18 indicates that upgrades on the network aredirectly related to technical performance indicators CMLand CI

In Table 3 the techniques applied and their contributionto CAPEX during the evaluation period from 2015 to 2047 bya demand scenario are shown

422 DECC2 2015ndash2047 Within this experiment the mostrelevant results to analyse the evolutionary network statesare presented

In Figure 19 the trend of CAPEX and OPEX from 2015to 2047 is shown There is a significant increase of CAPEXin the years 2024 to 2026 at the beginning of RIIO-ED2due to the necessary implementation of new techniques toreach low-carbon targets The evolution of CML and CI ispresented in Figure 20 linking larger investment years whenmajor reductions are found The contribution of each solutiontechnique is presented in Table 3

423 Comparison between DECC2 and DECC4 for 2015ndash2047 Figure 21 presents a multidimensional parallel coor-dinate representation of the feasible network statersquoscombinations that produced a 2015ndash2047 investment path-way for the evaluated area It bundles economic indicatorsie CAPEX and OPEX with technical performance indica-tors providing valuable insights on the number of traditionalreinforcements utilised to heal falling network states Resultsare also clustered by the two demand scenarios assessedduring the experiments

Solutions of DECC2 (represented in orange and green)and DECC4 (in black and blue) are clustered by the per-centage of traditional reinforcements utilised as well asthe utilisation factor

Solutions in green and in black represent those wheresmart grids were more used whereas orange and blue repre-sent the cheapest (CAPEX and OPEX) less utilised and theworst responding to the decrease of CML CI and lossesand the ones using more traditional reinforcement to fixnetwork states

Besides the fact that DECC2 smart solutions are between16 and 28 more expensive than DECC4 traditional

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

CAPEXOPEX

442444648

552

Mill

ions

pound p

er an

num

54

Figure 13 CAPEX and OPEX DECC2 2015ndash2023

12 Complexity

reinforcement solutions (green solutions versus blue solu-tions in Figure 21) DECC2 traditional solutions are in therange of costs of smart solutions of DECC4 (orange andblack solutions in Figure 21) It can also be concluded thatinvestment pathways using less smart techniques provide a

cheaper response to technical performance evaluators suchas utilisation CML CI and losses

43 Summary Experiment evaluation runs from the2015ndash2047 period present lower investment rates for the

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

PU

Figure 14 CML and CI DECC2 2015ndash2023

CAPEXOPEX

DAR -transformer

Meshednetworks

Traditional -transformer

Traditional -cable

0

20000

40000

60000

80000

100000

120000

140000

pound

Figure 15 Average cost disaggregation per technique DECC2 2015ndash2023

Figure 16 Average CML and CI improvement per technique DECC2 2015ndash2023

13Complexity

2015ndash2023 period than if the evaluation is just performedwith a 2015ndash2023 time frame This occurs as a result of thechallenging low-carbon targets up to 2047 and a myopicplanning with short-term lookahead For an evaluationperiod four times larger the CAPEX and TOTEX increasedjust 18 compared to 2015ndash2023 concluding that betweenpound52M and pound68M the evaluation area regardless of thetime horizon for the investment or the demand scenarioconsidered is required

Furthermore it can be inferred comparing Tables 2 and 3that for less smart interventions the short-term planningis used compared to the long-term horizon planningDAR-cable and DAR-transformer experienced a significantimplementation variation between the two evaluationperiods for both demand scenarios as well as CAPEX allo-cated in cable upgrades falling from 89ndash91 (2015ndash2023)to 29ndash66 in the long term 2015ndash2047 run Both technicalperformance evaluators CML and CI respond to theirrespective CAPEX curve shape Due to a more incentivisedsmart grid technique during RIIO-ED2 and ED3 the per-centages of smart techniques implemented varied notablyFor DECC4 in 2023rsquos outlook the share of techniques is

25 for smart grid interventions whereas for 2050rsquos out-look the share is increased up to 58 In the same wayfor DECC2 in 2023rsquos outlook the share of smart interven-tions is 23 whereas in 2050rsquos outlook the share increasesup to 69

Feasible solutions characterising the solution space(Figure 21) differ in the degree of investment required andtechnical performance evaluators Each of the solution pathrepresented in the multidimensional solution space inFigure 21 represents the intersection of that dimension witheach axis Parallel coordinates [35] are low complexityworking for any N-dimension In the case of Figure 21it has 8 dimensions treating every variable uniformlyEach intersection of one solution path with each axis isthe value of that solution for that axis As for axis DECC2and DECC4 it represents the feasible solutions for eachdemand scenario Making a query by the percentage oftraditional reinforcement used in that solution we have thepartition between orange and green for DECC2 and blueand black being the first solutions that use more traditionalreinforcement compared with the ones that use more smarttechniques represented in green (DECC2) and black(DECC4) From Figure 21 we can argue that solutions thatuse more smart techniques (green and blacks) are moreexpensive in terms of CAPEX and OPEX than their peers(orange and blues) when compared by a demand scenarioIt can also be stated that solutions with a higher use of tradi-tional reinforcements are the ones that decrease the mostCML CI and losses

Disaggregating results by network state of the six 11 kVprimaries and by the feeder if necessary for further granulardebugging can discern locational capacity Under bothdemand scenarios Secklow Gate was seen to be the one withgreater capacity being necessary to apply fewer techniquesover the evaluation period 2015ndash2047 On the other handNewport Pagnell exceeds the capacity as soon as 2015requiring a high number of network state evaluation to befixed that happens for each subsequent year hence the largenumber of network states is presented in Table 4

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

pound p

er an

num

Figure 17 CAPEX and OPEX DECC4 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

pu

Figure 18 CML and CI DECC4 2015ndash2047

14 Complexity

5 Conclusions

Power flow analysis using a nodal network model is essentialwhen determining the benefits of trailed smart interventions

because the interactions and implications are highly specificto a particular location and scenario

This suggests that while the bottom-up approach isonerous in terms of data handling and manipulation this isworthwhile for strategic planning and policy evaluationComparing both investment strategies investment strategyadjustments will be necessary in future regulation periodsif an over-invested network behaves as displayed in theshort-term section of this study

Traditional reinforcement will continue to be the mainmethod by which network issues are mostly resolvedfollowed by dynamic asset rating and meshed networks

The assessment of the six novel smart interventions in theFALCON 11kV primary test area in Milton Keynes hasproved the suitability of three techniques able to fix failuresimproving the quality of service and their readiness to bedeployed in the near future These techniques are DAR forcables and transformers and meshed networks Meshednetworks have been repeatedly selected as a feasible tech-nique because using it will reduce CML CI and power losses

Table 3 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2047

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 18 5 31 6

DAR-transformer 32 16 32 6

ALT 0 0 0 0

Mesh 8 3 6 3

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 25 44 9 16

TRAD-cable 15 29 20 66

TRAD-transfer load 1 1 1 1

TRAD-new feeder 1 2 1 2

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

s pound p

er an

num

Figure 19 CAPEX and OPEX DECC2 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

PU

Figure 20 CML and CI DECC2 2015ndash2047

15Complexity

improving the quality of service and the efficiency whilebeing a cost-effective solution

The initial capacity at primary substations differedsignificantly and this affected the number and complexityof interventions required by the SIM Due to the loadscenarios showing significant peak load increases DAR wasoften a temporary measure that would delay but not removethe eventual need for traditional reinforcement

Implementing DAR for cables and transformers themonitoring of assets when their peak capacity is increasedwas analysed On the other hand it was observed thattraditional reinforcements still play a key role in keepingthe electricity distribution networks free of constraintsTRAD techniques such as transformer and cable replace-ments are able to fix the majority of failures and will beessential also in the future

The comparison between traditional reinforcements andnovel smart techniques has provided a new knowledge aboutthe suitability of each technique to be applied in termsof costs electrical performance failures fixed and assetreplaced Cable replacement is the most costly techniquehowever its use is unavoidable in a number of cases Further-more the applicability of each technique regarding costsinvolved improvements on power quality and efficiency

and failures solved lead to new questions to be analysed suchas the lack of these techniques to provide flexible capacitywithin the trailed area

To sum up this study has performed a comparativeanalysis of novel smart intervention techniques providinginsights for future investments in electricity distributionplanning Further work can focus on scaling up the analysisto include a larger section of the network or a constrainedarea to evaluate national applicability of the current findings

Data Availability

Some of the data relevant to this submission is proprietaryfrom Western Power Distribution It can be accessed forresearch purposes under a signed collaboration agreement

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors would like to thank the rest of the WesternPower Distribution Innovation team particularly the other

2400

2200

2000

350

360

370

380

390

400

1400

1300

1200

1100

1000DECC 4

50

55

60

12

11

10

9

8

4400

4200

4000

3800

3600

2400

CAPEX (in kpound) OPEX (in kpound) Utilisation Traditional reinforcements () DECC 2 998796 CML 998796 CI 998796 Losses

2200

2000

1800

Figure 21 Parallel coordinate visualisation of technoeconomic performance indicators under DECC4 and DECC2

Table 4 Summary of network states and results for demand scenario DECC4 and DECC2

Primary 11 kV substation Feeders Year TechniquesResultsDECC4

NSDECC4

ResultsDECC2

NSDECC2

Fox Milne 13 2047 Smart and traditional 54 276 47 259

Newport Pagnell 9 2047 Smart and traditional 48 317 31 318

Secklow Gate 9 2047 Smart and traditional 27 29 15 16

Bletchley 19 2047 Smart and traditional 32 40 21 48

Marlborough Street 11 2047 Smart and traditional 31 52 19 37

Childs Way 17 2047 Smart and traditional 67 398 54 181

16 Complexity

members of the FALCON project as well as Paul CharletonStuart Fowler Ilja Orlovs Dan Taylor and Steve IngramAlso the authors want to acknowledge OFGEM and theLowCarbon Network Fund for funding this piece of research

References

[1] European Commission ldquoCommunication from the Commis-sion to the European Parliament the Council the EuropeanEconomic and Social Committee and the Committee of theRegionsrdquo 2011 December 2016 httpseur-lexeuropaeulegal-contentENTXTqid=1537473508968ampuri=CELEX52016PC076721

[2] F G N Li and E Trutnevyte ldquoInvestment appraisal ofcost-optimal and near-optimal pathways for the UK elec-tricity sector transition to 2050rdquo Applied Energy vol 189pp 89ndash109 2017

[3] G Strbac I Konstantelos M Pollitt and R Green DeliveringFuture-Proof Energy Infrastructure National InfrastructureCommission 2016

[4] Ofgem ldquoGB Network regulation - the RIIO modelrdquo 2010December 2014 httpswwwofgemgovuknetwork-regulation-riio-model

[5] R Poudineh and T Jamasb ldquoDistributed generation storagedemand response and energy efficiency as alternatives togrid capacity enhancementrdquo Energy Policy vol 67 pp 222ndash231 2014

[6] J Yang X Bai D Strickland L Jenkins and A M CrossldquoDynamic network rating for low carbon distribution networkoperationmdashA UK applicationrdquo IEEE Transactions on SmartGrid vol 6 no 2 pp 988ndash998 2015

[7] M Behnke W Erdman S Horgan et al ldquoSecondary net-work distribution systems background and issues relatedto the interconnection of distributed resourcesrdquo NationalRenewable Energy Laboratory (NREL) Tech Rep NRELTP-560-38079 2005

[8] J Crispim J Braz R Castro and J Esteves ldquoSmart grids in theEU with smart regulation experiences from the UK Italy andPortugalrdquo Utilities Policy vol 31 pp 85ndash93 2014

[9] Tnei ldquoIPSA Power software for power systemsrdquo March 2016httpwwwipsa-powercom

[10] ETAP ldquoETAP PSrdquo 2018 April 2016 httpsetapcomsoftwareproduct-overview-main

[11] DINIS ldquoDINIS distribution network information sys-temsrdquo 2002 March 2016 httpsetapcomsoftwareproduct-overview-main

[12] Western Power Distribution ldquoSIM final reportrdquo 2015November 2016 httpwwwwesternpowerinnovationcoukProjectsFalconaspx

[13] Western Power Distribution ldquoProject FALCONrdquo 2015November 2017 httpwwwdiniscom

[14] EA Technology ldquoTransform modelrdquo March 2016 httpwwweatechnologycomproducts-and-servicescreate-smarter-gridstransform-model

[15] L Sigrist E Lobato L Rouco M Gazzino and M CantuldquoEconomic assessment of smart grid initiatives for islandpower systemsrdquo Applied Energy vol 189 pp 403ndash415 2017

[16] J A Momoh ldquoSmart grid design for efficient and flexiblepower networks operation and controlrdquo in 2009 IEEEPESPower Systems Conference and Exposition pp 1ndash8 SeattleWA USA 2009

[17] N Good K A Ellis and P Mancarella ldquoReview and classifica-tion of barriers and enablers of demand response in the smartgridrdquo Renewable and Sustainable Energy Reviews vol 72pp 57ndash72 2017

[18] Z Yang J Xiang and Y Li ldquoDistributed consensus basedsupplyndashdemand balance algorithm for economic dispatchproblem in a smart grid with switching graphrdquo IEEE Trans-actions on Industrial Electronics vol 64 no 2 pp 1600ndash1610 2017

[19] SuSTAINABLE ldquoSmart distribution network operation formaximising the integration of renewable generationrdquo 2015March 2016 httpwwwsustainableprojecteuHomeaspx

[20] S Mohtashami D Pudjianto and G Strbac ldquoStrategic distri-bution network planning with smart grid technologiesrdquo IEEETransactions on Smart Grid vol 8 no 6 pp 2656ndash2664 2017

[21] Smart Grid Forum ldquoSmart grid forum work stream 7rdquo2015 March 2016 httpwwwsmarternetworksorgprojectnia_nget015425

[22] ETI ldquoEnergyPath networksrdquo March 2016 httpwwweticoukprojectenergypath

[23] C Mateo Domingo T Gomez San Roman Aacute Sanchez-Miralles J P Peco Gonzalez and A Candela Martinez ldquoAreference network model for large-scale distribution planningwith automatic street map generationrdquo IEEE Transactions onPower Systems vol 26 no 1 pp 190ndash197 2011

[24] R Bolton and T J Foxon ldquoInfrastructure transformation as asocio-technical process mdash implications for the governanceof energy distribution networks in the UKrdquo TechnologicalForecasting and Social Change vol 90 pp 538ndash550 2015

[25] UK Power Networks ldquoDNO guide to future Smart man-agement of distribution networks Summary reportrdquo 2014httpsinnovationukpowernetworkscoukinnovationenProjectstier-2-projectsLow-Carbon-London-(LCL)Project-DocumentsLCL20Learning20Report20-20SR20-20Summary20Report20-20DNO20Guide20to20Future20Smart20Management20of20Distribution20Networkspdf

[26] A Botea J Rintanen and D Banerjee ldquoOptimal reconfigura-tion for supply restoration with informed AlowastSearchrdquo IEEETransactions on Smart Grid vol 3 no 2 pp 583ndash593 2012

[27] T Logenthiran D Srinivasan and T Z Shun ldquoDemand sidemanagement in smart grid using heuristic optimizationrdquoIEEE Transactions on Smart Grid vol 3 no 3 pp 1244ndash1252 2012

[28] J Saacutenchez-Cuadrado J de Lara and E Guerra ldquoBottom-upmeta-modelling an interactive approachrdquo in Model DrivenEngineering Languages and Systems pp 3ndash19 Springer2012

[29] Y H Kim F J Sting and C H Loch ldquoTop-down bottom-upor both Toward an integrative perspective on operationsstrategy formationrdquo Journal of Operations Managementvol 32 no 7-8 pp 462ndash474 2014

[30] C Bohringer ldquoThe synthesis of bottom-up and top-down inenergy policy modelingrdquo Energy Economics vol 20 no 3pp 233ndash248 1998

[31] T G R Bower ldquoRepetition in human developmentrdquo Merrill-Palmer Quarterly of Behavior and Development vol 20no 4 pp 303ndash318 1974

[32] H Mintzberg and J A Waters ldquoOf strategies deliberate andemergentrdquo Strategic Management Journal vol 6 no 3pp 257ndash272 1985

17Complexity

DECC2 is with DECC3 the most challenging scenario interms of electrification and low-carbon technology integra-tion The procedure to run the experiments in the SIM isshown in Figure 6 The inputs to be sent to the SIM aredetailed in Figure 5 the selected network techniques evalu-ation period demand scenario and cost model whereas theoutputs of the SIM we obtain are techniques used failuressolved assets fixed and electrical performance indicatorsThe SIM address mentioned outputs for long-term plan-ning reinforcements to optimise investment asset planningresolving network constraints The performance criteria eval-uated were capital expenditures (CAPEX) operating expen-ditures (OPEX) utilisation of assets CMLs CIs and lossesThese parameter values are delivered by the SIM after eachsimulation In order to look for the optimal solution amongthe feasible solution pathways the SIM allows a granularitystudy of each network state

4 Results

This section presents the assessment of the six smart gridinterventions along with traditional reinforcements in thetrial area compounded by six 11 kV primaries in MiltonKeynes UK for two different evaluation periods DECC2and DECC4 Subsection 41 introduces the results for the2015ndash2023 period as short-term planning and Subsection42 presents the results for a long-term evaluation period2015ndash2047

41 Short-Term Planning This section presents the assess-ment of the six smart grid interventions along with tradi-tional reinforcements in the trial area compounded by six11 kV primaries in Milton Keynes UK for two differentdemand scenarios DECC2 and DECC4

411 DECC4 2015ndash2023 Figure 9 shows the trends ofCAPEX and OPEX Note that in the year 2015 there is aCAPEXrsquos peak due to the application of more techniquesand OPEX increments over time from 2017 to 2023

Despite the investment the increase is compensated bya benefit in the electricity distribution network with areduction in CML and CI as shown in Figure 10

The summary of the proportion of installations requiredduring this evaluation period and by capital expenditures pertechnique is presented in Table 2 for both DECC scenarios

The average yearly price of each technique implementedto fix a network state is key for future decision-makingconsiderations

Figure 11 displays the average price of each techniquedisaggregated by CAPEX and OPEX

Performance indicators are key parameters for futuredecision-making within electricity distribution planning asquantifiers due to their influence on the quality of service

Therefore as shown in Figure 12 the only smart tech-nique used in combination with traditional reinforcementsin this scenario and evaluation period that slightly influenceslightly CML and CI is meshed networks

412 DECC2 2015ndash2023 Figure 13 plots the trend ofCAPEX and OPEX over the evaluation period It is important

to highlight that there is a CAPEX peak in 2015 due to anincrease of techniques applied in this period

This CAPEX increment produces a benefit in terms ofCML and CI as observed in Figure 14

The distribution of techniques applied and capital expen-ditures per technique are shown in Table 2 The average priceof each technique implementation in this demand scenariofor this evaluation period is shown in Figure 15

As displayed in Figure 12 for DECC4rsquos simulation theonly smart grid technique that improves the quality of serviceis the meshed network In Figure 16 the contribution ofsmart techniques on CML and CI for DECC2 demandevaluation is captured

413 Comparison between DECC2 and DECC4 for 2015ndash2023 The results presented may facilitate improvementsin electricity distribution network operations and planningresulting in better-informed decision-making when upgrad-ing current electricity distribution networks The assess-ment of the six smart grid techniques discovered thatonly three of them were selected as part of optimal

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

Mill

ions

pound p

er an

num

CAPEXOPEX

442444648

55254

Figure 9 CAPEX and OPEX DECC4 2015ndash2023

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

pu

Figure 10 CML and CI DECC4 2015ndash2023

10 Complexity

solutions for DECC4 and DECC2 as described in Table 2These three techniques applied are DAR for cables andtransformers and meshed networks

Comparing the cost trends of the two assessed scenariosit is notable that in both demand scenarios DECC4 and

DECC2 the CAPEX peak occurs in 2015 (Figures 9 and13) reflecting the more difficult nature of network statesto be feasible in a demanding scenario whereas DECC2shows a higher improvement of CI and CML performanceindicators as can be seen in Figures 10 and 14

Table 2 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2023

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 15 2 14 2

DAR-transformer 7 1 5 1

ALT 0 0 0 0

Mesh 3 1 4 1

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 8 3 7 2

TRAD-cable 60 89 65 91

TRAD-transfer load 6 2 4 1

TRAD-new feeder 1 2 1 2

DAR - cable DAR -transformer

Meshednetworks

TRAD -transformer

TRAD - cable

CAPEXOPEX

0

20000

40000

60000

80000

100000

pound

Figure 11 Average cost disaggregation per technique DECC4 2015ndash2023

Figure 12 Average CML and CI improvement per technique DECC4 2015ndash2023

11Complexity

The techniques applied are the key consideration to beassessed It is necessary to analyse the number of interven-tions applied by type the effect of these techniques in theelectric grid solving failures and fixing assets Figures 11and 15 show average prices of techniques used in eachdemand scenario Smart techniques have a lower TOTEXthan traditional reinforcements These novel techniquesabove are not frequently able to fix failures and thereforeproduce feasible network states In terms of CML and CIthe only technique able to moderately contribute to theirimprovement is meshed networks as shown in Figures 12and 16 Results attribute the majority of fixes to traditionalreinforcements with a comparatively smaller number offailures being solved by smart techniques (Table 2) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention forDECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

For DECC4 the three mentioned techniques along withtraditional cable and transformer replacement are applied(Table 2) whereas for DECC2 two of them are appliedDAR for cables and meshed networks (Table 2) Comparingthe cost trends of the two assessed scenarios it is notable thatin DECC4 the CAPEX peak occurs in 2015 (Figure 7)whereas in DECC2 besides the peak in 2015 there is alsoone in 2019 (Figure 11) reflecting the more difficult natureof network states to be feasible in a demanding scenarioHowever DECC2 shows a higher improvement of electricalperformance indicators as can be seen in Figure 12

In addition traditional cable replacement DAR forcables and meshed networks are able to fix the cable whereastraditional transformer replacement and DAR for trans-formers are able to fix transformer issues (Figure 16) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention for

DECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

42 Long-Term Planning This section evaluates two experi-ments namely characterising the DECC2 and DECC4 sce-narios for 2015ndash2047 evaluation period For each demandscenario expected investments disaggregating CAPEX andOPEX are presented the evolution of electrical performanceindicators and the number of techniques applied

421 DECC4 2015ndash2047 In this experiment the mostsignificant results to analyse the suitability of each techniqueare presented

Figure 17 shows the trend of CAPEX and OPEX from2015 to 2047

There are CAPEX peaks in the year 2018 and during thebeginning of RIIO-ED2 in the years 2025 to 2026 due to theapplication of more techniques to fulfill low-carbon targets

Figure 18 indicates that upgrades on the network aredirectly related to technical performance indicators CMLand CI

In Table 3 the techniques applied and their contributionto CAPEX during the evaluation period from 2015 to 2047 bya demand scenario are shown

422 DECC2 2015ndash2047 Within this experiment the mostrelevant results to analyse the evolutionary network statesare presented

In Figure 19 the trend of CAPEX and OPEX from 2015to 2047 is shown There is a significant increase of CAPEXin the years 2024 to 2026 at the beginning of RIIO-ED2due to the necessary implementation of new techniques toreach low-carbon targets The evolution of CML and CI ispresented in Figure 20 linking larger investment years whenmajor reductions are found The contribution of each solutiontechnique is presented in Table 3

423 Comparison between DECC2 and DECC4 for 2015ndash2047 Figure 21 presents a multidimensional parallel coor-dinate representation of the feasible network statersquoscombinations that produced a 2015ndash2047 investment path-way for the evaluated area It bundles economic indicatorsie CAPEX and OPEX with technical performance indica-tors providing valuable insights on the number of traditionalreinforcements utilised to heal falling network states Resultsare also clustered by the two demand scenarios assessedduring the experiments

Solutions of DECC2 (represented in orange and green)and DECC4 (in black and blue) are clustered by the per-centage of traditional reinforcements utilised as well asthe utilisation factor

Solutions in green and in black represent those wheresmart grids were more used whereas orange and blue repre-sent the cheapest (CAPEX and OPEX) less utilised and theworst responding to the decrease of CML CI and lossesand the ones using more traditional reinforcement to fixnetwork states

Besides the fact that DECC2 smart solutions are between16 and 28 more expensive than DECC4 traditional

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

CAPEXOPEX

442444648

552

Mill

ions

pound p

er an

num

54

Figure 13 CAPEX and OPEX DECC2 2015ndash2023

12 Complexity

reinforcement solutions (green solutions versus blue solu-tions in Figure 21) DECC2 traditional solutions are in therange of costs of smart solutions of DECC4 (orange andblack solutions in Figure 21) It can also be concluded thatinvestment pathways using less smart techniques provide a

cheaper response to technical performance evaluators suchas utilisation CML CI and losses

43 Summary Experiment evaluation runs from the2015ndash2047 period present lower investment rates for the

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

PU

Figure 14 CML and CI DECC2 2015ndash2023

CAPEXOPEX

DAR -transformer

Meshednetworks

Traditional -transformer

Traditional -cable

0

20000

40000

60000

80000

100000

120000

140000

pound

Figure 15 Average cost disaggregation per technique DECC2 2015ndash2023

Figure 16 Average CML and CI improvement per technique DECC2 2015ndash2023

13Complexity

2015ndash2023 period than if the evaluation is just performedwith a 2015ndash2023 time frame This occurs as a result of thechallenging low-carbon targets up to 2047 and a myopicplanning with short-term lookahead For an evaluationperiod four times larger the CAPEX and TOTEX increasedjust 18 compared to 2015ndash2023 concluding that betweenpound52M and pound68M the evaluation area regardless of thetime horizon for the investment or the demand scenarioconsidered is required

Furthermore it can be inferred comparing Tables 2 and 3that for less smart interventions the short-term planningis used compared to the long-term horizon planningDAR-cable and DAR-transformer experienced a significantimplementation variation between the two evaluationperiods for both demand scenarios as well as CAPEX allo-cated in cable upgrades falling from 89ndash91 (2015ndash2023)to 29ndash66 in the long term 2015ndash2047 run Both technicalperformance evaluators CML and CI respond to theirrespective CAPEX curve shape Due to a more incentivisedsmart grid technique during RIIO-ED2 and ED3 the per-centages of smart techniques implemented varied notablyFor DECC4 in 2023rsquos outlook the share of techniques is

25 for smart grid interventions whereas for 2050rsquos out-look the share is increased up to 58 In the same wayfor DECC2 in 2023rsquos outlook the share of smart interven-tions is 23 whereas in 2050rsquos outlook the share increasesup to 69

Feasible solutions characterising the solution space(Figure 21) differ in the degree of investment required andtechnical performance evaluators Each of the solution pathrepresented in the multidimensional solution space inFigure 21 represents the intersection of that dimension witheach axis Parallel coordinates [35] are low complexityworking for any N-dimension In the case of Figure 21it has 8 dimensions treating every variable uniformlyEach intersection of one solution path with each axis isthe value of that solution for that axis As for axis DECC2and DECC4 it represents the feasible solutions for eachdemand scenario Making a query by the percentage oftraditional reinforcement used in that solution we have thepartition between orange and green for DECC2 and blueand black being the first solutions that use more traditionalreinforcement compared with the ones that use more smarttechniques represented in green (DECC2) and black(DECC4) From Figure 21 we can argue that solutions thatuse more smart techniques (green and blacks) are moreexpensive in terms of CAPEX and OPEX than their peers(orange and blues) when compared by a demand scenarioIt can also be stated that solutions with a higher use of tradi-tional reinforcements are the ones that decrease the mostCML CI and losses

Disaggregating results by network state of the six 11 kVprimaries and by the feeder if necessary for further granulardebugging can discern locational capacity Under bothdemand scenarios Secklow Gate was seen to be the one withgreater capacity being necessary to apply fewer techniquesover the evaluation period 2015ndash2047 On the other handNewport Pagnell exceeds the capacity as soon as 2015requiring a high number of network state evaluation to befixed that happens for each subsequent year hence the largenumber of network states is presented in Table 4

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

pound p

er an

num

Figure 17 CAPEX and OPEX DECC4 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

pu

Figure 18 CML and CI DECC4 2015ndash2047

14 Complexity

5 Conclusions

Power flow analysis using a nodal network model is essentialwhen determining the benefits of trailed smart interventions

because the interactions and implications are highly specificto a particular location and scenario

This suggests that while the bottom-up approach isonerous in terms of data handling and manipulation this isworthwhile for strategic planning and policy evaluationComparing both investment strategies investment strategyadjustments will be necessary in future regulation periodsif an over-invested network behaves as displayed in theshort-term section of this study

Traditional reinforcement will continue to be the mainmethod by which network issues are mostly resolvedfollowed by dynamic asset rating and meshed networks

The assessment of the six novel smart interventions in theFALCON 11kV primary test area in Milton Keynes hasproved the suitability of three techniques able to fix failuresimproving the quality of service and their readiness to bedeployed in the near future These techniques are DAR forcables and transformers and meshed networks Meshednetworks have been repeatedly selected as a feasible tech-nique because using it will reduce CML CI and power losses

Table 3 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2047

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 18 5 31 6

DAR-transformer 32 16 32 6

ALT 0 0 0 0

Mesh 8 3 6 3

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 25 44 9 16

TRAD-cable 15 29 20 66

TRAD-transfer load 1 1 1 1

TRAD-new feeder 1 2 1 2

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

s pound p

er an

num

Figure 19 CAPEX and OPEX DECC2 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

PU

Figure 20 CML and CI DECC2 2015ndash2047

15Complexity

improving the quality of service and the efficiency whilebeing a cost-effective solution

The initial capacity at primary substations differedsignificantly and this affected the number and complexityof interventions required by the SIM Due to the loadscenarios showing significant peak load increases DAR wasoften a temporary measure that would delay but not removethe eventual need for traditional reinforcement

Implementing DAR for cables and transformers themonitoring of assets when their peak capacity is increasedwas analysed On the other hand it was observed thattraditional reinforcements still play a key role in keepingthe electricity distribution networks free of constraintsTRAD techniques such as transformer and cable replace-ments are able to fix the majority of failures and will beessential also in the future

The comparison between traditional reinforcements andnovel smart techniques has provided a new knowledge aboutthe suitability of each technique to be applied in termsof costs electrical performance failures fixed and assetreplaced Cable replacement is the most costly techniquehowever its use is unavoidable in a number of cases Further-more the applicability of each technique regarding costsinvolved improvements on power quality and efficiency

and failures solved lead to new questions to be analysed suchas the lack of these techniques to provide flexible capacitywithin the trailed area

To sum up this study has performed a comparativeanalysis of novel smart intervention techniques providinginsights for future investments in electricity distributionplanning Further work can focus on scaling up the analysisto include a larger section of the network or a constrainedarea to evaluate national applicability of the current findings

Data Availability

Some of the data relevant to this submission is proprietaryfrom Western Power Distribution It can be accessed forresearch purposes under a signed collaboration agreement

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors would like to thank the rest of the WesternPower Distribution Innovation team particularly the other

2400

2200

2000

350

360

370

380

390

400

1400

1300

1200

1100

1000DECC 4

50

55

60

12

11

10

9

8

4400

4200

4000

3800

3600

2400

CAPEX (in kpound) OPEX (in kpound) Utilisation Traditional reinforcements () DECC 2 998796 CML 998796 CI 998796 Losses

2200

2000

1800

Figure 21 Parallel coordinate visualisation of technoeconomic performance indicators under DECC4 and DECC2

Table 4 Summary of network states and results for demand scenario DECC4 and DECC2

Primary 11 kV substation Feeders Year TechniquesResultsDECC4

NSDECC4

ResultsDECC2

NSDECC2

Fox Milne 13 2047 Smart and traditional 54 276 47 259

Newport Pagnell 9 2047 Smart and traditional 48 317 31 318

Secklow Gate 9 2047 Smart and traditional 27 29 15 16

Bletchley 19 2047 Smart and traditional 32 40 21 48

Marlborough Street 11 2047 Smart and traditional 31 52 19 37

Childs Way 17 2047 Smart and traditional 67 398 54 181

16 Complexity

members of the FALCON project as well as Paul CharletonStuart Fowler Ilja Orlovs Dan Taylor and Steve IngramAlso the authors want to acknowledge OFGEM and theLowCarbon Network Fund for funding this piece of research

References

[1] European Commission ldquoCommunication from the Commis-sion to the European Parliament the Council the EuropeanEconomic and Social Committee and the Committee of theRegionsrdquo 2011 December 2016 httpseur-lexeuropaeulegal-contentENTXTqid=1537473508968ampuri=CELEX52016PC076721

[2] F G N Li and E Trutnevyte ldquoInvestment appraisal ofcost-optimal and near-optimal pathways for the UK elec-tricity sector transition to 2050rdquo Applied Energy vol 189pp 89ndash109 2017

[3] G Strbac I Konstantelos M Pollitt and R Green DeliveringFuture-Proof Energy Infrastructure National InfrastructureCommission 2016

[4] Ofgem ldquoGB Network regulation - the RIIO modelrdquo 2010December 2014 httpswwwofgemgovuknetwork-regulation-riio-model

[5] R Poudineh and T Jamasb ldquoDistributed generation storagedemand response and energy efficiency as alternatives togrid capacity enhancementrdquo Energy Policy vol 67 pp 222ndash231 2014

[6] J Yang X Bai D Strickland L Jenkins and A M CrossldquoDynamic network rating for low carbon distribution networkoperationmdashA UK applicationrdquo IEEE Transactions on SmartGrid vol 6 no 2 pp 988ndash998 2015

[7] M Behnke W Erdman S Horgan et al ldquoSecondary net-work distribution systems background and issues relatedto the interconnection of distributed resourcesrdquo NationalRenewable Energy Laboratory (NREL) Tech Rep NRELTP-560-38079 2005

[8] J Crispim J Braz R Castro and J Esteves ldquoSmart grids in theEU with smart regulation experiences from the UK Italy andPortugalrdquo Utilities Policy vol 31 pp 85ndash93 2014

[9] Tnei ldquoIPSA Power software for power systemsrdquo March 2016httpwwwipsa-powercom

[10] ETAP ldquoETAP PSrdquo 2018 April 2016 httpsetapcomsoftwareproduct-overview-main

[11] DINIS ldquoDINIS distribution network information sys-temsrdquo 2002 March 2016 httpsetapcomsoftwareproduct-overview-main

[12] Western Power Distribution ldquoSIM final reportrdquo 2015November 2016 httpwwwwesternpowerinnovationcoukProjectsFalconaspx

[13] Western Power Distribution ldquoProject FALCONrdquo 2015November 2017 httpwwwdiniscom

[14] EA Technology ldquoTransform modelrdquo March 2016 httpwwweatechnologycomproducts-and-servicescreate-smarter-gridstransform-model

[15] L Sigrist E Lobato L Rouco M Gazzino and M CantuldquoEconomic assessment of smart grid initiatives for islandpower systemsrdquo Applied Energy vol 189 pp 403ndash415 2017

[16] J A Momoh ldquoSmart grid design for efficient and flexiblepower networks operation and controlrdquo in 2009 IEEEPESPower Systems Conference and Exposition pp 1ndash8 SeattleWA USA 2009

[17] N Good K A Ellis and P Mancarella ldquoReview and classifica-tion of barriers and enablers of demand response in the smartgridrdquo Renewable and Sustainable Energy Reviews vol 72pp 57ndash72 2017

[18] Z Yang J Xiang and Y Li ldquoDistributed consensus basedsupplyndashdemand balance algorithm for economic dispatchproblem in a smart grid with switching graphrdquo IEEE Trans-actions on Industrial Electronics vol 64 no 2 pp 1600ndash1610 2017

[19] SuSTAINABLE ldquoSmart distribution network operation formaximising the integration of renewable generationrdquo 2015March 2016 httpwwwsustainableprojecteuHomeaspx

[20] S Mohtashami D Pudjianto and G Strbac ldquoStrategic distri-bution network planning with smart grid technologiesrdquo IEEETransactions on Smart Grid vol 8 no 6 pp 2656ndash2664 2017

[21] Smart Grid Forum ldquoSmart grid forum work stream 7rdquo2015 March 2016 httpwwwsmarternetworksorgprojectnia_nget015425

[22] ETI ldquoEnergyPath networksrdquo March 2016 httpwwweticoukprojectenergypath

[23] C Mateo Domingo T Gomez San Roman Aacute Sanchez-Miralles J P Peco Gonzalez and A Candela Martinez ldquoAreference network model for large-scale distribution planningwith automatic street map generationrdquo IEEE Transactions onPower Systems vol 26 no 1 pp 190ndash197 2011

[24] R Bolton and T J Foxon ldquoInfrastructure transformation as asocio-technical process mdash implications for the governanceof energy distribution networks in the UKrdquo TechnologicalForecasting and Social Change vol 90 pp 538ndash550 2015

[25] UK Power Networks ldquoDNO guide to future Smart man-agement of distribution networks Summary reportrdquo 2014httpsinnovationukpowernetworkscoukinnovationenProjectstier-2-projectsLow-Carbon-London-(LCL)Project-DocumentsLCL20Learning20Report20-20SR20-20Summary20Report20-20DNO20Guide20to20Future20Smart20Management20of20Distribution20Networkspdf

[26] A Botea J Rintanen and D Banerjee ldquoOptimal reconfigura-tion for supply restoration with informed AlowastSearchrdquo IEEETransactions on Smart Grid vol 3 no 2 pp 583ndash593 2012

[27] T Logenthiran D Srinivasan and T Z Shun ldquoDemand sidemanagement in smart grid using heuristic optimizationrdquoIEEE Transactions on Smart Grid vol 3 no 3 pp 1244ndash1252 2012

[28] J Saacutenchez-Cuadrado J de Lara and E Guerra ldquoBottom-upmeta-modelling an interactive approachrdquo in Model DrivenEngineering Languages and Systems pp 3ndash19 Springer2012

[29] Y H Kim F J Sting and C H Loch ldquoTop-down bottom-upor both Toward an integrative perspective on operationsstrategy formationrdquo Journal of Operations Managementvol 32 no 7-8 pp 462ndash474 2014

[30] C Bohringer ldquoThe synthesis of bottom-up and top-down inenergy policy modelingrdquo Energy Economics vol 20 no 3pp 233ndash248 1998

[31] T G R Bower ldquoRepetition in human developmentrdquo Merrill-Palmer Quarterly of Behavior and Development vol 20no 4 pp 303ndash318 1974

[32] H Mintzberg and J A Waters ldquoOf strategies deliberate andemergentrdquo Strategic Management Journal vol 6 no 3pp 257ndash272 1985

17Complexity

solutions for DECC4 and DECC2 as described in Table 2These three techniques applied are DAR for cables andtransformers and meshed networks

Comparing the cost trends of the two assessed scenariosit is notable that in both demand scenarios DECC4 and

DECC2 the CAPEX peak occurs in 2015 (Figures 9 and13) reflecting the more difficult nature of network statesto be feasible in a demanding scenario whereas DECC2shows a higher improvement of CI and CML performanceindicators as can be seen in Figures 10 and 14

Table 2 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2023

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 15 2 14 2

DAR-transformer 7 1 5 1

ALT 0 0 0 0

Mesh 3 1 4 1

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 8 3 7 2

TRAD-cable 60 89 65 91

TRAD-transfer load 6 2 4 1

TRAD-new feeder 1 2 1 2

DAR - cable DAR -transformer

Meshednetworks

TRAD -transformer

TRAD - cable

CAPEXOPEX

0

20000

40000

60000

80000

100000

pound

Figure 11 Average cost disaggregation per technique DECC4 2015ndash2023

Figure 12 Average CML and CI improvement per technique DECC4 2015ndash2023

11Complexity

The techniques applied are the key consideration to beassessed It is necessary to analyse the number of interven-tions applied by type the effect of these techniques in theelectric grid solving failures and fixing assets Figures 11and 15 show average prices of techniques used in eachdemand scenario Smart techniques have a lower TOTEXthan traditional reinforcements These novel techniquesabove are not frequently able to fix failures and thereforeproduce feasible network states In terms of CML and CIthe only technique able to moderately contribute to theirimprovement is meshed networks as shown in Figures 12and 16 Results attribute the majority of fixes to traditionalreinforcements with a comparatively smaller number offailures being solved by smart techniques (Table 2) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention forDECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

For DECC4 the three mentioned techniques along withtraditional cable and transformer replacement are applied(Table 2) whereas for DECC2 two of them are appliedDAR for cables and meshed networks (Table 2) Comparingthe cost trends of the two assessed scenarios it is notable thatin DECC4 the CAPEX peak occurs in 2015 (Figure 7)whereas in DECC2 besides the peak in 2015 there is alsoone in 2019 (Figure 11) reflecting the more difficult natureof network states to be feasible in a demanding scenarioHowever DECC2 shows a higher improvement of electricalperformance indicators as can be seen in Figure 12

In addition traditional cable replacement DAR forcables and meshed networks are able to fix the cable whereastraditional transformer replacement and DAR for trans-formers are able to fix transformer issues (Figure 16) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention for

DECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

42 Long-Term Planning This section evaluates two experi-ments namely characterising the DECC2 and DECC4 sce-narios for 2015ndash2047 evaluation period For each demandscenario expected investments disaggregating CAPEX andOPEX are presented the evolution of electrical performanceindicators and the number of techniques applied

421 DECC4 2015ndash2047 In this experiment the mostsignificant results to analyse the suitability of each techniqueare presented

Figure 17 shows the trend of CAPEX and OPEX from2015 to 2047

There are CAPEX peaks in the year 2018 and during thebeginning of RIIO-ED2 in the years 2025 to 2026 due to theapplication of more techniques to fulfill low-carbon targets

Figure 18 indicates that upgrades on the network aredirectly related to technical performance indicators CMLand CI

In Table 3 the techniques applied and their contributionto CAPEX during the evaluation period from 2015 to 2047 bya demand scenario are shown

422 DECC2 2015ndash2047 Within this experiment the mostrelevant results to analyse the evolutionary network statesare presented

In Figure 19 the trend of CAPEX and OPEX from 2015to 2047 is shown There is a significant increase of CAPEXin the years 2024 to 2026 at the beginning of RIIO-ED2due to the necessary implementation of new techniques toreach low-carbon targets The evolution of CML and CI ispresented in Figure 20 linking larger investment years whenmajor reductions are found The contribution of each solutiontechnique is presented in Table 3

423 Comparison between DECC2 and DECC4 for 2015ndash2047 Figure 21 presents a multidimensional parallel coor-dinate representation of the feasible network statersquoscombinations that produced a 2015ndash2047 investment path-way for the evaluated area It bundles economic indicatorsie CAPEX and OPEX with technical performance indica-tors providing valuable insights on the number of traditionalreinforcements utilised to heal falling network states Resultsare also clustered by the two demand scenarios assessedduring the experiments

Solutions of DECC2 (represented in orange and green)and DECC4 (in black and blue) are clustered by the per-centage of traditional reinforcements utilised as well asthe utilisation factor

Solutions in green and in black represent those wheresmart grids were more used whereas orange and blue repre-sent the cheapest (CAPEX and OPEX) less utilised and theworst responding to the decrease of CML CI and lossesand the ones using more traditional reinforcement to fixnetwork states

Besides the fact that DECC2 smart solutions are between16 and 28 more expensive than DECC4 traditional

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

CAPEXOPEX

442444648

552

Mill

ions

pound p

er an

num

54

Figure 13 CAPEX and OPEX DECC2 2015ndash2023

12 Complexity

reinforcement solutions (green solutions versus blue solu-tions in Figure 21) DECC2 traditional solutions are in therange of costs of smart solutions of DECC4 (orange andblack solutions in Figure 21) It can also be concluded thatinvestment pathways using less smart techniques provide a

cheaper response to technical performance evaluators suchas utilisation CML CI and losses

43 Summary Experiment evaluation runs from the2015ndash2047 period present lower investment rates for the

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

PU

Figure 14 CML and CI DECC2 2015ndash2023

CAPEXOPEX

DAR -transformer

Meshednetworks

Traditional -transformer

Traditional -cable

0

20000

40000

60000

80000

100000

120000

140000

pound

Figure 15 Average cost disaggregation per technique DECC2 2015ndash2023

Figure 16 Average CML and CI improvement per technique DECC2 2015ndash2023

13Complexity

2015ndash2023 period than if the evaluation is just performedwith a 2015ndash2023 time frame This occurs as a result of thechallenging low-carbon targets up to 2047 and a myopicplanning with short-term lookahead For an evaluationperiod four times larger the CAPEX and TOTEX increasedjust 18 compared to 2015ndash2023 concluding that betweenpound52M and pound68M the evaluation area regardless of thetime horizon for the investment or the demand scenarioconsidered is required

Furthermore it can be inferred comparing Tables 2 and 3that for less smart interventions the short-term planningis used compared to the long-term horizon planningDAR-cable and DAR-transformer experienced a significantimplementation variation between the two evaluationperiods for both demand scenarios as well as CAPEX allo-cated in cable upgrades falling from 89ndash91 (2015ndash2023)to 29ndash66 in the long term 2015ndash2047 run Both technicalperformance evaluators CML and CI respond to theirrespective CAPEX curve shape Due to a more incentivisedsmart grid technique during RIIO-ED2 and ED3 the per-centages of smart techniques implemented varied notablyFor DECC4 in 2023rsquos outlook the share of techniques is

25 for smart grid interventions whereas for 2050rsquos out-look the share is increased up to 58 In the same wayfor DECC2 in 2023rsquos outlook the share of smart interven-tions is 23 whereas in 2050rsquos outlook the share increasesup to 69

Feasible solutions characterising the solution space(Figure 21) differ in the degree of investment required andtechnical performance evaluators Each of the solution pathrepresented in the multidimensional solution space inFigure 21 represents the intersection of that dimension witheach axis Parallel coordinates [35] are low complexityworking for any N-dimension In the case of Figure 21it has 8 dimensions treating every variable uniformlyEach intersection of one solution path with each axis isthe value of that solution for that axis As for axis DECC2and DECC4 it represents the feasible solutions for eachdemand scenario Making a query by the percentage oftraditional reinforcement used in that solution we have thepartition between orange and green for DECC2 and blueand black being the first solutions that use more traditionalreinforcement compared with the ones that use more smarttechniques represented in green (DECC2) and black(DECC4) From Figure 21 we can argue that solutions thatuse more smart techniques (green and blacks) are moreexpensive in terms of CAPEX and OPEX than their peers(orange and blues) when compared by a demand scenarioIt can also be stated that solutions with a higher use of tradi-tional reinforcements are the ones that decrease the mostCML CI and losses

Disaggregating results by network state of the six 11 kVprimaries and by the feeder if necessary for further granulardebugging can discern locational capacity Under bothdemand scenarios Secklow Gate was seen to be the one withgreater capacity being necessary to apply fewer techniquesover the evaluation period 2015ndash2047 On the other handNewport Pagnell exceeds the capacity as soon as 2015requiring a high number of network state evaluation to befixed that happens for each subsequent year hence the largenumber of network states is presented in Table 4

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

pound p

er an

num

Figure 17 CAPEX and OPEX DECC4 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

pu

Figure 18 CML and CI DECC4 2015ndash2047

14 Complexity

5 Conclusions

Power flow analysis using a nodal network model is essentialwhen determining the benefits of trailed smart interventions

because the interactions and implications are highly specificto a particular location and scenario

This suggests that while the bottom-up approach isonerous in terms of data handling and manipulation this isworthwhile for strategic planning and policy evaluationComparing both investment strategies investment strategyadjustments will be necessary in future regulation periodsif an over-invested network behaves as displayed in theshort-term section of this study

Traditional reinforcement will continue to be the mainmethod by which network issues are mostly resolvedfollowed by dynamic asset rating and meshed networks

The assessment of the six novel smart interventions in theFALCON 11kV primary test area in Milton Keynes hasproved the suitability of three techniques able to fix failuresimproving the quality of service and their readiness to bedeployed in the near future These techniques are DAR forcables and transformers and meshed networks Meshednetworks have been repeatedly selected as a feasible tech-nique because using it will reduce CML CI and power losses

Table 3 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2047

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 18 5 31 6

DAR-transformer 32 16 32 6

ALT 0 0 0 0

Mesh 8 3 6 3

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 25 44 9 16

TRAD-cable 15 29 20 66

TRAD-transfer load 1 1 1 1

TRAD-new feeder 1 2 1 2

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

s pound p

er an

num

Figure 19 CAPEX and OPEX DECC2 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

PU

Figure 20 CML and CI DECC2 2015ndash2047

15Complexity

improving the quality of service and the efficiency whilebeing a cost-effective solution

The initial capacity at primary substations differedsignificantly and this affected the number and complexityof interventions required by the SIM Due to the loadscenarios showing significant peak load increases DAR wasoften a temporary measure that would delay but not removethe eventual need for traditional reinforcement

Implementing DAR for cables and transformers themonitoring of assets when their peak capacity is increasedwas analysed On the other hand it was observed thattraditional reinforcements still play a key role in keepingthe electricity distribution networks free of constraintsTRAD techniques such as transformer and cable replace-ments are able to fix the majority of failures and will beessential also in the future

The comparison between traditional reinforcements andnovel smart techniques has provided a new knowledge aboutthe suitability of each technique to be applied in termsof costs electrical performance failures fixed and assetreplaced Cable replacement is the most costly techniquehowever its use is unavoidable in a number of cases Further-more the applicability of each technique regarding costsinvolved improvements on power quality and efficiency

and failures solved lead to new questions to be analysed suchas the lack of these techniques to provide flexible capacitywithin the trailed area

To sum up this study has performed a comparativeanalysis of novel smart intervention techniques providinginsights for future investments in electricity distributionplanning Further work can focus on scaling up the analysisto include a larger section of the network or a constrainedarea to evaluate national applicability of the current findings

Data Availability

Some of the data relevant to this submission is proprietaryfrom Western Power Distribution It can be accessed forresearch purposes under a signed collaboration agreement

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors would like to thank the rest of the WesternPower Distribution Innovation team particularly the other

2400

2200

2000

350

360

370

380

390

400

1400

1300

1200

1100

1000DECC 4

50

55

60

12

11

10

9

8

4400

4200

4000

3800

3600

2400

CAPEX (in kpound) OPEX (in kpound) Utilisation Traditional reinforcements () DECC 2 998796 CML 998796 CI 998796 Losses

2200

2000

1800

Figure 21 Parallel coordinate visualisation of technoeconomic performance indicators under DECC4 and DECC2

Table 4 Summary of network states and results for demand scenario DECC4 and DECC2

Primary 11 kV substation Feeders Year TechniquesResultsDECC4

NSDECC4

ResultsDECC2

NSDECC2

Fox Milne 13 2047 Smart and traditional 54 276 47 259

Newport Pagnell 9 2047 Smart and traditional 48 317 31 318

Secklow Gate 9 2047 Smart and traditional 27 29 15 16

Bletchley 19 2047 Smart and traditional 32 40 21 48

Marlborough Street 11 2047 Smart and traditional 31 52 19 37

Childs Way 17 2047 Smart and traditional 67 398 54 181

16 Complexity

members of the FALCON project as well as Paul CharletonStuart Fowler Ilja Orlovs Dan Taylor and Steve IngramAlso the authors want to acknowledge OFGEM and theLowCarbon Network Fund for funding this piece of research

References

[1] European Commission ldquoCommunication from the Commis-sion to the European Parliament the Council the EuropeanEconomic and Social Committee and the Committee of theRegionsrdquo 2011 December 2016 httpseur-lexeuropaeulegal-contentENTXTqid=1537473508968ampuri=CELEX52016PC076721

[2] F G N Li and E Trutnevyte ldquoInvestment appraisal ofcost-optimal and near-optimal pathways for the UK elec-tricity sector transition to 2050rdquo Applied Energy vol 189pp 89ndash109 2017

[3] G Strbac I Konstantelos M Pollitt and R Green DeliveringFuture-Proof Energy Infrastructure National InfrastructureCommission 2016

[4] Ofgem ldquoGB Network regulation - the RIIO modelrdquo 2010December 2014 httpswwwofgemgovuknetwork-regulation-riio-model

[5] R Poudineh and T Jamasb ldquoDistributed generation storagedemand response and energy efficiency as alternatives togrid capacity enhancementrdquo Energy Policy vol 67 pp 222ndash231 2014

[6] J Yang X Bai D Strickland L Jenkins and A M CrossldquoDynamic network rating for low carbon distribution networkoperationmdashA UK applicationrdquo IEEE Transactions on SmartGrid vol 6 no 2 pp 988ndash998 2015

[7] M Behnke W Erdman S Horgan et al ldquoSecondary net-work distribution systems background and issues relatedto the interconnection of distributed resourcesrdquo NationalRenewable Energy Laboratory (NREL) Tech Rep NRELTP-560-38079 2005

[8] J Crispim J Braz R Castro and J Esteves ldquoSmart grids in theEU with smart regulation experiences from the UK Italy andPortugalrdquo Utilities Policy vol 31 pp 85ndash93 2014

[9] Tnei ldquoIPSA Power software for power systemsrdquo March 2016httpwwwipsa-powercom

[10] ETAP ldquoETAP PSrdquo 2018 April 2016 httpsetapcomsoftwareproduct-overview-main

[11] DINIS ldquoDINIS distribution network information sys-temsrdquo 2002 March 2016 httpsetapcomsoftwareproduct-overview-main

[12] Western Power Distribution ldquoSIM final reportrdquo 2015November 2016 httpwwwwesternpowerinnovationcoukProjectsFalconaspx

[13] Western Power Distribution ldquoProject FALCONrdquo 2015November 2017 httpwwwdiniscom

[14] EA Technology ldquoTransform modelrdquo March 2016 httpwwweatechnologycomproducts-and-servicescreate-smarter-gridstransform-model

[15] L Sigrist E Lobato L Rouco M Gazzino and M CantuldquoEconomic assessment of smart grid initiatives for islandpower systemsrdquo Applied Energy vol 189 pp 403ndash415 2017

[16] J A Momoh ldquoSmart grid design for efficient and flexiblepower networks operation and controlrdquo in 2009 IEEEPESPower Systems Conference and Exposition pp 1ndash8 SeattleWA USA 2009

[17] N Good K A Ellis and P Mancarella ldquoReview and classifica-tion of barriers and enablers of demand response in the smartgridrdquo Renewable and Sustainable Energy Reviews vol 72pp 57ndash72 2017

[18] Z Yang J Xiang and Y Li ldquoDistributed consensus basedsupplyndashdemand balance algorithm for economic dispatchproblem in a smart grid with switching graphrdquo IEEE Trans-actions on Industrial Electronics vol 64 no 2 pp 1600ndash1610 2017

[19] SuSTAINABLE ldquoSmart distribution network operation formaximising the integration of renewable generationrdquo 2015March 2016 httpwwwsustainableprojecteuHomeaspx

[20] S Mohtashami D Pudjianto and G Strbac ldquoStrategic distri-bution network planning with smart grid technologiesrdquo IEEETransactions on Smart Grid vol 8 no 6 pp 2656ndash2664 2017

[21] Smart Grid Forum ldquoSmart grid forum work stream 7rdquo2015 March 2016 httpwwwsmarternetworksorgprojectnia_nget015425

[22] ETI ldquoEnergyPath networksrdquo March 2016 httpwwweticoukprojectenergypath

[23] C Mateo Domingo T Gomez San Roman Aacute Sanchez-Miralles J P Peco Gonzalez and A Candela Martinez ldquoAreference network model for large-scale distribution planningwith automatic street map generationrdquo IEEE Transactions onPower Systems vol 26 no 1 pp 190ndash197 2011

[24] R Bolton and T J Foxon ldquoInfrastructure transformation as asocio-technical process mdash implications for the governanceof energy distribution networks in the UKrdquo TechnologicalForecasting and Social Change vol 90 pp 538ndash550 2015

[25] UK Power Networks ldquoDNO guide to future Smart man-agement of distribution networks Summary reportrdquo 2014httpsinnovationukpowernetworkscoukinnovationenProjectstier-2-projectsLow-Carbon-London-(LCL)Project-DocumentsLCL20Learning20Report20-20SR20-20Summary20Report20-20DNO20Guide20to20Future20Smart20Management20of20Distribution20Networkspdf

[26] A Botea J Rintanen and D Banerjee ldquoOptimal reconfigura-tion for supply restoration with informed AlowastSearchrdquo IEEETransactions on Smart Grid vol 3 no 2 pp 583ndash593 2012

[27] T Logenthiran D Srinivasan and T Z Shun ldquoDemand sidemanagement in smart grid using heuristic optimizationrdquoIEEE Transactions on Smart Grid vol 3 no 3 pp 1244ndash1252 2012

[28] J Saacutenchez-Cuadrado J de Lara and E Guerra ldquoBottom-upmeta-modelling an interactive approachrdquo in Model DrivenEngineering Languages and Systems pp 3ndash19 Springer2012

[29] Y H Kim F J Sting and C H Loch ldquoTop-down bottom-upor both Toward an integrative perspective on operationsstrategy formationrdquo Journal of Operations Managementvol 32 no 7-8 pp 462ndash474 2014

[30] C Bohringer ldquoThe synthesis of bottom-up and top-down inenergy policy modelingrdquo Energy Economics vol 20 no 3pp 233ndash248 1998

[31] T G R Bower ldquoRepetition in human developmentrdquo Merrill-Palmer Quarterly of Behavior and Development vol 20no 4 pp 303ndash318 1974

[32] H Mintzberg and J A Waters ldquoOf strategies deliberate andemergentrdquo Strategic Management Journal vol 6 no 3pp 257ndash272 1985

17Complexity

The techniques applied are the key consideration to beassessed It is necessary to analyse the number of interven-tions applied by type the effect of these techniques in theelectric grid solving failures and fixing assets Figures 11and 15 show average prices of techniques used in eachdemand scenario Smart techniques have a lower TOTEXthan traditional reinforcements These novel techniquesabove are not frequently able to fix failures and thereforeproduce feasible network states In terms of CML and CIthe only technique able to moderately contribute to theirimprovement is meshed networks as shown in Figures 12and 16 Results attribute the majority of fixes to traditionalreinforcements with a comparatively smaller number offailures being solved by smart techniques (Table 2) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention forDECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

For DECC4 the three mentioned techniques along withtraditional cable and transformer replacement are applied(Table 2) whereas for DECC2 two of them are appliedDAR for cables and meshed networks (Table 2) Comparingthe cost trends of the two assessed scenarios it is notable thatin DECC4 the CAPEX peak occurs in 2015 (Figure 7)whereas in DECC2 besides the peak in 2015 there is alsoone in 2019 (Figure 11) reflecting the more difficult natureof network states to be feasible in a demanding scenarioHowever DECC2 shows a higher improvement of electricalperformance indicators as can be seen in Figure 12

In addition traditional cable replacement DAR forcables and meshed networks are able to fix the cable whereastraditional transformer replacement and DAR for trans-formers are able to fix transformer issues (Figure 16) Theresults obtained in terms of traditional reinforcement sharefor 2015ndash2023 show a 60 proportion of intervention for

DECC4 and a 65 proportion of intervention for DECC2which is close to the 59 forecasted by the transform modelfor this evaluation period

42 Long-Term Planning This section evaluates two experi-ments namely characterising the DECC2 and DECC4 sce-narios for 2015ndash2047 evaluation period For each demandscenario expected investments disaggregating CAPEX andOPEX are presented the evolution of electrical performanceindicators and the number of techniques applied

421 DECC4 2015ndash2047 In this experiment the mostsignificant results to analyse the suitability of each techniqueare presented

Figure 17 shows the trend of CAPEX and OPEX from2015 to 2047

There are CAPEX peaks in the year 2018 and during thebeginning of RIIO-ED2 in the years 2025 to 2026 due to theapplication of more techniques to fulfill low-carbon targets

Figure 18 indicates that upgrades on the network aredirectly related to technical performance indicators CMLand CI

In Table 3 the techniques applied and their contributionto CAPEX during the evaluation period from 2015 to 2047 bya demand scenario are shown

422 DECC2 2015ndash2047 Within this experiment the mostrelevant results to analyse the evolutionary network statesare presented

In Figure 19 the trend of CAPEX and OPEX from 2015to 2047 is shown There is a significant increase of CAPEXin the years 2024 to 2026 at the beginning of RIIO-ED2due to the necessary implementation of new techniques toreach low-carbon targets The evolution of CML and CI ispresented in Figure 20 linking larger investment years whenmajor reductions are found The contribution of each solutiontechnique is presented in Table 3

423 Comparison between DECC2 and DECC4 for 2015ndash2047 Figure 21 presents a multidimensional parallel coor-dinate representation of the feasible network statersquoscombinations that produced a 2015ndash2047 investment path-way for the evaluated area It bundles economic indicatorsie CAPEX and OPEX with technical performance indica-tors providing valuable insights on the number of traditionalreinforcements utilised to heal falling network states Resultsare also clustered by the two demand scenarios assessedduring the experiments

Solutions of DECC2 (represented in orange and green)and DECC4 (in black and blue) are clustered by the per-centage of traditional reinforcements utilised as well asthe utilisation factor

Solutions in green and in black represent those wheresmart grids were more used whereas orange and blue repre-sent the cheapest (CAPEX and OPEX) less utilised and theworst responding to the decrease of CML CI and lossesand the ones using more traditional reinforcement to fixnetwork states

Besides the fact that DECC2 smart solutions are between16 and 28 more expensive than DECC4 traditional

0005

01015

02025

03035

04045

05

2015 2016 2017 2018 2019 2020 2021 2022 2023

CAPEXOPEX

442444648

552

Mill

ions

pound p

er an

num

54

Figure 13 CAPEX and OPEX DECC2 2015ndash2023

12 Complexity

reinforcement solutions (green solutions versus blue solu-tions in Figure 21) DECC2 traditional solutions are in therange of costs of smart solutions of DECC4 (orange andblack solutions in Figure 21) It can also be concluded thatinvestment pathways using less smart techniques provide a

cheaper response to technical performance evaluators suchas utilisation CML CI and losses

43 Summary Experiment evaluation runs from the2015ndash2047 period present lower investment rates for the

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

PU

Figure 14 CML and CI DECC2 2015ndash2023

CAPEXOPEX

DAR -transformer

Meshednetworks

Traditional -transformer

Traditional -cable

0

20000

40000

60000

80000

100000

120000

140000

pound

Figure 15 Average cost disaggregation per technique DECC2 2015ndash2023

Figure 16 Average CML and CI improvement per technique DECC2 2015ndash2023

13Complexity

2015ndash2023 period than if the evaluation is just performedwith a 2015ndash2023 time frame This occurs as a result of thechallenging low-carbon targets up to 2047 and a myopicplanning with short-term lookahead For an evaluationperiod four times larger the CAPEX and TOTEX increasedjust 18 compared to 2015ndash2023 concluding that betweenpound52M and pound68M the evaluation area regardless of thetime horizon for the investment or the demand scenarioconsidered is required

Furthermore it can be inferred comparing Tables 2 and 3that for less smart interventions the short-term planningis used compared to the long-term horizon planningDAR-cable and DAR-transformer experienced a significantimplementation variation between the two evaluationperiods for both demand scenarios as well as CAPEX allo-cated in cable upgrades falling from 89ndash91 (2015ndash2023)to 29ndash66 in the long term 2015ndash2047 run Both technicalperformance evaluators CML and CI respond to theirrespective CAPEX curve shape Due to a more incentivisedsmart grid technique during RIIO-ED2 and ED3 the per-centages of smart techniques implemented varied notablyFor DECC4 in 2023rsquos outlook the share of techniques is

25 for smart grid interventions whereas for 2050rsquos out-look the share is increased up to 58 In the same wayfor DECC2 in 2023rsquos outlook the share of smart interven-tions is 23 whereas in 2050rsquos outlook the share increasesup to 69

Feasible solutions characterising the solution space(Figure 21) differ in the degree of investment required andtechnical performance evaluators Each of the solution pathrepresented in the multidimensional solution space inFigure 21 represents the intersection of that dimension witheach axis Parallel coordinates [35] are low complexityworking for any N-dimension In the case of Figure 21it has 8 dimensions treating every variable uniformlyEach intersection of one solution path with each axis isthe value of that solution for that axis As for axis DECC2and DECC4 it represents the feasible solutions for eachdemand scenario Making a query by the percentage oftraditional reinforcement used in that solution we have thepartition between orange and green for DECC2 and blueand black being the first solutions that use more traditionalreinforcement compared with the ones that use more smarttechniques represented in green (DECC2) and black(DECC4) From Figure 21 we can argue that solutions thatuse more smart techniques (green and blacks) are moreexpensive in terms of CAPEX and OPEX than their peers(orange and blues) when compared by a demand scenarioIt can also be stated that solutions with a higher use of tradi-tional reinforcements are the ones that decrease the mostCML CI and losses

Disaggregating results by network state of the six 11 kVprimaries and by the feeder if necessary for further granulardebugging can discern locational capacity Under bothdemand scenarios Secklow Gate was seen to be the one withgreater capacity being necessary to apply fewer techniquesover the evaluation period 2015ndash2047 On the other handNewport Pagnell exceeds the capacity as soon as 2015requiring a high number of network state evaluation to befixed that happens for each subsequent year hence the largenumber of network states is presented in Table 4

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

pound p

er an

num

Figure 17 CAPEX and OPEX DECC4 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

pu

Figure 18 CML and CI DECC4 2015ndash2047

14 Complexity

5 Conclusions

Power flow analysis using a nodal network model is essentialwhen determining the benefits of trailed smart interventions

because the interactions and implications are highly specificto a particular location and scenario

This suggests that while the bottom-up approach isonerous in terms of data handling and manipulation this isworthwhile for strategic planning and policy evaluationComparing both investment strategies investment strategyadjustments will be necessary in future regulation periodsif an over-invested network behaves as displayed in theshort-term section of this study

Traditional reinforcement will continue to be the mainmethod by which network issues are mostly resolvedfollowed by dynamic asset rating and meshed networks

The assessment of the six novel smart interventions in theFALCON 11kV primary test area in Milton Keynes hasproved the suitability of three techniques able to fix failuresimproving the quality of service and their readiness to bedeployed in the near future These techniques are DAR forcables and transformers and meshed networks Meshednetworks have been repeatedly selected as a feasible tech-nique because using it will reduce CML CI and power losses

Table 3 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2047

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 18 5 31 6

DAR-transformer 32 16 32 6

ALT 0 0 0 0

Mesh 8 3 6 3

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 25 44 9 16

TRAD-cable 15 29 20 66

TRAD-transfer load 1 1 1 1

TRAD-new feeder 1 2 1 2

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

s pound p

er an

num

Figure 19 CAPEX and OPEX DECC2 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

PU

Figure 20 CML and CI DECC2 2015ndash2047

15Complexity

improving the quality of service and the efficiency whilebeing a cost-effective solution

The initial capacity at primary substations differedsignificantly and this affected the number and complexityof interventions required by the SIM Due to the loadscenarios showing significant peak load increases DAR wasoften a temporary measure that would delay but not removethe eventual need for traditional reinforcement

Implementing DAR for cables and transformers themonitoring of assets when their peak capacity is increasedwas analysed On the other hand it was observed thattraditional reinforcements still play a key role in keepingthe electricity distribution networks free of constraintsTRAD techniques such as transformer and cable replace-ments are able to fix the majority of failures and will beessential also in the future

The comparison between traditional reinforcements andnovel smart techniques has provided a new knowledge aboutthe suitability of each technique to be applied in termsof costs electrical performance failures fixed and assetreplaced Cable replacement is the most costly techniquehowever its use is unavoidable in a number of cases Further-more the applicability of each technique regarding costsinvolved improvements on power quality and efficiency

and failures solved lead to new questions to be analysed suchas the lack of these techniques to provide flexible capacitywithin the trailed area

To sum up this study has performed a comparativeanalysis of novel smart intervention techniques providinginsights for future investments in electricity distributionplanning Further work can focus on scaling up the analysisto include a larger section of the network or a constrainedarea to evaluate national applicability of the current findings

Data Availability

Some of the data relevant to this submission is proprietaryfrom Western Power Distribution It can be accessed forresearch purposes under a signed collaboration agreement

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors would like to thank the rest of the WesternPower Distribution Innovation team particularly the other

2400

2200

2000

350

360

370

380

390

400

1400

1300

1200

1100

1000DECC 4

50

55

60

12

11

10

9

8

4400

4200

4000

3800

3600

2400

CAPEX (in kpound) OPEX (in kpound) Utilisation Traditional reinforcements () DECC 2 998796 CML 998796 CI 998796 Losses

2200

2000

1800

Figure 21 Parallel coordinate visualisation of technoeconomic performance indicators under DECC4 and DECC2

Table 4 Summary of network states and results for demand scenario DECC4 and DECC2

Primary 11 kV substation Feeders Year TechniquesResultsDECC4

NSDECC4

ResultsDECC2

NSDECC2

Fox Milne 13 2047 Smart and traditional 54 276 47 259

Newport Pagnell 9 2047 Smart and traditional 48 317 31 318

Secklow Gate 9 2047 Smart and traditional 27 29 15 16

Bletchley 19 2047 Smart and traditional 32 40 21 48

Marlborough Street 11 2047 Smart and traditional 31 52 19 37

Childs Way 17 2047 Smart and traditional 67 398 54 181

16 Complexity

members of the FALCON project as well as Paul CharletonStuart Fowler Ilja Orlovs Dan Taylor and Steve IngramAlso the authors want to acknowledge OFGEM and theLowCarbon Network Fund for funding this piece of research

References

[1] European Commission ldquoCommunication from the Commis-sion to the European Parliament the Council the EuropeanEconomic and Social Committee and the Committee of theRegionsrdquo 2011 December 2016 httpseur-lexeuropaeulegal-contentENTXTqid=1537473508968ampuri=CELEX52016PC076721

[2] F G N Li and E Trutnevyte ldquoInvestment appraisal ofcost-optimal and near-optimal pathways for the UK elec-tricity sector transition to 2050rdquo Applied Energy vol 189pp 89ndash109 2017

[3] G Strbac I Konstantelos M Pollitt and R Green DeliveringFuture-Proof Energy Infrastructure National InfrastructureCommission 2016

[4] Ofgem ldquoGB Network regulation - the RIIO modelrdquo 2010December 2014 httpswwwofgemgovuknetwork-regulation-riio-model

[5] R Poudineh and T Jamasb ldquoDistributed generation storagedemand response and energy efficiency as alternatives togrid capacity enhancementrdquo Energy Policy vol 67 pp 222ndash231 2014

[6] J Yang X Bai D Strickland L Jenkins and A M CrossldquoDynamic network rating for low carbon distribution networkoperationmdashA UK applicationrdquo IEEE Transactions on SmartGrid vol 6 no 2 pp 988ndash998 2015

[7] M Behnke W Erdman S Horgan et al ldquoSecondary net-work distribution systems background and issues relatedto the interconnection of distributed resourcesrdquo NationalRenewable Energy Laboratory (NREL) Tech Rep NRELTP-560-38079 2005

[8] J Crispim J Braz R Castro and J Esteves ldquoSmart grids in theEU with smart regulation experiences from the UK Italy andPortugalrdquo Utilities Policy vol 31 pp 85ndash93 2014

[9] Tnei ldquoIPSA Power software for power systemsrdquo March 2016httpwwwipsa-powercom

[10] ETAP ldquoETAP PSrdquo 2018 April 2016 httpsetapcomsoftwareproduct-overview-main

[11] DINIS ldquoDINIS distribution network information sys-temsrdquo 2002 March 2016 httpsetapcomsoftwareproduct-overview-main

[12] Western Power Distribution ldquoSIM final reportrdquo 2015November 2016 httpwwwwesternpowerinnovationcoukProjectsFalconaspx

[13] Western Power Distribution ldquoProject FALCONrdquo 2015November 2017 httpwwwdiniscom

[14] EA Technology ldquoTransform modelrdquo March 2016 httpwwweatechnologycomproducts-and-servicescreate-smarter-gridstransform-model

[15] L Sigrist E Lobato L Rouco M Gazzino and M CantuldquoEconomic assessment of smart grid initiatives for islandpower systemsrdquo Applied Energy vol 189 pp 403ndash415 2017

[16] J A Momoh ldquoSmart grid design for efficient and flexiblepower networks operation and controlrdquo in 2009 IEEEPESPower Systems Conference and Exposition pp 1ndash8 SeattleWA USA 2009

[17] N Good K A Ellis and P Mancarella ldquoReview and classifica-tion of barriers and enablers of demand response in the smartgridrdquo Renewable and Sustainable Energy Reviews vol 72pp 57ndash72 2017

[18] Z Yang J Xiang and Y Li ldquoDistributed consensus basedsupplyndashdemand balance algorithm for economic dispatchproblem in a smart grid with switching graphrdquo IEEE Trans-actions on Industrial Electronics vol 64 no 2 pp 1600ndash1610 2017

[19] SuSTAINABLE ldquoSmart distribution network operation formaximising the integration of renewable generationrdquo 2015March 2016 httpwwwsustainableprojecteuHomeaspx

[20] S Mohtashami D Pudjianto and G Strbac ldquoStrategic distri-bution network planning with smart grid technologiesrdquo IEEETransactions on Smart Grid vol 8 no 6 pp 2656ndash2664 2017

[21] Smart Grid Forum ldquoSmart grid forum work stream 7rdquo2015 March 2016 httpwwwsmarternetworksorgprojectnia_nget015425

[22] ETI ldquoEnergyPath networksrdquo March 2016 httpwwweticoukprojectenergypath

[23] C Mateo Domingo T Gomez San Roman Aacute Sanchez-Miralles J P Peco Gonzalez and A Candela Martinez ldquoAreference network model for large-scale distribution planningwith automatic street map generationrdquo IEEE Transactions onPower Systems vol 26 no 1 pp 190ndash197 2011

[24] R Bolton and T J Foxon ldquoInfrastructure transformation as asocio-technical process mdash implications for the governanceof energy distribution networks in the UKrdquo TechnologicalForecasting and Social Change vol 90 pp 538ndash550 2015

[25] UK Power Networks ldquoDNO guide to future Smart man-agement of distribution networks Summary reportrdquo 2014httpsinnovationukpowernetworkscoukinnovationenProjectstier-2-projectsLow-Carbon-London-(LCL)Project-DocumentsLCL20Learning20Report20-20SR20-20Summary20Report20-20DNO20Guide20to20Future20Smart20Management20of20Distribution20Networkspdf

[26] A Botea J Rintanen and D Banerjee ldquoOptimal reconfigura-tion for supply restoration with informed AlowastSearchrdquo IEEETransactions on Smart Grid vol 3 no 2 pp 583ndash593 2012

[27] T Logenthiran D Srinivasan and T Z Shun ldquoDemand sidemanagement in smart grid using heuristic optimizationrdquoIEEE Transactions on Smart Grid vol 3 no 3 pp 1244ndash1252 2012

[28] J Saacutenchez-Cuadrado J de Lara and E Guerra ldquoBottom-upmeta-modelling an interactive approachrdquo in Model DrivenEngineering Languages and Systems pp 3ndash19 Springer2012

[29] Y H Kim F J Sting and C H Loch ldquoTop-down bottom-upor both Toward an integrative perspective on operationsstrategy formationrdquo Journal of Operations Managementvol 32 no 7-8 pp 462ndash474 2014

[30] C Bohringer ldquoThe synthesis of bottom-up and top-down inenergy policy modelingrdquo Energy Economics vol 20 no 3pp 233ndash248 1998

[31] T G R Bower ldquoRepetition in human developmentrdquo Merrill-Palmer Quarterly of Behavior and Development vol 20no 4 pp 303ndash318 1974

[32] H Mintzberg and J A Waters ldquoOf strategies deliberate andemergentrdquo Strategic Management Journal vol 6 no 3pp 257ndash272 1985

17Complexity

reinforcement solutions (green solutions versus blue solu-tions in Figure 21) DECC2 traditional solutions are in therange of costs of smart solutions of DECC4 (orange andblack solutions in Figure 21) It can also be concluded thatinvestment pathways using less smart techniques provide a

cheaper response to technical performance evaluators suchas utilisation CML CI and losses

43 Summary Experiment evaluation runs from the2015ndash2047 period present lower investment rates for the

2015 2016 2017 2018 2019 2020 2021 2022 2023

CICML

0010203040506070809

PU

Figure 14 CML and CI DECC2 2015ndash2023

CAPEXOPEX

DAR -transformer

Meshednetworks

Traditional -transformer

Traditional -cable

0

20000

40000

60000

80000

100000

120000

140000

pound

Figure 15 Average cost disaggregation per technique DECC2 2015ndash2023

Figure 16 Average CML and CI improvement per technique DECC2 2015ndash2023

13Complexity

2015ndash2023 period than if the evaluation is just performedwith a 2015ndash2023 time frame This occurs as a result of thechallenging low-carbon targets up to 2047 and a myopicplanning with short-term lookahead For an evaluationperiod four times larger the CAPEX and TOTEX increasedjust 18 compared to 2015ndash2023 concluding that betweenpound52M and pound68M the evaluation area regardless of thetime horizon for the investment or the demand scenarioconsidered is required

Furthermore it can be inferred comparing Tables 2 and 3that for less smart interventions the short-term planningis used compared to the long-term horizon planningDAR-cable and DAR-transformer experienced a significantimplementation variation between the two evaluationperiods for both demand scenarios as well as CAPEX allo-cated in cable upgrades falling from 89ndash91 (2015ndash2023)to 29ndash66 in the long term 2015ndash2047 run Both technicalperformance evaluators CML and CI respond to theirrespective CAPEX curve shape Due to a more incentivisedsmart grid technique during RIIO-ED2 and ED3 the per-centages of smart techniques implemented varied notablyFor DECC4 in 2023rsquos outlook the share of techniques is

25 for smart grid interventions whereas for 2050rsquos out-look the share is increased up to 58 In the same wayfor DECC2 in 2023rsquos outlook the share of smart interven-tions is 23 whereas in 2050rsquos outlook the share increasesup to 69

Feasible solutions characterising the solution space(Figure 21) differ in the degree of investment required andtechnical performance evaluators Each of the solution pathrepresented in the multidimensional solution space inFigure 21 represents the intersection of that dimension witheach axis Parallel coordinates [35] are low complexityworking for any N-dimension In the case of Figure 21it has 8 dimensions treating every variable uniformlyEach intersection of one solution path with each axis isthe value of that solution for that axis As for axis DECC2and DECC4 it represents the feasible solutions for eachdemand scenario Making a query by the percentage oftraditional reinforcement used in that solution we have thepartition between orange and green for DECC2 and blueand black being the first solutions that use more traditionalreinforcement compared with the ones that use more smarttechniques represented in green (DECC2) and black(DECC4) From Figure 21 we can argue that solutions thatuse more smart techniques (green and blacks) are moreexpensive in terms of CAPEX and OPEX than their peers(orange and blues) when compared by a demand scenarioIt can also be stated that solutions with a higher use of tradi-tional reinforcements are the ones that decrease the mostCML CI and losses

Disaggregating results by network state of the six 11 kVprimaries and by the feeder if necessary for further granulardebugging can discern locational capacity Under bothdemand scenarios Secklow Gate was seen to be the one withgreater capacity being necessary to apply fewer techniquesover the evaluation period 2015ndash2047 On the other handNewport Pagnell exceeds the capacity as soon as 2015requiring a high number of network state evaluation to befixed that happens for each subsequent year hence the largenumber of network states is presented in Table 4

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

pound p

er an

num

Figure 17 CAPEX and OPEX DECC4 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

pu

Figure 18 CML and CI DECC4 2015ndash2047

14 Complexity

5 Conclusions

Power flow analysis using a nodal network model is essentialwhen determining the benefits of trailed smart interventions

because the interactions and implications are highly specificto a particular location and scenario

This suggests that while the bottom-up approach isonerous in terms of data handling and manipulation this isworthwhile for strategic planning and policy evaluationComparing both investment strategies investment strategyadjustments will be necessary in future regulation periodsif an over-invested network behaves as displayed in theshort-term section of this study

Traditional reinforcement will continue to be the mainmethod by which network issues are mostly resolvedfollowed by dynamic asset rating and meshed networks

The assessment of the six novel smart interventions in theFALCON 11kV primary test area in Milton Keynes hasproved the suitability of three techniques able to fix failuresimproving the quality of service and their readiness to bedeployed in the near future These techniques are DAR forcables and transformers and meshed networks Meshednetworks have been repeatedly selected as a feasible tech-nique because using it will reduce CML CI and power losses

Table 3 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2047

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 18 5 31 6

DAR-transformer 32 16 32 6

ALT 0 0 0 0

Mesh 8 3 6 3

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 25 44 9 16

TRAD-cable 15 29 20 66

TRAD-transfer load 1 1 1 1

TRAD-new feeder 1 2 1 2

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

s pound p

er an

num

Figure 19 CAPEX and OPEX DECC2 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

PU

Figure 20 CML and CI DECC2 2015ndash2047

15Complexity

improving the quality of service and the efficiency whilebeing a cost-effective solution

The initial capacity at primary substations differedsignificantly and this affected the number and complexityof interventions required by the SIM Due to the loadscenarios showing significant peak load increases DAR wasoften a temporary measure that would delay but not removethe eventual need for traditional reinforcement

Implementing DAR for cables and transformers themonitoring of assets when their peak capacity is increasedwas analysed On the other hand it was observed thattraditional reinforcements still play a key role in keepingthe electricity distribution networks free of constraintsTRAD techniques such as transformer and cable replace-ments are able to fix the majority of failures and will beessential also in the future

The comparison between traditional reinforcements andnovel smart techniques has provided a new knowledge aboutthe suitability of each technique to be applied in termsof costs electrical performance failures fixed and assetreplaced Cable replacement is the most costly techniquehowever its use is unavoidable in a number of cases Further-more the applicability of each technique regarding costsinvolved improvements on power quality and efficiency

and failures solved lead to new questions to be analysed suchas the lack of these techniques to provide flexible capacitywithin the trailed area

To sum up this study has performed a comparativeanalysis of novel smart intervention techniques providinginsights for future investments in electricity distributionplanning Further work can focus on scaling up the analysisto include a larger section of the network or a constrainedarea to evaluate national applicability of the current findings

Data Availability

Some of the data relevant to this submission is proprietaryfrom Western Power Distribution It can be accessed forresearch purposes under a signed collaboration agreement

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors would like to thank the rest of the WesternPower Distribution Innovation team particularly the other

2400

2200

2000

350

360

370

380

390

400

1400

1300

1200

1100

1000DECC 4

50

55

60

12

11

10

9

8

4400

4200

4000

3800

3600

2400

CAPEX (in kpound) OPEX (in kpound) Utilisation Traditional reinforcements () DECC 2 998796 CML 998796 CI 998796 Losses

2200

2000

1800

Figure 21 Parallel coordinate visualisation of technoeconomic performance indicators under DECC4 and DECC2

Table 4 Summary of network states and results for demand scenario DECC4 and DECC2

Primary 11 kV substation Feeders Year TechniquesResultsDECC4

NSDECC4

ResultsDECC2

NSDECC2

Fox Milne 13 2047 Smart and traditional 54 276 47 259

Newport Pagnell 9 2047 Smart and traditional 48 317 31 318

Secklow Gate 9 2047 Smart and traditional 27 29 15 16

Bletchley 19 2047 Smart and traditional 32 40 21 48

Marlborough Street 11 2047 Smart and traditional 31 52 19 37

Childs Way 17 2047 Smart and traditional 67 398 54 181

16 Complexity

members of the FALCON project as well as Paul CharletonStuart Fowler Ilja Orlovs Dan Taylor and Steve IngramAlso the authors want to acknowledge OFGEM and theLowCarbon Network Fund for funding this piece of research

References

[1] European Commission ldquoCommunication from the Commis-sion to the European Parliament the Council the EuropeanEconomic and Social Committee and the Committee of theRegionsrdquo 2011 December 2016 httpseur-lexeuropaeulegal-contentENTXTqid=1537473508968ampuri=CELEX52016PC076721

[2] F G N Li and E Trutnevyte ldquoInvestment appraisal ofcost-optimal and near-optimal pathways for the UK elec-tricity sector transition to 2050rdquo Applied Energy vol 189pp 89ndash109 2017

[3] G Strbac I Konstantelos M Pollitt and R Green DeliveringFuture-Proof Energy Infrastructure National InfrastructureCommission 2016

[4] Ofgem ldquoGB Network regulation - the RIIO modelrdquo 2010December 2014 httpswwwofgemgovuknetwork-regulation-riio-model

[5] R Poudineh and T Jamasb ldquoDistributed generation storagedemand response and energy efficiency as alternatives togrid capacity enhancementrdquo Energy Policy vol 67 pp 222ndash231 2014

[6] J Yang X Bai D Strickland L Jenkins and A M CrossldquoDynamic network rating for low carbon distribution networkoperationmdashA UK applicationrdquo IEEE Transactions on SmartGrid vol 6 no 2 pp 988ndash998 2015

[7] M Behnke W Erdman S Horgan et al ldquoSecondary net-work distribution systems background and issues relatedto the interconnection of distributed resourcesrdquo NationalRenewable Energy Laboratory (NREL) Tech Rep NRELTP-560-38079 2005

[8] J Crispim J Braz R Castro and J Esteves ldquoSmart grids in theEU with smart regulation experiences from the UK Italy andPortugalrdquo Utilities Policy vol 31 pp 85ndash93 2014

[9] Tnei ldquoIPSA Power software for power systemsrdquo March 2016httpwwwipsa-powercom

[10] ETAP ldquoETAP PSrdquo 2018 April 2016 httpsetapcomsoftwareproduct-overview-main

[11] DINIS ldquoDINIS distribution network information sys-temsrdquo 2002 March 2016 httpsetapcomsoftwareproduct-overview-main

[12] Western Power Distribution ldquoSIM final reportrdquo 2015November 2016 httpwwwwesternpowerinnovationcoukProjectsFalconaspx

[13] Western Power Distribution ldquoProject FALCONrdquo 2015November 2017 httpwwwdiniscom

[14] EA Technology ldquoTransform modelrdquo March 2016 httpwwweatechnologycomproducts-and-servicescreate-smarter-gridstransform-model

[15] L Sigrist E Lobato L Rouco M Gazzino and M CantuldquoEconomic assessment of smart grid initiatives for islandpower systemsrdquo Applied Energy vol 189 pp 403ndash415 2017

[16] J A Momoh ldquoSmart grid design for efficient and flexiblepower networks operation and controlrdquo in 2009 IEEEPESPower Systems Conference and Exposition pp 1ndash8 SeattleWA USA 2009

[17] N Good K A Ellis and P Mancarella ldquoReview and classifica-tion of barriers and enablers of demand response in the smartgridrdquo Renewable and Sustainable Energy Reviews vol 72pp 57ndash72 2017

[18] Z Yang J Xiang and Y Li ldquoDistributed consensus basedsupplyndashdemand balance algorithm for economic dispatchproblem in a smart grid with switching graphrdquo IEEE Trans-actions on Industrial Electronics vol 64 no 2 pp 1600ndash1610 2017

[19] SuSTAINABLE ldquoSmart distribution network operation formaximising the integration of renewable generationrdquo 2015March 2016 httpwwwsustainableprojecteuHomeaspx

[20] S Mohtashami D Pudjianto and G Strbac ldquoStrategic distri-bution network planning with smart grid technologiesrdquo IEEETransactions on Smart Grid vol 8 no 6 pp 2656ndash2664 2017

[21] Smart Grid Forum ldquoSmart grid forum work stream 7rdquo2015 March 2016 httpwwwsmarternetworksorgprojectnia_nget015425

[22] ETI ldquoEnergyPath networksrdquo March 2016 httpwwweticoukprojectenergypath

[23] C Mateo Domingo T Gomez San Roman Aacute Sanchez-Miralles J P Peco Gonzalez and A Candela Martinez ldquoAreference network model for large-scale distribution planningwith automatic street map generationrdquo IEEE Transactions onPower Systems vol 26 no 1 pp 190ndash197 2011

[24] R Bolton and T J Foxon ldquoInfrastructure transformation as asocio-technical process mdash implications for the governanceof energy distribution networks in the UKrdquo TechnologicalForecasting and Social Change vol 90 pp 538ndash550 2015

[25] UK Power Networks ldquoDNO guide to future Smart man-agement of distribution networks Summary reportrdquo 2014httpsinnovationukpowernetworkscoukinnovationenProjectstier-2-projectsLow-Carbon-London-(LCL)Project-DocumentsLCL20Learning20Report20-20SR20-20Summary20Report20-20DNO20Guide20to20Future20Smart20Management20of20Distribution20Networkspdf

[26] A Botea J Rintanen and D Banerjee ldquoOptimal reconfigura-tion for supply restoration with informed AlowastSearchrdquo IEEETransactions on Smart Grid vol 3 no 2 pp 583ndash593 2012

[27] T Logenthiran D Srinivasan and T Z Shun ldquoDemand sidemanagement in smart grid using heuristic optimizationrdquoIEEE Transactions on Smart Grid vol 3 no 3 pp 1244ndash1252 2012

[28] J Saacutenchez-Cuadrado J de Lara and E Guerra ldquoBottom-upmeta-modelling an interactive approachrdquo in Model DrivenEngineering Languages and Systems pp 3ndash19 Springer2012

[29] Y H Kim F J Sting and C H Loch ldquoTop-down bottom-upor both Toward an integrative perspective on operationsstrategy formationrdquo Journal of Operations Managementvol 32 no 7-8 pp 462ndash474 2014

[30] C Bohringer ldquoThe synthesis of bottom-up and top-down inenergy policy modelingrdquo Energy Economics vol 20 no 3pp 233ndash248 1998

[31] T G R Bower ldquoRepetition in human developmentrdquo Merrill-Palmer Quarterly of Behavior and Development vol 20no 4 pp 303ndash318 1974

[32] H Mintzberg and J A Waters ldquoOf strategies deliberate andemergentrdquo Strategic Management Journal vol 6 no 3pp 257ndash272 1985

17Complexity

2015ndash2023 period than if the evaluation is just performedwith a 2015ndash2023 time frame This occurs as a result of thechallenging low-carbon targets up to 2047 and a myopicplanning with short-term lookahead For an evaluationperiod four times larger the CAPEX and TOTEX increasedjust 18 compared to 2015ndash2023 concluding that betweenpound52M and pound68M the evaluation area regardless of thetime horizon for the investment or the demand scenarioconsidered is required

Furthermore it can be inferred comparing Tables 2 and 3that for less smart interventions the short-term planningis used compared to the long-term horizon planningDAR-cable and DAR-transformer experienced a significantimplementation variation between the two evaluationperiods for both demand scenarios as well as CAPEX allo-cated in cable upgrades falling from 89ndash91 (2015ndash2023)to 29ndash66 in the long term 2015ndash2047 run Both technicalperformance evaluators CML and CI respond to theirrespective CAPEX curve shape Due to a more incentivisedsmart grid technique during RIIO-ED2 and ED3 the per-centages of smart techniques implemented varied notablyFor DECC4 in 2023rsquos outlook the share of techniques is

25 for smart grid interventions whereas for 2050rsquos out-look the share is increased up to 58 In the same wayfor DECC2 in 2023rsquos outlook the share of smart interven-tions is 23 whereas in 2050rsquos outlook the share increasesup to 69

Feasible solutions characterising the solution space(Figure 21) differ in the degree of investment required andtechnical performance evaluators Each of the solution pathrepresented in the multidimensional solution space inFigure 21 represents the intersection of that dimension witheach axis Parallel coordinates [35] are low complexityworking for any N-dimension In the case of Figure 21it has 8 dimensions treating every variable uniformlyEach intersection of one solution path with each axis isthe value of that solution for that axis As for axis DECC2and DECC4 it represents the feasible solutions for eachdemand scenario Making a query by the percentage oftraditional reinforcement used in that solution we have thepartition between orange and green for DECC2 and blueand black being the first solutions that use more traditionalreinforcement compared with the ones that use more smarttechniques represented in green (DECC2) and black(DECC4) From Figure 21 we can argue that solutions thatuse more smart techniques (green and blacks) are moreexpensive in terms of CAPEX and OPEX than their peers(orange and blues) when compared by a demand scenarioIt can also be stated that solutions with a higher use of tradi-tional reinforcements are the ones that decrease the mostCML CI and losses

Disaggregating results by network state of the six 11 kVprimaries and by the feeder if necessary for further granulardebugging can discern locational capacity Under bothdemand scenarios Secklow Gate was seen to be the one withgreater capacity being necessary to apply fewer techniquesover the evaluation period 2015ndash2047 On the other handNewport Pagnell exceeds the capacity as soon as 2015requiring a high number of network state evaluation to befixed that happens for each subsequent year hence the largenumber of network states is presented in Table 4

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

pound p

er an

num

Figure 17 CAPEX and OPEX DECC4 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

pu

Figure 18 CML and CI DECC4 2015ndash2047

14 Complexity

5 Conclusions

Power flow analysis using a nodal network model is essentialwhen determining the benefits of trailed smart interventions

because the interactions and implications are highly specificto a particular location and scenario

This suggests that while the bottom-up approach isonerous in terms of data handling and manipulation this isworthwhile for strategic planning and policy evaluationComparing both investment strategies investment strategyadjustments will be necessary in future regulation periodsif an over-invested network behaves as displayed in theshort-term section of this study

Traditional reinforcement will continue to be the mainmethod by which network issues are mostly resolvedfollowed by dynamic asset rating and meshed networks

The assessment of the six novel smart interventions in theFALCON 11kV primary test area in Milton Keynes hasproved the suitability of three techniques able to fix failuresimproving the quality of service and their readiness to bedeployed in the near future These techniques are DAR forcables and transformers and meshed networks Meshednetworks have been repeatedly selected as a feasible tech-nique because using it will reduce CML CI and power losses

Table 3 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2047

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 18 5 31 6

DAR-transformer 32 16 32 6

ALT 0 0 0 0

Mesh 8 3 6 3

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 25 44 9 16

TRAD-cable 15 29 20 66

TRAD-transfer load 1 1 1 1

TRAD-new feeder 1 2 1 2

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

s pound p

er an

num

Figure 19 CAPEX and OPEX DECC2 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

PU

Figure 20 CML and CI DECC2 2015ndash2047

15Complexity

improving the quality of service and the efficiency whilebeing a cost-effective solution

The initial capacity at primary substations differedsignificantly and this affected the number and complexityof interventions required by the SIM Due to the loadscenarios showing significant peak load increases DAR wasoften a temporary measure that would delay but not removethe eventual need for traditional reinforcement

Implementing DAR for cables and transformers themonitoring of assets when their peak capacity is increasedwas analysed On the other hand it was observed thattraditional reinforcements still play a key role in keepingthe electricity distribution networks free of constraintsTRAD techniques such as transformer and cable replace-ments are able to fix the majority of failures and will beessential also in the future

The comparison between traditional reinforcements andnovel smart techniques has provided a new knowledge aboutthe suitability of each technique to be applied in termsof costs electrical performance failures fixed and assetreplaced Cable replacement is the most costly techniquehowever its use is unavoidable in a number of cases Further-more the applicability of each technique regarding costsinvolved improvements on power quality and efficiency

and failures solved lead to new questions to be analysed suchas the lack of these techniques to provide flexible capacitywithin the trailed area

To sum up this study has performed a comparativeanalysis of novel smart intervention techniques providinginsights for future investments in electricity distributionplanning Further work can focus on scaling up the analysisto include a larger section of the network or a constrainedarea to evaluate national applicability of the current findings

Data Availability

Some of the data relevant to this submission is proprietaryfrom Western Power Distribution It can be accessed forresearch purposes under a signed collaboration agreement

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors would like to thank the rest of the WesternPower Distribution Innovation team particularly the other

2400

2200

2000

350

360

370

380

390

400

1400

1300

1200

1100

1000DECC 4

50

55

60

12

11

10

9

8

4400

4200

4000

3800

3600

2400

CAPEX (in kpound) OPEX (in kpound) Utilisation Traditional reinforcements () DECC 2 998796 CML 998796 CI 998796 Losses

2200

2000

1800

Figure 21 Parallel coordinate visualisation of technoeconomic performance indicators under DECC4 and DECC2

Table 4 Summary of network states and results for demand scenario DECC4 and DECC2

Primary 11 kV substation Feeders Year TechniquesResultsDECC4

NSDECC4

ResultsDECC2

NSDECC2

Fox Milne 13 2047 Smart and traditional 54 276 47 259

Newport Pagnell 9 2047 Smart and traditional 48 317 31 318

Secklow Gate 9 2047 Smart and traditional 27 29 15 16

Bletchley 19 2047 Smart and traditional 32 40 21 48

Marlborough Street 11 2047 Smart and traditional 31 52 19 37

Childs Way 17 2047 Smart and traditional 67 398 54 181

16 Complexity

members of the FALCON project as well as Paul CharletonStuart Fowler Ilja Orlovs Dan Taylor and Steve IngramAlso the authors want to acknowledge OFGEM and theLowCarbon Network Fund for funding this piece of research

References

[1] European Commission ldquoCommunication from the Commis-sion to the European Parliament the Council the EuropeanEconomic and Social Committee and the Committee of theRegionsrdquo 2011 December 2016 httpseur-lexeuropaeulegal-contentENTXTqid=1537473508968ampuri=CELEX52016PC076721

[2] F G N Li and E Trutnevyte ldquoInvestment appraisal ofcost-optimal and near-optimal pathways for the UK elec-tricity sector transition to 2050rdquo Applied Energy vol 189pp 89ndash109 2017

[3] G Strbac I Konstantelos M Pollitt and R Green DeliveringFuture-Proof Energy Infrastructure National InfrastructureCommission 2016

[4] Ofgem ldquoGB Network regulation - the RIIO modelrdquo 2010December 2014 httpswwwofgemgovuknetwork-regulation-riio-model

[5] R Poudineh and T Jamasb ldquoDistributed generation storagedemand response and energy efficiency as alternatives togrid capacity enhancementrdquo Energy Policy vol 67 pp 222ndash231 2014

[6] J Yang X Bai D Strickland L Jenkins and A M CrossldquoDynamic network rating for low carbon distribution networkoperationmdashA UK applicationrdquo IEEE Transactions on SmartGrid vol 6 no 2 pp 988ndash998 2015

[7] M Behnke W Erdman S Horgan et al ldquoSecondary net-work distribution systems background and issues relatedto the interconnection of distributed resourcesrdquo NationalRenewable Energy Laboratory (NREL) Tech Rep NRELTP-560-38079 2005

[8] J Crispim J Braz R Castro and J Esteves ldquoSmart grids in theEU with smart regulation experiences from the UK Italy andPortugalrdquo Utilities Policy vol 31 pp 85ndash93 2014

[9] Tnei ldquoIPSA Power software for power systemsrdquo March 2016httpwwwipsa-powercom

[10] ETAP ldquoETAP PSrdquo 2018 April 2016 httpsetapcomsoftwareproduct-overview-main

[11] DINIS ldquoDINIS distribution network information sys-temsrdquo 2002 March 2016 httpsetapcomsoftwareproduct-overview-main

[12] Western Power Distribution ldquoSIM final reportrdquo 2015November 2016 httpwwwwesternpowerinnovationcoukProjectsFalconaspx

[13] Western Power Distribution ldquoProject FALCONrdquo 2015November 2017 httpwwwdiniscom

[14] EA Technology ldquoTransform modelrdquo March 2016 httpwwweatechnologycomproducts-and-servicescreate-smarter-gridstransform-model

[15] L Sigrist E Lobato L Rouco M Gazzino and M CantuldquoEconomic assessment of smart grid initiatives for islandpower systemsrdquo Applied Energy vol 189 pp 403ndash415 2017

[16] J A Momoh ldquoSmart grid design for efficient and flexiblepower networks operation and controlrdquo in 2009 IEEEPESPower Systems Conference and Exposition pp 1ndash8 SeattleWA USA 2009

[17] N Good K A Ellis and P Mancarella ldquoReview and classifica-tion of barriers and enablers of demand response in the smartgridrdquo Renewable and Sustainable Energy Reviews vol 72pp 57ndash72 2017

[18] Z Yang J Xiang and Y Li ldquoDistributed consensus basedsupplyndashdemand balance algorithm for economic dispatchproblem in a smart grid with switching graphrdquo IEEE Trans-actions on Industrial Electronics vol 64 no 2 pp 1600ndash1610 2017

[19] SuSTAINABLE ldquoSmart distribution network operation formaximising the integration of renewable generationrdquo 2015March 2016 httpwwwsustainableprojecteuHomeaspx

[20] S Mohtashami D Pudjianto and G Strbac ldquoStrategic distri-bution network planning with smart grid technologiesrdquo IEEETransactions on Smart Grid vol 8 no 6 pp 2656ndash2664 2017

[21] Smart Grid Forum ldquoSmart grid forum work stream 7rdquo2015 March 2016 httpwwwsmarternetworksorgprojectnia_nget015425

[22] ETI ldquoEnergyPath networksrdquo March 2016 httpwwweticoukprojectenergypath

[23] C Mateo Domingo T Gomez San Roman Aacute Sanchez-Miralles J P Peco Gonzalez and A Candela Martinez ldquoAreference network model for large-scale distribution planningwith automatic street map generationrdquo IEEE Transactions onPower Systems vol 26 no 1 pp 190ndash197 2011

[24] R Bolton and T J Foxon ldquoInfrastructure transformation as asocio-technical process mdash implications for the governanceof energy distribution networks in the UKrdquo TechnologicalForecasting and Social Change vol 90 pp 538ndash550 2015

[25] UK Power Networks ldquoDNO guide to future Smart man-agement of distribution networks Summary reportrdquo 2014httpsinnovationukpowernetworkscoukinnovationenProjectstier-2-projectsLow-Carbon-London-(LCL)Project-DocumentsLCL20Learning20Report20-20SR20-20Summary20Report20-20DNO20Guide20to20Future20Smart20Management20of20Distribution20Networkspdf

[26] A Botea J Rintanen and D Banerjee ldquoOptimal reconfigura-tion for supply restoration with informed AlowastSearchrdquo IEEETransactions on Smart Grid vol 3 no 2 pp 583ndash593 2012

[27] T Logenthiran D Srinivasan and T Z Shun ldquoDemand sidemanagement in smart grid using heuristic optimizationrdquoIEEE Transactions on Smart Grid vol 3 no 3 pp 1244ndash1252 2012

[28] J Saacutenchez-Cuadrado J de Lara and E Guerra ldquoBottom-upmeta-modelling an interactive approachrdquo in Model DrivenEngineering Languages and Systems pp 3ndash19 Springer2012

[29] Y H Kim F J Sting and C H Loch ldquoTop-down bottom-upor both Toward an integrative perspective on operationsstrategy formationrdquo Journal of Operations Managementvol 32 no 7-8 pp 462ndash474 2014

[30] C Bohringer ldquoThe synthesis of bottom-up and top-down inenergy policy modelingrdquo Energy Economics vol 20 no 3pp 233ndash248 1998

[31] T G R Bower ldquoRepetition in human developmentrdquo Merrill-Palmer Quarterly of Behavior and Development vol 20no 4 pp 303ndash318 1974

[32] H Mintzberg and J A Waters ldquoOf strategies deliberate andemergentrdquo Strategic Management Journal vol 6 no 3pp 257ndash272 1985

17Complexity

5 Conclusions

Power flow analysis using a nodal network model is essentialwhen determining the benefits of trailed smart interventions

because the interactions and implications are highly specificto a particular location and scenario

This suggests that while the bottom-up approach isonerous in terms of data handling and manipulation this isworthwhile for strategic planning and policy evaluationComparing both investment strategies investment strategyadjustments will be necessary in future regulation periodsif an over-invested network behaves as displayed in theshort-term section of this study

Traditional reinforcement will continue to be the mainmethod by which network issues are mostly resolvedfollowed by dynamic asset rating and meshed networks

The assessment of the six novel smart interventions in theFALCON 11kV primary test area in Milton Keynes hasproved the suitability of three techniques able to fix failuresimproving the quality of service and their readiness to bedeployed in the near future These techniques are DAR forcables and transformers and meshed networks Meshednetworks have been repeatedly selected as a feasible tech-nique because using it will reduce CML CI and power losses

Table 3 Number of interventions and CAPEX involved DECC4 and DECC2 2015ndash2047

TechniqueDECC4 DECC2

Proportion of interventions () CAPEX () Proportion of interventions () CAPEX ()

DAR-cable 18 5 31 6

DAR-transformer 32 16 32 6

ALT 0 0 0 0

Mesh 8 3 6 3

Batteries 0 0 0 0

DSM 0 0 0 0

DG 0 0 0 0

TRAD-transformer 25 44 9 16

TRAD-cable 15 29 20 66

TRAD-transfer load 1 1 1 1

TRAD-new feeder 1 2 1 2

2015 2023 2031

CAPEXOPEX

2039 20470

50

100

150

200

250

300

Thou

sand

s pound p

er an

num

Figure 19 CAPEX and OPEX DECC2 2015ndash2047

CI

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

CML

0010203040506070809

PU

Figure 20 CML and CI DECC2 2015ndash2047

15Complexity

improving the quality of service and the efficiency whilebeing a cost-effective solution

The initial capacity at primary substations differedsignificantly and this affected the number and complexityof interventions required by the SIM Due to the loadscenarios showing significant peak load increases DAR wasoften a temporary measure that would delay but not removethe eventual need for traditional reinforcement

Implementing DAR for cables and transformers themonitoring of assets when their peak capacity is increasedwas analysed On the other hand it was observed thattraditional reinforcements still play a key role in keepingthe electricity distribution networks free of constraintsTRAD techniques such as transformer and cable replace-ments are able to fix the majority of failures and will beessential also in the future

The comparison between traditional reinforcements andnovel smart techniques has provided a new knowledge aboutthe suitability of each technique to be applied in termsof costs electrical performance failures fixed and assetreplaced Cable replacement is the most costly techniquehowever its use is unavoidable in a number of cases Further-more the applicability of each technique regarding costsinvolved improvements on power quality and efficiency

and failures solved lead to new questions to be analysed suchas the lack of these techniques to provide flexible capacitywithin the trailed area

To sum up this study has performed a comparativeanalysis of novel smart intervention techniques providinginsights for future investments in electricity distributionplanning Further work can focus on scaling up the analysisto include a larger section of the network or a constrainedarea to evaluate national applicability of the current findings

Data Availability

Some of the data relevant to this submission is proprietaryfrom Western Power Distribution It can be accessed forresearch purposes under a signed collaboration agreement

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors would like to thank the rest of the WesternPower Distribution Innovation team particularly the other

2400

2200

2000

350

360

370

380

390

400

1400

1300

1200

1100

1000DECC 4

50

55

60

12

11

10

9

8

4400

4200

4000

3800

3600

2400

CAPEX (in kpound) OPEX (in kpound) Utilisation Traditional reinforcements () DECC 2 998796 CML 998796 CI 998796 Losses

2200

2000

1800

Figure 21 Parallel coordinate visualisation of technoeconomic performance indicators under DECC4 and DECC2

Table 4 Summary of network states and results for demand scenario DECC4 and DECC2

Primary 11 kV substation Feeders Year TechniquesResultsDECC4

NSDECC4

ResultsDECC2

NSDECC2

Fox Milne 13 2047 Smart and traditional 54 276 47 259

Newport Pagnell 9 2047 Smart and traditional 48 317 31 318

Secklow Gate 9 2047 Smart and traditional 27 29 15 16

Bletchley 19 2047 Smart and traditional 32 40 21 48

Marlborough Street 11 2047 Smart and traditional 31 52 19 37

Childs Way 17 2047 Smart and traditional 67 398 54 181

16 Complexity

members of the FALCON project as well as Paul CharletonStuart Fowler Ilja Orlovs Dan Taylor and Steve IngramAlso the authors want to acknowledge OFGEM and theLowCarbon Network Fund for funding this piece of research

References

[1] European Commission ldquoCommunication from the Commis-sion to the European Parliament the Council the EuropeanEconomic and Social Committee and the Committee of theRegionsrdquo 2011 December 2016 httpseur-lexeuropaeulegal-contentENTXTqid=1537473508968ampuri=CELEX52016PC076721

[2] F G N Li and E Trutnevyte ldquoInvestment appraisal ofcost-optimal and near-optimal pathways for the UK elec-tricity sector transition to 2050rdquo Applied Energy vol 189pp 89ndash109 2017

[3] G Strbac I Konstantelos M Pollitt and R Green DeliveringFuture-Proof Energy Infrastructure National InfrastructureCommission 2016

[4] Ofgem ldquoGB Network regulation - the RIIO modelrdquo 2010December 2014 httpswwwofgemgovuknetwork-regulation-riio-model

[5] R Poudineh and T Jamasb ldquoDistributed generation storagedemand response and energy efficiency as alternatives togrid capacity enhancementrdquo Energy Policy vol 67 pp 222ndash231 2014

[6] J Yang X Bai D Strickland L Jenkins and A M CrossldquoDynamic network rating for low carbon distribution networkoperationmdashA UK applicationrdquo IEEE Transactions on SmartGrid vol 6 no 2 pp 988ndash998 2015

[7] M Behnke W Erdman S Horgan et al ldquoSecondary net-work distribution systems background and issues relatedto the interconnection of distributed resourcesrdquo NationalRenewable Energy Laboratory (NREL) Tech Rep NRELTP-560-38079 2005

[8] J Crispim J Braz R Castro and J Esteves ldquoSmart grids in theEU with smart regulation experiences from the UK Italy andPortugalrdquo Utilities Policy vol 31 pp 85ndash93 2014

[9] Tnei ldquoIPSA Power software for power systemsrdquo March 2016httpwwwipsa-powercom

[10] ETAP ldquoETAP PSrdquo 2018 April 2016 httpsetapcomsoftwareproduct-overview-main

[11] DINIS ldquoDINIS distribution network information sys-temsrdquo 2002 March 2016 httpsetapcomsoftwareproduct-overview-main

[12] Western Power Distribution ldquoSIM final reportrdquo 2015November 2016 httpwwwwesternpowerinnovationcoukProjectsFalconaspx

[13] Western Power Distribution ldquoProject FALCONrdquo 2015November 2017 httpwwwdiniscom

[14] EA Technology ldquoTransform modelrdquo March 2016 httpwwweatechnologycomproducts-and-servicescreate-smarter-gridstransform-model

[15] L Sigrist E Lobato L Rouco M Gazzino and M CantuldquoEconomic assessment of smart grid initiatives for islandpower systemsrdquo Applied Energy vol 189 pp 403ndash415 2017

[16] J A Momoh ldquoSmart grid design for efficient and flexiblepower networks operation and controlrdquo in 2009 IEEEPESPower Systems Conference and Exposition pp 1ndash8 SeattleWA USA 2009

[17] N Good K A Ellis and P Mancarella ldquoReview and classifica-tion of barriers and enablers of demand response in the smartgridrdquo Renewable and Sustainable Energy Reviews vol 72pp 57ndash72 2017

[18] Z Yang J Xiang and Y Li ldquoDistributed consensus basedsupplyndashdemand balance algorithm for economic dispatchproblem in a smart grid with switching graphrdquo IEEE Trans-actions on Industrial Electronics vol 64 no 2 pp 1600ndash1610 2017

[19] SuSTAINABLE ldquoSmart distribution network operation formaximising the integration of renewable generationrdquo 2015March 2016 httpwwwsustainableprojecteuHomeaspx

[20] S Mohtashami D Pudjianto and G Strbac ldquoStrategic distri-bution network planning with smart grid technologiesrdquo IEEETransactions on Smart Grid vol 8 no 6 pp 2656ndash2664 2017

[21] Smart Grid Forum ldquoSmart grid forum work stream 7rdquo2015 March 2016 httpwwwsmarternetworksorgprojectnia_nget015425

[22] ETI ldquoEnergyPath networksrdquo March 2016 httpwwweticoukprojectenergypath

[23] C Mateo Domingo T Gomez San Roman Aacute Sanchez-Miralles J P Peco Gonzalez and A Candela Martinez ldquoAreference network model for large-scale distribution planningwith automatic street map generationrdquo IEEE Transactions onPower Systems vol 26 no 1 pp 190ndash197 2011

[24] R Bolton and T J Foxon ldquoInfrastructure transformation as asocio-technical process mdash implications for the governanceof energy distribution networks in the UKrdquo TechnologicalForecasting and Social Change vol 90 pp 538ndash550 2015

[25] UK Power Networks ldquoDNO guide to future Smart man-agement of distribution networks Summary reportrdquo 2014httpsinnovationukpowernetworkscoukinnovationenProjectstier-2-projectsLow-Carbon-London-(LCL)Project-DocumentsLCL20Learning20Report20-20SR20-20Summary20Report20-20DNO20Guide20to20Future20Smart20Management20of20Distribution20Networkspdf

[26] A Botea J Rintanen and D Banerjee ldquoOptimal reconfigura-tion for supply restoration with informed AlowastSearchrdquo IEEETransactions on Smart Grid vol 3 no 2 pp 583ndash593 2012

[27] T Logenthiran D Srinivasan and T Z Shun ldquoDemand sidemanagement in smart grid using heuristic optimizationrdquoIEEE Transactions on Smart Grid vol 3 no 3 pp 1244ndash1252 2012

[28] J Saacutenchez-Cuadrado J de Lara and E Guerra ldquoBottom-upmeta-modelling an interactive approachrdquo in Model DrivenEngineering Languages and Systems pp 3ndash19 Springer2012

[29] Y H Kim F J Sting and C H Loch ldquoTop-down bottom-upor both Toward an integrative perspective on operationsstrategy formationrdquo Journal of Operations Managementvol 32 no 7-8 pp 462ndash474 2014

[30] C Bohringer ldquoThe synthesis of bottom-up and top-down inenergy policy modelingrdquo Energy Economics vol 20 no 3pp 233ndash248 1998

[31] T G R Bower ldquoRepetition in human developmentrdquo Merrill-Palmer Quarterly of Behavior and Development vol 20no 4 pp 303ndash318 1974

[32] H Mintzberg and J A Waters ldquoOf strategies deliberate andemergentrdquo Strategic Management Journal vol 6 no 3pp 257ndash272 1985

17Complexity

improving the quality of service and the efficiency whilebeing a cost-effective solution

The initial capacity at primary substations differedsignificantly and this affected the number and complexityof interventions required by the SIM Due to the loadscenarios showing significant peak load increases DAR wasoften a temporary measure that would delay but not removethe eventual need for traditional reinforcement

Implementing DAR for cables and transformers themonitoring of assets when their peak capacity is increasedwas analysed On the other hand it was observed thattraditional reinforcements still play a key role in keepingthe electricity distribution networks free of constraintsTRAD techniques such as transformer and cable replace-ments are able to fix the majority of failures and will beessential also in the future

The comparison between traditional reinforcements andnovel smart techniques has provided a new knowledge aboutthe suitability of each technique to be applied in termsof costs electrical performance failures fixed and assetreplaced Cable replacement is the most costly techniquehowever its use is unavoidable in a number of cases Further-more the applicability of each technique regarding costsinvolved improvements on power quality and efficiency

and failures solved lead to new questions to be analysed suchas the lack of these techniques to provide flexible capacitywithin the trailed area

To sum up this study has performed a comparativeanalysis of novel smart intervention techniques providinginsights for future investments in electricity distributionplanning Further work can focus on scaling up the analysisto include a larger section of the network or a constrainedarea to evaluate national applicability of the current findings

Data Availability

Some of the data relevant to this submission is proprietaryfrom Western Power Distribution It can be accessed forresearch purposes under a signed collaboration agreement

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

The authors would like to thank the rest of the WesternPower Distribution Innovation team particularly the other

2400

2200

2000

350

360

370

380

390

400

1400

1300

1200

1100

1000DECC 4

50

55

60

12

11

10

9

8

4400

4200

4000

3800

3600

2400

CAPEX (in kpound) OPEX (in kpound) Utilisation Traditional reinforcements () DECC 2 998796 CML 998796 CI 998796 Losses

2200

2000

1800

Figure 21 Parallel coordinate visualisation of technoeconomic performance indicators under DECC4 and DECC2

Table 4 Summary of network states and results for demand scenario DECC4 and DECC2

Primary 11 kV substation Feeders Year TechniquesResultsDECC4

NSDECC4

ResultsDECC2

NSDECC2

Fox Milne 13 2047 Smart and traditional 54 276 47 259

Newport Pagnell 9 2047 Smart and traditional 48 317 31 318

Secklow Gate 9 2047 Smart and traditional 27 29 15 16

Bletchley 19 2047 Smart and traditional 32 40 21 48

Marlborough Street 11 2047 Smart and traditional 31 52 19 37

Childs Way 17 2047 Smart and traditional 67 398 54 181

16 Complexity

members of the FALCON project as well as Paul CharletonStuart Fowler Ilja Orlovs Dan Taylor and Steve IngramAlso the authors want to acknowledge OFGEM and theLowCarbon Network Fund for funding this piece of research

References

[1] European Commission ldquoCommunication from the Commis-sion to the European Parliament the Council the EuropeanEconomic and Social Committee and the Committee of theRegionsrdquo 2011 December 2016 httpseur-lexeuropaeulegal-contentENTXTqid=1537473508968ampuri=CELEX52016PC076721

[2] F G N Li and E Trutnevyte ldquoInvestment appraisal ofcost-optimal and near-optimal pathways for the UK elec-tricity sector transition to 2050rdquo Applied Energy vol 189pp 89ndash109 2017

[3] G Strbac I Konstantelos M Pollitt and R Green DeliveringFuture-Proof Energy Infrastructure National InfrastructureCommission 2016

[4] Ofgem ldquoGB Network regulation - the RIIO modelrdquo 2010December 2014 httpswwwofgemgovuknetwork-regulation-riio-model

[5] R Poudineh and T Jamasb ldquoDistributed generation storagedemand response and energy efficiency as alternatives togrid capacity enhancementrdquo Energy Policy vol 67 pp 222ndash231 2014

[6] J Yang X Bai D Strickland L Jenkins and A M CrossldquoDynamic network rating for low carbon distribution networkoperationmdashA UK applicationrdquo IEEE Transactions on SmartGrid vol 6 no 2 pp 988ndash998 2015

[7] M Behnke W Erdman S Horgan et al ldquoSecondary net-work distribution systems background and issues relatedto the interconnection of distributed resourcesrdquo NationalRenewable Energy Laboratory (NREL) Tech Rep NRELTP-560-38079 2005

[8] J Crispim J Braz R Castro and J Esteves ldquoSmart grids in theEU with smart regulation experiences from the UK Italy andPortugalrdquo Utilities Policy vol 31 pp 85ndash93 2014

[9] Tnei ldquoIPSA Power software for power systemsrdquo March 2016httpwwwipsa-powercom

[10] ETAP ldquoETAP PSrdquo 2018 April 2016 httpsetapcomsoftwareproduct-overview-main

[11] DINIS ldquoDINIS distribution network information sys-temsrdquo 2002 March 2016 httpsetapcomsoftwareproduct-overview-main

[12] Western Power Distribution ldquoSIM final reportrdquo 2015November 2016 httpwwwwesternpowerinnovationcoukProjectsFalconaspx

[13] Western Power Distribution ldquoProject FALCONrdquo 2015November 2017 httpwwwdiniscom

[14] EA Technology ldquoTransform modelrdquo March 2016 httpwwweatechnologycomproducts-and-servicescreate-smarter-gridstransform-model

[15] L Sigrist E Lobato L Rouco M Gazzino and M CantuldquoEconomic assessment of smart grid initiatives for islandpower systemsrdquo Applied Energy vol 189 pp 403ndash415 2017

[16] J A Momoh ldquoSmart grid design for efficient and flexiblepower networks operation and controlrdquo in 2009 IEEEPESPower Systems Conference and Exposition pp 1ndash8 SeattleWA USA 2009

[17] N Good K A Ellis and P Mancarella ldquoReview and classifica-tion of barriers and enablers of demand response in the smartgridrdquo Renewable and Sustainable Energy Reviews vol 72pp 57ndash72 2017

[18] Z Yang J Xiang and Y Li ldquoDistributed consensus basedsupplyndashdemand balance algorithm for economic dispatchproblem in a smart grid with switching graphrdquo IEEE Trans-actions on Industrial Electronics vol 64 no 2 pp 1600ndash1610 2017

[19] SuSTAINABLE ldquoSmart distribution network operation formaximising the integration of renewable generationrdquo 2015March 2016 httpwwwsustainableprojecteuHomeaspx

[20] S Mohtashami D Pudjianto and G Strbac ldquoStrategic distri-bution network planning with smart grid technologiesrdquo IEEETransactions on Smart Grid vol 8 no 6 pp 2656ndash2664 2017

[21] Smart Grid Forum ldquoSmart grid forum work stream 7rdquo2015 March 2016 httpwwwsmarternetworksorgprojectnia_nget015425

[22] ETI ldquoEnergyPath networksrdquo March 2016 httpwwweticoukprojectenergypath

[23] C Mateo Domingo T Gomez San Roman Aacute Sanchez-Miralles J P Peco Gonzalez and A Candela Martinez ldquoAreference network model for large-scale distribution planningwith automatic street map generationrdquo IEEE Transactions onPower Systems vol 26 no 1 pp 190ndash197 2011

[24] R Bolton and T J Foxon ldquoInfrastructure transformation as asocio-technical process mdash implications for the governanceof energy distribution networks in the UKrdquo TechnologicalForecasting and Social Change vol 90 pp 538ndash550 2015

[25] UK Power Networks ldquoDNO guide to future Smart man-agement of distribution networks Summary reportrdquo 2014httpsinnovationukpowernetworkscoukinnovationenProjectstier-2-projectsLow-Carbon-London-(LCL)Project-DocumentsLCL20Learning20Report20-20SR20-20Summary20Report20-20DNO20Guide20to20Future20Smart20Management20of20Distribution20Networkspdf

[26] A Botea J Rintanen and D Banerjee ldquoOptimal reconfigura-tion for supply restoration with informed AlowastSearchrdquo IEEETransactions on Smart Grid vol 3 no 2 pp 583ndash593 2012

[27] T Logenthiran D Srinivasan and T Z Shun ldquoDemand sidemanagement in smart grid using heuristic optimizationrdquoIEEE Transactions on Smart Grid vol 3 no 3 pp 1244ndash1252 2012

[28] J Saacutenchez-Cuadrado J de Lara and E Guerra ldquoBottom-upmeta-modelling an interactive approachrdquo in Model DrivenEngineering Languages and Systems pp 3ndash19 Springer2012

[29] Y H Kim F J Sting and C H Loch ldquoTop-down bottom-upor both Toward an integrative perspective on operationsstrategy formationrdquo Journal of Operations Managementvol 32 no 7-8 pp 462ndash474 2014

[30] C Bohringer ldquoThe synthesis of bottom-up and top-down inenergy policy modelingrdquo Energy Economics vol 20 no 3pp 233ndash248 1998

[31] T G R Bower ldquoRepetition in human developmentrdquo Merrill-Palmer Quarterly of Behavior and Development vol 20no 4 pp 303ndash318 1974

[32] H Mintzberg and J A Waters ldquoOf strategies deliberate andemergentrdquo Strategic Management Journal vol 6 no 3pp 257ndash272 1985

17Complexity

members of the FALCON project as well as Paul CharletonStuart Fowler Ilja Orlovs Dan Taylor and Steve IngramAlso the authors want to acknowledge OFGEM and theLowCarbon Network Fund for funding this piece of research

References

[1] European Commission ldquoCommunication from the Commis-sion to the European Parliament the Council the EuropeanEconomic and Social Committee and the Committee of theRegionsrdquo 2011 December 2016 httpseur-lexeuropaeulegal-contentENTXTqid=1537473508968ampuri=CELEX52016PC076721

[2] F G N Li and E Trutnevyte ldquoInvestment appraisal ofcost-optimal and near-optimal pathways for the UK elec-tricity sector transition to 2050rdquo Applied Energy vol 189pp 89ndash109 2017

[3] G Strbac I Konstantelos M Pollitt and R Green DeliveringFuture-Proof Energy Infrastructure National InfrastructureCommission 2016

[4] Ofgem ldquoGB Network regulation - the RIIO modelrdquo 2010December 2014 httpswwwofgemgovuknetwork-regulation-riio-model

[5] R Poudineh and T Jamasb ldquoDistributed generation storagedemand response and energy efficiency as alternatives togrid capacity enhancementrdquo Energy Policy vol 67 pp 222ndash231 2014

[6] J Yang X Bai D Strickland L Jenkins and A M CrossldquoDynamic network rating for low carbon distribution networkoperationmdashA UK applicationrdquo IEEE Transactions on SmartGrid vol 6 no 2 pp 988ndash998 2015

[7] M Behnke W Erdman S Horgan et al ldquoSecondary net-work distribution systems background and issues relatedto the interconnection of distributed resourcesrdquo NationalRenewable Energy Laboratory (NREL) Tech Rep NRELTP-560-38079 2005

[8] J Crispim J Braz R Castro and J Esteves ldquoSmart grids in theEU with smart regulation experiences from the UK Italy andPortugalrdquo Utilities Policy vol 31 pp 85ndash93 2014

[9] Tnei ldquoIPSA Power software for power systemsrdquo March 2016httpwwwipsa-powercom

[10] ETAP ldquoETAP PSrdquo 2018 April 2016 httpsetapcomsoftwareproduct-overview-main

[11] DINIS ldquoDINIS distribution network information sys-temsrdquo 2002 March 2016 httpsetapcomsoftwareproduct-overview-main

[12] Western Power Distribution ldquoSIM final reportrdquo 2015November 2016 httpwwwwesternpowerinnovationcoukProjectsFalconaspx

[13] Western Power Distribution ldquoProject FALCONrdquo 2015November 2017 httpwwwdiniscom

[14] EA Technology ldquoTransform modelrdquo March 2016 httpwwweatechnologycomproducts-and-servicescreate-smarter-gridstransform-model

[15] L Sigrist E Lobato L Rouco M Gazzino and M CantuldquoEconomic assessment of smart grid initiatives for islandpower systemsrdquo Applied Energy vol 189 pp 403ndash415 2017

[16] J A Momoh ldquoSmart grid design for efficient and flexiblepower networks operation and controlrdquo in 2009 IEEEPESPower Systems Conference and Exposition pp 1ndash8 SeattleWA USA 2009

[17] N Good K A Ellis and P Mancarella ldquoReview and classifica-tion of barriers and enablers of demand response in the smartgridrdquo Renewable and Sustainable Energy Reviews vol 72pp 57ndash72 2017

[18] Z Yang J Xiang and Y Li ldquoDistributed consensus basedsupplyndashdemand balance algorithm for economic dispatchproblem in a smart grid with switching graphrdquo IEEE Trans-actions on Industrial Electronics vol 64 no 2 pp 1600ndash1610 2017

[19] SuSTAINABLE ldquoSmart distribution network operation formaximising the integration of renewable generationrdquo 2015March 2016 httpwwwsustainableprojecteuHomeaspx

[20] S Mohtashami D Pudjianto and G Strbac ldquoStrategic distri-bution network planning with smart grid technologiesrdquo IEEETransactions on Smart Grid vol 8 no 6 pp 2656ndash2664 2017

[21] Smart Grid Forum ldquoSmart grid forum work stream 7rdquo2015 March 2016 httpwwwsmarternetworksorgprojectnia_nget015425

[22] ETI ldquoEnergyPath networksrdquo March 2016 httpwwweticoukprojectenergypath

[23] C Mateo Domingo T Gomez San Roman Aacute Sanchez-Miralles J P Peco Gonzalez and A Candela Martinez ldquoAreference network model for large-scale distribution planningwith automatic street map generationrdquo IEEE Transactions onPower Systems vol 26 no 1 pp 190ndash197 2011

[24] R Bolton and T J Foxon ldquoInfrastructure transformation as asocio-technical process mdash implications for the governanceof energy distribution networks in the UKrdquo TechnologicalForecasting and Social Change vol 90 pp 538ndash550 2015

[25] UK Power Networks ldquoDNO guide to future Smart man-agement of distribution networks Summary reportrdquo 2014httpsinnovationukpowernetworkscoukinnovationenProjectstier-2-projectsLow-Carbon-London-(LCL)Project-DocumentsLCL20Learning20Report20-20SR20-20Summary20Report20-20DNO20Guide20to20Future20Smart20Management20of20Distribution20Networkspdf

[26] A Botea J Rintanen and D Banerjee ldquoOptimal reconfigura-tion for supply restoration with informed AlowastSearchrdquo IEEETransactions on Smart Grid vol 3 no 2 pp 583ndash593 2012

[27] T Logenthiran D Srinivasan and T Z Shun ldquoDemand sidemanagement in smart grid using heuristic optimizationrdquoIEEE Transactions on Smart Grid vol 3 no 3 pp 1244ndash1252 2012

[28] J Saacutenchez-Cuadrado J de Lara and E Guerra ldquoBottom-upmeta-modelling an interactive approachrdquo in Model DrivenEngineering Languages and Systems pp 3ndash19 Springer2012

[29] Y H Kim F J Sting and C H Loch ldquoTop-down bottom-upor both Toward an integrative perspective on operationsstrategy formationrdquo Journal of Operations Managementvol 32 no 7-8 pp 462ndash474 2014

[30] C Bohringer ldquoThe synthesis of bottom-up and top-down inenergy policy modelingrdquo Energy Economics vol 20 no 3pp 233ndash248 1998

[31] T G R Bower ldquoRepetition in human developmentrdquo Merrill-Palmer Quarterly of Behavior and Development vol 20no 4 pp 303ndash318 1974

[32] H Mintzberg and J A Waters ldquoOf strategies deliberate andemergentrdquo Strategic Management Journal vol 6 no 3pp 257ndash272 1985

17Complexity

[33] K Moslehi and R Kumar ldquoA reliability perspective of thesmart gridrdquo IEEE Transactions on Smart Grid vol 1 no 1pp 57ndash64 2010

[34] F Rahimi and A Ipakchi ldquoDemand response as a marketresource under the smart grid paradigmrdquo IEEE Transactionson Smart Grid vol 1 no 1 pp 82ndash88 2010

[35] A Inselberg Parallel Coordinates Visual MultidimensionalGeometry and Its Applications Springer 2009

18 Complexity

Hindawiwwwhindawicom Volume 2018

MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Mathematical Problems in Engineering

Applied MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Probability and StatisticsHindawiwwwhindawicom Volume 2018

Journal of

Hindawiwwwhindawicom Volume 2018

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawiwwwhindawicom Volume 2018

OptimizationJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Engineering Mathematics

International Journal of

Hindawiwwwhindawicom Volume 2018

Operations ResearchAdvances in

Journal of

Hindawiwwwhindawicom Volume 2018

Function SpacesAbstract and Applied AnalysisHindawiwwwhindawicom Volume 2018

International Journal of Mathematics and Mathematical Sciences

Hindawiwwwhindawicom Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Hindawiwwwhindawicom Volume 2018Volume 2018

Numerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisAdvances inAdvances in Discrete Dynamics in

Nature and SocietyHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Dierential EquationsInternational Journal of

Volume 2018

Hindawiwwwhindawicom Volume 2018

Decision SciencesAdvances in

Hindawiwwwhindawicom Volume 2018

AnalysisInternational Journal of

Hindawiwwwhindawicom Volume 2018

Stochastic AnalysisInternational Journal of

Submit your manuscripts atwwwhindawicom

Hindawiwwwhindawicom Volume 2018

MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Mathematical Problems in Engineering

Applied MathematicsJournal of

Hindawiwwwhindawicom Volume 2018

Probability and StatisticsHindawiwwwhindawicom Volume 2018

Journal of

Hindawiwwwhindawicom Volume 2018

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawiwwwhindawicom Volume 2018

OptimizationJournal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom Volume 2018

Engineering Mathematics

International Journal of

Hindawiwwwhindawicom Volume 2018

Operations ResearchAdvances in

Journal of

Hindawiwwwhindawicom Volume 2018

Function SpacesAbstract and Applied AnalysisHindawiwwwhindawicom Volume 2018

International Journal of Mathematics and Mathematical Sciences

Hindawiwwwhindawicom Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Hindawiwwwhindawicom Volume 2018Volume 2018

Numerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisAdvances inAdvances in Discrete Dynamics in

Nature and SocietyHindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Dierential EquationsInternational Journal of

Volume 2018

Hindawiwwwhindawicom Volume 2018

Decision SciencesAdvances in

Hindawiwwwhindawicom Volume 2018

AnalysisInternational Journal of

Hindawiwwwhindawicom Volume 2018

Stochastic AnalysisInternational Journal of

Submit your manuscripts atwwwhindawicom