MARKED BASED OPERATION & CONTROL OF EMERGING ELECTRICITY DISTRIBUTION SYSTEMS AS COMPLEX

SYSTEMSEttore F. Bompard

Power system and critical infrastructures senior scientist Joint Research Centre of the EC, Institute for Energy and Transport - Netherlands

Energy and Environment (E&E) Seminars - Judge Business School - University of Cambridge

Cambridge, November 12th 2012

OUTLINE

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MARKET BASED OPERATION & CONTROL OF EES

EU KEY OBJECTIVE IN (ELEC.) ENERGY

　 Competiveness as the key to efficiency (economic,energetic,…)

　 Emerging Distribution Systems as an environmentfor competition to achieve sustainability, efficiencyand security

SUSTAINABILITY

COMPETITIVENESS

SECURITY

• European Commission’s Green Paper “A European strategy forsustainable, competitive and secure energy” (2006)

Sustainability is the ability to meet the present and future energy (electricity) needs without compromising, in the short and long term, fundamental resources (air, water, food, etc.) while achieving social, economic and technical levels of performance.

“Security” is the ability to keep the power system feasible under normal and emergency conditions.

“Quality ” is the ability to provide electricity at the consumers’ locations with predefined technical features (continuity of supply, waveform,..).

SUSTAINABILITY, SECURITY, 

CONTEXT

• Distributed decision making: a multitude of self-interested individuals considering specific psychologicaland social profiles, interacting among themselves andwith the environment to produce global systemperformance (economic, environmental, energetic).

• Competition and markets, in Western world, key wordsto achieve the goal (economic efficiency, social welfare(education, health, social security), satisfaction ofenergy needs, reduction of environmental impacts.)

• A huge amount of information, coming from differentlevels of the system, has been made available todecision makers through massive uses of the ICTtechnology

• The context is complex not complicated; what arousescomplexity is the interplay of all the "layers" and“players” involved

CONTROL AND SYSTEM GOVERNANCE

• Decision making at the regulatory level (policy decisionmaking) may affect the sought global performance,"exciting" it with rules, constraints, incentives,…, and letthe system evolve (hopefully in the right direction).

• The decision makers making proper rules for driving the“complex interacting sub-systems” toward the desiredperformance need both theoretical framework andsimulation tools for assessing the impacts of new decisions/rules ex-ante, through simulating “in vitro” the overallenvironment.

• In complex systems both "economic" and "physical" layersare coexistent. Physical variables and constraints arerelated to physical laws that rule the world; they are"nature based" and cannot be altered. Economic variablesare "man-made", a little bit more artificial and, to a certainextent, can be decided and changed.

• Market based control is the design andimplementation of a set of regulatorystructures and price signals by “macro-players” (policy decision makers, retailers,DSO) to “micro-players” (consumers,prosumers, small generators,..) foroptimizing global system performanceaccording to a predefined set of objectivesthrough the induced behaviors of micro-players.

MARKET BASED  OPERATION & CONTROL OF EES

CONTROL SCHEME

WHAT IS COMPLEXITY

DEFINITIONS OF COMPLEXITY

“Complexity is that property of a model which makes it difficult to formulate its overall behaviour in a given language, even when given reasonably complete information about its atomic components and their inter-relations.”

-- Bruce Edmonds, Syntactic Measures of Complexity [doctoral dissertation], Manchester Univ. 1999

“Complexity: the greater the extent of inter-connections between components of a system, the more difficult it is to decompose the system without changing its behaviour.”

-- RAMAMOORTHY, CV. An analysis of graphs by connectivity considerations. Journal of the Association of Computing Machinery, 1966, 13, 211-222.

“Complexity in economics has simply meant not assuming that an economic agent acted as if it had the computational resources to completely cope with the demand placed on it by its environment.”

-- HOLM, HJ. Complexity in Economic Theory. Lund, Sweden: University of Lund: Lund Economic Studies, 1993.

“Complexity is the relations weaving the parts together that turn the system into a complex, producing emergent properties.”

--Francis Heylighen. Complexity: 5 questions, Automatic Press/vip, 2008

“The philosophy of complexity is that this is in general impossible: complex systems...has properties -- emergence properties -- that cannot be reduced to the mere properties of their parts.”

--Francis Heylighen. Complexity and self-organization. Encyclopedia of libraryand information sciences, 2008.

“Complexity can emerge in a system when the whole cannot be fully understood by analyzing its components.”

-- P. Cilliers. Complexity and post modernism: understanding complexsystems.Psychology press, 1998.

“Complexity is concerned with how the nature of a system may be characterized with reference to its constituent parts in a non-reductionist manner.”

--S.M. Manson. Simplifying complexity: a review of complexity theory.Geoforum 32, 405-414

DEFINITIONS OF COMPLEXITY

OUR UNDERSTANDING OF COMPLEXITY

“A system, that can be decomposed in a set of

elementary parts with autonomous behaviors, goals

and attitudes and an environment, is complex if its

modeling and related simulation tools cannot be

done resorting to a set of whichever type of

equations expressing the overall performance of the

system, in terms of quantitative metrics, or of a

function on the basis of state variables and other

quantitative inputs.”

COMPLEXITY IN EES

EMERGING PARADIGM OF EES

Level 1: Generation (centralised, large scale –hundreds of MW) + Transmission

Level 2: Distribution + Utilisation (small scale –down to kW)

　 Traditional paradigm: four subsystems.　Generation (centralized)　 transmission 　 distribution 　 utilisation. The first threesubsystems are devoted to assure “quality electricity” to the fourth.

　 Emerging paradigm, “generation” associated also with subsystem　as the users become “prosumer” (producer/consumers - huge numberof small-sized generators from renewable sources). Subsystem 　becomes active (capable of injecting power) with the possibility ofbidirectional power flows.

COMPLEXITY IN EES‐LEVEL 1

• The physical layer is the electrical power grid(wires, transformers, circuit breakers…) to transferelectrical power from generators to customers.

• A widespread change of the electrical network mayresult from decisions of system operators made inthe decision-making layer.

• The cyber layer acts as an interface between thedecision-making and the physical layers and viceversa.

• Electricity markets require efficiently exploitingavailable resources to supply customers, whichcauses more complex interactions within the abovelayers.

• The performance of the power system dependson a multitude of self-interested decision makers,each of them acting on a portion of the EUinterconnected power transmission grid.

COMPLEXITY IN EES‐LEVEL 1

COMPLEXITY IN EES‐LEVEL 2

COMPLEXITY IN EES‐LEVEL 2

• Distributed generations from renewable energyresources such as wind power, solar energy, fuel celland so on are drastically emerging

• Shift in the paradigm from “passive” distribution,unidirectional flow (generation - final users) to“active” distribution with bidirectional flows withactive users (prosumers).

• Emergence of bilateral power flow in Level 2 hasenormously incorporated complexity into thephysical layer.

• Initiatives have been laid upon the shoulder oftraditional passive end-users, among whomtremendous social complexity can arise as a result.

• Appropriate sets of policies and coordination rulesneed to be set for the whole social welfare (economicgrowth, security and environment sustainability).

COMPLEX ISSUES IN EES

• Multitude of self-interested individualswith different expectations and utilityfunctions that provide a distributeddecision making context with differentgoals.

• Policy makers with considerations forglobal environment, energetic problems,social expectations, economic efficiencyand security of supply, to create sets oftargets, laws, rules and instruments forachieving global goals.

COMPLEX ISSUES IN EES

• Individuals with constraints from policiesand technical possibilities, and withconsiderations of economic terms from theother parts of the system decide thebehaviour of himself in terms of powerinjection/withdrawal to get electricity asboth easy and economic as possible.

• The system operator, under constraints frompolicy makers and operational feasibility,considering gathered information andexpectations from individuals, conduct themost reliable and economical operation forthe system.

COMPLEX ISSUES IN EES

• The distributed decision making interacts withthe network structure, with physical (Kirchhoff’slaw) and operational constraints, defining its(active and reactive) flows (flow networks).

• The states of the system (feasible/unfeasible,secure/unsecure, reliable and unreliable, stableand instable, vulnerable and resilient, …) built inreal time and in medium/long term are basedon those distributed devices over its physicallayer with a set of communication/controlchannels provided by its cyber layer.

COMPLEX ISSUES IN EES

• The modelling of the system comprises themodel of each individual player (TSO, DSO,prosumer…) for the technical (power profile,ICT channel …) and economic (profit, cost),defining utilities and interactions amongthemselves and with the cyber and physicallayers. Providing study case and runningsimulation on the interactions with desiredtime frame, the global performance can bederived.

DISTRIBUTION EES AS MULTI-LAYER(COMPLEX) SYSTEMS

EES  AS MULTI‐LAYER INTERACTING CMX SYSTEMS 

• EES and performances are related to variousinteracting aspects that may be social,psychological, technical, economic andenvironmental

• EES can be schematized by three layers: social,cyber and physical.

• The layers interact among themselves andwith external inputs to determine the overallperformance of the system that can be measuredby a set of meaningful metrics (energy savings,environmental pollution, market efficiency …).

• The overall “system control” can be exertedonly in terms of policy actions, implemented bylaws and regulations (compelling, prohibiting,incentivising or de-incentivizing) to influence thebehaviour of the various players.

MULTILAYER EES WITH EXTERNAL INTERACTIONS

LAYERS IN EES: PHYSICAL

• Physical layer is the layer in which power is flowing

• In the layer, are included power grids (radial ormeshed) with (active and reactive) powerinjection/withdrawal at specific locations (nodes)which generated (real and reactive) power flowdepending on the “electrical” topology of thenetwork in terms of connections among nodes andtheir admittances.

• The grid needs to be operated under a set of strictoperational constraints (voltage profile, max lineflow limits, steady state and dynamic securityconstraints)

LAYERS IN EES: CYBER

• Cyber layer is the layer in which information fortechnical/economic operation are flowing over ICTsupports.

• The operation of the grid relays on an ICTcommunication/command/control systems thattransfer technical data for the field, in terms ofdigital and analogue information tohuman/automatic decision makers and, vice versa,provide command and control action to the field.

• The information exchange is also key in a smoothfunctioning of electricity markets for real time priceinformation (retail market) and power exchangeoperation (wholesale market)

LAYERS IN EES: SOCIAL

• Social layer is the layer in which, individually and

within a social network, people make decisions

• The decision making incorporates human and

automatic procedure to control the status of the

players and their interactions with the system at

various levels (from a national Transmission

System Operator to a single prosumer) with

reference to the physical/economic flows.

TIMEFRAMES FOR MULTILAYERS EES

• In the short term EES interact with an externalenvironment, in terms of social, economic(market) and environmental conditions, subjectto some constraints and incentives provided bythe policy/regulation.

• In the mid/long term the change in thepolicy/regulation, considering possible changesin the environment strives for an improvementof the expected performances (economicefficiency, energy sustainability, security ofsupply).

EXTERNAL INTERCATIONS TO MULTILAYER EES

ENVIRONMENT AND POLICY/REGULATION

• Environment: encompasses various aspects thatcharacterizes the external condition that affect theperformance of the power system at a given point intime. Those conditions may evolve more or less rapidlyover the time.

• Some of the most relevant are: technology (optionsavailable for production, communication…), marketissues (structure and design, billing and customerservices) social (expectations, values and attitudes),natural context (natural renewable energy flows,weather conditions...).

• Policy/regulation: basically provides “the rules of thegame” to the various decision makers in the powersystem, in terms of constraints (security levels,emergency operations, environment of pollution,…) andincentives (rewards for green energy and quality ofservice)

PERFORMANCE

• Performance: is the expected outcome of thepower system and can be measured by a set ofproper metrics related mainly to economic,energetic and environmental issues.

• Some examples of metrics are social surplus,allocation of surplus among producers andconsumers, average price, market power indices(economic), system losses, production mix,share of renewable, energy intensity, to beclean or green (energetic), level of “criteriapollutants” such as nitrogen dioxide (NOx),carbon monoxide, ozone, lead, sulphur dioxide(SOx) and particulate matter (environmental).

COMPLEX SCIENCE APPROACHES FOR EES

WHY WE NEED COMPLEX SYSTEM METHODOLOGIES

• Difficult to capture all the interactions withtraditional “closed form” models (analyticalequations).

• Traditional models are mainly focused only on onelayer or in one of its subsets.

• Need for cross-boundary analysis in which thefocus is more on the interactions (connectingvariables) among the layers than on the layeritself.

• Provide a realistic simulation of the EES and theirinteractions (internal and external) as a tool forpolicy decision making support.

• Testing and assessment “in vitro” of legislative andregulation measures ex-ante

WHAT WE CAN DO WITH COMPLEX SYSTEM METHODOLOGIES

• Capture the interactions between the prosumers and thegrid in EES, taking into account thesocial/technical/economic factors.

• The goals is to link the social behavior (psycho/economic)to the network impacts (technical).

• Simulation of complex EES with a large number ofprosumers deciding for their own loads and distributedgenerations at the node where they are connected.

• Capture prosumer behaviors under different social,technical, and economical conditions, and updating ofattitudes through interactions among themselves.

• See what system performance would be with theseprosumers’ autonomous behaviors and especially with largepenetration of distributed generations.

• Decide what regulation rules should be implemented andhow to react to prosumers behaviors and optimize systemperformance and social surplus.

COMPLEX SCIENCE AS THE KEY FOR MODELING AND POLICY  DM

MULTI-AGENT SIMULATION AND ANALYSIS OF COMPLEX EES

COMPLEXITY FO EES AND COMPLEX AGENT BASED TECHNIQUES

THE FRAMEWORK FOR THE SIMULATION

• The simulation framework is built upon astage named ESTS (Environmental SocialTechnical System);

• Various players whose actions form theprogress of the play, act on that stage.

• Directors provide rules or instructions forthe players, to direct player actions, guidingthe whole play towards their idealorientations (optimizations)

• Looking to the performance on the stage thedirectors can assess the progress of the playand provide new directions to the players

THE STAGE: ESTS – ENVIRONVENTAL SOCIAL TECHNICAL SYSTEMS

• The ESTS provide inputs to players defines thepossibility of interaction and get the impacts of theplay.

• (Natural) Environment: the weather conditions,states of natural resources, emission amounts,…

• Social: the network of people, their status ofprofession/ economy/ technique/ psychology, andinteractions among them...

• Technical: the facilities available for productionelectricity, for controlling the electric plants (smartbuilding,…), the communication facilities

• The ESTS is the stage over which the playersinteract according to the rules set by the directors.

ESTS DIRECTORS (MACRO‐PLAYERS)

• Regulator (REG – regulating agent ). Public body

in charge of issuing the rules and exerting the

control over the electricity (and more generally

energy) markets

• Policy Decision Makers (PDM – top decision

agent). They are represented by the institutions,

such as parliament, governments, ministries that

fix the general goals and decide the policies

ESTS PLAYERS

Prosumers (individual agents): persons, companies, institutions andorganization that are connected to the distribution grid (MV and LV)in at least one point and that exchange bi-directional energyaccording to given power profiles continuously.Distribution System Operators (DSO- system agent): privateorganization that operated the distribution system with the goal tokeep it feasible assuring some quality standards (continuity ofsupply, voltage distortion,…). They charge to prosumers andmarketers fees for “transporting” power/energy to/from theprosumers according to some pre-defined quality standards. Thequality standards are fixed buy the Regulator and improvement ofworsening of the standards may result in prizes or penalty to theDSO.Retailers (RET - market agents): companies whose core business isselling at the retail market of prosumer electricity. They may ownsome power capacity and purchase electricity on the wholesalemarket.Public acquiring companies: State owned companies that provideincentivized prices.

INDIVIDUAL AGENT

• Action set: constrained by available technical options and social status, that define the dimension of the action set.

• Communication with outside: it makes materially possible to get information from other agents or environment willing to share their information; the communication represent the “fresh” knowledge that can be used to pursue the objective.

• “Intelligence” different degrees of intelligence, in terms of modeling, optimal decision making and learning are possible (zero intelligence agents choose purely random).

POPULATION OF AGENTS

• Properties of a population of agents are firstly decided by the initial types and share of each type of agent.

• With time evolution, aggregated actions from a population of agents can be fed back into the ESTS, performances can then be tested under scenarios.

• Interaction among agents of the same type, can be competition and cooperation; under social, economical, technical or political contents.

• Intelligence of a population of agents results into a system level optimization, under proper regulation.

• Performance of a population of agents can be in the sense of aggregated objective, or in the sense of ESTS optimization.

PERFORMANCE EVALUATION FOR AGENTS

To Prosumers

To Distribution sys

To Transmission sys

To Regulator

- Bill savings- Continuity of service- Comfort level

- LV & MV network losses- Voltage profile- System overload- Operational costs- Security levels

- HV network losses- Voltage profile- System overload- Operational costs- Security levels

- Share of renewable generation- Energy efficiency - Market efficiency- Average prices- GHG reduction

MULTI‐LAYER MODELING

• The model is intended to capture the interactionbetween the prosumers and the MV/LVdistribution system operator and regulator,taking into account the social interaction ofprosumers, the physical specificity of thenetwork as well as regulation methods providedby operator.

SOCIAL DYNAMIC MODEL OF THE  INTERACTIONS OF PROSUMERS

[48]

Neighborhood circle: circle of prosumersconnected at the same bus of the network

Social circle: circle of prosumersinterrelated by some social links (workplace, clubs, churches,…)

INDIVIDUAL BEHAVIOR AND SOCIAL DYNAMICS OF PROSUMERS

[ 3D view with both Physical and Social layers ]

[ Physical ]Distribution system layer

[ social ]Prosumer layer

[ prosumerslinking with their nodes ]

PROSUMERS WITH DG IN DS SIMULATION RUNNING

[ Social Layer ] [ Social Dynamic and Convergence ]

[ Physical Layer ] [ Unbalanced generation and consumption ]

/ /

/ /

A SIMPLE EXAMPLE OF MARKET BASED CONTROL OF A MULTILAYER DISTRIBUTION

EES

• Community of prosumers with individual andsocial behavior

• Regulator that provides to the DSO differentoptions in terms of network charges on thedistribution network

• DSO is the one exerting the market basedcontrol in terms of selecting the net chargeschemes

• Objective of the control: optimize globaldistribution network “technical” performance interms of network losses, voltage profile and linepower flows.

A SIMPLE EXAMPLE: BASIC ASSUMPTIONS

• Uniform Pricing (UP): providing commonnetwork charging prices all over the network,computed from averaged network cost.

• Nodal Pricing (NP): providing network chargingprices to each individual node, computed fromnetwork cost (cost caused in branches)introduced by power withdraw or injection onthe node.

• Responsive Market Pricing (RMP): providingnetwork charging prices to prosumersaccording to market-based control.

DSO NET CHARGING  METHODS

• Technical performance: network losses,voltage deviation from rated values, lineoverload

• Economic performance: average prosumes'utility, DSO revenue

SYSTEM PERFORMANCE

PROSUMER ‘S INDIVIDUAL/SOCIAL  CHARACTERIZATION

• Individual/social “physiological” characterization:• 1) attitude toward economic

benefit μ, in terms of avoidingcost from consumption ormaximizing earnings frompower injection (economicdimension);

• 2) attitude toward comfort φ,in terms of desire or willingnessto use appliances and devices tosatisfy its leaving standards(physiological/social dimension).

PROSUMER POWER CHARACTERIZATION

• Power interactions with the network (at a given time point)

• power withdrawn (En>0) or injection (En<0) as:

• En = dn - gn = Dn [ 1-n µn (1-n) ] - Gn [ 1-n µn (1-n) ]

•• Individual utility:

Un = [Bn , Cn]

• Economic benefit (n = -n )

Bn = -n.En = (Dn + Gn ) µn (1-n)n2- (Dn + Gn ) n

• Comfort attitude

Cn = n (1-µn)

(n , n ) selling buying price for prosumer n

PROSUMERS’ SOCIAL INTERACTIONS AND POWER WITHDRAWAL/INJECTION

• The update is based on the evaluation and comparison of theirutilities with those from the other prosumers:∆Un = µn ∆Bn + n ∆Cn

• In the next time step, prosumer n will decide their powerinjection/ withdrawn En according to µn

t+1and nt+1with a pair

of updated price [nt+1,n

t+1] :En

t+1=Dn [1-nt+1 µn

t+1 (1-nt+1)]- Gn [1-n

t+1 µnt+1 (1-

nt+1)]

• prosumers continuously update theirattitudes through their socialinteractions with other prosumers

• the overall set of prosumers’ attitudeis dynamically changing over thetime.

DSO PREDICTION OF THE PROSUMERS’ BEHAVIOR

• Prosumers’ attitude (µ'n ,'n) can be estimated bythe DSO, at a given time step, according to theprice and power at the previous time step

En = Dn [ 1-n µ'n (1-'n) ] - Gn [ 1-n µ'n (1-'n) ]

µ'n (1-'n)

B'n = (Dn + Gn ) µ'n (1-'n)n2- (Dn + Gn ) n

• The estimated attitudes of prosumers, allow forcomputing, at given prices [ , ] utilities andpower injection/ withdraw

RESPONSIVE MARKET PRICE DETERMINATION BY DSO

• Prices for eachprosumer (n, n) arethe decision variables

• The DSO decides theprices for eachprosumer trying toalign its objectives withthe estimated utilitiesof the prosumers underconstraints of thedistribution network

max,

, ∗ ∗

s.t.∑ 1fi = 0

Vimin≤ Vi ≤ Vi

max

│Sl│≤ Slmax

TECHNICAL PERFORMANCE: NETWORK LOSSES

• RMP gets the best performance in terms of least network losses. • At the same time, other network performances such as voltage

profile and line currents can also be assured under RMP, with a standard deviation.

• DSO revenues were compared with the increasing value of prices

for network charge (both nodal pricing and MBC pricing charge

differently among buses, here only compares averaged values). But the

prosumer profit were not affected by RMP’s higher charging level

ECONOMIC  PERFORMANCE: DSO REVENUE

• The RMP is beneficial to the DSO and at the same time to the

prosumers; their economic benefits grow both along the

timeframe

ECONOMIC  PERFORMANCE: PROSUMER BENEFIT

RETAILER PRICING

time-of-use

SOCIAL CONVERGENCE

clustering

DSO CHARGING

uniformcharging

nodal charging

RMP

ROBUSTNESS OF MARKET BASED CONTROL W.R.T RETAIL PRICE

ROBUSTNESS OF MARKET BASED CONTROL W.R.T RETAIL PRICE

ROBUSTNESS OF MARKET BASED CONTROL W.R.T RETAIL PRICE

• The change in retail price does not affect the technical

performance of the network (losses, voltages and overflows). For

economic performance, it does affect the DSO revenues; while

the prosumers' economic benefit and comfort are not harmed.

ROBUSTNESS OF MARKET BASED CONTROL W.R.T RETAIL PRICE

• Different society may present different social dynamic behavior in termof the convergence of the prosumer to a small number of typicalbehaviors (prosumers’ clustering) in terms of accepted level ofeconomic benefit and comfort.

• Putting together the two aspects (retailer and prosumers decisions) theoutcomes of the RMP do not change considerably from the resultspresented.

ROBUSTNESS OF MARKET BASED CONTROL W.R.T SOCIAL CLUSTERING

• In emerging electricity distribution systems the overallsystem performance is related to the interplay ofmacro-players (Regulator, DSOs, Retailers) and micro-players such as prosumers with different global orindividual goals and utilities.

• The global utilities pursued by the macro-player interms of environmental control, energetic efficiency ortechnical feasibility of the network can be pursuedproviding proper price signals to the micro-players anddevicing strategies that would align the global goalswith the individual utilities.

CONCLUDING REMARKS

• To choose a proper regulatory strategy,comprehensive models of emerging distributionsystems able to incorporate both social and technicallayers are needed and can be used to test ex-ante thestrategies

• MBC suggested collectively considering both electricand social behaviors; managing self-interesteddistributed participants; simultaneously optimizingperformance in multiple aspects, network and marketaspects seems a promising way to go toward.

CONCLUDING REMARKS