tJan Willem Eising , Tom van Onna , Floortje Alkemade

bUtrecht University, Copernicus Institute of Sustainable Development, Heidelberglaan 2, 3584 CS Utrecht, The Netherlands

h i g h l i g h t s

rationssociatevel usiimprov

ation method that assesses electricity demand and supply on the neighbour-

1. Introduction

electric vehicles is combined with the deployment of renewableand other low-carbon energy technologies such as solar panelsand wind turbines [1,2]. This low-carbon transition requires an

integration of the energy and transport supply chains. In the newfrom a one-way(smart) grpply andell as a

option.Such smart grid solutions are also necessary to ensu

functioning when a large number of electric vehicles will be con-nected to the existing grid. While several earlier studies assumethat the diffusion of electric vehicles will be slow enough for capac-ity planning to respond adequately, and that vehicle charging willcoincide with the supply of energy from renewable sources or takeplace during off-peak hours [3,4], the diffusion of EV is outpacingthe implementation of smart grid technology in many countriesand concerns about grid functioning are increasing [58]. For

Corresponding author. Tel.: +31 629454179.

Applied Energy 123 (2014) 448455

Contents lists availab

Applied

journal homepage: www.elseE-mail address: janwillem.eising@alliander.com (J.W. Eising).The electrication of transport is widely considered a promisingoption for the transition to a low carbon energy and transport sys-tem. The envisioned benets are largest when the deployment of

integrated supply chain the energy grid changeshierarchical grid to a two-way, more interactivedistributed renewable energy as a source of suvehicles as a (exible) source of demand as w0306-2619 2014 The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license.http://dx.doi.org/10.1016/j.apenergy.2013.12.017id withelectricstorage

re gridSmart gridsEnergy managementEnergy transitionEnergy securityDistribution networks

hood level. The paper combines local level electric vehicle diffusion forecasts, with neighbourhood leveldata about the grid additional capacity. Application of the model to the Netherlands shows that risks forgrid functioning already appear as early as 2015. More specically, the diffusion of electric vehicles isfound to compromise the functioning of the grid on the short term in densely populated areas such asAmsterdam. In these neighbourhoods early and fast adoption of electric vehicles coincides with the pres-ence of an older grid with less additional capacity. The model provides insights for grid operators as wellas for policy makers that seek to stimulate the transition to sustainable energy and transport systems,and can be used as a strategic tool to plan (smart) grid investments.

2014 The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license.Keywords:Electric vehicles

present a GIS-based simul Grid capacity problems due to EV integ A GIS-based simulation assesses risks a Risks are assessed on neighbourhood l Grid operators can use our method to

a r t i c l e i n f o

Article history:Received 27 June 2013Received in revised form 28 November 2013Accepted 16 December 2013Available online 10 January 2014expected in 2015 in urban areas.ed with large scale EV integration.ng GIS data.e investment planning.

a b s t r a c t

This paper identies the risks for the functionality and reliability of the grid that arise from the integra-tion of the transport and supply chain. The electrication of transport is a promising option for the tran-sition to a low carbon energy and transport system. But on the short term, the electrication of transportalso creates risks. More specically, when promising technological such as vehicle-to-grid and smart-grids are not yet available on a large scale, the rapid diffusion of electric vehicles and the rechargingbehaviour of consumers can create risks for grid functioning. In order to assess these risks, this papera Liander N.V., Utrechtseweg 68, 6812 AH Arnhem, The NetherlandsTowards smart grids: Identifying the risksof energy and transport supply chains

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example in the Netherlands, the diffusion of electric vehicles israpidly growing,1 supported by ambitious policy scenarios [9,10]while recharging data shows that the early adopters mostly rechargetheir electric vehicles during times of existing peak demand.2 More

J.W. Eising et al. / Applie Eneresearch is needed on the possibilities to inuence the rechargingbehaviour of electric vehicle adopters, but on the short term the ef-fect of electric vehicle diffusion could thus be a substantial increasein peak demand. On the middle- and long-term, smart grid solutionsare a promising option to mitigate these effects. However, grid fail-ures in the early days of electric vehicle diffusion might also nega-tively affect user acceptance and thereby the diffusion of thesetechnologies on the long term [11]. Identifying the risks that arisefrom the integration of the energy and transport system is necessaryin order to plan the location and timing of grid investments and theimplementation of smart grid technologies and thereby ensuringgrid reliability on the long term.

Current country-level scenarios for the diffusion of electricvehicles may underestimate these risks for grid reliability as theydo not take into account local heterogeneity. More specically,the local diffusion of electric vehicles is characterised by large dif-ferences between geographical areas and this heterogeneity on thelocal level is expected to increase in the future [1214]. The ex-pected additional energy demand from electric vehicles as pre-dicted in the national scenarios is thus unevenly distributed,with areas of very high as well as areas with low increases in addi-tional energy demand. This heterogeneity could amplify the risksfor the functioning and reliability of the grid. Moreover, the load-availability of the grid is also characterised by an unbalanced geo-graphical distribution. As a consequence, the risks for grid func-tioning and reliability are expected to be higher and moreimminent when neighbourhoods with lower grid additional capac-ity are facing a faster local diffusion of electric vehicles than the na-tional-scale scenarios [6].

In order to assess these risks, the impact of the diffusion of elec-tric vehicles needs to be assessed on a smaller scale than in the cur-rent studies [3,7,14]. This paper therefore combines local leveldiffusion forecasts (estimated from current local diffusion andneighbourhood characteristics), with local level GIS data aboutthe grid additional capacity in order to identify the energy securityrisks that arise during the transition of the transport system. Wethereby extend earlier studies of the local diffusion of electric vehi-cles [15] by also taking into account supply side characteristics,and earlier stylized simulation models [1620] by integrating GISbased data in our simulation. We present our method using a un-ique dataset provided by Dutch grid operator Liander. Liander isthe largest grid operator of the Netherlands, supplying electricityto 37% of the households in the Netherlands through over 40,000medium voltage/low voltage transformer (MV/LV) substations.We base our simulation on current data about EV diffusion and en-ergy demand, for two reasons. First, this conservative scenariogives us an accurate description of short term risks. Second, theoutcomes of the simulations will provide guidance to the relativeadvantages of the local implementation of smart grid technologiesand infrastructure investments, and to the relative disadvantagesof local diffusion of fast-charging stations. In summary, the pro-posed method will help grid operators to better plan the locationand timing of (smart) investments in order to mitigate the risksassociated with the diffusion of electric vehicles.

The remainder of the paper is organised as follows. Section 2

1 NL Agency [Internet]. Cijfers Elektrisch Vervoer. Ministry of Economic Affairs.[update 2013 May 14; cited 2013 June 10] available from: . Dutch.2 E-laad [Internet]. Opladen elektrische autos zorgt voor piekbelastingen [update2013 May 16; cited 2013 June 10] Available from: . Dutch.,

d3. Data

3.1. Infrastructure

Electric vehicles are charged using charging stations. In the2. The diffusion of infrastructure dependent technologies

Innovation diffusion processes often follow an S-shaped patternas described by Fisher and Pry (FP) [21]. The FP model predicts thediffusion of a technology using the following model:

f 1=21 tanhat th 1where a is half the annual fractional growth in the early years of dif-fusion, th is the time at which the diffusion has reached 50% and f isthe fraction of the potential market that is substituted by the newtechnology. Grbler [22] has applied the FP model to the diffusionof infrastructure dependent technologies, such as vehicles. Usingempirical data, he showed that the diffusion of a successful newtechnology and its infrastructure are interdependent and follow asimilar S-shaped curve of diffusion, but that the diffusion of infra-structure often precedes the of the technology. Insights in the diffu-sion patterns of EV may thus also provide insights regarding thedesired patterns of (smart grid) infrastructure investments.

The cumulative adoption curve as modelled by FP is the out-come of the aggregation of the individual adoption decisions ofconsumers. As consumers are heterogeneous, the exact shape ofthe curve may vary considerably according to geographical area.In his work on individual adoption processes Rogers [23] has iden-tied ve consecutive adopter categories, ranked by innovative-ness: innovators, early adopters, early majority, late majority andlaggards. He thereby models innovation as a social process whereearlier adopters inuence later adopters. Rogers has empiricallyidentied the following group sizes for the different adopter cate-gories: 2.5% innovators, 13.5% early adopters, 34% early majority,34% late majority and 16% laggards. Generally the innovators andthe early adopters, the rst consumers that adopt a technology,are characterised by a higher income and a higher social status[23]. This was also found to be true for the early adopters of electricvehicles [15].

One of the mechanisms through which early adopters of aninnovation inuence the adoption decision of later adopters isthrough the increased observability of the innovation [23]; con-sumers are more likely to adopt a technology they can actually ob-serve in their environment. Spatial remoteness betweenconsumers, as in sparsely populated rural areas, decreases theobservability of a technology and thereby negatively inuencesthe adoption decision of a consumer [24]. Urbanisation, in contrast,has a positive inuence on the adoption decision of a consumer, aswas also observed for the diffusion of plug-in electric vehicles [15].An additional factor is that for clean fuel cars in general, and forelectric vehicles in particular, adoption is more likely if the house-hold already owns another vehicle [25,26]. In addition, Schuitemaet al. [25] suggest that the refuelling range of a vehicle, a consider-able disadvantage of current electric vehicles, is of less importantwhen buying a second car compared to a rst car.provides a brief introduction to the diffusion of infrastructuredependent technologies. Sections 3 and 4 presents the data andthe methods used. Section 5 presents the results of the simulation.Section 6 concludes and discusses the presented results andmethod.

rgy 123 (2014) 448455 449Netherlands the early diffusion of electric vehicles was stimulatedas initially most models were sold including a complementaryprivate charging station or a free public charging station. These

registration date, vehicle tyThese models share that they have pe,and postcode (4-digit level) of the vehicle owner.

r us

450 J.W. Eising et al. / Applied Enecharging stations are usually located in residential areas, near thehome of the vehicles owner. The charging stations use the existinggrid for their energy supply. The electricity grid is thus a new com-ponent in the transport supply chain although it is already animportant and well developed component in the electricity supplychain. Compared to other countries, the Dutch grid is one of thebest performing grids, as indicated by the low number of interrup-tions per year per customer [27]. Although the overall quality ofthe grid is excellent, the additional capacity of the grid varies con-siderably between MV/LV substations.

In order to identify to what extent current local grids can sup-port the expected local diffusion of electric vehicles, and the corre-sponding additional demand of electricity, we identify thecurrently available local additional capacity of the grid. This paperuses grid capacity data provided by Liander N.V., the largest distri-bution service operator in the Netherlands. For each MV/LV substa-tion, our dataset gives the 2012 values for the peak load in kVA,this is the highest peak demand of energy, the nominal capacityin kVA (this is the installed capacity), and the geographical loca-tion. The additional capacity depends on the nominal capacity ofthe MV/LV substation and the registered peak load. The peak de-mand and the installed capacity differ per MV/LV substation andtherefore the additional capacity differs also per MV/LV substation.The peak demand is kept constant for the duration of the simula-tion. This is in line with the trend for the Netherlands over the past

Fig. 1. S-shaped diffusion scenarios for electric vehicles in the service area of Liandetargets (FP estimate for a is 0.2 and th is 2027).six years [28].The additional capacity uctuates over time. Unfortunately, the

current peak demand of electric vehicle charging stations coincideswith current overall peak demand. This is because electric vehicleowners are mostly charging their vehicles when they arrive athome at the end of the day.3 Therefore this paper will assess the im-pact of the electric vehicle during the current peak of energydemand.

3.2. Technology

Two main types of electric vehicles can be distinguished: plug-in hybrid electric vehicles and full electric vehicles, and currentlymore than 20 different models are available on the market. Thesemodels share that they have a battery storage system of 4 kWh ormore, a means of recharging the battery from an external source,and the ability to drive at least 10 miles in all electric mode [29, p.544]. The electric vehicle eet has been rapidly growing from

3 E-laad [Internet]. Opladen elektrische autos zorgt voor piekbelastingen [update2013 May 16; cited 2013 June 10] Available from: . Dutch.The rapid growth gures create positive expectations about thefurther diffusion of electric vehicles and are in line with Dutch pol-icy goals. Current policy targets are set at 20,000 electric vehiclesin 2015, 200,000 in 2020 and 1,000,000 in 2025 [9]. This paper usesthree scenarios for future EV diffusion: high growth, low growthand medium growth, Fig. 1 shows the corresponding FP curves.The scenarios are adapted to the size of the service area of Liander[9,31]. The medium growth scenario is in line with Dutch policytargets (FP estimate for a is 0.2 and th is 2027). The high and lowgrowth scenarios use the same parameter values but differ with re-spect to the nal diffusion of electric vehicles.

3.3. Neighbourhood level of adoption determinants

In order to analyse the local consumer characteristics that inu-ence electric vehicle adoption, we use neighbourhood level data onincome, social status, remoteness (urbanisation), second car own-1063 vehicles in 2011 to 6244 vehicles in 2012. In this researchwe use a dataset provided by the Dutch vehicle-licensing agency(RDW), which includes all electric vehicles and plug-in hybridvehicles registered in the Netherlands up to and including Decem-ber 2012 [30]. For each vehicle, the dataset gives information on:

ing the FisherPry model. The medium growth scenario is in line with Dutch policy

rgy 123 (2014) 448455ership and observability of electric vehicles provided by StatisticsNetherlands and the Netherlands Institute for Social Research[3234]. The number of electric vehicles in each neighbourhoodin 2011 will be used as an indicator for the local observability ofelectric vehicles (RDW [30]). Using the 4-digit postcode classica-tion, the Netherlands is divided into 4103 neighbourhoods, 1596 ofwhich lie within the Liander service area. On average, a neighbour-hood consists of 1830 households and 4095 inhabitants.

Table 1 shows the inuence of the different determinants dis-cussed in Section 3.2 on electric vehicle diffusion using a multiplestepwise-regression model. In this model the car eet has the high-est explanatory effect as shown by the R2 variable value of 0.216,this corresponds for the neighbourhoods with a coefcient of0.000290. The level of electric vehicles can thus be best explainedby the amount of vehicles registered in that neighbourhood. Theother determinants have considerably lower R-square values butcombined they account for almost 24% of the overall explanatoryeffect of this regression model (Table 1). The positive values ofthe standardised coefcients in Table 1 show that all signicantand substantial determinants have a positive effect on adoption.These coefcients are in Section 4.2 used to determine a normaldistribution of the different neighbourhoods. The determinant so-

cial status does not have signicant and substantial effect on theadoption of electric vehicles and is therefore excluded from oursimulations. These determinants are incorporated in the model de-scribed in the next section.

4. Methodology

Fig. 2 presents an overview of the methodology. In order to as-sess the risks for the functioning and reliability of the electricitygrid through the large-scale adoption of electric vehicles we rstcalculate the additional capacity per neighbourhood (Section 4.1)and then simulate the expected local demand (Section 4.2)

Table 1Stepwise regression model illustrating the relation between household and neighbourhoo

Model Independent variables R2 Total R2 Variable

1 Car eet 0.216 0.2162 Observability 0.249 0.0333 Income 0.265 0.0164 Second car 0.269 0.0045 Urbanisation 0.283 0.0146 Social status

Dependent variable: electric vehicles per neighbourhood (postcode 4-level) sold in 2012N: 3378.

J.W. Eising et al. / Applied Ene4.1. Supply side

We calculate the additional capacity per MV/LV substationusing data on the current peak-load (Pmax) and nominal capacity(Pn) per MV/LV substation. Currently, electric vehicles are chargedusing single-phase charging stations with a maximum capacity of4 kVA (PEV). As not every electric vehicle is charged at the exactsame time, it is common in grid planning to use a coincidence fac-tor. The grid operators in the Netherlands are currently using acoincidence factor (g) of 0.4 for electric vehicles indicating that atFig. 2. Overview of the methodology, which comprises three main steps: calculat-ing the additional capacity per neighbourhood, simulation of expected localdemand and a comparison of the results to classify the risks.most 2 out of 5 charging stations are used simultaneously. Thisleads to the following equation for the carrying capacity of a sub-station (#EVsub):

#EV sub Pn Pmax=g PEV 2Using the geographical locations of the MV/LV substations we

then aggregate the substation capacities to calculate the carryingcapacity per neighbourhood.

4.2. GIS-based simulation model

Fisher and Pry (1972) explain that the diffusion of technologiesfollows an S-curve pattern, where the diffusion is determined by thand a in Eq. (1). This equation will also be used to determine localdiffusion S-curves of electric vehicles. The average size of postcodeareas makes it possible to apply the S-curve methodology at the lo-cal level. The F&P model is a model for fractional growth; we willuse the following equation to calculate the absolute growth perneighbourhood:

Diffusiont 1=21 tanhat th local adoptionmax 3The next step is to determine the parameters th, a and local

adoptionmax for every neighbourhood. Our dataset also providesthe nation number of internal combustion vehicles (national careet) and the vehicles that are registered in a neighbourhood (localcar eet). This number and the maximum national adoption (totaladoptionmax), which for the three scenarios can be derived fromFig. 1, are used to determine the maximum local adoption (localadoptionmax) for each neighbourhood:

Local adoptionmax total adoptionmax=national car fleet local car fleet 4

The year at which the diffusion has reached 50% (th) depends onthe start time (t0) of the diffusion and the duration (T) of the diffu-sion process:

th t0 0:5T 5Following the scenarios depicted in Fig. 1 and the Dutch policy

d characteristics and electric vehicle diffusion.

% of Total F-value Coefcients Signicance

76.33 932.109 0.000290 0.00011.66 560.136 0.510625 0.0005.65 405.886 0.022952 0.0001.41 310.964 2.642072 0.0004.95 266.422 0.000165 0.000

0.671

.

rgy 123 (2014) 448455 451goals we set T at 37 years (until 2050) This is in line with researchon transitions that shows that transitions often take decades tocomplete [22]. The start time for the diffusion process in eachneighbourhood is derived from the dataset as follows: Neighbour-hoods without any electric vehicles are randomly assigned a starttime in the next 3 years (e.g., 2013, 2014, or 2015). As explainedin Section 2, the local diffusion of electric vehicles is inuencedby the observability of a technology, but this observability doesof course not stop at the border of the neighbourhood. Thereforethe starting time of neighbourhoods without electric vehicles isalso inuenced by the situation in adjacent neighbourhoods. Whenadjacent neighbourhoods have already adopted an electric vehicle,this positively inuences the model t0 of the neighbourhoods with-out electric vehicles by reducing the randomly assigned startingtime. Thereby adjacent neighbourhoods, which have adopted elec-

Table 2Adoption threshold and corresponding a values per adopter category.

Adopter category (% of total) Adoption threshold score a

Innovator (2.5%) ATP 2.83 0.10Early-adopter (13.5%) 2.83 > ATP 2.44 0.15Early-majority (34%) 2.44 > ATP 2.25 0.175Late-majority (34%) 2.25 > ATP 2.06 0.225Laggards (16%) 2.06 > ATP 0.85 0.25

Fig. 3. Overview of the risks for the functioning of the electricity grid that arise from the diffusion of electric vehicles for different scenarios.

Table 3Overview of main parameters used in the simulation model.

Parameter Value (s)

t0 20082015T 37 yearsa 0.10.25Total adoptionmax 0.5, 1.1 and 1.8 millionTotal careet 3.2 million

452 J.W. Eising et al. / Applied Energy 123 (2014) 448455

tric vehicles already in 2008, will reduce this starting time by2 years and adjacent neighbourhoods, which have adopted the rstelectric vehicles in 2009, will reduce this by 1 year.

The next step is to determine the value of a, which determinesthe slope of the S-curve, for each neighbourhood. This is done withthe help of adoption thresholds. The determinants of the adoptionof electric vehicles by consumers as identied in Table 1 differ perneighbourhood and the local values will be used to determine the afor each neighbourhood. The coefcients of the variables income(I), second car (SC) and urbanisation (U) (Table 1) were used to cre-ate a positive adoption threshold (AT) for every neighbourhoodusing the following equation:

AT 0:022952 I 2:642072 SC 0:000165 U 6The resulting normal distribution of individual thresholds is

used to divide the neighbourhoods into the ve adopter-categoriesas proposed by Rogers [23]. These categories are then used to as-sign the neighbourhoods different a values with an overall averageof 0.2, corresponding to the a of the national level scenario (Ta-ble 2). The value for a is thus lower in neighbourhoods which arecharacterised by a higher income and/or more second cars and/ora higher degree of urbanisation. As a consequence, the S-curve issteeper for these neighbourhoods.

Eq. (3)and the corresponding values of the neighbourhoods areinput for the GIS-based simulation model, and are used to forecastthe local demand resulting from electric vehicle adoption. Thesimulation model was run for 37 time steps, corresponding to

1 year, and 20 runs of the experiments were performed for eachscenario. Table 3 gives an overview of the main parameters usedin the simulation model for the service area of Liander.

5. Results

This section compares the outcome of the simulation modelwith local grid capacity, in order to assess the risks for the electric-ity grid. The section starts with an overview of the entire servicearea of Liander (approximately 37% of households in the Nether-lands). In order to illustrate the regional risks for the supply chainwe also present an overview of the impact on the province ofNorth-Holland; one of the largest and most densely populatedprovinces with 2.7 million inhabitants, including the capital cityof the Netherlands, Amsterdam. Section 5.3 further zooms in onthe local risks for the supply chain and presents results for themunicipality of Amsterdam.

5.1. The Netherlands

Fig. 3 and Table 4 present the overall results of our analysis. Theadditional capacity of the MV/LV stations of Liander is approxi-mately the equivalent of 1.77 million electric vehicles. Under theassumption of a homogeneous geographical distribution of thesevehicles as used in the country level scenarios this number wouldnot be reached until 2036 in the high growth scenario. Our local

Table 4Summary of simulation outcomes for the Netherlands. In some neighbourhoods problems for the grid due to electric vehicle diffusion may already arise on the short term.

Time High Scenario Medium Scenario Low Scenario

# Of overloadedpostcode areas

% Of overloadedpostcode areas

# Of overloadedpostcode areas

% Of overloadedpostcode areas

# Of overloadedpostcode areas

% Of overloadedpostcode areas

2015 7 0.4 5 0.3 2 0.1

s fo

ning

% Of overloaded # Of overloaded % Of overloaded

J.W. Eising et al. / Applied Energy 123 (2014) 448455 4532020 34 2.1 192025 172 10.8 942030 412 26.5 274

Total number of postcodes areas: 1596.

Table 5Summary of simulation outcomes for North-Holland. In some neighbourhoods problem

Time High Scenario Medium Scenario

# Of overloadedpostcode areas

% Of overloadedpostcode areas

# Of overloadedpostcode areas

2015 5 1.1 42020 26 5.6 122025 145 31.0 772030 278 59.4 219

Total number of postcodes areas: 468.

Table 6Summary of simulation outcomes for Amsterdam. The short term risks for the functio

Time High Scenario Medium Scenario

# Of overloadedpostcode areas

% Of overloadedpostcode areas

# Of overloadedpostcode areas

2015 5 6.2 42020 14 17.3 82025 31 38.3 23

2030 40 49.4 40

Total number of postcodes areas: 81.postcode areas postcode areas postcode areas

4.9 2 2.59.9 5 6.228.4 18 23.3Low Scenario

% Of overloadedpostcode areas

# Of overloadedpostcode areas

% Of overloadedpostcode areas

0.9 2 0.42.6 5 1.1

16.5 40 8.546.8 119 25.4

of the grid due to electric vehicle diffusion are highest in this area.

Low Scenario1.2 8 0.55.9 53 3.3

17.2 144 9.0

r the grid due to electric vehicle diffusion may already arise on the short term.49.4 29 35.8

half are located in North Holland. This can be explained by the rel-

Eneatively high population density combined with an older grid archi-tecture. Technologies such as smart-grids and vehicle-to-gridsolutions that mitigate these risks are currently seen as a viable op-tion for the middle to long term and the simulation outcomes for2030 underline the need for these measures. The grid capacityproblems may be aggravated if fast-charging takes off and con-sumer charging behaviour remain unchanged.

5.3. Amsterdam

Fig. 3 and Table 6 present the simulation results for the city ofAmsterdam. The additional capacity of the MV/LV stations of Lian-der in Amsterdam is approximately the equivalent of 15,000 elec-tric vehicles divided over 81 postcode areas. This is a relatively lowcapacity for a municipality with more than 800,000 inhabitants.Amsterdam gives a good insight when on the short term an aboveaverage local diffusion scenario is combined with an older gridarchitecture. Therefore the postcode areas where substations areoverloaded in 2015 are mainly situated in the municipality ofAmsterdam. The showed percentages on the short term are there-fore also substantially higher in comparison with the percentagesof the entire service area and the province.

6. Discussion and conclusions

In this paper we presented a model to identify the risks for thefunctioning and reliability of the electricity grid that arise from theintegration of the transport and energy supply chains. More specif-ically, we introduced a GIS-based simulation model and combinedthis with an analysis of the reliability of the existing electricity dis-tribution network. The paper thereby translates national level dif-fusion scenarios to the neighbourhood level. The simulations showthe importance of this new approach as on this neighbourhood le-vel problems for grid reliability may already arise as early as 2015.More specically, in densely populated areas such as the city ofAmsterdam the additional demand from electric vehicles may al-ready exceed the capacity of the local electricity grid on the veryshort term, i.e., as early as 2015. This is explained by the fact thatin these neighbourhoods early and fast adoption of electric vehiclescoincides with the presence of an older grid with less capacity.

Our simulations use a conservative set of assumptions, keeping,technological development of the electric vehicle, charging infra-level results presented in Table 4, however, indicate that problemscan already be expected as early as 2015, even when following alow growth scenario. The results also indicate that in most post-code areas the quality of the grid is sufcient to support the diffu-sion of electric vehicles, but Fig. 3 shows that there are clearregional differences between North-Holland and other parts ofthe Netherlands.

5.2. North-Holland

Fig. 3 and Table 5 show the overall results for North-Holland.The additional capacity of the MV/LV stations of Liander inNorth-Holland is approximately the equivalent of 220,000 electricvehicles divided over 468 postcode areas. For a province whichhouses 16.3% of the 16.7 million Dutch citizens, this is a relativelylow capacity. In line with the result for the entire service area, Ta-ble 5 shows that capacity problems occur as early as 2015 in allscenarios. In 2020 the capacity problems rapidly growing and ofthe overloaded postcode areas in the entire service area, more than

454 J.W. Eising et al. / Appliedstructure and electricity infrastructure at the current level. As aconsequence, our model outcomes are most relevant for the shortterm, and the long term outcomes should be considered as indica-tors for the local priorities of grid investments. Several on-goingdevelopments can either alleviate or aggravate the problems withgrid functioning on the long term: On the one hand, advances insmart grid technology, investments in the electricity infrastruc-ture, vehicle to grid technology, and incentives targeting consumerbehaviour in smart grids can signicantly reduce the risks for gridfunctioning [7,8,35]. Such measures may increase grid capacity di-rectly, or indirectly by reducing peak demand and stimulatingcharging during off-peak periods. A challenge hereby is to createthe (price) incentives for consumers in such a way that these willnot reduce user acceptance of electric vehicles and thereby slowdown diffusion.

On the other hand, advances in charging technology and the dif-fusion of other energy -intensive technologies such as heat pumpsmight aggravate problems for grid functioning. The current stan-dard for the charging of electric vehicles is 1-phase charging witha maximum load capacity of 4 kVA. Consumers consider the longcharging time of electric vehicles as a disadvantage and the expec-tation is that charging technology will increasingly use a 3-phasesystem with a maximum load capacity of 12 kVA. Without changesin consumer behaviour this could increase the possible risksconsiderably. This transformation of the charging infrastructureis currently on-going whereas risk mitigating technologies arenot yet available on the market.

The simulation model provides useful insights for gridoperators who are responsible for grid functioning and reliabilityand allows them to strategically direct investments of commontechnological solutions. As the Dutch grid internationally standsout for its relatively low level of service disruptions, the proposedmethod might also be particularly valuable for countries with alower grid quality. If such countries would experience a rapid dif-fusion of electric vehicles the risks could be more imminent than inthe case of the Netherlands.

References

[1] Lindfeldt EG, Saxe M, Magnusson M, Mohseni F. Strategies for a road transportsystem based on renewable resources the case of an import-independentSweden in 2025. Appl Energy 2010;87:183645.

[2] Liu W, Hu W, Lund H, Chen Z. Electric vehicles and large-scale integration ofwind power the case of Inner Mongolia in China. Appl Energy2013;104:44556.

[3] Rahman S, Shrestha GB. An investigation into the impact of electric vehicleload on the electric utility distribution system. Power Delivery IEEE Trans1993;8(2):5917.

[4] Webster R. Can the electricity distribution network cope with an inux ofelectric vehicles? J Power Sources 1999;80:21725.

[5] Brouwer AS, Kuramochi T, van den Broek M, Faaij A. Fullling the electricitydemand of electric vehicles in the long term future: an evaluation ofcentralized and decentralized power supply systems. Appl Energy2013;107:3351 [ISSN 0306-2619].

[6] Mu Y, Wu J, Jenkins N, Jia H, Wang C. A spatialtemporal model for grid impactanalysis of plug-in electric vehicles. Appl Energy 2014;114:45665 [ISSN0306-2619].

[7] Clement-Nyns K, Haesen E, Driesen J. The impact of vehicle-to-grid on thedistribution grid. Elect Power Syst Res 2011;81(1):18592.

[8] Rmer B, Reichhart P, Kranz J, Picot A. The role of smart metering anddecentralized electricity storage for smart grids: the importance of positiveexternalities. Energy Policy 2012;50:48695.

[9] Hoen A, Koetse MJ. Rijden op elektriciteit, waterstof of biobrandstoffen, watwil de automobilist?, Den Haag: Planbureau voor de Leefomgeving; 2012. p. 8.World Wide Web: [Dutch].

[10] International Energy Agency. Technology Roadmap; Electric and plug-inhybrid electric vehicles, update; June 2011. World Wide Web: .

[11] Budde F, Weber M, Alkemade F. Expectations as a key to understanding actorstrategies in the eld of fuel cell and hydrogen vehicles. Technol ForecastingSoc Change 2012;79(6):107283.

[12] Higgins A, Paevere P, Gardner J, Quezada G. Combining choice modelling andmulti-criteria analysis for technology diffusion: an application to the uptake ofelectric vehicles. Technol Forecasting Soc Change 2012;79:1399412.

rgy 123 (2014) 448455[13] Zubaryeva A, Thiel C, Barbone E, Mercier A. Assessing factors for theidentication of potential lead markets for electried vehicles in Europe:expert opinion elicitation. Technol Forecasting Soc Change 2012;79:162237.

[14] Hadley SW. Impact of plug-in hybrid vehicles on the electric grid. Technicalreport ORNL/TM-2006/554. TN: Oak Ridge National Laboratory; 2006.

[15] Saarenp J, Kolehmainen M, Niska H. Geodemographic analysis andestimation of early plug-in hybrid electric vehicle adoption. Appl Energy2013;107:45664.

[16] van der Vooren A, Alkemade F. Managing the diffusion of low emissionvehicles. IEEE Trans Eng Manage 2012;99:113.

[17] Bekkering J, Broekhuis AA, van Gemert WJT, Hengeveld EJ. Balancing gassupply and demand with a sustainable gas supply chain a study based oneld data. Appl Energy 2013;111:84252.

[18] Devlin G, Talbot B. Deriving cooperative biomass resource transport supplystrategies in meeting co-ring energy regulations: a case for peat and woodbre in Ireland. Appl Energy 2014;113:17009.

[19] Ebadian M, Sowlati T, Sokhansanj S, Townley-Smith L, Stumborg M. Modelingand analysing storage systems in agricultural biomass supply chain forcellulosic ethanol production. Appl Energy 2013;102:8409.

[20] Yu S, Wei Y, Guo H, Ding L. Carbon emission coefcient measurement of thecoal-to-power energy chain in China. Appl Energy 2014;114:290300.

[21] Fisher JC, Pry RH. A simple substitution model of technological change.Technol Forecasting Soc Change 1972;3:7588.

[22] Grbler. The rise and fall of infrastructures: dynamics of evolution andtechnological change in transport. Heidelberg, Germany: Physica-Verlag;1990.

[23] Rogers EM. Diffusion of innovations. Free Press; 1962.[24] Hagerstrand T. Innovation diffusion as a spatial process. Postscript and

translation by Allan Pred, Translated with the assistance of Greta Haag. TheUniversity of Chicago Press; 1968.

[25] Schuitema G, Anable J, Skippon S, Kinnear N. The role of instrumental, hedonicand symbolic attributes in the intention to adopt electric vehicles. TransportRes Part A: Policy Pract 2013;48:3949.

[26] Skippon S, Garwood M. Responses to battery electric vehicles: UK consumerattitudes and attributions of symbolic meaning following direct experience toreduce psychological distance. Transport Res Part D: Transport Environ2011;16:52531.

[27] CEER. 5th CEER benchmarking-report on the quality of electricity supply 2011,Council of European Energy Regulators; April 2012. World Wide Web: .

[28] Dril AWN van, Gerdes J, Marbus S, Boelhouwer M. Energie Trends 2012, ECNBeleidsstudies; 2012. World Wide Web: [Dutch].

[29] Green II RC, Wang L, Alam M. The impact of plug-in hybrid electric vehicles ondistribution networks: a review and outlook. Renew Sust Energy Rev2011;15:54453.

[30] RDW. Voertuiggegevens raadplegen, voertuiginformatie voor zakelijke markt,c2011: (cited 06.02.13) [Dutch].

[31] Shell, Shell Energy Scenarios to 2050. Shell International BV; 2008. WorldWide Web: .

[32] The Netherlands Institute for Social Research. Statusontwikkeling van wijkenin Nederland 19982010 [Internet]. [updated 28.11.12; cited 18.04.13] [Dutch].

[33] Statistics Netherlands. Kerncijfers postcodegebieden, 20082010 [Internet]. [updated05.01.12; cited 18.04.13] [Dutch].

[34] Statistics Netherlands. Gemiddelde persoonlijke en besteedbarehuishoudinkomen naar postcodegebied, 20062009 [Internet]. [Updated 19.12.11; cited 18.04.13] [Dutch].

[35] Javed F, Arshad N, Wallin F, Vassileva I, Dahlquist E. Forecasting for demandresponse in smart grids: an analysis on use of anthropologic and structuraldata and short term multiple loads forecasting. Appl Energy 2012:96.

J.W. Eising et al. / Applied Energy 123 (2014) 448455 455

Towards smart grids: Identifying the risks that arise from the integration of energy and transport supply chains1 Introduction2 The diffusion of infrastructure dependent technologies3 Data3.1 Infrastructure3.2 Technology3.3 Neighbourhood level of adoption determinants

4 Methodology4.1 Supply side4.2 GIS-based simulation model

5 Results5.1 The Netherlands5.2 North-Holland5.3 Amsterdam

6 Discussion and conclusionsReferences