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Impacts of Plug-in Vehicles and Distributed Storage

on Electric Power Delivery Networks

Peter B. Evans, New Power TechnologiesLos Altos Hills, CA [email protected]

Soorya Kuloor, PhD, Optimal Technologies (US),Inc.

Raleigh, NC USA

[email protected]

Benjamin Kroposki, PhD, P.E., National Renewable Energy Laboratory

Golden, CO [email protected]

Abstract This paper discusses studies funded by the National

Renewable Energy Laboratory (NREL) via SunPower, Inc. and

the California Energy Commission (CEC), performed by NewPower Technologies and Optimal Technologies that showed that

high-penetrations of distribution-connected storage devices or

plug-in vehicles can have adverse grid impacts due to their

charging loads. Randomly-located or unmanaged additions, such

as plug-in vehicles, can also have greater impacts at lower

penetrations when compared to managed additions such as

utility-sponsored storage. The studies also found that potential

adverse impacts from such charging loads are highly localized,

and once identified are readily managed. The studies also show

the use of a high-definition Energynet power system simulation

and AEMPFAST power system optimization software for

identifying and managing the potential impacts of distribution-

connected storage.

Keywords: Plug-in Vehicles; Battery Electric Vehicles; Plug-in

Hybrid Vehicles; V2G; Electrical Storage; Load Flow Analysis;Optimal Control; Power Distribution; Power System Simulation;

Power Transmission; Distributed Storage, Distributed Generation

I. INTRODUCTION

The Department of Energy (DOE) Renewable SystemInterconnection (RSI) Study addressed barriers to theexpansion of renewable energy technologies associated withthe possible impacts on the electricity grid of high penetrationsof these technologies. The distributed generation element of theRSI Study is to address technologies that connect to the grid atthe distribution level, including solar photovoltaic (PV),distributed wind, vehicle-to-grid (V2G), and distribution-connected storage. Among the issues related to renewable

energy grid integration to be addressed under the RSI Studyare:

Understanding and developing solutions to utilityconcerns about large penetration of renewable generationresources [including storage] connected to the electric powersystem, and

Conducting detailed analysis of renewable energysystem performance and grid effects through electrical

transmission and distribution (T&D) system modeling andsimulation.

Electric power storage connected to the power deliverynetwork has the potential to improve the utilization of preferredgeneration sources and the performance of the grid itself bysupplying incremental capacity and energy on demand, e.g.,during periods of peak demand or system stress. Otherinvestigators have demonstrated the use of capacity derivedfrom demand response as a grid resource such as spinningreserve; storage offers similar potential, but with far greater

practical operational range. Vehicle-to-grid (V2G) refers to thedual-use of plug-in battery electric or hybrid vehicles as storagecapacity available to the grid for such purposes. The appeal ofV2G is the reduction in the effective cost to the power delivery

network of the storage asset.Electric storage also places a corresponding charging load

on the network. Even where there are adequate electric supplyresources to serve charging loads off-peak, delivering the

power to these loads may introduce localized problems with inthe delivery network. Further, plug-in vehicles place a chargingload on the network whether or not they are used as V2Gstorage resources. Lastly, rapid charging, a clear customervalue for plug-in vehicles, translates to higher charging loads.

The focus of this paper is the potential impacts of suchcharging loads on the power delivery network. For a given

power delivery network, we sought to determine thepenetration level at which the charging load of plug-in vehiclesmight begin to adversely impact the performance of the power

delivery network. We also sought to assess the role of the sizeof individual storage additions on their impact on network

performance.

A fundamental distinction of plug-in vehicles and V2Grelative to electrical storage generally is that their locationwithin the power delivery network is far less likely to beplanned by the power delivery network operator. So if power

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delivery networks are sensitive to charging loads at particularlocations, it is important for network operators have the meansto proactively identify those locations as well as the penetrationlevels at which these loads become a concern.

Customer-side distributed storage and plug-in vehicles areboth likely to be connected at customer service voltages andpotentially at electrically-remote locations within the power

delivery network. Meaningful impacts of the charging loads ofsuch devices could be highly localized, thus their assessmentrequires the ability to see the power delivery system at thecircuit element level as well as system wide. For this work weused a high-definition Energynet model of the subjectsystem, comprised of over 100,000 nodes and integratingregional and local transmission, sub-transmission with alldistribution substations, line segments, and elementscharacterized. This model was developed using ordinarily-available data sources provided by the utility and is supported

by an enhanced version of Positive Sequence Load Flow(PSLF) provided for our use by GE Energy. We also usedOptimal Technologies AEMPFAST power systemoptimization software which provides an assessment of the real

(active) and reactive power resource deficiency relative tooptimal at each individual node, which, in turn, can be usedto identify those locations where additions of storage capacity(and charging load) will move the system closer to a specifiedoptimum condition.

The following summarizes our experiences and findings inusing these tools to study the impacts of the charging load ofdistributed storage and plug-in vehicles on the subject powerdelivery network, including the impact of size, penetration, andwhether the placement of these devices is unmanaged (e.g.,with plug-in vehicles) or managed (e.g. with utility-directedstorage deployments).

II. THE HOBBYHIGH-DEFINITION SYSTEM MODEL

The subject system for this study was Southern CaliforniaEdisons Hobby system in Southern California as it stood inmid-November, 2005. As part of a larger project funded by theCEC we developed a high-definition Energynet model of theHobby system that provides system-wide scope withdistribution element-level granularity; this model enables thedirect observation of the impacts of any simulated change,addition, or removal on each individual element in the networkand on the network as a whole. The Hobby system consists of58 local transmission and sub-transmission substations, and215 radial distribution circuits with voltages ranging from 33kV to 2.4 kV. This system was modeled in full elementaldetail, comprising nearly 100,000 buses, and integrated into a

power flow model of the western United States provided by the

Western Electricity Coordinating Council (WECC). The Hobbysystem has a peak load of about 1,300 MW and servesapproximately 275,000 customers at about 46,000 individualdistribution transformers, each characterized by customer class.

The model includes loads derived from actual data recordedunder a variety of operating conditions; for this study themodeled loads were from on-peak and off-peak hours of a daythat saw one of the highest load peaks recorded for the year.The served-load modeled on-peak totaled 1,343.42 MW and

651.809 MVAR, and off-peak totaled 492.45 MW and 222.40MVAR. Importantly for a storage study, the Hobby systemsoff-peak load demands over 850 MW less from existinggeneration resources than the systems on-peak load.

Within the Hobby system are 102 voltage-regulatingdevices (transformer taps and voltage regulators), 839 reactive

power sources (capacitors), 30 embedded generation resources,

some of which are also sources of reactive power, and 4,684individually-addressable demand response resources, so thesimulated system provides a great deal of operational control.We considered all of these resources as individuallycontrollable to obtain the highest grid performance and toaccommodate the addition of storage devices. In each case,these resources were dispatched using AEMPFAST with anoptimization objective of minimizing both real and reactive

power losses while minimizing voltage deviation from nominalat each point in the Hobby portion of the network model. Asvoltage is also a factor in losses it tends to have a stronginfluence with this objective. Also, with this objective overloadrelief is a factor, but only indirectly as it relates to losses.

For budgetary reasons this storage analysis was built from

studies already completed in which we populated the subjectsystem with an Optimal DER Portfolio consisting ofcapacitors and demand response resources (and distributedgeneration in other studies). Each of these resources wasideally sized and sited to contribute to the optimizationobjective stated above, subject to external limits such as theamount of demand response or distributed generation a

particular type and size of customer would ordinarily host, anda circuit-level distributed generation limit that would avoidunintended post-contingency islanded operation. We alsoincorporated in the model all of the systems existing demandresponse as discrete resources. So under on-peak conditions themodel incorporated 104 capacitor additions and under on-peakconditions reflected the dispatch of existing and added demand

response comprising about 2.9% of load.One consequence of the use of this initial model is the

remaining benefit to system performance of the addition ofincremental capacity under on-peak conditions, even at ideallocations, is attenuated. Accordingly, the potential grid benefitsof storage and V2G as an incremental capacity resource on-

peak are understated in these results.

III. INITIALSTORAGECHARGINGLOADSTUDIES

As an extension of the study to identify the Optimal DERPortfolio of distributed energy resource additions, weinvestigated the addition of distributed storage representing 2%of the subject systems peak load, nominally network operator-managed and sited and sized to provide capacity valuable to thenetwork under on-peak conditions while minimizing the impacton the network off-peak,. We postulated a storage devicecapable of discharging for at least an hour at its rated capacity,and that would charge at 1.25x its discharge rating.Specifically, we simulated the addition of storage capacityincrements one by one up to the 2%-of-peak-load limit, in eachcase at the individual node within the system where the active

power resource deficiency on-peak netted against the activepower resource deficiency (or added to the active power

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resource surplus) off-peak was maximized, both as determinedin an AEMPFAST analysis. Storage capacity at these locationswould yield the greatest net benefit to the network or system

benefit relative to our stated optimization objective ofminimizing losses while minimizing voltage deviation fromnominal. These benefits to the power delivery network would

be in addition to any bulk system capacity or load management

benefits provided by this storage capacity.We found that even relatively small (

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TABLE III. 10,500V2GADDITIONS VOLTAGE AND LOSS IMPACTS

Operating Condition Vmin Real Losses Reactive Losses

On-Peak + 0.0228 PU - 21.023 MW - 61.357 MVAR

Off-Peak - 0.0513 PU + 7.051 MW + 17.833 MVAR

However, at a penetration level of 10,500 V2G units, thesystem-wide minimum voltage under off-peak conditions is notmaintained above the 0.95 PU threshold due to the chargingload, even with optimization of available voltage supportcontrols.

We also evaluated a penetration level of 35,112 V2Gadditions. In terms of customer sites this represents about 76%

penetration. At this penetration level the V2G devices representa nominal total of 877.8 MW discharging or on-peak capacity.Under on-peak conditions the capacity represented by theseunits raises Vmin by 0.011 PU, reduces real losses by 24.979MW, and reduces reactive losses by 114.45 MVAR, in eachcase relative to the system with no storage additions.

These 35,112 V2G units would represent 561.8 MW ofcharging load off-peak. This is still less than the 850 MWdifferential in initial system demand between on-peak and off-

peak loads. However, the important finding is that we found nofeasible power flow solution under off-peak conditions withthis added charging load. The system collapses and is unable tohandle the charging load of these units.

So we may conclude from the foregoing that even at lowpenetration levels of 1-2% and small (< 20 kW) sizes,randomly-placed charging loads associated with distributedstorage or plug in vehicles has a measurable effect on systemvoltage and losses, and at penetration levels around 20% suchloads could have a unacceptable impact on system voltage.Also, it is possible, though at possibly unreasonable penetrationlevels, for distributed charging loads to collapse a powerdelivery system even if supply resources remain.

It is important to note again that the on-peak impacts orbenefits of storage capacity presented here are probablyunderstated. With the initial system model populated with largeamounts of ideally-placed distributed resources, as describedabove, the potential benefits from incremental capacity on-peakare attenuated.

V. CHARGING LOAD IMPACTS OF MANAGED DISTRIBUTED

STORAGE ADDITIONS

We performed a separate set of studies of the impact of

network storage additions in which the placement of eachaddition within the power delivery network is activelymanaged by the network operator, and in this case targeted tominimize the off-peak burden to the network as well asmaximize the on-peak network benefit. These studies shed lightnot only on the value of managed placement of storage, butalso on the roles of unit size and penetration in the grid impactsof off-peak charging loads generally, whether distributedstorage or plug-in vehicles. Most importantly, these studies

provide a more granular view of the evidently highly-localizedimpacts of such charging loads on grid voltage.

In each study we began with the initial cases and conditionsdescribed in Table I. As with the studies described in SectionIII, we postulated a storage increment or device with thecapability to discharge for at least an hour at its nominalcapacity rating, and that would charge for at least an hour at

1.25x its nominal rating. Again, these are postulated asmanaged placements we sited these devices using a dualAEMPFAST optimization that identified each successivestorage site as the one at which the net on-peak network benefitless the off-peak network dis-benefit is maximized. As before,the optimization objective was the simultaneous minimizationof real and reactive power losses and voltage deviation fromnominal. We also allowed multiple increments at each location.This allowed us to determine to some extent the ideal size ofthe storage project at each location that would provide thesystem benefits we sought through the stated optimizationobjective. As with the prior studies we limited the impact ofcharging loads through a 0.95 PU limit for the lowest voltage atany point system-wide under off-peak condition.

A. Impact of Size

The initial studies described in Section III showed that thecharging loads of larger storage increments had greater systemimpacts under off-peak conditions. We thus attempted toidentify a safe storage increment under managed placement.We define this as the largest individual storage increment thatcould be added to the subject system at locations chosen tomaximize the net on-peak and off-peak network performance

benefit that would not also individually cause an off-peakvoltage reduction that would violate the 0.95 PU off-peak low-voltage limit. For this study we specified the total nominalcapacity of storage additions at a modest amount, e.g., 1-3% of

peak load. In practice this simulation might represent a network

operators distributed storage strategy, and an overall storagebudget of 1-3% of peak load might be indicative of the level ofembedded storage a utility might incorporate in a powerdelivery system for operational purposes such as regulation.Our focus in this study was on the system impact of individualstorage additions rather than the cumulative impact on thesystem of high storage penetrations.

We found that the Hobby system could accommodatestorage additions in individual increments of up to 70 kWwhile maintaining an off-peak minimum voltage of 0.95 PU orhigher at every point. In experimenting with larger minimumincrements, we found that the system could accommodatestorage additions in increments as large as 100 kW only if theacceptable off-peak low voltage limit was allowed to fall to

0.90 PU or lower, and that the system could accommodateadditions in increments as large as 90 kW with an off-peakvoltage at .93 PU or lower. In all cases we re-dispatched allavailable controls according to the same optimization objectiveto compensate for the charging load impact. So for this system,minimum storage increments of 90 or 100 kW are too large to

permit the system to maintain the 0.95 PU specified minimumvoltage at every point even with targeted placement accordingto our criteria, and thus would not meet our criterion for asafe maximum increment.

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More importantly, we found that the voltage impact of thecharging loads of these storage additions is not a generalsystem-level phenomenon, but is actually highly localized. Wefound that there are four or five specific locations within themodeled network that are the most sensitive to the addition ofreal power load off-peak in terms of voltage reduction. Thesefew locations effectively define the largest single storage

increment that can be added to the system while maintaining apre-specified minimum off-peak system-wide voltage.

This finding implies a reasonable conclusion that thereisnt really one maximum storage and charging load size thatcan be placed in a system without concern for causing a voltageviolation off-peak. Each location has a different voltagetolerance for the addition of real load under off-peakconditions. So framing the question the way we did forces theidentification of a small number of controlling buses.

We also found that there is very small number of locationsin the subject system with off-peak real power resource surplusconditions in other words, locations where the addition ofstorage charging load would actually improve network

performance relative to the objective. Specifically, these are

locations with positive P Indices greater than about 0.20 asmeasured by AEMPFAST. In this system these locations arevery rare, representing only about 0.4% of the buses in thesubject system, and these buses are located on only fourcircuits. We also found that the addition of one storage project(and its charging load) on one of these circuits appears toreverse the off-peak real power resource surplus condition onthe rest of that circuits buses and on other circuits as well. Thestorage locations on these few circuits are of courseconsistently the highest-ranked sites within our screen nettingon-peak system benefit and off-peak dis-benefit as storage atthese sites yields benefits under both conditions; they are alsothe most sensitive in terms of off-peak voltage impact.

The following example will illustrate how the addition of a

storage device in an otherwise beneficial location may be size-limited. It also shows the localized nature of storage impactsand thus the importance of evaluating storage impacts on thecircuit element level as well as at the system level.

Loon circuit is one of four 33 kV circuits in the Hobbysystem served from Tree 115/33 kV substation. Loon extendsfrom Tree substation toward Grape substation. Figure 1 showsTree and Grape substations, and Figure 2 shows Loon circuitextending between them from Tree substation. Loon in turnserves three lower-voltage substations, Puffin, Crane, andPenguin. Penguin substation, in turn, serves Cormorant circuit.Figure 3 shows Penguin substation and Cormorant circuit.

Figure 1. Tree and Grape Substations. Loon circuit lies between Tree

substation (lower right) and Grape substation (upper left).

Figure 2. Loon Circuit. Loon circuit extends from Tree substation to Grape

substation.

Figure 3. Penguin Substation and Cormorant Circuit. Loon circuit serves

three lower-voltage substations, Puffin, Crane, and Penguin. Penguinsubstation, in turn, serves Cormorant circuit

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Figure 4 shows the active power resource deficit or surplus(P Index) under both on-peak and off peak conditions beforeany storage additions, along a single path from Tree substationat the left axis, through Loon circuit, Penguin substation andCormorant circuit. Cormorant circuit has a P resource deficit,or negative P Index, on-peak. It is also one of the areas with aresource surplus, or positive P Index, off-peak. This means the

incremental on-peak capacity and the incremental off-peak loadof a storage device would improve network performancerelative to the optimization objective under both on-peak andoff-peak conditions. Accordingly, Cormorant is one of the mosthighly-ranked storage locations in the entire system under ourscreening approach.

-2

-1.5

-1

-0.5

0

0.5

1

Loon and Cormorant CircuitsP Deficiency/Surplus Before Storage Additions

Off-peak P Index

Peak P Index

Penguin

P.T.Substation

Loon Circuit

(Penguin Path)

Cormorant Circuit

(1361E Path)

Figure 4. Loon-to-Cormorant Path Initial Off-peak and On-peak Active

Power Resource Deficit/Surplus. Cormorant circuit has attractive storage sites

having both an active power resource deficit (negative P Index) on-peak andan active power resource surplus (positive P Index) off-peak.

The specific location on Cormorant where a storageaddition would yield the greatest net on-peak and off-peaksystem benefit is at device 1361E, which is at the end of pathshown in Figure 4. Figure 5 shows the off-peak voltage profilein per-unit terms along the same Loon-to-Cormorant path

before any storage additions, and also after the addition of four70 kW storage devices at chosen beneficial locations within theHobby system, including a single 70 kW storage device at1361E on Cormorant. Before any storage additions the off-peakvoltage at the end of Cormorant is slightly elevated, consistentwith a real power resource surplus. After the four storageadditions the off-peak voltage at the end of Cormorant dropsdue to the charging load of the storage devices, including theone at that location. In fact, the voltage at that point touches the.95 PU low-voltage limit. Note as well that the Cormorant

circuit voltage change at the substation is modest, thus theimpact of these charging loads on Cormorant circuit voltage isonly visible at the sub-circuit level.

0.9

0.925

0.95

0.975

1

1.025

1.05

Loon and Cormorant CircuitsOff Peak Voltage Profile

Before Storage Additions

Four 70 kW Storage Add s Area-wide

PenguinP.T.Substation

Loon Circuit

(Penguin Path)Loon Circuit

(1316E Path)

Figure 5. Loon-to-Cormorant Path Off-peak Voltage Profile before and after

Four 70 kW Storage Additions. The charging loads of 70 kW storageadditions drive Cormorant circuit voltage from above nominal to 0.95 PU.

This indicates that even though device 1361E on Cormorantis an advantageous location for a storage device for its systemimpacts under both on-peak and off-peak conditions, if thecharging load is larger than the 87.5 kW associated with anominal 70 kW storage device, the off-peak voltage will fall

below the acceptable range at that location. More importantly,this indicates the 70 kW safe maximum storage increment forthis system might be established by a single point in a networkcomprised of over 100,000 nodes.

The addition of 500 such storage increments of 70 kW(nominal) each represents 35 MW of incremental on peakcapacity, or about 2.6% of peak load. It also represents 43.75MW of charging load off peak. The condition of the systemwith the addition of this amount of storage at optimal locationsrelative to the system with no storage additions is tabulated inTable IV. Under off-peak conditions, real power losses andreactive power consumption both increase, as expected. Thereis also a clear impact on voltagethe system-wide minimumvoltage decreases to just at the 0.95 PU low-voltage limit. Thisis the influence of the 70 kW size of the additions acting on oneor more of the sensitive locations as described above.

Under on-peak conditions there is effectively no impact onvoltage. We believe this is due to the large amount of ideally-

place capacity associated with the Optimal DER Portfolioresources already incorporated in the model in these studies.On-peak losses decrease, and in this case more than with the 35MW of randomly-placed V2G storage addition shown aboveand in Table II. The greater loss reduction for an equal amountof incremental capacity is due to the targeted placement of the70 kW storage additions in this case.

TABLE IV. 50070KWSTORAGE ADDITIONS VOLTAGE AND LOSSIMPACTS


On-Peak - 0.006 PU - 4.537 MW - 12.829 MVAR


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B. Impact of Penetration

We also sought to identify the greatest penetration ofstorage projects where, if added to the subject system in safesizes and again at optimal locations chosen to maximize thenet on-peak and off-peak network performance benefittheircumulative impact was such that the system was still able tomaintain the 0.95 PU low-voltage limit. In practice this might

represent a network operators distributed storage deploymentgoing beyond the 1-3% of peak load range described above.

Here one of the findings from the previously-describedstudy comes in to play. We found that apart from the few verysensitive locations previously identified, this system is capableof absorbing a large amount of storage charging load whilemaintaining voltage off-peak within the limits we set, as longas the storage increments are optimally-placed.

We added a total of 3,000 storage increments of 70 kW(nominal). These additions represent incremental on-peakcapacity of 210 MW, or 15.6% of peak load, and charging loadof 262.5 MW. These increments are again sited at optimallocations to maximize net on-peak and off-peak network

benefits according to the screening method described above.We permitted multiple increments of 70 kW at a single locationif that was shown to yield network benefits. Thus we arrived ata set of storage projects at 2,514 different locations withinthe network and having a range of sizes. 2,281 of the projects(about 91%) are individual-increment 70 kW projects, and thelargest projects are nominally 420 kW (6 individualincrements). The sites of these projects are widelydistributed, located on 174 different circuits and in onesubstation.

The impact of these 2,514 storage projects relative to thesystem with no storage additions is tabulated in Table V. Underoff-peak conditions, real power losses and reactive powerconsumption are increased. The system-wide minimum voltage

remains at the limit even with the additional charging load,though AEMPFAST results indicate that there is an increasingnumber of buses near this limit. Under on-peak conditions real

power losses and reactive power consumption are reduced. Thesystem-wide minimum voltage is again largely unchanged.

TABLE V. 3,00070KWSTORAGE ADDITIONS (2,514PROJECTS)VOLTAGE AND LOSS IMPACTS


On-Peak - 0.010 PU - 21.720 MW - 54.439 MVAR


We continued to increase the storage penetration to thepoint where there were no remaining sites for a 70kW storageaddition that would not result in a violation of the .95 PU lowvoltage limit off-peak. This is effectively the penetration limitfor projects of this size. At this point we had added a total of12,540 units of 70 kW (nominal) of storage at 5,564 differentproject locations within the network. Again, these projectsare highly distributed; there are storage projects on nearly all ofthe subject systems circuits. The majority, 3,904 sites (70%),are single-increment 70 kW projects. The largest projects at

locations within the distribution portion of the subject systemare 420 kW. The largest projects overall were on 26 of the 115kV local transmission buses and represented 4.2 MW each.These projects represent 877.8 MW of incremental capacity on-

peak and charging load of 1,097.25 MW off-peak.

The impact of these 5,564 projects relative to the systemwith no storage additions is tabulated in Table VI. Under off-

peak conditions real power losses and reactive powerconsumption increase dramatically relative to the initialconditions. The system-wide minimum voltage remains at thelimit. Under on-peak conditions real power losses and reactive

power consumption are reduced and the system-wide minimumvoltage is slightly increased.

TABLE VI. 12,54070KWSTORAGE ADDITIONS (5,564PROJECTS)VOLTAGE AND LOSS IMPACTS


On-Peak - 0.013 PU - 33.689 MW - 130.371 MVAR


These off-peak conditions represent very significantloading of the system due to the charging of the added storagedevices; the total load is actually greater than the total on-peakload in the initial pre-storage conditions. The 1,097.25 MWcharging load is also substantially more than the 561.8 MWcharging load associated with the addition of 35,112 V2G unitsdescribed above which caused voltage collapse. Yet in this casenot only can the system support the load, but the low-voltagelimit is maintained at every location. This shows that thesystem can absorb much more charging load if the placement isdirected by the informed network operator.

The incremental capacity available on-peak from these5,564 projects is identical to the 877.8 MW of on-peak capacity

provided by the 35,112 V2G units described above. However,in this case the real power loss reduction of 33.689 MW farexceeds the real power loss reduction of 24.979 MW associatedwith the V2G units. Again, the difference is due to the targeted

placement of the storage projects in this case.

The makeup of these 5,564 storage projects also supportssome conclusions. These results do confirm that whilemaximum safe addition increment for this system remains at70 kW (nominal), there are sites that can accept and benefitfrom larger storage additions. However, recall that each storageincrement was placed according its net on-peak and off peaknetwork benefit relative to the optimization objective. That themajority of the projects in these results are single-increment70 kW nominal projects indicates that few sites would derive

additional network benefits from larger projects. Further, thelarger substation-sited and transmission-level storage projectsdid not emerge until very high penetration levels were reached,indicating that such projects yield relatively less in terms of netsystem benefits than the smaller, more distributed projects that

prevail at lower penetration levels.

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VI. IMPLICATIONS FOR A SMART GRID

The results in Section IV suggest that on a partial-capacitydispatch basis, plug-in vehicle batteries in a V2G deploymentcan represent a meaningful amount of incremental distributedcapacity for a power delivery system as penetration grows. Theresults in Sections IV and V show loss reductions and somevoltage benefits from incremental capacity under on-peak

conditions; further, as stated elsewhere, the results presentedhere probably understate the potential system benefits from thisincremental capacity under on-peak conditions.

In terms of charging load system impacts, the resultspresented in Sections III, IV, and V indicate that charging loadsassociated with storage projects and plug-in vehicles are a realconcern in terms of their impact on the power delivery system,and worthy of close evaluation. The results in Section IV showthat system was unable to maintain off-peak minimum voltagewith V2G charging loads at 20% of customer sites, andcollapsed with V2G charging loads representing less than thecapability of available supply resources. The grid impacts of

plug-in vehicles at a given penetration level might be greaterstill if their charging loads are concentrated in more

electrically-remote residential locations.

The results of Section V indicate that larger discrete sizes ofcharging loads can have larger impacts, and the small size ofV2G loads postulated here is likely mitigates impacts. Theresults of Section V also indicate that charging load impactscan be substantially reduced through management of the

placement of these loads, this may not be a realistic mitigationapproach when it comes to plug-in vehicles.

The results of Section V also indicate that the systemimpacts of charging loads are most dramatic at a very smallnumber of discrete locations individual buses within a fewindividual circuits, at least in this system. Moreover, the rest ofthis particular system demonstrated the capability to absorb

very large charging loads.This suggests that electric power storage and plug-in

vehicles relying on off-peak charging even at high-kW ratesremains viable from a power delivery system standpoint, andthat it may not be necessary for the grid operator to dictate the

placement of the charging load of each individual storageproject or vehicle. It may be sufficient for the grid operator,having identified the delivery systems sensitive locations, tomanage the charging rates of a few storage devices or vehiclesonly if connected to the grid in those few known sensitivelocations and possibly only under some conditions.

Device-level storage charging rate control would alsoenable device-level storage dispatch and its related benefits. Acomparison of the on-peak loss impacts of 12,540 70 kW

storage additions in Section V and 35, 112 V2G additions inSection IV clearly shows that an equal amount of incrementalcapacity on-peak yields greater results from specific, targetedlocations. Dispatch of storage capacity to relieve overloads orunder post-contingency conditions would be even morelocation-specific. Thus even a network operator that does notspecify the network location of plug-in vehicles may seekdevice-level control of V2G capacity associated with thosevehicles. Also, the use of plug-in vehicles as V2G storage

might be more successful if the dispatch parameters andlimitations can be tailored for each individual device and/orcustomer. We have, somewhat arbitrarily, specified the V2Gcapacity that might be available from a plug-in vehicle batteryof a given size for purposes of these studies; clearly thisrelation is one with many, many factors.

Device-level management of distributed storage or V2G

resources could be supported through utility DistributionManagement Systems or Advanced Metering Infrastructure,

provided a) these systems are designed to accommodate suchfunctions, and b) the storage devices themselves areindividually identifiable by and interoperable with thesesystems. Use of this device-level management capability willalso require advanced system modeling and analytical tools thatallow network operators to see conditions in their networksat the distribution element level and to gauge the impact ofeach of a very large number of resources locally and system-wide.

VII.

CONCLUSIONDistribution-connected electric storage, including plug-in

vehicles as V2G, can provide benefits in terms of powerdelivery network performance, including loss reduction andvoltage profile improvement. These benefits are in addition toother storage benefits such as load management improvedresource utilization or ancillary services. These studies are notcomprehensive in terms of their examination of all the potential

benefits of storage, and the benefits shown here are likelyunderstated.

Charging loads of electric storage and plug-in vehicles havetwo distinct sets of impacts on the electric power system;impacts on the power delivery system itself must be consideredin addition to the impacts of incremental demand on electric

supply resources. Plug-in vehicles present a fundamentallydifferent challenge in that the network operator has little directcontrol over the placement of these devices within the network.

Electrical storage or plug-in vehicle charging loads haveobservable impacts on voltage and losses even at low

penetration levels. These impacts are highly-localized charging load impacts appear at the sub-circuit or distributionelement level. Even if these impacts are visible at the systemlevel, assessment of charging load impacts at the system levelonly or even at the circuit level may not provide sufficientdetail to reveal the nature, extent, or location of meaningful oractionable impacts. Our re-optimization of all of these casesshows that these impacts cannot be overcome solely throughthe redispatch of available system controls and resources.

V2G can be a feasible approach to deploying electricstorage, yielding both real benefits from incremental capacityand providing real charging load issues to manage. A V2G

profile in which a 65kWh plug-in vehicle battery is capable ofdischarging a portion of its capacity at a rate 25 kW for an hourand charges at a rate of 16 kW for several hours representsenough incremental capacity and charging load to visiblyimpact the performance of the power delivery network both

beneficially on-peak and adversely off-peak even at low

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penetration levels of around 2%. As penetration grows theincremental capacity serves to reduce system losses on peak,and the incremental charging load serves to increase losses andreduce the overall system-wide minimum voltage off-peak. Wefound that due to the charging load, for this power deliverysystem there is a penetration level at which the system cannotmaintain a specified 0.95 PU minimum voltage at every point,

even with compensating capacitor or tap adjustments. There isa higher penetration level at which the system collapses evenwith supply resources remaining.

The location of storage within a power delivery network isa key factor in its impacts on the network. Storage additions

placed at or dispatched from specific locations where their neton-peak and off-peak system impacts are shown to bemaximized yield greater system benefits on-peak and havereduced impacts off-peak for a given nominal capacity. Thecharging load carrying capability of a network is far greater ifthe placement of those loads is managed to maximize netsystem benefits.

The size of each storage increment is a factor. There is amaximum size of storage addition that can be placed in certain

of a systems otherwise attractive storage sites. If thisincrement is exceeded the system will be unable to maintain itsvoltage within specifications at every location. In these studiesthis maximum was well over the size of the V2G additions, andthe probable size of plug-in vehicle charging loads likelymitigates their impact. At the same time, where storageadditions were sited and sized for their net impact on network

performance, the sites showing benefits from very large storageadditions were fewer and lower-ranking in terms of system

benefits than those showing benefits from smaller, moredistributed additions.

The management of storage impacts is a highly-localizedaffair. These studies show the voltage impact of charging loadscan be highly localized in this system the system-wide

voltage impact of charging loads was determined by the impactof charging loads in a few discrete locations on a few circuits.If charging loads exceeded a given threshold in these sensitivelocations the system was unable to maintain its voltage withinspecifications. At the same time, if the critical charging loadlevels at these locations are not exceeded, the system overallshows great capability to absorb charging loads without furtheradverse voltage impact. Therefore, the identification of thesesensitive locations, and possibly the management of individualcharging load sizes or rates at those locations, frees up theability of the rest of the system to support significant amountsof high-kW rate charging loads. Advanced power systemmodeling that allows engineers to see the system in element-level detail and advanced analytics that allow engineers to

accurately assess the resource deficiency or surplus at eachindividual node in the system can be of great value inidentifying and effectively managing around these sensitivelocations within a system.

Utility Distribution Management Systems or AdvancedMetering Infrastructure systems designed to manage individual

distributed storage units or plug-in vehicles (both charging andproviding capacity as V2G devices), and storage devicesdesigned to be visible to and interoperate with these systems,may be of significant value in the wide deployment of electricalstorage. This work demonstrates the integration needs for V2Gand distributed storage as the Smart Grid of the futurecontinues to evolve.

ACKNOWLEDGMENT

This technical effort was performed in support of theDepartment of Energys Renewable System Interconnectioninitiative under a subcontract to SunPower, Inc. A portion ofthis work was performed in support of the California EnergyCommissions Public Interest Energy Research in energysystems integration under PIER Contract 500-04-008.

The authors also acknowledge the in-kind support ofSouthern California Edison for substantial technical review andgenerous availability of system data.

DISCLAIMER

This report was prepared as a result of work sponsored bythe California Energy Commission (Energy Commission). Itdoes not necessarily represent the views of the EnergyCommission, its employees, or the State of California. TheEnergy Commission, the State of California, its employees,contractors, and subcontractors make no warranty, express orimplied, and assume no legal liability for the information inthis report; nor does any party represent that the use of thisinformation will not infringe upon privately owned rights. Thisreport has not been approved or disapproved by the EnergyCommission nor has the Energy Commission passed upon theaccuracy or adequacy of the information in this report.

REFERENCES

[1] Kroposki, B.; Margolis, R.; Kuswa, G., Torres, J.; Bower, W.; Key, T.;Ton, D.; Renewable Systems Interconnection: Executive Summary,NREL Technical Report (NREL/TP-581-42292); February, 2008.Available at: http://www1.eere.energy.gov/solar/solar_america/rsi.html

[2] Eto, J.; Demand response spinning reserve demonstration; EarnestOrlando Lawrence Berkeley National Laboratory, EnvironmentalEnergy Technologies Division; LBNL-62761; May, 2007.

[3] Kirby, B.; Ancillary services: technical and commercial insights;prepared for Wrtsil; July, 2007.

[4] Evans, P.; Optimal Portfolio Methodology for assessing distributedenergy resources for the Energynet; CEC-500-2005-096, March, 2005.Available at http://www.energy.ca.gov/2005publications/CEC-500-2005-096/CEC-500-2005-096.pdf

[5]

Evans, P.; Hamilton, S.; Dossey, T.;High-definition modeling ofelectric power delivery networks; Conference Proceedings,DistribuTECH Conference and Exhibition, Feb. 3-5, 2009, San Diego,CA; Pennwell Corporation, February, 2009.

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