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Abstract—As the potential economic and environmental

benefits of plug-in electric vehicles (PEVs) become apparent and as strong interest develops among automakers, seamless integration of PEVs to the grid will be a critical step to encourage utility support for PEV commercialization. Technological barriers continue to fall, but taking advantage of opportunities to increase revenue and improve power system efficiency using PEVs will require active participation by utilities and OEMs in how the vehicles are introduced to customers. PEVs are transformational in that they introduce electricity as a meaningful automotive fuel to a potentially very large market. This paper will focus on some of the most pressing technical issues associated with the act of plugging these vehicles into the electric grid. This paper presents key impact results using the EPRI study methodology [1] for one representative Hydro-Québec distribution feeders. This study is part of the multiyear collaborative project geared towards evaluating the localized impacts of plug-in electric vehicles on individual utility's distribution systems.

Index Terms—Plug-in Hybrid Vehicle, distribution system, measurement, deterministic models, probabilistic models, spatial distribution, temporal distribution, thermal loading, charge profile

I. INTRODUCTION

Electric transportation has the opportunity to produce societal and economic benefits. This includes decreasing greenhouse gas emissions, reducing dependence on imported petroleum, and potentially providing consumers a lower cost alternative to gasoline. Adopting electricity as a "transportation fuel" may have significant impact on distribution circuits and distribution operations. Utilities need to understand these impacts and need to adapt planning and operations procedures to supply this new load reliably. EPRI has initiated a multi-year project to understand PEV system impacts with several utilities in the United States, Canada, as well as a few European utilities. The purpose of the project is to identify, define, and calculate the impact of PEV on specific utility distribution systems considering total penetration levels as well as localized concentrations. The basic premise of this project is to conduct a comprehensive evaluation of PEV loading influence on distribution systems

operations using real distribution circuits and measured data. This study targets distribution system loading impacts from near-term projections (1-5 years) of PEV market penetration. Prior studies [2-4] have focused primarily on the adequacy of the generation capacity to handle the additional PEVs loads as it continues to grow. From these studies, it is concluded that the initial PEVs penetration will be contained within the system off-peaks without affecting the peak demand. However, such system wide expectations may not be as easy to achieve at localized distribution levels due to the following reasons: - PEVs loads will not grow uniformly in the whole utility

service area. Based on a recent study it is concluded that the initial penetrations will most likely be clustered in certain areas. This could result in significant overload on those distribution feeders (localized), even though the utility system as a whole as a whole have sufficient or excess generation capacity.

- Unless creation and wide-spread adoption of a two-way communication system (“smart charging”) between utility and the PEVs are implemented, it would be very difficult to shift the charging of these PEVs completely to off-peak periods.

- Traditional methods of load forecasting will not be possible due to spatial uncertainty in PEV penetration and temporal uncertainty in charging patterns and habits

- No historical data exists that can accurately project PEV adoption along specific distribution systems

- Traditional techniques cannot adequately capture the “true” electricity consumption profiles for these mobile devices along

In order to adequately address system adequacy, it would be necessary to do a micro level analysis in order to study the impact of PEVs loading on the distribution system operation. Scope of such analysis will cover the distribution system, including substation transformer, primary distribution, distribution transformer, and secondary system up to service entrance. Given the spatial diversity of the PEVs throughout the network, total penetration levels and local concentrations of electric transportation vehicles are key factors for determining distribution system impacts. The electrical characteristics of chargers and charging profiles are contributing factors.

Grid Impacts of Plug-in Electric Vehicles on Hydro Quebec’s Distribution System Arindam Maitra1, Kyung Soo Kook 1, Jason Taylor1, Angelo Giumento2

1Electric Power Research Institute (EPRI), USA 2Hydro-Québec Distribution, Montréal, Québec, Canada

978-1-4244-6547-7/10/$26.00 © 2010 IEEE

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A prior paper [1] detailed the overall study methodology. A companion paper [5] provided selected results for two representative Hydro-Québec distribution feeders as PEV penetration levels increase. Partial results of Component Level Analysis and System Level Impact Analysis were provided in conjunction with characteristics of the two distribution system feeders. Stochastic evaluations were not provided. This paper summarizes key results obtained from this study including deterministic capacity analysis, system level analysis and stochastic impact analysis. The assessment framework was developed using the Open Distribution Systems Simulator (OpenDSS) software. As purely electric vehicles are not expected to have a significant market share across the studied circuit during this time frame, the study only considers plug-in-hybrid electric vehicles (PHEV). Additionally, the study does not consider PHEV technologies which will not be available for the first generation of electric vehicles. In particular, PEVs acting as distributed generation and two-way communication controls are not evaluated. Hence, each modeled PEV is treated solely as a load whose behavior is determined by projected customer behaviors rather than external control settings.

II. HYDRO QUEBEC’S PERSPECTIVE Hydro-Québec updates its 15 year distribution planning scenarios on a yearly basis, such that the utility can plan ahead for shifting technological, economical or societal patterns which may affect the load on the distribution network. These planning scenarios include different solution scenarios onto which different control schemes, smart grid options, and infrastructure investments are studied in order to respond as effectively as possible to the growing load. Due to Québec’s particular geography and extensive use of electric space heating, Hydro-Québec is mostly a winter peaking utility. The distribution network is designed in such a way to be able to withstand a cold load pickup following a prolonged winter outage. Hydro-Québec is concerned with the possibility of a PEV clustering limiting the network’s ability to withstand cold load pickup.

III. CIRCUIT CHARACTERISTICS AND MODELING This feeder (shown in Figure 1) supplies is a highly urban circuit containing a very large population density and lots of growth. The majority of the loads represent multifamily homes or high rise condos.

• Number of customers – 2801, 44% (1220 customers) of which are living at high rise condos which are served out of 3-phase transformers

• Territory – 2731 residential customers, 69 commercial customers, 95% underground.

• Operating voltage – 25KV. • Load factor – 48% • Load density – 209 • Primary circuit length – 13.4 miles

• Loading – Winter Peaking Utility. Peak occurs on 2/28/2007. The first peak occurs at 8am and the second peak occurs at 7pm.

• Charging Scenario – Evening and Night recharging

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Figure 1: Feeder Circuit Diagram

An earlier paper [5] summarizes the modeling and analysis approach used in evaluating PEV loading impacts on this circuit. The analysis approach incorporates identified PEV and consumer charging characteristics with the “base case” electrical model of the specific distribution circuit. Distribution system impacts are characterized and quantified through the model’s response to specific or randomly generated PEV loading scenarios. The analysis targeted PEV adoption at multi-family homes, high rise condos, and single-family residences along the feeder. As such, only those customers specified as residential and served by single or three-phase transformers were treated as potential candidates for PEV adoption in the analysis. A peak load flow model of the circuit, additional circuit data for transformers and services, and typical customer class load profiles provided by HQ personnel were used to develop a full electrical circuit model from the substation to each individual customer served from the circuit. The developed model and simulation platform (EPRI’s OpenDSS) allow for hourly simulation of the long-term dynamic behavior of the circuit as it serves the associated loads as they vary through 8760 hours in a calendar year. As discussed in [1], certain specific analyses may utilize all or part of the model. Historical substation and feeder measurements were used to validate the

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model. As shown in the real power comparison plots of Figure 2, the model sufficiently captures the annual power flows on the circuit.

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Figure 2. Feeder Under Study – Simulated and Historical Loading Data

The circuit model was evaluated for any voltage excursions outside of acceptable tolerances across this circuit as well as any devices which are already overloaded before the application of additional loading representing PEVs. During the peak hour the modeled 25 kV voltages along the circuit are well within acceptable limits as illustrated by the circuit’s voltage profile provided in Figure 3. In this figure, the voltage is each primary node and each phase is denoted by its own color. It was also determined that none of the circuit’s devices were overloaded during the peak hour for this load allocation. Therefore, the modeled circuit does not represent an abnormal condition which requires further validation.

Figure 3. Feeder Under Study – Voltage Profile Along the Circuit at Peak Hour

IV. PEV CHARACTERISTICS AND CLUSTERING A key component in accurately assessing PEV impact on the distribution system is accurately representing the nature of the PEV loads themselves. This includes not only sufficiently accounting for the electrical characteristics of the loads but the customer behavior which inherently drives the PEV charging demand. Paper [5] details the characteristics that were considered for this study. In relation to penetration of PEVs

along the studied feeder this study assumes 2-8% as a likely vehicle market penetration range given the near-term time frame for this analysis. However this range is extended as high as 25% when performing stochastic analysis. All subsequent analyses are performed using the specified market penetration without reference to time period which permits interpretation of the results even in light of changing market conditions. Note that before the specified market penetration level is used in the analysis, it is converted into an equivalent utility customer PEV adoption percentage using relevant Department of Transportation data [5]. Even for low overall customer PEV adoption rates, PEV clusters can still occur on distribution system assets increasing the risk for negative impacts. Based on system configuration and the assumed customer adoption probabilities, clusters will occur randomly throughout the system for each case. For example, PEV clusters are visible in the daisy plot shown in Figure 4. Each PEV is represented by the circle and as PEVs are introduced at the same location they are spaced in a similar fashion as petals on a flower. Higher penetration rates, of course, increase the potential for larger cluster sizes and more frequent occurrences. It should be noted, that PEV clustering alone does not signify a likely adverse impact as the other PEV load characteristics must be taken into account as well.

8% penetration of PHEV

Blue triangles: D/S transformers

Daisy: PHEV locations

Figure 4 Example Daisy Plots Illustrating Clustering at 8% Penetration Levels Depending upon design practices, the closer a circuit component is to the loads the more susceptible it will be to PEV clusters as the potential benefit of spatial diversity decreases. This relationship is illustrated in Figure 5 which shows the number of PEVs per customer served from each system component (line segment, transformer, etc.) in the stochastic analyses. The plot shows that the higher cluster sizes occur for components serving fewer customers. However, for devices serving large number of customer this PEV/customer ratio actually represents the original customer adoption rate assumed in the analysis.

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Figure 5 Relationship between Cluster Size and Customers Served off System Elements

V. SYSTEM IMPACTS ASSESSMENT In addition to the PEV characteristics, the temporal and spatial variations in PEV loading (when and where devices are charged) also determine the effect these devices will have on the electrical system.

A. Evaluated Impacts The following PHEV impacts are evaluated for various PHEV characteristic combinations:

• Thermal loading to what extent are component normal and emergency ratings exceeded (number of occurrences, typically overload asset classes, duration and magnitudes)

• Voltage to what extent does PHEV loading adversely impact system voltage regulation. (Voltage excursions, regulator operations, cap operations, etc.)

• Unbalance potential for disproportionate penetration on particular phase and results on system unbalance

• Losses impact on distribution system losses

B. Framework To reasonably represent the effect of all these variations, the three stage analysis framework [1, 5] shown in Figure 6 has been developed to provide both potential impacts through sensitivity analysis and likely impacts from assumed probabilities. Each analysis stage serves as a tool for examining system response from a different conditional perspective as detailed further in the following sections. Results from the three analyses can be used separately or in conjunction to provide a complete picture concerning the nature and relationship between PHEV loads and system impacts. Additional, details of each of the three assessment types are provided in [1, 5]. The focus in this paper is to provide key findings and results related to the HQ circuit.

Figure 6 Impact Analysis Framework

C. Feeder Loading Based on the assumed PEVs characteristics, the total PEV demand seen at the LV side of the substation transformer serving the feeder under study was estimated from the stochastic simulations. For instance, the additional average near-term PEV hourly demand for the high penetration case, shown in Figure 7, is expected to be less than 0.483MW. This additional loading on the transformer represents only 1% of the 45.3 MVA transformer’s rating.

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VI. SUMMARY AND FUTURE WORK

VII. REFERENCES [1] J. Taylor, A. Maitra, M. Alexander, D. Brooks, M. Duvall, Evaluation of

the impact of PEV Loading on Distribution system operations, IEEE Power Engineering Society, Calgary, July, 2009

[2] M. K. Meyers, K. Schneider, R. Pratt, “Impacts Assessment of Plug-in Hybrid Vehicles on Electric Utilities and Regional US Power Grids Part

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1: Technical Analysis,” Pacific Northwest National Laboratory, Nov 2007.

[3] M. J. Scott, M. K. Meyers, D. B. Elliott, W. M. Warwick, “Impacts Assessment of Plug-in Hybrid Vehicles on Electric Utilities and Regional US Power Grids Part 2: Economic Assessment,” Pacific Northwest National Laboratory, Nov 2007.

[4] S. W. Hadley, A. Tsvetkova, “Potential Impacts of Plug-in Hybrid Electric Vehicles on Regional Power Generation,” ORNL/TM-2007/150, Jan 2008.

[5] A. Maitra, K. Kook, J. Taylor, A. Giumento, „Evaluation of PEV Loading on Hydro-Quebec’s Distribution System Operations, EVS24, Stavanger, Norway May 13-16, 2009

VIII. BIOGRAPHIES Arindam Maitra (M’1995) received his BSEE, MS, and Ph.D. degrees from R.E.C. Nagpur and Mississippi State University in 1995, 1997 and 2002, respectively. He is currently a senior project manager in System Studies Group at EPRI where he is responsible for conducting and managing activities associated with distribution and transmissions studies. His research interests are in the areas of modeling and simulation techniques for power system harmonics, power system transients, distribution reliability, load modeling, computer applications in power systems, and power system control and protection. He has authored and co-authored numerous technical papers on such topics. Kyung Soo Kook received his B.S. and M.S. degrees from Korea University, South Korea, in 1996 and 1998, and Ph.D. from Virginia Polytechnic Institute and State University (Virginia Tech.), USA, in 2007, respectively, all in Electrical Engineering. From 1998 to 2004, he was a senior researcher at Korea Electro-technology Research Institute (KERI) where he leadingly performed various research projects related to FACTS, transmission system planning, and electricity market operation. In May 2007, he joined the Electric Power Research Institute (EPRI) where he is a senior project engineer and has performed studies for wide-area power system frequency monitoring, renewable energy resources interconnection, power grid efficiency improvement, and PHEV impacts on distribution systems. Jason Taylor (M’1998) received his B.S. and M.S. degrees in electrical engineering from Mississippi State University and his Ph.D. from Auburn University. He is currently a senior project engineer in the systems analysis and studies group at EPRI, Knoxville, where he conducts power system engineering analytical studies spanning transmission, distribution, and system operations. His research interests include the areas of power system modeling, system identification and optimization, as well as controls. Angelo Giumento (B.Eng’2004) received his B.Eng degree in electrical engineering from McGill university in Montreal. He is currently a distribution planning engineer at Hydro-Québec Distribution strategic planning division in Montreal, where he conducts different distribution planning studies. His research interests include load impact studies as well as modelling. Before joining Hydro-Québec, Angelo was a program manager at CEATI International.

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