1
Uncoordinated Charging Impacts of Electric Vehicles on Electric Distribution Grids: Normal and Fast Charging Comparison E. Akhavan-Rezai, Student Member, IEEE, M. F. Shaaban, Student Member, IEEE, E. F. El-Saadany, Senior Member, IEEE, and A. Zidan, Student Member, IEEE
Abstract-- Plug-in electric vehicles (PEVs) have uncertain penetration in electric grids due to uncertainties in charging and discharging patterns. This uncertainty together with various driving habits makes it difficult to accurately assess the effects on local distribution network. Extra electrical loads due uncoordinated charging of electric vehicles have different impacts on the local distribution grid. This paper proposes a method to evaluate the impacts of uncoordinated PEVs charging on the distribution grid during peak period. Two PEVs charging scenarios are studied, including normal and fast charging. The impact analysis is evaluated in terms of voltage violations, power losses and line loading, which is implemented on a real distribution system in Canada. The results of the analysis indicate that there are significant impacts on distribution networks due to PEVs charging, which limits the accommodation of desired penetration levels of PEVs. Index Terms-- Distribution networks, fast charging, load flow, loss analysis, plug-in electric vehicle, state of charge.
I. INTRODUCTION
P
lug-in electric vehicles introduce a new concept of benefiting from electricity as the economic clean transportation technology. They have considerably less contribution in green house gases, and they can reduce the dependency of expensive fossil fuels. However, due to the effects of PEVs on power sector and the electric network operation, utilities need to concern reliable and safe operation of the network in presence of PEVs. A number of studies have discussed economic costs and benefits of PEVs on customers [1-4]. While, recently the more important question grabs electric utilities awareness: “What impacts will PEVs have on the electric distribution network?”. According to [5], the following aspects of PEVs impact on electric network: • • • •
Driving Pattern Charging Characteristics (Vehicle demand Profile) Charging timing (When they plug-in and the magnitude and duration of charging cycle) Vehicles penetration
The authors are with the Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada (e-mail:
[email protected]).
978-1-4673-2729-9/12/$31.00 ©2012 IEEE
Recently, a number of researches attempt on studying the expected PEVs impacts on electric distribution grids. Previous studies focused primarily on either technical impacts on distribution grids, or economical assessment and economic incentives of PEVs. Researches that attempt on PEV’s impact develop analytical methods to evaluate the following features of the distribution network [1-4], [6-23]: • • • • • • • •
System thermal loading Voltage profile Losses Unbalanced Transformer loss of life Harmonic distortion Demand response Resource scheduling with PEVs and renewable generation, and CHPs.
Among those, a number of studies focused on the impacts of charging the PEVs on lines’ loading, such as in [8, 16, 17, 19], where a typical distribution network is analyzed considering daily trip of the vehicles in different seasons. In [16], a method is proposed to evaluate the load profile imposed on a power system by grid-charging of the electric vehicles using a large database of field-recorded driving cycles, and parking times and locations. It applies fuzzy-logic inference to imitate the decision-making process of a driver when deciding to charge the electric vehicle. Another methodology is proposed in [17] to examine the impacts of charging PEVs on distribution transformer loading under different charging scenarios. It proposes demand side management solutions to overcome harmful peaks due PEV’s charging. The proposed demand management strategies enjoy benefitting from advanced metering infrastructure (AMI) to monitor household loads, together with a PEV control unit and remote switches which control the ON/OFF status of PEV outlets and household loads. The vehicles and trip characteristics are extracted in [8] from available travel survey data. It applies three charging scenarios, including off-peak charging, coordinated peak charging, and price based charging, where various PEV charging load profiles are generated based on the raw data. When such information is obtained through real raw data, it can be used for deducing the vehicle-to-grid potentials.
2
However, there is a lack of enough recorded data on PEVs as they rarely substitute the conventional vehicles so far. Impacts on different charging scenarios are studied by [19] as well, where the results indicate that without a comprehensive and effective PEV charging control, there will be undesirable increases in peak demands, and the existing electric network cannot respond to these requests. In addition of system loading behavior, number of studies investigates loss analysis in their research [9, 14, 18]. A load management solution is proposed by [9] based on coordinating the charging of PEVs in a smart grid system, using a real-time control strategy for energy loss minimization. A coordinated charging method is proposed in [14] to minimize the power losses and to maximize the main grid load factor using a stochastic programming to forecast household loading. The results of optimal coordinated analysis indicate that although the coordinated charging of PEVs can improve power losses and voltage deviations, in some cases grid reinforcements will be necessary. A feasibility study is performed in [18] to analyze optimal utilization of a real grid potential for charging the PEVs in terms of power loss. As the start charging time of the PEV has significant impact on the distribution network, especially in peak demand periods, it is necessary to either control the charging start time by the energy price tariffs and rates, or smart charging algorithms and infrastructures. Studies also address various strategies that apply demand side management techniques such as electricity ratings and smart PEV charging to provide policy makers and stakeholders with peak control and reduction. The impacts of time-of-use (TOU) electricity pricing schemes on customer behaviors during peak time in presence of PEVs are analyzed in [6]. However, the work in [6] discuses the customer behavior based on different factors such as seasons, PEV penetration levels and PEV charging manners. A method of smart charging is developed in [23] using an aggregator profit maximization to select the optimal set point for a PEV to start charging. The algorithm is simulated on a hypothetical group of cars using local transport, load, and price data. Each study on PEVs-related issues, as they are addressed in the previous works, provides recommendations for utilities on how to better optimize the integration of PEVs into the distribution network. For the analysis to be more effective and applicable, it is required to investigate a system-specific approach. This paper is aimed at comparing normal and fast charging scenarios of the PEVs in terms of network performance. Although, the normal charging which takes long charging period slightly affects the electric network operation, fast charging (with higher energy demand) needs to be considered in areas with severe weather conditions such as Canada to avoid battery depletion. The method proposed here applies load flow analysis in both charging scenarios with increasing the PEV’s penetration to examine how the network performance would be violated by PEVs penetration. The proposed method is implemented in a real distribution system
in Ontario, Canada. The rest of the paper is organized as description of the methodology used for study of the impacts of EV in section II. It is followed by modeling of the system and plug in electric vehicle in sections III. Eventually, the implementation results and discussions are presented in IV. II. METHODOLOGY A. Problem Statement One of the major questions being faced by the electric utilities today is whether the existing distribution network infrastructure would be able to serve mass introduction of PEVs and if not, what are the necessary network requirements and reinforcement. PEVs have uncertain penetration in electric grids due to uncertainties in charging and discharging patterns. This uncertainty together with various driving habits makes it difficult to accurately assess the effects on local distribution network. The uncoordinated and random charging activities of PEVs could significantly stress the distribution system causing: • Severe voltage fluctuations and violations, • Degraded system efficiency and economy, • Increasing the likelihood of blackouts due to network overloads. Furthermore, the charging level of the PEVs would distress the distribution grid to some extent. Therefore, the planners should evaluate the maximum possible penetration of PEVs in order to maintain seamless operation of the present network without violating its technical constraints. B. Proposed Method This study considers the impact of uncoordinated charging of electrical vehicles during daily charging cycle. The uncoordinated charging means that the batteries of the vehicles starts charging immediately when they are plugged in or after an adjustable fixed start delay which is defined by the user. The grid performances are evaluated in terms of voltage violation, power losses and line loading due to different PEVs penetration levels and charging scenarios. The charging scenarios include normal and fast charging. The analysis starts with a base case, which is zero penetration of PEVs, and follows by increasing the PEVs penetration levels up to 30%. Consequently, the distribution grid performance is evaluated according to voltage violation and power losses, as well as, lines loading levels. This process is applied to two different normal and fast charging scenarios to illustrate how different charging patterns might effect on system performance. Eventually, the optimum possible penetration of uncoordinated PEV charging is obtained based on the evaluations for each scenario. Fig. 1 shows the flowchart of the proposed method, which is implemented on a real distribution system in Canada.
3
Fig. 2. Topology of the test system TABLE I Test System Loading Information Fig. 1. Flowchart of the proposed method
III. SYSTEM AND MODELING This section presents the implementation process of applying the proposed method to a practical distribution system. A brief explanation of the test system, including topology, line and loading data is presented here. It is followed by the details of the PEVs model, including PEVs characteristics and charging levels. A. System under Study The system under study is a practical rural distribution system with a peak load of 16.18 MVA. The main substation at bus 1 is used to feed a rural area, and the maximum feeder capacity is 300 A. The topology of the test system is illustrated in Fig. 2. The detailed data about line parameters and the transformers are available in [24, 25]. Table I represent the active and reactive power as well as the number of households correspond to each load point of the system. Since it is not easy to have the exact number of households in each load points of the distribution feeder, it is estimated based on the statistics in [26]. According to [26], typical power requirement of a residential household is 5-7kW. The authors assume the average of 6kW for each household in this study. As this is a rural distribution system, it is further assumed that the residential loads are the dominant load type and only the beginning of the feeder includes all three load types of residential, commercial, and industrial. While the remained part covers the residential loads only. B. PEV Characteristics According to National Household Travel Survey (NHTS), there are four different PEV types [27]. The type name, the percentage, and the total charging energy demands are shown in Table II considering 20, 30, and 40 miles possible driving with electric energy.
Bus # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
Load (MW) 0 0 0 6.41346 0 0.90306 0 3.18725 0 0.576 0 0 0.019 0.34675 0 0 0 0 0 0 0 0.0475 0.0095 0 0.28975 0 0.152 0 0 0.19475 0.51775 0 0 0.20425 0 0.8075 0.1045 0 0 0 0.71193
Load (Mvar) 0 0 0 2.108 0 0.51179 0 1.0476 0 0.50798 0 0 0 0.11397 0 0 0 0 0 0 0 0.01561 0.00312 0 0.09524 0 0.04996 0 0 0.06401 0.17018 0 0 0.06713 0 0.02654 0.03434 0 0 0 0.71193
# of Households 0 0 0 320.673 0 45.153 0 531.2083333 0 96 0 0 3.166666667 57.79166667 0 0 0 0 0 0 0 7.916666667 1.583333333 0 48.29166667 0 25.33333333 0 0 32.45833333 86.29166667 0 0 34.04166667 0 134.5833333 17.41666667 0 0 0 118.655
TABLE II Different Type of PEVs with Charging Power Demand PEV-20 PEV-30 PEV-40 Type Percentage (kWh) (kWh) (kWh) Compact Sedan 60.85% 6.51 9.765 13.02 Mid-size Sedan 11.94% 7.21 10.815 14.42 Mid-size SUV 13.1% 8.75 13.125 17.5 Full-size Sedan 14.11% 10.15 15.225 20.3
4
C. PEV Chargin ng Levels For estimating the power consumption of the PEV, the traavel patterns sh hould be taken n into account. EPRI introdu uced thrree levels of ch harging standaard which is ap pplicable in No orth Am merica [28]. Charging leveel has a direct effect on the chharging time length. Three ch harging levels of EPRI stand dard is illustrated in Table III. Thiis study consid ders the two first f o which aree the standard household outtlets chharging levels only off 110 V/15 A and 240 V/30 0 A, known ass normal and fast f chharging respectively. Accordiing to PEVs manufacturers m su uch as Chevrolet [29 9], 2nd level charger c is preeferred for sev vere weeather conditio ons, and is exp pected to be th he most comm mon chharger used in Canada. C This iss due to the fasst depletion of the veehicle battery as a result off extra loading g of keeping the veehicle in comfo ortable temperaature for the paassengers. Besides, vehiicles with high her state of ch harge (SOC) –the – peercentage of rem mained charge when the vehiicle arrives from ma daaily trip– are charged c in a shorter s time. In I this study it is asssumed that thee SOC is zero and a there is no o charge remain ned whhen the vehiclee is plugged in.. TAB BLE III EPRI Standard on Threee Charging Level of o PEV Charging Specification Level 1
120 VAC, 15 5A (12A), Single-p phase, 1.44 kW/h
2
240 VAC, 40A 4 (30A), Singlee-phase, 6kW/h
3
480 VAC C, Three-phase, 60 0-150 kW/h
D. Number of PE EV Estimation and Charging g Time Nuumber of vehiccles per househ hold is another factor that sho ould bee considered in this anallysis. Accordiing to Natio onal Hoousehold Trav vel Survey (N NHTS), [30], there are 1.86 1 veehicles per houssehold. On the other hand, chargin ng start time, at a which vehiccles aree plugged in, impacts i the neetwork perform mance. The NH HTS exxtracted the arriival statistics of o the vehicles in North Amerrica whhich is illustratted in Fig. 3, [3 30]. This statisstics indicates that t moore vehicles arrrive between 4pm-8pm. Th his interval meeets thee peak electricity demands which happen n around 6-9p pm. Thherefore, it is essential to be b taken in to o account in the annalysis as well.
Fig. 3. Arriv val time distributio on of vehicles in North N America
Conssidering all thhe mentionedd assumptionss, the extra loading due PEV charging in eachh residential lload can be expresseed as (1):
Pres,ii = PLi + PPEVi
(1)
Where, Presi represennts the total ppower demandd in the ith residenttial load, andd PLi and PPEEVi represents the power demandd of residentiall load and thee correspond P PEV of that load. wer demand oof the PEVs would vary Moreeover, the pow based onn the PEV chaarging level annd PEVs penetration. This variationn is mathematiically represennted in (2):
⎧1.44 × A% × N PEV ,i for normal ch argging PPEV ,i = ⎨ for ffast ch arg ing ⎩6× A%×N PPEV ,i
(2)
Where A A% is the PEV V penetration, aand NPEV,i is thhe number of vehicle per household which is equaal 1.86 accordinng to [30]. V. IMPLEMENTTATION RESULTTS AND DISCUSSSIONS IV The results of im mplementation the proposedd method is presenteed in this sectiion. The load fflow analysis iis performed based oon Newton-Raaphson methood to assess the voltage deviatioons and the pow wer losses in tthe distributionn test system due unccoordinated PEVs chargingg, including tw wo charging level sceenario: 1st leveel (12A, 1.44 kkW/h), and 2ndd level (30A, 6 kW//h). The anaalysis consideers the worsst case of uncoorddinated PEVs ccharging in peaak demand onlyy. It staarts with the baase case load fflow on the syystem, which is normaal loading in ppeak demand w with zero penettration of the PEVs annd follows by increasing the PEV’s penetration as 5%, 15%, 255%, and 30%.. The load flow w analysis is repeated for both noormal and fastt charging levels. Figs. 4-6 address the results oof the load flow w analysis. Accoording to Figg. 4, maximum m voltage deeviation due normal charging of P PEVs (level 1) reaches up too 6% in few load poiints of the systtem (load poinnts at the end oof the system only), annd in 15% PEV V penetration oor more. Whilee, increasing the pennetration of PE EVs in fast chharging scenarrio (level 2) indicatees a significantt increase in vooltage digressiions. As it is illustrateed in Fig. 5, ffor the penetraation of 25% and 30% of PEV unncoordinated chharging the volltage drops 6% %-8% in 50% of loadd points whicch is significcantly violatedd from the minimum um acceptable vvoltage threshoold. Furthhermore, Fig. 6 compares thhe maximum vvoltage drop due diffferent PEV ppenetrations iin both norm mal and fast chargingg scenarios. T The maximum m voltage dropp in normal chargingg reaches to 00.943 p.u. Whiile, for the fastt charging it drops drramatically to 00.931 p.u.
5
the disstribution systtem to mainntain the dessirable grid perform mance. The loadd flow simulattion includes nnetwork loss analysiss as well to dem monstrate how the network w would affects of PEV charging in terrms of power lloss. As can bee seen in Fig. 7, the neetwork losses iincrease signifficantly with inncreasing the PEV peenetration due 2nd level charrging (fast chaarging). The ratio of the losses to tthe total energyy demand of thhe system is summarrized in Table IV. Regardingg the engineerring practice of maxiimum 3% for acceptable loosses in the neetwork, it is deducedd that the norm rmal charging does not viollate the loss thresholld. However, in case of faast charging tthe network losses rreach to more than 5% in hhigh penetratioon of PEVs (30% peenetration).
Fig. 4. Results of load flow anaalysis: Normal Chaarging Scenario
Fig. 7. Networkk power losses duee different penetrattion TABLE IV Loss to total energy demand ratio PEV C Charging level 1 Charging llevel 2 P Penetration (N Normal Charging)) (Fast Charrging)
ults of load flow an nalysis: Fast Charg ging Scenario Fig. 5. Resu
0%
2.72%
2.72% %
5%
2.79%
3.04% %
15%
2.95%
3.76% %
25%
3.12%
4.57% %
30%
3.19%
5.2%
Besiddes of load fflow analysis, the line loading of the system due two chharging scenarrios and diffferent PEVs penetrattion are furth ther examinedd to assess impacts of uncoorddinated charginng in peak tim me on total enerrgy demand. Consideering 300 A and 27.5 kV as the base current and voltage of the test sysstem and 14.5 kW peak dem mand (as can be seenn in Fig. 8), itt is simply viisible that in uunity power factor, the system is operating in full load with zero penetrattion of PEVs (ssee (3)).
P = 3 V . I . cosφ Fig. 6. Maximum voltage drop p due different PE EV penetration
Not only the voltage deviattions but also the power lossses, off the grid voltaage are essentiaal parameter fo or the operatorr of
(3)
As itt is illustratedd in Fig. 8, thhe line loadingg reaches to around 18 kW and 200 kW due fast charging of 255% and 30% PEV peenetration respectively. Thiis condition exposes the system tto huge loadinng during the ppeak demand. T This loading amount violates the acceptable shhort period liine loading.
6
W While, in normal charging the maximum loading reaches only o to less than 16 kW W in PEV peneetration of 30% %. From the persspective of feeeder loading th he fast charging g is praactical only for less than n 15% penetrration of PE EVs. Hoowever, for no ormal charging g up to 25% PE EV penetration n is appplicable for a short perio od of chargin ng. These resu ults or fast chargin ng scenario th he uncoordinaated inddicate that fo chharging is not applicable a in wiide range of PE EV penetration ns.
[5] [6]
[7]
[8] [9]
[10] [11]
[12] [13] Fig. 8. Netwo ork total demand due d different penetrration of PEVs. [14]
V. CON NCLUSION This study considered the integration off plug-in elecctric veehicles to the distribution grid d. The impacts of uncoordinaated chharging were ev valuated on a real test feedeer in Canada. The T annalysis was performed in term ms of voltage drop, d power lossses annd line loading to address how w different charrging scenarioss of PE EV charging would w violate the network performance. To revveal a true picture p of grradually variattions on systtem peerformance, different penettrations of PEVs P which are unncoordinated charging c in peak p demand were taken into i account. ysis illustrateed that in high h The results of the analy peenetration of PEVs the un ncoordinated charging c violaated system performaance and it iss not applicab ble for a pressent work. The find dings also high hlighted the need disstribution netw forr applying loaad managemen nt practices forr residential PEV P chharging, especcially in fast charging sccenario to av void exxcessive system m stresses and losses. VI. REFFERENCES [1]
[2]
[3] [4]
A. Simpson, “Cost-Benefit Anallysis of Plug-In Hy ybrid Electric Veh hicle wable Energy Laboratory, L in 22nd 2 Technology”, National Renew d Fuel Cell Electric Vehicle Sympossium International Battery, Hybrid and hama, Japan, Octob ber 23–28, 2006. and Exhibition (EVS-22), Yokoh M Kintner-Meyerr, D. B. Elliot, and a W. M. Warw wick, M. J. Scott, M. “Impacts assesssment of plug-in n hybrid electric vehicles on elecctric utilities and reg gional U.S. power grids: Part 2: Econ nomic assessment”, in 10th Annual EU UEC Conference, Tucson, AZ, Jan. 2007. 2 S. L. Judd, T. J. Overbye, “An Evaluation of PH HEV Contribution ns to m Disturbances an nd Economics”, 40th 4 North Amerrican Power System Power Symposiium, 2008. M Hajian, H. Zareipour, W. D. Rosehart, “Envirronmental Benefitts of Plug-in Hybrid Electric Vehicles: the Case of Albeerta”, IEEE Power and Energy Society General Meeting,, 2009.
[15] [16] [17] [18]
[19]
[20] [21] [22] [23]
R. C C. Green II, L. W Wang, M. Alam, “The Impact of P Plug-in Hybrid Elecctric Vehicles on Distribution Netw works: a Review and Outlook”, IEEE E Power and Enerrgy Society Generaal Meeting, 2010. S. S hao, T. Zhang, M M. Pipattanasomporrn,, S. Rahman, “IImpact of TOU Ratees on Distributioon Load Shapes in a Smart Griid with PHEV Peneetration”, in Proc . IEEE Transmisssion and Distribuution Conf. and Expoosition, New Orleaans, LA, April 20110. S. B Babaei, D. Steen, L. A. Tuan, O. C Carlson, L. Bertliing, “Effects of Plugg-in Electric Vehhicles on Distribuution Systems: A Real Case of Gothhenburg”, IEEE C Conf., Innovative SSmart Grid Technoologies Europe, 20100. Z. D Darabi, M. Ferdow wsi, “Aggregated Impact of Plug-in Hybrid Electric Vehhicles on Electriccity Demand Proofile”, IEEE Tranns. Sustainable Enerrgy, VOL. 2, NO. 4, October 2011, ppp. 501-508. S. D Deilami, A. S. Massoum, P. S. Mosess, M. A. S. Masouum, “Real-Time Cooordination of Plugg-In Electric Vehiicle Charging in Smart Grids to Minnimize Power Lossses and Improvee Voltage Profile””, IEEE Trans. NO. 3, September 2011, pp. 456-4677. Smaart Grid, VOL. 2, N A. A Ashtari, E. Bibeauu, S. Shahidinejaad, T. Molinski, ““PEV Charging Proffile Prediction andd Analysis Based on Vehicle Usagge Data”, IEEE Tranns. Smart Grid, 20011. Pp. 1-10. V. M Marano, G. Rizzooni, “Energy and E Economic Evaluaation of PHEVs and their Interaction with Renewable Energy Sources and the Power nference on Vehicuular Electronics Gridd”, in Proc. IEEE IInternational Conf and Safety, Columbuss, OH, USA. Septeember 2008. Tulpule, V. Maranno, S. Yurkovichy, G. Rizzoni, “Ennergy Economic P. T Anaalysis of PV basedd Charging Stationn at Workplace Paarking Garage”, in Prroc. IEEE Energytthech, 2011. J. Taaylor, A. Maitra, M M. Alexander, D. Brooks, M. Duvaall, “Evaluation of tthe Impact of Pllug-in Electric V Vehicle Loading oon Distribution Systtem Operations”, IIEEE Power and E Energy Society Geeneral Meeting, 20099. K. C Clement-Nyns, E. H Haesen, J. Driesenn, “The Impact of Charging PlugIn H Hybrid Electric Veehicles on a Residdential Distributioon Grid”, IEEE Tranns.Power Systems,, VOL. 25, NO. 1, February 2010, ppp. 371-380. J. G G. Vlachogiannis, ““Probabilistic Connstrained Load Floow Considering Vehicles”, IEEE Integgration of Wind Power Generation and Electric V Tranns.Power Systems,, VOL. 24, NO. 4, November 2009, pp. 1808-1817. S. S Shahidinejad, S. Fiilizadeh, E. Bibeaau, “Profile of Chaarging Load on Grid Due to Plug-iin Vehicles”, IEEE E Trans. Smart Grrid, 2011, pp. 1the G 7. Shao, M. Pipattanaasomporn, Saifur Rahman, “Challeenges of PHEV S. S Peneetration to the Residential Distributtion Network”, IE EEE Power and Enerrgy Society Generral Meeting, 2009. A. H Hajimiragha, C. A A. Canizares, M. W W. Fowler, A. Elkamel, “Optimal Trannsition to Plug-Inn Hybrid Electricc Vehicles in Onntario, Canada, Connsidering the Elecctricity-Grid Limittations”, IEEE Trrans, Industrial Elecctronics, VOL. 57,, NO. 2, February 2010, pp. 690-7011. Leonnor Trovao, Hum mberto M. Jorge, “Power Demand Impacts of the Charrging of Electric Vehicles on the P Power Distributionn Network in a Resiidential Area”, IE EEE Proceedings of the 3rd Interrnational Youth Conf nference on Energeetics, 2011. M. M Mallette, G. Venkkataramanan, “Thee Role of Plug-In H Hybrid Electric Vehhicles in Demand Response and Beeyond”, IEEE Traansmission and Disttribution Conferennce and Expositionn, 2010. A. Y Y. Saber, G. K. Venayagamoorthyy, “Resource Schheduling Under Unccertainty in a Smaart Grid with Rennewables and Plug-in Vehicles”, IEEE E System Journal,, 2011, pp. 1-7. S. A Acha, T. C. Greenn, N. Shah, “Effeccts of Optimized Plug-in Hybrid Vehhicle Charging Strrategies on Electriic Distribution Neetwork Losses”, IEEE E Transmission annd Distribution Coonference and Expposition, 2010. E. S Sortomme, M. A.. El-Sharkawi,” O Optimal Chargingg Strategies for Uniddirectional Vehiclee-to-Grid”, IEEE Trans. Smart Gridd, VOL. 2, NO. 1, MAR RCH 2011, pp. 131-1138.
[24] Y. M M. Atwa, E. F. El-Saadany, M. M M. A. Salama, R R. Seethapathy, “Opptimal Renewable Resources Mix for Distribution S System Energy wer Systems, VOL. 25, NO. 1, Losss Minimization”, IEEE Trans.Pow Febrruary 2010, pp. 3660-370. [25] Y. M M. Atwa, “ Distribbution System Plannning and Reliabillity Assessment undeer High DG Pennetration”, PhD tthesis, Electrical and Computer Engiineering Universiity of Watreloo, 20010. [26] M. U Uzunoglu, M. S. A Alam, Senior Mem mber, IEEE, “Dynaamic Modeling, Desiign, and Simulaation of a Com mbined PEM F Fuel Cell and Ultraacapacitor System m for Stand-Alone Residential Appliications”, IEEE
7
[27] [28] [29] [30]
Trans. Energy Conversion, VOL. 21, NO. 3, September 2006, pp. 767775. 2001 National Household Travel Survey User Guide, 2004. Plug-in electrical Vehicle Infrastructure Installation Guidelines, EPRI report, Volume 1: Multi-Family Dwellings, September 2009. Chevrolet plug in electric vehicle website, URL is available at: http://www.chevrolet.com/volt-electric-car/ 2009 National Household Travel Survey: Summary of Travel Trends, 2011.
VII. BIOGRAPHIES Elham Akhavan-Rezai(S’08) was born in Tehran, Iran, 1982. She received her BS from Gilan University, Rasht, Iran in 2005, and her M.Sc from Islamic Azad University, Tehran-South Branch, Iran, in electrical engineering. She is currently pursuing the Ph.D. degree in the Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada. Her research interests include power distribution system reliability, plug-in electric vehicles, smart grid and distribution automation, and data mining applications in distribution grids. Mostafa F. Shaaban(S’11) was born in Virginia, USA, 1982. He received the B.Sc. and M.Sc. degrees from Ain Shams University, Cairo, Egypt in 2004 and 2008 respectively, both in electrical engineering. He is currently pursuing the Ph.D. degree in the Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada. His research interests include electrical machines, reliability, renewable DG, distribution system planning, electric vehicles, storage systems and smart grid. E. F. El-Saadany (SM’05) was born in Cairo, Egypt, in 1964. He received the B.Sc. and M.Sc. degrees in electrical engineering from Ain Shams University, Cairo, Egypt, in 1986 and 1990, respectively, and the Ph.D. degree in electrical engineering from the University of Waterloo, Waterloo, ON, Canada, in 1998. Currently, he is a professor in the Department of Electrical and Computer Engineering, University of Waterloo. His research interests are distribution system control and operation, power quality, distributed generation, power electronics, digital signal processing applications to power systems, and mechatronics. AboelsoodZidan(S’11) was born in Sohag, Egypt, in 1982. He received B.Sc. and M.Sc. degrees from AssiutUniversity, Assiut, Egypt, in 2004 and 2007, respectively, both in electrical engineering. He is currently pursuing a Ph.D. degree in the Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada. His research interests include distribution automation, renewable DG, distribution system planning, and smart grids.
