IEEE TRANSACTIONS ON SMART GRID, VOL. 3, NO. 1, MARCH 2012 559

Autonomous Distributed V2G (Vehicle-to-Grid)Satisfying Scheduled Charging

Yutaka Ota, Member, IEEE, Haruhito Taniguchi, Tatsuhito Nakajima, Member, IEEE,Kithsiri M. Liyanage, Senior Member, IEEE, Jumpei Baba, Member, IEEE, and Akihiko Yokoyama, Member, IEEE

AbstractTo integrate large scale renewable energy sources inthe power grid, the battery energy storage performs an importantrole for smoothing their natural intermittency and ensuring grid-wide frequency stability. Electric vehicles have not only large intro-duction potential but also much available time for control becausethey are almost plugged in the home outlets as distributed batteryenergy storages. Therefore, vehicle-to-grid (V2G) is expected to beone of the key technologies in smart grid strategies. This paper pro-poses an autonomous distributed V2G control scheme. A grid-con-nected electric vehicle supplies a distributed spinning reserve ac-cording to the frequency deviation at the plug-in terminal, whichis a signal of supply and demand imbalance in the power grid. Asa style of EV utilization, it is assumed that vehicle use set nextplug-out timing in advance. In such assumption, user convenienceis satisfied by performing a scheduled charging for the plug-out,and plug-in idle time is available for the V2G control. Thereforea smart charging control is considered in the proposed scheme.Satisfaction of vehicle user convenience and effect to the load fre-quency control is evaluated through a simulation by using a typ-ical two area interconnected power grid model and an automotivelithium-ion battery model.Index TermsElectric vehicle, load frequency control, smart

charging, smart grid, state-of-charge, vehicle-to-grid.

I. INTRODUCTION

I NTERMITTENT renewable energy sources (RES) requireadditional dispatching resources such as thermal powergenerations, adjustable speed pumped storages, and batteryenergy storages. Smart grid strategies are expected to utilizedistributed generations and controllable loads in the demandside. Authors have proposed the ubiquitous power grid conceptin Fig. 1. Controllable RESs, heat pump water heaters, andbattery energy storages are integrated in the load frequencycontrol (LFC) of the grid and the regional energy managementsystem (EMS) of the distribution grid [1][4].

Manuscript received April 02, 2011; revised August 06, 2011; acceptedAugust 30, 2011. Date of publication October 28, 2011; date of current versionFebruary 23, 2012. This work was supported by Grant-in-Aid for ScientificResearch (B), Grant-in-Aid for Young Scientists (B) form Japan Society forthe Promotion of Science, Specially Promoted Research Grant from PowerAcademy of Japan, and Ubiquitous Power Grid Endowed Chair of Center forAdvanced Power & Environmental Technology (APET) of the University ofTokyo. Paper no. TSG-00133-2011.Y. Ota, H. Taniguchi, and T. Nakajima are with the University of Tokyo,

Tokyo 113-8656, Japan (e-mail: yota@upn.t.u-tokyo.ac.jp; taniharu@upn.t.u-tokyo.ac.jp; nakajima@p-front.t.u-tokyo.ac.jp).K. M. Liyanage is with the University of Peradeniya, Peradeniya 20400, Sri

Lanka (e-mail: liya@ee.pdn.ac.lk).J. Baba and A. Yokoyama are with the University of Tokyo, Chiba 277-8568,

Japan (e-mail: baba@asc.t.u-tokyo.ac.jp, yokoyama@syl.t.u-tokyo.ac.jp).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TSG.2011.2167993

Large scale integration of electric vehicles (EV) and plug-inhybrid vehicles (PHV) for the transportation electrificationbrings large potential of vehicle-to-grid (V2G) [5][7]. Aggre-gated V2G pool consisted by huge EVs contributes greatly tothe supply and demand dispatch, and each EV user may obtainthe incentive cost [8], [9]. V2G control strategies in the LFCand the regional EMS have been proposed in the ubiquitouspower grid concept [10], [11].This paper proposes an autonomous distributed V2G control

scheme providing a distributed spinning reserve for the unex-pected intermittency of the RESs. A droop control based on thefrequency deviation at plug-in terminal realizes a fast and syn-chronized response among multiple vehicles. Battery state-of-charge (SOC) is managed by using a balance control. And asmart charging control is applied for satisfying the scheduledcharging request by the vehicle user.Proposed V2G control scheme is explained in Section II,

then verified by a simulation using a two area interconnectedpower grid model and an automotive lithium-ion battery modelin Sections III and IV.

II. V2G CONTROL SCHEME

A. Autonomous Distributed V2G

Supply and demand imbalance of the power grid can beobserved from the frequency deviation detected at home outlet[12][14]. Therefore V2G power is controlled withdroop characteristics against the frequency deviation asfollows and shown in Fig. 2 [15]:

(1)

where maximum V2G power is limited by the specifi-cations of the home outlet, and V2G gain is decidedconsidering a tradeoff between effect for the LFC and the fluc-tuation range of the battery SOC.When the SOC is near to full (empty), a high-power charging

(discharging) should not be implemented for preventing over-charge (overdischarge). During long-term V2G cycles, the SOCis concerned to be full or empty because a mean value of thefrequency deviation is not always zero and there is a loss of thebattery. Considering these features, a balance control is installedas the following equation on the premise that the accurate SOCestimation is realized [15]:

(2)

1949-3053/$26.00 2011 IEEE

560 IEEE TRANSACTIONS ON SMART GRID, VOL. 3, NO. 1, MARCH 2012

Fig. 1. V2G in ubiquitous power grid.

Fig. 2. V2G control with droop against frequency deviation.

where is maximum V2G gain. , ,, , and are designed as the SOC is balanced

around 50% as shown in Fig. 3.

B. Smart Charging

For satisfying the scheduled charging, the V2G control isswitched to a smart charging control with a charging offset ofhalf the maximum V2G power and a half droop gainagainst the frequency deviation as follows and shown in Fig. 2:

(3)

Fig. 3. Battery SOC balance control.

If the frequency deviation falls below a minimum thresholdvalue , the maximum discharge is instantly supplied forthe grid.Necessary energy for charging to the destination SOC

is estimated by using the battery model as (6) explained in thenext chapter. Considering the mean value of the frequency devi-ation would be zero, the duration for the smart chargingis estimated by taking the charging offset into account as thefollowing equation:

(4)

When the estimated duration for the smart charging islonger than the actual duration to the plug-out time, the V2Gcontrol is switched to the smart charging control.

OTA et al.: AUTONOMOUS DISTRIBUTED V2G (VEHICLE-TO-GRID) SATISFYING SCHEDULED CHARGING 561

Fig. 4. Power grid model for calculating frequency of Japanese 50 Hz systems.

TABLE IPARAMETERS OF POWER GRID MODEL

III. V2G AND POWER GRID MODEL

A. Power Grid Model

Fig. 4 shows the power grid model for calculating the fre-quency of Japanese 50 Hz systems [16]. IEEJ East 10-machinesystems [17] are aggregated to a two area interconnected powergrid by using two inertia constants of thermal, hydro, and nu-clear power generation, two damping coefficients consisted byfrequency dependent characteristics of an aggregated load, anda synchronized power coefficient between two grids.Regarding the thermal power generator, 5 [%] of its rated

output is reserved for the governor-free control, and 1.5 [%]of the grid load capacity is reserved for the LFC. The loaddispatching center allocates area requirements (AR) to eachthermal power generator, flat frequency control (FFC) for thegrid-A and tie-line bias control (TBC) for the grid-B. Delaysof the frequency detection and the AR calculation are modeledas first-order lags. Communication delay from the load dis-patching center is modeled as a dead time. The parameters aresummarized in Table I.

TABLE IIPARAMETERS OF V2G CONTROL

TABLE IIISPECIFICATIONS OF BATTERY MODEL

Power fluctuations of the RES are generated by the normaldistributions. Their frequency bands are limited by the low passfilter (LPF) considering smoothing effect of the RESs.

B. V2G Model

Parameters of the V2G control are summarized in Table II.The maximum V2G power is 5[kW] assuming200[V]/25[A] home outlet, and maximum V2G gainis 200[kW/Hz], that is, the maximum V2G power is suppliedwhen the frequency deviation is 0.025[Hz]. The SOC balancecontrol is same as in Fig. 3. Ten minutes margin is consideredfor estimating the duration for the smart charging be-cause there is uncertainty such as a current dependent loss bythe internal resistance.In this paper, 20 000 vehicles are aggregated to a V2G pool

for simplicity of analysis. In the grid-A, there are two V2Gpools. First pool consists of EVs with middle size battery (EV1),and second one consists of EVs with large size battery (EV2).On the other hands, the grid-B has a V2G pool consists of PHVs(PHV1) with small size battery assuming the grid-B locates inthe countryside.

C. Battery Model

In this paper, a simplified battery model consists of voltagesource expressing open circuit voltage (OCV) and internal re-sistance is assumed [15].The battery OCV is defined as the following Nernst equation:

(5)

where and are nominal voltage and capacity, respec-tively. is gas constant, Faraday constant, and batterytemperature, respectively. is a sensitivity parameter between

562 IEEE TRANSACTIONS ON SMART GRID, VOL. 3, NO. 1, MARCH 2012

Fig. 5. Simulation results of V2G control under satisfying charging request. (a) Frequency deviation in grid-A. (b) Frequency deviation in grid-B. (c) Tie linepower flow deviation from grid-A to grid-B. (d) Power outputs of thermal power generation and RES in grid-A. (e) Power outputs of thermal power generationand RES in grid-B. (f) V2G power output of EV1. (g) V2G power output of EV2. (h) V2G power output of PHV1. (i) Battery SOCs of EV1, EV2, and PHV1.

the SOC and the OCV. Necessary energy from the presentSOC to the destination SOC is calculated byintegrating the OCV as follows:

(6)

During charge or discharge with current , battery CCV(Closed Circuit Voltage) and the V2G power arecalculated as follows:

(7)(8)

After all, the battery SOC is updated by the V2G power asthe following differential equation:

(9)

where is current efficiency of the battery.

OTA et al.: AUTONOMOUS DISTRIBUTED V2G (VEHICLE-TO-GRID) SATISFYING SCHEDULED CHARGING 563

TABLE IVQUALITY OF FREQUENCY DEVIATION IN GRID-A

TABLE VFLUCTUATION RANGE OF BATTERY SOC

Three types of automotive lithium-ion battery, Mitsubishii-MiEV [18] (EV1), Nissan Leaf [19] (EV2), Toyota Prius PHV[20] (PHV1), are assumed as Table III. Internal resistances donot necessarily clear, and common value is assumed.

IV. SIMULATION RESULTS

As an assumption of EV utilization, EV1 is plugged into thegrid-A at 2 h with initial SOC as 20[%]. Then EV1 is scheduledto be plugged out with destination SOC as 90[%] after eighthours. On the other hands, EV2 and PHV1 work as the V2Gpool maintaining 50% SOC during the simulation.Simulation results are summarized in Fig. 5. From two hours,

frequency fluctuations caused by the RES fluctuations are com-pensated by the V2G control in both grids. Battery SOC of theEV1 is firstly lifted up to the balanced SOC (50%) by the smartcharging. Then the EV1 supplies charge and discharge cyclesfor the grid by the V2G control from 3.8 hours to 7.2 hours. Fi-nally, the battery SOC achieves the destination SOC (90[%]) bythe second smart charging from 7.2 h to 9.9 h. Quality of the fre-quency is found to be not so degraded because of half droop gainagainst the frequency deviation even during the smart chargingof EV1. The smart charging control of the EV1 does not remark-ably affect the thermal power generation because the amount ofthe charging offset (50[MW]) is relatively smaller than the fluc-tuation components of the thermal power generation. After EV1is done for charging at 9.9 h, EV1 cannot supply any spinningreserve for the grid. However, quality of the frequency is main-tained by the rest of the vehicles plugged-into the grid, PHV1and EV2.Table IV summarizes maximum values, minimum values,

and root mean square (RMS) values of the frequency deviationin the grid-A. Advantage of the proposed V2G control havingfaster response than the governor-free control of the thermalpower generation is numerically confirmed. Table V showsfluctuation ranges of each battery SOC. Fluctuation range of thePHV1 with the small size battery is within 6% or 7%. Thereforethe capacity of the PHV battery is found to be enough for theapplication as the distributed spinning reserve through the homeoutlet. When the medium speed or quick charger in which highpower charge and discharge is assumed, the capacity of thebattery would be more critical for the fluctuation range of thebattery SOC.

V. CONCLUSION

The proposed V2G control is effective for a distributed spin-ning reserve without system-wide information exchange and in-terfering the conventional LFC by the thermal power gener-ations. And the proposed smart charging control satisfies thescheduled charging by the vehicle user. The combined controlscheme of the V2G and smart charging contribute to move to-ward low carbon energy systems through the large-scale inte-gration of intermittent renewable energy sources.A centralized control scheme allocating the LFC signals to

the thermal power generations and EVs have been proposed[10]. It is expected to coordinate the autonomous distributedV2G as a primary control and the centralized V2G as a sec-ondary control. The EVs have a potential for vehicle-to-home(V2H) dispatching rooftop photovoltaic generations andvehicle-to-building (V2B). There is further challenge in man-aging the V2G, V2H, and V2B and then creating synergy effectthroughout the power grids.The proposed control scheme could be easily embedded into

automotive power electronics circuits or household chargingunits to facilitate plug-and-play operation. However, there areresearch subjects on efficiency of the proposed V2G control,impact to the battery life, secure interconnection method to thegrid, and so on.

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Yutaka Ota (M04) was born in Nagano, Japan. Hereceived the B.S., M.S., and Ph.D.Eng. degrees fromNagoya Institute of Technology, Japan, in 1998, 2000and 2003, respectively.He is currently a Project Assistant Professor of

Ubiquitous Power Grid Endowed Chair in the Centerfor Advanced Power and Environmental Technology(APET) of the University of Tokyo, Japan. His re-search interests include vehicle-to-grid technology,modeling of batteries, and application of phasormeasurement unit based wide area measurement

system to power system monitoring, protection, and control.Prof. Ota is a member of CIGRE.

Haruhito Taniguchi was born in Japan. He receivedthe B.S, M.S., and Ph.D. degrees in electrical en-gineering from Kyoto University, Kyoto, Japan, in1973, 1975 and 1994, respectively.In 1975, he joined the Central Research Institute of

Electric Power Industry (CRIEPI). He was Directorof Power System Department, Director of SystemEngineering Research Laboratory, CRIEPI. He iscurrently a Project Professor, Ubiquitous Power GridEndowed Chair, Center for Advanced Power andEnvironmental Technology (APET), the University

of Tokyo, Japan, since 2008. He has been engaged in research mainly on

planning, operation, and control of power systems as well as new technologydevelopment.Prof. Taniguchi is a distinguished member of CIGRE.

Tatsuhito Nakajima (M87) was born in Tokyo,Japan. He received the B.S., M.S., and Dr.Eng.degrees from the University of Tokyo, Japan, in1985, 1987, and 1990, respectively.He joined Tokyo Electric Power Company

(TEPCO) in 1990. He has been with Power Engi-neering R&D Center of TEPCO. He is currentlya Project Associate Professor in the Center forAdvanced Power and Environmental Technology(APET) of the University of Tokyo. His researchinterests include application of power electronics for

power systems.Prof. Nakajima is a member of CIGRE.

Kithsiri M. Liyanage (M93SM10) was bornin Sri Lanka. He obtained B.Sc.Eng (Hons) fromUniversity of Peradeniya, Sri Lanka, in 1983 and theM.Eng. and Dr.Eng. degrees from the University ofTokyo, Japan, in 1988 and 1991, respectively.He has held positions at the University of Tokyo,

Japan, the University of Washington, and the Uni-versities of Ruhuna and Peradeniya in Sri Lanka.From September 2008 to August 2010, he was withthe Center for Advanced Power and EnvironmentalTechnology (APET) of the University of Tokyo,

Japan, as a Visiting Research Fellow on sabbatical leave from the Universityof Peradeniya, where he is a Professor currently. His current research interestsinclude making use of ICT to create an environmental friendly energy sector.

Jumpei Baba (S00M01) was born in Japan. Hereceived the B.Eng., M.Eng., and Ph.D.Eng degreesfrom the University of Tokyo, Tokyo, Japan in 1996,1998, and 2001, respectively.He has been with the Department of Electrical

Engineering, Tokyo University of Science, since2001, and with the Department of Advanced Energy,Graduate School of Frontier Sciences, University ofTokyo, since 2003. He is currently an Associate Pro-fessor of Department of Advanced Energy, GraduateSchool of Frontier Sciences, University of Tokyo.

Akihiko Yokoyama (M78) was born in Osaka,Japan. He received the B.Eng., M.Eng., and Dr.Eng.degrees from the University of Tokyo, Tokyo, Japan,in1979, 1981, and 1984, respectively.He has been with the Department of Electrical En-

gineering, University of Tokyo, since 1984 and is cur-rently a Professor in charge of power system engi-neering. He was a Visiting Research Fellow at theUniversity of Texas, Arlington, and the Universityof California, Berkeley, from February 1987 to Feb-ruary 1989.