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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
Abstract—To integrate large scale renewable energy sources in the power grid, the battery energy storage performs an important role for smoothing their natural intermittency and ensuring gridwide frequency stability. Electric vehicles have not only large introduction potential but also much available time for control because they are almost plugged in the home outlets as distributed battery energy storages. Therefore, vehicle-to-grid (V2G) is expected to be one of the key technologies in smart grid strategies. This paper proposes an autonomous distributed V2G control scheme. A grid-connected electric vehicle supplies a distributed spinning reserve according to the frequency deviation at the plug-in terminal, which is a signal of supply and demand imbalance in the power grid. As a style of EV utilization, it is assumed that vehicle use set next plug-out timing in advance. In such assumption, user convenience is satisfied by performing a scheduled charging for the plug-out, and plug-in idle time is available for the V2G control. Therefore a smart charging control is considered in the proposed scheme. Satisfaction of vehicle user convenience and effect to the load frequency control is evaluated through a simulation by using a typical two area interconnected power grid model and an automotive lithium-ion battery model. Index Terms—Electric vehicle, load frequency control, smart charging, smart grid, state-of-charge, vehicle-to-grid.
Large scale integration of electric vehicles (EV) and plug-in hybrid vehicles (PHV) for the transportation electrification brings large potential of vehicle-to-grid (V2G) [5]–[7]. Aggregated V2G pool consisted by huge EVs contributes greatly to the supply and demand dispatch, and each EV user may obtain the incentive cost [8], [9]. V2G control strategies in the LFC and the regional EMS have been proposed in the ubiquitous power grid concept [10], [11]. This paper proposes an autonomous distributed V2G control scheme providing a distributed spinning reserve for the unexpected intermittency of the RESs. A droop control based on the frequency deviation at plug-in terminal realizes a fast and synchronized response among multiple vehicles. Battery state-ofcharge (SOC) is managed by using a balance control. And a smart charging control is applied for satisfying the scheduled charging request by the vehicle user. Proposed V2G control scheme is explained in Section II, then verified by a simulation using a two area interconnected power grid model and an automotive lithium-ion battery model in Sections III and IV. II. V2G CONTROL SCHEME
I. INTRODUCTION
A. Autonomous Distributed V2G
I
NTERMITTENT renewable energy sources (RES) require additional dispatching resources such as thermal power generations, adjustable speed pumped storages, and battery energy storages. Smart grid strategies are expected to utilize distributed generations and controllable loads in the demand side. Authors have proposed the ubiquitous power grid concept in Fig. 1. Controllable RESs, heat pump water heaters, and battery energy storages are integrated in the load frequency control (LFC) of the grid and the regional energy management system (EMS) of the distribution grid [1]–[4]. Manuscript received April 02, 2011; revised August 06, 2011; accepted August 30, 2011. Date of publication October 28, 2011; date of current version February 23, 2012. This work was supported by Grant-in-Aid for Scientific Research (B), Grant-in-Aid for Young Scientists (B) form Japan Society for the Promotion of Science, Specially Promoted Research Grant from Power Academy of Japan, and Ubiquitous Power Grid Endowed Chair of Center for Advanced Power & Environmental Technology (APET) of the University of Tokyo. Paper no. TSG-00133-2011. Y. Ota, H. Taniguchi, and T. Nakajima are with the University of Tokyo, Tokyo 113-8656, Japan (e-mail:
[email protected];
[email protected];
[email protected]). K. M. Liyanage is with the University of Peradeniya, Peradeniya 20400, Sri Lanka (e-mail:
[email protected]). J. Baba and A. Yokoyama are with the University of Tokyo, Chiba 277-8568, Japan (e-mail:
[email protected],
[email protected]). 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
Supply and demand imbalance of the power grid can be observed from the frequency deviation detected at home outlet [12]–[14]. Therefore V2G power is controlled with droop characteristics against the frequency deviation as follows and shown in Fig. 2 [15]: (1) where maximum V2G power is limited by the specifications of the home outlet, and V2G gain is decided considering a tradeoff between effect for the LFC and the fluctuation range of the battery SOC. When the SOC is near to full (empty), a high-power charging (discharging) should not be implemented for preventing overcharge (overdischarge). During long-term V2G cycles, the SOC is concerned to be full or empty because a mean value of the frequency deviation is not always zero and there is a loss of the battery. Considering these features, a balance control is installed as the following equation on the premise that the accurate SOC estimation is realized [15]:
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(2)
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Fig. 1. V2G in ubiquitous power grid.
Fig. 3. Battery SOC balance control.
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 is switched to a smart charging control with a charging offset of half the maximum V2G power and a half droop gain against the frequency deviation as follows and shown in Fig. 2:
(3)
If the frequency deviation falls below a minimum threshold value , the maximum discharge is instantly supplied for the grid. Necessary energy for charging to the destination SOC is estimated by using the battery model as (6) explained in the next chapter. Considering the mean value of the frequency deviation would be zero, the duration for the smart charging is estimated by taking the charging offset into account as the following equation: (4) When the estimated duration for the smart charging is longer than the actual duration to the plug-out time, the V2G control is switched to the smart charging control.
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TABLE II PARAMETERS OF V2G CONTROL
TABLE III SPECIFICATIONS OF BATTERY MODEL
Fig. 4. Power grid model for calculating frequency of Japanese 50 Hz systems. TABLE I PARAMETERS OF POWER GRID MODEL
III. V2G AND POWER GRID MODEL A. Power Grid Model Fig. 4 shows the power grid model for calculating the frequency of Japanese 50 Hz systems [16]. IEEJ East 10-machine systems [17] are aggregated to a two area interconnected power grid by using two inertia constants of thermal, hydro, and nuclear power generation, two damping coefficients consisted by frequency dependent characteristics of an aggregated load, and a 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 load dispatching center allocates area requirements (AR) to each thermal power generator, flat frequency control (FFC) for the grid-A and tie-line bias control (TBC) for the grid-B. Delays of the frequency detection and the AR calculation are modeled as first-order lags. Communication delay from the load dispatching center is modeled as a dead time. The parameters are summarized in Table I.
Power fluctuations of the RES are generated by the normal distributions. Their frequency bands are limited by the low pass filter (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] assuming 200[V]/25[A] home outlet, and maximum V2G gain is 200[kW/Hz], that is, the maximum V2G power is supplied when the frequency deviation is 0.025[Hz]. The SOC balance control is same as in Fig. 3. Ten minutes margin is considered for estimating the duration for the smart charging because there is uncertainty such as a current dependent loss by the 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 V2G pools. 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 in the countryside. C. Battery Model In this paper, a simplified battery model consists of voltage source expressing open circuit voltage (OCV) and internal resistance is assumed [15]. The battery OCV is defined as the following Nernst equation:
(5) and are nominal voltage and capacity, respecwhere tively. is gas constant, Faraday constant, and battery temperature, respectively. is a sensitivity parameter between
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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 line power 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 generation and 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 SOC to the destination SOC integrating the OCV as follows:
from the present is calculated by
During charge or discharge with current , battery CCV (Closed Circuit Voltage) and the V2G power are calculated as follows: (7) (8) After all, the battery SOC is updated by the V2G power as the following differential equation: (9)
(6)
where
is current efficiency of the battery.
OTA et al.: AUTONOMOUS DISTRIBUTED V2G (VEHICLE-TO-GRID) SATISFYING SCHEDULED CHARGING
TABLE IV QUALITY OF FREQUENCY DEVIATION IN GRID-A
TABLE V FLUCTUATION RANGE OF BATTERY SOC
Three types of automotive lithium-ion battery, Mitsubishi i-MiEV [18] (EV1), Nissan Leaf [19] (EV2), Toyota Prius PHV [20] (PHV1), are assumed as Table III. Internal resistances do not necessarily clear, and common value is assumed. IV. SIMULATION RESULTS As an assumption of EV utilization, EV1 is plugged into the grid-A at 2 h with initial SOC as 20[%]. Then EV1 is scheduled to be plugged out with destination SOC as 90[%] after eight hours. On the other hands, EV2 and PHV1 work as the V2G pool maintaining 50% SOC during the simulation. Simulation results are summarized in Fig. 5. From two hours, frequency fluctuations caused by the RES fluctuations are compensated by the V2G control in both grids. Battery SOC of the EV1 is firstly lifted up to the balanced SOC (50%) by the smart charging. Then the EV1 supplies charge and discharge cycles for the grid by the V2G control from 3.8 hours to 7.2 hours. Finally, the battery SOC achieves the destination SOC (90[%]) by the second smart charging from 7.2 h to 9.9 h. Quality of the frequency is found to be not so degraded because of half droop gain against the frequency deviation even during the smart charging of EV1. The smart charging control of the EV1 does not remarkably affect the thermal power generation because the amount of the charging offset (50[MW]) is relatively smaller than the fluctuation components of the thermal power generation. After EV1 is done for charging at 9.9 h, EV1 cannot supply any spinning reserve for the grid. However, quality of the frequency is maintained by the rest of the vehicles plugged-into the grid, PHV1 and EV2. Table IV summarizes maximum values, minimum values, and root mean square (RMS) values of the frequency deviation in the grid-A. Advantage of the proposed V2G control having faster response than the governor-free control of the thermal power generation is numerically confirmed. Table V shows fluctuation ranges of each battery SOC. Fluctuation range of the PHV1 with the small size battery is within 6% or 7%. Therefore the capacity of the PHV battery is found to be enough for the application as the distributed spinning reserve through the home outlet. When the medium speed or quick charger in which high power charge and discharge is assumed, the capacity of the battery would be more critical for the fluctuation range of the battery SOC.
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V. CONCLUSION The proposed V2G control is effective for a distributed spinning reserve without system-wide information exchange and interfering the conventional LFC by the thermal power generations. And the proposed smart charging control satisfies the scheduled charging by the vehicle user. The combined control scheme of the V2G and smart charging contribute to move toward low carbon energy systems through the large-scale integration 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 distributed V2G as a primary control and the centralized V2G as a secondary control. The EVs have a potential for vehicle-to-home (V2H) dispatching rooftop photovoltaic generations and vehicle-to-building (V2B). There is further challenge in managing the V2G, V2H, and V2B and then creating synergy effect throughout the power grids. The proposed control scheme could be easily embedded into automotive power electronics circuits or household charging units to facilitate plug-and-play operation. However, there are research subjects on efficiency of the proposed V2G control, impact to the battery life, secure interconnection method to the grid, and so on.
REFERENCES [1] Y. Nishizaki, H. Irie, A. Yokoyama, and Y. Tada, “Coordinated control of blade pitch angle of wind turbine generators and battery for frequency regulation and the battery capacity reduction,” in Proc. Int. Conf. Electr. Eng., Jul. 2008, pp. 1–6. [2] H. Irie and A. Yokoyama, “Modeling for frequency control analysis of power system with a large penetration of wind power generation by a lot of controllable heat pump systems and battery systems,” in Proc. Int. Conf. Power Syst. Technol., Oct. 2008. [3] T. Masuta, A. Ykoyama, and Y. Tada, “System frequency control by heat pump water heaters (HPWHs) on customer side based on statistical HPWH model in power system with a large penetration of renewable energy sources,” in Proc. Int. Conf. Power Syst. Technol., Oct. 2010, pp. 1–7. [4] K. M. Liyanage, A. Yokoyama, Y. Ota, T. Nakajima, and H. Taniguchi, “Evaluating the impact of battery energy storage systems capacity on the performance of coordinated control of elements in ubiquitous power networks,” in Proc. Int. Conf. Industrial Information Syst., Aug. 2010, pp. 469–474. [5] W. Kempton, V. Udo, K. Huber, K. Komara, S. Letendre, S. Baker, D. Brunner, and N. Pearre, “A test of vehicle-to-grid (V2G) for energy storage and frequency regulation in the PJM system,” Publications of MAGICC (Mid-Atlantic Grid Interface Cars Consortium) [Online]. Available: http://www.magicconsortium.org/_Media/test-v2g-in-pjm-jan09.pdf, Jan. 2009 [6] “Smart garage charrette report,” Rocky Mountain Institute [Online]. Available: http://move.rmi.org/files/smartgarage/SmartGarageCharretteReport_2.10.pdf, Dec. 2008 [7] J. A. P. Lopes, F. J. Soares, and P. M. R. Almeida, “Integration of electric vehicles in the electric power system,” Proc. IEEE, vol. 99, no. 1, pp. 168–183, Jan. 2011. [8] A. Brooks, E. Lu, D. Reicher, C. Spirakis, and B. Weihl, “Demand dispatch,” IEEE Power Eng. Mag., vol. 8, no. 3, pp. 20–29, May 2010. [9] H. Sekyung, H. Soohee, and K. Sezaki, “Development of an optimal vehicle-to-grid aggregator for frequency regulation,” IEEE Trans. Smart Grid, vol. 1, no. 1, pp. 65–72, Jun. 2010. [10] K. Shimizu, T. Masuta, Y. Ota, and A. Yokoyama, “Load frequency control in power system using vehicle-to-grid system considering the customer convenience of electric vehicles,” in Proc. Int. Conf. Power Syst. Technol., Oct. 2010, pp. 1–8.
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[11] K. M. Liyanage, A. Yokoyama, Y. Ota, T. Nakajima, and H. Taniguchi, “Impacts of communication delay on the performance of a control scheme to minimize power fluctuations introduced by renewable generation under varying V2G vehicle pool size,” in Proc. IEEE Int. Conf. Smart Grid Commun., Oct. 2010, pp. 85–90. [12] Z. Zhong, C. Xu, B. J. Billian, L. Zhan, S.-J. Steven Tsai, R. W. Conners, V. A. Centen, A. G. Phadke, and Y. Liu, “Power system frequency monitoring network (FNET) implementation,” IEEE Trans. Power Syst., vol. 20, no. 4, pp. 1914–1921, Nov. 2005. [13] O. Samuelsson, M. Hemmingsson, A. H. Nielsen, K. O. H. Pedersen, and J. Rasmussen, “Monitoring of power system events at transmission and distribution level,” IEEE Trans. Power Syst., vol. 21, no. 2, pp. 1007–1008, May 2006. [14] Y. Ota, T. Hashiguchi, H. Ukai, M. Sonoda, Y. Miwa, and A. Takeuchi, “Monitoring of interconnected power system parameters using PMU based WAMS,” in Proc. IEEE PowerTech Conf., Jul. 2007, pp. 1718–1722. [15] Y. Ota, H. Taniguchi, T. Nakajima, K. M. Liyanage, J. Baba, and A. Yokoyama, “Autonomous distributed V2G (vehicle-to-grid) considering charging request and battery condition,” in Proc. IEEE PES Innov. Smart Grid Technol. Conf. Eur., Oct. 2010, pp. 1–6. [16] M. Arita, A. Yokoyama, and Y. Tada, “A basic study on suppression of power flow deviation on interconnecting transmission line between FFC and TBC networks using battery system as energy storage,” Transl.:Japanese IEEJ Trans. PE, vol. 128, no. 7, pp. 953–960, Jul. 2008. [17] “Japanese power system models,” Institute of Electrical Engineers of Japan [Online]. Available: http://www2.iee.or.jp/ver2/pes/23st_model/english/index.html, 2007 [18] T. Hosokawa, K. Tanihata, and H. Miyamoto, “Development of i MiEV next-generation electric vehicle (second report),” Mitsubishi Motors Tech. Rev., no. 20, pp. 52–59, 2008. [19] N. Yoshioka, “New Era of Mass-Produced BEV’s,” in Proc. Adv. Automotive Battery Conf., Jan. 2011. [20] T. Yoda, “Development of battery pack & system for plug-in hybrid,” in Proc. Adv. Automotive Battery Conf., May 2010. Yutaka Ota (M’04) was born in Nagano, Japan. He received the B.S., M.S., and Ph.D.Eng. degrees from Nagoya Institute of Technology, Japan, in 1998, 2000 and 2003, respectively. He is currently a Project Assistant Professor of Ubiquitous Power Grid Endowed Chair in the Center for Advanced Power and Environmental Technology (APET) of the University of Tokyo, Japan. His research interests include vehicle-to-grid technology, modeling of batteries, and application of phasor measurement 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 received the B.S, M.S., and Ph.D. degrees in electrical engineering from Kyoto University, Kyoto, Japan, in 1973, 1975 and 1994, respectively. In 1975, he joined the Central Research Institute of Electric Power Industry (CRIEPI). He was Director of Power System Department, Director of System Engineering Research Laboratory, CRIEPI. He is currently a Project Professor, Ubiquitous Power Grid Endowed Chair, Center for Advanced Power and Environmental 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 technology development. Prof. Taniguchi is a distinguished member of CIGRE.
Tatsuhito Nakajima (M’87) was born in Tokyo, Japan. He received the B.S., M.S., and Dr.Eng. degrees from the University of Tokyo, Japan, in 1985, 1987, and 1990, respectively. He joined Tokyo Electric Power Company (TEPCO) in 1990. He has been with Power Engineering R&D Center of TEPCO. He is currently a Project Associate Professor in the Center for Advanced Power and Environmental Technology (APET) of the University of Tokyo. His research interests include application of power electronics for power systems. Prof. Nakajima is a member of CIGRE.
Kithsiri M. Liyanage (M’93–SM’10) was born in Sri Lanka. He obtained B.Sc.Eng (Hons) from University of Peradeniya, Sri Lanka, in 1983 and the M.Eng. and Dr.Eng. degrees from the University of Tokyo, Japan, in 1988 and 1991, respectively. He has held positions at the University of Tokyo, Japan, the University of Washington, and the Universities of Ruhuna and Peradeniya in Sri Lanka. From September 2008 to August 2010, he was with the Center for Advanced Power and Environmental Technology (APET) of the University of Tokyo, Japan, as a Visiting Research Fellow on sabbatical leave from the University of Peradeniya, where he is a Professor currently. His current research interests include making use of ICT to create an environmental friendly energy sector.
Jumpei Baba (S’00–M’01) was born in Japan. He received the B.Eng., M.Eng., and Ph.D.Eng degrees from 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, since 2001, and with the Department of Advanced Energy, Graduate School of Frontier Sciences, University of Tokyo, since 2003. He is currently an Associate Professor of Department of Advanced Energy, Graduate School of Frontier Sciences, University of Tokyo.
Akihiko Yokoyama (M’78) 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 Engineering, University of Tokyo, since 1984 and is currently a Professor in charge of power system engineering. He was a Visiting Research Fellow at the University of Texas, Arlington, and the University of California, Berkeley, from February 1987 to February 1989.
