Decentralized Vehicle-to-Grid Control for Primary ... - IEEE Xplore

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IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 28, NO. 3, AUGUST 2013

Decentralized Vehicle-to-Grid Control for Primary Frequency Regulation Considering Charging Demands Hui Liu, Member, IEEE, Zechun Hu, Member, IEEE, Yonghua Song, Fellow, IEEE, and Jin Lin, Member, IEEE

Abstract—Vehicle-to-grid (V2G) control has the potential to provide frequency regulation service for power system operation from electric vehicles (EVs). In this paper, a decentralized V2G control (DVC) method is proposed for EVs to participate in primary frequency control considering charging demands from EV customers. When an EV customer wants to maintain the residual state of charge (SOC) of the EV battery, a V2G control strategy, called battery SOC holder (BSH), is performed to maintain the battery energy around the residual SOC along with adaptive frequency droop control. If the residual battery energy is not enough for next trip, the customer needs to charge the EV to higher SOC level. Then, a smart charging method, called charging with frequency regulation (CFR), is developed to achieve scheduled charging and provide frequency regulation at the same time. Simulations on a two-area interconnected power system with wind power integration have shown the effectiveness of the proposed method. Index Terms—Adaptive droop control, electric vehicle (EV), primary frequency control, scheduled charging, vehicle-to-grid (V2G).

NOMENCLATURE

Low state of charge of the th EV battery. Initial state of charge of the th EV battery at plug-in time. Expected state of charge of the th EV battery at plug-out time. SOC is normally distributed with the mean value and the variance . Charging droop of the th EV at time . Discharging droop of the th EV at time . Maximum V2G droop of the EVs. Maximum V2G Power at the battery side of the EVs (kW). V2G power at the battery side of the th EV at time (kW). V2G power at the power grid side of the EV at time , in kW.

State of charge of the th EV battery at time . Maximum state of charge of the th EV battery. Minimum state of charge of the th EV battery. High state of charge of the th EV battery.

Manuscript received October 14, 2012; revised December 27, 2012, February 03, 2013, and March 05, 2013; accepted March 05, 2013. Date of publication March 27, 2013; date of current version July 18, 2013. This work was supported in part by the National Natural Science Foundation of China under Grant 51107054 and Grant 51107060 and the National High Technology Research and Development of China 863 Program under Grant 2011AA05A110. Paper no. TPWRS-01160-2012. H. Liu is with the Department of Electrical Engineering, Tsinghua University, Beijing, China, and also with the School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China (e-mail: hughlh@126. com). Z. Hu, Y. Song and J. Lin are with the Department of Electrical Engineering, Tsinghua University, Beijing 100084, China (e-mail: [email protected]; [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/TPWRS.2013.2252029 0885-8950/$31.00 © 2013 IEEE

Scheduled charging power at the battery side of the th EV for achieving the charging demand (kW). Constant scheduled charging power at the battery side of the th EV for achieving the charging demand (kW). Tie-line power change between areas A and B. Regulation signal for load frequency control. Mechanical power change. Charging efficiency of the EVs. Discharging efficiency of the EVs. Rated capacity of the th EV battery (kWh). Energy variation of the (kWh).

EV battery

Scheduled charging duration for achieving charging demand of the (s).

EV

LIU et al.: DECENTRALIZED V2G CONTROL FOR PRIMARY FREQUENCY REGULATION CONSIDERING CHARGING DEMANDS

Plug-in time of the

EV (h).

Plug-out time of the

EV (h).

Synchronizing torque coefficient between areas A and B (p.u./Hz). Frequency deviation at time . Minimum frequency deviation. Frequency bias factor (p.u./Hz). Inertia constant (p.u. s). Load damping coefficient (p.u./Hz).

I. INTRODUCTION

I

N recent years, electric vehicles (EVs) is fast developing mainly due to environmental and energy security concerns. As a new type of load to a power system, large-scale integration of EVs will have a significant impact on power system operation and planning [1]–[5]. At the same time, EVs can also act as controllable loads and mobile storage devices. The implementation of vehicle-to-grid (V2G), which achieves bidirectional power flow between EVs and a power grid, will bring new applications for optimal operation of power systems. One of the most important applications of V2G is to offer frequency regulation service [6]–[9]. When the system frequency goes downwards, EV charging load reduction or EVs acting as power producers can prevent further frequency drop. On the other hand, EVs could absorb the power from the grid to prevent from further increase in frequency. This type of V2G control has been reported to be suited for electricity balancing markets to provide regulation services [10], [11]. Published literature has addressed the general concept and description of frequency regulation by EVs [12], [13]. Some studies have discussed the benefits of V2G to EV owners and utilities [6]–[9], [14]. In [15], how much power can be released from EVs to a power grid, called achievable power capacity, was estimated in a probabilistic manner. Some researchers paid attention to the V2G control supporting secondary frequency control (SFC) of a power system [16]–[18]. Centralized V2G control methods have been proposed to participate in the SFC in [16] and [17], but how to send load frequency control signal to EVs was not considered. In [18], the method for dispatching the load frequency control signal was presented according to the response speed and the controllable capacity. However, all EVs joining in load frequency control were assumed to have 0.85 state of charge (SOC), thus fulfilling charging demands of the EV customers was not considered. Except for the SFC, it is of great interest for EVs to be involved in primary frequency control (PFC). This is because the frequency signal is available at any location of a power system where an EV is connected. The impacts of EVs on PFC as well as battery SOC (BSOC) were evaluated considering high penetration of intermittent wind power generation in [19] and [20]. Autonomous distributed V2G control (ADC) was

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proposed to suppress the frequency fluctuation of a ubiquitous power grid concept in [21]–[23]. In order to satisfy charging demand, scheduled charging power in V2G control was estimated on the basis of historic frequency deviation in [21] and expressed with standard deviation of the system frequency dominated by normal distribution in [22]. Being different from [21] and [22], half-maximum V2G power was allocated beforehand as scheduled charging power in [23]. However, in [21]–[23], scheduled charging duration of the V2G control was not calculated on the basis of the actual plug-in duration. Once the scheduled charging duration is longer than the actual plug-in duration, it will fail to achieve the charging demand. In addition, it is inconvenient and complicated for the ADC to maintain different levels of residual battery energy. This is because the SOC limits needed for ADC must be set case by case for different residual SOCs. In this paper, we focus on the V2G control participating in PFC. When the remaining battery energy is low, the customer has to charge the EV to a higher SOC level. On the other hand, if the residual SOC is sufficiently high in the day time, the EV customer is generally willing to maintain the SOC and recharge the EV in the night considering the low off-peak electricity price. Therefore, except for frequency droop control, it is necessary for the V2G control strategy to include both maintaining the SOC and achieving charging demand. In this work, a decentralized V2G control (DVC) is proposed. In order to maintain the residual battery energy, a V2G control strategy, called BSOC holder (BSH), is proposed with adaptive frequency droop control. In addition, another V2G control strategy, called charging with frequency regulation (CFR), is designed to achieve scheduled charging and participate in frequency regulation at the same time. The remainder of this paper is organized as follows. In Section II, the framework of the DVC is presented to join in the PFC of a power grid. In Section III, the existing V2G control is addressed. In Section IV, the DVC is proposed to achieve frequency regulation as well as scheduled charging. A simulation system is introduced in Section V. Simulations and discussions illustrate the performances of the proposed V2G control in Section VI. Finally, this paper is concluded in Section VII. II. DESCRIPTIONS OF DVC JOINING IN PFC A. System Framework Fig. 1 illustrates the framework of DVC for EVs to join in the PFC of a power system. When EVs are connected to a power grid, the bidirectional energy exchange can be achieved by charging/discharging devices. The system frequency is monitored by the frequency detection block in real time. The V2G controller makes decisions based on real-time frequency and BSOC sent from the frequency detection block and the battery management system, respectively. The real-time command is produced and sent from the V2G controller to the charging/discharging device. The charging/discharging device controls the power interchange between the power grid and

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Fig. 2. BSOC balance control (the SOC is maintained at around 0.5 by setting , , , and , , , and the SOC is kept around 0.7 by setting , and ).

question in [21]–[23]. In order to compare with the DVC method proposed in Section IV, the V2G control strategy in [23] is specially analyzed. Fig. 1. Framework of DVC participating in PFC.

A. Balance Control (BC) the EV battery to suppress frequency fluctuation and achieve charging demand simultaneously.

BC of the ADC method [23] is used to maintain the residual battery energy by adjusting the charging/discharging power according to

B. Descriptions of the DVC As illustrated in Fig. 1, frequency regulation and charging demand are two important concerns that need to be handled in the DVC. From the point of view of a power grid, EVs may take part in frequency regulation service as mobile storage devices, while for EVs there is some restrictions imposed by desired SOCs for transportation purposes. In addition, EV customers pay more attention to BSOCs than frequency regulation. Therefore, the principal challenge of the DVC is the balance between frequency regulation from the power grid side and requirements from the EV customers. When an EV gets to the working or commercial place, the customer will check whether or not the residual BSOC is sufficient. If the battery energy at plug-in time is sufficient for the next trip, the EV customer usually tends to maintain the residual battery energy. This is because the customer is more willing to charge the EV at home considering the low off-peak electricity price at night. Therefore, although the residual BSOCs are different from EV to EV, the requirements from the EV customers can be generally categorized into two types: maintaining BSOC and achieving charging demand. In a market environment, the EVs that would like to join in the PFC can be organized by an aggregator to take part in market competition. The aggregator should estimate the regulation capability of the EV fleet and choose an optimal bidding strategy. After market clearing, the aggregator will inform the chosen EVs to participate in the PFC. Detailed market design and operation is beyond the scope of this paper. III. EXISTING V2G CONTROL METHOD As stated in Section II, the coordination between frequency regulation and charging demand is of great importance. Therefore, the ADC methods have been developed to deal with such

(1)

where

(2)

In [23], the BSOC is maintained at around 0.5. However, it , , is necessary to reset the BSOC limits, i.e., , and , when different BSOC levels need to be kept as illustrated in Fig. 2. As a result, the V2G control has to change the SOC limits, once the BSOC at plug-in time is changed resulting from the different electricity consumption of each drive. Therefore, it is necessary for the ADC to develop a kind of algorithm in order to automatically calculate the SOC limits. B. Smart Charging (SC) A SC strategy in [23] is used to achieve the charging demand with the follow setting: (3)

The SC consists of frequency droop control and scheduled charging power . With half-maximum V2G droop, frequency droop control responding to the frequency deviation signal is to suppress frequency fluctuation. Half-maximum V2G power is allocated beforehand in order to achieve the charging

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Fig. 3. Adaptive droop control loop of the BSH for the EV charging/discharging power.

Fig. 5. BSOC holder with adaptive droop control.

1) If

, then (5)

2) If

, then

Fig. 4. Adaptive droop of the BSH for maintaining the initial SOC.

(6) demand. In this condition, scheduled charging duration can be estimated by

3) If

, then

(7)

(4) It can be seen that the scheduled charging duration is independent on the actual plug-in duration of an EV. Therefore, it is uncertain whether or not charging demand will be met by the SC. In addition, it is not flexible to use half-maximum V2G power as scheduled charging power for all EVs. This is because the actual plug-in duration and the initial SOCs are different for different EVs. IV. PROPOSED DVC METHOD Here, the proposed DVC method that mainly includes BSH and CFR for EVs to participate in PFC will be introduced. A. BSOC Holder The BSH is designed for those EVs that need to maintain their SOC levels while joining in PFC. With the adaptive droop, frequency control loop of the BSH is constructed in Fig. 3. As the EV battery can absorb/inject power from/to a power grid, a saturation block with upper and lower limits must be included. In addition, a dead-band is added to reduce the charging/discharging operations on the EV battery. As depicted in Fig. 4, adaptive droop of the BSH depends on the real-time SOC and the initial SOC at plug-in time. Therefore, it is flexible for the BSH to maintain different initial SOC levels. When the real-time SOC is higher than the initial SOC, adaptive droop is used to block the increase of the BSOC. On the contrary, adaptive droop will impel the increase of the BSOC. According to the schematic diagram shown in Fig. 4, adaptive droop is calculated as follows.

4) If

, then

(8)

With the adaptive droop, the BSH is illustrated in Fig. 5. When the system frequency deviation is out of the predefined dead-band, the power is exchanged between the EV and the power grid to suppress frequency fluctuation. With the change of the real-time SOC, adaptive droop will rotate around point 1/2 between zero and to adjust the charging/discharging power as shown in Fig. 5. From the (5)–(8), the adaptive droop has the following relationship: (9) Therefore, the smaller the discharging droop is, then the larger the charging droop is. When the real-time SOC is lower will be larger than the initial SOC, charging droop than discharging droop , as illustrated in Fig. 4. In this condition, more power will be absorbed from the power grid than injected to the power grid, as illustrated in Fig. 5. The BSOC level will be forced to lift up. On the other hand, if the real-time SOC is higher than the initial SOC, charging droop will be smaller than discharging droop , and then more power will be injected to the power grid. Therefore, by the BSH, the charging/discharging power is automatically

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Fig. 6. Droop control loop of the CFR (red part represents frequency droop control and blue part is scheduled charging power).

Fig. 7. Charging with frequency regulation with maximum V2G droop.

adjusted to maintain the initial SOC level along with adaptive frequency droop control. B. Charging With Frequency Regulation Necessary energy must be supplied to an EV if the residual BSOC is not sufficient for the next trip or the EV owner wants to charge the battery overnight. The CFR is proposed to meet charging demand and suppress frequency deviation at the same time. When charging demand is considered in the V2G control, scheduled charging power may be described as (10)

If the scheduled charging power is constant, (10) can be simplified as (11) In (11), the plug-out time as well as the expected SOC should be provided by the EV customer in advance. It should be pointed out that an EV will not participate in PFC when the scheduled charging power is equal to or larger than the maximum charging power. With scheduled charging power, frequency droop control loop is constructed in Fig. 6, where constant droop is used. As indicated in Fig. 6, the CFR is mainly consisted of frequency droop control and scheduled charging power. Frequency droop control is used to improve frequency quality, responding to the frequency deviation signal. Scheduled charging power is to achieve charging demand, calculated by (11). Although charging demands may be different from EV to EV, the CFR is designed on the basis of the specific charging demand of each EV. Considering the scheduled charging power, the CFR shown in Fig. 7 is proposed to suppress frequency fluctuation. When frequency deviation lies in the predefined dead-band, just scheduled charging power works. Once frequency deviation is out of the dead-band, both scheduled charging power and frequency droop control will work. Taking the charging and discharging efficiencies into account, the power at connecting point of a charging/discharging device from/to the power grid has the following form: (12)

Fig. 8. Simulation system for DVC.

C. Control Strategy Switching The CFR is designed to achieve charging demand of the EV customer and provide frequency support as much as possible. The BSOC of an EV may be filled to the required level before the schedule duration or the actual plug-out time. In order to utilize the full capability of the EV for PFC in this case, the CFR will be automatically switched to the BSH, and then the BSOC will be held around the expected SOC. V. SIMULATION SYSTEM The simulation system is consisted of a two-area interconnected power system, as illustrated in Fig. 8, where the “Thermal” block is represented in Fig. 9 (please see [24] and [25] for reference), and the “EVs” block is shown in Fig. 10. The power system model is used to simulate frequency fluctuation resulted from random load and wind power variations. In EV model, real-time BSOC model is built to acquire the dynamic change of the battery energy during the course of the DVC. A. Power System Model Our main concerns are focused on area A, where EV integration and wind power generation are considered as shown in Fig. 8. Five percent of maximum load capacity of each area is


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TABLE I PARAMETERS OF POWER GRID MODEL

Fig. 9. Thermal power plant block for frequency regulation.

Fig. 10. EV V2G control block for frequency regulation.

Fig. 11. Load fluctuation in a time series with 45-s intervals.

TABLE II V2G SIMULATION PARAMETERS

values of and are kept constant in the following simulations for simplicity. B. V2G Simulation Parameters

Fig. 12. Load fluctuation in a time series with 4-s intervals.

It is assumed that the V2G regulation is considered to be free from the ramp rate limitations compared with that of conventional generators. As stated in Section II, the requirements from the EV customers usually include maintaining battery SOCs and achieving charging demands. They are named as TYPE 1 and TYPE 2, respectively. The V2G simulation parameters are listed in Table II. C. Real-Time BSOC Model During the charging/discharging process, the battery energy variation can be expressed as [18], [28], [29] (13)

Fig. 13. Wind power in a time series of 1 s.

reserved for the governor regulation control. Tie-line bias control (TBC) [26] is used for the interconnected power system, i.e., area control error is considered in load frequency control (LFC). Random load in a time series is used for frequency regulation simulation [27], which consists of a slow base component with large amplitude and a fast fringe component with small amplitude, as shown in Figs. 11 and 12. Intermittent wind power data in a time series with 1-s intervals is illustrated in Fig. 13, which is from real historical data. The detailed parameters of the power grid model are summarized in Table I. It should be noted that the

As the BSOC, usually expressed as a percentage of the rated capacity, is defined as the available capacity of the battery, the BSOC at time has the following form: (14)

VI. SIMULATIONS AND DISCUSSIONS Simulations are implemented under MATLAB environment to examine the effectiveness of the proposed DVC.

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TABLE III SIMULATION SCENARIOS OF THE EVS WITH NORMALLY DISTRIBUTED SOC

Fig. 15. Tie-line power deviation from area-A to area-B. (a) EVs integrated in area-A. (b) EVs integrated in area-B.

COMPARISONS

TABLE IV FREQUENCY AND TIE-LINE POWER DEVIATIONS DIFFERENT SIMULATION SCENARIOS

OF

IN

Fig. 14. Frequency deviation in area-A. (a) EVs integrated in area-A. (b) EVs integrated in area-B.

A. Normally Distributed Scenario It is assumed that the battery SOC is normally distributed within the limits, as shown in Table III. The normally distributed SOCs of 50 000 EVs are obtained by Monte Carlo simulation method, and they are modeled under a Simulink environment. In China, the customers usually start to work at 8:00 a.m., and depart from working places at nearly 5:00 p.m. [30]. Therefore, we assume that the plug-in duration of EVs is from 8:00 a.m. to 5:00 p.m. In order to examine the effects of the proposed V2G control strategy, the EVs are respectively integrated into area-A and area-B, where wind power generation is installed in area-A. As illustrated in Fig. 14, when the DVC is used, system frequency quality is improved because of the fast adjustments of charging/discharging power of EVs. When EVs are integrated into area-A, tie-line power deviation is suppressed as shown in Fig. 15(a). From Fig. 15(b), with EVs integrated in area-B, the tie-line power deviation becomes larger in contrast to the scenario without EV integration. This is because the tie-line is the only way for EVs in area-B to suppress the frequency fluctuation mainly sourced in area-A. In addition, maximum values (Max), minimum values (Min), and root mean square values (RMS) of frequency and tie-line power deviations are listed in Table IV, respectively. It can be seen that system frequency quality is always improved by the V2G control. However, when EVs and wind power generation are in different areas, the fluctuation of tie-line power deviation will be increased due to the inter-area support. We randomly choose four EVs for illustration shown in Fig. 16 and Fig. 18. Actually, all of the SOC curves are similar to each other. This is because the proposed V2G control is

Fig. 16. Real-time SOCs of four EVs in TYPE 1.

Fig. 17. Total V2G power at the power grid side of TYPE 1.

decentralized and run independently. In other words, the V2G control of each EV independently regulates the charging/discharging power according to the real-time change of its SOC and the system frequency deviation. It is effective for the DVC to maintain the battery energy with different initial SOCs, which is shown in Fig. 16 and Fig. 17. It is of interest for the EV customers to project the plug-out time conservatively. Therefore, it is assumed that the end time of scheduled charging is at 4:00 p.m. As shown in Figs. 18 and 19, the battery energy can be lifted to the expected SOC level at


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Fig. 18. Real-time SOCs of four EVs in TYPE 2.

Fig. 21. Comparisons between the BSH and the BC. The sSolid line represents the BSH, and the dotted line is the BC.

TABLE VI MAXIMUM V2G DROOPS OF THE ADC AND THE DVC Fig. 19. Total V2G power at the power grid side of TYPE 2.

TABLE V SIMULATION SCENARIO OF THE EVS TABLE VII QUALITY OF FREQUENCY DEVIATION IN AREA-A

TABLE VIII TIE-LINE POWER DEVIATION FROM AREA-A TO AREA-B

Fig. 20. Simulations of TYPE 1 with same V2G droops. (a) Real-time SOCs; (b) total V2G power at the power grid side.

about 4:00 p.m. When charging demands are achieved, the CFR of the DVC is automatically switched to the BSH. B. Comparisons and Discussions In order to compare the DVC with the ADC in [23], the simulation scenario is assumed in Table V, and both EVs and wind power generation are in area-A. The EVs with normally distributed SOCs are not considered because it is tedious to calculate the SOC limits for each EV needed in the ADC. When the DVC and the ADC are used respectively, the BSOC can be held around 0.7, as shown in Fig. 20. The V2G power of the ADC is bigger than that of the DVC. This is because the V2G droop of the BC of ADC is at 0.7 SOC, whereas the V2G droop of the BSH of DVC is 0.5Kmax, which is illustrated in Fig. 21. As a result, the simulation condition will be different when comparing the effectiveness between the BC

and the BSH. Therefore, it is necessary for the BSH to set bigger maximum V2G droop than that of the BC. In this simulation, the V2G droops of the BC and the BSH are equally set at 0.7 SOC for comparisons, and the maximum V2G droops are given in Table VI. When maximum V2G droops shown in Table VI are considered, Max, Min, and RMS of frequency and tie-line power deviation are respectively summarized in Tables VII and VIII. It can be seen that frequency deviation and tie-line power deviation are suppressed when the V2G control is used. Compared with the ADC, the DVC is more beneficial for suppressing system frequency fluctuation and tie-line power deviation. 1) Discussions of TYPE 1: As shown in Fig. 22, the BSOC may be maintained around 0.7, when the V2G droops shown in Table VI are used, respectively. The DVC performs better in contrast to the ADC, because it can acquire the greater difference between charging droop and discharging droop around 0.7 SOC as illustrated in Fig. 21. 2) Discussions of TYPE 2: As shown in Fig. 23(a), battery energy can be lifted to the expected level when the ADC and the DVC are used, respectively. The V2G power of the ADC is not released before charging demand is achieved–see Fig. 23(b). Compared with the ADC, the V2G power of the DVC is released altogether with scheduled charging as illustrated in Fig. 23(c).

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ADC is independent of the actual plug-in duration. However, the DVC is designed based on the actual plug-in duration. Therefore, charging demand can be achieved by the DVC unless the actual plug-in duration is too short. VII. CONCLUSION

Fig. 22. Simulations of TYPE 1 with different V2G droops. (a) Real-time SOC. (b) total V2G power at the power grid side.

In this paper, we pay attention to the V2G control participating in PFC of power systems. The DVC is proposed to suppress system frequency fluctuation while simultaneously achieve charging demand. With adaptive frequency droop control, a V2G control strategy, called BSH, is designed based on the initial SOC to maintain the residual battery energy. It is flexible for the BSH to hold different initial SOC levels altogether with frequency regulation. In order to achieve charging demand of the EV customer, another V2G control strategy, called CFR, is developed on the basis of the actual plug-in duration and the expected SOC. The CFR is designed considering the specific charging demand of each EV, so it is flexible. Simulations on a two-area interconnected power grid show that suppressing frequency fluctuation as well as satisfying charging demand is achieved by the DVC. Compared with the ADC in [23], the DVC is more flexible and effective to improve frequency quality and satisfy charging demand. REFERENCES

Fig. 23. Simulations of TYPE 2 with the same charging scenario. (a) Real-time SOC. (b) Total V2G Power at the power grid side with the ADC. (c) Total V2G Power at the power grid side with the DVC.

Fig. 24. Comparisons of scheduled charging of the ADC and the DVC with the same charging scenario.

Therefore, the DVC is more favorable to suppress system frequency fluctuation than the ADC during the scheduled charging. In Fig. 24, charging demand is achieved by the ADC at 11:39:18 a.m. Once the actual plug-out time of the EV customer is earlier than 11:39:18 a.m., the ADC will fail to achieve charging demand. This is because scheduled charging duration of the

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Hui Liu (M’12) was born in Sichuan, China. He received the M.S. and Ph.D. degrees from the College of Electrical Engineering, Guangxi University, Guangxi, China, in 2004 and in 2007, respectively. Currently, he is pursuing postdoctoral research with the Department of Electrical Engineering, Tsinghua University, Beijing, China. He is also an Associate Professor with the School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China. His research interests include power system control and electric vehicles.

Zechun Hu (M’09) was born in Nanjing, China. He received the B.S. and Ph.D. degrees from Xi’an Jiao Tong University, Shanxi, China, in 2000 and 2006, respectively. He was with Shanghai Jiao Tong University after graduation and then with the University of Bath as a Research Officer from 2009 to 2010. He joined the Department of Electrical Engineering, Tsinghua University, Beijing, China, in 2010, where he is now an Associate Professor. His major research interests include optimal planning, operation of power systems, and electric vehicles.

Yonghua Song (F’08) received the B.Eng. degree from Chengdu University of Science and Technology, Chengdu, China, in 1984, the Ph.D. degree from the China Electric Power Research Institute, Beijing, China, in 1989, and the D.Sc. degree from Brunel University, Middlesex, U.K., in 2002. He was a Postdoctoral Fellow with Tsinghua University, Beijing, China, from June 1989 to March 1991. He then held various positions with Bristol University, Bath University, and John Moores University from 1991 to 1996. In January 1997, he was appointed a Professor of power systems with Brunel University, Middlesex, U.K., where he was Pro-Vice Chancellor for Graduate Studies from August 2004. In January 2007, he took up a Pro-Vice Chancellorship and Professorship of Electrical Engineering with the University of Liverpool, Liverpool, U.K. He returned to Tsinghua University in February 2009 as a Professor with the Department of Electrical Engineering. In April 2009, he was appointed Assistant President of the University and Deputy Director of the Laboratory of Low-Carbon Energy.His research areas include Smart Grid, electricity economics, and operation and control of power systems. Prof. Song is a Fellow of the Royal Academy of Engineering (U.K.). In June 2009, he was elected Vice-President of Chinese Society for Electrical Engineering (CSEE) and appointed Chairman of the International Affairs Committee of the CSEE.

Jin Lin (M’12) was born in 1985. He received the B.S. degree in electrical engineering from Tsinghua University, Beijing, China, in 2007, where he is currently working toward the Ph.D. degree. He was sponsored by Chinese Scholarship Council to be a Visiting Student with Risø Laboratory, Roskilde, Denmark. His research interests are the grid integration technology of wind power and power system simulation technology.