Vehicle-to-Grid Control for Supplementary Frequency ... - IEEE Xplore

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Vehicle-to-Grid Control for Supplementary Frequency Regulation Considering Charging Demands Hui Liu, Member, IEEE, Zechun Hu, Member, IEEE, Yonghua Song, Fellow, IEEE, Jianhui Wang, Senior Member, IEEE, and Xu Xie

Abstract—Electric vehicles (EVs) as distributed storage devices have the potential to provide frequency regulation services due to the fast adjustment of charging/discharging power. In our previous research, decentralized vehicle-to-grid (V2G) control methods for EVs were proposed to participate in primary frequency control. In this paper, our attention is on bringing a large number of EVs into the centralized supplementary frequency regulation (SFR) of interconnected power systems. An aggregator is the coordinator between EVs and the power system control center. The aggregator calculates the total frequency regulation capacity (FRC) and expected V2G (EV2G) power of EVs based on the data communicated between the aggregator and individual EVs or EV charging stations. With FRC and EV2G power, a V2G control strategy is proposed for the aggregator to dispatch regulation requirements to EVs and EV charging stations. In individual EV charging stations, the FRC is calculated on the basis of the V2G power at present time, and EV2G power is presented considering both frequency regulation and charging demands. Besides, V2G control strategies are developed to distribute regulation requirements to each EV. Simulations on an interconnected power grid based on a practical power grid in China have demonstrated the effectiveness of the proposed strategies. Index Terms—Charging demand, electric vehicle (EV), frequency regulation capacity (FRC), supplementary frequency regulation (SFR), vehicle-to-grid (V2G).

NOMENCLATURE Frequency bias factor. Rated capacity of the th EV battery. Energy variation of the th EV battery. Frequency deviation. Manuscript received April 29, 2014; revised September 05, 2014 and November 11, 2014; accepted December 12, 2014. This work was supported in part by the National Natural Science Foundation of China (51107054, 51107060) and in part by the National High Technology Research and Development of China 863 Program (2011AA05A110). Paper no. TPWRS-00455-2014. H. Liu is with the School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, China, and also with the Department of Electrical Engineering, Tsinghua University, Beijing, China (e-mail: [email protected]). Z. Hu and Y. Song are with the Department of Electrical Engineering, Tsinghua University, Beijing, China (e-mail: [email protected]; [email protected]). J. Wang is with the Energy Systems Division, Argonne National Laboratory, Argonne, IL 60439 USA (e-mail: [email protected]). X. Xie is with the North China Branch, State Grid Corporation of China, 100053 Beijing, China. 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.2014.2382979

Total EV2G power of EVs at time for regulation down. Total EV2G power of EVs at time for regulation up. EV2G power of EVs holding battery SOC levels of the th EV charging station at time for regulation down. EV2G power of EVs holding battery SOC levels of the th EV charging station at time for regulation up. EV2G power of EVs adjusting battery SOC levels of the th EV charging station at time for regulation down. EV2G power of EVs adjusting battery SOC levels of the th EV charging station at time for regulation up. Scheduled power at the battery side of the th EV for adjusting battery SOC levels. Constant scheduled power at the battery side of the th EV for adjusting battery SOC levels. V2G power at the battery side of the th EV at time . Maximum V2G power at the battery side of EVs. Tie-line power deviation. SOC of the th EV battery at time . Maximum SOC of the th EV. Minimum SOC of the th EV. Initial SOC of the th EV at plug-in time. Expected SOC of the th EV battery at plug-out time. Regulation-down FRC of the th EV charging station at time . Regulation-up FRC of the th EV charging station at time . Regulation task of the th EV charging station at time for maintaining battery energy. Regulation task of the th EV charging station at time for adjusting battery SOC levels. Regulation task of the th EV charging station at time . Regulation task of all EVs at time . Plug-in time of the th EV. Plug-out time of the th EV.

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IEEE TRANSACTIONS ON POWER SYSTEMS

Charging efficiency of EVs. Discharging efficiency of EVs. Number of EVs in the th EV charging station for maintaining battery SOC levels. Number of EVs in the th EV charging station for adjusting battery SOC levels. Number of EVs in the th EV charging station. Number of EV charging stations. Decision variable for EVs to join in the SFR . Regulation-up factor, . Regulation-down factor,

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I. INTRODUCTION

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N increasing demand for electric vehicles (EVs) is expected worldwide to address energy crises and reduce environmental pollution. Large-scale integration of EVs will bring challenges to secure and economic operation of power systems [1]–[4]. As a new type of load, EVs have impacts on different aspects of a power grid, such as generation planning [5] and distribution network losses [6]. On the other hand, as one type of distributed energy storage, EVs may complement large-scale energy storage devices in a power grid through vehicle-to-grid (V2G) which enables the bidirectional power flow between EVs and a power grid. A significant amount of research has been carried out on the techniques and applications of V2G, such as peak shaving [7], [8], spinning reserve [9], frequency regulation [10], and system stability [11]. In particular, frequency regulation services provided by EVs have been focused on the fast adjustments of V2G power. This type of V2G control suited for regulation services in electricity markets has been reported in [12]. Published literatures have investigated the economic feasibility of V2G control performing frequency regulation services [13]–[15]. As the primary objective of EV integration into a power grid is to charge batteries to satisfy their transportation usage, V2G control strategies must consider both frequency regulation and charging demands at the same time. Research on EV charging strategies combing these two aspects has been largely missing in the literatures. Some research efforts have been devoted to the primary frequency regulation (PFR) of an interconnected power grid because the system frequency can be measured at any location [16]–[19]. Considering intermittent wind power, with expected state of charge (SOC) levels of batteries, the contribution of EVs to PFR was evaluated in [16]. V2G control strategies were presented for EVs to participate in PFR [17], [18]. However, due to the lack of information on actual plug-in durations, it is difficult for these V2G control strategies to fulfill the charging demands of EVs [19]. EVs participating in the supplementary frequency regulation (SFR) of an interconnected power grid have been analyzed in [15], [20]–[24]. The use of EVs for SFR only makes sense if a large number of EVs are jointly considered. Therefore, a new entity, i.e., the so-called aggregators, is required [20]. The V2G control for SFR has been discussed in [21]–[24]. In [21], achiev-

able power capacity which can be released from EVs to a power grid is estimated in a probabilistic manner and an optimal contract power capacity for providing frequency regulation is analyzed. In [22], an aggregated EV-based battery storage model is built for load frequency control (LFC) simulations. Simulation results showed the effectiveness of V2G to suppress area control error (ACE) for the West Denmark power system. In [23], controllable loads such as EVs and household appliances were used to follow LFC signals. Although V2G control strategies were developed for EVs to take part in SFR in [22] and [23], the dispatch of regulation requirements from the control center to EVs is not considered. In [24], according to the different response speed and controllable capacity of loads, V2G strategies were designed for the control center to dispatch LFC signals to battery energy storage systems, EVs and heat pump water heaters. Simulations with a large-scale integration of photovoltaic generation and wind power generation show the effectiveness of the LFC method for suppressing the frequency fluctuation in the power system. However, all EVs are assumed to have the same SOC levels. Therefore, different charging demands of EVs were not well considered. In this paper, we focus on the control strategies for EVs to participate in SFR. With the form of aggregation of EVs, a systematic framework including an EV aggregator, individual EVs and EV charging stations is presented for EVs to participate in the SFR. The EV aggregator communicates with Automatic Generation Control (AGC) or LFC system and EV charging stations or individual EVs. At the same time, the aggregator is responsible for calculating the total frequency regulation capacity (FRC) and expected V2G (EV2G) power of EVs. In order to dispatch regulation requirements to EV charging stations or individual EVs, a V2G control strategy is proposed for the EV aggregator based on FRC and EV2G power. After an EV charging station receives a regulation signal from the EV aggregator, a control command will be generated and sent to EVs according the presented V2G control methods. At each charging station, the FRC aggregated to the EV aggregator is calculated on the basis of the V2G power at present time. Although the EV aggregator makes decisions according to the aggregated FRC and EV2G power without any SOC information of EV batteries, the expected battery SOC levels of EV customers can be achieved because the EV2G power of each EV is determined according to the battery SOC level. The rest of this paper is organized as follows. In Section II, the V2G control framework is presented for EVs to participate in SFR. In Section III, V2G control strategies are proposed considering both frequency regulation and charging demands. The simulation system is introduced in Section IV. In Section V, simulations and discussions illustrate the effectiveness of the proposed V2G control. Finally, this paper is concluded in Section VI. II. V2G CONTROL FRAMEWORK FOR SUPPLEMENTARY FREQUENCY REGULATION A. Overview of SFR With EVs When EVs are considered to participate in the SFR of interconnected power grids, the primary objective for these EVs is to

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regulation tasks to EVs with the “V2G Controller” block. Besides, in order to achieve the expected battery SOC levels of EVs, EV2G power which is used to coordinate regulation-up and regulation-down tasks is calculated based on the real-time battery SOC levels of EVs through the “EV2G Power” block, and then sent to the aggregator. When an EV is connected to a power system, the bidirectional energy exchange can be implemented by interface circuit (IC) including an information management system (IMS) and a charging/ discharging device. Battery SOC information from the Battery Management System (BMS) and charging demands of EV customers are transmitted to EV charging stations through the IMS of IC. A charging/ discharging device is used to charge/discharge an EV according to the control signal from an EV charging station. C. Control Objectives

Fig. 1. Framework of V2G control for EVs to participate in SFR.

adjust their charging/discharging according to the AGC signals. Therefore, the following aspects related to V2G control for SFR should be considered: 1) The SFR stems from the power mismatch in a power system. Usually, the outputs of generating units are controlled in real time to neutralize the power mismatch according to AGC signals. Compared to the power of generating units, the power capacity of an individual EV can be nearly neglected. Therefore, an aggregator as a “middleman” is expected to connect between a certain number of EVs and the control center. 2) Similar to generating units, the FRC from EVs must be known by the control center in order to correctly dispatch AGC signals to EVs. Therefore, the aggregator is responsible for calculating the total FRC and allocates the regulation tasks assigned by the control center to EVs. 3) Being different from generating units, the battery SOC levels of EVs have to be high enough for the transportation usage. However, participating in frequency regulation may deviate battery SOC levels of EVs from the expected due to frequent charging/discharging. Therefore, the tradeoff between regulation capability and achieving expected SOC level of an EV is of great crucial. B. Proposed Framework of SFR With EVs With the discussions above, the proposed framework for EVs to participate in SFR is illustrated in Fig. 1. Because an individual EV can be considered as an EV charging station with only one EV, individual EVs will not be mentioned specially in the following. The EV aggregator is responsible for communicating with both the AGC system and EV charging stations. According the AGC signals, the aggregator makes decisions to dispatch regulation tasks to EV charging stations based on the “FRC Calculation” block, the “V2G Controller” block and the “EV2G Power” block. EV charging stations calculate the FRC of regional EVs by the “FRC Calculation” block and simultaneously distribute

From the point of view of frequency regulation, EVs may undertake the regulation task as mobile storage devices. Therefore, properly undertaking the regulation task is the control objective of V2G strategies for EVs to participate in the SFR of interconnected power grids. On the other hand, there are some restrictions on EVs due to the customers' use of the EVs for transportation. When EVs arrive at the parking spot in the daytime, EV users will firstly check whether or not the residual battery energy is enough for the next transportation usage. If the residual battery energy is sufficient, EV customers may be more willing to hold than increase the battery SOC levels considering the low off-peak electricity price at night. Thus, charging behaviors can be generally categorized into maintaining battery SOC levels and adjusting battery energy levels, even if charging demands of EV customers may be different from EV to EV. Certainly, in power market environment, decisions of EV users may be more dependent on electricity price. Therefore, the other control objective of V2G strategies is that the battery SOC levels of EVs have to satisfy the requirements of users. III. V2G CONTROL STRATEGIES FOR SFR WITH EVS A. Principles on the EV2G Power of EVs As stated in Section II, charging behaviors may be categorized into maintaining battery SOC levels and adjusting battery energy levels. Therefore, we should respectively calculate the EV2G power for EVs in the two aspects. Suppose that the initial battery energy at plug-in time is sufficient for the next transportation usage and the EV user chooses to maintain the battery SOC level, a coordination method is proposed for an EV to take part in SFR and hold battery SOC level with the principle shown in Fig. 2. When the real-time battery SOC is higher than the initial battery SOC, a V2G power for regulation up is expected to set higher than the one for regulation down. This is because in order to hold the battery SOC level, it is expected for EVs to release more power to interconnected power grids rather than draw more power from the power grid. On the contrary, the regulation-up power is expected to set less than the regulation-down power if the real-time battery SOC level is lower than the initial battery SOC level. Particularly, the regulation-up power is expected to be the same as the regulation-down power when the



shown in Fig. 2, and the corresponding mathematical model can be described as follows: (2) where

and

are determined as follows:

(3) Fig. 2. Expected V2G power of an EV for maintaining battery SOC.

(4)

(5)

(6) Fig. 3. Expected V2G power of an EV for adjusting battery SOC level.

real-time battery SOC level is at the initial SOC level as plugged in. In order to build the relationship stated above, i.e., the mapping from battery SOC to a V2G power, a parabolic function is used as illustrated in Fig. 2 and the corresponding mathematical model will be given in (5) and (6). If an EV customer has to increase battery energy level for the next trip, the EV2G power is calculated based on the principle illustrated in Fig. 3. The charging/discharging power can be decomposed into scheduled charging power for satisfying charging demand and expected regulation dispatch. Here, the regulation-up and regulation-down power is set to be symmetric at scheduled charging power. For the case that an EV customer discharges power back to the grid, EV2G power can be calculated in a similar way. B. V2G Strategies for an EV Charging Station 1) FRC Calculation: Calculating the FRC is the key for EVs to participate in the SFR of interconnected power grids. During the course of frequency regulation, the charging/discharging power changes in real time, so the FRC varies. Based on the V2G power at the present time, the FRC at next time interval is calculated as follows:

With the characteristic of parabolic functions shown in (5) and (6), V2G power and battery SOC can be coordinated to participate in frequency regulation and simultaneously maintain battery SOC level as illustrated in Fig. 2. In all situations described from (2) to (6), the EV2G power of the ith EV has the following relationship: (7) Thus the expected regulation up and down demonstrate a negative correlation which is dependent on the battery SOC level according to the principle illustrated in Fig. 2. b) Adjusting the Battery SOC Levels of EVs: To guarantee the transportation usage, EV users expect that the residual battery energy should be enough. Therefore, an EV user can set the expected battery energy level and the leaving time. It should be pointed out that the expected SOC level can be lower than the initial value. This means that the EV user wants to sell electricity back to the power system. Scheduled power for increasing/decreasing battery SOC level can be calculated by (8) If a constant scheduled power is considered, (8) can be rewritten as

(1) (9) 2) EV2G Power Calculation: a) Holding the Battery SOC Levels of EVs: For EVs maintaining battery SOC levels, the principle to the EV2G power is

In this case, the EV2G power may be calculated based on the constant scheduled charging/discharging power. Therefore, the


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expected regulation-up and regulation-down power of an EV can be expressed as (10) c) Total EV2G Power in EV Charging Stations: Considering (2) and (10), total EV2G power of the whole EV charging station is calculated as (11) 3) V2G Control: a) Holding the Battery SOC Levels of EVs: Based on the FRC aggregated from EV charging stations, the EV aggregator dispatches regulation task to individual EV charging stations, where the regulation task is allocated to EVs holding battery SOC levels according to (12) The regulation task is distributed proportionally according to the EV2G power of the two types of EVs at time . With the regulation task assigned by (12), the command will be sent out by the V2G controller from EV charging station to individual EVs. For each EV that holds its SOC level, the V2G regulation power at time is allocated proportionally with the following strategy:

Fig. 4. Correlation between regulation task and expected V2G power.

(16) 2) Total EV2G Power: According to the EV2G power uploaded by EV charging stations, total EV2G power of EVs is calculated as (17) 3) V2G Control: Reducing ACE is usually chosen as a control objective for SFR. Once ACE goes beyond the dead-band, EVs and generating units participating in SFR will respond to regulation signals to suppress the ACE fluctuation. The V2G control strategies for EVs should take both regulation and EV users' requirements into consideration. A coordinated method is proposed and described as follows: (18)

(13) b) Adjusting the Battery SOC Levels of EVs: The regulation task for an EV adjusting SOC level is allocated as (14)

where and denote EV2G power for regulation up and regulation down, respectively; is a constant resulted from the EV2G power in (7) and (10); is the ratio of regulation task, which is decided based on the and . For clarity, the (18) can be rewritten as (19)

With the EV2G power calculated according to (10), the regulation power can be assigned proportionally to individual EVs as

(15) It can be seen from (15) that the V2G power consists of two parts: scheduled charging/discharging power to satisfy the expected SOC level and allocated power for SFR. C. V2G Strategies for EV Aggregator 1) FRC Calculation: For the EV aggregator, the total FRC can be summed as follows:

According to (19), the regulation-up task is positive correlation with regulation-up power and negative correlation with regulation-down power considering (18). The correlation between regulation-up and regulation-down tasks and regulation-up power of EVs is illustrated in Fig. 4. It can be seen that the greater the regulation up is, the more the regulation-up task and the less the regulation-down task, and vice versa. In this way, regulation-up and regulation-down tasks will be dynamically coordinated to reach expected battery SOC levels of EVs based on the EV2G power. Considering (18), V2G control strategy is to allocate regulation task for EV charging stations with the following form: (20)



Fig. 6. Total wind power output.

Fig. 7. Random load.

TABLE I PARAMETERS OF THE SIMULATION SYSTEM Fig. 5. Simulation system for the SFR with EVs.

where

. , EVs will not participate in the SFR of In (20), when interconnected power grids; when , EVs will undertake the full regulation task as possible. Regulation task is dispatched based on the EV2G power and FRC. The battery energy is taken into account with the dynamically change coefficient . According to the EV2G power uploaded by EV charging stations, with the (20), the V2G regulation of the EV charging station can be distributed proportionally as (21) The rest of regulation tasks will be undertaken by generating units with the expression (22) IV. SIMULATION SYSTEM A two-area interconnected power system is used to simulate SFR with EVs' participation. The schematic control diagram is illustrated in Fig. 5, where an EV aggregator, EV charging stations, and EV modules are included. A. Two-Area Interconnected Power Grid Operation data from practical interconnected power grids in China is used to build a two-area power grid model. Considering the practical interconnected mode, Tie-line Bias Control (TBC) and Flat Tie-line Control (FTC) are used for area-A and area-B,

respectively, i.e., and are respectively considered in LFC. Our main concerns are focused on area-A which includes EVs and wind power integration. In area-A, 73 generating units are controlled by the control center. Among them, 30 generating units take part in SFR. The other generating units only follow their generation plans. In area-B, power supply and demand are assumed to be well matched. Therefore, the frequency fluctuation in area-B is caused by the power oscillation in the tie-line, resulted from supply-demand mismatch in area-A. Control strategies of practical power grid in China are used in the “AGC strategies” block to dispatch the power to generating units. Intermittent wind power data in a time series with 1 s interval is illustrated in Fig. 6, which is also from real historical data. Practical power load with second intervals is shown in Fig. 7. Besides, the basic parameters of the simulation system are summarized in Table I. B. V2G Simulation Model The battery SOC at time

can be written as [24] (23)

where denotes the battery energy variation during the charging/discharging process, and satisfies


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TABLE II V2G SIMULATION PARAMETERS

Fig. 8. Real-time battery SOCs of four EVs in TYPE 1 of charging station 1.

TABLE III SIMULATION SCENARIOS

OF THE EVS WITH BATTERY SOCS

NORMALLY DISTRIBUTED


(24) Due to the fast regulation and response characteristics of EVs, the V2G regulation may be considered to be free from the ramp rate limitations in comparison to that of conventional generating units [19]. According to the system framework presented in Section III, an EV aggregator and two EV charging stations are constructed for EVs to take part in the SFR. As stated in Section II, charging behaviors of EV customers may be divided into maintaining battery SOC levels and adjusting battery energy levels. For the sake of convenience, they are respectively named as TYPE 1 and TYPE 2. The V2G simulation parameters are listed in Table II.


V. SIMULATIONS AND DISCUSSIONS A. Simulation Scenarios Considering the randomness of EV customers, normally distributed scenarios within the up-limit and down-limit are assumed to describe the battery SOC levels of EVs as shown in Table III. It is assumed that EV owners start to work at about 8:00 a.m. and return from working places to homes at about 5:00 p.m. [8], [19]. Therefore, it is rational that the plug-in duration of EVs is assumed from 8:00 a.m. to 5:00 p.m. for the sake of convenience. In order to simulate under the Simulink environment, the Monte Carlo sampling method is used to obtain the normally distributed battery SOC level for each EV. B. Simulations and Discussions For simplicity, the proposed V2G strategy is called as CS. By the CS, maximum values (Max), minimum values (Min), and root mean square values (RMS) of ACE and frequency deviations are listed in Tables IV and V, respectively. As shown in Tables IV and V, when V2G control strategy is used, frequency fluctuation and ACE are suppressed because of the fast


adjustments of charging/discharging power of EVs. We randomly choose SOC data of four EVs for illustrations shown in Figs. 8–11. Actually, the SOC curves of all EVs are similar, because the similar strategies are used for each EV. It can be seen from Figs. 8 and 10 that the CS can maintain battery SOC levels effectively. As illustrated in Figs. 9 and 11, the expected battery SOC levels can also be achieved by the CS. The main reason is that with the CS, regulation-up and regulation-down tasks can be dynamically coordinated according to battery SOC levels, which is illustrated in Fig. 12. In order to demonstrate the contributions of generating units and EVs on suppressing ACE, the ACE curves, the total power contribution of generating units, and the total FRC and V2G power of EVs are illustrated in Figs. 13–15, respectively. As



Fig. 12. Real-time coordination for regulation up and regulation down.

Fig. 16. Output power of a generating unit not participating in SFR.

TABLE IV QUALITY OF ACE IN AREA-A

Fig. 17. Output power of a generating unit participating in SFR.

C. Influences of V2G on Generating Units

Fig. 13. ACE results with and without V2G.

Fig. 14. Total power allocated to generating units for suppressing ACE.

From the point of view of SFR, the integration of EVs only influences the output of conventional generating units participating in SFR. This is because EVs and these generating units jointly undertake the SFR. However, when the PFC responding to system frequency deviations is considered, the outputs of generating units not participating in SFR will also be slightly influenced. In order to demonstrate the influences of EVs participating in SFR on generating units, we randomly choose two generating units: one does not take part in the SFR; the other is the generating unit participating in SFR. The output power of the two generating units is respectively illustrated in Figs. 16 and 17. From Fig. 16, the output power of the generating unit not participating in the SFR is slightly influenced. This is because PFC is actively responding to frequency deviations. As shown in Fig. 17, the output of the other generator is changed more obviously, since part of the SFR requirements is undertaken by EVs. VI. CONCLUSIONS

Fig. 15. Total FRC and V2G power of EVs for suppressing ACE (red curve for regulation-up FRC, green curve for regulation-down FRC, and blue curve for V2G power).

shown in Figs. 14 and 15, the regulation responsibility of generating units is reduced resulting from the participation of EVs in SFR. From Fig. 15, the regulation-up and regulation-down FRCs are dynamically maintained at certain levels.

In order to enable EVs' participation in the SFR of interconnected power grids, a framework for V2G control is constructed in the form of aggregation of EVs in this paper. The framework includes an EV aggregator, individual EVs and EV charging stations. The EV aggregator communicates with the AGC system and EV charging stations or individual EVs. In an EV aggregator, two aspects are involved: one is to calculate the total FRC and EV2G power from all EVs; the other is to develop a V2G strategy to dispatch regulation requirements to EV charging stations or individual EVs. Considering ACE, FRC and EV2G Power, a V2G strategy is proposed to dispatch regulation task from the EV aggregator to EV charging stations. In EV charging stations, a FRC calculation method is proposed on the basis of the V2G power at present time, and EV2G power is presented considering both frequency regulation and charging


TABLE V QUALITY OF FREQUENCY DEVIATION IN AREA-A

demands. Besides, V2G control strategies are developed for EV charging stations to distribute regulation task to EVs. Simulations on a two-area interconnected power grid model built according to practical interconnected power grids in China are used to examine the effectiveness of the proposed strategies. Simulation results show that the fluctuation of ACE and system frequency can be effectively suppressed. By balancing between regulation-up and regulation-down tasks, battery energy can be achieved at expected SOC levels. Considering the fast response of EVs for regulation, faster dispatching cycle may be implemented for EVs in comparison to that for generating units to achieve better AGC performance. This will be our further research topic.

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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, China, in 2004 and in 2007, respectively. He worked in Tsinghua University as a postdoctoral fellow from 2011 to 2013. He is an Associate Professor with the School of Electrical and Information Engineering, Jiangsu University, China. Currently, he is visiting the Energy Systems Division at Argonne National Laboratory, Argonne, IL, USA, as a visiting scholar. His research interests include power system control and electric vehicles.

Zechun Hu (M'09) received the B.S. and Ph.D. degrees from Xi'an Jiao Tong University, Shanxi, China, in 2000 and 2006, respectively. He worked in Shanghai Jiao Tong University after graduation and also worked in the University of Bath as a research officer from 2009 to 2010. He joined the Department of Electrical Engineering at Tsinghua University 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 in 1984 and the Ph.D. degree from China Electric Power Research Institute in 1989. He was a Postdoctoral Fellow at Tsinghua University from June 1989 to March 1991. He then held various positions at Bristol University, Bath University, and John Moores University from 1991 to 1996. In January 1997, he was appointed Professor of Power Systems at Brunel University, 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 at the University of Liverpool. He returned to Tsinghua University in February 2009 as a Professor at 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. Mr. Song was awarded the D.Sc. degree by Brunel University for his original achievements in power system research in 2002. In 2004, he was elected Fellow of the Royal Academy of Engineering (UK). In June 2009, he was elected VicePresident of Chinese Society for Electrical Engineering (CSEE) and appointed Chairman of the International Affairs Committee of the CSEE.


Jianhui Wang (M'07–SM'12) received the Ph.D. degree in electrical engineering from Illinois Institute of Technology, Chicago, IL, USA, in 2007. Presently, he is a Section Lead — Advanced Grid Modeling with the Energy Systems Division at Argonne National Laboratory, Argonne, IL, USA. He is also an affiliate professor at Auburn University and an adjunct professor at the University of Notre Dame. Dr. Wang is the secretary of the IEEE Power & Energy Society (PES) Power System Operations Committee. Before being promoted and elected to this position, he was the chair of the IEEE PES Power System Operation Methods Subcommittee for six years. He is an editor of the IEEE TRANSACTIONS ON POWER SYSTEMS, the IEEE TRANSACTIONS ON SMART GRID, an associate editor of the Journal of Energy Engineering, an editor of the IEEE PES LETTERS, and an associate editor of Applied Energy. He is an IEEE PES Distinguished Lecturer.

Xu Xie was born in Chongqing, China. He received the B.S. degree from North China Electric Power University, Baoding City, Hebei Province, and the M.S. degree from Tsinghua University, Beijing, China. His is now working with the North China Branch of State Grid Corporation of China. His special fields of interest include network modeling, energy management system (EMS) applications support, automatic generation control (AGC), automatic voltage control (AVC), and dispatcher training.