An optimal dispatching strategy for V2G aggregator participating in supplementary frequency regulation considering EV driving demand and aggregator’s benefits

Applied Energy 190 (2017) 591–599

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Applied Energy journal homepage: www.elsevier.com/locate/apenergy

An optimal dispatching strategy for V2G aggregator participating in supplementary frequency regulation considering EV driving demand and aggregator’s benefits q Chao Peng, Jianxiao Zou ⇑, Lian Lian, Liying Li School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China

h i g h l i g h t s
A dispatching strategy for V2G aggregator participating in frequency regulation.
Satisfying EV driving demand and maximizing benefits of aggregator simultaneously.
A driving demand calculation module is used to obtain the required EV battery SOC.
An optimal regulation power calculation model is designed to optimize aggregator profits.
A regulation power allocation model is built to allocate regulation power to EVs.

a r t i c l e

i n f o

Article history: Received 1 July 2016 Received in revised form 11 December 2016 Accepted 12 December 2016

Keywords: V2G aggregator Dispatching strategy Supplementary frequency regulation EV driving demand Economic benefits

a b s t r a c t With the development of Vehicle-to-Grid (V2G) technology and increasing number of electric vehicles (EVs) integrating in power grid, supplementary frequency regulation service provided by V2G aggregator has been seen as the most promising grid ancillary service provided by the integrated EVs. In this paper, an optimal dispatching strategy of V2G aggregator is proposed to satisfy the driving demand of EV owners and maximize the economic benefits of aggregator simultaneously when it participates in supplementary frequency regulation. A judgment module is designed to determine EVs in aggregator whether participating in frequency regulation according to EV battery SOC for EVs’ driving demand, which is calculated by EVs’ daily driving distance. An optimal regulation power calculation model is built to optimize profits of aggregator and tracking performance of frequency load control signal from grid operator. A fair regulation power allocation module is designed to avoid over-discharging of EVs in aggregator. Finally, the proposed strategy is implemented in the simulation experiments to demonstrate its effectiveness. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction Since 90s of last century, the environment pollution problems caused by traditional vehicle exhaust have attracted extensive public attentions. Especially, in recent years, the continuous increasing of vehicle makes these problems more and more serious. With the rapid development of technology of electric vehicles (EV), using EV instead of traditional vehicle has been considered as one of the most effective solutions [1]. Meanwhile, the amount of global EVs has increase dramatically year by year.

q This work was supported by National Natural Science Foundation of China under Grant No. 61201010. ⇑ Corresponding author. E-mail address: [email protected] (J. Zou).

http://dx.doi.org/10.1016/j.apenergy.2016.12.065 0306-2619/Ó 2016 Elsevier Ltd. All rights reserved.

With the rapid increasing of EVs, the integrating of such large scale of EVs would bring great challenges to power grid operation. To promote the coordinated development of power grid and EVs, the concept of vehicle-to-grid (V2G) was proposed in 1997 [2]. In the past near twenty years, V2G technology has been received extensive researches and has become mature. By using V2G technology, EV chargers could be operated in bidirectional mode, i.e., EV batteries can be charged when they are plugged in power grid, the energy stored in the EV batteries can also be delivered back to the power grid [3]. Now, the EVs integrating in grid not only are charging loads, but also could be used as power generation and energy storage units in power grid [4]. They could be utilized to participate in grid ancillary services to maintain power grid’s stability and security, such as frequency regulation [5], voltage regulation [6], spinning reserve

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Nomenclature Ropt ðtÞ Rnopt ðtÞ Preg ðtÞ Pem ðtÞ N SOC n ðtÞ SOC nexp C nmax

optimal regulation power of aggregator at time t optimal regulation power of nth EV at time t frequency regulation price at time t market electricity price at time t the total number of EVs managed by EV aggregator battery SOC of nth EV at time t battery SOC of driving demand of nth EV owner rated capacity of nth EV battery

[7], load peak shifting [8] and so on. However, the capacity of single EV is too little to participate in these grid ancillary services. Thus, a concept of V2G aggregator is proposed, which introduces a centralized control system to control a large scale of EVs to provide grid ancillary services [9]. Considering the communication delay between grid operator and V2G aggregator, communication delay between V2G aggregator and each EVs controlled by aggregator, V2G aggregator commonly is used to participate in the supplementary frequency regulation service, which requires V2G aggregator to follow a load frequency control (LFC) signal [10–12], which is within the achievable power capacity of V2G aggregator. The dispatching strategy is the key for aggregator, which regulates the charging and discharging power of each EV to participate in grid frequency regulation. The dispatching strategy determines the feasibility and economics of V2G aggregator participating in supplementary frequency regulation. Currently, the dispatching strategy for V2G aggregator participating in supplementary frequency regulation has become a research hotspot. Some dispatching strategies are proposed considering on the problem of satisfying EV’s charging demand or driving demand when EV aggregator participating in supplementary frequency regulation [13–17]. To satisfy charging demand of EVs in aggregator, a dispatching strategy considering charging demands is proposed in [14]. To reduce the EV owner’s cost and obtain the desired EV battery SOC when EV departs, a model predictive control based dispatching strategy is proposed in [15]. A dispatching strategy considering the operational status and mobility needs is proposed in [16]. In [17], a dispatching strategy is proposed to maximize the profit for the EV owner and satisfy EV charging demand during the whole parking time. Meanwhile, many dispatching strategies are proposed to optimize the aggregator’s profit when it participating in supplementary frequency regulation [17–22]. A dispatching strategy is proposed to maximize the revenue obtained from aggregator participating in the supplementary frequency regulation in [18]. To optimize aggregator’s profit under electricity price uncertainty, the dispatching strategies considering the electricity price is proposed in [19,20]. In [21], a dispatching strategy is proposed to minimize the operating cost of aggregator when it participating in frequency regulation. In [22], a real-time welfare-maximizing regulation allocation based dispatching strategy is proposed to fairly allocate the regulation power capacity among the EVs. In [23], a dispatching strategy is proposed to optimize the aggregator’s profit considering the fair allocation of charging or discharging power of EVs. A stochastic dynamic programming based dispatching strategy is proposed to optimize the charging and frequency regulation capacity bids of EVs in [24]. In [25], a robust frequency regulation scheduling algorithm is proposed to maximize the revenue of aggregator under the frequency regulation performance-based compensation scheme. According to the mentioned above, the satisfying driving demand of EV and optimization of benefits of aggregator are the two main problems faced by EV aggregator dispatching strategy

Rich

max

Ridisch max SðtÞ

battery maximum charging power limitation of nth EV battery maximum discharging power limitation of nth EV frequency regulation command signal at time t

participating in supplementary frequency regulation. The first problem refers to the satisfaction of EV owners, the second problem refers to the attraction for V2G aggregator participating in frequency regulation. The above existing dispatching strategies have not considered on both of them comprehensively. The contribution of this paper is the development of a novel optimal dispatching strategy for V2G aggregator participating in supplementary frequency regulation, to (I) optimizes the tracking performance of load frequency control signal, (II) satisfy EV owner’s driving demand and maximize the economic benefits of V2G aggregator as much as possible in same time. The SOC for EV owner’s driving demand is calculated by the daily driving distance information. A judge module is designed to determine which EVs in aggregator participating in frequency regulation. An optimal regulation power participating in frequency regulation calculation module is built for aggregator to optimize the profits of aggregator and tracking performance of frequency regulation command signal. A regulation power allocation module is designed to fairly allocate the charging or discharging power of each EV participating in frequency regulation based on their capacity. The rest of this paper is organized as follows. In Section 2, the structure of V2G aggregator participating in supplementary frequency regulation is described. In Section 3, the principle and design objective of the proposed dispatching strategy are proposed. The detailed design of the proposed strategy is presented in Section 4. The results of simulation experiments are discussed in Section 5. Finally, the conclusion is presented in Section 6.

2. V2G aggregator participating in supplementary frequency regulation V2G aggregator is a control center of EVs, which manages the charging and discharging behavior of each EV in aggregator. The system structure of V2G aggregator participating in supplementary frequency regulation is shown in Fig. 1. As seen in Fig. 1, when aggregator participates in supplementary frequency regulation, aggregator receives load frequency control signal from grid operator and sends its participating power capacity information back to grid operator. Meanwhile, aggregator dispatches the charging or discharging power command to each EV according to the load frequency control signal, information of EV owner’s requirements and state information of each EV. The aggregator plays a connector role between EVs and power grid.

3. The proposed dispatching strategy 3.1. Principle The proposed dispatching strategy consists of driving demand calculation module, EVs participating frequency regulation judge module, optimal regulation power participating in frequency

C. Peng et al. / Applied Energy 190 (2017) 591–599

593

Fig. 1. System structure of V2G aggregator participating in supplementary frequency regulation.

regulation calculation module and regulation power allocation module. Its scheme is shown in Fig. 2. The driving demand calculation module calculates the EV battery SOC for EV owner driving demand according to each EV daily driving distance. The EVs participating frequency regulation judgment module determines which EVs in aggregator participating in frequency regulation. The optimal regulation power calculation module calculates the regulation power of aggregator participating in frequency regulation according to the SOC for driving demand of EV owners and the current SOC state of EV battery. The regulation

power allocation module dispatches the charging and discharging power of each EV according to the regulation power of aggregator participating in frequency regulation and SOC state of each EV. 3.2. Objectives When aggregator participates in supplementary frequency regulation, grid operator would ask aggregator to track the load frequency control signal. However, charging service provided aggregator is prior to participating in supplementary frequency

Fig. 2. Scheme of the proposed dispatching strategy.

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regulation for EV owners. Their EVs in aggregator should be charged to meet their driving demand when they departure. Meanwhile, the attraction for aggregator is its economy benefits obtained from participating in supplementary frequency regulation. Thus, the objectives of the proposed dispatching strategy of V2G aggregator could be given as follows,

ticipating in supplementary frequency regulation and optimization of the tracking performance of load frequency control signal. They are given as follows,

8 Ireg ðtÞ < max Ropt ðtÞ

ð4Þ

: min Dsig ðtÞ Ropt ðtÞ

(1) The SOC of each EV in aggregator should satisfy the driving demand of EV owners. (2) Optimizing the economy profits of aggregator and tracking performance of load frequency signal from grid operator as much as possible. 4. Design of the proposed dispatching strategy 4.1. Driving demand calculation module The calculation model for driving demand calculation module is built based on the EV daily driving distance, EV battery energy consumption and additional energy for uncertainty of driving and battery safety. It is written as follow,

DE ¼ M d  Em SOC exp ¼ SOC g  20% þ

ð1Þ DE 1 þ Em  S S

ð2Þ

where DE is the energy for driving demand. Md is the daily driving distance of EV. Em is the energy consumption per kilometer. SOCgexp is the battery SOC for driving demand, SOCg is upper bound SOC of EV battery. S is the maximum capacity of battery. Considering on the battery working temperature, current limits, the SOC range of EV battery always are selected between 20% and 95% in the calculation.

F ¼

1; ðSOC n ðtÞ > SOC nexp Þ 0; ðSOC n ðtÞ 6 SOC nexp Þ

;

ðn ¼ 1; 2; . . . NÞ

ð5Þ

where RðtÞ is the regulation power of aggregator participating in supplementary frequency regulation, Preg ðtÞ and Pcha ðtÞ are frequency regulation energy price and market electricity price at time t respectively, Dt is the time interval of load frequency control signal from grid operator. The indices of tracking error square could be used to measure the tracking performance of load frequency control signal from grid. Thus, Dsig ðtÞ could be written as follow,

Dsig ðtÞ ¼ ðRðtÞ  SðtÞÞ2

ð6Þ

To simplify the calculation, the multi-objective in Eq. (4) could be transformed into the following single objective, Ropt ðtÞ

The EVs participating frequency regulation judgment module compares the current SOC of each EVs in aggregator with their SOC for driving demand of EV owners. It would select which EVs in V2G aggregator to participate in supplementary frequency regulation. Its selection condition is whether current SOC of nth EV is above its SOC for driving demand, which is given as follows,

(

Ireg ðtÞ ¼ Preg ðtÞRðtÞDt þ ðsgnðSðtÞÞÞPem ðtÞRðtÞDt

max UðtÞ ¼ aP reg ðtÞRopt ðtÞDt þ bðsgnðRopt ðtÞÞÞP em ðtÞRopt ðtÞDt

4.2. EVs participating frequency regulation judgment module

n

where Ropt ðtÞ is the optimal regulation power of aggregator participating in supplementary frequency regulation, Ireg ðtÞ is the total economy benefits obtained from participating in frequency regulation, Dsig ðtÞ is the tracking error between load frequency control signal and actual regulation power participating in supplementary frequency regulation. Considering on the income of aggregator participating in supplementary frequency regulation, charging income and discharging cost of EVs in the process of participating in frequency regulation, economy benefits Ireg ðtÞ could be written as follow,

ð3Þ

where Fn is a flag to represent whether nth EV in aggregator participates in frequency regulation. Fn = 1 represents nth EV would participate in frequency regulation, Fn = 0 represents nth EV would not participate in frequency regulation. 4.3. Optimal regulation power participating frequency regulation calculation module Optimal regulation power participating frequency regulation calculation module calculates the regulation power of aggregator participating in supplementary frequency regulation according to the SOC for driving demand of EV in aggregator, load frequency control signal from grid operator, electricity price and frequency regulation price. An optimal regulation power calculation model is used in this module and its optimization objectives and constraints are given bellow. 4.3.1. Optimization objectives The optimization objectives of optimal regulation power calculation model are maximizing economy benefits obtained from par-

 cP reg ðtÞðRopt ðtÞ  SðtÞÞ2

ð7Þ

where UðtÞ is the total benefits of EVs participating in supplementary frequency regulation, a is the weighting factor for income of aggregator participating in supplementary frequency regulation, b is the weighting factor for income of EVs charging or cost of EVs discharging in the process of frequency regulation, c is the weighting factor for tracking performance of load frequency signal from grid operator. 4.3.2. Constraints There are two constraints in optimal regulation power calculation model. (1) The satisfaction of driving demand of EV owners. When aggregator participates in up frequency regulation, the EVs participating in frequency would be discharged. However, their SOC should not be below the SOC for driving demand. N N X X F n SOC n ðtÞ  Ropt ðtÞDt P F n SOC nexp n¼1

ð8Þ

n¼1

(2) The limitation of max SOC. When aggregator participates in down frequency regulation, EVs would be charged. However, considering on battery safety, their SOC should not be charged above the maximum of SOC N N X X F n SOC n ðtÞ  Ropt ðtÞDt 6 F n SOC nmax n¼1

n¼1

ð9Þ

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To sum up, the optimal regulation power calculation for aggregator participating in frequency regulation could be given as follow,

(4) The limitation of EV battery charging/discharging rates, i.e., the regulation power of each EV would be limited in its normal range.

max UðtÞ ¼ aPreg ðtÞRopt ðtÞDt þ bðsgnðRopt ðtÞÞÞPem ðtÞRopt ðtÞDt

F n ðSOC n ðtÞ þ Rn ðtÞDtÞ 6 F n SOC nmax

Ropt ðtÞ

 cðRopt ðtÞ  SðtÞÞ

2

ð10Þ

s.t. N N X X F n SOC n ðtÞ  Ropt ðtÞDt P F n SOC nexp n¼1

ð11Þ

N N X X F n SOC n ðtÞ  Ropt ðtÞDt 6 F n SOC nmax n¼1

To sum up, the allocated regulation power calculation model could be given as follow,

min JðtÞ ¼ n R ðtÞ

 n  N X  ðtÞSOC n ðt1Þ F n SOC  SOC n SOC n n¼1

max

exp

N N X X jSOC n ðtÞ  SOC n ðt  DtÞj ¼ Rn ðtÞDt

n¼1

ð12Þ

ð17Þ

n¼1

!

ð18Þ

n¼1

s.t.

n¼1

N X Rn ðtÞ ¼ Ropt ðtÞ

4.4. Regulation power allocation module

ð19Þ

n¼1

Regulation power allocation module allocates the optimal regulation power to each EV in aggregator. To allocate the optimal regulation power fairly and reduce the over-discharging of one EV battery, a calculation model based on the available capacity of EV battery is used. Its objective function and constraints are given bellow. 4.4.1. Objective function The objective of regulation power allocation calculation model is calculating the regulation power of each EV participating in supplementary frequency regulation according to their available SOC range and make the SOC change of EVs small as much as possible when they participate in frequency regulation. The objective function could be given as follows,

  n n   N X n SOC ðtÞ  SOC ðt  1Þ min JðtÞ ¼ F   n n  SOC max  SOC exp  Rn ðtÞ n¼1

ð13Þ

where SOC n ðtÞ  SOC n ðt  DtÞ ¼ Rn ðtÞDt, Rn ðtÞ is the allocated regulation power of nth EV participating in frequency regulation, J(t) represents the SOC change of EVs participating in frequency regulation. 4.4.2. Constraints There are four constraints in regulation power allocation calculation model. (1) The sum of regulation power of EVs participating in frequency regulation must equal the calculated optimal regulation power. N X F n Rn ðtÞDt ¼ Ropt ðtÞ

ð14Þ

n¼1

(2) The satisfaction of driving demand, i.e., the battery SOC of each EV participating in frequency regulation would not be bellow the SOC of driving demand of EV owner.

F

n

SOC nexp

n

n

n

6 F ðSOC ðtÞ  R ðtÞDtÞ

ð15Þ

(3) The limitation of EV battery capacity, i.e., the battery SOC of each EV participating in frequency regulation would not be above its maximum SOC limitation.

F n ðSOC n ðtÞ þ Rn ðtÞDtÞ 6 F n SOC nmax

ð16Þ

SOC nexp 6 SOC n ðtÞ  Rn ðtÞDt

ð20Þ

SOC n ðtÞ þ Rn ðtÞDt 6 SOC nmax

ð21Þ

Rnmin 6 Rn ðtÞ 6 Rnmax

ð22Þ

5. Simulation and performance analysis 5.1. Simulation experiments In this section, the proposed dispatching strategy will be implemented in an EV aggregator participating in supplementary frequency regulation simulation experiment. An EV aggregator with 10 EVs of three different types will be used in the simulation experiment. The types of EVs are Mini-E, Nissan Leaf and i-MiEV, their battery capacities are 35 kWh [26], 24 kWh [27], 16 kWh [28] respectively. The initial SOC of EV batteries are random. The expected SOCs of driving demand are calculated based on daily travel distances information of EVs from statistical data in [29]. The detail parameters of the EVs in aggregator are shown in Table 1. In the simulation experiment, the frequency regulation price, electricity price and load frequency control signal are selected from New York Independent System Operator (NYISO) market in March 11, 2012. The time interval of frequency regulation DT is 15 min. The frequency regulation price and electricity price are shown in Fig. 3. To illustrate the effectiveness of the proposed dispatching strategy, two dispatching strategies are compared in the simulation experiment: (a) the proposed dispatching strategy, (b) the traditional economic benefits based dispatching strategy [18]. 5.2. Simulation results discussions 5.2.1. Load frequency control signal tracking performance Power grid operator sends the load frequency signal control signal to V2G aggregator at every 15 min. The tracking results of load frequency signal are shown in Fig. 4. As seen in Fig. 4, dispatching strategy (b) only considers on maximizing economic benefits of aggregator, which leads it could not track load frequency control signal well. The tracking error of the proposed strategy in this paper, i.e., dispatching strategy (a) is within ±3%, which is much smaller than that of dispatching strategy (b).

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Table 1 Parameters of EVs. EV No

Capacity/kWh

SOCinit

SOCexp

Power of charge/kW

Power of regulation/kW

EV1 EV2 EV3 EV4 EV5 EV6 EV7 EV8 EV9 EV10

35 35 35 24 24 24 24 16 16 16

0.342 0.632 0.098 0.279 0.547 0.838 0.965 0.158 0.533 0.957

0.372 0.532 0.284 0.504 0.578 0.550 0.526 0.517 0.627 0.606

0–12 0–12 0–12 0–10 0–10 0–10 0–10 0–8 0–8 0–8

14 14 14 12 12 12 12 10 10 10

to to to to to to to to to to

14 14 14 12 12 12 12 10 10 10

35 market electricity price Frequency regulation price

30

Price ($/MWh)

25 20 15 10 5 0

0

1

2

3

4

5

6

7

8

9

10

11

12

Time (h) Fig. 3. Regulation capacity price and energy price from NYISO.

40 load frequency signal dispatching strategy a) dispatching strategy b)

Regulation Power (kW)

30 20 10 0 -10 -20 -30 0

1

2

3

4

5

6

7

8

9

10

11

12

Time (h) Fig. 4. Load frequency control signal tracking results.

5.2.2. Economic benefits The economy benefits of V2G aggregator obtained from participating in supplementary frequency regulation could be calculated by following equation, which considers the benefits from participating supplementary frequency regulation, the charging benefits from EV owners, the aggregator penalty imposed by grid operator for load frequency control signal tracking violation [30]. In ¼

M X ðP reg ðt þ kDTÞRðt þ kDTÞDT þ ðsgnðRðt þ kDTÞÞPem ðt þ kDTÞ k¼1

 Rðt þ kDTÞDT  kPreg ðt þ kDTÞjRðt þ kDTÞ  Sðt þ kDTÞjÞ

ð23Þ

where k is the penalty coefficient of load frequency control signal tracking violation, is selected as 0.45. According to the load frequency signal tracking results in Fig. 4 and calculation of income in Eq. (23), the income by using the proposed strategy, i.e., dispatching strategy (a) is $45.03, which is 7% more than that obtained by dispatching strategy (b). This shows that the economic benefits of aggregator participating in supplementary frequency obtained by using the proposed dispatching strategy is better than that obtained by using dispatching strategy (b), when considering the frequency load control signal tracking violation penalty imposed by grid operation.

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1

SOC

0.8

0.6

0.4

0.2

0

1

2

3

4

5

6

7

8

9

10

11

12

Time (h) 1

SOC

0.8

0.6

0.4

0.2

0

1

2

3

4

5

6

7

8

9

10

11

12

Time (h) 1

SOC

0.8

0.6

0.4

0.2

0

1

2

3

4

5

6

7

8

9

10

11

12

Time (h) 1

SOC

0.8

0.6

0.4

0.2

0

2

4

6

8

10

12

Time (h) Fig. 5. The simulation experiment results of EVs’ SOC curve (‘O-’ is the SOC change obtained by dispatching strategy a), ‘⁄-’ is the SOC change obtained by dispatching strategy b), ‘+-’ is SOC of EV owner driving demand).

5.2.3. Battery SOC and driving demand of EVs The maximum, minimum and change range of EVs’ battery SOCs during aggregator participating in supplementary frequency regulation are shown in Table 2. As seen in Table 2, there is no

obvious differences in SOC maximums of EVs by using these two dispatching strategies. The SOC minimums of all EVs are above 50% by using the proposed dispatching strategy. By using dispatching strategy (b), the SOC minimums of EVs are between 26.37% and

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Table 2 Comparison of EVs’ SOC change. EV No

EV1 EV2 EV3 EV4 EV5 EV6 EV7 EV8 EV9 EV10

SOC maximum

SOC minimum

SOC change range

(a)

(b)

(a)

(b)

(a)

(b)

93.63% 95.00% 90.60% 86.55% 91.52%. 93.28% 94.52% 92.47% 88.65% 94.28%

91.35% 93.50% 91.28% 89.64% 90.92% 88.49% 94.70% 91.31% 83.42% 94.24%

55.32% 53.18% 56.33% 51.79% 52.48% 50.77% 52.57% 50.39%. 58.63% 60.55%

29.16% 26.37% 35.82% 42.86% 38.90% 33.65% 26.61% 28.25%. 39.49% 26.58%

38.31% 41.80% 34.27% 34.76% 39.04% 42.51% 41.95% 42.08% 30.20% 33.73%

62.19% 67.13% 55.46% 46.78% 52.02% 54.84% 68.09% 63.06% 43.93% 67.66%

42.86%. The SOC change ranges by using the proposed dispatching strategy are between 41.80% and 30.20%. It is obvious that the SOC change ranges by using the proposed dispatching strategy are less than the those by using dispatching strategy (b) during the process of aggregator participating in supplementary frequency regulation. The proposed dispatching strategy could reduce the SOC change ranges of EVs and avoid over-discharging when aggregator participating in supplementary frequency regulation. The simulation experiment results of EV battery SOC change curve of four randomly selected EVs are shown in Fig. 5. As seen in Fig. 5, the SOCs of four EVs by using strategy (a) are above their expected SOC for driving demand in most time. However, by using strategy (b), the SOCs are below the expected SOC for driving demand in some hours. Especially, at times of 2nd to 5th hour and 8th hour to 11th hour, when aggregator participates in up frequency regulation. To sum up, by using strategy (a), the SOCs of EVs could satisfy the driving demand of EV owner during the most time of process of aggregator participating in frequency regulation. By using strategy (b), the SOCs of EVs could not satisfy the driving demand of EV owner during the near half time of process of aggregator participating in frequency regulation.

6. Conclusion To satisfy the driving demand of EV owners and maximize the economic benefits of EV aggregator obtained from participating in frequency regulation service simultaneously, a novel dispatching strategy for V2G aggregator is proposed in this paper. In the proposed strategy, a driving demand calculation module is designed to calculate the battery SOC for EV owner’s driving demand. An optimal regulation power participating frequency regulation calculation module and a regulation power allocation module are built to optimize the economic benefits of aggregator and aggregator’s tracking performance of load frequency control signal from grid operator. Finally, the simulation experiment is conducted to demonstrate the effectiveness of the proposed dispatching strategy. The simulation results show that the proposed dispatching strategy not only could optimize the economy benefits of aggregator participating in supplementary frequency regulation, but also make the battery SOC of EVs in aggregator satisfying the driving demand of EV owners and avoid over-discharging of EVs.

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Chao Peng received the M.S. degree and Ph.D. degree in Automation from University of Electronic Science and Technology of China in 2007 and 2012, respectively. Currently, he is a lecturer with school of automation engineering in Electronic Science and Technology of China. His research interests are smart grid, Intelligent information processing and control technologies.

Jianxiao Zou received the M.S. degree and Ph.D. degree in Automation from University of Electronic Science and Technology of China in 2003 and 2009, respectively. Currently, he is a professor with school of automation engineering in Electronic Science and Technology of China. His research interests are smart grid and renewable power generation technologies.

599 Liying Li received Ph.D. degree in Automation from University of Electronic Science and Technology of China in 2010, respectively. Currently, she is an associate professor with school of automation engineering in Electronic Science and Technology of China. Her research interests are smart grid and its communication technologies.

Lian Lian received the B.S. degree in Automatic Control from Southeast University, China, in 2012. She is currently work toward the M.S. degree in school of automation engineering in Electronic Science and Technology of China. Her research interests are smart grid and charging and discharging technology for electric vehicle.