Demand Side Energy Management with PSO and Regulated Electric Vehicles Behaviours Yimin Zhou1 , Guoqing Xu2 Abstract— A real-time optimal appliance usage strategy based on binary particle swarm algorithm is presented in the paper with the participation of both energy suppliers and endusers. Under the multi end-users and time-of-use electricity prices incentive circumstances, the total electricity bills of endusers can be generated with different appliance usage patterns: random or optimized modes, resulting in different load curves. Moreover, the function of electric vehicles to the grid (V2G) is investigated and analyzed considering its characteristics as both energy consumption load and energy resource. Matlab simulation tool is used to perform the experiments to prove that the load shifting, energy saving and energy supply efficiency enhancement can be achieved with particle swarm optimization. Key words–Home energy management, Particle swarm optimization, Electric vehicles, Residential appliances
I. I NTRODUCTION With the development of economics and scientific technologies, the power grid has been enduring infrastructure reform called smart grid construction due to the energy crisis and air pollution issues worldwide [1]. The prominent characteristic of smart grid is to use advanced information and communication technology (ICT) to flexibly integrate demand side resources and renewable energy resources combined with traditional energy resources to achieve the mutual interaction of information and electric energy. Demand side management (DSM) is a basic and important function in modern power grid for dispatching and control, which radically changes the traditional mode of purely relying on increasing the supply of energy to meet energy demand growth [2][3]. It also plays an important strategic role in electric power industry and economic development and environmental protection, which is a necessary measure to be taken so as to improve energy saving capacity at the users’ terminal. In paper [4], it targets the issue of increasingly difference growth of electricity usage in peak-valley periods. Neural network optimal algorithm is used for industrial users to save the energy and reduce peak-valley difference [5]. An electricity load management model is proposed in [6] for single user or multi users based on linear integer programming, which takes into account renewable energy and distributed battery storage. Mixed linear programming method is applied in paper [7], where the developed mathematical model is used to obtain the least electricity bill objective. Dynamic 1 Y. Zhou is with Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China, 518055 and also with the the Chinese University of Hong Kong, Hong Kong, China.
Email:
[email protected]
2 G. Xu is with Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China. Email:
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pricing is considered to be an optimal method satisfying not only the users need but also the minimum tariff [8]. Particle swarm method is used to achieve the minimum exhaustion and economical load assignment so as to improve the energy utilization efficiency and energy conservation and emission reduction [9]. As for the electricity suppliers, they expect to improve power supply efficiency and avoid unnecessary energy waste [10]. From the end-user perspective, time-of-use (ToU) of the electricity, total tariff and obtained comfort are their mostly concerned [11]. A mechanism is needed to manage their usage of electricity taking into account the interests of electricity suppliers and user side power management to seek an optimum solution. The cultivation and development of all kinds of electric vehicles (EVs), i.e., hybird EVs as one of the strategic emerging industries, has received much attention due to the environmental pollution and energy storage consideration [12]. It is regarded as the most potential transportation tool in the twenty-first century. Along with the installation of electric car charging stations becoming clearly designated and implemented, the construction of electric vehicle charging infrastructure has begun to enter its substantive development application stage. The popularity of the electric vehicles will bring significant impact on the future power systems. The charging and discharging behaviours of the EVs will bring effects on the power generation, stability reduction in the operation and transmission power network systems [13]. when a large amount of EVs are being used, the required charging energy will cause a heavy load in the distributed grid system, especially during the high-peak load periods [14][15]. However, the EVs have the capability of energy storage via the batteries, which have positive functions in the smart grid realization. On the one hand, the load of EVs will take heavy percentage in the whole load of the energy network system. When the EVs are charging as one of the loads, the charging time can be arranged for orderly charging management so as to avoid impulse to the grid, increase operational efficiency and reduce effect on grid safety. If the power batteries are used as storage device, the energy stored in the batteries are considered as backup power to provide energy feed back to the grid and optimize network operation so that the reliability of the system can be increased [16][17]. Based on the impact of the charging and discharging behaviour to the load, paper [18] studies the EV charging/discharging behaviours based on the statistical characteristics and develops the economic scheduling model for EVs
and wind power generation considering their uncertainties. Through the analytical inference, the stochastic problem can be transferred into a deterministic problem and the application of interior point method is used to solve it. Paper [19] investigates the EVs load frequency control and proposes the model predictive control for EVs. In paper [20], it adopts the centralized control strategy to evaluate its reliability, effectiveness and benefit via Monte Carlo model. Under the condition that the EV charging/discharging time distribution is determined, paper [21] discusses the impact of their bebaviours on the grid via the comparison of stochastic charging mode without any constraints and the electricity price regulated charging mode. The charging and discharging behaviours of the EVs have two different effects on the power system. Thus the charging/discharging prices as main incentive play an important role in the power supply market. In this paper, the behaviours of the rechargeable batteries of the EVs are analyzed from the economic perspective. The cost function of the EV users, grid and power sources are developed so that the cost factors will be determined. A binary particle swarm method is used to optimize the appliances with the minimized tariff objective considering the electrical appliances operating characteristics. The remainder of the paper is organized as follows. In section II, the system and the details of the binary particle swarm algorithm is described. V2G technologies and involved battery state estimation are discussed in Section III. Simulation experiments are performed to testify the effectiveness of the particle swarm optimization in Section IV. Conclusions are given in Section V. II. B INARY PARTICLE S WARM O PTIMAL A LGORITHM A. System description Due to various characteristics of residential electrical appliances, i.e., refrigerator, washing machine, there are diverse ways of operation for each type of the appliances. So does the personal habit for each family. Here, one family is regarded as an end-user for discussion. One day (24-hour) is partitioned evenly by 24 intervals. From the end user point of view, an optimal algorithm should be developed with the objective of minimum tariff considering the personal habit and appliance characteristics. Under the time-of-use price circumstances, the least tariff can be obtained via the operational period adjustment for each appliance. A group of 24 × 1 metrics with 0,1 elements are used to describe the appliance working status, where 0 denotes that the appliance is on ‘off’ status and 1 is ‘on’ status. Similarly to other swarm intelligence optimization methods, a binary particle swarm method can obtain the optimal solution in N dimension globally. The searching process starts from one solution set, i.e., the system is composed of a swarm of particles. Information sharing mechanism is used in the searching process. Besides, the swarm particle optimization has simple principle and less parameters, which is easy to be realized with fast convergency speed.
The domestic appliances can be classified into three categories. The 1st group is the appliances that their working time cannot be shiftable with determined working period, such as refrigerator that has to be powered in 24 hours. The 2nd group includes shiftable appliances, i.e., washing machine and dish washer. Although their working time can be shiftable, they have to be worked continuously without being interrupted otherwise more electricity bills will be generated. The 3rd type is those that can be switched on and off at any time, such as humidifier, air-conditioner and electrical charging system. Suppose xi (i ∈ R) denotes the ith domestic appliance. Ti is the plan table to represent n possibilities for the appliance xi per day. The elements are composed of ‘0’ and ‘1’. For instance, xi belongs to the 1st category, and there is only one possibility in the time table, i.e., Ti ∈ 24 × 1. If xi belongs to the 2nd category with continuous working hour 1 < mi < 24, thus there are (24 − mi + 1) possibilities in the 24-hour time slots, i.e., Ti ∈ (24 × (24 − mi + 1)). For example, mi = 2, then, ⎞ ⎛ 1 0 0 ··· 1 ⎜ 1 1 0 ··· 0 ⎟ ⎟ ⎜ ⎜ 0 1 1 ··· 0 ⎟ ⎟ ⎜ Ti = ⎜ . . . (1) . ⎟ ⎜ .. .. .. · · · .. ⎟ ⎟ ⎜ ⎝ 0 0 0 ··· 0 ⎠ 0 0 0 · · · 1 24×23 If xi is in the 3rd category and it should satisfy the whole power requirement Hi . Then the time table can be designed based on the appliance nominal power and whole power requirement. Suppose the working hour for this appliance ti ti possibilities, i.e., Ti with C24 ×24 ti ≤ 24, thus there are C24 dimension. Let the time-of-use L = (L1 , L2 , L3 , . . . , L24 ) the electricity prices per day in 24 time slots. According to the working matrix Ti of each appliance xi (i ∈ R), n schemes of the power matrix Pin can be obtained. Only one scheme will be selected each time for the user to satisfy the least tariff expectation. B. A binary particle swarm optimization algorithm Study shows that birds often abruptly change their directions, i.e., spreading, aggregating during their flights. Although their immediate flight behaviours are unpredictable, they keep consistency as a whole and each individual maintains optimum distance to the other individuals. Through research on similar behaviours of biological populations, a social information sharing mechanism exists in the swarms, which is the origin of the particle swarm optimization (PSO) algorithm. When PSO algorithm is used to solve optimal issues, the solution is to seek the best position to the objective, which is called ’particle’. Each particle can fly freely in Ddimensional space. Four factors should be discussed in the optimized procedure: the current position Xi of each particle, the current speed Vi of each particle, the best searched
position Pi , the global best position of all particles Pg . The current speed Vi decides the direction and distance of its flight. Pi and Pg are all determined by a evaluation function f (the function to be optimized). Therefore, in D-dimensional solution space, each particle will update themselves by tracking two ”extremes” Pi and Pg at each iteration. Assuming particle i = (1, 2, · · · , M ) is in the D-dimensional space, M particles are composed of a population, then Xi = (xi1 , xi2 , · · · , xiD ) , Vi = (vi1 , vi2 , · · · , viD ) , Pi = (pi1 , pi2 , · · · , piD ) and Pg = (pg1 , pg2 , · · · , pgD ) denote the position of each particle, speed of each particle, current optimized position and global optimized position of the group, respectively. The binary particle swarm law of each particle will be updated in ‘0’ or ‘1’ values according to the following formula velocity and position: vid = ωvid + c1 r1 (pid − xid ) + c2 r2 (pgd − xid ) 0 else xid = 1 r ≥ 1/ (1 + exp(−vid ))
(2) (3)
where d ∈ (1, 2, · · · , D) denotes the space dimension, ω is the inertia coefficient; c1 and c2 are the learning factors; r1 , r2 and r are the random numbers in the range of [0, 1]. In order to manage the household appliances via binary PSO algorithm, here, assume D = 24, M = 40, c1 = c2 = 2, ω = 0.9, the maximum iteration time is 2000 and the fitness function is L × Xi (ToU price × particle position). Thus the control of the appliances in the 2nd and 3rd categories can be optimized via PSO based on their inherent characteristics. 40 particles are updated in 2000 times to seek the minimum solution, where the fitness function L × Xi is regarded as the fitness value and Pi , Pg are computed at the same time which are brought to the next iteration till the process completion.The diagram of the calculation procedure is depicted in Fig.1.
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Fig. 1.
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The diagram of the PSO operational procedure
III. V2G T ECHNOLOGIES A. Description of V2G The EV entered into the grid technology (i.e. Vehicle-toGrid, V2G) is the mutual interaction and exchange for the energy of EVs, which is an important part in the ’Smart Grid Technology’. V2G describes a novel grid technology, where EVs can be regarded as power consumption customers but also distributed power suppliers when EVs are under idle state through battery discharging. By using the formation of scale access of the electric vehicles, with the support of V2G technology, the electric vehicle batteries can provide power for EVs but are used as mobile storage unit to access the gird for the purpose of peak shaving, spinning reservation to enhance the power supply flexibility, reliability and energy efficiency. The application of V2G technology has obvious social and economic benefits. The vehicles from the V2G can complete their own driving function, at the same time, they are also encouraged to participate the V2G service via the idle storage capacity. So the EV users can make profit and the investment for the fixed storage equipment can be reduced. Moreover, the power generation factories could reduce their power yield to increase the economic performance, and win-win results can be achieved among power factory, grid, and electric vehicles users. V2G embodies the energy flow between vehicles and power grid in mutual, controllable and real-time status. The equipment of the charging and discharging are interacted between the grid and vehicles. Several assumptions are made to analyze the impact of V2G. 1) The SOC of the EV battery can be acquired from the terminal, so that it can be used for regulation. 2) The nominal power of each EV is 30 kW/h. 3) The driving time of the EV in the morning is random distributed among 6h-8h; and the driving time in the evening is distributed in [18-22]h. 4) The energy in EVs are fed to the grid between [9-17]h for energy storage and as buffer energy. 5) The charging time for EVs are [0-6]h, [0-7]h or [0-8]h and SOC of the battery should be 0.9 or above. 6) The discharging periods during night are [18-23]h, [1923]h, [20-23]h,[21-23]h or [22-23]h. 7) During 9-17h, if SOC is lower than 0.5 then charging starts till SOC reaches 0.9 or if SOC is higher than 0.5 then discharging occurs till SOC is 0.5. 8) When EV is connected to the grid during the night, i.e., 18h, 19h, 20h, 21h or 22h, the SOC of the EV battery are detected. While the discharging and driving behaviours in the daytime, the battery SOCs are between 0.2-0.9. If the current SOC is > 0.5, it is assumed that the discharging behaviour is profitable or no further action will be taken. To alleviate the high peak load situation, the battery discharge will initiate and stop till SOC equal to 0.2.
TABLE I
B. Estimation of SOC
T HE CATEGORY OF THE APPLIANCES AND THEIR CHARACTERISTICS
The definition of battery SOC (state of charge) is: the ratio between the rest of the power and the nominal power under the same condition after certain discharging rate, i.e., SOC =
Qc Ct
(4)
where Qc is the remainder power and Ct is the possessed capacity when the battery discharges with constant current I. In recent years, there are many methods to estimate the SOC battery, which can be classified into four main methods, power accumulation method, resistance measurement method, voltage measurement method and intelligent algorithm [22][23][24]. The power accumulation method is also called AH method, which is the most used method for SOC estimation. It can be applied in all kinds of electric vehicles and it is suitable for charging process and discharging process which is a simple and reliable SOC estimation method. However, AH method has higher SOC estimation error under the high temperature and huge current fluctuation. Besides, the error can be accumulated with the increase of battery cycling times. In this paper, the involved charging and discharging processes of EVs are disposable current processes, where the battery data will be collected each time before any further action is taken. Hence, AH method can be used for SOC estimation which is simple and effective without much error. Suppose the initial reminder power of a battery is SOC0 , then the SOC after Δt discharging time is SOC1 = SOC0 −
Idi Δt ηd Ci
(5)
where Idi is the discharging power of the ith EV; ηd is the discharging efficiency of the EV. If a EV charges in Δt time, the varied SOC of the ith EV is: Ici Δt (6) SOC1 = SOC0 + ηc Ci where Ici is the charging current of a EV; ηc is the charging efficiency of a EV. IV. S IMULATION E XPERIMENTS A ND R ESULTS A NALYSIS In order to test the effectiveness of PSO algorithm and the electric vehicle V2G model of a power grid, simulation experiments have been performed. There are a lot of appliances in one family and people have different living habits with various inherent characteristics of the appliances. The appliances included in the 2nd and 3rd categories are used for particle swarm optimization. The optimal position of the appliances are obtained at the final calculation procedure. Eleven typical residential appliances are selected and listed in Table I. Since the refrigerator x1 belongs to the 1st category, therefore only one working time-table is obtained with (24 × 1) matrix, T1 = [1, 1, 1, · · · , 1]T . As for the TV,
Type 1st
Residential appliances Refrigerator x1 TV x2
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Electric frying pot x3 Washing machine x4 Dish washer x5 Rice cookers x6 Heating water x7
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Vacuum cleaners x8 Heating x9 EV Charger x10 Humidifier x11
Characteristics and requirement 24 hour working time Power: 0.12 kw/h Working time:[20:00-22:00] Power output: 0.15kw/h Working time:[17,18,19,20]h Power output: 2kw/h Continuous 2 working hours Power output: 0.5 kw/h Continuous 1 working hours Power output: 2 kw/h Working time:[17,18,19,20]h Power output: 1 kw/h Working time:[18,19,20,21,22,23]h Power output: 1.5 kw/h Working time:[8,9,10,11,12,13,14,15]h Power output: 1.5 kw/h Working time: 10 hours per day Power output: 1.5 kw/h Working time: 8 hours per day Power output: 0.8 kw/h Working time: 12 hours per day Power output: 0.1 kw/h
4 patterns are usually adopted: 18 ∼ 23h, 19 ∼ 22h, 20 ∼ 22h, 21 ∼ 22h. The electric frying pot, washing machine, dish washer and rice cooker, x3 , x4 , x5 and x6 , belong to the 2nd category, where their working patterns can be chosen freely based on personal necessities. For example, the washing machine can work in any 2 continuous hours and the dish washer can work in any 1 uninterrupted working hour. The appliances in the 3rd category can be worked in any time and can be interrupted at any time as long as the required working time are satisfied. The ToU prices L = [0.8, 0.8, 0.8, 0.5, 0.5, 0.5, 0.8, 0.8, 0.8, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1.5, 1.5, 1.5]Yuan (Chinese Unit¥). The supply curve of a region is composed of two parts, one part is the residential electricity supply and the other is for the commercial power supply. The experiments are based on the assumption that the supply curve of the industrial and commercial load power is known, as shown in Fig. 2. Suppose there are three hundred home users in one region. A series of scenarios are designed in three working modes for residential energy management. Mode 1: all the end-users use random pattern for the household electrical appliances without any human interference or disturbance. Mode 2: part of the stochastic model, PSO optimization algorithm and V2G management are adopted by some users. Mode 3: PSO optimization and V2G management are adopted by all users. Therefore, the load curve of home users varies according to different ways of working pattern of appliances. Mode 1: Each user has eleven home appliances listed in Table I, and their usage should be followed their own operating characteristics. With the satisfaction of the working feature of each appliance, their working time can be selected randomly which is called random pattern. When the random pattern is adopted by the three hundred home users, it
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becomes the random mode from the user side. Assume each family has one electric vehicle and the battery rated capacity is 30kWh. Under the stochastic mode, the electric vehicle battery does not participate into PSO and V2G management. It is also assumed that the starting SOC of each EV battery is random and unknown, and the charging behavior is accomplished till SOC equals 0.9 at any time during one day. If demand load curves are composed of business users and home users, the load curve for business users is depicted in Fig.3 with red line curve. The red curve shows the daily load distribution map of the area (300 home users and business users), where the total electricity bill of all users is ¥64323.8 yuan. The maximum power demand is 2769.4 kW and the minimum power 1441.8 kW. Mode 2: Some of 300 home users use random pattern to regulate their EV charging and discharging behaviours and the rest of the users adopt the PSO and V2G management for the EV battery regulation. The selection of the optimal management participation for the users is based on the initial status of the battery SOC. Fig. 3 shows the daily load distribution map of the area (including both business users and home users) with blue stared curve. The total electricity of all users is ¥56362.2 yuan. The maximum power demand is 2256.2 kW and the minimum power 1445.9 kW. Mode 3: Suppose all three hundred home users participate the energy management with optimal electricity price algorithm of PSO and V2G. Fig.3 also shows the daily load distribution map of the area (business users and home users) with purple circled curve. The total electricity of all users is ¥54935.5 yuan. The maximum power demand is 2218 kW and the minimum power 1580.0 kW. Through the analysis of experimental results demonstrated in Fig.3, it is shown that the generated electricity bill is the highest if random mode is adopted by all 300 home users. The difference between the high peak and low valley is also maximum and the peak load is the maximum. If part of the users participate the energy regulation, the generated electricity bills from the user side is lower than that in Mode 1. Furthermore, if the PSO and V2G energy policy are adopted by all home users, the electricity bills is the
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least with minimum peak-valley difference and peak load. It indicates that both suppliers and users can benefit from the energy management between vehicles and grid. However, it should be noted that the maximum power demand from all home users’ participation has not reduced greatly compared to that in Mode 2 and Mode 3. One main reason is that the designed strategy for EV charging is too strict for their required time periods. No additional or unexpected situations are considered in the strategy. Another worthy notifying result is that the minimum power in Mode 3 is surprisingly larger than that in Mode 2. This phenomenon brings the attention that the strategy in V2G has negative effects on the grid operation. Moreover, the factors of battery power and battery endurance during the charging and discharging processes have not been included in the algorithm. Further discussion should be considered in the performance enhancement in the optimal V2G management. It can be expected that there will be a lot of electric vehicles accessing to the grid for charging and discharging with the popularization of electric vehicles in the future. If there is no corresponding policies and measures to guide the charge/discharge behavior, the mass disordered charge/discharge behavior of electric vehicles will have a serious negative impact on the power grid operation. The orderly management of charging and discharging electric vehicles of the end-users’s behaviours has to be studied from demand side perspective. V. C ONCLUSIONS In this paper, particle swarm optimization algorithm is used in home energy management. With the development of electric vehicles widely connected to the grid randomly, the orderly management of electric vehicle load becomes necessary and urgent. According to the characteristics of electric vehicle charging and discharging load model, the paper studies the management of EVs and provides reference for load management of a large number of electric vehicle access of the grid, together with other residential electrical appliances. The simulation results prove that the effectiveness of the adopted strategy for energy management. Further research will discuss the time-of-unit price settings
for electric vehicle charging and discharging behavior and station scheduling model. ACKNOWLEDGMENT This work is supported under the Shenzhen Science and Technology Innovation Commission Project Grant Ref. JCYJ20140417113430574. R EFERENCES [1] Shamshiri M., Gan C K. and Tan C W., ‘A review of recent development in smart grid and micro-grid laboratories’, Power Engineering and Optimization Conference (PEOCO), 2012 IEEE International. IEEE, 2012: 367-372. [2] Zhong J., Kang C. and Liu K., ‘Demand side management in China’, Power and Energy Society General Meeting, 2010 IEEE. IEEE, 2010: 1-4. [3] Malik A S. and Bouzguenda M., ‘Smart grid capacity and energy saving potential-A case study of Oman’, Innovative Smart Grid Technologies-Middle East (ISGT Middle East), 2011 IEEE PES Conference on. IEEE, 2011: 1-6. [4] Babu P R, Divya S., Srikanth P, et al., ‘Neural Network and DSM techniques applied to a Industrial consumer a Case Study’, Compatibility in Power Electronics, 2007. CPE’07. IEEE, 2007: 1-4. [5] Babu P R. and Divya S., ’Application of ANN and DSM Techniques for peak load management a Case study’ Sustainable Energy Technologies, 2008. ICSET 2008. IEEE International Conference on. IEEE, 2008: 384-388. [6] Barbato A, Capone A, Carello G, et al., ‘House energy demand optimization in single and multi-user scenarios’, Smart Grid Communications (SmartGridComm), 2011 IEEE International Conference on. IEEE, 2011: 345-350. [7] Zhang D., Papageorgiou L G., Samsatli N J., et al., ‘Optimal scheduling of smart homes energy consumption with microgrid’, ENERGY 2011, The First International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies. 2011: 70-75. [8] Hatami S. and Pedram M., ‘Minimizing the electricity bill of cooperative users under a quasi-dynamic pricing model’,Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference on. IEEE, 2010: 421-426. [9] Singh H P., Brar Y S. and Kothari D P., ‘Multiobjective load dispatch using Particle Swarm Optimization’, Industrial Electronics and Applications (ICIEA), 2013 8th IEEE Conference on. IEEE, 2013: 272-277. [10] Erol-Kantarci M. and Mouftah H T., ‘Tou-aware energy management and wireless sensor networks for reducing peak load in smart grids’, Vehicular Technology Conference Fall (VTC 2010-Fall), 2010 IEEE 72nd. IEEE, 2010: 1-5. [11] Gottwalt S., Ketter W., Block C., et al., ‘Demand side management-A simulation of household behavior under variable prices’ Energy Policy, 2011, 39(12): 8163-8174. [12] Clement-nyns K., Van Reusel K. and Driesen J., ‘The consumption of electrical energy of plug-in hybrid electric vehicles in Belgium’, Proceedings of the 2nd Eurpoean Ele-Drive Transportation Conference, Belgium, 2007:1-8. [13] Puterus G.A., Suwanaping P., et.al., ‘Impact of electric vehicles on power distribution networks’, Proceedings of hte Vehicle Power and Propulsion Conference, Sep., 2009, USA:827-831. [14] Denholm P. and Short W., ‘An evaluation of utility system impacts and benefits of optimally dispatched plug-in hybrid electric vehicles’, Golden, CO., USA, National Renewable Energy Laboratory, 2006. [15] Lopes J., Soares F. J. and Alemida P., ‘Integration of electric vehicles in electric power system’, Proceedings of the IEEE, 2001, 99(1):168183. [16] Kempton W. and Dhanju A., ‘Vehicle-to-grid power implementation:from stabilizing the grid to supporting larg-scale renewable energy’, Journal of Power Sources, 2005, 144(1):280-294. [17] Kempton W. and Dhanju A., ‘Electric vehicles with V2G: stroage for large-scale wind power’, Windtech International, 2006, 2(1):18-21. [18] Zhao J., Wen F. et.al., ‘Power system stochastic econmic dispatch considering uncertain outputs from plug-in electric vehicles and wind generators’, Automation of Electric Power Systems, 2010, 34(20):2229.
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