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收稿日期：2012-10-25。作者简介：

刘念(1981)，男，博士，讲师，主要研究方向为智能配用电系统、微电网与电动汽车等，nian_liu@163.com；

唐霄(1989)，女，硕士研究生，主要研

刘念

究方向为微电网优化规划和电动汽车；

段帅(1987)，男，硕士研究生，主要研究方向为电力系统优化规划和电动汽车；

张建华(1952)，男，教授，博士生导师，主要研究方向为电力系统分析运行与控制、安全评价和智能电网等。

(责任编辑王剑乔)

Extended Summary 正文参见pp.34-44

Capacity Optimization Method for PV-based Battery Swapping Stations

Considering Second-use of Electric Vehicle Batteries

LIU Nian, TANG Xiao, DUAN Shuai, ZHANG Jianhua

(North China Electric Power University)

KEY WORDS: electric vehicle batteries; PV-based battery swapping station; capacity optimization; second-use

The integration of photovoltaic (PV) power generation technology and electric vehicle (EV) charging infrastructure can effectively improve the efficiency of clean energy utilization and environmental benefits. The concept has been widely recognized and gradually carried out for demonstration. Among EV charging modes, the battery swapping mode can facilitate the unified purchase of electric vehicle batteries (EVBs). There have been some researches on the integrated applications of PV generation systems and EV charging infrastructure. However, according to the demand of battery exchange and considering second-use of EVBs, how to optimize the capacities of components in PV-based battery swapping stations (BSSs) is still a problem to be solved.

In this paper, Section I is a brief system description, focusing on system structure and energy exchange strategy. In Section II, the mathematical model is established with the system's maximum annual profit as the objective function, and constraints are analyzed from the aspects of decision variables, system power and energy, as shown in (1).

maxP=max[I?(CS+CE+CB+CEC+CBC)]

?0≤NPVP≤NPVP.max,0≤NE≤NE.max?0≤N≤N

BB.max,0≤NEC≤NEC.max?

?0≤NBC≤NBC.max,0<θ<90°

?0≤P(t)≤10Nη,?10N≤P(t)≤10Nη

EECECBCBBCBC (1) ??

s.t.?PE(t)PB(t)

PB(t)>0+=PS(t),?ηηBC

?EC

PE(t)?

PtPt()+(?())=η,PB(t)<0SBBC?ηEC???0≤WE(t)≤WEN,(1?cDOD)WBN≤WB(t)≤WBN

calculation method of the system's annual profit is

analyzed in detail. Section V is the case study for capacity optimization of the PV-based BSS in a certain typical area. The optimal scheme is obtained by the DE algorithm, as shown in Tab. 1.

Tab. 1 Capacity optimization results

Items

The number of PV cells in parallel The number of energy storage batteries

The number of EVBs The number of modules 2 The number of modules 3

Optimal

Rated

Annual costs of components/(105 RMB) 10.709 00 55.903 00

results capacity 445

1 365 kW 8.442 90

172 2 476 kW?h270 6 480 kW?h57 49

570 kW 0.403 53 490 kW 0.520 33

—

Maximum system annual profit/(106 RMB) 4.611 9 —

In Section III, the calculation model of second-use

capacity of EVBs is obtained by the Monte Carlo simulation method on the basis of the capacity degradation model of EVBs and the statistical model of the vehicle's daily driving distance. Section IV introduces the method for solving the optimization model. Since the model is a nonlinear problem containing multiple decision variables, it can be solved by the differential evolution (DE) algorithm. The

The system power and energy variation are given during 24 hours of a typical day in summer. The optimal results of EVBs, energy storage batteries, PV power generation systems, and DC/DC modules 2 and 3 are analyzed, verifying the rationality of the capacity optimization results of system components. The related analysis also verifies that the optimal results could satisfy the system power and energy constraints.

In the effect analysis of second-use of EVBs, firstly, the second-use capacity of EVBs in each year during the system span is analyzed, verifying the feasibility of the calculation model of second-use capacity and reflecting the significance of the introduction of the second-use strategy. Then the influences of second-use on the system's annual profit and the utilization rate of PV power generation are analyzed, showing that compared with the system not considering second-use, the indicators are greatly improved. The influence of the unit price of battery swapping service on the system's annual profit is analyzed, presenting an approximate linear relationship between them.

The calculation results verify the reasonableness of the model discussed in this paper. It can provide a reference for the planning and designing of EV charging infrastructure.