大规模电动汽车充放电优化控制及容量效益分析

建立了电动汽车充放电两阶段优化模型,并采用整数规划方法求解,以获取通过优化控制降低的峰荷量及车辆到电网(V2G)的放电量。考虑电动汽车起始充电时间、起始荷电状态的随机分布,采用拉丁超立方抽样方法进行抽样,以有效减小计算规模。以中国2020年和2030年电动汽车充电负荷为例,对电动汽车可控比例不同情景下的有序充电优化、V2G优化问题进行了仿真计算,并对成本效益进行了分析。考虑到未来参数的不确定性,对成本效益相关参数进行了灵敏度分析。分析结果表明,所提出的两阶段充放电优化控制模型能有效降低峰荷并平滑负荷曲线。同时可知,装机成本和电池寿命损耗成本的变化将对V2G的效益产生较大的影响,上网电价的变化则对V2G效益的影响较小。

Coordinated Charging and Discharging of Large-scale Plug-in Electric Vehicles with Cost and Capacity Benefit Analysis

A two-stage optimal model is proposed to obtain the peak load shaving and the discharging energy in the vehicle-to-grid scenario.The Latin hypercube sampling method is adopted to sample the starting charging time and arrival state of charge to reduce the computation burden.The charging load of plug-in electric vehicles in 2020 and 2030 in China is simulated and the load profiles are compared with and without optimal control.Cost-benefit analysis in different scenarios are performed.Sensitivity analysis is performed to investigate the influence of several key parameters’ uncertainties on the cost and benefit of plug-in electric vehicle charging and discharging coordination.Simulation results show that the method proposed can effectively reduce the peak load and smooth the load curve.The installed cost and the battery life loss cost will significantly affect the capacity benefit of the charging and discharging control while the network power price will not. This work is supported by National High Technology Research and Development Program of China(863 Program)(No.2011AA05A110,No.2012AA050211) and National Natural Science Foundation of China(No.51107060).