【摘要】 面对全球环境污染和能源紧缺,减排节能迫在眉睫,其中减少汽车尾气排放是重要的途径之一。电动汽车的投产与应用有利于实现节能减排,但电动汽车大量入网充电,对整个配电网产生巨大的影响。从配电网中电动汽车充放电优化问题的研究出发,通过典型日历史负荷,预测当天常规负荷。利用网格选取法隐含变压器不过载条件,同时分配电动汽车充放电时段。利用分时电价构建电费成本最低的目标函数,电池特性为约束条件,通过交叉遗传粒子群算法获取最优网格,通过V2G策略实现收益最大化,利用蒙特卡洛仿真法进行仿真,结果表明:V2G优化控制策略可以实现降低峰谷差和电费成本,提高充电设施的利用率。更多还原

【Abstract】 In the case of global environmental pollution and energy shortages,energy conservation and emission reduction is imminent.One of the important ways is reducing vehicle exhaust emissions.Electric vehicle production and application is conducive to the realization of energy conservation and emission reduction.However,masses of electric vehicles charging network has a huge impact on the entire distribution network.The main goal of this paper is to maximize benefits through the V2 Gstrategy.Based on the study of charge and discharge optimization of electric vehicles in the distribution network,the typical daily load is predicted by the typical daily load.Using grid selection method implied transformer overload conditions and distributed electric vehicle charging and discharging time.Using the time-sharing price to construct the objective function with the lowest electricity cost,the optimal grid is obtained by cross genetic particle swarm optimization(PSO),and the Monte Carlo simulation method is used to simulate and validate it.The results showed that the V2 Goptimal control strategy reduce the peak-valley difference and the cost of electricity,and improve the utilization rate of the charging facilities.更多还原