大规模电动汽车无序充电易造成电网负荷"峰上加峰"等后果,这严重影响电网安全运行,因此合理调度充电行为至关重要。基于分时电价制度和电动汽车可入网的情况,针对电动汽车调度机构建立了计及电网负荷波动及用户成本的多目标优化模型,采用交叉遗传粒子群算法求解得到次日优化充放电计划。基于某商用楼宇负荷数据进行算例仿真,对比分析了分时电价与固定电价下的仿真结果及不同分时电价对调度策略的影响,结果表明:分时电价引导下的调度策略在减小电网峰谷差与提高用户经济性上都具有更大优势;受峰谷电价差增大与尖峰电价的影响,新的分时电价下电网调峰效果更加明显,但用户成本却因平均电价上浮而增高。
The out-of-order charging of electric vehicles(EV) more likely cause the much higher peak load that seriously affects the secure operation of power grid,so it is of crucial importance to reasonably dispatch the behavior of EV charging.Based on the time-of-use(TOU) price mechanism and vehicle-to-grid(V2G) mode,a multi-objective optimization model for EV dispatching department,in which the load fluctuation in power grid and the user cost are taken into account,is established,this model is solved by cross inheritance particle swarm optimization to obtain the optimized charging and discharging scheduling for the next day.Taking the load data of a certain business building as the case,the simulation results under TOU price and that under fixed electricity price as well as the influences of different TOU price on scheduling strategy are contrasted and analyzed.Analysis results show that the TOU price-guided scheduling strategy possesses greater superiority in reducing the valley-to-peak difference of power grid and improving user's economy;due to the influences of increased valley-to-peak difference and peak load price,the effect of peak load regulation under new TOU price will be more obvious,however the user cost will be increased due to the rising of average electricity price.