基于多群组均衡协同搜索算法的电动汽车充放电多目标优化

摘要

The uncoordinated charging strategy of massive electric vehicles (EVs) will threaten the safe operation of the grid, this situation can be relieved if coordinated charging/discharging strategy is developed.Based on classical battery-wear model and time-of-use price, a multi-objective optimal model for EVs` charging/discharging process is proposed to reduce the daily load fluctuation and the charging cost considering EV`s charging demand.The Pareto front and the compromise solution are calculated by equilibrium-inspired multiple group search optimization with synergistic learning (EMGSS).Rolling optimization is adapted to deal with the day and night random variance of charging demand and reach the double-win of grid and EV owners.The simulation results demonstrate that daily load fluctuation is reduced effectively and EVs` charging cost is also decreased.%大规模电动汽车无序充电将会给电网安全运行带来巨大压力,合理利用V2G(vehicle to grid)技术制定最优充放电策略可以有效改善电网运行状况.在满足电动汽车充电需求的基础上,基于经典电池损耗模型和分时电价,以日负荷曲线波动最小和计及电池放电成本的用户充电成本最小为目标建立了电动汽车充放电多目标优化模型,采用多群组均衡协同搜索算法(EMGSS)进行帕累托前沿和最优折中解的求取,以滚动优化的方式满足综合考虑日间/夜间不同的随机的充电需求并进行优化计算,最大限度地实现电网侧和用户侧的双赢.通过仿真案例验证了该模型可以有效地平抑日负荷曲线波动并且降低用户充电成本.