规模化电动汽车有序充电分层控制策略研究

电动汽车作为一种特殊电力负荷大规模投入电网后,对其有序充电策略进行研究,能够降低充电负荷对电网的冲击、平抑负荷波动,降低用户充电费用。文中以电动私家车为研究对象、居民小区为应用场景,提出了一种上层根据居民小区的充电需求与负荷方差最小的优化目标确定功率指导曲线、下层同时考虑下发的指导曲线以及充电站峰谷差最小的优化目标来满足用户充电需求的有序充电优化方法。分析充电模式等因素对电动汽车(Electric Vehicle,EV)充电负荷的影响,并基于蒙特卡洛方法进行充电负荷预测的计算;提出了电动汽车有序充电的分层控制架构,并建立了能够平抑总体负荷波动、降低变电站负荷峰谷差的双层优化模型,采用粒子群算法进行计算;以某居民小区为例进行仿真计算,对比电动汽车在不同渗透率下使用该优化充电方法前后的结果。结果表明,该控制策略使充电负荷的高峰期往后推迟至基础负荷的用电低谷期,实现了负荷曲线的“削峰填谷”,且随着渗透率的增加,在降低负荷峰谷差、平抑负荷波动方面的优化效果更好;在充电费用方面,有序充电情况下充电费用减少了29.0%,降低了电动汽车用户的充电负担。

Research on hierarchical control strategy of orderly charging of large-scale electric vehicles

After electric vehicles are put into the power grid on a large scale as a special power load,the study of their orderly charging strategy can reduce the impact of the charging load on the power grid,smooth load fluctuations,and reduce user charging costs.Taking electric private cars as the research object and residential quarters as the application scenarios,this paper proposes an orderly charging optimization method that upper-level determines the power guidance curve based on the charging demand of the residential quarters and optimization target of the minimum load variance,and the lower-level also considers the issued guidance curve and the optimization goal of the minimum valley difference to meet the charging needs of users.Firstly,this paper analyzes the impact of charging mode and other factors on the charging load of electric vehicles(EV),and calculates the charging load prediction based on the Monte Carlo method;then,a hierarchical control architecture for orderly charging of electric vehicles is proposed,and a double-layer optimization model that can smooth the overall load fluctuations and reduce the peak-valley difference of the substation load is established,and the particle swarm algorithm is used to calculate;finally,taking a residential area as an example for simulation calculation,this paper compares the results before and after using the optimized charging method for electric vehicles under different penetration rates.The results show that the control strategy delays the peak period of the charging load to the trough period of the base load,and achieves the"peak-reduction and valley-filling"of the load curve,and as the penetration rate increases,the optimization effect of reducing load peak-valley difference and suppressing load fluctuations is better;in terms of charging costs,the charging costs are reduced by 29.0%under the orderly charging situation,which reduces the charging burden of electric vehicle users.