基于阶段分时电价响应的电动汽车充电负荷优化策略

Optimization Strategy of Electric Vehicle Charging LoadBased on Stage-Sharing Tariff Response

针对电动汽车的充电负荷优化问题,提出一种基于阶段分时电价响应的电动汽车充电负荷优化策略。该策略在考虑电动汽车负荷因素的前提下,建立电动汽车充电负荷模型,并通过蒙特卡洛算法模拟电动汽车无序充电的负荷变化。在峰、谷、平段电价弹性矩阵的基础上,细分不同的等分时间段建立阶段分时电价模型,构建以用户充电总费用和系统总负荷均方差最小为优化目标的电动汽车分时电价优化模型,采用改进的混沌多目标遗传算法对模型进行求解优化。实验对比结果验证了该策略的有效性和经济性,在给定数据条件下用户充电总费用和系统总负荷均方差较传统分时电价方案分别降低了0.4091万元和37.151 MW。

Aiming at the charging load optimization problem of electric vehicles,this paper proposes a charging load optimization strategy for electric vehicles based on stage-sharing tariff response.The strategy establishes an electric vehicle charging load model and simulates the load variation of uncontrolled charging of electric vehicles by Monte Carlo algorithm,taking into account the electric vehicle load factor.On the basis of the elasticity matrix of peak-valley leveling tariffs,the stage time-sharing tariff model is established by subdividing different equal time periods,and the optimization model of time-sharing tariffs for electric vehicles is constructed with the optimization objectives of minimizing the mean square deviation of the total charging costs of the users and the total load of the system,and the improved chaotic multi-objective genetic algorithm is used to solve and optimize the model.Finally,the effectiveness and economy of the strategy are verified by the experimental comparison results,where the total user charging cost and the total system load mean squared deviation are reduced by 4091 yuan and 37.151 MW respectively,compared with the traditional time-sharing tariff scheme under the given data conditions.