基于用户响应画像的居民小区电动汽车充放电优化策略研究

针对电动汽车大规模接入电网系统带来供电可靠性及多方经济性问题，提出一种针对居民小区的电动汽车充放电优化策略。首先，考虑“深度+潜力”因素，采用CRITIC-MABAC法对用户进行分级评估，识别得到按调度参与深度和响应潜力划分的用户群体画像特征；然后，根据画像特征制定分群体的差异化充电目标与调度模式，建立电动汽车充放电优化调度模型；最后，以负荷波动及充电成本加权组合的适应度最小为优化目标，使用精英遗传算法完成调度求解。实证分析证明所研究策略可实现平抑负荷与降低充电成本的综合优化目标。

Charging and Discharging Optimization Strategy for Electric Vehicles in Residential Community Based on User Response Portrait

Targeting the problems of power supply reliability and multi-economy caused by large-scale electric vehicles connected to power grid system， the paper proposes a charging and discharging optimization strategy for electric vehicle in residential areas. Firstly，considering the factor of depth and potential， the CRITIC-MABAC method is used to evaluate the users， and the user group portrait features are obtained according to the scheduling participation depth and response potential. Then based on the portrait characteristics，the discrepant charging objectives and scheduling modes of different groups are formulated， and the optimal scheduling model of the charging and discharging for the electric vehicle is established. Finally，with the optimization goal of minimizing the load fluctuation and charging cost weighted fitness，the elite genetic algorithm is used to complete the solution. The empirical analysis proves that the strategy can achieve the comprehensive optimization goal of leveling load and reducing charging cost.