V2G (Vehicle to Grid) technology can adjust the grid load through the unified control of the charging and discharging of electric vehicles (EVs), and achieve peak shaving and valley filling to smooth load fluctuations. Aiming at the random and uncertain problem of EV users travel and behavior decision-making, this paper proposes a V2G multi-objective dispatching strategy based on user behavior. First, a V2G behavior model was established based on user behavior questionnaire surveys, and the effective effect of EV load was simulated through Monte Carlo simulation. Then, combined with the regional daily load curve and peak-valley time-of-use electricity prices, with the goal of stabilizing grid load fluctuations and increasing the benefits of EV users, a multi-objective optimal dispatching model for EV clusters charging and discharging is established. Finally, Considering the needs of EV users and the operation constraints of the microgrid, the genetic algorithm is used to obtain the Pareto optimal solution. The results show that when dispatching with the maximum benefit of users, the peak-to-valley ratio of the grid side can be reduced by 2.99%, and the variance can be reduced by 9.52%. The optimization strategy can use peak and valley time-of-use electricity prices to guide the intelligent charging and discharging of EVs while meeting user needs, so as to achieve the optimal multi-objective benefit of V2G participation in power response.

V2G（Vehicle to Grid）技术可以通过电动汽车（EV）的充放电统一控制来调节电网负荷，实现削峰填谷，平滑负荷波动。针对电动汽车用户出行和行为决策的随机性和不确定性问题，提出一种基于用户行为的V2G多目标调度策略。首先，基于用户行为问卷调查建立V2G行为模型，通过蒙特卡罗模拟模拟EV负载的有效效果。然后，结合区域日负荷曲线和峰谷分时电价，以稳定电网负荷波动，增加电动汽车用户收益为目标，建立了电动汽车集群充放电多目标优化调度模型。最后，考虑到电动汽车用户的需求和微电网的运行约束，采用遗传算法获得帕累托最优解。结果表明，在以用户利益最大化调度时，电网侧峰谷比可降低2.99%，方差可降低9.52%。该优化策略可以在满足用户需求的同时，利用峰谷分时电价引导电动汽车的智能充放电，从而实现V2G参与电力响应的多目标最优收益。遗传算法用于获得帕累托最优解。结果表明，在以用户利益最大化调度时，电网侧峰谷比可降低2.99%，方差可降低9.52%。该优化策略可以在满足用户需求的同时，利用峰谷分时电价引导电动汽车的智能充放电，从而实现V2G参与电力响应的多目标最优收益。遗传算法用于获得帕累托最优解。结果表明，在以用户利益最大化调度时，电网侧峰谷比可降低2.99%，方差可降低9.52%。该优化策略可以在满足用户需求的同时，利用峰谷分时电价引导电动汽车的智能充放电，从而实现V2G参与电力响应的多目标最优收益。