计及用户响应度的电动汽车充放电优化调度策略

Optimal Scheduling Strategy for Electric Vehicles Charging and Discharging Considering User Responsiveness

针对传统电动汽车集群调度过程中未能充分考虑用户响应度及其影响因素对可调度容量影响的问题,提出了计及用户响应度的电动汽车充放电调度策略。首先基于用户出行数据,对电动汽车集群的充电负荷模型进行建模;其次建立了基于韦伯-费希纳定律的电动汽车用户响应度模型,并综合考虑聚合商设定的充放电价和车辆荷电状态(state of charge,SOC)对用户充放电响应度的影响;最终将聚合商设定的充放电价与电动汽车的充放电功率作为决策变量,统筹考虑电网、聚合商以及电动汽车用户的三方收益,构建以最小化配电网负荷波动、最小化用户充电成本和最大化聚合商收益为目标的电动汽车充放电优化调度模型,采用粒子群优化算法(particle swarm optinization,PSO)求解该优化问题。通过算例结果表明,该模型能够在实现削峰填谷的同时,保证了聚合商以及电动汽车用户的利益。

This paper proposes an electric vehicle(EV)charging and discharging scheduling strategy that takes into account the user responsiveness in the scheduling process of traditional EV clusters,which fails to fully consider the impact of user responsiveness and its influencing factors on the schedulable capacity.Firstly,the charging load model of EV clusters based on user travel data is built.Secondly,an EV user responsiveness model is established based on Weber-Fechner's law,and the influence of the charging and discharging price set by the aggregator and the vehicle state of charge(SOC)on user charging and discharging responsiveness is comprehensively considered.Finally,the charging and discharging price set by aggregators and the charging and discharging power of EVs are taken as decision variables.Overall considering of the benefit of the power grid,aggregators and EV users,the EV charg⁃ing and discharging optimal scheduling strategy model is designed to minimize the load fluctuation of the distribution network and the charging cost of the user,maximize the revenue of the aggregator.And the optimal problem is solved by particle swarm optimization(PSO)algorithm.The example results show that the model can achieve peak-shaving and valley-filling while ensuring the benefits of aggregators and EV users.