基于自适应变异粒子群算法的电动汽车换电池站充电调度多目标优化

Multi-Objective Optimization of Charging Dispatching for Electric Vehicle Battery Swapping Station Based on Adaptive Mutation Particle Swarm Optimization

大规模电动汽车用户的无序充电行为会对电网造成"峰上加峰"等影响,因此电动汽车规模化应用迫切要求实现对充电行为的引导和调度。电动汽车换电站具有受可调度时间约束影响小等特点,与个体电动汽车相比较易实现充电调度。根据换电站的特点以换电站各时刻的充电功率为控制对象,建立多目标的调度策略数学模型,并采用自适应变异的粒子群算法求解以减小标准粒子群容易早熟对优化结果的影响,得到次日优化充电计划。基于某地区负荷曲线进行算例仿真,验证了算法的有效性,比较了单目标优化和多目标优化的调度策略对负荷曲线的影响。结果表明,换电站充电调度策略采用多目标优化时能够克服单目标优化填充"最低谷"效果差的问题,有效地降低电网峰谷差,达到平稳负荷波动的效果。

The out-of-order charging behavior of largescale electric vehicle（EV） users will make the peak load condition of power grid more severe,so it is urgently to implement the guidance and dispatching of the charging behavior under large-scale utilization of EV.Due to the feature that the battery swapping station of EV is less impacted by the scheduling time constraint,it is easier to implement the charging dispatching by battery swapping station of EV than the scheduling of individual EVs.According to the characteristics of battery swapping station,taking the charging power of battery swapping station in different time intervals as controlled object a multi-objective dispatching strategy model is built and solved by adaptive mutation particle swarm optimization to reduce the influence of premature of standard particle swarm algorithm on optimization result to achieve optimized charging scheme for the next day.Based on the load curve of a certain region,the proposed method is simulated to verify the effectiveness of the proposed algorithm and the influences of single-objective optimization and multi-objective optimization on load curve are compared.Simulation results show that when multi-objective optimization is applied in charging strategy of battery swapping station the poor effect of filling the lowest valley portion in the load curve by singleobjective optimization can be remedied and the peak-valley difference in the load curve can be effectively reduced.