基于萤火虫与粒子群混合优化算法的移动储能调度

近年来，受规模化电动汽车无序充电、高比例新能源功率波动等因素影响，配电网存在负荷峰谷差较大、网损较高、配电网运行成本高的问题。提出基于萤火虫与粒子群混合优化算法的移动储能调度方法。分别建立电动汽车、移动储能车、氢燃料发电车的移动储能模型，建立负荷峰谷差、配电网网损和配电网运行成本多目标函数，为了降低排名异常的概率，引入Tent混沌映射、柯西变异算子、萤火虫算法中的模糊自适应惯性权值，求解多类型移动储能共同参与调度的最优方案。算例分析结果表明，所提方法能够有效减少负荷峰谷差、降低配电网网损和降低配电网运行成本。

Mobile Energy Storage Scheduling Based on Firefly and Particle Swarm Optimization Algorithm

In recent years， due to the disorderly charging of large-scale electric vehicles and the power fluctuation of a high proportion of renewable energy， there are problems such as large load peak valley difference， high network loss and high distribution network operation cost in power grid. This paper proposes a mobile energy storage scheduling method based on the hybrid optimization algorithm of firefly and particle swarm optimization. The mobile energy storage models of electric vehicles， mobile energy storage vehicles and hydrogen fuel power generation vehicles are respectively established， and the multi-objective functions of the load peak valley difference， distribution network loss and distribution network operation cost are established. In order to reduce the probability of ranking abnormality， Tent chaotic map，Cauchy mutation operator and the fuzzy adaptive inertia weight in the firefly algorithm are introduced to solve the optimal scheme for multi type mobile energy storage participating in the scheduling. The results show that the load peak valley difference，network loss and distribution network operation cost can be effectively reduced by the proposed method.