基于双重粒子群算法的电动汽车参与配网优化调度

研究电动汽车参与含分布式光伏电源的配电系统的协同调度问题. 首先,建立了分布式光伏和电动汽车充电负荷的数学模型,给出了两种协同调度策略:电动汽车充电位置优化和电动汽车充电时段优化. 优化模型以配电系统网损最小为目标,分别就充电地址不固定和固定两种情况进行优化. 然后,使用蒙特卡洛法对电动汽车充电负荷情况进行模拟,使用双重粒子群算法对优化模型进行最优求解. 最后,用一个33节点系统作为算例系统对所提出的调度策略进行仿真验证,证明了优化策略能平抑电网波动,减少配电系统网损.

Optimization Algorithm for Electric Vehicle Participation in DistributionNetwork Scheduling Based on Multiparticle Swarm

In this paper,we use the double particle swarm optimization algorithm to solve the two models for the coordinated operation of electric vehicle in distribution system with distributed photovoltaic power supply. This paper firstly establishes the mathematical model of the distributed load of photovoltaic and electric vehicle. Then two kinds of cooperative scheduling strategies are given:optimizing the charging position of electric vehicle and optimizing the dispatching strategy of electric vehicle charging time. The optimal model is based on the minimum loss of the distribution system,and the two cases are optimized for the fixed charging address and the fixed address,Monte Carlo method is used to simulate the charging load of electric vehicle. The optimal solution is optimized by using the double particle swarm optimization algorithm. At last,a 33-node system is used as an example to simulate the proposed scheduling strategy,and it is proved that the optimization strategy can stabilize the fluctuation of the power grid and reduce the network loss of the distribution system.