基于多目标粒子群算法分布式电源的最优配置策略

Optimal Configuration Strategy for DG Based on Multi-objective Particle Swarm Optimization Algorithm

随着各种可再生能源发电技术逐渐成熟,将多类型分布式电源(Distributed Generation,DG)接入配电网(Distribution Network,DN)是促进电力系统能源转型的快捷方式。但是,大量的DG接入DN会导致DN运行稳定性下降,迫切寻找到解决方法。为此本文考虑了PV(Photovoltaic,PV)、风力发电(Wind Power Generation,WPG)和燃料电池(Fuel Cell,FC)三种DG,并以DG的经济性指标、电压偏差、电压波动以及网络损耗作为优化目标,采用多目标粒子优化算法(Multi-objective Particle Swarm Optimization,MOPSO)确定了三种DG接入DN的最优位置已经最佳容量,并与多目标蝗虫优化算法(Multi-objective Grasshopper Optimization Algorithm,MOGOA)进行了比较。基于IEEE-33节点仿真测试算例表明与未配置DG相比,通过MOPSO算法配置DG后,平均电压波动降低了0.055 p.u.(5.01%),网络损耗减少了646.65 kW(15.92%),并且,MOPSO配置成本更低,具有更好经济性。

With the gradual maturity of various renewable energy generation technologies,connecting multiple types of distributed generation(DG)to the distribution network(DN)is a quick way to promote the energy transformation of the power system.However,a large number of DG access to DN will lead to the decline of DN operation stability,so it is urgent tofind a solution.Photovoltaic(PV),wind power generation(WPG),fuel cell(FC)are considered in this paper,and the economic index,voltage deviation,voltagefluctuation and network loss of DG are taken as optimization objectives.The multi-objective particle swarm optimization(MOPSO)algorithm was used to determine the optimal location and capacity of three kinds of DG access to DN.It is compared with multi-objective grasshopper optimization algorithm(MOGOA).Based on the IEEE-33 node simulation test example,the average voltage fluctuation is reduced by 0.055 p.u.(5.01%),and the network loss is reduced by 646.65kW(15.92%)after DG is configured by MOPSO algorithm.In addition,MOPSO configuration costs less and has better economics.