基于改进遗传算法的光伏发电并网优化配置  

Optimal Allocation of Grid-Connected Photovoltaic Generation Based on Improved Genetic Algorithm

作　　者：包广清[1] 杨国金[1] 杨勇 常勇[1] 

机构地区：[1]甘肃省工业过程先进控制重点实验室(兰州理工大学),兰州市730050 [2]甘肃省电力科学研究院,兰州市730050

出　　处：《电力建设》2014年第6期13-17,共5页Electric Power Construction

基　　金：国家自然科学基金项目(51267011);甘肃省杰出青年基金项目(1111RJDA007)

摘　　要：光伏发电在未来将成为新增分布式发电系统的主流,为此研究了光伏发电并网的优化配置问题。建立了光伏电源选址和定容的配电网络损耗最小、节点电压偏移最小和接入费用最小的多目标优化模型;提出了一种改进的基于遗传算法的并列选择法;突出优化重点目标,对各目标函数区别对待,在前推回推法计算配电网潮流的基础上,采用该改进的算法求得模型的最优解;最后通过IEEE33节点算例系统对模型和算法进行了测试,结果表明所建模型的正确性和改进算法的优越性。Photovoltaic generation will become the mainstream of new distributed generation systems in the future. This paper studied the optimal allocation of grid-connected photovoltaic generation. The multi-objective optimization model was established for the position and capacity of photovoltaic power, with minimum network loss, minimum node voltage offset and minimum connecting cost; the improved parallel selection method was proposed based on genetic algorithm （GA） ; the key objectives of optimization were highlighted, each objective function was treated differently, and the optimal solution of model was obtained with using the improved algorithm, based on the power flow calculation with using forward-backward sweeping method. Finally, the model and algorithm were tested through the IEEE33 node example system, whose results proved the correctness of the model and the superiority of the improved algorithm.

关 键 词：光伏发电 配电网 潮流计算 遗传算法 优化配置 

分 类 号：TM71[电气工程—电力系统及自动化]