考虑风光荷时序波动特性的配电网投资策略多目标优化  

Multi-objective optimization of distribution network investment strategy considering the time series fluctuation of wind power generation, photovoltaic power generation and power load

作　　者：樊晓伟 王瑞妙 朱小军 姚龙 周兴华 张晓 Fan Xiaowei;Wang Ruimiao;Zhu Xiaojun;Yao Long;Zhou Xinghua;Zhang Xiao(State Grid Chongqing Electric Power,Chongqing 400014,China;State Grid Chongqing Electric Power Research Institute,Chongqing 401123,China;Beijing Join Bright Digital Power Technology Co.,Ltd.,Beijing 100085,China;School of Electrical and Electronic Engineering,North China Electric Power University,Baoding 071003,China)

机构地区：[1]国网重庆市电力公司,重庆400014 [2]国网重庆市电力公司电力科学研究院,重庆401123 [3]北京中恒博瑞数字电力科技有限公司,北京100085 [4]华北电力大学电气与电子工程学院,河北保定071003

出　　处：《可再生能源》2023年第2期268-276,共9页Renewable Energy Resources

基　　金：国网重庆市电力公司科技项目资助(2021渝电科技19#)。

摘　　要：风电、光伏等分布式电源出力和用电负荷具有明显的时序特性,常用的恒定功率模型无法准确体现这一特点。文章构建了风力、光伏发电出力概率模型,应用蒙特卡洛方法对其发电出力和常规用电负荷进行模拟仿真,生成大量场景;采用改进K中心点聚类算法对仿真场景进行缩减,构建典型风光荷联合时序场景集;从各典型场景发生概率出发,以光伏、风力电站投资回报率最大和配电网各节点电压偏差最小为目标,采用改进多目标遗传算法求解光伏、风力电站最优安装位置和容量。最后,以IEEE 33配电系统进行算例分析,验证了所提方法的有效性。The output of distributed power sources such as wind power and photovoltaic and the size of different types of conventional loads have obvious timing characteristics, which can not be accurately reflected by the commonly used constant power model. Build the probability model of wind power and photovoltaic power generation output, and use Monte Carlo method to simulate its power generation output and conventional load to generate a large number of scenarios;The improved k-medoids clustering algorithm is used to reduce the simulation scene and construct the typical scenery load time series scene set. Based on the occurrence probability of each typical scenario, aiming at maximizing the return on investment of photovoltaic and wind power plants and minimizing the voltage deviation of each node of distribution network, the improved multi-objective genetic algorithm is used to solve the optimal installation location and capacity of photovoltaic and wind power stations. Finally, an example of IEEE 33 distribution system is analyzed to verify the feasibility of the proposed algorithm.

关 键 词：风光荷 时序特性 蒙特卡洛 改进K中心聚类 场景概率 投资策略 多目标遗传算法 

分 类 号：TK81[动力工程及工程热物理—流体机械及工程] TK51[电气工程—电力系统及自动化] TM743