基于长鼻浣熊算法的分布式光伏选址定容多目标规划  

Distributed photovoltaic location and capacity planning based on multi-target coati algorithm

作　　者：慕佳敏 韩兴臣 张智晟 MU Jiamin;HAN Xingchen;ZHANG Zhisheng(College of Electrical Engineering,Qingdao University,Qingdao 266071,China;State Grid Shandong Electric Power Company,Qingdao Power Supply Company,Qingdao 266002,China)

机构地区：[1]青岛大学电气工程学院,山东青岛266071 [2]国网山东省电力公司青岛供电公司,山东青岛266002

出　　处：《电力需求侧管理》2025年第2期75-81,共7页Power Demand Side Management

基　　金：国网山东省电力公司科技项目(520602220004)。

摘　　要：在配电网向低碳化转型的背景下,高比例绿色能源和以电能为动力的电动汽车呈持续增长趋势,配电网的运行和规划面临着新的挑战与机遇,因此提出了一种分布式光伏选址定容规划模型。首先,对电动汽车充电负荷和分布式光伏时序特性进行建模;然后,兼顾配电网的经济性、低碳性和稳定性,以配电公司年综合效益、碳排放量和负荷方差为目标函数构建多目标规划模型,并采用多目标长鼻浣熊算法对上述模型进行求解,基于帕累托占优原则保留全局帕累托最优解集,引入均衡度决策函数进行评价最优解的分布均衡性。最后在IEEE-33节点配电系统上进行算例分析,验证所提模型和方法的可行性。Under the background of the transformation of distribution network to low-carbon,the high proportion of green energy and electric vehicles powered by electric energy are developing continuously,and the operation and planning of distribution network are facing new challenges and opportunities.Based on this,an optimal planning model of distribution network including electric vehicle charging load and distributed photovoltaic is proposed.First,the electric vehicle charging load and distributed photovoltaic timing characteristics are modeled.Second,considering the economy,low carbon and stability of the distribution network,a multi-objective planning model is constructed with the annual comprehensive benefit,carbon emission and load variance of the distribution company as the objective function,and the multi-objective coati algorithm is used to solve the above model,and the global Pareto optimal solution set is retained based on the Pareto dominant principle.The equilibrium decision function is introduced to evaluate the distribution equilibrium of the optimal solution.Finally,the feasibility of the proposed model and method is verified by an example analysis on IEEE-33 node power distribution system.

关 键 词：分布式光伏 多目标长鼻浣熊算法 帕累托 电动汽车 配电网规划 

分 类 号：TM715[电气工程—电力系统及自动化] TK018[动力工程及工程热物理]