摘要
为减少分布式光伏不确定性给配电网规划带来的不利影响,本文提出了一种考虑光伏出力及基础负荷不确定性的分布式光伏选址定容方法。首先采用K均值聚类算法建立典型情景高效处理分布式光伏出力的不确定性;其次构建包含购电成本、分布式光伏投资成本和网络损耗的综合运行成本目标函数,为了解决粒子群算法(PSO)易陷入局部最优的缺点,建立了惯性权值和学习因子的自适应调整策略;最后以IEEE33节点配电网为研究对象,通过不同方案运行结果的对比,证明了该方法的有效性。
In order to mitigate the negative effects of distributed photovoltaic uncertainty on distribution network planning,this paper proposes a distributed photovoltaic location and sizing method considering the uncertainty of photovoltaic output and basic load.Firstly,the k-means clustering algorithm is used to establish a typical scenario to efficiently deal with the uncertainty of distributed photovoltaic output.Secondly,a comprehensive operation cost objective function including power purchase cost,distributed photovoltaic investment cost and network loss is constructed.In order to solve the problem that particle swarm optimization(PSO)is easy to fall into local optimum,an adaptive adjustment strategy of inertia weight and learning factor is established.Finally,taking IEEE33 node distribution network as the research object,the effectiveness of this method is proved by comparing the operation results of different schemes.
作者
韩胜峰
王文宾
李征
范曾
HAN Shengfeng;WANG Wenbin;LI Zheng;FAN Zeng(State Grid Hebei Electric Power Co.,Ltd.Xingtai Power Supply Branch,,Xingtai 054000,China)
出处
《河北电力技术》
2023年第2期24-29,共6页
Hebei Electric Power
基金
国家电网有限公司科技项目(5204XT20000P)。
关键词
分布式光伏
不确定性
K均值聚类算法
选址定容
改进粒子群算法
distributed photovoltaic
uncertainty
k-means clustering algorithm
location and sizing
improved particle swarm optimization algorithm
作者简介
韩胜峰(1977-),男,高级工程师,主要从事配电网规划研究工作。
