In order to make the grid connection planning of distributed power sources more reasonable,the uncertainty issues of intermittent distributed power generation output and load forecasting are included in the solution process.Firstly,multi scenario analysis is introduced to transform the source load uncertainty problem into a deterministic problem.The Latin hyperpower square sampling method is used to generate the initial planning scenario,and the density peak clustering idea and elbow method are used to improve the K-means clustering algorithm and reduce the scenario.Secondly,a distributed power grid optimization configuration model is constructed with the objective function of minimizing the annual comprehensive cost.Finally,in response to the slow convergence speed and susceptibility to local optima in particle swarm optimization,an adaptive inertia weight factor was adopted,and the particle swarm algorithm was improved by combining genetic mutation ideas.The effectiveness of the established model and proposed method was verified through IEEE 33 node standard simulation examples.
关键词
分布式电源/不确定性/多场景分析/改进粒子群算法/K-means聚类算法
Key words
distributed power generation/uncertainty/multi-scenario analysis/improved particle swarm optimization algorithm/K-means clustering algorithm
维普
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