基于多场景分析的分布式电源优化配置研究  

Research on Optimization Configuration of Distributed Power Sources Based on Multi-Scenario Analysis

作　　者：李楠 LI Nan(Digital Operations Center,Eighth Oil Extraction Plan,Daqing Field Co.,Ltd.,Daqing,Heilongjiang 163000,China)

机构地区：[1]大庆油田有限责任公司采油八厂数字化运维中心,黑龙江大庆163000

出　　处：《自动化应用》2024年第17期185-188,共4页Automation Application

摘　　要：为了使分布式电源并网规划更合理,将间歇性分布式电源出力和负荷预测存在的不确定性问题纳入求解过程中。首先,引入多场景分析将源荷不确定性问题转化为确定性问题,采用拉丁超力方抽样法生成初始规划场景,将密度峰值聚类思想和手肘法用于改进K-means聚类算法,并用于场景缩减;其次,以年综合费用最小为目标函数构建分布式电源并网优化配置模型;最后,针对粒子群算法收敛速度慢及易陷入局部最优的问题,采用自适应惯性权重因子,同时结合遗传变异思想改进粒子群算法,通过IEEE 33节点标准仿真算例验证所建模型和所提方法的有效性。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 

分 类 号：TM77[电气工程—电力系统及自动化]