基于分类概率综合多场景分析的分布式电源多目标规划

为了在配电网分布式电源规划中更加准确合理地考虑分布式电源出力及负荷需求的不确定性，基于风电、光伏和负荷的随机性分布特征差异，提出分类概率综合多场景分析方法以实现更合理的多场景生成，并融合K-Means聚类方法和层次凝聚聚类(HAC)算法形成H-K复合聚类场景压缩方法，实现更高效的场景压缩；以年均收益率和配电系统电压分布改善率最大化为目标构建多场景分布式电源多目标规划模型，并采用基于HAC种群截断策略的改进非劣排序复合微分进化算法对模型进行求解；以IEEE33节点配电系统为例进行了分布式电源多目标规划，仿真结果验证了所提方法的有效性和优越性。

Multi-objective DG planning based on classified probability integration multi-scenario analysis

In order to consider the uncertainties of DG(Distributed Generation) output and load demand more accurately and reasonably in DG planning of distribution network, based on the difference of random distribution characteristics among wind power, photovoltaic and load, the classified probability integration multi-scenario analysis method is proposed to realize multi-scenario creation more reasonably, and the H-K compound clustering compression method is constructed with the combination of K-Means clustering method and HAC(Hierarchical Aggregation Clustering) algorithm to achieve more efficient scene compression. A multi-scenario and multi-objective DG planning model is built with the objective of maximum average annual profit rate and voltage distribution improvement rate, which is solved by the INSCDE(Improved Non-dominated Sorting Compound Differential Evolution) algorithm based on HAC algorithm population truncation strategy. The IEEE 33-bus distribution system is taken as an example for multi-objective DG planning, and the simulative results verify the effectiveness and superiority of the proposed method.