基于改进K-means算法的多场景分布式电源规划

Multi-Scene Distributed Power Planning Based on Improved K-Means Algorithm

针对恒定调度模型分布式电源选址定容的配置方案实用性差的缺陷,考虑分布式电源(DG:Distributed Generation)出力和负荷需求的时序性与不确定性,建立以配电网系统总投资成本、总电压偏差和系统网损最小化的多目标调度模型。首先,通过蒙特卡洛算法随机模拟全年风-负荷场景,并采用K-means聚类法对全年场景进行缩减。其次,引入轮廓系数对其改进以获取最优的聚类数。最后,通过快速非支配排序遗传算法(NSGA-Ⅱ)与无偏折中策略进行优化处理。以IEEE33节点配电系统为例与标准遗传算法做对比仿真实验,验证了所提算法的有效性和优越性。

In view of the poor practicality of the constant scheduling model for distributed power supply site selection and constant volume configuration, considering the time-series and uncertainty of DG （Distributed Generation） output and load demand, based on the total investment cost of the distribution network system, total voltage deviation and system network loss minimization, multi-objective scheduling model is established. Firstly, the Monte-Carlo algorithm was used to simulate the wind-load scenario all year round, and the K-means clustering method is used to reduce the year-round scenarios. To solve the problem that the number of clusters in the K-means algorithm is difficult to set, the contour factor is introduced and improved to get the optimal number of clusters. Finally, the NSGA-I and non-biased strategy are used to optimize the process. In order to verify the validity of the method, taking the IEEE33 node distribution system as an example, the comparison experiment with the standard genetic algorithm validates the validity and superiority of the proposed algorithm.