基于K-means聚类粒子群算法的多点PV-DG日前分配计划  

Particle Swarm Optimization Based on K-means Cluster for Day-ahead Allocation Plan of Multiple Photovoltaic Distributed Generation

作　　者：李磊[1] 王俊熙[1] 贺易 詹鹏[1] 刘方方[1] 汤弋[1] 

机构地区：[1]国网湖北省电力公司信息通信公司,武汉430077

出　　处：《高电压技术》2017年第4期1263-1270,共8页High Voltage Engineering

摘　　要：针对光伏分布式电源(PV-DG)将大量接入配电网的场景,提出了基于K-means聚类粒子群算法(PSO)的PV-DG日前出力优化算法。该算法通过K-means聚类法根据PV-DG依次接入不同配电网的节点每小时网损的分析对节点进行分类,结合设计的分配公式初始化并网节点的PV-DG出力,将此出力作为初始化粒子引入粒子群优化算中。将分时系数自回归滑动平均(ARMA)模型预测方法与常规ARMA预测方法进行了比较,仿真结果表明分时系数ARMA模型预测方法提高了预测精度;并将K-means聚类的粒子群算法与粒子群算法及模糊粒子群算法分别进行了比较,对比结果说明提出的优化方法进一步降低了网损。In allusion to the scene that multiple photovoltaic distributed generation （PV-DG） will be connected in the distribution power system, particle swarm optimization （PSO） based on K-means cluster for day-ahead allocation plan of multiple PV-DG is proposed. In this algorithm, the hourly network loss of nodes connected with PV-DG is alternately combined with the designed formulas to initially allocate the penetration level of multiple PV-DG connected with nodes, this penetration is used to initialize the particle in the PSO algorithm. Moreover, the ARMA is compared with various predicting parameters the traditional ARMA. The simulation results indicate that the proposed ARMA can improve the predicting accuracy. The comparison is carried on among the PSO based K-means cluster, classical PSO, and fuzzy PSO, the results show that PSO-based K-means cluster can reduce more network loss.

关 键 词：光伏分布式电源 自回归滑动平均 分时预测系数 基于K-means的粒子群优化算法 网损 

分 类 号：TM714.3[电气工程—电力系统及自动化]