摘要
大量分布式光伏并网可能会给配电网的网损、电流三相不平衡度和电能质量带来负面影响。为合理地规划分布式光伏并网容量,提出了一种综合考虑配电网三相不平衡度、系统经济性、电压偏差和静态电压稳定裕度的规划方法。利用动态时间弯曲距离来描述时间序列的相似性,采用改进K-means聚类算法,得到规划区负荷有功功率及与规划区纬度相同区域已知光伏出力的典型场景。利用博弈论的思想基于改进层次分析法和熵权法得到综合权重,将多目标优化问题转换成单目标优化问题,并采用改进的量子粒子群算法对其进行求解。最后利用改进的IEEE33节点配网系统和某地区实际配网系统进行了仿真验证,研究结果表明所提分布式光伏并网容量规划方法的合理性和有效性。
Massive amount of distributed photovoltaics connect to distribution network would bring side-effects to net loss,three-phase unbalance of current and power quality.In order to reasonably plan the grid-connected capacity of distributed photovoltaics,a planning method comprehensively considering the three-phase unbalance of distribution network,system economy,voltage deviation and static voltage stability margin is proposed.The dynamic time wrapping distance is used to describe the similarity of the time series,and the improved K-means clustering algorithm is used to obtain the typical scenarios of the load active power in the planning area and the known photovoltaic output in the area of same latitude to the planning area.Using the idea of game theory to obtain the comprehensive weight based on the improved AHP and entropy weight method,the multi-objective optimization problem is converted into a single-objective optimization problem,which is solved by the improved quantum particle swarm optimization algorithm.Finally,the improved IEEE33 node distribution system and actual distribution network system in a certain area are used to verify the simulation.The simulation results show that the proposed distributed photovoltaic grid-connected capacity planning method is reasonable and effective.
作者
杨晴
罗平
李俊杰
YANG Qing;LUO Ping;LI Junjie(Hangzhou Dianzi University,Hangzhou 31008,China)
出处
《杭州电子科技大学学报(自然科学版)》
2023年第6期42-51,共10页
Journal of Hangzhou Dianzi University:Natural Sciences
基金
浙江省自然科学基金(LY20E070004)
国家自然科学基金资助项目(62073108)。
关键词
分布式光伏并网
配电网三相不平衡度
动态时间弯曲距离
量子粒子群算法
多目标优化
grid-connection distributed photovoltaic
three-phase unbalance of distribution network
dynamic time warping distance
quantum particle swarm algorithm
multi-objective optimization problem