摘要
Abstract
The growing penetration of new energy sources in the power system has significantly increases the uncertainties in the grid,asking for higher requirements for the planning,opera-tion and control of the distribution network.The distribution network planning is an important cornerstone for the safe and stable operation of the power system.The traditional distribu-tion network planning,in which all parameters are determined in advance,lacks adaptability to uncertainties.In view of this,we proposed a method for distribution network planning based on probabilistic power flow analysis.The source-load output model was firstly established according to the quantitative mod-eling of uncertainties in the distribution network by using our method.Secondly,we utilized the rank correlation coefficient matrix to characterize the correlation between wind speed,light intensity and load,and developed a semi-invariant probabilistic power flow calculation method with correlation taken into ac-count.Finally,with the objective function of reducing the com-prehensive cost,we constructed a distribution network plan-ning model with the constraints of feeder capacity,node voltage,tidal balance,and radial structure of the grid.And the particle swarm algorithm was improved by optimization of in-ertia parameters and incorporation of variational operations.The improved algorithm was employed to solve the planning model.Simulations were conducted taking a 33-node system as an instance,and the results confirms the effectiveness of our method in reducing network loss and network planning costs.关键词
配电网规划/不确定性/相关性/概率潮流/粒子群算法Key words
distribution network planning/uncertainty/cor-relation/probabilistic power flow/particle swarm optimiza-tion分类
信息技术与安全科学
