Based on the severe wind curtailment situation and promising development trend of dispersed wind farm,this paper proposes a multi-objective dispersed wind farm planning method.The power flow entropy is introduced to describe the balance degree of system flow,and the planning objective is to minimize both the overall operation cost and power flow entropy.This paper applies Latin hypercube sampling (LHS) to deal with the uncertainty and correlation of wind speed and load,and uses the chance constrained programming to restrict the curtailment rate and power rationing after establishing the static constraint model of traditional power system.Besides,an intelligent optimization algorithm is employed in wind farm locating and sizing,the influence of different factors on planning result is studied as well.The simulation of IEEE 9 system shows that the proposed method can guarantee the economic benefit and wind power utilization,and makes the power grid more adaptable to load changes.The result also indicates the great impact of unit climbing and wind speed correlation on the planning capacity of wind power.%结合我国当前严峻的弃风形势和分散式风电场良好的发展趋势,提出了一种分散式风电场多目标规划方法.引入潮流熵描述系统潮流均衡度,以系统整体运行成本和潮流熵最小作为目标函数.采用拉丁超立方抽样(Latinhypercube sampling,LHS)处理风速、负荷的不确定性和相关性,在建立传统电力系统静态约束模型的同时,对弃风率及限电次数进行机会约束.将智能优化算法应用到风电场的选址定容中,并研究了不同因素对规划结果的影响.IEEE 9节点系统算例表明:所提方法能够兼顾经济效益、风电利用率,同时使电网具备对负荷变化更强的适应性,结果同时体现了常规机组爬坡能力和风速相关性对风电规划容量的重要影响.
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