确定风电厂最优落点的概率特征值灵敏度方法

Probability Eigenvalue Sensitivity Indices for Determining Optimal Access Points of Wind Farms

随着电力系统中风电渗透率的不断提高,电力系统潮流分布的复杂性和不确定性随之增加。为解析含风电互联电力系统的稳定运行特性,提升接纳间歇性可再生能源发电的能力,文章提出利用概率灵敏度指标优化配置风电落点。首先,针对互联系统多种运行方式,研究系统状态矩阵与留数之间关系,构建系统概率灵敏度指标,辨别引起系统低频振荡的相关因素和强相关电机组。之后,采用特征根和动态时域仿真,对比分析“增加”和“替换”风电两种配置方案对系统振荡特性的影响,并确定风电入网的最优配置落点。最后,采用算例对所提方法的可行性与有效性进行仿真验证。

With the ever-increasing penetration of wind power in an interconnected power system,the power flow distribution is becoming more and more complicated and uncertain.To analyze the stable operating characteristics of an interconnected power system with wind power integrated and enhance the accommodation capability for intermittent renewable generation,this paper applies probabilistic eigenvalue sensitivity indices to determine optimal access points of wind farms.Under multiple operation conditions of an interconnected power system,the relationship between the system state matrix and the residual index is studied,the probability sensitivity indices are developed,and the correlation factors and strongly related units that cause the low-frequency oscillation of the system are identified.The eigenvalue analysis method and dynamic time-domain simulation are used to analyze and compare the influences of“adding”and“replacing”wind power configuration schemes on the system oscillation characteristics,and to determine the optimal access points of wind farms.Finally,two numerical examples are employed to demonstrate the feasibility and efficiency of the proposed approach.