基于多场景变权多目标优化的UPFC在风电并网系统中的配置方案研究

Allocation Scheme Research of UPFC Based on Multiple Scenarios With Different Weighting Coefficients and Multi-target Optimization in Wind Power Integrated System

风电出力与负荷的双向随机性使得电网潮流存在较大的不确定性,易造成电压失稳和线路传输功率过载等问题。统一潮流控制器(unified power flow controller,UPFC)可以通过控制线路潮流和稳定节点电压,使风电并网系统运行的安全性、经济性大大增强。针对现有UPFC配置方法存在的未能充分考虑系统运行场景的不确定性及多场景下权重差异性的缺点,建立UPFC多场景变权多目标优化配置模型。利用K-means聚类方法对风电出力-负荷进行场景划分,以有功网损、电压偏移率和负载均衡度以及UPFC安装成本为优化目标,利用NSGA-II算法对UPFC的安装数量、位置、容量进行优化,并提出多场景熵权-层次分析法对Pareto解集排序,得出UPFC配置的最优方案。算例对IEEE 30节点系统进行仿真计算,验证了该模型和算法的正确性和有效性。

Output of wind power and bi-directional randomness make system load flow more uncertain, producing problems, such as voltage instability and overload of transmission lines. UPFC can greatly improve safety and economy of operation of wind power integrated system. Aiming at the deficiencies of not considering uncertainty of system operating status and the difference of weighting coefficients in different scenes in current UPFC collocation method, the article sets up multiple UPFC scenarios with different weighting coefficients and a multi-target optimization allocation model, partitioning the scenes of wind power output-load, taking active power loss, voltage deviation rate, load balance degree and installation cost of UPFC controller as the targets, and utilizing NSGA-II algorithm to optimize UPFC installation location and capacity. Besides, based on multi-scenario entropy coefficient-AHP, the Pareto solution set is disaggregated and sorted and the best plan of UPFC allocation is reached. A study example is simulated to calculate IEEE30-node system, verifying validity and efficiency of the proposed model and algorithm.