负荷建模的多目标优化

改进了传统负荷参数辨识的目标函数，将现有负荷模型参数辨识的单目标优化问题转化成多目标优化问题，并在改进强度Pareto进化算法的基础上引入并行遗传算法的思想，进行多目标参数辨识，力求克服目前困扰负荷建模及其参数辨识中收敛速度慢、易发散等问题。解决了以前算法只能辨识出一组参数的问题，便于决策者根据不同侧重进行参数选取。高效、高精度的并行算法为网格平台下的负荷建模做了前期准备。最后，对上海地区的负荷进行实测建模，结果表明所述建模策略的可行性。

Multi-objective Optimization for Load Modeling

Parameter identification for load modeling is analyzed and the conventional single-objective optimization model for load modeling is modified with a multi-objective optimization model.Then the improved strength Pareto evolutionary algorithm(SPEA2) combined with the parallel genetic algorithm(PGA) is used to identify the parameters.Compared with the conventional one, the proposed algorithm has such features as fast convergence and powerful optimization capability.It can obtain multiple sets of parameters for load modeling, which makes it convenient for customers to choose an appropriate solution according to their own specific demands.This efficient and high-precision algorithm is also a preparation for load modeling on the grid platform.Finally,the proposed method is validated by successful tests in the Shanghai power system.