含风电机组的配电网多目标无功规划  

Multi-objective reactive power planning in distribution system incorporating with wind turbine generation

作　　者：张沈习[1] 程浩忠[1] 张立波[1] 陈楷[2] 龙禹[2] 

机构地区：[1]电力传输与功率变换控制教育部重点实验室(上海交通大学),上海200240 [2]南京供电公司,江苏南京210019

出　　处：《电力系统保护与控制》2013年第1期40-46,共7页Power System Protection and Control

基　　金：国家重点基础研究发展计划项目(973项目)(2009CB219703)~~

摘　　要：考虑风电机组(Wind Turbine Generation,WTG)出力的随机性以及负荷的不确定性,以网损最小和无功补偿设备投资费最小作为目标,利用机会约束规划方法建立了配电网多目标无功规划模型。采用基于拉丁超立方采样的蒙特卡洛模拟嵌入非支配排序遗传算法(Non-dominated Sorting Genetic Algorithm,NSGA-II)对规划模型进行求解,得到配电网多目标无功机会约束规划问题的Pareto最优解集,避免了传统加权求解方法中权重确定的主观影响。改进的33节点配电网的仿真和分析验证了所提模型的合理性以及算法的有效性。Based on the chance constrained programming method, a multi-objective reactive power planning model for distribution system is proposed, which can take into account the randomness of the wind turbine generation （WTG） as well as the uncertainty of the load. The planning objective is to minimize both the power loss and the investment of the compensators. And the model is solved by non-dominated sorting genetic algorithm （NSGA-II） combined with Latin hypercube sampling-based Monte Carlo simulation, and the pareto-optimal solutions are obtained, which can avoid the subjective impact of the traditional weighted methods in determining the weights. The feasibility of the model and the effectiveness of the algorithm are proved by the simulation and analysis of a modified 33-bus distribution system.

关 键 词：配电网 多目标无功规划 机会约束规划 拉丁超立方采样 PARETO最优解集 非支配排序遗传算法 

分 类 号：TM715[电气工程—电力系统及自动化]