基于一种改进粒子群算法的含风电场电力系统动态经济调度  

Dynamic economic dispatch considering wind power penetration based on IPSO

作　　者：姜文[1] 严正[1] 

机构地区：[1]上海交通大学电子信息与电气工程学院,上海200240

出　　处：《电力系统保护与控制》2010年第21期173-178,183,共7页Power System Protection and Control

基　　金：国家863专项基金项目(2007AA05Z458)

摘　　要：随着风力发电在电力系统中比重的持续增加,在电力系统经济调度中需要考虑风电场的影响。提出了一种改进的粒子群优化算法,用来求解含风电场的电力系统动态经济调度问题。优化模型中引入了正、负旋转备用约束,以应对风电功率预测误差给系统调度带来的影响,并在目标函数中计及了常规机组的发电效应带来的能耗成本。以经典的10机系统为算例,通过与基本的粒子群算法和遗传算法进行比较,验证了所提算法的可行性和有效性。该方法可以节省较多的发电成本,具有较高的实用价值。With the increase of wind power in power systems,the influence of wind farms penetration should be considered in economic dispatch.In this paper,a n improved particle swarm optimization（I PSO） i s proposed for solving the problem of dynamic economic dispatch（D ED）.In this optimization model,the constraints of up spinning reserve and down spinning reserve are introduced to deal with the influence of wind power forecast errors on DED,and energy cost of routine unitis is considered in the objective function.The case studies are conducted based on a typical 10 units test power system.The effectiveness and feasibility of the proposed method are demonstrated by comparing its performance with that of other approaches.This proposed method can save much fuel cost and has high application value.

关 键 词：风电场 动态经济调度 粒子群算法 旋转备用 

分 类 号：TM734[电气工程—电力系统及自动化]