协进化粒子群算法在含有风电的电力系统动态经济调度中的应用  

Application of Co-Evolutionary Particle Swarm Optimization Algorithm to Dynamic Economic Dispatch of Power Systems with Wind Power

作　　者：夏晨杰 陈永强[1] 蒋正华[2] 俞博[1] 黄颖姝 谢翩[1] 

机构地区：[1]西华大学电气信息学院,成都610039 [2]四川大学吴玉章学院,成都610065

出　　处：《电测与仪表》2014年第17期123-128,共6页Electrical Measurement & Instrumentation

摘　　要：为了解决电力系统中含有风电的动态经济调度问题,文章在考虑了实际运行中的机组爬坡率、运行约束和旋转备用约束等多种约束条件后,利用风速求出了风电24小时的功率曲线,将风电的投资和维护成本折算成风电的发电成本,提出了一个含有常规机组阀点效应的发电成本、风电发电成本和系统备用成本的目标函数和所有约束条件的罚函数,应用提出的协进化粒子群优化算法求解该问题,该算法通过两个种群的自主进化和交互信息,得到了全局最优解和最佳罚因子。最后通过实例的仿真结果验证了该算法具有良好的搜索性能和收敛特性,获得的解得质量明显好于其它算法。To solve the problem of dynamic economic dispatch for power systems with wind power,the paper proposes a co-evolutionary particle swarm optimization algorithm. Considering the constraints such as the unit ramp rate,the operation constraints and the spinning reserve in actual operation,the paper first calculates 24 hours’wind power curve using wind speed. Then with wind power generation cost converted from wind power investment and maintenance cost,an objective function including conventional power generation cost with valve point effect,wind power generation cost,system spinning reserve cost and all constraints is presented. The problem is solved using the proposed co-evo-lutionary particle swarm optimization algorithm,which can achieve global optimal solution and optimal penalty factor through the autonomous evolution and interaction information of the two populations. Finally,simulation result of an actual example shows that the algorithm has good searching and convergence performance,and the quality of the solu-tion is significantly better than other algorithms.

关 键 词：阀点效应 风电成本 协进化粒子群算法 罚函数 

分 类 号：TM73[电气工程—电力系统及自动化]