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文章摘要
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
the application of co-evolutionary particle swarm optimization algorithm in dynamic economic dispatch of power system incorporated wind power
Received:April 14, 2014  Revised:April 14, 2014
DOI:
中文关键词: 阀点效应  风电成本  协进化粒子群算法  罚函数  
英文关键词: valve point effect  wind power generation cost  co-evolutionary particle swarm optimization algorithm  penalty function  
基金项目:
Author NameAffiliationE-mail
Xia Chenjie School of Electrical and Information Engineering,Xihua University,Chengdu xiachenjie1989@qq.com 
Chen Yongqiang* School of Electrical and Information Engineering,Xihua University,Chengdu  
Jiang Zhenghua School of Wu Yuzhang,Sichuan University,Chengdu  
Yu Bo School of Electrical and Information Engineering,Xihua University,Chengdu  
Huang Yingshu School of Electrical and Information Engineering,Xihua University,Chengdu  
Xie Pian School of Electrical and Information Engineering,Xihua University,Chengdu  
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中文摘要:
      为了解决电力系统中含有风电的动态经济调度问题,本文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。
英文摘要:
      In order to solve the power system dynamic economic dispatch incorporated wind power, after taking into account the unit of ramp rate in actual operating, the operating constraints, the spinning reserve constraints an, etc. Combined 24 hours’ power curve of the wind power calculated by using wind speed, power generation costs transformed by wind power generation investments and maintenance,an objective function including conventional power generation cost with valve point effect, wind power generation cost, system spinning reserve cost, and penalty function with all constraints, solved by co-evolutionary particle swarm optimization algorithm. The algorithm has achieved global optimal solution and optimal penalty factor through two populations of autonomous evolution and interaction information. At last, simulation result of the example shows that the algorithm has a good search performance, convergence features and the quality of the solution is significantly better than other algorithms.
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