改进粒子群优化算法在电源规划中的应用

摘要/Abstract

摘要： 电源规划是一类复杂、非线性组合优化问题.传统的方法随着规划期的延长,考虑因素的增多,难以有效的进行优化,在实际应用中作用有限.首先,对电源规划优化问题进行了建模.然后,对于粒子群(PSO)的迭代策略进行改进,在此基础上,运用遗传粒子群(GPHA)混合优化算法进行了优化尝试.考虑到电源规划中相关参数众多,在优化过程中引入了虚拟变量对电源规划中的问题进行了简化描述;GHPA算法的适应度评价函数设计中,运用了罚函数的思想,以提高算法优化的效果.最后本文使用某省实际负荷预测和系统负荷实际数据,进行了电源规划方案优化,得到了优化后的电源规划方案,并与普通的遗传算法、粒子群算法以及传统的动态规划算法得到的结果进行了比较.比较的结果显示出了本文提出的算法在优化结果和速度方面具有明显效果.

关键词: 遗传算法, 粒子群算法, 电源规划, 罚函数, 虚拟变量

Abstract: The generation expansion planning is a complex non-linear and combinatorial optimization problem.With the plan time lengthening and the factors considered increasing,the traditional optimization methods can not make the satisfactory result.And also so,it's applications in the practice are limited.Firstly,the model of the generation expansion was set up.Then,the paper improved the iteration tactic of the particle swarm optimization.It proposed a hybrid algorithm which combined genetic algorithm with particle swarm optimizaion(GPHA).Considering the various factors concerned,the paper introduced fictitious variables to briefly describe generation expansion planning problem in the optimization process.For the fitness function,the paper used penalty function to enhance the effect.In the end of this paper,it chose actual load forecast and the system load actual data of some province,quoted in the similar question regarding correlation parameter supposition,and compared the algorithm with the ordinary genetic algorithms,particle swarm optimization algorithm and the traditional dynamic programming algorithm.The result proves that the new algorithm has done well in the aspects of optimization and speed.

Key words: genetic algorithm, particle swarm optimization algorithm, generation expansion planning, penalty function, fictitious variable

中图分类号:

F713.50

李翔, 牛东晓, 杨尚东. 改进粒子群优化算法在电源规划中的应用[J]. 中国管理科学, 2006, (6): 113-118.

LI Xiang, NIU Dong-xiao, YANG Shang-dong. Application of the Improved Particle Swarm Optimization Algorithm in the Generation Expansion Planning[J]. Chinese Journal of Management Science, 2006, (6): 113-118.

参考文献

[1] 侯煦光.电力系统最优规划[M].武汉:华中理工大学出版社,1991.
[2] 叶泽.电力竞争[M].中国电力出版社,北京:2004.
[3] Climaco J.,C.H.,Martins A.G.,er al.A multiple objective linear programming model for power generation expansion planning[J].International Journal of Energy Research,1995,19(5):419-432.
[4] Antunes C.,Martins A.,Gomes,et al.A multiple objective mixed integer linear programming model for power generation expansion planning[J].Energy,2004,29(4):613-627.
[5] Jia N.,Yokoyama R.,Zhou Y.,et al.Optimal generation expansion planning under the deregulated market based on an improved DP approach[C].Power Plants and Power Systems Control 2000,Proceedings volume from the IFAC Symposium,2000,251-255.
[6] 韩新阳,胡兆光,李敬如.考虑环保因素的最小费用电源规划研究[J].国际电力,2003,7(3):14-17.
[7] 吴耀武,侯云鹤,熊信艮,等.基于遗传算法的电力系统电源规划模型[J].电网技术,1999,23(2):10-14.
[8] Chung T.S.,Li Y.Z.,Wang Z.Y..Optimal generation expansion planning via improved genetic algorithm approach[J].International Journal of Electrical Power and Energy System,2004,26(8):655-659.
[9] Kannan S.,Slochanal S.,Mary Raja,et al.Application of particle swarm optimization technique and its variants to generation expansion planning problem[J].Electric Power Systems Research,2004,70(3):203-210.
[10] Tsukada T.,Tamura T.,Kitagawa S.,et al.Optimal operational planning for cogeneration system using particle swarm optimization[C].Proceedings of the 2003 IEEE Swarm Intelligence Symposium.SIS' 03 (Cat.No.03EX706),2003,138-43.
[11] Sevilgen S.H.,Erdem H.H.,Cetin B..et al.Effect of economic parameters on power generation expansion planning[J].Energy Conversion and Management,2005,46(11):1780-1789.
[12] 黄席樾,胡小兵,何传江,等.现代智能算法理论与应用[M].北京:科学出版社,2004.
[13] Kennedy J.,Eberhart R.C..Particle swarm optimization.IEEE.Proceedings of The 6th conference on neural networks[C].NJ Piscataway:IEEE Service center,1995:1942-1948.
[14] M.Clerc,J.Kennedy.The particle swarm:explosion stability and convergence in a multi-dimensional complex spaee[J].Evolutionary Computing,2002,6 (1):58-73.
[15] Angeline Peter J..Using selection to improve particle swarm optimization[C].IEEE Int Conf Comput Intell,2003:84-86.