基于改进自适应遗传算法的配电网光伏容量优化配置
|
摘要
为解决配电网中分布式光伏电源的容量优化配置问题,构建了含有分布式光伏电源运行建设费用、配网损耗费用、购电成本效益和环保效益的目标函数,并以配电网中节点电压、功率平衡等为约束条件,通过改进自适应遗传算法求解模型最优解。IEEE 33节点算例中,传统遗传算法在迭代至第77次用时32.72 s收敛至最优解,自适应遗传算法迭代至第39次用时16.57 s收敛至最优解,改进的自适应遗传算法迭代至第10次用时4.78 s就已经收敛至最优解,验证了模型和算法的合理性。
In order to solve the problem of optimal capacity allocation of distributed photovoltaic power supply in distribution network, this paper constructs the objective function containing distributed PV power supply operation and construction cost, distribution network loss cost, electricity purchase cost-benefit and environmental benefit. The node voltage and power balance in the distribution network are taken as constraints to get the optimal solution of the mode through improved adaptive genetic algorithm. In the IEEE33 node example,the traditional genetic algorithm converges to the optimal solution in 32.72 seconds when iterating to the seventy-seventh time, and the adaptive genetic algorithm converges to the optimal solution in 16.57 seconds when iterating to the thirty-ninth time. The improved adaptive genetic algorithm converges to the optimal solution in4.78 seconds when iterating to the tenth time, which verifies the rationality of the model and the algorithm.
In order to solve the problem of optimal capacity allocation of distributed photovoltaic power supply in distribution network, this paper constructs the objective function containing distributed PV power supply operation and construction cost, distribution network loss cost, electricity purchase cost-benefit and environmental benefit. The node voltage and power balance in the distribution network are taken as constraints to get the optimal solution of the mode through improved adaptive genetic algorithm. In the IEEE33 node example,the traditional genetic algorithm converges to the optimal solution in 32.72 seconds when iterating to the seventy-seventh time, and the adaptive genetic algorithm converges to the optimal solution in 16.57 seconds when iterating to the thirty-ninth time. The improved adaptive genetic algorithm converges to the optimal solution in4.78 seconds when iterating to the tenth time, which verifies the rationality of the model and the algorithm.
引文
[1]王守相,王慧,蔡声霞.分布式发电优化配置研究综述[J].电力系统自动化,2009,33(18):110-115.
[2]王敏,丁明.分布式发电及其效益[J].合肥工业大学学报(自然科学版),2204,27(4):354-358.
[3]王志群,朱守真,周双喜,等.分布式发电对配电网电压分布的影响[J].电力系统自动化,2004,28(16):56-60.
[4]陈海焱,陈金富,段献忠.含分布式电源的配电网潮流计算[J].电力系统自动化,2006,30(l):35-40.
[5]郑睿,吴红斌.基于遗传算法的配电网中分布式电源选型与选址[J].合肥工业大学学报(自然科学版),2012,35(3):308-311.
[6]GRIFFIN T,TOMSOVIC K,SECREST D,et al.Placement of Dispersed Generations Systems for Reduced Losses[C]//Proceedings of the 33rd Hawaii International Conference on System Sciences.[s.l.]:[s.n.]2000:1-9.
[7]刘波,张焰,杨娜.改进的粒子群优化算法在分布式电源选址和定容中的应用[J].电工技术学报,2008,23(2):103-108.
[8]丁明,张颖媛,茆美琴,等.包含钠硫电池储能的微网系统经济运行优化[J].中国电机工程学报,2011,31(4):7-14.
[9]车仁飞,李仁俊.一种少环配电网三相潮流计算新方法[J]中国电机工程学报,2003,23(1):74-79.
[10]何仰赞,温增银.电力系统分析(下)[M].武汉:华中科技大学出版社,2002.
[11]ALGORITHM with a Cooperative Model[C]//Proceedings of IEEE international Symposium on Industrial Electronics.[s.l.]:[s.n.]2001.
[12]NARA K,SHIIOSE A,KITAGWA M,et al.Implementation of genetic algorithm for distribution systems loss minimum reconfiguration[J].IEEE Trans on Power Systems,1992,7(3):1044-1051.
[13]李登峰,谢开贵,胡博,等.基于净效益最大化的微电网电源优化配置[J].电力系统保护与控制,2013,41(20):20-26.
[14]刘军,刘学军.MATLAB在电力系统分析中的应用[J].电力系统及其自动化学报,2000,12(2):23-26.
[15]张立梅,唐巍.计及分布式电源的配电网前推回代潮流计算[J].电工技术学报,2010,25(8):123-130.
[16]陶来华,李俊峰,陈士安.基于遗传算法与综合性能协调的车辆悬架参数优化方法[J].浙江水利水电学院学报,2018,30(2):71-77.
[17]赵连朋,王立颖,毛少苗.一种求解多项式方程复数根的新方法[J].渤海大学学报(自然科学版),2017,38(4):363-369.
[18]谢海波,武小梅,林翔,等.含分布式电源的配电网无功优化研究综述[J].广东电力,2017,30(2):102-109.
[2]王敏,丁明.分布式发电及其效益[J].合肥工业大学学报(自然科学版),2204,27(4):354-358.
[3]王志群,朱守真,周双喜,等.分布式发电对配电网电压分布的影响[J].电力系统自动化,2004,28(16):56-60.
[4]陈海焱,陈金富,段献忠.含分布式电源的配电网潮流计算[J].电力系统自动化,2006,30(l):35-40.
[5]郑睿,吴红斌.基于遗传算法的配电网中分布式电源选型与选址[J].合肥工业大学学报(自然科学版),2012,35(3):308-311.
[6]GRIFFIN T,TOMSOVIC K,SECREST D,et al.Placement of Dispersed Generations Systems for Reduced Losses[C]//Proceedings of the 33rd Hawaii International Conference on System Sciences.[s.l.]:[s.n.]2000:1-9.
[7]刘波,张焰,杨娜.改进的粒子群优化算法在分布式电源选址和定容中的应用[J].电工技术学报,2008,23(2):103-108.
[8]丁明,张颖媛,茆美琴,等.包含钠硫电池储能的微网系统经济运行优化[J].中国电机工程学报,2011,31(4):7-14.
[9]车仁飞,李仁俊.一种少环配电网三相潮流计算新方法[J]中国电机工程学报,2003,23(1):74-79.
[10]何仰赞,温增银.电力系统分析(下)[M].武汉:华中科技大学出版社,2002.
[11]ALGORITHM with a Cooperative Model[C]//Proceedings of IEEE international Symposium on Industrial Electronics.[s.l.]:[s.n.]2001.
[12]NARA K,SHIIOSE A,KITAGWA M,et al.Implementation of genetic algorithm for distribution systems loss minimum reconfiguration[J].IEEE Trans on Power Systems,1992,7(3):1044-1051.
[13]李登峰,谢开贵,胡博,等.基于净效益最大化的微电网电源优化配置[J].电力系统保护与控制,2013,41(20):20-26.
[14]刘军,刘学军.MATLAB在电力系统分析中的应用[J].电力系统及其自动化学报,2000,12(2):23-26.
[15]张立梅,唐巍.计及分布式电源的配电网前推回代潮流计算[J].电工技术学报,2010,25(8):123-130.
[16]陶来华,李俊峰,陈士安.基于遗传算法与综合性能协调的车辆悬架参数优化方法[J].浙江水利水电学院学报,2018,30(2):71-77.
[17]赵连朋,王立颖,毛少苗.一种求解多项式方程复数根的新方法[J].渤海大学学报(自然科学版),2017,38(4):363-369.
[18]谢海波,武小梅,林翔,等.含分布式电源的配电网无功优化研究综述[J].广东电力,2017,30(2):102-109.