基于二阶锥规划与改进遗传算法的配网重构
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摘要
提出一种结合二阶锥规划的遗传算法应用于配网重构,降低了网损,消除了过电压。采用基于环路的十进制遗传编码规则,缩短了染色体长度,并利用开关环路矩阵和节点分层策略进行可行性判定。通过精英保留和动态控制变异率,避免了早熟,提高了收敛速度。通过等效变换和有效松弛,把原始的潮流方程转化为可以利用现有解算器高效求得全局最优解的二阶锥规划模型。应用所提算法对IEEE33节点配电网进行算例分析,仿真结果验证了所提出算法的可行性和计算高效性。
An improved genetic algorithm combined with second-order cone programing is proposed for distribution network reconfiguration to minimize power losses and eliminate voltage violations. The chromosome length is shortened by using the decimal genetic coding rule based on the loop. For eliminating the infeasible solutions generated during the optimization, the infeasible solution judgment method based on the switch loop-node matrix and the node layer strategy is adopted in this paper. Through the elite reserve and dynamic control mutation rate, the problem about the premature convergence of the genetic algorithm is effectively solved. Then, some equivalent conversion and relaxation are presented to cast the initial nonlinear power flow equation into a second-order cone model, which can reliably and efficiently solve the global optimality by using the available commercial software. Finally, case studies on IEEE 33-node test feeder are conducted and the results demonstrate the validity as well as effectiveness of the proposed method.
An improved genetic algorithm combined with second-order cone programing is proposed for distribution network reconfiguration to minimize power losses and eliminate voltage violations. The chromosome length is shortened by using the decimal genetic coding rule based on the loop. For eliminating the infeasible solutions generated during the optimization, the infeasible solution judgment method based on the switch loop-node matrix and the node layer strategy is adopted in this paper. Through the elite reserve and dynamic control mutation rate, the problem about the premature convergence of the genetic algorithm is effectively solved. Then, some equivalent conversion and relaxation are presented to cast the initial nonlinear power flow equation into a second-order cone model, which can reliably and efficiently solve the global optimality by using the available commercial software. Finally, case studies on IEEE 33-node test feeder are conducted and the results demonstrate the validity as well as effectiveness of the proposed method.
引文
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[12]邓海潮,毛弋,彭文强,等.基于改进人工蜂群算法的配电网重构[J].电力系统及其自动化学报,2016,28(7):125-129.Deng H C,Mao G,Peng W Q,et al.Distribution Network Reconfiguration Based on Improved Artificial Bee Colony Algorithm[J].Proceedings of the CSU-EPSA,2016,28(7):125-129.
[13]董海鹰,魏占宏,赵香桂,等.基于多种群遗传算法的电动变桨系统的变论域模糊控制[J].控制工程,2014,21(2):182-188.Dong H Y,Wei Z H,Zhao X G,et al.Variable universe fuzzy control of electric pitch control system based on multiple population genetic algorithm[J].Control Engineering of China,2014,21(2):182-188.
[2]麻秀范,张粒子.基于十进制编码的配网重构遗传算法[J].电工技术学报,2004,19(10):65-69.Ma X F,Zhang L Z.Distribution Network Reconfiguration Based on Genetic Algorithm Using Decimal Encoding[J].Transactions of China Electrotechnical Society,2004,19(10):65-69.
[3]Jabr,R.A.Radial Distribution Load Flow Using Conic Programming[J].IEEE Transactions on Power Systems,2006,21(3):1458-1459.
[4]全然,韦化,简金宝.求解大规模机组组合问题的二阶锥规划方法[J].中国电机工程学报,2010,30(25):101-107.Quan R,Wei H,Jian J B.Solution of Large Scale Unit Commitment by Second-order Cone Programming[J].Proceedings of The Chinese Society for Electrical Engineering,2010,30(25):101-107.
[5]刘一兵,吴文传,张伯明,等.基于混合整数二阶锥规划的三相有源配电网无功优化[J].电力系统自动化,2014,38(15):58-64.Liu Y B,Wu W C,Zhang B M,et.al.Reactive power optimization for three-phase distribution networks with distributed generators based on mixed integer seconder-order cone programming[J].Automation of Electric Power Systems,2014,38(15):58-64.
[6]Taylor J A,Hover F S.Convex Models of Distribution System Reconfiguration[J].IEEE Transactions on Power Systems,2012,27(3):1407-1413.
[7]Jabr R A,Singh R,Pal B C.Minimum loss network reconfiguration using mixed-integer convex programming[J].Power Systems,IEEE Transactions on,2012,27(2):1106-1115.
[8]李洪美,崔翰韬,万秋兰.考虑电动汽车充电策略的配网重构二阶锥规划模型[J].中国电机工程学报,2015,35(18):4674-4681.Li H M,Cui H T,Wan Q L.Distribution Network Reconfiguration based on Second-order Conic Programming Considering EVCharging Strategy[J].Proceedings of The Chinese Society for Electrical Engineering,2015,35(18):4674-4681.
[9]何坚勇.运筹学基础[M].北京:清华大学出版社,2000.He J Y.Operational research fundamental[M].Beijing:Tsinghua University Press,2000.
[10]关万琳.分布式电源优化配置与配电网重构[D].湖南大学,2014.Guan W L.Distributed generation optimal allocation and distributionnetwork reconfiguration[D].Hu Nan University,2014.
[11]刘斌,雷霞,孔祥清,等.遗传灾变算法在配电网络重构中的应用[J].电力系统及其自动化学报,2013,25(2):31-35.Liu B,Lei X,Kong X Q,et al.Application of genetic catastrophic algorithm to power distribution system reconfiguration[J].Proceedings of the CSU-EPSA,2013,25(2):31-35.
[12]邓海潮,毛弋,彭文强,等.基于改进人工蜂群算法的配电网重构[J].电力系统及其自动化学报,2016,28(7):125-129.Deng H C,Mao G,Peng W Q,et al.Distribution Network Reconfiguration Based on Improved Artificial Bee Colony Algorithm[J].Proceedings of the CSU-EPSA,2016,28(7):125-129.
[13]董海鹰,魏占宏,赵香桂,等.基于多种群遗传算法的电动变桨系统的变论域模糊控制[J].控制工程,2014,21(2):182-188.Dong H Y,Wei Z H,Zhao X G,et al.Variable universe fuzzy control of electric pitch control system based on multiple population genetic algorithm[J].Control Engineering of China,2014,21(2):182-188.