Variable scaling hybrid differential evolution for solving network reconfiguration of distribution systems

This paper presents an effective method-variable scaling hybrid differential evolution (VSHDE)-for solving the network reconfiguration for power loss reduction and voltage profit enhancement of distribution systems. The network reconfiguration of distribution systems is to beneficially recognize load transfers so that the objective function composed of power losses is minimized and the prescribed voltage limits are satisfied. The variable scaling factor based on the 1/5 success rule is used in the VSHDE method to overcome the drawback of the fixed and random scaling factor and alleviate the problem of the selection of a mutation operator in the hybrid differential evolution (HDE). One three-feeder distribution system from the literature and one practical distribution network of Taiwan Power Company (TPC) are used to compare the performance of the proposed method with the HDE, genetic algorithms (GAs), and simulated annealing (SA). Numerical results show that the performance of the proposed method is better than the other methods.

[1]  J. J. Grainger,et al.  Distribution feeder reconfiguration for loss reduction , 1988 .

[2]  D. Shirmohammadi,et al.  Reconfiguration of electric distribution networks for resistive line losses reduction , 1989 .

[3]  Felix F. Wu,et al.  Network Reconfiguration in Distribution Systems for Loss Reduction and Load Balancing , 1989, IEEE Power Engineering Review.

[4]  H. Chiang,et al.  Optimal network reconfigurations in distribution systems. I. A new formulation and a solution methodology , 1990 .

[5]  Thomas Bäck,et al.  A Survey of Evolution Strategies , 1991, ICGA.

[6]  A. Y. Chikhani,et al.  Feeder reconfiguration for loss reduction: an application of distribution automation , 1991 .

[7]  William L. Goffe,et al.  SIMANN: FORTRAN module to perform Global Optimization of Statistical Functions with Simulated Annealing , 1992 .

[8]  M. Kitagawa,et al.  Implementation of genetic algorithm for distribution systems loss minimum re-configuration , 1992 .

[9]  S. K. Basu,et al.  A new algorithm for the reconfiguration of distribution feeders for loss minimization , 1992 .

[10]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[11]  Nikos D. Hatziargyriou,et al.  Distribution network reconfiguration to minimize resistive line losses , 1993 .

[12]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.

[13]  H. E. Shaalan,et al.  Time varying load analysis to reduce distribution losses through reconfiguration , 1993 .

[14]  Hong-Chan Chang,et al.  Network reconfiguration in distribution systems using simulated annealing , 1994 .

[15]  T.-H. Chen,et al.  Simplified bidirectional-feeder models for distribution-system calculations , 1995 .

[16]  Rainer Storn,et al.  Minimizing the real functions of the ICEC'96 contest by differential evolution , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[17]  Ching-Tzong Su,et al.  A new fuzzy-reasoning approach to optimum capacitor allocation for primary distribution systems , 1996, Proceedings of the IEEE International Conference on Industrial Technology (ICIT'96).

[18]  Y. H. Song,et al.  Distribution network reconfiguration for loss reduction using fuzzy controlled evolutionary programming , 1997 .

[19]  Ching-Tzong Su,et al.  Optimal selection of capacitors in distribution systems , 1999, PowerTech Budapest 99. Abstract Records. (Cat. No.99EX376).

[20]  Feng-Sheng Wang,et al.  Hybrid method of evolutionary algorithms for static and dynamic optimization problems with application to a fed-batch fermentation process , 1999 .

[21]  Feng-Sheng Wang,et al.  Plant scheduling and planning using mixed-integer hybrid differential evolution with multiplier updating , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[22]  J.-P. Chiou,et al.  Estimation of Monod model parameters by hybrid differential evolution , 2001 .