A novel Particle Swarm method for distribution system optimal reconfiguration

Searching for the optimal radial configuration of large distribution systems is a problem leading to combinatorial explosion of the number of configurations to analyze. Several deterministic and heuristic methods have been applied in order to endeavor near-optimal solutions. However, no method can ensure that the optimal solution is found in a finite time, so that developing and testing new heuristics is an open and challenging task. This paper illustrates the development of an original Particle Swarm Optimization (PSO) method applied to the minimum losses reconfiguration of large distribution systems. The classical scheme of the PSO has been revisited in order to develop an efficient heuristic, able to take into account the system radial configurations and the various operational constraints of real distribution systems. The resulting PSO method has been successfully applied to large real urban distribution systems. A hybrid formulation including some steps performed with a deterministic iterative improvement method has shown the best performances. The results obtained by using the proposed method on a large real urban MV distribution system are shown and compared to the ones obtained from other methods such as deterministic iterative improvement and simulated annealing.

[1]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[2]  Vladimiro Miranda,et al.  EPSO-evolutionary particle swarm optimization, a new algorithm with applications in power systems , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[3]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[4]  Yonghua Song,et al.  Optimisation techniques for electrical power systems. II. Heuristic optimisation methods , 2001 .

[5]  G. Chicco,et al.  Ant-colony search-based minimum losses reconfiguration of distribution systems , 2004, Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference (IEEE Cat. No.04CH37521).

[6]  G. Chicco,et al.  Comparing deterministic and simulated annealing-based algorithms for minimum losses reconfiguration of large distribution systems , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[7]  Y. Mishima,et al.  Loss minimum re-configuration of distribution system by tabu search , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[8]  Y. Ogita,et al.  A parallel tabu search based method for reconfigurations of distribution systems , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[9]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[10]  G. Peponis,et al.  Reconfiguration of radial distribution networks: application of heuristic methods on large-scale networks , 1995 .

[11]  R. F. Chang,et al.  Feeder reconfiguration for load factor improvement , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[12]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[13]  A. Augugliaro,et al.  Genetic, simulated annealing and tabu search algorithms: Three heuristic methods for optimal reconfiguration and compensation of distribution networks , 1999 .

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

[15]  P. Suganthan.  Particle swarm optimiser with neighbourhood operator , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[16]  Felix F. Wu,et al.  Network reconfiguration in distribution systems for loss reduction and load balancing , 1989 .

[17]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[18]  Enrico Carpaneto,et al.  Performance of the simulated annealing-based algorithms for the optimal reconfiguration of distribution systems , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

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

[20]  Mark M. Millonas,et al.  Swarms, Phase Transitions, and Collective Intelligence , 1993, adap-org/9306002.

[21]  C. N. Lu,et al.  Feeder reconfiguration for power quality requirement and feeder service quality matching , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

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

[23]  Hans-Georg Beyer,et al.  Toward a Theory of Evolution Strategies: Self-Adaptation , 1995, Evolutionary Computation.