Study of Reconfiguration for the Distribution System With Distributed Generators

This paper proposes a reconfiguration methodology based on an Ant Colony Algorithm (ACA) that aims at achieving the minimum power loss and increment load balance factor of radial distribution networks with distributed generators. A 33-bus distribution system and a Tai-Power 11.4-kV distribution system were selected for optimizing the configuration and to demonstrate the effectiveness of the proposed methodology for solving the optimal switching operation of distribution systems. The simulation results have shown that lower system loss and better load balancing will be attained at a distribution system with distributed generation (DG) compared to a system without DG. Furthermore, the simulation results also satisfy and suitability reference merits of the proposal method.

[1]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[2]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[3]  Jae-Chul Kim,et al.  Integration operation of dispersed generations to automated distribution networks for network reconfiguration , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[4]  V. Calderaro,et al.  Maximizing DG penetration in distribution networks by means of GA based reconfiguration , 2005, 2005 International Conference on Future Power Systems.

[5]  M Dorigo,et al.  Ant colonies for the travelling salesman problem. , 1997, Bio Systems.

[6]  J. Nahman,et al.  A new algorithm for service restoration in large-scale urban distribution systems , 1994 .

[7]  T. Stützle,et al.  MAX-MIN Ant System and local search for the traveling salesman problem , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[8]  Jae-Chul Kim,et al.  Network reconfiguration at the power distribution system with dispersed generations for loss reduction , 2000, 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077).

[9]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[10]  Corso Elvezia,et al.  Ant colonies for the traveling salesman problem , 1997 .

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

[12]  Jingjing Zhang,et al.  The Application of Hybrid Genetic Particle Swarm Optimization Algorithm in the Distribution Network Reconfigurations Multi-Objective Optimization , 2007, Third International Conference on Natural Computation (ICNC 2007).

[13]  Yu-Lung Ke.  Distribution feeder reconfiguration for load balancing and service restoration by using G-nets inference mechanism , 2004, IEEE Transactions on Power Delivery.

[14]  Shokri Z. Selim,et al.  Integrating genetic algorithms, tabu search, and simulated annealing for the unit commitment problem , 1999 .

[15]  C. Su,et al.  Variable scaling hybrid differential evolution for solving network reconfiguration of distribution systems , 2005 .

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

[17]  Bala Venkatesh,et al.  Optimal reconfiguration of radial distribution systems to maximize loadability , 2004 .