Multiobjective reconfiguration for loss reduction and service restoration using simulated annealing

This paper presents an approach to the reconfiguration of radial distribution networks, for both loss reduction and service restoration, using the same meta-heuristic tool: the simulated annealing algorithm. Besides the main objective of each problem (minimizing losses or load not supplied) minimization of the total number of switching device operations is also included as a possible criterion. Due to these conflicting goals, the network reconfiguration is a multi-objective optimization problem. The paper shows how to generate efficient solutions for the problem and discusses the associated decision issues. A 52-bus distribution network is used to illustrate the methodology.

[1]  H. Chiang,et al.  Fast service restoration for large-scale distribution systems with priority customers and constraints , 1997, Proceedings of the 20th International Conference on Power Industry Computer Applications.

[2]  M. M. Adibi,et al.  Distribution Feeder Reconfiguration for Service Restoration and Load Balancing , 2000 .

[3]  V. Susheela Devi,et al.  Optimal restoration of power supply in large distribution systems in developing countries , 1995 .

[4]  S. S. Venkata,et al.  An expert system operational aid for restoration and loss reduction of distribution systems , 1988 .

[5]  Hsiao-Dong Chiang,et al.  Optimal network reconfigurations in distribution systems. II. Solution algorithms and numerical results , 1990 .

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

[7]  Seung-Jae Lee,et al.  Service restoration of primary distribution systems based on fuzzy evaluation of multi-criteria , 1998 .

[8]  Whei-Min Lin,et al.  A new approach for distribution feeder reconfiguration for loss reduction and service restoration , 1998 .

[9]  C. McDiarmid SIMULATED ANNEALING AND BOLTZMANN MACHINES A Stochastic Approach to Combinatorial Optimization and Neural Computing , 1991 .

[10]  M. M. Adibi,et al.  An Expert System Operational Aid for Restoration and Loss Reduction of Distribution Systems , 2000 .

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