Multi-Objective Distribution Network Reconfiguration Based on Pareto Front Ranking

Abstract

Electrical distribution system reconfiguration is frequently addressed as a multi-objective problem, typically taking into account the system losses together with other objectives, among which reliability indicators are widely used. In the multi-objective context, Pareto front analysis enables the operator handling conflicting and even non-commensurable objectives without needing the use of additional hypotheses or weights. This paper provides advances on the application of Pareto front analysis to multi-objective distribution network reconfiguration. Starting from previous results in which genetic algorithms were effectively adopted to find the best-known Pareto front, a version of the multi-objective binary particle swarm optimization (MOBPSO) customized for distribution network reconfiguration has been developed by exploiting the internal ranking of the solutions (based on a multi-criteria decision making method in the selection of the local best) and the network topology. Furthermore, the Pareto front mismatch metric (already used by the authors to compare different methods for small networks for which the complete Pareto front can be calculated) has been generalized to be used with large systems for which only the best-known Pareto front is found. Applications to a test network and to a real urban distribution network are discussed, showing the consistent superiority of the customized MOBPSO version with respect to the application of genetic algorithms and of a more classical version of the particle swarm optimization method.