A Comprehensive Review of Metaheuristic Methods for the Reconfiguration of Electric Power Distribution Systems and Comparison with a Novel Approach Based on Efficient Genetic Algorithm

Abstract

The distribution system reconfiguration (DSR) is a complex large-scale optimization problem, which is usually formulated with one or more objective functions and should satisfy multiple sets of linear and non-linear constraints. As the exploration of feasible solutions in large and nonconvex search space of DSR is typically hard, it is important to develop efficient algorithms and methods for finding optimal solutions for DSR problem in reasonably short computational times. In traditional DSR, the configuration of distribution network can be changed by opening and closing sectional and tie switches, where active power losses are minimized, while radial network configuration and supply to all connected loads are both preserved. Accordingly, this paper provides a comprehensive review of a number of existing metaheuristic reconfiguration methods and introduces a novel efficient genetic algorithm (efficient GA) for DSR with loss minimization. In order to demonstrate benefits and effectiveness of the proposed efficient GA for DSR, the paper also provides a detailed comparison of results with an improved genetic algorithm (improved GA) for several test systems and real distribution networks. The obtained simulation results clearly show higher accuracy and improved convergence performance of the proposed efficient GA method, compared to the improved GA and other considered reconfiguration methods.