Integrating relevant aspects of MOEAs applied to service restoration in Distribution Systems

Abstract

Network reconfiguration for service restoration (SR) in Distribution Systems (DSs) is usually formulated as a nonlinear, multi-objective and multi-constrained optimization problem, which requires a solution in real-time. Recently two approaches were proposed to solve this problem in a very efficient way, even to DSs with thousands of buses and switches. Both of them use a new tree encoding, called Node-Depth Encoding (NDE), which enables the elimination of several of the usual constraint equations of the problem. One of those approaches, named MEAN, uses NDE together with a Multi-Objective Evolutionary Algorithm (MOEA) based on subpopulation tables. The other, named NSDE, uses NDE together with a modified version of the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). This paper proposes to combine the best characteristics of these two approaches to generate a new powerful approach to solve service restoration problem in DSs. Basically, the idea is to incorporate elements from the NSGA-II into the MEAN. In order to do this, the proposed approach, named MEAN-NDS (MEAN with Non-Dominated Solutions), uses additional subpopulations tables to store the non-dominated solutions from Pareto fronts, calculated as the NSGA-II does. As a consequence, the MEAN-NDS explores the space of the objective solutions better than the NSDE, approximating better the Pareto-optimal front. This statement has been demonstrated by several simulations results with DSs ranging from 632 to 1,277 switches.