Integrating several subpopulation tables with node-depth encoding and strength Pareto for service restoration in large-scale distribution systems

Abstract

Network reconfiguration for service restoration in distribution systems is a combinatorial complex optimization problem that usually involves multiple non-linear constraints and objectives functions. For large scale distribution systems, no exact algorithm has found adequate restoration plans in real-time. On the other hand, the combination of Multi-objective Evolutionary Algorithms (MOEAs) with the Node-Depth Encoding (NDE) has been able to efficiently generate adequate restoration plans for relatively large distribution systems (with thousands of buses and switches). The method called MEAN-NDS results from the combination of NDE with a technique of MOEA based on subpopulation tables and the MOEA called NSGA-II. In order to obtain a more efficient MOEA to treat service restoration problem in large scale distribution systems, this paper proposes a new method, which results from the combination of MEAN-NDS with the MOEA called SPEA-2. The idea is to improve the capacity of MEAN-NDS to explore both the search and objective spaces. Simulations results with distribution systems ranging from 632 to 1,277 switches, have shown that the proposed method found the configurations of lower switching operations, and explores the space of the objective solutions better than the MEAN-NDS, approximating better the Pareto-optimal front.