A Multi-objective Evolutionary Algorithm with Efficient Data Structure and Heuristic Initialization for Fault Service Restoration

Abstract

Service restoration in energy distribution systems is a complex optimization problem with many restrictions. After a fault occurrence, the challenge is obtain a service restoration plan reconnecting all the healthy out-of-service areas satisfying all the operational and technical constraints. Recent works have study the use of meta-heuristics in order to find a sub-optimal solution with low computational complexity. One of these works include the use of a multi-objective algorithm with a new data structure called node depth encoding. This paper proposes and analyses the use of a new heuristic initialization procedure to be used with node depth encoding which guarantees the analysis of all possible solutions considering only a restricted number of switches incident to the out-of-service areas. The proposed methodology is evaluated by applying it to the real and large-scale distribution system of Londrina city (Brazil). The results showed that the new heuristic improves the overall performance reducing the number of switches operations to reconfigure the distribution system.