A Multiobjective Evolutionary Algorithm with Node-Depth Encoding for Energy Restoration

On last decades, evolutionary algorithms (EA) have been utilized in many engineer´s applications. A raising application in electrical engineer has been in network reconfiguration. It can be performed to restore electricity for out-of-service areas after a fault has been identified and isolated. This paper presents an application of multiobjective EA for energy restoration in large-scale distribution networks. To improve EA performance for energy restoration, two techniques are employed: (1) the use of an efficient data structure that produces only feasible configurations, save RAM memory and running time; (2) a multiobjective method based on subpopulation tables. The proposed methodology provides an efficient alternative for network reconfiguration. Moreover, it can be used in problems which require online solutions.