Optimized Self-Healing of Networked Microgrids using Differential Evolution Algorithm

Abstract

A framework for the independent Networked Microgrids (NMGs) in an transformative manner was proposed in this work. NMGs comprises of multiple Microgrids (MGs) are linked by a typical bus for supporting and interchanging the electric power with one other in this suggested configuration. It consists of normal and selfhealing mode. These NMGs contain a two layer cyber communication network for information exchange and coordinate the self-healing process. Each MG contain a lower layer and operation of each MG are scheduled by energy management system (EMS).A number of EMS are linked by the upper layer for global optimization and communication. The aim of the first (normal) mode of performance is to schedule the dispatchable distributed generators (DGs) and loads to minimizing the operating cost and to minimize the supply and demand imbalance of each MG. This scheme switches to self-healing (next) mode when a fault or deficiency in generation happens in any one MG. The intention of self-healing mode of operation is to support the faulted portion of the system by the local power production capabilities of other normally operating MGs in an optimal cost. A Differential Evolution (DE) Algorithm is utilized to share the portion of required power support to each MG in a scattered manner. The demanded power support will be provided by the resultant overall power output of NMGs. The efficiency of the scheduled methodology was demonstrated by the following test cases.