Optimization of Fuzzy Energy-Management System for Grid-Connected Microgrid Using NSGA-II

This article proposes a fuzzy logic-based energy-management system (FEMS) for grid-connected microgrid with renewable energy sources (RESs) and storage (ESS). The objectives of the FEMS are reducing average peak load (APL) operating cost through arbitrage operation ESS. These achieved by controlling charge discharge rate ESS based on state ESS, power difference between RES, electricity market price. effectiveness logic greatly depends membership functions (MFs). MFs optimized offline using Pareto-based multiobjective evolutionary algorithm, nondominated sorting genetic algorithm (NSGA-II). best compromise solution is selected as final implemented in fuzzy-logic controller. A comparison other control strategies similar carried out at simulation level. proposed experimentally validated real test bed Newcastle University, U.K.