Multi objective optimization of a fuzzy logic controller for energy management in microgrids

This paper presents a novel power flow optimization strategy in Micro Grids (MGs) connected to the main grid. When the MG includes stochastic energy sources, such as photovoltaic and micro eolic-generators, it is very useful to rely on Energy Storage Systems (ESSs) to buffer energy. In fact, an ESS can be employed to perform several functionalities, related to different user requirements, such as power stability, peak shaving, optimal energy trading, etc. The Energy Management System is based on a Fuzzy Logic Controller (FLC) optimized by a Multi-Objective Genetic Algorithm in order to maximize both the total profit in energy trading with the main grid and the State of Health (SOH) of the ESS. The FLC manages the neat aggregate energy deficit and surplus inside the MG, analyzing in real time the state of the MG (aggregated energy demand and production, State of Charge of the ESS, energy sale and purchase prices). The FLC is tested on a MG composed by a photovoltaic solar generator, a domestic user and a Li-ion battery. A multi-objective genetic algorithm is in charge to find the set of solutions on the Pareto front. The results are compared with the same FLC optimized by a mono-objective Genetic Algorithm (GA) minimizing in a first case only the total profit and in the second case a convex linear combination of the total profit and a measure of the battery stress.