A Comparison of Fuzzy-Based Energy Management Systems Adjusted by Nature-Inspired Algorithms

The growing energy demand around the world has increased usage of renewable sources (RES) such as photovoltaic and wind energies. combination traditional power systems RESs generated diverse problems due especially to stochastic nature RESs. Microgrids (MG) arise address these types increase penetration RES utility network. A microgrid includes an management system (EMS) operate its components efficiently. objectives pursued by EMS are usually economically related minimizing operating costs MG or maximizing income. However, new regulations network operators, a objective minimization peaks fluctuations in profile exchanged with taken great interest recent years. In this regard, EMSs based on off-line trained fuzzy logic control (FLC) have been proposed alternative approach those on-line optimization mixed-integer linear (or nonlinear) programming reduce computational efforts. procedure adjust FLC parameters barely addressed. This parameter adjustment is problem itself that can be formulated terms cost/objective function susceptible being solved metaheuristic nature-inspired algorithms. particular, paper evaluates methodology for adjusting residential aims minimize through two algorithms, namely particle swarm differential evolution. definition cost optimized. Numerical simulations specific example presented compare evaluate performances also including comparison other ones addressed previous works Cuckoo search approach. These further used extract useful conclusions off-line-trained designs.