Generic fuzzy-based optimal energy management strategy for efficient performance of realistic micro-grids with battery lifetime enhancement under climate changes

Energy management is receiving more attention from economic and technical viewpoints. This study proposes a hybrid fuzzy linear programming based energy management strategy considering adaptive multi-linear membership functions of fuzzy models. The fuzzy models are established for the grid’s power outputs, Photovoltaic power, load power, the power grid’s cost, and both power and energy of the involved batteries into the microgrids. The multi-linear membership functions emulate the objectives’ physical characteristics and operational constraints. Additionally, with the objective function of the minimum power’s cost, the parameters of membership functions are optimized by the Tunicate Swarm algorithm. Two realistic microgrid applications from Bangladesh and South France are employed to prove the efficiency and accuracy of the proposed strategy under different climate changes, which are divided into two cases for clear and cloudy days. The implementation of the proposed methodology is carried out using SimScape/Matlab software. Compared to the conventional methods, the simulation results demonstrate that the proposed strategy leads to more economical energy management with significant savings of up to 14%–27%, and enhances the battery’s lifetime as the number of charging/discharging per clear/cloudy day compared to the heuristic method. Therefore, the proposed strategy can effectively provide a generic energy management strategy under important uncertainty and climate changes.