Development of a smart energy community with fuzzy-enhanced electrical management system

Energy communities play a crucial role in enhancing energy sustainability and self-sufficiency by fostering local renewable generation, storage systems, and consumption. However, the inherent variability in renewable power generation and consumption presents challenges. This work proposes the development of a fuzzy-enhanced Energy Management System (EMS) for a smart energy community integrating photovoltaic generation, battery storage, and local load. The EMS is modelled in Python using real consumption and production datasets, enabling the calculation of battery State of Charge (SoC), energy flows, and system losses. A Fuzzy Logic Controller (FLC) is implemented to optimise charging and discharging strategies under variable operating conditions. Simulation results show improved self-consumption, reduced grid dependence, and more efficient battery operation. The proposed framework provides a functional basis for future optimisation and deployment in community-scale energy systems.