Ricerc@Sapienza

The efficient integration of Renewable Energy Sources (RES) in the actual electrical grid has gained recently a high attention in the Smart Grids (SGs) research topic. The evolution of existing electric distribution networks into SGs can be accomplished gradually and conveniently through the installation of local grid-connected Microgrids (MGs), usually installed nearby the RESs and provided by Energy Storage Systems (ESSs). Each MG is in charge to manage connected RES, assuring the local power demand, as well as the safety and stability of the electric grid. To this aim, the Energy Management System (EMS) must provide intelligent decision making in fixing both MG configuration and energy flows between each subsystem in real time, according to some objective functions. In this work, it is proposed a MG EMS based on a Fuzzy Inference System (FIS) optimized through a custom implementation of Multi Objective Genetic Algorithm (MO-GA). In particular, the EMS is based on a three inputs FIS and it has been designed in order to reduce the fluctuations of energy exchanged with the grid (i.e. the grid stress) and to maximize the energy auto-consumption by employing an efficient utilization of the ESS. Results show that it is possible to improve considerably the auto-consumption performance, and at the same time to reduce grid stress, improving peak shaving concerning the maximum power request from the main grid.