Ricerc@Sapienza

Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in improving goods and services. In this paper we present an interesting application of the fuzzy-GA paradigm to the problem of energy flows management in microgrids, concerning the design, through a data driven synthesis procedure, of an Energy Management System (EMS).

With the capillary spread of multi-energy systems such as microgrids, nanogrids, smart homes and hybrid electric vehicles, the design of a suitable Energy Management System (EMS) able to schedule the local energy flows in real time has a key role for the development of Renewable Energy Sources (RESs) and for reducing pollutant emissions. In the literature, most EMSs proposed are based on the implementation of energy systems prediction which enable to run a specific optimization algorithm.

Microgrids (MGs) development is one of the most pursued solution for the electric grid modernization into smart grids, as an effective approach to achieve the European Funding Program Horizon 2020 targets. In particular, residential grid-connected MGs demand the active role of the customer into the electric market, the fulfilment of Demand Response (DR) services, and a local control of the distribution energy balance.

Grid-connected Microgrids (MGs) have a key role for bottom-up modernization of the electric distribution network forward next generation Smart Grids, allowing the application of Demand Response (DR) services, as well as the active participation of prosumers into the energy market. To this aim, MGs must be equipped with suitable Energy Management Systems (EMSs) in charge to efficiently manage in real time internal energy flows and the connection with the grid. Several decision making EMSs are proposed in literature mainly based on soft computing techniques and stochastic models.