Microgrid energy management by ANFIS supported by an ESN based prediction 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. In order to take advantage of the local production (such as a photovoltaic generator) and of the Energy Storage System (ESS), a MG needs an Energy Management System (EMS) able to decide how to efficiently redistribute in real time the energy flows among the energy systems, in order to satisfy the customer needs which are expressed through a suitable objective function. This work focuses on a new version of an Adaptive Neural Fuzzy Inference System (ANFIS) as the core of a MG EMS, supported by an Echo State Network (ESN) based predictor. The overall training algorithm is designed to maximize the profit generated by the energy exchange with the grid, by assuming a Time Of Use (TOU) energy price policy. The main objective of this work is focused on studying the impact of the prediction system on the EMS performances. Results show that EMS performances improve of about 30% for prediction time horizons over 10 hours.