Ricerc@Sapienza

This paper presents a decentralized Model Predictive Control (MPC) for Plug-in Electric Vehicles (PEVs) charging, in presence of both network and drivers' requirements. The open loop optimal control problem at the basis of MPC is modeled as a consesus with regularization optimization problem and solved by means of the decentralized Alternating Direction Method of Multipliers (ADMM).

The proposed study concerns the implementation of new strategies aimed at saving energy and reducing the global warming through the reduction of carbon emissions. For this purpose, the implementation of synergic procedures, contemporarily applied to microgrid and prosumers, are proposed and validated in the paper. The main implemented methodologies allow the real time management of different energy flows present inside buildings and, at the same time at higher hierarchic level, enable the supervision and control of the energy managed by the microgrid.

A novel energy management system (EMS) synthesis procedure based on adaptive neurofuzzy inference systems (ANFISs) by hyperplane clustering is investigated in this paper. In particular, since it is known that clustering input–output samples in hyperplane space does not consider clusters’ separability in the input space, a Min–Max classifier is applied to properly cut and update those hyperplanes defined on scattered clusters in order to refine the ANFIS membership functions.