Alessandro Di Giorgio

TL;DR: Results provide a proof of concept about the consumers benefits coming from the use of local energy management systems and the relevance of automated Demand Side Management for the general target of efficient and cost effective operation of electric networks.

TL;DR: In this article, an event driven model predictive control (MPC) framework for managing charging operations of electric vehicles (EV) in a smart grid is presented, where the objective is to minimize the cost of energy consumption while respecting EV drivers' preferences, technical bounds on the control action (in compliance with the IEC 61851 standard) and both market and grid constraints (by seeking the tracking of a reference load profile defined by the grid operator).

TL;DR: In this article, an event driven model predictive control approach for a local energy management system, enabling residential consumers to the automated participation in demand side management (DSM) programs, is presented, where resources are coordinated according to the needs of maximizing selfconsumption and minimizing the cost of energy consumption.

TL;DR: In this article, a model predictive control (MPC) strategy is proposed to control the energy flows in a distribution network node equipped with an electric storage system (ESS) and serving a portion of the grid with high penetration of renewable energy sources (RES).

TL;DR: The proposed approach allows the consumer to minimize the daily energy cost in scenarios characterized by Time of Use tariffs and Demand Side Management, by dynamically evaluating the best time to run of the appliances and the optimal evolution of the battery level of charge.