Ricerc@Sapienza

This article presents an economic model predictive control (EMPC) algorithm for reducing losses and increasing the resilience of medium-voltage electricity distribution grids characterized by high penetration of renewable energy sources and possibly subject to natural or malicious adverse events. The proposed control system optimizes grid operations through network reconfiguration, control of distributed energy storage systems (ESSs), and on-load tap changers.

The new concept of renewable energy communities introduced by the Revised European Directive on the promotion of renewable sources (2018/2001) has opened new possibilities for microgrids. In fact, it permits to enhance the value of the energy produced by renewable sources sharing it inside an 'energy community' and to increase the social welfare. In this article, the authors investigated about the actual legislation framework on energy communities at the European and Italian level, highlighting regulatory problems and barriers that are delaying their constitutions.

The recent evolution in electricity distribution systems has led to the development of new intelligent structures, better known as Smart Grids, Microgrids and Prosumers. The consequent advantages regard mainly both economy aspects and environmental safeguard. In a microgrid, producers and consumers can exchange energy in peer-to-peer way. Normally, the microgrid is connected to a Smart Grid in a single common point. In this case, the microgrid can be easily disconnected from the external grid and continue to work in 'island' mode.

The new concept of renewable energy communities introduced by the Revised European Directive on the promotion of renewable sources (2018/2001) has opened new possibilities for microgrids. In fact, it permits to enhance the value of the energy produced by renewable sources sharing it inside an 'energy community' and to increase the social welfare. In the present paper, the authors investigated about the actual legislation framework on energy communities at European and Italian level, highlighting regulatory problems and barriers that are delaying their constitutions.

A critical aspect of distributed generation systems focuses on the installation of Electrical Energy Storage Systems in customer-side facilities. In this scenario, flywheel technology is challenged to provide high levels of safety, compactness and competitive cost. This work presents a novel, one-body flywheel scheme based on a switched reluctance machine, whose laminated rotor fulfils both the motor/generator and energy storage functions. The one-body architecture enhances compactness and robustness, whereas the laminated rotor ensures high safety.

The worldwide power grid can be thought as a System of Systems deeply embedded in a time-varying, non-deterministic and stochastic environment. The availability of ubiquitous and pervasive technology about heterogeneous data gathering and information processing in the Smart Grids allows new methodologies to face the challenging task of fault detection and modeling. In this study, a fault recognition system for Medium Voltage feeders operational in the power grid in Rome, Italy, is presented.

In smart grids and microgrids, time series prediction is a fundamental tool for enabling intelligent energy resource management and advanced interactions between heterogeneous agents. In this work, we propose a solution to the energy forecasting problem based on two machine learning techniques: Convolutional Neural Network and Long Short-Term Memory Network. These techniques are combined with a new embedding format to appropriately feed the time series to the stacked network architecture.

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.

n this paper we present an interesting application of the Decision Support System, known as the OCC_System, designed for faults recognition and classification within the real-world Medium Voltage power grid of Rome, Italy, managed by the Azienda Comunale Energia e Ambiente (ACEA) company. Given a historical data set consisting of fault patterns described by heterogeneous features related to endogenous and exogenous factors, the recognition system is trained to classify fault states assigning them a probability of fault.