Ricerc@Sapienza

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.

This article deals with the implementation of an automated simulator for the optimization of power-flows in blocks of buildings (prosumers) by controlling local generation, storage and consumptions in real-time and simultaneously. Among the advantages of the proposed multi-agent saver, a special emphasis is given to the benefits that may be achieved in carbon reduction and consequent environmental protection. The simulator can be operated in either 'Economy' mode to maximize economic profit, or 'Energy' mode, for better energy saving.

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.

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.

The efficient integration of Renewable Energy Sources (RES) in the actual electrical grid has gained recently a high attention in the Smart Grids (SGs) research topic. The evolution of existing electric distribution networks into SGs can be accomplished gradually and conveniently through the installation of local grid-connected Microgrids (MGs), usually installed nearby the RESs and provided by Energy Storage Systems (ESSs). Each MG is in charge to manage connected RES, assuring the local power demand, as well as the safety and stability of the electric grid.

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.

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.

The World energy demand is progressively growing, so that many different Renewable Energy Sources (RESs) are exploited to meet the user needs and to reduce the Global Warming. In this context, an emerging RES is the sea wave energy, because it can offer a better continuity in the energy production by taking advantage of the stationary nature of the waves. Research in the energy harvesting from waves has led to the development of Wave Energy Converters (WECs). The adoption of Computational Intelligence techniques become crucial for maximizing the WECs efficiency.

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.