Ricerc@Sapienza

Power Grids face significant variability in their operation, especially where there are high proportions of non-programmable renewable energy sources constituting the electricity mix. An accurate and up-to-date knowledge of operational data is essential to guaranteeing the optimal management of the network, and this aspect will be even more crucial for the full deployment of Smart Grids.

Increasing electrification of final uses can be a viable solution towards low-carbon energy systems, when coupled with local renewable power generation. Mountain areas can already benefit from high shares of hydro-power generation, but, at the same time, rely on oil products for transport and for the heating sector in remote areas where natural gas infrastructures are not available.

Bringing the cellular connectivity in rural zones is a big challenge, due to the large installation costs that are incurred when a legacy cellular network based on fixed Base Stations (BSs) is deployed. To tackle this aspect, we consider an alternative architecture composed of UAV-based BSs to provide cellular coverage, ground sites to connect the UAVs with the rest of the network, Solar Panels (SPs) and batteries to recharge the UAVs and to power the ground sites, and a ring of optical fiber links to connect the installed sites.

We focus on the problem of managing the energy consumption of a cellular network tailored to cover rural and low-income areas. The considered architecture exploits Unmanned Aerial Vehicles (UAVs) to ensure wireless coverage, as well as Solar Panels (SPs) and batteries installed in a set of ground sites, which provide the energy required to recharge the UAVs. We then target the maximization of the energy stored in the UAVs and in the ground sites, by ensuring the coverage of the territory through the scheduling of the UAV missions over space and time.

During the last few years, European Union has supported many energy projects to enhance the role of renewables in the European Countries, in order to reduce the energy consumption and CO2 emissions of the residential sectors. In this context, the implementation of the cogeneration plant and district heating seems to be a good solution, especially thanks to the possibility of coupling those systems with renewable sources. In literature, the development of a district heating supplied by Renewable Energy Sources (RES) was investigated by several researches.

Renewable Energy Sources based (RES-based) Dispersed Generation (DG) and Electrical Vehicles (EVs) charging systems diffusion is in progress in many Countries around the word. They have huge effects on the distribution grids planning and operation, particularly on MV and LV distribution grids. Many studies on their impact on the power systems are ongoing, proposing different approaches of managing. The present work deals with a real application case of integration of EVs charging stations with ES-based DG.

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 larger integration worldwide of Renewable Energy Sources (RES) in the electricity distribution system is certainly desirable, to reduce CO2 emissions and to contribute to a sustainable development. However, the increasing penetration of renewable energy is a challenge for the system performance, because it affects the Power Quality (PQ) and the load management, forecasting and scheduling. To reduce the impact of intermittent energy sources on network security, it is mandatory to predict with reasonable accuracy the renewable energy variations.

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.

Small cells (SCs) mounted on top of unmanned aerial vehicles (UAVs) are a promising solution to boost the capacity in hotspot areas. However, the adoption of UAV-SCs involves the planning of their missions over time, which includes the scheduling of recharging actions of each UAV-SC at ground sites. Typically, the energy needed to recharge UAV-SCs is derived from the grid, which can be coupled with microgeneration exploiting renewable energy sources (e.g., solar panels).