With the Clean Energy Package, the European Union introduced the concept of Renewable Energy Communities: groups of citizens, small and medium enterprises and local authorities that decide to join forces to equip themselves with systems to produce and share energy from renewable energy sources. The Italian legislation recently started an experimental phase in which renewable energy communities receive an incentivising tariff for the energy produced and shared within the community. This paper faces the problem of creating a new renewable energy community in two steps. First, a mathematical model of the energy flows among the members of the community is characterised according to the Italian schema. This model is used to find the optimal portfolio for the energy community, given energy requests and local source availability. Secondly, the Shapley value, a particular solution of cooperative games known to be the most fair method to allocate costs and profits of shared infrastructures, is proposed to distribute benefits among community members. The methodology has been applied to a case study based on a real low voltage network, and the economics for consumers and producers in participating to the project have been evaluated. The proposed solution, simulated adopting real economic parameters defined in the Italian regulatory framework, results to be economically viable from the point of view of the investors with a profitability index of 1.36 and, at the same time, aligned with the social purposes of the energy communities.

Off-grid power systems based on photovoltaic and battery energy storage systems are becoming a solution of great interest for rural electrification. The storage system is one of the most crucial components since inappropriate design can affect reliability and final costs. Therefore, it is necessary to adopt reliable models able to realistically reproduce the working condition of the application. In this paper, different models of lithium-ion battery are considered in the design process of a microgrid. Two modeling approaches (analytical and electrical) are developed based on experimental measurements. The derived models have been integrated in a methodology for the robust design of off-grid electric power systems which has been implemented in a MATLAB-based computational tool named Poli.NRG (POLItecnico di Milano—Network Robust desiGn). The procedure has been applied to a real-life case study to compare the different battery energy storage system models and to show how they impact on the microgrid design.

Energy communities (EC) are expected to have a pivotal role to reach European decarbonization targets. One of the key aspects is the regulatory framework adopted by each Member State to properly manage such new customers’ aggregation. The paper firstly provides an updated overview of the EC regulation, focusing on the current Italian legislation. Next, a novel methodology for the design and management of energy community initiatives is proposed. The procedure firstly solves a design and operation optimization problem to calculate the best size of energy assets (boiler, heat pump, photovoltaic, thermal storage) to be installed. Second, a Shapley value-based approach is exploited to distribute a part of the community’s incomes to members, based on their contribution to the overall welfare. Results demonstrate that the adopted methodology is effective in ensuring a proper cash flow for the community, while pushing its members towards energy efficient behaviors.

Currently, distribution system operators (DSOs) are asked to operate distribution grids, managing the rise of the distributed generators (DGs), the rise of the load correlated to heat pump and e-mobility, etc. Nevertheless, they are asked to minimize investments in new sensors and telecommunication links and, consequently, several nodes of the grid are still not monitored and tele-controlled. At the same time, DSOs are asked to improve the network’s resilience, looking for a reduction in the frequency and impact of power outages caused by extreme weather events. The paper presents a machine learning GIS-based approach to estimate a secondary substation’s load profiles, even in those cases where monitoring sensors are not deployed. For this purpose, a large amount of data from different sources has been collected and integrated to describe secondary substation load profiles adequately. Based on real measurements of some secondary substations (medium-voltage to low-voltage interface) given by Unareti, the DSO of Milan, and georeferenced data gathered from open-source databases, unknown secondary substations load profiles are estimated. Three types of machine learning algorithms, regression tree, boosting, and random forest, as well as geographic information system (GIS) information, such as secondary substation locations, building area, types of occupants, etc., are considered to find the most effective approach.

Energy Communities (EC) are becoming a major driver to foster the energy transition in Europe and the regulatory framework adopted by each Member State (MS) plays a key role for a prosperous deployment of ECs. This paper is thus divided into two layers. The first layer of this paper addresses the current regulations introduced by MSs regarding ECs, providing a critical comparison of each solution used. The second layer of research concerns the introduction of a Mixed Integer Linear Programming (MILP) optimization algorithm early studied by some of the authors furtherly developed to assess the conditions that favour prosumers’ participation to ECs. Both these models have been tested on a case study located in the city of Magliano Alpi, in the north of Italy. The results demonstrate that the proposed methodology correctly evaluates the key parameters influencing participation of citizens in ECs and indicate that for the Italian EC under study, there is the possibility to further expand the capacity installed without undermining the profitability of investment.