Tommaso Robbiano

As Renewable Energy Communities (RECs) drive a shift toward decentralized power systems, innovative solutions to manage the inherent intermittency of distributed energy resources are essential. This paper investigates the potential of electric vehicles (EVs) as dynamic flexibility providers within the REC framework. By leveraging smart charging and Vehicle-to-Everything (V2X) technologies, EV fleets can act as key assets to facilitate the transition toward active distribution networks by providing upward and downward power reserves within local flexibility markets. This study presents a Mixed-Integer Linear Programming (MILP)-based Energy Management System (EMS) to optimally manage a case study REC in Northern Italy, characterized by renewable power plants and V2X-enabled EV charging stations for both electric cars and electric trucks. The proposed EMS model aims to simultaneously maximize the energy virtually shared within the REC and the provision of upward and downward reserves by the EV fleet over the considered time horizon. The EMS optimal results are analyzed for two distinct periods of the year, namely one week in spring and one in autumn, demonstrating that the flexibility guaranteed by the EVs can significantly impact the energy-sharing mechanism of the REC while at the same time providing additional revenues to the REC members.

Renewable Energy Communities (RECs), introduced by the RED-II EU Directive, represent a promising mechanism to facilitate the integration of Renewable Energy Sources (RESs) in compliance with European targets. Based on the principles of virtually shared energy and geographical proximity, RECs enable energy exchange among members without direct physical connections, relying instead on simultaneous energy injection and withdrawal through the public distribution network. This paper presents a comprehensive methodology for both the optimal design and management of RECs, improving their technical and economic performances. The optimal design identifies the best mix of REC members and determines the appropriate sizing of Photovoltaic (PV) and Battery Energy Storage Systems (BESS), aiming to minimise the total costs (purchase and installation costs, operation and maintenance costs, electricity costs) and to maximise the incentives on the shared energy, considering also Electric Vehicle (EV) charging infrastructure. Regarding the optimal management, a bi-level Energy Management System (EMS) is proposed. The upper level, controlled by the REC manager, seeks to maximize the shared energy, while the lower-level EMSs, located at members’ premises, perform detailed resource dispatch under both cooperative and non-cooperative behavioural models. Optimization results highlight that including mainly larger members improves economic viability, while the inclusion of a large number of residential users may not always generate benefits, due to increased REC management costs.

One of the ways to decarbonize cities and to enhance grid stability is to convert existing buildings into prosumers equipped with power plants able to supply electrical and thermal energy. The simulation of such multi-energy systems permits the analysis of their performance in steady-state and dynamic conditions, with the aim of defining effective operating strategies able to reduce emissions and costs. The present paper describes a dynamic simulation model, implemented in the Matlab/Simulink R2025a environment, developed to simulate the daily and weekly operation of a prosumer building equipped with a small-sized cogeneration unit, a Photovoltaic (PV) plant, a back-up boiler, a thermal storage system and some charging points for Electric Vehicles (EVs). The mathematical model is reported in detail, and the main results of the study are described, referring to operating days characterized by different weather conditions. Then, energy, economic and environmental performance indicators are defined and calculated for the different simulated scenarios. Over the considered time horizons, the simulation results highlight a significant increase in the electrical self-sufficiency of the facility up to 91.1% and an important reduction in total net operating costs up to 59.8%, compared to the AS-IS case (i.e., without the newly installed technologies).

The national energy sectors are undergoing a significant transformation due to the increased interest in renewable energy. This revolution has a significant impact on electrical distribution networks, which are increasingly challenged by the complexities of balancing demand, managing production fluctuations, and adapting to regulatory updates. Through Load Flow (LF) simulations carried out over a one-year period, this paper investigates the impact of Distributed Generation (DG) on a Low-Voltage (LV) network composed of 25 nodes and connecting 23 end-users, both prosumers and consumers, 10 of which are members of a Renewable Energy Community (REC) located in the north of Italy. Additionally, calculations of the economic incentives recognized to the shared energy (i.e., virtual energy self-consumption) within the REC are carried out, comparing two different methods. The presence of both prosumers and consumers leads to a reduction in the lines’ loading, improved network efficiency and lower active and reactive power losses (up to 27% and 42%, respectively) with respect to a network consisting of only consumers.

The present paper proposes an Energy Management System (EMS), based on a Mixed-Integer Linear Programming (MILP) optimization problem, aiming to optimally manage a hybrid prosumer building, located in the north of Italy. The facility is connected to the Low-Voltage (LV) distribution network by a single Point of Delivery (POD) and is equipped with Photovoltaic (PV) and Wind Turbine (WT) units, a Combined Heat and Power (CHP) system, a heat pump, smart and manageable loads, and a charging station for an Electric Vehicle (EV) enabled to provide Vehicle-to-Everything (V2X) service. The objective function of the EMS considers the net operating costs of the building related to the power exchange with the network, the curtailment costs associated with the production from PV and WT units and the fuel costs for the CHP unit. The paper describes the mathematical model of the EMS and, in the results section, a sensitivity analysis on the prices for selling energy to the network has been conducted, showing a 6.6% decrease in the overall costs of the facility and a 51% reduction in the energy bought from the network.

The increasing penetration of Electric Vehicles (EVs) will pose issues in both transmission and distribution networks, especially when coupled to Renewable Energy Resources (RESs). This paper aims to present an Energy Management System (EMS) for the optimal operation of a prosumer building, owned by a freight delivery company, equipped with Photovoltaic (PV) and Wind Turbine (WT) units and with a charging stations dedicated to an EV fleet, composed of 10 electric vans. The EMS, modeled as a Mixed-Integer Linear Programming (MILP) problem, aims to minimize the net operating costs of the facility related to the exchange of electricity with the distribution network. As a novelty, the participation of prosumers in a flexibility market, by offering upward and downward active power reserves through their EV fleets, is explored. Additionally, the application of Vehicle-to-Building (V2B) technology is explored. The results of the tailored EMS demonstrated that coupling the V2B technique with the ability to offer upward and downward reserves can contribute to a reduction in the facility's annual net costs of up to 30 %, which may be of great interest to the prosumer.

This paper presents an Energy Management System (EMS) for a grid-tied Low-Voltage Direct Current (LVDC) Microgrid (MG) equipped with Photovoltaic (PV) units, Battery Energy Storage Systems (BESSs), and AC and DC loads. The objective of this EMS is to minimize the facility's operational costs associated with the energy exchange with the external network. Optimal Power Flow (OPF) equations are included in the EMS model in non-linear and linear forms. In the former case, the optimization problem is defined as a MixedInteger Quadratically Constrained Programming (MIQCP) problem, while the latter is a Mixed-Integer Linear Programming (MILP) problem. The case study is a 3-bus MG, for which the deviation between the results obtained with the two formulations of OPF equations is evaluated. In this way, the impact of the linearized approach on electrical, economic, and computational performances is assessed. A sensitivity analysis carried out by varying the parameters of the lines shows the limits and the validity domain of the approximation introduced by linear OPF equations, also focusing on computational aspects. When considering the base length of the lines, the results prove a negligible impact on Mean Absolute Percentage Error (MAPE) in voltages ($\text{0. 0 1 \%}$) and power ($\text{1. 7 1 \%}$) between the non-linear and linear implementations. On the other hand, the findings show a growing trend in terms of MAPE when increasing the length of the lines and higher differences in the OPF results. However, the computation time of the linear implementation (0.79 s) is significantly lower in comparison to the non-linear approach (632.4 s).

This paper proposes a mixed-integer linear programming model for the energy management system of the new research laboratory that will be installed in the Savona Campus of the University of Genova, benefitting from the fundings of the Italian National Center for Sustainable Mobility. This energy management system is able to daily manage the nanogrid feeding the laboratory which will be equipped with a photovoltaic plant and a battery energy storage system, and will have to feed electric loads of the laboratory and a set of autonomous mobile robots used to execute different delivery tasks within the campus. The proposed energy management system optimizes power flows and robot task scheduling in order to minimize the operating costs, the curtailment of the photovoltaic source and the number of unperformed tasks. The application of the proposed mathematical formulation to the real case of the Savona Campus is presented and discussed in the paper.