The integration of renewable energy sources in the European power system is one of the main goals set by the European Union. In order to ease this integration, in recent years, Renewable Energy Communities (RECs) have been introduced that aim to increase the exploitation of renewable energy at the local level. This paper presents an Energy Management System (EMS) for an industrial microgrid owned and operated by a greentech company located in the north of Italy. The company is a member of an REC. The microgrid is made of interconnected busbars, integrating photovoltaic power plants, a fleet of electric vehicles, including company cars and delivery trucks supporting Vehicle-to-Grid (V2G), dedicated charging stations, and a centralized battery energy storage system. The industrial site includes two warehouses, an office building, and a connection to the external medium-voltage network. The EMS is designed to optimize the operation of the microgrid and minimize the operating costs related to the sale and purchase of energy from the external network. Furthermore, as the company is a member of an REC, the EMS must try to follow a desired power exchange profile with the grid, suggested by the REC manager, with the purpose of maximizing the energy that is shared within the community and incentivized. The results demonstrate that, when minimizing only costs, local self-consumption is favored, leading to a Self-Sufficiency Rate (SSR) of 65.37%. On the other hand, when only the adherence to the REC manager’s desired power exchange profile is considered in the objective function, the SSR decreases to 56.43%, net operating costs increase, and the energy shared within the REC is maximized.

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.

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).

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.