The European Union’s vision for energy transition not only foresees decarbonization of the electricity sector, but also requires commitment across different sectors such as gas, heating, and cooling through an integrated approach. It also sets local energy communities at the center of the energy transition as a bottom-up approach to achieve these ambitious decarbonization goals. The energy hub is seen as a promising conceptual model to foster the optimization of multi-carrier energy systems and cross-sectoral interaction. Especially in the context of local energy communities, the energy hub concept can enable the optimal design, management, and control of future integrated and digitalized networks where multiple energy carriers operate seamlessly and in complementarity with each other. In that sense, the optimal design and operation of energy hubs are of critical importance, especially under the effect of multiple objectives taking on board not only technical, but also other aspects that would enable the sustainability of local energy communities, such as economic and environmental. This paper aims to provide an in-depth review of the literature surrounding the existing state-of-the-art approaches that are related to the design and operation optimization of energy hubs by also exploring their interaction with the external network and multiple markets. As the planning and operation of an energy hub is a multifaceted research topic, this paper covers issues such as the different optimization methods, optimization problems formulation including objective functions and constraints, and the hubs’ optimal market participation, including flexibility mechanisms. By systematizing the existing literature, this paper highlights any limitations of the approaches so far and identifies the need for further research and enhancement of the existing approaches.

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.

Electrical load forecasting has a fundamental role in the decision-making process of energy system operators. When many users are connected to the grid, high-performance forecasting models are required, posing several problems associated with the availability of historical energy consumption data for each end-user and training, deploying and maintaining a model for each user. Moreover, introducing new end-users to an existing network poses problems relating to their forecasting model. Global models, trained on all available data, are emerging as the best solution in several contexts, because they show higher generalization performance, being able to leverage the patterns that are similar across different time series. In this work, the lodging/residential electricity 1-h-ahead load forecasting of multiple time series for smart grid applications is addressed using global models, suggesting the effectiveness of such an approach also in the energy context. Results obtained on a subset of the Great Energy Predictor III dataset with several global models are compared to results obtained with local models based on the same methods, showing that global models can perform similarly to the local ones, while presenting simpler deployment and maintainability. In this work, the forecasting of a new time series, representing a new end-user introduced in the pre-existing network, is also approached under specific assumptions, by using a global model trained using data related to the existing end-users. Results reveal that the forecasting model pre-trained on data related to other end-users allows the attainment of good forecasting performance also for new end-users.

Aiming at integrating different energy sectors and exploiting the synergies coming from the interaction of different energy carriers, sector coupling allows for a greater flexibility of the energy system, by increasing renewables’ penetration and reducing carbon emissions. At the local level, sector coupling fits well in the concept of an integrated local energy community (ILEC), where active consumers make common choices for satisfying their energy needs through the optimal management of a set of multi-carrier energy technologies, by achieving better economic and environmental benefits compared to the business-as-usual scenario. This paper discusses the stochastic operation optimization of the smart Savona Campus of the University of Genoa, according to economic and environmental criteria. The campus is treated as an ILEC with two electrically interconnected multi-energy hubs involving technologies such as PV, solar thermal, combined heat and power systems, electric and geothermal heat pumps, absorption chillers, electric and thermal storage. Under this prism, the ILEC can participate in the day-ahead market (DAM) with proper bidding strategies. To assess the renewables’ uncertainties, the roulette wheel method is used to generate an initial set of scenarios for solar irradiance, and the fast forward selection algorithm is then applied to preserve the most representative scenarios, while reducing the computational load of the next optimization phase. A stochastic optimization model is thus formulated through mixed-integer linear programming (MILP), with the aim to optimize the operation strategies of the various technologies in the ILEC, as well as the bidding strategies of the ILECs in the DAM, considering both energy costs and carbon emissions through a multi-objective approach. Case study results show how the optimal bidding strategies of the ILEC on the DAM allow minimizing of the users’ net daily cost, and, as in the case of environmental optimization, the ILEC operates in self-consumption mode. Moreover, in comparison to the current operation strategies, the optimized case allows reduction of the daily net energy cost in a range from 5 to 14%, and the net daily carbon emissions in a range from 6 to 18%.

Recently, hybrid multi-energy systems consisting of multiple generation, conversion, and storage technologies have been receiving great attention as a promising option to meet the multi-energy demands of residential end-users, by transforming them from passive consumers to active prosumers, who both produce and consume energy. The design problem of such systems is challenging due to the large number of degrees of freedom in the design and operation phases, so the system as a whole must be optimized. Moreover, both economic and low-carbon priorities should be considered in the design problem to foster an effective implementation and deployment. The aim of this paper was to present a methodology for the optimal design of multi-energy nanogrids (MENs) operating in grid-connected and islanded modes. Based on a pre-defined MEN superstructure, a multi-objective linear problem was established to find the types and sizes of the technologies in the MEN, with the aim to reduce the total annual cost and the fossil primary energy input, while satisfying the assigned time-varying user multi-energy demand. With reference to the latter, the thermal behavior of the building was simulated by using the dynamic simulation software TRNSYS. The Pareto frontier was found by minimizing a weighted sum of the total annual cost and fossil primary energy input, and the problem was solved by using branch-and-cut. In the numerical testing, a single-family house of 200 m² located in Italy was considered as the residential end-user. Results show the effectiveness of the model for providing good balancing solutions for end-users based on economic and energetic priorities. Moreover, it was found that the MEN operating in grid-connected mode showed economic and environmental performances much better than those found for the configuration operating in islanded mode.