In recent years, a rapid increase in the adoption of renewable energy sources and in the transition from a centralized electricity generation system to an increasingly distributed one has occurred. Within this scenario, in line with the European directives for achieving the objectives in the field of energy transition and climate change, energy communities are seen as potential contributors. The purpose of this work is to analyze the application potential of the energy community concept associated with district heating networks, leading to better overall energy-economic performance. This was demonstrated for a specific energy community in Italy, and it can be achieved by maximizing internal energy sharing—resulting from the electricity surplus generated by the photovoltaic system—and adopting different strategies that include heat pumps in order to maximize self-consumption and self-sufficiency, as well as to evaluate the most efficient investment in economic terms by exploiting the incentive tariff on shared energy. The results show that the performance of the system can be improved with the proposed design, achieving a significant reduction in the system’s energy demand, emissions and costs: compared to the reference case, the use of photovoltaics reduces primary energy demand by approximately 11%, while the addition of the energy community configuration allows emissions to be reduced by nearly 12%, with no additional investment.

The European and national regulations in the decarbonisation path towards 2050 promote district heating in achieving the goals of efficiency, energy sustainability, use of renewables, and reduction of fossil fuel use. Improved management and optimisation, use of RES, and waste heat/cold sources decrease the overall demand for primary energy, a condition that is further supported by building renovations and new construction of under (almost) zero energy buildings, with a foreseeable decrease in the temperature of domestic heating systems. Models for the simulation of efficient thermal networks were implemented and described in this paper, together with results from a real case study in Italy, i.e., University Campus of Parma. Activities include the creation and validation of calculation codes and specific models in the Modelica language (Dymola software), aimed at investigating stationary regimes and dynamic behaviour as well. An indirect heat exchange substation was coupled with a resistive-capacitive model, which describes the building behaviour and the thermal exchanges by the use of thermos-physical parameters. To optimise indoor comfort conditions and minimise consumption, dynamic simulations were carried out for different operating sets: modulating the supply temperature in the plant depending on external conditions (Scenario 4) decreases the supplied thermal energy (−2.34%) and heat losses (−8.91%), even if a lower temperature level results in higher electricity consumption for pumping (+12.96%), the total energy consumption is reduced by 1.41%. A simulation of the entire heating season was performed for the optimised scenario, combining benefits from turning off the supply in the case of no thermal demand (Scenario 3) and from the modulation of the supply temperature (Scenario 4), resulting in lower energy consumption (the thermal energy supplied by the power plant −3.54%, pumping +7.76%), operating costs (−2.40), and emissions (−3.02%). The energy balance ex-ante and ex-post deep renovation in a single user was then assessed, showing how lowering the network operating temperature at 55 °C decreases the supplied thermal energy (−22.38%) and heat losses (−22.11%) with a slightly higher pumping consumption (+3.28%), while maintaining good comfort conditions. These promising results are useful for evaluating the application of low-temperature operations to the existing district heating networks, especially for large interventions of building renovation, and confirm their potential contribution to the energy efficiency targets.

The performance of the innovative configurations of the “efficient” thermal networks is a key topic in scientific research, focusing on distribution temperatures and integration with high-efficiency plants and renewable sources. As it already happens for the electricity prosumers, a thermal prosumer may feed the district heating network through a bidirectional exchange substation with the excess of the locally produced thermal energy (e.g., by means of solar thermal plant) or with the waste heat recovered in the industrial processes. The Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA) and the Alma Mater Studiorum University of Bologna (UNIBO) designed a bidirectional substation prototype, based on a return-to-supply configuration, and tested steady-state and dynamic conditions to evaluate performances and optimization measures. In this paper, the Modelica language and Dymola software were used to run a multi-domain simulation and model-based design of the substation, starting from a new heat exchanger model featuring variable efficiency, based on the thermal resistance scaling method. Control systems and components were customized from models in standard libraries in order to reproduce the substation behavior under defined operating settings, and the model was validated on the abovementioned experimental tests. Numerical results in terms of exchanged powers, temperatures and flow rates were systematically compared to experimental data, demonstrating a sufficient agreement. In particular, the absolute mean deviation—in terms of temperature—between experimental and numerical data assessed over the entire tests remains contained in +/−1 °C. As further step of the analysis, an optimized model could be included as a component in a district heating network for further investigations on the prosumers’ effects on an existing traditional grid (e.g., in case of deep renovation of urban areas connected to district heating and/or creation of micro energy communities).

The constant attention to sustainability aimed at reconciling economic and social development with environmental protection is the driving force of the continuous growth of renewable energy in the energy sector. Among the numerous actions taken by the European Commission (EC) in this direction, an important initiative towards the complete decarbonization is represented by the Renewable Energy Communities (RECs). According to the EC, “energy communities enable collective and citizen-driven energy actions to support the clean energy transition. They can contribute to increasing public acceptance of renewable energy projects and make it easier to attract private investments in the clean energy transition”. At the European level, numerous energy communities are emerging, although they are all based on photovoltaic production and, consequently, focus only on electricity flows. The aim of this paper is to define a thermal energy community in which thermal energy sharing can be achieved by exploiting the concept of the smart district heating network. Starting from a small existing district heating network, its conversion into a smart one will be analyzed and optimized with the aim of studying the sharing of thermal energy between the various prosumer and non-prosumer users connected to the district heating network.

The heating and cooling sector accounts for nearly half of Europe’s energy consumption and remains heavily dependent on fossil fuels, emphasizing the urgent need for decarbonization. Simultaneously, the global shift toward renewable energy is accelerating, alongside growing interest in decentralized energy systems where prosumers play a significant role. In this context, district heating and cooling networks, serving nearly 100 million people, are strategically important. In next-generation systems, thermal prosumers can feed-in locally produced or industrial waste heat into the network via bidirectional substations, allowing energy flows in both directions and enhancing system efficiency. The complexity of these networks, with numerous users and interacting heat flows, requires advanced predictive models to manage large volumes of data and multiple variables. This work presents the development of a predictive model based on artificial neural networks (ANNs) for forecasting excess thermal renewable energy from a bidirectional substation. The numerical model of a substation prototype designed by ENEA provided the physical data for the ANN training. Thirteen years of simulation results, combined with extensive meteorological data from ECMWF, were used to train and to test a multi-step ANN capable of forecasting the six-hour thermal power feed-in horizon using data from the preceding 24 h, improving operational planning and control strategies. The ANN model demonstrates high predictive capability and robustness in replicating thermal power dynamics. Accuracy remains high for horizons up to six hours, with MAE ranging from 279 W to 1196 W, RMSE from 662 W to 3096 W, and R² from 0.992 to 0.823. Overall, the ANN satisfactorily reproduces the behavior of the bidirectional substation even over extended forecasting horizons.