Renovating buildings with cultural heritage significance is an important step toward achieving sustainability in our cities. The benefits are not only energy-related but also encompass social aspects that make these renovations a high apriority. The present work investigates the renovation process of a cultural heritage building in the Municipality of Trento in Italy, specifically focusing on achieving energy savings and renewable energy integration by implementing various renovation actions. These renovation actions include improvements to the building envelope, such as roof insulation and window replacements. Additionally, the renovation actions for active systems involve the installation of a ground-source heat pump for heating/cooling coupled with a borehole thermal energy storage system, which is an innovative technology for the renovation of cultural heritage buildings. The electrical systems of the building are upgraded through the addition of standard rooftop photovoltaics, innovative building-integrated photovoltaics (shingles), and the installation of an LED lighting system. The baseline and the renovation scenarios are studied using the dynamic simulation tool INTEMA.building, written in the programming language Modelica. This tool simulates both the building envelope and the energy systems with a high level of detail, using advanced control systems and adjustable time steps. According to the simulation analysis, the primary energy demand is reduced by 30.49%, the final energy demand by 36.74%, and the net electricity demand by 8.72%. Results from this study can be useful to interested stakeholders (e.g., building owners, architects, construction companies, public agents, and urban planners) dealing with the renovation of cultural heritage and protected buildings. Also, the results can be exploited for estimating energy savings by applying advanced renovation strategies for cultural heritage buildings.

Building renovation is a key initiative to promote energy efficiency, the integration of renewable energy sources (RESs), and a reduction in CO₂ emissions. Supporting these goals, emerging research is dedicated to energy communities and positive energy districts. In this work, an urban neighborhood of six buildings in Trento (Italy) is considered. Firstly, the six buildings are modeled with the Urban Modeling Interface tool to evaluate the energy performances in 2024 and 2050, also accounting for the different climatic conditions for these two time periods. Energy demands for space heating, domestic hot water, space cooling, electricity, and transport are computed. Then, EnergyPLAN coupled with a multi-objective evolutionary algorithm is used to investigate 12 different energy decarbonization scenarios in 2024 and 2050 based on different boundaries for RESs, energy storage, hydrogen, energy system integration, and energy community incentives. Two conflicting objectives are considered: cost and CO₂ emission reductions. The results show, on the one hand, the key role of sector coupling technologies such as heat pumps and electric vehicles in exploiting local renewables and, on the other hand, the higher costs in introducing both electricity storage to approach complete decarbonization and hydrogen as an alternative strategy in the electricity, thermal, and transport sectors. As an example of the quantitative valuable finding of this work, in scenario S1 “all sectors and EC incentive” for the year 2024, a large reduction of 55% of CO₂ emissions with a modest increase of 11% of the total annual cost is identified along the Pareto front.

In the context of decarbonizing the energy system, the European Union has promoted a package of eight different directives known as the Clean Energy Package. Among them, Directive 2018/2001/EU, also known as RED II, has proposed a new legal framework for the development of renewable energy sources (RES) and, more importantly, for increasing citizen participation in the energy transition through two new tools: collective self-consumption systems (CSC) and renewable energy communities (REC). The realization, management, and optimization of these new realities of widespread energy production and consumption require the use of ICT technologies, without which the integrated production, distribution, and efficient use of renewable energy would not be possible. However, the massive use of ICT technologies essential for the operation of such systems brings with it the risk of threats from malicious actors, whose objectives may include undermining the stability, operation, and reliability of the community itself. This paper aims, in the first chapter, to explain the concept of REC and, in the second chapter, to outline the cybersecurity aspects of REC. The third chapter outlines the legal frameworks and privacy issues connected to the community itself. At the end, it will provide an overview of the existing attack surfaces in the energy community and possible mitigations through prevention and defence strategies.

This study explores the decarbonization of the district heating network in Riva del Garda. The existing system (baseline) was modeled in EnergyPLAN, and future configurations were optimized using a Multi-Objective Evolutionary Algorithm (MOEA) to minimize both CO₂ emissions and annual costs. Nine decision variables were assessed under defined boundary conditions to generate alternative future scenarios grouped into five types. In Type A, a large deep geothermal cogeneration plant combined with a small biomass boiler achieved the only zero-emission solution, with lower annual costs than the baseline but high capital needs. Excluding deep geothermal cogeneration (Type B) led to dominance of the biomass boiler and waste heat recovery from the Alto Garda Power (AGP) plant; full decarbonization remained possible only with extensive biomass use at a higher cost. Removing biomass (Type C), the solar thermal plant, and the shallow geothermal heat pump enabled deep but costly decarbonization, including grid electricity dependence. Types D and E, dominated, respectively, by shallow geothermal heat pump and electric boiler, provided moderate emission reductions and further increase in costs. Across all types, thermal storage improved operational flexibility. These analyses were also extended to assess potential district heating network expansions within Riva del Garda and into the neighboring municipality of Arco.

Building renovation to improve energy efficiency is crucial for reducing CO₂ emissions, aligning with the goal of achieving net-zero emissions by 2050. This task requires a holistic approach that encompasses retrofitting outdated systems, enhancing thermal insulation, and integrating renewable energy sources. Simulating different indoor environmental conditions and technological systems within Digital Twin (DT) before interventions is crucial for optimizing energy efficiency. Simulations can support the proper installation of heating and cooling devices and facilitate the deployment of advanced technologies, including smart Heating, Ventilation, and Air Conditioning (HVAC) systems, energy-efficient lighting, and automated energy management solutions. The use of Artificial Intelligence (AI) in simulations allows for the precise sizing of HVAC systems, including heat pumps and related devices, by accurately modelling demand profiles and optimizing sensor placement based on the geometries of DTs. This study, conducted as part of the Horizon Europe InCUBE project1, explores a real-world use-case at the Centro Servizi Culturali Santa Chiara in Trento, Italy. It introduces an innovative approach that integrates 3D surveying, computational fluid dynamics (CFD), and digital twin (DT) geometries to enhance the analysis of indoor heat distribution. The proposed data-driven pipeline optimizes sensor placement within indoor spaces, ensuring precise system design, improving performance and energy efficiency, and minimizing energy waste while preventing the oversizing of technological systems.

This study investigates the decarbonization of the Oslo district heating network by, firstly, modeling its current framework using EnergyPLAN and, afterwards, exploring optimized solutions, in terms of CO2 emissions and total annual cost, combining EnergyPLAN with a multi-objective evolutionary algorithm. Alternative scenarios are explored based on nine decision variables (energy technologies), each constrained by upper and lower boundaries defined by a specific set of criteria. In this work, the exploration of 20,000 solutions is performed following a four-step approach that starting from a broad availability of the decision variables, step by step adds new constraints. In the first step, heat pumps and waste heat recovery were recognized as the most cost-effective decarbonization solutions. The second step, avoiding the use of heat pumps, shifted the focus to more costly electric boilers. The third step, excluding also electric boilers, resulted in favoring biomass boilers, but with an additional cost increase. In the final step, carbon capture and storage became the only feasible decarbonization option and highlighted the difficult decarbonization with a steep Pareto front. Overall, this study confirms that, in line with recent system-oriented literature but extending beyond technology- or case-specific studies, waste heat recovery and electrification-based sector coupling emerge as the dominant cost-optimal decarbonization pathways at the system level.