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.

The design of renewable energy communities requires multi-disciplinary approaches to identify the most promising solutions from technical, economic, and environmental perspectives. Indeed, different simulations tools can be adopted, ranging from urban modelling to energy planning methods. In this framework, this research focused on the Santa Chiara district in Trento, Italy, to assess the performance of different decarbonization strategies, encompassing fossil and renewable energy systems. To achieve this goal, the district energy balance, CO2 emissions, total costs, and impact of incentives for energy communities were analyzed for each scenario. Furthermore, the effects of current and future hot climatic conditions were investigated, coupling urban building energy modelling (umi) and energy planning codes (EnergyPLAN).Results highlighted major modifications to the energy balance of the district due to climate change, with an important increase of space cooling needs. Heat pumps coupled with photovoltaic and solar thermal panels were identified as the most suitable solution, effectively contributing to the transformation of the considered district into an energy community. Finally, the adopted methodology pointed out the relevant role played by different calculation tools when used in an integrated workflow, allowing for a more comprehensive understanding of the available urban decarbonization strategies and more robust design choices.

In the contemporary landscape, roughly one-fourth of worldwide carbon dioxide emissions stem from industrial energy usage. In the industrial sector, improving the efficient and flexible coupling among different energy demands (electricity, heating, and cooling) and exploiting the integration of Renewable Energy Sources (RESs) and waste heat can lead to a drastic reduction in CO2 emissions, these are also the goals of the EU founded Horizon Europe FLEXIndustries project. This study aims to establish a cost-optimized decarbonization strategy for an energy-intensive industry, focusing on an Italian pharmaceutical company. It delves into the exploration of potential pathways and diverse energy mix configurations. The approach undertaken involves coupling a customized energy system simulation framework, specifically designed for the industrial site, with a Multi-Objective Evolutionary Algorithm (MOEA). The study, conducted with a focus on the year 2024, involves a comparative analysis of three distinct scenarios. Within the intricate and challenging constraints of the industrial demo site, 13 technologies were investigated. The outcomes of each scenario identify a set of 500 Pareto optimal solutions, obtained through 40,000 simulations. These results shed light on the compelling potential of hybrid solutions, showcasing the feasibility of achieving substantial decarbonization with only moderate increases in costs. The availability of land for RES technologies, along with the existence of a biomass supply chain in the region, emerge as pivotal determinants.

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.