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.

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.