Abstract
In response to the growing need for sustainable urban development, energy systems modelling must provide long-term carbon-neutral solutions at the city scale while balancing competing criteria. This work introduces a multi-objective optimization approach addressing technical, economic, and environmental criteria for urban smart energy systems designed to achieve 100% renewable energy integration. The analysis incorporates climate change impacts on both energy demand and production. Two optimization strategies are evaluated using Bozen-Bolzano, Italy, as a case study. Specifically, the energy systems were modelled using EnergyPLAN, integrated with Python for automation. Grid search and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) were adopted as optimization methods to compare the advantages and limitations of two different approaches. The results show that both methods produce similar solutions on the Pareto front, with the grid search slightly outperforming due to the consideration of extreme input ranges. However, NSGA-II generated a significantly larger number of Pareto solutions, demonstrating its effectiveness in exploring the solution space more comprehensively. This study underscores the importance of incorporating climate change into multi-objective optimization for robust decision-making in the design of smart urban energy systems for sustainable development.