Optimizing Renewable Energy Community Management Through Multi-Objective Approaches

Multi-objective optimization plays a significant role in optimizing the sizing and operation of Renewable Energy Communities (RECs), facilitating informed decision-making through precise Pareto curves. In this study, we extend a model to incorporate thermal loads and explore the effectiveness of the A-AUGMECON2 algorithm and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for solving it. Through comparative analysis, we aim to assess the performance and robustness of different optimization approaches in enhancing the sustainability and efficiency of REC operation.