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.
Optimizing Renewable Energy Community Management Through Multi-Objective Approaches
Thomopulos D.;Raugi M.
2024-01-01
Abstract
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.File in questo prodotto:
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