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In the context of Energy Communities (ECs), where energy flows among PV generators, batteries and loads have to be optimally managed not to waste a single drop of energy, relying on robust optimization algorithms is mandatory. The purpose of this work is to reasonably investigate the performance of the Fuzzy Inference System - Multi-Objective - Genetic Algorithm model (MO-FIS-GA), synthesized for achieving the optimal Energy Management strategy for a docked e-boat. The MO-FIS-GA performance is compared to a model composed of the same FIS implementation related to the former work but optimized by a Differential Evolution (DE) algorithm – instead of the GA – on the same optimization problem. Since the aim is not evaluating the best-performing optimization algorithm, it is not necessary to push their capabilities to the max. Rather, a good meta-parameter combination is found for the GA and the DE such that their performance is acceptable according to the technical literature. Results show that the MO-FIS-GA performance is similar to the equivalent MO-FIS-DE model, suggesting that the former could be worth developing. Further works will focus on proposing the aforementioned comparison on different optimization problems for a wider performance evaluation, aiming at implementing the MO-FIS-GA on a wide range of real applications, not only in the nautical field.

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On the Performance of Multi-Objective Evolutionary Algorithms for Energy Management in Microgrids

  • 会议论文

  • 发表日期：

    2025年

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  • Antonino Capillo,
  • Enrico De Santis,
  • Fabio Massimo Frattale Mascioli,
  • Antonello Rizzi

摘要

In the context of Energy Communities (ECs), where energy flows among PV generators, batteries and loads have to be optimally managed not to waste a single drop of energy, relying on robust optimization algorithms is mandatory. The purpose of this work is to reasonably investigate the performance of the Fuzzy Inference System - Multi-Objective - Genetic Algorithm model (MO-FIS-GA), synthesized for achieving the optimal Energy Management strategy for a docked e-boat. The MO-FIS-GA performance is compared to a model composed of the same FIS implementation related to the former work but optimized by a Differential Evolution (DE) algorithm – instead of the GA – on the same optimization problem. Since the aim is not evaluating the best-performing optimization algorithm, it is not necessary to push their capabilities to the max. Rather, a good meta-parameter combination is found for the GA and the DE such that their performance is acceptable according to the technical literature. Results show that the MO-FIS-GA performance is similar to the equivalent MO-FIS-DE model, suggesting that the former could be worth developing. Further works will focus on proposing the aforementioned comparison on different optimization problems for a wider performance evaluation, aiming at implementing the MO-FIS-GA on a wide range of real applications, not only in the nautical field.