Optimizing Renewable Energy Community Management Through Multi-Objective Approaches

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings - 24th EEEIC International Conference on Environment and Electrical Engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024
EditorsZbigniew Leonowicz, Erika Stracqualursi
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350355185
DOIs
Publication statusPublished - 1 Jan 2024
Event24th EEEIC International Conference on Environment and Electrical Engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024 - Rome, Italy
Duration: 17 Jun 202420 Jun 2024

Publication series

NameProceedings - 24th EEEIC International Conference on Environment and Electrical Engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024

Conference

Conference24th EEEIC International Conference on Environment and Electrical Engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024
Country/TerritoryItaly
CityRome
Period17/06/2420/06/24

Keywords

  • Mixed Integer Linear Programming (MILP)
  • Multi-objective
  • Renewable Energy Communities (REC)

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