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<p>This paper proposes a methodology to solve the profit-based unit commitment (PBUC) problem under uncertainties in renewable energy generation. The PBUC addresses a generation company (GENCO) that operates thermal generators, energy storage systems, wind and solar power plants. A hybrid optimization method is proposed, combining a specialized genetic algorithm with classical optimization techniques for solving the PBUC problem. Uncertainties are incorporated into the formulation through representative daily scenarios obtained via the m-ISODATA method for clustering a historical dataset, which automatically obtains an adequate number of scenarios that represents the dataset. The resulting solution is notably robust and feasible across all scenarios. The method addresses uncertainties linked to intermittent energy sources, offering an efficient solution for energy generation companies striving for enhanced decision-making processes. To evaluate the performance of the proposed methodology, three case studies are conducted on a test system comprising ten thermal generators, two wind power plants, three photovoltaic solar systems, and two energy storage systems. Case studies demonstrate the value of ESS, showing that their inclusion increased the GENCO’s daily total profit from $199,223.00 to $202,423.21, totaling an expected annual revenue of over $1 million directly from the operation of the ESS. The proposed approach provides an efficient and robust scheduling solution, feasible across all scenarios, for GENCOs in competitive markets.</p>

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Hybrid optimization for profit-based unit commitment in uncertain renewable scenarios

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月30日

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  • Lucas Santiago Nepomuceno,
  • Arthur Neves de Paula,
  • Edimar José de Oliveira,
  • Ramon Abritta Aguiar Santos,
  • Ivo Chaves da Silva Junior

摘要

This paper proposes a methodology to solve the profit-based unit commitment (PBUC) problem under uncertainties in renewable energy generation. The PBUC addresses a generation company (GENCO) that operates thermal generators, energy storage systems, wind and solar power plants. A hybrid optimization method is proposed, combining a specialized genetic algorithm with classical optimization techniques for solving the PBUC problem. Uncertainties are incorporated into the formulation through representative daily scenarios obtained via the m-ISODATA method for clustering a historical dataset, which automatically obtains an adequate number of scenarios that represents the dataset. The resulting solution is notably robust and feasible across all scenarios. The method addresses uncertainties linked to intermittent energy sources, offering an efficient solution for energy generation companies striving for enhanced decision-making processes. To evaluate the performance of the proposed methodology, three case studies are conducted on a test system comprising ten thermal generators, two wind power plants, three photovoltaic solar systems, and two energy storage systems. Case studies demonstrate the value of ESS, showing that their inclusion increased the GENCO’s daily total profit from $199,223.00 to $202,423.21, totaling an expected annual revenue of over $1 million directly from the operation of the ESS. The proposed approach provides an efficient and robust scheduling solution, feasible across all scenarios, for GENCOs in competitive markets.