Comparative Study of Unit Commitment Methods: Classical vs. Nature-Inspired Algorithms

The Unit Commitment Problem (UCP), which plays a crucial role in ensuring the secure and costeffective operation of power systems, is recognized as one of the most challenging optimization problems due to its complex mix of variables and constraints. To address this challenge, a wide range of solution methodsspanning both deterministic and stochastic search algorithms have been proposed, each with its own strengths and limitations. This paper presents a comprehensive literature review of the algorithms developed for solving the UCP and compares their performance using results from established benchmark problems. The insights gained from this survey and comparative analysis aim to support ongoing research efforts in the field of power engineering.

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Publication Details

Journal
International Journal of Electrical Engineering and Computer Science
Published
2025-11-20
DOI
https://doi.org/10.37394/232027.2025.7.23
Primary Topic
Electric Power System Optimization
Type
article
Field-Weighted Citation Impact
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article

Comparative Study of Unit Commitment Methods: Classical vs. Nature-Inspired Algorithms

Sunghyuk Im, E. S. Ali
International Journal of Electrical Engineering and Computer Science
Electric Power System Optimization
article

Comparative Study of Unit Commitment Methods: Classical vs. Nature-Inspired Algorithms

Sunghyuk Im, E. S. Ali
article en

Abstract

The Unit Commitment Problem (UCP), which plays a crucial role in ensuring the secure and costeffective operation of power systems, is recognized as one of the most challenging optimization problems due to its complex mix of variables and constraints. To address this challenge, a wide range of solution methodsspanning both deterministic and stochastic search algorithms have been proposed, each with its own strengths and limitations. This paper presents a comprehensive literature review of the algorithms developed for solving the UCP and compares their performance using results from established benchmark problems. The insights gained from this survey and comparative analysis aim to support ongoing research efforts in the field of power engineering.

International Journal of Electrical Engineering and Computer ScienceVol. 7
Jazan University (SA)
Openalex Percentile: Top 65%
Electric Power System Optimization
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