Unit Commitment is one of the most complex and difficult optimization problem in power systems. The objective of the optimal commitment is to determine the on/off states of the units in the system to meet the load demand and spinning reserve requirements at each time period, such that the overall cost of generation is minimized, while satisfying the various operational constraints. This paper presents the application of an improved genetic algorithm (GA) with crossover, mutation, and advanced operators such as repair and swap mutation to determine the commitment order of the thermal units in power generation. The proposed GA has been successfully applied to 10 and 26 generating-unit systems. Robustness of the proposed GA is demonstrated by comparison to the dynamic programming algorithm. The comparison results show the effectiveness of the proposed GA in solving the unit commitment problem with sufficient accuracy and low computational time.
Ibrahim, M. I. (2020). A Genetic Based Algorithm for Short-Term Thermal Generating Unit Commitment.. MEJ- Mansoura Engineering Journal, 30(2), 1-10. doi: 10.21608/bfemu.2020.131358
MLA
Mohamed Ibrahim El-said Ibrahim. "A Genetic Based Algorithm for Short-Term Thermal Generating Unit Commitment.". MEJ- Mansoura Engineering Journal, 30, 2, 2020, 1-10. doi: 10.21608/bfemu.2020.131358
HARVARD
Ibrahim, M. I. (2020). 'A Genetic Based Algorithm for Short-Term Thermal Generating Unit Commitment.', MEJ- Mansoura Engineering Journal, 30(2), pp. 1-10. doi: 10.21608/bfemu.2020.131358
VANCOUVER
Ibrahim, M. I. A Genetic Based Algorithm for Short-Term Thermal Generating Unit Commitment.. MEJ- Mansoura Engineering Journal, 2020; 30(2): 1-10. doi: 10.21608/bfemu.2020.131358
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