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Short-Term Hydro-Thermal-Solar Scheduling with CCGT Based on Self-Adaptive Genetic Algorithm

Author

Listed:
  • Borche Postolov

    (Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University, Ruger Boshkovikj 18, P.O. Box 574 Skopje, North Macedonia)

  • Nikolay Hinov

    (Faculty of Electronic Engineering and Technologies, Technical University of Sofia, 8 Kl. Ohridski Blvd, 1000 Sofia, Bulgaria)

  • Atanas Iliev

    (Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University, Ruger Boshkovikj 18, P.O. Box 574 Skopje, North Macedonia)

  • Dimitar Dimitrov

    (Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University, Ruger Boshkovikj 18, P.O. Box 574 Skopje, North Macedonia)

Abstract

This paper presents a new metaheuristic approach based on a self-adaptive genetic algorithm (SAGA) for solving the short-term hydro-thermal-solar scheduling with combined-cycle (CCGT) units. First of all, the proposed approach is applied to a test system with different characteristics, considering the valve-point effect. The simulation results obtained from the new SAGA are compared with the results obtained from some other metaheuristic methods, such as AIS, DE, and EP to reveal the validity and verify the feasibility of the proposed approach. The test results show that the proposed metaheuristic approach proves the effectiveness and superiority of the SAGA algorithm for solving the short-term hydro-thermal-solar scheduling (SHTSS) problem.

Suggested Citation

  • Borche Postolov & Nikolay Hinov & Atanas Iliev & Dimitar Dimitrov, 2022. "Short-Term Hydro-Thermal-Solar Scheduling with CCGT Based on Self-Adaptive Genetic Algorithm," Energies, MDPI, vol. 15(16), pages 1-25, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:5989-:d:891838
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    References listed on IDEAS

    as
    1. Maturana, Jorge & Riff, Maria-Cristina, 2007. "Solving the short-term electrical generation scheduling problem by an adaptive evolutionary approach," European Journal of Operational Research, Elsevier, vol. 179(3), pages 677-691, June.
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    4. Sirote Khunkitti & Neville R. Watson & Rongrit Chatthaworn & Suttichai Premrudeepreechacharn & Apirat Siritaratiwat, 2019. "An Improved DA-PSO Optimization Approach for Unit Commitment Problem," Energies, MDPI, vol. 12(12), pages 1-23, June.
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