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Optimizing Generation Maintenance Scheduling Considering Emission Factors

Author

Listed:
  • Panit Prukpanit

    (Department of Electrical and Computer Engineering, Faculty of Engineering, Thammasat University, Klong Luang, Pathum Thani 12120, Thailand)

  • Phisan Kaewprapha

    (Department of Electrical and Computer Engineering, Faculty of Engineering, Thammasat University, Klong Luang, Pathum Thani 12120, Thailand)

  • Nopbhorn Leeprechanon

    (Department of Electrical and Computer Engineering, Faculty of Engineering, Thammasat University, Klong Luang, Pathum Thani 12120, Thailand)

Abstract

Conventional generation maintenance scheduling (GMS) is a solution to increase the reliability of power systems and minimize the operation and maintenance costs paid by generation companies (GenCos). Nonetheless, environmental aspects, such as zero carbon emissions, have attracted global attention, leading to emission costs being paid by electricity generators. Therefore, to obtain GMS plans that consider these factors, this paper proposes multi-objective GMS models to minimize operation, maintenance, and emission costs by using lexicographic optimization as a mathematical tool. A demand response program (DRP) is also adapted to decrease emission generation and operational expenditures. The probability that no generation unit (GU) fails unexpectedly and the average net reserve value, comprising the system reliability with and without considering the GU failure rate, are demonstrated. Numerical examples are implemented for the IEEE 24-bus reliability test system. A GMS algorithm presented in a published work is run and compared to verify the robustness of the proposed GMS models. Our results indicate that this paper provides comprehensive approaches to the multi-objective GMS problem focusing on operation, maintenance, carbon, and DRP costs in consideration of technical and environmental aspects. The use of lexicographic optimization allows for the systematic and hierarchical consideration of these objectives, leading to significant benefits for GenCos.

Suggested Citation

  • Panit Prukpanit & Phisan Kaewprapha & Nopbhorn Leeprechanon, 2023. "Optimizing Generation Maintenance Scheduling Considering Emission Factors," Energies, MDPI, vol. 16(23), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7775-:d:1287805
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    References listed on IDEAS

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    1. Leroutier, Marion, 2022. "Carbon pricing and power sector decarbonization: Evidence from the UK," Journal of Environmental Economics and Management, Elsevier, vol. 111(C).
    2. Rokhforoz, Pegah & Montazeri, Mina & Fink, Olga, 2023. "Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    3. Lovcha, Yuliya & Perez-Laborda, Alejandro & Sikora, Iryna, 2022. "The determinants of CO2 prices in the EU emission trading system," Applied Energy, Elsevier, vol. 305(C).
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