IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i23p7775-d1287805.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/23/7775/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/23/7775/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Missbach, Leonard & Steckel, Jan Christoph & Vogt-Schilb, Adrien, 2024. "Cash transfers in the context of carbon pricing reforms in Latin America and the Caribbean," World Development, Elsevier, vol. 173(C).
    2. Osorio, Sebastian & Pietzcker, Robert C. & Pahle, Michael & Edenhofer, Ottmar, 2020. "How to deal with the risks of phasing out coal in Germany," Energy Economics, Elsevier, vol. 87(C).
    3. Wanlin Yu & Jinlong Luo, 2022. "Impact on Carbon Intensity of Carbon Emission Trading—Evidence from a Pilot Program in 281 Cities in China," IJERPH, MDPI, vol. 19(19), pages 1-19, September.
    4. Abrell, Jan & Kosch, Mirjam & Rausch, Sebastian, 2022. "How effective is carbon pricing?—A machine learning approach to policy evaluation," Journal of Environmental Economics and Management, Elsevier, vol. 112(C).
    5. Su, Chi Wei & Wei, Shenkai & Wang, Yan & Tao, Ran, 2024. "How does climate policy uncertainty affect the carbon market?," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    6. Oktay Ozkan & Salah Abosedra & Arshian Sharif & Andrew Adewale Alola, 2024. "Dynamic volatility among fossil energy, clean energy and major assets: evidence from the novel DCC-GARCH," Economic Change and Restructuring, Springer, vol. 57(3), pages 1-19, June.
    7. Böhringer, Christoph & Rosendahl, Knut Einar, 2022. "Europe beyond coal – An economic and climate impact assessment," Journal of Environmental Economics and Management, Elsevier, vol. 113(C).
    8. Friedrich, Marina & Lin, Yicong, 2024. "Sieve bootstrap inference for linear time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 239(1).
    9. Rafaty, R. & Dolphin, G. & Pretis, F., 2020. "Carbon pricing and the elasticity of CO2 emissions," Cambridge Working Papers in Economics 20116, Faculty of Economics, University of Cambridge.
    10. Ryan Rafaty & Geoffroy Dolphin & Felix Pretis, 2020. "Carbon pricing and the elasticity of CO2 emissions," Working Papers EPRG2035, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    11. Shaolong Zeng & Qinyi Fu & Danni Yang & Yihua Tian & Yang Yu, 2023. "The Influencing Factors of the Carbon Trading Price: A Case of China against a “Double Carbon” Background," Sustainability, MDPI, vol. 15(3), pages 1-24, January.
    12. Blanchard, Olivier Jean & Gollier, Christian & Tirole, Jean, 2022. "Fighting the war against climate change," TSE Working Papers 22-1360, Toulouse School of Economics (TSE).
    13. Huang, Wenyang & Wang, Huiwen & Wei, Yigang, 2023. "Identifying the determinants of European carbon allowances prices: A novel robust partial least squares method for open-high-low-close data," International Review of Financial Analysis, Elsevier, vol. 90(C).
    14. Gugler, Klaus & Haxhimusa, Adhurim & Liebensteiner, Mario, 2023. "Carbon pricing and emissions: Causal effects of Britain's carbon tax," Energy Economics, Elsevier, vol. 121(C).
    15. Fujii, Hidemichi & Webb, Jeremy & Mundree, Sagadevan & Rowlings, David & Grace, Peter & Wilson, Clevo & Managi, Shunsuke, 2024. "Priority change and driving factors in the voluntary carbon offset market," MPRA Paper 120657, University Library of Munich, Germany.
    16. Umberto Desideri & Alaa Krayem & Eva Thorin, 2023. "The Unprecedented Natural Gas Crisis in Europe: Investigating the Causes and Consequences with a Focus on Italy," Energies, MDPI, vol. 16(16), pages 1-21, August.
    17. Niklas Döbbeling-Hildebrandt & Klaas Miersch & Tarun M. Khanna & Marion Bachelet & Stephan B. Bruns & Max Callaghan & Ottmar Edenhofer & Christian Flachsland & Piers M. Forster & Matthias Kalkuhl & Ni, 2024. "Systematic review and meta-analysis of ex-post evaluations on the effectiveness of carbon pricing," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    18. Philippe Quirion, 2022. "Economic tools to promote ecological transition and their distributive effects [Les outils économiques pour favoriser la transition écologique et leurs effets distributifs]," Post-Print hal-04457612, HAL.
    19. Abban, Olivier Joseph & Xing, Yao Hong & Nuţă, Alina Cristina & Nuţă, Florian Marcel & Borah, Prasad Siba & Ofori, Charles & Jing, Yao Jing, 2023. "Policies for carbon-zero targets: Examining the spillover effects of renewable energy and patent applications on environmental quality in Europe," Energy Economics, Elsevier, vol. 126(C).
    20. Pier Basaglia & Sophie Behr & Moritz A. Drupp, 2023. "De-Fueling Externalities: How Tax Salience and Fuel Substitution Mediate Climate and Health Benefits," Discussion Papers of DIW Berlin 2041, DIW Berlin, German Institute for Economic Research.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7775-:d:1287805. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.