IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i23p7751-d1286926.html
   My bibliography  Save this article

Modified Genetic Algorithm for the Profit-Based Unit Commitment Problem in Competitive Electricity Market

Author

Listed:
  • Lucas Santiago Nepomuceno

    (Department of Electrical Energy, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil)

  • Layon Mescolin de Oliveira

    (Department of Electrical Energy, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil)

  • Ivo Chaves da Silva Junior

    (Department of Electrical Energy, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil)

  • Edimar José de Oliveira

    (Department of Electrical Energy, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil)

  • Arthur Neves de Paula

    (Department of Electrical Energy, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil)

Abstract

This article proposes a solution to the Profit-Based Unit Commitment (PBUC) problem to maximize the profit of a power generation company that owns thermal units and compressed air energy storage (CAES) systems, considering the Day-Ahead market. The proposed methodology is more realistic as it considers a mixed-integer nonlinear formulation of the PBUC. The problem is solved through two stages, with Stage 1 dedicated to obtaining the operational state of the generating units (On or Off) and the operation mode of the storage system (energy exchange: charging, discharging, idle). Stage 2 determines the dispatch of power from the thermoelectric units and the energy exchange in the storage system. The analysis of the system consisting of 20 thermoelectric units and three storage systems shows the efficiency of the proposed method in making decisions for the power generation company and is therefore promising for real-world applications.

Suggested Citation

  • Lucas Santiago Nepomuceno & Layon Mescolin de Oliveira & Ivo Chaves da Silva Junior & Edimar José de Oliveira & Arthur Neves de Paula, 2023. "Modified Genetic Algorithm for the Profit-Based Unit Commitment Problem in Competitive Electricity Market," Energies, MDPI, vol. 16(23), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7751-:d:1286926
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/23/7751/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/23/7751/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Abdi, Hamdi, 2021. "Profit-based unit commitment problem: A review of models, methods, challenges, and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    2. Anand, Himanshu & Narang, Nitin & Dhillon, J.S., 2018. "Profit based unit commitment using hybrid optimization technique," Energy, Elsevier, vol. 148(C), pages 701-715.
    3. Mostafa Nasouri Gilvaei & Mahmood Hosseini Imani & Mojtaba Jabbari Ghadi & Li Li & Anahita Golrang, 2021. "Profit-Based Unit Commitment for a GENCO Equipped with Compressed Air Energy Storage and Concentrating Solar Power Units," Energies, MDPI, vol. 14(3), pages 1-20, January.
    4. Budt, Marcus & Wolf, Daniel & Span, Roland & Yan, Jinyue, 2016. "A review on compressed air energy storage: Basic principles, past milestones and recent developments," Applied Energy, Elsevier, vol. 170(C), pages 250-268.
    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. Hossein Lotfi & Mohammad Hasan Nikkhah, 2024. "Multi-Objective Profit-Based Unit Commitment with Renewable Energy and Energy Storage Units Using a Modified Optimization Method," Sustainability, MDPI, vol. 16(4), pages 1-29, February.
    2. Ann-Kathrin Klaas & Hans-Peter Beck, 2021. "A MILP Model for Revenue Optimization of a Compressed Air Energy Storage Plant with Electrolysis," Energies, MDPI, vol. 14(20), pages 1-21, October.
    3. Mostafa Nasouri Gilvaei & Mahmood Hosseini Imani & Mojtaba Jabbari Ghadi & Li Li & Anahita Golrang, 2021. "Profit-Based Unit Commitment for a GENCO Equipped with Compressed Air Energy Storage and Concentrating Solar Power Units," Energies, MDPI, vol. 14(3), pages 1-20, January.
    4. Chen, Long Xiang & Xie, Mei Na & Zhao, Pan Pan & Wang, Feng Xiang & Hu, Peng & Wang, Dong Xiang, 2018. "A novel isobaric adiabatic compressed air energy storage (IA-CAES) system on the base of volatile fluid," Applied Energy, Elsevier, vol. 210(C), pages 198-210.
    5. Houssainy, Sammy & Janbozorgi, Mohammad & Ip, Peggy & Kavehpour, Pirouz, 2018. "Thermodynamic analysis of a high temperature hybrid compressed air energy storage (HTH-CAES) system," Renewable Energy, Elsevier, vol. 115(C), pages 1043-1054.
    6. Cheayb, Mohamad & Marin Gallego, Mylène & Tazerout, Mohand & Poncet, Sébastien, 2022. "A techno-economic analysis of small-scale trigenerative compressed air energy storage system," Energy, Elsevier, vol. 239(PA).
    7. Dib, Ghady & Haberschill, Philippe & Rullière, Romuald & Revellin, Rémi, 2021. "Modelling small-scale trigenerative advanced adiabatic compressed air energy storage for building application," Energy, Elsevier, vol. 237(C).
    8. Guo, Cong & Xu, Yujie & Zhang, Xinjing & Guo, Huan & Zhou, Xuezhi & Liu, Chang & Qin, Wei & Li, Wen & Dou, Binlin & Chen, Haisheng, 2017. "Performance analysis of compressed air energy storage systems considering dynamic characteristics of compressed air storage," Energy, Elsevier, vol. 135(C), pages 876-888.
    9. Bossink, Bart A.G., 2017. "Demonstrating sustainable energy: A review based model of sustainable energy demonstration projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1349-1362.
    10. Xu, Ying & Ren, Li & Zhang, Zhongping & Tang, Yuejin & Shi, Jing & Xu, Chen & Li, Jingdong & Pu, Dongsheng & Wang, Zhuang & Liu, Huajun & Chen, Lei, 2018. "Analysis of the loss and thermal characteristics of a SMES (Superconducting Magnetic Energy Storage) magnet with three practical operating conditions," Energy, Elsevier, vol. 143(C), pages 372-384.
    11. Nicolas Martinez & Youssef Benchaabane & Rosa Elvira Silva & Adrian Ilinca & Hussein Ibrahim & Ambrish Chandra & Daniel R. Rousse, 2019. "Computer Model for a Wind–Diesel Hybrid System with Compressed Air Energy Storage," Energies, MDPI, vol. 12(18), pages 1-18, September.
    12. Li, Chengchen & Wang, Huanran & He, Xin & Zhang, Yan, 2022. "Experimental and thermodynamic investigation on isothermal performance of large-scaled liquid piston," Energy, Elsevier, vol. 249(C).
    13. Bravo, Rafael Rivelino Silva & De Negri, Victor Juliano & Oliveira, Amir Antonio Martins, 2018. "Design and analysis of a parallel hydraulic – pneumatic regenerative braking system for heavy-duty hybrid vehicles," Applied Energy, Elsevier, vol. 225(C), pages 60-77.
    14. Li, Yi & Yu, Hao & Tang, Dong & Li, Yi & Zhang, Guijin & Liu, Yaning, 2022. "A comparison of compressed carbon dioxide energy storage and compressed air energy storage in aquifers using numerical methods," Renewable Energy, Elsevier, vol. 187(C), pages 1130-1153.
    15. Qin, Chao (Chris) & Loth, Eric, 2021. "Isothermal compressed wind energy storage using abandoned oil/gas wells or coal mines," Applied Energy, Elsevier, vol. 292(C).
    16. Dzido, Aleksandra & Krawczyk, Piotr & Wołowicz, Marcin & Badyda, Krzysztof, 2022. "Comparison of advanced air liquefaction systems in Liquid Air Energy Storage applications," Renewable Energy, Elsevier, vol. 184(C), pages 727-739.
    17. Meng, Hui & Wang, Meihong & Olumayegun, Olumide & Luo, Xiaobo & Liu, Xiaoyan, 2019. "Process design, operation and economic evaluation of compressed air energy storage (CAES) for wind power through modelling and simulation," Renewable Energy, Elsevier, vol. 136(C), pages 923-936.
    18. Diaa Salman & Mehmet Kusaf, 2021. "Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting," Sustainability, MDPI, vol. 13(24), pages 1-22, December.
    19. Hyrzyński, Rafał & Ziółkowski, Paweł & Gotzman, Sylwia & Kraszewski, Bartosz & Ochrymiuk, Tomasz & Badur, Janusz, 2021. "Comprehensive thermodynamic analysis of the CAES system coupled with the underground thermal energy storage taking into account global, central and local level of energy conversion," Renewable Energy, Elsevier, vol. 169(C), pages 379-403.
    20. He, Yang & MengWang, & Chen, Haisheng & Xu, Yujie & Deng, Jianqiang, 2021. "Thermodynamic research on compressed air energy storage system with turbines under sliding pressure operation," Energy, Elsevier, vol. 222(C).

    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:7751-:d:1286926. 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.