IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i7p1585-d1364093.html
   My bibliography  Save this article

Research on Decision Optimization and the Risk Measurement of the Power Generation Side Based on Quantile Data-Driven IGDT

Author

Listed:
  • Zhiwei Liao

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Bowen Wang

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Wenjuan Tao

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Ye Liu

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Qiyun Hu

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

Abstract

In an environment marked by dual carbon goals and substantial fluctuations in coal market prices, coal power generation enterprises face an urgent imperative to make scientifically informed decisions regarding production management amidst significant market uncertainties. To tackle this challenge, this paper proposes a methodology for optimizing electricity generation side market decisions and assessing risks using quantile data-driven information-gap decision theory (QDD-IGDT). Initially, a dual-layer decision optimization model for electricity production is formulated, taking into account coal procurement and blending processes. This model optimizes the selection of spot coal and long-term contract coal prices and simplifies the dual-layer structure into an equivalent single-layer model using the McCormick envelope and Karush–Kuhn–Tucker (KKT) conditions. Subsequently, a quantile dataset is generated utilizing a short-term coal price interval prediction model based on the quantile regression neural network (QRNN). Interval constraints on expected costs are introduced to develop an uncertainty decision risk measurement model grounded in QDD-IGDT, quantifying decision risks arising from coal market uncertainties to bolster decision robustness. Lastly, case simulations are executed by using real production data from a power generation enterprise, and the dual-layer decision optimization model is solved by employing the McCormick–KKT–Gurobi approach. Additionally, decision risks associated with coal market uncertainties are assessed through a one-dimensional search under interval constraints on expected cost volatility. The findings demonstrate the effectiveness of the proposed research methodology in cost optimization within the context of coal market uncertainties, underscoring its validity and economic efficiency.

Suggested Citation

  • Zhiwei Liao & Bowen Wang & Wenjuan Tao & Ye Liu & Qiyun Hu, 2024. "Research on Decision Optimization and the Risk Measurement of the Power Generation Side Based on Quantile Data-Driven IGDT," Energies, MDPI, vol. 17(7), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1585-:d:1364093
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/7/1585/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/7/1585/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dominik Bongartz & Alexander Mitsos, 2017. "Deterministic global optimization of process flowsheets in a reduced space using McCormick relaxations," Journal of Global Optimization, Springer, vol. 69(4), pages 761-796, December.
    2. Shu, Tony & Papageorgiou, Dimitri J. & Harper, Michael R. & Rajagopalan, Srinivasan & Rudnick, Iván & Botterud, Audun, 2023. "From coal to variable renewables: Impact of flexible electric vehicle charging on the future Indian electricity sector," Energy, Elsevier, vol. 269(C).
    3. Tiedemann, Silvana & Müller-Hansen, Finn, 2023. "Auctions to phase out coal power: Lessons learned from Germany," Energy Policy, Elsevier, vol. 174(C).
    4. Li, Hui & Wu, Zixuan & Yuan, Xing & Yang, Yixuan & He, Xiaoqiang & Duan, Huiming, 2022. "The research on modeling and application of dynamic grey forecasting model based on energy price-energy consumption-economic growth," Energy, Elsevier, vol. 257(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. Huster, Wolfgang R. & Schweidtmann, Artur M. & Mitsos, Alexander, 2020. "Globally optimal working fluid mixture composition for geothermal power cycles," Energy, Elsevier, vol. 212(C).
    2. Srivastav, Sugandha & Zaehringer, Michael, 2023. "The Economics of Coal Phaseouts," INET Oxford Working Papers 2023-17, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    3. Jaromił Najman & Dominik Bongartz & Alexander Mitsos, 2021. "Linearization of McCormick relaxations and hybridization with the auxiliary variable method," Journal of Global Optimization, Springer, vol. 80(4), pages 731-756, August.
    4. Artur M. Schweidtmann & Alexander Mitsos, 2019. "Deterministic Global Optimization with Artificial Neural Networks Embedded," Journal of Optimization Theory and Applications, Springer, vol. 180(3), pages 925-948, March.
    5. Sorknæs, Peter & Thellufsen, Jakob Zinck & Knobloch, Kai & Engelbrecht, Kurt & Yuan, Meng, 2023. "Economic potentials of carnot batteries in 100% renewable energy systems," Energy, Elsevier, vol. 282(C).
    6. Rosa, Carmen Brum & Francescatto, Matheus Binotto & Neuenfeldt Júnior, Alvaro Luiz & Bernardon, Daniel Pinheiro & dos Santos, Laura Lisiane Callai, 2023. "Regulatory analysis of E-mobility for Brazil: A comparative review and outlook," Utilities Policy, Elsevier, vol. 84(C).
    7. Chrysoula D. Kappatou & Dominik Bongartz & Jaromił Najman & Susanne Sass & Alexander Mitsos, 2022. "Global dynamic optimization with Hammerstein–Wiener models embedded," Journal of Global Optimization, Springer, vol. 84(2), pages 321-347, October.
    8. Tsay, Calvin & Pattison, Richard C. & Zhang, Yue & Rochelle, Gary T. & Baldea, Michael, 2019. "Rate-based modeling and economic optimization of next-generation amine-based carbon capture plants," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    9. Matthew E. Wilhelm & Chenyu Wang & Matthew D. Stuber, 2023. "Convex and concave envelopes of artificial neural network activation functions for deterministic global optimization," Journal of Global Optimization, Springer, vol. 85(3), pages 569-594, March.
    10. Göke, Leonard & Weibezahn, Jens & Kendziorski, Mario, 2023. "How flexible electrification can integrate fluctuating renewables," Energy, Elsevier, vol. 278(PA).
    11. Liu, Zhiyuan & Li, Yan & Sun, Yong & Feng, Fang & Tagawa, Kotaro, 2023. "Preparation of biochar-based photothermal superhydrophobic coating based on corn straw biogas residue and blade anti-icing performance by wind tunnel test," Renewable Energy, Elsevier, vol. 210(C), pages 618-626.
    12. Matthew E. Wilhelm & Matthew D. Stuber, 2023. "Improved Convex and Concave Relaxations of Composite Bilinear Forms," Journal of Optimization Theory and Applications, Springer, vol. 197(1), pages 174-204, April.
    13. Jaromił Najman & Alexander Mitsos, 2019. "Tighter McCormick relaxations through subgradient propagation," Journal of Global Optimization, Springer, vol. 75(3), pages 565-593, November.

    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:17:y:2024:i:7:p:1585-:d:1364093. 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.