IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i10p2658-d1660917.html
   My bibliography  Save this article

A Probabilistic Reserve Decision-Making Method Based on Cumulative Probability Approximation for High-Penetration Renewable Energy Power System

Author

Listed:
  • Yun Yang

    (Power Dispatching and Control Center Guangdong Power Grid Corporation, Guangzhou 510335, China)

  • Zichao Meng

    (Power Dispatching and Control Center Guangdong Power Grid Corporation, Guangzhou 510335, China)

  • Guobing Wu

    (Power Dispatching and Control Center Guangdong Power Grid Corporation, Guangzhou 510335, China)

  • Zhanxin Yang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Ruipeng Guo

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

Probabilistic modeling of net load forecast errors is an important approach for reserve decision-making in power systems with a high penetration of renewable energy. However, existing probabilistic modeling methods face issues such as insufficient estimation accuracy in the small probability interval of the tails or increased complexity in probability decision-making problems. A probabilistic reserve decision-making method based on cumulative probability approximation is proposed. By using key points on the cumulative probability distribution curve of net load forecast error samples, this method enhances the fitting accuracy of the normal distribution model in the small probability interval of the tail, resulting in an optimal reserve outcome with the desired comprehensive expected profit. Using relevant renewable energy output and load data from actual transmission networks in Guangdong Province, China, the proposed method demonstrates good practical value.

Suggested Citation

  • Yun Yang & Zichao Meng & Guobing Wu & Zhanxin Yang & Ruipeng Guo, 2025. "A Probabilistic Reserve Decision-Making Method Based on Cumulative Probability Approximation for High-Penetration Renewable Energy Power System," Energies, MDPI, vol. 18(10), pages 1-13, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:10:p:2658-:d:1660917
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/10/2658/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/10/2658/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Min, C.G. & Park, J.K. & Hur, D. & Kim, M.K., 2016. "A risk evaluation method for ramping capability shortage in power systems," Energy, Elsevier, vol. 113(C), pages 1316-1324.
    2. Kemal Dinçer Dingeç & Wolfgang Hörmann, 2022. "Efficient Algorithms for Tail Probabilities of Exchangeable Lognormal Sums," Methodology and Computing in Applied Probability, Springer, vol. 24(3), pages 2093-2121, September.
    3. Yishan Shi & Ruipeng Guo & Yuchen Tang & Yi Lin & Zhanxin Yang, 2023. "Integrated Transmission Network Planning by Considering Wind Power’s Uncertainty and Disasters," Energies, MDPI, vol. 16(14), pages 1-25, July.
    4. Navid Shirzadi & Fuzhan Nasiri & Ramanunni Parakkal Menon & Pilar Monsalvete & Anton Kaifel & Ursula Eicker, 2023. "Smart Urban Wind Power Forecasting: Integrating Weibull Distribution, Recurrent Neural Networks, and Numerical Weather Prediction," Energies, MDPI, vol. 16(17), pages 1-17, August.
    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. Zhou Su & Guoqing Yang & Lixiao Yao & Qingqing Zhou & Yuhan Zhang, 2024. "Optimization of Provincial Power Source Structure Planning in Northwestern China Based on Time-Series Production Simulation," Energies, MDPI, vol. 17(19), pages 1-14, September.
    2. Tang, Yang & Liu, Qingyou & Jing, Jiajia & Yang, Yan & Zou, Zhengwei, 2017. "A framework for identification of maintenance significant items in reliability centered maintenance," Energy, Elsevier, vol. 118(C), pages 1295-1303.
    3. Chang-Gi Min & Mun-Kyeom Kim, 2017. "Flexibility-Based Evaluation of Variable Generation Acceptability in Korean Power System," Energies, MDPI, vol. 10(6), pages 1-12, June.
    4. Chang-Gi Min & Mun-Kyeom Kim, 2017. "Flexibility-Based Reserve Scheduling of Pumped Hydroelectric Energy Storage in Korea," Energies, MDPI, vol. 10(10), pages 1-13, September.
    5. Niu, Wen-jing & Feng, Zhong-kai & Cheng, Chun-tian, 2018. "Optimization of variable-head hydropower system operation considering power shortage aspect with quadratic programming and successive approximation," Energy, Elsevier, vol. 143(C), pages 1020-1028.
    6. Changgi Min, 2020. "Impact Analysis of Transmission Congestion on Power System Flexibility in Korea," Energies, MDPI, vol. 13(9), pages 1-11, May.
    7. Chang-Gi Min & Mun-Kyeom Kim, 2017. "Net Load Carrying Capability of Generating Units in Power Systems," Energies, MDPI, vol. 10(8), pages 1-13, August.
    8. Chang-Gi Min & Mun-Kyeom Kim, 2017. "Impact of the Complementarity between Variable Generation Resources and Load on the Flexibility of the Korean Power System," Energies, MDPI, vol. 10(11), pages 1-13, October.
    9. Carnero, María Carmen & Gómez, Andrés, 2017. "Maintenance strategy selection in electric power distribution systems," Energy, Elsevier, vol. 129(C), pages 255-272.
    10. Ziya Demirkol & Faruk Dayi & Aylin Erdoğdu & Ahmet Yanik & Ayhan Benek, 2025. "A Techno-Economic Analysis of Power Generation in Wind Power Plants Through Deep Learning: A Case Study of Türkiye," Energies, MDPI, vol. 18(10), pages 1-40, May.
    11. Dongran Song & Xiao Tan & Qian Huang & Li Wang & Mi Dong & Jian Yang & Solomin Evgeny, 2024. "Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023," Energies, MDPI, vol. 17(6), pages 1-22, March.
    12. Dimitrios Michos & Francky Catthoor & Dimitris Foussekis & Andreas Kazantzidis, 2024. "Ultra-Short-Term Wind Power Forecasting in Complex Terrain: A Physics-Based Approach," Energies, MDPI, vol. 17(21), pages 1-20, November.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:18:y:2025:i:10:p:2658-:d:1660917. 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.