IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v345y2025i1d10.1007_s10479-024-06236-6.html
   My bibliography  Save this article

Modelling a storage system of a wind farm with a ramp-rate limitation: a semi-Markov modulated Brownian bridge approach

Author

Listed:
  • Abel Azze

    (CUNEF Universidad)

  • Guglielmo D’Amico

    (University G. d’Annunzio of Chieti–Pescara)

  • Bernardo D’Auria

    (University of Padova)

  • Salvatore Vergine

    (Marche Polytechnic University)

Abstract

We propose a new methodology to simulate the discounted penalty applied to a wind-farm operator by violating ramp-rate limitation policies. It is assumed that the operator manages a wind turbine plugged into a battery, which either provides or stores energy on demand to avoid ramp-up and ramp-down events. The battery stages, namely charging, discharging, or neutral, are modeled as a semi-Markov process. During each charging/discharging period, the energy stored/supplied is assumed to follow a modified Brownian bridge that depends on three parameters. We prove the validity of our methodology by testing the model on 10 years of real wind-power data and comparing real versus simulated results.

Suggested Citation

  • Abel Azze & Guglielmo D’Amico & Bernardo D’Auria & Salvatore Vergine, 2025. "Modelling a storage system of a wind farm with a ramp-rate limitation: a semi-Markov modulated Brownian bridge approach," Annals of Operations Research, Springer, vol. 345(1), pages 39-57, February.
    • Handle: RePEc:spr:annopr:v:345:y:2025:i:1:d:10.1007_s10479-024-06236-6
    DOI: 10.1007/s10479-024-06236-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-024-06236-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-024-06236-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Cui, Yang & Chen, Zhenghong & He, Yingjie & Xiong, Xiong & Li, Fen, 2023. "An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events," Energy, Elsevier, vol. 263(PC).
    2. Cui, Yang & He, Yingjie & Xiong, Xiong & Chen, Zhenghong & Li, Fen & Xu, Taotao & Zhang, Fanghong, 2021. "Algorithm for identifying wind power ramp events via novel improved dynamic swinging door," Renewable Energy, Elsevier, vol. 171(C), pages 542-556.
    3. Guglielmo D’Amico & Filippo Petroni & Salvatore Vergine, 2022. "Ramp Rate Limitation of Wind Power: An Overview," Energies, MDPI, vol. 15(16), pages 1-15, August.
    4. Khalid, M. & Savkin, A.V., 2010. "A model predictive control approach to the problem of wind power smoothing with controlled battery storage," Renewable Energy, Elsevier, vol. 35(7), pages 1520-1526.
    5. Aleka A. Papadopoulou & George Tsaklidis & Sally McClean & Lalit Garg, 2012. "On the Moments and the Distribution of the Cost of a Semi Markov Model for Healthcare Systems," Methodology and Computing in Applied Probability, Springer, vol. 14(3), pages 717-737, September.
    6. Razmjoo, A. & Gakenia Kaigutha, L. & Vaziri Rad, M.A. & Marzband, M. & Davarpanah, A. & Denai, M., 2021. "A Technical analysis investigating energy sustainability utilizing reliable renewable energy sources to reduce CO2 emissions in a high potential area," Renewable Energy, Elsevier, vol. 164(C), pages 46-57.
    7. Guglielmo D'Amico & Filippo Petroni & Flavio Prattico, 2013. "Wind speed modeled as an indexed semi‐Markov process," Environmetrics, John Wiley & Sons, Ltd., vol. 24(6), pages 367-376, September.
    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. Busola D. Akintayo & Oluwafemi E. Ige & Olubayo M. Babatunde & Oludolapo A. Olanrewaju, 2023. "Evaluation and Prioritization of Power-Generating Systems Using a Life Cycle Assessment and a Multicriteria Decision-Making Approach," Energies, MDPI, vol. 16(18), pages 1-18, September.
    2. Tang, Jie & Brouste, Alexandre & Tsui, Kwok Leung, 2015. "Some improvements of wind speed Markov chain modeling," Renewable Energy, Elsevier, vol. 81(C), pages 52-56.
    3. Muhammad, Gul & Potchamyou Ngatcha, Ange Douglas & Lv, Yongkun & Xiong, Wenlong & El-Badry, Yaser A. & Asmatulu, Eylem & Xu, Jingliang & Alam, Md Asraful, 2022. "Enhanced biodiesel production from wet microalgae biomass optimized via response surface methodology and artificial neural network," Renewable Energy, Elsevier, vol. 184(C), pages 753-764.
    4. Cui, Yang & Chen, Zhenghong & He, Yingjie & Xiong, Xiong & Li, Fen, 2023. "An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events," Energy, Elsevier, vol. 263(PC).
    5. Feng, Xinzhen & Zhou, Dequn & Hussain, Tufail, 2024. "An investigation of fintech governance, natural resources and government stability on sustainability: Policy suggestions under the SDGs theme," Resources Policy, Elsevier, vol. 96(C).
    6. Wang, Xiaodi & Hao, Yan & Yang, Wendong, 2024. "Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy," Energy, Elsevier, vol. 297(C).
    7. Nirbheram, Joshi Sukhdev & Mahesh, Aeidapu & Bhimaraju, Ambati, 2025. "Feasibility study of a PV-grid-assisted charging station for electric and hydrogen fuel cell vehicles under uncertain arrivals," Energy, Elsevier, vol. 322(C).
    8. Silviu Nate & Yuriy Bilan & Mariia Kurylo & Olena Lyashenko & Piotr Napieralski & Ganna Kharlamova, 2021. "Mineral Policy within the Framework of Limited Critical Resources and a Green Energy Transition," Energies, MDPI, vol. 14(9), pages 1-32, May.
    9. Feng Xiong & HuiDong Mo, 2023. "How can new energy development reduce CO2 emissions: Empirical evidence of inverted U-shaped relationship in China," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-19, November.
    10. Guglielmo D’Amico & Filippo Petroni & Salvatore Vergine, 2022. "Ramp Rate Limitation of Wind Power: An Overview," Energies, MDPI, vol. 15(16), pages 1-15, August.
    11. Ma, Changwei & Dong, Wei & Shi, Yongtao & Zhang, Fan & Yang, Qiang, 2025. "A fuzzy logic dispatching method based on model predictive control for adaptively addressing uncertain operation scenarios in multi-energy systems," Energy, Elsevier, vol. 322(C).
    12. Vaziri Rad, Mohammad Amin & Kasaeian, Alibakhsh & Niu, Xiaofeng & Zhang, Kai & Mahian, Omid, 2023. "Excess electricity problem in off-grid hybrid renewable energy systems: A comprehensive review from challenges to prevalent solutions," Renewable Energy, Elsevier, vol. 212(C), pages 538-560.
    13. Meng, Anbo & Zhang, Haitao & Yin, Hao & Xian, Zikang & Chen, Shu & Zhu, Zibin & Zhang, Zheng & Rong, Jiayu & Li, Chen & Wang, Chenen & Wu, Zhenbo & Deng, Weisi & Luo, Jianqiang & Wang, Xiaolin, 2023. "A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN," Energy, Elsevier, vol. 283(C).
    14. Mousavi, Seyed Ali & Toopshekan, Ashkan & Mehrpooya, Mehdi & Delpisheh, Mostafa, 2023. "Comprehensive exergetic performance assessment and techno-financial optimization of off-grid hybrid renewable configurations with various dispatch strategies and solar tracking systems," Renewable Energy, Elsevier, vol. 210(C), pages 40-63.
    15. Alya AlHammadi & Nasser Al-Saif & Ameena Saad Al-Sumaiti & Mousa Marzband & Tareefa Alsumaiti & Ehsan Heydarian-Forushani, 2022. "Techno-Economic Analysis of Hybrid Renewable Energy Systems Designed for Electric Vehicle Charging: A Case Study from the United Arab Emirates," Energies, MDPI, vol. 15(18), pages 1-20, September.
    16. Weitzel, Timm & Glock, Christoph H., 2018. "Energy management for stationary electric energy storage systems: A systematic literature review," European Journal of Operational Research, Elsevier, vol. 264(2), pages 582-606.
    17. Nikolas Schöne & Boris Heinz, 2023. "Semi-Systematic Literature Review on the Contribution of Hydrogen to Universal Access to Energy in the Rationale of Sustainable Development Goal Target 7.1," Energies, MDPI, vol. 16(4), pages 1-42, February.
    18. Soares M.C. Borba, Bruno & Szklo, Alexandre & Schaeffer, Roberto, 2012. "Plug-in hybrid electric vehicles as a way to maximize the integration of variable renewable energy in power systems: The case of wind generation in northeastern Brazil," Energy, Elsevier, vol. 37(1), pages 469-481.
    19. Azizi, Askar & Nourisola, Hamid & Shoja-Majidabad, Sajjad, 2019. "Fault tolerant control of wind turbines with an adaptive output feedback sliding mode controller," Renewable Energy, Elsevier, vol. 135(C), pages 55-65.
    20. Junwei Fu & Yuna Ni & Yuming Ma & Jian Zhao & Qiuyi Yang & Shiyi Xu & Xiang Zhang & Yuhua Liu, 2023. "A Visualization-Based Ramp Event Detection Model for Wind Power Generation," Energies, MDPI, vol. 16(3), pages 1-16, January.

    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:spr:annopr:v:345:y:2025:i:1:d:10.1007_s10479-024-06236-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.