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Dynamic dimensioning approach for operating reserves: Proof of concept in Belgium

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  • De Vos, K.
  • Stevens, N.
  • Devolder, O.
  • Papavasiliou, A.
  • Hebb, B.
  • Matthys-Donnadieu, J.

Abstract

This article discusses a new method for the sizing of operating reserves by electric power system operators. Operating reserves are used by system operators to deal with unexpected variations of demand and generation, and maintain a secure operation of the system. This becomes increasingly challenging due to the increasing share of renewable generation based on variable resources. This paper revisits the current sizing method applied in Belgium, which is based on a static approach that determines the required capacity once a year. The presented dynamic sizing method determines the required capacity on a daily basis, using the estimated probability of facing a system imbalance during the next day. This risk is estimated based on historical observations of system conditions by means of machine learning algorithms. A proof of concept is presented for the Belgian system, and demonstrates that the proposed methodology improves reliability management while decreasing the average capacity to be contracted. The method is compliant with European market design, and the corresponding regulatory framework, and is of particular interest for systems with a high share of renewable generation. For these reasons a gradual implementation in Belgium towards 2020 has been decided based on the results of this study.

Suggested Citation

  • De Vos, K. & Stevens, N. & Devolder, O. & Papavasiliou, A. & Hebb, B. & Matthys-Donnadieu, J., 2019. "Dynamic dimensioning approach for operating reserves: Proof of concept in Belgium," Energy Policy, Elsevier, vol. 124(C), pages 272-285.
    • Handle: RePEc:eee:enepol:v:124:y:2019:i:c:p:272-285
    DOI: 10.1016/j.enpol.2018.09.031
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    3. Anthony Papavasiliou, 2021. "An Overview of Probabilistic Dimensioning of Frequency Restoration Reserves with a Focus on the Greek Electricity Market," Energies, MDPI, vol. 14(18), pages 1-19, September.
    4. Rancilio, G. & Rossi, A. & Falabretti, D. & Galliani, A. & Merlo, M., 2022. "Ancillary services markets in europe: Evolution and regulatory trade-offs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    5. Hermans, Mathias & Bruninx, Kenneth & Van den Bergh, Kenneth & Poncelet, Kris & Delarue, Erik, 2021. "On the temporal granularity of joint energy-reserve markets in a high-RES system," Applied Energy, Elsevier, vol. 297(C).
    6. Silva-Rodriguez, Lina & Sanjab, Anibal & Fumagalli, Elena & Virag, Ana & Gibescu, Madeleine, 2022. "Short term wholesale electricity market designs: A review of identified challenges and promising solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    7. Lina Silva-Rodriguez & Anibal Sanjab & Elena Fumagalli & Ana Virag & Madeleine Gibescu, 2020. "Short Term Electricity Market Designs: Identified Challenges and Promising Solutions," Papers 2011.04587, arXiv.org.
    8. Li, Binghui & Feng, Cong & Siebenschuh, Carlo & Zhang, Rui & Spyrou, Evangelia & Krishnan, Venkat & Hobbs, Benjamin F. & Zhang, Jie, 2022. "Sizing ramping reserve using probabilistic solar forecasts: A data-driven method," Applied Energy, Elsevier, vol. 313(C).
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    10. Bhavsar, S. & Pitchumani, R. & Ortega-Vazquez, M.A., 2021. "Machine learning enabled reduced-order scenario generation for stochastic analysis of solar power forecasts," Applied Energy, Elsevier, vol. 293(C).

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