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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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    1. Tuohy, Aidan & Meibom, Peter & Denny, Eleanor & O'Malley, Mark, 2009. "Unit commitment for systems with significant wind penetration," MPRA Paper 34849, University Library of Munich, Germany.
    2. PAPAVASILIOU, Anthony & OREN, Shmuel & ROUNTREE, Barry, 2015. "Applying high performance computing to transmissions-consstrained stochastic unit commitment for renewable energy integration," LIDAM Reprints CORE 2679, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Anthony Papavasiliou & Shmuel S. Oren, 2013. "Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network," Operations Research, INFORMS, vol. 61(3), pages 578-592, June.
    4. Hirth, Lion & Ziegenhagen, Inka, 2015. "Balancing power and variable renewables: Three links," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1035-1051.
    5. Michael L. Telson, 1975. "The Economies of Alternative Levels of Reliability for Electric Power Generation Systems," Bell Journal of Economics, The RAND Corporation, vol. 6(2), pages 679-694, Autumn.
    6. PAPAVASILIOU, Anthony & OREN, Schmuel S., 2013. "Multiarea stochastic unit commitment for high wind penetration in a transmission constrained network," LIDAM Reprints CORE 2500, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    Cited by:

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    8. 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).
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