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An Overview of Probabilistic Dimensioning of Frequency Restoration Reserves with a Focus on the Greek Electricity Market

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  • Anthony Papavasiliou

    (Center for Operations Research and Econometrics, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium)

Abstract

The dynamic dimensioning of frequency restoration reserves based on probabilistic criteria is becoming increasingly relevant in European power grid operations, following the guidelines of European legislation. This article compares dynamic dimensioning based on k -means clustering to static dimensioning on a case study of the Greek electricity market. It presents a model of system imbalances which aims to capture various realistic features of the stochastic behavior of imbalances, including skewed distributions, the dependencies of the imbalance distribution on various imbalance drivers, and the contributions of idiosyncratic noise to system imbalances. The imbalance model was calibrated in order to be consistent with historical reserve requirements in the Greek electricity market. The imbalance model was then employed in order to compare dynamic dimensioning based on probabilistic criteria to static dimensioning. The analysis revealed potential benefits of dynamic dimensioning for the Greek electricity market, which include a reduction in average reserve requirements and the preservation of a constant risk profile due to the adaptive nature of probabilistic dimensioning.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5719-:d:633196
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    References listed on IDEAS

    as
    1. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
    2. Bessa, Ricardo J. & Miranda, V. & Botterud, A. & Zhou, Z. & Wang, J., 2012. "Time-adaptive quantile-copula for wind power probabilistic forecasting," Renewable Energy, Elsevier, vol. 40(1), pages 29-39.
    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. Papavasiliou, Anthony & Bouso, Alberte & Apelfröjd, Senad & Wik, Ellika & Gueuning, Thomas & Langer, Yves, 2021. "Multi-Area Reserve Dimensioning using Chance-Constrained Optimization," LIDAM Reprints CORE 3190, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Kristof De Vos & Nicolas Stevens & Olivier Devolder & Anthony Papavasiliou & Bob Hebb & James Matthys-Donnadieu, 2019. "Dynamic dimensioning approach for operations reserves: proof of concept in Belgium," LIDAM Reprints CORE 2993, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. 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.
    7. 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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