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The value of flexibility in power markets

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  • Goutte, Stéphane
  • Vassilopoulos, Philippe

Abstract

The concept of flexibility is not one you find in standard microeconomics textbooks, yet it already plays a major role in the remuneration of the resources that generate and consume electricity every day and is likely to play an even larger role with the penetration of large intermittent renewable capacities. In this paper we attempt to quantify the net revenues that can be captured by a flexible resource able to react to the short term price variations on the day-ahead and intraday markets in Germany. We find that the difference between day-ahead and intraday revenues for a flexible resource has been increasing (although the profitability has been decreasing on both markets). This difference is more pronounced once 15 mn price variations can be captured by a flexible resource. The net revenues from the local 15 mn auction (which is held 3 h after the hourly “coupled” day-ahead auction) are more than eight times higher than the day-ahead hourly auction but below the net revenues that can be captured with the high prices from the continuous market. The results of the backward-looking empirical estimations allow us to distinguish and quantify two components of flexibility: (1) the “immediacy” value as we are approaching real-time and the urgency of the delivery increases (this value is revealed during the continuous intraday process and is highly linked to the stochastic nature of power supply and demand (i.e., wind/solar forecasts, forced outages of thermal generation,…) forecast error risk), and (2) the “ramping capability” component based on the technical characteristics as a resource can react to variations of shorter granularity (15 mn vs. 60 mn). We model and quantify the ramping capability component using a geometric brownian motion with jumps.

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  • Goutte, Stéphane & Vassilopoulos, Philippe, 2019. "The value of flexibility in power markets," Energy Policy, Elsevier, vol. 125(C), pages 347-357.
  • Handle: RePEc:eee:enepol:v:125:y:2019:i:c:p:347-357
    DOI: 10.1016/j.enpol.2018.10.024
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    7. Loisel, Rodica & Simon, Corentin, 2021. "Market strategies for large-scale energy storage: Vertical integration versus stand-alone player," Energy Policy, Elsevier, vol. 151(C).
    8. Ward, K.R. & Green, R. & Staffell, I., 2019. "Getting prices right in structural electricity market models," Energy Policy, Elsevier, vol. 129(C), pages 1190-1206.
    9. Joel Alpízar-Castillo & Laura Ramirez-Elizondo & Pavol Bauer, 2022. "Assessing the Role of Energy Storage in Multiple Energy Carriers toward Providing Ancillary Services: A Review," Energies, MDPI, vol. 16(1), pages 1-31, December.
    10. Heffron, Raphael & Körner, Marc-Fabian & Wagner, Jonathan & Weibelzahl, Martin & Fridgen, Gilbert, 2020. "Industrial demand-side flexibility: A key element of a just energy transition and industrial development," Applied Energy, Elsevier, vol. 269(C).
    11. Pusceddu, Elian & Zakeri, Behnam & Castagneto Gissey, Giorgio, 2021. "Synergies between energy arbitrage and fast frequency response for battery energy storage systems," Applied Energy, Elsevier, vol. 283(C).
    12. 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).
    13. Mays, Jacob, 2021. "Missing incentives for flexibility in wholesale electricity markets," Energy Policy, Elsevier, vol. 149(C).
    14. 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.
    15. Palaniyappan, Balakumar & T, Vinopraba & Chandrasekaran, Geetha, 2023. "Solving electric power distribution uncertainty using deep learning and incentive-based demand response," Utilities Policy, Elsevier, vol. 82(C).
    16. Yuanyuan, Zhang & Huiru, Zhao & Bingkang, Li, 2023. "Distributionally robust comprehensive declaration strategy of virtual power plant participating in the power market considering flexible ramping product and uncertainties," Applied Energy, Elsevier, vol. 343(C).
    17. Crampes, Claude & Renault, Jérôme, 2018. "Supply flexibility in electricity markets," TSE Working Papers 18-964, Toulouse School of Economics (TSE).
    18. Paul Schott & Johannes Sedlmeir & Nina Strobel & Thomas Weber & Gilbert Fridgen & Eberhard Abele, 2019. "A Generic Data Model for Describing Flexibility in Power Markets," Energies, MDPI, vol. 12(10), pages 1-29, May.
    19. Sasaki, Kento & Aki, Hirohisa & Ikegami, Takashi, 2022. "Application of model predictive control to grid flexibility provision by distributed energy resources in residential dwellings under uncertainty," Energy, Elsevier, vol. 239(PB).
    20. Oğuz Saygın & Ömer İskenderoğlu, 2022. "Does the level of financial development affect renewable energy? Evidence from developed countries with system generalized method of moments (System‐GMM) and cross‐sectionally augmented autoregressive," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(5), pages 1326-1342, October.
    21. Zakeri, Behnam & Cross, Samuel & Dodds, Paul.E. & Gissey, Giorgio Castagneto, 2021. "Policy options for enhancing economic profitability of residential solar photovoltaic with battery energy storage," Applied Energy, Elsevier, vol. 290(C).
    22. Briest, Gordon & Lauven, Lars-Peter & Kupfer, Stefan & Lukas, Elmar, 2022. "Leaving well-worn paths: Reversal of the investment-uncertainty relationship and flexible biogas plant operation," European Journal of Operational Research, Elsevier, vol. 300(3), pages 1162-1176.
    23. Fridgen, Gilbert & Michaelis, Anne & Rinck, Maximilian & Schöpf, Michael & Weibelzahl, Martin, 2020. "The search for the perfect match: Aligning power-trading products to the energy transition," Energy Policy, Elsevier, vol. 144(C).

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    More about this item

    Keywords

    Intra day; Flexibility; Ramping capability; Auction; Spot; Volatility; Market design;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C57 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Econometrics of Games and Auctions
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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