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Automatic frequency restoration reserve market prediction: Methodology and comparison of various approaches

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  • Merten, Michael
  • Rücker, Fabian
  • Schoeneberger, Ilka
  • Sauer, Dirk Uwe

Abstract

In continental Europe, automatic Frequency Restoration Reserve (aFRR) is the second fastest control reserve market. Due to the complex auction design, market entrance barriers for new players are high and the market is dominated by few operators of conventional power plants. However, a rising share of renewable technologies requires their integration into this market in order to assure future grid stability. Due to the high market complexity, operators and traders of such technologies are currently lacking a tool to estimate earning potentials. Both a market prediction methodology as well as a bidding strategy are required to estimate the earning potentials and to participate in the aFRR market. To encourage participation of new technologies, this paper first provides a detailed market description and then presents a market prediction methodology for estimating revenue potentials and to assist in creating bidding strategies for auction participation. For any potential bid, the acceptance probability within the auction is derived. Both statistical and machine learning based models are used for predicting key market quantities. A model comparison reveals a steadier and usually better performance of statistical models. Exogenous data sources such as weather, electrical loads or market data did not improve the prediction performance.

Suggested Citation

  • Merten, Michael & Rücker, Fabian & Schoeneberger, Ilka & Sauer, Dirk Uwe, 2020. "Automatic frequency restoration reserve market prediction: Methodology and comparison of various approaches," Applied Energy, Elsevier, vol. 268(C).
    • Handle: RePEc:eee:appene:v:268:y:2020:i:c:s0306261920304906
    DOI: 10.1016/j.apenergy.2020.114978
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    as
    1. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. Jesús Miguel & Pilar Olave, 1999. "Bootstrapping forecast intervals in ARCH models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 8(2), pages 345-364, December.
    3. Snyder, Ralph D & Ord, J Keith & Koehler, Anne B, 2001. "Prediction Intervals for ARIMA Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 217-225, April.
    4. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    5. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
    6. Peng, Lu & Liu, Shan & Liu, Rui & Wang, Lin, 2018. "Effective long short-term memory with differential evolution algorithm for electricity price prediction," Energy, Elsevier, vol. 162(C), pages 1301-1314.
    7. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    8. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    9. Russell Davidson & James MacKinnon, 2000. "Bootstrap tests: how many bootstraps?," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 55-68.
    10. Janczura, Joanna & Trück, Stefan & Weron, Rafał & Wolff, Rodney C., 2013. "Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling," Energy Economics, Elsevier, vol. 38(C), pages 96-110.
    11. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 744-763.
    12. Landon, Joshua & Singpurwalla, Nozer D., 2008. "Choosing a Coverage Probability for Prediction Intervals," The American Statistician, American Statistical Association, vol. 62, pages 120-124, May.
    13. John Asker & Estelle Cantillon, 2008. "Properties of scoring auctions," RAND Journal of Economics, RAND Corporation, vol. 39(1), pages 69-85, March.
    14. Knaut, Andreas & Obermüller, Frank & Weiser, Florian, 2017. "Tender Frequency and Market Concentration in Balancing Power Markets," EWI Working Papers 2017-4, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    15. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    16. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    17. de Oliveira, Erick Meira & Cyrino Oliveira, Fernando Luiz, 2018. "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, Elsevier, vol. 144(C), pages 776-788.
    18. Kristiansen, Tarjei, 2012. "Forecasting Nord Pool day-ahead prices with an autoregressive model," Energy Policy, Elsevier, vol. 49(C), pages 328-332.
    19. Clements, Michael P. & Taylor, Nick, 2001. "Bootstrapping prediction intervals for autoregressive models," International Journal of Forecasting, Elsevier, vol. 17(2), pages 247-267.
    20. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    21. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    22. Fabian Ocker & Karl‐Martin Ehrhart & Marion Ott, 2018. "Bidding strategies in Austrian and German balancing power auctions," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 7(6), November.
    Full references (including those not matched with items on IDEAS)

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    5. Zakeri, Behnam & Gissey, Giorgio Castagneto & Dodds, Paul E. & Subkhankulova, Dina, 2021. "Centralized vs. distributed energy storage – Benefits for residential users," Energy, Elsevier, vol. 236(C).
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