IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v268y2020ics0306261920304906.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920304906
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.114978?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. 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.
    3. 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.
    4. John Asker & Estelle Cantillon, 2008. "Properties of scoring auctions," RAND Journal of Economics, RAND Corporation, vol. 39(1), pages 69-85, March.
    5. Kristiansen, Tarjei, 2012. "Forecasting Nord Pool day-ahead prices with an autoregressive model," Energy Policy, Elsevier, vol. 49(C), pages 328-332.
    6. 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.
    7. 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.
    8. Russell Davidson & James MacKinnon, 2000. "Bootstrap tests: how many bootstraps?," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 55-68.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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).
    14. 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.
    15. 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.
    16. Clements, Michael P. & Taylor, Nick, 2001. "Bootstrapping prediction intervals for autoregressive models," International Journal of Forecasting, Elsevier, vol. 17(2), pages 247-267.
    17. 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.
    18. 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.
    19. 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).
    20. 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.
    21. 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.
    22. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nitsch, Felix & Deissenroth-Uhrig, Marc & Schimeczek, Christoph & Bertsch, Valentin, 2021. "Economic evaluation of battery storage systems bidding on day-ahead and automatic frequency restoration reserves markets," Applied Energy, Elsevier, vol. 298(C).
    2. 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).
    3. Tamás Orosz & Anton Rassõlkin & Pedro Arsénio & Peter Poór & Daniil Valme & Ádám Sleisz, 2024. "Current Challenges in Operation, Performance, and Maintenance of Photovoltaic Panels," Energies, MDPI, vol. 17(6), pages 1-22, March.
    4. 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).
    5. Kristina Pandžić & Ivan Pavić & Ivan Andročec & Hrvoje Pandžić, 2020. "Optimal Battery Storage Participation in European Energy and Reserves Markets," Energies, MDPI, vol. 13(24), pages 1-21, December.
    6. Fabian Rücker & Michael Merten & Jingyu Gong & Roberto Villafáfila-Robles & Ilka Schoeneberger & Dirk Uwe Sauer, 2020. "Evaluation of the Effects of Smart Charging Strategies and Frequency Restoration Reserves Market Participation of an Electric Vehicle," Energies, MDPI, vol. 13(12), pages 1-31, June.
    7. Kuttner, Leopold, 2022. "Integrated scheduling and bidding of power and reserve of energy resource aggregators with storage plants," Applied Energy, Elsevier, vol. 321(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
    3. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    4. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    5. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    6. Meira, Erick & Cyrino Oliveira, Fernando Luiz & Jeon, Jooyoung, 2021. "Treating and Pruning: New approaches to forecasting model selection and combination using prediction intervals," International Journal of Forecasting, Elsevier, vol. 37(2), pages 547-568.
    7. 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.
    8. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    9. G P Girish & Aviral Kumar Tiwari, 2016. "A comparison of different univariate forecasting models forSpot Electricity Price in India," Economics Bulletin, AccessEcon, vol. 36(2), pages 1039-1057.
    10. Trond Husby & Hans Visser, 2021. "Short- to medium-run forecasting of mobility with dynamic linear models," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 45(28), pages 871-902.
    11. Hess, Alexander & Spinler, Stefan & Winkenbach, Matthias, 2021. "Real-time demand forecasting for an urban delivery platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    12. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    13. Grzegorz Marcjasz & Tomasz Serafin & Rafał Weron, 2018. "Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 11(9), pages 1-20, September.
    14. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    15. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1520-1532.
    16. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    17. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    18. Kath, Christopher & Ziel, Florian, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Energy Economics, Elsevier, vol. 76(C), pages 411-423.
    19. Evangelos Spiliotis & Fotios Petropoulos & Vassilios Assimakopoulos, 2023. "On the Disagreement of Forecasting Model Selection Criteria," Forecasting, MDPI, vol. 5(2), pages 1-12, June.
    20. Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting: Part II — Probabilistic forecasting," Energy Economics, Elsevier, vol. 79(C), pages 171-182.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:268:y:2020:i:c:s0306261920304906. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.