IDEAS home Printed from https://ideas.repec.org/a/spr/comgts/v16y2019i4d10.1007_s10287-019-00347-3.html
   My bibliography  Save this article

Arbitrage conditions for electricity markets with production and storage

Author

Listed:
  • Raimund M. Kovacevic

    (Vienna University of Technology)

Abstract

This work analyzes the notion of arbitrage for market models with electricity production from fuel. Several generators, fuel storage, and the related fuel and storage costs are considered. Based on stochastic optimization in Banach spaces, a necessary and a sufficient no-arbitrage condition in terms of stochastic discount factors are derived and analyzed further in the context of (potentially nonlinear) vector-autoregressive price models with additive residuals. For this large class of statistical models, it is found that both the necessary and the sufficient condition can be rejected only in very rare cases. Based on these results, it is demonstrated how absence of arbitrage can be used to construct superhedging based valuation formulas for physical electricity delivery contracts, and that absence of arbitrage in tree-based stochastic programming can be ensured easier in the context of electricity production than in a purely financial context.

Suggested Citation

  • Raimund M. Kovacevic, 2019. "Arbitrage conditions for electricity markets with production and storage," Computational Management Science, Springer, vol. 16(4), pages 671-696, October.
    • Handle: RePEc:spr:comgts:v:16:y:2019:i:4:d:10.1007_s10287-019-00347-3
    DOI: 10.1007/s10287-019-00347-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10287-019-00347-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10287-019-00347-3?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. Back, Kerry, 2010. "Asset Pricing and Portfolio Choice Theory," OUP Catalogue, Oxford University Press, number 9780195380613.
    2. Geyer, Alois & Hanke, Michael & Weissensteiner, Alex, 2010. "No-arbitrage conditions, scenario trees, and multi-asset financial optimization," European Journal of Operational Research, Elsevier, vol. 206(3), pages 609-613, November.
    3. 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.
    4. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    5. Georg Pflug & Alois Pichler, 2015. "Dynamic generation of scenario trees," Computational Optimization and Applications, Springer, vol. 62(3), pages 641-668, December.
    6. Raimund M. Kovacevic & Georg Ch. Pflug & Maria Teresa Vespucci (ed.), 2013. "Handbook of Risk Management in Energy Production and Trading," International Series in Operations Research and Management Science, Springer, edition 127, number 978-1-4614-9035-7, September.
    Full references (including those not matched with items on IDEAS)

    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. Raimund M. Kovacevic, 2019. "Valuation and pricing of electricity delivery contracts: the producer’s view," Annals of Operations Research, Springer, vol. 275(2), pages 421-460, April.
    2. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    3. Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Pradhan, Biswajeet & Mai, Ngoc-Luan & Vu, Diep-Anh, 2021. "Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms," Resources Policy, Elsevier, vol. 73(C).
    4. Zoran Gligorić & Svetlana Štrbac Savić & Aleksandra Grujić & Milanka Negovanović & Omer Musić, 2018. "Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process," Energies, MDPI, vol. 11(7), pages 1-17, July.
    5. Maciej Kostrzewski & Jadwiga Kostrzewska, 2021. "The Impact of Forecasting Jumps on Forecasting Electricity Prices," Energies, MDPI, vol. 14(2), pages 1-17, January.
    6. Nazila Pourhaji & Mohammad Asadpour & Ali Ahmadian & Ali Elkamel, 2022. "The Investigation of Monthly/Seasonal Data Clustering Impact on Short-Term Electricity Price Forecasting Accuracy: Ontario Province Case Study," Sustainability, MDPI, vol. 14(5), pages 1-14, March.
    7. Gianfreda, Angelica & Ravazzolo, Francesco & Rossini, Luca, 2020. "Comparing the forecasting performances of linear models for electricity prices with high RES penetration," International Journal of Forecasting, Elsevier, vol. 36(3), pages 974-986.
    8. 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.
    9. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices," Energies, MDPI, vol. 13(14), pages 1-19, July.
    10. Joanna Janczura & Aleksandra Michalak, 2020. "Optimization of Electric Energy Sales Strategy Based on Probabilistic Forecasts," Energies, MDPI, vol. 13(5), pages 1-16, February.
    11. Luis M. López-Manrique & E. V. Macias-Melo & O. May Tzuc & A. Bassam & K. M. Aguilar-Castro & I. Hernández-Pérez, 2018. "Assessment of Resource and Forecast Modeling of Wind Speed through An Evolutionary Programming Approach for the North of Tehuantepec Isthmus (Cuauhtemotzin, Mexico)," Energies, MDPI, vol. 11(11), pages 1-22, November.
    12. Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019. "Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1533-1547.
    13. Gabrielli, Paolo & Wüthrich, Moritz & Blume, Steffen & Sansavini, Giovanni, 2022. "Data-driven modeling for long-term electricity price forecasting," Energy, Elsevier, vol. 244(PB).
    14. Derek W. Bunn & Angelica Gianfreda & Stefan Kermer, 2018. "A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market," Energies, MDPI, vol. 11(10), pages 1-13, October.
    15. Croonenbroeck, Carsten & Stadtmann, Georg, 2019. "Renewable generation forecast studies – Review and good practice guidance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 312-322.
    16. Riccardo De Blasis & Giovanni Batista Masala & Filippo Petroni, 2021. "A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm," Energies, MDPI, vol. 14(2), pages 1-16, January.
    17. Bohlayer, Markus & Fleschutz, Markus & Braun, Marco & Zöttl, Gregor, 2020. "Energy-intense production-inventory planning with participation in sequential energy markets," Applied Energy, Elsevier, vol. 258(C).
    18. Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023. "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, vol. 125(C).
    19. Jethro Browell & Ciaran Gilbert, 2022. "Predicting Electricity Imbalance Prices and Volumes: Capabilities and Opportunities," Energies, MDPI, vol. 15(10), pages 1-7, May.
    20. Roberto Baviera & Giuseppe Messuti, 2020. "Daily Middle-Term Probabilistic Forecasting of Power Consumption in North-East England," Papers 2005.13005, arXiv.org, revised Oct 2020.

    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:spr:comgts:v:16:y:2019:i:4:d:10.1007_s10287-019-00347-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.