IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v177y2023ics0301421523001374.html
   My bibliography  Save this article

Electricity price spike formation and LNG prices effect under gross bidding scheme in JEPX

Author

Listed:
  • Rassi, Samin
  • Kanamura, Takashi

Abstract

This study empirically analyzes the price spikes that occurred in the Japan Electric Power Exchange (JEPX) market in January 2021 and draws energy policy implications from the results. The contributions of this research are as follows. First, we propose a novel structural model to capture electricity price spikes based on the buy and sell bid curves of the JEPX order book and incorporate the effect of LNG spot price fluctuations on the price of electricity. Second, the results of our empirical analysis show that the model can adequately capture the price spike in the JEPX market that occurred in January 2021. Third, the estimation of the model parameters shows that the natural gas spot price immediately shifts the price curve upward. As an important energy policy implication from the results, we propose that to manage the risk of electricity price spikes, the LNG effect on the market players’ bidding behavior, rather than the marginal cost of the supply curve, be considered; from this perspective, the LNG spot price, rather than the trend of the LNG price a few months earlier, must be monitored, and the institutional design of the JEPX market should take this into account.

Suggested Citation

  • Rassi, Samin & Kanamura, Takashi, 2023. "Electricity price spike formation and LNG prices effect under gross bidding scheme in JEPX," Energy Policy, Elsevier, vol. 177(C).
    • Handle: RePEc:eee:enepol:v:177:y:2023:i:c:s0301421523001374
    DOI: 10.1016/j.enpol.2023.113552
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421523001374
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.enpol.2023.113552?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. Ziel, Florian & Steinert, Rick, 2016. "Electricity price forecasting using sale and purchase curves: The X-Model," Energy Economics, Elsevier, vol. 59(C), pages 435-454.
    2. Tashpulatov, Sherzod N., 2013. "Estimating the volatility of electricity prices: The case of the England and Wales wholesale electricity market," Energy Policy, Elsevier, vol. 60(C), pages 81-90.
    3. Zareipour, Hamidreza & Bhattacharya, Kankar & Canizares, Claudio A., 2007. "Electricity market price volatility: The case of Ontario," Energy Policy, Elsevier, vol. 35(9), pages 4739-4748, September.
    4. Florian Ziel & Rick Steinert, 2015. "Electricity Price Forecasting using Sale and Purchase Curves: The X-Model," Papers 1509.00372, arXiv.org, revised Aug 2016.
    5. Sakaguchi, Makishi & Fujii, Hidemichi, 2021. "The impact of variable renewable energy penetration on wholesale electricity prices in Japan," MPRA Paper 110554, University Library of Munich, Germany.
    6. Kanamura, Takashi & Bunn, Derek W., 2022. "Market making and electricity price formation in Japan," Energy Economics, Elsevier, vol. 107(C).
    7. Ioannidis, Filippos & Kosmidou, Kyriaki & Savva, Christos & Theodossiou, Panayiotis, 2021. "Electricity pricing using a periodic GARCH model with conditional skewness and kurtosis components," Energy Economics, Elsevier, vol. 95(C).
    8. Kun Li & Joseph D. Cursio & Yunchuan Sun & Zizheng Zhu, 2019. "Determinants of price fluctuations in the electricity market: a study with PCA and NARDL models," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 32(1), pages 2404-2421, January.
    9. Nakajima, Tadahiro, 2013. "Inefficient and opaque price formation in the Japan Electric Power Exchange," Energy Policy, Elsevier, vol. 55(C), pages 329-334.
    10. M. T. Barlow, 2002. "A Diffusion Model For Electricity Prices," Mathematical Finance, Wiley Blackwell, vol. 12(4), pages 287-298, October.
    11. 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.
    12. Li, Kun & Cursio, Joseph D. & Jiang, Mengfei & Liang, Xi, 2019. "The significance of calendar effects in the electricity market," Applied Energy, Elsevier, vol. 235(C), pages 487-494.
    13. Manner, Hans & Türk, Dennis & Eichler, Michael, 2016. "Modeling and forecasting multivariate electricity price spikes," Energy Economics, Elsevier, vol. 60(C), pages 255-265.
    14. Aitor Ciarreta & Peru Muniainy & Ainhoa Zarraga, 2017. "Modelling Realized Volatility in Electricity Spot Prices: New insights and Application to the Japanese Electricity Market," ISER Discussion Paper 0991, Institute of Social and Economic Research, Osaka University.
    15. Lester Hadsell & Hany A. Shawky, 2006. "Electricity Price Volatility and the Marginal Cost of Congestion: An Empirical Study of Peak Hours on the NYISO Market, 2001-2004," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 157-180.
    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. Krisztina Katona & Christina Sklibosios Nikitopoulos & Erik Schlögl, 2023. "A Hyperbolic Bid Stack Approach to Electricity Price Modelling," Risks, MDPI, vol. 11(8), pages 1-39, August.
    2. Lucía Inglada-Pérez & Sandra González y Gil, 2024. "A Study on the Nature of Complexity in the Spanish Electricity Market Using a Comprehensive Methodological Framework," Mathematics, MDPI, vol. 12(6), pages 1-21, March.

    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. Smith, Michael Stanley & Shively, Thomas S., 2018. "Econometric modeling of regional electricity spot prices in the Australian market," Energy Economics, Elsevier, vol. 74(C), pages 886-903.
    2. Sergei Kulakov, 2020. "X-Model: Further Development and Possible Modifications," Forecasting, MDPI, vol. 2(1), pages 1-16, February.
    3. Ismail Shah & Francesco Lisi, 2020. "Forecasting of electricity price through a functional prediction of sale and purchase curves," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 242-259, March.
    4. Nadja Klein & Michael Stanley Smith & David J. Nott, 2023. "Deep distributional time series models and the probabilistic forecasting of intraday electricity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 493-511, June.
    5. 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.
    6. Nadja Klein & Michael Stanley Smith & David J. Nott, 2020. "Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices," Papers 2010.01844, arXiv.org, revised May 2021.
    7. 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.
    8. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    9. 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.
    10. Sergei Kulakov, 2019. "X-model: further development and possible modifications," Papers 1907.09206, arXiv.org.
    11. Kuttner, Leopold, 2022. "Integrated scheduling and bidding of power and reserve of energy resource aggregators with storage plants," Applied Energy, Elsevier, vol. 321(C).
    12. Hain, Martin & Kargus, Tobias & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2022. "An electricity price modeling framework for renewable-dominant markets," Working Paper Series in Production and Energy 66, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    13. Micha{l} Narajewski & Florian Ziel, 2021. "Optimal bidding in hourly and quarter-hourly electricity price auctions: trading large volumes of power with market impact and transaction costs," Papers 2104.14204, arXiv.org, revised Feb 2022.
    14. Daniel Manfre Jaimes & Manuel Zamudio López & Hamidreza Zareipour & Mike Quashie, 2023. "A Hybrid Model for Multi-Day-Ahead Electricity Price Forecasting considering Price Spikes," Forecasting, MDPI, vol. 5(3), pages 1-23, July.
    15. Tschora, Léonard & Pierre, Erwan & Plantevit, Marc & Robardet, Céline, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Applied Energy, Elsevier, vol. 313(C).
    16. 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.
    17. Ethem Çanakoğlu & Esra Adıyeke, 2020. "Comparison of Electricity Spot Price Modelling and Risk Management Applications," Energies, MDPI, vol. 13(18), pages 1-22, September.
    18. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    19. Joanna Janczura & Aleksandra Michalak, 2020. "Optimization of Electric Energy Sales Strategy Based on Probabilistic Forecasts," Energies, MDPI, vol. 13(5), pages 1-16, February.
    20. Hain, Martin & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2017. "An Electricity Price Modeling Framework for Renewable-Dominant Markets," Working Paper Series in Production and Energy 23, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).

    More about this item

    Keywords

    Electricity spot price spike; Structural price model; JEPX; LNG spot price; Fundamental price model;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • L98 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Government Policy

    Statistics

    Access and download statistics

    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:enepol:v:177:y:2023:i:c:s0301421523001374. 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/locate/enpol .

    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.