IDEAS home Printed from https://ideas.repec.org/p/zbw/esprep/276957.html
   My bibliography  Save this paper

Modelling Natural Gas Markets: Could We Learn from our Mistakes in the Past? - A Reality Check for MAGELAN

Author

Listed:
  • Seeliger, Andreas

Abstract

MAGALAN is a global gas market model developed in 2005 that provided forecasts until 2030 for gas production, international trade and other relevant parameters. As investors, policy makers and also researchers have a need of reliable model results, models should be faced with reality from time to time. This paper presents a reality check for some results from MAGELAN for the year 2020. Some forecasts show a very high accordance with actual figures (such as aggregated global gas production or total LNG trades), some other are far from recent developments in the real world (e.g. gas production of individual countries or realised capacities of specific pipeline projects). The paper finish with a brief discussion about reasons for the divergence between model and reality. Beside some improvements are likely with some updates to the model, major exogenous shocks will always be crucial for gas market modelling (respectively modelling in general). In that specific case, the “shale gas revolution” in the USA was such a major shock, that disturbed a wide range of gas market parameters.

Suggested Citation

  • Seeliger, Andreas, 2023. "Modelling Natural Gas Markets: Could We Learn from our Mistakes in the Past? - A Reality Check for MAGELAN," EconStor Preprints 276957, ZBW - Leibniz Information Centre for Economics.
    • Handle: RePEc:zbw:esprep:276957
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/276957/1/E2%20WP%201_2023%20Seeliger.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Richard C. Grinold, 1983. "Model Building Techniques for the Correction of End Effects in Multistage Convex Programs," Operations Research, INFORMS, vol. 31(3), pages 407-431, June.
    3. Hecking, Harald & Panke, Timo, 2012. "COLUMBUS - A global gas market model," EWI Working Papers 2012-6, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    4. Paul L. Joskow, 2013. "Natural Gas: From Shortages to Abundance in the United States," American Economic Review, American Economic Association, vol. 103(3), pages 338-343, May.
    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. 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.
    2. Monica Santillan Vera & Lilia Garcia Manrique & Isabel Rodriguez Pena & Angel de la Vega Navarro, 2021. "Drivers of Electricity GHG Emissions and the Role of Natural Gas in Mexican Energy Transition," Working Paper Series 1021, Department of Economics, University of Sussex Business School.
    3. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2020. "Large Time-Varying Volatility Models for Electricity Prices," Working Papers No 05/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    4. Karol Pilot & Alicja Ganczarek-Gamrot & Krzysztof Kania, 2024. "Dealing with Anomalies in Day-Ahead Market Prediction Using Machine Learning Hybrid Model," Energies, MDPI, vol. 17(17), pages 1-20, September.
    5. Duque, Valentina & Gilraine, Michael, 2022. "Coal use, air pollution, and student performance," Journal of Public Economics, Elsevier, vol. 213(C).
    6. Cardinale, Roberto, 2019. "The profitability of transnational energy infrastructure: A comparative analysis of the Greenstream and Galsi gas pipelines," Energy Policy, Elsevier, vol. 131(C), pages 347-357.
    7. Schulte, Simon & Weiser, Florian, 2017. "Natural Gas Transits and Market Power - The Case of Turkey," EWI Working Papers 2017-6, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI), revised 15 Aug 2017.
    8. Nagl, Stephan, 2013. "Prices vs. Quantities: Incentives for Renewable Power Generation - Numerical Analysis for the European Power Market," EWI Working Papers 2013-4, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    9. Uniejewski, Bartosz & Maciejowska, Katarzyna, 2023. "LASSO principal component averaging: A fully automated approach for point forecast pooling," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1839-1852.
    10. Rubaszek Michal & Karolak Zuzanna & Kwas Marek & Uddin Gazi Salah, 2020. "The role of the threshold effect for the dynamics of futures and spot prices of energy commodities," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(5), pages 1-20, December.
    11. Zuo, Na & Schieffer, Jack & Buck, Steven, 2019. "The effect of the oil and gas boom on schooling decisions in the U.S," Resource and Energy Economics, Elsevier, vol. 55(C), pages 1-23.
    12. Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2022. "Short-term risk management of electricity retailers under rising shares of decentralized solar generation," Energy Economics, Elsevier, vol. 109(C).
    13. Li, Fengyun & Zheng, Haofeng & Li, Xingmei & Yang, Fei, 2021. "Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model," Applied Energy, Elsevier, vol. 303(C).
    14. Sabet, Amir H. & Agha, Mahmoud & Heaney, Richard, 2018. "Value of investment: Evidence from the oil and gas industry," Energy Economics, Elsevier, vol. 70(C), pages 190-204.
    15. Savitski, David W. & Nuryyev, Guych, 2018. "Enhancing electric reliability with storage-field generators," Energy Policy, Elsevier, vol. 121(C), pages 611-620.
    16. Santillán Vera, Mónica & García Manrique, Lilia & Rodríguez Peña, Isabel & De La Vega Navarro, Angel, 2023. "Drivers of electricity GHG emissions and the role of natural gas in mexican energy transition," Energy Policy, Elsevier, vol. 173(C).
    17. Christian Growitsch & Harald Hecking & Timo Panke, 2014. "Supply Disruptions and Regional Price Effects in a Spatial Oligopoly—An Application to the Global Gas Market," Review of International Economics, Wiley Blackwell, vol. 22(5), pages 944-975, November.
    18. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.
    19. Krishna Prakash N. & Jai Govind Singh, 2023. "Electricity price forecasting using hybrid deep learned networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1750-1771, November.
    20. 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.

    More about this item

    Keywords

    Long term gas market modelling; Linear optimisation; Gas demand and supply; LNG;
    All these keywords.

    JEL classification:

    • Q31 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Demand and Supply; Prices
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:zbw:esprep:276957. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/zbwkide.html .

    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.