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Balancing Generation from Renewable Energy Sources: Profitability of an Energy Trader

Author

Listed:
  • Christopher Kath

    () (RWE Supply & Trading GmbH, 45141 Essen, Germany
    House of Energy Markets and Finance, University of Duisburg-Essen, 45141 Essen, Germany)

  • Weronika Nitka

    () (Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
    Faculty of Pure and Applied Mathematics, Wrocław University of Science and Technology, 50-370 Wrocław, Poland)

  • Tomasz Serafin

    () (Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
    Faculty of Pure and Applied Mathematics, Wrocław University of Science and Technology, 50-370 Wrocław, Poland)

  • Tomasz Weron

    () (Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
    Faculty of Pure and Applied Mathematics, Wrocław University of Science and Technology, 50-370 Wrocław, Poland)

  • Przemysław Zaleski

    () (Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
    Department of Finance and Strategic Analysis, EkoPartner Recykling Sp. z o.o., 59-300 Lubin, Poland)

  • Rafał Weron

    () (Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland)

Abstract

Motivated by a practical problem faced by an energy trading company in Poland, we investigate the profitability of balancing intermittent generation from renewable energy sources (RES). We consider a company that buys electricity generated by a pool of wind farms and pays their owners the day-ahead system price minus a commission, then sells the actually generated volume in the day-ahead and balancing markets. We evaluate the profitability (measured by the Sharpe ratio) and market risk faced by the energy trader as a function of the commission charged and the adopted trading strategy. We show that publicly available, country-wide RES generation forecasts can be significantly improved using a relatively simple regression model and that trading on this information yields significantly higher profits for the company. Moreover, we address the issue of contract design as a key performance driver. We argue that by offering tolerance range contracts, which transfer some of the risk to wind farm owners, both parties can bilaterally agree on a suitable framework that meets individual risk appetite and profitability expectations.

Suggested Citation

  • Christopher Kath & Weronika Nitka & Tomasz Serafin & Tomasz Weron & Przemysław Zaleski & Rafał Weron, 2020. "Balancing Generation from Renewable Energy Sources: Profitability of an Energy Trader," Energies, MDPI, Open Access Journal, vol. 13(1), pages 1-15, January.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:1:p:205-:d:304260
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    References listed on IDEAS

    as
    1. 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.
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    Cited by:

    1. Lu Zhu & Lanli Hu & Serhat Yüksel & Hasan Dinçer & Hüsne Karakuş & Gözde Gülseven Ubay, 2020. "Analysis of Strategic Directions in Sustainable Hydrogen Investment Decisions," Sustainability, MDPI, Open Access Journal, vol. 12(11), pages 1-19, June.

    More about this item

    Keywords

    electricity price; day-ahead market; balancing market; RES generation; wind power forecast; profitability; Sharpe ratio; Value-at-Risk; trading strategy; contract design;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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