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High-Resolution Electricity Spot Price Forecast for the Danish Power Market

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  • Jannik Schütz Roungkvist

    (Department of Business Development and Technology, Centre for Energy Technologies, Aarhus University, Aarhus BSS, Birk Centerpark 15, DK-7400 Herning, Denmark)

  • Peter Enevoldsen

    (Department of Business Development and Technology, Centre for Energy Technologies, Aarhus University, Aarhus BSS, Birk Centerpark 15, DK-7400 Herning, Denmark)

  • George Xydis

    (Department of Business Development and Technology, Centre for Energy Technologies, Aarhus University, Aarhus BSS, Birk Centerpark 15, DK-7400 Herning, Denmark)

Abstract

Energy markets with a high penetration of renewables are more likely to be challenged by price variations or volatility, which is partly due to the stochastic nature of renewable energy. The Danish electricity market (DK1) is a great example of such a market, as 49% of the power production in DK1 is based on wind power, conclusively challenging the electricity spot price forecast for the Danish power market. The energy industry and academia have tried to find the best practices for spot price forecasting in Denmark, by introducing everything from linear models to sophisticated machine-learning approaches. This paper presents a linear model for price forecasting—based on electricity consumption, thermal power production, wind production and previous electricity prices—to estimate long-term electricity prices in electricity markets with a high wind penetration levels, to help utilities and asset owners to develop risk management strategies and for asset valuation.

Suggested Citation

  • Jannik Schütz Roungkvist & Peter Enevoldsen & George Xydis, 2020. "High-Resolution Electricity Spot Price Forecast for the Danish Power Market," Sustainability, MDPI, vol. 12(10), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:10:p:4267-:d:361744
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    References listed on IDEAS

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    Cited by:

    1. Meng, Anbo & Wang, Peng & Zhai, Guangsong & Zeng, Cong & Chen, Shun & Yang, Xiaoyi & Yin, Hao, 2022. "Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization," Energy, Elsevier, vol. 254(PA).
    2. 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.
    3. Shah, Muhammad Ibrahim & Kirikkaleli, Dervis & Adedoyin, Festus Fatai, 2021. "Regime switching effect of COVID-19 pandemic on renewable electricity generation in Denmark," Renewable Energy, Elsevier, vol. 175(C), pages 797-806.

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