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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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    1. Ketterer, Janina C., 2014. "The impact of wind power generation on the electricity price in Germany," Energy Economics, Elsevier, vol. 44(C), pages 270-280.
    2. Hong, Tao & Pinson, Pierre & Fan, Shu, 2014. "Global Energy Forecasting Competition 2012," International Journal of Forecasting, Elsevier, vol. 30(2), pages 357-363.
    3. Kristiansen, Tarjei, 2012. "Forecasting Nord Pool day-ahead prices with an autoregressive model," Energy Policy, Elsevier, vol. 49(C), pages 328-332.
    4. 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.
    5. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 744-763.
    6. Russell, Jeffrey R. & Engle, Robert F., 2005. "A Discrete-State Continuous-Time Model of Financial Transactions Prices and Times: The Autoregressive Conditional Multinomial-Autoregressive Conditional Duration Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 166-180, April.
    7. Janczura, Joanna & Weron, Rafal, 2010. "An empirical comparison of alternate regime-switching models for electricity spot prices," Energy Economics, Elsevier, vol. 32(5), pages 1059-1073, September.
    8. Moral-Carcedo, Julián & Pérez-García, Julián, 2017. "Integrating long-term economic scenarios into peak load forecasting: An application to Spain," Energy, Elsevier, vol. 140(P1), pages 682-695.
    9. Karakatsani Nektaria V & Bunn Derek W., 2010. "Fundamental and Behavioural Drivers of Electricity Price Volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-42, September.
    10. Jónsson, Tryggvi & Pinson, Pierre & Madsen, Henrik, 2010. "On the market impact of wind energy forecasts," Energy Economics, Elsevier, vol. 32(2), pages 313-320, March.
    11. Tryggvi Jónsson & Pierre Pinson & Henrik Aa. Nielsen & Henrik Madsen, 2014. "Exponential Smoothing Approaches for Prediction in Real-Time Electricity Markets," Energies, MDPI, vol. 7(6), pages 1-23, June.
    12. Orhan Altuğ Karabiber & George Xydis, 2019. "Electricity Price Forecasting in the Danish Day-Ahead Market Using the TBATS, ANN and ARIMA Methods," Energies, MDPI, vol. 12(5), pages 1-29, March.
    13. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
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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. 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.
    3. 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.

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