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

Jannik Schütz Roungkvist, Peter Enevoldsen and George Xydis
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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

Sustainability, 2020, vol. 12, issue 10, 1-19

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.

Keywords: electricity spot price forecast; power markets; renewable energy; wind power; big data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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