Economic implications of forecasting electricity generation from variable renewable energy sources

Oliver Ruhnau, Patrick Hennig and Reinhard Madlener

Renewable Energy, 2020, vol. 161, issue C, 1318-1327

Abstract: Short-term forecasting of electricity generation from variable renewable energy sources is not an end in itself but should provide some net benefit to its user. In the case of electricity trading, which is in the focus of this paper, the benefit can be quantified in terms of an improved economic outcome. Although some effort has been made to evaluate and to improve the profitability of electricity forecasts, the understanding of the underlying effects has remained incomplete so far. In this paper, we develop a more comprehensive theoretical framework of the connection between the statistical and the economic properties of day-ahead electricity forecasts. We find that, apart from the accuracy and the bias, which have already been extensively researched, the correlation between the forecast errors and the market price spread determines the economic implications - a phenomenon which we refer to as ‘correlation effect’. Our analysis is completed by a case study on solar electricity forecasting in Germany which illustrates the relevance and the limits of both our theoretical framework and the correlation effect.

Keywords: Forecasting evaluation; Renewable energy; Electricity markets; Balancing costs; Artificial neural network; Clear sky model; Germany (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:161:y:2020:i:c:p:1318-1327

DOI: 10.1016/j.renene.2020.06.110

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