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Economic implications of forecasting electricity generation from variable renewable energy sources

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  • Ruhnau, Oliver
  • Hennig, Patrick
  • Madlener, Reinhard

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.

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  • Ruhnau, Oliver & Hennig, Patrick & Madlener, Reinhard, 2020. "Economic implications of forecasting electricity generation from variable renewable energy sources," Renewable Energy, Elsevier, vol. 161(C), pages 1318-1327.
    • Handle: RePEc:eee:renene:v:161:y:2020:i:c:p:1318-1327
    DOI: 10.1016/j.renene.2020.06.110
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    Cited by:

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    2. Cheng, Lilin & Zang, Haixiang & Wei, Zhinong & Zhang, Fengchun & Sun, Guoqiang, 2022. "Evaluation of opaque deep-learning solar power forecast models towards power-grid applications," Renewable Energy, Elsevier, vol. 198(C), pages 960-972.
    3. Musawenkosi Lethumcebo Thanduxolo Zulu & Rudiren Pillay Carpanen & Remy Tiako, 2023. "A Comprehensive Review: Study of Artificial Intelligence Optimization Technique Applications in a Hybrid Microgrid at Times of Fault Outbreaks," Energies, MDPI, vol. 16(4), pages 1-32, February.
    4. Shelare, Sagar D. & Belkhode, Pramod N. & Nikam, Keval Chandrakant & Jathar, Laxmikant D. & Shahapurkar, Kiran & Soudagar, Manzoore Elahi M. & Veza, Ibham & Khan, T.M. Yunus & Kalam, M.A. & Nizami, Ab, 2023. "Biofuels for a sustainable future: Examining the role of nano-additives, economics, policy, internet of things, artificial intelligence and machine learning technology in biodiesel production," Energy, Elsevier, vol. 282(C).
    5. Leonard Burg & Gonca Gürses-Tran & Reinhard Madlener & Antonello Monti, 2021. "Comparative Analysis of Load Forecasting Models for Varying Time Horizons and Load Aggregation Levels," Energies, MDPI, vol. 14(21), pages 1-16, November.
    6. Liu, Tingting & Xu, Jiuping, 2021. "Equilibrium strategy based policy shifts towards the integration of wind power in spot electricity markets: A perspective from China," Energy Policy, Elsevier, vol. 157(C).

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