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Modelling power prices in markets with high shares of renewable energies and storages—The Norwegian example

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  • Scheben, Heike
  • Hufendiek, Kai

Abstract

Though power markets evolved into high shares of renewable energies markets, typical optimisation models, designed for thermal electricity markets, lead to prices with low volatility when applied to the Norwegian market. The objective of this work is to adapt typical optimisation models, so that they can depict price structures in markets with high shares of renewables and flexibilities. Additionally, also the sensitivity of the prices to the smallest parameter variations of the influencing factors shall be highlighted. For this purpose, the key factors which are qualitatively examined in the literature are selected. These are implemented in a standard electricity market model and subsequently analysed quantitatively. It is demonstrated that adding uncertainty and storage restrictions is sufficient to adequately represent typical price structures in Norway. Furthermore, it is shown that price structures are highly dependent on the parameters. E.g., limiting the maximum availability of storage power plants to 63%–69% leads to significant price peaks, resulting in a maximum price increase of up to 160 €/MWh. This indicates that the influencing factors identified here should be taken into consideration when modelling future power systems, although great diligence is recommended during parameterisation of these factors.

Suggested Citation

  • Scheben, Heike & Hufendiek, Kai, 2023. "Modelling power prices in markets with high shares of renewable energies and storages—The Norwegian example," Energy, Elsevier, vol. 267(C).
    • Handle: RePEc:eee:energy:v:267:y:2023:i:c:s0360544222033370
    DOI: 10.1016/j.energy.2022.126451
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    1. Löschenbrand, Markus & Wei, Wei & Liu, Feng, 2018. "Hydro-thermal power market equilibrium with price-making hydropower producers," Energy, Elsevier, vol. 164(C), pages 377-389.
    2. Miseta, Tamás & Fodor, Attila & Vathy-Fogarassy, Ágnes, 2022. "Energy trading strategy for storage-based renewable power plants," Energy, Elsevier, vol. 250(C).
    3. Gabrielli, Paolo & Wüthrich, Moritz & Blume, Steffen & Sansavini, Giovanni, 2022. "Data-driven modeling for long-term electricity price forecasting," Energy, Elsevier, vol. 244(PB).
    4. Coester, Andreas & Hofkes, Marjan W. & Papyrakis, Elissaios, 2018. "Economics of renewable energy expansion and security of supply: A dynamic simulation of the German electricity market," Applied Energy, Elsevier, vol. 231(C), pages 1268-1284.
    5. Prüggler, Natalie & Prüggler, Wolfgang & Wirl, Franz, 2011. "Storage and Demand Side Management as power generator’s strategic instruments to influence demand and prices," Energy, Elsevier, vol. 36(11), pages 6308-6317.
    6. Heike Scheben & Nikolai Klempp & Kai Hufendiek, 2020. "Impact of Long-Term Water Inflow Uncertainty on Wholesale Electricity Prices in Markets with High Shares of Renewable Energies and Storages," Energies, MDPI, vol. 13(9), pages 1-21, May.
    7. Ward, K.R. & Green, R. & Staffell, I., 2019. "Getting prices right in structural electricity market models," Energy Policy, Elsevier, vol. 129(C), pages 1190-1206.
    8. 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.
    9. 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).
    10. Graabak, I. & Korpås, M. & Jaehnert, S. & Belsnes, M., 2019. "Balancing future variable wind and solar power production in Central-West Europe with Norwegian hydropower," Energy, Elsevier, vol. 168(C), pages 870-882.
    11. Pape, Christian & Hagemann, Simon & Weber, Christoph, 2016. "Are fundamentals enough? Explaining price variations in the German day-ahead and intraday power market," Energy Economics, Elsevier, vol. 54(C), pages 376-387.
    12. Antonio Bello & Derek Bunn & Javier Reneses & Antonio Muñoz, 2016. "Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices," Energies, MDPI, vol. 9(11), pages 1-15, November.
    13. Xiong, Xiaoping & Qing, Guohua, 2023. "A hybrid day-ahead electricity price forecasting framework based on time series," Energy, Elsevier, vol. 264(C).
    Full references (including those not matched with items on IDEAS)

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