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Modelling the impact of wind generation on electricity market prices in Ireland: An econometric versus unit commitment approach

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  • Denny, Eleanor
  • O'Mahoney, Amy
  • Lannoye, Eamonn

Abstract

The aim of this research is to assess the impact of wind generation on electricity market prices using two different approaches: an econometric regression model and a unit commitment model (the latter being a methodological approach favoured by the engineering discipline). Overall, the findings of this paper indicate that wind generation reduces the marginal price on the case study electricity system, a result which is consistent across both methodological approaches. The level of savings is non-trivial and in the order of 4–5.4% of total dispatch cost. It is also found that the relationship between wind and prices is linear. The majority of literature in this area uses either an econometric or a unit commitment approach but not both, thus this paper allows for the isolation of the impact of modelling approach on the results. It finds that the results from both methods are comparable and the choice of modelling approach influences the results by just 1.4%. Thus, depending on the type of analysis required, an ex-post econometric model may be preferred by policy makers to an ex-post unit commitment model given the significant reduction in data requirements and computational time involved.

Suggested Citation

  • Denny, Eleanor & O'Mahoney, Amy & Lannoye, Eamonn, 2017. "Modelling the impact of wind generation on electricity market prices in Ireland: An econometric versus unit commitment approach," Renewable Energy, Elsevier, vol. 104(C), pages 109-119.
    • Handle: RePEc:eee:renene:v:104:y:2017:i:c:p:109-119
    DOI: 10.1016/j.renene.2016.11.003
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    4. Khoshrou, Abdolrahman & Dorsman, André B. & Pauwels, Eric J., 2019. "The evolution of electricity price on the German day-ahead market before and after the energy switch," Renewable Energy, Elsevier, vol. 134(C), pages 1-13.
    5. Woo-Cheol Jeong & Da-Han Lee & Jae Hyung Roh & Jong-Bae Park, 2022. "Scenario Analysis of the GHG Emissions in the Electricity Sector through 2030 in South Korea Considering Updated NDC," Energies, MDPI, vol. 15(9), pages 1-12, May.
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    7. Shao, Jing & Chen, Huanhuan & Li, Jinke & Liu, Guy, 2022. "An evaluation of the consumer-funded renewable obligation scheme in the UK for wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    8. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression," Applied Energy, Elsevier, vol. 239(C), pages 610-625.
    9. Schleifer, Anna H. & Murphy, Caitlin A. & Cole, Wesley J. & Denholm, Paul, 2022. "Exploring the design space of PV-plus-battery system configurations under evolving grid conditions," Applied Energy, Elsevier, vol. 308(C).
    10. Doering, Kenji & Sendelbach, Luke & Steinschneider, Scott & Lindsay Anderson, C., 2021. "The effects of wind generation and other market determinants on price spikes," Applied Energy, Elsevier, vol. 300(C).
    11. Moreno, Blanca & Díaz, Guzmán, 2019. "The impact of virtual power plant technology composition on wholesale electricity prices: A comparative study of some European Union electricity markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 99(C), pages 100-108.
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    More about this item

    Keywords

    Renewable resources; Marginal pricing; Wind; Merit order; Unit commitment;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions

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