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Quantifying the effect of renewable generation on day–ahead electricity market prices: The Spanish case

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  • Sánchez de la Nieta, A.A.
  • Contreras, J.

Abstract

The penetration of renewable generation has grown since the electricity sector has been deregulated. To account for that, this paper proposes a methodology to estimate the downward effect of renewable generation participation upon the day-ahead electricity market prices, since such an effect is quite intuitive observing the merit order of the generating units. The European Electricity Market Matching Algorithm (EMMA) is currently based on Euphemia (Price Coupling of Regions), though there are several differences among countries across Europe. The new algorithm proposed uses market orders, which include aggregate hourly orders such as aggregate supply and demand curves. These orders are simple orders and the marginal price is affected by complex orders, especially by the minimum income condition (MIC) used in the Iberian Electricity Market and considered in our proposed algorithm. A case study of the Spanish day-ahead electricity market is evaluated for 2015, for which a daily generation sample is composed of 16 days in 2015. The sample is created following the characteristics of thermal production, renewable production and inframarginal production. The conclusions are drawn comparing the simulations of the real marginal prices and the new marginal prices after incorporating renewable generation participation into the aggregate demand curve at the maximum price.

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  • Sánchez de la Nieta, A.A. & Contreras, J., 2020. "Quantifying the effect of renewable generation on day–ahead electricity market prices: The Spanish case," Energy Economics, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:eneeco:v:90:y:2020:i:c:s014098832030181x
    DOI: 10.1016/j.eneco.2020.104841
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    2. Li, Yanxue & Xie, Yun & Zhang, Xiaoyi & Xiao, Fu & Gao, Weijun, 2024. "Grid variability and value assessment of long-duration energy storage under rising photovoltaic penetration: Evidence from Japan," Energy, Elsevier, vol. 307(C).
    3. Mahler, Valentin & Girard, Robin & Kariniotakis, Georges, 2022. "Data-driven structural modeling of electricity price dynamics," Energy Economics, Elsevier, vol. 107(C).
    4. Divényi, Dániel & Polgári, Beáta & Sleisz, Ádám & Sőrés, Péter & Raisz, Dávid, 2021. "Investigating minimum income condition orders on European power exchanges: Controversial properties and enhancement proposals," Applied Energy, Elsevier, vol. 281(C).
    5. Graf, Christoph & Quaglia, Federico & Wolak, Frank A., 2021. "(Machine) learning from the COVID-19 lockdown about electricity market performance with a large share of renewables," Journal of Environmental Economics and Management, Elsevier, vol. 105(C).
    6. Rinne, Sonja, 2024. "Estimating the merit-order effect using coarsened exact matching: Reconciling theory with the empirical results to improve policy implications," Energy Policy, Elsevier, vol. 185(C).
    7. Valentin Mahler & Robin Girard & Georges Kariniotakis, 2021. "Data-driven Structural Modeling of Electricity Price Dynamics," Working Papers hal-03445396, HAL.
    8. Arango-Aramburo, Santiago & Bernal-García, Sebastián & Larsen, Erik R., 2021. "Renewable energy sources and the cycles in deregulated electricity markets," Energy, Elsevier, vol. 223(C).

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