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Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices

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  • Madadkhani, Shiva
  • Ikonnikova, Svetlana

Abstract

The growing share of renewables, the retirement of coal generation, and the increasing significance (and price) of carbon emissions continue to reshape electricity market dynamics. Ongoing power sector transformations, along with recurrent energy price shocks, bring new challenges to electricity price prediction and projection. Adept at capturing nonlinear and time-varying dynamics of the power market, machine learning (ML) methods can significantly enhance modeling and projection capabilities. This paper leverages ML modeling to (1) analyze the day-ahead electricity price dynamics and (2) develop future price projections under varying market conditions. First, we offer a methodology for identifying and ranking the factors determining day-ahead power price behavior. Guided by the understanding of power price drivers, we then develop an ensemble ML model for high-resolution (i.e., for each day of the next year) electricity price projection. Hence, the first part of our analysis helps relate fundamental modeling approaches to ML studies, and the second part extends the existing literature, which is limited to high aggregation level (e.g., yearly basis) projections. High-resolution projections provide valuable insights into within-year price dynamics and help answer highly debated questions raised by the growing share of renewables and the current energy market turmoil. We demonstrate our methodology on the German power market, using data on 80 explanatory variables from 2015 to 2021. We identify the most critical power price drivers and develop an ML model to study the within-year impact of high fuel and carbon prices. Our findings indicate that the often-neglected interaction variables may significantly impact price projections and should be included in day-ahead electricity price modeling. We confirm the efficacy of our approach with a forecasting exercise based on day-ahead electricity prices in the first two quarters of 2022.

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  • Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    • Handle: RePEc:eee:eneeco:v:129:y:2024:i:c:s0140988323007399
    DOI: 10.1016/j.eneco.2023.107241
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