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Mapping price dynamics across electricity market designs: A functional data approach with STL decomposition

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  • Yu, Miao
  • Sibbertsen, Philipp

Abstract

This study develops a functional data analysis (FDA) framework to investigate how electricity price dynamics respond to changes in energy generation across countries with different market structures. Using Seasonal-Trend decomposition using Loess (STL) decomposition, electricity prices are separated into trend, seasonal, and irregular components to distinguish market-driven cycles from policy-driven signals. Functional principal component analysis (FPCA) is applied to extract shared and country-specific seasonal features, while multivariate functional response analysis (FRA) quantifies the time-varying influence of energy generation on price. The analysis covers six countries, Germany, the United Kingdom, the United States, France, China, and Brazil, which represent a spectrum of liberalized, semi-regulated, and regionally fragmented electricity markets. The results reveal that renewables in liberalized systems drive immediate price volatility, while policy coordination in semi-regulated systems leads to delayed and smoothed price adjustments. By linking institutional design with functional elasticity patterns, the study offers a unified approach to evaluating the interplay between price signals and energy generation in diverse regulatory contexts.

Suggested Citation

  • Yu, Miao & Sibbertsen, Philipp, 2026. "Mapping price dynamics across electricity market designs: A functional data approach with STL decomposition," Applied Energy, Elsevier, vol. 407(C).
    • Handle: RePEc:eee:appene:v:407:y:2026:i:c:s0306261926000425
    DOI: 10.1016/j.apenergy.2026.127390
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    References listed on IDEAS

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