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Performing price scenario analysis and stress testing using quantile regression: A case study of the Californian electricity market

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  • Westgaard, Sjur
  • Fleten, Stein-Erik
  • Negash, Ahlmahz
  • Botterud, Audun
  • Bogaard, Katinka
  • Verling, Trude Haugsvaer

Abstract

This paper uses quantile regression to demonstrate how electricity price distributions are linked to fundamental supply and demand variables. It investigates the California electricity market (zone SP15) for selected trading hours using data from January 8, 2013 to September 24, 2016. The approach quantifies a non-linear relationship between the fundamentals and electricity prices, just as predicted by the merit order curve. Natural gas, greenhouse gas allowance prices and load all have a positive effect on electricity prices, with the effect increasing with the quantiles. In contrast, solar production and wind production both have a negative effect on electricity prices. The effect of solar production increases with quantiles, whereas the effect of wind production decreases with quantiles. This paper also includes a stress testing case study in which a producer faces the risk of high solar and wind production, and investigates the effect on the lower tail of the price distribution. Overall, the results demonstrate how the proposed approach can be a helpful risk management tool for participants in the electricity market.

Suggested Citation

  • Westgaard, Sjur & Fleten, Stein-Erik & Negash, Ahlmahz & Botterud, Audun & Bogaard, Katinka & Verling, Trude Haugsvaer, 2021. "Performing price scenario analysis and stress testing using quantile regression: A case study of the Californian electricity market," Energy, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:energy:v:214:y:2021:i:c:s0360544220319034
    DOI: 10.1016/j.energy.2020.118796
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    Cited by:

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    2. Hakan Acaroğlu & Fausto Pedro García Márquez, 2021. "Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy," Energies, MDPI, vol. 14(22), pages 1-23, November.
    3. Liu, Luyao & Bai, Feifei & Su, Chenyu & Ma, Cuiping & Yan, Ruifeng & Li, Hailong & Sun, Qie & Wennersten, Ronald, 2022. "Forecasting the occurrence of extreme electricity prices using a multivariate logistic regression model," Energy, Elsevier, vol. 247(C).
    4. Lei, Guowen & Hagspiel, Verena & Stanko, Milan, 2023. "Price stress testing in offshore oil field development planning," Energy, Elsevier, vol. 263(PD).
    5. Janczura, Joanna & Wójcik, Edyta, 2022. "Dynamic short-term risk management strategies for the choice of electricity market based on probabilistic forecasts of profit and risk measures. The German and the Polish market case study," Energy Economics, Elsevier, vol. 110(C).
    6. Jun Dong & Xihao Dou & Aruhan Bao & Yaoyu Zhang & Dongran Liu, 2022. "Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    7. Zheng, Kedi & Chen, Huiyao & Wang, Yi & Chen, Qixin, 2022. "Data-driven financial transmission right scenario generation and speculation," Energy, Elsevier, vol. 238(PC).
    8. Natalia Iwaszczuk & Jacek Wolak & Aleksander Iwaszczuk, 2021. "Turkmenistan’s Gas Sector Development Scenarios Based on Econometric and SWOT Analysis," Energies, MDPI, vol. 14(10), pages 1-18, May.

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