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Probabilistic Day-Ahead Wholesale Price Forecast: A Case Study in Great Britain

Author

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  • Stephen Haben

    (Energy Systems Catapult, Cannon House, Birmingham B4 6BS, UK
    Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK)

  • Julien Caudron

    (Energy Systems Catapult, Cannon House, Birmingham B4 6BS, UK)

  • Jake Verma

    (Energy Systems Catapult, Cannon House, Birmingham B4 6BS, UK)

Abstract

The energy sector is moving towards a low-carbon, decentralised, and smarter network. The increased uptake of distributed renewable energy and cheaper storage devices provide opportunities for new local energy markets. These local energy markets will require probabilistic price forecasting models to better describe the future price uncertainty. This article considers the application of probabilistic electricity price forecasting models to the wholesale market of Great Britain (GB) and compares them to better understand their capabilities and limits. One of the models that this paper considers is a recent novel X-model that predicts the full supply and demand curves from the bid-stack. The advantage of this model is that it better captures price spikes in the data. In this paper, we provide an adjustment to the model to handle data from GB. In addition to this, we then consider and compare two time-series approaches and a simple benchmark. We compare both point forecasts and probabilistic forecasts on real wholesale price data from GB and consider both point and probabilistic measures.

Suggested Citation

  • Stephen Haben & Julien Caudron & Jake Verma, 2021. "Probabilistic Day-Ahead Wholesale Price Forecast: A Case Study in Great Britain," Forecasting, MDPI, vol. 3(3), pages 1-37, August.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:3:p:38-632:d:623967
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    References listed on IDEAS

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    Cited by:

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    3. Diankai Wang & Inna Gryshova & Mykola Kyzym & Tetiana Salashenko & Viktoriia Khaustova & Maryna Shcherbata, 2022. "Electricity Price Instability over Time: Time Series Analysis and Forecasting," Sustainability, MDPI, vol. 14(15), pages 1-24, July.

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