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Hourly Electricity Price Prediction for Electricity Market with High Proportion of Wind and Solar Power

Author

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  • Yangrui Zhang

    (Marketing Service Center, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China)

  • Peng Tao

    (Marketing Service Center, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China)

  • Xiangming Wu

    (State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China)

  • Chenguang Yang

    (State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China)

  • Guang Han

    (State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China)

  • Hui Zhou

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Yinlong Hu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

Abstract

In an open electricity market, increased accuracy and real-time availability of electricity price forecasts can help market parties participate effectively in market operations and management. As the penetration of clean energy increases, it brings new challenges to electricity price forecasting. An electricity price forecasting model is constructed in this paper for markets containing a high proportion of wind and solar power, where the scenario with a high coefficient of variation (COV) caused by the high frequency of low electricity prices is particularly concerned. The deep extreme learning machine optimized by the sparrow search algorithm (SSA-DELM) is proposed to make predictions on the model. The results show that wind–load ratio and solar–load ratio are the key input variables for forecasting in power markets with high proportions of wind and solar energy. The SSA-DELM possesses better electricity price forecasting performance in the scenario with a high COV and is more suitable for disordered time series models, which can be confirmed in comparison with LSTM.

Suggested Citation

  • Yangrui Zhang & Peng Tao & Xiangming Wu & Chenguang Yang & Guang Han & Hui Zhou & Yinlong Hu, 2022. "Hourly Electricity Price Prediction for Electricity Market with High Proportion of Wind and Solar Power," Energies, MDPI, vol. 15(4), pages 1-13, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1345-:d:748307
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    References listed on IDEAS

    as
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    Cited by:

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    2. Di Zhu & Yinghong Wang & Fenglin Zhang, 2022. "Energy Price Prediction Integrated with Singular Spectrum Analysis and Long Short-Term Memory Network against the Background of Carbon Neutrality," Energies, MDPI, vol. 15(21), pages 1-20, October.
    3. Laiqing Yan & Zutai Yan & Zhenwen Li & Ning Ma & Ran Li & Jian Qin, 2023. "Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm," Energies, MDPI, vol. 16(13), pages 1-18, July.
    4. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2024. "Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting," Applied Energy, Elsevier, vol. 353(PA).

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