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Bidding strategy with forecast technology based on support vector machine in the electricity market

Author

Listed:
  • Gao, Ciwei
  • Bompard, Ettore
  • Napoli, Roberto
  • Wan, Qiulan
  • Zhou, Jian

Abstract

The participants in the electricity market are concerned very much with the market price evolution. Various technologies have been developed for price forecasting. The SVM (Support Vector Machine) has shown its good performance in market price forecasting. Two approaches for forming the market bidding strategies based on SVM are proposed. One is based on the price forecasting accuracy, with which the rejection risk is defined. The other takes into account the impact of the producer’s own bid. The risks associated with the bidding are controlled by the parameter settings. The proposed approaches have been tested on a numerical example.

Suggested Citation

  • Gao, Ciwei & Bompard, Ettore & Napoli, Roberto & Wan, Qiulan & Zhou, Jian, 2008. "Bidding strategy with forecast technology based on support vector machine in the electricity market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3874-3881.
    • Handle: RePEc:eee:phsmap:v:387:y:2008:i:15:p:3874-3881
    DOI: 10.1016/j.physa.2008.02.080
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    References listed on IDEAS

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    1. Gao, Ciwei & Bompard, Ettore & Napoli, Roberto & Cheng, Haozhong, 2007. "Price forecast in the competitive electricity market by support vector machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 98-113.
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    Cited by:

    1. Li, Gong & Shi, Jing & Qu, Xiuli, 2011. "Modeling methods for GenCo bidding strategy optimization in the liberalized electricity spot market–A state-of-the-art review," Energy, Elsevier, vol. 36(8), pages 4686-4700.
    2. Andr s Oviedo-G mez & Sandra Milena Londo o-Hern ndez & Diego Fernando Manotas-Duque, 2021. "Electricity Price Fundamentals in Hydrothermal Power Generation Markets Using Machine Learning and Quantile Regression Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 66-77.
    3. Arim Jin & Dahan Lee & Jong-Bae Park & Jae Hyung Roh, 2023. "Day-Ahead Electricity Market Price Forecasting Considering the Components of the Electricity Market Price; Using Demand Decomposition, Fuel Cost, and the Kernel Density Estimation," Energies, MDPI, vol. 16(7), pages 1-19, April.

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