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Machine Learning Techniques for Decarbonizing and Managing Renewable Energy Grids

Author

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  • Muqing Wu

    (Beijing Laboratory of Advanced Information Networks and Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Qingsu He

    (Beijing Laboratory of Advanced Information Networks and Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China
    State Grid Gansu Elect Power Co., Lanzhou 730060, China)

  • Yuping Liu

    (State Grid Gansu Elect Power Co., Lanzhou 730060, China)

  • Ziqiang Zhang

    (State Grid Gansu Elect Power Co., Lanzhou 730060, China)

  • Zhongwen Shi

    (State Grid Gansu Elect Power Co., Lanzhou 730060, China)

  • Yifan He

    (University of California, Santa Cruz, CA 95064, USA)

Abstract

Given the vitality of the renewable-energy grid market, the optimal allocation of clean energy is crucial. An optimal dispatching method for source–load coordination of renewable-energy grid is proposed. An improved K-means clustering algorithm is used to preprocess the source data and historical load data. A support vector machine is used to predict the cluster of renewable-energy grid resources and load data, and typical scenarios are selected from the prediction results. Taking typical scenarios as a representative, the probability distribution of wind power output is accurately obtained. An optimization model of the total operation cost of the renewable-energy grid is established. The experimental results show that the algorithm reduces the error between the predicted value and the actual value. Our method can improve the real-time prediction accuracy of the renewable-energy grid system and increase the economic benefits of the renewable energy grid.

Suggested Citation

  • Muqing Wu & Qingsu He & Yuping Liu & Ziqiang Zhang & Zhongwen Shi & Yifan He, 2022. "Machine Learning Techniques for Decarbonizing and Managing Renewable Energy Grids," Sustainability, MDPI, vol. 14(21), pages 1-13, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:13939-:d:954220
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    References listed on IDEAS

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