Machine Learning Techniques for Decarbonizing and Managing Renewable Energy Grids

Muqing Wu, Qingsu He, Yuping Liu, Ziqiang Zhang, Zhongwen Shi and Yifan He ()
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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
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

Sustainability, 2022, vol. 14, issue 21, 1-13

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.

Keywords: machine learning; renewable-energy grid system; probability distribution of wind power output; dispatching optimization model; short-term prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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