Inherent spatiotemporal uncertainty of renewable power in China

Jianxiao Wang, Liudong Chen, Zhenfei Tan, Ershun Du, Nian Liu, Jing Ma, Mingyang Sun, Canbing Li, Jie Song, Xi Lu (), Chin-Woo Tan () and Guannan He ()
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Jianxiao Wang: Peking University
Liudong Chen: Columbia University
Zhenfei Tan: Shanghai Jiao Tong University
Ershun Du: Tsinghua University
Nian Liu: North China Electric Power University
Jing Ma: North China Electric Power University
Mingyang Sun: Zhejiang University
Canbing Li: Shanghai Jiao Tong University
Jie Song: Peking University
Xi Lu: Tsinghua University
Chin-Woo Tan: Stanford University
Guannan He: Peking University

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: Abstract Solar and wind resources are vital for the sustainable energy transition. Although renewable potentials have been widely assessed in existing literature, few studies have examined the statistical characteristics of the inherent renewable uncertainties arising from natural randomness, which is inevitable in stochastic-aware research and applications. Here we develop a rule-of-thumb statistical learning model for wind and solar power prediction and generate a year-long dataset of hourly prediction errors of 30 provinces in China. We reveal diversified spatiotemporal distribution patterns of prediction errors, indicating that over 60% of wind prediction errors and 50% of solar prediction errors arise from scenarios with high utilization rates. The first-order difference and peak ratio of generation series are two primary indicators explaining the uncertainty distribution. Additionally, we analyze the seasonal distributions of the provincial prediction errors that reveal a consistent law in China. Finally, policies including incentive improvements and interprovincial scheduling are suggested.

Date: 2023
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DOI: 10.1038/s41467-023-40670-7

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