Inherent spatiotemporal uncertainty of renewable power in China

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. Renewable uncertainty analysis is vital for stochastic-aware research. This study generates a benchmark dataset of year-long hourly renewable prediction errors in China, and reveals the law of the spatiotemporal distribution of renewable uncertainty.

Automated summary

This study examines the statistical characteristics and spatiotemporal distribution of prediction errors for wind and solar power. A statistical learning model is developed to generate a year-long dataset of hourly prediction errors in 30 provinces in China. The study reveals the sources of prediction errors in scenarios with high utilization rates and analyzes the seasonal distribution of provincial prediction errors.