Wind power forecast uncertainty co-varies across Korean regions through asymmetric heavy-tailed connectedness networks

Rapid wind power expansion is vital for global decarbonization, yet its supply uncertainty challenges grid stability. While forecasting can mitigate variability, how multidimensional forecast errors co-vary across interconnected systems remains unclear. Here we develop a framework integrating retrospective statistical diagnosis with forward-looking network modeling to characterize wind power uncertainty along higher-order dimensions – volatility, skewness, and kurtosis – and trace systemic co-dependence through spatially heterogeneous networks. When applied to hourly forecast data from the Republic of Korea’s major wind regions, the framework reveals heavy-tailed error distributions and asymmetric co-dependence. Daily volatility concentrates in dominant sources while extreme risks are distributed across multi-polar hubs, assigning regions differentiated roles as transmitters, receivers, or peripheral nodes. These findings demonstrate that wind power uncertainty is a systemic phenomenon requiring coordinated management rather than isolated forecasting improvements. The framework provides a generalizable toolkit for grid operations, risk-informed investment, and policy design, accelerating a resilient, cost-effective renewable transition.