揭示青藏高原理论风能潜力：一种针对威布尔双变量分布的新型贝叶斯-蒙特卡洛框架

Abstract Understanding the regional theoretical wind potential is crucial for wind power planning and construction. Previous studies have faced challenges including inconsistent wind speed data quality, unquantified uncertainties in distribution parameters, and flawed methods for estimating theoretical wind potential. Therefore, this study introduced a Hierarchical Bayesian-Monte Carlo framework that processed multi-year and multi-source wind speed data in a probabilistic and hierarchical manner. It could quantify the uncertainties associated with wind speed distributions and their parameters and reduce prediction errors by integrating the historical data. Moreover, the effects of wind speed and air density variations over the blade sweep height and the maximum possible power coefficient were considered on the traditional method of estimating theoretical wind potential. The results showed that the wind speed distributions in the Qinghai-Tibetan Plateau followed Weibull functions, with the prior distributions of their parameters k and λ being gamma functions. Using the Metropolis-Hastings algorithm to simulate the posterior distributions indicated that the overall standard deviations after merging the two chains of k and λ were less than 0.0193 and 0.0244 m/s, respectively. The uncertainties of k and λ were less than 0.08 and 0.097 m/s, respectively. The discrepancies between the predicted and actual wind speeds were less than 0.089 m/s. These findings confirmed the validity and reliability of the Hierarchical Bayesian-Monte Carlo model. Furthermore, in the Qinghai-Tibetan Plateau, 19.31 % of the area had the maximum theoretical wind potential, 21.43 % a high level, and 19.78 % a moderate level. Consequently, the flexible methodological framework established by this study can effectively support the identification of optimal locations for wind power development across regions.