Saudi Arabia has been seeking to reduce its dependence on oil by diversifying its energy portfolio, including the largely underused energy potential from wind. However, extreme winds can possibly disrupt the wind turbine operations, thus preventing the stable and continuous production of wind energy. In this study, we assess the risk of disruptions of wind turbine operations, based on return levels with a hierarchical spatial extreme modeling approach for wind speeds in Saudi Arabia. Using a unique Weather Research and Forecasting dataset, we provide the first high-resolution risk assessment of wind extremes under spatial non-stationarity over the country. We account for the spatial dependence with a multivariate intrinsic autoregressive prior at the latent Gaussian process level. The computational efficiency is greatly improved by parallel computing on subregions from spatial clustering, and the maps are smoothed by fitting the model to cluster neighbors. Under the Bayesian hierarchical framework, we measure the uncertainty of return levels from the posterior Markov chain Monto Carlo samples, and produce probability maps of return levels exceeding the cut-out wind speed of wind turbines within their lifetime. The probability maps show that locations in the South of Saudi Arabia and near the Red Sea and the Persian Gulf are at very high risk of disruption of wind turbine operations.

沙特阿拉伯一直在寻求通过多样化能源组合来减少对石油的依赖，包括广泛利用的风能潜力。但是，极端风可能会扰乱风力涡轮机的运行，从而阻碍稳定持续产生风能。在这项研究中，我们根据回报水平和沙特阿拉伯风速的分层空间极端建模方法，评估了风力涡轮机运行中断的风险。使用独特的天气研究和预报数据集，我们提供了全国空间不稳定状态下极端风的高分辨率高分辨率风险评估。我们用潜在高斯过程水平的多元固有自回归来解释空间依赖性。通过对来自空间聚类的子区域进行并行计算，大大提高了计算效率，并且通过将模型拟合到聚类邻居来平滑地图。在贝叶斯层次结构框架下，我们测量后马尔可夫链蒙托卡洛样本的收益水平的不确定性，并生成收益水平超过风力涡轮机使用寿命内极限风速的概率图。概率图显示，沙特阿拉伯南部，红海和波斯湾附近的位置极有可能破坏风力发电机的运行。我们从后马尔可夫链Monto Carlo样本中测量返回水平的不确定性，并生成返回水平的概率图，该概率图超过了风力涡轮机使用寿命内的极限风速。概率图显示，沙特阿拉伯南部，红海和波斯湾附近的位置极有可能破坏风力发电机的运行。我们从后马尔可夫链Monto Carlo样本中测量返回水平的不确定性，并生成返回水平的概率图，该概率图超过了风力涡轮机使用寿命内的极限风速。概率图显示，沙特阿拉伯南部，红海和波斯湾附近的位置极有可能破坏风力发电机的运行。