Aggregated stochastic characteristics of geographically distributed wind generation will provide valuable information for secured and economical system operation in electricity markets. This paper focuses on the uncertainty set prediction of the aggregated generation of geographically distributed wind farms. A Spatio-temporal model is proposed to learn the dynamic features from partial observation in near-surface wind fields of neighboring wind farms. We use Bayesian LSTM, a probabilistic prediction model, to obtain the uncertainty set of the generation in individual wind farms. Then, spatial correlation between different wind farms is presented to correct the output results. Numerical testing results based on the actual data with 6 wind farms in northwest China show that the uncertainty set of aggregated wind generation of distributed wind farms is less volatile than that of a single wind farm.

翻译：地理分布式风力发电的综合随机特征将为电力市场安全、经济的系统运行提供宝贵信息,本文件侧重于对地理分布式风力农场总发电量的不确定性预测,建议采用时空空间模型,从邻近风力农场近地风力场局部观测中学习动态特征,我们使用概率预测模型Bayesian LSTM, 获取单个风力农场中发电量的不确定性。然后,介绍不同风力农场之间的空间相关性,以纠正产出结果。根据中国西北部6个风力农场的实际数据得出的数值测试结果显示,分布式风力农场总风力发电量的不确定性小于单一风力农场的不确定性。