  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.
