Abstract
With the increasing penetration of renewable resources such as wind and
solar, especially in terms of large-scale integration, the operation and
planning of power systems are faced with great risks due to the inherent
stochasticity of natural resources. Although this uncertainty can be
anticipated, the timing, magnitude, and duration of fluctuations cannot
be predicted accurately. In addition, the outputs of renewable power
sources are correlated in space and time, and this brings further
challenges for predicting the characteristics of their future behavior.
To address these issues, this paper describes an unsupervised
distribution learning method for renewable scenario forecasts that
considers spatiotemporal correlation based on generative adversarial
network (GAN), which has been shown to generate realistic time series
for stochastic processes. We first utilize an improved GAN to learn
unknown data distributions and model the dynamic processes of renewable
resources. We then generate a large number of forecasted scenarios using
stochastic constrained optimization. For validation, we use power
generation data from the National Renewable Energy Laboratory wind and
solar integration datasets. The simulation results show that the
generated trajectories not only reflect the future power generation
dynamics, but also correctly capture the temporal, spatial, and
fluctuant characteristics of the real power generation processes. The
experimental comparisons verify the superiority of the proposed method
and indicate that it can reduce at least 50% of the training iterations
of the generative model for scenario forecasts.