Abstract
With the increasing penetration of renewable resources, such as wind and
solar, the operation and planning of power systems, especially in
large-scale integration, are faced with great risks due to the inherent
stochasticity of natural resources. Although this uncertainty is
anticipated, their timing, magnitude and duration cannot be predicted
accurately. In addition, the renewable power outputs are correlated in
space and time and bring further challenges in characterizing their
behaviors. To address these issues, this paper provides a data-driven
method to forecast renewable scenarios considering its spatiotemporal
correlations based on generative adversarial networks (GANs), which has
the ability to generated realistic samples from an unknown distribution
making them one of the hottest areas in artificial intelligence
research. We first utilize GANs to learn the intrinsic patterns and
model the dynamic processes of renewable energy sources. Then by solving
an optimization problem, we are able to generate large number of
day-ahead forecasting scenarios. For validation, we use power generation
data from NREL wind and solar integration data sets. The experimental
results of this present research accord with the expectations.