Abstract
With the increasing penetration of renewable resources such as wind and
solar, the operation and planning of power systems, especially in terms
of large-scale integration, 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 method for
renewable scenario forecasts that considers spatiotemporal correlations
based on generative adversarial networks (GANs), which have been shown
to generate high-quality samples. We first utilized an improved GAN to
learn unknown data distributions and model the dynamic processes of
renewable resources. We then generated a large number of forecasted
scenarios using stochastic constrained optimization. For validation, we
used power-generation data from the National Renewable Energy Laboratory
wind and solar integration datasets. The experimental results validated
the effectiveness of our proposed method and indicated that it has
significant potential in renewable scenario analysis.