  The operation and planning of large-scale power systems are becoming more
challenging with the increasing penetration of stochastic renewable generation.
In order to minimize the decision risks in power systems with large amount of
renewable resources, there is a growing need to model the short-term generation
uncertainty. By producing a group of possible future realizations for certain
set of renewable generation plants, scenario approach has become one popular
way for renewables uncertainty modeling. However, due to the complex spatial
and temporal correlations underlying in renewable generations, traditional
model-based approaches for forecasting future scenarios often require extensive
knowledge, while fitted models are often hard to scale. To address such
modeling burdens, we propose a learning-based, data-driven scenario forecasts
method based on generative adversarial networks (GANs), which is a class of
deep-learning generative algorithms used for modeling unknown distributions. We
firstly utilize an improved GANs with convergence guarantees to learn the
intrinsic patterns and model the unknown distributions of (multiple-site)
renewable generation time-series. Then by solving an optimization problem, we
are able to generate forecasted scenarios without any scenario number and
forecasting horizon restrictions. Our method is totally model-free, and could
forecast scenarios under different level of forecast uncertainties. Extensive
numerical simulations using real-world data from NREL wind and solar
integration datasets validate the performance of proposed method in forecasting
both wind and solar power scenarios.
