Long-Term Hourly Scenario Generation for Correlated Wind and Solar Power
  combining Variational Autoencoders with Radial Basis Function Kernels
Julio Alberto Silva Dias
Accurate generation of realistic future scenarios of renewable energy
generation is crucial for long-term planning and operation of electrical
systems, especially considering the increasing focus on sustainable energy and
the growing penetration of renewable generation in energy matrices. These
predictions enable power system operators and energy planners to effectively
manage the variability and intermittency associated with renewable generation,
allowing for better grid stability, improved energy management, and enhanced
decision-making processes. In this paper, we propose an innovative method for
generating long-term hourly scenarios for wind and solar power generation,
taking into consideration the correlation between these two energy sources. To
achieve this, we combine the capabilities of a Variational Autoencoder (VAE)
with the additional benefits of incorporating the Radial Basis Function (RBF)
kernel in our artificial neural network architecture. By incorporating them, we
aim to obtain a latent space with improved regularization properties. To
evaluate the effectiveness of our proposed method, we conduct experiments in a
representative study scenario, utilizing real-world wind and solar power
generation data from the Brazil system. We compare the scenarios generated by
our model with the observed data and with other sets of scenarios produced by a
conventional VAE architecture. Our experimental results demonstrate that the
proposed method can generate long-term hourly scenarios for wind and solar
power generation that are highly correlated, accurately capturing the temporal
and spatial characteristics of these energy sources. Taking advantage of the
benefits of RBF in obtaining a well-regularized latent space, our approach
offers improved accuracy and robustness in generating long-term hourly
scenarios for renewable energy generation.