  Greater direct electrification of end-use sectors with a higher share of
renewables is one of the pillars to power a carbon-neutral society by 2050.
However, in contrast to conventional power plants, renewable energy is subject
to uncertainty raising challenges for their interaction with power systems.
Scenario-based probabilistic forecasting models have become a vital tool to
equip decision-makers. This paper presents to the power systems forecasting
practitioners a recent deep learning technique, the normalizing flows, to
produce accurate scenario-based probabilistic forecasts that are crucial to
face the new challenges in power systems applications. The strength of this
technique is to directly learn the stochastic multivariate distribution of the
underlying process by maximizing the likelihood. Through comprehensive
empirical evaluations using the open data of the Global Energy Forecasting
Competition 2014, we demonstrate that this methodology is competitive with
other state-of-the-art deep learning generative models: generative adversarial
networks and variational autoencoders. The models producing weather-based wind,
solar power, and load scenarios are properly compared in terms of forecast
value by considering the case study of an energy retailer and quality using
several complementary metrics. The numerical experiments are simple and easily
reproducible. Thus, we hope it will encourage other forecasting practitioners
to test and use normalizing flows in power system applications such as bidding
on electricity markets, scheduling power systems with high renewable energy
sources penetration, energy management of virtual power plan or microgrids, and
unit commitment.
