Online Decision Making for Trading Wind Energy
This work addresses the challenge of optimizing wind energy trading in dynamic electricity markets, representing an incremental improvement through a hybrid method.
The authors tackled the problem of trading wind energy in electricity markets by developing an online learning algorithm that combines adaptive gradient descent with a feature-driven newsvendor model, resulting in significant economic gains and better adaptability to nonstationary conditions.
We propose and develop a new algorithm for trading wind energy in electricity markets, within an online learning and optimization framework. In particular, we combine a component-wise adaptive variant of the gradient descent algorithm with recent advances in the feature-driven newsvendor model. This results in an online offering approach capable of leveraging data-rich environments, while adapting to the nonstationary characteristics of energy generation and electricity markets, also with a minimal computational burden. The performance of our approach is analyzed based on several numerical experiments, showing both better adaptability to nonstationary uncertain parameters and significant economic gains.