Online Decision Making for Trading Wind Energy
Machine Learning
2023-05-22 v3 Systems and Control
Systems and Control
Abstract
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.
Keywords
Cite
@article{arxiv.2209.02009,
title = {Online Decision Making for Trading Wind Energy},
author = {Miguel Angel Muñoz and Pierre Pinson and Jalal Kazempour},
journal= {arXiv preprint arXiv:2209.02009},
year = {2023}
}