In this section
@article{Miguel:2023:10.1007/s10287-023-00462-2,
author = {Miguel, Angel M and Pinson, P and Kazempour, J},
doi = {10.1007/s10287-023-00462-2},
journal = {Computational Management Science},
pages = {1--31},
title = {Online decision making for trading wind energy},
url = {http://dx.doi.org/10.1007/s10287-023-00462-2},
volume = {20},
year = {2023}
}
TY - JOUR
AB - 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.
AU - Miguel,Angel M
AU - Pinson,P
AU - Kazempour,J
DO - 10.1007/s10287-023-00462-2
EP - 31
PY - 2023///
SN - 1619-697X
SP - 1
TI - Online decision making for trading wind energy
T2 - Computational Management Science
UR - http://dx.doi.org/10.1007/s10287-023-00462-2
UR - https://link.springer.com/article/10.1007/s10287-023-00462-2
UR - http://hdl.handle.net/10044/1/105217
VL - 20
ER -
Dyson School of Design Engineering
Imperial College London
25 Exhibition Road
South Kensington
London
SW7 2DB
design.engineering@imperial.ac.uk
Tel: +44 (0) 20 7594 8888