Environmental Data Science · 2025
Robust machine-learned algorithms for efficient grid operation
Environmental Data Science
Can learned dispatch policies remain reliable under distribution shift?
RobustML combines a learned dispatch algorithm with a bound on worst case cost. A combined cycle cogeneration case study examines dispatch and ramping costs as renewable penetration increases, including performance under distribution shift.
Research themes
Connections
Guarantees meet physical infrastructure
Aggregate flexibility communicates feasible actions for coordinated charging. Robust dispatch and safe water control connect performance guarantees to operational costs and physical constraints.
Cite this paper
Nicolas Christianson, Christopher Yeh, Tongxin Li, Mehdi Hosseini, Mahdi Torabi Rad, Azarang Golmohammadi, Adam Wierman. Robust machine-learned algorithms for efficient grid operation. Environmental Data Science, 2025. https://doi.org/10.1017/eds.2024.28
BibTeX
@article{tongxin-robust-grid,
title = {{Robust machine-learned algorithms for efficient grid operation}},
author = {Nicolas Christianson and Christopher Yeh and Tongxin Li and Mehdi Hosseini and Mahdi Torabi Rad and Azarang Golmohammadi and Adam Wierman},
year = {2025},
journal = {Environmental Data Science},
url = {https://doi.org/10.1017/eds.2024.28},
doi = {10.1017/eds.2024.28}
}
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