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Robustifying machine-learned algorithms for efficient grid operation
Nicolas Christianson · Christopher Yeh · Tongxin Li · Mahdi Torabi Rad · Azarang Golmohammadi · Adam Wierman
Event URL: https://www.climatechange.ai/papers/neurips2022/19 »

We propose a learning-augmented algorithm, RobustML, for operation of dispatchable generation that exploits the good performance of a machine-learned algorithm while providing worst-case guarantees on cost. We evaluate the algorithm on a realistic two-generator system, where it exhibits robustness to distribution shift while enabling improved efficiency as renewable penetration increases.

Author Information

Nicolas Christianson (California Institute of Technology)
Christopher Yeh (California Institute of Technology)
Tongxin Li (The Chinese University of Hong Kong (Shenzhen))
Mahdi Torabi Rad (Beyond Limits)
Azarang Golmohammadi (Beyond Limits, Inc.)
Adam Wierman (Caltech)

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