Robustifying machine-learned algorithms for efficient grid operation
Nicolas Christianson · Christopher Yeh · Tongxin Li · Mahdi Torabi Rad · Azarang Golmohammadi · Adam Wierman
Abstract
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.
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