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Robustifying machine-learned algorithms for efficient grid operation (Papers Track)

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 (California Institute of Technology)

Paper PDF Slides PDF Recorded Talk NeurIPS 2022 Poster Topia Link Cite

Uncertainty Quantification & Robustness Power & Energy Reinforcement Learning

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.

Recorded Talk (direct link)

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