Machine Learning for Equitable Load Shedding: Real-time Solution via Learning Binding Constraints
<|reference_start|>Machine Learning for Equitable Load Shedding: Real-time Solution via Learning Binding Constraints: Timely and effective load shedding in power systems is critical for maintaining supply-demand balance and preventing cascading blackouts. To eliminate load shedding bias against specific regions in the system, optimization-based methods are uniquely positioned to help balance between economical and equity considerations. However, the resulting optimization problem involves complex constraints, which can be time-consuming to solve and thus cannot meet the real-time requirements of load shedding. To tackle this challenge, in this paper we present an efficient machine learning algorithm to enable millisecond-level computation for the optimization-based load shedding problem. Numerical studies on both a 3-bus toy example and a realistic RTS-GMLC system have demonstrated the validity and efficiency of the proposed algorithm for delivering equitable and real-time load shedding decisions.<|reference_end|>
@article{zhou2024machine,
    title={Machine Learning for Equitable Load Shedding: Real-time Solution via
  Learning Binding Constraints},
    author={Yuqi Zhou, Joseph Severino, Sanjana Vijayshankar, Juliette
  Ugirumurera, and Jibo Sanyal},
    journal={arXiv preprint arXiv:2407.18989},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18989},
    primaryClass={eess.SY cs.AI cs.SY}
}