Machine Learning for Equitable Load Shedding: Real-time Solution via Learning Binding Constraints

• URL: http://arxiv.org/abs/2407.18989v2
• Date: Mon, 30 Sep 2024 04:19:10 GMT
• Title: Machine Learning for Equitable Load Shedding: Real-time Solution via Learning Binding Constraints
• Authors: Yuqi Zhou, Joseph Severino, Sanjana Vijayshankar, Juliette Ugirumurera, Jibo Sanyal,
• Abstract summary: 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.
• Score: 1.3345486884341395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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