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arXiv · 2407.18989

Machine Learning for Fairness-Aware Load Shedding: A Real-Time Solution via Identifying Binding Constraints

Yuqi Zhou·Joseph Severino·Sanjana Vijayshankar·Juliette Ugirumurera·Jibo Sanyal
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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 economic and fairness 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 fairness-aware and real-time load shedding decisions.

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Yuqi Zhou, Joseph Severino, Sanjana Vijayshankar, Juliette Ugirumurera, Jibo Sanyal. 2024-07-25. Machine Learning for Fairness-Aware Load Shedding: A Real-Time Solution via Identifying Binding Constraints. https://arxiv.org/abs/2407.18989

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