Machine Learning for Fairness-Aware Load Shedding: A Real-Time Solution via Identifying Binding Constraints
This work addresses the problem of preventing biased load shedding in power systems for grid operators, though it is incremental as it builds on existing optimization-based methods.
The paper tackles the challenge of achieving real-time, fairness-aware load shedding in power systems by developing an efficient machine learning algorithm that reduces computation time to millisecond levels, as validated on a toy example and a realistic system.
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.