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

TL;DR

This paper introduces a machine learning approach that enables real-time, fairness-aware load shedding in power systems by efficiently solving complex optimization problems within milliseconds, ensuring supply-demand balance and preventing blackouts.

Contribution

The paper presents a novel machine learning algorithm that significantly accelerates load shedding optimization, making fairness-aware decisions feasible in real-time power system operations.

Findings

Algorithm achieves millisecond-level computation times.

Validated on both toy and realistic power system models.

Demonstrates effectiveness in fairness and speed for load shedding.

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.