Scalable Learning for Optimal Load Shedding Under Power Grid Emergency Operations

TL;DR

This paper introduces a decentralized neural network-based method for optimal load shedding that enables fast, scalable, and effective emergency response in power grids, improving resilience during contingencies.

Contribution

It presents a novel offline training approach for neural networks to generate decentralized load shedding decisions applicable to large-scale power grids.

Findings

Neural network decisions are fully decentralized.

The method achieves real-time response capabilities.

Numerical studies validate effectiveness on IEEE 14-bus system.

Abstract

Effective and timely responses to unexpected contingencies are crucial for enhancing the resilience of power grids. Given the fast, complex process of cascading propagation, corrective actions such as optimal load shedding (OLS) are difficult to attain in large-scale networks due to the computation complexity and communication latency issues. This work puts forth an innovative learning-for-OLS approach by constructing the optimal decision rules of load shedding under a variety of potential contingency scenarios through offline neural network (NN) training. Notably, the proposed NN-based OLS decisions are fully decentralized, enabling individual load centers to quickly react to the specific contingency using readily available local measurements. Numerical studies on the IEEE 14-bus system have demonstrated the effectiveness of our scalable OLS design for real-time responses to severe grid emergency events.