Dispatch-Aware Deep Neural Network for Optimal Transmission Switching: Toward Real-Time and Feasibility Guaranteed Operation

TL;DR

This paper introduces a dispatch-aware deep neural network that rapidly predicts optimal transmission switching configurations, ensuring feasibility and scalability for real-time power grid operations.

Contribution

It presents a novel DA-DNN model that enforces physical constraints during training and inference, enabling fast, feasible solutions for large-scale transmission switching problems.

Findings

DA-DNN predicts feasible topologies in real-time.

The method maintains physical feasibility throughout training.

It captures economic benefits of optimal transmission switching.

Abstract

Optimal transmission switching (OTS) improves optimal power flow (OPF) by selectively opening transmission lines, but its mixed-integer formulation increases computational complexity, especially on large grids. To deal with this, we propose a dispatch-aware deep neural network (DA-DNN) that accelerates DC-OTS without relying on pre-solved labels. DA-DNN predicts line states and passes them through a differentiable DC-OPF layer, using the resulting generation cost as the loss function so that all physical network constraints are enforced throughout training and inference. In addition, we adopt a customized weight-bias initialization that keeps every forward pass feasible from the first iteration, which allows stable learning on large grids. Once trained, the proposed DA-DNN produces a provably feasible topology and dispatch pair in the same time as solving the DCOPF, whereas conventional mixed-integer solvers become intractable. As a result, the proposed method successfully captures the economic advantages of OTS while maintaining scalability.