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

• URL: http://arxiv.org/abs/2507.17194v1
• Date: Wed, 23 Jul 2025 04:39:29 GMT
• Title: Dispatch-Aware Deep Neural Network for Optimal Transmission Switching: Toward Real-Time and Feasibility Guaranteed Operation
• Authors: Minsoo Kim, Jip Kim,
• Abstract summary: We propose a dispatch-aware deep neural network (DA-DNN) that accelerates DC-OTS without relying on pre-solved labels.<n>DA-DNN predicts line states and passes them through a differentiable DC-OPF layer.<n>It produces a provably feasible topology and dispatch pair in the same time as solving the DCOPF.
• Score: 3.3894236476098185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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