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arXiv · 2510.16064

Residual Correction Models for AC Optimal Power Flow Using DC Optimal Power Flow Solutions

Muhy Eddin Za'ter·Bri-Mathias Hodge·Kyri Baker
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Abstract

Solving the nonlinear AC optimal power flow (AC OPF) problem remains a major computational bottleneck for real-time grid operations. In this paper, we propose a residual learning paradigm that uses fast DC optimal power flow (DC OPF) solutions as a baseline, and learns only the nonlinear corrections required to provide the full AC-OPF solution. The method utilizes a topology-aware Graph Neural Network with local attention and two-level DC feature integration, trained using a physics-informed loss that enforces AC power-flow feasibility and operational limits. Evaluations on OPFData for 57-, 118-, and 2000-bus systems show around 25% lower MSE, up to 3X reduction in feasibility error, and up to 13X runtime speedup compared to conventional AC OPF solvers. The model maintains accuracy under N-1 contingencies and scales efficiently to large networks. These results demonstrate that residual learning is a practical and scalable bridge between linear approximations and AC-feasible OPF, enabling near real-time operational decision making.

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Muhy Eddin Za'ter, Bri-Mathias Hodge, Kyri Baker. 2025-10-17. Residual Correction Models for AC Optimal Power Flow Using DC Optimal Power Flow Solutions. https://arxiv.org/abs/2510.16064

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