Input Convex Neural Network as a Surrogate in Stability-Constrained Optimization for IBR-dominated Power Systems

Wangkun Xu; Hongyang Jia; Yi Wang; Ning Zhang; Fei Teng

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arXiv · 2606.26446

Input Convex Neural Network as a Surrogate in Stability-Constrained Optimization for IBR-dominated Power Systems

Wangkun Xu·Hongyang Jia·Yi Wang·Ning Zhang·Fei Teng

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Abstract

Input convex neural networks (ICNNs) are increasingly used as surrogates for stability indices and embedded as constraints in power-system optimization. This letter clarifies two recurring formulation limitations that can negate ICNN convexity benefits: (i) applying generic Big-$M$ mixed-integer reformulations introduces auxiliary binaries that are unnecessary for enforcing ICNN sublevel constraints; and (ii) reversing the stability inequality transforms a convex sublevel set into a generally nonconvex superlevel set, invalidating global-convergence guarantees of cut-based methods. After clarifying the limitations, we provide (i) an exact LP-based epigraph reformulation for ReLU-ICNNs, (ii) an outer-approximation scheme with global guarantees under the sublevel convention, and (iii) a feasibility-preserving inner-approximation scheme for the superlevel convention, with simulations on IEEE 14- and 118-bus unit commitment instances.

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Wangkun Xu, Hongyang Jia, Yi Wang, Ning Zhang, Fei Teng. 2026-06-24. Input Convex Neural Network as a Surrogate in Stability-Constrained Optimization for IBR-dominated Power Systems. https://arxiv.org/abs/2606.26446

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