Structured Differentiable Optimization for Efficient Decision-focused Learning in Power Systems
2026arXiv preprint arXiv:2608.04189
Wangkun Xu
Wangkun Xu
Research Associate · Imperial College London
I develop learning and optimisation methods for efficient, robust, and secure power-system operation under high penetrations of renewable energy.
Learning-augmented optimisation Decision-focused learning Power-system security System dynamics
Selected publications
Recent work spanning learning-augmented decision-making including efficient differentiable optimization for power system decision-focused learning and constraint learning for stability-constrained power system optimizations.
IEEE Transactions on Power Systems
* Corresponding author
arXiv preprint arXiv:2603.20472
IEEE Transactions on Power Systems
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Latest news
Delivered the guest lecture An Open-Source Toolbox for Embedding Neural Stability Surrogates in Stability-Constrained Optimization at the University of Strathclyde, Glasgow. Course details.
Open-sourced NCET, a toolbox for embedding neural stability surrogates in stability-constrained optimization. NCET package.
Presented Input Convex Neural Network as a Surrogate in Stability-Constrained Optimization for IBR-dominated Power Systems.
Presented Structured Differentiable Optimization for Efficient Decision-Focused Learning in Power Systems at the EPICS Bimonthly Meeting, covering theory, software, and IEEE 118-bus evidence. Slides.
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Featured software
Reusable scientific packages, research implementations, and open teaching resources.
Neural stability surrogates · Stability-constrained optimization
Differentiable optimisation · Decision-focused learning
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Current teaching
ELEC97077 · Smart Grid Technology
Machine Learning and Data Science for Power Systems
Guest Lecturer · Lecturing, coursework, demonstrations, tutorials, and marking · 5 hours of lectures + 1-hour tutorial
MECH70039 · Data Science and Digitalisation
Introduction to Machine Learning for Energy Systems
Guest Lecturer · Lecture and hands-on tutorial · 2-hour lecture + 2-hour tutorial
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