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Lookup NU author(s): Jie Zhu, Dr Shahab DehghanORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2026 The Author(s). Renewable power-to-hydrogen (ReP2H) systems have emerged as an effective solution for local renewable consumption, but their economic scheduling remains challenging. On one hand, the nonlinear efficiency of electrolyzers introduce strong nonconvexities into the optimization problem. On the other hand, ensuring reliable operation of low-inertia ReP2H systems requires enforcing nonlinear transient frequency-security constraints, which significantly increase computational complexity. To address these challenges, this paper proposes a novel frequency-constrained scheduling framework tailored for ReP2H systems based on self-supervised learning (SSL). A deep neural network (DNN)-based frequency dynamics surrogate (FDS) is first developed and trained through an accelerated structure function-enhanced training strategy, which balance the high predictive accuracy and training efficiency under limited budgets of labeled data generation. The trained FDS is then embedded within an SSL-based primal–dual learning SSL (PDL-SSL) optimization framework, where the primal and dual DNNs are jointly trained to learn a scheduling proxy that preserves the nonlinear dynamics of electrolyzers and allows end-to-end training without relying on pre-solved optimal decisions. Finally, case studies on 14-node and 33-node ReP2H systems demonstrate that the proposed method achieves near-optimal scheduling results while satisfying frequency-security constraints with substantially reduced computation time.
Author(s): Zhu J, Dehghan S
Publication type: Article
Publication status: Published
Journal: Electric Power Systems Research
Year: 2027
Volume: 263
Print publication date: 01/02/2027
Online publication date: 14/07/2026
Acceptance date: 02/04/2018
Date deposited: 04/08/2026
ISSN (print): 0378-7796
ISSN (electronic): 1873-2046
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.epsr.2026.113784
DOI: 10.1016/j.epsr.2026.113784
Data Access Statement: Data will be made available on request.
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