Li, Yuan, Lu, Shuai, Gu, Wei, Yu, Ruizhi, Yao, Shuai  ORCID: https://orcid.org/0000-0002-7202-7961, Xu, Yijun, Zhang, Suhan and Huang, Zhikai
2026.
Accelerating electricity-gas energy flow analysis using global linear port model of natural gas network.
Applied Energy
424
, 128485.
10.1016/j.apenergy.2026.128485
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Abstract
The deepening interdependence between power and natural gas systems renders combined energy flow analysis (CEFA) indispensable for operational security. However, efficient CEFA faces significant impediments due to the intractable nonlinear dynamics of gas networks and the unavailability of accurate physical parameters in practical engineering. To overcome these challenges, this paper proposes a novel CEFA framework for the integrated electricity-gas system (IEGS) utilizing a global linear port model (GLPM) of the natural gas network. First, we derive a global linear representation of pipeline gas dynamics by transforming the governing nonlinear partial differential equations (PDEs) into a high-dimensional linear system via Koopman operator theory. Second, we develop a reduced-order GLPM that exclusively preserves the port variables of the gas network by applying the Schur complement technique. Furthermore, to address data scarcity, we establish a stability-constrained parameter identification model for the GLPM using limited boundary measurements and propose a physics-embedded machine learning algorithm for its solution. Ultimately, the reduced-order GLPM is integrated into the CEFA framework, facilitating the use of standard iterative solvers for rapid analysis. Case studies on multi-scale systems demonstrate the superiority of the proposed method over traditional approaches, achieving a computational speedup of over 70% while maintaining a relative error below 0.6%.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Engineering |
| Additional Information: | RRS policy applied |
| Publisher: | Elsevier |
| ISSN: | 0306-2619 |
| Date of First Compliant Deposit: | 29 July 2026 |
| Date of Acceptance: | 15 July 2026 |
| Last Modified: | 29 Jul 2026 09:00 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/188516 |
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