Learning-based spatially-cascaded distributed coordination of shared transmission systems for renewable fuels and refined oil with quasi-optimality preservation under uncertainty

Wang, Shengshi, Fang, Jiakun, Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602, Ai, Xiaomeng, Cui, Shichang, Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714, Gan, Wei, Xue, Xizhen, Huang, Danji, Zhang, Hongyu and Wen, Jinyu 2025. Learning-based spatially-cascaded distributed coordination of shared transmission systems for renewable fuels and refined oil with quasi-optimality preservation under uncertainty. Applied Energy 381 , 125085. 10.1016/j.apenergy.2024.125085 Item availability restricted.

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Abstract

This paper focuses on the distributed optimal coordination framework for energy conservation in the emerging shared transmission systems for renewable fuels and refined oil (STS-RRs) while realizing secure operation with uncertain factors during the energy transition. Specifically, we first propose a practical model for distributed coordination of wide-area pump stations considering sequential transmission features in an STS-RR and variable speed pumps with individual piece-wise linear prejudgment functions (PLPFs) to achieve spatially-cascaded splitting. In the pre-schedule stage, to obtain scenarios-and-spatiality-perceiving slopes of the PLPFs for the stations as well as preserving optimality, a spatial gradient learning method, inspired by the approximate dynamic programming, is designed to acquire prior knowledge from error distribution. In the real-time stage, the models are executed by pump stations based on the real-time measurement information. Both stages are implemented in a spatially-cascaded distributed fashion. The proposed framework was validated using two real-world STS-RRs, demonstrating its feasibility, superior performance, full optimality in ideal conditions, and quasi-optimality under stochastic scenarios, along with good scalability.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0306-2619
Funders:	National Natural Science Foundation
Date of First Compliant Deposit:	31 January 2025
Date of Acceptance:	2 December 2024
Last Modified:	03 Feb 2025 11:45
URI:	https://orca.cardiff.ac.uk/id/eprint/175798

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