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

Shengshi Wang, Jiakun Fang, Jianzhong Wu, Xiaomeng Ai, Shichang Cui, Yue Zhou, Wei Gan, Xizhen Xue, Danji Huang, Hongyu Zhang and Jinyu Wen

Applied Energy, 2025, vol. 381, issue C, No S0306261924024693

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.

Keywords: Spatially-cascaded distributed coordination; Shared transmission system for renewable fuels and refined oil; Multi-product sequential transmission; Uncertain parameter; Spatial gradient learning method; Quasi-optimality preservation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2024.125085

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