Addressing Spatiotemporal Mismatch via Hourly Pipeline Scheduling: Regional Hydrogen Energy Supply Optimization

Lei Yu, Xinhao Lin, Yinliang Liu, Shuyin Duan, Lvzerui Yuan, Yiyong Lei, Xueyan Wu and Qingwei Li ()
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Lei Yu: CSG Electric Power Research Institute Co., Ltd., Guangzhou 510663, China
Xinhao Lin: CSG Electric Power Research Institute Co., Ltd., Guangzhou 510663, China
Yinliang Liu: CSG Electric Power Research Institute Co., Ltd., Guangzhou 510663, China
Shuyin Duan: CSG Electric Power Research Institute Co., Ltd., Guangzhou 510663, China
Lvzerui Yuan: CSG Electric Power Research Institute Co., Ltd., Guangzhou 510663, China
Yiyong Lei: China Southern Power Grid Co., Ltd., Guangzhou 510663, China
Xueyan Wu: CSG Electric Power Research Institute Co., Ltd., Guangzhou 510663, China
Qingwei Li: CSG Electric Power Research Institute Co., Ltd., Guangzhou 510663, China

Energies, 2025, vol. 18, issue 21, 1-38

Abstract: The rapid adoption of hydrogen fuel cell vehicles (HFCVs) in the Beijing–Tianjin–Hebei (BTH) hub accentuates the mismatch between renewable-based hydrogen supply in Hebei and concentrated demand in Beijing and Tianjin. We develop a mixed-integer linear model that co-configures a hydrogen pipeline network and optimizes hourly flow schedules to minimize annualized cost and CO 2 emissions simultaneously. For 15,000 HFCVs expected in 2025 (137 t d −1 demand), the Pareto-optimal design consists of 13 production plants, 43 pipelines and 38 refueling stations, delivering 50 767 t yr −1 at 68% pipeline utilization. Hebei provides 88% of the hydrogen, 70% of which is consumed in the two megacities. Hourly profiles reveal that 65% of electrolytic output coincides with local wind–solar peaks, whereas refueling surges arise during morning and evening rush hours; the proposed schedule offsets the 4–6 h mismatch without additional storage. Transport distances are 40% < 50 km, 35% 50–200 km, and 25% > 200 km. Raising the green hydrogen share from 10% to 70% increases total system cost from USD 1.56 bn to USD 2.73 bn but cuts annual CO 2 emissions from 142 kt to 51 kt, demonstrating the trade-off between cost and decarbonization. The model quantifies the value of sub-day pipeline scheduling in resolving spatial–temporal imbalances for large-scale low-carbon hydrogen supply.

Keywords: mixed-integer linear programming (MILP); spatiotemporal optimization; pipeline scheduling; renewable energy integration; hydrogen economy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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