TY - JOUR
T1 - A data-driven approach for the optimization of future two-level hydrogen supply network design with stochastic demand under carbon regulations
AU - Ge, Xianlong
AU - Jin, Yuanzhi
AU - Ren, Jingzheng
N1 - Funding Information:
This research was funded by the General Project of Chongqing Natural Science Foundation [grant number cstc2020jcyj-msxmX0108 ], and the Key scientific and technological innovation project of “Construction of Chengdu-Chongqing Economic Circle” of Chongqing Municipal Education Commission [grant number KJCXZD2020031 ].
Publisher Copyright:
© 2022
PY - 2022/9/10
Y1 - 2022/9/10
N2 - Reducing emission of conventional vehicle exhaust has many benefits for developing low-carbon cities. The transition from on-road conventional passenger cars to Hydrogen Fuel Cell Vehicles (HFCVs) may enhance air quality and health significantly. Aiming at constructing low-cost and environmentally friendly hydrogen supply networks (HSNs) in urban areas, a basic model that considers hydrogen plant and refueling station decisions is presented for the Two-level Hydrogen Supply Network Design (THSND) with stochastic demand. By coupling the basic model and carbon regulations, four extended models are established to evaluate carbon reduction on total cost of HSNs. A data-driven approach that does not require the full information of target areas is developed to solve the models. It introduces a data acquisition method of Points of Interests (POIs), a clustering-based method to select alternative refueling stations, a trip simulation method to determine demand uncertainty, and the application of solvers. The developed data-driven approach is shown to be robust and effective in terms of applicability and generalizability according to the simulations. Meanwhile, the best suitable decision result on plants and stations is both cost-effective and environmentally successful under the model considering carbon cap and trade. The internal relationship between the models indicates that the hybrid carbon emission policy is superior to naive carbon regulations.
AB - Reducing emission of conventional vehicle exhaust has many benefits for developing low-carbon cities. The transition from on-road conventional passenger cars to Hydrogen Fuel Cell Vehicles (HFCVs) may enhance air quality and health significantly. Aiming at constructing low-cost and environmentally friendly hydrogen supply networks (HSNs) in urban areas, a basic model that considers hydrogen plant and refueling station decisions is presented for the Two-level Hydrogen Supply Network Design (THSND) with stochastic demand. By coupling the basic model and carbon regulations, four extended models are established to evaluate carbon reduction on total cost of HSNs. A data-driven approach that does not require the full information of target areas is developed to solve the models. It introduces a data acquisition method of Points of Interests (POIs), a clustering-based method to select alternative refueling stations, a trip simulation method to determine demand uncertainty, and the application of solvers. The developed data-driven approach is shown to be robust and effective in terms of applicability and generalizability according to the simulations. Meanwhile, the best suitable decision result on plants and stations is both cost-effective and environmentally successful under the model considering carbon cap and trade. The internal relationship between the models indicates that the hybrid carbon emission policy is superior to naive carbon regulations.
KW - Point of interest
KW - Data-driven approach
KW - Hydrogen supply network
KW - Stochastic demand
KW - Carbon regulations
UR - https://www.sciencedirect.com/science/article/abs/pii/S0959652622023320
UR - http://www.scopus.com/inward/record.url?scp=85132902184&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2022.132734
DO - 10.1016/j.jclepro.2022.132734
M3 - Journal article
SN - 0959-6526
VL - 365
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 132734
ER -