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Energy supply infrastructure LCA model for electric and hydrogen transportation systems

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  • Lucas, Alexandre
  • Neto, Rui Costa
  • Silva, Carla Alexandra

Abstract

Many transportation environmental life cycle analyses neglect the contribution of the energy supply infrastructures. In alternative light duty vehicle technologies, it has been shown through case studies that this can be a relevant factor. However, no model that can generalise the evaluation of energy and emissions from construction, maintenance and decommissioning of such infrastructure to analyse different scenarios currently exists. A model is proposed, focussing on electricity and on hydrogen supply through centralised steam methane reforming (H2(a)) and on-site electrolysis (H2(b)). The model outputs are in gCO2eq/MJ and MJeq/MJ of the final energy. Model main inputs are the region's electricity mix, the annual distance driven, supply chain losses and the number of vehicles per station or chargers. The evaluation of the number of vehicles served per each charger/station as a function of annual distance driven is presented. The uncertainty is estimated by using the pedigree matrix, impact uncertainty and literature estimates. The model shows consistency in the results and uncertainty range. Charging policies that minimise the electricity infrastructure burden should incentivise approximately 37% of normal charging. H2(a) pipeline lifetime should be extended. Efforts in the electrolyser should be undertaken to approximate the ratio of vehicles per station with a conventional one.

Suggested Citation

  • Lucas, Alexandre & Neto, Rui Costa & Silva, Carla Alexandra, 2013. "Energy supply infrastructure LCA model for electric and hydrogen transportation systems," Energy, Elsevier, vol. 56(C), pages 70-80.
    • Handle: RePEc:eee:energy:v:56:y:2013:i:c:p:70-80
    DOI: 10.1016/j.energy.2013.04.056
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    References listed on IDEAS

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    Cited by:

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    2. Niancheng Zhou & Jiajia Wang & Qianggang Wang & Nengqiao Wei & Xiaoxuan Lou, 2014. "Capacity Calculation of Shunt Active Power Filters for Electric Vehicle Charging Stations Based on Harmonic Parameter Estimation and Analytical Modeling," Energies, MDPI, vol. 7(8), pages 1-19, August.
    3. Ribau, João P. & Sousa, João M.C. & Silva, Carla M., 2015. "Reducing the carbon footprint of urban bus fleets using multi-objective optimization," Energy, Elsevier, vol. 93(P1), pages 1089-1104.
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    5. Comodi, Gabriele & Bevilacqua, Maurizio & Caresana, Flavio & Paciarotti, Claudia & Pelagalli, Leonardo & Venella, Paola, 2016. "Life cycle assessment and energy-CO2-economic payback analyses of renewable domestic hot water systems with unglazed and glazed solar thermal panels," Applied Energy, Elsevier, vol. 164(C), pages 944-955.
    6. Marcelo Moya & Javier Martínez-Gómez & Esteban Urresta & Martín Cordovez-Dammer, 2022. "Feature Selection in Energy Consumption of Solar Catamaran INER 1 on Galapagos Island," Energies, MDPI, vol. 15(8), pages 1-17, April.
    7. Shen, Jiayu, 2020. "An environmental supply chain network under uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    8. Martin Khzouz & Evangelos I. Gkanas & Jia Shao & Farooq Sher & Dmytro Beherskyi & Ahmad El-Kharouf & Mansour Al Qubeissi, 2020. "Life Cycle Costing Analysis: Tools and Applications for Determining Hydrogen Production Cost for Fuel Cell Vehicle Technology," Energies, MDPI, vol. 13(15), pages 1-19, July.

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