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Impacts of emission reduction and external cost on natural gas distribution
•A new method based on external cost is developed to optimize gas distribution.•Impacts of gas distribution on external cost and CO2 emission are studied.•Sensitivity analysis is been done to study the key parameters in the model.•China is selected as case study, results provide insights to other st...
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Published in: | Applied energy 2017-12, Vol.207, p.553-561 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | •A new method based on external cost is developed to optimize gas distribution.•Impacts of gas distribution on external cost and CO2 emission are studied.•Sensitivity analysis is been done to study the key parameters in the model.•China is selected as case study, results provide insights to other studies about energy systems.
Natural gas, as a cleaner fossil fuel energy resource, is playing an increasingly important role in the future energy mix to achieve emission reduction target globally. In this study, a new method based on the external cost is developed to identify an optimal solution for natural gas distribution. China is selected for a case study. Both the economic cost and the external cost of pollutant emissions have been considered. The provincial distribution of natural gas and other energy resources is optimized with the aim to minimize both economic and external costs. Results show that the supply of natural gas should be prioritized for Beijing, Tianjin and Shanghai, owing to higher external costs and the optimized distribution can reduce the overall external cost by 4% in China. The optimization of natural gas distribution will also influence CO2 emissions, therefore, the determination of the reduction target for each province should consider the external cost. Sensitivity study also shows that the minimum energy demand, the maximum natural gas supply and the minimum natural gas demand are the key parameters that impact the optimized distribution for each province. |
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ISSN: | 0306-2619 1872-9118 1872-9118 |
DOI: | 10.1016/j.apenergy.2017.06.005 |