Journal
APPLIED ENERGY
Volume 261, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2019.114398
Keywords
Carbon footprint; Distributed energy system; Life cycle assessment; Multi-objective mathematical programming; Floating Production Storage and Offloading
Categories
Funding
- National Natural Science Foundation of China [51874325, 2016zx0503004-004]
- National Science and technology major project [19K15260]
- Japan Ministry of Education, Culture, Sports, Science and Technology
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Deep-sea oil and gas fields are acting as a vital role by providing substantial oil and gas resource, and Floating Production Storage and Offloading is an indispensable tool for the development of offshore oil and gas fields effectively. Here, Life Cycle Assessment is applied to evaluate environmental loads in the whole life cycle of the deep-sea oil and gas production. This paper explores the carbon footprint of Floating Production Storage and Offloading as the time axis. It is found that Floating Production Storage and Offloading is a conceptual product at the design stage and does not generate carbon emission, while the operational stage releases considerable emission by the fuel combustion process, accounting for 88.2% of the entire life cycle. To decrease this part of carbon emission, distributed energy system is considered as a promising choice because it integrates different energy resources and provides an economic and environmental energy allocation scheme to meet the energy demand. For the operation stage, this paper establishes a Multi-objective Mathematical Programming model to determine the selection and capacity of facilities with minimum annual total cost and carbon emissions by considering the energy balance and technical constraints. The model is validated by an example and solved by the weight method. According to designer's demand, distributed energy system can optimize economic objectives in a maximum range of 14.6%, and a maximum emission reduction of 4.53% can be expected compared with the traditional scheme. Sensitivity analysis shows that cost is more sensitive to natural gas price.
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