Robust chance-constrained gas management for a standalone gas supply system based on wind energy

Remote areas are often rich in renewable energy resources, which can be used to produce synthetic natural gas (SNG) at power-to-gas conversion facilities. A balance between gas supply and demand is vital for the efficient and reliable operation of a standalone gas supply system. However, it is gener...

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Bibliographic Details
Published in:Energy (Oxford) 2020-12, Vol.212, p.118723, Article 118723
Main Authors: Xu, Xiao, Hu, Weihao, Du, Yuefang, Liu, Wen, Liu, Zhou, Huang, Qi, Chen, Zhe
Format: Article
Language:English
Subjects:
Alternative energy sources
Distributionally robust optimization
Electric power generation
Energy resources
Energy sources
Environmental management
Gas management
Integer programming
Linear programming
Mixed integer
Mixed-integer linear programming
Natural gas
Objective function
Optimization
Power-to-gas conversion
Renewable energy
Robustness
Substitute natural gas
Wind power
Wind power generation
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Summary:Remote areas are often rich in renewable energy resources, which can be used to produce synthetic natural gas (SNG) at power-to-gas conversion facilities. A balance between gas supply and demand is vital for the efficient and reliable operation of a standalone gas supply system. However, it is generally difficult to guarantee such a balance in a standalone gas supply system. Therefore, a transport tank vehicle is considered as an option for transporting SNG from other areas with sufficient natural gas. Existing studies in this domain have not considered a separate gas supply system for remote areas and fail to provide a robust gas management strategy in an uncertain environment. Thus, this study first introduces a chance-constrained gas management model for the standalone gas supply system. The objective function is to minimize the total operational cost of the gas supply system. An ambiguity set based on the historical wind power data is employed to address the uncertainty caused by the wind power generation. The chance-constrained gas management model is processed via distributionally robust optimization. Then, the gas management model can be reformulated as a mixed-integer linear programming problem. Finally, the proposed model is validated using a case study.
ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2020.118723