Modeling and optimization of natural gas distribution networks for new supplier projects. (August 2022)

• Record Type: Journal Article
• Authors: Alves, O.; Fontes, C.
• Is Part Of: Energy conversion and management. X. Volume 15(2022)
• Publication Date: 2022-08
• Subjects: Natural gas -- Distribution networks -- Mixing processes -- Dynamic optimization -- Quality control
• DOI: 10.1016/j.ecmx.2022.100240
• Languages: English
• ISSNs: 2590-1745

Abstract

Highlights: NG distribution networks involving suppliers with unspecified streams. Cost minimization and specification assurance for the consumer market. Real case study involving all the alternatives for receiving NG for distribution. Feasibility and flexibility of applying the model in different configurations. Reduction in the total cost of distribution of 22.65%. Abstract: The opening of the market and the possibility of the participation of several suppliers in Natural Gas (NG) distribution networks has raised the level of complexity associated with the quality control of the final stream to be made available to the consumer market. There are suppliers that offer lower prices but supply a lower quality of fuel which can contribute to an out-of-spec final stream (after mixing the different suppliers). On the other hand, it is not always economically viable to build Processing Units and acquire analytical equipment for gas quality control. In general, simulations in natural gas distribution networks focus on the evaluation of physical criteria and energy balance. This work presents a dynamic optimization model, validated by real case studies, for the monitoring and quality control of a natural gas stream mixing process that involves several suppliers providing varying quality levels (including out of specification streams) and prices. The results show the feasibility of executing projects for new suppliers, as well as supplying natural gas to customers who use it as a raw material and who establish more restrictive specification limits than those established by the regulatory agency. Comparing with the conventional operating strategy, the results obtained by the proposed optimization model show a reduction in the cost of distribution equal to 13.5% and 22.6% in the two case studies analyzed, respectively.