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Operation Configuration Optimization of Power Gas Energy Hub System Considering NOx Emission

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Artificial Intelligence and Security (ICAIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13338))

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Abstract

Since industrialization, people’s demand for energy has reached an unprecedented level, and is increasing with the development of the times. In the 21st century, countless environmental and energy problems are in front of us. The shortage of resources, environmental degradation and global warming all remind us that we need to make changes to deal with the coming energy disaster. In the process of energy supply, it will not only consume energy, but also emit a large number of polluting gases, such as carbon dioxide and nitrogen oxides. In order to improve the efficiency of energy utilization and reduce the emission of polluting gases, energy hub (EH) is proposed. Energy hub is an important model to analyze the coupling effect of multiple energy sources. It can improve the utilization efficiency of multiple energy systems and reduce the emission of polluting gases. However, in the related research of many energy hubs, few optimization schemes take into account the emission of nitrogen oxide n0x. In this paper, particle swarm optimization (PSO) will be used to construct a method considering nitrogen oxide. The optimization model of x emission electric gas energy hub is used to solve this problem.

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Correspondence to Xudong Wang .

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Zhang, X., Wang, X., Duan, J., Chen, W., Sun, X., Xia, J. (2022). Operation Configuration Optimization of Power Gas Energy Hub System Considering NOx Emission. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13338. Springer, Cham. https://doi.org/10.1007/978-3-031-06794-5_3

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  • DOI: https://doi.org/10.1007/978-3-031-06794-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06793-8

  • Online ISBN: 978-3-031-06794-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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