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Carbon-Energy Composite Flow for Transferred Multi-searcher Q-Learning Algorithm with Reactive Power Optimization

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Soft Computing in Data Science (SCDS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1771))

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Abstract

In the conventional carbon emission computation paradigm, the primary obligation falls on the electricity-generating side. However, according to the theory of carbon emission flow, the grid side and the user side are the primary producers of carbon emissions and must carry the majority of the obligation. To minimize carbon dioxide emissions, it is required to apply the carbon emission flow analysis approach to move the carbon footprint from the power generation side to the grid side and the user side in order to create more efficient energy saving and emission reduction plans. In order to accomplish the low-carbon, energy-saving, and cost-effective operation of the power system, the carbon-energy composite flow is included in the objective function of reactive power optimization in this study. In order to solve the reactive power optimization model of carbon-energy composite flow and to demonstrate the superiority of the Q-learning algorithm of migration multi-searcher, this paper designs a carbon-energy composite flow optimization model on the IEEE 118 node system and adds six algorithms, such as the genetic algorithm, in order to solve the reactive power optimization model of carbon-energy composite flow. Come in for a simulation exercise, including comparison. The simulation and verification outcomes of an example demonstrate that the suggested model and algorithm may achieve economical, low-carbon, and secure functioning of the power system.

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Acknowledgement

This research was made possible with funding from the National Natural Science Foundation of China (No.61862051), the Science and Technology Foundation of Guizhou Province (No.ZK[2022]549, No.[2019]1299), the Top-notch Talent Program of of Guizhou Province (No.KY[2018]080), the Natural Science Foundation of Education of Guizhou Province (No.[2019]203), and the Funds of Qiannan Normal University for Nationalities (No. Qnsy2018003, No. Qnsy2019rc09, No. Qnsy2018JS013, No. Qnsyrc201715).

Author information

Authors and Affiliations

  1. School of Computer and Information, Qiannan Normal University for Nationalities, DuyunGuizhou, 558000, China

    Jincheng Zhou & Hongyu Xue

  2. School of Computer Sciences, Baoji University of Arts and Sciences, Baoji, 721007, China

    Hongyu Xue

  3. Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou, DuyunGuizhou, 558000, China

    Jincheng Zhou

Authors
  1. Jincheng Zhou
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  2. Hongyu Xue
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Corresponding author

Correspondence to Jincheng Zhou .

Editor information

Editors and Affiliations

  1. Universiti Teknologi MARA, Shah Alam, Malaysia

    Marina Yusoff

  2. Baoji University of Arts and Sciences, Baoji, China

    Tao Hai

  3. Universiti Teknologi MARA, Shah Alam, Malaysia

    Murizah Kassim

  4. Universiti Teknologi MARA, Shah Alam, Malaysia

    Azlinah Mohamed

  5. Institute of Liberal Arts and Sciences, Nagoya, Japan

    Eisuke Kita

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Zhou, J., Xue, H. (2023). Carbon-Energy Composite Flow for Transferred Multi-searcher Q-Learning Algorithm with Reactive Power Optimization. In: Yusoff, M., Hai, T., Kassim, M., Mohamed, A., Kita, E. (eds) Soft Computing in Data Science. SCDS 2023. Communications in Computer and Information Science, vol 1771. Springer, Singapore. https://doi.org/10.1007/978-981-99-0405-1_3

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  • DOI: https://doi.org/10.1007/978-981-99-0405-1_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-0404-4

  • Online ISBN: 978-981-99-0405-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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