Proceedings of the 3rd International Conference on Public Management and Big Data Analysis, PMBDA 2023, December 15–17, 2023, Nanjing, China

Research Article

Simulation of Carbon Footprint Calculation Model of Power Enterprises Based on Particle Swarm Optimization

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  • @INPROCEEDINGS{10.4108/eai.15-12-2023.2345349,
        author={Fangzhao  Deng and Xingwu  Guo and Zhenli  Deng and Meng  Yang},
        title={Simulation of Carbon Footprint Calculation Model of Power Enterprises Based on Particle Swarm Optimization},
        proceedings={Proceedings of the 3rd International Conference on Public Management and Big Data Analysis, PMBDA 2023, December 15--17, 2023, Nanjing, China},
        publisher={EAI},
        proceedings_a={PMBDA},
        year={2024},
        month={5},
        keywords={particle swarm optimization; carbon footprint; power enterprises},
        doi={10.4108/eai.15-12-2023.2345349}
    }
    
  • Fangzhao Deng
    Xingwu Guo
    Zhenli Deng
    Meng Yang
    Year: 2024
    Simulation of Carbon Footprint Calculation Model of Power Enterprises Based on Particle Swarm Optimization
    PMBDA
    EAI
    DOI: 10.4108/eai.15-12-2023.2345349
Fangzhao Deng1,*, Xingwu Guo1, Zhenli Deng1, Meng Yang1
  • 1: State Grid Henan Economic Research Institute
*Contact email: dengfangzhao@foxmail.com

Abstract

The purpose of this study is to develop a carbon footprint calculation model of power enterprises based on PSO(particle swarm optimization) to help power enterprises quantify their carbon emissions and optimize their carbon emission reduction strategies. In this study, considering the user-side carbon emission quota constraint model, a Fuzzy SPSO (Fuzzy Self-Correcting PSO) is proposed to solve this optimization problem. Fuzzy SPSO is an optimization algorithm that combines PSO and fuzzy logic to improve the adaptability and global search ability of the algorithm. Through the introduction of fuzzy rules, the algorithm can better adapt to the characteristics of different problems, deal with uncertainty and diversity, and find a better solution. The simulation results show that the algorithm can effectively deal with multiple influencing factors, including power supply structure, energy efficiency and carbon emission factors, so as to find the best emission reduction strategy. This study provides a powerful tool, which is helpful for power enterprises to achieve carbon emission reduction targets, promote sustainable development, and provide valuable experience and methods for research and practice in the field of carbon emission reduction. This is of great significance for coping with climate change and achieving the goal of carbon neutrality.