Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27–29, 2023, Tianjin, China

Research Article

The Diffusion Path of Distributed Photovoltaic Power Generation Technology driven by Individual Behavior

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  • @INPROCEEDINGS{10.4108/eai.27-10-2023.2341959,
        author={Xiuchun  Wang and Xuedong  He and Xiaoqian  Sun and Meicui  Qin and Ruiping  Pan and Yuanyuan  Yang},
        title={The Diffusion Path of Distributed Photovoltaic Power Generation Technology driven by Individual Behavior},
        proceedings={Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27--29, 2023, Tianjin, China},
        publisher={EAI},
        proceedings_a={ICEMBDA},
        year={2024},
        month={1},
        keywords={technology diffusion; distributed photovoltaic power generation; agent-based modeling; adoption willingness},
        doi={10.4108/eai.27-10-2023.2341959}
    }
    
  • Xiuchun Wang
    Xuedong He
    Xiaoqian Sun
    Meicui Qin
    Ruiping Pan
    Yuanyuan Yang
    Year: 2024
    The Diffusion Path of Distributed Photovoltaic Power Generation Technology driven by Individual Behavior
    ICEMBDA
    EAI
    DOI: 10.4108/eai.27-10-2023.2341959
Xiuchun Wang1, Xuedong He1, Xiaoqian Sun1,*, Meicui Qin1, Ruiping Pan1, Yuanyuan Yang1
  • 1: Customer Service Center of State Grid Co. , Ltd.
*Contact email: 396600845@qq.com

Abstract

The widespread adoption of distributed photovoltaic (PV) power generation technologies among electricity consumers is a crucial factor in enabling the power system's low-carbon transition. While extensive research has explored consumers' willingness to adopt this technology, prior studies have primarily focused on static psychological factors. This study, however, takes a heterogeneous behavioral perspective by examining the dynamic effects of individual behavioral interactions on technology diffusion. We construct a technology diffusion model for distributed PV power generation, simulate the changes in user adoption willingness, and assess the impact of external economic interventions. Our simulation results indicate that residential environment constraints, resulting from individual behavioral differences, can influence the diffusion potential of technology. Furthermore, non-mandatory promotion methods are more effective in enhancing user adoption willingness. Interestingly, we found that free installation interventions tend to reduce the diffusion effect in later stages and should not be implemented in isolation. These insights can contribute to enhancing the diffusion of distributed PV power generation technology and furthering the development of low-carbon electricity.