Resilience assessment of power grid’s key material supply chain based on cloud model

Jiahao Xu; Chaohong Xie; Yu Gu; Yicong Xi

doi:10.1117/12.3045047

16 January 2025 Resilience assessment of power grid’s key material supply chain based on cloud model

Jiahao Xu, Chaohong Xie, Yu Gu, Yicong Xi

Author Affiliations +

Proceedings Volume 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024); 134474E (2025) https://doi.org/10.1117/12.3045047
Event: International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 2024, Wuhan, China

Abstract

To enhance the resilience of the power grid’s key material supply chain and ensure the safety, stability, and robust development of the power industry, the paper employs the Wuli-Shili-Renli (WSR) Methodology to construct a resilience evaluation system comprising both quantitative and qualitative indicators. The paper utilizes the cloud model to facilitate the conversion between qualitative concepts and quantitative parameters, employing comprehensive method to determine the weights of indicators. Moreover, the study evaluates the supply chain resilience of critical and representative enterprises through the example of the cable supply chain. The findings indicate that the cable supply chain achieves the highest score in the 'Renli' primary indicator, with other two primary indicators and overall resilience performing well. However, shortcomings are identified in the secondary indicators related to 'supply chain network structure' and 'human resource management.' Based on these evaluation results, the paper proposes corresponding strategies for enhancing resilience, offering guidance for the State Grid Corporation of China.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiahao Xu, Chaohong Xie, Yu Gu, and Yicong Xi "Resilience assessment of power grid’s key material supply chain based on cloud model", Proc. SPIE 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 134474E (16 January 2025); https://doi.org/10.1117/12.3045047

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