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Digital Transformation Strategies for Power Grid Enterprises Aimed at Improving Management Efficiency

Year: 2025
Type: conference-paper
Abstract: In promoting digital transformation in power grid enterprises, cutting-edge information technologies’ integrated applications have become crucial in supporting system performance and management efficiency. This study analyzes the comprehensive framework of digital transformation in power grid enterprises, clarifies their core driving mechanisms, and identifies key technological paths. the study establishes a data-driven power system model based on advanced approaches, including the Internet of Things, edge computing, artificial intelligence, and cloud computing. Each technology not only demonstrates its domain-specific advantages but also achieves synergistic effects through interpenetration within the system architecture, forming a multidimensional data closed-loop. After implementation, the system demonstrates remarkable performance improvements. Experimental data reveal substantial efficiency gains; management efficiency shows notable enhancement with a 65.7% reduction in response time, equipment failure rates decrease by 53.1%, and resource allocation efficiency improves by 25.5%. Regarding power supply capacity, system reliability increases by 7.5% while average downtime reduces by 68.3%. These indicator improvements collectively validate the effectiveness of technological integration. The findings further illustrate that introducing intelligent technological systems during digital transformation yields substantial benefits for power grid enterprises. This approach optimizes operation and maintenance management structures; it also provides viable paths and empirical evidence for enhancing system resilience and intelligent capabilities. The study offers both technical support and practical foundations for advancing power systems toward higher levels of automation and intelligence in future development.
Authors: Jiaxin Ma, Yibo Wang, Jing Zuo, Ruonan Xu, Wenjiao Du, Fang'e Duan
Language: English
Open Access status: gold
OpenAlex ID: W4414425100
FWCI: 2.358
Cites: 12
Cited by: 1
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