Huang Jisheng, Zhou Shanshan, Li Guangming, Shen Qiang
Lincang Power Supply Bureau, Yunnan Power Grid Co., Ltd., Lincang, 677000, Yunnan, China.
Kunming Sonar Media Technology Co., Ltd., Kunming, 650000, Yunnan, China.
Sci Rep. 2025 Jul 10;15(1):24890. doi: 10.1038/s41598-025-07592-4.
This paper presents a comprehensive framework for real-time monitoring and optimization of user-side energy management systems leveraging edge computing technology. The proposed approach addresses key challenges in traditional centralized energy management by bringing computation and data processing closer to end devices. The framework encompasses three main components: an edge computing-based system architecture for data acquisition and processing, real-time monitoring methods for energy consumption and power quality, and optimization techniques for demand response and distributed energy resource coordination. Through case studies and experimental analysis, we demonstrate that the proposed framework achieves significant improvements in energy efficiency, response time, and cost reduction compared to conventional centralized approaches. The results show up to 30% increase in renewable energy utilization and 25% reduction in operating costs across various deployment scenarios. This work provides valuable insights into the application of edge computing for next-generation energy management systems while highlighting remaining challenges and future research directions.
本文提出了一个利用边缘计算技术对用户侧能源管理系统进行实时监测和优化的综合框架。所提出的方法通过将计算和数据处理更靠近终端设备，解决了传统集中式能源管理中的关键挑战。该框架包括三个主要组件：用于数据采集和处理的基于边缘计算的系统架构、能源消耗和电能质量的实时监测方法，以及需求响应和分布式能源资源协调的优化技术。通过案例研究和实验分析，我们证明，与传统的集中式方法相比，所提出的框架在能源效率、响应时间和成本降低方面取得了显著改进。结果表明，在各种部署场景中，可再生能源利用率提高了30%，运营成本降低了25%。这项工作为边缘计算在下一代能源管理系统中的应用提供了有价值的见解，同时突出了剩余的挑战和未来的研究方向。
