arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2402.01664cs.NIcs.DC
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智能电网中的边缘卸载

Edge Offloading in Smart Grid

Gabriel Ioan Arcas, Tudor Cioara, Ionut Anghel, Dragos Lazea, Anca Hangan

AI总结:

本文综述智能电网中的边缘卸载,分析边缘-雾-云等架构及优化算法,并通过SWOT分析支持决策,以应对低延迟和高可靠性挑战。

摘要:

能源转型支持向更可持续的能源替代方案转变,推动迈向去中心化的智能电网,在这种电网中,能源在更靠近使用点的地方产生。去中心化的智能电网预见了新型数据驱动的低延迟应用,用于提高弹性和响应能力,例如点对点能源交易、微电网控制、故障检测或需求响应。然而,传统的基于云的智能电网架构无法满足新兴应用的需求,如低延迟和高可靠性,因此需要采用边缘、雾或混合等替代架构。此外,边缘卸载可以在下一代智能电网AI应用中发挥关键作用,因为它能够实现计算资源的有效利用,并应对物联网设备产生的日益增长的数据所带来的挑战,优化响应时间、能耗和网络性能。然而,需要全面概述当前研究状态,以支持关于将能源相关应用从云卸载到雾或边缘的合理决策,重点关注智能电网的开放性挑战和潜在影响。在本文中,我们深入探讨了智能电网和计算分布架构,包括边缘-雾-云模型、编排架构和无服务器计算,并分析了决策变量和优化算法,以评估边缘卸载的效率。最后,这项工作有助于全面理解智能电网中的边缘卸载,提供了SWOT分析以支持决策制定。

英文摘要:

The energy transition supports the shift towards more sustainable energy alternatives, paving towards decentralized smart grids, where the energy is generated closer to the point of use. The decentralized smart grids foresee novel data-driven low latency applications for improving resilience and responsiveness, such as peer-to-peer energy trading, microgrid control, fault detection, or demand response. However, the traditional cloud-based smart grid architectures are unable to meet the requirements of the new emerging applications such as low latency and high-reliability thus alternative architectures such as edge, fog, or hybrid need to be adopted. Moreover, edge offloading can play a pivotal role for the next-generation smart grid AI applications because it enables the efficient utilization of computing resources and addresses the challenges of increasing data generated by IoT devices, optimizing the response time, energy consumption, and network performance. However, a comprehensive overview of the current state of research is needed to support sound decisions regarding energy-related applications offloading from cloud to fog or edge, focusing on smart grid open challenges and potential impacts. In this paper, we delve into smart grid and computational distribution architec-tures, including edge-fog-cloud models, orchestration architecture, and serverless computing, and analyze the decision-making variables and optimization algorithms to assess the efficiency of edge offloading. Finally, the work contributes to a comprehensive understanding of the edge offloading in smart grid, providing a SWOT analysis to support decision making.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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