功率-通信耦合智能电网时效性驱动集成传感、传输、计算和控制

IF 17.2
Haijun Liao;Hongxu Yan;Wen Zhou;Wenxuan Che;Haodong Liu;Zhenyu Zhou;Shahid Mumtaz
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引用次数: 0

摘要

6G、云雾计算和物联网(IoT)的快速发展彻底改变了智能电网的控制模式。由于通信和功率域之间的紧密耦合,控制性能在很大程度上依赖于电网状态信息的及时、安全的感知、传输和计算。将四个扇区作为独立子系统的传统方法收敛速度慢,甚至存在级联控制振荡。本文研究了传感-传输-计算-控制集成优化的关键问题,以使总体电压偏差最小化。提出了一种时效性驱动的集成优化算法,根据信息时效性在感知、传输和计算过程中的演变及其对控制精度的影响,主动优化通信资源自适应和功率域控制决策。特别地,提出了一种基于自惩罚的代价函数来量化通信域资源分配与电压控制偏差之间的不匹配。此外,引入了一种新的时效性指标——可信信息年龄(AoTI)来捕捉比例积分(PI)共识控制稳定裕度上的时效性-可信度性能损失。在此基础上对共识权值进行了优化,进一步提高了收敛速度和控制精度。仿真结果表明,该算法显著提高了功率域控制稳定性,验证了AoTI作为控制信息重要度的重要指标的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Timeliness-Driven Integrated Sensing, Transmission, Computing, and Control for Power-Communication Coupling Smart Grid
The rapid advancement of 6G, cloud-fog computing, and internet of things (IoT) has revolutionized the control paradigm of smart grid. With the closed coupling between communication and power domains, control performance heavily relies on timely and secure sensing, transmission, and computing of grid state information. Conventional approaches which treat the four sectors as separate subsystems suffer from slow convergence and even cascading control oscillations. In this paper, we address the key research problem of sensing-transmission-computing-control integrated optimization to minimize the overall voltage deviation. A timeliness-driven integrated optimization algorithm is proposed, where proactive optimization of communication resource adaptation and power-domain control decisions is conducted based on the evolution of information timeliness loss in sensing, transmission, and computing, as well as its impact on control accuracy. Particularly, a self-penalty based cost function is developed to quantify the mismatch between communication-domain resource allocation and voltage control deviation. Moreover, a novel timeliness indicator, named age of trustworthy information (AoTI), is introduced to capture timeliness-trustworthiness performance loss on proportional-integral (PI) consensus control stability margin. Consensus weights are optimized based on AoTI to further enhance convergence speed and improve control accuracy. Simulation results demonstrate that the proposed algorithm significantly improves power-domain control stability, validating the efficiency of AoTI as a critical indicator for control information importance.
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