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Edge Computing- and H∞-Switching-Based Networked Control for Frequency Control in Multi-Microgrids with Time Delays
Author(s) -
Yang Peng,
Wei Guo,
Guanghua Wu,
Cong Wang,
Kai Zhang,
Ran Zhang
Publication year - 2021
Publication title -
wireless communications and mobile computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.42
H-Index - 64
eISSN - 1530-8677
pISSN - 1530-8669
DOI - 10.1155/2021/6670591
Subject(s) - computer science , control (management) , enhanced data rates for gsm evolution , edge computing , automatic frequency control , computer network , distributed computing , real time computing , telecommunications , artificial intelligence
The frequency stability of multi-microgrids is easily affected by random load fluctuations and intermittent renewable resources. Additionally, geographically distributed generation equipment usually cannot adopt the “point-to-point” dedicated communication scheme to realize the information exchange considering the construction and computation costs. Therefore, a H∞-switching frequency control strategy for multi-microgrids based on edge computing framework is proposed in this paper. Firstly, an edge computing device is set up in each microgrid to collect the operation statuses of local participating equipment and generate the control instructions to ensure the real-time local frequency stability. Secondly, the multihop data transmission process in edge computing environment is described as a cascade queuing model. Then, the frequency control system in each microgrid is described as a switching model dependent on the varying time delays. Finally, via constructing a Lyapunov function, the constraints of the controller gains ensuring the H∞-damping performance for external load demands and the renewable outputs are derived at the same time. Simulation results show that compared with the traditional centralized control schemes, the peak value of our proposed edge computing framework is reduced by 32.51% compared with the traditional centralized control scheme. Moreover, under the same edge computing framework, the integral of absolute error (IAE) of frequency with the proposed H∞ control strategy can be reduced by 37.19% at least. Therefore, a better transient performance can be obtained with our proposed method.

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