arrow
返回

A Power-Grid-Mapping Edge Computing Structure for Digital Distributed Distribution Networks

delete2024-07-01
delete0
PRE
AI
Z
Zhiqi Xu
蒋玮 封面图
蒋玮 (Wei Jiang) *
J
Junjun Xu
Y
Yifan Deng
Junbo Zhao 封面图
Junbo Zhao (Junbo Zhao)
DOI:10.1109/TSG.2023.3343373delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
To achieve comprehensive awareness and efficient regulation, various types of intelligent electronic devices (IEDs) and new information and communication technologies (ICTs) are involved in distribution networks, leading to the evolution of traditional distribution networks into digital ones. Unfortunately, the traditional centralized management approach suffers from an excessive computational and communication burden at the master station in large-scale digital distribution networks. Driven by this motivation, this paper proposes a power-grid-mapping edge computing structure to engine the emerging digital distributed distribution networks (DDDNs). In the proposed structure, the distributed IEDs are used as edge computing nodes to perform computations. Based on the specific nature of the computation tasks, the edge nodes create a communication network that mirrors the topology of the power grid. Computation tasks are then executed through a novel relay computing scheme. In this scheme, each edge node receives a computation task from its upstream neighbor, decomposes it into a local sub-task and a downstream sub-task, and forwards the downstream sub-task to the downstream neighbor. The voltage sensitivity analysis is used as an illustrative example of the proposed relay computing scheme. Case studies demonstrate the effectiveness of the proposed computing structure and highlight its benefits.
Keyword:
Distributed energy resources
digitalization
edge computing
intelligent electronic device
smart distribution grid

期刊

IEEE Transactions on Smart Grid 封面图
IEEE Transactions on Smart Grid
IF:
9.8
论文数:
5.7K
被引数:
4.3W

机构

S
southeast university - china
学者数:
5.3W
论文数: 4.9W
被引数: 57
U
University of Connecticut
学者数:
2.4W
论文数: 2.2W
被引数: 2.5W
引用论文

引用论文

err分享
err收藏
err分享
err收藏
An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks
err2021-03-01
err102
errOAAI
errHan, Tao; Muhammad, Khan; Hussain, Tanveer; Lloret, Jaime; Baik, Sung Wook
err分享
err收藏
学者 查看更多内容