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智能电网综述:挑战与机遇

A Comprehensive Review on Smart Grids: Challenges and Opportunities.

机构信息

Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Zacatenco, Instituto Politécnico Nacional, Ciudad de México 07340, Mexico.

Escuela Superior de Turismo, Instituto Politécnico Nacional, Ciudad de México 07630, Mexico.

出版信息

Sensors (Basel). 2021 Oct 21;21(21):6978. doi: 10.3390/s21216978.

DOI:10.3390/s21216978
PMID:34770285
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8587637/
Abstract

Recently, the operation of distribution systems does not depend on the state or utility based on centralized procedures, but rather the decentralization of the decisions of the distribution companies whose objectives are the efficiency of interconnectivity. Therefore, distribution companies are exposed to greater risks, and due to this, the need to make decisions based on increasingly reliable models has grown up considerably. Therefore, we present a survey of key aspects, technologies, protocols, and case studies of the current and future trend of Smart Grids. This work proposes a taxonomy of a large number of technologies in Smart Grids and their applications in scenarios of Smart Networks, Neural Networks, Blockchain, Industrial Internet of Things, or Software-Defined Networks. Therefore, this work summarizes the main features of 94 research articles ranging the last four years. We classify these survey, according Smart Grid Network Topologies, because it can group as the main axis the sensors applied to Smart Grids, as it shows us the interconnection forms generalization of the Smart Networks with respect to the sensors found in a home or industry.

摘要

最近,配电系统的运行不再依赖于基于集中式程序的州或公用事业,而是依赖于配电公司决策的分散化,其目标是互联互通的效率。因此,配电公司面临更大的风险,因此,需要根据越来越可靠的模型做出决策。因此,我们对智能电网的当前和未来趋势的关键方面、技术、协议和案例研究进行了调查。这项工作提出了智能电网中大量技术及其在智能网络、神经网络、区块链、工业物联网或软件定义网络场景中的应用的分类法。因此,这项工作总结了过去四年中 94 篇研究文章的主要特点。我们根据智能电网网络拓扑结构对这些调查进行分类,因为它可以将应用于智能电网的传感器作为主要轴进行分组,因为它向我们展示了智能网络相对于家庭或工业中发现的传感器的互联形式的概括。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdf6/8587637/b13f8bea4014/sensors-21-06978-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdf6/8587637/126d1bfb0538/sensors-21-06978-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdf6/8587637/b0111e587e3f/sensors-21-06978-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdf6/8587637/9dc9e7f4820e/sensors-21-06978-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdf6/8587637/fb1d24f4ea23/sensors-21-06978-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdf6/8587637/739d7a837e76/sensors-21-06978-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdf6/8587637/b13f8bea4014/sensors-21-06978-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdf6/8587637/126d1bfb0538/sensors-21-06978-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdf6/8587637/b0111e587e3f/sensors-21-06978-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdf6/8587637/9dc9e7f4820e/sensors-21-06978-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdf6/8587637/fb1d24f4ea23/sensors-21-06978-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdf6/8587637/739d7a837e76/sensors-21-06978-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdf6/8587637/b13f8bea4014/sensors-21-06978-g006.jpg

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