Mobile Emergency Power Source Configuration Scheme considering Dynamic Characteristics of a Transportation Network

Recently, with the large scale of power grids and the increase in frequency of extreme weather, the safe and stable operation of power systems is facing great challenges. Therefore, mobile emergency power source (MEPS) are a promising and feasible way to deal with extreme weather and reduce economic...

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Autores principales:	Tianen Huang, Jian Tang, Zhenjie Wu, Yuantao Wang, Xiang Li, Shuangdie Xu, Wenguo Wu, Yajun Mo, Tao Niu, Hang Dong, Fan Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	mobile emergency power source (MEPS) dynamic road traffic index (DRTI) wavelet neural network (WNN) A-Star algorithm (AS) distribution network Chemical technology TP1-1185 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/4afd5d5787bc4401a5b62a3c56f32603
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spelling	oai:doaj.org-article:4afd5d5787bc4401a5b62a3c56f326032021-11-25T18:50:34ZMobile Emergency Power Source Configuration Scheme considering Dynamic Characteristics of a Transportation Network10.3390/pr91119352227-9717https://doaj.org/article/4afd5d5787bc4401a5b62a3c56f326032021-10-01T00:00:00Zhttps://www.mdpi.com/2227-9717/9/11/1935https://doaj.org/toc/2227-9717Recently, with the large scale of power grids and the increase in frequency of extreme weather, the safe and stable operation of power systems is facing great challenges. Therefore, mobile emergency power source (MEPS) are a promising and feasible way to deal with extreme weather and reduce economic losses. However, the current urban power grid and transportation network are closely coupled, and the congested traffic hinders the rapid configuration of MEPSs. Therefore, this paper proposes an MEPS configuration scheme considering real-time traffic conditions. Firstly, the dynamic road traffic index (DRTI) is defined, which can fully describe the dynamic characteristics of traffic. The wavelet neural network (WNN) is used to predict the traffic flow. Then, combined with the knowledge of graph theory, an A-Star algorithm (AS) is used to determine the optimal path. Secondly, the optimal installation location of MEPSs is determined by forward–backward sweep method in distribution network. Finally, the feasibility, accuracy, and time cost of the proposed method are verified by numerical simulations, which can meet the requirements of online application.Tianen HuangJian TangZhenjie WuYuantao WangXiang LiShuangdie XuWenguo WuYajun MoTao NiuHang DongFan LiMDPI AGarticlemobile emergency power source (MEPS)dynamic road traffic index (DRTI)wavelet neural network (WNN)A-Star algorithm (AS)distribution networkChemical technologyTP1-1185ChemistryQD1-999ENProcesses, Vol 9, Iss 1935, p 1935 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	mobile emergency power source (MEPS) dynamic road traffic index (DRTI) wavelet neural network (WNN) A-Star algorithm (AS) distribution network Chemical technology TP1-1185 Chemistry QD1-999
spellingShingle	mobile emergency power source (MEPS) dynamic road traffic index (DRTI) wavelet neural network (WNN) A-Star algorithm (AS) distribution network Chemical technology TP1-1185 Chemistry QD1-999 Tianen Huang Jian Tang Zhenjie Wu Yuantao Wang Xiang Li Shuangdie Xu Wenguo Wu Yajun Mo Tao Niu Hang Dong Fan Li Mobile Emergency Power Source Configuration Scheme considering Dynamic Characteristics of a Transportation Network
description	Recently, with the large scale of power grids and the increase in frequency of extreme weather, the safe and stable operation of power systems is facing great challenges. Therefore, mobile emergency power source (MEPS) are a promising and feasible way to deal with extreme weather and reduce economic losses. However, the current urban power grid and transportation network are closely coupled, and the congested traffic hinders the rapid configuration of MEPSs. Therefore, this paper proposes an MEPS configuration scheme considering real-time traffic conditions. Firstly, the dynamic road traffic index (DRTI) is defined, which can fully describe the dynamic characteristics of traffic. The wavelet neural network (WNN) is used to predict the traffic flow. Then, combined with the knowledge of graph theory, an A-Star algorithm (AS) is used to determine the optimal path. Secondly, the optimal installation location of MEPSs is determined by forward–backward sweep method in distribution network. Finally, the feasibility, accuracy, and time cost of the proposed method are verified by numerical simulations, which can meet the requirements of online application.
format	article
author	Tianen Huang Jian Tang Zhenjie Wu Yuantao Wang Xiang Li Shuangdie Xu Wenguo Wu Yajun Mo Tao Niu Hang Dong Fan Li
author_facet	Tianen Huang Jian Tang Zhenjie Wu Yuantao Wang Xiang Li Shuangdie Xu Wenguo Wu Yajun Mo Tao Niu Hang Dong Fan Li
author_sort	Tianen Huang
title	Mobile Emergency Power Source Configuration Scheme considering Dynamic Characteristics of a Transportation Network
title_short	Mobile Emergency Power Source Configuration Scheme considering Dynamic Characteristics of a Transportation Network
title_full	Mobile Emergency Power Source Configuration Scheme considering Dynamic Characteristics of a Transportation Network
title_fullStr	Mobile Emergency Power Source Configuration Scheme considering Dynamic Characteristics of a Transportation Network
title_full_unstemmed	Mobile Emergency Power Source Configuration Scheme considering Dynamic Characteristics of a Transportation Network
title_sort	mobile emergency power source configuration scheme considering dynamic characteristics of a transportation network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4afd5d5787bc4401a5b62a3c56f32603
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_version_	1718410645314994176

Mobile Emergency Power Source Configuration Scheme considering Dynamic Characteristics of a Transportation Network

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