東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Data-driven Algorithms for Distribut...

Zhang, Yue.

Linked to FindBook

Google Book

Amazon

博客來

Data-driven Algorithms for Distribution System Operation and Control.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-driven Algorithms for Distribution System Operation and Control./
Author:	Zhang, Yue.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	151 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-08, Section: B.
Contained By:	Dissertations Abstracts International81-08B.
Subject:	Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13885697
ISBN:	9781687973566

Data-driven Algorithms for Distribution System Operation and Control.
Zhang, Yue.

Data-driven Algorithms for Distribution System Operation and Control. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 151 p.

Source: Dissertations Abstracts International, Volume: 81-08, Section: B.

Thesis (Ph.D.)--Washington State University, 2019.

This item must not be sold to any third party vendors.

Transactive energy systems are emerging as a transformative solution for the problems that distribution system operators face due to an increase in the use of distributed energy resources and rapid growth in the scalability of managing active distribution system. To meet the reliability, sustainability and security requirements, efficient management and control of distributed generators, energy storage systems, and demand response is necessary to provide services such as reactive power compensation, voltage control, energy balancing, peak load reduction. These edge resources can also be used for system restoration after the extreme events and enabling resiliency of the electric grid. While there are numbers of related resources focused on solving these problems, availability of additional data resources has not been utilized to its potential to enable the reliable, sustainable and resilient electric grid. In order to address these challenges, data-driven algorithms have been proposed in this dissertation to utilize those new data to effectively monitor and control the energy storage system, distributed energy systems, and demand response.

ISBN: 9781687973566Subjects--Topical Terms:

586835
Engineering.

Data-driven Algorithms for Distribution System Operation and Control.
LDR:02113nmm a2200289 4500 001 2269508
005 20200911122831.5
008 220629s2019 ||||||||||||||||| ||eng d
020 $a 9781687973566
035 $a (MiAaPQ)AAI13885697
035 $a AAI13885697
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhang, Yue. $3 1297056
245 1 0 $a Data-driven Algorithms for Distribution System Operation and Control.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 151 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-08, Section: B.
500 $a Advisor: Srivastava, Anurag.
502 $a Thesis (Ph.D.)--Washington State University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Transactive energy systems are emerging as a transformative solution for the problems that distribution system operators face due to an increase in the use of distributed energy resources and rapid growth in the scalability of managing active distribution system. To meet the reliability, sustainability and security requirements, efficient management and control of distributed generators, energy storage systems, and demand response is necessary to provide services such as reactive power compensation, voltage control, energy balancing, peak load reduction. These edge resources can also be used for system restoration after the extreme events and enabling resiliency of the electric grid. While there are numbers of related resources focused on solving these problems, availability of additional data resources has not been utilized to its potential to enable the reliable, sustainable and resilient electric grid. In order to address these challenges, data-driven algorithms have been proposed in this dissertation to utilize those new data to effectively monitor and control the energy storage system, distributed energy systems, and demand response.
590 $a School code: 0251.
650 4 $a Engineering. $3 586835
690 $a 0537
710 2 $a Washington State University. $b Electrical Engineering. $3 2098665
773 0 $t Dissertations Abstracts International $g 81-08B.
790 $a 0251
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13885697