東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Methods for Optimal Charging of Larg...

Le Floch, Caroline.

FindBook

Google Book

Amazon

博客來

Methods for Optimal Charging of Large Fleets of Electric Vehicles.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Methods for Optimal Charging of Large Fleets of Electric Vehicles./
作者:	Le Floch, Caroline.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	147 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Contained By:	Dissertation Abstracts International79-07B(E).
標題:	Alternative Energy. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10600396
ISBN:	9780355576344

Methods for Optimal Charging of Large Fleets of Electric Vehicles.
Le Floch, Caroline.

Methods for Optimal Charging of Large Fleets of Electric Vehicles. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 147 p.

Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2017.

Today's electric grid must be transformed to meet modern consumption behaviors and safely integrate renewable energy sources. This has led to major efforts to develop grid-scale energy management solutions and ensure safety and reliability of our modern power network. In particular, large penetrations of Plug-in Electric Vehicles (PEVs) are expected increase energy needs and peak consumption, which would bring new challenges for utilities and grid operators.

ISBN: 9780355576344Subjects--Topical Terms:

1035473
Alternative Energy.

Methods for Optimal Charging of Large Fleets of Electric Vehicles.
LDR:03026nmm a2200325 4500 001 2160227
005 20180724121429.5
008 190424s2017 ||||||||||||||||| ||eng d
020 $a 9780355576344
035 $a (MiAaPQ)AAI10600396
035 $a (MiAaPQ)berkeley:17108
035 $a AAI10600396
040 $a MiAaPQ $c MiAaPQ
100 1 $a Le Floch, Caroline. $3 3348138
245 1 0 $a Methods for Optimal Charging of Large Fleets of Electric Vehicles.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 147 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
500 $a Adviser: Scott Moura.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2017.
520 $a Today's electric grid must be transformed to meet modern consumption behaviors and safely integrate renewable energy sources. This has led to major efforts to develop grid-scale energy management solutions and ensure safety and reliability of our modern power network. In particular, large penetrations of Plug-in Electric Vehicles (PEVs) are expected increase energy needs and peak consumption, which would bring new challenges for utilities and grid operators.
520 $a In this work, we develop optimization methods to coordinate the charging of large fleets of PEVs in distribution grids. We show that different methods should be applied, based on the infrastructure requirements and the objective of the controller.
520 $a The first Chapter Optimal Charging of Fleets of Electric Vehicles with Discrete Charging rates: PDE Modeling and Control Techniques presents a continuum modeling framework to coordinate PEV charging with discrete charging rates. We consider PEVs as loads, which diffuse along the State Of Energy (SOE) axis, and can be in three different categories: charging, discharging or idle. We use a discretized form of Partial Differential Equations (PDEs) to model the dynamics of the system and control the transitions between each category. The second Chapter Dual Splitting Framework for Optimal Charging of Fleets of Electric Vehicles with Continuous Charging rates proposes a tailored distributed optimization method to coordinate PEV charging for load shaping. Three iteration methods are presented and their convergence characteristics are detailed. The third Chapter Electric Vehicle Charging in the Smart Grid: Plug-and-Play Model Predictive Control techniques studies a voltage-regulation scenario for PEV charging. Power flow and distribution grid constraints are modeled, and PEV charging is controlled with Plug-and-Play Model Predictive Control. Finally, the final chapter Behavioral study of Demand Response programs studies the impact of non rational choices on energy consumption and on the success of Demand Response programs.
590 $a School code: 0028.
650 4 $a Alternative Energy. $3 1035473
650 4 $a Applied mathematics. $3 2122814
690 $a 0363
690 $a 0364
710 2 $a University of California, Berkeley. $b Civil and Environmental Engineering. $3 1043687
773 0 $t Dissertation Abstracts International $g 79-07B(E).
790 $a 0028
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10600396