Grid Optimal Integration of Electric Vehicles: Examples with Matlab Implementation

Andrés Ovalle

ISBN 10: 3319731769 / ISBN 13: 9783319731766

Published by Springer International Publishing, 2018

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book is a compilation of recent research on distributed optimization algorithms for the integral load management of plug-in electric vehicle (PEV) fleets and their potential services to the electricity system. It also includes detailed developed Matlab scripts. These algorithms can be implemented and extended to diverse applications where energy management is required (smart buildings, railways systems, task sharing in micro-grids, etc.). The proposed methodologies optimally manage PEV fleets' charge and discharge schedules by applying classical optimization, game theory, and evolutionary game theory techniques. Taking owner's requirements into consideration, these approaches provide services like load shifting, load balancing among phases of the system, reactive power supply, and task sharing among PEVs. The book is intended for use in graduate optimization and energy management courses, and readers are encouraged to test and adapt the scripts to their specific applications.

Seller Inventory # 9783319731766

Bibliographic Details

• Title: Grid Optimal Integration of Electric Vehicles: Examples with Matlab Implementation
• Publisher: Springer International Publishing
• Publication Date: 2018
• Language: English
• ISBN 10: 3319731769
• ISBN 13: 9783319731766
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 19D millimeters 241 millimeters width by 160 millimeters height by 19 millimeters depth
• Weight: 524 grams

About this title

Synopsis

This book is a compilation of recent research on distributed optimization algorithms for the integral load management of plug-in electric vehicle (PEV) fleets and their potential services to the electricity system. It also includes detailed developed Matlab scripts. These algorithms can be implemented and extended to diverse applications where energy management is required (smart buildings, railways systems, task sharing in micro-grids, etc.). The proposed methodologies optimally manage PEV fleets’ charge and discharge schedules by applying classical optimization, game theory, and evolutionary game theory techniques. Taking owner’s requirements into consideration, these approaches provide services like load shifting, load balancing among phases of the system, reactive power supply, and task sharing among PEVs. The book is intended for use in graduate optimization and energy management courses, and readers are encouraged to test and adapt the scripts to their specific applications.