Grid optimal integration of electric vehicles
Series: Studies in systems, decision and control ; 137Publication details: Cham: Springer, 2018Description: xv, 219pISBN: 9783319892382Subject(s): Mathematical optimization | Renewable energy sources | Game theory | Clean energy industries | Computational intelligence | Computer programsUDC classification: 629.064.5:681.3.06M Summary: 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.| Item type | Current library | Collection | Call number | Status | Date due | Barcode |
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Book
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Indian Institute of Technology Delhi - Central Library Central Library | General | 629.064.5:681.3.06M OVA-G (Browse shelf(Opens below)) | Available | 176769 |
Includes bibliographical references.and index (215-219p.)
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.
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