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Grid Optimal Integration of Electric Vehicles: Examples with Matlab Implementation [electronic resource] / by Andrés Ovalle, Ahmad Hably, Seddik Bacha.

By: Ovalle, Andrés [author.].
Contributor(s): Hably, Ahmad [author.] | Bacha, Seddik [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Systems, Decision and Control: 137Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XV, 219 p. 90 illus., 88 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319731773.Subject(s): Computational intelligence | Renewable energy sources | Mathematical optimization | Game theory | Computational Intelligence | Renewable Energy | Optimization | Game TheoryAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction -- Centralized Approach -- Dynamic Programming and Potential Game Approach -- Evolutionary Game Theory Approach Part I: Mixed Strategist Dynamics -- Evolutionary Game Theory Approach Part II: Escort Dynamics.
In: Springer Nature eBookSummary: 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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Introduction -- Centralized Approach -- Dynamic Programming and Potential Game Approach -- Evolutionary Game Theory Approach Part I: Mixed Strategist Dynamics -- Evolutionary Game Theory Approach Part II: Escort Dynamics.

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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