Fast Convergence in Electric Vehicle Smart Charging

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We address the problem to control the charging schedules in a large population of plug-in electric vehicles, considered as heterogeneous noncooperative agents, with different strongly convex cost functions weakly coupled by a common pricing signal, convex charging constraints, e.g. plug-in times, deadlines and capacity limits. We assume a minimal information structure through which a central control unit can broadcast incentive signals to coordinate the decentralized optimal responses of the agents. We propose a dynamic control that, based on fixed-point operator theory, ensures global exponential convergence to an aggregative equilibrium, independently on the population size. We illustrate the benefits of the proposed control via numerical simulations, in scenarios where the aggregate charging demand tends to fill the overnight demand valley. Finally, we touch upon the a generalized setup with convex, separable, joint constraints, e.g. transmission line constraints and shared network resources.
Original languageEnglish
Title of host publicationIntelligent Integrated Energy Systems
Subtitle of host publicationThe PowerWeb Program at TU Delft
EditorsPeter Palensky, Milos Cvetkovic, Tamas Keviczky
Place of PublicationCham, Switzerland
ISBN (Electronic)978-3-030-00057-8
ISBN (Print)987-3-030-00056-1
Publication statusPublished - 2019

Bibliographical note

"Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public."


  • Smart charging
  • Plug-in electric vehicles
  • Computational game theory
  • Fixed-point operator theory


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