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Mean field constrained charging policy for large populations of plug-in electric vehicles


F. Parise, M. Colombino, S. Grammatico, J. Lygeros

IEEE Conference on Decision and Control, Los Angeles, California, USA

Constrained charging control of large populations of Plug-in Electric Vehicles (PEVs) is addressed using mean field game theory. We consider PEVs as heterogeneous agents, with different charging constraints (plug-in times and deadlines). The agents minimize their own charging cost, but are weakly coupled via a common electricity price. We propose an iterative algorithm that, in the case of an infinite population, converges to the Nash equilibrium of a related decentralized optimization problem. This allows us to approximate the centralized optimal solution, which in the unconstrained case fills the overnight power demand valley, via a decentralized procedure. The benefits of the proposed formulation in terms of convergence behavior and overall charging cost are illustrated through numerical simulations.


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J. Lygeros

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% Autogenerated BibTeX entry
@InProceedings { ParEtal:2014:IFA_4744,
    author={F. Parise and M. Colombino and S. Grammatico and J. Lygeros},
    title={{Mean field constrained charging policy for large
	  populations of plug-in electric vehicles}},
    booktitle={IEEE Conference on Decision and Control},
    address={Los Angeles, California, USA},
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