We study the interdependence between transportation and power systems considering decentralized renewable generators and electric vehicles (EVs). We formulate the problem in a stochastic multiagent optimization framework considering the complex interactions between EV/conventional vehicle drivers, renewable/conventional generators, and independent system operators, with locational electricity and charging prices endogenously determined by markets. We show that the multiagent optimization problems can be reformulated as a single convex optimization problem and prove the existence and uniqueness of the equilibrium. To cope with the curse of dimensionality, we propose the alternating direction method of multipliers (ADMM)-based decomposition algorithm to facilitate parallel computing. Numerical insights are generated using standard test systems in the transportation and power system literature.


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    Titel :

    A Stochastic Multiagent Optimization Framework for Interdependent Transportation and Power System Analyses


    Beteiligte:
    Guo, Zhaomiao (Autor:in) / Afifah, Fatima (Autor:in) / Qi, Junjian (Autor:in) / Baghali, Sina (Autor:in)


    Erscheinungsdatum :

    01.09.2021


    Format / Umfang :

    1884496 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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