Ahstract-Transforming to a low-carbon future requires massive efforts from both transport and power systems. Electric vehicles (EVs) can reduce CO2 emission in road transport through eco-routing while providing carbon intensity service for power systems via vehicle-to-grid (V2G) scheduling. This paper studies the coordinated effect of routing and scheduling problems of EVs via a novel model-free multi-agent reinforcement learning (MARL) method. In this context, EVs do not reply on any knowledge of the simulated environment and are capable of handling the system with various uncertainties and dynamics during the learning process, which can lead to timely decision making and better privacy protection. Extensive case studies based on a virtual 7-node 10-edge transportation network are developed to demonstrate the effectiveness of the proposed MARL method on reducing carbon emissions in the transportation system and providing carbon intensity service in the power system.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Multi-agent reinforcement learning for electric vehicles joint routing and scheduling strategies


    Beteiligte:
    Wang, Yi (Autor:in) / Qiu, Dawei (Autor:in) / Strbac, Goran (Autor:in)


    Erscheinungsdatum :

    2022-10-08


    Format / Umfang :

    482760 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Multi-agent reinforcement learning for autonomous vehicles: a survey

    Joris Dinneweth / Abderrahmane Boubezoul / René Mandiau et al. | DOAJ | 2022

    Freier Zugriff

    Multi-agent reinforcement learning for autonomous vehicles: a survey

    Dinneweth, Joris / Boubezoul, Abderrahmane / Mandiau, René et al. | Springer Verlag | 2022

    Freier Zugriff

    Multi-Agent Reinforcement Learning for Autonomous On Demand Vehicles

    Boyali, Ali / Hashimoto, Naohisa / John, Vijay et al. | IEEE | 2019