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    Optimal Design of Energy Storage System to Buffer Charging Infrastructure in Smart Cities

    Source: Journal of Management in Engineering:;2020:;Volume ( 036 ):;issue: 002
    Author:
    Dong Zhao
    ,
    Navwant Thakur
    ,
    Jiayu Chen
    DOI: 10.1061/(ASCE)ME.1943-5479.0000742
    Publisher: ASCE
    Abstract: A parallel trend of vehicle automation and electrification represents smart mobility in smart cities. Dramatic growth of electric vehicles (EVs) on roads is projected in the next decade. The pivotal challenge to infrastructure is how to fill the charging-capacity gap for the increased number of EVs. The challenge is twofold: one is the energy gap in charging stations due to peak demands, and the other is shortage of charging infrastructure owing to high construction costs. As an emerging solution, energy storage technology provides stable and reliable electricity buffers during peak hours; however, it is unknown how to effectively integrate energy storage to charging stations while obtaining the lowest cost. The objective of this paper is to develop a simulation model that determines the optimal design of the energy storage system (ESS) for a given network of charging stations. The model is made novel by integrating the charging station network and energy storage system as a whole. The optimal ESS design informs the configuration and distribution of battery type, size, amount, and location. A case study of the Detroit area in Michigan indicates the model is robust and provides efficient decision support for planners, designers, and engineers to construct energy storage systems. Strategies retrieved from the case suggest large-sized batteries and microgrids for cross-station energy exchange, which leads to a potential 20%–36% of cost savings for energy storage development.
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      Optimal Design of Energy Storage System to Buffer Charging Infrastructure in Smart Cities

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4266060
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    contributor authorDong Zhao
    contributor authorNavwant Thakur
    contributor authorJiayu Chen
    date accessioned2022-01-30T19:50:07Z
    date available2022-01-30T19:50:07Z
    date issued2020
    identifier other%28ASCE%29ME.1943-5479.0000742.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4266060
    description abstractA parallel trend of vehicle automation and electrification represents smart mobility in smart cities. Dramatic growth of electric vehicles (EVs) on roads is projected in the next decade. The pivotal challenge to infrastructure is how to fill the charging-capacity gap for the increased number of EVs. The challenge is twofold: one is the energy gap in charging stations due to peak demands, and the other is shortage of charging infrastructure owing to high construction costs. As an emerging solution, energy storage technology provides stable and reliable electricity buffers during peak hours; however, it is unknown how to effectively integrate energy storage to charging stations while obtaining the lowest cost. The objective of this paper is to develop a simulation model that determines the optimal design of the energy storage system (ESS) for a given network of charging stations. The model is made novel by integrating the charging station network and energy storage system as a whole. The optimal ESS design informs the configuration and distribution of battery type, size, amount, and location. A case study of the Detroit area in Michigan indicates the model is robust and provides efficient decision support for planners, designers, and engineers to construct energy storage systems. Strategies retrieved from the case suggest large-sized batteries and microgrids for cross-station energy exchange, which leads to a potential 20%–36% of cost savings for energy storage development.
    publisherASCE
    titleOptimal Design of Energy Storage System to Buffer Charging Infrastructure in Smart Cities
    typeJournal Paper
    journal volume36
    journal issue2
    journal titleJournal of Management in Engineering
    identifier doi10.1061/(ASCE)ME.1943-5479.0000742
    page04019048
    treeJournal of Management in Engineering:;2020:;Volume ( 036 ):;issue: 002
    contenttypeFulltext
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