With the rapid development of electric vehicles (EVs), aggregators can use reliable technology to aggregate widely distributed lithium batteries, enabling deterministic centralized output and effectively participating in multi-time scale grid demand response, thereby achieving significant economic benefits. This paper analyzes demand response across different time scales, such as minutes and hours, and studies the multi-time matching technology of probabilistic profiling techniques. The SOM neural network clustering method is used to aggregate EV loads, and aggregators control the unified charging and discharging behavior of EV batteries. A mixed-integer linear programming optimization algorithm is employed to allocate the aggregated battery resources, ensuring optimal energy distribution. Finally, simulations based on typical daily EV random operation scenarios show an improvement in the revenue and operational satisfaction of EV aggregators, validating the effectiveness of the aggregation technology.


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    Title :

    Multi-time Scale Probabilistic Aggregation Technology of Large-scale Electric Vehicle Energy Storage Batteries Participating in Grid Demand Response


    Contributors:
    Liu, Liming (author) / Gao, Liping (author) / She, Yanjie (author) / He, Chunqi (author) / Ren, Jinglei (author) / Li, Jiang (author)


    Publication date :

    2024-10-10


    Size :

    421794 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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