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EV Hosting Capacity Enhancement in a Community Microgrid Through Dynamic Price Optimization-Based Demand Response.

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    This study introduces a dynamic pricing demand response (DR) scheme to manage electric vehicle (EV) charging in community microgrids. The approach ensures microgrids can securely host more EVs by optimizing energy consumption.

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    Area of Science:

    • Electrical Engineering
    • Computer Science
    • Energy Systems

    Background:

    • Community microgrids enhance smart grid resiliency.
    • Increasing electric vehicle (EV) adoption strains microgrid capacity.
    • Uncontrolled EV charging poses operational challenges for electric networks.

    Purpose of the Study:

    • To propose an efficient demand response (DR) scheme using dynamic pricing for microgrids.
    • To enhance the microgrid's capacity for hosting a large number of EVs securely.
    • To develop a hierarchical optimization framework for managing EV charging.

    Main Methods:

    • A hierarchical two-level optimization framework was developed.
    • The upper level optimizes dynamic pricing using an evolutionary algorithm.
    • The lower level uses mixed-integer linear programming for user energy optimization.

    Main Results:

    • The proposed DR scheme effectively manages EV charging in microgrids.
    • Numerical experiments validated the scheme's effectiveness against benchmark policies.
    • The distributed energy scheduling enhances scalability.

    Conclusions:

    • The dynamic pricing DR scheme successfully enhances microgrid capacity for EVs.
    • The hierarchical optimization framework provides an efficient solution for EV integration.
    • The approach offers a scalable and effective method for managing EV charging in smart grids.