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

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.