Optimal scheduling strategy of electric vehicle based on improved NSGA-III algorithm

Area of Science:

• Electrical Engineering
• Optimization Theory
• Sustainable Energy Systems

Background:

• Unmanaged electric vehicle charging increases distribution network load.
• Disorderly charging leads to low satisfaction among electric vehicle owners.
• Balancing grid stability with user needs is crucial for electric vehicle integration.

Purpose of the Study:

• To propose an optimal electric vehicle scheduling model considering vehicle owner satisfaction.
• To address the challenges of load increase and low owner satisfaction in distribution networks.
• To optimize charging station income and grid load stability.

Main Methods:

• Developed an optimal scheduling model using dynamic electricity prices and EV charging/discharging states.
• Incorporated vehicle owner satisfaction, charging station income, and load fluctuation as optimization objectives.
• Utilized an improved NSGA-III algorithm (DJM-NSGA-III) for multi-objective, high-dimensional problem-solving.

Main Results:

• The proposed model enhances vehicle owner satisfaction and charging station revenue.
• Effectively mitigates the conflict between owner interests and operational demands.
• Demonstrates improved initial population quality and avoidance of local optima in the optimization algorithm.

Conclusions:

• The DJM-NSGA-III algorithm effectively solves complex electric vehicle scheduling problems.
• The model successfully balances economic benefits for charging stations and satisfaction for owners.
• Ensures safe and stable operation of the distribution network with increased electric vehicle penetration.