Multi-objective operation optimization method of microgrid considering the influence of electric vehicle

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Smart Grids

Background:

• Large-scale electric vehicle (EV) integration into microgrids can disrupt system stability due to unmanaged charging.
• Existing microgrid operational models often do not account for the dynamic load impact of EVs.

Purpose of the Study:

• To propose an optimized operational strategy for grid-connected microgrids that incorporates electric vehicles.
• To mitigate the negative impacts of disorderly EV charging on microgrid stability and economic efficiency.

Main Methods:

• Developed a grid-connected microgrid model including EVs.
• Utilized Monte Carlo simulations to model disorderly and orderly EV charging/discharging load profiles.
• Implemented a time-of-use pricing strategy for orderly EV charging and discharging.
• Formulated a multi-objective optimization problem (minimizing cost and peak-valley difference) and solved it using a particle swarm optimization algorithm.

Main Results:

• Simulations compared three scenarios: no EVs, disorderly EV charging, and orderly EV charging/discharging.
• Orderly EV charging and discharging significantly reduced microgrid operating costs.
• The proposed strategy effectively lowered the peak-valley load difference.

Conclusions:

• Orderly charging and discharging of electric vehicles is a viable strategy for enhancing microgrid performance.
• This approach improves clean energy utilization and reduces operational expenses.
• The method offers practical value for stable and economical microgrid operation with high EV penetration.