Optimal dispatching of regional power grid considering vehicle network interaction

Area of Science:

• Power Systems Engineering
• Renewable Energy Integration
• Smart Grids

Background:

• Uncontrolled charging of large-scale electric vehicles (EVs) poses significant risks to power system stability and security.
• Existing grid management strategies struggle to accommodate the dynamic and often unpredictable nature of EV charging demands.
• The integration of EVs necessitates advanced scheduling methods to balance grid load and leverage EV flexibility.

Purpose of the Study:

• To develop a regional power network optimization scheduling method that incorporates vehicle-grid interaction.
• To establish a robust model for EV charging and discharging behavior, considering user and technical characteristics.
• To create a multi-objective optimization framework addressing economic efficiency, new energy consumption, and grid demand-side curve smoothing.

Main Methods:

• Developed an EV charging and discharging behavior model based on user and vehicle characteristics.
• Utilized Monte Carlo sampling to define scheduling potentials and limits for EVs.
• Formulated deterministic and interval multi-objective optimization models, solved using NSGA-II and improved NSGA-II algorithms.
• Employed the Analytic Hierarchy Process (AHP) to select the optimal scheduling scheme.

Main Results:

• The proposed method effectively models EV charging/discharging behavior and quantifies scheduling potential.
• The interval multi-objective optimization model demonstrated superior adaptability, practicality, and robustness against uncertainties compared to deterministic models.
• Price incentives successfully guided EV users towards optimal charging and discharging patterns, enhancing grid flexibility.

Conclusions:

• The developed optimization scheduling method, considering vehicle-grid interaction and uncertainties, significantly enhances regional power network stability.
• The interval multi-objective approach provides a more resilient solution for managing EV integration in power grids.
• This research offers a practical framework for optimizing EV participation in grid services, promoting economic benefits and renewable energy utilization.