Electric Vehicles Participate in the Economic Optimal Scheduling Method of Virtual Power Plant Considering Time-of-Use Tariff

The rapid growth in electric vehicle ownership will significantly impact the safe and stable operation of distribution networks due to the uncertain charging and discharging behavior of these vehicles. The development of smart power grids has facilitated the flexible mobilization of demand-side resources, playing an increasingly crucial role in balancing supply and demand. Research findings indicate that electric vehicle loads are highly sensitive and responsive to electricity prices. To address these challenges, we propose a virtual power plant economic optimization scheduling method based on incentive demand response strategies with dynamic load compensation for electric vehicles under time-of-use pricing. Firstly, historical data is analyzed using three-times exponential smoothing methods to predict wind/solar power station output, baseline load for electric vehicles, and adjustable capacity for power users to ensure economic compensation for charging users' demand response. Then, considering system constraints such as power balance, new energy output from wind/solar sources, and operating costs associated with energy storage systems while incorporating time-of-use pricing, we establish an optimal scheduling model for maximizing virtual power plant economic benefits. Finally, we validate the accuracy and effectiveness of our proposed method using actual data from an electric vehicle charging station located in Zhongmu County, Zhengzhou City in Henan Province.