A Hierarchical Integrated Management Method for the Aggregated Control of Large-Scale Renewable Resources in Virtual Power Plants
A method for dynamic aggregation and regulation management of large-scale resources in a virtual power plant (VPP) is proposed. By determining the peak periods, off-peak periods, and normal periods of regional electricity consumption, the power system load demand data, flexible load characteristic data, and price data for each period are obtained. The operation objective function of VPP under the market mechanism is proposed, and its participation in the market operation mechanism is established. The types of flexible resources are identified, and a multi-flexible resource dynamic aggregation model of VPP is constructed. The improved wolf pack algorithm (WPA) is used to optimize the satisfaction factor of the satisfactory decision. The results show that compared with traditional methods, the improved WPA algorithm can significantly reduce costs, enabling VPP to meet electricity demand through gas turbines and energy storage equipment during peak periods, thereby enhancing the flexibility and economy of the power system.