An Optimal VPP Grouping Method for Deep Peak Regulation Ancillary Service of Power Grid

With a high proportion of renewable energy connected to the power grid, it is necessary to optimize the grouping of flexible resources on the demand side to facilitate regulation and control in order to solve the shortcomings of decentralized and poor direct control of flexible resources on the demand side and meet the demand for deep peak regulation ancillary services of the power grid. In this paper, an optimized grouping method of virtual power plant (VPP) is proposed, which takes into account the peak regulation profits balance index of VPPs and the modularity structure index based on electrical distance. Firstly, the flexibility adjustment ability of various demand side flexible resources is analyzed, and the peak regulation profits balance index is proposed and combined with the modularity index reflecting the structural characteristics of the VPP. Then, the maximum weighted sum of the two indexes is used as the objective function to establish the VPP optimized formation model, and the genetic algorithm is used to solve it. Among them, the peak regulation profits balance index is obtained by simulating the participation of VPPs in the peak regulation ancillary services market based on typical day scenarios and calculating the peak regulation profits of each VPP. Finally, the rationality and effectiveness of the proposed method are verified by an example.