Flexible Resource Data Acquisition and Dynamic Characterization Techniques for Virtual Power Plants with a High Percentage of New Energy Access

To address the worsening environmental pollution and the scarcity of fossil fuels, and to achieve the “dual carbon” goals, a high proportion of renewable energy is integrated into the power grid. Due to the significant fluctuations in renewable energy output, in order to alleviate the phenomenon of wind and light abandonment, distributed energy sources, energy storage systems, and controllable loads are integrated and co-dispatched through virtual power plants (VPPs). Aiming at the difficulties of the VPPs, such as the wide variety of flexible resources and their different characteristics, this paper proposes a data acquisition and dynamic characterization technique for flexible resources in VPPs. The method first establishes different resource models within the VPP, and then performs load decomposition and feature extraction of different resource objects from various dimensions. Finally, based on the dynamic performance identification technique using time-period Markov models, the dynamic performance parameters of the resource cluster are parameterized. Case studies demonstrate that the proposed method can effectively extract flexible resource response characteristics and accurately portray aggregated resources' dynamic performance within VPPs.