Dynamic Construction Strategy for Virtual Power Plants Considering Distributed Energy Resource Interaction Uncertainty and Response Recovery Process

The interaction uncertainty of Distributed Energy Resources (DERs) during grid dispatch poses a challenge to the efficient and economical construction of Virtual Power Plants (VPPs). To address this, this paper proposes a dynamic construction strategy for VPPs that considers the interaction uncertainty of DERs and the response recovery process. First, the interaction effects of DER power variations are quantified through Seasonal Trend Decomposition (STL) and cross-correlation analysis, and an linkage matrix is constructed to depict the uncertainty. Then, a comprehensive evaluation method is proposed, which not only accounts for the direct economic cost of response scheduling but also innovatively introduces the optimization of the post-scheduling response recovery process, using Model Predictive Control (MPC) to minimize the total recovery cost. On this basis, a dynamic construction strategy using a Generative Adversarial Network (GAN)-Copula function is proposed. For resources with significant interaction, a joint probability distribution model is established to accurately predict the power interaction impacts during the scheduling process, thereby dynamically adjusting the final aggregation set and improving its economy. Simulation results based on data from 247 charging stations in Shenzhen and 8,497 communication base stations in Hefei indicate that, compared to existing methods, this approach can reduce scheduling response costs by approximately 12.71%.