Privacy-preserving multi-VPPs scheduling for peak ramp minimization

The increasing integration of distributed energy resources (DERs) has driven the transformation of active distribution systems. A large volume of small-capacity DERs results in various distribution system operational challenges, such as ramping events, over-voltage issues, privacy concerns, etc. The virtual power plant (VPP) emerges as a promising solution. Effective coordination between power distribution networks and multi-VPPs (MVPPs) is imperative for mitigating peak ramp. This paper introduces a novel peak ramp minimization model for MVPP systems in active distribution networks. The proposed model incorporates location-aware MVPP power exchanges, reducing distribution losses and operational costs. By integrating the Karush–Kuhn–Tucker condition into the Alternating Direction Method of Multipliers (ADMM), we propose a novel ADMM-like algorithm for decentralized energy management. The ADMM-like algorithm enables local optimization for each VPP and preserves privacy. Numerical simulations demonstrate that the proposed approach effectively minimizes the peak ramp, reduces power losses, and mitigates computational and communication burdens.