Vertex-Scenario Robust P2P Energy Trading for Multi-Energy VPPs

This paper investigates the peer-to-peer (P2P) trading framework in a multi-energy virtual power plant (VPP). The aim is to deal with the uncertainty of renewable energy. To solve this problem, a vertex-scenario robust optimization (VRO) method is developed in this paper, which, through Taguchi's orthogonal array testing method, can effectively reduce the number of scenarios and increase the computation efficiency. On this basis, this paper establishes a P2P trading model of multi-energy VPPs, and uses the tracker-based distributed algorithm (TBDA) to solve this optimization problem, which can increase the consumption rate of renewable energy and realize collaborative optimization among multi-energy VPPs. Finally, the rationality and availability of the advanced model are testified in case studies.