Distributed robust optimization strategy for multi-energy virtual power plant clusters

As renewable energy penetration continues to rise, the demand for coordinated optimization of decentralized source-load-storage. Virtual power plant (VPP) addresses this need by aggregating and coordinating diverse resources. However, effective mechanisms for multi-agent VPP coordination remain limited. To address this, this paper proposes a distributed robust optimization strategy for multi-energy VPP clusters in high-altitude regions. This strategy combines a dual-norm uncertainty set with a Nash bargaining mechanism to coordinate multi-agent interactions and mitigate operational risks. First, addressing the integrated planning-operation problem for multi-energy VPPs, a two-stage distributed robust optimization model is established. The dual-norm uncertainty set characterizes scenario probability uncertainties. Second, aiming to minimize interaction costs, a bargaining model is proposed based on Nash bargaining theory. Finally, case studies are conducted on three multi-energy VPPs. Results demonstrate that the proposed optimization strategy effectively enhances overall system revenue, environmental benefits, and source-load matching capability while ensuring fair competition among VPPs.