Year: 2025
Type: article
Abstract: 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.
Source: Scientific Reports
Authors: Ziyang Wang, Hao Guo, Ruijin Zhu, Zixuan Liu
Institutions: State Grid Corporation of China (China), Hohai University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Award: G7887146239
OpenAlex ID: W4417442902