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

Ziyang Wang1, Hao Guo2, Ruijin Zhu3

  • 1College of Clean Energy and Electrical Engineering, Xizang Agricultural and Animal Husbandry University, Nyingchi, 860000, China.

Scientific Reports
|December 17, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a robust optimization strategy for coordinating multiple virtual power plants (VPPs) in high-altitude regions. The method enhances system revenue, environmental benefits, and energy matching while ensuring fair VPP competition.

Keywords:
Binomial constraintDistributed robust optimizationMulti energy virtual power plant clusterNash negotiations

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Area of Science:

  • Energy Systems Engineering
  • Optimization Theory
  • Renewable Energy Integration

Background:

  • Increasing renewable energy penetration necessitates coordinated optimization of decentralized energy resources.
  • Virtual Power Plants (VPPs) aggregate diverse resources, but effective multi-agent coordination mechanisms are limited.
  • High-altitude regions present unique challenges for energy management.

Purpose of the Study:

  • To propose a distributed robust optimization strategy for multi-energy VPP clusters in high-altitude regions.
  • To address the integrated planning-operation problem for multi-energy VPPs.
  • To develop a coordination mechanism that mitigates operational risks and minimizes interaction costs.

Main Methods:

  • Developed a two-stage distributed robust optimization model for integrated planning-operation.
  • Utilized a dual-norm uncertainty set to characterize scenario probability uncertainties.
  • Incorporated a Nash bargaining mechanism to coordinate multi-agent interactions and minimize costs.

Main Results:

  • The proposed strategy effectively enhances overall system revenue for VPP clusters.
  • Demonstrated significant improvements in environmental benefits and source-load matching capability.
  • Ensured fair competition and equitable outcomes among participating VPPs.

Conclusions:

  • The distributed robust optimization strategy provides an effective solution for coordinating multi-energy VPP clusters.
  • The approach successfully balances operational efficiency, risk mitigation, and inter-VPP fairness.
  • This methodology is particularly relevant for renewable energy integration in challenging geographical areas like high-altitude regions.