Multi-VPPs power-carbon joint trading optimization considering low-carbon operation mode

Xinrui Liu*, Yulu Ni, Yufei Sun, Jiawei Wang, Rui Wang, Qiuye Sun

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


With the continuous increase of demand-side penetration of solar power generation and energy storage systems, the power system is no longer able to directly and centrally manage the massive distributed energy resources (DERs). Moreover, the joint trading of electricity and carbon under the “dual carbon” target is becoming a trend. In this paper, the virtual power plant (VPP) modeling was first introduced to manage and control the massive heterogeneous DERs in adjacent geographical locations. Then, a low-carbon operation mode of the VPP based on emission arbitrage was proposed. Under the cooperative guidance of dynamic carbon-emission-factors and time-of-use electricity price from the grid, flexible loads are adjusted to participate in demand response activities. In addition, carbon emission arbitrage is carried out by changing the utilization time of renewable power using the storage system. Those two cooperative demand response actions can jointly contribute to the active low-carbon scheduling of VPPs. Finally, considering the resource complementarity characteristics among VPPs, an optimization model of multi-VPPs power-carbon joint trading under the low-carbon operation mode was developed. Such a model can motivate users to reduce the carbon actively with the goal of reducing operating costs and carbon emissions. The original-dual-ADMM distributed optimization algorithm was adopted for an iterative solution. The case study utilized the operation data of VPPs with 6 different DERs characterized by a typical hourly profile in northern China in a winter day, and the simulation results verified that the proposed model can effectively improve the economy and decarbonization of multi-VPPs.

Original languageEnglish
Article number110786
Number of pages16
JournalJournal of Energy Storage
Early online date6 Feb 2024
Publication statusE-pub ahead of print - 6 Feb 2024

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