Optimal Scheduling of Data Center Virtual Power Plant in Electricity-Carbon Joint Market Under Uncertainty

Ziwei Zhao; Chen Yang; Chen Liang; Dan Xu; Yilin Zhang; Junjie Tang

doi:10.35833/MPCE.2025.000414

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Optimal Scheduling of Data Center Virtual Power Plant in Electricity-Carbon Joint Market Under Uncertainty

Original Paper | 更新时间：2026-09-28

- Optimal Scheduling of Data Center Virtual Power Plant in Electricity-Carbon Joint Market Under Uncertainty
- Optimal Scheduling of Data Center Virtual Power Plant in Electricity-Carbon Joint Market Under Uncertainty
- MPCE 2026年14卷第5期页码：1896-1908
- 作者机构：
  
  1.State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China
  2.Electric Power Research Institute of State Grid Gansu Electric Power Company, Lanzhou 730070, China
  3.Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610213, China
  4.Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology, China Electric Power Research Institute, Beijing 100192, China
- 作者简介：
  
  Ziwei Zhao received the B.S. degree from China University of Mining and Technology, Beijing, China, in 2023. He is currently pursuing the Ph.D. degree in electrical engineering at Chongqing University, Chongqing, China. His research interests include energy management and market strategy of virtual power plant. Email:zhaoziwei@stu.cqu.edu.cn
  Chen Yang received the M.S. degree from Chongqing University, Chongqing, China, in 2022. He is an Engineer in Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu, China. His research interests include optimal operation of low-carbon power system and virtual power plant. Email: yangchen@tsinghua-eiri.org
  Chen Liang received the M.S. degree in electrical engineering from Xi’an Jiaotong University, Xi’an, China, in 2010. He is a Professor-level Senior Engineer in Electric Power Research Institute of State Grid Gansu Electric Power Company, Lanzhou, China. He is currently pursuing the Ph.D. degree in electrical engineering at Chongqing University, Chongqing, China. His interests include energy conservation and emission reduction in power system. Email:liangchen034@163.com
  Dan Xu received the B.S. and M.S. degrees in electrical engineering from China Agricultural University, Beijing, China, in 2007 and 2009, respectively, and the Ph.D. degree in electrical engineering from China Electric Power Research Institute, Beijing, China, in 2022. He is a Senior Engineer in China Electric Power Research Institute. His interests include optimal operation of large-scale power system and renewable energy integration. Email:xudan@epri.sgcc.com.cn
  Yilin Zhang received the bachelor degree from Hubei University of Technology, Wuhan, China, in 2023. He is currently pursuing the Ph.D. degree in electrical engineering at Chongqing University, Chongqing, China. His research interest includes application of artificial intelligence in power system. Email:zhangyilin@stu.cqu.edu.cn
  Junjie Tang received the Ph.D. degree in electrical engineering from the Institute for Automation of Complex Power Systems, E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany, in 2014. He is currently an Associate Professor with the State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, China. His research interests include complex power system, uncertainty quantification analysis, risk assessment and optimization management of hybrid AC/DC grid, and application of machine learning in power system. Email:tangjunjie@cqu.edu.cn
- 基金信息：
  
  the National Natural Science Foundation of China(52177071);the Science and Technology Project from State Grid Corporation of China(5400-202433214A-1-1-ZN)
- DOI：10.35833/MPCE.2025.000414
  中图分类号：
- 收稿：2025-05-09，
  
  修回：2025-09-05，
  
  录用：2025-12-29，
  
  网络首发：2026-03-30，
  
  纸质出版：2026-09-15
- 稿件说明：
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Ziwei Zhao, Chen Yang, Chen Liang, 等. Optimal Scheduling of Data Center Virtual Power Plant in Electricity-Carbon Joint Market Under Uncertainty[J]. MPCE, 2026,14(5):1896-1908.

Zhao Ziwei,Yang Chen,Liang Chen,et al.Optimal Scheduling of Data Center Virtual Power Plant in Electricity-Carbon Joint Market Under Uncertainty[J].Journal of Modern Power Systems and Clean Energy,2026,14(05):1896-1908.
Ziwei Zhao, Chen Yang, Chen Liang, 等. Optimal Scheduling of Data Center Virtual Power Plant in Electricity-Carbon Joint Market Under Uncertainty[J]. MPCE, 2026,14(5):1896-1908. DOI： 10.35833/MPCE.2025.000414.

Zhao Ziwei,Yang Chen,Liang Chen,et al.Optimal Scheduling of Data Center Virtual Power Plant in Electricity-Carbon Joint Market Under Uncertainty[J].Journal of Modern Power Systems and Clean Energy,2026,14(05):1896-1908. DOI： 10.35833/MPCE.2025.000414.

摘要

Abstract

As an emerging demand response (DR) resource

data centers (DCs) have garnered significant attention due to their ability to adapt to stochastic renewable energy fluctuations. To fully exploit the price signals from diverse markets and achieve the flexible complementarity between DCs and virtual power plant (VPP) resources

this paper proposes a coordinated framework for the data center virtual power plant (DCVPP) participating in the electricity

carbon joint market under uncertainty. Specifically

a refined model of power consumption for cooling system in DC is developed based on the second-order equivalent thermal parameter (ETP) theory

which enhances the accuracy of load modeling and maximizes the potential for DR. To address the penalty risks associated with forecast uncertainties in renewable energy generation and load demands

a risk-based reserve scheme is developed by integrating the Gaussian copula with two-sided superquantile theory. This scheme provides a coordinated mechanism for quantifying the correlations between forecast errors. On this basis

an optimal scheduling strategy of DCVPPs is formulated in the

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=119199355&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=119199378&type=

14.98600006

2.96333337

carbon joint market

which coordinates DC load flexibility with distributed energy resources to optimize economic performance and reduce carbon emissions. Case studies demonstrate that the proposed refined model achieves an root mean square error (RMSE) reduction ranging from 57.9% to 68.8%

and the risk-based reserve scheme reduces penalty costs by 24.4%. Furthermore

the integration of the carbon trading mechanism in the proposed optimal scheduling strategy results in an increase of $259.9 in economic benefits and an average reduction of 14% in carbon emissions.

关键词

Keywords

references

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