Matching-based Energy and Carbon Sharing for Prosumers Considering Social Behavior and Low-carbon Preferences

Lu Tan; Nian Liu; Jie Huang; Haonan Sun; Kai Jiang

doi:10.35833/MPCE.2025.000690

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Matching-based Energy and Carbon Sharing for Prosumers Considering Social Behavior and Low-carbon Preferences

Original Paper | 更新时间：2026-09-28

- Matching-based Energy and Carbon Sharing for Prosumers Considering Social Behavior and Low-carbon Preferences
- Journal of Modern Power Systems and Clean Energy Vol. 14, Issue 5, Pages: 1869-1881(2026)
- 作者机构：
  
  1.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University , Beijing, China
  2.School of Control and Computer Engineering, North China Electric Power University, Beijing, China
  3.State Grid Electric Power Research Institute (NARI Group Corporation), Nanjing, China
- 作者简介：
  
  Lu Tan is currently pursuing the Ph.D. degree with the School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China. Her research interests include prosumer, cyber-physical-social system, and energy sharing. Email:tan123@ncepu.edu.cn
  Nian Liu received the Ph.D. degree in electrical engineering from North China Electric Power University, Beijing, China, in 2009. He is currently a Professor and the Vice Dean of the School of Electrical and Electronic Engineering, North China Electric Power University. He is the Director of Research Section for Multi-information Fusion and Integrated Energy System Optimization, and with the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources. He is a Member of the Standardization Committee of Power Supply and Consumption in the Power Industry of China. He was a Visiting Research Fellow with the Royal Melbourne Institute of Technology University, Melbourne, Australia, from 2015 to 2016. His main research interests include multi-energy system integration, microgrid, cyber-physical energy system, and renewable energy integration. Email:nianliu@ncepu.edu.cn
  Jie Huang received the Ph.D. degree from Nanjing University of Science and Technology, Nanjing, China, in 2011. He is currently a Professorate Senior Engineer with State Grid Electric Power Research Institute (NARI Group Corporation), Nanjing, China. His research interests include carbon market and carbon emission risk management. Email:huangjie1@sgepri.sgcc.com.cn
  Haonan Sun
  Kai Jiang received the Ph.D. degree in electrical engineering from North China Electric Power University, Beijing, China, in 2024. He is currently a Lecturer with the College of Electrical and Electronic Engineering, North China Electric Power University. His main research interests include electricity and carbon markets. Email:kai_jiang@ncepu.edu.cn
- 基金信息：
- DOI：10.35833/MPCE.2025.000690
  CLC：
- Received：28 July 2025，
  
  Revised：2025-10-20，
  
  Accepted：23 January 2026，
  
  Online First：11 February 2026，
  
  Published：15 September 2026
- 稿件说明：
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Tan Lu,Liu Nian,Huang Jie,et al.Matching-based Energy and Carbon Sharing for Prosumers Considering Social Behavior and Low-carbon Preferences[J].Journal of Modern Power Systems and Clean Energy,2026,14(05):1869-1881.
DOI：

Tan Lu,Liu Nian,Huang Jie,et al.Matching-based Energy and Carbon Sharing for Prosumers Considering Social Behavior and Low-carbon Preferences[J].Journal of Modern Power Systems and Clean Energy,2026,14(05):1869-1881. DOI： 10.35833/MPCE.2025.000690.

摘要

Abstract

Peer-to-peer (P2P) energy and carbon sharing among prosumers promotes the local decarbonization

yet it faces new challenges due to the dynamic roles and heterogeneous individual characteristics of prosumers. This paper considers the social behavior and low-carbon preferences of prosumers

and proposes a novel matching-based energy and carbon sharing scheme. First

a bidirectional carbon emission obligation transfer model via sensitivity coefficients is developed

enabling dynamic allocation of emission obligation based on the energy sharing strategies of prosumers. Second

the hybrid preference model of prosumers in P2P energy and carbon sharing is formulated

which integrates

besides economic-based preferences

the considerations of social behavior and low-carbon preferences. Third

a supply-demand ratio (SDR) based proposal method combined with a modified stable matching algorithm is developed

achieving faster market clearing than conventional alternating direction method of multipliers (ADMM) methods while guaranteeing weak Pareto-optimality. Implemented on an IEEE 33-bus system with 32 prosumers

the proposed matching-based energy and carbon sharing scheme can enhance the participation of prosumers

encourage prosumers to choose cleaner energy

and then reduce the total carbon emissions of cluster.

关键词

Keywords

references

J. Wang, H. Jia, X. Jin et al. , “A decentralized peer-to-peer framework for integrated electricity-heat-carbon sharing among multiple microgrids,” Journal of Modern Power Systems and Clean Energy , vol. 13 , no. 5 , pp. 1787 - 1799 , Sept. 2025.

Y. Zhou, Z. Ma, T. Wang et al. , “Joint energy and carbon trading for multi-microgrid system based on multi-agent deep reinforcement learning,” IEEE Transactions on Power Systems , vol. 39 , no. 6 , pp. 7376 - 7388 , Nov. 2024 .

X. Yu and Y. Xue, “Smart grids: a cyber-physical systems perspective,” Proceedings of the IEEE , vol. 104 , no. 5 , pp. 1058 - 1070 , May 2016.

Y. Xue and X. Yu, “Beyond smart grid: cyber-physical-social system in energy future,” Proceedings of the IEEE , vol. 105 , no. 12 , pp. 2290 - 2292 , Dec. 2017 .

X. Yu, N. Liu, and Y. Xue, “The role of cyber-physical-social systems in smart energy future,” IEEE Transactions on Industrial Cyber-Physical Systems , vol. 2 , pp. 35 - 42 , Jan. 2024 .

J. Li, S. Ge, Z. Xu et al. , “A network-secure peer-to-peer trading framework for electricity-carbon integrated market among local prosumers,” Applied Energy , vol. 335 , p. 120420 , Apr. 2023 .

W. Xu, F. Lin, R. Jia et al. , “Game-based pricing for joint carbon and electricity trading in microgrids,” IEEE Internet of Things Journal , vol. 11 , no. 16 , pp. 27732 - 27743 , Aug. 2024 .

J. Wang, X. Jin, H. Jia et al. , “Joint electricity and carbon sharing with PV and energy storage: a low-carbon DR-based game theoretic approach,” IEEE Transactions on Sustainable Energy , vol. 15 , no. 4 , pp. 2703 - 2717 , Oct. 2024 .

X. Liu, Y. Yao, T. Li et al. , “Data-driven peer-to-peer energy trading based on prosumer-driven carbon-aware distribution locational marginal price,” Journal of Modern Power Systems and Clean Energy , vol. 13 , no. 5 , pp. 1836 - 1848 , Sept. 2025.

M. Y. Yan, M. Shahidehpour, A. Alabdulwahab et al. , “Blockchain for transacting energy and carbon allowance in networked microgrids,” IEEE Transactions on Smart Grid , vol. 12 , no. 6 , pp. 4702 - 4714 , Nov. 2021 .

Z. Zhu, K. W. Chan, S. Bu et al. , “Nash equilibrium estimation and analysis in joint peer-to-peer electricity and carbon emission auction market with microgrid prosumers,” IEEE Transactions on Power Systems , vol. 38 , no. 6 , pp. 5768 - 5780 , Nov. 2023 .

S. N. A. U. Nambi and R. V. Prasad, “Toward the development of a techno-social smart grid,” IEEE Communications Magazine , vol. 54 , no. 11 , pp. 202 - 209 , Nov. 2016 .

X. Cheng, Y. Zheng, Y. Lin et al. , “Hierarchical operation planning based on carbon-constrained locational marginal price for integrated energy system,” International Journal of Electrical Power & Energy Systems , vol. 128 , pp. 1 - 10 , Jun. 2021 .

C. Kang, T. Zhou, Q. Chen et al. , “Carbon emission flow from generation to demand: a network-based model,” IEEE Transactions on Smart Grid , vol. 6 , no. 5 , pp. 2386 - 2394 , Sept. 2015.

Z. Lu, L. Bai, J. Wang et al. , “Peer-to-peer joint electricity and carbon trading based on carbon-aware distribution locational marginal pricing,” IEEE Transactions on Power Systems , vol. 38 , no. 1 , pp. 835 - 852 , Jan. 2023 .

W. Hua, J. Jiang, H. Sun et al. , “A blockchain based peer-to-peer trading framework integrating energy and carbon markets,” Applied Energy , vol. 279 , p. 115539 , Dec. 2020 .

S. Perdan and A. Azapagic, “Carbon trading: current schemes and future developments,” Energy Policy , vol. 39 , no. 10 , pp. 6040 - 6054 , Oct. 2011 .

Y. Cheng, N. Zhang, B. Zhang et al. , “Low-carbon operation of multiple energy systems based on energy-carbon integrated prices,” IEEE Transactions on Smart Grid , vol. 11 , no. 2 , pp. 1307 - 1318 , Mar. 2020 .

S. Cui, Y. Wang, Y. Shi et al. , “An efficient peer-to-peer energy-sharing framework for numerous community prosumers,” IEEE Transactions on Industrial Informatics , vol. 16 , no. 12 , pp. 7402 - 7412 , Dec. 2020 .

L. Tan, N. Liu, H. Sun et al. , “A cyber-physical-social system approach for peer-to-peer energy sharing,” IEEE Transactions on Smart Grid , vol. 15 , no. 4 , pp. 4010 - 4022 , Jul. 2024 .

G. C. Okwuibe, K. Vinogradova, S. Klingner et al. , “Pooling platform: a decentralized local energy market platform based on clustered prosumer’s preferences,” in Proceedings of the 2023 International Conference on Future Energy Solutions , Vaasa, Finland, Jun. 2023 , pp. 1 - 6 .

H. Yao, Y. Xiang, J. Liu et al. , “Peer-to-peer energy trading considering low-carbon preference,” in Proceedings of the 2023 Panda Forum on Power and Energy , Chengdu, China, Apr. 2023 , pp. 1018 - 1023 .

T. Morstyn and M. McCulloch, “Multiclass energy management for peer-to-peer energy trading driven by prosumer preferences,” IEEE Transactions on Power Systems , vol. 34 , no. 5 , pp. 4005 - 4014 , Sept. 2019.

S. Talari, M. Khorasany, R. Razzaghi et al. , “Mechanism design for decentralized peer-to-peer energy trading considering heterogeneous preferences,” Sustainable Cities and Society , vol. 87 , p. 104182 , Dec. 2022 .

Z. Zhao, F. Luo, J. Yang et al. , “A peer-to-peer energy trading system considering participants’ social relationships and multi-class preferences,” in Proceedings of the 2022 IEEE Sustainable Power and Energy Conference , Perth, Australia, Dec. 2022 , pp. 1 - 5 .

Z. Zhao, F. Luo, C. Zhang et al. , “A social relationship preference aware peer-to-peer energy market for urban energy prosumers and consumers,” IET Renewable Power Generation , vol. 16 , pp. 688 - 699 , Mar. 2022 .

C. Will, N. Lehmann, N. Baumgartner et al. , “Consumer understanding and evaluation of car bon-neutral electric vehicle charging services,” Applied Energy , vol. 313 , p. 118799 , May 2022.

A. Longo, A. Markandya, and M. Petrucci, “The internalization of externalities in the production of electricity: willingness to pay for the attributes of a policy for renewable energy,” Ecological Economics , vol. 67 , pp. 140 - 152 , Sept. 2008.

J. Zorić and N. Hrovatin, “Household willingness to pay for green electricity in Slovenia,” Energy Policy , vol. 47 , pp. 180 - 187 , Aug. 2012 .

Q. Zhang, H. Wang, Z. Feng et al. , “Many-to-many matching theory-based dynamic bandwidth allocation for UAVs,” IEEE Internet of Things Journal , vol. 8 , no. 12 , pp. 9995 - 10009 , Jun. 2021 .

Z. Zhou, K. Ota, M. Dong et al. , “Energy-efficient matching for resource allocation in D2D enabled cellular networks,” IEEE Transactions on Vehicular Technology , vol. 66 , no. 6 , pp. 5256 - 5268 , Jun. 2017 .

L. Einav, C. Farronato, and J. Levin, “Peer-to-peer markets,” Annual Review of Economics , vol. 8 , no. 1 , pp. 615 - 635 , Oct. 2016 .

Y. Zeng, X. Wei, Y. Yao et al. , “Determining the pricing and deployment strategy for virtual power plants of peer-to-peer prosumers: a game-theoretic approach,” Applied Energy , vol. 345 , p. 121349 , Jun. 2023 .

N. Liu, M. Cheng, X. Yu et al. , “Energy-sharing provider for PV prosumer clusters: a hybrid approach using stochastic programming and Stackelberg game,” IEEE Transactions on Industrial Electronics , vol. 65 , no. 8 , pp. 6740 - 6750 , Aug. 2018 .

A. Esmat, M. de Vos, Y. G. Farrolhfal et al. , “A novel decentralized platform for peer-to-peer energy trading market with blockchain technology,” Applied Energy , vol. 282 , p. 116123 , Jan. 2021 .

L. Chen, Q. Xu, Y. Yang et al. , “Community integrated energy system trading: a comprehensive review,” Journal of Modern Power Systems and Clean Energy , vol. 10 , no. 6 , pp. 1445 - 1458 , Nov. 2022 .

H. J. Kim, Y. S. Chu ng, S. J. Kim et al. , “Pricing mechanisms for peer-to-peer energy trading: towards an integrated understanding of energy and network service pricing mechanisms,” Renewable and Sustainable Energy Reviews , vol. 183 , p. 113435 , Sept. 2023.

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