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A robust framework for peer-to-peer energy trading with transmission costs consideration: A fuzzy possibilistic programming model

Author

Listed:
  • Talebi, Ehsan
  • Mehdinejad, Mehdi
  • Mohammadi-Ivatloo, Behnam
  • Abapour, Mehdi
  • Tohidi, Sajjad

Abstract

The restructuring of traditional electricity market paradigms and the transition to decentralized energy systems, aligned with advancements in communication and control technologies, have led to the emergence of innovative trading mechanisms. Peer-to-peer (P2P) energy trading as a decentralized approach, enables direct transactions between producers and consumers without intermediaries. Large producers and consumers, as independent entities in wholesale markets, can participate in bilateral exchanges at the transmission network level. This paper designs a fully decentralized P2P energy trading framework for direct transactions between large producers and consumers within the transmission network. In this trading system, market players freely and independently select their trading peers to maximize their social welfare. Since energy exchanges occur within the network’s physical layer, incorporating technical network constraints becomes inevitable. Hence, in this study, the power transfer distribution factor (PTDF) method is developed to fairly and distributively allocate power loss costs and network utilization fees based on the network’s physical topology and the electrical distance between players. In this research, consumers’ demand is considered uncertain. To model this uncertainty, the robust possibilistic programming (RPP) method, based on fuzzy theory, is applied. RPP is efficient in scenarios without sufficient historical data, operating fully decentralized and proactive, avoiding decision-maker (DM) reliance for defining confidence levels of uncertain constraints. Finally, an alternating direction method of multipliers (ADMM) approach is employed to clear the proposed decentralized market, ensuring high rate convergence, participants’ privacy, and an optimal solution. Case studies on a 9-bus transmission network demonstrated the feasibility and effectiveness of the proposed decentralized market model. The simulation results indicate that considering network utilization fees in player transactions reduces the overall market social welfare by 20.1 %, accounting for power loss costs decreases it by 8.6 %, and applying both costs results in a 28.8 % reduction compared to the baseline scenario.

Suggested Citation

  • Talebi, Ehsan & Mehdinejad, Mehdi & Mohammadi-Ivatloo, Behnam & Abapour, Mehdi & Tohidi, Sajjad, 2025. "A robust framework for peer-to-peer energy trading with transmission costs consideration: A fuzzy possibilistic programming model," Applied Energy, Elsevier, vol. 398(C).
    • Handle: RePEc:eee:appene:v:398:y:2025:i:c:s0306261925011092
    DOI: 10.1016/j.apenergy.2025.126379
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    References listed on IDEAS

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    1. Sim, Jisu & Lee, Deok-Joo & Yoon, Kiho, 2025. "Incentive-compatible double auction for Peer-to-Peer energy trading considering heterogeneous power losses and transaction costs," Applied Energy, Elsevier, vol. 377(PC).
    2. Izanlo, Ali & Sheikholeslami, Abdolreza & Gholamian, S. Asghar & Kazemi, Mohammad Verij & Hosseini, S. Naghi, 2024. "A combination of MILP and game theory methods for P2P energy trading by considering network constraints," Applied Energy, Elsevier, vol. 374(C).
    3. Tarashandeh, Nader & Karimi, Ali, 2024. "Peer-to-peer energy trading under distribution network constraints with preserving independent nature of agents," Applied Energy, Elsevier, vol. 355(C).
    4. Wang, Zibo & Dong, Lei & Shi, Mengjie & Qiao, Ji & Jia, Hongjie & Mu, Yunfei & Pu, Tianjiao, 2023. "Market power modeling and restraint of aggregated prosumers in peer-to-peer energy trading: A game-theoretic approach," Applied Energy, Elsevier, vol. 348(C).
    5. Seyed Amin Sedgh & Hossein Aghamohammadloo & Hassan Khazaei & Mehdi Mehdinejad & Somayeh Asadi, 2023. "A New Design for the Peer-to-Peer Electricity and Gas Markets Based on Robust Probabilistic Programming," Energies, MDPI, vol. 16(8), pages 1-19, April.
    6. Zhang, Xihai & Ge, Shaoyun & Liu, Hong & Zhou, Yue & He, Xingtang & Xu, Zhengyang, 2023. "Distributionally robust optimization for peer-to-peer energy trading considering data-driven ambiguity sets," Applied Energy, Elsevier, vol. 331(C).
    7. Feng, Chen & Liu, Andrew L., 2025. "Peer-to-peer energy trading of solar and energy storage: A networked multiagent reinforcement learning approach," Applied Energy, Elsevier, vol. 383(C).
    8. Liu, Jia & Zhou, Yuekuan & Yang, Hongxing & Wu, Huijun, 2022. "Uncertainty energy planning of net-zero energy communities with peer-to-peer energy trading and green vehicle storage considering climate changes by 2050 with machine learning methods," Applied Energy, Elsevier, vol. 321(C).
    9. Jiang, Zhisen & Guo, Ye & Wang, Jianxiao, 2025. "Dynamic operating envelopes embedded peer-to-peer-to-grid energy trading," Applied Energy, Elsevier, vol. 377(PB).
    10. Mehdinejad, Mehdi & Shayanfar, Heidarali & Mohammadi-Ivatloo, Behnam, 2022. "Peer-to-peer decentralized energy trading framework for retailers and prosumers," Applied Energy, Elsevier, vol. 308(C).
    11. Mehdinejad, Mehdi & Shayanfar, Heidarali & Mohammadi-Ivatloo, Behnam, 2022. "Decentralized blockchain-based peer-to-peer energy-backed token trading for active prosumers," Energy, Elsevier, vol. 244(PA).
    12. Dukovska, Irena & Slootweg, J.G. (Han) & Paterakis, Nikolaos G., 2023. "Introducing user preferences for peer-to-peer electricity trading through stochastic multi-objective optimization," Applied Energy, Elsevier, vol. 338(C).
    13. Hoque, Md Murshadul & Khorasany, Mohsen & Azim, M. Imran & Razzaghi, Reza & Jalili, Mahdi, 2024. "A framework for prosumer-centric peer-to-peer energy trading using network-secure export–import limits," Applied Energy, Elsevier, vol. 361(C).
    14. Zhang, Chenxi & Yang, Yi & Wang, Yunqi & Qiu, Jing & Zhao, Junhua, 2024. "Auction-based peer-to-peer energy trading considering echelon utilization of retired electric vehicle second-life batteries," Applied Energy, Elsevier, vol. 358(C).
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