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March 25, 2026Computers0 citationsOpen Access

RepuTrade: A Reputation-Based Deposit Consensus Mechanism for P2P Energy Trading in Smart Environments

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XYXingyu YangMonash UniversityBCBen ChenCaulfield HospitalHCHui CuiQinhuangdao Second Hospital

Key Points

  • The research aims to address participant trust issues and transaction failures in decentralized P2P energy trading.
  • Proposed RepuTrade framework using a blockchain-based approach
  • Implemented a reputation-based consensus mechanism
  • Developed a dynamic collateral management scheme linked to participant reputations
  • Utilized a reputation-driven matching algorithm to enhance pairing of users
  • Conducted simulation with 200 users across 8 trading rounds
  • Achieved trade success rates of 92–99%, compared to 83–95% in the baseline
  • Reduced transaction defaults by more than 40%
  • Found a strong negative correlation between user reputation and default probability
  • Improved transaction reliability and execution efficiency
  • Lowered consensus validation latency observed

Abstract

Current peer-to-peer (P2P) energy trading systems face important challenges in decentralised trading environments, particularly in managing participant trustworthiness, preventing dishonest behaviour, and mitigating transaction defaults. These limitations reduce transaction reliability and weaken trust among participants in community-scale energy trading markets. Although P2P energy trading enables communities to exchange locally generated renewable energy in smart environments, existing platforms often lack effective mechanisms to regulate participant behaviour and support reliable transactions. This paper proposes RepuTrade, a blockchain-based P2P energy trading platform tailored for community-scale microgrids. The proposed framework integrates a reputation-based consensus mechanism and a dynamic collateral management scheme that is directly linked to participant reputations such that trading reliability can be strengthened through behavioural incentives. In addition, a reputation-driven matching algorithm preferentially pairs highly reputable participants to improve market stability and trust. Simulation-based evaluation, involving 200 users across 8 trading rounds, shows that the RepuTrade framework consistently achieves higher trade success rates (92–99% compared to 83–95% in the baseline) and reduces defaults by more than 40% (27–44 vs. 55–72 per run). The results further reveal a strong negative correlation between user reputation and default probability, indicating that higher reputation is associated with a lower likelihood of dishonest behaviour. Overall, under the simulated settings considered in this study, the proposed framework improves transaction reliability and execution efficiency by reducing failed trades and lowering consensus validation latency. These findings contribute to the design of trust-aware decentralised energy trading mechanisms and provide simulation-based insights for developing more reliable and transparent community-scale renewable energy markets.

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Cite This Study

Yang et al. (2026) studied this question.

synapsesocial.com/papers/69c37be2b34aaaeb1a67ebd6https://doi.org/10.3390/computers15030199
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