Distributed Power Trading Mechanism Based on Blockchain Smart Contracts

Lihua Zhang (), Rui Huang, Menglian Jiao, Qingyu Lu and Yi Yang
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Lihua Zhang: School of Information and Software Engineering, East China Jiaotong University, Nanchang 330000, China
Rui Huang: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330000, China
Menglian Jiao: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330000, China
Qingyu Lu: School of Information and Software Engineering, East China Jiaotong University, Nanchang 330000, China
Yi Yang: School of Information and Software Engineering, East China Jiaotong University, Nanchang 330000, China

Energies, 2025, vol. 18, issue 8, 1-27

Abstract: To address the challenges of inefficiency, vulnerability to attacks, and the lack of transparency and fairness in distributed power trading, a distributed power trading mechanism based on blockchain smart contracts is proposed. Firstly, a strategy for matching distributed power trading orders is designed, and a model for managing user credit is constructed using the entropy weight method and deviation cost. Secondly, a smart contract for distributed power trading is compiled using the Solidity language. Finally, the designed smart contract is deployed, tested, and a distributed power trading network is simulated. The security analysis indicates that the Merkle Tree structure and PoW consensus mechanism effectively ensure the data and system security of distributed power transactions. Simulation test results demonstrate that the transaction mechanism can reliably complete transaction settlements, safeguard the interests of trustworthy users, and enhance the economic benefits of both parties involved in the transaction.

Keywords: distributed electricity; blockchain; smart contract; trading mechanism; trading order matching strategy; credit management; Solidity (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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