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Blockchain-Enabled Smart Grids for Optimized Electrical Billing and Peer-to-Peer Energy Trading

Author

Listed:
  • Jalalud Din

    (School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Hongsheng Su

    (School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

Abstract

This research investigates the integration of blockchain technology into smart grids, focusing on optimizing both electrical billing and peer-to-peer energy trading between producers and consumers. Using blockchain smart contracts, the system automates and secures energy consumption recording, bill calculation, payment processing, and energy transactions. In the electrical billing framework, a blockchain-based approach was developed to model these functionalities, utilizing an EnergyBilling smart contract to calculate bills and an EnergyPayment smart contract to ensure payment accuracy. Validation using actual consumption data from Sinoma Handan’s project site confirmed the system’s accuracy and reliability when cross-verified with mathematical models. Simultaneously, the study explores peer-to-peer energy trading, where producers (represented by Askari Cement Plant.Nizampur, Pakistan) and consumers (Sinoma Handan Ltd, Handan, China.) conduct automated, transparent transactions. Blockchain’s decentralized nature ensures transparency, data immutability, and a secure, tamper-proof record of transactions. The system eliminates intermediaries, enhancing operational efficiency and reducing costs. Key outcomes demonstrate successful transaction execution with detailed settlements, ensuring financial accountability. Our research highlights blockchain’s transformative potential in revolutionizing electrical billing and energy trading. It offers a secure, transparent, and efficient solution while acknowledging scalability, transaction costs, and regulatory hurdles. Future work could focus on real-world implementation, integration with IoT devices for real-time data collection, and scaling these technologies for broader industrial applications in global energy markets.

Suggested Citation

  • Jalalud Din & Hongsheng Su, 2024. "Blockchain-Enabled Smart Grids for Optimized Electrical Billing and Peer-to-Peer Energy Trading," Energies, MDPI, vol. 17(22), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5744-:d:1522669
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    References listed on IDEAS

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    1. Pallonetto, Fabiano & De Rosa, Mattia & Milano, Federico & Finn, Donal P., 2019. "Demand response algorithms for smart-grid ready residential buildings using machine learning models," Applied Energy, Elsevier, vol. 239(C), pages 1265-1282.
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