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Blockchain-Driven Real-Time Incentive Approach for Energy Management System

Author

Listed:
  • Aparna Kumari

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India)

  • Riya Kakkar

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India)

  • Rajesh Gupta

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India)

  • Smita Agrawal

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India)

  • Sudeep Tanwar

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India)

  • Fayez Alqahtani

    (Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia)

  • Amr Tolba

    (Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia)

  • Maria Simona Raboaca

    (Doctoral School, University Politehnica of Bucharest, Splaiul Independentei Street No. 313, 060042 Bucharest, Romania
    National Research and Development Institute for Cryogenic and Isotopic Technologies—ICSI Rm. Vâlcea, Uzinei Street, No. 4, 240050 Râmnicu Vâlcea, Romania)

  • Daniela Lucia Manea

    (Faculty of Civil Engineering, Technical University of Cluj-Napoca, Constantin Daicoviciu Street, No. 15, 400020 Cluj-Napoca, Romania)

Abstract

In the current era, the skyrocketing demand for energy necessitates a powerful mechanism to mitigate the supply–demand gap in intelligent energy infrastructure, i.e., the smart grid. To handle this issue, an intelligent and secure energy management system (EMS) could benefit end-consumers participating in the Demand–Response (DR) program. Therefore, in this paper, we proposed a real-time and secure incentive-based EMS for smart grid, i.e., RI-EMS approach using Reinforcement Learning (RL) and blockchain technology. In the RI-EMS approach, we proposed a novel reward mechanism for better convergence of the RL-based model using a Q-learning approach based on the greedy policy that guides the RL-agent for faster convergence. Then, the proposed RI-EMS approach designed a real-time incentive mechanism to minimize energy consumption in peak hours and reduce end-consumers’ energy bills to provide incentives to the end-consumers. Experimental results show that the proposed RI-EMS approach induces end-consumer participation and increases customer profitabilities compared to existing approaches considering the different performance evaluation metrics such as energy consumption for end-consumers, energy consumption reduction, and total cost comparison to end-consumers. Furthermore, blockchain-based results are simulated and analyzed with the help of deployed smart contracts in a Remix Integrated Development Environment (IDE) with the parameters such as transaction efficiency and data storage cost.

Suggested Citation

  • Aparna Kumari & Riya Kakkar & Rajesh Gupta & Smita Agrawal & Sudeep Tanwar & Fayez Alqahtani & Amr Tolba & Maria Simona Raboaca & Daniela Lucia Manea, 2023. "Blockchain-Driven Real-Time Incentive Approach for Energy Management System," Mathematics, MDPI, vol. 11(4), pages 1-17, February.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:928-:d:1065954
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    References listed on IDEAS

    as
    1. Mota, Bruno & Faria, Pedro & Vale, Zita, 2022. "Residential load shifting in demand response events for bill reduction using a genetic algorithm," Energy, Elsevier, vol. 260(C).
    2. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    3. Zhang, Wenyi & Wei, Wei & Chen, Laijun & Zheng, Boshen & Mei, Shengwei, 2020. "Service pricing and load dispatch of residential shared energy storage unit," Energy, Elsevier, vol. 202(C).
    4. Wen, Lulu & Zhou, Kaile & Li, Jun & Wang, Shanyong, 2020. "Modified deep learning and reinforcement learning for an incentive-based demand response model," Energy, Elsevier, vol. 205(C).
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    Cited by:

    1. Ma, Chao-Qun & Lei, Yu-Tian & Ren, Yi-Shuai & Chen, Xun-Qi & Wang, Yi-Ran & Narayan, Seema, 2024. "Systematic analysis of the blockchain in the energy sector: Trends, issues, and future directions," Telecommunications Policy, Elsevier, vol. 48(2).

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