skip to main content
10.1145/3622896.3622927acmotherconferencesArticle/Chapter ViewAbstractPublication PagesccrisConference Proceedingsconference-collections
research-article

A microgrid power trading framework based on blockchain and deep reinforcement learning

Published: 03 October 2023 Publication History

Abstract

With the development of renewable energy technologies and the emergence of distributed power generation devices, traditional centralized power trading markets no longer meet people's transactional needs. Peer-to-peer (P2P) electricity trading within microgrids has become the future direction. However, in distributed trading scenarios, both parties in P2P transactions lack a foundation of trust and incentive mechanisms. To address these issues, we have designed an electricity trading framework based on blockchain and deep reinforcement learning. Users utilize deep reinforcement learning for load prediction and power planning to maximize their own interests. We propose a proof-of-work (POW) consensus algorithm based on reputation values to further improve consensus efficiency and block creation time. By introducing the InterPlanetary File System (IPFS), offloaded transactions are uploaded to IPFS for storage, enabling the unloading of unnecessary information and improving the resource utilization efficiency of blocks. Our framework is beneficial for reducing the costs for blockchain users, implementing credit management for P2P e-commerce transactions, and thereby enhancing the stability and efficiency of transactions.

References

[1]
Vieira, Guilherme, and Jie Zhang. Peer-to-peer energy trading in a microgrid leveraged by smart contracts’ Renewable and SustainableEnergy Reviews 143 (2021): 110900
[2]
Zhou Y, Lund P D. Peer-to-peer energy sharing and trading of renewable energy in smart communities- trading pricing models, decision-makingand agent-based collaboration[J]. Renewable Energy, 2023.
[3]
Korpaas, Magnus, Arne T. Holen, and Ragne Hildrum. ”Operation and sizing of energy storage for wind power plants in a market system.”International Journal of Electrical Power & Energy Systems 25.8 (2003):599-606.
[4]
Nakamoto, Satoshi, and A. Bitcoin. ”A peer-to-peer electronic cash system.” Bitcoin.–URL: https://bitcoin. org/bitcoin. pdf 4.2 (2008).
[5]
Zheng Z, Xie S, Dai H N, Blockchain challenges and opportunities: A survey[J]. International journal of web and grid services, 2018, 14(4):352-375.
[6]
Keenan, Thomas P. ”Alice in blockchains: surprising security pitfalls in PoW and PoS blockchain systems.” 2017 15th Annual Conference on Privacy, Security and Trust (PST). IEEE, 2017.
[7]
Lepore, Cristian, ”A survey on blockchain consensus with a performance comparison of PoW, PoS and pure PoS.” Mathematics 8.10(2020): 1782.
[8]
Bach, Leo Maxim, Branko Mihaljevic, and Mario Zagar. ”Comparative analysis of blockchain consensus algorithms.” 2018 41st international convention on information and communication technology, electronics and microelectronics (MIPRO). Ieee, 2018.
[9]
Yuxi Li. Deep reinforcement learning: An overview. arXiv.
[10]
Dimitri Bertsekas. Dynamic programming and optimal control: Volume I, volume 1. Athena sci-entific, 2012.
[11]
Nagabandi, Anusha, ”Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning.” 2018 IEEE international conference on robotics and automation (ICRA). IEEE, 2018.
[12]
Juan Benet. Ipfs-content addressed, versioned p2p file system. arXiv preprint arXiv:1407.3561,2014.

Index Terms

  1. A microgrid power trading framework based on blockchain and deep reinforcement learning
          Index terms have been assigned to the content through auto-classification.

          Recommendations

          Comments

          Information & Contributors

          Information

          Published In

          cover image ACM Other conferences
          CCRIS '23: Proceedings of the 2023 4th International Conference on Control, Robotics and Intelligent System
          August 2023
          215 pages
          ISBN:9798400708190
          DOI:10.1145/3622896
          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          Published: 03 October 2023

          Permissions

          Request permissions for this article.

          Check for updates

          Qualifiers

          • Research-article
          • Research
          • Refereed limited

          Funding Sources

          • Science & Technology Project of State Grid Zhejiang Electric Power Co.,Ltd

          Conference

          CCRIS 2023

          Contributors

          Other Metrics

          Bibliometrics & Citations

          Bibliometrics

          Article Metrics

          • 0
            Total Citations
          • 23
            Total Downloads
          • Downloads (Last 12 months)11
          • Downloads (Last 6 weeks)0
          Reflects downloads up to 10 Feb 2025

          Other Metrics

          Citations

          View Options

          Login options

          View options

          PDF

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          HTML Format

          View this article in HTML Format.

          HTML Format

          Figures

          Tables

          Media

          Share

          Share

          Share this Publication link

          Share on social media