Xinning Jia
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.
- Vieira, Guilherme, and Jie Zhang. Peer-to-peer energy trading in a microgrid leveraged by smart contracts’ Renewable and SustainableEnergy Reviews 143 (2021): 110900Google Scholar
- 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.Google Scholar
- 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.Google ScholarCross Ref
- Nakamoto, Satoshi, and A. Bitcoin. ”A peer-to-peer electronic cash system.” Bitcoin.–URL: https://bitcoin. org/bitcoin. pdf 4.2 (2008).Google Scholar
- 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.Google Scholar
- 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.Google ScholarCross Ref
- Lepore, Cristian, ”A survey on blockchain consensus with a performance comparison of PoW, PoS and pure PoS.” Mathematics 8.10(2020): 1782.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- Yuxi Li. Deep reinforcement learning: An overview. arXiv.Google Scholar
- Dimitri Bertsekas. Dynamic programming and optimal control: Volume I, volume 1. Athena sci-entific, 2012.Google Scholar
- 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.Google ScholarDigital Library
- Juan Benet. Ipfs-content addressed, versioned p2p file system. arXiv preprint arXiv:1407.3561,2014.Google Scholar
Index Terms
- A microgrid power trading framework based on blockchain and deep reinforcement learning
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