A Multi-Agent Reinforcement Learning Approach for Blockchain-based Electricity Trading System

2021 IEEE Global Communications Conference (GLOBECOM) Pub Date : 2021-12-01 DOI:10.1109/GLOBECOM46510.2021.9685510

Yifan Cao, Xiaoxu Ren, Chao Qiu, Xiaofei Wang, Haipeng Yao, F. Yu

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引用次数: 2

Abstract

In microgrid, peer-to-peer (P2P) electricity trading has quickly ascended to the spotlight and gained enormous popularity. However, there are inevitable credit problems and system security problems. Besides, the current model in the electricity trading system cannot balance the utilities of multiple trading entities. In this paper, we propose a blockchain-based distributed P2P electricity trading system. We define elecoins as currency in circulation within our trading system. In order to jointly optimize the utilities of both parties in the elecoins trading, we formulate the elecoins purchasing problem as a hierarchical Stackelberg game. Then, we design a distributed multi-agent utility-balanced reinforcement learning (DMA-UBRL) algorithm to search the Nash equilibrium. Finally, we factually build a blockchain system with a blockchain explorer and deploy an electricity trading smart contract (ETSC) on Ethereum, with a website interface for operating. The numerical results and the implemented realistic system show the advantages of our work.

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基于区块链的电力交易系统的多智能体强化学习方法

在微电网中，点对点(P2P)电力交易迅速成为人们关注的焦点，并获得了极大的普及。但是，信用问题和制度安全问题是不可避免的。此外，现行的电力交易系统模型无法平衡多个交易主体的效用。本文提出了一种基于区块链的分布式P2P电力交易系统。我们将电子币定义为在我们的交易系统中流通的货币。为了共同优化电子币交易双方的效用，我们将电子币购买问题表述为分层Stackelberg博弈。然后，我们设计了一种分布式多智能体效用平衡强化学习(DMA-UBRL)算法来搜索纳什均衡。最后，我们实际上用区块链浏览器构建了一个区块链系统，并在以太坊上部署了一个电力交易智能合约(ETSC)，并提供了一个网站操作界面。数值结果和实现的现实系统显示了我们工作的优势。

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2021 IEEE Global Communications Conference (GLOBECOM)

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