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Blockchain Based Optimized Energy Trading for E-Mobility Using Quantum Reinforcement Learning
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DOI：10.1109/TVT.2022.3225524.png)
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摘要
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Using microgrids to charge Electric Vehicles (EVs) is a significant step toward achieving Electric mobility. The microgrids generate electricity for self-use and sell surplus energy locally in Peer-to-Peer (P2P) manner, where seller and buyer meet to trade electricity directly on agreed terms without any intermediary. The energy trading decision for the microgrid is a challenging issue due to uncertainty of renewable energy yield, the electric demands of EVs and without knowing the offers of other competitive microgrids together makes it hard to decide the selling price of energy per unit. Further, there is a need to audit and verify the energy trading and store energy transactions securely in distributed manner to avoid collapse of the system in case of single point of failure. In this context, this paper presents a Blockchain and Quantum Reinforcement Learning based optimized Energy Trading (BQL-ET) model for E-mobility. Firstly, a double-auction mechanism is proposed to set optimal market-trading price by observing the selling price of each microgrid and the demand of EV's. Secondly, using smart contracts, consortium blockchain is deployed for the evaluation of overall utility, which includes energy supply, demand, and cost for both microgrids and EVs. Finally, Utility maximization problem is transformed into a Markov Decision Process (MDP), and in order to develop the learning policy and maximize overall utility, a QRL optimization for solving the MDP problem is proposed. Convergence analysis and performance results attest that BQL-ET convergences faster, maximizes the utility of both microgrids an EVs with lower transaction confirmation time and setting of the optimal market-trading price compared to state-of-the art models.
Keyword:
Microgrids
Blockchains
Security
Convergence
Privacy
Costs
Uncertainty
Blockchain
electric vehicles
energy trading
microgrids
quantum reinforcement learning
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
引用论文
A Scalable Privacy-Preserving Multi-Agent Deep Reinforcement Learning Approach for Large-Scale Peer-to-Peer Transactive Energy Trading用于大规模对等交易能量交易的可扩展的隐私保护多代理深度强化学习方法
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