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Cited by 14Electric Vehicles and InfrastructureBlockchain Technology Applications and SecuritySmart Grid Energy Management

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Multi-Agent Deep Reinforcement Learning for Blockchain-Based Energy Trading in Decentralized Electric Vehicle Charger-Sharing Networks

Open access
Han, Yinjie, Meng, Jingyi, Luo, Zihang·2024·Electronics
DOI 10.3390/electronics13214235
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Abstract

With The integration of renewable energy sources into smart grids and electric vehicle (EV) charger-sharing networks is essential for achieving the goal of environmental sustainability. However, the uneven distribution of distributed energy trading among EVs, fixed charging stations (FCSs), and mobile charging stations (MCSs) introduces challenges such as inadequate supply at FCSs and prolonged latencies at MCSs. In this paper, we propose a multi-agent deep reinforcement learning (MADRL)-based auction algorithm for energy trading that effectively balances charger supply with energy demand in distributed EV charging markets, while also reducing total charging latency. Specifically, this involves a MADRL-based hierarchical auction that dynamically adapts to real-time conditions, optimizing the balance of supply and demand. During energy trading, each EV, acting as a learning agent, can refine its bidding strategy to participate in various local energy trading markets, thus enhancing both individual utility and global social welfare. Furthermore, we design a cross-chain scheme to securely record and verify transaction results of energy trading in decentralized EV charger-sharing networks to ensure integrity and transparency. Finally, experimental results show that the proposed algorithm significantly outperforms both the second-price and double auctions in increasing global social welfare and reducing total charging latency.

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Crossref · 10.3390/electronics13214235

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Research works printed but not linked: 32

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Metadata

Authors
Han, Y., Meng, J., & Luo, Z.
Affiliations
  • Xi’an Jiaotong-Liverpool University (CN)
  • University of Illinois Urbana-Champaign (US)
  • Nanyang Technological University (SG)
Corresponding author
Han, Yinjie
Venue
Electronics
Publisher
MDPI AG
Published
2024-10-29
Volume
13
Issue
21
Pages
4235
ISSN
2079-9292 (ISSN-L)
DOI
10.3390/electronics13214235
OpenAlex
W4403869792
Cited by
14
References
0
Citations by year
2026: 9 · 2025: 5
FWCI
1.45
Type
journal article
Language
en
Open access
Licence
Locations
  • Electronics (publishedVersion, cc-by)
  • DR-NTU (Nanyang Technological University) (publishedVersion, cc-by)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
  • Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
Keywords
electric vehicle charging, multi-agent deep reinforcement learning, auction algorithm, social welfare (welfare economics), decentralized energy markets, bidding strategy

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