Hierarchical Hybrid Multi-Agent Deep Reinforcement Learning for Peer-to-Peer Energy Trading Among Multiple Heterogeneous Microgrids

Wu, Yuxin; Zhao, Tianyang; Yan, Haoyuan; Liu, Min; Liu, Nian

doi:10.1109/tsg.2023.3250321

Cited by 23 publications

(2 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…156 Decentralized Bi-level Framework Stackelberg Game Theory Optimization of flexible energy resources in the MMG network subjected to ramp-up constraints 3 Wu et al. 157 Multi-Agent Architecture ADMM To make MMG more adaptable and reliable, the P2P control architecture is proposed. – Zhang et al.…”

Section: Discussionmentioning

confidence: 99%

State-of-the-art review on energy sharing and trading of resilient multi microgrids

Kumar,

Singh,

Raghav

et al. 2024

iScience

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

State-of-the-art review on energy sharing and trading of resilient multi microgrids

Kumar,

Singh,

Raghav

et al. 2024

iScience

View full text Add to dashboard Cite

“…Researchers have started exploring the direct transaction connection between electricity consumers and generators, studying the potential advantages and challenges. Zhang et al [16] and Wang et al [17] provided comprehensive reviews of energy applications and P2P energy trading in microgrids, while Abdella et al [18] analyzed P2P energy trading in smart grids, and Wu et al [19] proposed multi-agent-based P2P energy trading in microgrids.…”

Section: A Related Workmentioning

confidence: 99%

Multi-Party Audit and Regulatory Mechanism for P2P Electricity Transaction Based on Distributed Traceable Linkable Group Signature

Li,

Zhao,

Guo

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Peer-to-Peer (P2P) electricity transaction is an emerging power trading model, which can effectively solve the drawbacks of traditional power trading that requires the exist of intermediaries. As the application spreads, P2P electricity transaction faces some regulatory issues. At present, some existing regulatory models are based on centralized trusteeship centers. There is no distributed regulatory mechanism, and no dynamic addition and deletion mechanism to prevent malicious collusion of regulators. In this article, we propose a multi-party audit and regulatory mechanism for P2P electricity transaction, which is based on a dynamic distributed traceable linkable group signature (DDT-LGS) scheme. This solution realizes the distributed trace and audit for P2P electricity transaction, and further realizes the dynamic permission of regulators to join and exit. We give the system architecture of multi-party audit and regulatory mechanism, formally define the algorithm process, and give a specific DDT-LGS construction. Security and performance analysis shows that the proposed scheme holds strong security, more comprehensive functions and better performance, that is suitable for P2P power trading scenarios.

show abstract

A decentralized approach for time-of-use network utilization pricing in peer-to-peer energy markets

Liu,

Gao,

Borghetti

et al. 2024

Applied Energy

View full text Add to dashboard Cite

Hierarchical Hybrid Multi-Agent Deep Reinforcement Learning for Peer-to-Peer Energy Trading Among Multiple Heterogeneous Microgrids

Cited by 23 publications

References 63 publications

State-of-the-art review on energy sharing and trading of resilient multi microgrids

State-of-the-art review on energy sharing and trading of resilient multi microgrids

Multi-Party Audit and Regulatory Mechanism for P2P Electricity Transaction Based on Distributed Traceable Linkable Group Signature

A decentralized approach for time-of-use network utilization pricing in peer-to-peer energy markets

Contact Info

Product

Resources

About