Name
Title | ||
|---|---|---|
A Scalable Privacy-Preserving Multi-Agent Deep Reinforcement Learning Approach for Large-Scale Peer-to-Peer Transactive Energy Trading |
Abstract | ||
|---|---|---|
Peer-to-peer (P2P) transactive energy trading has emerged as a promising paradigm towards maximizing the flexibility value of prosumers’ distributed energy resources (DERs). Despite reinforcement learning constitutes a well-suited model-free and data-driven methodological framework to optimize prosumers’ energy management decisions, its application to the large-scale coordinated management and P2P... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TSG.2021.3103917 | IEEE Transactions on Smart Grid |
Keywords | DocType | Volume |
HVAC,Peer-to-peer computing,Transactive energy,Scalability,Uncertainty,Reinforcement learning,Production | Journal | 12 |
Issue | ISSN | Citations |
6 | 1949-3053 | 2 |
PageRank | References | Authors |
0.38 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yujian Ye | 1 | 17 | 2.86 |
| Yi Tang | 2 | 12 | 6.04 |
| Huiyu Wang | 3 | 2 | 0.38 |
| Xiao-Ping Zhang | 4 | 6 | 2.79 |
| Goran Strbac | 5 | 75 | 19.95 |