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- Publisher Website: 10.1109/TSG.2024.3359289
- Scopus: eid_2-s2.0-85184310803
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Article: Graph Reinforcement Learning for Carbon-Aware Electric Vehicles in Power-Transport Networks
Title | Graph Reinforcement Learning for Carbon-Aware Electric Vehicles in Power-Transport Networks |
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Authors | |
Keywords | carbon intensity Electric vehicles multi-agent reinforcement learning power-transport network |
Issue Date | 29-Jan-2024 |
Publisher | Institute of Electrical and Electronics Engineers |
Citation | IEEE Transactions on Smart Grid, 2024, v. 15, n. 4, p. 3919-3935 How to Cite? |
Abstract | Transitioning towards a low-carbon future necessitates massive efforts from both the transport and power sectors. Electric vehicles (EVs) have emerged as a promising approach to realize this objective, leveraging their smart routing strategies and vehicle-to-grid (V2G) techniques. Previous studies have addressed various challenges in EV routing and scheduling through model-based optimization methods while ignoring the system uncertainties and dynamics. This paper focuses on studying the carbon-aware EV joint routing and scheduling problem within a coupled power-transport network that can enable EV recharging behaviors within the transport network while concurrently delivering carbon-intensity services within the power network. Specifically, a carbon emission flow model is introduced as a mechanism for tracing and calculating the nodal carbon intensity signals tailored for EVs to provide their carbon services. To solve this problem, we propose a model-free multi-agent reinforcement learning method that harnesses graph convolutional networks to capture essential network features and employs a parameter-sharing framework to learn large-scale control policies. The efficacy and scalability of the proposed method in achieving cost-effective and low-carbon transitions are verified through case studies involving two power-transport networks with 100 and 1,000 EVs, respectively. |
Persistent Identifier | http://hdl.handle.net/10722/346307 |
ISSN | 2023 Impact Factor: 8.6 2023 SCImago Journal Rankings: 4.863 |
DC Field | Value | Language |
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dc.contributor.author | Qiu, Dawei | - |
dc.contributor.author | Wang, Yi | - |
dc.contributor.author | Ding, Zhaohao | - |
dc.contributor.author | Wang, Yi | - |
dc.contributor.author | Strbac, Goran | - |
dc.date.accessioned | 2024-09-14T00:30:27Z | - |
dc.date.available | 2024-09-14T00:30:27Z | - |
dc.date.issued | 2024-01-29 | - |
dc.identifier.citation | IEEE Transactions on Smart Grid, 2024, v. 15, n. 4, p. 3919-3935 | - |
dc.identifier.issn | 1949-3053 | - |
dc.identifier.uri | http://hdl.handle.net/10722/346307 | - |
dc.description.abstract | Transitioning towards a low-carbon future necessitates massive efforts from both the transport and power sectors. Electric vehicles (EVs) have emerged as a promising approach to realize this objective, leveraging their smart routing strategies and vehicle-to-grid (V2G) techniques. Previous studies have addressed various challenges in EV routing and scheduling through model-based optimization methods while ignoring the system uncertainties and dynamics. This paper focuses on studying the carbon-aware EV joint routing and scheduling problem within a coupled power-transport network that can enable EV recharging behaviors within the transport network while concurrently delivering carbon-intensity services within the power network. Specifically, a carbon emission flow model is introduced as a mechanism for tracing and calculating the nodal carbon intensity signals tailored for EVs to provide their carbon services. To solve this problem, we propose a model-free multi-agent reinforcement learning method that harnesses graph convolutional networks to capture essential network features and employs a parameter-sharing framework to learn large-scale control policies. The efficacy and scalability of the proposed method in achieving cost-effective and low-carbon transitions are verified through case studies involving two power-transport networks with 100 and 1,000 EVs, respectively. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.relation.ispartof | IEEE Transactions on Smart Grid | - |
dc.subject | carbon intensity | - |
dc.subject | Electric vehicles | - |
dc.subject | multi-agent reinforcement learning | - |
dc.subject | power-transport network | - |
dc.title | Graph Reinforcement Learning for Carbon-Aware Electric Vehicles in Power-Transport Networks | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TSG.2024.3359289 | - |
dc.identifier.scopus | eid_2-s2.0-85184310803 | - |
dc.identifier.volume | 15 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 3919 | - |
dc.identifier.epage | 3935 | - |
dc.identifier.eissn | 1949-3061 | - |
dc.identifier.issnl | 1949-3053 | - |