TY - GEN
T1 - Multi-Agent Graph Convolutional Reinforcement Learning for Dynamic Electric Vehicle Charging Pricing
AU - Zhang, Weijia
AU - Liu, Hao
AU - Han, Jindong
AU - Ge, Yong
AU - Xiong, Hui
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/8/14
Y1 - 2022/8/14
N2 - Electric Vehicles (EVs) have been emerging as a promising low-carbon transport target. While a large number of public charging stations are available, the use of these stations is often imbalanced, causing many problems to Charging Station Operators (CSOs). To this end, in this paper, we propose a Multi-Agent Graph Convolutional Reinforcement Learning (MAGC) framework to enable CSOs to achieve more effective use of these stations by providing dynamic pricing for each of the continuously arising charging requests with optimizing multiple long-term commercial goals. Specifically, we first formulate this charging station request-specific dynamic pricing problem as a mixed competitive-cooperative multi-agent reinforcement learning task, where each charging station is regarded as an agent. Moreover, by modeling the whole charging market as a dynamic heterogeneous graph, we devise a multi-view heterogeneous graph attention networks to integrate complex interplay between agents induced by their diversified relationships. Then, we propose a shared meta generator to generate individual customized dynamic pricing policies for large-scale yet diverse agents based on the extracted meta characteristics. Finally, we design a contrastive heterogeneous graph pooling representation module to learn a condensed yet effective state action representation to facilitate policy learning of large-scale agents. Extensive experiments on two real-world datasets demonstrate the effectiveness of MAGC and empirically show that the overall use of stations can be improved if all the charging stations in a charging market embrace our dynamic pricing policy.
AB - Electric Vehicles (EVs) have been emerging as a promising low-carbon transport target. While a large number of public charging stations are available, the use of these stations is often imbalanced, causing many problems to Charging Station Operators (CSOs). To this end, in this paper, we propose a Multi-Agent Graph Convolutional Reinforcement Learning (MAGC) framework to enable CSOs to achieve more effective use of these stations by providing dynamic pricing for each of the continuously arising charging requests with optimizing multiple long-term commercial goals. Specifically, we first formulate this charging station request-specific dynamic pricing problem as a mixed competitive-cooperative multi-agent reinforcement learning task, where each charging station is regarded as an agent. Moreover, by modeling the whole charging market as a dynamic heterogeneous graph, we devise a multi-view heterogeneous graph attention networks to integrate complex interplay between agents induced by their diversified relationships. Then, we propose a shared meta generator to generate individual customized dynamic pricing policies for large-scale yet diverse agents based on the extracted meta characteristics. Finally, we design a contrastive heterogeneous graph pooling representation module to learn a condensed yet effective state action representation to facilitate policy learning of large-scale agents. Extensive experiments on two real-world datasets demonstrate the effectiveness of MAGC and empirically show that the overall use of stations can be improved if all the charging stations in a charging market embrace our dynamic pricing policy.
KW - charging station dynamic pricing
KW - graph contrastive learning
KW - graph neural networks
KW - multi-agent reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85137144415&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137144415&partnerID=8YFLogxK
U2 - 10.1145/3534678.3539416
DO - 10.1145/3534678.3539416
M3 - Conference contribution
AN - SCOPUS:85137144415
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 2471
EP - 2481
BT - KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
Y2 - 14 August 2022 through 18 August 2022
ER -