DClEVerNet: Deep Combinatorial Learning for Efficient EV Charging Scheduling in Large-scale Networked Facilities
Author:
Affiliation:
1. Khalifa University, United Arab Emirates
2. Khalifa University, United Arab Emirates and Division of Engineering, New York University Abu Dhabi, United Arab Emirates
3. Australian National University, Australia
Funder
Khalifa University of Science, Technology and Research
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3575813.3595205
Reference44 articles.
1. Adaptive Control of Plug-in Electric Vehicle Charging with Reinforcement Learning
2. Online EV Scheduling Algorithms for Adaptive Charging Networks with Global Peak Constraints
3. Electric vehicle optimum charging-discharging scheduling with dynamic pricing employing multi agent deep neural network
4. A review of EVs charging: From the perspective of energy optimization, optimization approaches, and charging techniques
5. The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid
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1. Efficient Trading of Aggregate Bidirectional EV Charging Flexibility with Reinforcement Learning;The 15th ACM International Conference on Future and Sustainable Energy Systems;2024-05-31
2. Advancing Communication Efficiency in Electric Vehicle Systems: A Survey of Generative AI and Distributed Machine Learning Strategies;SSRN Electronic Journal;2024
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