Context-aware optimal charging distribution using deep reinforcement learning

M Sharif, CB Heendeniya, AS Muhammad… - Proceedings of the …, 2020 - dl.acm.org
The expansion of charging infrastructure and the optimal utilization of existing infrastructure
are key influencing factors for the future growth of electric mobility. The main objective of this …

Multi-Agent Deep Reinforcement Learning for Charging Coordination of Electric Vehicles

Z Hu, R Huang, C Han - 2023 IEEE 3rd International …, 2023 - ieeexplore.ieee.org
Virtual power plant (VPP) is a power system control center that mediates among the energy
market, power grid, distributed energy resources, power storage units, controllable power …

A Novel Real Time Electric Vehicles Smart Charging Approach Based on Artificial Intelligence

M Boulakhber, I Sebbani, Y Oubail, I Aboudrar… - … Conference on Digital …, 2023 - Springer
Uncontrolled charging of electric vehicles (EVs) is likely to generate issues for power
distribution networks. As the use of EVs grows, smart charging approaches that prevent such …

AROA: Adam Remora Optimization Algorithm and Deep Q network for energy harvesting in Fog-IoV network

S Lohat, S Jain, R Kumar - Applied Soft Computing, 2023 - Elsevier
Electric Vehicles (EV) has gained immense popularity due to the increasing awareness
amongst people regarding low carbon emission. Smart vehicles have become a central part …

Multi-agent reinforcement learning for intelligent V2G integration in future transportation systems

J Dong, A Yassine, A Armitage… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Electric vehicles (EVs) are the backbone of the future intelligent transportation system (ITS).
They are environmentally friendly and can also be integrated as distributed energy …

[HTML][HTML] Multi-agent deep reinforcement learning-based optimal energy management for grid-connected multiple energy carrier microgrids

F Monfaredi, H Shayeghi, P Siano - … Journal of Electrical Power & Energy …, 2023 - Elsevier
Multi-agent deep reinforcement learning (MA-DRL) method provides a groundbreaking
approach to tackling computational problems in power systems, particularly for distributed …

Low Carbon Operation Method for Electric Vehicle Charging Stations Using Improved Deep Reinforcement Learning

C Zhou, Y Zhang, W Liu, G Shen… - … Conference on Power …, 2023 - ieeexplore.ieee.org
In response to the current problem for large scale electric vehicles with disorderly charging
behavior was not fully leveraging their low-carbon benefits, the paper provide a carbon …

Real-time coordinated operation of power and autonomous electric ride-hailing systems

A Bagherinezhad, MM Hosseini… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Integration of self-driving functions in electric vehicles is radically changing the
transportation systems, and representing an opportunity for power utilities to develop …

Smart navigation and energy management framework for autonomous electric vehicles in complex environments

G Raja, G Saravanan, SB Prathiba… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
Autonomous electric vehicles (AEVs) are revolutionizing the world of smart city
transportation due to their low-resource consumption, improved traffic efficiency, zero carbon …

Optimizing Electric Vehicle Charging Recommendation in Smart Cities: A Multi-Agent Reinforcement Learning Approach

P Suanpang, P Jamjuntr - World Electric Vehicle Journal, 2024 - mdpi.com
As global awareness for preserving natural energy sustainability rises, electric vehicles
(EVs) are increasingly becoming a preferred choice for transportation because of their ability …