[HTML][HTML] Reinforcement learning for electric vehicle applications in power systems: A critical review

D Qiu, Y Wang, W Hua, G Strbac - Renewable and Sustainable Energy …, 2023 - Elsevier
Electric vehicles (EVs) are playing an important role in power systems due to their significant
mobility and flexibility features. Nowadays, the increasing penetration of renewable energy …

Reinforcement learning-based optimal scheduling model of battery energy storage system at the building level

H Kang, S Jung, H Kim, J Jeoung, T Hong - Renewable and Sustainable …, 2024 - Elsevier
Installing the battery energy storage system (BESS) and optimizing its schedule to effectively
address the intermittency and volatility of photovoltaic (PV) systems has emerged as a …

Towards microgrid resilience enhancement via mobile power sources and repair crews: A multi-agent reinforcement learning approach

Y Wang, D Qiu, F Teng, G Strbac - IEEE transactions on power …, 2023 - ieeexplore.ieee.org
Mobile power sources (MPSs) have been gradually deployed in microgrids as critical
resources to coordinate with repair crews (RCs) towards resilience enhancement owing to …

A review of research on reinforcement learning algorithms for multi-agents

K Hu, M Li, Z Song, K Xu, Q **a, N Sun, P Zhou, M **a - Neurocomputing, 2024 - Elsevier
In recent years, multi-agent reinforcement learning techniques have been widely used and
evolved in the field of artificial intelligence. However, traditional reinforcement learning …

Unbiased cross-validation kernel density estimation for wind and PV probabilistic modelling

M Wahbah, B Mohandes, THM EL-Fouly… - Energy Conversion and …, 2022 - Elsevier
Uncertainties associated with power generation from wind energy systems and Photovoltaic
(PV) power systems present a major challenge for power system planners and operators. To …

[HTML][HTML] Hierarchical multi-agent reinforcement learning for repair crews dispatch control towards multi-energy microgrid resilience

D Qiu, Y Wang, T Zhang, M Sun, G Strbac - Applied Energy, 2023 - Elsevier
Extreme events are greatly impacting the normal operations of microgrids, which can lead to
severe outages and affect the continuous supply of energy to customers, incurring …

[HTML][HTML] Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning

DJB Harrold, J Cao, Z Fan - Applied Energy, 2022 - Elsevier
To reduce global greenhouse gas emissions, the world must find intelligent solutions to
maximise the utilisation of carbon-free renewable energy sources. In this paper, multi-agent …

Data-driven energy management system for flexible operation of hydrogen/ammonia-based energy hub: A deep reinforcement learning approach

D Wen, M Aziz - Energy Conversion and Management, 2023 - Elsevier
In the context of carbon neutrality, multi-energy systems are being designed to enhance the
integration of renewable energy, and the deployment of large-scale energy storage …

Strategic dispatch of electric buses for resilience enhancement of urban energy systems

X Zhang, Z Dong, F Huangfu, Y Ye, G Strbac, C Kang - Applied Energy, 2024 - Elsevier
The increasing frequency of the occurrence of high impact low probability (HILP) disruptive
events has posed huge threats to the power system. Therefore, power system resilience …

A review of the latest trends in technical and economic aspects of EV charging management

P Alaee, J Bems, A Anvari-Moghaddam - Energies, 2023 - mdpi.com
The transition from internal combustion engines to electric vehicles (EVs) has received
significant attention and investment due to its potential in reducing greenhouse gas …