[HTML][HTML] Reinforcement learning for electric vehicle applications in power systems: A critical review
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 …
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
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 …
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
Mobile power sources (MPSs) have been gradually deployed in microgrids as critical
resources to coordinate with repair crews (RCs) towards resilience enhancement owing to …
resources to coordinate with repair crews (RCs) towards resilience enhancement owing to …
A review of research on reinforcement learning algorithms for multi-agents
In recent years, multi-agent reinforcement learning techniques have been widely used and
evolved in the field of artificial intelligence. However, traditional reinforcement learning …
evolved in the field of artificial intelligence. However, traditional reinforcement learning …
Unbiased cross-validation kernel density estimation for wind and PV probabilistic modelling
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 …
(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
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 …
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
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 …
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
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 …
integration of renewable energy, and the deployment of large-scale energy storage …
Strategic dispatch of electric buses for resilience enhancement of urban energy systems
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 …
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 …
significant attention and investment due to its potential in reducing greenhouse gas …