[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 …
[HTML][HTML] Leveraging machine learning for efficient EV integration as mobile battery energy storage systems: Exploring strategic frameworks and incentives
The emergence of electric vehicles is resha** the energy landscape, requiring the
development of innovative energy integration mechanisms to engage prosumers. However …
development of innovative energy integration mechanisms to engage prosumers. However …
A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning
Multi-agent deep reinforcement learning (MADRL) approaches are at the forefront of
contemporary research in optimum electric vehicle (EV) charging scheduling challenges …
contemporary research in optimum electric vehicle (EV) charging scheduling challenges …
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 …
Coordinated electric vehicle active and reactive power control for active distribution networks
The deployment of renewable energy in power systems may raise serious voltage
instabilities. Electric vehicles (EVs), owing to their mobility and flexibility characteristics, can …
instabilities. Electric vehicles (EVs), owing to their mobility and flexibility characteristics, can …
Quantifying the energy trilemma in China and assessing its nexus with smart transportation
Purpose Mitigating the energy trilemma (ET) is of great importance for dealing with climate
change and realizing carbon neutrality. To this end, effectively assessing the level of the ET …
change and realizing carbon neutrality. To this end, effectively assessing the level of the ET …
Routing and charging scheduling for EV battery swap** systems: Hypergraph-based heterogeneous multiagent deep reinforcement learning
This work studies the joint electric vehicle (EV) routing and battery charging scheduling
problem in a transportation network with multiple battery swap** stations (BSSs) under …
problem in a transportation network with multiple battery swap** stations (BSSs) under …
Physics-model-free heat-electricity energy management of multiple microgrids based on surrogate model-enabled multi-agent deep reinforcement learning
Reliability and cost-effectiveness in the operation of the multiple microgrid (MMG) system
depend on the skillful management of its energy resources. Traditional energy management …
depend on the skillful management of its energy resources. Traditional energy management …
Counterfactual-attention multi-agent reinforcement learning for joint condition-based maintenance and production scheduling
Maintenance and production scheduling are interactive activities that should be considered
simultaneously to maintain production systems' reliability and high production delivery rate …
simultaneously to maintain production systems' reliability and high production delivery rate …
Multi-microgrid collaborative optimization scheduling using an improved multi-agent soft actor-critic algorithm
J Gao, Y Li, B Wang, H Wu - Energies, 2023 - mdpi.com
The implementation of a multi-microgrid (MMG) system with multiple renewable energy
sources enables the facilitation of electricity trading. To tackle the energy management …
sources enables the facilitation of electricity trading. To tackle the energy management …