[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 …
A review of deep reinforcement learning for smart building energy management
Global buildings account for about 30% of the total energy consumption and carbon
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
Federated reinforcement learning: Techniques, applications, and open challenges
This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL),
an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of …
an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of …
[HTML][HTML] Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach
The large-scale integration of distributed energy resources into the energy industry enables
the fast transition to a decarbonized future but raises some potential challenges of insecure …
the fast transition to a decarbonized future but raises some potential challenges of insecure …
Reinforcement learning in deregulated energy market: A comprehensive review
The increasing penetration of renewable generations, along with the deregulation and
marketization of power industry, promotes the transformation of energy market operation …
marketization of power industry, promotes the transformation of energy market operation …
Deep reinforcement learning for smart grid operations: algorithms, applications, and prospects
With the increasing penetration of renewable energy and flexible loads in smart grids, a
more complicated power system with high uncertainty is gradually formed, which brings …
more complicated power system with high uncertainty is gradually formed, which brings …
A novel model-free deep reinforcement learning framework for energy management of a PV integrated energy hub
This paper utilizes a fully model-free and data-driven deep reinforcement learning (DRL)
framework to develop an intelligent controller that can exploit information to optimally …
framework to develop an intelligent controller that can exploit information to optimally …
Dynamic energy scheduling and routing of multiple electric vehicles using deep reinforcement learning
The demand on energy is uncertain and subject to change with time due to several factors
including the emergence of new technology, entertainment, divergence of people's …
including the emergence of new technology, entertainment, divergence of people's …
Artificial intelligence Internet of Things: A new paradigm of distributed sensor networks
KP Seng, LM Ang… - International Journal of …, 2022 - journals.sagepub.com
The advances and convergence in sensor, information processing, and communication
technologies have shaped the Internet of Things of today. The rapid increase of data and …
technologies have shaped the Internet of Things of today. The rapid increase of data and …
An assessment of multistage reward function design for deep reinforcement learning-based microgrid energy management
Reinforcement learning based energy management strategy has been an active research
subject in the past few years. Different from the baseline reward function (BRF), the work …
subject in the past few years. Different from the baseline reward function (BRF), the work …