Reinforcement learning algorithms: A brief survey
Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
[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] Review of online learning for control and diagnostics of power converters and drives: Algorithms, implementations and applications
Power converters and motor drives are playing a significant role in the transition towards
sustainable energy systems and transportation electrification. In this context, rich diversity of …
sustainable energy systems and transportation electrification. In this context, rich diversity 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 …
Federated reinforcement learning for decentralized voltage control in distribution networks
Multi-agent reinforcement learning (MARL) with “centralized training & decentralized
execution” framework has been widely investigated to implement decentralized voltage …
execution” framework has been widely investigated to implement decentralized voltage …
Energy-transport scheduling for green vehicles in seaport areas: A review on operation models
Internal combustion engine vehicles, although conventionally playing essential roles in
seaport logistic operation, are major sources of carbon emissions. To guarantee the “green …
seaport logistic operation, are major sources of carbon emissions. To guarantee the “green …
An improved two-stage deep reinforcement learning approach for regulation service disaggregation in a virtual power plant
Managing numerous distributed energy resources (DERs) within the virtual power plant
(VPP) is challenging due to inaccurate parameters and unknown dynamic characteristics. To …
(VPP) is challenging due to inaccurate parameters and unknown dynamic characteristics. To …
Feasibility constrained online calculation for real-time optimal power flow: A convex constrained deep reinforcement learning approach
Due to the increasing uncertainties of renewable energy and stochastic demands, quick-
optimal control actions are necessary to retain the system stability and economic operation …
optimal control actions are necessary to retain the system stability and economic operation …