[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] A systematic review on power systems planning and operations management with grid integration of transportation electrification at scale
Transportation electrification plays a crucial role in mitigating greenhouse gas (GHG)
emissions and enabling the decarbonization of power systems. However, current research …
emissions and enabling the decarbonization of power systems. However, current research …
Distributed robust model predictive control-based energy management strategy for islanded multi-microgrids considering uncertainty
A microgrid is considered to be a smart power system that can integrate local renewable
energy effectively. However, the intermittent nature of renewable energy causes operating …
energy effectively. However, the intermittent nature of renewable energy causes operating …
Safe reinforcement learning for real-time automatic control in a smart energy-hub
Nowadays, multi-energy systems are receiving special attention from smart grid community
owing to their high flexibility potentials integrating with multiple energy carriers. In this …
owing to their high flexibility potentials integrating with multiple energy carriers. In this …
Multi-agent attention-based deep reinforcement learning for demand response in grid-responsive buildings
Integrating renewable energy resources and deploying energy management devices offer
great opportunities to develop autonomous energy management systems in grid-responsive …
great opportunities to develop autonomous energy management systems in grid-responsive …
Resilient distribution networks by microgrid formation using deep reinforcement learning
Resilience becomes vital for power grids facing the increasingly frequent extreme weather
events. Microgrid formation is a promising way to achieve resilient distribution networks …
events. Microgrid formation is a promising way to achieve resilient distribution networks …
Deep reinforcement learning-based model-free on-line dynamic multi-microgrid formation to enhance resilience
Multi-microgrid formation (MMGF) is a promising solution for enhancing power system
resilience. This paper proposes a new deep reinforcement learning (RL) based model-free …
resilience. This paper proposes a new deep reinforcement learning (RL) based model-free …
[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 …
A novel model-free deep reinforcement learning framework for energy management of a PV integrated energy hub
A Dolatabadi, H Abdeltawab… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …
Multi-agent deep reinforcement learning for coordinated energy trading and flexibility services provision in local electricity markets
Y Ye, D Papadaskalopoulos, Q Yuan… - … on Smart Grid, 2022 - ieeexplore.ieee.org
Local electricity markets (LEM) have recently attracted great interest as an effective solution
to the challenging problem of distributed energy resources'(DER) management. However …
to the challenging problem of distributed energy resources'(DER) management. However …