Reinforcement learning and its applications in modern power and energy systems: A review
With the growing integration of distributed energy resources (DERs), flexible loads, and
other emerging technologies, there are increasing complexities and uncertainties for …
other emerging technologies, there are increasing complexities and uncertainties for …
Multi-agent deep reinforcement learning for HVAC control in commercial buildings
In commercial buildings, about 40%-50% of the total electricity consumption is attributed to
Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic …
Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic …
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 …
Big data analytics for future electricity grids
This paper provides a survey of big data analytics applications and associated
implementation issues. The emphasis is placed on applications that are novel and have …
implementation issues. The emphasis is placed on applications that are novel and have …
On machine learning-based techniques for future sustainable and resilient energy systems
Permanently increasing penetration of converter-interfaced generation and renewable
energy sources (RESs) makes modern electrical power systems more vulnerable to low …
energy sources (RESs) makes modern electrical power systems more vulnerable to low …
Learning to run a power network challenge: a retrospective analysis
Power networks, responsible for transporting electricity across large geographical regions,
are complex infrastructures on which modern life critically depend. Variations in demand …
are complex infrastructures on which modern life critically depend. Variations in demand …
Online reconfiguration scheme of self-sufficient distribution network based on a reinforcement learning approach
With increasing number of distributed renewable energy sources integrated in power
distribution networks, network security issues such as line overloading or bus voltage …
distribution networks, network security issues such as line overloading or bus voltage …
Physics-constrained vulnerability assessment of deep reinforcement learning-based SCOPF
The decarbonization of energy systems has posed unprecedented challenges in system
complexity and operational uncertainty that render it imperative to exploit cutting-edge …
complexity and operational uncertainty that render it imperative to exploit cutting-edge …
Winning the l2rpn challenge: Power grid management via semi-markov afterstate actor-critic
Safe and reliable electricity transmission in power grids is crucial for modern society. It is
thus quite natural that there has been a growing interest in the automatic management of …
thus quite natural that there has been a growing interest in the automatic management of …