Reinforcement learning for selective key applications in power systems: Recent advances and future challenges
With large-scale integration of renewable generation and distributed energy resources,
modern power systems are confronted with new operational challenges, such as growing …
modern power systems are confronted with new operational challenges, such as growing …
Grid-connected photovoltaic battery systems: A comprehensive review and perspectives
Due to the target of carbon neutrality and the current energy crisis in the world, green,
flexible and low-cost distributed photovoltaic power generation is a promising trend. With …
flexible and low-cost distributed photovoltaic power generation is a promising trend. With …
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 …
Physics informed neural networks for control oriented thermal modeling of buildings
Buildings constitute more than 40% of total primary energy consumption worldwide and are
bound to play an important role in the energy transition process. To unlock their potential, we …
bound to play an important role in the energy transition process. To unlock their potential, we …
Distributed energy scheduling for integrated energy system clusters with peer-to-peer energy transaction
Surplus electricity energy from renewable sources can be efficiently utilized by converting it
into other forms of energy in the integrated energy system (IES). Local electricity energy …
into other forms of energy in the integrated energy system (IES). Local electricity energy …
Operational optimization for off-grid renewable building energy system using deep reinforcement learning
With the application of renewable energy in single office buildings, an increasing number of
power grids require building systems coupled with renewable energy to realize off-grid …
power grids require building systems coupled with renewable energy to realize off-grid …
Site demonstration and performance evaluation of MPC for a large chiller plant with TES for renewable energy integration and grid decarbonization
Thermal energy storage (TES) for a cooling plant is a crucial resource for load flexibility.
Traditionally, simple, heuristic control approaches, such as the storage priority control which …
Traditionally, simple, heuristic control approaches, such as the storage priority control which …
Comparison of online and offline deep reinforcement learning with model predictive control for thermal energy management
This paper proposes a comparison between an online and offline Deep Reinforcement
Learning (DRL) formulation with a Model Predictive Control (MPC) architecture for energy …
Learning (DRL) formulation with a Model Predictive Control (MPC) architecture for energy …
[HTML][HTML] A taxonomy of machine learning applications for virtual power plants and home/building energy management systems
A Virtual power plant is defined as an information and communications technology system
with the following primary functionalities: enhancing renewable power generation …
with the following primary functionalities: enhancing renewable power generation …
[HTML][HTML] Energy management in residential microgrid using model predictive control-based reinforcement learning and Shapley value
This paper presents an Energy Management (EM) strategy for residential microgrid systems
using Model Predictive Control (MPC)-based Reinforcement Learning (RL) and Shapley …
using Model Predictive Control (MPC)-based Reinforcement Learning (RL) and Shapley …