Reinforcement learning in sustainable energy and electric systems: A survey
The dynamic nature of sustainable energy and electric systems can vary significantly along
with the environment and load change, and they represent the features of multivariate, high …
with the environment and load change, and they represent the features of multivariate, high …
Reinforcement learning for electric power system decision and control: Past considerations and perspectives
In this paper, we review past (including very recent) research considerations in using
reinforcement learning (RL) to solve electric power system decision and control problems …
reinforcement learning (RL) to solve electric power system decision and control problems …
Adaptive dynamic programming: An introduction
In this article, we introduce some recent research trends within the field of
adaptive/approximate dynamic programming (ADP), including the variations on the structure …
adaptive/approximate dynamic programming (ADP), including the variations on the structure …
Optimal home energy management under dynamic electrical and thermal constraints
F De Angelis, M Boaro, D Fuselli… - IEEE Transactions …, 2012 - ieeexplore.ieee.org
The optimization of energy consumption, with consequent costs reduction, is one of the main
challenges in present and future smart grids. Of course, this has to occur keeping the living …
challenges in present and future smart grids. Of course, this has to occur keeping the living …
Reinforcement learning algorithms with function approximation: Recent advances and applications
X Xu, L Zuo, Z Huang - Information sciences, 2014 - Elsevier
In recent years, the research on reinforcement learning (RL) has focused on function
approximation in learning prediction and control of Markov decision processes (MDPs). The …
approximation in learning prediction and control of Markov decision processes (MDPs). The …
An event-triggered approach for load frequency control with supplementary ADP
The modern power system is evolving towards a new generation of smart grid, with
significant benefits from the latest computer-based communication network technologies …
significant benefits from the latest computer-based communication network technologies …
Artificial neural networks for control of a grid-connected rectifier/inverter under disturbance, dynamic and power converter switching conditions
S Li, M Fairbank, C Johnson… - IEEE transactions on …, 2013 - ieeexplore.ieee.org
Three-phase grid-connected converters are widely used in renewable and electric power
system applications. Traditionally, grid-connected converters are controlled with standard …
system applications. Traditionally, grid-connected converters are controlled with standard …
A brief survey on nonlinear control using adaptive dynamic programming under engineering-oriented complexities
Nonlinear dynamics is frequently encountered in practical applications. Adaptive dynamic
programming (ADP), which is implemented via actor/critic neural networks with excellent …
programming (ADP), which is implemented via actor/critic neural networks with excellent …
Dynamic, stochastic, computational, and scalable technologies for smart grids
GK Venayagamoorthy - IEEE Computational Intelligence …, 2011 - ieeexplore.ieee.org
The smart electric power grid will evolve into a very complex adaptive system under semi-
autonomous distributed control. Its spatial and temporal complexity, non-convexity, non …
autonomous distributed control. Its spatial and temporal complexity, non-convexity, non …
Wide-area measurement based dynamic stochastic optimal power flow control for smart grids with high variability and uncertainty
J Liang, GK Venayagamoorthy… - IEEE Transactions on …, 2012 - ieeexplore.ieee.org
To achieve a high penetration level of intermittent renewable energy, the operation and
control of power systems need to account for the associated high variability and uncertainty …
control of power systems need to account for the associated high variability and uncertainty …