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 …
[PDF][PDF] Reinforcement learning for decision-making and control in power systems: Tutorial, review, and vision
With large-scale integration of renewable generation and distributed energy resources
(DERs), modern power systems are confronted with new operational challenges, such as …
(DERs), modern power systems are confronted with new operational challenges, such as …
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 …
Optimal operable power flow: Sample-efficient holomorphic embedding-based reinforcement learning
The nonlinearity of physical power flow equations divides the decision-making space into
operable and non-operable regions. Therefore, existing control techniques could be …
operable and non-operable regions. Therefore, existing control techniques could be …
Deep reinforcement learning-based adaptive voltage control of active distribution networks with multi-terminal soft open point
The integration of highly penetrated distributed generators (DGs) aggravates the rise of
voltage violations in distribution networks. Connected by multi-terminal soft open points (M …
voltage violations in distribution networks. Connected by multi-terminal soft open points (M …
Navigating the landscape of deep reinforcement learning for power system stability control: A review
The widespread penetration of inverter-based resources has profoundly impacted the
electrical stability of power systems (PSs). Deepening grid integration of photovoltaic and …
electrical stability of power systems (PSs). Deepening grid integration of photovoltaic and …
Optimal coordination for multiple network-constrained VPPs via multi-agent deep reinforcement learning
This paper proposes a multi-agent deep reinforcement learning method to coordinate
multiple microgrids owned virtual power plants (VPPs) connected in the active distribution …
multiple microgrids owned virtual power plants (VPPs) connected in the active distribution …
Optimal operation and maintenance of energy storage systems in grid-connected microgrids by deep reinforcement learning
The operation of microgrids, ie, energy systems composed of distributed energy generation,
local loads and energy storage capacity, is challenged by the variability of intermittent …
local loads and energy storage capacity, is challenged by the variability of intermittent …
Demand-side joint electricity and carbon trading mechanism
Decarbonization of the whole energy chain has been recognized as a measure to tackle the
global challenge of climate change, and significant progress has already been made on the …
global challenge of climate change, and significant progress has already been made on the …
Sequential reconfiguration of unbalanced distribution network with soft open points based on deep reinforcement learning
Z Yin, S Wang, Q Zhao - Journal of Modern Power Systems and …, 2022 - ieeexplore.ieee.org
With the large-scale distributed generations (DGs) being connected to distribution network
(DN), the traditional day-ahead reconfiguration methods based on physical models are …
(DN), the traditional day-ahead reconfiguration methods based on physical models are …