Distributional deep reinforcement learning-based emergency frequency control
Emergency frequency control is one of the most critical approaches to maintain power
system stability after major disturbances. With the increasing number of grid-connected …
system stability after major disturbances. With the increasing number of grid-connected …
Pi-ars: Accelerating evolution-learned visual-locomotion with predictive information representations
Evolution Strategy (ES) algorithms have shown promising results in training complex robotic
control policies due to their massive parallelism capability, simple implementation, effective …
control policies due to their massive parallelism capability, simple implementation, effective …
Multi-agent graph-attention deep reinforcement learning for post-contingency grid emergency voltage control
Grid emergency voltage control (GEVC) is paramount in electric power systems to improve
voltage stability and prevent cascading outages and blackouts in case of contingencies …
voltage stability and prevent cascading outages and blackouts in case of contingencies …
Deep Reinforcement Learning Based Active Network Management and Emergency Load-Shedding Control for Power Systems
H Zhang, X Sun, MH Lee, J Moon - IEEE Transactions on Smart …, 2023 - ieeexplore.ieee.org
This paper presents two novel deep reinforcement learning (DRL) approaches aimed at
solving complex power system control problems in a data-driven sense to maintain the …
solving complex power system control problems in a data-driven sense to maintain the …
Physics-informed evolutionary strategy based control for mitigating delayed voltage recovery
In this work we propose a novel data-driven, real-time power system voltage control method
based on the physics-informed guided meta evolutionary strategy (ES). The main objective …
based on the physics-informed guided meta evolutionary strategy (ES). The main objective …
Safe reinforcement learning for emergency load shedding of power systems
The paradigm shift in the electric power grid necessitates a revisit of existing control
methods to ensure the grid's security and resilience. In particular, the increased …
methods to ensure the grid's security and resilience. In particular, the increased …
Distribution System Optimization to Manage Distributed Energy Resources (DERs) for Grid Services
The proliferation of distributed energy resources (DERs) and the deployment of advanced
sensing and control technologies in electric power distribution systems calls for coordinated …
sensing and control technologies in electric power distribution systems calls for coordinated …
Barrier function-based safe reinforcement learning for emergency control of power systems
Under voltage load shedding has been considered as a standard and effective measure to
recover the voltage stability of the electric power grid under emergency and severe …
recover the voltage stability of the electric power grid under emergency and severe …
Smart sampling for reduced and representative power system scenario selection
With increasing penetration of renewable energy and active market participation, power
system operation scenarios and patterns have increased exponentially. This has led to …
system operation scenarios and patterns have increased exponentially. This has led to …
Scalable voltage control using structure-driven hierarchical deep reinforcement learning
This paper presents a novel hierarchical deep reinforcement learning (DRL) based design
for the voltage control of power grids. DRL agents are trained for fast, and adaptive selection …
for the voltage control of power grids. DRL agents are trained for fast, and adaptive selection …