• Corpus ID: 219980661

Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control

@article{Huang2020AcceleratedDR,
  title={Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control},
  author={Renke Huang and Yujiao Chen and Tianzhixi Yin and Xinya Li and Ang Li and Jie Tan and Qiuhua Huang},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.12667}
}
Load shedding has been one of the most widely used and effective emergency control approaches against voltage instability. With increased uncertainties and rapidly changing operational conditions in power systems, existing methods have outstanding issues in terms of either speed, adaptiveness, or scalability. Deep reinforcement learning (DRL) was regarded and adopted as a promising approach for fast and adaptive grid stability control in recent years. However, existing DRL algorithms show two… 

Figures and Tables from this paper

Safe Reinforcement Learning for Emergency Load Shedding of Power Systems

TLDR
A novel method for safe RL-based load shedding of power systems that can enhance the safe voltage recovery of the electric power grid after experiencing faults is introduced.

Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systems

TLDR
A novel safe RL method for emergency load shedding of power systems, that can enhance the safe voltage recovery of the electric power grid after experiencing faults and can be applied to other safety-critical control problems.

Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning

TLDR
Fast adaptation of the meta-trained DRL polices with latent variables to new operating conditions and scenarios using the proposed DMRL algorithm achieve superior performance compared to the state-of-the-art DRL and model predictive control (MPC) methods.

Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning

TLDR
This paper uses the area-wise division structure of the power system to propose a hierarchical DRL design that can be scaled to the larger grid models, and employs an enhanced augmented random search algorithm that is tailored for the voltage control problem in a twolevel architecture.

PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations

TLDR
Predictive Information Augmented Random Search (PI-ARS) is developed, which combines a gradient-based representation learning technique, Predictive Information (PI), with an gradient-free ES algorithm, Augmented random Search (ARS), to train policies that can process complex robot sensory inputs and handle highly nonlinear robot dynamics.

Safe Reinforcement Learning for Grid Voltage Control

TLDR
A couple of novel safe RL approaches are discussed, namely constrained optimization approach and Barrier function-based approach, that can safely recover voltage under emergency events and can be applied to other safety-critical control problems.

Reinforcement Learning for Battery Energy Storage Dispatch augmented with Model-based Optimizer

  • Gayathri KrishnamoorthyA. Dubey
  • Engineering, Computer Science
    2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
  • 2021
TLDR
A novel approach to synergistically combine the physics-based models with learning-based algorithms using imitation learning to solve distribution-level OPF problems to solve a specific case of battery storage dispatch in the power distribution systems.

References

SHOWING 1-10 OF 42 REFERENCES

Adaptive Power System Emergency Control Using Deep Reinforcement Learning

TLDR
An open-source platform named Reinforcement Learning for Grid Control (RLGC) has been designed for the first time to assist the development and benchmarking of DRL algorithms for power system control.

Load Shedding Scheme with Deep Reinforcement Learning to Improve Short-term Voltage Stability

In this paper, a novel load shedding scheme against voltage instability with deep reinforcement learning (DRL). The convolutional neural networks (CNNs) are chosen to automatically learn the features

A Hierarchical Data-Driven Method for Event-Based Load Shedding Against Fault-Induced Delayed Voltage Recovery in Power Systems

TLDR
A hierarchical data-driven method is proposed for the online prediction of event-based load shedding (ELS) against fault-induced delayed voltage recovery, which is very accurate in prediction with excellent control performance.

Model-Free Emergency Frequency Control Based on Reinforcement Learning

TLDR
With the aid of reinforcement learning, novel model-free control (MFC)-based emergency control schemes are presented and a deep deterministic policy gradient (DDPG) algorithm is adopted to learn near-optimal solutions.

A Multi-Agent Deep Reinforcement Learning Method for Cooperative Load Frequency Control of a Multi-Area Power System

TLDR
Numerical simulations on a three-area power system and the fully-modeled New-England 39-bus system demonstrate that the proposed method can effectively minimize control errors against stochastic frequency variations caused by load and renewable power fluctuations.

Data-Driven Load Frequency Control for Stochastic Power Systems: A Deep Reinforcement Learning Method With Continuous Action Search

This letter proposes a data-driven, model-free method for load frequency control (LFC) against renewable energy uncertainties based on deep reinforcement learning (DRL) in continuous action domain.

A Novel Online Load Shedding Strategy for Mitigating Fault-Induced Delayed Voltage Recovery

This paper develops a novel online fast load shedding strategy aimed at shedding the most effective load to mitigate fault-induced delayed voltage recovery (FIDVR). Induction motor kinetic energy

Fast and Robust Determination of Power System Emergency Control Actions

This paper outlines an optimization framework for choosing fast and reliable control actions in a transmission grid emergency situation. We consider contractual load shedding and generation