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Found 11 papers, 2 papers with code
Toward Intelligent Emergency Control for Large-scale Power Systems: Convergence of Learning, Physics, Computing and Control
Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56000 scenarios.
Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning
We propose a novel model-based-DRL framework where a deep neural network (DNN)-based dynamic surrogate model, instead of a real-world power-grid or physics-based simulation, is utilized with the policy learning framework, making the process faster and sample efficient.
Safe Reinforcement Learning for Grid Voltage Control
Under voltage load shedding has been considered as a standard approach to recover the voltage stability of the electric power grid under emergency conditions, yet this scheme usually trips a massive amount of load inefficiently.
Physics-informed Evolutionary Strategy based Control for Mitigating Delayed Voltage Recovery
Reinforcement learning methods have been developed for the same or similar challenging control problems, but they suffer from training inefficiency and lack of robustness for "corner or unseen" scenarios.
Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systems
However, this scheme usually trips a massive amount of load which can be unnecessary and harmful to customers.
Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning
We exploit the area-wise division structure of the power system to propose a hierarchical DRL design that can be scaled to the larger grid models.
Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning
In this paper, we mitigate these limitations by developing a novel deep meta reinforcement learning (DMRL) algorithm.
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control
Load shedding has been one of the most widely used and effective emergency control approaches against voltage instability.
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression
We propose a new forecasting method for predicting load demand and generation scheduling.
The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms
Consequently, benchmarking studies using the seminal AC Optimal Power Flow (AC-OPF) problem have emerged as the primary method for evaluating these emerging methods.
Optimization and Control
Adaptive Power System Emergency Control using Deep Reinforcement Learning
Power system emergency control is generally regarded as the last safety net for grid security and resiliency.
