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Found 15 papers, 1 papers with code
Transmission Line Outage Probability Prediction Under Extreme Events Using Peter-Clark Bayesian Structural Learning
The Bayesian network is a probabilistic model that is very effective for predicting line outages under weather-related uncertainties.
Differential Predictive Control of Residential Building HVACs for Maximizing Renewable Local Consumption and Supporting Fast Voltage Control
In addition, a detailed controller-building-grid co-simulation platform is developed and utilized for analyzing the potential impacts of the proposed control scheme on both the buildings and distribution system.
Quantifying and Predicting Residential Building Flexibility Using Machine Learning Methods
Residential buildings account for a significant portion (35\%) of the total electricity consumption in the U. S. as of 2022.
Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations
Improving system-level resiliency of networked microgrids is an important aspect with increased population of inverter-based resources (IBRs).
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.
Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning
This paper presents a novel federated reinforcement learning (Fed-RL) methodology to enhance the cyber resiliency of networked microgrids.
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.
Learning to run a power network with trust
We first advance an agent with the ability to send to the operator alarms ahead of time when the proposed actions are of low confidence.
Improving Safety in Deep Reinforcement Learning using Unsupervised Action Planning
One of the key challenges to deep reinforcement learning (deep RL) is to ensure safety at both training and testing phases.
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.
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.
