A review of safe reinforcement learning methods for modern power systems
Due to the availability of more comprehensive measurement data in modern power systems,
there has been significant interest in developing and applying reinforcement learning (RL) …
there has been significant interest in developing and applying reinforcement learning (RL) …
Deep Reinforcement learning for resilient power and energy systems: Progress, prospects, and future avenues
M Gautam - Electricity, 2023 - mdpi.com
In recent years, deep reinforcement learning (DRL) has garnered substantial attention in the
context of enhancing resilience in power and energy systems. Resilience, characterized by …
context of enhancing resilience in power and energy systems. Resilience, characterized by …
Spatio-temporal graph convolutional neural networks for physics-aware grid learning algorithms
This paper proposes novel architectures for spatio-temporal graph convolutional and
recurrent neural networks whose structure is inspired by the physics of power systems. The …
recurrent neural networks whose structure is inspired by the physics of power systems. The …
Spatiotemporal Deep Learning for Power System Applications: A Survey
M Saffari, M Khodayar - IEEE Access, 2024 - ieeexplore.ieee.org
Understanding spatiotemporal correlations in power systems is crucial for maintaining grid
stability, reliability, and efficiency. By discerning connections between spatial and temporal …
stability, reliability, and efficiency. By discerning connections between spatial and temporal …
Graph Reinforcement Learning for Power Grids: A Comprehensive Survey
The rise of renewable energy and distributed generation requires new approaches to
overcome the limitations of traditional methods. In this context, Graph Neural Networks are …
overcome the limitations of traditional methods. In this context, Graph Neural Networks are …
Network-Constrained Reinforcement Learning for Optimal EV Charging Control
This paper introduces a comprehensive control model that integrates aggregate electric
vehicle (EV) charging demand with power grid systems operations, capitalizing on the …
vehicle (EV) charging demand with power grid systems operations, capitalizing on the …
Robust Optimal Power Flow Against Adversarial Attacks: A Tri-Level Optimization Approach
SM Khamaneh, T Wu - arXiv preprint arXiv:2411.08618, 2024 - arxiv.org
In power systems, unpredictable events like extreme weather, equipment failures, and
cyberattacks present significant challenges to ensuring safety and reliability. Ensuring …
cyberattacks present significant challenges to ensuring safety and reliability. Ensuring …
Transferable Learning of GCN Sampling Graph Data Clusters from Different Power Systems
Contemporary neural network (NN) detectors for power systems face two primary
challenges. First, each power system requires individual training of NN detectors to …
challenges. First, each power system requires individual training of NN detectors to …