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) …
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 …
Review of machine learning techniques for optimal power flow
H Khaloie, M Dolanyi, JF Toubeau… - Available at SSRN …, 2024 - papers.ssrn.com
Abstract The Optimal Power Flow (OPF) problem is the cornerstone of power systems
operations, providing generators' most economical dispatch for power demands by fulfilling …
operations, providing generators' most economical dispatch for power demands by fulfilling …
[HTML][HTML] Surrogate Modeling for Solving OPF: A Review
The optimal power flow (OPF) problem, characterized by its inherent complexity and strict
constraints, has traditionally been approached using analytical techniques. OPF enhances …
constraints, has traditionally been approached using analytical techniques. OPF enhances …
Constrained reinforcement learning for predictive control in real-time stochastic dynamic optimal power flow
Deep Reinforcement Learning (DRL) has emerged as a favored approach for resolving
control challenges in power systems. Traditional DRL guides the agent through exploration …
control challenges in power systems. Traditional DRL guides the agent through exploration …
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 …
End-to-End Reinforcement Learning of Curative Curtailment with Partial Measurement Availability
H Wolf, L Böttcher, S Bouchkati, P Lutat… - arXiv preprint arXiv …, 2024 - arxiv.org
In the course of the energy transition, the expansion of generation and consumption will
change, and many of these technologies, such as PV systems, electric cars and heat pumps …
change, and many of these technologies, such as PV systems, electric cars and heat pumps …
[PDF][PDF] Learning on graphs from theory to industrial application in power management of distribution grids
H Wolf - publications.rwth-aachen.de
Learning on graphs has strong ties to theoretical computer science, as some algorithms
used for learning are rooted in graph theory. Furthermore, expressivity of learning methods …
used for learning are rooted in graph theory. Furthermore, expressivity of learning methods …