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Applications of physics-informed neural networks in power systems-a review
The advances of deep learning (DL) techniques bring new opportunities to numerous
intractable tasks in power systems (PSs). Nevertheless, the extension of the application of …
intractable tasks in power systems (PSs). Nevertheless, the extension of the application of …
Opportunities for quantum computing within net-zero power system optimization
Optimized power system planning and operation are core to delivering a low-cost and high-
reliability transition path to net-zero carbon emissions. The major technological changes …
reliability transition path to net-zero carbon emissions. The major technological changes …
[PDF][PDF] Predicting ac optimal power flows: Combining deep learning and lagrangian dual methods
Abstract The Optimal Power Flow (OPF) problem is a fundamental building block for the
optimization of electrical power systems. It is nonlinear and nonconvex and computes the …
optimization of electrical power systems. It is nonlinear and nonconvex and computes the …
[HTML][HTML] Physics-informed neural networks for ac optimal power flow
This paper introduces, for the first time to our knowledge, physics-informed neural networks
to accurately estimate the AC-Optimal Power Flow (AC-OPF) result and delivers rigorous …
to accurately estimate the AC-Optimal Power Flow (AC-OPF) result and delivers rigorous …
Deepopf: A deep neural network approach for security-constrained dc optimal power flow
We develop DeepOPF as a Deep Neural Network (DNN) approach for solving security-
constrained direct current optimal power flow (SC-DCOPF) problems, which are critical for …
constrained direct current optimal power flow (SC-DCOPF) problems, which are critical for …
DeepOPF: A feasibility-optimized deep neural network approach for AC optimal power flow problems
To cope with increasing uncertainty from renewable generation and flexible load, grid
operators need to solve alternative current optimal power flow (AC-OPF) problems more …
operators need to solve alternative current optimal power flow (AC-OPF) problems more …
Learning to run a power network challenge: a retrospective analysis
Power networks, responsible for transporting electricity across large geographical regions,
are complex infrastructures on which modern life critically depend. Variations in demand …
are complex infrastructures on which modern life critically depend. Variations in demand …
Self-supervised primal-dual learning for constrained optimization
This paper studies how to train machine-learning models that directly approximate the
optimal solutions of constrained optimization problems. This is an empirical risk minimization …
optimal solutions of constrained optimization problems. This is an empirical risk minimization …
[HTML][HTML] Operations research in optimal power flow: A guide to recent and emerging methodologies and applications
The fields of power system engineering and operations research are growing rapidly and
becoming increasingly entwined. This survey aims to strengthen the connections between …
becoming increasingly entwined. This survey aims to strengthen the connections between …
Topology-aware graph neural networks for learning feasible and adaptive AC-OPF solutions
Solving the optimal power flow (OPF) problem is a fundamental task to ensure the system
efficiency and reliability in real-time electricity grid operations. We develop a new topology …
efficiency and reliability in real-time electricity grid operations. We develop a new topology …