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 …
Artificial intelligence-based methods for renewable power system operation
Carbon neutrality goals are driving the increased use of renewable energy (RE). Large-
scale use of RE requires accurate energy generation forecasts; optimized power dispatch …
scale use of RE requires accurate energy generation forecasts; optimized power dispatch …
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 …
DeepOPF-FT: One deep neural network for multiple AC-OPF problems with flexible topology
We propose-as an embedded training approach to design one deep neural network (DNN)
for solving multiple AC-OPF problems with flexible topology and line admittances …
for solving multiple AC-OPF problems with flexible topology and line admittances …
Physics embedded graph convolution neural network for power flow calculation considering uncertain injections and topology
Probabilistic analysis tool is important to quantify the impacts of the uncertainties on power
system operations. However, the repetitive calculations of power flow are time-consuming …
system operations. However, the repetitive calculations of power flow are time-consuming …
Optimal operable power flow: Sample-efficient holomorphic embedding-based reinforcement learning
The nonlinearity of physical power flow equations divides the decision-making space into
operable and non-operable regions. Therefore, existing control techniques could be …
operable and non-operable regions. Therefore, existing control techniques could be …
Physics-informed neural networks for minimising worst-case violations in dc optimal power flow
Physics-informed neural networks exploit the existing models of the underlying physical
systems to generate higher accuracy results with fewer data. Such approaches can help …
systems to generate higher accuracy results with fewer data. Such approaches can help …
Learning provably stable local Volt/Var controllers for efficient network operation
This paper develops a data-driven framework to synthesize local Volt/Var control strategies
for distributed energy resources (DERs) in power distribution grids (DGs). Aiming to improve …
for distributed energy resources (DERs) in power distribution grids (DGs). Aiming to improve …
Opf-learn: An open-source framework for creating representative ac optimal power flow datasets
Increasing levels of renewable generation motivate a growing interest in data-driven
approaches for AC optimal power flow (AC OPF) to manage uncertainty; however, a lack of …
approaches for AC optimal power flow (AC OPF) to manage uncertainty; however, a lack of …
Multi-scale spatio-temporal transformer: A novel model reduction approach for day-ahead security-constrained unit commitment
M Liu, X Kong, K **ong, J Wang, Q Lin - Applied Energy, 2025 - Elsevier
Security-constrained unit commitment (SCUC) in large-scale power systems faces
significant computational challenges, particularly with increasing renewable energy …
significant computational challenges, particularly with increasing renewable energy …