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Found 4 papers, 1 papers with code
Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning
Recent years have seen a rich literature of data-driven approaches designed for power grid applications.
Exascale Grid Optimization (ExaGO) toolkit: An open-source high-performance package for solving large-scale grid optimization problems
This paper introduces the Exascale Grid Optimization (ExaGO) toolkit, a library for solving large-scale alternating current optimal power flow (ACOPF) problems including stochastic effects, security constraints and multi-period constraints.
Neuro-physical dynamic load modeling using differentiable parametric optimization
In this work, we investigate a data-driven approach for obtaining a reduced equivalent load model of distribution systems for electromechanical transient stability analysis.
Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem
The dynamics-aware economic dispatch (DED) problem embeds low-level generator dynamics and operational constraints to enable near real-time scheduling of generation units in a power network.
