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Found 5 papers, 0 papers with code
Learning Regionally Decentralized AC Optimal Power Flows with ADMM
One potential future for the next generation of smart grids is the use of decentralized optimization algorithms and secured communications for coordinating renewable generation (e. g., wind/solar), dispatchable devices (e. g., coal/gas/nuclear generations), demand response, battery & storage facilities, and topology optimization.
Risk-Aware Control and Optimization for High-Renewable Power Grids
The transition of the electrical power grid from fossil fuels to renewable sources of energy raises fundamental challenges to the market-clearing algorithms that drive its operations.
Uncertainty Quantification
Vocal Bursts Intensity Prediction
Data-Driven Time Series Reconstruction for Modern Power Systems Research
A critical aspect of power systems research is the availability of suitable data, access to which is limited by privacy concerns and the sensitive nature of energy infrastructure.
Spatial Network Decomposition for Fast and Scalable AC-OPF Learning
This paper proposes a novel machine-learning approach for predicting AC-OPF solutions that features a fast and scalable training.
High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
It is non-convex and NP-hard, and computationally challenging for large-scale power systems.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
