45 citations · 67 across the 29 of their papers we have counts for
5 papers · 1 filter
Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments
Seonho Park, Wenbo Chen, Dahye Han +2
Reliability Assessment Commitment (RAC) Optimization is increasingly important in grid operations due to larger shares of renewable generations in the generation mix and increased…
Just-In-Time Learning for Operational Risk Assessment in Power Grids
Oliver Stover, Pranav Karve, Sankaran Mahadevan +4
In a grid with a significant share of renewable generation, operators will need additional tools to evaluate the operational risk due to the increased volatility in load and genera…
Bucketized Active Sampling for Learning ACOPF
Michael Klamkin, Mathieu Tanneau, Terrence W. K. Mak +1
This paper considers optimization proxies for Optimal Power Flow (OPF), i.e., machine-learning models that approximate the input/output relationship of OPF. Recent work has focused…
Learning Regionally Decentralized AC Optimal Power Flows with ADMM
Terrence W. K. Mak, Minas Chatzos, Mathieu Tanneau +1
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.…
Risk-Aware Control and Optimization for High-Renewable Power Grids
Neil Barry, Minas Chatzos, Wenbo Chen +10
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 operatio…