4 citations · 5 across the 4 of their papers we have counts for
5 papers
Chance-Constrained DC Optimal Power Flow Using Constraint-Informed Statistical Estimation
Tianyang Yi, D. Adrian Maldonado, Anirudh Subramanyam
Chance-constrained optimization has emerged as a promising framework for managing uncertainties in power systems. This work advances its application to the DC Optimal Power Flow (D…
The statistical spread of transmission outages on a fast protection time scale based on utility data
Ian Dobson, D. Adrian Maldonado, Mihai Anitescu
When there is a fault, the protection system automatically removes one or more transmission lines on a fast time scale of less than one minute. The outaged lines form a pattern in…
Scalable Multi-Period AC Optimal Power Flow Utilizing GPUs with High Memory Capacities
Sungho Shin, Vishwas Rao, Michel Schanen +2
This paper demonstrates the scalability of open-source GPU-accelerated nonlinear programming (NLP) frameworks -- ExaModels.jl and MadNLP.jl -- for solving multi-period alternating…
Data-Driven Estimation of Failure Probabilities in Correlated Structure-Preserving Stochastic Power System Models
Hongli Zhao, Tyler E. Maltba, D. Adrian Maldonado +2
We propose a data-driven approach for propagating uncertainty in stochastic power grid simulations and apply it to the estimation of transmission line failure probabilities. A redu…
Data-driven Closures & Assimilation for Stiff Multiscale Random Dynamics
Tyler E. Maltba, Hongli Zhao, D. Adrian Maldonado
We introduce a data-driven and physics-informed framework for propagating uncertainty in stiff, multiscale random ordinary differential equations (RODEs) driven by correlated (colo…