6 citations · 9 across the 10 of their papers we have counts for
11 papers
Nodal Frequency Stability-Constrained UC & ED for Renewable-Dominated Power Systems
Marena Trujillo, David Cole, Bri-Mathias Hodge
In modern power systems with high shares of renewable, inverter-based resources (IBRs), frequency stability becomes more complex due to the fast dynamics of IBRs and frequency traj…
Analyzing Performance and Scalability of Benders Decomposition for Generation and Transmission Expansion Planning Models
David L. Cole, Michael Lau, Xinliang Dai +2
Generation and Transmission Expansion Planning (GTEP) problems co-optimize generation and transmission expansion, enabling them to provide better planning decisions than traditiona…
A Graph-Based, Distributed Memory, Modeling Abstraction for Optimization
David L. Cole, Jordan Jalving, Jonah Langlieb +1
We present a general, flexible modeling abstraction for building and working with distributed optimization problems called a RemoteOptiGraph. This abstraction extends the OptiGraph…
Benders Decomposition using Graph Modeling and Multi-Parametric Programming
Parth Brahmbhatt, David L. Cole, Victor M. Zavala +1
Benders decomposition is a widely used method for solving large optimization problems, but its performance is often hindered by the repeated solution of subproblems. We propose a f…
Graph-Based Modeling and Decomposition of Hierarchical Optimization Problems
David L. Cole, Filippo Pecci, Omar J. Guerra +3
We present a graph-theoretic modeling approach for hierarchical optimization that leverages the OptiGraph abstraction implemented in the Julia package Plasmo.jl. We show that the a…
PlasmoData.jl -- A Julia Framework for Modeling and Analyzing Complex Data as Graphs
David L Cole, Victor M Zavala
Datasets encountered in scientific and engineering applications appear in complex formats (e.g., images, multivariate time series, molecules, video, text strings, networks). Graph…
Contextual Reinforcement Learning for Offshore Wind Farm Bidding
David Cole, Himanshu Sharma, Wei Wang
We propose a framework for applying reinforcement learning to contextual two-stage stochastic optimization and apply this framework to the problem of energy market bidding of an of…
Hierarchical Graph Modeling for Multi-Scale Optimization of Power Systems
David L. Cole, Harsha Gangammanavar, Victor M. Zavala
Hierarchical optimization architectures are used in power systems to manage disturbances and phenomena that arise at multiple spatial and temporal scales. We present a graph modeli…
A Graph-Based Modeling Framework for Tracing Hydrological Pollutant Transport in Surface Waters
David L. Cole, Gerardo J. Ruiz-Mercado, Victor M. Zavala
Anthropogenic pollution of hydrological systems affects diverse communities and ecosystems around the world. Data analytics and modeling tools play a key role in fighting this chal…
Exploiting GPU/SIMD Architectures for Solving Linear-Quadratic MPC Problems
David Cole, Sungho Shin, François Pacaud +2
We report numerical results on solving constrained linear-quadratic model predictive control (MPC) problems by exploiting graphics processing units (GPUs). The presented method red…
A Julia Framework for Graph-Structured Nonlinear Optimization
David L Cole, Sungho Shin, Victor Zavala
Graph theory provides a convenient framework for modeling and solving structured optimization problems. Under this framework, the modeler can arrange/assemble the components of an…