42 citations · 42 across the 1 of their papers we have counts for
9 papers
Spatial Network Decomposition for Fast and Scalable AC-OPF Learning
Minas Chatzos, Terrence W. K. Mak, Pascal Van Hentenryck
This paper proposes a novel machine-learning approach for predicting AC-OPF solutions that features a fast and scalable training. It is motivated by the two critical considerations…
High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
Minas Chatzos, Ferdinando Fioretto, Terrence W. K. Mak +1
The AC Optimal Power Flow (AC-OPF) is a key building block in many power system applications. It determines generator setpoints at minimal cost that meet the power demands while sa…
Bilevel Optimization for Differentially Private Optimization in Energy Systems
Terrence W. K. Mak, Ferdinando Fioretto, Pascal Van Hentenryck
This paper studies how to apply differential privacy to constrained optimization problems whose inputs are sensitive. This task raises significant challenges since random perturbat…
Lagrangian Duality for Constrained Deep Learning
Ferdinando Fioretto, Pascal Van Hentenryck, Terrence WK Mak +3
This paper explores the potential of Lagrangian duality for learning applications that feature complex constraints. Such constraints arise in many science and engineering domains,…
Privacy-Preserving Obfuscation for Distributed Power Systems
Terrence W. K. Mak, Ferdinando Fioretto, Pascal Van Hentenryck
This paper considers the problem of releasing privacy-preserving load data of a decentralized operated power system. The paper focuses on data used to solve Optimal Power Flow (OPF…
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods
Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck
The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems. It is nonlinear and nonconvex and computes the generator setp…