Mixed-integer optimization with constraint learning
We establish a broad methodological foundation for mixed-integer optimization with learned
constraints. We propose an end-to-end pipeline for data-driven decision making in which …
constraints. We propose an end-to-end pipeline for data-driven decision making in which …
Neur2SP: Neural two-stage stochastic programming
Stochastic Programming is a powerful modeling framework for decision-making under
uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely …
uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely …
Modeling the AC power flow equations with optimally compact neural networks: Application to unit commitment
Nonlinear power flow constraints render a variety of power system optimization problems
computationally intractable. Emerging research shows, however, that the nonlinear AC …
computationally intractable. Emerging research shows, however, that the nonlinear AC …
Neur2RO: Neural two-stage robust optimization
Robust optimization provides a mathematical framework for modeling and solving decision-
making problems under worst-case uncertainty. This work addresses two-stage robust …
making problems under worst-case uncertainty. This work addresses two-stage robust …
[PDF][PDF] Review of machine learning techniques for optimal power flow
ABSTRACT The Optimal Power Flow (OPF) problem is the cornerstone of power systems
operations, providing generators' most economical dispatch for power demands by fulfilling …
operations, providing generators' most economical dispatch for power demands by fulfilling …
Deep-quantile-regression-based surrogate model for joint chance-constrained optimal power flow with renewable generation
Joint chance-constrained optimal power flow (JCC-OPF) is a promising tool for managing
distributed renewable generation uncertainties. However, existing works are usually based …
distributed renewable generation uncertainties. However, existing works are usually based …
Closing the loop: A framework for trustworthy machine learning in power systems
Deep decarbonization of the energy sector will require massive penetration of stochastic
renewable energy resources and an enormous amount of grid asset coordination; this …
renewable energy resources and an enormous amount of grid asset coordination; this …
Capturing electricity market dynamics in strategic market participation using neural network constrained optimization
In competitive electricity markets, the optimal bid or offer problem of a strategic agent is
commonly formulated as a bi-level program and solved as a mathematical program with …
commonly formulated as a bi-level program and solved as a mathematical program with …
Optimization with Neural Network Feasibility Surrogates: Formulations and Application to Security-Constrained Optimal Power Flow
In many areas of constrained optimization, representing all possible constraints that give rise
to an accurate feasible region can be difficult and computationally prohibitive for online use …
to an accurate feasible region can be difficult and computationally prohibitive for online use …
Computational tradeoffs of optimization-based bound tightening in relu networks
The use of Mixed-Integer Linear Programming (MILP) models to represent neural networks
with Rectified Linear Unit (ReLU) activations has become increasingly widespread in the …
with Rectified Linear Unit (ReLU) activations has become increasingly widespread in the …