07-28, 17:00–17:30 (UTC), JuMP
This work discusses some of the requirements for deploying non-convex nonlinear optimization methods to solve large-scale problems in practice. AC Optimal Power Flow is proposed as a proxy-application for testing the viability of nonlinear optimization frameworks for solving such problems. The current performance of several Julia frameworks for nonlinear optimization is evaluated using a standard benchmark library for AC Optimal Power Flow.
The AC Optimal Power Flow problem (AC-OPF) is one of the most foundational optimization problems that arises in the design and operations of power networks. Mathematically the AC-OPF is a large-scale, sparse, non-convex nonlinear continuous optimization problem. In practice AC-OPF is most often solved to local optimality conditions using interior point methods. This project proposes AC-OPF as proxy-application for testing the viability of different nonlinear optimization frameworks, as performant solutions to AC-OPF has proven to be a necessary (but not always sufficient) condition for solving a wide range of industrial network optimization tasks.
Objectives
- Communicate the technical requirements for solving real-world continuous non-convex mathematical optimization problems.
- Highlight scalability requirements for the problem sizes that occur in practice.
- Provide a consistent implementation for solving AC-OPF in different nonlinear optimization frameworks.
AC-OPF Implementations
This work adopts the mathematical model and data format that is used in the IEEE PES benchmark library for AC-OPF, PGLib-OPF. The Julia package PowerModels is used for parsing the problem data files and making standard data transformations.
The implementations of the AC-OPF problem in various Julia NonLinear Programming (NLP) frameworks are available in Rosetta-OPF project, which currently includes implementations in JuMP, NLPModels, Nonconvex, Optim and Optimization. This work reports on the solution quality and runtime of solving the PGLib-OPF datasets with each of these NLP frameworks.
Carleton Coffrin is a staff scientist in Los Alamos National Laboratory’s Advanced Network Science Initiative. His research interests focus on how optimization methods can be used to solve applications in infrastructure networks. His background spans many forms of optimization including mathematical programing, constraint programming, and local search. Recently Carleton has been exploring the potential of novel computing architectures such as, quantum computers, neuromorphic processors and memristors to solve optimization applications.