Carleton Coffrin

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](https://github.com/power-grid-lib/pglib-opf). The Julia package [PowerModels](https://github.com/lanl-ansi/PowerModels.jl) 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](https://github.com/lanl-ansi/rosetta-opf) project, which currently includes implementations in [JuMP](https://github.com/jump-dev/JuMP.jl), [NLPModels](https://github.com/JuliaSmoothOptimizers/NLPModels.jl), [Nonconvex](https://github.com/JuliaNonconvex/Nonconvex.jl), [Optim](https://github.com/JuliaNLSolvers/Optim.jl) and [Optimization](https://github.com/SciML/Optimization.jl). This work reports on the solution quality and runtime of solving the PGLib-OPF datasets with each of these NLP frameworks.

Carleton Coffrin

Talks:

Benchmarking Nonlinear Optimization with AC Optimal Power Flow

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