Copyright Georgia Tech 2022-2024

PGLearn

Instance generator for various OPF problems.

Installation instructions

This repository is a non-registered Julia package.

• Option 1: install as a Julia package. You can use it, but not modify the code julia using Pkg Pkg.add(url="git@github.com:AI4OPT/PGLearn.jl.git")

• Option 2: clone the repository. Use this if you want to change the code bash git clone git@github.com:AI4OPT/PGLearn.jl.git To use the package after cloning the repo bash $ cd PGLearn $ julia --project=. julia> using PGLearn

  If you are modifying the source code, it is recommened to use the package Revise.jl so that you can use the changes without having to start Julia. Make sure you load Revise before loading PGLearn in your julia session. julia using Revise using PGLearn

Using HSL solvers (ma27, ma57)

Please refer to Ipopt.jl installation instructions for how to install non-default linear solvers, e.g., HSL, Pardiso, etc... Note that a specific license may be required.

To use HSL linear solvers when solving OPF instances, set the parameter "linearsolver" to "ma27" or "ma57" in the config file. The recommended solver for Ipopt is ma27. ```toml solver.name = "Ipopt" solver.attributes.linearsolver = "ma27" ```

Quick start

Building and solving an OPF problem

```julia using PGLib, PowerModels using PGLearn using Ipopt

Load data from PGLib case

pmnetwork = PowerModels.makebasicnetwork(pglib("14ieee")) data = PGLearn.OPFData(pm_network)

Build OPF optimization model

To switch formulations, replace ACOPF with, e.g., DCOPF or SOCOPF

opf = PGLearn.build_opf(PGLearn.ACOPF, data, Ipopt.Optimizer)

Solve and extract result

PGLearn.solve!(opf) res = PGLearn.extract_result(opf) ```

Generating random OPF instances

```julia using Random using PGLib, PowerModels using PGLearn

Load data from PGLib case

pmnetwork = PowerModels.makebasicnetwork(pglib("14ieee")) data = PGLearn.OPFData(pm_network)

Load scaler using global scaling + uncorrelated LogNormal noise

config = Dict( "load" => Dict( "noisetype" => "ScaledUniform", "l" => 0.8, "u" => 1.2, "sigma" => 0.10,
) ) opfsampler = SimpleOPFSampler(data, config)

Generate a new instance

rng = MersenneTwister(42) newdata = rand(rng, opfsampler)

data.pd[1] # old 0.217

new_data.pd[1] # new 0.21480423013573954 # ⚠️ exact value depends on Julia version) ```

To generate multiple instances, run the above code in a loop julia dataset = [ PGLearn.rand(rng, opf_sampler) for i in 1:100 ]

Generating datasets

A script for generating multiple ACOPF instances is given in exp/sampler.jl.

It is called from the command-line as follows: bash julia --project=. exp/sampler.jl <path/to/config.toml> <seed_min> <seed_max> where * <path/to/config.toml> is a path to a valid configuration file in TOML format (see exp/config.toml for an example) * <seed_min> and <seed_max> are minimum and maximum values for the random seed. Must be integer values. The script will generate instances for values smin, smin+1, ..., smax-1, smax.

Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. 2112533 NSF AI Institute for Advances in Optimization (AI4OPT). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.