222042015 / opf

OPF

Code used in Optimal Power Flow Using Graph Neural Networks and Unsupervised Optimal Power Flow Using Graph Neural Networks. To cite our work please use the following biblatex citation. The version used for ICASSP is archived in a different branch.

@misc{owerko2022opf,
  doi = {10.48550/ARXIV.2210.09277},
  url = {https://arxiv.org/abs/2210.09277},
  author = {Owerko, Damian and Gama, Fernando and Ribeiro, Alejandro},
  title = {Unsupervised Optimal Power Flow Using Graph Neural Networks},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@inproceedings{owerko2020opf,
  author={Owerko, Damian and Gama, Fernando and Ribeiro, Alejandro},
  booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Optimal Power Flow Using Graph Neural Networks}, 
  year={2020},
  pages={5930-5934},
  doi={10.1109/ICASSP40776.2020.9053140}
}

Reproducing Results

The code on the main branch is much more recent than used in the paper. To make reproducing results easier, I have created a branch

Unsupervised Optimal Power Flow Using Graph Neural Networks

The code from the paper is archived in the unsupervised branch. Below I provide instructions on how to reproduce the results for the IEEE-30 system.

Data

Generated data is available on Google Drive. Download the data and place in data/, relative to the repo root.

• IEEE-30
• IEEE-118

Alternatively use generate.py to generate your own data.

python -m opf.generate --help

Training

To train the model with the hyperparameters from the paper, run the following command.

python scripts/train.py --F 32 --K 8 --L 2 --activation leaky_relu --adj_threshold 0.01 --batch_size 256 --case_name case30 --constraint_features 0 --cost_weight 0.01 --enforce_constraints 0 --eps 0.0001 --gpus 1 --gradient_clip_val 0 --log 1 --lr 0.0003 --max_epochs 1000 --patience 1000 --readout local --s 10 --t 500 --wandb 0

Using the provided checkpoint

The branch unsupervided contains a checkpoint trained on the IEEE-30 system. It is inside models/. There is a checkpoint file and a yaml file specifying all the hyperparameters needed to load the model. You can create an instance of OPFLogBarrier using the hyperparameters and load the weights using load_state_dict.

Optimal Power Flow Using Graph Neural Networks

The code from the paper is archived in the icassp branch. I do not recommend using this branch as it is outdated.

Installation

Python

This project uses poetry for dependency managemnt. To install the project and all the dependencies simply call use poetry install. Poetry will create a virtualenv for you or alternatively you can use,

poetry config virtualenvs.create false --local

to supress virtualenv creation for the project.

Julia

You will need to install Julia 1.9.3 (as of October 2023). You can download it from the julia website. Then install the dependencies. Run the following command in the repo root.

julia --project=. -e 'using Pkg; Pkg.instantiate()'

Optionally, test the PowerModels installation. This can take a while.

julia --project=. -e 'using Pkg; Pkg.test("PowerModels")'

HSL Solver

The HSL linear solver for IPOPT dramatically speeds up solving the problem by taking advantage of BLAS and LAPACK. PowerModels.jl (benchmarks)[https://lanl-ansi.github.io/PowerModels.jl/stable/experiment-results/] use the HSL_MA57 solver, which I use as well. If you do not want to use the HSL solver then in the repo root run the following.

julia --project=OPFHelpers -e 'using Pkg; Pkg.rm("HSL_jll")'

If you want to use HSL you will have to download it yourself from (here)[https://licences.stfc.ac.uk/product/julia-hsl]. You need a license to do so, but it is free for academic use. Once you obtain the archive download it and extract to a location of your choice. Then run the following with PATH_TO_HSL_JL replaced by the path to the extracted archive.

julia --project=OPFHelpers -e 'using Pkg; Pkg.develop(path="PATH_TO_HSL_JL")' 

Code Overview

Files

• train.py is the main file used for experiments. It uses Weights and Biases for cloud based logging. You can change the hyperparameters by editing the hyperparameter dict.
• power.py wraps pandapower in two classes and provides several helper functions.
• generate.py is a utility for generating a dataset for test cases.

Classes

• load_case loads cases from the pandapower.networks module.
• adjacency_from_net creates an adjacency matrix based off of the imported network object.
• NetWrapper provides a set of functions to manipulate the PandaPower data structure. Pandapower uses pandas to store information about the electrical network in tables, this class allows you to easily manipulate those as numpy matrices.
• LoadGenerator is a utility for generating synthetic load profiles based on real data or sampled from a uniform distribution.

Conventions

Units

We follow the conventions from PowerModels.jl. All quantities are expressed in the per-unit system. This simplifies computations and typically normalizes values.

All angles are expressed in radians where possible. This is contrary to the PandaPower convention.

Bus Values

We can describe the state of the buses in the grid by two complex quantities:

• the voltage at each node
• and the power injected at the node (PandaPower values have the opposite sign). This can be represented by a Nx4 matrix containing the real and imaginary components of both quantities.

Test Cases

Not of pandapower test cases converge during optimal power flow. Some have missing parameters, necessary for OPF computation, others just fail to converge using PYPOWER. Below is a list of tested test cases.

Working:

• case6ww
• case30
• case57
• case118

Broken:

• case4gs
• case5

Load profiles

Though currently unused, data for generating "realistic" load profile based on historical data is available here.