eiv-grid-id

Distribution network identification via error-in-variable models.

Getting started

This project relies on Python 3.8 or above. To get started, first install python and pip. Then download or clone this repository, and unzip the archive load_profiles.zip. It should give you two large .npy files that you need to place in the folder data/profiles/.

Make sure you are in the correct venv (if your python install is encapsulated in one) using

source venv/bin/activate

if python is installed in a virtual environment.

Then, clone the repository and run the following in the root folder:

pip install -r requirements.txt

It will install all the required packages.

If you have pyCUDA and CUpy installed, you may benefit from GPU-accelerated computations. The corresponding packages are commented in requirements.txt

Running the scripts

The script simulate_network.py simulates a network based on P,Q load profiles. The results are saved in data/simulations_output. The script identify_network.py executes several identification methods to estimate the parameters of a network based on the measurements in data/simulations_output/[NETWORK_NAME].npz. The script plot_results.py displays the results of identify_network.py in the data folder if they are available.

The following commands provides details on exact parameters.

python simulate_network.py --help
python identify_network.py --help
python plot_results.py --help

The files in the [ROOT_REPO]/conf folder also provide variables that can be modified. Comments in their contents explain what they do.

Simulation for CDC21 manuscript

Simulations for the manuscript Bayesian error-in-variable models for the identification of distribution grids (available here) have been generated using the following commands.

python simulate_network.py -vle --network cigre_mv_feeder1
python identify_network.py -vlw --network cigre_mv_feeder1 --max-iterations 50
python plot_results.py -v --network cigre_mv_feeder1 --color-scale 1.0

The parameters in [ROOT_REPO]/conf need however to be adjusted as follows.

conf.simulation.days to 1
conf.simulation.time_steps to 500
conf.identification.lambda_eiv = 4e7
conf.identification.lambdaprime = 20
conf.identification.use_tls_diag = True

DISCLAIMER: Due to newly introduced seed resets for reproductibility and potential differences in load profiles, the results may not be exactly the same as in the paper.

Simulation for TSG manuscript

Simulations for the manuscript Bayesian error-in-variable models for the identification of distribution grids (to be submitted in Transactions on Smart Grids) have been generated using the following commands.

python simulate_network.py -vqte --network ieee123center
python identify_network.py -vwq --max-iterations 20 --network ieee123center --phases 012
python plot_results.py -v --network ieee123center