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https://github.com/ai4opt/pglearn-texas7k

https://github.com/ai4opt/pglearn-texas7k

Science Score: 26.0%

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Repository

Basic Info
  • Host: GitHub
  • Owner: AI4OPT
  • License: mit
  • Language: MATLAB
  • Default Branch: main
  • Size: 245 MB
Statistics
  • Stars: 0
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Created about 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme License

README.md

PGLearn Texas7k data sources

This repository contains raw data for the PGLearn Texas7k dataset.

The original Matpower and time series data files are part of the Texas A&M University Electric Grid Datasets, namely the 6716-bus Texas synthetic grid. If you use this dataset in your work, please cite the appropriate papers.

Installation instructions

  1. Make sure you have julia installed
  2. Instantiate the current environment bash julia --project=. -e 'using Pkg; Pkg.instantiate()'

Quick start

Processing

Data processing is not required if you cloned the repository.

This code is included for reproducibility. 1. Download raw demand data files from TAMU (see here) 2. Copy the following files into the data/ folder * Texas7k_20210804.m * TX7kMW2020.csv * TX7kMvar2020.csv 3. Rename matpower file bash mv data/Texas7k_20210804.m data/texas7k_TAMU_20210804.m 3. Execute the data processing script bash julia --project=. process.jl

Interpolation

It is recommended to execute this script with multiple threads bash julia --project=. --threads=4 interpolate.jl

By default, the demand data is interpolated down to 5-min granularity. While the underlying code supports other granularities (see interpolate_demand function in interpolate.jl), this functionality is not exposed from the command line. Please open an issue if you'd like to request this feature.

Owner

  • Name: AI4OPT
  • Login: AI4OPT
  • Kind: organization

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