(P)lan for (R)ap(I)d (DE)carbonization (PRIDE)

This repository contains analysis tools, models, and publications associated with planning for rapid decarbonization.

Publication: 2020-fairhurst-hydrogen-production

This repository holds:

• data of the fuel consumed by the MTD and UI fleet.
• analysis of the hydrogen required by those fleet to become carbon free.
• information of different methods to produce hydrogen.

Publication: 2020-dotson-optimal-sizing

This repository holds the data analysis, figures, that will lead to quantitative recommendations for the optimal reactor size.

Multiple scenarios will be addressed:

1. The reactor itself is free (significant reduction in capital cost).
2. The reactor still has a price tag and higher capital cost.
3. Increasing penetration of variable renewable energy sources.
4. Add grid flexibility in the form of H2 and thermal storage.

Instructions to Run TEMOA

TEMOA is an open source modeling tool available on GitHub. Follow the installation instructions here.

After creating a database in sql, navigate to the directory with your database:

sqlite3 [filename].sqlite < [filename].sql

if you don't have sqlite installed, run:

sudo apt-get install sqlite or sudo apt-get install sqlite3

TEMOA models can be run from the command line, current iterations use the online model platform at model.temoacloud.com.

Instructions to Run TEMOA scenarios

To run a single TEMOA scenario first add the path to Temoa to your ~/.bashrc:

echo "export TEMOA=/path/to/temoa" >> ~/.bashrc

for example:

echo "export TEMOA=/home/roberto/github/temoa" >> ~/.bashrc

Remember to either close and open the terminal, or run source ~/.bashrc. Then, write the following commands in the terminal:

cd temoa-uiuc
source activate temoa-py3
# Example scenario
sqlite3 data_files/bau_uiuc.sqlite < data_files/bau_uiuc.sql
yes | python $TEMOA/temoa_model/ --config=data_files/run_bau.txt

The data processing must be done separately. Figures can be produced using tools in data_parser.py. An example of how this is done can be found in mga_analysis.ipynb.

To run all scenarios (except for MGA, which must be run individually):

snakemake must be installed.

cd temoa-uiuc
source activate temoa-py3
pip install snakemake
snakemake --cores=4
# if the build fails due to file system latency, try
# snakemake --cores=4 --latency-wait=10

This automatically generates figures in the /figures/ folder.

Instructions to Run the Jupyter Notebooks

Generating typical time histories was done by using RAVEN an open source tool from INL. This repository should be in a folder adjacent to raven. See directory map below for an example.

To install RAVEN follow the instructions from INL.

Instructions to Obtain the Data

Some of the data has not yet been cleared for publication so a shared link cannot yet be provided. Shared links for data that is already publicly available have been provided below.

In order to execute the jupyter notebooks the data files should be downloaded to your computer in a folder called data such that your directories look like:

home
|
|--2020-dotson-optimal-sizing
|
|--raven
|
|--data

Data:

• Champaign County (Willard Airport)
• Logan County (Lincoln Airport)
• UIUC Solar Farm
• Solar Irradiance (At UIUC Solar Farm)
• UIUC Steam Demand : Not cleared for public access
• UIUC Electricity Demand : Not cleared for public access