OnSSET for Mini-Grids (OMG)

Quick techno-economic analysis of potential mini-grid sites. The script is based on OnSSET model with modifications to accommodate specifications related to the overall viability of mini-grids.

Content

This repository contains:

An environment file (onssetmg_env.yml) needed for setting up a fully functioning python 3.7 environment necessary for the model.
The OMG code in ipynb format. The file contains necessary steps in order to reproduce results.
Input/Output sample data for testing.

Installing and running the clustering notebook

Requirements

The OMG module (as well as all supporting scripts in this repo) has been developed in Python 3. We recommend installing Anaconda's free distribution as suited for your operating system.

Install the repository from GitHub

After installing Anaconda you can download the repository directly or clone it to your designated local directory using:

> conda install git
> git clone https://github.com/SEforALL-IEAP/OMG.git

Once installed, open anaconda prompt and move to your local "OMG" directory using:

> cd ..\OMG

To be able to run the come (.ipynb) you have to install all necessary packages. "onssetmg_env.yml" contains all of these and can be easily set up by creating a new virtual environment using:

conda env create --name onssetmg_env --file onssetmg_env.yml

This might take some time. When complete, activate the virtual environment using:

conda activate onssetmg_env

With the environment activated, you can now move to the main directory and start a "jupyter notebook" session by simply typing:

..\OMG> jupyter notebook

Changelog

28-February-2024: Original code base developed
27-August-2024: Updated code base published

Resources

Documentation is available here

Credits

Funding SEforALL
Conceptualization: Alexandros Korkovelos, Andreas Sahlberg
Methodology: Andreas Sahlberg, Alexandros Korkovelos, Davide Mazzoni
Software: Andreas Sahlberg, Alexandros Korkovelos, Julian Cantor
Validation: Alexandros Korkovelos, Andreas Sahlberg, Julian Cantor, Cristina Dominguez
Supervision and Advisory support: Nishant Narayan, Irene Calvé Saborit