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geosemosys_kenya

GEOSeMOSYS

https://github.com/kth-desa/geosemosys_kenya

Science Score: 62.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
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    Academic publication links
    Links to: sciencedirect.com
  • ○
    Academic email domains
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    Institutional organization owner
    Organization kth-desa has institutional domain (www.energy.kth.se)
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (13.3%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

GEOSeMOSYS

Basic Info
Statistics
  • Stars: 0
  • Watchers: 4
  • Forks: 0
  • Open Issues: 1
  • Releases: 2
Created almost 6 years ago · Last pushed over 2 years ago
Metadata Files
Readme Contributing License Citation

README.md

GEOSeMOSYS_Kenya

This repository is for the paper

Increasing spatial and temporal resolution in energy system optimisation model – The case of Kenya

Nandi Moksnes (1) *, Mark Howells (2,3), William Usher (1) 1) KTH Royal Institute of Technology 2) STEER Centre, Department of Geography & Environment, Loughborough University, UK 3) Imperial College, London, United Kingdom

To be able to run the model you need to have approx 256 GB RAM. This model has been run on a High performance cluster at KTH. The shell file is therefor applicable for the HPC at PDC Dardel from SNIC (Swedish National Infrastructure for Computing).

The whole workflow is run in Jupyter Notebook. The work flow is only tested on a Windows computer, therefore there might be small adjustements needed for other OS.

image

Python dependencies

The workflow has a number packages that needs to be installed.

The easiest way to install the Python packages is to use miniconda.

Obtain the miniconda package (https://docs.conda.io/en/latest/miniconda.html): 1) Add the conda-forge channel: conda config --add channels conda-forge 2) Create a new Python environment: conda env create -f environment.yml 3) Activate the new environment: conda activate geosemosys

R

To download the capacityfactors for solar and wind you need to have R on your computer. You can download R for free https://www.r-project.org/ You also need to install the package "curl" which you install through the R commander 

install.packages("curl")

Required accounts (free to register)

To run the code you need to create accounts in the following places: - https://www.renewables.ninja/ and get the token to download several files per hour - https://payneinstitute.mines.edu/eog/nighttime-lights/ and the password is entered in the first cell in the notebook

Run the model

To run the code go the src folder and write: *juputer notebook* and then open the src/GEOSeMOSYS - exploring linear programming geospatial modelling.ipynb

Run the code step by step. The building of the model takes about 24 hours to run. This is related to several aspects. One of the functions clips the vector file of the 11 kV transmission lines which consists of several hundreds of lines. The renewables.ninja account only allows for 50 download per hour which (if you have many locations and technologies) makes the run longer.

Owner

  • Name: KTH division of Energy Systems
  • Login: KTH-dESA
  • Kind: organization
  • Location: Sweden

Citation (CITATION.cff) 

cff-version: "1.2.0"
authors:
- family-names: "Moksnes"
  given-names: "Nandi"
  orcid: "https://orcid.org/0000-0002-8641-564X"
title: "GEOSeMOSYS_Kenya"
version: 1.0.0
date-released: 2022-06-07
url: "https://github.com/KTH-dESA/GEOSeMOSYS_Kenya"
message: "If you use this software, please cite our article in Energy Strategy Reviews."
preferred-citation:
  authors:
  - family-names: "Moksnes"
    given-names: "Nandi"
    orcid: "https://orcid.org/0000-0002-8641-564X"
  - family-names: "Howells"
    given-names: "Mark"
  - family-names: "Usher"
    given-names: "William"
    orcid: "https://orcid.org/0000-0001-9367-1791"
  issn: ISSN 2211-467X
  date-published: 2024-01
  journal: Energy Strategy Reviews
  publisher:
    name: Elsevier
  start: 101263
  title: "Increasing spatial and temporal resolution in energy system optimisation model – The case of Kenya"
  type: article
  url: "https://www.sciencedirect.com/science/article/pii/S2211467X23002134"
  volume: 51

GitHub Events

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Last synced: about 2 years ago

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  • Total issues: 2
  • Total pull requests: 54
  • Average time to close issues: about 7 hours
  • Average time to close pull requests: about 12 hours
  • Total issue authors: 1
  • Total pull request authors: 2
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.04
  • Merged pull requests: 52
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 6
  • Average time to close issues: N/A
  • Average time to close pull requests: 1 minute
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 6
  • Bot issues: 0
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Top Authors
Issue Authors
  • NMoksnes (2)
Pull Request Authors
  • NMoksnes (59)
  • willu47 (1)
Top Labels
Issue Labels
enhancement (2)
Pull Request Labels

Dependencies

environment.yml conda
  • affine 2.3.0
  • alabaster 0.7.12
  • appdirs 1.4.4
  • argon2-cffi 20.1.0
  • async_generator 1.10
  • attrs 20.3.0
  • babel 2.9.1
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  • backports 1.0
  • backports.functools_lru_cache 1.6.4
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  • boto3 1.17.60
  • botocore 1.20.60
  • brotlipy 0.7.0
  • bzip2 1.0.8
  • ca-certificates 2021.5.30
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  • click 7.1.2
  • click-plugins 1.1.1
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  • configargparse 1.4
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  • decorator 4.4.2
  • defusedxml 0.7.1
  • docutils 0.17.1
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  • geopandas 0.9.0
  • geos 3.8.1
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  • git 2.30.2
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  • icu 67.1
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  • importlib-metadata 4.0.1
  • intel-openmp 2021.2.0
  • ipykernel 5.5.3
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  • python 3.7.8
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  • python_abi 3.7
  • pytz 2021.1
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