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osembe_ecemf

This repository contains the scenarios modelled in OSeMBE throughout the H2020 project ECEMF -European Climate and Energy Modelling Forum.

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

Science Score: 36.0%

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

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    3 of 3 committers (100.0%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (13.8%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

This repository contains the scenarios modelled in OSeMBE throughout the H2020 project ECEMF -European Climate and Energy Modelling Forum.

Basic Info
  • Host: GitHub
  • Owner: KTH-dESA
  • License: apache-2.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 13.5 MB
Statistics
  • Stars: 8
  • Watchers: 5
  • Forks: 4
  • Open Issues: 21
  • Releases: 0
Created almost 5 years ago · Last pushed about 1 year ago
Metadata Files
Readme License

README.md

OSeMBE_ECEMF

This repository contains the scenarios modelled in OSeMBE throughout the H2020 project ECEMF - European Climate and Energy Modelling Forum.

The repo contains a workflow to run OSeMOSYS models from datapackage to results file in IAMC format.

OSeMOSYS workflow

This workflow allows to run one or multiple scenarios. Starting from an OSeMOSYS datapackage going through all steps, running the model with the Gurobi solver and producing the results in IAMC format.

In addition to the standard OSeMOSYS outputs, the workflow can produce CSV files containing the dual values for all constraints in the model.

Installation

Install snakemake using conda into a new environment called snakemake:

bash conda install -c conda-forge mamba mamba create -c bioconda -c conda-forge -n snakemake snakemake-minimal

The workflow manages dependencies through conda environments. Dependencies are defined per rule and are installed upon first running the workflow.

Configuring the workflow

  1. Place the script resultify.py from the repo osemosys2iamc in the root folder of the project.
  2. For the resultify.py script to run using the .append method it is required to use Pandas <2, since the .append is no longer supported by Pandas version 2 or newer. By using pandas less than 2 (such as 1.5) one must use a Python < 3.11. After running the snakemake workflow and specifying --use-conda in the shell command, the openentrance-env will install all dependencies. When this is completed, run: pip install -e git+https://github.com/openENTRANCE/openentrance.git@main#egg=openentrance in the terminal while in the openentrance-env environment.

Adding new scenarios

  1. Place datapackage(s) in the folder input_data. Each datapackage should be placed in a folder that is named after the scenario, e.g. baseline.
  2. Check that the file config.yaml, which defines the conversion of OSeMOSYS results to IAMC format is suitable for the model.

Running the workflow

  1. Optional: To retrieve dual values from your model you need to edit the list of constraints in the file run.py.
  2. Open terminal or command prompt and change to the directory of the snakefile.
  3. Optional: Perform a dry run to test snakemake with the command: snakemake -n
  4. Start the scenario runs with the command snakemake --cores <number of cores to be used> --use-conda

Owner

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

GitHub Events

Total
  • Watch event: 1
  • Push event: 18
  • Commit comment event: 4
Last Year
  • Watch event: 1
  • Push event: 18
  • Commit comment event: 4

Committers

Last synced: 11 months ago

All Time
  • Total Commits: 208
  • Total Committers: 3
  • Avg Commits per committer: 69.333
  • Development Distribution Score (DDS): 0.558
Past Year
  • Commits: 22
  • Committers: 1
  • Avg Commits per committer: 22.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Emir Fejzic f****c@k****e 92
HauHe h****h@k****e 82
Will Usher w****r@k****e 34
Committer Domains (Top 20 + Academic)
kth.se: 3

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 33
  • Total pull requests: 48
  • Average time to close issues: about 2 months
  • Average time to close pull requests: about 1 month
  • Total issue authors: 4
  • Total pull request authors: 3
  • Average comments per issue: 1.06
  • Average comments per pull request: 0.65
  • Merged pull requests: 43
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: 1 day
  • Issue authors: 1
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 2.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • HauHe (19)
  • willu47 (8)
  • Timon-R (3)
  • EmiFej (2)
Pull Request Authors
  • HauHe (26)
  • EmiFej (21)
  • willu47 (3)
Top Labels
Issue Labels
enhancement (7) bug (4)
Pull Request Labels
New scenario (3) enhancement (2) bug (1)