This dataset refers to the modelling exercise (version01_210616RCLEWs). The dataset contains the OSeMOSYS code used to run the modelling exercise, the model input data, the scenarios model data files, and the results. The code for the results visualization is available at https://github.com/KTH-dESA/teaching-CLEWs_visualization.

This is an update of version 01_210827 available at: https://doi.org/10.5281/zenodo.5293834

This repository consists of the following datasets

1. EAPP_reference scenario_datafile.DD- This dataset is a model file that needs to be used with the code available in this GitHub link. This data file (in concurrence with the OSeMOSYS code) can be used to create a linear programming file (LP file) to be solved using any mathematical optimisation solver like GLPSOL/C-PLEX/GUROBI/CBC.

2. Main article_EAPP_data for figures.xlsx- This excel file contains the base data used to illustrate the figures in the main article.

3. Supplementary article_EAPP_data for figures.xlsx- This excel file contains the base data used to illustrate the figures in the supplementary article.

Hands-on exercise 14 part of the Energy System Modelling using OSeMOSYS Course.

By the end of this exercise, you will be able to do the following in OSeMOSYS: Model the energy efficiency policy implementation on a starter kit. Identifying and modifying the data/parameters affected by policy inclusions. Implementing technology specific constraints to model real world alike energy transitions.

This dataset contains the LEAP and OSeMOSYS output data for the manuscript Karamaneas et al., submitted to Renewable & Sustainable Energy Transition in September 2022.

All of the Data_Prep Files for the 2024 Update of the OSeMOSYS Open University Course, using the User Interface instead of ClicSAND.

Techno-economic dataset for long-term energy systems modelling in Morocco. This includes data on electricity capacity by source, power plants costs, fossil fuel reserves, renewable energy potential and green hydrogen export demand.

The Kenya-CLEWS model involves a model developed in the Open Source Energy Modeling System, OSeMOSYS. The use of GIS data to have an approximation of different land uses such as artificial surfaces, cropland, grassland, and tree covers, among others. Sectors include the cooking sector for urban and rural areas since its direct interconnection with forest land, i.e., wood and charcoal for cooking. The cooking sector is included because 55.1 percent of households in Kenya still use wood as their primary fuel for cooking. Firewood and charcoal supply 80 percent of the 6.2 million households that use a single fuel source [2]. Other cooking technologies such as gas, kerosene, and electric stoves are also included in the model. Regarding crops, this model version incorporates crops that significantly impact the food value chain, such as wheat and maize.

These documents provide a tutorial of translating a hypothetical policy into constraints in clicSAND. The policy involves setting a target for the generation of certain renewable energy sources to reach 50% from 2040 onwards.

This dataset presents the input and results for the study on the increase of Solar and Wind energy generation in the Brazilian Power System, as a way to achieve the NetZero by 2050. This study was conducted using the OSeMOSYS and Flextool as a deliverable of the Energy Modelling Platform for Latin America and The Caribbean Course - EMP-LAC 2023.

These are files with the outputs for each scenario in the paper "Analyzing Carbon Emissions Policies for the Bolivian Electric Sector" submitted to the journal Renewable and Sustainable Energy Transition.

Seven clicSAND scenario files for Beyond the Dams: Combatting Hydropower Over-reliance & Securing Pathways for a Low-carbon Future for Laos' Electricity Sector using OSeMOSYS (Open-Source Energy Modelling System).

How to Visualise Results Online and Offline outline the steps required to re-run the scenarios on OSeMOSYS Cloud

Scenario Short Note outlines the steps to replicate the analysis and rebuild the scenarios

Annex - Input Data and Assumptions listing the data sources and assumptions in the scenarios

A starter data kit for Thailand

A starter data kit for Ecuador

A starter data kit for Bolivia

This dataset details a scenario simulated using the Open Source energy MOdelling SYStem for Greece (OSeMOSYS-GR). The scenario assumes that natural gas, coupled with carbon capture and storage (CCS), will remain part of the electricity mix until 2050. Please note that natural gas and lignite power plants are modelled in an aggregated manner due to the confidentiality of their specific technoeconomic data.

This slide deck helps you troubleshoot in OSeMOSYS. It goes through the main culprits behind failing models and how to fix them. These include 1) unintended edits to the SAND file during editing, 2) errors during model development, such as when adding technologies, changing time slices, etc., and 3) errors during scenario development which create logical contradictions, such as when adding and changing constraints.

A starter data kit for Botswana

This dataset underpins the report provided to the project JRC-TEMBA - African decarbonisation pathways.

The report provides insights into energy supply and demand, power generation, investments and total system costs, water consumption and withdrawal as well as carbon dioxide emissions for the African continent.

The energy supply systems of forty-seven African countries are modelled individually and connected via gas and electricity trade links to identify the cost-optimal solution to satisfy each country´s total final energy demand for the period 2015-2065. In this analysis, The Electricity Model Base for Africa (TEMBA) was extended to include a simple representation of the full energy system. It was also updated to include new data. It is run using the medium- to long-term Open Source Energy Modelling System tool (OSeMOSYS).

The TEMBA model produces aggregate energy, and detailed power system results in each country in the African continent. The power sector results are also reported with power pool aggregation.

The OSeMOSYS model and input data used to produce these results can be found at https://github.com/KTH-dESA/jrc_temba.