Scientific Software

The Energy Program is developing a suite of tools and scientific software packages to facilitate the analysis of climate-change mitigation scenarios in the context of sustainable development.

Screenshot from the IAMC 1.5°C Scenario Explorer hosted by IIASA (https://data.ene.iiasa.ac.at/iamc-1.5c-explorer)

Screenshot from the IAMC 1.5°C Scenario Explorer hosted by IIASA (https://data.ene.iiasa.ac.at/iamc-1.5c-explorer)

The open-source MESSAGEix framework and the ixmp package for scenario data management

MESSAGEix is a versatile and flexible framework that can be used to develop and run a broad range of energy system models, taking into account emissions, environmental dimenions and a variety of sustainable-development indicators. Applications of the MESSAGEix framework can range from very stylized national exercises to the global MESSAGEix-GLOBIOM integrated-assessment model, which is described in detail here.

The MESSAGEix framework is fully integrated with IIASA’s ix modeling platform (ixmp), a data warehouse for high-powered numerical scenario analysis. The platform supports an efficient workflow between original input data sources, the implementation of the mathematical model formulation, and the analysis of numerical results. The platform can be accessed via a web-based user interface and application programming interfaces (API) to the scientific programming languages Python and R. The platform also includes a generic data exchange API to GAMS for numerical computation.

More information: https://docs.messageix.org

The Scenario Explorer and ixmp server infrastructure

The Scenario Explorer is a web-based user interface to access and manage scenario data. It provides intuitive visualizations and display of timeseries data and download of the data in multiple formats.

 The Scenario Explorer also has an interface for uploading and managing scenario data as a central data repository in multi-institution model comparison exercises. The infrastructure includes data version control and allows to execute scenario post-processing on any uploaded data (e.g., validation, consistency, meta-analysis).

 More information: https://software.ene.iiasa.ac.at/ixmp-server

pyam: Open-source Python Package for Scenario Analysis and Visualization

This package provides a suite of tools and functions for analyzing and visualizing input data (i.e., assumptions, parametrization) and results (model output) of integrated-assessment scenarios, energy systems analysis, and sectoral studies.

The package is based on the timeseries data format developed by the Integrated Assessment Modeling Consortium (IAMC), but it supports additional features such as sub-annual time resolution.

Documentation: pyam-iamc.readthedocs.io
GitHub repository: github.com/IAMconsortium/pyam
Community forum: pyam.groups.io

iam-units: Open-source Python Package for Common Unit Conversion in Integrated-assessment Research

This package provides a simple and intuitive way to convert units frequently used in research on integrated assessment, energy systems research, climate change mitigation and sustainable development. It includes conversion of greenhouse gas species to carbon-dioxide equivalent using any Global Warming Potential (GWP) factors determined in recent IPCC assessment reports.

GitHub repository: github.com/IAMconsortium/units





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Last edited: 08 September 2020

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Software

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PUBLICATIONS

Hunt, J. , Zakeri, B. , Leal Filho, W., Schneider, P.S., de Assis Brasil Weber, N., Vieira, L.W., Ermel, C., de Castro, N.J., et al. (2021). Swimming pool thermal energy storage, an alternative for distributed cooling energy storage. Energy Conversion and Management 230, E113796. 10.1016/j.enconman.2020.113796.

Vieira, L.W., Marques, A.D., Schneider, P.S., José da Silva Neto, A., Viana, F.A.C., Abdel-jawad, M., Hunt, J. , & Siluk, J.C.M. (2020). Methodology for ranking controllable parameters to enhance operation of a steam generator with a combined Artificial Neural Network and Design of Experiments approach. Energy and AI, e100040. 10.1016/j.egyai.2020.100040. (In Press)

Jie, D., Xu, X., & Guo, F. (2020). The future of coal supply in China based on non-fossil energy development and carbon price strategies. Energy, e119644. 10.1016/j.energy.2020.119644. (In Press)

Awais, M., Parkinson, S., Zakeri, B. , McPherson, M., & Riahi, K. (2020). Leveraging new open-source modelling tools to rapidly prototype pathways for achieving SDG7 and mid-century climate targets in Pakistan. In: AGU Fall Meeting, 11-17 December 2018.

Vinca, A. , Parkinson, S., Riahi, K. , Byers, E. , Siddiqi, A., Muhammad, A., Ilyas, A., Yogeswaran, N., et al. (2020). Transboundary cooperation a potential route to sustainable development in the Indus basin. Nature Sustainability 10.1038/s41893-020-00654-7.

Smith, S.J., Klimont, Z. , Drouet, L., Harmsen, M., Luderer, G., Riahi, K. , van Vuuren, D.P., & Weyant, J.P. (2020). The Energy Modeling Forum (EMF)-30 study on short-lived climate forcers: introduction and overview. Climatic Change 163 (3), 1399-1408. 10.1007/s10584-020-02938-5.

McKenna, C.M., Maycock, A.C., Forster, P.M., Smith, C., & Tokarska, K.B. (2020). Stringent mitigation substantially reduces risk of unprecedented near-term warming rates. Nature Climate Change 10.1038/s41558-020-00957-9. (In Press)

Pusceddu, E., Zakeri, B. , & Castagneto Gissey, G. (2020). Synergies between energy arbitrage and fast frequency response for battery energy storage systems. Applied Energy, e116274. 10.1016/j.apenergy.2020.116274. (In Press)

Rose, S.K., Bauer, N., Popp, A., Weyant, J., Fujimori, S., Havlik, P. , Wise, M., & van Vuuren, D.P. (2020). An overview of the Energy Modeling Forum 33rd study: assessing large-scale global bioenergy deployment for managing climate change. Climatic Change 10.1007/s10584-020-02945-6. (In Press)

Gibson, M., Rao, N. , Slade, R.B., Pereira, J.P., & Rogelj, J. (2020). The role of energy in mitigating grain storage losses in India and the impact for nutrition. Resources, Conservation and Recycling 163, e105100. 10.1016/j.resconrec.2020.105100.

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