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

CONTACT DETAILS

Daniel Huppmann

Research Scholar

Energy

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Volker Krey

Deputy Program Director

Energy

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Keywan Riahi

Program Director

Energy

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Peter Kolp

Programmer

Transitions To New Technologies

Energy

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Software

https://software.ene.iiasa.ac.at/

PUBLICATIONS

Gibson M, Rao N , Slade RB, Pereira JP, & 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. DOI:10.1016/j.resconrec.2020.105100.

Ghadaksaz H & Saboohi Y (2020). Energy supply transformation pathways in Iran to reduce GHG emissions in line with the Paris Agreement. Energy Strategy Reviews 32: e100541. DOI:10.1016/j.esr.2020.100541.

Millward-Hopkins J, Steinberger JK, Rao N , & Oswald Y (2020). Providing decent living with minimum energy: A global scenario. Global Environmental Change 65: e102168. DOI:10.1016/j.gloenvcha.2020.102168.

Huppmann D (2020). We don’t need any more open-source models! In: Energy Modelling Seminar Series, Aalto Energy Platform, Aalto University, Finland.

Hunt J , Zakeri B, Nascimento A, Garnier B, Pereira MG, Bellezoni RA, de Assis Brasil Weber N, Schneider PS, et al. (2020). High velocity seawater air-conditioning with thermal energy storage and its operation with intermittent renewable energies. Energy Efficiency DOI:10.1007/s12053-020-09905-0. (In Press)

Andrijevic M, Schleussner C-F, Gidden M, McCollum D, & Rogelj J (2020). COVID-19 recovery funds dwarf clean energy investment needs. Science 370 (6514): 298-300. DOI:10.1126/science.abc9697.

Marshall LR, Smith C, Forster PM, Aubry TJ, Andrews T, & Schmidt A (2020). Large Variations in Volcanic Aerosol Forcing Efficiency Due to Eruption Source Parameters and Rapid Adjustments. Geophysical Research Letters 47 (19): e2020GL090241. DOI:10.1029/2020GL090241.

Andrade Furtado GCd, Amarante Mesquita AL, Morabito A, Hendrick P, & Hunt J (2020). Using hydropower waterway locks for energy storage and renewable energies integration. Applied Energy 275: e115361. DOI:10.1016/j.apenergy.2020.115361.

Jiang X, Wang T, Jiang M, Xu M, Yu Y, Guo B, Chen D, Hu S, et al. (2020). Assessment of Plastic Stocks and Flows in China: 1978-2017. Resources, Conservation and Recycling 161: e104969. DOI:10.1016/j.resconrec.2020.104969.

Kuzemko C, Bradshaw M, Bridge G, Goldthau A, Jewell J , Overland I, Scholten D, Van de Graaf T, et al. (2020). Covid-19 and the politics of sustainable energy transitions. Energy Research & Social Science 68: e101685. DOI:10.1016/j.erss.2020.101685.

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