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energy analysis toolbox in R
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energyRt: energy analysis toolbox in R

energyRt is a package for R to develop Reference Energy System (RES) models and analyze energy-technologies.

energyRt package provides tools to build RES ("Bottom-Up"), technological, linear cost-minimizing models, which can be solved using GAMS and GLPK. The RES model has similarities with TIMES/MARKAL, OSeMOSYS, but has its own specifics, f.i. definition of technologies.

energyRt package is a set of classes, methods, and functions in R which are designed to:

  • handle data, assist in defining RES models,
  • helps to analyze data, check for errors and bugs before parsing it into solver,
  • parses your dataset to GAMS or GLPK and runs them to solve the model,
  • reads the solution and imports results back to R,
  • assists with an analysis of results and reporting.


  • minimize time of development and application of RES/BottomUp models,
  • boost learning curve in energy modeling,
  • improve transparency and understanding of energy models,
  • use power of open-source to improve energy models and their application,
  • making reproducible research (see [Reproducible Research with R and R Studio] ( by @christophergandrud and/or [Dynamic Documents with R and knitr] ( by @yihui) accessible in RES-modeling,
  • integration with other models and software.

Development status

The project is in an preparation of the first official release, which includes the documentation and a set of examples (expected - Nov 2018). Current functionality allows development of multi-regional RES models with trade, time-slices, and variety of technologies, an integration with GIS (via sp and choropleth packages), pivot tables (via rPivotTable), authomatic pdf-reports for models and scenarios, analysis of levelized costs of tehcnologies, and other features.



R and RStudio

Assuming that R is already installed (if not, please download and install from, we also recommend RStudio (, a powerful IDE (Integrated Development Environment) for R. It simplifies usage of R, provides number of features such as reproducible research (integration with Markdown, Sweave), integration with version control (github, svn).

GAMS or GLPK to solve the model

The cost-minimising linear programming model (the set of equation for LP problem), emboddied into energyRt package requires additional software to solve it. Currently energyRt model code is written in GAMS and GLPK, Julia version is in progress, at least one of them is required to solve the model.

The General Algebraic Modeling System (GAMS, is a powerful proprietary modeling system. Suitable LP solvers: CBC (included in the basic GAMS version, very powerful open source solver) or CPLEX. Others LP solvers have not been tested, but may work as well.

GAMS path should be also added to the environmental variables in your operating system.

GLPK is an open source Linear Programming Kit which includes powerful LP and MIP solver, and basic language for creating mathematical programming models (Mathprog or GMPL – for details see

GLPK/GMPL is an open source alternative to GAMS, but only for LP and MIP problems. GLPK/GMPL is a bit slower than GAMS for small models, and significantly slower for large models, partially because of the slower Mathprog (GMPL) language processor. Installing GLPK on PC/Windows systems

Download GLPK binaries for Windows:

Installing GLPK on Mac systems

We are not familiar if there are any GLPK-binaries/installers for Mac OSx. Therefore the following example is for installed from source with a standard procedure: gzip -d glpk-4.57.tar.gz
tar -x < glpk-4.57.tar
cd glpk-4.57
make check
make install
make distclean

After installation check:
which glpsol
or glpsol -v

Response from glpsol will be an indicator of successful installation.

Alternatively, GLPK is included in homebrew-science installer library.
See: and for details.


Required to generate authomatic reports.
For Windows: (other options are possible but not tested)
For Mac: (other options are possible but not tested)


Currently the package is hosted only on GitHub. To install the package:

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