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https://github.com/energyRt/energyRt

Making Energy Systems Modeling as simple as a linear regression in R
https://github.com/energyRt/energyRt

energy-models gams glpk julia pyomo

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Making Energy Systems Modeling as simple as a linear regression in R

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README 

        
## energyRt Logo-search



**energyRt** (*energy* system modeling *R-t*oolbox /ˈɛnərdʒi ɑrt/) is a

set of classes, methods, and functions that define a macro-language for

energy system modeling within the R environment. This package offers a

high-level, user-friendly interface that simplifies the development and

analysis of complex energy models. By abstracting much of the underlying

complexity, **energyRt** allows users to concentrate on strategic and

analytical aspects rather than the technical details of coding.



**Key Features:**



-   **User-Friendly Interface: energyRt** enables users to define energy

    systems, input data, and configure scenarios using intuitive,

    domain-specific commands. It is designed to be accessible for both

    experienced modelers and those new to the field.

-   **Seamless R Integration:** The package integrates seamlessly with

    R’s extensive ecosystem of packages, allowing users to utilize

    powerful data handling and visualization tools within their energy

    modeling projects.

-   The **energyRt** optimization

    [model](https://energyrt.github.io/book/model.html) is implemented

    in four widely-used mathematical programming languages, both

    proprietary and open-source: [GAMS](http://www.gams.com/),

    [GLPK/Mathprog](https://www.gnu.org/software/glpk/),

    [Python/Pyomo](http://www.pyomo.org/),

    [Julia/JuMP](http://www.juliaopt.org/JuMP.jl/stable/). The package

    is designed to work seamlessly with any of these versions, allowing

    users to solve models using their preferred software while ensuring

    consistent and equivalent results across all platforms.

-   **Modular Model Construction: energyRt** supports the construction

    of models in a modular fashion, enabling incremental development,

    individual component testing, and code reuse across different

    projects. This modularity, combined with R’s interactive

    environment, promotes an iterative approach to modeling where

    assumptions can be tested, and results explored in real-time.

-   **Applications: energyRt** is designed to facilitate the creation of

    sophisticated energy system models, offering both flexibility and

    depth for detailed analysis. It is an essential tool for

    researchers, policymakers, and industry professionals engaged in

    long-term energy system planning, energy transition, and

    decarbonization efforts.



The package website: \

Documentation in progress: 



### Development status



**energyRt** is currently in preparation for its first release and

publication on [CRAN](https://cran.r-project.org/). The major milestone

for the package is the version **v0.50** (*"half-way-there"*), a proof

of concept with a full-featured and efficient model written in four

math-prog languages, with R-interface for the model design, processing

results, and producing reports. This version will have frozen model

code, classes and methods. Any updates will address only potential fixes

and new features with minimal impact on already existing modeling

projects.



Further development, versions starting from **v0.9** towards the

**v1.0** will have fully reviewed model and classes with the goal to

further increase efficiency, reduce memory footprint and computational

burden for both the model and its R interface, and significantly extend

features.



## Installation



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. The installation of the package is done via the

`pak` or `remotes` packages:



`pak::pkg_install("energyRt/[email protected]")`\

or\

`remotes::install_github("energyRt/energyRt", ref = "v0.50")`



The next step would be to install at least one of the solvers: GAMS,

GLPK, Python/Pyomo, Julia/JuMP. Please refer to the respective websites

for installation instructions. More detaileds is available on the

[IDEEA](https://ideea-model.github.io/IDEEA/articles/install.html) model

website, a project based on the **energyRt** package.