# \[ANN\] AnyMOD.jl v0.1.8

**URL:** <https://discourse.julialang.org/t/ann-anymod-jl-v0-1-8/85831>\
**Category:** Package Announcements\
**Tags:** package, jump\
**Created:** [August 16, 2022, 3:16pm UTC](https://discourse.julialang.org/t/ann-anymod-jl-v0-1-8/85831 "2022-08-16T15:16:35Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![lgo](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/lgo/32/48751_2.png) [@lgo](https://discourse.julialang.org/u/lgo)\
**Post date:** [August 16, 2022, 3:16pm UTC](https://discourse.julialang.org/t/ann-anymod-jl-v0-1-8/85831/1 "2022-08-16T15:16:36Z")

</div>

Although v0.1.0 was released more than 2 years ago, I finally want to share our package here.

[AnyMOD.jl](https://github.com/leonardgoeke/AnyMOD.jl) builds on JuMP.jl and creates capacity expansion problems optimizing the transformation of macro-energy systems towards renewable energy. Compared to similar Python tools, AnyMOD.jl puts an emphasis on innovative methods that achieve high temporal detail to capture fluctuations of renewables while maintaining a comprehensive scope in terms of regions and sectors. These methods often require extensive pre-processing and depend on Julia’s performance to be practical.

There are [two related working papers and six peer-reviewed publications](https://www.researchgate.net/project/AnyMODjl-Methods-and-applications), but highlights from a Julia perspective are:

- A [SoftwareX paper](https://www.sciencedirect.com/science/article/pii/S2352711021001382) introducing the package and describing how it stores JuMP.jl objects in DataFrames to efficiently construct linear problems using SQL-like commands.

- A recent [working paper](https://arxiv.org/abs/2208.07078) on stochastic optimization with AnyMOD.jl proposing a novel refinement for Benders decomposition.

There is also a [documentation](https://leonardgoeke.github.io/AnyMOD.jl/stable/) and a short [JuliaCon presentation](https://www.youtube.com/watch?v=QE_tNDER0F4) of the tool.