[7thaccount]

Small world. I've looked through these libraries before and have chatted with NREL staff at conferences (didn't expect to talk on HN though). I've enjoyed some of the more recent papers on inertia and frequency response as well and will have to look into this study in more detail.

I tend to stick more to Python + Numpy and ScyPy, but do check in on Julia from time to time. I'm still questioning whether the sparse matrix routines have matured enough in Julia (necessary for the truly large systems). On the optimization side, has all of NREL switched to Julia + JuMP, or is the native Python API for GUROBI used and Julia for the network pieces?

[kdheepak]

I was surprised to see this on HN too.

Definitely not all of NREL has switched to Julia + JuMP. From what I can tell, Python, MATLAB etc still are quite prominent across the laboratory. And, NREL is a large organization and we are a small team; we don't have much insight into what tools developers decide to choose and why. If anything, it is possible that we've set the precedent that Julia + JuMP can be used for this sort of work.

[7thaccount]

Ah, yeah I think that makes more sense.

I know Matlab is a good R&D tool (like Mathematica), but it is a little painful for the end user and far too expensive for a lot of industrial users who don't work at a company already entrenched with the ecosystem. I don't want to pay $5k for a database toolbox if you know what I mean. If the code is only for a study though...it probably doesn't matter a whole lot.

Python seems like a good lingua franca and Julia isn't far behind overall. What makes me excited about Julia is that (at least in theory) I can write some blazing fast code without being a systems level programmer and also get the ability to look at the assembly output (just all around cool) and write macros (a la lisp). I doubt I'd ever use macros on serious code, but having the opportunity is a plus. It's a neat design.

Best of luck in your future endeavors.