Case Study: Invenia optimizes the electrical grid with Julia Computing

Invenia Technical Computing is scaling up its energy intelligence system using Julia

Invenia Technical Computing, a North American energy intelligence company, needed to scale its electrical grid optimization platform and move beyond the limits of its existing MATLAB, Python, and C codebase. The team wanted a language that could handle more data, run more simulations faster, and offer better flexibility for programming style, parallelization, and interoperability. Julia Computing and Julia were selected for this effort.

With Julia Computing’s ecosystem and the Julia language, Invenia was able to expand its Energy Intelligence System to improve day-ahead planning and support more efficient grid optimization. The shift helped address previous constraints around speed, threading, library availability, and compatibility, while also enabling Invenia to contribute to Julia’s core and build multiple open-source packages. As a result, Invenia is using Julia to make electrical grids more efficient, reliable, and secure.

Invenia