Joint Chance Constraints achieve a good trade-off between cost and robustness for optimization under uncertainty. This talk proposes a Julia implementation of Joint Chance Constraints and algorithms to solve the programming problems for different types of multivariate probabilities. The library focuses on unit commitment problems for decentralized, renewable-powered microgrids, connected to an unreliable higher-level, grid-balancing unit, but can be applied to similarly-structured problems.