In a Smart Grid context, the increasing penetration of embedded generation units leads to a greater complexity in the management of production units. In this arti- cle, we focus on the impact of the introduction of decentralized generation for the unit commitment problem (UC). Unit Commitment Problems consist in finding the optimal schedules and amounts of power to be generated by a set of gen- erating units in response to an electricity demand forecast. While this problem have received a significant amount of attention, classical approaches assume these problems are centralized and deterministic. However, these two assumptions are not realistic in a smart grid context. Indeed, finding the optimal schedules and amounts of power to be generated by multiple distributed generator units is not trivial since it requires to deal with distributed computation, privacy, stochastic planning, ... In this paper, we focus on smart grid scenarios where the main source of complexity comes from the proliferation of distributed generating units. In solving this issue, we consider distributed stochastic unit commitment prob- lems. We introduce a novel distributed gradient descent algorithm which allow us to circumvent classical assumptions. This algorithm is evaluated through a set of experiments on real-time power grid simulator.