We present a distributed computing architecture for smart grid management, composed of two applications at two different levels of the grid. At the high voltage level, we optimize operations using a stochastic unit commitment (SUC) model with hybrid time resolution. The SUC problem is solved with an asynchronous distributed subgradient method, for which we propose stepsize scaling and fast initialization techniques. The asynchronous algorithm is implemented in a high-performance computing cluster and benchmarked against a deterministic unit commitment model with exogenous reserve targets in an industrial scale test case of the Central Western European system (679 buses, 1037 lines, and 656 generators). At the distribution network level, we manage demand response from small clients through distributed stochastic control, which enables harnessing residential demand response while respecting the desire of consumers for control, privacy, and simplicity. The distributed stochastic control scheme is successfully tested on a test case with 10,000 controllable devices. Both applications demonstrate the potential for efficiently managing flexible resources in smart grids and for systematically coping with the uncertainty and variability introduced by renewable energy
