In this second portion of a two-part analysis of a scalable computational approach to stochastic unit commitment, we focus on solving stochastic mixed-integer programs in tractable run-times. Our solution technique is based on Rockafellar and Wets’ progressive hedging algorithm, a scenario-based decomposition strategy for solving stochastic programs. To achieve high-quality solutions in tractable run-times, we describe critical, novel customizations of the progressive hedging algorithm for stochastic unit commitment. Using a variant of the WECC-240 test case with 85 thermalgeneration units, we demonstrate the ability of our approach to solve realistic, moderate-scale stochastic unit commitment problems with reasonable numbers of scenarios in no more than 15 minutes of wall clock time on commodity compute platforms. Further, we demonstrate that the resulting solutions are high-quality, with costs typically within 1-2.5% of optimal. For larger test cases with 170 and 340 thermal generators, we are able to obtain solutions of identical quality in no more than 25 minutes of wall clock time. A major component of our contribution is the public release of the optimization model, associated test cases, and algorithm results, in order to establish a rigorous baseline for both solution quality and run times of stochastic unit commitment solvers.