Distributed and on-site energy generation and distribution systems employing renewable energy sources and energy storage devices (referred to as microgrids) have been proposed as a new design approach to meet our energy needs more reliably and with lower carbon footprint. Management of such a system is a multi-scale decision-making problem encompassing hourly dispatch, daily unit commitment (UC), and yearly sizing for which efficient formulations and solution algorithms are lacking thus far. Its dynamic nature and high uncertainty are additional factors in limiting efficient and reliable operation. In this study, two-stage stochastic programming (2SSP) for day-ahead UC and dispatch decisions is combined with a Markov decision process (MDP) evolving at a daily timescale. The one-day operation model is integrated with the MDP by using the value of a state of commitment and battery at the end of a day to ensure longer term implications of the decisions within the day are considered. In the MDP formulation, capturing daily evolving exogenous information, the value function is recursively approximated with sampled observations estimated from the daily 2SSP model. With this value function capturing all future operating costs, optimal sizing of the wind farm and battery devices is determined based on a surrogate function optimization. Meanwhile, a multi-scale wind model consistent from seasonal to hourly is developed for the connection of the decision hierarchy across the scales. The results of the proposed integrated approach are compared to those of the daily independent 2SSP model through a case study and real wind data.