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This paper is motivated by a power system with one main grid (arbiter) and multiple microgrids (MGs) (agents). The MGs are equipped to control their local generation and demands in the presence of uncertain renewable generation and heterogeneous energy management settings. We propose an extension to the classical two-stage stochastic programming model to capture these interactions by modeling the arbiter’s problem as the first-stage master problem and the agent decision problems as second-stage subproblems. To tackle this problem formulation, we propose a sequential sampling-based optimization algorithm that does not require a priori knowledge of probability distribution functions or selection of samples for renewable generation. The subproblems capture the details of different energy management settings employed at the agent MGs to control heating, ventilation, and air conditioning systems, home appliances, industrial production, plug-in electrical vehicles, and storage devices. Our computational experiments conducted on the U.S. western interconnect (WECC-240) data set illustrate that the proposed algorithm is scalable and the solutions are statistically verifiable. Our results also show that the proposed framework can be used as a systematic tool to gauge: 1) the impact of energy management settings in efficiently utilizing renewable generation and 2) the role of flexible demands in reducing system costs. [ABSTRACT FROM AUTHOR]