Active participation of customers in the management of demand, and renewable energy supply, is a critical goal of the Smart Grid vision. However, this is a complex problem with numerous scenarios that are difficult to test in field projects. Rich and scalable simulations are required to develop effective strategies and policies that elicit desirable behavior from customers. We present a versatile agent-based "factored model" that enables rich simulation scenarios across distinct customer types and varying agent granularity. We formally characterize the decisions to be made by Smart Grid customers as a multiscale decision-making problem and show how our factored model representation handles several temporal and contextual decisions by introducing a novel "utility optimizing agent." We further contribute innovative algorithms for (i) statistical learning-based hierarchical Bayesian timeseries simulation, and (ii) adaptive capacity control using decision-theoretic approximation of multiattribute utility functions over multiple agents. Prominent among the approaches being studied to achieve active customer participation is one based on offering customers financial incentives through variable-price tariffs; we also contribute an effective solution to the problem of "customer herding" under such tariffs. We support our contributions with experimental results from simulations based on real-world data on an open Smart Grid simulation platform.