A bottom-up method to generate synthetic residential loads realistically, but with minimal computational resources, is presented. Six energy services, associated with high electricity use, are considered. Each energy service is characterized by number of events on a given day, event start time, and event duration. Distributions for number of events, start time and duration are proposed for four demographic categories: singles, couples, families and retired people. The distributions are augmented by elasticity parameters that allow load control and shaping. The distributions are based on information from focus groups and online surveys. In principle, the method can produce data at arbitrary temporal and topological resolution, and is thus suitable for a range of applications from machine learning of energy consumption patterns to detailed transient power flow analysis. Data can be aggregated as needed, for example by meter, by distribution transformer, or by substation transformer. In the present framework, loads for individual appliances, associated with individual electric meters, are generated at 1 Hz resolution, to explore two important applications that are relevant to the development of control paradigms for distribution microgrids. In such microgrids, a distribution feeder may be islanded from the bulk grid. The applications considered are aggregated real-time power dispatch and load shedding, both of which are needed for effective management of distributed energy resources in a microgrid setting. It is shown that aggregated loads can be shaped to follow a desired signal, for example to balance intermittent solar generation. Significant load reduction achieved by residents' behavioral response is also demonstrated. Such load reductions could be invoked in the case of low-probability, high-consequence events, and could contribute to increased energy resilience at the community level. (C) 2019 Published by Elsevier B.V.