Actively engaging flexible electric loads in power systems operation can reduce the inefficiency, cost, and environmental impact of the system. However, the sensing and communication requirements of many existing algorithms to schedule and control distributed loads are significant, and would be expensive to implement. In this talk I will describe our efforts to infer the behavior (models, states, and outputs) of aggregations of electric loads from incomplete measurements, and how our results enable optimal reserve scheduling and real-time control.