  Demand response (DR) programs engage distributed demand-side resources, e.g.,
controllable residential and commercial loads, in providing ancillary services
for electric power systems. Ensembles of these resources can help reducing
system load peaks and meeting operational limits by adjusting their electric
power consumption. To equip utilities or load aggregators with adequate
decision-support tools for ensemble dispatch, we develop a Markov Decision
Process (MDP) approach to optimally control load ensembles in a
privacy-preserving manner. To this end, the concept of differential privacy is
internalized into the MDP routine to protect transition probabilities and,
thus, privacy of DR participants. The proposed approach also provides a
trade-off between solution optimality and privacy guarantees, and is analyzed
using real-world data from DR events in the New York University microgrid in
New York, NY.
