  Utilities use demand response to shift or reduce electricity usage of
flexible loads, to better match electricity demand to power generation. A
common mechanism is peak pricing (PP), where consumers pay reduced (increased)
prices for electricity during periods of low (high) demand, and its simplicity
allows consumers to understand how their consumption affects costs. However,
new consumer technologies like internet-connected smart thermostats simplify
real-time pricing (RP), because such devices can automate the tradeoff between
costs and consumption. These devices enable consumer choice under RP by
abstracting this tradeoff into a question of quality of service (e.g., comfort)
versus price. This paper uses a principal-agent framework to design PP and RP
rates for heating, ventilation, and air-conditioning (HVAC) to address adverse
selection due to variations in consumer comfort preferences. We formulate the
pricing problem as a stochastic bilevel program, and numerically solve it by
reformulation as a mixed integer program (MIP). Last, we compare the
effectiveness of different pricing schemes on reductions of peak load or load
variability. We find that PP pricing induces HVAC consumption to spike high
(before), spike low (during), and spike high (after) the PP event, whereas RP
achieves reductions in peak loads and load variability while preventing large
spikes in electricity usage.
