Designing Real-Time Prices to Reduce Load Variability with HVAC

TL;DR

This paper designs real-time and peak pricing schemes for HVAC demand response, using a principal-agent model and mixed integer programming, to reduce load variability and peak demand effectively.

Contribution

It introduces a stochastic bilevel programming approach for designing pricing schemes that account for consumer comfort preferences and compares their effectiveness.

Findings

RP reduces peak loads and load variability.

PP causes consumption spikes before and after peak periods.

RP prevents large electricity usage spikes.

Abstract

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.