Behavioral and Appliance-Specific Determinants of the Effectiveness of Residential Electricity

Behavioral and Appliance-specific Determinants of the Effectiveness of Residential Electricity Demand Response Programs

L. Zhao, The Johns Hopkins University, Baltimore U.S.A., +1-302-442-0564, [email protected] B.F. Hobbs, The Johns Hopkins University, Baltimore U.S.A., + 1-410-516-4681, [email protected]

Overview

Electricity demand response (DR) programs use price incentives to induce a change in customer electricity demand in response to system conditions, usually with several hours or a day’s notice. Effective DR programs can reduce system costs and, in some cases, reduce the exercise of market power by generation. In particular, dynamic pricing provides an incentive for customers to reduce demand during peak load or high congestion hours; lessening the need for costly energy from peaking generators. DR can also mitigate supplier market power by increasing the elasticity of demand [1]. The analysis of this paper identifies the economic and behavioral aspects of customers’ response to DR programs while considering their interaction with household characteristics.

Past studies measured customers’ response to price incentives through demand models that relate hourly demand for electricity to various types of dynamic prices [2-10]. We hypothesize that there are also more subtle reactions to DR programs. One example is changes in load during off peak hours in addition to peak hours due to cross-price elasticities or spillover effects. Others are non-price behavioral responses to information and other cues. Our analysis addresses both price and behavioural (non-price) time-varying influences on demand.

We also address influences that do not vary with time. With a few exceptions [6-10], past demand response studies have not analyzed how customer response depends on demographic characteristics and appliance ownership patterns. The analysis of this paper addresses some of those dependencies as well.

Methods

The dataset used in this study comes from the Statewide Pricing Pilot (SPP) conducted in California in 2003 and 2004 involving around 2,500 customers [6]. This pilot tested the traditional time-of-use (TOU) rate and two varieties of critical peak pricing (CPP). One CPP tariff charges high peak price for 2 P.M. to 7 P.M. for a small number of announced days per summer (19 days in our data set). CPP prices could be as high as five times the normal level. During non-CPP event days, the tariff applied to customers is the TOU rate. The other CPP price applies a high peak price to a variable length peak period within the 2 P.M. to 7 P.M. time frame, ranging from two to five hours. The experiment also included a group of customers that remained on a standard tariff. Also, to test the usefulness of information in inducing a demand change, a subset of the customers on the standard tariff received notifications for CPP days but did not have CPP rates charged during CPP days.

Using this data set, this paper estimates the demand for electricity as a response to prices and notifications conditioned on the following variables:

 ownership of air conditioning,  ownership of common household electric appliances,  the size of the residence measured in square feet,  temperature (as gauged by cooling-degree days), and  the number of members in a household.

Finally, we investigate the influence of information in the form of a CPP event notification.

Two independent variables were studied: average load during peak hours and average load during off-peak hours. The relationship between these two average loads and price during peak hours has been determined to study the self- and cross-elasticity of demand. Appliance related dependent variables considered are ownership of A/C, Square Footage of a house, Cooling Degree Days, number of household appliances, and the size of the household. In addition, to study customers’ response information, the effect feedback to the customer in the form of electricity bill has been existed. These variables include: the amount billed weighted as a percentage of income, a dummy variable for receiving a bill, and whether the bill was an increase or decrese from the average of the three previous bills.

Results

For demand response to be effective, it is important to understand all the factors that influence demand. As this study shows, both appliance specific factors and behavior matter. Policymakers or utilities that would like to conduct such program should know well the implications of the demographics of its customers and of the stock of appliances its customer. We found higher peak prices to lead to reduced peak demand and reduced off-peak demand as well. However, our results also show that focusing on the effect of price is not sufficient. Instead, demographic factors such the number of people in a household and their attitude towards the environment can influence the effectiveness of demande response programs. Furthermore, the role of information can also matter. We found that receiving a bill that indicates increased spending as compared to the past 3 billing cycles can lead to a reduction in electricity demand. Similarly, notifiying customers of a critical peak day can induce them to reduce load, given that they have an environment friendly attitude.

References

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