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Real-Time Pricing: Ready for the Meter? An Empirical Study of Customer Response

Fortnightly Magazine - November 1 1998

likely to affect how customers respond.

Moreover, customers are dynamic optimizers. They don't aim to maximize efficiency or minimize costs at just a single moment. A customer decides on power purchases in part by comparing the current hourly price to anticipated future hourly prices. Thus a customer presently facing a high hourly price might decide to postpone consumption until a later hour, when lower hourly prices are expected, provided that the inconvenience does not outweigh the savings. Hence, past consumption also will affect present and future consumption. Also, high RTP prices usually last for four to six hours or more. The typical industrial process usually takes at least a couple of hours to come back on line once it has been interrupted. In such cases, most of the lost production is usually rescheduled for the next day, giving further illustration that changes in customer demand may lag price changes to a significant degree.

Study and Method

The data used in measuring these demand elasticities are taken from Pareto Electric Corporation (name changed to protect confidentiality of the utility). Pareto has 360 industrial customers, eight of which take service under RTP tariffs. Pareto introduced its one-part RTP program in June 1994. The data set consists of hourly price information and hourly consumption of electricity as metered by Pareto for a representative sample of four out of the eight industrial customers for the period June 1, 1995, to September 30, 1995.

Table 1 shows the characteristics of the customers included in the study. (Customers are identified only by SIC number and product line.) "Hours of operation" denotes the number of hours a day and number of days a week the customer consumes electricity. "Price threshold" marks the price at which the customer switches to self-generation or shuts down. "Peak load" is the maximum demand placed by the customer on Pareto Electric's generating system.

The model estimated is: Y = f (P, Yt-1, T), where Y represents the quantity of electricity, P equals price, and T equals temperature. The independent variable Yt-1 is included because the measurement of the consumption of electricity is lagged by 24 hours (minus one day) to account for the fact, noted above, that customers tailor consumption decisions in part to reflect not only the current hourly price but the anticipated future hourly price.

The econometric method consists of sets of regressions using the Ordinary Least Squares (OLS) method. In the first set of regressions the data for all the months is pooled together for each customer and the demand equation is estimated for each customer. However, the customers are not all pooled together, since some information on differences between the customer responses to RTP might be lost in aggregation. (See Table 2.) In the second and third sets, the demand equation is applied to estimate prices both below and above 6 cents per kWh, the price threshold at which self-generation becomes a truly viable option, and at which point one might expect to see some variation in customer response. (See Tables 3 and 4.)

Analyzing the Results

Table 2 shows