THE RESPONSE OF INDUSTRIAL CUSTOMERS TO ELECTRIC RATES BASED UPON DYNAMIC MARGINAL COSTS BY Joseph A. Herriges, S. Mostafa Baladi, Douglas W. Caves and Bernard F. Neenan Presented by: Ugonna

Objective The purpose of this paper is to describe the results from an analysis of a real-time pricing experiment recently conducted at Niagara Mohawk Power Corporation. The objective of the experiment is to measure the response of large industrial customers to dynamic, fully time- differentiated, marginal cost-based electricity rates over a broad range of industries.

The electric power industry has historically relied upon rate schedules with little or no variability over time to reflect the dynamic nature of its marginal costs. Recent advances in technology and in the theory of utility operations and planning have led to an increased interest in time differentiated rate options. Real-time pricing (RTP) of electricity encompasses a range of possible service options, featuring prices that reflect the constantly changing costs of supplying electricity. Compared to conventional or time-of-use rates, real-time prices more accurately represent marginal costs at each point in time. To the extent that customers alter usage in response to frequent price changes, RTP offers significant benefits to both utilities and their customers.

The Experiment Interest in real-time pricing for large electricity customers comes only a few years after the small-user time-of-use pricing experiments that were conducted in the 1970s and 1980s. However, there are fundamental differences between the earlier experiments(small-user time-of-use pricing experiments ) and current RTP experiments. First, because RTP experiments involve large users, both the utility and the customer risk substantial funds, often measured in the millions of dollars. This is because, A sample involving more than a few large customers will defeat the experiment's purpose of testing response before risking substantial revenues. Hence, a pricing experiment for large users must obtain the maximum amount of information from a small sample of customers.

Second, the RTP studies are targeted at customers who possess the commercial and political wherewithal to frustrate mandatory participation. Hence customers in each experiment are volunteers, creating the potential for self-selection bias.

Next is a summary of the rate and experimental designs underlying Niagara Mohawk's Hourly Integrated Pricing Program (HIPP).

Rate Design The cost of production can change due to changing weather conditions and capacity availability. The appropriate price signal in this situation is a time- differentiated rate, reflecting the variation in marginal costs, Steiner (1957) and Boiteux (1960). The HIPP tariff was designed to provide customers with hourly price signals set as close as possible to marginal cost. HIPP is a two-part tariff, consisting of a marginal cost based hourly energy price ($/kWh) and an access charge that is independent of the customer's current usage.

Draw Graph Figure 1: illustrates the pattern of energy prices during the first eight months of the HIPP program, compared to the standard time-of- use tariff, which has fixed on-peak and off-peak rates.

Experimental Design The target population for the experiment is Niagara Mohawk's large commercial and industrial class, which is generally billed according to the utility's mandatory large customer time-of-use tariff. Large users were chosen because they are most likely to generate RTP benefits that exceed metering, communications, and administration costs. Second, they are more likely to invest the resources required to understand and evaluate the HIPP tariff. Third, Niagara Mohawk has historical load research data for these firms, which are necessary for calculating the HIPP access charge and for analyzing customer response to RTP.

The design of the HIPP experiment is shown in figure 2. There are two customer groups and two time periods. During the baseline period all customers were on the standard time-of-use rate. For the test period, customers who volunteered for the HIPP tariff were randomly assigned to either the control or treatment groups. It is the volunteer control group that provides the basis for evaluating customer response to real- time pricing. Among RTP experiments, the HIPP experiment is unique in its use of a volunteer control group.

The first set of statistics in table 2 shows that the load growth of the control group, 5.1%, surpassed that of the test group, 1.5%. Thus, HIPP did not result in an increase in total energy. The second set of descriptive statistics compares the average price of electricity under the HIPP and standard rates for both test and control customers using usage levels from the eight test months. As expected, given the revenue neutrality of the HIPP rate, there is little difference in the average price of electricity under the two tariffs for control customers. The test customers‘ average price is over 6% lower under HIPP than under the standard rate. Given their modest load growth of 1.5%, this difference in average price indicates an ability to shift loads away from high priced hours. The third set of statistics examines test period usage relative to the baseline loads during high priced hours. The hour of greatest interest to the utility has traditionally been the hour of system peak. Table 2 shows that during this hour the average test customer reduced loads from their baseline level by 13.2%, while the average control customer increased loads by 4.5%. The hour of highest marginal cost did not coincide with the hour of system peak. At the hour of the peak HIPP price, test customers reduced usage by over 36%, while the control customers reduced usage by less than 5%.

Response indexes, for each customer (test and control) for each month, are listed in table 3. While the indexes range in value from to 1.018, there is a higher incidence of response (i.e., R < 1) among the test customers than among the control group. Monthly averages across customers indicate a response index below in five of the eight months for test customers, but in only one month for the control customers.

Table 4 provides the relative frequencies of the response indexes. Responses are classified into four categories: strong (R < 0.990), moderate (0.990 < R < 0.995), weak (0.995 < R < 1), and none (R 2 1). Again, the results suggest that test customers responded to HIPP prices by shifting loads. The individual customer monthly results show that 32% of the test customers monthly results fall into the moderate to strong response categories, while only 14% of the control customers fall into these categories. The average monthly and average customer results show this same pattern of response. The relative frequency of observations in the no response category is consistently higher for the control group. While the pattern of responses in tables 3 and 4 support the hypothesis that test customers did alter their usage patterns in response to HIPP rates, the differences may be due to random variation.

The top panel in table 5 presents the results from testing the hypothesis that the proportion of test group customers with a moderate to strong response index (i.e., R < 0.995) is less than or equal to that of the control group. Based on all observed response indexes, 32% of the test group and 14% of the control group have values of R below 0.995, with value of t equal to The critical level for the hypothesis is 2%, suggesting its rejection at the usual confidence levels. Columns 3 and 4 of table 5 test the robustness of the conclusion using monthly average and customer average response indexes. Using the eight monthly averages for the test and the eight monthly averages for the control customers, the test statistic is recomputed in column 3. Again, the critical level is less than 5%, indicating that the test customers are significantly more likely to have a moderate or stronger response index. Finally, average response indexes for each of the fifteen test and control customers are used to test the hypothesis. Twenty-two percent of the test mean response indexes exceed 0.995, while none of the control mean indexes surpass this level. The critical level for rejecting the null hypothesis is 20%. This latter test assumes that the customer observations are independent, but that the monthly observations for any individual customer are perfectly correlated. The true critical level is likely to lie somewhere between the results in columns 2 and 4.

Conclusions Technology has become available in recent years to provide electric utility customers with rapidly changing price signals that reflect changes in the marginal cost of production over time. The potential gains from implementing such rate structures depend upon the ability of customers to understand and respond to the price signals. The analysis supports the conclusion that some firms are able to shift their usage patterns in response to real-time rates, and in particular at the hour of system peak.

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