A case study shows how today's typical tariffs can force some industrial electric customers to subsidize others.
There ought to be a better way for electric utilities to set prices for...
regulation and load following, we obtained detailed data from a U.S. control-area operator. Specifically, we obtained 30-second data on generation and system loads, as well as the loads for several large industrial customers. These data cover a 12-day period in February 1999. Figure 1 shows the industrial, nonindustrial and total system loads for three weekdays and the weekend (with industrial load defined as the sum of the large, individually metered loads). The data for total system and nonindustrial loads show the expected winter patterns with morning and evening peaks, and loads lower (by about 10 percent) on the weekend days. The industrial load, on the other hand, remains relatively constant from hour to hour. Its volatility is about half that of the nonindustrial load.
The next step required us to separate regulation from load following.[Fn.6] To do that, we compared the volatility of system load and generation at the one-half-, one-, two- and four-minute levels. We found that the two sets of patterns were roughly similar with the two-minute averages of load and generation. We defined load following on the basis of the 30-minute rolling averages of these two-minute data and regulation on the basis of the difference between the two-minute load averages and the 30-minute rolling average. Based on this split between the two services, we set the following definitions:
* Load Following. The difference (in megawatts) between the maximum and minimum values within each hour of the 30-minute rolling average of these two-minute load data.
* Regulation. The standard deviation of the 30 individual regulation values in each hour.[Fn.7]
Case A (Regulation):
Volatility, Not Size
Our case study illustrates how the need for regulation is a function of load volatility, not size of load, indicating that demand-based methods are inappropriate for billing this ancillary service.
After reviewing several possible metrics, we focused on the standard deviation (in megawatts) of the 30 values in each hour to measure system-level regulation using the two-minute regulation values noted above. Figure 2 shows the hour-to-hour patterns in regulation magnitude for weekdays. This graph shows that the industrial loads have much greater volatility than do the nonindustrial loads. Indeed, as a share of total load, the industrial loads require about six times as much regulation as do the nonindustrial loads. Overall, the regulation standard deviation is 1.3 percent of the total load.
In our study, the correlation coefficients between load itself and regulation are very low for total load, nonindustrial load, and industrial load, suggesting that load is a poor predictor of regulation requirements. Therefore, load is a poor billing determinant to use in assessing charges for regulation.
We next developed a method to allocate fairly the total regulation requirement between any two loads (and, by extension, among several loads). Such an allocation method should meet certain objectives. First, it should yield results that are independent of any subaggregations. In other words, the assignment of regulation to load L should not depend on whether L is billed independently of other loads for regulation or is part of a larger group of loads. In addition, the