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Electricity Prices and the Tariff Analysis Project (TAP)

Much of the work done in the Environmental Energy Technologies Division (EETD) at Lawrence Berkeley National Laboratory (Berkeley Lab) involves analyzing the costs and benefits of energy-saving technologies and energy-efficiency measures. For the consumer, who pays to implement these measures, their economic benefits depend heavily on the price of electricity that the consumer pays at the margin, i.e., for the next kilowatt-hour (kwh) of electricity.

If a consumer is to choose energy-saving technologies wisely, s/he needs information about the economic benefits and the conditions under which savings will be maximized. To be accurate, this information should be based on actual utility tariffs. Although many public utility tariffs are available on the web, the complexity and diversity of tariff structures make it difficult to apply them systematically to analyze costs and benefits of a specific energy-efficiency investment. To address this problem, the Energy-Efficiency Standards group's Tariff Analysis Project (TAP) has created a database and web tools that facilitate the use of actual utility tariffs in cost-benefit analysis.

The TAP database is designed to handle tariffs for any kind of utility service and contains data from around the world although the focus to date has been on U.S. electricity prices. The effort was originally undertaken for the U.S. Department of Energy (DOE) as part of an analysis of efficiency standards for air conditioning and distribution transformers. That analysis highlighted the need for tools to accurately and efficiently analyze end-use-dependent prices. Both air conditioners and distribution transformers have peaky loads that strongly correlate to overall system loads. Although the cost of serving peaky loads will obviously be higher than the annual average cost of producing each kwh of electricity, just how much higher and how the price is affected by differing conditions cannot be determined without considerable attention to detail. Moreover, the question of whether retail prices adequately capture these additional costs at the margin is very difficult to answer when tariff structures are complicated, as most non-residential tariffs are. So another goal of TAP is to define analytically robust methods of summarizing price information and to provide researchers with relatively easy access to accurate tariff data.

TAP web site screenshot

Figure 1. Screen shot of the TAP web site. This view shows information for a TOU tariff, including season and hour type definitions.

Tariffs Included in TAP

For the DOE analysis, the TAP research group developed a statistically representative sample of 90 electric utilities reflecting industry characteristics that may correlate with electricity rates, including location, ownership type, and company size. Regional variation implicitly encompasses variables such as climate, demographics, historical development, and market structures, so location is one of the most important single factors influencing price. The region definitions used to construct the TAP sample are based on combining nine census divisions with the nine climate divisions defined by the National Oceanic and Atmospheric Administration (NOAA). Different market structures were accounted for by separating out Texas, Florida, New York, California, and the Pennsylvania/New Jersey/ Maryland (PJM) area. Within each region, the number of utilities chosen was determined by the relative population living in that area and the proportion of customers served by privately versus publicly owned companies.

Electricity tariffs are typically classed as residential or non-residential. Non-residential may be further subdivided into general service and special-use tariffs (special-use tariffs include street-lighting and agriculture, for example). For the general service category, some utilities explicitly distinguish between commercial and industrial, but most do not. There are usually several general-service tariffs for different customer sizes (size is defined by the value of the annual peak load), and each size class generally has a default tariff. TAP currently contains the default tariff for each customer size and market, including time-of-use (TOU) tariffs whenever they are mandatory. The result is a collection of 247 tariffs for the 90 utilities in the sample; about 30 of the 247 are TOU tariffs. The database also contains the primary voltage (usually applicable only to very large customers) and residential tariffs for the utility sample as well as agricultural tariffs for California.

The TAP website also includes a simple bill calculator, which allows the user to input energy consumption and demand data and view the bill calculation and breakdown by different types of charges (see Figure 1).

Other tools currently on line include a web interface that allows a user to enter and review data using custom-designed forms and a Simple Object Access Protocol (SOAP) server that enables a direct link between web-based applications and the TAP database.

Calculating Electricity Prices: an Example using Commercial Buildings

Using data from the Commercial Building Energy Consumption Survey (CBECS), we created a representative set of customers for use in calculating average and marginal prices by region. Based on these data, the average effective marginal prices by region were calculated as a function of the marginal load factor, as illustrated in Figure 2. The steep rise in marginal price at low load factors is due to the increasing importance of demand charges as the marginal load factor drops. More information about these calculations can be found in a longer version of this article at the TAP website http://tariffs.lbl.gov.

TAP chart

Figure 2. Effective marginal price as a function of marginal load factor. The load factor decreases towards the right. The North Mountain, East South Central, and West North Central regions are very similar to the West South Central curve, so they are not plotted.

What Exactly is a Tariff?

Rather than a list of prices, a tariff should be thought of as an algorithm that generates a customer's bill from information about their energy use. Given the necessary input data and the bill calculation algorithm, a variety of prices can be defined as needed. The key thing to remember is that the price of electricity depends on the combination of customer data and the tariff. If the tariff consisted simply of a fixed charge per kwh of electricity consumed, this would not be the case, but tariff structures are typically quite complex. Our primary interest for efficiency standards is the calculation of what we call the effective marginal price, defined as the total change in the bill under some scenario, divided by the total change in energy consumption. Typically, the dollar value of the benefits associated with an efficiency measure is calculated by multiplying the estimated energy savings by a price for energy. By definition, the effective marginal price is the correct value to use in such a calculation. Any under- or over-estimate of benefits that results from using a different price can be directly determined by comparing that price to the effective marginal price.

An alteration in a customer's energy use patterns will generally result in a change to both average hourly consumption and peak hourly demand, with the ratio of the two determined by the end use or efficiency measure being considered. One of the findings of our analysis is that the marginal price that a customer sees depends very strongly on this ratio, which we call the marginal load factor. In practice, electricity prices depend on the customer size and type (which determines the tariff assigned to the customer), the baseline energy use (which determines where within the tariff the customer's margin will be), and the end use or efficiency measure being considered (which determines the marginal load factor). These characteristics may vary systematically for different types of electricity consumers; when we consider policy measures that impact specific subpopulations of the consumer base, ignoring these differences can translate into substantial inaccuracies in the benefits estimation.

Modeling Tariffs in TAP

The tariff data model used in TAP is a set of linked tables designed so that any utility rate structure can be represented by assigning values to predefined variables in a generic format. This format captures most of the features of real rate schedules, including demand charges, seasonal rates, variable block rates, and TOU rates. Having a single consistent tariff model streamlines the data-entry process and facilitates the comparison of rate structures across different tariffs and utilities. Except for some approximations made to reduce the complexity of tariff data, TAP represents the tariff directly and includes the ability to link together all information (for example linkage of seasonal rates to season definitions).

Conclusions

As the electricity industry continues to deregulate, tariff structures continue to get more complex, and it is important for the analytic methods used in efficiency cost-benefit estimation to keep up. The tools developed as part of TAP allow a great deal of complex information to be managed and used efficiently so that the real benefits of energy efficiency can be counted accurately.

— Katie Coughlin

For more information, contact:

  • Katie Coughlin
  • (510) 486-5949; Fax (510) 486-6996

The TAP website.

This research is funded by the U.S. Department of Energy's Appliance Standards Program.

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