Public Utilities Reports

PUR Guide 2012 Fully Updated Version

Available NOW!
PUR Guide

This comprehensive self-study certification course is designed to teach the novice or pro everything they need to understand and succeed in every phase of the public utilities business.

Order Now

Competitive Efficiency: A Ranking of U.S. Electric Utilities

Fortnightly Magazine - June 15 1997

3 companies

Upper Bangor

Washington Southwestern Maine Peninsula Hydro

Water Public Public Energy Electric

AEP Power Service Service Corp. Co.

Total Sales (MWh) 116,196,875 10,558,467 19,084,259 664,623 808,215 1,725,870

% Residential Sales 24% 29% 13% 26% 31% 30%

% Industrial Sales 36% 15% 39% 20% 28% 51%

Average System Rate 0.05 0.04 0.04 0.09 0.07 0.10

Salary per employee 45,755 48,301 43,412 37,006 46,468 40,278

Total Sales (MWh)/Employee 6,408 10,335 9,403 3,675 1,495 3,469

Plant in Service ($1000s)/MWh 0.16 0.14 0.12 0.12 0.20 0.16

Percent Purchased Power 4% 42% 2% 84% 81% 81%

Operating Expense ($1000s)/MWh 0.03 0.03 0.03 0.07 0.06 0.08

Load Factor 0.63 0.60 0.63 0.64 0.71 0.76

Table 3. Comparison of Efficiency by Various Categories

Number

of utilities Average

Variable Category in category Efficiency

Size (Average MWh) Small = Quartile 1 23 86.7%

Medium= Quartile 2 24 90.8%

Large = Quartile 3 23 92.0%

Very Large = Quartile 4 24 92.4%

Region Northwest 4 98.5%

West 16 89.6%

North-central 21 92.2%

Central / Midwest 9 90.5%

South / Southeast 13 93.9%

East / Northeast 31 87.3%

Nuclear fuel: 0% 39 91.1%

Percent of total fuel cost 1-10% 23 91.0%

10-20% 19 89.7%

20-30% 7 88.6%

30-40% 5 89.0%

over 40% 1 88.7%

Purchase Power - % of total sales 25-50% 32 89.6%

50-75% 5 86.8%

over 75% 5 81.3%

Utility with gas sales Yes 47 90.7%

No 47 90.3%

Percent Industrial 0-20% 22 89.1%

20-40% 60 91.0%

over 40% 12 90.0%

Holding Company Yes 30 91.6%

No 64 90.0%

Hydro electric % of sales 0 39 90.0%

0-10% 47 91.0%

over 10% 8 90.0%

1Several econometric techniques have been developed for obtaining the measurement of each component. The computational procedures, however, are complex and inexact.

2One study employing this technique was published in PUBLIC UTILITIES FORTNIGHTLY. (See, "The Efficient Utility: Labor, Capital, & Profit," by D. Thomas Taylor and Russell G. Thompson, Sept. 1, 1995, p. 25.) That study used Data Envelopment Analysis, a mathematical programming technique, to estimate relative efficiencies of 13 investor-owned utilities. Some of that study's flaws and certain weaknesses of its methodology were later noted by Matthew Morey and L. Dean Hiebert. (See, "Measuring Utility Efficiency: A New Frontier" [letter to editor], PUBLIC UTILITIES FORTNIGHTLY, Jan. 1, 1996, p. 7.)

3The estimated equation was formulated as:

Ln(Yit) = Siai +SjbjLn(Xijt) + LFit + Îit where Ln(Yit) is the natural logarithm of total output in megawatt hours, Ln(Xijt) is natural logarithm of a set of j inputs (labor, capital, fuel and material), LF is the load factor, and T is a trend variable with values of 1 to 6 representing each year of data from 1990 to 1995. Index i refers to utilities, and index t refers to time periods.

Ît is an error term representing two elements: statistical noise (vit) and inefficiency (ui): Îit = vit + ui). The decomposition of the error term into its two components may be done in several ways. The fixed effects approach assumes differences in the efficiency of different utilities are captured in their respective intercepts by the term