A data envelopment analysis of the levels and determinants of coal-fired electric power generation performance

Utilities Policy 9 (2000) 47–59 www.elsevier.com/locate/utilpol

A data envelopment analysis of the levels and determinants of coal-fired electric power generation performance Williams O. Olatubi a

a,*

, David E. Dismukes

b

Center for Energy Studies, Louisiana State University, Baton Rouge, LA 70803-030, USA b Econ One Research Inc., 1004 Prairie Street, Suite 200, Houston, TX 77002, USA Received 1 December 1999; accepted 1 January 2001

Abstract Greater levels of competition in electric power markets offer the promise of increased efficiency, with lower costs to consumers. Yet despite these perceived benefits, little empirical work has been conducted to quantify existing power plant performance characteristics. In the past, empirical work has focused on average determinations of cost performance, and their associated scale implications, and not on measures of best practice (i.e., cost efficiency). This paper attempts to measure cost efficiency opportunities for coalfired electric generation facilities. We apply non-parametric measurement techniques to plant-specific information. Our approach also partitions cost efficiency into its component parts and considers the influence that fuel type, technology, vintage and size has on operating efficiency. Our results show considerable opportunities for cost reduction in the industry that could result in price reductions to electricity consumers.  2001 Elsevier Science Ltd. All rights reserved. JEL classification: L5; Q4 Keywords: Utilities; Power generation; Restructuring; Efficiency; Data envelopment; Stochastic frontier

1. Introduction Over the past two decades, developments in the US electric industry have shown that competition can be sustained in the generation portion of the industry. Orders 888 and 889 issued by the Federal Energy Regulatory Commission (FERC) in 1996 were primarily designed to promote and enhance competition in wholesale markets for electricity. A beneficial side effect of this policy movement has been the imposition of greater cost efficiency in the provision of retail, and mostly regulated, services. It is often argued that full-scale competition at both the wholesale and retail level will be beneficial to society since markets will provide strong incentives to firms to minimize costs and operate in the most efficient manner. Ultimately, it is expected that these cost efficiencies will be translated into lower prices * Corresponding author. Tel.: +1-225-388-3592; fax: +1-225-3884541. E-mail addresses: [email protected] (W.O. Olatubi), ddismukes @econone.com (D.E. Dismukes).

for end users. Utilities also benefit from industry restructuring by enhancing their operational efficiency and taking full advantage from innovations often associated with competitive markets. Recently, these perceived benefits have accelerated the pace of retail competition. While the theoretic possibility of increased generation efficiency and other related benefits of competition has been recognized, limited empirical work has been conducted to estimate the degree of inefficiency that currently exists in the generation portion of the industry. Most empirical work over the years has focused on measuring the degrees of scale economies that have arguably either existed or not existed. Unfortunately, many of these studies have focussed on firm level measures of economies of scale and not plant specific investigations into efficient operations. In the few instances where efficiency has been investigated, efficiency determinants have been left unanswered (Christensen and Greene, 1976; Atkinson and Halvorsen, 1984; Granderson and Linvill, 1998). The increasing movement towards electric restructuring has amplified the interest in how these potential competitive gains may be achieved. Therefore,

0957-1787/01/$ - see front matter  2001 Elsevier Science Ltd. All rights reserved. PII: S 0 9 5 7 - 1 7 8 7 ( 0 1 ) 0 0 0 0 4 - 2

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this study not only estimates existing generator-specific performance, but also tries to ascertain the underlying sources and factors contributing to these plant specific performance profiles.

2. Empirical methods for estimating utility performance Methods outlined by Farrell (1957), based on earlier work by Knight (1933), Debreu (1951) and Koopmans (1951), laid the foundation for current approaches to measuring production efficiency (or inefficiency) by developing a system of equations known as distance functions. While Farrell’s work enables theoretical estimation of productive efficiency in a primal framework, the development of duality theorem by Shephard (1953) establishes the link between production and costs. Cost function approaches to evaluating firm performance are particularly relevant in a regulatory environment that constrains output or price. Implementation of Farrell’s idea has evolved along three lines: linear programming, deterministic frontiers, and stochastic frontiers. The advantage of the linear programming and the deterministic frontier approach is that they do not require functional or distribution assumptions like most stochastic approaches. However, these approaches have the disadvantage of not taking into account random errors, which are facilitated through stochastic analyses. Most past cost analyses of the electric utility industry have focussed on scale economies, with a few investigations of technical change using parametric approaches. These estimates of scale economies and/or technical change led to inferences about a firm’s optimal (efficient) location on the long run cost function. For instance, Christensen and Greene (1976) first challenged the assumption of economies of scale in electric generation, and concluded that the economies of scale present in 1955 were exhausted by 1970. Other empirical works (Huettner and Landon, 1974; Bopp and Costello, 1990; Kamerschen and Thompson, 1993; Atkinson and Halvorsen, 1984; Thompson and Wolf, 1993) have more or less corroborated these findings. These subsequent studies included extensions such as the estimation of own and cross price elasticities of substitution, and fuel-type cost comparisons (i.e., nuclear versus coal). An important exception is the study by Fare et al. (1985), which examined relative performance of public versus private electric utilities using a non-parametric approach. The problem with previous studies relying on a traditional econometric estimate of a production function is their incongruence with the economic theory of production (or costs). Generally, in the context of traditional production function, the estimated function traces out the least cost locus for varying output, the ‘average’ output

for varying input levels, or minimum cost given output levels. Hence, the average production function represents maximum output levels given fixed input levels. Underlying this methodological approach is the assumption that all firms are operating efficiently.1 Another line of empirical work takes a slightly different route using stochastic cost or production frontiers. Users of stochastic frontier measures of efficiency assume inefficiency of the firm is embedded in the error term of the traditional econometric regression (i.e. a composed error). The structure of the error term and the validity of the assumption about its distribution thus influence the measured efficiency of the firm. A methodological alternative to the regression to the mean analysis of past utility cost analysis was presented by Charnes et al. (1978), who coined the term data envelopement analysis, and introduced a mathematical programming framework into Farrell (1957). DEA uses a linear programming method to search for the optimal combinations of outputs and inputs. In cost applications, DEA seeks the minimum cost associated with the highest level of outputs. The method optimizes on each individual observation with the objective of calculating a discrete piecewise frontier in contrast to the focus on averages and estimation of parameters with statistical approaches. DEA is similar to the parametric frontier analysis in the sense that it uses best practice observations to trace out a least-cost operating locus. Its main difference, however, is that as a deterministic method, it makes no adjustments for random noise, and can be sensitive to outlier observations.2 In a DEA analysis, some attention must be given to assumptions regarding cost returns. Although Charnes et al. (1995) considered only constant returns to scale (CRS) assumption, other studies have included a variety of approaches such as variable returns to scale (VRS), and even non-increasing returns to scale (NIRS). Our analysis will assume variable returns to scale given the increasingly competitive nature of the power industry and the host of prior studies that have undermined the natural monopoly assumption. From a practical perspective, the use of variable returns to scale permits the estimation of efficiency scores that are not confounded by scale effects. In Fig. 1, we illustrate the differences between the traditional parametric approach and the DEA in measuring efficiency. While most parametric approaches using linear functions seek to optimize a single plane (AB)

1

Under certain conditions a fitted (average) function can permit a ranking of observations by technical efficiency. Nevertheless, it cannot provide any quantitative information on technical inefficiency for any observation in the sample (Schmidt and Lovell, 1979). 2 Since the true level of efficiency or inefficiency is not known, it is not possible to ascertain which of the assumptions underlying the stochastic frontier or the DEA dominates the other.

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49

Therefore, scale efficiency is DE/DF. In contrast, all these information are subsumed under the ubiquitous ‘average’ function or line AB in a parametric framework. The typical cost function frontier used in most parametric studies of efficiency in the electric industry can be generalized as: TC(q,w)⫽min{w⬘x:f(x)ⱖq}

Fig. 1.

Comparison of average function and DEA frontiers.

through the data, the DEA approach takes an alternative route by optimizing on individual observation.3 The goal of the DEA is to estimate a discrete piecewise frontier (an envelope) as determined by the given data set of Pareto-efficient firms. We have presented an illustration of this frontier in Fig. 1. For our example, we have assumed a one-input, one-output firm, although the analyses can be easily extended to multiple-output, multiple-input situations. Depending on the particular assumption about returns to scale, a frontier that envelopes the data may be given by the CRS projection (OC), the VRS projection (JK), or non-increasing returns (NIRS) projection (OL).4 Thus, in the parametric average function it is assumed that each individual firm takes on the score of the ‘average’ performance unit or firm. In contrast, the DEA focus is on each individual firm’s score in relation to the best performer such that each firm lies on or below the extreme frontier. Each firm not on the relevant frontier (OC, JK, or OL) is scaled against a convex combination of the firm or unit of observation on the frontier closest to it. All firms located below the relevant frontier are thus technically inefficient and hence, cannot be minimizing cost since they will be, by definition, using inputs excessively given those inputs’ prices. Consider the estimation of technical and scale efficiency in a one-input (x) and one-output (y) situation. Under CRS, the input-oriented technical inefficiency of G is the distance GE, that is DE/DG. However, under VRS, the equivalent measure is GF, that is DF/DG. 3

Most empirical parametric approaches have assumed linear functions. However, under non-linearity optimization is over multiple planes. 4 This illustration combines three different frontiers and the traditional ‘average’ function (AB) into one graph so readers can easily grasp the differences. Each of the three frontiers, OC, JK, or OL is only relevant for the return-to-scale it represents, and hence, the data points they envelope.

(1)

where TC is total cost, w is the vector of exogenously determined input prices, and x is a vector of inputs. This equation defines the minimum expenditure needed to produce a given level of output, q. Specific functional forms applied to this relationship usually include Cobb– Douglas, constant elasticity of substitution (CES), or more flexible functional forms like translog cost functions form. Traditionally, the estimation of specific form of Eq. (1) is followed by the calculation of elasticities of substitution and the estimation of scale economies (SCE) that can be given by: SCE⫽1⫺∂lnTC/∂lnNET

(2)

where NET is net output, and the second term in Eq. (3) represents the elasticity of total cost with respect to output. Indirect inferences are then often made about a firm’s efficiency based on their SCE estimate. There are two significant benefits of the DEA approach, which overcomes the limitations in using Eqs. (1) and (2) above while extending the latitude of information that can be extracted. The first benefit is the freedom from predetermined functional relationships for production. The second benefit is the relative ease in using multiple inputs and outputs. Assume there are data on K inputs and N outputs for each of M firms. For the ith firm these are represented by the column vectors xi and yi, respectively. Thus the (K×M) input matrix, X, and the (N×M) output matrix, Y, represents data for all M firms. The linear program to estimate technical efficiency (TE) for each firm is: minq,l,q Subject to:

(3)

−yi⫹Yl ⱖ 0, qxi⫹Xl ⱖ 0, M1⬘l

ⱖ 1,

l

ⱖ 0.

where M is an (m×1) vector of ones, and q (ⱕ1) is a positive scalar defining the efficiency score for the ith firm. The convexity restriction M1⬘lⱖ1 ensures that an inefficient firm is only benchmarked against firms of similar size. In a VRS assumption context the third constraint is reformulated as M1⬘l=1.

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In addition to the above, a cost minimization DEA is also estimated as: minl,x∗i ,w⬘ix∗i Subject to:

(4)

−yi⫹Yl ⱖ 0, qxi∗⫹Xl ⱖ 0, M⬘l

ⱖ 1,

l

ⱖ 0.

where wi is a vector of input prices for the ith firm and xi∗ is the cost minimizing vector of input quantities for the ith firm, given the input prices wi and the output levels, yi. For VRS assumption applications, the third constraint is again reformulated as M⬘1l=1 The total cost efficiency or economic efficiency (EE) is then calculated as: EE⫽

w⬘xi∗ w⬘xi

and the allocative efficiency (AE) is estimated thus: EE AE⫽ TE Which implies: EE⫽AE⫻TE In our model, NET represents output (Y); fuel inputs, labor, and capital comprise the input matrix (X); fuel prices and the prices of labor and capital are the elements of the input price matrix (W).

3. Data sources and descriptive stastics Data for the analyses were obtained from the 1996 Steam Electric Generating Facility Database published by the Utility Data Institute (UDI), supplemented with data from the US Energy Information Administration (IEA) report Financial Statistics for Privately Owned US Electric Utilities. The data, while published by a private source, is a recompilation of plant-specific information filed in the Form 1 by all Federal Energy Regulatory Commission (FERC) regulated investor-owned utilities (IOUs). Although this data contains information on all types of electric generation, this study concentrated on coal-fired steam plants. Because of missing data, 10 observations were removed. Our data set represents a very large sample of the universal population of electric plants in the US that rely on coal as primary fuel for generation. In fact, our sample is larger than most of the previous studies that have examined this subject. A detailed definition of the variables considered in our

analysis has been presented in Appendix A while their descriptive statistics are presented in Table 1. Taken together, output and main input use gives some indications of the wide variability in scales of operation of steam electric generation plants in the US. The low sample variance in each case gives an indication of tight range of input costs across generating facilities. In addition to the primary fuel coal, natural gas and oil are used at coal generating facilities for start up and some operational purposes. The fuel price statistics are standardize to cost per British Thermal Unit (BTU) and shows that gas and oil prices per BTU are twice and four times the cost of coal, respectively. Average total variable cost was 64.2 million dollars while other variables such as heat rate, and regional and specific operational dummies have been included in the analysis of cost efficiency. Heat rate, a measure of the fuel use efficiency of a generating plant, averages about 11,000 composite BTU per kWh for all fuels, and exhibits a relatively low range of variability. Most plants are self-owned and operated as indicated by the ownership/operator indicator variable OPFD.

4. Empirical results Table 2 presents the results of the DEA approach. Two general observations can be made from these results. First, there are potential opportunities for cost efficiency gains for US IOU coal generating facilities. Second, looking at the two components of cost efficiency, we observe that there are differential impacts or contributions to the overall inefficiency. The mean cost efficiency is 0.65, comprising of 0.66 in allocative efficiency and 0.93 in technical efficiency. Therefore, most of the inefficiencies in electric power generation for this period arise mainly from allocative inefficiency rather than technical inefficiency. The few opportunities in technical efficiency are not surprising given the nature of coal plant operations. Most steam generating plants, whether they are coal, nuclear, gas or oil, are typically ‘baseload’ facilities. This entails that they are run at a near continuous rate to meet the base level of system demand. Steam plants are run in this manner for a number of different reasons. First, from a technical perspective, steam plants cannot be turned on and off instantaneously. It can take anywhere from 12 to 16 h for a steam generating plant to reach temperatures necessary to run their respective electrical generators. In addition, most steam plants are characterized as having relatively high capital costs and low operating costs. Running these plants at a continuous rate allows utilities to recover these large capital costs. The important empirical result, however, is related to the opportunities for overall cost reduction at most coal generating units via changes in allocative efficiency. The

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Table 1 Descriptive statistics Variable NET (million kWh) LAB W K (’000 $/kWh) R COAL (million MMBTU) CP OIL(MBTU) OP GAS (MBTU) GP TC AVFVTG HTR (MBTU) AVFAGE CAPF MKTFSHR (%) REGEXPFOP ($) ECAR ERCOT MAAC MAIN MAAP NPCC SERC SPP WSCC ALTFD FGDFD OPFD DSFD HSFD OEFPPFD OSSFD N=313

Mean

Median

Standard development Minimum value

Maximum value

4.43 153.79 40.69 0.85 442.92

3.03 126.00 40.72 0.63 358.31

4.29 107.79 8.18 0.71 273.57

0.01 2.00 22.86 0.01 70.00

21.80 537.00 76.98 3.56 2,309.40

44.84

30.97

43.00

0.09

222.38

1.39 266.94 4.56 0.26 3.10 64.22 29.63 10.91 29.62 54.62 11.50 3,030,817 0.26 0.03 0.08 0.15 0.08 0.04 0.18 0.08 0.11 0.24 0.16 0.89 0.35 0.52 0.42 0.05

1.37 35.53 4.58 0.00 2.85 42.72 30.50 10.49 30.50 57.42 9.38 1,496,324 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 1.00 0.00 0.00

0.39 1131.11 0.76 1391.19 0.99 63.84 11.40 3.16 11.40 18.21 10.07 3,482,901 0.44 0.17 0.26 0.36 0.28 0.17 0.39 0.26 0.30 0.42 0.36 0.30 0.48 0.50 0.49 0.23

0.48 0.00 1.76 0.00 1.22 0.16 0.00 0.00 0.00 2.47 0.1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2.83 10,157.75 8.47 20.75 9.57 387.75 60.50 58.50 60.50 90.16 60.34 14,675,088 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

estimates show that on average, coal-generating facilities could reduce costs by as much as 35%. While some plants are operating at their most cost efficient point, our DEA analysis reveals that the least efficient plant has the opportunity to reduce costs by 100%. In Table 3, we present some summary statistics on the efficiency measures in terms of quartile distribution over the entire sample of plants. The result shows plants that are quite technically efficient at the topmost quartile (1%) but 13% inefficient in terms of cost and allocative efficiency. While technical efficiency at the lowest quartile is similar to the top quartile, there is a sharp decline in allocative and overall cost efficiency. Cost and allocative inefficiency are 72% and 71%, respectively. The implication is that though plants are choosing the right inputs to maximize output, the mix of inputs given their prices are significantly sub-optimal for most plants. The dramatic efficiency gains required of plants in the lowest quartile indicate that they will face considerable hurdles

in a competitive market. These plants could be candidates for plant closure in a restructured environment. Our analyses allow us to identify the optimal levels of inputs and the implied associated minimum costs which are presented in Tables 4 and 5. Results in Table 4 show that, for the top 50 most efficient plants, actual coal use is closest to the expected minimum (optimal) levels. For the selected sample, we observe that 86% of the plants are above their expected optimal coal use levels. For other inputs, 18% of the plants are over their optimal use for labor, 28% are over optimal for capital, 12% are over optimal for oil, and none of the plants are over their optimal levels for gas. Cost implications of these input use levels are depicted in Table 5. For coal usage, the minimum cost is above actual cost for only 22% of the plants. For other inputs, actual costs are above long run minimum for 70% of the plants for labor, 76% for capital, 100% for gas, and 86% for oil. The results indicate that given the price of inputs, capital is

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W.O. Olatubi, D.E. Dismukes / Utilities Policy 9 (2000) 47–59

Table 2 Frequency and summary statistics of efficiency by sources Range

0.99–0.94 0.93–0.88 0.87–0.82 0.81–0.76 0.75–0.70 0.69–0.64 0.63–0.58 0.57–0.52 0.51–0.46 0.45–0.40 0.39–0.34 0.33–0.28 0.27–0.22 0.21–0.16 0.15–0.10 0.10 and below ALL: Mean Std. Dev. Range

Cost efficiency

Allocative efficiency

Technical efficiency

Number of firms

% Dist.

Number of firms

% Dist.

No of firms

% Dist.

19 8 19 87 73 26 26 5 3 0 1 0 0 0 0 42 313 0.653 0.016 1

0.061 0.026 0.061 0.278 0.233 0.083 0.083 0.016 0.010 0.000 0.003 0.000 0.000 0.000 0.000 0.134 1.000

19 8 25 101 64 26 20 3 0 1 0 0 0 0 0 42 313 0.664 0.016 1

0.061 0.026 0.080 0.323 0.204 0.083 0.064 0.010 0.000 0.003 0.000 0.000 0.000 0.000 0.000 0.134 1.000

211 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 313 0.983 0.001 0.096

0.674 0.016 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000

overutilized by most plants. This result is consistent with the familiar Averch–Johnson effect, which posits that firms operating in regulated industries tend to overcapitalize to maximize profits. Other studies (Nelson, 1985; Rhine, 1998) have found similar results. One expected gain in a restructured power market is the possible reduction of cost and a downward shift in the industry supply curve. In Fig. 2, we develop a combined least cost dispatch supply curve for the SPP and SERC regions. The upper curve dispatches all coal generating facilities in these two regions at their existing costs while the lower curve dispatches the same set of plants under the assumption that all DEA estimated cost efficiency opportunities are exploited. The DEA curve shows a dramatic downward shift in the regional (coal generation) supply curve. At $15/MWh, the actual supply shows a dispatch of about 21 GWh of electricity. The DEA derived supply curve shows increased output of 37 GWh, to a level of 58 GWh. Alternatively, the market-clearing price for 21 GWh of electricity at the actual supply curve is $15/MWh, while the clearing price for the DEA derived supply curve is $11/MWh. The cost/price reduction implied by these efficiency gains is 27%.

5. Estimated determinants of inefficiency The preceding DEA results by themselves do not provide information on the empirical determinants of plant level inefficiencies. Thus, policy uses of DEA results can

be limiting. However, one approach to identifying determinants of inefficiency is to run DEA scores a dependent variable in a second stage regression estimation against a number of hypothesized inefficiency determinants. In most previous studies this regression is run assuming plants or producing units produce conditional on the levels of inputs and the given technology. In cases where the conditioning assumption is valid, the regression results are likely to be plausible. On the other hand, in those cases where the technology in use may vary between plants or producing units in time or across time periods, inefficiency determinants often employed may not be truly exogenous. For example, regressing the (in)efficiency scores from the DEA in our case against seemingly exogenous determinants of (in)efficiency such as FGDFD, AVFVTG, or ALTFD will not be correct. This is because these variables are somewhat ‘confounding’ given that electric plants may use different technology and hence, these variables cannot be the determinants of (in)efficiency. To remove the effects of technology-related variables from the estimated DEA scores, we estimated an intermediate regression that approximates the impacts of these variables. To obtain the true (in)efficiency scores, the estimated impact of these variables are subtracted from the DEA scores. The estimated equation and the results are as follow:

Mean Std. Dev. Range No. of firms below mean(of quartile) No. of firms above mean(of quartile) No. of firms below mean(%) No. of firms above mean(%) Number of observations

0.8745 0.0080 0.2040 47 31 60.26 39.74 78

0.8707 0.0082 0.2120 46

32

58.97

41.03

78

78

66.67

33.33

52

0.9990 0.0001 0.0030 26

78

53.85

46.15

42

0.7649 0.0014 0.0490 36

78

50.00

50.00

39

0.7764 0.0012 0.0400 39

Allocative efficiency

Cost efficiency

Technical efficiency

Cost efficiency

Allocative efficiency

Second Quartile

First Quartile

Table 3 Summary statistics on sources of efficiency by quartile distribution

78

52.56

47.44

41

0.9927 0.0003 0.0080 37

Technical efficiency

74

60.81

44.59

45

0.7045 0.0029 0.0970 33

Cost efficiency

74

63.51

41.89

47

0.7245 0.0029 0.0850 31

Allocative efficiency

Third Quartile

78

52.56

47.44

41

0.9813 0.0005 0.0140 37

Technical efficiency

79

46.84

53.16

37

0.2763 0.0335 0.6380 42

Cost efficiency

79

46.84

53.16

37

0.2869 0.0348 0.6680 42

Allocative efficiency

Fourth Quartile

79

62.03

37.97

49

0.9586 0.0015 0.0700 30

Technical efficiency

W.O. Olatubi, D.E. Dismukes / Utilities Policy 9 (2000) 47–59 53

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W.O. Olatubi, D.E. Dismukes / Utilities Policy 9 (2000) 47–59

Table 4 Analysis of top 50 plants: Input usage levels Number

Plant

State

Labor use differencea

Capital use difference

Coal use difference

Gas use difference

Oil use difference

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

HARRISON MILLER SCHERER WN CLARK MILL CREEK ARMSTRONG BIG BEND PLEASANTS GHENT BARRY HATFIELDS FERRY CHESWICK GASTON DUNKIRK SIBLEY GASTON EW BROWN PINEVILLE GANNON PRESQUE ISLE POWERTON BOWEN CR HUNTLEY CANE RUN GORGAS TWO WILL COUNTY WILLOW ISLAND STATE LINE TNP ONE KINCAID GADSDEN NEW TOLK NAUGHTON TNP ONE TNP ONE ALBRIGHT HARRINGTON LIMESTONE ALLEN S KING PORT WASHINGTON NORTHEASTERN 3 and 4 VALMONT DOLET HILLS EDGEWATER PIRKEY CRYSTAL RIVER 4 and 5 GREEN RIVER NORTH BRANCH BRANDON SHORES MARSHALL

WV AL GA CO KY PA FL WV KY AL PA PA AL NY MO AL KY KY FL MI IL GA NY KY AL IL WV IL TX IL AL TX WY TX TX WV TX TX MN WI OK CO LA WI TX FL KY WV MD NC

0.000 0.000 0.000 0.000 0.113 ⫺0.053 ⫺0.158 ⫺0.104 ⫺0.181 ⫺0.047 ⫺0.044 ⫺0.079 ⫺0.066 ⫺0.165 ⫺0.193 ⫺0.143 ⫺0.113 ⫺0.217 ⫺0.222 1.694 ⫺0.219 ⫺0.028 ⫺0.186 ⫺0.158 ⫺0.975 ⫺0.368 ⫺0.201 ⫺0.438 0.189 ⫺0.294 ⫺0.277 0.159 ⫺0.141 0.070 0.181 ⫺0.347 0.125 0.027 ⫺0.225 ⫺0.352 0.194 ⫺0.036 ⫺0.046 0.732 0.040 0.327 ⫺0.378 ⫺0.469 0.088 0.242

0.000 0.000 0.000 0.000 ⫺0.112 ⫺0.023 ⫺0.017 ⫺0.040 ⫺0.026 ⫺0.092 ⫺0.097 ⫺0.084 ⫺0.127 ⫺0.080 ⫺0.113 ⫺0.081 ⫺0.142 0.011 ⫺0.158 ⫺0.130 ⫺0.225 ⫺0.024 ⫺0.197 ⫺0.212 0.023 ⫺0.274 ⫺0.275 ⫺0.316 ⫺0.003 ⫺0.425 ⫺0.312 ⫺0.030 0.033 ⫺0.007 ⫺0.015 ⫺0.308 0.050 ⫺0.067 0.008 ⫺0.424 0.041 0.009 ⫺0.074 ⫺0.029 0.015 0.006 ⫺0.398 ⫺0.318 ⫺0.011 0.015

0.000 0.000 0.000 0.000 0.001 0.090 ⫺0.006 0.008 0.021 ⫺0.012 0.024 0.067 0.058 0.062 0.016 0.062 0.053 0.449 ⫺0.006 ⫺0.047 ⫺0.032 0.028 0.045 0.057 0.083 0.016 0.116 0.033 0.071 0.014 0.074 0.034 0.033 0.036 0.077 0.097 0.026 0.075 ⫺0.017 ⫺0.004 0.020 0.119 0.010 0.144 0.018 0.036 0.092 0.834 0.017 0.036

na na na na na na na na na na na na na na na na na na na na na na na na na na na na ⫺4.689 na na ⫺5.406 ⫺4.957 ⫺5.119 ⫺4.918 na ⫺5.886 ⫺3.972 ⫺5.888 na ⫺5.506 ⫺5.162 ⫺4.933 na ⫺4.948 na na na na na

nab na na na na na na na na na na na na na na na na na na ⫺4.772 na ⫺1.515 na na 2.018 na na na na na na na na na na na na na na na na na na ⫺3.793 na ⫺5.419 na na ⫺5.430 ⫺5.183

a b

Difference means actual minus minimum input use na not applicable i.e. firm did not use this input

ES= − 0.0741

− 0.0841∗ALTFD

(0.0507)

(0.0243)

+ 0.0132∗AVFVTG

− 0.1996∗FGDFD

(0.0011)

(0.0738)

+ 0.0076∗AGE∗FGDFD (0.0033)

(5)

R2: 0.31 Standard Error: 0.14 where ES indicates the efficiency score and the figures in parenthesis are standard errors. All parameters, except the constant, are significant at the 5% level.5

5 The discussion in this section draws largely from the suggestions of one of our anonymous referees.

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Table 5 Comparison of top 50 plants: Input cost levels Number

Plant

State

Labor cost differencea

Capital cost difference

Coal cost difference

Gas cost difference

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

HARRISON MILLER SCHERER WN CLARK MILL CREEK ARMSTRONG BIG BEND PLEASANTS GHENT BARRY HATFIELDS FERRY CHESWICK GASTON DUNKIRK SIBLEY GASTON EW BROWN PINEVILLE GANNON PRESQUE ISLE POWERTON BOWEN CR HUNTLEY CANE RUN GORGAS TWO WILL COUNTY WILLOW ISLAND STATE LINE TNP ONE KINCAID GADSDEN NEW TOLK NAUGHTON TNP ONE TNP ONE ALBRIGHT HARRINGTON LIMESTONE ALLEN S KING PORT WASHINGTON NORTHEASTERN 3 and 4 VALMONT DOLET HILLS EDGEWATER PIRKEY CRYSTAL RIVER 4 and 5 GREEN RIVER NORTH BRANCH BRANDON SHORES MARSHALL

WV AL GA CO KY PA FL WV KY AL PA PA AL NY MO AL KY KY FL MI IL GA NY KY AL IL WV IL TX IL AL TX WY TX TX WV TX TX MN WI OK CO LA WI TX FL KY WV MD NC

⫺0.0005 0.0005 0.0000 0.0007 ⫺0.1796 0.0863 0.2514 0.1681 0.2852 0.0724 0.0730 0.1223 0.1044 0.2767 0.3048 0.2181 0.1750 0.3359 0.3531 ⫺2.7717 0.3775 0.0443 0.3112 0.2513 1.5402 0.6348 0.3254 0.8259 ⫺0.2910 0.5077 0.4375 ⫺0.2514 0.2079 ⫺0.1077 ⫺0.2787 0.5631 ⫺0.1973 ⫺0.0451 0.3665 0.5756 ⫺0.2945 0.0587 0.0699 ⫺1.1405 ⫺0.0629 ⫺0.5039 0.5843 0.7825 ⫺0.1481 ⫺0.3946

0.0005 ⫺0.0013 ⫺0.0002 0.0011 0.2911 0.0654 0.0457 0.1125 0.0634 0.2399 0.2349 0.2147 0.3130 0.2100 0.2747 0.2109 0.3381 ⫺0.0262 0.3731 0.3381 0.5645 0.0555 0.5261 0.5402 ⫺0.0559 0.6614 0.7127 0.7759 0.0100 1.0237 0.8170 0.0826 ⫺0.0869 0.0229 0.0483 0.8014 ⫺0.1264 0.1562 ⫺0.0230 1.0855 ⫺0.1064 ⫺0.0229 0.2125 0.0726 ⫺0.0432 ⫺0.0171 0.9341 0.8096 0.0321 ⫺0.0325

0.0000 ⫺0.0001 0.0000 0.0000 0.0000 ⫺0.0090 0.0016 ⫺0.0002 ⫺0.0062 0.0006 ⫺0.0033 ⫺0.0074 ⫺0.0164 ⫺0.0081 ⫺0.0043 ⫺0.0005 ⫺0.0042 ⫺0.0150 0.0018 0.0093 0.0141 ⫺0.0039 ⫺0.0069 ⫺0.0026 ⫺0.0145 ⫺0.0071 ⫺0.0088 ⫺0.0142 ⫺0.0097 ⫺0.0026 ⫺0.0217 ⫺0.0113 ⫺0.0022 ⫺0.0050 ⫺0.0105 ⫺0.0029 ⫺0.0060 ⫺0.0010 0.0008 0.0006 ⫺0.0018 ⫺0.0115 ⫺0.0015 ⫺0.0114 ⫺0.0002 ⫺0.0107 ⫺0.0008 ⫺0.1193 ⫺0.0031 ⫺0.0058

nab na na na na na na na na na na na na na na na na na na na na na na na na na na na 1.7399 na na 2.2794 1.6074 2.0013 1.9739 na 2.5009 1.6285 2.1188 na 2.6192 1.6846 2.1593 na 2.4521 na na na na na

a b

Difference means actual minus minimum cost na not applicable i.e. firm did not use this input

The results also show that when these estimated impacts are subtracted from the initial DEA efficiency scores, the level of overall inefficiency declined substantially. The mean of the corrected inefficiency score was 0.74, a 39% reduction from the DEA average level of

inefficiency. This adjustment is even more dramatic when one considers that the maximum inefficiency score from the corrected analysis is 0.75, compared to 0.97 DEA estimate. The differential entails that the most inefficient plant is actually 75% inefficient compared to the

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W.O. Olatubi, D.E. Dismukes / Utilities Policy 9 (2000) 47–59

Fig. 2.

Estimated coal generating plant supply curves (SPP and SERC regions).

DEA estimate of 97% once the scores are adjusted for technology. Having obtained the adjusted inefficiency scores, we proceed to estimate the impacts of the selected potential determinants of inefficiency. Our approach follows the discussion in Pitt and Lee (1981). The relationship between DEA scores and inefficiency determinants can be formed as: m∗i ⫽f(Zi,y)

(6)

where Zi is a (p×1) vector of determinant variables; and y is a vector of parameters to be estimated. In this case, m∗i , the ‘true’ inefficiency scores, is regressed on a number of potentially influencing exogenous factors such as degree of plant ownership, market-share, regulatory expenses, heat rate and other variables that may affect choice of input-mix and other managerial operations.. However, because q is bounded [0,1], OLS is a biased estimator for Eq. (6). To avoid this potential bias we estimate a tobit model of the inefficiency scores using maximum likelihood techniques and assuming a logistic distribution for the errors. The specific vector of variables included in ZI, CAPF, HSFD, HTR, OPFD, REGEXPFOP, MKTFSHR; incentive dummies, OEFPPFD, OSSFD; and FERC regional dummies.6 The results of the Tobit regression of allocative inefficiency on the selected determinant variables are shown

6

For a detail explanation and justification for including these variables, see Joskow and Schmalensee (1987), Knittel (1999) and Welch et al. (2000).

in Table 6. Some limited insights into the sources of inefficiency can be presented. The Specific vector of variables included in Zi relates to such factors as the capacity utilization of the plant (CAPF), whether the plant is operated within a holding company structure (HSFD), the heat rate of the plant (HTR), the ownership percentage of the plant (OPFD), the annual reglatory expenditures for the utility operator of the plant (REGEXPFOP), or the regional generation market share (MKTFSHR) of the utility operator. Table 6 Tobit regression equation estimatesa Variable

Coefficient

Std. Error

z-statistics

Constant CAPF HSFD HTR OPFD REGEXPFOP MKTFSHR ECAR ERCOT MAAC MAAP MAIN NPCC SERC SPP OEFPPFD OSSFD R2 S.E. of Est. Log likelihood

0.5908 ⫺0.0065 0.0075 0.0000 0.0825 0.0000 ⫺0.0056 ⫺0.1699 0.0944 ⫺0.0218 ⫺0.0141 ⫺0.0405 0.0240 ⫺0.0896 ⫺0.1027 ⫺0.0329 ⫺0.0373 0.53 0.14 38.84

0.0871 0.0005 0.0188 0.0000 0.0286 0.0000 0.0010 0.0366 0.0626 0.0429 0.0400 0.0361 0.0478 0.0368 0.0384 0.0181 0.0421

6.7836 ⫺12.0498*** 0.3987 2.0784** 2.8818** ⫺2.0156*** ⫺5.6300*** ⫺4.6363*** 1.5083 ⫺0.5079 ⫺0.3539 ⫺1.1217 0.5024 ⫺2.4350** ⫺2.6742** ⫺1.8151** ⫺0.8875

a

***, **, * Significant at 1%, 5% and 10%, respectively.

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These results show that a number of variables have a statistically significant impact on our adjusted inefficiency scores. One of the largest impacts is associated with the ownership characteristics of the plant (OPFD). Variable is equal to one if the plant is more than 50% owned by its operator. The results indicate that the inefficiency score can increase by as much as 8% relative to those plants which have more diffuse ownership characteristics. This is an interesting result which essentially states that principalagent problems are not as problematic for power plant operations as conventional wisdom might lead one to believe. The other large and significant variable impacting inefficiency scores appears to be related to the presence of a certain type of incentive regulation associated with operating and safety performance of a utility’s power plant. The incentive regulation variable (OEFPPFD) shows that inefficiency scores decrease by approximately 3% for those plants under these incentive mechanisms. Incentives for off system sales (OSS-D), however, while of the appropriate sign, do not appear to have a stastically significant impact on generator performance. Increasing the capacity factor of a generating facility (CAPF) also has significant opportunity of reducing inefficiency scores. Heat rate (HTR) and regulatory expenditures (REGEXPFOP) have statically significant, but very small impacts, on inefficiency scores. Of the region variables examined, it appears that ECAR and SERC have unique regional characteristics that allow their coal-fired plant to be operated in a more efficient manner than the base region; the SWCC. Our model also examined the impacts that market share (MKTFSHR) has on plant performance. The results indicate that greater levels of regional generator ownership tend to result in lower inefficeincy scores. This has interesting implications for areas of the country that are considering electric restructuring but are grappling with the issue of market power and forced divestiture. Our result would seem to indicate that some, albeit small, efficiency losses could be associated with this regulatory strategy.

6. Conclusions and implications The findings in this study lead to at least two significant conclusions. First, most coal-fired electric generation plants have almost exhausted their potential for technical efficiency. That is, given the current regulatory environment, future gains in a competitive environment should be expected on allocative capabilities of a plant’s operations. Second, specific attention should be paid to

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the use of capital. Our results showed clearly that among the non-fuel inputs, capital is the most misused. The suboptimal use of capital may be due to often-cited Averch– Johnson effect that posits that utilities have incentives to overcapitalize in regulated markets. In addition, we can also draw two major implications for the work that we have presented here. First, from a methodological perspective, we have presented a competing approach to benchmarking generation cost performance in the electric power industry. In the past, most attention was placed on determining scale economies, and utilities were ranked by either having them, or not having them. OLS-determined average cost function provides limited firm-specific information that may be crucial for firms in preparing strategies and plans for a deregulated regime. In other words, firms and regulatory commissions will find the DEA approach offers more relevant information on the degree of cost and operating inefficiency. Understanding the relative cost performance of electrical generators has significant implications in determining the benefits associated with a shift towards competition. The non-parametric frontier approach provides an avenue to uncover sources and determinants of inefficiency. For example, the information that regional location may influence efficiency may represent the difference between successful and unsuccessful regulation. From a practical perspective, two observations can be made. The fact that allocative inefficiency is the most important source of inefficiency for the period under consideration is significant for future market competition in electric generation. This is because allocative efficiency is highly dependent on factor prices. Hence, the success or failure of firms in a full competition environment may be dependent on their changed reaction to prevailing factor prices. Also, because allocative inefficiency is highly influenced by heat rates and vintage, technology may force the early closure of several older less efficient plants. The large number of combined cycle natural gas merchant facilities announced across the US in the past several years could be a real world harbinger of this prediction. DEA method offers an important contribution to forecasting potential gains from competition. Over the past few years, consultants and scholars have invested considerable time and effort in creating multi-area dispatch models to forecast regional power prices. The efficiency estimate from DEA models can be incorporated into these models along with interconnection constraints, line losses, and other engineering-related constraints to regional power systems to generate a more robust quantifiable estimate of regional market clearing prices.

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Appendix A Table 7 Variable definition and description Variable

Label

NUM NET LAB W K R COAL CP OIL OP G GP TC AVFVTG HTR AGE REGEXPFOP CAPF MKTFSHARE Incentive Regulation Indicator OSSFD OEFPPFD Regional Indicators: ECAR ERCOT MAAC MAIN MAAP NPCC SERC SPP WSCC Other Indicators: AltFD FgdFD OPFD HSFD

Identification Number of Plant Net Generation produced in reporting year (mega watts hours) Labor input, average number of employees at plant site in 1996 (full time) Average wage (’000 $) Capital input, gross nameplate capacity (mega watts) Cost per gross Kilo watts of installed capacity ($/kilo watts) Quantity of coal burned at site (million btu) Coal price, average cost of coal burned per million btu ($) Quantity of oil burned at site (million btu) Oil price, average cost of oil burned per million btu ($) Quantity of gas burned at site (million btu) Oil price, average cost of gas burned per million btu ($) Total variable cost (million $) Average vintage (=the year of initial operation minus 1918) Average plant heat rate: composite btu per net kilo watts hour of all fuels Estimated as (=calendar year minus year of initial operation) Level of annual regulatory expenditures Capacity factor of plant Percent of total generating assets relative to the operator’s NERC region Off-system sales Operating efficiency, safety and power plant performance East Central Area Reliability Coordination Agreement Electric Reliability Council of Texas Mid Atlantic Area Council Mid America Interconnected Network Mid Continent Area Power Pool North East Power Coordinating Council South Eastern Electric Reliability Council Southwest Power Pool Western Systems Coordinating Council Dummy Dummy Dummy Dummy

for for for for

alternative fuel operating FGD scrubber at site percentage ownership of operating company whether a plant is under a holding company

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