Technology choice in electricity generation under different market conditions

Resources

and Energy

TECHNOLOGY

12 (1990) 231-251.

North-Holland

CHOICE IN ELECTRICITY GENERATION DIFFERENT MARKET CONDITIONS* Hadi DOWLATABADI Resources for the Future,

Received

March

and Michael Washingron,

1989, final version

UNDER

TOMAN

DC 20036, USA

received

November

1989

In this paper we present a multi-period, multi-region model of cost-minimizing new investment in electricity generation capacity, efficient plant dispatch subject to environmental constraints, and interregional power exchanges. We illustrate the model’s use by applying it to a nine-state region in the eastern U.S. A key feature of the modeling framework is a detailed stochastic sensitivity analysis. The main conclusions from our application are that some ‘new’ generation technologies appear to have significant cost advantages over technologies now being widely used, and that expanded interregional power exchanges may convey significant cost savings if the requisite transmission capacity is available.

1. Introduction and summary Electricity generation markets in the United States have experienced dramatic changes over the past two decades. The historical pattern of steady demand growth and stable fuel prices, coupled with ever larger plant sizes, has been interrupted. Cost economies from increasing plant sizes or greater thermodynamic efficiency have become elusive.’ In addition, environmental constraints have become more stringent, and fuel prices and demand growth have become more unstable. The overall impact of these factors has been to confront electric utilities with significant uncertainties about the size of new *We are grateful for comments and other assistance from many people including John Ahearne, Douglas Bohi, Oliver Gildersleeve, Robert Hahn, and Roy Marsden. Partial financial support for the research was provided by grants to Resources for the Future by the Edison Electric Institute and the Electric Power Research Institute. However, the views represented in this paper are entirely our own. ‘Traditional design sought economies through larger size, reflecting engineering facts such as the increase in fluid volume relative to boiler surface area as plant size grew. However, this approach became stymied by limits to the properties of materials used for plant construction. Thus, while larger steam plants were cheaper to construct they also were less reliable and costlier to operate. The alternative of pushing thermodynamic limits through ‘supercritical’ technology also proved disappointing due to plant failures resulting from impurities deposition in boiler tubes. Both large-size and supercritical plants also have suffered greatly from thermal cycling when attempting to follow load. The result has been an absence of scale returns beyond 600 MW and a move away from supercritical technology. For further discussion see Joskow and Schmalensee (1985) and Joskow and Rose (1985). 01654572/90/$03.50

0

199sElsevier

Science Publishers

B.V. (North-Holland)

232

H. Dowlatabadi

and M. Toman, Electricity

technology

choice

investments in generation capacity and the choice of technologies for these investments. In this study we examine cost-minimizing investments in generation capacity and marginal supply costs under different market conditions. We use a multi-period, multi-region model of capacity investment, plant dispatch and interregional power exchanges that allows us to examine tradeoffs among different technologies, particularly the relative advantages of conventional versus advanced technologies. Model-based analysis of technology choice and capacity investment is not new. Increasingly sophisticated applications of programming and process models have been used for individual utility planning, industry impact assessment, and national policy analysis for over a quarter century.’ The models have been broadened to include regional disaggregation of supply, optimization of investment over time, environmental constraints, interactions between supply and demand, and the impacts of economic regulation3 Our focus in this study is on generation technology choices and capacity investment decisions which could minimize the economic cost of electricity supply over time under different market conditions. We do not attempt to predict actual utility behavior, including the impacts of changing economic regulation, or to address interactions between electricity supply and demand. Nor do we offer sweeping normative conclusions. Within the stated scope, however, our analysis synthesizes and augments numerous elements found in previous studies. The model we use describes adjustment of investment decisions over time for multiple regions of the U.S. The model also includes an endogenous representation of interregional power exchanges. The menu of technology choices for new investment includes ‘new’ technologies currently at the pilot or demonstration stage, as well as technologies already being widely used. Both new investment and plant operation decisions are constrained by controls on sulfur oxide emissions, and the model incorporates sulfur control retrofits on existing plant as well as new investment in lower-emission technologies. Perhaps the most significant extension embodied in our analysis is a very Rather than using a few point detailed stochastic sensitivity analysis.

*There also is an extensive econometric literature on electricity supply, mainly efforts to estimate the parameters of cost functions from historical data. Because this method cannot readily be used to analyze choices among new technologies. we use a programming approach and do not dwell on econometric models here. 3For an early example see Masse and Gilbert (1964). A fairly comprehensive model was undertaken by Baughman, Joskow and Kamat (1979) who also provide citations for numerous other examples. A more recent illustration is Morrison and Rubin (1985). Many other frameworks have been developed at the Department of Energy, the Electric Power Research Institute, ICF inc., Carnegie-Mellon University, and other organizations

H. Dowlatabadi and M. Toman, Electricity technology choice

233

estimates for the values of the input parameters, we posit (subjective) probability distributions for the inputs. The shapes of the distributions reflect judgments about the nature of the uncertainties that surround the central parameter values. The model then maps these input probability distributions to probability distributions over the model outputs (e.g., investment levels and costs). Statistical analysis of these distributions helps us to identify those inputs in the model which have the strongest impact on the outputs. We emphasize that the focus of this study is on discerning cost-minimizing generation options in an engineering-economic sense, and in assessing the sensitivity of these decisions to changes in market conditions and environmental regulation. We also wish to demonstrate the usefulness of stochastic sensitivity analysis for studying the electricity generation industry. Our limited goals are reflected in the numerous caveats regarding the scope of our analysis which are noted in the next section of the paper. Despite these caveats, several interesting conclusions emerge from the study. We find that for a wide range of assumptions about market conditions, technology performance, and environmental constraints, various advanced generation technologies - particularly natural gas-tired plants appear to be more cost-effective than more conventional alternatives for new capacity investment. We also find that the potential cost savings and reduction in investment requirements from expanded interstate power trading could be substantial, though reaping these benefits would likely require an expansion of existing transmission capacity. The balance of the paper is organized into four parts. Section 2 describes our model and other aspects of our analytical framework. Section 3 contains while section 4 presents our main a sketch of our input assumptions, modeling results. Section 5 offers concluding remarks. Because of space limitations many particulars of our analysis are compressed or omitted in this paper. Details are in a longer report [Dowlatabadi and Toman (1990)].

2. Analytical

framework

In this section we first describe the basic structure of the model. We then discuss the scope of our analysis in terms of technologies, time horizon, and regions; our procedure for representing bulk power trading among states and regions; and our sensitivity analysis methodology.

2.1. The model The model

we use is known

as the Planning

and

Dispatch

for Reduced

234

H. Dowlatabadi

and M. Toman, Electricity

technology

choice

Emissions (PADRE) framework. 4 PADRE simulates investment and patch decisions by solving a cost-minimization problem.’ The problem be summarized by the following listing of objective, choice variables, constraints6

discan and

Assumptions/constraints Specifications of present annual demand profiles for these regions, and changes over time in these profiles (average growth rates, changes in peakto-average demand ratios, impacts of industrial cogeneration, etc.); costs and characteristics of fuels consumed in each region;’ costs and characteristics of generation technologies, including specifications of plant life;’ costs, efficiency, and other characteristics of wet flue gas desulfurization (FGD) pollution control technology; transmission costs and a characterization of delivered costs for bulk power imports; and specifications of state or regional environmental constraints. Decisions/outputs Timing, type, location, and size of generation capacity expansions FGD retrofits; plant dispatch order and quantities of fuels consumed; marginal cost of electricity generation by region, period, and demand and volume of new interstate energy transfers.

and

level;

“This model was originally developed at CarnegieeMellon University as part of a larger study of how environmental constraints would affect electricity generation. It was derived from the Multi-Period Multi-State (MPMS) model, which integrated the many modules in the Advanced Utility Simulation Model (AUSM). It has undergone further refinement and testing at RFF. 5PADRE is formulated as a linear program. The sensitivity analysis procedure overcomes the problem of ‘knife-edge’ LP solutions by examining results over a wide range of input assumptions. To decompose the problem computationally we solve backwards in time, using the observed fact that capital stock and environmental stringency have increased monotonically over the years. 6The basic structure of the model is derived from the discussion in Turvey and Anderson (I 977, Chapter 13). ‘Some preprocessing of the coal data is required since PADRE can accommodate only three coal types per state or region out of the more than 100 types recorded in our database that could be used. Moreover, the types of coal likely to be used in practice - and their prices - will vary with the severity of environmental constraints. To accommodate these conditions, we run a second LP model designed to select cost-minimizing coal purchases given diRerent power supplies and environmental constraints. The three coal types in each state with the highest indicated consumption then are used in the PADRE simulations. ‘Dowlatabadi (1984) describes another model in which plant life extension and retirement decisions are endogenous.

H. Dowlatabadi and M. Toman, Electricity technology choice

Objective - Minimize the present value of total supply transmission and operating and maintenance 2.2.

costs, including (O&M) costs.’

235

capital,

fuel,

Time horizon and technology options

The model extends from 1985 to 2010. This choice of time horizon allows us to include both commercially available technology and options which are at the demonstration stage of development today. By including the latter we broaden the relevance of the study to encompass technology development issues as well as capacity planning decisions, while avoiding the even greater uncertainties associated with more speculative technology options. The technologies we consider for new capacity investment in the model are appropriate for utilities and larger ‘independent power producers’ (IPPs), with unit sizes of 200-500 MW. Because our focus here is not on issues of industrial organization, power supply contracting, and economic regulation, we simply refer to all plant operators as ‘utilities’.” Contributions to power supply by industrial cogenerations, small power producers, and other suppliers are not included directly in the analysis of cost-minimizing choices because of difficulties in parametrizing these technologies for incorporation in the model. These suppliers are included indirectly by exogenously modifying the load profiles faced by ‘utilities’ in the model. The analysis of capacity investment covers both proved, conventional technologies and more advanced technologies which in some cases still are in the development and demonstration stage today. Of the plethora of advanced technologies being developed, we include three options for new capacity investment which seem to have more economic promise and less technical uncertainty within our assumed time frame. These are atmospheric fluidized bed combustion (AFBC) coal plants, gas turbine combined-cycle (GTCC) plants, and integrated gasification combined-cycle (IGCC) plants - a combination of GTCC with a coal gasifier. ” The conventional alternative for new investment is pulverized coal (PC) plants with wet flue gas ‘Use of a cost-minimizing framework is typical in studies of this type, though even the most fervent proponents would agree that institutional and regulatory complications make this framework an incomplete description of actual utility behavior. Nevertheless, cost-minimization is likely to grow in future importance with the rise in competitive pressures in the electricity industry. “‘Joskow and Schmalensee (1983) provide a general discussion of issues related to the structure and regulation of the electricity industry. Toman and Darmstadter (1988) and Joskow (1989) discuss proposals and prospects for changes in the regulation of wholesale generation markets, including entry by non-traditional suppliers. “Optimization in the model over successive periods backwards in time forces us to treat construction of GTCC and IGCC plants as unrelated events. This causes an overestimation of IGCC costs and an underestimation of IGCC penetration, since in practice IGCC plants can be build in stages starting as GTCC, followed by the addition of a gasifier - with a lower overall capital cost.

236

H. Dowlatabadi and M. Toman, Electricity technology choice

desulfurization (FGD) equipment to meet environmental the decision framework of the model we also include existing coal plants to meet emission constraints.r2

standards. Within FGD retrofits of

2.3. Spatial scope of the analysis To strengthen our conclusions about the technology frontier in electricity generation, including the impacts of power trading, we need to exercise the model for more than a single utility. However, to expand the scope of the analysis while keeping it manageable requires several simplifying assumptions. In this study we exercise the model using stylized representations of two adjacent regions with significant differences in the age and technology of existing plants; the susceptibility of existing plants to tightened environmental regulation; demand growth; capacity margins; and degree on intraregional coordination. The first region is a five-state area in the east central part of the U.S. consisting of Indiana, Kentucky, Michigan, Ohio, and West Virginia. The second region is a four-state area in the middle Atlantic par of the U.S. that includes Delaware, Maryland, New Jersey, and Pennsylvania. We refer to these regions throughout the study as ‘Region E’ and ‘Region M’, respectively. In this study of investment options we use the model to examine separately the cost-minimizing technology options for each state in Region E, reflecting the relative loose degree of regional coordination in this area. The state is treated as the unit of analysis in these assessments, with the implicit assumption of perfect coordination within each Region E state. This is an obvious but necessary oversimplification to achieve manageability. The whole of Region M is treated as a single unit of analysis, reflecting the participation of utilities in these states in a ‘tight’ power ~001.‘~ To run the model we use baselines for generation capacity, demand, and existing trade for each state based on actual experience in the early 1980s.r4 Because our interest is in qualitative conclusions rather than precise numerical projections, we have not tried to use more recent baselines.15 To emphasize this point while limiting possible misinterpretation of our findings, we do not report state-level findings with actual state names. Instead, we refer to the states as El-E5 and Ml-M4. “See Dowlatabadi and Toman (1990, Chapter 3) for further discussion and justification of our technology selection. 13A tight pool involves joint planning for cost-minimizing plant use and new investment among participating utilities, with established rules for sharing returns and costs. ‘&See Dowlatabadi and Toman (1990, Chapter 2) for details. “In any event, more recent data of sufftcient detail are proprietary. An alternative strategy we could have followed would entail creating artificial states and baseline inputs, but we feel that tying our conclusions more closely to actual experience strengthens them.

H. Dowlatabadi and M. Toman, Electricity technology choice

231

2.4. Bulk power trading Expanded long-term bulk power imports provide an alternative to ‘domestic’ capacity additions for the Region E states and the Region M pool. To specify the cost of imported power, we first run the model under two artificial conditions. In the first case we do not permit any new interstate bulk power transfers among Region E states and between Region E and Region M beyond existing contractual volumes, while continuing to treat Region M as a tight pool. In the other extreme case we run the model to minimize joint costs for both regions. This is equivalent to perfect coordination among all the states in Region E and Region M. In this run the delivered cost of imported power is equal to the sum of marginal generation cost and an estimate of long-run marginal cost for transmission.16 For states which are net importers under perfect coordination, the difference between the delivered marginal cost of imports with perfect coordination and the marginal cost of in-state generation with no new trade is an upper bound on the value of access to imports. To implement bulk power transfers in the absence of perfect coordination - the case of primary interest in the study - we assume that the exporting state retains half the trading rent. To do this, we set transmission charges equal to marginal cost plus half of this trading rent for all states found to be exporters in the jointdispatch scenario. States that are importers in that scenario correspondingly are given the option of purchasing power from the exporters at a delivered cost equal to marginal generation cost plus this transmission charge.” In defining the trading regime, we continue to treat the Region M states as a single pool. Thus, power exchanges among Region M states reflect a transmission charge equal to marginal cost; the charge defined above applies to Region M imports from states in Region E. The outputs of the model used in the sensitivity analysis of investment options are produced with this trading regime. This approach implicitly assumes that any economic barriers to expanded transmission capacity can be overcome by a price equal to long-run marginal cost, and that noneconomic barriers also can be disposed of. These assumptions are important in that our model results with trade involve larger volumes of interstate power transfer than existing line capacity. In section 4 below, we discuss how

16The estimate we use, 5 mills/KWH for each 100 miles of transfer, reflects the cost of a double-circuit 345 KV line operating at a typical loading of 50 percent, with a 14-year amortization and a 20 percent add-on for transformers and other equipment. The typical interstate transmission distances used in the study reflect the spatial distribution of plants within each state. See Dowlatabadi and Toman (1990, Chapter 4) for details, “This pricing formula is broadly similar to the split-savings rules frequently observed in interutility economy exchanges, though our focus here is on long-term trading versus short-term economy transactions. Of course, this is only one of a continuum of rules we could have employed for specifying the delivered cost of imports.

238

our conclusions opportunities.

2.5. Stochastic

H. Dowlatahadi and M. Toman, Electricity technology choice

are affected

sensitivity

by less expansive

assumptions

about

trading

analysis

Determining the future values for the input parameters of PADRE is fraught with uncertainty. Moreover, the knife-edge behavior of the LP solutions makes the results of any single scenario of limited value. The sensitivity analysis explores the consequences of changes in input assumptions. The input combinations considered in this analysis are a random sample drawn from subjective probability distributions described in section 3 below.18 This allows an explicit representation of uncertainty about future states of the world. The shapes of the distributions reflect judgments about the nature of the uncertainties surrounding different parameters. For example, where the literature suggests a best modal estimate a triangular distribution could be used; a uniform distribution could be used where the literature specifies a range of values without singling out any point estimates. Running the model with these input assumptions generates corresponding probability distributions over the model outputs. Because the precise numerical outputs convey limited information we apply two non-parametric statistical tests to ascertain the relative importance of different input assumptions in determining the model outputs. l9 Note that in carrying out this procedure, there is the risk of falsely concluding that a particular input assumption is not important when that assumption does not in fact show enough variability across the sample for its importance to be detected. We return to this point in section 4.

3. Input parameter distributions Input parameters describing demand, technology, fuel costs, and other factors are discussed below. Here we provide a summary of the model inputs.20 The inputs are specified as (subjective) probability distributions to facilitate the sensitivity analysis. Wherever possible, we have tried to specify ranges for these distributions which span forecasts by organizations such as the Energy Information Administration (EIA), Data Resources Incorporated (DRI), the Electric Power Research Institute (EPRI), and the North American Electricity Reliability Council (NERC). Where sources for para“To generate the combinations we use the so-called Latin Hypercube Sampling [Iman and Helton (1985)]. “The procedures are Kendall’s T and Spearman’s p. [Downie and Heath (1965), (1975)]. *‘See Dowlatabadi and Toman (1990, Chapter 4) for details.

Scheme Lehman

H. Dowlatabadi

and M. Toman, Electricity

technology

choice

239

meter specifications are not otherwise specified, the choices reflect the authors’ own judgments. It also should be noted that except for fuel prices and environmental constraints, the input parameters in this study are assumed to be independent of one another. In practice, of course, fuel prices and the level of electricity demand are related. In focusing on supply decisions rather than overall market behavior, our approach does not exploit knowledge about how demand responds to prices. Fuel costs also influence the unit construcFurthermore, much of the technology tion cost of generation plants. employed in the generation of electricity is shared between different plant designs, so that there are correlations between the construction cost and thermodynamic efficiency of various technologies. The scope of this study did not allow us to address these issues2r

3.1. Demand Four-block load duration curves (LDCs) are posited for each state.22 The evolution of demand over time is assumed to be composed of four components. The ‘basic’ annual growth rates are uniformly distributed between 1.5 and 3.0 percent, using information from NERC (1987) and EIA (1988). These rates are modified by the possible load reduction over the period of the study from cogeneration and strategic conservation, which is taken to have a translated lognormal distribution with median of zero, 10th percentile of -6 percent, and 90th percentile of + 14 percent using information from EPRI (1986a). The LDC shape also is assumed to respond to load management (LM). This response is through peak and shoulder loads being shifted to cycling and base periods. The magnitude of this shift out to 2010 is modeled by a translated lognormal distribution with median of 2 percent, 10th percentile of -2 percent, and 90th percentile of 9 percent, again drawing upon EPRI (1986a).

3.2.

Supply technologies

Table 1 gives ‘best-estimate’ values for engineering-economic of the four technologies considered for incremental capacity.

characteristics Uncertainties

*‘For discussion of a more sophisticated sensitivity analysis that does address these points, given the requisite data, see Dowlatabadi and Evans (1986). By not taking the correlations into account we are, in effect, placing too much weight on unlikely parameter combinations. The result is more diffuse probability distributions for model outputs than could be obtained with more discriminatory input sampling. *‘The blocks correspond to peak, shoulder, cycling, and base demand.

H. Dowlatabadi

240

and M. Toman, Electricity Table

Engineering-economic

characteristics

technology

choice

1 of various

GkM

costsb

generation

technologies.

Availability

type

Heat rate’ (Rtu/ KWH)

Atmospheric fluidized bed combustion (10.2)

Coal

10.045

7.8

81.5

81.3

1,508

Gas turbine combinedcycle (44.2)

Gas

8.480

2.1

95.0

90.3

527

Integrated coal gasification combinedcycle (14.2)

Coal

9,115

3.2

94.7

83.2d

1,411

Pulverized coal with FGD (5.2)

Coal

9,800

2.0’

71.9

71.2

1,106’

Technology and (TAG code)

Fuel

(Mills/ KWH)

Maximum (%)

Minimum (%)

Capital costb (S/KWY

aRepresents average annual heat rate. ‘All costs are in December 1984 dollars. ‘Unit construction costs for ‘overnight construction’ (no interest expenses for funds used during construction are included). dThe minimum availability figure is assumed to be close to the equivalent availability figure published in EPRI’s TAG [EPRI (1986b)]. A more precise calculation of this figure using the methodology outlined in EPRI (1979) would yield a figure of 81 percent. ‘Does not include the operating cost of the FGD unit, which is computed endogenously (see text). ‘Includes costs of both the PC plant and the FGD unit. Source: EPRI (1986b).

about the technologies’ heat rates and unit construction costs are shown in table 2 and table 3. The distributions are triangular in form, with longer upper tails to reflect possible cost overruns. All of these figures assume a 500 MW unit size for conventional PC/FGD plants, and 200 MW unit sizes for the other technologies. The modal figures for new plant cost and performance are taken from EPRI’s Technical Assessment Guide [EPRI (1986b)], a standard industry reference. However, the uncertainty bounds in table 2 and 3 reflect our own judgments, informed partly by EPRI figures and partly by analogies we have drawn with uncertainties regarding manufacturing processesz3 The GTCC and PC/FGD technologies are mature, with the least uncertainty; variability of cost or technical attributes for these technologies primarily reflects variations in contractor or operator performance. IGCC is treated as a more uncertain demonstration technology, while AFBC is treated as the least certain pilot technology. Because there is disagreement within the industry %ee Merrow, Phillips, and Myers (1981). Of course, to the extent that manufacturing and electricity generating processes face different types of uncertainties this analogy is a distortion.

H. Dowlatabadi and M. Toman, Electricity technology choice

241

Table 2 Heat rate uncertainties

for various

technologies.”

Heat rate (Btu/KWH)

Technology and (TAG code)

status

Low

Modal

High

AFBC (10.2) GTCC (44,2) IGCC (14.2) PC/FGD (5.2)

Pilot Mature Demonstration Mature

9,750 8,230 9,490 9,760

10,045 8,480 9,775 9,800

11,000 10,050 9,980 10,350

“The form of the assumed distribution is asymmetric triangular. The modal value of heat rates presented here coincides with figures published in EPRI (1986b). There are no published data on the uncertainty bounds for the heat rate of various generation technologies. Here we have used part load plant efftciencies to mimic these bounds, the lower bound being that for a 50 percent load and the upper bound for a 100 percent load. These figures are approximations and do not reflect EPRI data on actual plant performance uncertainties.

Table 3 Unit construction

Technology and (TAG code) AFBC (10.2) GTCC (44.2) IGCC (14.2) PC/FGD (5.2)

cost uncertainties

Unit construction

for various

capital

technologies.”

cost distribution

(1985 $/KW)

Minimum

Mode

Median

Maximum

1,206 474 1,199 995

1,508 527 1,411 1,100

1,669 538 1,511 1,128

2,412 620 1,987 1,301

“The form of the assumed distribution is asymmetric triangular. The figures presented here represent the authors subjective assessment of the range of unit construction costs for various technologies. The modal values coincide with the figures presented in EPRI (1986b). However, the uncertainty in the cost distribution also has been derived from other findings, in particular Merrow, Phillips and Myers (1981).

about the relative performance of AFBC and IGCC technologies, we also exercise the model for two special cases involving more favorable assumptions about AFBC plants. In one case the probability distribution for AFBC construction cost is shifted to the left to reflect the presence of some scale economies in building 500 MW versus 200 MW units. In the other case we reverse the assumed degrees of riskiness for IGCC and AFBC costs and performance, making AFBC the less risky of the two. As already noted, the model also includes FGD retrofits for existing coal plants. The cost of FGD retrofit for each existing coal plant in the model’s database is calculated using an adaptation of Rubin’s (1983) model. While there is thus no generic FGD retrofit unit, the retrofits generally cost something over $2OO/KW to install, 9-11 cents per KWH to operate and require around 8 percent of net plant energy output to run.

H. Dowlatabadi and M. Toman, Electricity technology choice

242

Table 4 EIA real oil and gas price per-annum

Fuel

198771990 (%)

growth

rate projections.

199G2000 (“i,)

1987-2000 (%)

6.4 5.1

5.6 3.8

5.8 4.9

4.9 2.3

7.1 6.6

6.3 5.8

Base case Natural gas Residual fuel oil

3.4 -0.2

Low world oil price/high growth case Natural gas Residual fuel oil

2.1 -5.6

High world oil price/high growth case Natural gas Residual fuel oil

3.8 3.2

Sources: EIA (1988, tables Al, Bl, Cl). All prices are delivered to electric utilities.

3.3. Fuel prices, environmental

constraints,

costs

and discount rates

Low, modal, and high rates for oil and gas price growth from 1987-2000 are drawn from the three price scenarios in EIA (1988), as summarized in table 4. For each of the subperiods from 1987 to 1990 and from 1990 to 2000, high oil price growth rates are associated with high gas price growth rates and vice versa.24 Possible price growth rates from 2OOS2010 are assumed to be the same for oil and gas, with a range equal to possible oil price growth rates over 1987-2000. Over all of these ranges, asymmetric triangular distributions for the price growth rates are assumed with ranges and modal values given by the EIA projections. Price growth trends are assumed to be perfectly correlated across the three subperiods: high oil and gas price growth from 1987 to 1990 corresponds to high growth in later subperiods and vice versa. The EIA oil and gas price projections have been criticized for being too 10w,~’ and for being too bunched together and thus not showing sufficient sensitivity to the range of possible petroleum market conditions. While remaining agnostic on that debate here, we note that the gas price asumptions in table 4 give rise to 2010 gas prices ranging from SS.SO/ MMBtu to $g.OO/MMBtu (in 1985 dollars). Moreover, even with the lowest growth rate for gas prices the Btu-equivalent price of gas is 3.5 times that of coal in 2010, versus rough parity in 1987. We return to this point in the next section of the paper. 24More precisely, we sample the oil and gas price growth rates in parallel ranges so that the rates in the sample are perfectly correlated. ‘%ee, e.g., Hogan (1988).

fashion

across

their

H. Dowlatabadi and M. Toman, Electricity technology choice

243

Table 5 Alternative

Case Case Case Case

SO,

1 2 3 4

emission constraints for the 31 states adjoining the Mississippi.”

east of and

1985

1990

1995

2ooo

2005

2010

14.5 14.5 14.5 14.5

14.5 14.5 12.5 12.5

14.5 12.5 9.5 4.5

14.5 9.5 4.5 4.5

14.5 4.5 4.5 2.5

14.5 4.5 4.5 2.5

“Million tons SO2 emissions from utility boilers per year. Source: Author assumptions (see text).

Table 6 Price growth

differentials

for different coal types under reduction cases.

Year differentials begin Case Case Case Case

1 2 3 4

2000 1995 1990

Sulfur content

alternative

emission

in (Ibs of SOJMMBTU)

< 1.2

1.2-1.8

1.8-3.0

73.0

0.0 1.0 1.5 1.5

0.0 0.50 0.75 1.5

0.0 0.0 0.0 0.5

0.0 0.0 0.0 0.0

Source: Derived from EPRI (1984). All entries represent differential price growth rates for each alternative specification of environmental control (see table 5).

Coal prices are treated separately from oil and gas prices in the sensitivity analysis. The growth in the delivered price of coal arises from transport price escalation and sulfur content premiums. The former are dependent on the mode of transport, while the latter are correlated with the severity of environmental restrictions. Four alternatives for sulfur emission control, assumed to be equally probable in the sensitivity analysis, are shown in table 5. Case 1 holds emissions constant at the level that would have been achieved in 1980 given compliance with the so-called State Implementation Plan (SIP) standard developed in response to the Clear Air Act.26 In Case 1 we assume for simplicity that there is no real growth in any minemouth coal prices; transport costs are assumed to grow at rates up to 1.5 percent per year, depending on the mode. Cases 24 in table 5 postulate accelerated sulfur emission reductions. This acceleration is assumed to produce corresponding differential increases in minemouth prices for coals with different sulfur content shown in table 6. Thus, in these cases the growth in delivered coal cost reflects the sum of transport cost growth and various ‘sulfur premia’. 26Actual

1980 SO, emissions

are estimated

to have been 2-3 million

tons above these levels.

H. Dowlatabadi and M. Toman, Electricity technology choice

244

I a GAS TURBINE

0

IO

mean=

0

IO

Mean=35 la and

14.5

30

lb. Plant

capacity

40

CYCLE

50

60

30

80

.I0

50

8 GW

70

8C

Capacity

histograms for intermediate Region E and Region M.

110

in GWe

COMBINED

60

100

90

Capacity

GASIFICATION

‘0

70

GW

INTEGRATED

lb

Figs.

20

COII6INED

CYCLE

90

IO0

I IO

In GWe

coordination

and

trade

between

Finally, the real discount rate in the sensitivity analysis is drawn from a symmetric truncated normal distribution with a median value of 9 percent [using information from FERC (1987)], and a range of 6-12 percent. We also consider a discount rate of 18 percent (with modal values of other parameters) in an experiment to assess the consequences of a high rate.

4. Model results 4.1. Basic findings Figs.

la-le

with expanded

show

probability

trade histograms

from

the model

for cumulative

H. Dowlatabadi and M. Toman, Electricity technology choice lc

CONVENTIONAL

COAL

WITH

WET

245

FGD

80

.60 s -

Capacity

Mean=2.4 GW Id TOTAL

NEW

CAPACITY

in GWe

INVESTMENTS

I 9

IO

Mean=52 Figs.

lc and

Id. Plant

capacity

20

30

40

50

60

70

7 GW histograms for intermediate Region E and Region M.

80

90

Capacity coordination

I IO

100

in GWe and

trade

between

capacity investments and FGD retrofits by 2010, given the option of expanded interstate trade as discussed in section 2. The relatively uniform distribution of total new capacity investments more closely resembles our assumed distribution for demand growth than our assumptions about cost and technology performance (which are single-peaked triangular distributions). The GTCC and IGCC distributions also are relatively uniform; the asymmetry on the low end of the IGCC distribution partially reflects the low probability that conventional PC/FGD plants are more cost-effective than IGCC for meeting baseload demand. The most striking result to emerge from these runs is the complete absence of AFBC investment; this is why no AFBC histogram is shown in fig. 1. This is a particularly surprising conclusion in light of the fact that AFBC is

H. Dowlatabadi and M. Toman, Electricity technology choice

246

Ie

WET

0

FGD

IO

RETROFITTED

20

30

40

TO EXISTING

50

60

70

COAL

80

90

Max. Mean=45

Fig.

le. Plant

capacity

histogram

6 GW

for intermediate and Region

Capacity coordination M.

PLANTS

100

I IO

Pbssible

in GWe and

trade

between

Region

E

favored as a new technology by many in the industry.” While the apparent cost-ineffectiveness of the AFBC technology no doubt is magnified by the knife-edge character of LP solutions in the PADRE model, its complete absence suggests that other forces are at work. Rerunning the model with a lower unit construction cost for AFBC plants, to reflect scale economies with 500 MW units, yields only a modest amount of AFBC investment (1.5 GW out of some 53 GW of total new investment calculated by the model). Rerunning the model with AFBC being less risky than IGCC fails to generate any AFBC investment, even in the tails of the output distributions. These findings suggest that AFBC investment becomes cost-effective in the model only with cost and performance assumptions that are significantly more favorable than the EPRI figures we use. The histogram for FGD retrofits (fig. le) also is of interest. The leftmost bar, indicating little FGD retrofit, reflects Case 1 scenario of relatively loose environmental constraints (see table 6). In the three other environmental scenarios considered (Cases 2-4 in table 6), the amount of retrofit is substantial - 40-80GW out of some 99 GW of plant capacity eligible for such treatment in Regions E and M. However, the results do not indicate three equiprobable bars corresponding to the three more severe environmental scenarios; these environmental constraints interact with other input assumptions in the determination of FGD retrofits. One other experiment of interest concerns the implications of a higher (18 *‘As noted previously, our optimization routine overstates the cost of IGCC investment by failing to allow for stated construction starting with a GTCC unit. Thus, the finding of the model that IGCC is more cost-effective than AFBC is in some ways conservative.

H. Dowlatabadi and M. Toman, Electricity technology choice

241

percent) real discount rate. As one would expect, the higher discount rate gives a preference to GTCC plants with lower capital cost and shorter leadtimes. In fact, the mean of the output distribution for GTCC investment is roughly double the mean level in fig. la, while mean IGCC investment falls by roughly a third relative to fig. lb.28

4.2. Technology

choice and trade

Detailed model results (not shown here) indicate that state E5 is the single largest power exporter, while states E4, M2, and M4 are the major importers. Marginal electricity supply costs for E4, M2, and M4 are reduced by imports which back out more expensive in-state capacity. Correspondingly, the marginal cost of in-state generation is higher in state E5 as capacity is used more intensively to meet both in-state and import demands; these higher costs are recompensed by importer payments. Expanded trade opportunities also are manifested in lower needs for construction of new capacity. The model results indicate that total capacity additions are about 13 percent lower with expanded trade opportunities; the decline in GTCC investment, mainly for meeting peak demands, is almost 20 percent. Other than these declines in total investment scale, however, the probability histograms for new investment in the absence of expanded trade are not fundamentally different from those shown in figs. la-ld.29 Thus, we feel that our technology choice results are not greatly sensitive to our assumptions about trade.30 4.3. Sensitivity

analysis

Table 7 summarizes the input assumptions which are statistically significant in explaining variations in new capacity investments across model runs. The columns denote the different technologies; the row numbers indicate the rank of the different explanatory factors; and the signs following the entries indicate the direction of the relationship between that input and the capacity investment in questions. 31 Many of these findings are fairly intuitive. For example, the bulk of new investment in the model is in GTCC and IGCC 28Since demand is the same in the two cases, total capacity additions essentially are unchanged. 29The histogram for FGD retrofits without expanded trade shifts to the left by 1OGW relative to the pattern shown in fig. le. This reflects the greater reliance on older plants with expanded trade, since new investment is lower, and thus a greater need for retrofits to meet environmental constraints. “‘We emphasize again, though, that our findings do not take into account various possible obstacles to expanding transmission capacity. 3’These findings derive from nonparametric tests, as discussed previously. The entries in the table have a significance level of 5 percent or greater in explaining variations in the model outputs across 60 model runs generated by the sample of input parameters.

R.E.

B

H. Dowlatabadi and M. Toman, Electricity technology choice

248

Table 7 Rank

of significant

Region E 1 2 3

factors

influencing investments 2010.”

GTCC?

IGCC’

Demand ( + )’ LM (-)” IGCC cost (+)

Demand LM (-)

( +)

Demand (+) LM (-) IGCC cost (+)

Demand LM (-)

(+)

in new capacity

in

PC/FGDd IGCC cost (+)’ PC HR (-)”

_

-

Region M

1 2 3

-

IGCC cost ( + ) PC cost (-)’ Demand ( +)

“Factors listed are signilicant at the live percent level in nonparametric test; see text for further discussion. “Incremental investments in gas turbine combined-cycle plants. ‘Incremental investments in integrated coal gasification combinedcycle plants. dIncremental investments in pulverized coal plants with FGD units. ‘Growth in electricity demand. ‘Unit construction cost for integrated coal gasification combined-cycle plants. Sccess in load management programs. hHeat rate of pulverized coal power plants. ‘Unit construction cost for pulverized coal plants.

plants, so the scale of these investments will be more heavily affected by total demand growth and by success in load management. In contrast, PC/FGD investments occur more sporadically in the model runs and are more heavily influenced by the cost and performance of alternative technology options. Conspicuous by their scarcity or absence from table 7 are inputs related to technology cost or performance and fuel prices. The lack of apparent significance of technology cost or performance in determining GTCC and IGCC investment reflects the dominance of these technologies over AFBC and PC/FGD in the model results. If we assumed an even wider range of parameter values for technology cost or performance that implied greater diversity in the model results, these factors presumably would appear in table 7. Similarly, the absolute price of coal has lesser impact on the choice of different coal-using technologies, and our assumptions about gas prices relative to coal prices do not yield substantial variability across the model runs in the choice between GTCC and IGCC for new baseload capacity. Consequently, fuel prices do not appear as significant in table 7. Natural gas prices would appear in the table only if we considered gas price growth paths so low that GTCC frequently competed with IGCC for satisfying baseload demands. Table 8 summarizes significant influences on marginal generation cost for all nine states considered in the study. In Region E, demand growth

H. Dowlatabadi

and M. Toman, Electricity

technology

choice

249

Table 8 Rank of significant factors influencing the average marginal cost of electricity generation in 2010.” State El

E2

E3

E4

E5

1 2 3

Demand ( +)b IGCC cost (+)’ Disc. rate (+)

Demand ( + ) Disc. rate ( +)d

Demand ( +) IGCC cost (+) O&G price ( +)’

Demand ( +) IGCC cost (+) O&G price (+)

Demand

Ml

M2

M3

M4

1 2 3

Disc. rate (+) IGCC cost (+) O&G price (+)

Disc. rate

(+) IGCC cost (+)

Disc. rate (+) IGCC cost (+) O&G price ( +)

Disc. rate (+) IGCC cost (+) Demand (+)

O&G

price (+)

( +)

-

“Factors listed are significant at the live percent level in nonparametric tests; see text for further discussion. bGrowth in electricity demand. “Unit construction cost for integrated coal gasification combined-cycle plants. dThe discount rate. ‘Price of oil and gas used for generation of electricity.

significantly influences cost by altering the utilization rates of all plants and the need for new plants. In contrast, Region M requires substantial investment for all demand scenarios, and the discount rate is an important factor through its impact on total capital cost. In both regions, IGCC cost is an important influence given the prevalence of this technology for new capacity investment.

5. Concluding remarks In this paper we describe a methodology for analyzing the economic cost-minimizing frontier in electricity generation, and we present some illustrative findings based on technology options currently under industry consideration. Our finding that advanced technologies appear to be more cost-effective may be conservative in that our analysis does not consider practical advantages accruing to these advanced technologies due to their modularity, e.g., lower risk of overinvestment and subsequent regulatory disallowance of investment costs. The model also indicates that gas-using technologies, including plants using coal-to-gas conversions, appear to have lower costs than fluidized bed units. We are not prepared to offer this result as a prediction in light of the many uncertainties plaguing the assessment of advanced technologies’ cost and performance. At the very least, however, our findings indicate the need for deeper scrutiny of these technologies’ properties and relative cost-effectiveness.

250

H. Dowlatabadi and M. Toman, Electricity technology choice

Our analysis does not directly reflect the impact on technology choice of risk aversion which could cause suppliers to prefer technologies with less performance uncertainty or technologies with greater cost controllability (e.g., coal-fired technologies less subject to the gyrations of world oil and gas markets). Our approach does assume asymmetric distributions in which mean costs exceed modal costs and mean performance is less than modal performance. However, this is an incomplete assessment of the issue; further research on how risk aversion affects the cost-effectiveness of different technologies as seen by utilities clearly is warranted. Our analysis includes the possibility of expanded interstate trade where long-term bulk power purchases are a lower-cost alternative to expanded interstate generation. We find that such trade may convey significant efficiency gains, as indicated by a lowering of requirements for new capacity and a narrowing of cost gaps among states. Whether these gains can (or should) be reaped in practice depends on questions related to the siting and pricing of transmission capacity which have not yet been answered. Finally, the results in this paper are a case study with a limited number of states, technologies, and environmental constraints. Expanding the scope of the analysis in any of these directions appears fruitful. References Baughman, M.L., P.L. Joskow and D.P. Kamat, 1979, Electric power in the United States: Models and policy analysis (MIT Press, Cambridge, MA). Dowlatabadi, H., 1984, Electricity interchange between integrated grid systems: Methods and case studies, Ph.D. thesis (Cambridge Energy Research Group, Cambridge University, Cambridge). Dowlatabadi, H. and N. Evans, 1986, Electricity trade in the U.K.: Economic prospects and future uncertainty, Energy Policy 14, 3547. Dowlatabadi, H. and M.A. Toman, 1990, Technology options for electricity generation: Economic and environmental factors (Resources for the Future, Washington, DC). Downie, N.M. and R.W. Heath, 1965, Basic statistical methods, 2nd ed. (Harper and Row, New York). Electric Power Research Institute, 1979, Uncertainty methods in comparing power plants, Report FFAS-1048 (EPRI, Palo Alto, CA) April. Electric Power Research Institute, 1984, Effects of resource depletion on future coal prices, Report EA-3733 (EPRI, Palo Alto, CA) Oct. Electric Power Research Institute, 1986a, Impact of demand side management on future customer electricity demand, Report EM-4815~SR (EPRI, Palo Alto, CA) Oct. Electric Power Research Institute, 1986b. Technical assessment guide, vol. 1, Electricity supply 1986, Report P-4463~SR (EPRI, Palo Alto, CA) Dec. Energy Information Administration (EIA), 1988, Anual energy outlook 1987, Document DOE/ EIA-0383 (87) (Government Printina Office. Washington, DC) March. Federal Energy Regulatory Commission (FERC), 1987,Generic determination of rate of return on common eouitv for utilities. Docket no. RM87-35-000 (FERC. Washington, DC). Hogan, W.W., 1988, Oil demand and OPEC’s recovery, Energy and Environmental Policy Center discussion paper E-88-02 (Harvard University, Cambridge, MA) June. Iman, R.L. and J.C. Helton, 1985, A comparison of uncertainty and sensitivity analysis techniques for computer models, NOREG/CR-3904 SAND84-1461 (Sandia National Laboratories, Albuquerque, NM) March.

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Joskow, P.L., 1989, Regulatory failure, regulatory reform, and structural change in the electrical power industry, in: Martin Neil Baily and Clifford Winston, eds. Brookings papers on economic activity: Microeconomics 1989 (Brookings Institution, Washington, DC). Joskow, P.L. and N.L. Rose, 1985, The effects of technical change, experience, and environmental regulation on the construction cost of coal-burning generating units, Rand Journal of Economics 16, 1-27. Joskow, P.L. and R. Schmalensee, 1983, Markets for power (MIT Press, Cambridge, MA). Joskow, P.L. and R. Schmalensee, 1985, The performance of coal-burning electric generation units in the United States: 196&1980, Working paper no. 377 (Department of Economics, Massachusetts Institute of Technology, Cambridge, MA) July. Lehman, E.L., 1975, Nonparametrics: Statistical methods based on ranks (Holden-Day, Oakland, CA). Masse, P. and R. Gilbert, 1964, Application of linear programming to investments in the electric cost pricing in practice (Prentice-Hall, power industry, in: R.J. Nelson, ed., Marginal Englewood Cliffs, NJ). cost growth and performance Merrow, E., K. Phillips and C. Myers, 1981, Understanding shortfalls in pioneer process plants (Rand Corporation, Santa Monica, CA). Morrison, M.D. and E.S. Rubin, 1985, A linear programming model for acid rain policy analysis, Journal of the Air Pollution Control Association 35, 1137-l 148. North American Electricity Reliability Council (NERC), 1987, 1987 electricity supply and demand: For 1987-1996 (NERC, Princeton, NJ). Rubin, ES., 1983, International pollution control costs of coal-fired power plants, Environmental Science and Technology 17, 366A-377A. Toman, M.A. and J. Darmstadter, 1988, Improving performance of wholesale electric generation markets. Energy and natural resources division discussion paper ENR88-03 (Resources for the Future, Washington, DC) Dec. Turvey, R. and D. Anderson, 1977, Electricity economics (World Bank, distributed by Johns Hopkins University Press, Baltimore, MD).