Simulating the spiral of impossibility in the electric utility industry

Simulating the spiral of impossibility in the electric utility industry

Andrew Ford and Annette Youngbiood

This article examines the 'spiral of impossibility' for different types of investor-owned eiectdc uti l i ty companies in the USA. The spiral begins with an increase in electricity price that discourages demand growth. The utility must spread its fixed costs over fewer kilowatt-hours of sales, which, in turn, leeds to still higher rates. Computer simulation techniques are used to quantify whether the spiral would pose sarious planning problems for utility companies, and the effect of policies that might mitigate the spiral's effects are tested. The conclusion is that the spiral poses substantial planning problems for utility companies building long lesd-time power plants, serving customers with high price elasticity of demand and quick response time, but those relying on oil or natural gas are less vulnerable.

Keywords: Electricity; Utilities; Price

The authors are with the Economics Group, Los Alamos National Lab- oratory, PO Box 1663, Los Alamos, NM 87545, USA.

t'A dark future for utilities', Business Week, No 2587, 28 May 1979, p 110. 2Lovins' remarks appear in 'New directions for utility resources and financial planning', an address delivered at the Stanford

continued on page 20

' A d a r k fu tu r e for ut i l i t ies ' was the prognos is for the na t ion ' s investor- o w n e d e lec t r i c ut i l i ty c o m p a n i e s in a Business Week (28 M a y 1979) a r t i c l e . Business Week's desc r ip t ion o f the indus t ry ' s many p rob lems i n c l u d e s t he fo l lowing sequence of events :

A return to the zooming rates of a few years ago could intensify the vicious circle in which an increasing number of utilities now find themselves. As higher rates discourage demand, a utility's capital costs must be spread over fewer kilowatt- hours which in turn leads to still higher rates. Already, some big industrial customers are considering building their own power plants or turning to cogenerat ion of power with process steam; even the loss of one such customer could be serious for some utilities. 1

A m o r y Lov ins , an o u t s p o k e n cri t ic o f the ut i l i ty indust ry , refers to this s e q u e n c e as t he 'Sp i ra l of Imposs ib i l i ty ' . In his ke yno t e speech at the 1980 S t a n f o r d S y m p o s i u m on ' E n e r g y eff iciency and the ut i l i t ies ' , M r Lovins d e s c r i b e s the p r o b l e m in cons ide rab l e deta i l .

But, I think it's a mistake to suggest that the financial position of the utilities is a result only of regulation, because the regulatory lag and the political sensitivities of regulators are only the tip of the iceberg. I 'd like to argue briefly that utility cash flow, given the present nature of the business, is inherently unstable. This is because of the extreme capital intensity of the business - about 100 times of the tradit ional direct-fuel systems on which our economy was built - and even more it 's because of the long lead time to build conventional power stations, much longer than the time it takes consumers to start responding to higher price. If you build a power plant, one way or another you'll have to raise the price to maintain your financial position during construction, even if you don't have CWIP. Then by the time the plant is built, or shortly thereafter, the higher price will dampen the demand growth below what you forecast. In general, demand forecasts are based on historic or rolled-in historical prices, not on what the prices are going to be once the plant is built, so you build more capacity than you'll be able to amortize from the revenues of those plants. When you have a shortfall in revenue to cover the fixed charges on the plant you just built, you have to raise the rates more, which dampens the demand growth further, and you get into the well-known "spiral of impossibili ty", which is familiar in places like railways and mass transit. 2

In th is a r t i c le , we t ake a c lose look at this so-ca l led spiral o f impossibi l i ty

0301-42151831010019-20503.00 © 1983 Butterworth & Co (Publishers) Ltd 1 g

Simulating the spiral of impossibility

continued from page 19 University Symposium on 'Energy effic- iency and the utilities: new directions', sponsored by the California Public Utility Commission, 18-19 April 1980, p 169. In the same address, Lovins makes it clear that the spiral poses serious planning problems for European utilities as well: 'Just recently there was the front-page story in The Times in London, "Electricity Prices Set to Rise Further as Profit Turns to Loss." And the Chairman of the Central Waste-Heat Generating Board (I call them after their principal product) said, "We face a disturbing prospect: a vicious circle of rising electricity prices, causing further reductions in demand, which in tum would push up prices still more'. Lovins' views on the financial problems of the electric utility industry are also explained in A.B. Lovins, 'Electric utility investments: excelsior or confetti?', Journal of Business Administra- tion, published by the Faculty of Commerce and Business Administration, The University of British Columbia, Vancouver, Canada and in A.B. Lovins, 'How to keep electric utilities solvent', The Energy Journal, forthcoming. 3E. Cazalet, C. Clark and T. Keelin, 'Costs and benefits of over/under capacity in electric power system planning', Report EA-927 of the Electric Power Research Institute, October 1978. 4A. Ford and I. Yabroff, 'Defending against uncertainty in the electric utility industry', Energy Systems and Policy, Vol 4, No 1 and 2, 1980, lap 57-98. SR. Boyd and R. Thompson, 'The effect of demand uncertainty on the relative economics of electrical generation technologies with differing lead times', Energy Systems and Policy, Vol 4, No 1 and 2, 1980, pp 99-124. 6A. Ford and A. Polyzou, 'Simulating the planning advantages of short lead time generating technologies under irregular demand growth', presented at the Western Economic Association Conference, San Francisco, CA, USA, 3 July 1981. ~'l'he Los Alamos review is given in A. Ford, 'Utility planning for an uncertain future: a review of capacity expansion studies', presented at the Institute for Gas Technology Symposium, Energy Modeling II1: 'Dealing with energy uncertainty', 4-8 August 1980, Chicago, IL. Readers may also be interested in a discussion of the way in which corporate planning models used by electric utility companies de or do not handle the 'spiral of impossibility'. For a case study comparison of a dozen corporate planning models, see Electric Power Research Institute report EA-2065, 'Case study comparison of utility corporate models', October 1981. The case study comparison showed that only one of the 12 models treated the 'spiral of impossibility' explicitly, and the study participants found that the unique model exhibited dramatic- ally different behaviour in the case study exercises.

using computer simulation analysis. We show that the spiral could give rise to unstable conditions for certain types of utility companies.

Strategies that company planners and state regulators might adopt to reduce the adverse effects of the spiral are tested with the simulation model. The special case of utility companies that depend on expensive oil or natural gas for a large part of their electricity generation is investi- gated. For some of these companies, the spiral of impossibility does not pose a significant planning or stability problem.

The computer simulation of the spiral of impossibility is part of a larger field of investigation in which mathematical models are used to help utility planning for additional generating capacity under uncertainty about the future growth in electricity demand. Before discussing the spiral of impossibility, related areas of interest and modelling efforts are reviewed.

Planning capacity additions under uncertainty Building a new power plant is a billion-dollar business based on uncertain information about the future. Large generating stations may take two to three years to license and eight to nine years to construct. If these plants are to be initiated in a timely manner, the utility must take action over a decade in advance of the anticipated need for electric power. To antici- pate the capacity which will be needed, utilities prepare 10- to 12-year forecasts of power demand. Unfortunately, these forecasts are highly prone to error because of uncertainties in the consumer response to electricity price increases, in the population growth and economic activity, and in the possible substitution of electricty for oil and natural gas.

During the past few years, a number of studies have been initiated on the premise that errors in demand forecasts are unavoidable. The goal of recent studies has been to test the effectiveness of different strategies that would minimize the adverse effects of uncertainty on the company and the ratepayer. In certain cases, the strategy of interest is to 'overbuild' generating capacity to hedge against the possibility that demand growth might accelerate in the coming years, a In other cases, the strategy is to increase utility investment in short lead-time generating technologies such as small coal plants, wind machines, and geothermal stations.4,s, 6

As part of Los Alamos research on electric utility planning issues, we have reviewed six studies in which mathematical models are used to analyse capacity expansion planning in the face of large demand uncertainty. The review showed that the studies employ several different techniques (such as decision analysis, dynamic simulation, deterministic simulation, stochastic simulation, and dynamic programming) to characterize utility company actions in the area of forecasting, planning, capacity construction and capacity operation.

This review also shows marked differences in the scope and detail of the model representations of company behaviour. However, when characterizing consumer behaviour, none of the six studies allowed for explicit representation of the price/demand vicious circle referred to as the spiral of impossibility.7 Thus, the analysis presented here not only addresses an important planning issue confronting utility executives, but also fills a void in the new field of capacity expansion modelling with long-term demand uncertainty.

20 E N E R G Y POLICY March 1983

sJ.W. Forrester, Industrial Dynamics, MIT Press, Cambridge, MA, 1961. 9M.R. Goodmen, Study Notes in System Dynamics, Wright-Allen Press, Cambridge, MA, 1974. 1OE.B. Roberts, Managerial Applicab'ons of System Dynamics, MIT Press, Cambridge, MA, 1978. HR.G. Coyle, Management System Dynamics, Wiley, New York and London, 1977, p 2. 12The relevance of control theory has been described by Amory Lovins as follows: 'You might want to think of this [the spiral] as a problem in control theory. Utility cash flow is a high-flux, long-lag system. Such systems are well known to overshoot and collapse. The only way to correct that is to reduce the time constants.' A. Lovins, 'New directions for utility resources and financial planning,' op cit, Ref 2. 'In short, a disparity of time constants between construction and price response makes cash-flow unstable-the classic control theory instability of any system with long lags. Reconciling the two time constants can cure the instability, but subsidies make it worse. It is like having a furnace controlled by a thermostat at the end of a long corridor; the corridor will over- heat before the thermostat can tell the furnace to shut off. Moving the thermostat up next to the furnace reduces the time lag and can eliminate the overshoot. Tuming up the thermostat or enlarging the fumace merely exacerbates it'. A. Lovins, 'How to keep electric utilities solvent', op cit, Ref 2. • 131. Yabroff and A. Ford, An Electric Utility Policy and Planning Analysis Mode/, Final Report from SRI International, December 1980.


Simulating electric utility company operations This investigation of the spiral makes use of a computer model designed to simulate the operations of a hypothetical, investor-owned electric utility company subject to rate-of-return regulation as practiced by state public service commissions. The model has been constructed using the System Dynamics techniques developed by Jay W. Forrester and his colleagues at the Massachusetts Institute of Technology.S, 9, lo System Dynamics has been described as 'that branch of control theory which deals with socio-economic systems, and that branch of Management Science which deals with problems of controllability'. 11 System Dynamics is well suited to this investigation because the spiral of impossibility may be characterized as a management and control problem. 12

Model description The specific model used here, known as EPPAM (Electric utility Policy and Planning Analysis Model), is described in full technical detail else- where. 13 Therefore the model description is limited to the following summaries of the different sectors of the model:

Electricity demand. The user specifies the annual growth in demand that would occur if there were no change in the real price of electricity. This growth rate is a proxy for the growth in population and economic activity in the service region. The model modifies the growth over time in response to changes in the real price of electricity. The adjustment is based on a user specified price elasticity of demand and the length of the consumer adjustment delay. The demand load factor is exogeneously specified and may be changed over time to simulate the effect of load management programmes. The load duration curve is analytically approximated by considering the values of the peak, average, and minimum demands for power.

Electricity production. Governed by a merit order, based on the relative operating costs of the 15 technologies used in the model. A simple derating method is used to 'fill in' the area under the load duration curve with generation from the different plants operating in the system.

Generating technologies. These include four coal-based technologies (plants with scrubbers, plants without scrubbers, fluidized-bed combustion, and combined cycle) and two nuclear plants (light water reactors and liquid metal fast breeder reactors). Other technologies include hydroelectric, biomass, wind machines, solar-thermal, and ocean thermal, as well as the conventional plants fired by heavy oil or natural gas. Peaking technologies include pumped storage and gas turbines.

Technology choice. This is determined on the basis of the relative costs of each technology when operating in either baseload or intermediate duty. A logit function is used to allocate investment among several technologies showing similar costs of generation. With the exception of turbines for peaking purposes, no new oil- or gas-fired plants are permitted in the model. The user must specify any conversions of existing oil and gas plants to coal.

Capacity expansion planning. This is based on an internally prepared

E N E R G Y P O L I C Y M a r c h 1 9 8 3 21


forecast of the future size and shape of the load duration curve. Planning long lead-time plants requires forecasts that look over a decade into the future. Two delays are imposed between plant initiation and installation- one for the time required to obtain permit approvals and one for construction. The amount of capacity being planned and under construction is monitored as part of the planning process. Should demand slow unexpectedly during the interval when a plant is receiving construction permits, construction may be cancelled in the model. Similarly, the lifetime of the existing plants may be shortened or extended, depending on the pattern of demand growth.

Construction and operating costs. These costs arise from each generating technology, as well as from the transmission and distribution equipment employed. Unit construction costs are specified in dollars per kW of capacity for new plant and new distribution equipment. The user- specified S/kWh of new annual demand is used to determine distribution expenditures. Interest during construction and escalation is handled internally by the model. Operational costs include operation and main- tenance (O&M) of existing plants, depreciation, property taxes, income taxes, and debt interest.

Capital assets. These are accumulated for each of the plants in the system, for transmission and distribution equipment, for construction work in progress (CWlP) and for the allowance for funds used during construction (AFDC). The common practice is to accumulate AFDC during plant construction and add it to the rate base once construction is completed.

Rate o f return regulation. Is represented by setting allowed revenues to cover annual costs plus an allowed return on investments. The allowed rate of return is set to the weighted cost of capital, and a regulatory delay is applied before any requested rate increase becomes effective.

Financial accounts are maintained; they include the non-cash entry due to AFDC. Both 'reported profits' (which include AFDC) and 'cash profits' are monitored by the model. Reported profits, less AFDC less the non-operating expenses (debt repayment, stock dividends, and adjust- ments to working capital) give the cash surplus. This surplus is used either to help finance new construction or to buy back common stock. If extemal financing is required, the model in order of preference: acquires new debt financing; sells new preferred stock; or sells new common stock. The sale of debt is limited by the interest coverage ratio, whereas the sale of preferred stock is limited by the desired debt-to-preferred-stock ratio. The model is designed to sell whatever common stock is required to finance the desired construction programme. Consequently, the model monitors the amount of outstanding common stock for signs of dilution.

Key feedback loops and delays

Crucial to an understanding of stability problems like the spiral of impossibility is an appreciation of information feedback. Therefore, the chief contribution of the computer simulation model is in the study of key feedback loops and delays at work in the utility industry. The obvious feedback loop to mention first would be the spiral, itself, which is shown in Figure 1. This diagram shows model variables interconnected by lines of causal influence. The signs at the end of each arrow represent the

22 ENERGY POLICY March 1983

Figure 1. Causal demand spiral loop.

diagram of the


Allowed

Economic activity Population /

for electricity

~ . Consumer

Demand Actual price spiral loop (+) of electricity

Indicated price /

+

of electricity ~ lag

revenues

polarity of effect. For example, Figure 1 shows that an increase in the actual price of electricity causes a decrease in the demand for electricity in the future.

A decline in the demand for electricity would then cause an increase in the indicated price of electricity that the utility must charge if it is to earn the allowed revenues. This, in turn, would cause the actual price of electricity to increase after a delay required for the regulatory body to complete hearings, thereby closing the chain of cause and effect. Of course, the positive feedback loop can work in the opposite direction if one considers an initial decline in the electricity price followed by increased consumer demand and an opportunity for the utility to lower rates still further while still covering its fixed costs.

Regardless of whether they work towards rapid growth or rapid decline, such closed chains of casual influence are called positive feedback loops. This is denoted the 'demand spiral loop', and note the two important delays that slow the action of the loop: the regulatory lag required for the state commission to alter rates; and the consumer lag in altering electricity consumption in response to a change in the price of electricity.

Figure 2 shows an important loop describing the utility company's response to change in the demand for electricity. Should demand increase, for example, the company's forecast of future capacity requirements would increase, and the company would initiate preconstruction planning on new units. After delays for planning and construction, these units would come on line and equate installed capacity with the utility's estimated requirements. This loop, the 'Construction Loop', acts to bring utility generating capacity into balance with consumer demand. However, there is no guarantee that ~his loop can maintain this balance because its actions are slowed substantially by the

ENERGY POLICY March 1983 23


Figure 2. Causal diagram of the power plant construction loop.

Figure 3. Causal diagram of the key loops in the utility/regulator/consumer system.

For- sted ~ Demand c;~aa;ity~ +~... . - - ' ' ' ' ' ~ for e~ectricity

•j•..,•,•.• required in future

+ Planning

j initiation Planning ~ rate ,e,o:/ Construction ~ initiation rate

Construction / delay 1 ..... \

Construction loop (-) }

C°rnn~;;ui°cti°: te /

capacity

long delays required for planning and construction. The demand spiral loop and the construction loop are interconnected

as utility planners and electricity consumers act over time to change the status of the system. This interaction is portrayed in Figure 3, which shows three of the feedback loops at work in the simulation model. A new feedback loop appears when one traces the causal influences around the outside of the diagram in Figure 3.

Suppose, for example, that the demand for electricity were to increase causing an increase in the company's forecast and an increase in the initiation of planning for capacity additions. After a delay for preconstruction planning and the delay for plant construction, the new units would come on line and enter the company rate base. This, in turn, increases the company's allowed revenues and increases the price of electricity that must be charged under the rules of the commission. After a regulatory delay, the actual price of electricity increases leading, in turn, to a decline in the demand for electricity. Thus, when one works through the closed chain of causation, the loop acts to eliminate the

Forecasted capacity _+ Demand for-

+ ~ required -- / e lec t r ic i ty in future / ~..

Planning ~ / initiation ~ /

~,( rote ~ / D e m a n d . . ~, + 1 N [ soirol Ioo "+" Actum price

~ . F , ~ Demand ~ sp p t. / of electricity ~or~rrucl'lon + . . . . control loop (-) ,n,t,at,on rate .... X ~ / / Construction ~ ~ ~.

loop (-) / t loop(-) J ~'~=.. Indicoted price

Con~ruction j - of electricity completion rate ~ J +

\ .. Company..-~'+ eve ues

~-.b..generoong "---"-"~+ rote base + capacity


14Studying the model's response under irregular demand growth is similar to a test of feedback system response under 'noisy' inputs by a control engineer. Such tests tell the investigator much more about the model than would be learned by examining the model output under smoothly growing, predictable conditions. l-~l'he description of model behaviour under irregular growth is given in A. Ford and A. Polyzou, 'Simulating the planning advantages of short lead time generating technologies under irregular demand growth', op cit, Ref 6. The high volatility in the demand growth rate used in this text was caused by major changes in economic activity and some weather fluctuations. These volatile conditions were used only to test the overall performance of the EPPAM model and are not used in any of the simulation results shown here. ~6The six demand studies that did not provide estimates of both long-term price elasticity and consumer response delay needed in the EPPAM analysis are the following: J.P. Acton and B.M. Mitchell, 'Evaluating time-of-day electricity rates for residential customers', Regulated Industries and Public Enterprise, 1980, pp 247-273; J.P. Acton, B.M. Mitchell, and R. Sohlberg, 'Estimating residential electricity demand under declining-block tariffs: an economic study using micro- data', Applied Economics, Vol 12, 1980, pp 145-161; W.S. Chern, R.E. Just and H.S. Chang, A Varying Elasticity Model of Electricity Demand with Given Appliance Saturation, ORNL/NUREG/I"M 438, Oak Ridge National Laboratory, manuscript completed December 1980; A. Faruqui, J. Aigner and R.T. Howard, Consumer Response to Time-of-Use Rates, Topic Paper 1, Electric Utility Rate Design Study, a Report to the National Association of Regulatory Utility Commissioners No 84, March 1981; L.A. Lillard and J.E. Acten, 'Seasonal electricity demand and pricing analysis with a variation response model', The Bell Journal of Economics, Spring 1981, pp 71-92; J.S. Maybee and N.D. Uri, 'A methodology for forecasting discrete approximations on the load duration curve', Energy Systems and Policy, Vol 4, No 2, 1978, pp 125-133; and M. Parti and C. Parti, 'The total and appliance-specific conditional demand for electricity in the household sector', The Bell Journal of Economics, Spring 1980, pp 309-321. Whese six studies are described in greater

detail in a Los Alamos working paper on the spiral of impossibility, A. Ford and A. Polyzou, 'Simulating the spiral of impossibility in the electric utility industry', Report LA-UR-81-3343, Los Alamos National Laboratory, November 1981. ~aThe 'demand adjustment delay' used in EPPAM is similar to the adjustment process in the Koyck Lag formulations often employed in econometric studies. With the Koyck Lag formulation, one can calculate both the long-term price elasticity and an



original change. This is characteristic of negative feedback loops that act to control system behaviour. Thus, we have named the third loop in Figure 3 the 'demand control loop'. Of the three loops, this is most encumbered by delays (ie regulatory lag, consumers' price response delay, preconstruction planning delay, and the capacity construction delay).

Note here that the model variables and interconnections shown in Figure 3 are only a subset of those included in the electric utility simulation model. Indeed, a complete picture of the total model would include hundreds of feedback loops; only the three most important loops for our investigation are shown in Figure 3.

Model behaviour

Often, the best way to gain an appreciation for a model's usefulness is to examine the behaviour of its principal outputs. In this article, we are particularly interested in the response of the model when demand grows in an unpredictable fashion, and the simulated utility company finds itself with the wrong amount or mix of generating capacity. Previous research with this model has shown that it responds in a reasonable fashion when used to simulate the likely reaction of a large, investor-owned electric utility to noisy 14 variations in the demand growth rate. In particular, the model's projection of capacity initiations, capacity under construction, system reserve margin, and price of electricity were found to behave in a reasonable (but somewhat volatile) fashion when the demand growth rate was set to follow an unpredictable pattern taken from the records of a small, southwestern utility company, is

Consumer response to higher prices A quantitative description of the spiral of impossibility requires a calculation of the likely consumer response to changes in electricity price. In the EPPAM model, consumer reaction is described through the price elasticity of demand and the time delay required for full response. To gain an appreciation for the likely range of values for these two model parameters, we have reviewed over a dozen studies of electricity demand. We found that roughly half of the studies do not provide estimates of both the long-term price elasticity and the consumer response delay needed in the EPPAM analysis.16 The useful studies are noted in Table I. In most cases, the studies used a Koyck Lag formulation with the average price of electricity triggering the consumer response. The six studies differ in their data sources as well as their geographic boundary and sector of the economy under study as indicated in Table 1.17

Figure 4 summarizes the results of recent studies by displaying the long-run price elasticity against the demand adjustment delay. The demand adjustment delay is defined in the EPPAM model as the interval of time required for 63% of the consumer response to occur after an increase in the price of electricity. This can be obtained from any study that offers both a short- and long-run price elasticity of demand, is

The results in Figure 4 indicate a wide range of values for both the long-run price elasticity of demand and the consumer response delay. The former range from -0.5 to -1.5 in the residential and commercial sectors and from around 0 to -2.5 in the industrial sector. The delay time for 63% of the consumer response to occur also varies substantially from I to 12 years.

E N E R G Y P O U C Y March 1983 25


Table 1. Summary of applicable electrlc~/demand sludle~

Short-, long-run Data Study Model specification elaedlclty, sector meuurement Data source Chern et al a Logarithmic Koyck lag Short- and long-run Average price Pooled time series

demand equation, elasticities for resi- (195,5-1974) and cross linear price equation dential, commercial, section (over the with quadratic terms, and industrial sectors, states in each region).

Chem et al b As above As above AS above As above, additional data for 1975 and 1976.

Houthakked: Logarithmic flow- Short- and long-run Marginal price Annual, pooled time adjustment model, elasticities for real- series for 48 states

dential sector. (1964-1978). Uri d Log-linear model. Long-run alastioities Average price Monthly, time series

for residential, com- (January 1972- mercial and industrial March 1978). sectors.

Chang and Chem e Double-logarithmic Short-run elasticities Average price Annual, time series Koyck lag model for 15 SIC industries (1959-1976).

and 'all remaining' industries.

Smith f Log-linear model. Long-run elasticities Average price Cross section for 27 for residential sector, utilities (1957-1972).

Sources: aW.S. Chem, R.E. Just, B.D. Holcomb, and H.D. Nguyen, Regional Econometric Model for Forecasting Electricity Demand by Sector and by State, ORNL/NUREG-49, Oak Ridge National Laboratory, October 1978. bW.S. Chem, J.W. Dick, C.A. Gallagher, B.D. Holcomb, R.E. Just, and H.D. Nguyen, The ORNL State-Level Electricity Demand Forecasting Mode/, ORNL/NUREG-63, Oak Ridge National Laboratory, 1980. cH.S. Houthakker, 'Residential electricity revisited', The Energy Journal, Vol 1, No 1, 1980, lop 29-41. dNoel D. Uri, 'Price expectations and the demand for electric energy', Energy Systems and Policy, Vol 3, No 1,1979, pp 73-82. eH.S. Chang and Wen S. Chem, 'Specification, estimation, and forecasts of industrial demand and price of electricity', Energy Systems and Policy, Vol 5, No 3, 1981, pp 219-242. fV.K. Smith, 'Estimating the price elasticity of US electricity demand', Energy Economics, Vol 2, No 2, April 1980, pp 81-85.

continued from page 25 'adjustment parameter', which represents the proportion of the total price response to occur in the first year. With an adjustment parameter of 0.5, for example, one would expect 50% of the total response to occur by the end of the first year, 75% after two years, 87.5% after three years, and so on. At this rate of adjustment, it would take roughly 1.5 years for 63% of the response to occur. Thus, a Koyck Lag 'adjustment parameter ' of 0.5 corresponds to an EPPAM 'demand adjustment delay' of 1.5 years.

With all the study results displayed together, one might conclude that there is no connection between the estimates of long-run price elasticity and the consumer response delay. However, certain studies in Figure 4 show that higher estimates of the long-run price elasticity are accompanied by higher estimates of the time required for consumer response. This connection is especially strong in the Houthakker residential sector results and version II of the Chern study of the commercial sector. One would expect such a connection to appear in these statistical studies as it is possible to explain a given reduction in electricity demand as either the initial portion of a large response or the majority portion of a small response.

In simulating the effects of the spiral of impossibility, those cases where the utility customers display a large price elasticity and a quick response time are of particular interest. Under these conditions, one would expect the demand spiral loop to be a dominant element in the system shown in Figure 3. Figure 4 indicates that such conditions are certainly possible. The study of specific industry groups by Chang and Chern, for example, shows price elasticities over -1.5 with response times as low as 1 year. Version II of the Chern study shows residential sector price elasticities around -1 .0 with consumer response times of 2 years. Because of the wide range of possible parameter values evident from Figure 4, we present a large number of simulations results with the intent of spanning the range of plausible demand parameters.

S i m u l a t i n g the spiral o f imposs ibi l i ty

To simulate the severity of the spiral, the EPPAM model is used to represent a large, investor-owned electric utility company with a desired reserve margin of 30% and a demand growth rate of 4.5% per year. As


Q

a

12

II

I0

9

8

7

6

5

4

3

2

I

0

I0

9

8

7

6

5

4

5

2

I

0

Residential

I •

I I l i I • I III liT lIIII ITT I

I I -0 .5 -I.0

Price elasticity -I .5

Commercial

] I

E ] t l ~ i I

I I -0.5 - I 0

Price elasticity

]I

]I

Tw I

II

I

-I.5

Industrial I0 9

8 7 6 5: • 2 I

I - K •

IT ° I • •

%~ • • • I I I /

- 0 5 -I .0 -I.5 - 2.0 Price elasticity

Figure 4. Comparison of price elasticities and adjustment delays from recent demand studies.17 Key: I Chem eta/, version I, 1978, regional

estimates lI Chern eta/, version II, 1980, regional

estimates • Other major studies cited by Chem eta/,

national estimates • Houthakker, 1980, national and

regional estimates • Chang and Chem, 1981, specific

industry groups

'gThe planning and construction delays are represented in mathematical form in the EPPAM model as third order exponential delays whose delay parameter is specified by the model user. Setting this parameter at



the simulation begins, the model has the desired amount of capacity in operation and a backlog of construction work in progress that is con- sistent with a demand forecast of 4.5% per year. To test the effects of the spiral, we simply assume that the demand growth rate declines from 4.5%/yr to 2%/yr after the first five years of the simulation. This unexpected, one-time drop in the demand growth rate will put the model into an over-invested status which will allow us to monitor the effects of the spiral of impossibility.

Model behaviour with long lead time plants

These simulation experiments are started with the price elasticity of demand set to zero. It is assumed that the utility is constructing mostly long lead time power plants with the average preconstruction planning delay set at 2 years and the construction delay set at 4 years.'9 Figure 5 shows the results of the first simple simulation experiment. The model begins the simulation with the reserve margin at the desired value of 30%. If demand were to continue to grow at the rate of 4.5%/yr, the model would always be successful in keeping total capacity at the desired level. The decline in demand growth occurs after year 5 causing the system reserve margin to increase above the target level. The unusually high reserves are only a temporary phenomenon, however, because the simulated utility adjusts its initiations and retirements to accommodate the lower rate of demand growth. Roughly ten years after the drop in demand, the utility is able to bring the system reserve margin back to the desired value of 30%. Overall, the model response in Figure 5 indicates only minor problems in adjusting to the slower demand growth rate.

To demonstrate the exasperating effect of the spiral of impossibility, we repeat the same simulation experiment in Figure 6 with the values of the price elasticity and demand adjustment delay from the middle region of Figure 4. Under these conditions, the utilities planning problems are much more severe.

Beginning in year five, the simulated company cuts new initiations to zero. Nevertheless, the system reserve margin grows to almost 50% due to the completion of construction of unnecessary units and the exasperating effect of the consumers' response to the price increases. The system reserve margin begins to decline after year 10, and the simulated company initiates planning on new units thought to be needed in the future. By years 18-20, the system reserve margin is below the target value of 30%, and the company is initiating planning of numerous units. After several more oscillations, the reserve margin finally converges on the target value, and the company's capacity initiation rate grows in a less volatile fashion.

Model behaviour with short lead time plants

Other studies have shown that utilities' planning problems under uncertain demand growth can be reduced if utility investments shift to shorter lead time power plants such as small coal plants, geothermal stations, or wind machines. 2°, 21, 22 Thus, our first variation on the results from Figures 5 and 6 is generated by shortening the power plant approvals delay from 2 to 1 years, and by reducing the length of the construction delay from 4 to 3 years. The final change in the model required to properly simulate a utility building short lead time plants is a reduction in the forecasting interval to correspond to the fact that this

E N E R G Y P O U C Y March 1983 27

Simulating the spiral of impossibility 60

Figure 5. Model response to a slowdown in demand growth with long lead time power plants and zero price elasticity of demand.

continued from page 27 roughly half the total time for a large plant to complete the planning and construction stages provides a close approximation of the construction dynamics of a multi-unit power plant. :OBoyd and Thompson, op cit, Ref 5. :1Ford and Yabroff, op cit, Ref4. 22Ford and Polyzou, o/3 cit, Ref 6.

~C

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E

~" 2o

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~and th rate

I I O0 10 20 30 40

Yeors

company would not have to forecast demand growth so far into the future.

Figure 7 shows the simulated utility's reserve margin with the assumption that the price elasticity of demand is zero. Overall, the model response in Figure 7 is quite similar to the response in Figure 5 - the utility is able to bring the reserve margin back to the target value within about 10 years after the initial drop in the demand growth rate. In the simulation shown in Figure 8, we assume a price elasticity of one, an 8-year demand adjustment delay, and the experiment with the demand slowdown is repeated. A comparison of system reserve margin and capacity initiation rates in Figures 6 and 8 shows that the switch to shorter lead time power plants reduces the volatility of model behaviour substantially.

Model behaviour with different demand parameters

The next variation is generated by altering the price elasticity of demand or the demand adjustment delay. The behaviour of the reserve margin from three simulations with different values of the price elasticity of demand are shown in Figure 9 while the results from three simulations with differing values of the demand adjustment delay are given in Figure 10. In all six simulation experiments, the simulated utility company is investing in long lead time power plants and experiences the simple slowdown in demand growth shown previously in Figures 5-8.

The results in Figure 9 show that the volatility of the reserve margin is increased as the price elasticity of demand increases. This is to be

20 - 60

Figure 6. Model response to the same slowdown in demand growth with long lead time power plants, a unitary price elasticity of demand, and a demand adjustment delay of eight years.

1 5 -

I0 - R g o

5 -

0 -

4O

-p

0 ; 0 10 20 30 40

Years

28 ENERGY POUCY March 1983

6 0


Figure 7. Model response to a slowdown in demand growth with short lead time power plants and a zero price elasticity of demand.

Figure 8. Model response to the same slowdown in demand growth with short lead time power plants, a unitary price elasticity of demand, and a demand adjustment delay of eight years.

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expected because a larger price elasticity implies a stronger demand spiral loop. In Figure 10, the price elasticity of demand is assumed to be - 1.0, and the delay time required for the majority of the price response to occur is varied from 4 years to 8 years to 12 years. These results show that reserve margin volatility is also increased if the delay for consumer response to higher electricity prices is shortened. This result is also expected because a shorter consumer delay makes the demand spiral feedback loop a more important element in the entire system.

A measure of system volatility

The reduction in volatility between Figures 6 and 8 and between the different simulations shown in Figures 9 and 10 is apparent from the smaller oscillations in reserve margin about the target value of 30% and f rom the faster rate of convergence to the 30% target value. To assist in summarizing other simulation results, we have defined a System Volatility Measure to serve as a quantitative measure of reserve margin volatility over an entire simulation.

The measure for a particular simulation is calculated by comparing the actual reserve margin with the target reserve margin for each year of the simulation. The relative differences are summed over the entire simulation, and the sum is divided by the number of years simulated. Thus, the system volatility measure shows the average, relative deviation of the simulated utility's reserve margin from its target. Should the company be successful in keeping reserve margin right at the target, the measure

20 - 6C

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0 - 0

~ Reserve

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Figure 8. Three model responses to a slowdown in demand growth with long lead time power plants, a demand adjustment delay of eight years, and varying values of the price elasticity of demand.

Figure 10. Three model responses to a slowdown in demand growth with long lead time power plants, a unitary price elasticity of demand, and varying values for the demand adjustment delay.

60

40

20

~ Price elasticity = -I.5

Price elasticity = _i ,O J~"'~J? t "X" " / Price elasticity = -0.5

0 I I I 0 I0 20 30 40

Years would be zero. To provide an example, we have reviewed the time varying behaviour of the nation's total reserve margin over the past 30 years (see Figure 11). Assuming a target value of roughly 20%, it can be seen from Figure 11 that the nation's reserves were somewhat low in the early 1950s (a problem sometimes attributed to the rapid growth in demand after the Second World War). By 1960, however, the nation's utilities (taken in aggregate) had increased the reserve margin to the 20% level, and later in the 1960s the nation had excess reserves. The reserve margin was near the target level by the end of the 1960s and during the early 1970s, but the slowdown in demand growth after 1973/74 caused the nation's reserve margin to increase well above the 20% value. Summing the relative deviations in Figure 11 over the entire 30-year interval yields a System Volatility Measure of 33%.

Summary of results By repeating the simulation experiments with other combinations of parameter values, we have learned that the spiral of impossibility can lead to volatile behaviour whenever the consumer response to price increases is strong and fast relative to the delay time required for the utility to complete planning and construction of the new capacity.

This conclusion is illustrated by displaying the System Volatility Measure from each of 37 different simulation experiments as shown in

60 ~ l Demand response

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; . _ . . . . .

\ \ / ~" 20 Demand response

delay = 8 delay = 12

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Years


4 0

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~ 2o

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0 I I 1950 1960 1970

Years 1980

Figure 11. Historical behaviour of the national reserve margin. Note: Average relative deviation of the actual reserve margin from an assumed target of 20% has been calculated to be 33%.


Figure 12. In this summary diagram, the triangles show the System Volatility Measure from different simulations in which the price elasticity of demand was set at - 1.5. The circles show results from simulations with a unitary price elasticity, and the squares show results when the price elasticity is -0 .5 . The results from the 37 computer simulations are arranged along the horizontal axis according to the ratio of the delay around the construction loop (Figure 3) relative to the delay around the demand spiral loop. Specifically, the position of the symbols in Figure 12 show the ratio of the utility company's capacity planning plus construction delay relative to the demand adjustment delay of the electricity consumers.

To interpret the summary results in Figure 12, consider the six simulation experiments in which the company's delay in adding capacity is exactly equal to the consumers' demand adjustment delay. Two of these six experiments were performed with a price elasticity of -1.5, and we observed a system volatility measure of around 60%. When the price

150 r I

Price elasticity = -1.5

IO0

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E

03

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Price elasticity = - I D

Figure 12. System volatility measure observed from 36 different simulation experiments with the model.

• • • Price elasticity = -0.5

I Company capacity additions delay Consumer's demand response delay



23A. Ford and A. Polyzou, 'Oil backout and the price of electricity', Energy- The International Journal, Vol 7, No 5, 1982, pp 429-448.

Figure 13. Causal diagram of the key loops in the utility/regulator/ consumer system for the case of an oil-dependent utility.

elasticity was reduced to -1 .0 , the system volatility was reduced to around 20-30%. And when the price elasticity was reduced still further, the system volatility was around 10--15%. Another interpretation of Figure 12 is to find several simulations that exhibit the same overall system volatility. For example, the system volatility is about 60% for two simulations with a - 1.5 price elasticity and two other simulations with a price elasticity of -1.0. In the case of the -1 .0 price elasticity simulations, however, the company's delays in adding new capacity are twice as long relative to the consumers' delays in responding to price increases.

Based on the summary results shown in Figure 12, we would conclude that the stability problems of the utility/consumer/regulator system can be substantial when the price elasticity of demand is large, especially if the delay for the utility to add capacity to the system is larger than the consumer's demand adjustment delay. If the price elasticity is around - 0 . 5 or smaller, however, we would expect much less severe planning problems, regardless of the length of the company's capacity additions delay or the consumer's demand adjustment delay.

S p e c i a l ca se for oil and gas d e p e n d e n t uti l it ies

Overbuilding and the oil-backout cost ratio

We turn now to those utility companies which rely on older oil- or gas-fired boilers for a significant portion of their steam generation. These companies must be examined separately in our investigation of the spiral of impossibility because overbuilding generating capacity does not necessarily lead to higher electric rates to their customers. If an oil- dependent utility builds more coal plants than would be needed to maintain a 20% reserve margin, for example, it could reduce the operation of the older oil-fired units. If the fuel cost savings from reduced oil consumption out-weighed the capital costs of the extra coal-fired capacity, the ratepayer would experience a lower electricity price due to the overbuilding. To account for the oil displacement, we have expanded the causal influence diagram of Figure 3 to include an 'oil displacement loop' as shown in Figure 13.

The relative importance of the oil displacement loop has been studied in considerable detail in a separate analysis with the EPPAM model. 23 It was found that a utility that chooses to overbuild capacity to hasten the phase out of oil use will experience higher or lower electric rates depending on the value of the so-called 'oil backout cost ratio'. This ratio is

Forecasted C,~n - capacity ~ Demand for

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l / rate . . . . N~ / Demand spiral Actual, ,~Hr~ T),- Construction \ i .... ,_, v .~

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from oil plants


24To illustrate the calculation of the oil backout cost ratio, consider a hypothetical, oil-dependent utility that expects oil to cost $50 per barrel in 1985 and expects to complete construction of new coal-fired units for $750 per kW (in 19855). For this company, a new 100MW coal plant would cost $75 million, would have a variable cost of about 30 mills/kWh, and an availability factor of around 65%. With oil costing $50 per barrel, the variable cost for the old oil plants would be around 90 mills/kWh. Thus, if each kWh of generation from a new 100MW coal plant were to displace a kWh of oil-fired generation, the net savings in variable costs would be roughly $37 million in 1985. In this case, the oil backout cost ratio would be $75 million divided by $37 million or 2.0. =sit is important to remember that all three companies are assumed to employ the same target reserve margin. One could argue, however, that the desired reserve margin target for the third company should be higher than for the other two because of the economics for overbuilding. A more complete analysis would include results for a fourth company whose desired reserve margin would be sat at a higher value because of the rate benefits of overbuilding. Although we have not performed an analysis for such a company, we suspect that its system volatility curve would lie between the results shown for the two oil- dependent companies shown in Figure 15.

Figure 14. Summary of the rate effects of overbuilding for a hypothetical electric utility company with moderate initial dependence on oil or gas and a high demand growth rate.


defined as the cost of constructing new coal or nuclear capacity relative to the estimated first year's fuel savings if each kWh of generation from the new plant displaces exactly one kWh of oil steam generation. 24 The ratio may be calculated once the capital cost, fuel cost, and availability factor of the new coal or nuclear plant is specified and the likely fuel cost of the older oil plant is defined.

Figure 14 displays the results of the separate analysis of the rate effects of overbuilding for a hypothetical utility company with moderate dependence on oil or natural gas. This company was assumed to face a demand growth rate of 4.7%/yr and to construct a mixture of new coal or nuclear plants to meet future demand. Each of the symbols appearing in Figure 14 corresponds to a simulation experiment with different values for the cost of oil or natural gas. To determine the price effect from overbuilding, the electricity prices from two simulation experiments were compared. In one experiment, the model expanded capacity with a desired reserve margin of 25%; in the other, the target reserve margin was set at 30%. This small difference in the desired reserve margin caused a small amount of overbuilding which allowed us to learn if the ratepayer would be penalized or rewarded by the overbuilding strategy. Symbols appearing above the horizontal axis in Figure 14 indicate that the simulated utility would penalize ratepayers through overbuilding; symbols located below the axis indicate cases where ratepayers would be rewarded through an overbuilding strategy. The two curves shown in Figure 14 show the rate effects in 1986-- the year just after the new plants come on line - and again in 1996. Based on these simulation results, it can be seen that this type of company could lower rates through overbuilding as long as the expected value of the oil backout cost ratio were less than 5-6.

Summary of simulation results

Figure 15 displays the system volatility measures observed in 12 simulation experiments designed to show the importance of the oil displacement loop. In each of these simulations, the price elasticity of demand was set at the high value of -1.5 and the demand adjustment delay was set at 8 years. In each simulation experiment, the company encounters an unexpected slowdown in demand growth in year 5 of the simulation, which causes the system reserve margin to increase above the desired value of 30% 2s The curves in Figure 15 show the volatility observed for three utility companies:

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I l I I I I I I I I l I I 2 3 4 5 6 7 8 9 I0 I I 12 13 14 15

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E N E R G Y P O L I C Y March 1983 33

Simulating the spiral of impossibility 150

;oal utility

I00

== =o E

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50

f Oil utility, overbuildin G penalty

Oil utility, overbuilding benefit

Figure 15. System volatility measure observed from 12 simulation experiments of the model.

I 00~" I 2

Company capacity additions delay Consumer's demand response delay

• Coal utility. This company was assumed to rely on oil or natural gas only for peaking purposes. Thus, any new coal or nuclear plants would displace older coal-fired power plants and would not lead to significant fuel cost savings. The system volatility measures observed in the four simulations for the coal utility are the same as shown previously in Figure 12.

• Oil utility, overbuilding penalty. This company was assumed to rely on oil-fired steam plants for over 60% of total generation at the start of the simulation. The set of assumptions on new capacity capital costs, availability, and fuel costs combined with the assumption on the oil price yielded an oil backout cost ratio of just under 11. From Figure 14, we see that overbuilding would tend to penalize ratepayers under these cost assumptions.

• Oil utility, overbuilding benefit. The third example is similar to the second, except that the combination of cost assumptions yields an oil



backout cost ratio of 3.5. Figure 14 shows that overbuilding capacity would lower electricity prices under these cost assumptions.

The volatility curves shown in Figure 15 show the importance of the oil backout cost ratio in determining the system planning problems under a simple slowdown in demand growth. If the oil backout cost ratio is high (around 11 in this example), we learn that the effect of the oil displacement loop is a moderate reduction in the volatility problems of the simulated utility company. However, if the oil backout cost ratio is low (3.5 in this example), we see that model volatility is substantially erased. Figure 15 also shows that the volatility problems of an oil-dependent utility with a low oil backout cost ratio are minimal even if the company's capacity additions delay is long relative to the consumers' demand response delay.

To understand the importance of the oil backout cost ratio in determining system stability, consider the events that would follow immediately after the 5th year in which the reduction in demand growth rate occurs. Both oil dependent utilities could experience an increase in their system reserve margin, and both companies would be able to reduce their usage of the older oil-fired plants. For the company with the low value of the oil backout cost ratio, the fuel cost savings would be sufficient to lower the price of electricity. This, in turn, would set the demand spiral loop working to raise the electricity demand, and this would bring the company reserve margin back toward the target level. However, for the company with a high value of the oil backout cost ratio, the fuel cost savings in the years immediately after the reduction in demand growth would not be sufficient to lower the price of electricity. Rather, the price of electricity would increase which, in turn, would cause the demand for electricity to decline, and the company's reserve margin would grow further away from the target value.

Based on the results summarized in Figure 15, the utility companies dependent on oil or natural gas for a significant portion of their steam generation are less likely to experience stability problems due to the spiral of impossibility. Stability problems would also be reduced substantially for those companies whose oil backout cost ratio is low.

Strategies for countering the spiral A computer-based analysis of the effectiveness of different strategies that might reduce the stability problems of the spiral of impossibility is now considered. First are the most important steps that could be taken to reduce the delays in the construction loop (Figure 2).

Short lead time power plants

It has been shown that the system stability problems could be reduced by a reduction in the company's delay for pre-construction planning and actual construction. If we take the results from Figure 12 for a price elasticity of -1 .0 , for example, we see that a 50% reduction in the capacity additions delay would lead to a 50% reduction in the overall volatility of the system.

It would, of course, be helpful if the planning and construction lead times of all generating technologies could be shortened, and utility companies and their representatives are working toward this goal. In the meantime, however, companies can shorten the lead times for their total



Z6A. Ford and T. Raim, An Economic and Environmental Analysis of Large and Small Electric Power Stalions in the Rocky Mountain West, Los Alamos Scientific Laboratory report LA-8033-MS, October 1979. ~7'The utilities are building small', Business Week, 17 March 1980, laP 148N-149. 2a'S. California Edison raises commitment to alternative energy in "major change" ,' Wall Street doumal, 20 October 1980.

package of investments by increasing the investment in specific technologies such as small coal plants or wind machines. The shorter lead times of small coal plants is explained in a detailed Los Alamos study, z6 and it appears to be the main reason for the increased interest by utility companies in small coal plants in the past few years. This is illustrated by the following excerpt from a news article entitled 'The utilities are building small':

Utilities are becoming wary of projects with longlead times; by the time a plant is finished, demand could be much lower than expected. If you're wrong with a big one, you're really wrong . . . . Uncertainty over demand is the main reason for the appeal of small plants. 27

Wind machines and geothermal stations offer the same advantages, as the next quotation from a news article about the plans of Southern California Edison Company illustrates:

The utility said that by 1990 about 30% of additional generationneeds will be met by renewable and alternate sources including wind, geothermal, solar power, fuel cells, hydro-electricity, and co-generation . . . . The fluxuation in demand and the long lead time required to build a plant have made nuclear power and coal much less attractive than they were 10 years ago . . . . It takes two or three years for a windmill or geothermal station to begin service, in contrast to a nuclear power plant which might take 10 years. 2s

Based on this analysis of the spiral of impossibility, it appears that the recent interest by certain utility companies in short lead time power plants is a good method of countering the stability problems associated with the spiral.

Regulatory policies The summary results shown in Figures 12 and 15 indicate that a lengthening of the consumers' demand adjustment delay would tend to reduce the stability problems associated with the spiral. Lengthening this delay slows the exasperating effects of the demand spiral loop in Figure 3. An examination of Figure 3 shows, however, that two loops act to slow the effects of the demand spiral loop- the consumers' delay in responding to higher prices, and the regulator's delay in setting them. From a system's point of view, the key factor is the total delay around the loop. Thus, we are motivated to enquire about the effect of increasing the length of the regulatory lag on the system stability.

Figure 16 shows the results of 22 simulation experiments in which the price elasticity of demand is set at the high value of - 1.5, the consumers' demand adjustment delay is set at 8 years, and the simulated utility is building long lead time power plants. The system volatility measures taken from these simulations show that a lengthening of the regulatory lag would reduce the stability problems of the system. We do not wish to seriously consider this remedy, however, because of the degradation in utility financial performance that would result from longer regulatory lags during periods of high inflation.

The second regulatory change examined here is to allow the company to count Construction Work in Progress (CWIP) in the rate base. In his keynote speech, Mr Lovins speculated that granting CWIP would make the company's planning problems worse: 'I think CWIP makes it [the spiral] worse because it gives longer for the price elasticity to work, so the


150 Simulating the spiral of impossibility

One year requlatory laq

I00

o

:>,

co

year requlatory laq

50

Figure 16. System volatility measures observed in 24 simulation experiments with the model.

0 b f J 0 I 2

Company capacity additions delay Consumer's demand response delay

29A. Lovins, 'New directions for utility resources and financial planning', op cit, Ref 2, p 170. 3OAn expanded representation of commission rate-making procedures is provided in a more recent version of file model described in A. Ford and A. Youngblood, 'Technical documentation of the electric utility policy and planning analysis model, version 4', Los Alamos National Laboratory report LA-9347-MS, April 1982.

shortfall in revenue is even greater than expected by the time the plant is completed'. 29 A close look at Figure 3 shows, however, that granting CWlP in the company rate base shortens the total delay around the demand control loop. Thus, the overall effect of this regulatory change might be to increase the controllability or manageability of the system. 30

A large number of simulation experiments have been repeated with CWlP counted in the company rate base, and the system volatility measure observed in the simulations show mixed results. In some cases, the granting of CWlP in the rate base improves the stability of the system; in others, the system is more stable with the traditional use of the Allowance for Funds Used During Construction (AFDC) as a non-cash income. Overall, our simulation results show no important connection between the CWlP rate-making issue and the severity of the spiral of impossibility. In future research with the model, we wish to study this linkage in more detail. It may be that the regulatory policy on CWlP may



have a significant effect on system stability when the simulation experiments are repeated with a more elaborate representation of utility rate- making procedures.

Conclusions

We have demonstrated that computer simulation techniques can be used to quantify the planning problems associated with the spiral of impossibility. The results show that the spiral can pose substantial planning problems for utility companies building long lead-time power plants and serving customers that react strongly and quickly to changes in the price of electricity. It has been shown that changes in the regulatory commission's treatment of Construction Work In Progress or in the length of their rate-setting lag are not likely to significantly affect or moderate the effects of the spiral. The best policy for minimizing the spiral's adverse effects is to shift company investments to generating technologies with shorter construction lead times.

The simulation results have also shown that the adverse effects of the spiral will be substantially less for companies burning oil or natural gas for a large share of their electricity generation. Indeed, for oil dependent companies with certain ranges of values of the oil-backout cost ratio, the spiral may pose no significant planning problem regardless of how strongly and how rapidly their customers react to changes in the price of electricity.


Simulating the spiral of impossibility in the electric utility industry

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