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Planning in an uncertain environment : a case study of gas turbine technology El-Ramly, Manal 1997

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PLANNING IN AN UNCERTAIN ENVIRONMENT: A CASE STUDY OF GAS TURBINE TECHNOLOGY by MANAL EL-RAMLY B A S c , The University of British Columbia, 1990 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER'S OF APPLIED SCIENCE in THE FACULTY OF GRADUATE STUDIES (Department of Mechanical Engineering) We accept this thesis as conforming to the required standard  The University of British Columbia September 1997 ©Manal El-Ramly, 1997  In  presenting  degree freely  at  this  the  thesis  in  University  of  available for  copying  of  .department  this or  publication of  partial  by  his  of  and study.  I further  of  or  her  this thesis for  financial  NWcUaoxccJ  DE-6 (2/88)  (QrYoW  H  that the  agree  representatives.: |t  The University of British Columbia Vancouver, Canada  Pate  requirements  scholarly purposes may be  gain shall not  permission.  Department  the  British Columbia, I agree  reference  thesis for  fulfilment  ;  l'HT-  l ( ^ i ^ f n ^ /  is  for  an  Library shall make  that permission for granted  advanced  by the  understood  that  it  extensive  head  of  copying  my or  be allowed. without my written  II  Abstract  The electric industry is in a time of rapid change, as it is moving from a regulated monopolistic environment with a guaranteed rate of return to a risky competitive market with many players. This change is introducing uncertainty with respect to supply, demand, pricing, input fuel costs and technology. This thesis proposes a methodology for financial decision making in such an uncertain environment. The objective of the model is to demonstrate an approach for solving the problem, not to provide a specific answer to the problem. Therefore, the model discussed here is simple; users who use the approach are capable of developing their own, more sophisticated, models.  A probabilistic cash flow model technique is employed in this thesis. The model is based on a Monte Carlo Simulation. The model determines the supply price of electricity to achieve a MARR (minimum attractive rate of return) on a gas turbine project. The model also compares two technologies, namely, F Series and G Series gas turbines, to determine which technology to implement depending on an organizations' appetite for risk.  The input variables for the model are associated with a large degree of uncertainty, particularly due to the rapid changes in the electric industry. Expert judgments was used to characterize this uncertainty, through a process of subjective elicitation. Based on the results of the 3 experts, if one were to invest in a new gas turbine facility, the technology to implement today, given the assumptions of the models, would be the G Series if there were no constraint on the ability to sell the demand. As the constraint on demand increases, the F Series becomes the better alternative, as its decreased capital costs becomes more advantageous than its lower efficiency. The large variations in the experts' opinions suggests that the conclusions are themselves uncertain. A different sampling of experts could very well result in a different conclusion.  Table of Contents  Abstract Table of Contents List of Tables List of Figures Acknowledgment INTRODUCTION  1. Background 1.1. The Traditional Utility 1.2. Deregulating the Electricity Industry 1.3. The Changing Market 1.4. Competition 1.5. Resource Planning  2. Technology 2.2. Gas Turbines - Range of Applications 2.3. Simple Cycle Vs. Combined Cycle 2.4. Developments in Gas Turbine Technology  3. Uncertainty in the Industry 3.1. Input Fuel Uncertainty 3.2. Competition, Marketability & Demand Uncertainty 3.3. Technology Uncertainty 3.4. Regulation Uncertainty 3.5. Environmental Uncertainty  iv 3.6. Transmission Uncertainty  .  45  3.7. Conclusions  .  4  4. The Model  5  46  4.1. Model Objective  .  4  6  4.2. Methodology of Risk Analysis Model  49  4.3. Model Development  50  4.4. Model Formulation  .  51  4.5. Example of N P V Calculation  54  4.6. Planning in an Uncertain Environment Program  60  4.7. The Data__  .  4.8. Conclusions  .  6  1  62  5. Results  63  5.1. Objective  6  5.2. Results of the Experts  .  3  63  5.3. Output from the M o d e l _ _  74  5.4. Conclusion  74  6. Discussion  84  6.1. Results from the Experts  .  84  6.2. Output from the Model_  .  89  6.3. Effect of Variables on Supply Price Electricity  94  6.4. Reflection on Experts' Results  102  7. Conclusion & Recommendations  106  7.1. Objective of thesis 7.2. Recommendations for Future Work_  :  106 .  111  V  Bibliography Appendix 1: Sample Copy of Excel Model Spreadsheet, Spreadsheet Formulas and Excel Macro Appendix 2. Expert Debriefer and Elicitation of Subjective Probabilities Questionnaire  113 120 124  vi  List of Tables Table 2-1 Comparison between simple cycle and combined cycle gas turbines.  27  Table 2-2 Comparison ofF&G  29  Series gas turbines.  Table 3-1 Cumulative Capacity Additions in California  38  Table 3-2 Comparison of price indices' characteristics  40  Table 5-1: GE Specifications forF &G Gas Turbine  64  Table 5-2 Expert 1: F Series CDF  65  Table 5-3 Expert 1, G Series CDF  66  Table 5-4 Expert 3, F Series CDF  68  Table 5-5 Expert 3, G Series CDF  69  Table 5-6 Expert 4, F Series CDF  70  Table 5-7 Expert 4, G Series CDF  71  Table 5-8 Distributions Used for Input Model  73  7ab/e 5-9 Results of Experts' Simulation Runs: F &G Series  75  Table 6-1 Average of 3 Experts Results  89  Table 6-2 Input Variables For Model  90  Table 6-3 Comparison of Minimums, Maximums and Means.  91  Table 6-4 Standard Deviations of the Means and Standard Deviation between Minimum, Maximum and Mean of Experts.  92  Table 6-5 Comparison of Standard Deviations for Experts 1, 3, and 4 for Project Phases  95  Table 6-6 Mean Cost of Phases and Relative Percentage Cost for Phases for Expert 1,3, and 4  96  vii List of Figures  Figure 1.1 WSCC 1996-2005 Generation Additions  17  Figure 2.1 Breakdown of Capacity by Type for CA, US & North America  18  Figure 2.2 Simple cycle arrangement.  23  Figure 2.3 Air Standard Brayton Cycle  24  Figure 2.4 Combined cycle arrangements  25  Figure 2.5 Evolution of GE Gas Turbine Technology  28  Figure 3.1 Average US Wellhead Natural Gas Prices Price of Natural Gas in the US 1940-1994  33  Figure 3.2 Henry Hub Gas Near Month Close from April 1990 to January 1997  .  34  Figure 3.3 Spot Market Comparison - Henry Hub Futures  35  Figure 3.4 WSCC Region Net Generation Additions: Actual Data & Projections Summer Capability  37  Figure 3.5 . Graphical comparison of price indices  41  Figure 4.1 Probability Distribution A & B  48  Figure 4.2 Project Life Cycle  52  Figure 4.3 Cash Flow for O&M and Revenue Phase  53  Figure 4.4 Sample Cash Flow  54  Figure 4.5 Sample Output Graph  59  Figure 5.1 Expert 1, Cash Flow Model  64  Figure 5.2 Pre-Operation Cost Components  67  Figure 5.3 Expert 3, Pre-Operation Project Phases  67  Figure 5.4 Comparison of Experts' Outlook on Capacity  76  Figure 5.5 Comparison of Experts' Outlook on Heat Rate  77  Figure 5.6 Comparison of Experts' Outlook on Pre-Operation Duration  78  Figure 5.7 Comparison of Experts' Outlook on Pre-Operation Costs  79  Figure 5.8 Comparison of Experts' Outlook on Operation Duration  ;  80  Figure 5.9 Comparison of Experts' Outlook on Operation & Maintenance Costs  81  Figure 5.10 Comparison of Experts' Outlook on Fuel Costs  82  Figure 5.11 Comparison of Experts' Output Supply Price of Electricity  83  viii Figure 6.1 Comparison Summary of Expert 1 Results  85  Figure 6.2 Comparison Summary of Expert 3 Results  86  Figure 6.3 Comparison Summary of Expert 4 Results  88  Figure 6.4 Summary Comparison of Results from the 3 Experts  91  Figure 6.5 Comparison of Uncertainties as Represented by Standard Deviation Figure 6.6 Comparison of Experts' Means for the F &G  forF&G  Series  Series 12% and 15% MARR runs  93 97  Figure 6.7 Comparison of Experts' Uncertainties Represented by Standard Deviation for the F & G Series 12% and 15% MARR runs.  97  Figure 6.8 Graph of mean Supply Price of Electricity for Average of 3 Experts For a Given Demand  99  Figure 6.9 Graph of Standard Deviation Supply Price of Electricity for A verage of 3 Experts for a Given Demand  99  ix  Acknowledgment I owe thanks to many. Firstly, I'd like to thank my parents. My mother, Salwa, for appreciation of her devotion and efforts over the years, it hasn't always been easy. My father, Zak, who provided academic direction, industry insight, fatherly guidance and motivational support. My brother, Aiman, who provided me with editorial comments along the way and who has always been there to help me meet my deadlines. I thank my supervisors Dr. Sheldon Green and Dr. Rene Abou-Rached in the Department of Mechanical Engineering, and Dr. Paul Bradley, in the Department of Economics for making this thesis possible. Special thanks go to Dr. Abou-Rached for believing in me; without him I probably would not be in this program, and for Dr. Green who dedicated his time and efforts guiding me, without his continued support this thesis wouldn't have been possible. Additionally I'd like to extend a thanks to Dr. Schajer, Grad Advisor, who guided me and ensured I was 'converging' along the way. I'd also like to thank my friends, Indra and Farrah, for spending hours of editorial time and endless moral support. Finally I would like to thank the countless members of the industry which provided me with comments, or advice along the way. These include Dr. Zak El-Ramly, ZE PowerGroup Inc. Mr Ken Epp of ZE PowerGroup Inc., Mr. Peter Caldler of BC Hydro, Mr Don Fairburn of Inland Pacific Energy Corp., Mr. Bill Hillock of G E Power Systems Canada. Mr Sohail Alyasin of Indianapolis Power and Light. Mr. Conrad Eustis of Portland General Electric, Mr. Stephen DeMarco of IOU in New York State Mr. George Gunter, New Brunswick, Mr. Michael Coberly of Tenaska. The utility industry is full of helpful and accommodating individuals, and I'm certain I must have received help from more than the above, sometimes not even knowing that I was getting the help. For those individuals I extend my thanks, and apology for failing to name them.  1 INTRODUCTION  With the recent trend towards competition in the energy commodity markets, more specifically electricity, the need for energy market participants to account for volatility and uncertainty becomes paramount.  Major changes are being manifested in the electric industry:  1.  There is a move to market-based prices.  2. There is a move towards price transparency. 3. The industry has become highly volatile and uncertain. 4. The industry has become customer focused. 5.  Service providers are unbundling and re-bundling electric products and services.  6. There are many competing service providers. 7.  There is a move towards integrating financial and physical markets.  8.  The industry is becoming technology driven.  The traditional vertically integrated electric utility operated within a secure regulated franchise area. Utilities practiced integrated resource planning, and rates were based on cost of service. Therefore, planning followed a known and straightforward procedure. Changes in the electric industry demand a new decision making matrix. Cost based pricing and secure franchise areas will become largely historical structures. As an example of these changes, consider the Bonneville Power Administration and Tenaska project described in the section labeled "Justification". This thesis develops a methodology for planning in an uncertain environment.  Decision Making in an Uncertain Environment In the emerging electricity market the engineering criteria used in the past to select a system are no longer valid. The marginal cost of the system and the cost of power is a lot more unpredictable than  2 previously. A similar uncertainty affects most of the variables that determine the economic rate of return, net present value, a financial return, or any other indicators that may be used to evaluate a project.  Historically, methods of analysis have included multi-criteria decision making, benefit cost analysis, integrated resource planning, or just 'prudent' engineering decisions.  Multi-criteria decision making (MCDM), (also referred to as the ratings & weights approach) is designed to improve the quality of decisions involving multi-attribute alternatives. Its goal is to make more explicit, rational and efficient decisions in multi-objective situations. An example of its use is to examine methods of incorporating environmental factors into the resource acquisition problem.  Cost benefit analysis is based on identifying all the costs and benefits in any project. The value of the benefits of the project is summed, and likewise all the costs. Finally the ratio of benefits to costs is calculated. Projects with a given benefit/cost ratio, such as a ratio greater than 1.5, are allowed to proceed.  Integrated Resource Planning (IRP) is a model used to help utilities and state regulatory commissions assess consistently a broad range of supply demand and resources to meet customer energy service needs cost-effectively. Key characteristics of this planning approach include: explicit consideration and fair treatment of a wide variety of demand and supply options, consideration of the environmental and other social costs of providing energy services, public participation in the development of the resource plan, and analysis of the uncertainties associated with different external factors and resource options. IRP differs from traditional planning in the types and scope of resources considered, the owners of the resources, the organizations involved in resource planning, and the criteria for resource selection . The main goal of IRP 1  is to minimize long-term societal costs.  The above methods of analysis are used to deliver the most likely values (MLV) or contingency values. Using contingency or MLV leads to biased results. In contingency evaluation an extra or "contingent" amount is added to all of the variable values. Therefore, the output of such an analysis is an overly  1  Eric Hirst, Guidelines for a "Good" Integrated Resource Plan, in Public Utilities Fortnightly. March 29, 1990.P.5  3 conservative result, which may make the decision maker veto an otherwise feasible project. Using a MLV analysis fails to take into account other values of the variables that may occur. Basing a decision on a single value of the decision variable causes a corporation to take more risk than intended.  Justification This is an emerging problem that is just surfacing. Even large low cost providers, such as Bonneville Power Administration (BPA), are beginning to feel the effects of the market, and having problems. BPA had to cancel a project with Tenaska on a half finished 270 MW high efficiency gas turbine . BPA also 2  canceled the Columbia River Treaty agreement for the purchase of approximately 500 MW from BC. BPA has always been a dominating utility in the market; with this changing environment the processes and decision making that were done in the past are no longer viable. The decisions were based on rigorous IRP processes that considered everything but the market; the market proved BPA wrong.  Risk Analysis The purpose of risk analysis is to eliminate the need for restricting one's judgment to a single optimistic, pessimistic, or "best" evaluation, by carrying through the analysis the complete possible range of each variable, and the likelihood of each value within this range . A common procedure for this complete 3  analysis is Monte Carlo Simulation , which is the approach adopted here. 4  Thesis Objective This preamble leads to the description of the thesis objectives. This work is proposed to provide the electric industry with a methodology for evaluating engineering economics of risk-intensive gas turbine power generation plants.  2  A E S P - N e t Group T h e News Tribune, T a c o m a , W a s h . Knight-Ridder/Tribune Business News  3  Louis Y Pouliquen, Risk Analysis in Project Appraisal. (Baltimore and London: T h e J o h n Hopkins University Press, 1970)2  4  College Park, History of Monte Carlo Method. http://www.geocities.com/CollegePark/Quad/2435, 1997.  4 Corporations vary on the performance parameter that they may use to evaluate the feasibility of a project. These may be the Internal Rate of Return (IRR), Net Present Value (NPV), or supply price of electricity. As the choice to build a facility is a function of the market price of electricity, it was chosen to use the supply price of electricity as the performance parameter for the model.  The objectives of the model developed here are to:  1.  Determine the expectation of supply price of electricity for a given MARR (minimum attractive rate of return) on a gas turbine project. The supply price of electricity is the average price of electricity such that revenue will offset costs to meet a minimum attractive rate of return (set to be 12% here)  2. Compare two technologies, namely 'F' series and 'G' series gas turbine, to determine which technology to implement based on the technology risk profile which best fits the organization's appetite for risk.  The focus of the work is to develop the viability of a decision making approach to evaluate a continuum of alternatives under uncertainty. It is expected that users who apply the methodology are likely to be sophisticated enough to modify their own existing analytical models to incorporate this methodology. Furthermore every situation differs and the data is both dynamic and changing, calling for a customized approach to the problem. The author intentionally developed a simple model to demonstrate and prove the point without getting tangled in engineering or economic details. It is therefore stressed that the reader should focus on the approach but not use or extrapolate the actual results. The results are intuitively reasonable but reflect the particular time period, region and the experts' judgments.  The contents of this thesis are as follows. This chapter, Chapter I, serves as the introduction to the study. Chapter II, provides background and reflects on regulatory and structural changes in the electric market. Chapter III, "Gas Turbine Technology," describes gas turbine technology from simple cycle, combined cycle to cogeneration. The chapter goes into detail regarding the evolution of the technology and its future in the electric industry. Chapter IV examines the uncertainties in the industry. Chapter V presents the Planning in an Uncertain Environment Model. It is a detailed chapter of the methodology of the model and  5 discusses the process of structuring expert judgments for representing uncertainties. Chapter VI and Chapter VII present the results and discussion of the expert opinions applied to the model. The final chapter, Chapter VIII, is a summary and includes recommendations for future work.  6  Chapter One 1.  Background  As a result of deregulation and threat thereof, the last few years have seen tremendous changes in the electric power industry. To illustrate, the idea for the thesis topic stemmed from the author's consulting work . During a conference in March 1995 , the topic of Electric Rate Derivatives was unfamiliar to most 5  s  utility people and the impact of the changes to come was not apparent or acknowledged. The principles that were presented at that conference have now become key elements in the restructuring of the energy industry.  During this period, the viability of a competitive environment in the electric industry was new or foreign to most. On December 15, 1994, the Province of British Columbia directed the British Columbia Utilities Commission ("BCUC", "Commission") to undertake a public review of the provincial electricity industry. In September of 1995 the Commission completed addressing the new challenges through an Electricity Market Review . The Commission's recommendations were to proceed slowly with wholesale wheeling in 7  8  BC, and concluded "the Commission finds that retail competition is neither desired nor necessary in 9  5  Z E PowerGroup, Author's consulting work from March 1994-present with Z E PowerGroup Inc. A strategic consulting company, www.ze.com/ze  6  Power Marketing Association, P M A , Electric Rate Derivatives, Scottsdale Arizona R e d Lion's L a P o s a d a March 16-17, 1995  7  British Columbia Utilities C o m m i s s i o n ( B C U C ) , T h e British Columbia Electricity Market Review Report and Recommendations to the Lieutenant Governor in Council Report and Recommendations to the Lieutenant Governor in Council, September 1995. (Vancouver: Sept. 1995).  8  Wholesale Wheeling, or wholesale competition, which any entity may obtain generation from power producers or other sources. Sale for resale.  9  Retail Wheeling, or retail competition, which refers to the end-user's ability to purchase power from any producer or marketer. Sale for end-use consumption.  7 British Columbia in the next few years . A little over a year later, following applications by West Kootenay 10  Power and BC large transmission class customers , the Commission decided to conduct a public generic 11  hearing on the issue of retail access for March 1997. Prior to this hearing, the Province of BC established a Task Force to look at the issues of restructuring, and directed the Commission to set the public generic hearing aside.  1.1.  The Traditional Utility  Electricity has been in commercial use since before the middle of the nineteenth century and today's physical electric industry owes its roots to the master innovator Thomas Edison. Not only did he invent the incandescent light bulb and other electric technologies still in common use, he also envisioned the physical electric distribution system much as it exists now. In 1880, one year after the invention of the light bulb, he patented his electric distribution system. The first privately owned utility, Pearl Street Station, began operation on September 4, 1882. By the close of the century electric utilities were a reality and competition was fierce. Early participants included Westinghouse and General Electric, both among today's most powerful corporations worldwide.  The introduction of the demand meter allowed for more accurate pricing of electricity. This was the beginning of cost-based rates. Rates charged were based both on initial capital investment and the operating cost of generating additional kWhs. Because electricity for the most part cannot be stored, utilities needed to establish the right mix of customers to use the full potential of generating output: load diversity. Economies of scale facilitated the introduction of forecasting and planning into the business function.  Large capital requirements, investor reluctance and the uncertain nature of municipal franchises identified for Samuel Insull, Edison's Clerk, the need for non-partisan control of industry practice. This led him  1 0  British Columbia Utilities C o m m i s s i o n ( B C U C ) , T h e British Columbia Electricity Market Review Report and Recommendations to the Lieutenant Governor in Council Report and Recommendations to the Lieutenant Governor in Council. September 1995. (Vancouver: Sept. 1995).  1 1  British Columbia Utilities C o m m i s s i o n ( B C U C ) . British Columbia T a s k Force on Electricity Reform. T e r m s of Reference. (Vancouver: May 1997) p.  8  directly to a proposal for state control of utilities. Insull made his appeal in 1898 before the National Electric Light Association, the predecessor of the Edison Electric Institute. The proposal rallied public support and allowed the industry to expand. Regulation was introduced and resulted in cheaper, more reliable, expanded and standardized electric service. In the next twenty years more than thirty states adopted regulatory control of electric utilities.  The new public utilities began to assume obligations as franchise areas were established. It was assumed that since natural monopolies eliminate redundancy, costs would be reduced. In return for their franchise areas, utilities were obligated to serve all customers within the franchise area equally and fairly (at reasonable cost). These guarantees and efficiencies fueled investment in utilities. Today, three quarters of all generation in North America is privately owned, the balance by federal and municipal governments and co-operatives.  In general the vertically integrated monopolies of the electric utilities were regulated such that rate payers were protected from market power and were charged rates that were just and reasonable, and at the same time provided utilities with a reasonable return on invested capital. This regulatory compact remained relatively unchanged for many decades, until the early 1970s when significant world and economic events and circumstances shook the industry. These events include, but are not limited to, the high capital cost over-runs and poor operating performances of nuclear power plants, the Three Mile Island shutdown, high fuel costs as a result of the OPEC oil crisis, and disappointing economy of scale benefits from new generation. All these events contributed to a significant increase in rates to consumers.  This increase led to a consumer revolt against the increases, a loss of confidence, and negative public reaction towards utilities. It also led to greater scrutiny by regulators to ensure prudency of investments, and eventually to significant regulatory reform to break up the utility monopoly and to encourage open competition as a means of achieving economic efficiency and reduced electricity costs.  There thus has been a fundamental reversal in position, in which now the competitive market is seen as preferential.  9 1.2.  Deregulating the Electricity Industry  The first of several US Acts to change the electricity industry was focused on the generation side. The Public Utilities Regulatory Act (PURPA) of 1978 required utilities to purchase power from "qualifying facilities" (QFs). These QFs are companies that install cogeneration equipment and certain small powerproduction facilities that make the use of renewable energy sources and a variety of waste fuels . 12  A large number of regulated monopolies have been deregulated to promote competition, including the telecommunication and airline industries. More recently, the gas industry has been deregulated, and players from the gas industry are promoting changes for the electric industry. The deregulation of the gas industry and introduction of competitive gas prices served as a catapult for the opening up of the electric industry. Begun more than a decade ago, the deregulation of gas brings to light the type of consumer and market benefits possible in electricity . 13  Federal and Provincial/State regulatory agencies have contributed to the opening of the market. In 1992, the US National Energy Policy Act was passed to promote competition at a wholesale level. It required 14  utilities to open up access to transmission and/or build new transmission for all market players.  Most recently, the US Federal Energy Regulatory Commission (FERC) has made a significant effort to achieve its objectives to eliminate monopoly power over transmission, remove impediments to competition in wholesale trade, and to lower the cost of power to US consumers.  Early in 1996 FERC released a Proposed Ruling, known as the MegaNOPR  15  The MegaNOPR covered  four topics:  1 2  P.L. Joskow, Regulatory Failure, Regulatory Reform, and Structural Change in the Electrical Power Industry, Brookings Papers: (Microeconomics, 1989), 163.  1 3  Z E PowerGroup Inc. (1997) NYMEX Futures: Gas and Electric. Report Prepared for A Multi-Client Study. (Vancouver, British Columbia, March 1997) 2-6.  1 4  Secretary of Energy, US National Energy Policy Act of 1992, (Public Law 102-486) Bill H R 7 7 6 .  1 5  Federal Energy Regulatory C o m m i s s i o n , Notice of Proposed Ruling, March 1995  10 1.  The FERC's jurisdictional powers to implement wholesale open access  2.  FERC's proposal for electric utilities to recover "legitimate and verifiable stranded costs" from departing wholesale customers (a small fraction of all stranded investment), and its belief that states should ensure recovery on retail bypass (the much larger share). Retail bypass is when customers attempt to bypass the historic costs of a utility, such as distribution, transmission and generation. This can be done by self-generation, or by using the facilities of someone other than the utility that was traditionally responsible for their service area.  3.  A range of measures to implement wholesale open access  4.  Market power in generation  16  Following the MegaNOPR in 1996, FERC released Orders 888 & 889 . Below is a summary of the 17  18  highlights of the FERC Orders.  FERC Order 888 requires open access transmission by all public utilities that own, operate or control interstate transmission assets. These utilities must file open access non-discriminatory transmission tariffs and take transmission services for their new sales/purchases under open access tariffs. The order also allows for recovery of legitimate verifiable stranded costs. (This order basically opens wholesale power sales to competition).  FERC Order 889 requires public utilities to develop and maintain an Open Access Same Time Information System (OASIS) so that potential users have the same information as the utility enjoys. It also mandates that public utilities separate transmission from the generating and marketing functions; however, corporate restructuring is not mandated.  1 6  Alex Henney, The Mega-NOPR,  1 7  Federal Energy Regulatory C o m m i s s i o n (1996) Final Rule Order No. 889.  1 8  Public Utilities Fortnightly. Vol. 1, July 1995: 29  Federal Energy Regulatory C o m m i s s i o n (1996) Final Rule Establishing Requirements  OASIS and Standards  (Order No. 889). http://www.energyonline.com/Restructuring/models/  of Conduct  Since not all interstate transmission facilities fall within FERC's jurisdiction, reciprocity provisions are included to preclude the possibility of non-open access utilities taking unfair advantage of open access utilities.  The FERC orders have ramifications that extend past the physical US/Canadian Border. In order to trade in the US and to comply with the Reciprocity Provision, Canadian Corporations must have wholesale wheeling rates acceptable to FERC, referred to as the pro-forma tariffs.  In Canada the most progressive province has been Alberta, which has instituted the first North American Power Pool. The power pool of Alberta began operation on January 1, 1996. Currently discussion on retail access or end-user choice, as it is referred to in Alberta, is on the table to bring in a higher level of competition to consumers in Alberta.  The California debate has also been proceeding for some time. The California Public Utilities Commission has ordered transformation to the California electricity structure. The final decision is the development of an Independent System Operator (ISO) and a California Pool, referred to as the Power Exchange. The ISO will facilitate the forward markets and is responsible for obtaining ancillary systems for system reliability. The Power Exchange will conduct a day-ahead and an hour-ahead auction for power generation as a separate electricity spot market, through which market participants may sell or purchase energy. California mandates a 10% rate reduction for some retail customers by January 1, 1998, with a goal of an additional 10% by 2002.  Retail access is being debated or implemented in all states but one.  Similarly, at the international level many countries have deregulated the power industry; examples include Norway, England, Argentina, Australia, and New Zealand. Many countries are also in the process of implementing new competitive structures; examples include Spain, Brazil, US and Canada.  12 The following diagram identifies some of the major regulatory steps that have specific relevance to understanding how deregulation was eventually introduced.  19  Z E PowerGroup Inc. (1997) NYMEX Futures: Gas and Electric. Report Prepared for A Multi-Client Study. (Vancouver, British Columbia, March 1997) 8-3.  Regulatory Timeline: Electricity211 1880  1898  1920  1935  1978  1992  1995  1996  1996  O n e y e a r after  Federal Water Act  T h e P u b l i c Utilities  F E R C Notice of  F E R C O r d e r 889 is  the invention of  of 1920 is e n a c t e d ,  Regulatory Policy A c t  P r o p o s e d Role  released, in part  the light bulb,  which in part  ( P U R P A ) , which in part  Making ( N O P R )  requiring utilities to  T h o m a s Edison  regulated  requires utilities to buy  which in part  implement conduct  patents his  hydroelectric power  power from  p r o p o s e s rules to  standards and to  electric  generation.  independent power  facilitate open  provide information  distribution  producers w h o s e  a c c e s s to  about transmission  system.  facilities have b e e n  transmission  availability on a real time  qualified by F E R C  services.  basis to all would-be  (QF's).  users. This is to be posted on an O p e n A c c e s s S a m e Time Information S y s t e m ( O A S I S ) , i.e. s a m e method the utilities gain the information.  Regulatory  S a m u e l Insull, formerly o n e of  T h e Federal P o w e r A c t is  T h e E n e r g y Policy A c t ( E P A ) is  F E R C O r d e r 888 r e l e a s e d , which in  E d i s o n ' s clerks, after m a n y y e a r s  e n a c t e d , which in part  e n a c t e d which in part o p e n s  part o p e n s wholesale power s a l e s  of lobbying a n d marketing o n  authorized the Federal  transmission s y s t e m s owned by  to competition. It requires public  behalf of the electric utilities,  Regulatory E n e r g y  private utilities to all wholesale  utilities to file non- discriminatory  r e c o m m e n d s state regulation in  C o m m i s s i o n to regulate  trade.  open a c c e s s tariffs with conditions  a n a d d r e s s before the National  interstate transmission  comparable to the service they  Light A s s o c i a t i o n .  commences.  provide t h e m s e l v e s .  Timeline  Z E PowerGroup Inc. (1997) NYMEX  Futures:  Gas and Electric. Report Prepared for A Multi-Client Study. (Vancouver, British Columbia, March 1997). 8.3  13  14 1.3.  The Changing Market  All these factors are causing the current evolving environment to be uncertain. The electric power industry in North America is experiencing change in structure, regulation, key players, business dynamics, pricing and contract dynamics. The value of electricity is becoming unstable, and the prices have begun fluctuating considering.  Participants with experience in the gas market are moving to the electric industry to take advantage of the new opportunities. Other key players in the market include utilities, electric utility affiliates, co-generators, independent power producers (IPPs), financial institutions, brokers and agents. It is the collective knowledge of all players in the industry that will shape the outcome of tomorrow.  Although electricity prices have declined or stabilized during the past few years, utilities continue to face fundamental changes. These changes include deregulation of electricity generation; greater access by utilities and others to the transmission systems of other utilities; competition for retail customers from other fuels and even from other electricity suppliers; changes in regulation of electricity prices; growing use of DSM programs as capacity and energy resources; increased concern with the environmental consequences of electricity production; growing public opposition to construction of power plants and transmission lines; and considerable uncertainty about future load growth, fossil-fuel prices and availability, and the costs and construction times of facilities needed to meet future energy needs . 21  1.4.  Competition  The opening of the market means competition in the electric industry. There are two broad forms of competition that are progressing steadily: wholesale wheeling and retail wheeling. Each of these have a major impact on the utility.  Eric Hirst, A G o o d Integrated Resource Plan: Guidelines for Electric Utilities and Regulators (Tennessee: U S O a k Ridge National Laboratory, 1994). 4  15 Wholesale wheeling gives the players more options on the supply side and enables electricity to be traded between two parties through the transmission systems of one or more parties. With wholesale wheeling, the utility is now highly uncertain of how much they need to generate on the supply side, although they are given another degree of freedom for resource planning. Wholesale wheeling has been emerging for quite sometime, since the EPA act of 1992.  Retail Wheeling affects the utility on the demand side; as utilities lose their customers they can no longer accurately estimate what their load pattern will be like. Retail wheeling is just starting; industrial customers in some jurisdictions are now realizing the opportunities arising from their new freedom to select an electricity supplier.  1.5.  Resource Planning  Historically, it has been the responsibility of the large vertically integrated utilities to provide for electric power generation. The forecasts of annual electricity use and peak demand are, in some respects, the starting point for resource planning. To a large extent these forecasts, when compared with the utility's existing and committed resources, determine the amounts, timing, and types of future resources that the utility will need during its planning period . Forecasters perform analyses on previous years and trends 22  and extrapolate trends for future use and peak demand. With a change in the environment there is a discontinuity in the process, which introduces uncertainty in forecasting.  Historic utility engineering economics was based on predictable and stable utility rates. Recent changes impact the way that engineers choose systems on both the supply and demand side.  On the supply side, integrated resource planning required the engineer to rank all the options available to the utilities for energy and capacity supply. The operating costs were fixed and predictable, as was the future value of the power, and therefore the analysis was quite simple. If the engineer made a mistake in  Eric Hirst, A G o o d Integrated Resource Plan: Guidelines for Electric Utilities and Regulators (Tennessee: U S O a k Ridge National Laboratory, 1994). 7  16 the analysis, the utility would not suffer. The utility would ask the regulatory commission for a rate increase, and the costs would be passed on to the consumer.  On the demand side, typically an engineer looked at enhancing the efficiency of a component, product or system. The engineer would compare the marginal cost of the more efficient equipment and value that against long term energy savings - which were possible to predict with some degree of certainty. Sometimes when the more efficient fixes failed the analyses, the utility jumped in to subsidize the costs for two reasons - social/environmental concerns, and secondly, because they were obligated to the provision of future power for the consumer.  Gas Technology  Over the years, jet engines - whether propelling airplanes or energizing electric utility grids - have become extremely efficient. Advancements in metallurgy, improved cooling technology, and advanced coatings for turbine blades and vanes let them operate at higher temperatures . Efficiency rates of 40% 23  to 50% have been attained for combined cycle operation, and GE claims that with its latest machines exceeds 60% efficiency. This efficiency, combined with lower capital costs, a shorter construction window, low fuel prices, a slow rate of growth in power demand, and the electric utilities' reluctance to make major capital expenditures as the prospect of stranded assets looms in the changing environment, have caused a revolutionary shift in the industry's approach to the production of electricity . Gas turbines are now the 24  new generation facilities of choice. For example, Figure 1.1 shows the Western System Coordinating Council (WSCC)Ten-Year Coordinated Plan Summary 1996-2005 Generation Additions for the WSCC region, a total of 4492 MW, or 49.2% of 25  generation additions between 1996-2005 are forecast to be combined cycle, and 1339 MW, or 14.7%  S Glasser, Deregulated  Industries, New Technology  Gave Rise to Utility Competition,  Energy in the News  Magazine, Fall 1995/Winter 1996 lssue.32 Ibid page 32. Western Systems Coordinating Council Ten-Year Report issued May 1996. 6.  Coordinated  Plan Summary  1996-2005, University of Utah,  17 cogeneration. Therefore, a total of 63.9% of the generation additions in the W S C C region will be from gas turbine technology.  Combustion Turbine  2.0%  Figure 1.1 WSCC 1996-2005 Generation  Additions  Because turbine technology is advancing at a rapid rate the developer must decide whether to use the more reliable current technology, or the more risky new technology. While starting this study, these technologies were the F & G Series respectively. However, the respective technologies are now the G & H Series turbines, which emphasize the rapid developments that have occurred in gas turbine technology.  This background was provided to allow the reader to understand the changes that have taken place and how the recent developments in gas technology necessitate a new decision making matrix. It is important to understand changes in the fundamental cost structure and the way the business economics will be in the deregulated market. Also we are seeing new business drivers. These drivers are being forced into the existing and rigid physical system. As competitive market elements converge in the electric industry we will see players adopt new methodology for planning in an uncertain environment.  18  Chapter Two 2.  Technology  T h e total electricity c o n s u m p t i o n in t h e U S retail m a r k e t is 2.9 billion M W h , worth roughly $ 2 0 0 billion at current utility rates. T h e w h o l e s a l e m a r k e t r e p r e s e n t s a b o u t two thirds of that m a r k e t .  A s electricity h a s  b e e n a v a i l a b l e for t h e last century, North A m e r i c a n s largely h a v e t a k e n that availability a n d the a s s o c i a t e d reliability of s e r v i c e f o r g r a n t e d . T h e r e c a n b e little d e b a t e that s o c i e t y c a n d o without electric p o w e r . T h e d e b a t e s that d o a r i s e a r e f o c u s e d o n the i s s u e of t h e type of plant u s e d to p r o d u c e this electricity, the a m o u n t of c o n s e r v a t i o n n e e d e d to r e d u c e the n e e d for n e w r e s o u r c e s , a n d h o w the electricity is b o u g h t a n d s o l d . P r e v i o u s l y , o b j e c t i v e s in r e s o u r c e a c q u i s i t i o n h a v e b e e n to e n s u r e reliability for a g i v e n s e r v i c e a r e a at t h e l o w e s t long t e r m c o s t . Historically, l a r g e facilities h a v e b e e n built, a s they p r o d u c e t h e lowest m a r g i n a l c o s t o f electricity. F i g u r e 2.1 s h o w s a b r e a k d o w n of c a p a c i t y by g e n e r a t i o n type for C a n a d a , the U S , a n d North A m e r i c a . G a s facilities, including g a s t u r b i n e s a n d c o m b i n e d c y c l e , contribute a m e r e 4 % of total c a p a c i t y in C a n a d a , a n d 6 % o f g e n e r a t i o n for U S a n d North A m e r i c a .  •  800,000  CAMW  _USMW • TotalMW  700,000 600,000 500,000 |  400,000 300,000 200,000 100,000  ±_  0 Fossil Steam  Geothermal  G a s (GT &  Hydroelectric  CC)  Figure 2.1 Breakdown of Capacity by Type for CA, US & North America  Nuclear Steam  Total  19 T h e B C Market Electric R e v i e w  2 6  identified the e l e m e n t s that a r e d i m i n i s h i n g the c o s t a d v a n t a g e of large  central g e n e r a t i o n . A m o n g t h e s e f a c t o r s a r e :  1.  T h e positive correlation b e t w e e n s c a l e of facility a n d t h e r m a l e f f i c i e n c y a p p e a r s to h a v e r e a c h e d its limits;  2.  T h e b e s t h y d r o s i t e s h a v e b e e n exploited ( e s p e c i a l l y in d e v e l o p e d c o u n t r i e s ) a n d c o m p e t i n g u s e s exist for the r e m a i n i n g g o o d sites;  3.  T r a n s m i s s i o n e x p a n s i o n is i n c r e a s i n g l y difficult a n d costly, in part b e c a u s e of land u s e conflicts, t h e r e b y i n c r e a s i n g the c o s t of large central station g e n e r a t o r s with their g e n e r a l r e q u i r e m e n t for associated transmission investments; and  4.  N e w e n v i r o n m e n t a l r e g u l a t i o n s a n d siting difficulties c a n affect the c o s t s of l a r g e - s c a l e t e c h n o l o g i e s disproportionately.  G a s t u r b i n e s a r e a n e w e v o l v i n g t e c h n o l o g y w h o s e popularity is steadily i n c r e a s i n g . T h e p r e d o m i n a n t r e a s o n for the i n c r e a s i n g utilization of g a s t u r b i n e s is that g a s t u r b i n e s h a v e b r o k e n the e c o n o m i e s of s c a l e ; s m a l l p o w e r plants ( u n d e r 100 M W ) p r o d u c e at p r i c e s lower than larger ( s e v e r a l 100 M W )  power  plants.  L a r g e plant e f f i c i e n c y h a s m o v e d up in the last 2 0 y e a r s but h a s r e a c h e d a p l a t e a u  a n d is n o w  s t a b l e at 3 6 - 3 8 % . O n the o t h e r h a n d ; g a s t u r b i n e s h a v e c l i m b e d in e f f i c i e n c y f r o m 2 0 % to a s high a s 42%  2 7  .  British Columbia Utilities C o m m i s s i o n ( B C U C ) , T h e British Columbia Electricity Market Review Report and Recommendations to the Lieutenant Governor in Council Report and Recommendations to the Lieutenant Governor in Council. September 1995. (Vancouver: Sept. 1995). Margaret E. Mclntire. Trigen Energy Services Corporation White Plains, NY. 2.  for the Mid-Sized  Industrial and Commercial  Market, Trigen Energy  20 The deregulation of the airline industry had indirect consequences for the electric industry. It was the deregulation of the airlines that forced an advancement in jet turbine technology, leading to more efficient jet engines. In the electric industry, standby generation units had been typically just combustion turbines attached directly to generators. Their lack of efficiency did not allow them to be financially viable as standalone units, but only as backup. However, the advancement of gas turbine technology led to the introduction of efficient combined cycle units, which in turn reduced costs and lead times for new project construction. This, coupled with utilities' risk aversion to capital intensive facilities and cheap gas prices, all conspired to create an environment which favored combined cycle gas fired generation over conventional generation facilities such as hydro, coal, and nuclear. In summary there are several reasons for the popularity of gas turbines: high efficiency, environmentally sound operation, on-line quick, retrofitting old plants, and new customers with special preferences. Each of these traits will be further expanded.  2.1.1. High Efficiency Gas turbines operate at a high efficiency, thus reducing power generating costs by increased efficiency. State-of-the-art gas-fired power plants achieve an efficiency of 41%, while hard-coal-fired plants reach 44%. Combining gas turbines and steam turbine cycles in power plants further boosts fuel utilization and hence efficiency. Gas turbine manufacturers are pushing the 60% total efficiency barrier, which translates to reduced electricity costs.  2.1.2. Environmentally Sound Operation Due to the improved technology and efficiency advantage, combined cycle plants produce notably lower levels of pollutant emissions than do steam turbine plants of comparable output. In addition, gas turbine fuels (natural gas and distillate) are nearly free of sulfur, meaning virtually no sulfur dioxide is emitted. The content of nitrogen compounds in natural gas is low and hybrid burners burn natural gas at low NOx emission levels, postcombustion measures for lowering the nitrogen oxide content of flue-gas emissions  21 a r e not n e c e s s a r y . 1 9 7 0 ' s g a s turbine p r o d u c e d 6 0 0 parts of nitrous o x i d e s ( N O x ) p e r million parts of 2 8  e x h a u s t while 1 9 9 6 g a s t u r b i n e s p r o d u c e 9 to 4 5 parts N O x p e r million p a r t s e x h a u s t (1.5 to 7 . 5 % o f t h e N O x of 1 9 7 0 t u r b i n e s ) . 29  2.1.3. Siting & Transmission Access S m a l l g a s t u r b i n e s a r e t h e least c o n t r o v e r s i a l r e g a r d i n g siting a n d a p p r o v a l c o m p a r e d to o t h e r g e n e r a t i n g t e c h n o l o g i e s . D u e to their s m a l l s i z e a n d low e m i s s i o n s , g a s turbine facilities c a n obtain siting a p p r o v a l in city c e n t e r s . T h e r e f o r e , g a s t u r b i n e s c a n b e strategically l o c a t e d in t r a n s m i s s i o n b o t t l e n e c k s a n d p e a k load regions.  2.1.4. On-LineFast G a s t u r b i n e s h a v e t h e s h o r t e s t l e a d time of all m a j o r p o w e r g e n e r a t i o n facilities. F i n a n c i n g a n d return o n i n v e s t m e n t i n c r e a s e d d u e to t h e fact that c o m b i n e d c y c l e plants c a n b e put into o p e r a t i o n in s t a g e s prior to final c o m p l e t i o n , starting with only t h e g a s turbine s e c t i o n . A s i m p l e c y c l e g a s turbine c a n b e built a n d r e a d y to run in 2 0 - 3 0 m o n t h s .  2.1.5. Upgrading Steam Power Plants by Adding Gas Turbines T h e s e r v i c e life o f b o i l e r s is m u c h s h o r t e r than that of s t e a m t u r b i n e s , t h e r e f o r e c o n v e r s i o n to c o m b i n e d c y c l e plants is a n alternative to r e p l a c i n g boilers. T h e e f f i c i e n c y of t h e plant i n c r e a s e s f r o m a typical 3 7 % to a l m o s t 5 0 % , while d o u b l i n g o r e v e n tripling t h e plant output.  Hans B o h m , Gas Turbines - Playing an Ever Greater Role on the Global Market, S i e m e n s Power Journal Vol. 2 , 1995.  6  Margaret E . Mclntire. Trigen Energy Services for the Mid-Sized Industrial and Commercial Mari<et, Trigen Energy Corporation White Plains, NY. 3.  22 2.1.6. New Market Results in New Customers T h e s u c c e s s o f g a s t u r b i n e s c a n b e attributed to its rapid t e c h n o l o g i c a l d e v e l o p m e n t a n d low fuel p r i c e s , 30 but c h a n g e s in the m a r k e t h a v e a l s o h a d a n effect. O n e of the m a i n g o a l s of P U R P A  in 1978 w a s to  lower electricity c o s t s by c r e a t i n g alternative s o u r c e s for bulk p o w e r for p u r c h a s e by e l e c t r i c utilities. Indirectly, P U R P A s p a w n e d the c r e a t i o n of I P P s . S i n c e P U R P A a n d the e n a c t m e n t o f the N a t i o n a l E n e r g y P o l i c y A c t o f 1 9 9 2 , the I P P industry h a s e x p a n d e d to i n c l u d e s o m e e x e m p t w h o l e s a l e g e n e r a t o r s ( E W G s ) , p o w e r m a r k e t e r s , e n e r g y b r o k e r s a n d m e r c h a n t plants ( m e r c h a n t plants a r e g e n e r a t i n g facilities that h a v e n o s p e c i f i c c u s t o m e r to p u r c h a s e p o w e r , a n d t h e r e f o r e , h a v e n o g u a r a n t e e d return o n i n v e s t m e n t ) . U s u a l l y f i n a n c e d by individuals or c o m p a n i e s with s u b s t a n t i a l e c o n o m i c r e s o u r c e s , m e r c h a n t plants a r e b e i n g d e v e l o p e d to s c a n a g g r e s s i v e l y potential m a r k e t s e g m e n t s for n e w b u l k - p o w e r c u s t o m e r s , in a n a t t e m p t to c o m p e t e in the m a r k e t p l a c e . M e r c h a n t plants a r e d e s i g n e d to sell w h o l e s a l e p o w e r into a c o m p e t i t i v e m a r k e t , r e a d y to s e r v e potential, unspecified customers.  2.2.  Gas Turbines - Range of Applications  O n e of the a d v a n t a g e s of g a s t u r b i n e s is the v a s t r a n g e of a p p l i c a t i o n s a v a i l a b l e to a n entity building s u c h a facility. G a s t u r b i n e s c a n b e built a s a g r e e n f i e l d facility, in o t h e r w o r d s , a n entirely n e w facility. A g r e e n f i e l d facility m a y b e a s i n g l e o r s i m p l e c y c l e g a s turbine, c o m b i n e d c y c l e , o r c o g e n e r a t i o n facility.  C o g e n e r a t i o n i n v o l v e s c o u p l i n g a turbine unit with a n industrial p r o c e s s . In c o g e n t h e s t e a m f r o m the turbine is u s e d in a n industrial p r o c e s s (a c o n f i g u r a t i o n c a l l e d a t o p p i n g c y c l e ) or the s t e a m u s e d in a n industrial p r o c e s s is u s e d to g e n e r a t e electric e n e r g y (a c o n f i g u r a t i o n c a l l e d a b o t t o m i n g c y c l e ) . C o g e n e r a t i o n i n c r e a s e s the o v e r a l l e f f i c i e n c y of the overall c y c l e . H o w e v e r c o g e n e r a t i o n a d d s a n e w d i m e n s i o n o f c o m p l e x i t y , a s the two s y s t e m s m u s t b e m a t c h e d to w o r k in c o n j u n c t i o n with o n e a n o t h e r . C o g e n e r a t i o n is b e y o n d the s c o p e of this t h e s i s .  Refer to Chapter 1 for details regarding P U R P A .  23 Another alternative to facility owners is to repower or refurbish existing facilities with gas turbines. Repowering is the addition to or replacement of existing power plant equipment, retaining serviceable permitted components to improve generation economics, extend life, improve environmental performance, enhance operability and maintainability, and more effectively use an existing site.  31  The options available with repowering include: repowering gas turbines, converting a free-standing gas turbine to combined cycle, or converting a steam plant to combined cycle (with boiler replacement or retaining an existing boiler). Repowering economics will not be covered in this thesis. There are many gas turbine manufacturers competitively trying to gain market share. GE, Westinghouse and Siemens are the leading gas turbine manufacturers. The remaining part of the chapter will discuss, in further detail, greenfield applications with gas turbines:  2.2.1. The Simple Gas Turbine  Figure 2.2 Simple cycle  arrangement.  The simple or single cycle combustion turbine (or gas turbine generator) is essentially a jet engine with a generator attached. The components of the system are simply a gas turbine and a generator. In a gas turbine, a compressor is used to compress air to pressures between 100 and 450 psi, depending on the engine design. Fuel (usually natural gas) is then injected and burned to raise the temperature of the compressed air to a value between 1500 and 2300 degrees Fahrenheit (F), again depending on the engine design. That high pressure, high temperature, air is then expanded to a lower pressure through a  3 1  Harry Stoll, R a u b W Smith, and Leroy O Tomilson, Performance  and Economic  Considerations  of  Repowering  24 turbine. In t h e p r o c e s s , it turns t h e turbine, giving u p e n e r g y in the f o r m o f m e c h a n i c a l p o w e r that c a n b e u s e d to turn a g e n e r a t o r . M o d e r n g a s turbine e n g i n e s r a n g e in output f r o m a f e w kilowatts to o v e r 2 5 0 , 0 0 0 k W . T h e e f f i c i e n c i e s o f m o d e r n large f r a m e g a s t u r b i n e s run a s high a s 3 3 % . M o d e r n aircraft derivative t u r b i n e s ( b a s e d o n jet e n g i n e s ) h a v e p o w e r o u t p u t s u p to 5 0 , 0 0 0 k W with e f f i c i e n c i e s a s high a s 3 6 %  3 2  .  T h e e x h a u s t o f g a s t u r b i n e s is v e r y hot, typically 8 5 0 to 1 1 5 0 d e g r e e s F a h r e n h e i t .  T h e i d e a c y c l e for s i m p l e g a s turbine is the B r a y t o n C y c l e . T h e B r a y t o n c y c l e p r o c e s s is s h o w n o n t h e P-v a n d T - s d i a g r a m s of F i g u r e 2 . 3  P  T  Figure 2.3 Air Standard Brayton Cycle  G a s turbine p r i c e s r a n g e f r o m $ 1 8 3 . 1 4 / k W to $ 8 2 8 . 7 3  ;  Steam Power Plants, G E Industrial and Power Systems Schenectady, N Y . 1. All efficiencies given are high heating value (HHV) b a s e d . That is the typical efficiency reported by utilities and fuel is generally valued in dollars per million B T U H H V . Turbine Systems Engineering, http://www.gas-turbines.com/TRADER7Manprice.htm  25 2.2.2. Combined Cycle  SINGLE SHAFT HRSG  <« MULTI SHAFT  iL  f HRSG GEM  it  GEN  1 iHRSG  GEN  Figure 2.4 Combined  cycle  arrangements  The combined cycle is a combustion turbine coupled with a steam turbine, with each unit attached to a generator. Sometimes as many as four gas turbines with individual boilers may be associated with a single steam turbine. The gas turbine, steam turbine, and generator may be arranged as a single-shaft design. Alternatively, a multi-shaft arrangement may be used with each gas turbine driving a generator and exhausting into its heat recovery boiler with all boilers supplying a separate steam turbine and generator . 34  The gas turbine combined cycle (GTCC) process is the same as a simple cycle unit; however, instead of the hot gas being exhausted to the atmosphere, it is used in a continuing process. The "waste heat" can be recovered by directing the hot exhaust through a steam boiler, commonly called a heat recovery steam generator (HRSG). Generally, steam pressures from HRSG's run from 600 to 1800 PSIG depending upon the design and the exhaust temperature of the gas turbine. This high pressure and high temperature can  World Bank, Gas Turbine  Design  r7ttp://gopher.worldbank.org/html/fpd/em/eminfo/EA/projdef/thrmtech/gascsubs.htm.  3-4  26 b e e x p a n d e d t h r o u g h a s t e a m turbine to g e n e r a t e p o w e r while letting t h e s t e a m p r e s s u r e d o w n to the lower p r e s s u r e s a n d t e m p e r a t u r e s n e e d e d for m o s t p r o c e s s a p p l i c a t i o n s . Alternatively, t h e s t e a m c a n b e e x h a u s t e d into a c o n d e n s e r o p e r a t i n g at a v a c u u m just a s c o n v e n t i o n a l s t e a m turbine p o w e r plants d o . T h i s u s e of "waste heat" i n c r e a s e s the p o w e r output of the facility by a p p r o x i m a t e l y 4 0 % to 6 0 % . S i n c e the plant p r o d u c e s m o r e p o w e r with t h e s a m e fuel, t h e efficiency is a l s o i n c r e a s e d .  T h e c o m b i n e d c y c l e c o n c e p t h a s b e e n u s e d in electric utility p o w e r g e n e r a t i o n (primarily in E u r o p e ) a n d p r o c e s s c o g e n e r a t i o n (in t h e U S A a n d worldwide) for o v e r 3 0 y e a r s . E x i s t i n g m o d e r n plants o f this type h a v e b e e n built in s i z e s to 1,750 M W a n d with t h e r m a l e f f i c i e n c i e s of 4 5 % . R e c e n t l y i n t r o d u c e d g a s t u r b i n e s with i m p r o v e d e f f i c i e n c i e s will p u s h G T C C p o w e r plant e f f i c i e n c i e s o v e r 6 5 % .  T h e r e a r e v a r i o u s c o m b i n e d c y c l e s y s t e m s , w h i c h i n c l u d e s i n g l e p r e s s u r e , two p r e s s u r e o r c o m b i n e d c y c l e with reheat. S i n g l e p r e s s u r e h a s t h e lowest capital c o s t , c a p a c i t y a n d least e f f i c i e n c y  of the v a r i o u s  s y s t e m s . T w o p r e s s u r e G T C C is e c o n o m i c for m i d - r a n g e a n d b a s e l o a d o p e r a t i o n . C o m b i n e d c y c l e with r e h e a t is t h e c y c l e w h i c h p r o v i d e s t h e m a x i m u m plant t h e r m a l efficiency, a l t h o u g h requiring t h e h i g h e s t capital i n v e s t m e n t .  2.2.3. Gas Turbine Design In g a s turbine d e s i g n , t h e firing t e m p e r a t u r e ( d u e to melting of t h e b l a d e s ) , c o m p r e s s i o n ratio, m a s s flow, a n d centrifugal s t r e s s e s a r e the f a c t o r s limiting both s i z e a n d efficiency. F o r e x a m p l e , e a c h 5 5 ° C ( 1 0 0 ° F ) firing t e m p e r a t u r e i n c r e a s e g i v e s a 1 0 - 1 3 % output i n c r e a s e a n d a 2 - 4 p e r c e n t e f f i c i e n c y i n c r e a s e . 3 5  2.3.  Simple Cycle Vs. Combined Cycle  F o r a n entity c h o o s i n g to build a g a s turbine s y s t e m , t h e c h o i c e of t h e s y s t e m is a function of the o b j e c t i v e s a n d n e e d s of t h e s y s t e m o p e r a t i o n .  World Bank, Gas Turbine Design http://gopher.worldbank.org/html/fpd/em/eminfo/EA/projdef/thrmtech/gascsubs.htm.  1  27 T a b l e 2-1  b e l o w d i s p l a y s p r e d o m i n a n t d i f f e r e n c e s b e t w e e n the s i m p l e c y c l e a n d c o m b i n e d c y c l e g a s  turbines. T h e c a p i t a l c o s t of the s i m p l e c y c l e is the lowest c o s t of the two alternatives b e c a u s e the C C i n c l u d e s a n H R S G a n d additional s t e a m t u r b i n e s a n d g e n e r a t o r . S i n c e in t h e c o m b i n e d c y c l e s y s t e m the e x h a u s t h e a t f r o m the g a s turbine is u s e d to f e e d the s t e a m turbine, the plant t h e r m a l e f f i c i e n c y is h i g h e r than in a s i m p l e c y c l e . T h e r e f o r e the o p e r a t i n g c o s t s of a s i m p l e c y c l e a r e h i g h e r t h a n a c o m b i n e d c y c l e s y s t e m , a l t h o u g h the capital c o s t s a r e lower. W i t h a c o m b i n e d c y c l e t h e r e is h i g h e r c a p a c i t y ability t h a n a s i m p l e c y c l e , a l t h o u g h the option d o e s exist to c o n v e r t a s i m p l e c y c l e to C C a n d h e n c e i n c r e a s e its c a p a c i t y a n d t h e r m a l efficiency.  A s a c o n s e q u e n c e of t h e s e d i f f e r e n c e s , s i m p l e c y c l e s a r e u s u a l l y u s e d a s p e a k i n g units, w h e r e they o p e r a t e l e s s frequently, d u e to the higher o p e r a t i n g c o s t s . C o m b i n e d c y c l e units s h o u l d b e run a s m u c h a s p o s s i b l e to s p r e a d the capital c o s t s o v e r m o r e k W h . S i n c e the c o m b i n e d c y c l e unit o p e r a t e s m o r e frequently a n d for a l o n g e r duration than a s i m p l e c y c l e , the c o s t s of c o m b i n e d c y c l e units a r e m o r e strongly a f f e c t e d by g a s p r i c e s .  Simple Cycle  Combined Cycle  Capital C o s t s  L o w e s t capital c o s t s  M o r e costly  Efficiency  L o w e r efficiency  M a x i m u m plant t h e r m a l e f f i c i e n c y  Capacity  Lower capacity  Highest capacity  Scale economies  Potential to i n c r e a s e c a p a c i t y to C C  L i m i t e d future e n h a n c e m e n t s  Table 2-1 Comparison between simple cycle and combined cycle gas turbines.  2.4.  Developments in Gas Turbine Technology  A s the industry m o v e s t o w a r d s c o m p e t i t i o n , it b e c o m e s c l e a r that the n e w e r e n t r a n t s with m o r e efficient plants m a y h a v e a d v a n t a g e s o v e r the o l d e r utilities. It is e s t a b l i s h e d that t h e g a s turbine is the m a r g i n a l c o s t unit of c h o i c e , a s it is the m o s t v i a b l e t e c h n o l o g y currently.  28 T h e c h a l l e n g e a r i s e s in w h i c h g a s turbine t e c h n o l o g y to i m p l e m e n t , a s the t e c h n o l o g y is still d e v e l o p i n g . F i g u r e 2.5 b e l o w s h o w s the evolution of g a s turbine t e c h n o l o g y o v e r the last half c e n t u r y . T h e m a j o r 3 6  m a n u f a c t u r e r s , S i e m e n s , W e s t i n g h o u s e , a n d G E , a r e h e a d to h e a d , a n d a i m to b r e a k the latest e f f i c i e n c y barrier. T o illustrate, in M a y 1 9 9 5 G E i n t r o d u c e d both the " G " a n d "H" s e r i e s g a s t u r b i n e s . T h e G s e r i e s will r e a c h a 5 8 % net t h e r m a l efficiency in c o m b i n e d c y c l e efficiency, a n d the a d v a n c e d H t e c h n o l o g y platform will b r e a c h the 6 0 % barrier in net t h e r m a l e f f i c i e n c y . 37  1940s  1950s  1960s  1970s  1980s  1990s  Figure 2.5 Evolution ofGE Gas Turbine Technology  F o r the p u r p o s e of this t h e s i s , the m o s t important d i f f e r e n c e b e t w e e n t e c h n o l o g i e s is in their efficiency. T h e v a l u e of e f f i c i e n c y a f f e c t s the g a s c o s t s a n d the e n v i r o n m e n t a l c o s t s . A s i n g l e p e r c e n t a g e point i n c r e a s e in e f f i c i e n c y c a n r e d u c e p o w e r - p l a n t o p e r a t i n g c o s t s by $ 1 5 million to $ 2 0 million o v e r the life of a typical 4 0 0 M W - 5 0 0 M W  plant.  T h e a i m of this t h e s i s is to c o m p a r e two t e c h n o l o g i e s , to d e t e r m i n e w h i c h t e c h n o l o g y to i m p l e m e n t b a s e d o n the t e c h n o l o g y risk profile w h i c h b e s t fits the o r g a n i z a t i o n ' s appetite for risk. It is the m e t h o d o l o g y of the  General Electric, G E G a s Turbines, T h e Power Plant for the Next Century is Here T o d a y . M S 7001 F A , Brochure. 6.  29 a n a l y s i s , a n d the t e c h n i q u e in w h i c h the v a l u e s of the v a r i a b l e s a r e d e t e r m i n e d , w h i c h is the h e a r t of the thesis.  T h e t e c h n o l o g i e s c o m p a r e d h e r e w e r e the F a n d G S e r i e s g a s t u r b i n e s . T a b l e 2-2  s h o w s the  m a n u f a c t u r e r ' s s p e c i f i c a t i o n s for the ' F ' S e r i e s a n d ' G ' S e r i e s m o d e l s . T h e F s e r i e s g a s turbine 7 0 0 0 S e r i e s turbine w a s i n t r o d u c e d in the early 1 9 9 0 s , while the G S e r i e s w a s i n t r o d u c e d in M a y 1995. T h e G s e r i e s w a s c h o s e n a s it is the n e w e s t m o s t efficient g a s turbine in the m a r k e t ; the H s e r i e s h a s yet to b e r e l e a s e d . C h a p t e r IV details the m e t h o d o l o g y of the m o d e l , a n d the c o m p a r i s o n of F & G S e r i e s c a n b e s e e n in C h a p t e r V , the R e s u l t s a n d A n a l y s i s C h a p t e r .  F Series  G Series  P G 7001  FA  G  Model  P G 7231  Capacity  253,500 k W  350,000 k W  Heat Rate  6,160  5,883 Btu/kWh  Efficiency  55.4%  Btu/kWh  Table 2-2 Comparison ofF&G  58%  Series gas turbines.  General Electric, G E Introduces A d v a n c e d G a s Turbine Technology Platform: First to R e a c h 6 0 % C o m b i n e d C y c l e Power Plant Efficiency. P r e s s Release, May 21, 1995.2.  30  Chapter Three 3.  Uncertainty in the Industry  T h i s c h a p t e r is not i n t e n d e d to p r o v i d e a c o m p l e t e s y n o p s i s of the c h a n g e s that a r e taking p l a c e in the industry a n d the u n c e r t a i n t i e s they c r e a t e . R a t h e r it is written to p r o v i d e the r e a d e r with a s e n s e o f w h a t is taking p l a c e in the industry a n d the n e e d for different a p p r o a c h e s to project d e v e l o p m e n t .  A s the r e g u l a t e d structure g u a r a n t e e i n g a rate o f return is b e i n g r e p l a c e d with c o m p e t i t i v e m a r k e t pricing, the risk a s s o c i a t e d with the d e v e l o p m e n t of n e w p o w e r projects h a s greatly i n c r e a s e d . R i s k , a s d e f i n e d by Webster's Dictionary,  38  is the possibility of l o s s o r injury.  B u s i n e s s e s i n v o l v e d in d e t e r m i n i n g  the viability o f building n e w g e n e r a t i o n facilities a r e c o n f r o n t e d with  m a n y u n k n o w n s . Q u e s t i o n s a n d i s s u e s that a r i s e in s u c h a n a s s e s s m e n t i n c l u d e :  1.  H o w m u c h is it g o i n g to c o s t m e to c o n s t r u c t it?  2.  C a n I g e t a p p r o v a l to build it?  3.  C a n I r u n , a s e x p e c t e d , the facility that I built?  4.  H o w m u c h c a n I e x p e c t for r e v e n u e s ?  5.  Will w e b e a b l e to b e a t the c o m p e t i t i o n ?  6.  Will a c c e s s to the m a r k e t b e a v a i l a b l e ?  T h e a n s w e r s to t h e s e q u e s t i o n s d e p e n d o n a n u m b e r of f a c t o r s . In a r e g u l a t e d e n v i r o n m e n t the a n s w e r s to t h e s e q u e s t i o n s w e r e not a m a j o r c o n c e r n , s i m p l y b e c a u s e the regulatory s t r u c t u r e p r o t e c t e d the  31 utilities f r o m m a n y o f t h e s e risks. F u r t h e r m o r e , in a r e g u l a t e d e n v i r o n m e n t s o m e of t h e s e risks m a y b e b u n d l e d , a n d t h u s not o b v i o u s .  In c o n t r a s t , in this a g e o f e l e c t r i c industry restructuring, e v e r y effort is n e e d e d to m a n a g e uncertainty. R i s k m a n a g e m e n t t e c h n i q u e s a r e not a n e w field. H o w e v e r , for electric industry p l a y e r s the a p p l i c a t i o n of t h e s e t e c h n i q u e s a r e n e w . A n u n d e r s t a n d i n g of t h e s e u n c e r t a i n t i e s is a n important p r e r e q u i s i t e for s o u n d d e c i s i o n m a k i n g . T h e p u r p o s e o f this t h e s i s is to p r e s e n t a m e t h o d that will allow p l a n n e r s to u s e risk m a n a g e m e n t and analysis techniques.  In the d e v e l o p e d m o d e l the u n c e r t a i n v a r i a b l e s dealt with explicitly i n c l u d e :  •  Heat Rate  •  Capacity  •  P r e - O p e r a t i o n Duration  •  Pre-Operation Cost  •  Operation & M a i n t e n a n c e Duration  •  Operation & Maintenance Cost  •  Fuel Costs  T h e s e v a r i a b l e s a r e d e s c r i b e d in C h a p t e r 5.  H o w e v e r , in the industry t h e r e a r e m o r e f a c t o r s than the a b o v e v a r i a b l e s that a r e a s s o c i a t e d with a high d e g r e e of uncertainty. T h e r e m a i n d e r of this c h a p t e r will d i s c u s s in m o r e detail the a r e a s o f uncertainty in the industry. T h e s e a r e a s a r e b r o k e n d o w n into 6 c a t e g o r i e s of uncertainty that a n entity will b e e x p o s e d to w h e n building a n e w facility:  •  Input F u e l U n c e r t a i n t y  Merriam Webster's Collegiate Dictionary, Tenth Edition: Merriam-Webster, Incorporated Springfield, Massachusetts, U S A Copyright 1996. 1011.  32 •  C o m p e t i t i o n , Marketability & D e m a n d U n c e r t a i n t y  •  T e c h n o l o g y Uncertainty  •  Regulation Uncertainty  •  Environmental Uncertainty  •  T r a n s m i s s i o n Uncertainty  3.1.  Input Fuel Uncertainty  F u e l c o s t s directly affect the profitability of a p o w e r project v e n t u r e . In the c a s e s t u d i e d h e r e , the natural input fuel a c c o u n t s for b e t w e e n 40-51 % of total project c o s t s in 1 9 9 6 $ (refer to C h a p t e r 5 R e s u l t s & A n a l y s i s ) . T h e fuel c o s t s directly affect the i n c r e m e n t a l c o s t of the unit, w h i c h t h e r e f o r e d e t e r m i n e s the unit c o m p e t i t i v e n e s s a n d its potential for d i s p a t c h .  T h e North A m e r i c a n natural g a s industry h a s u n d e r g o n e a rapid p r o c e s s o f e v o l u t i o n o v e r the p a s t 15 y e a r s . F i g u r e 3.1 b e l o w d i s p l a y s the A v e r a g e P r i c e o f N a t u r a l G a s in t h e U S f r o m 1 9 4 0 - 1 9 9 4 in J a n u a r y 1 9 9 5 D o l l a r s . D e r e g u l a t i o n w a s b e g u n in 1978 a n d initially r e s u l t e d in a m a j o r j u m p in g a s p r i c e s . T h i s 3 9  j u m p in g a s p r i c e s led to m o r e d e v e l o p m e n t a n d e x p l o r a t i o n . T h e m a r k e t b e c a m e truly liquid by the early 1 9 9 0 ' s , facilitated by the introduction of N Y M E X natural g a s futures. P r i c e s s i n c e t h e n h a v e s t a b i l i z e d or d e c r e a s e d , albeit with m a j o r volatility.  Northwest Power Planning Council, Fourthwest Transition Opportunities  Conservation  and Electric Power Plan Northwest Power in  and Risks (Draft) March 1996 - Publication 96-5. 6.  33  4.00  0.00 I I 1940  I I I II  1945  I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I1  1950  1955  I960  1965  1970  1975  1980  1985  1990  Figure 3.1 Average US Wellhead Natural Gas Prices Price of Natural Gas in the US 1940-1994  F i g u r e 3.2 s h o w s t h e variation of N Y M E X H e n r y H u b Natural G a s C o n t r a c t s for t h e N e a r M o n t h s f r o m April 1 9 9 0 to J a n u a r y 1 9 9 7 . It d e m o n s t r a t e s t h e volatility o f t h e fuel p r i c e . T h e y r e a c h e d a low of 1.2 $ / m m b t u to a high of a l m o s t 3 . 5 0 $ / m m b t u in this p e r i o d . In this e n v i r o n m e n t , m a r k e t participants h a v e f o u n d t h e m s e l v e s e x p o s e d to large price m o v e m e n t s .  34  Figure 3.2 Henry Hub Gas Near Month Close from April 1990 to January 1997  F i g u r e 3.3 s h o w s the variation of g a s p r i c e s at v a r i o u s delivery l o c a t i o n s (indices) c o m p a r e d to H e n r y H u b ( N Y M E X g a s futures). T h e figure d e m o n s t r a t e s h o w c o n d i t i o n s s u c h a s t h e c o l d winter of 1996 c o u l d result in m a j o r d i v e r s i o n s of g a s t r a n s m i s s i o n , with p r i c e s i n c r e a s i n g d r a m a t i c a l l y . T h e s e d i v e r s i o n s r e p r e s e n t b a s i s o r transportation c o s t volatility for natural g a s u s e r s , o v e r a n d a b o v e the c o m m o d i t y volatility.  +  Henry Hub Futures  G a s Daily Price at Henry Hub  G a s Daily Price at E l P a s o  G a s Daily Price at N W S u m a s  G a s Daily Price at C h i c a g o  Figure 3-3. Spot market comparison - Henry Hub futures  35  36  3.1.1. Long Term Fuel Contracts vs. Spot Market T h e volatility o f input fuel p r i c e s c r e a t e s a n u n k n o w n s t r e a m of c o s t s for project o p e r a t i o n . A n alternative to p u r c h a s i n g o v e r the s p o t m a r k e t is to e n g a g e in a long t e r m fuel c o n t r a c t . Naturally, with long t e r m fuel c o n t r a c t s , the s e l l e r of the c o n t r a c t will b e c a r r y i n g the fuel p r i c e risk. T o a c c e p t this risk, the s e l l e r i n c r e a s e s the c o s t of the long t e r m contract, to m a k e the v e n t u r e worthwhile. T h i s i n c r e a s e d i m i n i s h e s the profit m a r g i n of the p o w e r p r o d u c e r , p e r h a p s to the point w h e r e t h e r e is a n insufficient rate o f return to justify p r o c e e d i n g with the project.  Historically, s c e n a r i o a n a l y s e s h a d b e e n u s e d to a s s e s s the potential profits o f a project. D u e to the volatility o f the g a s p r i c e s , a n d the e x t e n d e d r a n g e o f v a l u e s that they m a y t a k e , a s s i g n i n g probabilities to the g a s p r i c e s will g i v e the d e c i s i o n m a k e r a better h a n d l e o n the r i s k s a n d r e w a r d s o f the project.  3.2.  Competition, Marketability & Demand Uncertainty  T h e e f f e c t s o f c o m p e t i t i o n m a n i f e s t t h e m s e l v e s o n the d e m a n d for the project output, a n d t h u s ultimately o n the r e v e n u e s t r e a m a n d profitability.  3.2.1. Demand If o n e c o n s t r u c t s a n e w p o w e r plant, o n e d o e s s o u n d e r the a s s u m p t i o n that t h e r e a r e c u s t o m e r s to w h o m the output c a n b e s o l d . I s s u e s that m u s t b e c o n s i d e r e d w h e n e v a l u a t i n g w h e r e t h e r e will b e sufficient d e m a n d are:  •  Is t h e r e a n e e d for n e w facilities or is the facility r e p l a c i n g / c o m p e t i n g with existing r e s o u r c e s ?  •  If the facility is c o m p e t i n g with existing r e s o u r c e s , c a n it, t h r o u g h its h i g h e r e f f i c i e n c y , h a v e r e d u c e d o p e r a t i n g c o s t s that c a n c o v e r a s h a r e of the capital c o s t ?  •  O n e m u s t c o n s i d e r the fact that the m a r k e t s o m e t i m e s h a s e n o u g h s u r p l u s to r e d u c e p r i c e s b e l o w the i n c r e m e n t a l c o s t , m a k i n g it not a l w a y s v i a b l e to o p e r a t e .  37 F i g u r e 3.4 d i s p l a y s a g r a p h of W S C C R e g i o n Net G e n e r a t i o n A d d i t i o n s , A c t u a l D a t a a n d P r o j e c t i o n s S u m m e r C a p a b i l i t y for e a c h y e a r f r o m 1986 to 2 0 0 5 . T h e g r a p h s h o w s that t h e r e is a n e e d for d e m a n d , a s the r e g i o n h a s not r e a c h e d s u p p l y / r e s o u r c e b a l a n c e .  Projected Actual  86 87 88 89 90 91 92 93 94 95 96 97 98 99 '00 '01 '02 '03 '04 '05  Figure 3.4 WSCC  Region Net Generation  Additions: Actual Data & Projections  Summer  Capability  T h e a m o u n t of output a n entity m a y sell will a l s o b e a function of the s e a s o n , particularly in the P a c i f i c Northwest, w h e r e t h e r e is a high p e r c e n t a g e of h y d r o . D u r i n g p e r i o d s of fish flush o r high precipitation, hydro electricity is p r o d u c e d in quantity a n d at low e x p e n s e , a n d t h e r e f o r e d i s p l a c e s all o t h e r f u e l s . C o m p e t i t i o n f r o m h y d r o d r i v e s the l o a d factor of the g a s facility w a y d o w n , t h e r e f o r e greatly r e d u c i n g the revenue.  In a c o m p r e h e n s i v e s t u d y p r e p a r e d for the C a l i f o r n i a E n e r g y C o m m i s s i o n  4 0  , the a u t h o r s a n a l y z e d the  e f f e c t s of restructuring in C a l i f o r n i a . T h i s s t u d y e s t i m a t e s the C u m u l a t i v e C a p a c i t y A d d e d for B a s e F u e l Costs C a s e  4 1  in C a l i f o r n i a , w h i c h is s h o w n in T a b l e 3-1 below. T h e a n a l y s i s for n e w m a r k e t entrants in  L C G Consulting.(1996) Modeling  Competitive  Energy Mari<et In California: Analysis  the California Energy C o m m i s s i o n , October 11, 1996.  of Restructuring,  3.17.  B a s e Fuel C o s t s C a s e : This is the primary c a s e which is used for comparison with other scenarios.  Prepared for  38 C a l i f o r n i a for the B F c a s e i n d i c a t e s up to 5,100 M W , 1 0 , 1 5 0 M W a n d 2 0 , 4 0 0 M W of n e w c o m b i n e d c y c l e units a r e e c o n o m i c a l l y v i a b l e in C a l i f o r n i a for 1998, 2001 a n d 2 0 0 6 r e s p e c t i v e l y .  1998  2001  2006  Location  Type  Capacity  Capacity (MW)  Capacity M W  Northern C a i  CC  -  -  500  Southern C a i  CC  -  -  4,000  R k y Mtn  CT  500  1,100  1,100  R k y Mtn  CC  -  -  900  Northwest  CC  2,850  4,800  6,850  Southwest  CT  350  850  1,350  Southwest  CC  1,400  3,400  5,700  5,100  10,150  20,400  Total  Table 3-1 Cumulative Capacity Additions in California  T h e r e f o r e , with t h e c h a n g e s in the electricity m a r k e t structure, the potential for a p o w e r p r o d u c e r to sell its future output f r o m its facility, p e r h a p s to C a l i f o r n i a , exists. In jurisdictions all a r o u n d the facility site, s u c h a s B C , A l b e r t a , C a l i f o r n i a , a n d the W S C C r e g i o n a s a w h o l e , g e n e r a t i o n output will b e n e e d e d a n d the c a p a c i t y of the p r o p o s e d project will h a v e a m a r k e t to sell into. T h e u n k n o w n is the l o a d factor, a n d w h e t h e r the r e v e n u e g e n e r a t e d will offset the c o s t s i n c u r r e d .  3.2.2. Pricing  T h e electric industry h a s a l s o b e c o m e m o r e liquid. W i t h the i n c r e a s i n g l y c o m p e t i t i v e m a r k e t , electricity p r i c e s h a v e b e c o m e volatile. Volatility brings larger m a r k e t risk, a n d t h u s the n e e d to m a n a g e the risk.  39 T h e r e a r e m a n y p r i c e i n d i c e s n o w a v a i l a b l e in the m a r k e t . Indices g i v e b u y e r s a n d s e l l e r s a p r i c e r e f e r e n c e reflecting the m a r k e t price of electricity a n d aid in p r i c e t r a n s p a r e n c y . T h e s e i n d i c e s a v a i l a b l e for u s e a r e D o w J o n e s , P o w e r M a r k e t s W e e k a n d A l b e r t a P o w e r P o o l , w h i c h a r e c o m p a r e d in T a b l e 3-2.  A c o m m o d i t i e s m a r k e t w a s a l s o d e v e l o p e d to m a n a g e that risk. N Y M E X Electricity f u t u r e s w e r e l a u n c h e d on March 29 , t h  1996.  A s the n u m b e r o f b u y e r s a n d s e l l e r s i n c r e a s e s , liquidity is g e n e r a t e d in the m a r k e t p l a c e ; liquidity a n d volatility i n d u c e the n e e d for a c t i v e c o s t a n d risk m a n a g e m e n t . W i t h f u t u r e s , b u y e r s a n d s e l l e r s c a n lock in the price of electricity, allowing t h e m to h e d g e t r a n s a c t i o n s . H o w e v e r , electricity f u t u r e s c a n only b e p u r c h a s e d 18 m o n t h s f o r w a r d , s o a n y long t e r m c o n t r a c t s g r e a t e r t h a n 18 m o n t h s c a n n o t b e h e d g e d with N Y M E X futures. S i n c e electricity is g e n e r a t e d by v a r i o u s f u e l s , s u c h a s c o a l , g a s a n d oil, the volatility in t h e s e m a r k e t s will b e reflected in electricity p r i c e s . H y d r o plants h a v e a n o p e r a t i n g c o m p e t i t i v e 4 2  a d v a n t a g e ; their i n c r e m e n t a l c o s t of g e n e r a t i o n is significantly l e s s t h a n o t h e r f u e l s , s u c h a s g a s , c o a l o r n u c l e a r , allowing t h e m to a c h i e v e priority d i s p a t c h i n g if r e q u i r e d . F i g u r e 3.5  displays sample data  from the indices discussed above. The relative duration of historical data available for each index can be seen. Also demonstrated is the long-term correlation between the indices as well as relative volatility. All i n d i c e s s h o w the  Z E PowerGroup Inc. (1997) NYMEX  s e v e r e volatility o f the m a r k e t .  Futures: Gas and Electric. Report Prepared for A Multi-Client Study.  (Vancouver, British Columbia, March 1997. 6-23.  Dow Jones Telerate Factor  COB  Class  Palo Verde  MidColombia  Power Markets Week  Alberta Pool  Palo Verde  Alberta Pool  COB  physical  MidColumbia  physical  physical  NYMEX COB  Palo Verde  primarily f i n a n c i a l , c o n t r a c t s c a n g o to p h y s i c a l delivery  Domain  point in  sub-  point in  point in  trans-  station  transmission system  mission  substation  within a  p r o v i n c e of  point in  transmission  transmission  Alberta  transmission  system  system  substation  system  system  Time Frame Updated  daily  w e e k l y a n d daily  hourly  monthly  next d a y  next p e r i o d  hourly  real time  Market Perspective  cash  cash  physical  financial  historical to p r e s e n t  historical to p r e s e n t  historical to  historical a n d 18 m o n t h s  present  forward, no present  all trade in  all N Y M E X futures t r a d e  Portion of trade quoted Cost to Access Introduced  a p p r o x i m a t e l y 2/3 of t r a d e —  not a p p l i c a b l e , qualitative p h o n e s u r v e y s  p a r t i e s c o n t r a c t u a l l y b o u n d to report f r e e o n the W e b  pool a c c e s s to historical d a t a is $ 5 0 , u p d a t e s  4 a  are $25/month June  M a r c h '96  M a y '96  O c t '94  O c t '94  f r e e o n the Web&  O c t '94  J a n '96  '95  Table 3-2 Comparison of price indices' characteristics  A c c e s s to D o w J o n e s T e l e r a t e and N Y M E X price data is available at  http://www.energyonline.com  A c c e s s to Alberta P o w e r P o o l price data available at http://www.powerpool.ab.ca a n d on the B B S at 403-263-6075 user n a m e - public' A c c e s s to N Y M E X price data is a l s o available at  http://www.nymex.com  40  f r e e o n the W e b  BBS A p r '96  A p r '96  41  -Nymex COB Futures Near Month Price -Nymex PV Futures Near Month Prices  63 00  Dow Jones COB Non-Firm On-Peak  56 00 49 00  —  Dow Jones Firm On-Peak PV  42 00 35 00 28 00 21 00 14 00 • i  7 00 -  ^-^i  i  1  50.00  - P M W COB  45.00  - P M W PALO VERDE  i  i  L  PMW Mid-Columbia  40.00 35.00 3 30.00 « 25.00 3  20.00 15.00 10.00  ^  ^  ^  ^  ^  5.00  Figure 3-5. Graphical comparison of price indices  1  1  1  1  1  1  42  3.2.3. Product W i t h the c h a n g e s in the m a r k e t , the n e e d s of the client a r e b e c o m i n g m o r e clearly d e f i n e d . Electricity is b e i n g u n b u n d l e d to reflect the n e e d s of the p u r c h a s e r s . S e r v i c e s n o w o f f e r e d to p u r c h a s e r s i n c l u d e : e n e r g y , c a p a c i t y , l o a d following, s h a p i n g , s p o t p u r c h a s e s a n d long t e r m p u r c h a s e s .  T o d a y ' s typical electric utility p r o v i d e s b u n d l e d s e r v i c e s to its c u s t o m e r s . H o w e v e r , in t h e e m e r g i n g m a r k e t all t h e s e s e r v i c e s will b e m a r k e t e d s e p a r a t e l y  4 4  a n d competitively, w h i c h a d d s to the c o m p l e x i t y of  e s t i m a t i n g potential r e v e n u e a n d profitability. T h i s a s p e c t of the p r o b l e m will not b e m o d e l e d in this t h e s i s .  3.3.  Technology Uncertainty  G a s turbine t e c h n o l o g y , a l t h o u g h a m a t u r e t e c h n o l o g y , is still i m p r o v i n g . T h e s e t e c h n o l o g y i m p r o v e m e n t s a r e the result of n e w m a t e r i a l s , i m p r o v e d d e s i g n s a n d d e c r e a s e d p r o d u c t i o n c o s t s .  In this w o r k two g a s turbine t e c h n o l o g i e s , n a m e l y " F " S e r i e s a n d " G " s e r i e s , a r e u s e d to d e m o n s t r a t e  how  risks c a n b e m o d e l e d . B y c o m p a r i n g the two s e r i e s , w e will try to d e t e r m i n e w h e t h e r the p r o m i s e d i n c r e a s e in e f f i c i e n c y for a n e w e r t e c h n o l o g y justifies the i n c r e a s e d risk of t e c h n o l o g i c a l difficulties a n d i n c r e a s e d capital c o s t s a s s o c i a t e d with it.  A n o t h e r i s s u e is the d i f f e r e n c e in g a s turbines a m o n g s u p p l i e r s . F o r e x a m p l e A B B & S i e m e n s h a v e substantially c h a n g e d their g a s turbine d e s i g n to b e c o m e c o m p e t i t i v e with G E & M i t s u b i s h i g a s turbines. T h e r e f o r e , t h e r e is m o r e risk a s s o c i a t e d with o n e of their F s e r i e s t u r b i n e s t h a n t h e c o m p a r a b l e G E a n d M i t s u b i s h i t u r b i n e s . T h i s is a n o t h e r uncertainty f a c e d by the p l a n n e r that will not b e t r e a t e d in this w o r k .  3.4.  Regulation Uncertainty  T h e p r o b l e m for c o r p o r a t i o n s in the electric industry is that the regulatory p r o c e s s e s a r e l o n g w i n d e d , involved decision making p r o c e s s e s . F o r e x a m p l e :  43 •  B C H W h o l e s a l e T r a n s m i s s i o n P o l i c y - T h e potential for s o m e f o r m of a c c e s s to t r a n s m i s s i o n o v e r the B C H y d r o s y s t e m h a s b e e n d e b a t e d in front of the British C o l u m b i a Utilities C o m m i s s i o n s i n c e at least 1 9 9 1 . A s of 1 9 9 7 the B C U C permitted B C H y d r o to u s e a n interm rate; the rates a r e c o n t r o v e r s i a l a s they a r e v i e w e d to b e o v e r p r i c e d . A h e a r i n g is s e t for the fall of this y e a r to d e t e r m i n e the final rates.  •  C P U C E l e c t r i c R e s t r u c t u r i n g in C a l i f o r n i a 4 y e a r p r o c e s s . In April 1 9 9 2 , the C a l i f o r n i a P u b l i c Utilities C o m m i s s i o n initiated a c o m p r e h e n s i v e review of c u r r e n t a n d future t r e n d s in the electric industry. O n D e c e m b e r 2 0 , 1 9 9 5 , the C o m m i s s i o n a p p r o v e d its p r o p o s e d transition to a c o m p e t i t i v e electric m a r k e t b e g i n n i n g J a n u a r y 1, 1998, with all c o n s u m e r s participating by 2001  4 5  T h e m a i n i m p a c t of t h e s e regulatory p r o c e s s e s is that the p r o d u c t d e v e l o p e r will h a v e to r e e x a m i n e c o n t i n u o u s l y the m a r k e t for project viability. S i n c e p r e - o p e r a t i o n time is v a r i a b l e , it m u s t b e f a c t o r e d in the a n a l y s i s . T h e u n c e r t a i n p r e - o p e r a t i o n time i m p a c t s o n the d e v e l o p e r ' s ability to s e c u r e l o n g e r t e r m c o n t r a c t s , t h u s i n c r e a s i n g e x p o s u r e to m a r k e t p r i c e s .  3.5.  Environmental Uncertainty  In the p a s t , a n y o n e c o u l d build a n y type of facility, without c o n c e r n to a n y pollution o r e n v i r o n m e n t a l h a z a r d s it will p r e s e n t . H o w e v e r , this is n o l o n g e r the c a s e . It is b e c o m i n g e x c e e d i n g l y difficult to g e t a p p r o v a l for a n y projects that m a y h a v e a n e g a t i v e i m p a c t o n the e n v i r o n m e n t . T h e n e g a t i v e i n f l u e n c e c o u l d i n c l u d e p r o b l e m s of land u s a g e , w a t e r a n d air quality, a n d n o i s e pollution. F e d e r a l , provincial o r state g o v e r n m e n t s , regulatory a g e n c i e s , a n d o t h e r s a r e i m p o s i n g n e w s t a n d a r d s a n d g r e a t e r restrictions  F E R C order 888 defined six ancillary services. T h e s e include energy services such as: back-up, imbalances, storage, shaping etc. T h e ancillary services make provision of the basic services of generating capacity, energy supply, and power delivery. California Public Utilities C o m m i s s i o n , California Restructuring Background, (California Public Utilities Commission D e c e m b e r 1995) P. 1  44  o n anything that r e l e a s e s pollutants into the air, f r o m m o b i l e to stationary  s o u r c e s . C o n s e q u e n t l y , there is  a threat to a n y entity w a n t i n g to build/own a g e n e r a t i n g facility that e n v i r o n m e n t a l limits m a y b e retroactively i m p o s e d o n .  R e d u c e d e m i s s i o n s a s s o c i a t e d with the d e v e l o p m e n t of n e w t e c h n o l o g y s e t s n e w e m i s s i o n s t a n d a r d s for s i m i l a r facilities. A s s u c h , t h e s e n e w t e c h n o l o g i e s m a k e existing plants e n v i r o n m e n t a l l y o b s o l e t e . F o r e x a m p l e , i m p r o v e m e n t s in c o a l facilities h a v e d e c r e a s e d the p e r m i s s i b l e a m o u n t of e m i s s i o n s . T h i s in turn f o r c e s utilities to i n c r e a s e capital e x p e n d i t u r e s to p u r c h a s e the t e c h n o l o g y n e c e s s a r y for r e d u c i n g emissions.  T h e regulatory a n d e n v i r o n m e n t a l p r o c e s s e s a r e lengthy a n d  i n v o l v e d for p o w e r s u p p l y projects. In B C  the E n v i r o n m e n t a l A s s e s s m e n t A c t (Bill 29) w a s i n t r o d u c e d o n M a y 5 ' 1996. T h i s a c t r e p l a c e s the E n e r g y th  P r o j e c t R e v i e w P r o c e s s for projects r e g u l a t e d u n d e r the Utilities C o m m i s s i o n A c t . F o r p o w e r projects, the Utilities C o m m i s s i o n A c t d e f i n e s r e g u l a t e d projects a s t h o s e with a c a p a c i t y o f 2 0 M W or greater, o r t h o s e that C a b i n e t d e s i g n a t e s a s r e g u l a t e d if t h e s e projects a r e "significant in the matter of e n e r g y . " T h e duration of a review c o u l d b e b e t w e e n 6 to 12 m o n t h s ; if it only n e e d s to g o to S t a g e T w o of the review p r o c e s s , 9 9 % of p r o j e c t s a r e a p p r o v e d by S t a g e 2. If a project r e q u i r e s a S t a g e 3 review, its duration is b e t w e e n 18 a n d 4 2 m o n t h s . T h e bottom line is that the c o n s t r a i n t s put o n o w n e r s of g e n e r a t i n g facilities, with r e s p e c t to e m i s s i o n s a n d e n v i r o n m e n t a l effects, a r e i n c r e a s i n g . A n e w regulation c o u l d c o s t a plant o w n e r millions of d o l l a r s by f o r c i n g the o w n e r to install n e w t e c h n o l o g y for e m i s s i o n s r e d u c t i o n , or e l s e to retire the plant early. T h e potential for n e w r e g u l a t i o n s m a k e s it v e r y difficult for a n y entity with a facility or wanting to build a facility to b e s u r e w h a t will b e the total c o s t of the plant o p e r a t i o n a n d m a i n t e n a n c e .  F o r entities that w a n t to build n e w facilities, t h e r e is a n a d d e d c o m p l i c a t i o n in that the p r o c e s s r e q u i r e d to g e t a project a p p r o v a l is l o n g a n d i n v o l v e d , w h i c h m a k e s it difficult to s c h e d u l e l o n g t e r m d e a l s .  45  3.6.  Transmission Uncertainty  F a i r m a r k e t a c c e s s is a n e c e s s a r y e l e m e n t b e f o r e effective c o m p e t i t i o n c a n thrive in the electric utility industry. E x c e s s i v e t r a n s m i s s i o n rates c a n b e u s e d to p r e v e n t a c c e s s to the t r a n s m i s s i o n s y s t e m .  All P o w e r p u r c h a s e r s d e p e n d u p o n the t r a n s m i s s i o n s y s t e m of the larger utilities for p o w e r d e l i v e r i e s . W i t h i n e a c h control a r e a , the t r a n s m i s s i o n function is both  a natural m o n o p o l y a n d a n e s s e n t i a l facility,  w h i c h is w h y the m o n o p o l y structure w a s s u p p o r t e d historically.  R e c e n t regulatory d e v e l o p m e n t r e m o v e d t r a n s m i s s i o n a c c e s s a s a m a i n c o n c e r n , a s t r a n s m i s s i o n is n o w o p e n a n d a v a i l a b l e . H o w e v e r , t r a n s m i s s i o n pricing r e m a i n s a m a j o r uncertainty. T h e d i f f e r e n c e in c o s t s b e t w e e n firm t r a n s m i s s i o n a c c e s s a n d non-firm t r a n s m i s s i o n a c c e s s c o u l d b e a factor of 2 or 3. T h i s d i f f e r e n c e i m p a c t s h o w the p r o d u c t d e v e l o p e r will p a c k a g e his r e s o u r c e s , a n d the n u m b e r of firm v e r s u s n o n firm s a l e s (portfolio m a n a g e m e n t ) . T h e i s s u e of portfolio m a n a g e m e n t is not dealt with in this t h e s i s a s it c o u l d b e indirectly f a c t o r e d into the c h o i c e of c o m m o d i t y pricing.  3.7.  Conclusions  T h e f e e d b a c k f r o m o n e of the e x p e r t s s u m m a r i z e s the a b o v e i s s u e s eloquently.  1.  P r o d u c e r s will b e taking m o r e risk, a s t h e r e is l e s s certainty in the m a r k e t p l a c e . It will be h a r d e r for p r o d u c e r s to s i g n long t e r m c o n t r a c t s .  2.  It will b e difficult for p r o d u c e r s to fix long t e r m r e v e n u e s .  3.  T h e inability to f o r e c a s t r e v e n u e will m a k e i n v e s t o r s d e m a n d h i g h e r returns.  T h e s e f a c t o r s s u g g e s t the n e e d for a f r a m e w o r k to a s s i s t d e c i s i o n m a k e r s in m a k i n g d e c i s i o n s in a n uncertain  environment.  46  Chapter Four 4.  The Model  4.1.  Model Objective  T h e first f o u r c h a p t e r s of this t h e s i s d e m o n s t r a t e d the n e e d for a f r a m e w o r k to a n a l y z e the feasibility of a project in a n u n c e r t a i n e n v i r o n m e n t . T h i s c h a p t e r d e s c r i b e s the m e t h o d o l o g y of the d e v e l o p e d m o d e l .  T o p r o c e e d with a n y project, the e c o n o m i c s of the project m u s t b e justified. T h e p r o c e s s for e v a l u a t i n g the e c o n o m i e s u s u a l l y i n v o l v e s running a n a l y s e s to c a l c u l a t e a p e r f o r m a n c e m e a s u r e a n d t h u s d e t e r m i n e the feasibility of a p r o p o s e d project. T h e project will p r o c e e d if the c a l c u l a t e d p e r f o r m a n c e m e a s u r e is a c c e p t a b l y g o o d . Naturally, the p e r f o r m a n c e m e a s u r e is a f f e c t e d by all the project c o s t c o m p o n e n t s a n d the future r e v e n u e s t r e a m . S o m e t i m e s c a l c u l a t i n g the p e r f o r m a n c e m e a s u r e c a n b e a c h a l l e n g e .  If the v a r i a b l e s affecting the p e r f o r m a n c e m e a s u r e w e r e d e t e r m i n i s t i c o r a s i n g l e "point" e s t i m a t e , the p r o b l e m w o u l d b e s i m p l e . All the i n v e s t o r h a s to d o is d e v e l o p a m o d e l that s h o w s the c a s h flow of the project; this c a n b e d o n e o n a n y s p r e a d s h e e t or m a t h e m a t i c a l p r o g r a m s u c h a s M a t h C a d . T h e output will b e a s i n g l e result, w h i c h e a s e s the d e v e l o p e r s ' d e c i s i o n m a k i n g .  S o m e t i m e s , the a n a l y s i s c a n b e b r o k e n d o w n into a n u m b e r of s c e n a r i o s : a high c o s t , m e d i u m c o s t , a n d low c o s t s c e n a r i o , for e x a m p l e . In this c a s e , the a n a l y s i s is c a r r i e d out a s for the d e t e r m i n i s t i c m o d e l . H o w e v e r , i n s t e a d of running the m o d e l o n c e , it m u s t b e run all t h r e e t i m e s to a c c o u n t for all the p o s s i b l e s c e n a r i o s . T h e output in this c a s e will b e a s e t of 3 points, giving the d e v e l o p e r a f e e l for the p o s s i b l e v a l u e s of the p e r f o r m a n c e m e a s u r e .  47 T h e nature of the electricity industry is that the v a r i a b l e s that affect the p e r f o r m a n c e m e a s u r e a r e a s s o c i a t e d with a l a r g e d e g r e e of uncertainty. T h e r e is a w i d e b a n d of variation, a n d t h e nature of the variation is r a n d o m . M o s t importantly, the v a r i a b l e s v a r y i n d e p e n d e n t l y of o n e a n o t h e r .  T h e r e f o r e , a n e w m e t h o d o l o g y of a n a l y s i s m u s t b e u s e d to a c c o u n t for all the variation o n the v a r i a b l e s . T h e s e f a c t o r s a r e w h a t led to the d e v e l o p m e n t of the p r o p o s e d m o d e l . T h e m o d e l is a probabilistic c a s h flow m o d e l , a n d the t e c h n i q u e p r o p o s e d in this t h e s i s is that of a M o n t e C a r l o S i m u l a t i o n ( M C S ) . S i m u l a t i o n is a t e c h n i q u e w h e r e b y a m o d e l , s u c h a s a n E x c e l w o r k s h e e t , is c a l c u l a t e d m a n y t i m e s with different input v a l u e s , with the intent of getting a c o m p l e t e r e p r e s e n t a t i o n of all p o s s i b l e s c e n a r i o s that might o c c u r in a n u n c e r t a i n s i t u a t i o n . M o n t e C a r l o r e f e r s to a traditional m e t h o d of s a m p l i n g r a n d o m 46  v a r i a b l e s in a s i m u l a t i o n m o d e l i n g .  T h e a p p r o a c h u s e d in the m o d e l w a s to d e t e r m i n e the s u p p l y p r i c e of electricity to m a k e a c o r p o r a t i o n ' s m i n i m u m attractive rate of return ( M A R R ) o n a g a s turbine project. T h e lower the s u p p l y p r i c e of electricity, the lower t h e risk to the d e v e l o p e r a n d the h i g h e r the probability of a c h i e v i n g the r e q u i r e d return.  T h e a p p r o a c h is a p p l i e d to c o m p a r e two t e c h n o l o g i e s , n a m e l y , F s e r i e s a n d G s e r i e s , g a s t u r b i n e s . T h e p u r p o s e is to e x a m i n e the effect of n e w t e c h n o l o g i e s o n the e c o n o m i c risk profile of the project. T h e m o d e l output w o u l d allow a n i n v e s t o r to c o m p a r e e a c h t e c h n o l o g y , a n d c h o o s e the t e c h n o l o g y b a s e d o n the risk profile that b e s t fits the o r g a n i z a t i o n ' s appetite for risk.  F o r e x a m p l e , F i g u r e 4.1 b e l o w illustrates a n e x a m p l e of two probability distributions, A a n d B. Probability distribution B r e p r e s e n t s g r e a t e r risk than A b e c a u s e the r a n g e , or s t a n d a r d d e v i a t i o n is larger. T h e r e f o r e the probability of o c c u r r e n c e r e p r e s e n t s a w i d e r r a n g e of v a l u e s for B than A . H o w e v e r in the c a s e that a lower v a l u e is m o s t d e s i r a b l e , Probability distribution B h a s a high probability of o c c u r r e n c e of a lower  Palisade Corporation, @ R i s k A d v a n c e d Risk Analysis for Spreadsheets (Palisade Corporation: Newfield, N Y September 1996). Glossary. 296.  48 v a l u e t h a n A . A l e s s risk a v e r s e c o r p o r a t i o n m a y prefer a s m a l l e r s p r e a d in the r e s u l t s , with m o s t of the probability a s s o c i a t e d with the d e s i r a b l e results. W h e r e a s , a l e s s risk a v e r s e taking c o r p o r a t i o n s m a y accept a greater spread.  10  20  30  40  50  60  70  80  20  30  40  50  60  70  80  B  10  Figure 4.1 Probability Distribution A & B  4.1.1. Assumptions T h e probabilistic c a s h flow m o d e l d e v e l o p e d is a s i m p l e m o d e l of the c a s h flows for a c o m b i n e d c y c l e plant. T h e a n a l y s i s did not t a k e all the v a r i a b l e s into a c c o u n t ; j u s t the m o s t important v a r i a b l e s w e r e a d d r e s s e d a n d a s s i g n e d probability distributions. T h e r e a s o n s for the simplifying a s s u m p t i o n s i n c l u d e the following:  1.  T h i s p r o b l e m is a c o m p l e x a n d n e w p r o b l e m . T h e t h e s i s d e m o n s t r a t e s a m e t h o d o l o g y for s o l v i n g t h e p r o b l e m , but d o e s not p r o v i d e a c o m p l e t e solution to t h e p r o b l e m .  2.  T h e u s e r s w h o will a p p l y this m e t h o d o l o g y a r e s o p h i s t i c a t e d u s e r s . E v e r y situation differs a n d t h e m a r k e t c h a n g e s e v e r y d a y , calling for a n e w c u s t o m i z e d a p p r o a c h to the  49 p r o b l e m . A c o r p o r a t i o n u n d e r g o i n g their o w n s t u d i e s will c u s t o m i z e the m o d e l to m e e t their s p e c i f i c criteria, n e e d s a n d a p p r o a c h to p l a n n i n g .  3.  E x p e r t s w e r e c o n s u l t e d for their e s t i m a t e s of c o s t s . T h e m o d e l that w a s d e v e l o p e d r e q u i r e d ten distributions for e a c h of the " F " a n d " G " S e r i e s r u n s , a n d t h u s twenty distributions w e r e r e q u i r e d in all. T h i s w a s a time c o n s u m i n g p r o c e s s , a n d t h e r e f o r e , f a c t o r s h a d to b e m i n i m i z e d a n d s i m p l i f i e d .  Methodology of Risk Analysis Model  4.2.  T h e m e t h o d o l o g y of the m o d e l is a probabilistic c a s h flow m o d e l . V a r i a b l e s with h i g h e r l e v e l s of uncertainty in the m o d e l a r e identified a n d a s s i g n e d probabilistic distributions. A M o n t e C a r l o s i m u l a t i o n that g e n e r a t e s r a n d o m n u m b e r s d r a w n f r o m t h e s e probability distributions, for e a c h of the v a r i a b l e s , is run. A p p r o x i m a t e s o l u t i o n s a r e in t e r m s of a r a n g e of v a l u e s , e a c h of w h i c h h a s a c a l c u l a t e d probability of b e i n g t h e s o l u t i o n . All the p o s s i b l e r a n g e s of v a l u e s that the v a r i a b l e s m a y t a k e a r e f a c t o r e d into the output p e r f o r m a n c e m e a s u r e . T h e result g i v e s the d e c i s i o n m a k e r a c o m p l e t e picture of all p o s s i b l e o u t c o m e s a n d their probabilities.  T h e m o d e l is run in a n E x c e l software called @ R i s k  4 8  4 7  s p r e a d s h e e t , a n d the s i m u l a t i o n is p e r f o r m e d u s i n g a p r e p a c k a g e d  . @ R i s k is a s p r e a d s h e e t a d d - i n for either E x c e l o r L o t u s .  4.2.1. Monte Carlo Simulation  T h e M o n t e C a r l o S i m u l a t i o n M e t h o d is n a m e d after the city in M o n a c o , f a m e d for its c a s i n o . T h e n a m e a n d the s y s t e m a t i c d e v e l o p m e n t of M o n t e C a r l o m e t h o d s d a t e b a c k to a b o u t 1 9 4 4 .  4 9  Microsoft Excel, Microsoft. Copyright 1993- 94. Trademark of Microsoft Corporation. BestFit, Palisade Corporation. Copyright 1993-95. Trademark of Palisade Corporation. Sabri Pllana, History of Monte Carlo Method. http://www.geocities.com/CollegePark/Quad/2435/,  1997.  50 M o n t e C a r l o s i m u l a t i o n s a m p l e s a r e c h o s e n c o m p l e t e l y at r a n d o m a c r o s s t h e r a n g e of distribution. T h u s it is n e c e s s a r y to t a k e l a r g e n u m b e r s of s a m p l e s for c o n v e r g e n c e of highly s k e w e d or long-tailed distributions . 50  4.3.  Model Development  T h e following o u t l i n e s the s t a g e s of m o d e l d e v e l o p m e n t :  1.  D e f i n e the p e r f o r m a n c e m e a s u r e for project a p p r o v a l  2.  D e f i n e the functional r e l a t i o n s h i p s b e t w e e n the p e r f o r m a n c e m e a s u r e a n d the v a r i a b l e s  3.  F o r m u l a t e the M o d e l  4.  Model Variable Behavior  5.  Perform a Monte Carlo Simulation  6.  Calculate and C o m p a r e Performance Measures  7.  Obtain Representative Data  E a c h will b e d i s c u s s e d in detail:  4.3.1. Performance measure T h e p u r p o s e of e v e r y m o d e l is to p r o d u c e a n output. T h e output of a m o d e l p r o v i d e s a p e r f o r m a n c e m e a s u r e to the d e c i s i o n m a k e r . T h i s m e a s u r e g i v e s the c o r p o r a t i o n a v a l u e to c o m p a r e different  Palisade Corporation, @ R i s k A d v a n c e d Risk Analysis for Spreadsheets (Palisade Corporation: Newfield, N Y  51 alternatives. In m o s t i n d u s t r i e s , t h e p e r f o r m a n c e m e a s u r e s a r e f i n a n c i a l m e a s u r e m e n t s . T h e m o s t m e a n i n g f u l to c o r p o r a t i o n e x e c u t i v e s i n c l u d e N e t P r e s e n t V a l u e ( N P V ) , I R R (internal rate o f return), a C o s t Estimate, or a Financial Return.  In t h e m o d e l d e v e l o p e d h e r e , t h e M A R R is t h e p e r f o r m a n c e m e a s u r e u s e d . T h e output of t h e m o d e l is the s u p p l y p r i c e o f electricity r e q u i r e d to m e e t t h e M A R R .  T h e r e a s o n s u p p l y price of electricity w a s c h o s e n a s the m o d e l output is that electricity is b e c o m i n g a c o m m o d i t y . T h e r e a r e m a n y p h y s i c a l i n d i c e s , including t h e N Y M E X futures, that m e a s u r e t h e m a r k e t ' s v a l u a t i o n of t h e worth of p o w e r . T h e r e f o r e , t h e r e a r e s i g n a l s a v a i l a b l e to t h e industry that s h o w w h e r e the m a r k e t v a l u e of electricity is. It is natural for industries to c o m p a r e t h e output s u p p l y p r i c e of electricity to the i n d i c e s a v a i l a b l e , in d e t e r m i n i n g w h e t h e r a project is f e a s i b l e .  4.4.  Model Formulation  T o build a m o d e l , t h e first s t e p is to d e f i n e t h e functional r e l a t i o n s h i p s b e t w e e n t h e v a r i o u s v a r i a b l e s . In o u r c a s e , a c a s h flow a n a l y s i s p r o v i d e s t h e r e q u i r e d functional relationship b e t w e e n t h e v a r i a b l e s .  A project is c o m p r o m i s e d of v a r i o u s p h a s e s , w h i c h t a k e n together, constitute the project life c y c l e . 5 1  F i g u r e 4 . 2 b e l o w d i s p l a y s t h e c a s h a n d r e v e n u e p h a s e s m o d e l e d . T h e p h a s e s illustrated b e l o w i n c l u d e : Pre-operation P h a s e , G a s Turbine Capital C o s t s P h a s e , O & M P h a s e , Fuel P h a s e and R e v e n u e P h a s e .  T h e x a x i s r e p r e s e n t s time, a n d t h e y a x i s r e p r e s e n t s dollar flows. F l o w s a b o v e a n d b e l o w t h e x a x i s a r e revenues, a n d costs, respectively.  September 1996). Glossary. 295. 5 1  A . D. Russell Review Notes for Civil 522 - Project & Construction Economics, Department of Civil Engineering U B C , September 1996.  52 $ i  Revenue w  O&M  PreOp  Time  Fuel  Gas Turbine  r  Figure 4.2 Project Life Cycle  T h e structure of the m o d e l  b r e a k s the c a s h flow a n a l y s i s into two c a s h flow s t r e a m s . T h e first s t r e a m is  the c o s t s t r e a m , w h i c h c a l c u l a t e s the life c y c l e c o s t s i n c u r r e d during the full o p e r a t i o n of the facility. T h e r e v e n u e s t r e a m c a l c u l a t e s e x p e c t e d r e v e n u e f r o m the turbine output for the life of the facility.  4.4.1. Functional Relationship T h e a p p r o a c h of the m o d e l is to s o l v e for the s u p p l y p r i c e of electricity, p, to d e t e r m i n e w h a t the r a n g e s of electricity p r i c e s a r e to a c h i e v e a g i v e n M A R R .  If all the v a r i a b l e s w e r e a s i n g l e d e t e r m i n i s t i c v a l u e , w e  c o u l d s o l v e for p s i m p l y . T h e following a r e the r e q u i r e d s t e p s :  1.  C a l c u l a t e the P r e s e n t V a l u e of E a c h P h a s e  T h i s is d o n e by c a l c u l a t i n g the p r e s e n t worth of the p h a s e , for e x a m p l e O & M illustrated in F i g u r e 4.3, by d i s c o u n t i n g the c a s h flow to t i m e z e r o .  Phase as  53  $  Revenue O&M  Time  T2  Tl  Figure 4.3 Cash Flow for O&M and Revenue Phase  T h e e q u a t i o n for this is a function o f t h e flow pattern, w h i c h w a s a s s u m e d to b e a c o n t i n u o u s flow for t h e full d u r a t i o n of t h e p h a s e .  F o r a c o n t i n u o u s flow, the p r e s e n t v a l u e of t h e O & M flow illustrated in F i g u r e 4 . 3 , will t a k e the f o r m :  T2  p  V = e  -marr-TlJ  C o s t  g-marr't  d  {  T1  W h e r e m a r r = m i n i m u m attractive rate of return T 1 = t i m e b e t w e e n t h e start of t h e p h a s e a n d time z e r o T2-T1  = duration of the p h a s e  C o s t = c o s t of t h e p h a s e (in d o l l a r s / t i m e ) 2.  S u m u p p r e s e n t v a l u e of all c o s t p h a s e s , this g i v e s t h e N e t P r e s e n t V a l u e o f c o s t s NPV  3.  C 0 S t s  = PVp -Op  +  re  PV asTurbine G  +  PVfj&M  +  PVVuel  S u m u p p r e s e n t v a l u e of t h e r e v e n u e p h a s e , a s illustrated in F i g u r e 4 . 3 . T h i s g i v e s t h e Net Present V a l u e of revenue N PV  r e v e  nue  —  P ^revenue  T2  T1  54 Where  p = t h e s u p p l y price of electricity in $ / M W h  q = e n e r g y s o l d in M W h  4.  S e t cost stream (NPV  5.  S o l v e f o r p, t h e s u p p l y p r i c e of electricity.  4.5.  C0Sts  ) to e q u a l t h e r e v e n u e s t r e a m (NPV  reven  ue)  Example of NPV Calculation  T h e following d e m o n s t r a t e s h o w to c a l c u l a t e N P V for t h e c o s t a n d r e v e n u e s t r e a m s illustrated in F i g u 4.4 f o r a d i s c o u n t rate o f 12%.  $ T = 4 years 2  C =30 M$ 2  Time •  1  C,=20 M$ T ! = 2 years  Figure 4.4 Sample Cash Flow  T1  T2  o NPV=  -r/ ) ( 1 - e T1  T1  r  \)/(-r) +  e \C-% )0-e  r  r  2  ")/(-r)  55  N P V = -( / )(1-e 2 0  2  ( 12  * /(-.12)+ e" * ( / ) ( 1 - e 2)  12  2  30  4  12  * )/(-.12) 4  NPV = $17.78-18.74  N P V = $0.96  T h e c u r r e n t e n v i r o n m e n t is s o m e w h a t m o r e c o m p l i c a t e d . S o m e of t h e v a r i a b l e s in t h e m o d e l d o not t a k e d e t e r m i n i s t i c v a l u e s . T h e s e v a r i a b l e s a r e a s s o c i a t e d with high l e v e l s of uncertainty. T h i s is w h e r e t h e M o n t e C a r l o S i m u l a t i o n t e c h n i q u e is r e q u i r e d . T h e s i m u l a t i o n s a m p l e s all t h e p o s s i b l e v a l u e s of t h e probabilistic v a r i a b l e s a n d t h e output d i s p l a y s t h e full r a n g e o f p o s s i b l e v a l u e s .  4.5.1. Variable Behavior A s d i s p l a y e d in F i g u r e 4 . 2 a b o v e , the m o d e l is b r o k e n into five p h a s e s : f o u r c o s t p h a s e s a n d o n e r e v e n u e p h a s e . T h e s e include:  1.  P r e - O p e r a t i o n Project C o s t s  2.  G a s Turbine Costs  3.  O & M Costs  4.  Fuel Costs  5.  Revenue Phase  T h e p r e s e n t v a l u e s o f p h a s e s a r e c a l c u l a t e d by integrating the c a s h flow function o v e r the p e r i o d . T o c a l c u l a t e t h e p r e s e n t worth for e a c h p h a s e , s e v e r a l v a r i a b l e s m u s t b e s p e c i f i e d . T h e s e i n c l u d e the length of time of t h e p h a s e , t h e c a s h flow function (or c o s t distribution o f t h e p h a s e , c(t)), a n d t h e c o s t of t h e p h a s e . F o r simplicity, t h e c a s h flow f u n c t i o n s w e r e a s s u m e d to b e c o n s t a n t t h r o u g h o u t t h e length of t h e p h a s e (c(t)=A). T h i s a s s u m p t i o n d o e s not greatly affect t h e N P V , but it a f f e c t s t h e c a s h flow of t h e project.  56  T h e r e f o r e , for e a c h of the p h a s e s , the only v a r i a b l e s that w e r e r e q u i r e d w e r e the length of the p h a s e a n d the c o s t of the p h a s e s .  O t h e r m o d e l v a r i a b l e s that d o not c h a n g e with the p h a s e i n c l u d e :  1.  MARR  2.  Capacity  3.  Heat Rate  P r o b a b i l i s t i c distributions w e r e a s s i g n e d for e a c h v a r i a b l e , e x c e p t for the M A R R . U s i n g quantitative a n d qualitative j u d g m e n t , distributions w e r e elicited f r o m e x p e r t s . T h i s is d e t a i l e d in S e c t i o n 4.6. T h e following s e c t i o n s detail the v a r i a b l e s a n d k e y a s s u m p t i o n s .  4.5.1.1. Pre-Operation Project Costs  T h i s p h a s e i n c l u d e s all the c o s t s a s s o c i a t e d with the project, e x c e p t for the direct a n d indirect c o n s t r u c t i o n a n d h a r d c o n s t r u c t i o n c o s t s . T h i s p h a s e i n c l u d e s the C o n c e p t , Feasibility, E n g i n e e r i n g , P r o c u r e m e n t a n d C o n s t r u c t i o n a n d C o m m i s s i o n i n g of the project. It is a s s u m e d that this p h a s e starts at time z e r o .  T h i s v a r i a b l e w a s g i v e n both a s a c o s t in millions of d o l l a r s , a n d a l s o a function of the c a p a c i t y of the g a s turbine.  4.5.1.2. Gas Turbine Costs  T h i s p h a s e i n c l u d e s the direct, indirect c o n s t r u c t i o n a n d e q u i p m e n t c o s t s i n c u r r e d d u r i n g c o n s t r u c t i o n of the project. T h i s p h a s e a l s o started at time z e r o , a n d its duration is the s a m e length a s the  pre-operation  d u r a t i o n . S o m e of the e x p e r t s c h o s e to relate this c o s t a s a $ p e r k W , while o t h e r s p r e f e r r e d to e x p r e s s the c o s t s in t e r m s of millions of dollars.  57  T w o of the e x p e r t s c h o s e to c o m b i n e P h a s e 1 & P h a s e 2, t h e r e b y giving only o n e distribution for the p e r i o d prior to o p e r a t i o n of the c o m b i n e d c y c l e .  4.5.1.3. O&M Costs  It is a s s u m e d that, following c o m p l e t i o n of the P r e - O p e r a t i o n a n d G a s T u r b i n e p h a s e s , the r e m a i n i n g o n e s a r e the O & M P h a s e , F u e l P h a s e a n d R e v e n u e P h a s e . It is a l s o a s s u m e d that the O & M P h a s e , F u e l P h a s e a n d R e v e n u e P h a s e will all h a v e the s a m e d u r a t i o n .  O & M C o s t s a r e the o p e r a t i o n a n d m a i n t e n a n c e c o s t s during the life of the project. T h e r e f o r e it a c c o u n t s for all the fixed c o s t s i n c u r r e d d u r i n g the o p e r a t i o n of the c o m b i n e d c y c l e .  4.5.1.4. Fuel Costs  T h e fuel c o s t s a c c o u n t for the v a r i a b l e c o s t s of the c o m b i n e d c y c l e o p e r a t i o n . T h e v a l u e s elicited a r e a v e r a g e r a n g e of fuel c o s t s that m a y o c c u r o v e r the life of the o p e r a t i o n of the turbine. All the e x p e r t s g a v e the fuel c o s t s in $ / M M B t u .  In the P a c i f i c N o r t h w e s t the electricity p r i c e s a n d g a s p r i c e s a r e i n d e p e n d e n t d u r i n g low d e m a n d p e r i o d s , a n d d e p e n d e n t in high d e m a n d p e r i o d s . T h e m o d e l treats the fuel a n d electricity c o s t s a s i n d e p e n d e n t . T h i s w o u l d b e true in the c a s e that the g a s c o n t r a c t s a r e l o n g e r t e r m c o n t r a c t s .  4.5.1.5. Revenue Phase  T h e r e v e n u e p h a s e c a l c u l a t e s the r e v e n u e g e n e r a t e d f r o m the s a l e of the c o m b i n e d c y c l e output a n d c a l c u l a t e s the s u p p l y p r i c e of electricity.  It is a s s u m e d that t h e s a l e of the electricity is for d e m a n d , not c a p a c i t y . Additionally, ancillary s e r v i c e s a r e a l s o i g n o r e d . Electricity c o n t r a c t s a r e at 1 0 0 % L o a d F a c t o r .  58  4.5.1.6. MARR  T h e M A R R is the m i n i m u m attractive rate of return. F o r the r u n s it w a s a s s u m e d to b e 1 2 % . T h i s v a r i a b l e is a d e t e r m i n i s t i c v a l u e . F o r sensitivity a n a l y s i s t h e m o d e l w a s a l s o run at a M A R R o f 1 5 % .  4.5.1.7. Capacity & Heat Rate  T h e m a n u f a c t u r e r ' s s p e c i f i c a t i o n s for the G a s T u r b i n e " F " a n d " G " S e r i e s u s e d in the m o d e l a r e the following:  F Series  G Series  5 2  Model  P G 7231  FA  P G 7001  Capacity  253,500 k W  350,000 k W  Heat Rate  6,160  5,883 B t u / k W h  Efficiency  55.4%  Btu/kWh  G  58%  T h e s e s p e c i f i c a t i o n s a r e b a s e d o n s p e c i f i c site c o n d i t i o n s .  T h e output o f t h e g a s turbine is very site  s p e c i f i c a n d will v a r y d e p e n d i n g o n the altitude a n d o p e r a t i n g c o n d i t i o n s . T h e m a n u f a c t u r e r g u a r a n t e e s a h e a t rate a n d c a p a c i t y within a g i v e n r a n g e . It is likely that the output a n d h e a t rate m a y v a r y o v e r the r a n g e g i v e n by the m a n u f a c t u r e r . T h e m o d e l t a k e s into a c c o u n t the uncertainty s u r r o u n d i n g t h e s e variables.  A m a j o r i s s u e r e g a r d i n g g a s turbine o p e r a t i o n is t h e d e g r a d a t i o n of the turbine. T h e turbine c a n n e v e r m a i n t a i n its h e a t rate o v e r its life of o p e r a t i o n . S o m e m a y b e r e c o v e r a b l e by a n o v e r h a u l ; h o w e v e r , s o m e m a y n e v e r b e r e c o v e r a b l e . T h e m o d e l a s s u m e s n o d e g r a d a t i o n of the turbine.  G E Power S y s t e m s C a n a d a ,  GE Power Generation Produce Line Summary,  ( G E C a n a d a , Edmonton, 1997). 1.  G E Basis: ISO, dry, natural g a s , standard inlet and exhaust pressure drops, three-pressure reheat steam cycle.  59  4.5.2. Output T h e output is a probability distribution of the p e r f o r m a n c e m e a s u r e . T h i s a l l o w s t h e d e c i s i o n m a k e r to s e e w h a t the p o s s i b l e r a n g e of v a l u e s the p e r f o r m a n c e m e a s u r e m a y fall in. A l t e r n a t i v e s a r e c h o s e n b a s e d u p o n c o r p o r a t i o n s ' a p p e t i t e s for risk.  F i g u r e 4 . 5 s h o w a n s a m p l e output for illustration.  Supply Price of Electricity Expert 1: F Series 0.16  Figure 4.5 Sample Output Graph  T h e r e s u l t s a n d a n a l y s e s will b e d i s c u s s e d in the following c h a p t e r .  4.5.3. Case Studies T w o c a s e s t u d i e s w e r e run for the m o d e l . O n e c a s e s t u d y w a s for a n " F " S e r i e s g a s turbine, a n d the s e c o n d w a s for a " G " s e r i e s g a s turbine. T h e objective is to d e t e r m i n e w h e t h e r the i n c r e a s e d e f f i c i e n c y with the n e w t e c h n o l o g y o f f s e t s the i n c r e a s e d risk of o p e r a t i o n .  60 4.6.  Planning in an Uncertain Environment Program 4.6.1. Software  T h e p r o g r a m w a s d e v e l o p e d u s i n g a n E x c e l s p r e a d s h e e t . A m a c r o , @ R i s k , w a s u s e d to p e r f o r m the risk analysis.  F o r the f r a m e w o r k to b e a d o p t e d by the industry, it m u s t b e run o n s o f t w a r e that m a n y entities h a v e a c c e s s to a n d e x p e r i e n c e with.  E x c e l w a s c h o s e n o v e r o t h e r s p r e a d s h e e t s a s it is t h e l e a d i n g W i n d o w s  s p r e a d s h e e t p r o g r a m . @ R i s k w a s c h o s e n b e c a u s e it c a n b e u s e d with E x c e l , w h i c h is a s o f t w a r e that m o s t entities in the industry h a v e . U s i n g E x c e l a n d @ R i s k is a low c o s t alternative to p u r c h a s i n g risk a n a l y s i s s o f t w a r e . A l t e r n a t i v e s to u s i n g E x c e l a n d @ R i s k w o u l d b e to u s e o t h e r risk a n a l y s i s t o o l s , s u c h as, A r e n a or S i m a n .  A p p e n d i x 1 d i s p l a y s a c o p y of the e x c e l m o d e l s p r e a d s h e e t a n d the s p r e a d s h e e t s h o w i n g the cell formulas.  T o c a l c u l a t e the c o s t of e a c h p h a s e in c u r r e n t d o l l a r s , a n e x c e l m o d u l e w a s written that d i s c o u n t s the c a s h flow u s i n g a u n i f o r m flow pattern. R e f e r to A p p e n d i x 1.  4.6.2. BestFit T h e v a l u e s elicited f r o m the e x p e r t s w e r e in the f o r m of c u m u l a t i v e probabilities. T h e v a l u e s that w e r e o b t a i n e d i n c l u d e the 0, 5 , 2 5 th  t h  50 , 75 , 95 th  th  t h  a n d the 1 0 0  t h  p e r c e n t i l e s . T h e next p r o c e s s w a s to find  w h i c h distribution b e s t fits the d a t a elicited. B e s t F i t , a s p r e a d s h e e t a d d - i n for E x c e l , w a s u s e d to fit the input d a t a to a s e l e c t e d statistical distribution.  61 4.7.  The Data 4.7.1. Elicitation of Subjective Probabilities from Experts  T h e input v a r i a b l e s for the m o d e l a r e a s s o c i a t e d with a large d e g r e e of uncertainty, particularly d u e to the r e c e n t c h a n g e s e v o l v i n g in the industry. T h e r e is n o historical d a t a a v a i l a b l e . A s s u c h , o b t a i n i n g u n c e r t a i n t i e s c a n n o t b e d o n e t h r o u g h the u s e o f c l a s s i c a l statistical t e c h n i q u e s . T h e r e f o r e , the u s e o f e x p e r t j u d g m e n t s is n e e d e d to c h a r a c t e r i z e a d e q u a t e l y a n d d e a l with this uncertainty. E x p e r t s w e r e u s e d to elicit s u b j e c t i v e j u d g m e n t s , in the f o r m of probability distributions, to treat explicitly t h e uncertainty.  F o u r e x p e r t s w e r e u s e d for the a n a l y s i s . T h e results f r o m e a c h of the e x p e r t s m a y reflect the proprietary k n o w l e d g e of their c o r p o r a t i o n . T o protect their interests, they will not b e n a m e d . T h e interviews l a s t e d for a c o u p l e of h o u r s in m a n y c a s e s .  E x p e r t s with different p o s i t i o n s a n d e x p e r i e n c e s in the industry w e r e  c h o s e n to reflect their different v i e w s .  E x p e r t 1 is a S e n i o r c o n s u l t a n t . H e h a s h a d m o r e t h a n 2 0 y e a r s of utility e x p e r i e n c e prior to his c o n s u l t i n g . E x p e r t 2 is a l s o a S e n i o r c o n s u l t a n t . H e h a d g r e a t e r t h a n 2 5 y e a r s of e x p e r i e n c e in a utility a n d a p o w e r m a r k e t i n g c o r p o r a t i o n prior to b e c o m i n g a c o n s u l t a n t . E x p e r t 2 w a s reluctant to s u p p l y probabilistic distributions at the time of the interview, s i n c e h e did not h a v e r e c e n t industry e x p e r i e n c e involving c h a n g e s in the industry. H o w e v e r , while this interview did not yield a full s e t of distributions, this interview w a s o n e of the m o s t t e c h n i c a l l y p r o d u c t i v e .  E x p e r t 3 is a n E x e c u t i v e D e v e l o p e r . H e i n d i c a t e d that his c o r p o r a t i o n is not i n v o l v e d in a n y g e n e r a t i o n projects, a s the m a r k e t e c o n o m i c s d o e s not s u p p o r t s u c h projects. E x p e r t 4 is a Utility S e n i o r M a n a g e r ; h e w o r k s for a l a r g e integrated utility. H i s c o r p o r a t i o n is currently i n v o l v e d in o n e g r e e n field project a n d o n e u p g r a d e project.  T h e p r o c e s s of elicitation of o p i n i o n s f r o m e x p e r t s i n c l u d e d interviews with e a c h of the f o u r e x p e r t s . Prior to the interview a briefer w a s s e n t to e a c h of the e x p e r t s to p r o v i d e t h e m with literature o n the m o d e l a n d to familiarize t h e m with the s u b j e c t i v e elicitation p r o c e s s . A c o p y of the d e b r i e f e r is in A p p e n d i x 2.  62 D u r i n g t h e interviews, s o m e time w a s s p e n t e x p l a i n i n g t h e b a c k g r o u n d o f t h e a u t h o r ' s w o r k a n d d e s c r i b i n g t h e a n a l y s i s . F o l l o w i n g this w a s a p e r i o d d i s c u s s i n g a n d e x p l a i n i n g t h e p s y c h o l o g i c a l literature r e g a r d i n g s u b j e c t i v e elicitation. T h e final s t a g e o f the interview i n c l u d e d eliciting distributions f r o m the e x p e r t s . T h e q u e s t i o n n a i r e u s e d for t h e elicitation p r o c e s s w a s a d a p t e d f r o m t h e article "Elicitation of S u b j e c t i v e Probabilities: A n Investigation," by M a l i k R a n a s i n g h e a n d A l a n D. R u s s e l l . R e f e r to 5 4  A p p e n d i x 2.  4.8.  Conclusions  T h e a i m of this c h a p t e r is to e x p l a i n the m e t h o d o l o g y of the P l a n n i n g in a n U n c e r t a i n E n v i r o n m e n t M o d e l . T h e following two c h a p t e r s p r e s e n t t h e results a n d d i s c u s s i o n of t h e m o d e l . C h a p t e r 6 p r e s e n t s the results f r o m E x p e r t s ' interviews a n d t h e statistical a n d g r a p h i c a l outputs f r o m t h e m o d e l . C h a p t e r 7 p r e s e n t s a d i s c u s s i o n of t h e m o d e l results a n d t h e a u t h o r ' s reflection o n t h e e x p e r t s ' results.  Malik Ranasinghe and Alan D Russell, Elicitation of Subjective Probabilities for Economic risk Analysis: An Investigation, in Construction Management and Economics. Vol. 11, 1993.  63  Chapter Five 5.  Results  5.1.  Objective  T h e p u r p o s e o f this c h a p t e r is to p r e s e n t the results of the m o d e l . T h e results a r e p r e s e n t e d in two f o r m s : the b a s i c d a t a a s elicited f r o m the e x p e r t s , a n d the p r o c e s s e d output f r o m the P l a n n i n g in a n U n c e r t a i n Environment Model.  5.2.  Results of the Experts  F o u r e x p e r t s w e r e interviewed. H o w e v e r , only t h r e e o f the e x p e r t s p r o v i d e d the d a t a u s e d in the m o d e l . A s e x p l a i n e d in the p r e v i o u s c h a p t e r , the m e t h o d o l o g y for quantifying the e x p e r t s ' belief w a s t h r o u g h eliciting the fifth, 2 5 , 5 0 t h  t h  (median), 75 , and 9 5  u n c e r t a i n input v a r i a b l e .  th  t h  p e r c e n t i l e s of the s u b j e c t i v e probability distribution for e a c h  T h e result is a c u m u l a t i v e distribution f u n c t i o n ( C D F ) for the u n c e r t a i n v a r i a b l e s .  T h e input v a r i a b l e s elicited i n c l u d e d : P r e - O p e r a t i o n D u r a t i o n , P r e - O p e r a t i o n C o s t , O & M D u r a t i o n , O & M C o s t , F u e l C o s t , H e a t R a t e , a n d C a p a c i t y for e a c h o f the ' F ' S e r i e s a n d the ' G ' S e r i e s . T h e results of the experts are presented below.  5.2.1. Manufacturers' Gas Turbine Specifications T a b l e 5-1 is e x t r a c t e d f r o m G E a n d d i s p l a y the m a n u f a c t u r e r s ' s p e c i f i c a t i o n s for the ' F ' a n d ' G ' S e r i e s g a s t u r b i n e s that w e r e u s e d in the m o d e l ; t h e s e a r e the s p e c i f i c a t i o n s that w e r e p r e s e n t e d to the E x p e r t s .  64  Model  PG 7231 FA  PG 7001 G  Capacity  253,500 kW  350,000 kW  Heat Rate  6,160 Btu/kWh  5,883 Btu/kWh  Efficiency  55.4%  58%  Table 5-1: GE Specifications forF & G Gas Turbine  5.2.2. Expert 1 T h e v a r i a b l e s elicited f r o m E x p e r t 1 w a s the P r e O p e r a t i o n C o s t & D u r a t i o n , O & M C o s t & D u r a t i o n , F u e l C o s t & D u r a t i o n , C a p a c i t y a n d H e a t R a t e . T h e e x p e r t s w e r e g i v e n the option of w h e t h e r to c o m b i n e all the P r e - O p e r a t i o n C o s t s , o r to s e p a r a t e t h e m . E x p e r t #1 c h o s e to c o m b i n e all the P r e - O p e r a t i n g c o s t s a n d r e l a y e d it a s o n e v a r i a b l e .  B e l o w is a s c h e m a t i c of the c a s h flow m o d e l that E x p e r t 1 u s e d .  $ •  Revenue P reOp  O&M  Time  Fuel  Figure 5.1 Expert 1, Cash Flow Model  B e l o w a r e the C u m u l a t i v e Distribution F u n c t i o n s ( C D F s ) elicited f r o m E x p e r t 1 for all the v a r i a b l e s , for both the F a n d G S e r i e s g a s turbine. T h e first c o l u m n is the probability, the r e m a i n i n g c o l u m n s d i s p l a y the v a r i o u s v a r i a b l e s a n d the v a l u e s that a r e a s s o c i a t e d with that probabilities.  65 5.2.2.1. F Series r h-- T T 7 .  Probabi Months  '....!.  ;  T  "  ™  . .- .  $/kW  Years  cents/kWh  Years  $/10 6 BTU  MW  Btu/KWh  A  lity 0  6  150  5  0.2  Same  1.2  202  5800  0.05  7  250  10  0.25  as  1.4  224  6000  0.25  12  300  12  0.3  O&M  1.8  235  6160  0.5  17  450  15  0.5  2.0  253  6500  0.75  20  600  20  0.7  2.5  257  6650  0.95  30  850  25  1  3.5  269  6800  1  96  1000  30  1.2  4.0  280  7000  Table 5-2 Expert 1: F Series CDF  E x p e r t 1 h a d the w i d e s t o u t l o o k o n the input v a r i a b l e s than the o t h e r E x p e r t s . F o r e x a m p l e for the F S e r i e s d a t a d i s p l a y e d in T a b l e 5-2, of all the v a r i a b l e s , the e x p e r t w a s m o s t c e r t a i n with r e s p e c t to the c a p a c i t y a n d h e a t rate. F o r t h e s e v a r i a b l e s , the 9 5 p e r c e n t i l e w a s only b e t w e e n 1.1 a n d 1.2 t i m e s g r e a t e r t h a n the 5 p e r c e n t i l e . In c o m p a r i s o n , the p h a s e v a r i a b l e s , s u c h a s p r e - o p e r a t i o n d u r a t i o n , p r e - o p e r a t i o n c o s t s , a n d O & M c o s t s , t h e 9 5 p e r c e n t i l e w a s b e t w e e n 3.5-4 t i m e s g r e a t e r t h a n the 5 p e r c e n t i l e . O v e r a l l , the w i d e s p r e a d s u b s e q u e n t l y results in a u n c e r t a i n r a n g e of v a l u e s for t h e s u p p l y p r i c e of electricity.  66  5.2.2.2. G Series  — i . - — iu,,:..;  v  Miiiiii  Probabi Months lity  $/kW  Years  cents/kWh  Years  $/10 6 B T U  MW  Btu/KWh  0  9  250  8  0.25  Same  1.2  310  5700  0.05  11  300  10  0.3  as  1.4  323  5800  0.25  15  450  15  0.4  O&M  1.8  335  5883  0.5  19  500  17  0.6  2.0  341  6080  0.75  24  700  20  0.75  2.5  350  6410  0.95  36  850  23  1  3.5  372  6700  1  96  1000  25  1.2  4.0  385  6900  A  Table 5-3 Expert 1, G Series CDF  E x p e r t 1's v i e w s o n the G S e r i e s a r e s h o w n in T a b l e 5-3. A g a i n , the E x p e r t a s s o c i a t e d a h i g h e r d e g r e e of uncertainty with the p h a s e v a r i a b l e s , s u c h a s c o s t s a n d duration, t h a n the g a s turbine s p e c i f i c a t i o n s , s u c h a s c a p a c i t y a n d h e a t rate.  E x p e r t 1 h a d a l o w e r m e a n a n d a w i d e r r a n g e of uncertainty for the F S e r i e s d a t a o v e r the G S e r i e s . T h i s m a y b e b e c a u s e E x p e r t 1 h a d n o b o u n d o n the h i g h e r level of uncertainty (i.e. the 9 5 percentile). In his v i e w s , t h e r e is n o limit to h o w b a d t h i n g s c a n g o ; e q u a l l y for F a n d G S e r i e s . F o r e x a m p l e , a facility b e i n g s h u t d o w n for pollution c o n t r o l . H o w e v e r , in his v i e w s , b e c a u s e of the lower initial c o s t s for F , the e x p e r t a s s o c i a t e d F S e r i e s with a l o w e r m i n i m u m for m a n y of t h e v a r i a b l e s . S u b s e q u e n t l y , F h a s a lower m e a n a n d a n artificially h i g h e r s t a n d a r d d e v i a t i o n .  67  5.2.3. Expert 3 In treating the p r e - o p e r a t i n g c o s t s E x p e r t #3 c h o s e to s e p a r a t e the p r e - o p e r a t i o n c o s t s . T h e first P r e O p e r a t i o n c o s t s i n c l u d e all the pre-permitting c o s t s , the a n a l y s i s a n d e v a l u a t i o n o f the project, a n d the p r e - d e s i g n a n d d e v e l o p m e n t . It i n c l u d e s all t h e d e s i g n a n d e n g i n e e r i n g c o s t s relating to the E n g i n e e r i n g , P r o c u r e m e n t a n d M a n a g e m e n t o f the project.  <  X Pre-design  X Commitment Book & place order  > Engineering  Figure 5.2 Pre-Operation Cost Components  T h e s e c o n d P r e - O p e r a t i o n c o s t s i n c l u d e all the direct a n d indirect c o n s t r u c t i o n a n d e q u i p m e n t c o s t s a s s o c i a t e d with t h e project. T h e e x p e r t a s s u m e d that this p h a s e duration will h a v e a d e t e r m i n i s t i c length of 2 y e a r s , a n d it will b e the last two y e a r s of the E n g i n e e r i n g , P r o c u r e m e n t a n d M a n a g e m e n t P h a s e . F i g u r e 5.3 b e l o w illustrates t h e s e a s s u m p t i o n s :  $  P r e - O p 1: Engineering, Procurement & Management C o s t s  Time  P r e - O p 2: Direct & Indirect Construction & Equipment Costs  2 years  Figure 5.3 Expert 3, Pre-Operation Project Phases  68  5.2.3.1. F Series  CDF  years  Million $  Years  $/MWh  Years  Million $  $/GJ  MW  Btu/kWh  0  3.5  10  2  650  15  5  1.9  200  6,000  0.05  3.75  11  2  675  18  5.5  Same  1.95  205  6,050  0.25  3.9  13  2  687  21.5  6  as  2  240  6,100  0.5  4  14  2  750  25  6.5  O & M  2.05  250  6,300  0.75  4.5  16  2  775  26  7.5  2.5  267  6,400  0.95  5  18  2  800  27  9  2.75  273  6,600  1  6  20  2  850  30  10  2.85  275  6,750  Table 5-4 Expert 3, F Series  CDF  E x p e r t 3 h a d a s m a l l r a n g e o f e x p e c t a t i o n s with r e s p e c t to t h e v a r i a b l e s . T h e l a r g e s t s p r e a d for the v a r i a b l e s r e s u l t e d in t h e 9 5 percentile h a v i n g d o u b l e the v a l u e of t h e 5 p e r c e n t i l e ( a s c o m p a r e d to 3.5-4 t i m e s f o r E x p e r t 1). T h e v a r i a b l e s exhibiting the l a r g e s t d e g r e e o f uncertainty w a s t h e Life C y c l e of t h e T u r b i n e ( O & M Duration) a n d the O & M C o s t . E x p e r t 3, similar to E x p e r t 1, w a s m o s t c e r t a i n r e g a r d i n g the c a p a c i t y a n d t h e h e a t rate. P e r h a p s the E x p e r t s a r e a s s u m i n g that t h e m a n u f a c t u r e r m a y a b s o r b s o m e o f the r i s k s of t h e g a s turbine p e r f o r m a n c e o r t h e t e c h n o l o g y is m a t u r e e n o u g h .  69  5.2.3.2. G Series  ' -t:. years  Million $  Years  $/MWh  Years  10 $  $/GJ  MW  Btu/KWh  0  3.5  10  2  650  15  5  1.9  275  5,730  0.05  3.75  11  2  675  18  5.5  Same  1.95  285  5,775  0.25  3.9  13  2  687  21.5  6  as  2.  330  5,825  0.5  4  14  2  750  25  6.5  O & M  2.05  350  6,015  0.75  4.5  16  2  775  26  7.5  2.5  370  6,110  0.95  5  18  2  800  27  9  2.75  378  6,300  1  6  20  2  850  30  10  2.85  380  6,445  6  Table 5-5 Expert 3, G Series CDF  E x p e r t 3 h a s a s i m i l a r o u t l o o k for the G S e r i e s a s the F S e r i e s . In fact, this E x p e r t w a s u n a b l e to d i s t i n g u i s h b e t w e e n all v a r i a b l e s , e x c e p t for C a p a c i t y a n d H e a t R a t e , for the F a n d G S e r i e s w h i c h a r e s p e c i f i e d by the m a n u f a c t u r e r .  5.2.4. Expert 4  E x p e r t 4 c h o s e to treat the p r e - o p e r a t i o n c o s t s the s a m e w a y that E x p e r t 1; that is c o m b i n i n g the Direct & Indirect E n g i n e e r i n g , P r o c u r e m e n t a n d M a n a g e m e n t c o s t s for the g a s turbine. E x p e r t #4 m i n i m i z e d the uncertainty a s s o c i a t e d with the h e a t rate a s h e explicitly a s s u m e d that the m a n u f a c t u r e r will a b s o r b the risk to m e e t their s p e c i f i c a t i o n s .  T a b l e 5-6 & T a b l e 5-7 a r e the C D F s for E x p e r t #4 for both the F & G S e r i e s t u r b i n e s .  70  5.2.4.1. F Series  Heat  Rate  ________ months  Million $  Years  $/MWh  $/GJ  MW  Btu/Kwh  0  16  187  10  3.6  1.6  253  6,000  0.05  18  190  12  4.0  Same  1.8  260  6,050  0.25  22  195  14  4.2  as  2  262  6,070  0.5  26  200  15  4.4  O & M  2.1  265  6,100  0.75  28  210  17  5.0  2.5  266  6,160  0.95  30  220  20  6.0  2.8  268  6,200  1  34  240  25  7.0  3  270  6,250  Table 5-6 Expert 4, F Series CDF  E x p e r t 4 ' s o u t l o o k o n t h e s p r e a d of the v a r i a b l e s w e r e s i m i l a r to E x p e r t 3's beliefs. E x p e r t 4 9 5 percentile w a s b e t w e e n 1.5-2 t i m e s that of the elicited 5 p e r c e n t i l e . T h e g r e a t e s t variation for this E x p e r t w a s for the O & M D u r a t i o n . T h e r a n g e of v a r i a b l e s that w e r e elicited w e r e a l m o s t V2 the s p r e a d of E x p e r t 1's s p r e a d s . T h i s i n d i c a t e s that E x p e r t 4 either did not a c c o u n t for the w i d e variation, or e l s e E x p e r t 1 o v e r e x a g g e r a t e d the variation in t h e industry.  71  5.2.4.2. G Series  ffiM' months  Million $  Years  $/MWh  0  16  233.75  10  3.6  0.05  18  237.5  12  0.25  22  243.75  0.5  26  0.75  • -j $/GJ  MW  Btu/Kwh  Same  1.6  320  5,800  4.0  as  1.8  325  5,830  14  4.2  O & M  2  330  5,850  250  15  4.4  2.1  340  5,870  28  262.5  17  5.0  2.5  345  5,900  0.95  30  275  20  6.0  2.8  350  5,950  1  34  300  25  7.0  3  360  6,000  Table 5-7 Expert 4, G Series CDF  E x p e r t 4, s i m i l a r to E x p e r t 3, v i e w s o n the s p r e a d of the G S e r i e s w a s s i m i l a r to that of the F S e r i e s . T h e E x p e r t w a s u n a b l e to differentiate b e t w e e n s o m e of the v a r i a b l e s for the F a n d G S e r i e s , t h e s e v a r i a b l e s w e r e the P r e - O p e r a t i o n D u r a t i o n , O p e r a t i o n D u r a t i o n , O & M C o s t s a n d F u e l C o s t s . F o r the v a r i a b l e s that E x p e r t 4 did differentiate b e t w e e n , the r a n g e of the v a r i a b l e s w e r e in the s a m e m a g n i t u d e .  5.2.5. Input Variable Distributions T h e c u m u l a t i v e distribution f u n c t i o n s elicited f r o m the e x p e r t s w e r e input into B e s t F i t . B e s t F i t is a 5 5  p r o g r a m that fits input d a t a with probability distributions a n d a n a l y z e the results. T h e results a r e g i v e n n u m e r i c a l l y a n d g r a p h i c a l l y , a n d a c o m p l e t e statistical report, including g o o d n e s s - o f - f i t , c o n f i d e n c e l e v e l s a n d target v a l u e s is p r o d u c e d . T h e r e a r e twenty-six a v a i l a b l e distributions.  BestFit, Palisade Corporation Copyright 1993-95. Trademark of Palisade Corporation  72 F o r e a c h distribution type, B e s t F i t first m a k e s a g u e s s o n the distribution's p a r a m e t e r s u s i n g m a x i m u m likelihood e s t i m a t o r s ( M L E s ) . T h e M L E s o f a distribution a r e the p a r a m e t e r s o f that f u n c t i o n that m a x i m i z e the likelihood of the distribution g i v e n a s e t o f o b s e r v a t i o n d a t a . T h e L e v e n b e r g - M a r q u a r d t M e t h o d A l g o r i t h m is t h a n u s e d to m a x i m i z e the g o o d n e s s - o f - f i t b e t w e e n a d a t a s e t a n d distribution f u n c t i o n . T h e m e t h o d t a k e s the initial p a r a m e t e r g u e s s e s of the p a r a m e t e r s of the distribution f u n c t i o n , a n d t h e n v a r i e s the p a r a m e t e r until it f i n d s a g o o d fit. T h e g o o d n e s s - o f - f i t test u s e d by B e s t F i t for o p t i m i z i n g a distribution is the c h i - s q u a r e t e s t . 5 6  F o r e a c h input v a r i a b l e , the s a m e probability distribution w a s c h o s e n to facilitate the c o m p a r i s o n b e t w e e n E x p e r t s . T h e distributions u s e d for all the v a r i a b l e s w e r e the B e t a Distribution a n d t h e L o g n o r m a l distribution.  T a b l e 5-8, s h o w the probability distributions that w e r e u s e d for e a c h o f the input v a r i a b l e s for the E x p e r t s .  BestFit, Palisade Corporation, Copyright 1993-95. p 2-9 to 2-11  If'  Capacity  Heat Rate  Pre O p Duration  F Series  G Series  F Series  G Series  F Series  G Series  (Beta(2.98,2.12)*  Beta(2.31,2.84)*  Beta(1.3,0.72)  Beta(1.28,0.66)*105 Beta(4.14,2.19)  Beta(1.85,2.2)  78+202)*1000  75+310  *75+200  + 275  *17+253)*1000  *40+320  Beta(1.84,1.68)  Beta(0.99,1.57)  Beta(1.11,1.79)  Beta(1.11,1.79)  Beta(1.97,2.34)  Beta(1.96,3.02)  *1200+5800  *1200+5700  *750+6000  *715+5730  *250+6000  Lognorm(18.33,9.7)  Lognorm(21.71,9.62)  Lognorm(4.22,0.45)  Same  (Beta(2.02,2.02)  Same  *18+16)/12  Pre O p C o s t  Lognorm(482,211)  Lognorm(559,185)  Lognorm(14.4,2.23)  Pre O p Construction  Beta(1.55,1.96)  Cost  *200+650  Same  Lognorm(203,10.41)  Lognorm(254,13.01)  Same  Operation Duration  Lognorm(16.23,5.53)  Lognorm(17.11,4.37)  Lognorm(23.7,3.38)  Same  Lognorm(15.6,2.63)  Same  O&M Costs  Beta(0.86,1.67)+.2  Beta(1.21,2.17)  Beta(1.3,2.21)*5+5  Same  Beta(1.36,2.9)*1.7+1.8  Same  Fuel C o s t s  (Beta(1.21,2.15)  Same  Beta(0.56,1.13) +1.9  Same  Beta(1.36,2.9)*3.4+3.6  *28+12)/10  Table 5-8 Distributions Used for Input Model  73  Same  74  5.3.  Output from the Model  T h e results f r o m the m o d e l m a y b e interpreted f r o m its statistical p a r a m e t e r s .  T h e m o d e l output is the s u p p l y price of electricity. T a b l e 5-9 lists the r e s u l t s for the input v a r i a b l e s a n d the s u p p l y p r i c e of electricity. T h e statistical p a r a m e t e r s s h o w n for the s u p p l y p r i c e of electricity a r e the m a x i m u m a n d m i n i m u m v a l u e s , m e a n s , v a r i a n c e s , a n d m e a s u r e s of s k e w n e s s a n d k u r t o s i s .  Probability distributions d i s p l a y g r a p h i c a l l y the s p r e a d a n d likelihood of p o s s i b l e r e s u l t s a n d p r o v i d e a v i s u a l m e a n s of interpreting the results. F i g u r e 6-1 to F i g u r e 6-8 illustrates the g r a p h i c a l output of the s u p p l y p r i c e o f electricity a n d all the input v a r i a b l e s . A l o n g the x - a x i s a r e input v a r i a b l e s a n d a l o n g the ya x i s the probability that t h e s e v a r i a b l e s will o c c u r . T h e input distributions a r e not the raw probability f u n c t i o n s ( s m o o t h ) , but the probability f u n c t i o n s u s e d in the M o n t e C a r l o S i m u l a t i o n .  5.4.  Conclusion  T h e a i m of this c h a p t e r w a s to d i s p l a y the different o u t l o o k s of e a c h of t h e e x p e r t s a n d their c o m p a r i s o n of the F a n d G S e r i e s T u r b i n e . T h e e x p e r t s w e r e c h o s e n d u e to their d i v e r s e a n d e x t e n s i v e e x p e r i e n c e in t h e electric industry. T h e results of the e x p e r t s w e r e elicited a s C D F s , a n d their w i d e b a n d s of results indicate their p e r s p e c t i v e o f t h e future. T h i s c h a p t e r a l s o d i s p l a y e d the m o d e l o u t p u t for e a c h E x p e r t s F a n d G S e r i e s D a t a . T h i s d a t a is e x p r e s s e d u s i n g v a r i o u s statistical p a r a m e t e r s , a n d t h r o u g h g r a p h i c a l r e p r e s e n t a t i o n of t h e s e p a r a m e t e r s .  T h e f o c u s of the  following c h a p t e r , C h a p t e r 7: D i s c u s s i o n ,  is to p r e s e n t a d e e p e r d i s c u s s i o n a n d a n a l y s i s  of the o u t p u t s . Probability a n a l y s i s g i v e s a d e c i s i o n m a r k e t a c o m p l e t e picture of the project, a n d e n a b l e s quantification of project risk. T h e c h a p t e r d i s c u s s e s the results o f the probability a n a l y s i s , a n d its i m p l i c a t i o n s in the c h a n g i n g electric industry.  75  Results of Simulation  Runs Expert 1 F Series  Supply Price of Electricity Minimum Maximum Mean Std Deviation = Variance = Skewness Kurtosis = Capacity Minimum Maximum Mean Std Deviation = Variance = Skewness Kurtosis Heat Rate Distribution Minimum Maximum Mean Std Deviation = Variance = Skewness = Kurtosis = Pre Op Duration Distribution Minimum Maximum Mean Std Deviation = Variance Skewness = Kurtosis = Pre Op Cost Distribution Minimum Maximum Mean Std Deviation = Variance Skewness Kurtosis =  G Series  Expert 3 F Series  ' G Series  S/MWh 11.47 73.36 29.11 8.87 78.72 0.93 4.32  VMWh 13.17 64.34 30.14 8.09 65.44 0.86 4.19  VMWh 18.62 59.76 31.80 5.57 31.06 0.69 3.82  VMWh 17.86 51.36 29.64 5.22 27.22 0.64 3.66  kW (Beta(2.98,2.12) * 78+202)"1000 C3 206,251 279,928 247,930 15,884 252,310,300 (0.25) 2.32 Btu/kWh Beta(1.84.1.68) "1200+5800 C4 5,814 6,972 6,430 283 79,830 (0.07) 2.02 years Lognorm(18.33,9 7) E7 0.32 10.21 1.57 0.89 0.79 2.15 13.24 $/kW Lognonn(482,211) G7 138.02 1,708.43 488.63 214.11 45,841.00 1.36 5.83  kW Beta(2.31,2.84)" 75+310 C3 310,802.30 381,282.30 344,095.10 15,391.91 236,910,900 0.15 2.29 Btu/kWh Beta<0.99,1.57> •1200+5700 C4 5,701 6,897 6,163 318.32 101,329.00 0.36 2.04 years Lognorm(21.71,9.62) E7 0.50 6.30 1.79 0.81 0.66 1.42 6.20 VkW Lognorm(559,185) G7 153.41 1.352.67 556.44 181.79 33,047.96 0.95 4.38  kW Beta(1.3,0.72) "75+200 C3 200,174 274,989 247.984 20.634 425,752,300 (0.51) 2.11 Btu/kWh B«ta(1.11,1.79) "750+6000 C4 6.000 6,740 6,288 186 34,427 0.37 2.16 years Lognorm(4.22,0.45) E7 3.03 6.06 4.22 0.45 0.20 0.25 3.07 Millions $ Lognorm(14.4,2.23) G7 8.77 25.47 14.34 2.22 4.94 0.50 3.59  kW Beta(1.28,0.66) " 105 + 275 C3 276,121 380,000 343.741 29,011 841,614.200 (D 2 Btu/kWh Beta(1.11,1.79) "715+5730 C4 5,730 6,436 5.998 176 31,080 0.44 2.23 years lognorm(4.22,0.45) E7 2.89 5.81 4.23 0.45 0.20 0.40 3.12 Millions $ 2 Distributions -- This is the Sum G7 198.10 334.64 269.10 26.60 707.80 (0.15) 2.54  Operation Duration Distribution Lognorm(16.23,5.53) E9 Minimum Maximum  years  years 5.29 46.31 16.26 5.59 31.28 1.01 4.82 VMWh  Lognorm(17.11,4.37) E9  15.59 36.38 23.68 Std Deviation = 3.33 11.09 Variance = 0.39 Skewness Kurtosis = 3.05 Million / Year O&M Costs Beta(1.21.2.17) Beta(1.3.2.21)"5+5 Distribution 8eta(0.86,1.67) +.2 G9 G9 G9 2.00 2.50 5.00 Minimum 11.93 12.21 9.93 Maximum 5.45 6.17 6.89 Mean 2.54 2.32 1.15 Std Deviation = 6.47 5.38 1 32 Variance = 0.57 0.44 0.37 Skewness = 2.31 2.22 2.26 Kurtosis = S/mmBtu VmmBtu VmmBtu Fuel Costs Distribution (Beta(1 21.2 15) *23+12)/10 (Beta(1.21,2.15) "28+12)/10 Beta(0.56,1.13) +1.9 G10 G10 G10 1.20 1.20 2.00 Minimum Maximum 3.87 3.92 3.06 2.23 2.22 2.36 Mean 0.66 0.65 0.31 Std Deviation = 0.43 0.42 0.10 Variance = 0.40 0.45 0.61 Skewness = 2.22 2.34 2.16 Kurtosis =  Table 5-9 Results of Experts' Simulation Runs: F& G Series  7.59 35.75 17.30 4.55 20.71 0.70 3.51 cents/kWh  years Lognorm(23.7,3.38) E9  Expert 4 F Series VMWh 23.71 47.90 33.49 3.70 13.70 0.36 3.04  14.25 35.72 23.67 3.41 11.60 0.36 3.26 Million / Year Beta(1.3.2.21)"5 + 5 G9 5.00 9.86 6.81 1.11 1.23 0.53 2.46 VmmBtu Beta(0.56,1.13) +1.9)"1054.35/10 G10 2.00 3.05 2.34 0.30 0.09 0.68 2.33  Series $/MWh 24.21 47.03 32.51 3.68 13.54 0.41 3.00  kW kW (Beta(4.14,2.19) "17+253)"1000 Beta(1.85,2.2) "40+320 C3 C3 320,213 254.532 269.799 359,775 264.065 338,262 2,900 8,864 8,407,592 78,570.900 (0.33) 0.14 2.59 2.16 Btu/kWh Btu/kWh Beta(1.97,2.34) "250+6000 Beta(1.96.3.02) C4 C4 6,002 5,802 6,240 5,987 6,114 5,878 53 39 2,849 1,543 0.19 0.37 2.27 2.39 years years (Beta(2.02,2.02) "18+ 16)/12 (Beta(2.02,2.02) "18+16)/1 E7 E7 1.36 1.34 2.82 2.81 2.10 2.08 0.34 0.33 0.11 0.11 0.00 0.02 2.16 2.22 Millions S Millions $ Lognorm(203,10.41) Lognorm(254,13.01) G7 G7 169.38 215.65 240.14 298.02 202.76 254.31 10.39 12.88 108.02 165.93 0.15 0.14 3.10 3.02  years Lognorm(23.7,3.38) E9  G  years Lognorm(15.6,2.63) E9  Beta(1.36,2.9)"1.7+1 8 G9  8.52 25.31 15.73 2.71 7.33 0,45 3.03 VMWh 3.60 6.79 4.68 0.67 0.45 0.52 2.53 VmmBtu  Beta(1.53,1.88) G10 1.69 3.12 2.35 0.35 0.13 0.14 2.05  years Lognorm(15.6,2.63) E9 9.56 26.54 15.63 2.66 7.05 0.44 3.28 VMWh Beta(1.36,2.9)"1.7+1.8 G9 3.60 6.75 4.71 0.71 0.50 0.50 2.36 $/mmBtu Beta(1.53,1.88) G10 1.71 3.12 2.34 0.35 0.12 0.21 2.09  76  Figure 5.4 Comparison of Experts' Outlook on Capacity  77  Heat Rate  Heat Rate  Expert 1 F Series  Expert 1 G Series  0.3  0.25 0.2 §3.15  b.15  CD O CC  °-0.1  0.1  J-  0.05  CO  w  CO  05  O  —  C  m m co co co  J  C  ^  J  C  O  '  *  m  _tufttWn^* 1^?0)  c  D  4 0  t  D  4 0  (  ^  -  ~  (  *°  0 1 0  0  >  ^  0  ESu/KWh (°10«S) «  Heat Rate Expert 3 G Series-  0.3  <o to  <D  <D  Heat Rate Expert 3 G Series  Qc15 m  o °0.1 0.05  5?  8  8  S  5  53/al _s it_tun!8red_  g  £  g  g  Heat Rate Expert 4 F Series  0.3  g  r ^ - r ^ c o a >  o  «  ID <° v<ffties"?n Hfindteds Heat Rate Expert 4 G Series  m n  w  —-  c M d M - ^ t f i t o r ^ c o c D O  0.2 -§0.15 m  o o. 0.1 0.05 -I  [ M i l l  I I I I I I I I I I I I I I I I I I  Values in Hundreds  Figure  5.5 Comparison  of Experts'  Outlook  on Heat  Rate  -I—E—•—— i •—I—•—•—I—•—I—I—•—•—I— Values in Hundreds  m  0  <°  1 0  «> ^  78  0.45  Pre-Op Duration  Pre O p D u r a t i o n  Expert 1 F Series  Expert 1 G Series  0.45  0.40 0.35 £.0.30  5 <  0  2  5  §0.20  a.  °-0.15  1  0.10 0.05  i ^ r t O c D c o p t q r o p c q c o o c D  Pre O p Duration -Expert 3 F Series  0.45  Pre O p D u r a t i o n Expert 3 G Series  0.40 0.35 £0.30 _25  -  g)20 • _.15 0.10  -  0.05 -  »—  T—  CM  Pre O p Duration  Pre O p D u r a t i o n  Expert 4 F Series  Expert 4 G Series  Figure 5.6 Comparison of Experts' Outlook on Pre-Operation Duration  O  CO  co  co cn  CM  79  Pre O p C o s t  Figure  5.7 Comparison  of Experts'  Outlook  Pre O p C o s t  on Pre-Operation  Costs  80  Operation Duration  Operation Duration  Expert 1 F Series  Expert 1 G Series  o r ^ i o C M O > r ^ ^ . - ; C » c q c o ^ c q i f i c o in r ^ o c o i / > c o * - ^ c o a > C M L r i r ^ o c o TT - CM TCar^ CM co co co ^ ^  t \ i o j r > ; ^ ^ c » c q c O T ^ c o co i r i c o i - . j - c D a J C M i r i r ^ T— T— »— cxi Y e a r s ' ^ CM co co co  Operation Duration  Operation Duration  Expert 3 F Series  Expert 3 G Series  0.20  _).15  3).15  _0.10  _010  0.05  0.05  CM CO CO CO ^-  arSM  !3reCM  ^  Operation Duration  Operation Duration Expert 4 F Series  0.25  0.2  .4.15  I lO O i—  Figure  CM  CO .—  5.8 Comparison  c» CO .—  W T  -  ^ O) r-* ^ CD IM Vfear^  of Experts'  1  Outlook  -t—'.—!—t—i—I—I—!—i— co co ^co cq CO O) C\i to O CO C M co co co f *r  on Operation  CM  Duration  Expert 4 G Series  CO  CO  CO *T  81  O&M Cost  O&M Cost  Expert 4 F Series  Expert 4 G Series  0.35  0.3  0.25  =  0.2  £0.15 a. 0.1  0.05  i I I 1111111111i  $  MIMioris $  Figure  5.9  Comparison  of Experts'  Outlook  S  on Operation  MilTion§"$  & Maintenance  Costs  M i l l  H-H-HH  82  Fuel C o s t  Fuel C o s t s 0.18  lilt  1 11 mfml  V  BP I PL •if WMS BHll|iipifi rin  Figure  o oi  PJ  5.10 Comparison  Expert 4 G Series  T M  f  r  ~  | |—|—\ | | | | j—j—|—|—|—|—|—i—|—\—|—\— T  -  .  r  NHUiorf!>$  of Experts'  Outlook  r <•>  ~  -  <  <*>  on Fuel  «  -  *  r •»  Costs  C  O •*  T  •*  N  MRIionBi$ "  n  83  Supply Price of Electricity  Suppy Price of Electricity Expert 1 F Series  J  1  -- 0.9  100%  0.2 0.18  •f  -  0.8  0.16  -  0.7  0.14  70%  -- 0.6  0_I2  + 60%  -- 0.5  |.1  50%  -- 0.4  <_08  40%  -  0.06  30%  - 0.2  0.04  20%  -- 0.1  0.02  -  0.3  0  10%  0  •l"l-M-l I  i O T - c o c M t - C M o o c o c n ^ t ' O L O T - C M C N C O C O ' ^ ' ^ " l O > O C D C D r ^  c o c N r - ~ < N c o c o a > T j <M CO CO $flU|VWl «» <0  s  Supply Price of Electricity ExpertJSG^Ser^  Supply Price of Electricity Expert 3 F Series  o  m  T~  <D  CM  r^-  —  —  CN  CN  CO  CO  CM  co  's/rSWrT  co m  G>  m  CD  a> co  C O C M t ^ C M C O C O O ) ^ -  m r»  N  Supply Price of Electricity Expert 4 F Series •r 100% - 90% - 80%  0.2  j  m  "S/WfWrf  -  -- 50%  |.1  -  -- 40%  <g08-  - 30%  0.06 --  - 60%  - - 20%  0.04  -  -  0.02  -  10%  -- 0%  °$/lflftVrr in co r  co  a> IO  co  O) to  0 -  to  r-  Figure 5.11 Comparison of Experts' Output Supply Price of Electricity  ^  Supply Price of Electricity Expert 4 G Series  0.18 0.16 -0.14  -- 70%  CM  90% 80%  °$/fflvVh'  ^  0%  84  Chapter Six 6.  Discussion  T h i s c h a p t e r p r e s e n t s a d i s c u s s i o n of the output of the P l a n n i n g in a n U n c e r t a i n Environment Analysis.  T h i s i n c l u d e s the individual results of the e x p e r t s a n d a  c o m p a r i s o n of the elicitations of the e x p e r t o p i n i o n s . S u b s e q u e n t l y , t h e r e is a d i s c u s s i o n of the i m p l i c a t i o n s of the results o n the p l a n n i n g function a n d o v e r a l l electric industry.  6.1.  Results from the Experts 6.1.1. Supply Price of Electricity  T h e output of the m o d e l is p r e s e n t e d a s the s u p p l y p r i c e of electricity. T h e s u p p l y price of electricity, in e c o n o m i c t e r m s , is the p r i c e of electricity that will p r o v i d e r e v e n u e e n o u g h to offset c o s t s to m e e t a particular m i n i m u m attractive rate of return (set to b e 1 2 % for the r u n s ) . T h e output s u p p l y price of electricity is the a v e r a g e s u p p l y p r i c e o v e r t h e life of the project. T h e l o w e r the s u p p l y p r i c e of electricity, the lower the risk for t h e d e v e l o p e r p r o c e e d i n g with the project a n d the h i g h e r the probability of a c h i e v i n g the r e q u i r e d return. T h e s i m u l a t i o n m o d e l w a s a l s o run at a 1 5 % M A R R to e x a m i n e the sensitivity of the r e s u l t s to the M A R R .  6.1.2. Expert 1 T h e e x p e r t s ' p r e d i c t i o n s indicate their different p e r s p e c t i v e s o n t h e future of the electric industry a n d their v i e w s o n the c h a r a c t e r i s t i c s of the ' F ' a n d ' G ' S e r i e s G a s T u r b i n e s . T h e b a s i c d i f f e r e n c e s b e t w e e n the F a n d G S e r i e s turbines is the e f f i c i e n c y a n d the c a p a c i t y . T h e F S e r i e s m o d e l is a P G 7231 6,160  F A M o d e , with a c a p a c i t y o f 2 5 3 M W a n d a h e a t rate of  B t u / k W h . T h e G S e r i e s m o d e l is P G 7001 G , with a c a p a c i t y of 3 5 0 M W a n d a h e a t  85  rate of 5 , 8 8 3 B t u / k W h . T h e d i f f e r e n c e in h e a t rate t r a n s l a t e s into a 2 . 6 % differential in o v e r a l l e f f i c i e n c y o r a n i n c r e a s e in e f f i c i e n c y of the G S e r i e s o v e r the F S e r i e s of 4 . 5 % .  80  H  70 60 -  IF Series IG Series  50 40 - 30  -  20 10 0 -  Minimum  Mean  Maximum  Standard Deviation  Figure 6.1 Comparison Summary of Expert 1 Results  T h e results for E x p e r t 1, a s illustrated in F i g u r e 6.1, s h o w that the m e a n s u p p l y price o f electricity is l e s s for the F S e r i e s than the G S e r i e s by a s m a l l a m o u n t . T h e F S e r i e s h a s a m e a n of 2 9 . 1 1 $ / M W h while the G S e r i e s h a s a m e a n of 3 0 . 1 4 $ / M W h , w h i c h is a 4 % d i f f e r e n c e . T h e r e f o r e , in his v i e w the i n c r e a s e d e f f i c i e n c y o f the G S e r i e s d o e s not offset the h i g h e r capital c o s t s .  Additionally, t h e m a x i m u m p r i c e of electricity for F S e r i e s is 11 mills h i g h e r t h a n that of G S e r i e s ( $ 7 3 . 3 6 / M W h a n d $ 6 4 . 3 4 / M W h for the F a n d G S e r i e s r e s p e c t i v e l y ) . T h i s is a n e x p e c t e d d i f f e r e n c e for the tails of the output. T h e m i n i m u m price o f electricity w a s  11.47  $ / M W h a n d $ 1 3 . 1 7 $ / M W h for the F a n d G S e r i e s r e s p e c t i v e l y .  In E x p e r t 1's outlook, in t e r m s of risk, the F S e r i e s exhibits a h i g h e r risk t h a n the G S e r i e s g a s turbine. T h e F S e r i e s s t a n d a r d deviation w a s  9%  g r e a t e r t h a n that o f t h e  G Series.  86  T h e s e results a r e not w h a t the a u t h o r h a d originally e x p e c t e d . T h e a u t h o r ' s e x p e c t a t i o n w a s that t h e  G S e r i e s w o u l d b e a s s o c i a t e d with a h i g h e r d e g r e e o f uncertainty, a n d a  l o w e r m e a n s u p p l y o f electricity. T h i s is b e c a u s e , a s the n e w e r s e r i e s , G S e r i e s t e c h n o l o g y is e v o l v i n g , a n d m a y h a v e p r o b l e m s a s s o c i a t e d with its first plants. H o w e v e r , its h i g h e r e f f i c i e n c y will d e c r e a s e the m e a n s u p p l y of electricity.  F r o m this E x p e r t ' s p e r s p e c t i v e , a c o r p o r a t i o n ' s attitude for risk will d e t e r m i n e the c h o i c e of t e c h n o l o g y to i m p l e m e n t . F o r the risk a v e r s e , G S e r i e s m a y b e the t e c h n o l o g y o f c h o i c e s i n c e it h a s a lower s t a n d a r d d e v i a t i o n a n d lower e x t r e m e m a x i m u m v a l u e s . H o w e v e r , for l e s s risk a v e r s e c o r p o r a t i o n s , the F S e r i e s m a y b e the t e c h n o l o g y o f c h o i c e a s it h a s a l o w e r m e a n s u p p l y p r i c e of electricity.  6.1.3. Expert 3 T h e r e s u l t s for E x p e r t 3 a r e illustrated in F i g u r e 6.2. In this e x p e r t ' s o p i n i o n the G S e r i e s h a s a 1 4 % a d v a n t a g e o v e r the F S e r i e s , a s reflected in the m e a n s u p p l y p r i c e of electricity ( 2 9 . 8 4 $ / M W h a n d 3 1 . 8 0 $ / M W h for G & F r e s p e c t i v e l y ) . E x p e r t 3 a l s o associated a  h i g h e r level of uncertainty with the F S e r i e s than the G S e r i e s . W h i c h is  reflected a s a n i n c r e a s e o f 6 % n the s t a n d a r d deviation for t h e s e g a s t u r b i n e s .  $60.00 $50.00  s <  $40.00  F Series G Series  $30.00 $20.00 $10.00 $Minimum  Figure 6.2 Comparison  Mean  Summary of Expert 3 Results  Maximum  Standard Deviation  87 O b s e r v i n g the m a x i m u m a n d m i n i m u m tails for E x p e r t 3, s h o w s that the s u p p l y o f electricity m i n i m u m v a l u e s for the F a n d G S e r i e s a r e c l o s e . H o w e v e r , the m a x i m u m s u p p l y p r i c e o f electricity v a r i e s far m o r e t h a n the m i n i m u m v a l u e s . T h e m i n i m u m s u p p l y p r i c e s o f electricity w e r e 18.62 $ / M W h a n d 17.86 $ / M W h for the F & G S e r i e s r e s p e c t i v e l y . T h e G S e r i e s m a y potentially o u t p e r f o r m the F s e r i e s by 4 % .  The maximum  s u p p l y p r i c e o f electricity for F S e r i e s w a s 5 9 . 7 6 $ / M W h , a n d for t h e G S e r i e s 5 1 . 3 6 $ / M W h . A s the u p p e r e n d of the r a n g e , the G S e r i e s m a y o u t p e r f o r m t h e F S e r i e s , in this c a s e by 1 4 % .  B a s e d o n this e x p e r t v i e w s , the G s e r i e s w o u l d b e the p r e f e r r e d t e c h n o l o g y i r r e s p e c t i v e o f the risk attitude o f the c o r p o r a t i o n . F o r both risk a v e r s e a n d risk taking b u s i n e s s e s G S e r i e s w o u l d b e the t e c h n o l o g y of c h o i c e s i n c e the s t a n d a r d d e v i a t i o n  a n d the m a x i m u m ,  m i n i m u m a n d m e a n s u p p l y o f electricity is lower for the G S e r i e s t h a n the F S e r i e s .  6.1.4. Expert 4 E x p e r t #4 h a d the l e a s t variation b e t w e e n the F a n d G S e r i e s g a s t u r b i n e s , a s illustrated in F i g u r e 6.3. T h e m e a n s for the F a n d G S e r i e s t u r b i n e s w e r e v e r y c l o s e , 3 3 . 4 9 $ / M W h a n d 32.51 $ / M W h r e s p e c t i v e l y . B e s i d e s the d e g r e e o f uncertainty  a s s o c i a t e d with both  the F a n d G S e r i e s g a s t u r b i n e s a r e v i e w e d to b e the s a m e for E x p e r t #4, with a difference.  1%  88  $50.00 $40.00  IF Series IG Series  $30.00  <  $20.00 $10.00 $-  Minimum  Figure 6.3 Comparison  Mean  Maximum  Standard Deviation  Summary of Expert 4 Results  T h e m a x i m u m s u p p l y p r i c e of electricity w a s h i g h e r for F S e r i e s t h a n G s e r i e s , 4 7 . 9 0 $ / M W h a n d 4 7 . 0 3 $ / M W h r e s p e c t i v e l y , h o w e v e r ; m i n i m u m s u p p l y p r i c e of electricity w a s lower 2 3 . 7 1 $ / M W h for t h e F S e r i e s a n d . 24.21 $ / M W h for the G S e r i e s  F o r this e x p e r t , t h e a n a l y s i s s u g g e s t s t h e r e is little to c h o o s e b e t w e e n t h e two technologies.  6.1.5. Average of 3 Experts J u d g i n g by t h e c o l l e c t e d results of t h e 3 E x p e r t s m a y l e a d a c o r p o r a t i o n to a different c o n c l u s i o n . T a b l e 6-1 b e l o w s h o w s t h e c o n v o l u t e d results for t h e 3 E x p e r t s . F r o m t h e s e results, in t h e integrated r e s o u r c e p l a n n i n g p r o c e s s o f the p a s t , t h e t h r e e e x p e r t s w o u l d b e c o n s i s t e n t r e g a r d i n g the t e c h n o l o g y to i m p l e m e n t . T h i s w o u l d b e t h e G s e r i e s , w h i c h is a s s o c i a t e d with a lower d e g r e e of uncertainty a n d a lower m e a n s u p p l y p r i c e of electricity than t h e F S e r i e s . T h e r e f o r e , t h e relative r i s k i n e s s o f the t e c h n o l o g y is t h e d r i v i n g f a c t o r f  for d e c i s i o n m a k i n g in t h e old r e g i m e .  89  F Series  G Series  Minimum  17.93  18.41  Maximum  60.34  54.25  31.47  30.83  6.05  5.66  Mean  Standard Deviation  Table 6-1 Average of 3 Experts  Results  B a s e d o n the 3 E x p e r t s results, in the c o m p e t i t i v e p l a n n i n g e n v i r o n m e n t o f t o d a y , the t e c h n o l o g y to i m p l e m e n t w o u l d a l s o b e the G S e r i e s , rather t h e F S e r i e s . H o w e v e r , in this c a s e , the d i f f e r e n c e in e x p e r t s ' v i e w s will i n f l u e n c e a c o r p o r a t i o n ' s d e c i s i o n o f w h e t h e r to p r o c e e d with the project. T h e d e c i s i o n m a k i n g  p r o c e s s is n o w b a s e d o n the risk profile of  the t e c h n o l o g y a n d the c o r p o r a t i o n ' s ability to a b s o r b a n d m a n a g e risk. A l t h o u g h the relative r i s k i n e s s of the projects a r e important, the driving f a c t o r n o w b e c o m e s the ability to m e e t t h e c h o s e n p e r f o r m a n c e m e a s u r e .  6.2.  Output from the Model  6.2.7. Input Variable Distributions A s d e s c r i b e d in the p r e v i o u s c h a p t e r s , t h e r e w e r e u p to 10 input v a r i a b l e s for e a c h o f the F a n d G S e r i e s g a s t u r b i n e s . T h e e x p e r t s did not a l w a y s d i s t i n g u i s h b e t w e e n the v a r i a b l e  90 distributions elicited for s o m e of the v a r i a b l e s b e t w e e n F & G . T h i s w a s either b e c a u s e they w e r e u n a b l e to d i s t i n g u i s h , o r e l s e they b e l i e v e d that t h e r e w a s n o d i f f e r e n c e . T a b l e 6-2 b e l o w lists the different v a r i a b l e s , a n d w h e t h e r the E x p e r t d i s t i n g u i s h e d b e t w e e n  the  distributions (different) or w a s u n a b l e to d i s t i n g u i s h ( s a m e ) .  HL_ia-v-  Ll^JliS©  S^-jaM  Capacity  Different  Different  Different  Heat Rate  Different  Different  Different  P r e - O p Duration  Different  Same  Same  Pre-Op Cost  Different  Same  Different  Operation  Different  Same  Same  O & M Costs  Different  Same  Same  Fuel Costs  Same  Same  Same  Duration  1  Table 6-2 Input Variables For Model  E x p e r t 1 w a s a b l e to d i s t i n g u i s h b e t w e e n all of the F & G S e r i e s d a t a , e x c e p t for F u e l C o s t s . E x p e r t 3 h a d the least variation for distribution v a r i a b l e s . J u s t C a p a c i t y a n d H e a t R a t e w e r e different; h o w e v e r ; they w e r e prorated the s a m e for both the F & G S e r i e s . E x p e r t 4 w a s a b l e to d i s t i n g u i s h b e t w e e n the c a p a c i t y , h a t e rate, a n d p r e - o p c o s t s . T h i s e x p l a i n s w h y e x p e r t 1 h a d the g r e a t e s t variation b e t w e e n his F a n d G S e r i e s c o m p a r e d to the o t h e r E x p e r t s .  6.2.2. Comparison of Minimum, Maximum & Means T a b l e 6-3 d i s p l a y s the output m i n i m u m , m a x i m u m , a n d m e a n v a l u e s of the s u p p l y p r i c e of electricity for E x p e r t s 1, 3, a n d 4 for both the F a n d G S e r i e s . A l t h o u g h the e x p e r t s  91 h a v e different e x p e r t i s e in the industry, e x a m i n a t i o n of their m e a n s i n d i c a t e s a high level of a g r e e m e n t a m o n g the e x p e r t s r e g a r d i n g their outlook of the m e d i a n s of the g a s t u r b i n e s . T h e i r m e a n s w e r e 29.11  $ / M W h , 3 1 . 8 0 $ / M W h a n d 3 3 . 4 9 $ / M W h for F S e r i e s ,  for E x p e r t s 1, 3, a n d 4 r e s p e c t i v e l y . T h e m e a n s w e r e 3 0 . 1 4 $ / M W h , 2 9 . 8 4 $ / M W h 32.51  and  $ / M W h for G S e r i e s , for E x p e r t s 1, 3, a n d 4 r e s p e c t i v e l y . A c o m p a r i s o n of the  relative s u p p l y price for F a n d G S e r i e s s u p p l y price m e a n s is d i s p l a y e d in F i g u r e 6.4  llllll^^ ,  •  F Series  G Series  F Series  G Series  F Series  G Series  F Series  G Series  Minimum  11.47  13.17  18.62  17.86  23.71  24.21  17.93  18.41  Maximum  73.36  63.34  59.76  51.36  47.90  47.03  60.34  54.25  29.11  30.14  31.80  29.84  33.49  32.51  31.47  30.83  Mean  Table 6-3 Comparison of Minimums, Maximums and Means.  Expert 1  Expert 3  Expert 4  Figure 6.4 Summary Comparison of Results from the 3 Experts  Average of 3  92 H o w e v e r , the e x p e r t o p i n i o n differed m o r e significantly o n the  tails o f t h e distribution; the  m a x i m u m s a n d m i n i m u m s . T h i s is reflected by the v e r y w i d e r a n g e o f o u t c o m e s that w e r e o b t a i n e d for the m a x i m u m a n d m i n i m u m v a l u e s . T h e r e is a h i g h e r a g r e e m e n t a m o n g a g i v e n E x p e r t s F a n d G s e r i e s , than a g r e e m e n t a m o n g different E x p e r t s ' F o r G S e r i e s . F o r e x a m p l e , F o r E x p e r t 1 the m i n i m u m v a l u e s for F & G S e r i e s a r e 11.47 $ / M W h a n d 13.17 $ / M W h , while the F S e r i e s m i n i m u m v a l u e is 11.47 $ / M W h , 18.62 $ / M W h , a n d 23.71 $ / M W h for E x p e r t s 1, 3, a n d 4 r e s p e c t i v e l y , a s d i s p l a y e d in T a b l e 6-3 a b o v e .  6.2.3. Standard Deviation of the Means vs. Standard Deviations from Experts T h e E x p e r t s w e r e c h o s e n for their i n v o l v e m e n t with the industry, particularly d u e to their d i v e r s e e x p e r t i s e . A n e x a m i n a t i o n of their s t a n d a r d deviation o f their m e a n s a n d the s t a n d a r d d e v i a t i o n s b e t w e e n the e x p e r t s y i e l d s s o m e interesting i n f o r m a t i o n . T a b l e 6-4 b e l o w d i s p l a y s the s t a n d a r d d e v i a t i o n s of the e x p e r t s m e a n s , a n d the s t a n d a r d d e v i a t i o n s between the experts' m i n i m u m , m a x i m u m a n d m e d i a n v a l u e s .  F i g u r e 6.5 illustrates the relative d i f f e r e n c e s in s t a n d a r d d e v i a t i o n for the E x p e r t s a n d the A v e r a g e of the 3 E x p e r t s .  Expert 1 S D  8.87  8.09  Expert 3 S D  5.57  5.22  Expert 4 S D  3.70  3.68  S D o f M i n of E x p e r t s  6.15  5.54  S D of M a x  12.74  9.01  S D of M e a n  2.21  1.46  S D of S D  2.62  2.24  Table 6-4 Standard Deviations of the Means and Standard Deviation between Minimum, Maximum and Mean of Experts.  93  $9.00 $8.00  El F Series Standard Deviatoin  +  • G Series Standard Deviation  $7.00 $6.00 |  $5.00  1  $4.00 $3.00 $2.00 $1.00 $Expert 1  Expert 3  Expert 4  Average of 3  Figure 6.5 Comparison of Uncertainties as Represented by Standard Deviation forF &G Series  E x a m i n a t i o n o f t h e s t a n d a r d d e v i a t i o n s reflects the variation b e t w e e n the e x p e r t s , the a n a l y s i s i n d i c a t e s that the variation b e t w e e n the e x p e r t s is l e a s t for the m e a n v a l u e s . T h e s t a n d a r d d e v i a t i o n for the m e a n v a l u e s is 2.21 a n d 1.46 for F a n d G S e r i e s r e s p e c t i v e l y . E x a m i n a t i o n o f the m e d i a n s t a n d a r d d e v i a t i o n d e m o n s t r a t e s a c o n s e n s u s a m o n g the E x p e r t s for t h e m e d i a n of the s u p p l y p r i c e o f electricity.  H o w e v e r , t h e r e is c o n s i d e r a b l e uncertainty a s s o c i a t e d with t h e m i n i m u m a n d m a x i m u m v a l u e s . T h i s is r e f l e c t e d by a w i d e r a n g e of s t a n d a r d d e v i a t i o n s b e t w e e n the e x p e r t s , the s t a n d a r d d e v i a t i o n s for the m i n i m u m s a r e 6 . 1 5 a n d 5.54 for the F a n d G S e r i e s r e s p e c t i v e l y , a n d the s t a n d a r d d e v i a t i o n s for the m a x i m u m s a r e the h i g h e s t , 1 2 . 7 4 a n d 9.01 for t h e F a n d G S e r i e s . T h e r a n g e o f o u t l o o k s a s s o c i a t e d with the m a x i m u m v a l u e s a r e a l m o s t 6 t i m e s g r e a t e r t h a n the r a n g e o f o u t l o o k s a s s o c i a t e d with the m e a n v a l u e s , reflecting the u n c e r t a i n s t a t u s of the industry.  94  T y p i c a l l y , a g r o u p of e x p e r t s w o u l d h a v e w i d e r r a n g e ( s p e c t r u m ) of o u t l o o k s o r e x p e c t a t i o n s than e a c h individually. H o w e v e r , in this c a s e this d o e s not h o l d true. In the c a s e of E x p e r t 1, his individual outlook o n uncertainty, r e p r e s e n t e d by the s t a n d a r d d e v i a t i o n , w a s g r e a t e r than the c o l l e c t i v e uncertainty of the 3 e x p e r t s . T h e only c a s e w h e r e this is not true, is for the o p i n i o n s of the m a x i m u m s ; in this c a s e the s t a n d a r d d e v i a t i o n s of the m a x i m u m s a r e g r e a t e r t h a n the s t a n d a r d d e v i a t i o n s for all t h r e e E x p e r t s . F o r the F S e r i e s , the a v e r a g e m a x i m u m s t a n d a r d deviation w a s 12.74 a n d the s t a n d a r d d e v i a t i o n s of the m a x i m u m s w e r e 8.87, 5.57 a n d 3.70 for E x p e r t s 1, 2, a n d 3 r e s p e c t i v e l y . F o r the G S e r i e s , this v a l u e w a s 9.01 a n d the s t a n d a r d d e v i a t i o n s for the m a x i m u m s w e r e 8.09, 5.22, a n d 3.68 for E x p e r t s 1, 2 a n d 3 r e s p e c t i v e l y .  T h e E x p e r t 1 results e n v e l o p e d the E x p e r t 3 a n d E x p e r t 4 results. H i s m i n i m u m w a s least of all m i n i m u m s , a n d his m a x i m u m w a s the g r e a t e s t of all m a x i m u m v a l u e s . T h e r e f o r e , E x p e r t 1 a c c o u n t e d for the w i d e s t r a n g e of o p i n i o n a m o n g the 3 e x p e r t s . T h i s i n d i c a t e s either that E x p e r t s 2 & 3 did not h a v e t a k e into a c c o u n t the e x t r e m e s that m a y o c c u r , o r that E x p e r t 1 is o v e r r e a c t i n g to the c h a n g e s taking p l a c e in the electricity industry.  6.3.  Effect of Variables on Supply Price Electricity  It m a y b e insightful to c o m p a r e the r a n g e of o p i n i o n s of the 3 E x p e r t s r e g a r d i n g their o u t l o o k s o n the v a r i o u s p h a s e s . T h e p h a s e s c a n b e v i e w e d a s t h r e e m a j o r c o s t p h a s e s : P r e - O p e r a t i o n P h a s e , O & M P h a s e , a n d the F u e l P h a s e . T h e v i e w s of t h e E x p e r t s w e r e c o n s i s t e n t r e g a r d i n g the relative i m p o r t a n c e of e a c h  p h a s e of the project life c y c l e . T a b l e  6-5 d i s p l a y s the c o m p a r i s o n of the s t a n d a r d d e v i a t i o n s for E x p e r t s 1,3 a n d 4 for the 3 cost phases.  C o n s i s t e n t l y , for all t h r e e E x p e r t s , there is the w i d e s t r a n g e of o u t l o o k s for the F u e l p h a s e . T h i s is reflected in the fact that the F u e l p h a s e h a s the h i g h e s t s t a n d a r d deviation of the p h a s e s .  95  IJSBIigjSSIff^  •  '  E x p e r t 1: F S e r i e s  47.58  33.04  60.38  E x p e r t 1: G S e r i e s  57.31  41.90  78.10  E x p e r t 3: F S e r i e s  19.3  8.44  38.91  E x p e r t 3: G S e r i e s  26.6  8.28  49.08  E x p e r t 4: F S e r i e s  9.62  9.76  29.4  E x p e r t 4: G S e r i e s  12.35  12.1  37.16  Table 6-5 Comparison of Standard Deviations for Experts 1, 3, and 4 for Project Phases  6.3.1. Relative Cost of Phases to Total Project Costs T a b l e 6-6 b e l o w d i s p l a y s the m e a n c o s t of e a c h p h a s e , a n d the p e r c e n t a g e of this c o s t relative to the total project c o s t , for e a c h of the E x p e r t s . T h e fuel c o m p o n e n t of the project w a s t h e g r e a t e s t c o s t c o m p o n e n t for all three of the E x p e r t s . O w i n g to this, if o n e w i s h e s to m i n i m i z e risk it is important to h e d g e fuel c o s t s a n d obtain l o n g t e r m c o n t r a c t s . T h e p r e - o p e r a t i o n p h a s e is a l s o relatively high c o s t c o m p o n e n t c o m p a r e d to the o v e r a l l project c o s t . A g a i n , this r e v e a l s the benefits of p a s s i n g o n the c o n s t r u c t i o n a n d p r e - o p e r a t i o n risk o n t o the c o n t r a c t o r . T h i s will r e d u c e the uncertainty of the potential total p r e - o p e r a t i o n c o s t . H o w e v e r , it m a y a l s o i n c r e a s e the c o s t to the builder, a s a c o n t r a c t o r willing to a c c e p t the p r e - o p e r a t i o n r i s k s will require a h i g h e r r e v e n u e to c a r r y that risk.  96  E x p e r t 1:  108  31%  65  18%  180  51%  G  172  34%  105  20%  236  46%  Expert 3  198  51%  33  9%  154  40%  G  269  53%  33  6%  205  40%  Expert 4  179  43%  59  14%  181  43%  G  254  46%  75  14%  222  40%  Table 6-6 Mean Cost of Phases and Relative Percentage Cost for Phases for Expert 1,3, and 4  6.3.2. Sensitivity Analysis: Results of Run for 15% MARR A sensitivity a n a l y s i s is p e r f o r m e d to a n a l y z e the effect of c h a n g i n g the M A R R o n the output s u p p l y price of electricity a n d in particular the relative merits of the F & G S e r i e s t u r b i n e s . A s e x p e c t e d , the results w a s a n i n c r e a s e in the e x p e c t e d s u p p l y p r i c e of electricity with i n c r e a s e d d i s c o u n t rate. T h i s is intuitive b e c a u s e with a n e x p e c t e d h i g h e r rate of return, the p r i c e of electricity to satisfy this return m u s t b e higher.  F i g u r e 6.6 a n d  F i g u r e 6.7 illustrate a c o m p a r i s o n of the output for F & G S e r i e s M e a n s for the 1 2 % a n d 1 5 % runs.  97  40 35 25  I  2  12% Runs: F Series  115% R u n s : F S e r i e s  •  12% Runs: G  115% R u n s : G S e r i e s  Series  :i  30 |  •  0 < 15 10 5 0 Expert 1  Expert 3  Expert 4  Figure 6.6 Comparison of Experts' Means for the F &G Series 12% and 15% MARR runs  10 9 8 7 6 5 4 3 2 1 0 Expert 1  Expert 3  •  12% Runs: F Series  •  15% Runs: F Series  •  12% Runs: G  Series  •  15% Runs: G  Series  Expert 4  Figure 6.7 Comparison of Experts' Uncertainties Represented by Standard Deviation for the F &G Series 12% and 15% MARR runs.  98  A n i n c r e a s e in the M A R R f r o m 1 2 % to 1 5 % results in a n i n c r e a s e in the e x p e c t e d m e a n s u p p l y p r i c e of electricity f r o m 2 - 3 $ / M W h o r of a b o u t 7 % - 1 1 % . A n i n c r e a s e in the M A R R f r o m 1 2 % to 1 5 % results in a n i n c r e a s e in the s t a n d a r d deviation f r o m .2-1 $ / M W h o r of a b o u t 3 - 1 2 % . B y i n c r e a s i n g the e x p e c t e d returns o n a project, the c o r r e s p o n d i n g s u p p l y p r i c e i n c r e a s e r a i s e s the risk for the d e v e l o p e r p r o c e e d i n g with the project a n d the l o w e r s the probability o f a c h i e v i n g the r e q u i r e d return.  6.3.3. The Effect of Demand on F &G Technology Choices: A s m e n t i o n e d p r e v i o u s l y , for a n y c o r p o r a t i o n to run the m o d e l t h e c o r p o r a t i o n m u s t e n s u r e all k e y v a r i a b l e s that m a y i n f l u e n c e the c o r p o r a t i o n ' s c h o i c e o f t e c h n o l o g y to i m p l e m e n t a r e c o n s i d e r e d . F o r e x a m p l e , a k e y v a r i a b l e that will directly i n f l u e n c e the s e l e c t i o n of a t e c h n o l o g y to i m p l e m e n t is that of the e x p e c t e d d e m a n d .  In a c o m p e t i t i v e e n v i r o n m e n t , c o s t a n d marketability b e c o m e the m o s t i m p o r t a n t f a c t o r s in d e t e r m i n i n g the potential for s a l e o f the output of a n y plant. T h e q u e s t i o n b e c o m e s : W h a t p r i c e is o n e willing to a c c e p t for the o u t p u t ? T h e P l a n n i n g in a n U n c e r t a i n E n v i r o n m e n t M o d e l a s s u m e s that the output of the plant, both F a n d G S e r i e s , c a n b e fully a b s o r b e d by the m a r k e t . T h e d e g r e e to w h i c h the full output is m a r k e t a b l e m a y affect the c h o i c e between F a n d G S e r i e s .  T h e P l a n n i n g in a n U n c e r t a i n M o d e l w a s run u n d e r v a r i o u s d e m a n d s c e n a r i o s to e x p l o r e the effect o f c o n s t r a i n e d output for d e m a n d m a y h a v e o n the c h o i c e b e t w e e n the F a n d G S e r i e s t e c h n o l o g i e s . T h e m o d e l w a s run limiting the a v a i l a b l e s a l e o f c a p a c i t y to 3 5 0 3 0 0 M W , 2 5 0 M W a n d lastly 2 0 0 M W .  MW,  T h i s is e s s e n t i a l l y the s a m e a s c h a n g i n g the l o a d  f a c t o r o f the g a s turbine. L o a d factor is the ratio of a v e r a g e g e n e r a t i o n output d i v i d e d by the c a p a c i t y o f t h e facility. T h e results a r e b a s e d o n d a t a r e p r e s e n t i n g the a v e r a g e o f the 3 e x p e r t s , to illustrate the e f f e c t s of limiting the d e m a n d , or l o a d factor.  99  T h e e f f e c t s of limiting the d e m a n d or l o a d factor w e r e not s u r p r i s i n g . F i g u r e s xy b e l o w illustrate the e x p e c t e d m e a n a n d s t a n d a r d deviation of the s u p p l y p r i c e of electricity of the F a n d G S e r i e s for t h e s e c a s e s .  Graph of Mean Supply Price of Electricity for Average of 3 Experts For A Given Demand  IF Series IG Series  350  MW  300  MW  250  MW  200  MW  Figure 6.8 Graph of mean Supply Price of Electricity for Average of 3 Experts For a Given Demand  Graph of Standard Deviation Supply Price of Electricity for Average of 3 Experts for A Given Demand  350 MW  300 MW  250 MW  200 MW  Figure 6.9 Graph of Standard Deviation Supply Price of Electricity for Average of 3 Experts for a Given Demand  100  A s the r e s u l t s illustrate, a s s u m i n g 1 0 0 % l o a d factor, or a s s u m i n g that the m a x i m u m d e m a n d a v a i l a b l e for s a l e is g r e a t e r than 3 7 5 M W , the G S e r i e s turbine h a s both a l o w e r e x p e c t e d s u p p l y p r i c e of electricity a n d a lower level of uncertainty t h a n the F S e r i e s . T h i s m a y b e d u e to s e v e r a l r e a s o n s , d i s c u s s e d in S e c t i o n x z b e l o w . A s a limit is i m p o s e d o n the a m o u n t o f d e m a n d that m a y b e s o l d , the results indicate that the F S e r i e s turbine m a y b e c o m e a better alternative than the G S e r i e s . Intuitively this is b e c a u s e the G S e r i e s l o s e s its h i g h e r e f f i c i e n c y a d v a n t a g e . A s its o p e r a t i o n is r e d u c e d the g r e a t e r c o n s t r u c t i o n c o s t s a s s o c i a t e d with the G S e r i e s is n o l o n g e r offset by r e d u c e d fuel c o s t s . A s the g r a p h s illustrate, a s the d e m a n d d e c r e a s e s , the F S e r i e s b e c o m e s the better alternative than the G S e r i e s . In the c a s e w h e r e t h e r e is only 2 0 0 M W a v a i l a b l e to s e l l , the F S e r i e s h a s a m e a n o f a l m o s t 8 $ / M W h l e s s than the G S e r i e s . Similarly, the d e g r e e o f uncertainty a s s o c i a t e d with the F S e r i e s is a l s o r e d u c e d .  T h e d e c i s i o n m a k e r m u s t c o n s i d e r the potential load f a c t o r of the facility. In the e x t r e m e c a s e , w h e r e a facility will b e o p e r a t i n g at a low load factor, " p e a k e r s " , o r s i n g l e c y c l e facilities, w h i c h a r e lower c o s t a n d low e f f i c i e n c y a r e u s u a l l y u s e d . A s s u m i n g that the full output o f the t u r b i n e s c a n b e s o l d will result in a different d e c i s i o n t h a n a s s u m i n g a limit o n the a m o u n t the m a r k e t c a n a b s o r b . W i t h o u t taking into c o n s i d e r a t i o n all t h e s e f a c t o r s , a d e c i s i o n m a k e r m a y not c h o o s e the b e s t alternative for the e n v i r o n m e n t .  6.3.4. The Effect of Varying Fuel Prices Yearly T h e m o d e l a s s u m e d that the fuel p r i c e s did not c h a n g e yearly; the v a l u e s w e r e elicited for a o v e r a l l a v e r a g e . S i n c e the m o d e l is not f o c u s e d o n a y e a r - t o - y e a r c a s h flow a n a l y s i s , the y e a r to y e a r fuel variation is not important. W h a t is important is the o v e r a l l m e a n v a l u e of the fuel p r i c e s .  T o v a l i d a t e t h e a b o v e o b s e r v a t i o n , the m o d e l w a s rerun by allowing the fuel p r i c e s to fluctuate yearly b a s e d o n the a v e r a g e e x p e r t v a l u e s a n d u s i n g a b e t a distribution. T h i s w a s a c h i e v e d by s a m p l i n g the fuel price e a c h y e a r for the full o p e r a t i o n o f the turbine, a s  101  o p p o s e d to t h e original c a s e in w h i c h t h e fuel price w a s s a m p l e d o n c e . T h e o v e r a l l affect w a s insignificant. T h e m e a n a n d s t a n d a r d d e v i a t i o n of t h e fuel p r i c e with fluctuation a n d without fluctuation w e r e t h e s a m e . F o r e x a m p l e , for t h e F S e r i e s , t h e m e a n w a s 32.11 $ / M W h a n d 3 1 . 6 0 $ / M W h a n d a s t a n d a r d deviation of 4.81 $ / M W h  a n d 4 . 7 0 $ / M W h for  the n o n fluctuating a n d fluctuating c a s e r e s p e c t i v e l y .  6.3.5. Market for Gas Turbines T h e E x p e r t s interviewed s u g g e s t e d with d e r e g u l a t i o n C a l i f o r n i a will s e e i n v e s t m e n t in g a s t u r b i n e s to r e p l a c e the existing s t o c k of o l d e r l e s s efficient units with high i n c r e m e n t a l c o s t of o p e r a t i o n . In c o n t r a s t , t h e r e is limited m a r k e t for t h e m in t h e P a c i f i c N o r t h w e s t b e c a u s e of t h e l o w e r i n c r e m e n t a l c o s t of h y d r o electric p o w e r there.  T h i s c o n c l u s i o n c o i n c i d e s with the results of a c o m p r e h e n s i v e s t u d y p r e p a r e d f o r t h e C a l i f o r n i a E n e r g y C o m m i s s i o n , M o d e l i n g C o m p e t i t i v e E n e r g y M a r k e t in C a l i f o r n i a : A n a l y s i s o f R e s t r u c t u r i n g t h e a u t h o r s ran a n u m b e r of c a s e s to a n a l y z e t h e e f f e c t s of restructuring in C a l i f o r n i a . T h e report e s t i m a t e w h a t t h e m i n i m u m c o s t r e c o v e r y r e q u i r e d 5 7  for N e w G e n e r a t i n g P l a n t s in C a l i f o r n i a w o u l d b e . T h e report f i n d s that c o m b i n e d c y c l e units a d d e d o u t s i d e C a l i f o r n i a require a c a p a c i t y f a c t o r g r e a t e r t h a n 6 7 . 5 % a n d m u s t m a i n t a i n a n a v e r a g e total c o s t of l e s s than 2 4 $ / M W h in U S 1 9 9 6 $ .  T h e s e r e s u l t s a g r e e with the r a n g e in t h e s u p p l y electricity p r i c e s f o r t h e t h r e e E x p e r t s . A t a 1.4% c u r r e n c y , 2 4 $ / M W h is 3 3 . 6 C A $ / M W h . T h i s v a l u e is c l o s e to t h e m e a n output p r i c e a s a s c e r t a i n e d by the E x p e r t s , a n d d i s p l a y e d in T a b l e 6 - 3 a b o v e .  L C G Consulting, Modeling Competitive Energy Market In California: Analysis of Restructuring October 11, 1996 (Revision 1) Modeling Competitive Energy Market in California: Analysis of Restructuring: October 11, 1996 (Revision 1) Prepared for California Energy Commission Principal Investigator Rajat K Deb Co-Investigators Richard S. Albert Lie-Long Hsue L C G Consulting  102 6.4.  Reflection on Experts' Results  A s m e n t i o n e d earlier, the results f r o m of the e x p e r t s ' w e r e not w h a t the a u t h o r e x p e c t e d . T h e a u t h o r e x p e c t e d that the F S e r i e s w o u l d b e p e r c e i v e d a s a m o r e c e r t a i n t e c h n o l o g y , a s it utilizes p r o v e n t e c h n o l o g y , while the G S e r i e s h a s yet to b e built in North A m e r i c a . A l t h o u g h ; d u e to its lower efficiency, it is e x p e c t e d that it w o u l d h a v e a h i g h e r m e a n s u p p l y o f electricity t h a n the G S e r i e s .  T h e r e s u l t s f r o m the e x p e r t s , h o w e v e r , indicate that the G S e r i e s w o u l d b e their o v e r a l l preferred  t e c h n o l o g y to i m p l e m e n t o v e r the F S e r i e s . T h i s is true for the individual  e x p e r t s results a n d naturally for their c o m b i n e d j u d g m e n t a s p r o d u c e d by the m o d e l .  T h e following a r e p o s s i b l e f a c t o r s for w h i c h the G S e r i e s s e e m s to b e m o r e f a v o r a b l e t h a n the F S e r i e s :  Technical Factors:  1.  E c o n o m i e s of S c a l e  2.  R e d u c e d F u e l C o s t s of G S e r i e s  3.  L o w e r F u e l Uncertainty a n d Increased C o m p e t i t i v e n e s s of G S e r i e s  4.  V e n d o r s ' Implicit A b s o r p t i o n of R i s k to E s t a b l i s h N e w S e r i e s  Modeling Factors  5.  Marketability of the O u t p u t  6.  D i s t i n g u i s h i n g b e t w e e n Input V a r i a b l e s  103  6.4.1. Economies of Scale T h e F S e r i e s output s p e c i f i c a t i o n s w e r e 2 5 3 M W . W h i l e the G S e r i e s output w a s s p e c i f i e d a s 3 5 0 M W . T h e r e f o r e ; p e r h a p s the r e a s o n the G s e r i e s m e a n is l o w e r t h a n that o f the F s e r i e s m a y b e e c o n o m i e s o f s c a l e . S i n c e the G S e r i e s h a s a n a d d i t i o n a l 100  MW  output, a s its a v e r a g e c o s t s d e c l i n e with s i z e . T o d e t e r m i n e w h a t the effect o f the g a s turbine output o n the m e a n s u p p l y p r i c e of electricity w o u l d b e to e q u a t e the c a p a c i t y for the two t u r b i n e s , t h e r e b y o n e c a n o b s e r v e the direct effect of the h e a t rate.  6.4.2. Reduced Fuel Costs of G Series T h e G S e r i e s m a y b e m o r e f a v o r a b l e than the F S e r i e s , d u e to its i n c r e a s e d e f f i c i e n c y . T h e F S e r i e s turbine is 5 5 . 4 % efficient, while the G S e r i e s turbine s p e c i f i c a t i o n s a r e 5 8 % efficient. T h i s is a 2 . 6 % d i f f e r e n c e b e t w e e n the F a n d G S e r i e s turbine. T h e 2 . 6 % i n c r e a s e in e f f i c i e n c y c a n r e d u c e the p o w e r - p l a n t o p e r a t i n g c o s t s by $ 1 0 million to $ 2 3 million, b a s e d o n g a s c o s t s of $ 2 / m m b t u to $ 5 / m m b t u for a 3 5 0 M W Plant.  T a b l e 6-6 a b o v e illustrates the relative p e r c e n t a g e of the c o s t of e a c h p h a s e to the total c o s t o f the project. T h e fuel p h a s e c o s t w a s b e t w e e n 4 0 - 5 1 % of the total c o s t o f the project o v e r its life.  6.4.3. Lower Fuel Uncertainty and Increased Competitiveness of G Series  T h e h i g h e r e f f i c i e n c y not only r e d u c e s the fuel c o s t s for the G S e r i e s turbine but a l s o results in l o w e r fuel c o s t s uncertainty. T h e G S e r i e s m a y b e a s s o c i a t e d with a h i g h e r level o f certainty, p e r h a p s the effect of u n c e r t a i n g a s c o s t s ( d u e to i n c r e a s e d efficiency) m a y h a v e a l e s s i m p a c t o n the G S e r i e s turbine than that of the uncertainty o f the t e c h n o l o g y .  S i n c e the fuel c o s t s of the G S e r i e s a r e lower than that of the F S e r i e s , the G S e r i e s will b e d i s p a t c h e d b e f o r e the F S e r i e s t u r b i n e s . T h e r e b y , r e d u c i n g the risk a s s o c i a t e d with the  104 G S e r i e s o p e r a t i o n . T h i s t r a n s l a t e s into a c o m p e t i t i v e a d v a n t a g e for the G S e r i e s , while t h e F S e r i e s h a s a threat of b e c o m i n g t e c h n o l o g i c a l l y o b s o l e t e .  A p p a r e n t l y the e x p e r t s a r e l e s s c o n c e r n e d with the uncertainty of the t e c h n o l o g y t h a n the uncertainty a s s o c i a t e d with the g a s p r i c e s . In a c o m p e t i t i v e w o r l d o n c e the s y s t e m s a r e built, it is the i n c r e m e n t a l c o s t s w h i c h d e t e r m i n e the d i s p a t c h i n g o r d e r .  M o r e o v e r , the  t e c h n o l o g y c o s t s a r e u n c e r t a i n o v e r the n e a r t e r m h o r i z o n . ( 3 y e a r s ) while t h e g a s c o s t s r e m a i n s for the l o n g e r t e r m of the life of the g a s turbine (19 y e a r s ) . A s o n e e x p e r t 5 8  i n d i c a t e d , "we d o not w a n t to build it t o d a y , a n d find o u r s e l v e s not c o m p e t i t i v e tomorrow". T h i s m i n d s e t is a r e v e r s a l f r o m the traditional c o n s e r v a t i v e attitude of the industry.  6.4.4. Vendors' Implicit Absorption of Risk to Establish New Series A c o u p l e of the e x p e r t s ' interviewed i n d i c a t e d that they w e r e m i n i m i z i n g s o m e of the b o u n d s of the v a r i a b l e s , s u c h a s the c a p a c i t y or h e a t rate, a s they a s s u m e d that the m a n u f a c t u r e r s ' m u s t g u a r a n t e e the p e r f o r m a n c e of the turbine. If the e x p e r t s did b a s e their r e s u l t s d u e to this a s s u m p t i o n , it is r e a s o n a b l e that their b i a s w o u l d b e reflected in a n i n c r e a s e d m e a n of the price.  T h e b i a s w o u l d affect the results if the e x p e r t s a s s u m e that the m a n u f a c t u r e r s a r e willing to a b s o r b m o r e of the risk a s s o c i a t e d with a n e w t e c h n o l o g y , to e n c o u r a g e i n v e s t o r s to c h o o s e t h e n e w u n c e r t a i n t e c h n o l o g y at l e a s t for early a d o p t e r s . T h e r e f o r e the E x p e r t s ' m a y b e implicitly a s s u m i n g that the m a n u f a c t u r e r s ' of the t u r b i n e s m a y b e willing to c a r r y a h i g h e r d e g r e e of the c o s t a s s o c i a t e d with the G t e c h n o l o g y to g e t it into t h e m a r k e t .  3 years is the overall mean of the pre-operation phase and 19 years is the overall g a s turbine operation p h a s e of the three experts.  105  6.4.5. Market Demand for Turbine Output O n e f a c t o r that w a s not i n c l u d e d in the a n a l y s i s w a s the i s s u e o f d e m a n d . In a c o m p e t i t i v e m a r k e t , it is s o m e t i m e s u n k n o w n w h e r e there is a m a r k e t to sell the output of the g a s turbine. T h e F S e r i e s output w a s 2 5 0 M W , while the G S e r i e s output w a s 3 5 0 M W . If this w a s c o n s i d e r e d in the a n a l y s i s , the  F S e r i e s m a y t h e n b e p e r c e i v e d to b e  m o r e f a v o r a b l e t h a n the G S e r i e s . T h e risk of finding a m a r k e t to sell the 2 5 0 M W is l e s s t h a n t h e risk of finding a m a r k e t to sell a 3 5 0 M W output. H a d this b e e n f a c t o r e d into the a n a l y s i s ; t h e results m a y h a v e exhibited s o m e d i f f e r e n c e .  6.4.6. Experts were not necessarily able to distinguish between input variables. A n o t h e r f a c t o r w h i c h w o u l d greatly effect the certainty of the v a r i a b l e s is the e x p e r t s ' abilities to differentiate b e t w e e n the input v a r i a b l e s . A s m e n t i o n e d in S e c t i o n 6.2.1 Input V a r i a b l e Distributions, the e x p e r t s w e r e not a l w a y s a b l e to (or not a c c u s t o m e d to) d i s t i n g u i s h b e t w e e n v a l u e s for s o m e o f the input v a r i a b l e s for F a n d G S e r i e s t u r b i n e s . A s a result, in the c a s e that the e x p e r t is u n a b l e to d i s t i n g u i s h b e t w e e n v a r i a b l e s , they a r e not taking into a c c o u n t or m a y b e o v e r a c c o u n t i n g for certainty in either the F o r G S e r i e s t u r b i n e s . T h e r e f o r e , in the c a s e of the G S e r i e s , the e x p e r t s m a y b e u n d e r v a l u i n g the risk of p r e o p e r a t i o n c o s t s a s s o c i a t e d with building the facility, t h e r e b y , the G S e r i e s a p p e a r to b e m o r e certain t h a n the F S e r i e s .  106  Chapter Seven 7.  Conclusion & Recommendations  7.1.  Summary  T h i s t h e s i s o n " P l a n n i n g in a n U n c e r t a i n E n v i r o n m e n t : A C a s e S t u d y of F & G S e r i e s T u r b i n e T e c h n o l o g y " illustrates the n e e d for n e w p l a n n i n g a n d d e c i s i o n m a k i n g m o d e l s in the e v o l v i n g m a r k e t . T h e stated o b j e c t i v e s w e r e to:  1.  D e t e r m i n e the e x p e c t e d s u p p l y price of electricity to m a k e a M A R R ( m i n i m u m attractive rate of return) o n a g a s turbine project. T h e s u p p l y price of electricity is the price of electricity s u c h that r e v e n u e will offset c o s t s to m e e t a m i n i m u m attractive rate of return (set h e r e to b e  2.  12%)  C o m p a r e two t e c h n o l o g i e s , n a m e l y , F S e r i e s a n d G S e r i e s g a s t u r b i n e s , to d e t e r m i n e w h i c h t e c h n o l o g y to i m p l e m e n t b a s e d o n the t e c h n o l o g y risk profile that b e s t fits a n o r g a n i z a t i o n s ' appetite for risk.  T h e d e c i s i o n to build a n e w g e n e r a t i o n plant is b a s e d o n f a c t o r s s u c h a s c o s t of c o n s t r u c t i o n , regulatory a p p r o v a l , m a r k e t d e m a n d , e x p e c t e d r e v e n u e s , c o m p e t i t i o n a n d m a r k e t a c c e s s . T h e r e is uncertainty inherent in e v e r y o n e of t h e s e f a c t o r s . S i x a r e a s of uncertainty c a n b e identified:  1.  Input F u e l P r i c e Uncertainty: F u e l a c c o u n t s for 40-51 % of total project c o s t s . (Natural g a s h a s historically b e e n the m o s t volatile c o m m o d i t y , a n d it is e x p e c t e d that the volatility in electricity will be greater)  2.  C o m p e t i t i o n , Marketability a n d D e m a n d : for project output.  C o m p e t i t i o n m a n i f e s t s itself a s d e m a n d  107 3.  T e c h n o l o g y : G a s t e c h n o l o g y c o n t i n u e s to i m p r o v e . It m a y b e difficult to m a k e a d e c i s i o n b e t w e e n u s i n g existing t e c h n o l o g y v e r s u s waiting for a n e x p e c t e d i m p r o v e m e n t in t e c h n o l o g y .  4.  R e g u l a t o r y Uncertainty: R e g u l a t i o n is a long a n d u n c e r t a i n p r o c e s s that c a n ultimately affect the viability of a project, e s p e c i a l l y w h e r e regulatory a p p r o v a l is not g u a r a n t e e d or is c o n d i t i o n a l .  5.  E n v i r o n m e n t a l Uncertainty: A g a i n , it is d e p e n d e n t o n a p p r o v a l f r o m f e d e r a l b o d i e s a n d the m a i n t e n a n c e of e n v i r o n m e n t a l s t a n d a r d s , w h i c h a r e s u b j e c t to change.  6.  T r a n s m i s s i o n Uncertainty: T r a n s m i s s i o n pricing will b e m a r k e t b a s e d a n d c o u l d drastically c h a n g e the price of d e l i v e r e d e n e r g y , r e d u c i n g the c o m p e t i t i v e n e s s of a project.  O l d m e t h o d s of d e c i s i o n m a k i n g i n c l u d e d Multi Criteria D e c i s i o n M a k i n g , Integrated R e s o u r c e P l a n n i n g , a n d S c e n a r i o A n a l y s i s . T h r o u g h the c o u r s e of the t h e s i s , it w a s d e m o n s t r a t e d that t h e s e old p l a n n i n g m e t h o d s c a n not m e e t the n e e d s of p l a n n i n g in a n u n c e r t a i n e n v i r o n m e n t , a s they d o not a d e q u a t e l y a c c o u n t for all the u n c e r t a i n t i e s p r e s e n t in the m o d e r n electricity g e n e r a t i o n m a r k e t .  T h e a b o v e m e t h o d s of a n a l y s i s a r e u s e d to deliver the m o s t likely v a l u e s ( M L V ) or c o n t i n g e n c y v a l u e s . U s i n g C o n t i n g e n c y or M L V l e a d s to b i a s e d results. In a c o n t i n g e n c y e v a l u a t i o n a n extra o r "contingent" a m o u n t is a d d e d to all of the v a r i a b l e v a l u e s . T h e r e f o r e , the output of s u c h a n a n a l y s i s is a n overly c o n s e r v a t i v e result, w h i c h m a y m a k e the d e c i s i o n m a k e r v e t o a n o t h e r w i s e f e a s i b l e project. U s i n g a M L V a n a l y s i s fails to t a k e into a c c o u n t o t h e r v a l u e s of the v a r i a b l e s that m a y o c c u r . B a s i n g a d e c i s i o n o n a s i n g l e v a l u e of the d e c i s i o n v a r i a b l e c a u s e s a c o r p o r a t i o n to t a k e m o r e risk t h a n i n t e n d e d .  108 T h e a p p r o a c h to T h e P l a n n i n g in a n U n c e r t a i n E n v i r o n m e n t m o d e l is risk a n a l y s i s . R i s k a n a l y s i s is a n y m e t h o d - qualitative or quantitative - for a s s e s s i n g the i m p a c t s of risk o n d e c i s i o n s . T h e p u r p o s e of risk a n a l y s i s is to eliminate the n e e d for restricting o n e ' s j u d g m e n t to a s i n g l e optimistic, p e s s i m i s t i c , or "best" e v a l u a t i o n , by c a r r y i n g t h r o u g h the a n a l y s i s the c o m p l e t e r a n g e of e a c h v a r i a b l e , a n d the likelihood of e a c h v a l u e within this range.  5 9  A c o m m o n p r o c e d u r e for this a n a l y s i s is M o n t e C a r l o S i m u l a t i o n , w h i c h is the 6 0  a p p r o a c h adopted here.  T h e f o c u s of the w o r k w a s to d e v e l o p a viable d e c i s i o n m a k i n g a p p r o a c h to e v a l u a t e a c o n t i n u u m of alternatives u n d e r uncertainty. It is e x p e c t e d that u s e r s w h o a p p l y the m e t h o d o l o g y a r e likely to b e s o p h i s t i c a t e d e n o u g h to modify their o w n existing analytical m o d e l s to i n c o r p o r a t e this m e t h o d o l o g y . F u r t h e r m o r e e v e r y situation differs a n d the d a t a is both d y n a m i c a n d c h a n g i n g , calling for a c u s t o m i z e d a p p r o a c h to the p r o b l e m . T h e a u t h o r intentionally d e v e l o p e d a s i m p l e m o d e l to d e m o n s t r a t e a n d p r o v e the point without getting t a n g l e d in e n g i n e e r i n g or e c o n o m i c details. It is therefore s t r e s s e d that the r e a d e r s h o u l d f o c u s o n the a p p r o a c h but not u s e or extrapolate the a c t u a l results. T h e results a r e intuitively r e a s o n a b l e but reflect the particular time p e r i o d , r e g i o n a n d the e x p e r t s ' judgments.  A probabilistic c a s h flow m o d e l is a t e c h n i q u e e m p l o y e d in this t h e s i s to facilitate d e c i s i o n m a k i n g in a n u n c e r t a i n e n v i r o n m e n t . T h e m o d e l is b a s e d o n a M o n t e C a r l o S i m u l a t i o n . T h e m o d e l is run in a n E x c e l s p r e a d s h e e t , a n d the s i m u l a t i o n is p e r f o r m e d u s i n g a p r e p a c k a g e d software called " @ R i s k " .  Louis Y Pouliquen, Risk Analysis in Project Appraisal. (Baltimore and London: The John Hopkins University Press, 1970) 2 James N. Siddall, Analytical Decision-Making in Engineering, (New Jersey: Prentice-Hall, Inc., 1972), P.52  109  T h e m o d e l is b r o k e n into 5 p h a s e s : P r e - o p e r a t i o n project c o s t s , g a s turbine c o s t s , O p e r a t i o n a n d M a i n t e n a n c e , F u e l , a n d R e v e n u e P h a s e s . U n c e r t a i n v a r i a b l e s in the m o d e l a r e identified a n d a s s i g n e d probabilistic distributions. A M o n t e C a r l o s i m u l a t i o n that g e n e r a t e s r a n d o m n u m b e r s d r a w n f r o m t h e s e probability distributions, for e a c h of the v a r i a b l e s , is run. T h e result is a probability distribution w h i c h g i v e s the d e c i s i o n m a k e r a c o m p l e t e picture of all p o s s i b l e o u t c o m e s a n d their probabilities.  T h e input v a r i a b l e s of the m o d e l (fuel price, p r e - o p e r a t i o n c o s t , etc.) a r e a s s o c i a t e d with a large d e g r e e of uncertainty. E x p e r t s w e r e relied o n to obtain r e p r e s e n t a t i v e d a t a , the p r o c e s s of extracting the d a t a w a s t h r o u g h elicitation of s u b j e c t i v e probabilities f r o m e x p e r t s . T h e results f r o m the e x p e r t s w e r e elicited a s C u m u l a t i v e Distribution F u n c t i o n s .  T h e m o d e l d e v e l o p e d is b a s e d o n the s y n t h e s i s a n d c o m p a r i s o n of the j u d g m e n t s of the four e x p e r t s elicited for the p r o c e s s , a n d the r e s e a r c h e r ' s s u b j e c t i v e e v a l u a t i o n of the information. T h e m a r k e t e x p e r t s a r e r e p r e s e n t a t i v e of the r a n g e of activity in the n e w m a r k e t e n v i r o n m e n t . T h e i r input w a s c o n s i s t e n t with r e s e a r c h c o n d u c t e d i n d e p e n d e n t l y (for e x a m p l e , the C a l i f o r n i a E n e r g y C o m m i s s i o n , M o d e l i n g C o m p e t i t i v e E n e r g y M a r k e t s in C a l i f o r n i a : A n A n a l y s i s of R e s t r u c t u r i n g S t u d y ) . T h e m o d e l output is a probability 6 1  distribution s h o w i n g the probability that a p r e s c r i b e d s u p p l y price of electricity will m e e t a 1 2 % M A R R r e q u i r e m e n t . F o r the F s e r i e s a v e r a g e of the 3 e x p e r t s , a s u p p l y p r i c e of $ 3 1 . 4 7 / M W h h a s a 5 0 % c h a n c e of m e e t i n g the M A R R , a n d is a s s o c i a t e d with a b a n d of uncertainty 6.05 $ / M W h , r e p r e s e n t e d by the s t a n d a r d d e v i a t i o n . F o r the G S e r i e s , a s u p p l y price of $ 3 0 . 8 3 h a s a 5 0 % c h a n c e of m e e t i n g the M A R R , a n d a s t a n d a r d deviation of 5.66  $/MWh.  O v e r a l l , the e s t i m a t e s f r o m the e x p e r t s imply that the G S e r i e s g a s turbine w o u l d b e their p r e f e r r e d t e c h n o l o g y . T h i s is b e c a u s e the G S e r i e s h a d both a m e a n lower than that of  LCG Consulting.( 1996) Modeling Competitive Energy Market In California: Analysis of Restructuring, Prepared for the California Energy Commission, October 11, 1996. 3.16.  110  the F S e r i e s , a n d a lower d e g r e e of uncertainty a s s o c i a t e d with the t e c h n o l o g y , reflected in the lower s t a n d a r d d e v i a t i o n . A p p a r e n t l y , the e x p e r t s a r e l e s s c o n c e r n e d with the uncertainty of the t e c h n o l o g y than the uncertainty a s s o c i a t e d with the g a s p r i c e s . In a c o m p e t i t i v e w o r l d , o n c e the s y s t e m s a r e built, it is the i n c r e m e n t a l c o s t s w h i c h d e t e r m i n e the d i s p a t c h i n g order.  M o r e o v e r , the t e c h n o l o g y c o s t s a r e u n c e r t a i n o v e r the n e a r t e r m  h o r i z o n ( 3 y e a r s ) while the g a s c o s t s r e m a i n s for the l o n g e r t e r m of the life of the g a s turbine (19 y e a r s ) . A s o n e e x p e r t i n d i c a t e d , "we d o not w a n t to build it today, a n d find 6 2  o u r s e l v e s not c o m p e t i t i v e tomorrow". T h i s m i n d s e t i s a r e v e r s a l f r o m the traditional c o n s e r v a t i v e attitude of the industry.  S c e n a r i o s that limit the d e m a n d for the output of the turbine w e r e a l s o i n c o r p o r a t e d . T h e results indicate that a s the ability to sell the output b e c o m e s limited, the a d v a n t a g e s of the i n c r e a s e d e f f i c i e n c y of the G S e r i e s turbine d i m i n i s h e s , a n d the F S e r i e s with its lower capital c o s t s b e c o m e s m o r e a d v a n t a g e o u s . A g a i n , this is intuitively c o r r e c t a n d the industry typically u s e s a s i n g l e c y c l e turbine (low c o s t , low efficiency) for intermittent p e a k i n g o p e r a t i o n . T h e utilization or l o a d factor at w h i c h the m o v e f r o m the F to G S e r i e s will b e e c o n o m i c a l l y justified will a g a i n b e e x p e c t e d to d e p e n d o n the v i e w s r e g a r d i n g future m a r k e t uncertainties. T h e a p p r o a c h of this t h e s i s s h o u l d a l s o b e u s e f u l in defining this b r e a k - e v e n point.  T h i s w o r k d e m o n s t r a t e s that u s i n g the M o n t e C a r l o s i m u l a t i o n in c o n j u n c t i o n with a structured  m o d e l c o u l d b e a v a l u a b l e tool for the investor. It e l i m i n a t e s the n e e d for  b a s i n g a d e c i s i o n o n a s i n g l e optimistic, p e s s i m i s t i c or m e a n v a l u e . In o r d e r to m a k e a d e c i s i o n , the results p r o v i d e d by s i m u l a t i o n m u s t be interpreted in the c o n t e x t of individual project c o n d i t i o n s . T h e s a m e results c a n b e interpreted differently b a s e d o n e a c h project's s p e c i f i c c o n d i t i o n s , a n d c o u l d l e a d to different c o u r s e s of a c t i o n . T h i s is  3 y e a r s is t h e o v e r a l l m e a n o f t h e p r e - o p e r a t i o n p h a s e a n d 19 y e a r s is t h e o v e r a l l operation p h a s e  of the  three experts.  g a s turbine  111 n e c e s s a r y b e c a u s e in a n u n c e r t a i n e n v i r o n m e n t , jurisdictional, r e g i o n a l a n d m a r k e t d i f f e r e n c e s c a n h a v e significant i m p a c t o n the viability of a project.  7.2.  Recommendations for Future Work  T h e r e a r e at least t h r e e d i r e c t i o n s in w h i c h further to e x p a n d the r e s e a r c h :  1.  T o t a k e the m o d e l to the next s t e p , by d e t e r m i n i n g the likelihood of the m a r k e t providing the s u p p l y price of electricity elicited f r o m the e x p e r t s . T h a t is, to c o m p a r e the s u p p l y p r i c e distribution to the forward m a r k e t e x p e c t e d price, w h i c h will itself h a v e a distribution. T h i s distribution w a s o b s e r v e d to v a r y with current a n d e x p e c t e d short t e r m price, future s u p p l y / r e s o u r c e b a l a n c e , inflation rate a n d , of c o u r s e , g a s fuel c o s t . T h e p r o b l e m is that 6 3  d e m a n d m e e t s s u p p l y in a p r o g r e s s i v e m a n n e r . T h a t is, initially d e m a n d e x c e e d s s u p p l y for only a f e w h o u r s during p e a k p e r i o d s a n d e x p a n d s to c o v e r m o r e h o u r s of the y e a r a s d e m a n d g r o w s . T h i s will result in g r a d u a l i n c r e a s e o n the utilization factor of the g a s t u r b i n e s , a d d i n g a n o t h e r e l e m e n t of uncertainty to the a n a l y s i s .  2.  T h e s e c o n d direction is to d e v e l o p a m o d e l that will t a k e into a c c o u n t a r e a s that h a v e b e e n simplified in this work, s u c h a s ancillary s e r v i c e s a n d t r a n s m i s s i o n i s s u e s . T h e ancillary s e r v i c e s h a v e a potential for i n c r e a s i n g the i n c o m e , a n d the t r a n s m i s s i o n m a y h a v e the effect of i n c r e a s i n g the c o s t s . B o t h of t h e s e i s s u e s a r e l a d e n with uncertainties. A n o t h e r simplification in the d e v e l o p e d m o d e l w a s that the v a r i a b l e s w e r e treated a s i n d e p e n d e n t , h o w e v e r , s o m e of the v a r i a b l e s m a y b e d e p e n d e n t a n d s h o u l d b e s o treated in a m o r e e l a b o r a t e m o d e l .  Based on proprietary work of ZE PowerGroup Inc.  112 3.  T o d e v e l o p a n interactive g e n e r i c p r o g r a m that will m a k e it s i m p l e for a p l a n n e r to d e v e l o p a probabilistic c a s h flow m o d e l . 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P r e s s R e l e a s e - November 22, 1995.  http://www.ino.com/gen/nymex/press/gas.html N e w Y o r k M e r c a n t i l e E x c h a n g e (1995) Natural Gas Futures Trading Activity  Exceeds  90,000. P r e s s R e l e a s e - N o v e m b e r 2 2 , 1 9 9 5 . http://www.ino.com/gen/nymex/press/activity.html N e w Y o r k M e r c a n t i l e E x c h a n g e (1996) Permian Basin Natural Gas Futures and Options. http://www.inm/gen/nymex/contract/ O F O R (1995) OFOR  Symposium:  October 1994 - "Futures Markets in the 21st Century".  http://gopher.ag.uiuc.edu/ACE/ofor/symp94.html P l l a n a , S a b r i . ( 1 9 9 7 ) History o f M o n t e C a r l o M e t h o d . http://www.geocities.com/CollegePark/Quad/2435. T u r b i n e S y s t e m s E n g i n e e r i n g Inc. (1997) G a s T u r b i n e M a n u f a c t u r e r s a n d $ p e r k W . Http://www.gas-turbines.com/TRADER/Manprice.htm Utilicorp ( 1 9 9 6 ) G O P Policy Makers Encourage to Speed up Electric Deregt7/af/o/7.http://www.utilicorp.com/ V a r n h o l t , B . ( 1 9 9 5 ) Six Recent Reports on Financial Derivatives:  A Critical Appraisal.  http://wwwfinance.wat.ch/genevapapers/paper2.html W e s t e r n S y s t e m s C o o r d i n a t i n g C o u n c i l (1996) Brief History of WSCC. http://www.wscc.com/history.html W o r l d B a n k , Gas Turbine Design r)ttp://gopher.worldbank.org/html/fpd/em/eminfo/EA/projdef/thrmtech/gascsubs.htm.  3-4  120  Appendix 1: Sample Copy of Excel Model Spreadsheet, Spreadsheet Formulas and Excel Macro  Planning in an Uncertain Environment Program Expert # 3: F Series Gas Turbine 248,267  Capacity  6,287  Heat Rate  kW Btu/kWh  0.12  MARR  Pre Operation Project Gas  Millions  Costs  0  4.22 years  2.22  2 years  14.4$  $  14 $  11.30  248,267 MW  $  183 $  124.85  23.70 kWh  $  162 $  32.41  $  762 $  152.15  $  321  248,267 kW  Turbine  Costs  Fuel Costs Net Present  738.32 $/kW Million/Y 6.85 ear $/mmBt  23.7 years  O&M Cosfs  Environmental  $  23.7 years  $  2.35 u  324.06 Btu  Costs Value  121  Capacity  kW  Heat Rata MARR  *{RiskBeta(1.3,0.72)' 750 * 75+* 6000 200)'1000 *RI$kB»ta(1.11.1.73) 0.12  Btu/kWh  Project Costs  0  =RtskLognorm(4.22.0.45) years  =RiskLognorm(14.4,2.23)  Millions $  =Capacity  kW  =E7-2  2  =RiskBeta(1.55,1.96) * 200 + 650  S/kW  =Capacity  MW  Million/Year  =E9  =G38  =PVuniform(K7,E38,MARR)  =G39"IS/10"6  =PVunlform(K8.E39,MARR) EXP(-MARR-C39)  WVh  =I9-G40  =PVimiform(K9,E41 ,MARR) EXP(-MARR E38)  Btu  -G41*I10  Gas Turbine Costs  years  O & M Costs  =RjskLognorm(23.7,3.38) years  =RiskBeta{1.3,2.21) * 5 * 5  Fuel Costs  =E9  =(RiskBeta(0.56,1.13) + 1.9)*1054.35/10 : $/mmBtu  years  A  =Cap«ctty*Heat  Rate-365-24-£9/l0*6/10*6  -  -  -  Not Present Value  =SUM(L7:L10,  '-  nnm  -  =PVuretom(K10,E41.MARR)'EXP(-MARR E38)  .  -E9  '  ;'-."!  :  years  11.17722B73I0IBS  ;  ""  _*'•'  s/MWIi  •  -  >mw*p«*y-E4WUO0  122  -.  MWI. |=G36M3S/10«6  ,••>*-'?>".  •  .  =(-4K36 / E40)" ((1 - EXP(-1 * MARR * E40)) / -MARR)"EXP(-MARR"E38)} - L42  -•  _•  123 'Excel Model ' P l a n n i n g i n an U n c e r t a i n  Environment  ' C a l c u l a t e t h e PV f o r U n i f o r m  Payment  F u n c t i o n P V u n i f o r m ( C o s t p h a s e , Tphase, r ) P V u n i f o r m = - ( C o s t p h a s e / Tphase) * ((1 - Exp(-1 End F u n c t i o n  * r * Tphase)) / -r)  S o l v e r Macro M a c r o r e c o r d e d 5/20/97 b y M a n a l Sub  Solver() Range("7:10,12:12").Select Range("A12").Activate Selection.Copy A c t i v e W i n d o w . S m a l l S c r o l l Down:=6 Range("A38").Select S e l e c t i o n . P a s t e S p e c i a l Paste:=xlValues, Operation:=xlNone, _ SkipBlanks:=False, Transpose:=False Sheets("Model").Select Rows("36:36").Select Selection.Copy Rows("44:44").Select S e l e c t i o n . P a s t e S p e c i a l Paste:=xlValues, Operation:=xlNone, SkipBlanks:=False, Transpose:=False Range("C3:C4").Select Application.CutCopyMode = False Selection.Copy Range("F3").Select S e l e c t i o n . P a s t e S p e c i a l Paste:=xlValues, Operation:=xlNone, _ SkipBlanks:=False, Transpose:=False SendKeys ("~") A p p l i c a t i o n . E x e c u t e E x c e l 4 M a c r o String:="'[SOLVER.XLA]SOLVER'!SOLVER.RESET()" Application.ExecuteExcel4Macro String:="" Application.ExecuteExcel4Macro String:="'[SOLVER.XLA]SOLVER'!SOLVER.OK(!R36C12,3,0,([Exp3 fd.xls]ModelIR36C7))" A p p l i c a t i o n . E x e c u t e E x c e l 4 M a c r o S t r i n g : = ' " [SOLVER.XLA]SOLVER' !SOLVER.SOLVE()" End Sub  124  Appendix 2. Expert Debriefer and Elicitation of Subjective Probabilities Questionnaire  125  Planning in an Uncertain Environment: A Case Study of Gas Turbine Technology Briefer for Expert Witnesses The Problem  Electric industry is in a time of rapid change and it is moving from a regulated monopolistic environment with a guaranteed rate of return to a risk competitive market with many players. This change is introducing uncertainty with respect to supply, demand, pricing, input fuel costs, and technology. The concept of risk comes about due to the recognition of future uncertainty, and it implies that a given action has more than one possible outcome, of which anyone may be injury or loss. I am developing a framework for decision making in an uncertain environment. This will allow engineers to use risk management and analysis techniques. Risk analysis is a method - qualitative and/or quantitative - for assessing the impacts of risks on decisions. The methodology of the framework is to model a probabilistic cash flow. Uncertain variables in the model are identified and assigned probabilistic distributions. A Monte Carlo simulation which generates random numbers drawn from these probability distributions, for each of the variables, is run. The result is a probability distribution which gives the decision maker a complete picture of all possible outcomes and their probabilities. The objective of the model is to determine the supply price of electricity to make the minimum attractive rate of return (MARR) on a gas turbine projects. Specifically to compare two technologies, 'F' and 'G' series gas turbine to determine which technology to implement based on the technology risk profile which best fits the organizations' appetite for risk. 13/10/97 Manal El-Ramly, MASc. UBC  126 Model  The model is a Probabilistic Cash Flow Model. Figure 1 is an illustration of the Deterministic Cash Flow Model. The uncertain variables in the model that will be represented with probabilistic distributions are the following: •  Capacity  •  Heat Rate  •  Pre-Operation Project Costs  •  Combined Cycle Capital Costs  •  O&M Costs  •  Fuel Costs  The methodology of the framework is to assign probabilistic distributions for each of the above cost and duration variables. A Monte Carlo simulation is then run, which generates random numbers for each of the variables. Figure 2 is attached to illustrate the Monte Carlo Simulation Process for Modeling Uncertainties. The following are the manufacturers specifications for the Gas Turbine 'F' Series and 'G' Series models: F Series  G Series  Model  PG 7231 FA  PG 7001 G  Capacity  253,500 kW  350,000 kW  Heat Rate  6,160 Btu/kWh  5,883 Btu/kWh  Efficiency  55.4%  58%  The pre-operation costs and the combined cycle costs may be combined into one phase if it is easier for the expert to express their beliefs on the values. The units may be in millions of dollars, or $/kw. The O&M costs represent yearly 13/10/97 Manal El-Ramly, MASc. UBC  127  operation & maintenance costs during the life expectancy of the facility. The units may be expressed in millions of dollars/year or dollars/kW. The fuel costs are the expected cost of providing the facility with fuel. This may be expressed in $/mmBtu. Subjective Elicitation The input variables for the model are associated with a large degree of uncertainty, particularly due to the changes evolving in the industry. There is no historical data available, as such, obtaining uncertainties cannot be done through the use of classical statistical techniques. Therefore; the use of expert judgments is needed to adequately characterize and deal with this uncertainty. Experts will be used to elicit subjective judgments in the form of probability distributions to clarify the variables (listed above) in the model to get an explicit treatment of uncertainty. The process for structuring expert judgments for representing uncertainties is conducted in a 5 phase process : These stages include: Motivating, Structuring, 1  Conditioning, Elicitation, and finally Verification.' Pre-elicitation The Pre-elicitation stage involved in using expert judgments effectively include: motivating, structuring, conditioning,. Motivating:  Motivating is the process involved in finding out the biases of the experts. They might have motivational reasons to give estimates that do not reflect their true beliefs. Additionally the analysis establishes a rapport with the expert and introduces the expert to the decision problem, the decision model, and the  1  C a r l S . S p e t z l e r a n d C a r l - A x e l S S t a e l v o n H o l s t e i n , "Probability E n c o d i n g in D e c i s i o n  Management  Science,  V o l . 2 2 , N o . 3 ( N o v e m b e r 1975): 3 4 0 - 3 5 8 .  13/10/97  Manal El-Ramly, MASc. UBC  Analysis,"  128  uncertain factors which affect the analysis. The analysis introduces the elicitation process, and the process of assessing uncertainty. Analysis points out the difference between deterministic and probabilistic prediction and that there is no commitment (firm projection) in a probability distribution  2  Structuring:  The second step is structuring; the objective of this stage is to structure the uncertain variables and determine how the expert thinks about it. It is necessary to define the variables, and to elicit all assumptions the expert may be applying to the variable. This stage is necessary to explain the relevance between the variables, the model and the necessity for the expert's cooperation. Conditioning  The third step is conditioning. The aim of this phase is to condition the expert to think fundamentally about his judgments and to avoid cognitive biases.  3  Elicitation (Encoding)  After the completion of the pre-elicitation stage the expert should be ready to quantify his belief about the uncertain variables. There are no right or wrong answers in eliciting value judgments and uncertainties. This step is encoding the data. This will be through the use of a cumulative distribution function (cdf) to get a visual representation of the experts' opinion. This involves asking for extreme levels from the expert; then working around the outside. This is to prevent anchoring of an answer, or basing your answers on previous answers, which narrows the band of solutions.  2  Ranasinghe and Russell  1993  3  Spetzler a n d Stael von Holstein,  1975  13/10/97 Manal El-Ramly, MASc. UBC  129 Verification The final step is verification of the data. This involves moving along all points of the cdf to find inconsistencies. Any inconsistencies should be discussed, and try to elicit the right answer  Psychological Aspects of Elicitation There are problems inherent in eliciting values from experts. The objective of this section is to bring to light some of the problems associated with elicitation, because if the experts are aware of them, they can be conscious not to make these common mistakes. First of all it is easy for the interviewer to anchor or steer the individual to a figure; basing your answers on previous answers, narrowing the band of solutions. To overcome the effects of heuristics of anchoring and adjustment the 4  elicitation is begun by establishing maximum and minimum credible values. The process is slightly iterative, and the interview must insure they are not molding an answer with each iteration. There are many types of biases which can make the encoded probability distributions inadequate representation of the subject's state of knowledge. These include motivational biases and cognitive biases. Motivational biases may arise when the encoded probabilities do not reflect the expert's conscious belief's. This may be due to the desire for reward or fear of punishment, which can be economic, psychological, or physical . 5  It is important to be in comfortable surroundings. Therefore for our meeting it is necessary to be in the expert's environment where the expert has easy access to the information that they may require.  4  A T v e r s k y a n d D K a h n e m a n , " J u d g m e n t U n d e r Uncertainty: H e u r i s t i c s a n d B i a s e s , "  185, 1 1 2 4 - 1 1 3 1 (1974)  13/10/97 Manal El-Ramly, MASc. U B C  Science  130  Bibliography: Louis Y. Pouliquen Risk Analysis in Project Appraisal, Published for the World Bank by the John Hopkins University Press Baltimore and London, World Bank Staff Occasional Papers Number 11, 1970 McNamee & Celona Probability Elicitation "Encoding a Probability Distribution" (Chapter 8 from the book) M. Granger Morgan and Max Henrion Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis Construction Management and Economics (1993) 11, 326-340 "Elicitation of subjective probabilities for economic risk analysis: An investigation" Malik Ranasinghe and Alan D. Russell, Peter Spinney and G Campbell Watkings, "Monte Carlo simulation techniques and electric utility resource decisions" Energy Policy, Vol. 24, No 2. Pp 155163, 1996. Copyright 1996 Elsevier Sciences Ltd. Printed in Great Britain M. Granger Morgan, Samuel C. Morris, Max Henrion, Deborah A. L. Amaral, and William R. Rish, "Technical Uncertainty in Quantitative Policy Analysis - A Sulfur Air Pollution Example" Risk Analysis, Vol 4, No. 3, 1984 201-215 Chelsey, G.R. (1975). Elicitation of Subjective Probabilities: a review, the Accounting Review, 50, 325-327 Seaver, D.A., Von Winterfeldt, D. and Edwards, W. (1978) Eliciting Subjective probability distributions on continuous variables, Organizational Behavior and Human Performance, 21, 379-91  5  M c N a m e e and Celona,  199?  13/10/97 Manal El-Ramly, MASc. UBC  131  Elicitation of Subjective Probabilities Questionnaire Questionnaire: The Questionnaire is here is as described in Elicitation of Subjective Probabilities: An Investigation, by Malik Ranasinghe and Alan D. Russell.  Question  1: W h a t in y o u r o p i n i o n is the s h o r t e s t p o s s i b l e duration to c o n s t r u c t the g a s turbine for  w h i c h the probability is s o s m a l l a s to e q u a l z e r o for practical p u r p o s e s ? ( V a l u e A )  Comment:  T h e pre-elicitation s t a g e s h o u l d clarify the t e r m s u s e d in the q u e s t i o n a n d e x p l a i n e d  the r a n g e of s c e n a r i o s the e x p e r t s s h o u l d c o n s i d e r in their quantification of j u d g m e n t s .  Question 2: Comment:  S o , A is in y o u r o p i n i o n the s h o r t e s t p o s s i b l e duration, is that c o r r e c t ?  A c h e c k to clarify the e x p e r t ' s thinking a b o u t the lower tail v a l u e of the distribution.  Question 3:  If A in y o u r o p i n i o n h a s a z e r o probability of not e x c e e d i n g the a c t u a l d u r a t i o n , w h a t  is the duration w h i c h w o u l d not e x c e e d a probability of 0 . 0 5 ? ( V a l u e C )  Comment:  H a v i n g e s t a b l i s h e d the point for z e r o probability the e x p e r t s h o u l d b e a b l e to give a  v a l u e for the fifth p e r c e n t i l e . T h i s v a l u e w o u l d b e a n c h o r e d to that of z e r o probability. H o w e v e r , the a n c h o r i n g is the result of f o r c i n g the e x p e r t to think of e x t r e m e o u t c o m e s to c o u n t e r a c t central bias.  Question 4:  S o , you associate a  1  in 2 0 c h a n c e that the a c t u a l duration will b e l e s s than C . Is that  correct?  Comment:  H e r e , o d d s a r e u s e d to c h e c k the c o n s i s t e n c y of the elicited fifth p e r c e n t i l e . T h i s is  helpful to verify the e x p e r t ' s thinking. If the e x p e r t c o n f i r m s his e s t i m a t e , g o to Q u e s t i o n 6, if not Q u e s t i o n 5.  Question 5:  If not, w h a t is the v a l u e for the a c t u a l duration that y o u c o n s i d e r to h a v e a 1 in 2 0  c h a n c e of not b e i n g e x c e e d e d ?  Comment:  A f o l l o w - u p q u e s t i o n to the c o n s i s t e n c y c h e c k a t t e m p t e d in Q u e s t i o n 4.  13/10/97  Manal El-Ramly, MASc. UBC  132 Question 6: W h a t in y o u r o p i n i o n is t h e l o n g e s t p o s s i b l e duration to c o n s t r u c t a g a s turbine for w h i c h t h e probability is s o large a s to b e e q u a l to o n e for practical p u r p o s e s ? ( V a l u e Z ) Comment: G o i n g f r o m o n e e x t r e m e to t h e o t h e r i n c r e a s e s t h e r a n g e a n d w o u l d r e d u c e e v e n m o r e the p o s s i b l e e f f e c t s o f c e n t r a l b i a s that m a y o c c u r w h e n the 2 5  t h  and 75  t h  p e r c e n t i l e s a r e elicited  after t h e m e d i a n v a l u e . Question 7: S o , Z is in y o u r o p i n i o n t h e l o n g e s t p o s s i b l e duration, is that c o r r e c t ? Comment: A c h e c k to clarify t h e e x p e r t ' s thinking a b o u t t h e u p p e r v a l u e o f t h e distribution. Question 8: If Z in y o u r o p i n i o n h a s a unit probability o f not e x c e e d i n g t h e a c t u a l duration, w h a t is the duration w h i c h w o u l d not e x c e e d a probability o f 0 . 9 5 ? ( V a l u e X )  Comment: S a m e a s for Q u e s t i o n 3 Question 9 : S o , y o u a s s o c i a t e 1 in 2 0 c h a n c e that t h e a c t u a l duration will b e a b o v e X . Is that correct? Comment: A g a i n , o d d s a r e u s e d to c h e c k t h e c o n s i s t e n c y o f the elicited 9 5  t h  p e r c e n t i l e . If t h e  e x p e r t c o n f i r m s h i s e s t i m a t e , g o to Q u e s t i o n 11, if not, a s k Q u e s t i o n 10. Question 10: If not, w h a t is t h e v a l u e for the a c t u a l duration that y o u c o n s i d e r to h a v e a 1 in 2 0 c h a n c e of being e x c e e d e d ?  Comment: A follow-up q u e s t i o n to 9. Question 11: W h a t in y o u r o p i n i o n is t h e v a l u e for a c t u a l duration s u c h that it is e q u a l l y likely to b e a b o v e a s it is to b e b e l o w ? ( V a l u e M ) Comment: T h i s q u e s t i o n w o u l d elicit t h e m e d i a n v a l u e o f the e x p e r t ' s s u b j e c t i v e probability distribution f o r duration to c o n s t r u c t a g a s turbine. Question 12: S o , y o u a r e willing to bet e q u a l o d d s that the a c t u a l duration is either a b o v e o r b e l o w M , is that c o r r e c t ? Comment: A c h e c k to clarify t h e e x p e r t ' s r e s p o n s e to t h e m e d i a n . Question 13: W h a t is t h e v a l u e for duration that y o u feel will divide t h e r e g i o n b e l o w M , t h u s it is just a s likely that duration will fall b e l o w this v a l u e a s it will b e b e t w e e n this v a l u e a n d M ? ( V a l u e L) Comment: T h e e x p e r t is a s k e d to b i s e c t t h e a r e a b e l o w the m e d i a n to g i v e a n e s t i m a t e f o r his 2 5 percentile v a l u e . Question 14: S o , y o u a s s o c i a t e a 1 in 4 c h a n c e that t h e a c t u a l duration will b e b e l o w L, is that correct?  13/10/97  Manal El-Ramly, MASc. UBC  t h  133 Comment: A c o n s i s t e n c y c h e c k to clarify that t h e e x p e r t is thinking a b o u t t h e 2 5  p e r c e n t i l e with  the b i s e c t e d v a l u e . If t h e e x p e r t c o n f i r m s his e s t i m a t e , g o to Q u e s t i o n 16, if not a s k Q u e s t i o n 15. Question 15: If not, w h a t is t h e v a l u e for the a c t u a l duration that y o u c o n s i d e r to h a v e a 1 in 4 c h a n c e of not b e i n g e x c e e d e d ?  Comment: A follow-up q u e s t i o n to 14. Question 16: N o w , c o n c e n t r a t e o n t h e c a s e w h e r e t h e duration c o u l d b e a b o v e M , w h i c h y o u felt w o u l d b e 5 0 % o f the time. W h a t is t h e v a l u e that y o u feel will divide t h e r e g i o n a b o v e M e q u a l l y , t h u s it is just a s likely that duration will b e a b o v e this v a l u e a s it will b e b e t w e e n this v a l u e a n d M ? (Value N). Comment: T h e e x p e r t is a s k e d to b i s e c t t h e a r e a a b o v e t h e m e d i a n to g i v e a n e s t i m a t e f o r his 75  t h  p e r c e n t i l e v a l u e . In a d d i t i o n , t h e e x p e r t is r e m i n d e d of his e s t i m a t e for t h e m e d i a n . T h i s g i v e s  him a further opportunity to c h a n g e o r c o n f i r m his e s t i m a t e for the m e d i a n . T h i s g i v e s h i m a further opportunity to c h a n g e o r c o n f i r m his e s t i m a t e for the m e d i a n , n o w that h e h a s g i v e n a n e s t i m a t e for the 2 5  t h  percentile.  Question 17: S o , y o u a s s o c i a t e a 1 in 4 c h a n c e that t h e a c t u a l duration will b e a b o v e N , is that correct? Comment: A c h e c k to clarify that the e x p e r t is thinking a b o u t t h e 7 5  t h  p e r c e n t i l e with t h e b i s e c t e d  v a l u e . If the e x p e r t c o n f i r m s his e s t i m a t e , s t o p t h e interview. If not a s k Q u e s t i o n 18. Question 18: If not, w h a t is t h e v a l u e for the a c t u a l duration that y o u c o n s i d e r to h a v e a 1 in 4 c h a n c e of being e x c e e d e d ?  Comment: A follow up to q u e s t i o n 17.  13/10/97  Manal El-Ramly, MASc. UBC  

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