ECONOMIC VALUATION OF MULTI- UNIT NUCLEAR PLANT PROGRAMS BASED ON REAL OPTIONS ANALYSIS. 32nd IAEE International Conference Arturo G.

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1 ECONOMIC VALUATION OF MULTI- UNIT NUCLEAR PLANT PROGRAMS BASED ON REAL OPTIONS ANALYSIS 32nd IAEE International Conference Arturo G. Reinking

2 Real Options Analysis What results does ROA deliver when evaluating a program instead evaluating a single plant or a sequence of units one by one? ROA vs Net present value: ROA can value the flexibility

3 Real Options Analysis 1. Identify sources of uncertainty 2. Recognize the flexibility embedded in a project 3. Anticipate relevant future decisions to respond to uncertainty 4. Develop a computational model aimed at estimating an expanded NPV reflecting the value of optimal decisions related to flexibilities available to management. Exp NPV = Conv NPV + Project Flexibility Value

4 Real Options Analysis Anticipated responses open to management when uncertainties evolve, are modeled in such a way that responses are only adopted if beneficial to the project, in the same way as financial options are rationally exercised only when profitable: the holder of such options has the right, but not the obligation, to exercise them.

5 Real Options Analysis The goal is evaluating a possible agreement with a contractor/vendor: allows four units to be ordered within fixed expiration dates, at progressively lower investment costs, both because repeated equipment orders, as well as by learning to build identical units more efficiently.

6 Computational model Simulates the rational behaviour of the utility management For a long term agreement/partnership with a contractor/vendor, Win/win for both utility and contractor/vendor

7 Computational model Beneficial to contractor/vendor possibility that its order books would be more likely to be filled up, utility is basically adopting a given reactor technology, so changing to some other would not be straightforward. Utility would expect to benefit from the contractor/vendor learning to build identical plants more efficiently repeated equipment and component manufacturing that would be reflected in lower costs for repeated units.

8 Computational model Agreement: commitment of a first unit at a given investment cost the right, but not the obligation, to order additional identical units at progressively lower costs within a given time frame. decide whether to place additional orders, that is, exercising the options as new information on the competitiveness of new unit operation becomes available.

9 Computational model Such agreement is similar to a very common transaction in the world of financial options: Contractor/vendor selling a call Utility buying such call

10 Computational model Binomial expansion Underlying value of the project depends on the parameter that the utility management doesn t control, but to which it can respond: electricity rates, as determined by alternate competing technologies.

11 Computational model The real option involved is a compound option: the value of an option depends on the value of a later option: model must consider both the incremental cash flow that exercising an option would generate, plus the values of the future real options that would be involved when exercising them later on.

12 Computational model Binomial expansion The call value of such compound option is reached by the Cox, Ross and Rubinstein valuation algorithm

13 Computational model Binomial expansion The foldback parameters involved in the Cox, Ross and Rubinstein call valuation scheme evolve as the valuation proceeds backwards from the lattice points in the future towards those closer to the present, and must be adjusted, in particular the risk neutral probability.

14 Results Base unit parameters PARAMETER VALUE UNITS Unit capacity 1,000 Mwe Overnight stand alone investment cost 3,250 Millions of US$ Capacity factor 90 % Operation, maintenance and fuel costs 20 US$/MWh Competing electricity price 55 US$/MWh Discount rate 12 % per year

15 Results ROA parameters PARAMETER VALUE UNITS Risk free rate 4 % per year Standard deviation of electricity price 4.5 % per year Length of binomial steps 4 years Number of steps in binomial lattice expansion 4 steps

16 Sample case Adopted: F1 = 0.1 and F2 = F3 = F4 = That is, the investment cost of the first unit of the program would be 10% higher than the investment of the stand alone unit,. The 2nd unit would be 5% lower than the $3,250 million. The 3rd unit s investment cost would be 5% lower than that of the 2nd unit and the 4th also 5% lower than the 3rd.

17 Sample case Binomial lattice $2,214 $4,074 $4,399 $3,046 $492 $1,374 $1,234 $0 $0 $0

18 Baseline for the sample case The exercise must be run again: this time for a sequence four stand alone units outside the possible utilility contractor/vendor agreement.

19 Sample case Baseline results $1,914 $3,250 $3,686 $2,583 $0 $691 $771 $0 $0 $0

20 Sample case Binomial lattice $2,214 $4,074 $4,399 $3,046 $492 $1,374 $1,234 $0 $0 $0

21 Sample case The difference of both ENPV: 2,214 M$ - 1, 914 M$ = 300 M$ would be the utility s flexibility quantification of the agreement with the contractor/vendor.

22 Sample case Matching premiums with flexibility values: For this exercise the premium = 10% of overnight investment cost of a standalone unit is 325 M$, is higher than the flexibility value! (300M$) The utility would reject such an agreement. (Such premium would have to amount to % of investment cost of a standalone unit in order to match the flexibility value of 312 M$).

23 THANK YOU