1. 1 Systems Analysis Advisory Committee (SAAC) Friday, February 7, 2003 Michael Schilmoeller John Fazio

2. Northwest Power Planning Council 2 Agenda Approval of the Dec 19 meeting minutes Review and questions from previous meetings –The Portfolio Model –Using the Portfolio Model and Other Council Tools to make Decisions –Metrics –Representations: Dispatchable plants (Beaver) –Price responsive demand –Renewables and conservation –Hydro –Loads –Natural gas prices –Representation of Transmission Congestion –Representation of Distributed Generation (to return) –Influence Diagram of Effects –Some statistical Results for Natural gas prices, electricity prices, load, temperature, aluminum prices, hydro, transmission congestion; Correlations among these

3. Northwest Power Planning Council 3 Agenda Review of Futures Uncertainty Representation of Planning Flexibility Representation of Aluminum Industry –Value of curtailment More Fun with Statistics! Lessons Learned from 2000-2001 Progress on Olivia

4. Northwest Power Planning Council 4 Agenda Approval of the Dec 19 meeting minutes Review and questions from the last meeting Review of Futures Uncertainty Representation of Planning Flexibility Representation of Aluminum Industry –Value of curtailment More Fun with Statistics! Lessons Learned from 2000-2001 Progress on Olivia

5. Northwest Power Planning Council 5 Agenda Approval of the Dec 19 meeting minutes Review and questions from the last meeting Review of Futures Uncertainty Representation of Planning Flexibility Representation of Aluminum Industry –Value of curtailment More Fun with Statistics! Lessons Learned from 2000-2001 Progress on Olivia

6. Northwest Power Planning Council 6 Agenda Approval of the Dec 19 meeting minutes Review and questions from the last meeting Review of Futures Uncertainty Representation of Planning Flexibility Representation of Aluminum Industry –Value of curtailment More Fun with Statistics! Lessons Learned from 2000-2001 Progress on Olivia

7. Northwest Power Planning Council 7 Represenation of Futures The purpose of this discussion is.... –Discuss how futures and their associated uncertainties are incorporated in the portfolio model –Review the mathematics –Clarify how the portfolio representation is related to decision-making –Distinguish between short-term variations and long-term uncertainties Futures Uncertainty

8. Northwest Power Planning Council 8 Some Terms Futures: ensemble of one or more sets of parameters over which the decision maker has no control: load growth, fuel price, electricity price, etc. Plans: ensemble of one or more sets of parameters over which the decision maker has control: technology choice, expansion schedules, etc. Scenario: A combination of one future and one plan Futures Uncertainty

9. Northwest Power Planning Council 9 The 40,000 Foot View correlations and volatilities decision variables interest rate, hours per period chronological structure of uncertainty conservation assumptions conservation calculations Futures Uncertainty

10. Northwest Power Planning Council 10 The 40,000 Foot View input calculation on-peakoff-peak random variables Futures Uncertainty

11. Northwest Power Planning Council 11 The 40,000 Foot View annual and study cost calculations and metrics Futures Uncertainty

12. Northwest Power Planning Council 12 The 40,000 Foot View correlations and volatilities chronological structure of uncertainty random variables Futures Uncertainty

13. Northwest Power Planning Council 13 Price Processes Futures Uncertainty

14. Northwest Power Planning Council 14 Price Processes Futures Uncertainty

15. Northwest Power Planning Council 15 Price Processes Futures Uncertainty

16. Northwest Power Planning Council 16 Price Processes Prices strongly correlated, one month to the next. Size of correlation matrix grows as n 2 As commodities, locations, and periods grow, so does the length n of the vector We have 64 variables (commodities and locations), not counting a potential for 61 additional load variables. To represent 60 months (or more) of future behavior results in a large correlation matrix, ({64+60}*60) 2 Futures Uncertainty

17. Northwest Power Planning Council 17 Zzzzzzzzzzzz Trick: Use principle factors We can typically capture 95%+ of correlation structure with the largest eigenvalues Futures Uncertainty

18. Northwest Power Planning Council 18 Zzzzzzzzzzzz Futures Uncertainty

19. Northwest Power Planning Council 19 Zzzzzzzzzzzz We can simulate random vectors with the correct volatilities and correlation structure using Efficiencies arise when m<<k Futures Uncertainty

20. Northwest Power Planning Council 20 Zzzzzzzzzzzz For a data vector representing a specific parameter at a specific location, the vectors associated with the largest eigenvalues often describe simple, typical changes to the data sequence over time. Futures Uncertainty

21. Northwest Power Planning Council 21 Zzzzzzzzzzzz Futures Uncertainty

22. Northwest Power Planning Council 22 Zzzzzzzzzzzz More elaborate contributions to future values are easy to incorporate Futures Uncertainty

23. Northwest Power Planning Council 23 Zzzzzzzzzzzz Example from the portfolio model Portfolio_24_with_Uncertainty_Graph.xls Futures Uncertainty

24. Northwest Power Planning Council 24 Time to Wake Up! We use the combined principle factors to help us represent different future scenarios The principle factor approach works well for discontinuities, but We may want to take a more parsimonious approach, such as making frequency and duration of jumps a random variable Futures Uncertainty

25. Northwest Power Planning Council 25 Important Decision makers will place greater value on contingency options that are likely to be needed. The probabilities we are assigning to “futures” are the likelihood of their occurrence. We are performing risk constrained least cost planning. Least-cost options for reducing risks to acceptable levels will be selected, even if they are not generally least cost. Otherwise, “robust” plans that make the greatest expected contribution to reducing costs will be selected. Futures Uncertainty

26. Northwest Power Planning Council 26 Important Although the principle factors are used to synthesize futures, we do not represent –Typical variations within periods or subperiods –Short-term correlations among parameters with periods and subperiods We represent these with period- and subperiod-specific volatilities and correlations. Futures Uncertainty

27. Northwest Power Planning Council 27 Important If longer-term periodicity in prices are present, our statistical techniques should detect them. In any case, we can include those in the simulations, if appropriate. Futures Uncertainty

28. Northwest Power Planning Council 28 Short-term Variations, Long-term Uncertainties correlations and volatilities decision variables interest rate, hours per period chronological structure of uncertainty conservation assumptions conservation calculations Futures Uncertainty

29. Northwest Power Planning Council 29 Agenda Approval of the Dec 19 meeting minutes Review and questions from the last meeting Review of Futures Uncertainty Representation of Planning Flexibility Representation of Aluminum Industry –Value of curtailment More Fun with Statistics! Lessons Learned from 2000-2001 Progress on Olivia

30. Northwest Power Planning Council 30 Representation of Planning Flexibility The purpose of this discussion is.... –Describe how we intend to model planning flexibility in the model –Discuss how planning flexibility creates value –Review the criterion used to signal resource investment –Get your ideas about improvements Planning Flexibility

31. Northwest Power Planning Council 31 Some Terms Planning Flexibility: The value imparted by some plans or resources by virtue of their flexibility with respect to being delayed or cancelled inexpensively. We discussed this before in the context of “real options.” Planning flexibility may also refer to modularity, or the ability to add small increments of capacity inexpensively. We focus of the former aspect today. Planning Flexibility

32. Northwest Power Planning Council 32 From the November 22 SAAC Planning Flexibility

33. Northwest Power Planning Council 33 New Portfolio Worksheet Function The function takes –Number of periods for project planning, for “optional” construction, and for committed construction; real levelized period costs for planning, construction, mothballing and cancellation; ramp rate of annual additions (number of units); and “return type” –Range of criteria (think prices) over periods in the study Planning Flexibility

34. Northwest Power Planning Council 34 New Portfolio Worksheet Function The function returns –A single column-range (or row- range) with total availability (units) in each period and summary of study costs in the last few entries, or –a block range with a report of decisions by vintage by period, or –a block range with a report of costs by vintage by period Planning Flexibility

35. Northwest Power Planning Council 35 New Portfolio Worksheet Function Cohort model Period-specific costs and rules User-specified criterion function indicates when construction is attractive to investors, how much foreknowledge they have Planning Flexibility

36. Northwest Power Planning Council 36 New Portfolio Worksheet Function Planning Flexibility

37. Northwest Power Planning Council 37 New Portfolio Worksheet Function Planning Flexibility

38. Northwest Power Planning Council 38 New Portfolio Worksheet Function Planning Flexibility

39. Northwest Power Planning Council 39 New Portfolio Worksheet Function Criterion function can be anything –I am relating it to an average over the last 18 month, plus six months in the future (“myopic” perfect foresight). Link to workbook with illustration of the planning function..\..\Portfolio Work\Modularity and Real Options\Modularity_01.xls Planning Flexibility

40. Northwest Power Planning Council 40 Agenda Approval of the Dec 19 meeting minutes Review and questions from the last meeting Review of Futures Uncertainty Representation of Planning Flexibility Representation of Aluminum Industry –Value of curtailment More Fun with Statistics! Lessons Learned from 2000-2001 Progress on Olivia

41. Northwest Power Planning Council 41 Representation of Load Curtailment The purpose of this discussion is.... –Describe how we intend to model the aluminum industry in the model –Discuss how voluntary curtailment of DSI load creates value –Review the criterion used to signal smelter shutdown and restart –Draw analogy with holding a put option on the aluminum-electricity price spread –Get your ideas about improvements Aluminum Industry

42. Northwest Power Planning Council 42 From the November 22 SAAC minimum shut-down period evalulation phase time wholesale electricity market aluminum-elec price spread expected price trend minimum restart period evalulation phase Aluminum Industry

43. Northwest Power Planning Council 43 DSI Loads Terry Morlan’s model Inspired by Robin Adams, Resource Strategies, CRU Group Aluminum Industry

44. Northwest Power Planning Council 44 DSI Loads Compute break-even price for each of nine PNW aluminum plants Assume plant will leave the system if the spread between aluminum prices and electricity cost component gets too small Examine the impact of 100 MW allocation of BPA power at various prices Aluminum Industry

45. Northwest Power Planning Council 45 DSI Loads Viable Smelter Loads 0 500 1000 1500 2000 2500 3000 3500 1100115012001250130013501400145015001550160016501700 Electricity Use Aluminum Price 20 25 28 30 32 35 40 $/Mw Aluminum Industry

46. Northwest Power Planning Council 46 The Rest of the Story During the crisis of 2000-2001, BPA found that they could make more money by having the DSIs leave the system and sharing the resold energy revenues with the DSIs. What are the economics here? Aluminum Industry

47. Northwest Power Planning Council 47 The Smelter’s Side At $37.88/MWh smelter rate Aluminum Industry

48. Northwest Power Planning Council 48 The Smelter’s Side So the smelter will shut down if offered Aluminum Industry

49. Northwest Power Planning Council 49 The Smelter’s Side If labor/other are considered “variable,” Aluminum Industry

50. Northwest Power Planning Council 50 BPA’s Side Why would BPA do this? Because they can make more than the lost DSI revenue and the payment to the DSI by reselling the DSI’s power the wholesale power market Aluminum Industry

51. Northwest Power Planning Council 51 Load Buy-back One interesting -- and for our purposes, useful – thing to note is that the assessment of whether it is beneficial for the smelter and BPA to enter into such an agreement does not depend on the smelter’s retail power rate! If rates are lower, BPA’s lost revenues are lower, but smelter’s profits and buyout price are higher. Aluminum Industry

52. Northwest Power Planning Council 52 Load Buy-back At $30/MWh rate: Aluminum Industry

53. Northwest Power Planning Council 53 Load Buy-back At $30/MWh rate: Aluminum Industry

54. Northwest Power Planning Council 54 Value of DSI Load For fixed aluminum price and rates, we have the following picture $0 Aluminum Industry

55. Northwest Power Planning Council 55 Value of DSI Load Retail DSI rate affects buyout and value, but not the switching price $0 Aluminum Industry

56. Northwest Power Planning Council 56 Value of DSI Load Combining with BPA’s (region’s) “enterprise” risk, we see the stabilizing effect $0 Aluminum Industry

57. Northwest Power Planning Council 57 DSI Load As Spread Option The actual situation is that aluminum prices are changing, too, so this is really what is referred to as a spread option Aluminum Industry

58. Northwest Power Planning Council 58 DSI Load As Spread Option Note that this is the payout function for a put option This is an example of a “physical option” Recall earlier examples of physical options –Thermal plants as spread call options –Tolling arrangements as spread call options –Fuel switching capability as a “rainbow” or “chooser” option Aluminum Industry

59. Northwest Power Planning Council 59 Value of DSI Load Of course, one of the big differences between a regular financial put option and the DSI load is that DSI load can not be flipped on and off like a switch. Typically, four to six months is the shortest amount of time an aluminum smelter may be left on or off To address this, we have created a new worksheet function.... Aluminum Industry

60. Northwest Power Planning Council 60 Another Portfolio Worksheet Function The function takes –Minimum number of periods for shutdown; minimum number of periods the smelt must remain active once it has been restarted; criteria thresholds for startup and for shutdown; a switch that indicates whether labor costs are considered variable; and “return type” –Ranges for wholesale electricity price, for aluminum prices, and for smelter-specific rates over periods in the study. Aluminum Industry

61. Northwest Power Planning Council 61 Another Portfolio Worksheet Function The function returns –A single column-range (or row- range) with total DSI load in each period, or –a block range with a report of decisions by smelter by period Aluminum Industry

62. Northwest Power Planning Council 62 From the November 22 SAAC minimum shut-down period evalulation phase time wholesale electricity market aluminum-elec price spread expected price trend minimum restart period evalulation phase Aluminum Industry

63. Northwest Power Planning Council 63 Value of DSI Load Example..\..\Portfolio Work\Loads\DSI Loads (Aluminum Industry)\Alum Model_MJS_03.xls Aluminum Industry

64. Northwest Power Planning Council 64 Other considerations If smelters have value as options, do we want to support them? If so, retail rates must be flexible to keep the smelters in business, but As we have seen, as rates decrease, option values decrease Tying the rates to aluminum assures smelters will stay in business, but puts BPA in the aluminum business. Aluminum Industry

65. Northwest Power Planning Council 65 Other considerations Being in the aluminum business may not be bad, if aluminum prices are uncorrelated to west-coast electricity rates If rates are to remain fixed, there is an “optimal” rate that gives BPA and the region the greatest put protection (Michael’s claim) Aluminum Industry

66. Northwest Power Planning Council 66 Agenda Approval of the Dec 19 meeting minutes Review and questions from the last meeting Review of Futures Uncertainty Representation of Planning Flexibility Representation of Aluminum Industry –Value of curtailment More Fun with Statistics! Lessons Learned from 2000-2001 Progress on Olivia

67. Northwest Power Planning Council 67 Statistics The purpose of this section is.... –Acquaint the SAAC with data acquisition and analysis that has taken place so far –Introduce the SAAC to the apparent scale of uncertainties with which we are dealing Statistics

68. Northwest Power Planning Council 68 Data Acquisition Electric wholesale power prices –Daily –COB 6/1995-12/2002, PV 1/1996-12/2002, Mid-C 1/1996-12/2002 –includes peak and off-peak, firm and non- firm prices and volumes CalISO loads –Hourly –4/1998 – 12/2002 Statistics

69. Northwest Power Planning Council 69 Data Acquisition WECC loads (FERC 714) –hourly –61 cities 1/1993-12/2000, 13 of which we have additional data for 1/2001 – 12/2001 HydroGeneration –daily, 1/1994-12/2002 –Albeni Falls, Big Cliff, Bonneville, Chandler, Chief Joseph, Cougar, Detroit, Dexter, Duncan, Dworshak, Foster, Grand Coulee, Green Peter, Hills Creek, Hungry Horse, Ice Harbor, John Day, Libby, Little Goose, Lookout Point, Lost Creek, Lower Granite, Lower Monumental, McNary, Priest Rapids, Rocky Reach, Roza Pump, The Dalles, Wanapum, Wells Statistics

70. Northwest Power Planning Council 70 Data Acquisition Natural Gas (Gas Daily) –daily –Henry Hub 12/1990 – 12/2002, Kern River/Opal 2/1992 – 12/2002, Malin 2/1998 – 12/2002, Malin 400 2/1992 – 2/1998, Malin 401 2/1992 – 2/1998, Nova (AECO) 1/1994 – 12/2002, Nova (field) 1/1991 – 10/1995, NW Stanfield 10/1995 – 12/2002, Sumas 12/1990 – 12/2002 Statistics

71. Northwest Power Planning Council 71 Data Acquisition Transmission –hourly –Interties AC 1/1996 – 11/2002, AC+DC 1/1997 – 12/2002, DC 1/1996-11/2002, BC 1/1996 – 11/2002 –Cutplanes West of Hatwaii 1/1998 – 11/2002, Idaho 1/1998 – 11/2002, Montana 2/1997 – 11/2002, MidPoint 1/1998 – 11/2002, West Side/North of John Day 1/1998 – 11/2002 Statistics

72. Northwest Power Planning Council 72 Data Acquisition CalISO Curtailments –daily 1/2001, 3/2001-11/2002 –includes systems, owner, plant name, unplanned or planned, unit max and curtailed, location Temperature –daily, min – max – midpoint – HDD – CDD –Boise 1/1928-8/2002, LA 1/1990 – 8/2002, Oakland 12/1997 – 10/2002, Portland 1/1928 – 10/2002, Sacramento 12/1997 – 8/2002, Seattle 1/1928 – 3/2002, Spokane 1/1928 – 8/2002 Statistics

73. Northwest Power Planning Council 73 Data Acquisition Aluminum Prices –daily, 1/1989 - 8/2002 CalISO net interchange –hourly, 7/1998 – 12/2002 CalPX prices –hourly, 4/1998-1/2001 Statistics

74. Northwest Power Planning Council 74 Some Preliminary Findings On peak Recall that we saw evidence that electricity prices are log normally distributed Statistics

75. Northwest Power Planning Council 75 Some Preliminary Findings Change in volatility is more than proportional Statistics

76. Northwest Power Planning Council 76 Some Preliminary Findings Some adjustment for seasonal volatility seems to be indicated Removing May 2000 - June 2001 has little impact on these patterns Statistics

77. Northwest Power Planning Council 77 Some Preliminary Findings Statistics

78. Northwest Power Planning Council 78 Some Preliminary Findings Statistics

79. Northwest Power Planning Council 79 Some Preliminary Findings Applying these daily volatilities to the the current NPPC gas and electricity price forecast gives us 1 standard deviation values close to those we postulated last fall. Statistics

80. Northwest Power Planning Council 80 NG price forecast Statistics

81. Northwest Power Planning Council 81 Mid-Columbia price forecast Average annual w/comparisons Statistics

82. Northwest Power Planning Council 82 Agenda Approval of the Dec 19 meeting minutes Review and questions from the last meeting Review of Futures Uncertainty Representation of Planning Flexibility Representation of Aluminum Industry –Value of curtailment More Fun with Statistics! Lessons Learned from 2000-2001 Progress on Olivia

83. Northwest Power Planning Council 83 Lessons Learned The purpose of this discussion is.... –Review risk management practices of market participants –Discuss mechanisms or practices that would have improved the outcome of participants Lessons Learned

84. Northwest Power Planning Council 84 Lessons Learned SCL –Exposure in the market –Slow response to market conditions, buying late –Raising debt rather than rates Would a stop-loss program helped? To what extent did management have access to frequent and periodic risk assessment reports? Was trading discipline a factor? Was volume management a factor? Lessons Learned

85. Northwest Power Planning Council 85 Agenda Approval of the Dec 19 meeting minutes Review and questions from the last meeting Review of Futures Uncertainty Representation of Planning Flexibility Representation of Aluminum Industry –Value of curtailment More Fun with Statistics! Lessons Learned from 2000-2001 Progress on Olivia

86. Northwest Power Planning Council 86 Olivia

87. Northwest Power Planning Council 87 Olivia’s Daughter correlations and volatilities decision variables interest rate, hours per period chronological structure of uncertainty conservation assumptions conservation calculations Olivia

88. Northwest Power Planning Council 88 Olivia Orientation changed to accommodate more resources/regions/subperiods Period and subperiod definition are user specified. Periods may be months or years; subperiods may be on-/off-peak or seasons within each year Workbooks created to user’s specification Olivia

89. Northwest Power Planning Council 89 Next Meeting February 27, 9:30AM, Council Offices Agenda –Results with Olivia –More discussion of statistics –Detailed assumptions around renewables and distributed generation (from the December SAAC) –Incentives for new generation