1
Systems Analysis Advisory Committee (SAAC)
Friday, February 7, 2003Michael Schilmoeller
John Fazio
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
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
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
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
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
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
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
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
Northwest Power Planning Council
10
The 40,000 Foot View
input input
calculation calculation
on-peak off-peakrandom
variables
Futures Uncertainty
Northwest Power Planning Council
11
The 40,000 Foot View
annual and study cost calculations and metrics
Futures Uncertainty
Northwest Power Planning Council
12
The 40,000 Foot View
correlations and volatilities
chronological structure of uncertainty
random variables
Futures Uncertainty
Northwest Power Planning Council
13
Price Processes
Gas Prices over the next three months
0.000.501.001.502.002.503.003.504.00
1 2 3 4 5 6
Forward month
$/M
MB
TU
Futures Uncertainty
Northwest Power Planning Council
14
Price Processes
2.35 <-price in March2.50 <-price in April2.35 <-price in May2.78 <-price in June3.02 <-price in July2.90 <-price in Aug
0.200.400.600.400.200.50
Futures Uncertainty
Northwest Power Planning Council
15
Price Processes
1.0 0.9 0.8 0.4 0.2 0.00.9 1.0 0.9 0.8 0.4 0.20.8 0.9 1.0 0.9 0.8 0.40.4 0.8 0.9 1.0 0.9 0.80.2 0.4 0.8 0.9 1.0 0.90.0 0.2 0.4 0.8 0.9 1.0
Futures Uncertainty
Northwest Power Planning Council
16
Price Processes
• Prices strongly correlated, one month to the next.
• Size of correlation matrix grows as n2
• 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
Northwest Power Planning Council
17
Zzzzzzzzzzzz
• Trick: Use principle factors
ii
i
i
kkk
1)(kiik)(k
eee
A
eeeeeeA
of transpose theis 'r eigenvectoth theis
eigenvalueth theis matrix symmetric a is
where''' 222111
• We can typically capture 95%+ of correlation structure with the largest eigenvalues
Futures Uncertainty
Northwest Power Planning Council
18
Zzzzzzzzzzzz
else. everywhere zeros anddiagonal on the entries
associated of deviationsstandard h thematrix wit
theis matrix Thematrices. symmetric are
iprelationsh by the definedmatrix n correlatio theand
))')(((matrix covariance The
S
RSS'
RuXuX
E
Futures Uncertainty
Northwest Power Planning Council
19
Zzzzzzzzzzzz
• We can simulate random vectors with the correct volatilities and correlation structure using
• Efficiencies arise when m<<k
factors" specific"randon of vector 1 a is variablesrandom ofmatrix 1 a is
rseigenvecto m ofmatrix theis means ofmatrix 1 their is
variablesrandom of vector 1 theis where
)(k)(mm)(k)(k)(k
εFLμX
εLFμX
Futures Uncertainty
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
Northwest Power Planning Council
21
Zzzzzzzzzzzz
Futures Uncertainty
Northwest Power Planning Council
22
Zzzzzzzzzzzz
• More elaborate contributions to future values are easy to incorporate
Futures Uncertainty
Northwest Power Planning Council
23
Zzzzzzzzzzzz
• Example from the portfolio model
• Portfolio_24_with_Uncertainty_Graph.xls
Futures Uncertainty
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
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
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
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
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
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
Northwest Power Planning Council
30
Representation ofPlanning 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
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
Northwest Power Planning Council
32
From theNovember 22 SAAC
construction phase
optional cancellation period
evalulation phase
time
wh
ole
sale
ele
ctri
city
ma
rke
t
price threshold
expected price trend
Planning Flexibility
Northwest Power Planning Council
33
New PortfolioWorksheet 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
Northwest Power Planning Council
34
New PortfolioWorksheet 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
Northwest Power Planning Council
35
New PortfolioWorksheet 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
Northwest Power Planning Council
36
New PortfolioWorksheet Function
Planning Flexibility
Northwest Power Planning Council
37
New PortfolioWorksheet Function
Planning Flexibility
Northwest Power Planning Council
38
New PortfolioWorksheet Function
Planning Flexibility
Northwest Power Planning Council
39
New PortfolioWorksheet 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
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
Northwest Power Planning Council
41
Representation ofLoad 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
Northwest Power Planning Council
42
From theNovember 22 SAAC
minimum shut-down period
evalulation phase
time
who
lesa
le e
lect
ricity
mar
ket
aluminum-elecprice spread
expected price trend
minimum restart period
evalulation phase
Aluminum Industry
Northwest Power Planning Council
43
DSI Loads
• Terry Morlan’s model
• Inspired by Robin Adams, Resource Strategies, CRU Group
Aluminum Industry
Northwest Power Planning Council
44
DSI LoadsAluminum Price 1550Premium Rate 0.03BPA Rate 23BPA Allocation 100
Mwh/Tonne 13.199Plant A
(modern prebake)Potential Demand 457Cost Components Alumina 403 Carbon 90 Labor/Other 400 Sustaining Capital 80
Electricity Cost Max 623.5
Electricity Price Max 47.24
Electricity Price$30
Demand @ Price 457
• 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
Northwest Power Planning Council
45
DSI Loads
Viable Smelter Loads
0
500
1000
1500
2000
2500
3000
3500
1100
1150
1200
1250
1300
1350
1400
1450
1500
1550
1600
1650
1700
Ele
ctr
icit
y U
se
Aluminum Price
20
25
28
30
32
35
40
$/Mw
Aluminum Industry
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
Northwest Power Planning Council
47
The Smelter’s Side
$/tonneAluminum 1500
Alumina 403Carbon 90Labor/Other 400Sustaining Capital 80
Electricity 500 @ 13.2 MWh/tonne
Profit (Loss) 27
• At $37.88/MWh smelter rate
Aluminum Industry
Northwest Power Planning Council
48
The Smelter’s Side
• So the smelter will shut down if offered$/tonne
Labor/Other 400Sustaining Capital 80Profit (Loss) 27
507so
507 $/tonne13.2 MWh/tonne
38.41 $/MWhand
38.41 $/MWh457 MW
17552.95 $/hr
or about $76.9M for six month's shutdown
Aluminum Industry
Northwest Power Planning Council
49
The Smelter’s Side
• If labor/other are considered “variable,”$/tonne
Labor/Other 0Sustaining Capital 80Profit (Loss) 27
107so
107 $/tonne13.2 MWh/tonne
8.11 $/MWhand
8.11 $/MWh457 MW
3704.47 $/hr
or about $16.2M for six month's shutdown
Aluminum Industry
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
Lost DSI revenues 37.88 $/MWhPayment to DSI 38.41 $/MWhMarket at least 76.29
Aluminum Industry
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
Northwest Power Planning Council
52
Load Buy-back
$/tonne
Aluminum 1500
Alumina 403
Carbon 90Labor/Other 400Sustaining Capital 80
Electricity 396 @ 13.2 MWh/tonne
Profit (Loss) 131.00
• At $30/MWh rate:
Aluminum Industry
Northwest Power Planning Council
53
Load Buy-back
• At $30/MWh rate:
$/tonne
Labor/Other 400
Sustaining Capital 80
Profit (Loss) 131
611so
611 $/tonne13 MWh/tonne
46.29 $/MWhand
46 $/MWh457 MW
21154 $/hr
Lost DSI revenues 30.00 $/MWhPayment to DSI 46.29 $/MWhMarket at least 76.29
Aluminum Industry
Northwest Power Planning Council
54
Value of DSI Load
• For fixed aluminum price and rates, we have the following picture
Value of DSI load ($)
Retail DSI rate equal wholesale market price
Buyout price
Wholesale market ($/MWh)$0
Aluminum Industry
Northwest Power Planning Council
55
Value of DSI Load
• Retail DSI rate affects buyout and value, but not the switching price
Value of DSI load ($)
Retail DSI rate equal wholesale market price
Buyout price
Wholesale market ($/MWh)
$0
Aluminum Industry
Northwest Power Planning Council
56
Value of DSI Load
• Combining with BPA’s (region’s) “enterprise” risk, we see the stabilizing effect
$0
$0
$0Aluminum Industry
Northwest Power Planning Council
57
DSI Load As Spread Option
Aluminum Prices ($/tonne)
Electricity Market Prices ($/MWh)Electric Rate ($/MWh)
The actual situation is that aluminum prices are changing, too, so this is really what is referred to as a spread option
Aluminum Industry
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
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
Northwest Power Planning Council
60
Another PortfolioWorksheet 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
Northwest Power Planning Council
61
Another PortfolioWorksheet 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
Northwest Power Planning Council
62
From theNovember 22 SAAC
minimum shut-down period
evalulation phase
time
who
lesa
le e
lect
ricity
mar
ket
aluminum-elecprice spread
expected price trend
minimum restart period
evalulation phase
Aluminum Industry
Northwest Power Planning Council
63
Value of DSI Load
• Example
• ..\..\Portfolio Work\Loads\DSI Loads (Aluminum Industry)\Alum Model_MJS_03.xls
Aluminum Industry
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
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
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
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
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
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
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
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
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
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
Northwest Power Planning Council
74
Some Preliminary FindingsAverage of ln of electricity prices, 1997-2002
0
0.5
1
1.5
2
2.5
3
3.5
4
1 2 3 4 5 6 7 8 9 10 11 12
Month
Ln
$/M
Wh
COB - TRUE
COB - FALSE
MidC - TRUE
MidC - FALSE
PV - TRUE
PV - FALSE
On peak
Recall that we saw evidence that electricity prices are log normally distributed
Statistics
Northwest Power Planning Council
75
Some Preliminary FindingsStd dev of ln of electricity prices
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
1 2 3 4 5 6 7 8 9 10 11 12
Month
ln $
/MW
h
COB - TRUE
COB - FALSE
MidC - TRUE
MidC - FALSE
PV - TRUE
PV - FALSE
Change in volatility is more than proportional
Statistics
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
Northwest Power Planning Council
77
Some Preliminary Findings
Average of ln of gas prices
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1 2 3 4 5 6 7 8 9 10 11 12
Month
ln $
/MM
BT
U
HENRY HUB KERN RIVER/OPAL PLANT
MALIN MALIN 400
MALIN 401 NOVA (AECO-C, NIT)
NOVA (FIELD) NW STANFIELD
SUMAS
Statistics
Northwest Power Planning Council
78
Some Preliminary Findings
Std Dev of Ln of Gas Price
0
0.1
0.2
0.3
0.4
0.5
0.6
1 2 3 4 5 6 7 8 9 10 11 12
Month
ln p
ric
e
HENRY HUB KERN RIVER/OPAL PLANT
MALIN MALIN 400
MALIN 401 NOVA (AECO-C, NIT)
NOVA (FIELD) NW STANFIELD
SUMAS
Statistics
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
Northwest Power Planning Council
80
NG price forecast
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1995 2000 2005 2010 2015 2020 2025
20
00
$/M
MB
tu
History
Low
Medlo
Medium
Medhi
High
EIA-Ref
EIA-Low
EIA-High
DRI-WEFA
GRI
CEC
ICF
Statistics
Northwest Power Planning Council
81
Mid-Columbia price forecastAverage annual w/comparisons
$0
$5
$10
$15
$20
$25
$30
$35
$40
$45
$50
$55
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Pri
ce
(2
00
0$
/MW
h)
Current Trends Hi Shape (092702)
5th Plan corrected transfer (062402).
Adequacy & Reliability Study (Feb 2000)
Statistics
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
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
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
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
Northwest Power Planning Council
86
Olivia
Olivia
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
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
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
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