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

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


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

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


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Agenda





6

Agenda





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

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

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


input input

calculation calculation

on-peak off-peakrandom

variables

Futures Uncertainty


11


annual and study cost calculations and metrics

Futures Uncertainty


12




random variables

Futures Uncertainty


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


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


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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


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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


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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


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


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


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

Zzzzzzzzzzzz

Futures Uncertainty


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Zzzzzzzzzzzz

• More elaborate contributions to future values are easy to incorporate

Futures Uncertainty


23

Zzzzzzzzzzzz

• Example from the portfolio model

• Portfolio_24_with_Uncertainty_Graph.xls

Futures Uncertainty


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

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


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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


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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


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Short-term Variations,Long-term Uncertainties


decision variables





Futures Uncertainty


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Agenda





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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


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.



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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



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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



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• 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



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• Cohort model

• Period-specific costs and rules

• User-specified criterion function indicates when construction is attractive to investors, how much foreknowledge they have



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37




38




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• 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



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Agenda





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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


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minimum shut-down period

evalulation phase

time

who

lesa

le e

lect

ricity

mar

ket

aluminum-elecprice spread


minimum restart period

evalulation phase

Aluminum Industry


43

DSI Loads

• Terry Morlan’s model

• Inspired by Robin Adams, Resource Strategies, CRU Group

Aluminum Industry


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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


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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


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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


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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


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• 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


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• 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


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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


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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


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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


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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


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


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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


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Value of DSI Load

• Combining with BPA’s (region’s) “enterprise” risk, we see the stabilizing effect

$0

$0

$0Aluminum Industry


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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


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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


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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


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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


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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


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minimum shut-down period

evalulation phase

time

who

lesa

le e

lect

ricity

mar

ket

aluminum-elecprice spread


minimum restart period

evalulation phase

Aluminum Industry


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Value of DSI Load

• Example

• ..\..\Portfolio Work\Loads\DSI Loads (Aluminum Industry)\Alum Model_MJS_03.xls

Aluminum Industry


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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


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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

Agenda





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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

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

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

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


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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


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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


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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


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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


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


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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


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


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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


79


• 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

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


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


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Agenda





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

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

Agenda





86

Olivia

Olivia


87

Olivia’s Daughter


decision variables





Olivia


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

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

Documents

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