10/4/20021 Systems Analysis Advisory Committee (SAAC) Friday, October 4, 2002 Michael Schilmoeller...

10/4/2002 1

Systems Analysis Advisory Committee (SAAC)

Friday, October 4, 2002Michael Schilmoeller

John Fazio

Northwest Power Planning Council

2

Agenda

• Background• Objectives• Basic Terms and Concepts• The Portfolio Model• Using the Portfolio Model and Other Council

Tools to make Decisions• Milestones for the SAAC Process• Action Items


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Background

• Description of decision framework, called Portfolio Analysis

• Overview– Review the approach and tools we

have used in the past to prepare power plans

– Describe what has changed– Describe the proposed approach

Background


4

Traditional Resource Planning

• Determine mix of resources (power plants and demand-side measures) that minimize expected cost of meeting requirements

• 20-year or longer time horizon• Regional perspective• Considered uncertainty or variability in

hydroelectric generation, loads, fuel prices, but did not value risk explicitly (Issac)

Background


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Existing Tools

• Aurora– systems analysis of Western System– market price prediction

• Genesys– reliability assessment focused on NW– addresses uncertainty in loads,

resource availability and hydro energy

Background


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What has changed?

• Market prices: – Before –

•Uncertainties small relative to other sources of uncertainty

•Prices assumed predictable from supply and demand

•Regional loads and resources determined reliability

Background


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What has changed?

• Market prices: – Now –

• Market prices seen as significantly more volatile, “stickier,” less predictable from supply and demand

• Most utilities exposed to market for part of load• Activities outside the region can be critical to market

price• Reliability can be influenced by credit problems• Available markets provide hedging and risk

management opportunities that are incorporated into capacity addition decisions

• Role of individual participants in the region was de-emphasized. Now we think of behavior as “bottom-up.” Relevance to the energy crisis.

Background


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Issues in the Fifth Plan

• incentives for generation capacity• price responsiveness of demand• sustained investment in efficiency• information for markets• fish operations and power• transmission and reliability• resource diversity and distributed generation• role of BPA• global change

• lessons learned from 2000-2001

Background


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Issues in the Fifth PlanThe role of risk

• incentives for generation capacity• price responsiveness of demand• sustained investment in efficiency• information for markets• fish operations and power• transmission and reliability• resource diversity and distributed generation• role of BPA• global climate change

• lessons learned from 2000-2001

Background


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Issues in the Fifth Plan

Examples• Incentives for resource development

– Energy value of a combustion turbine in traditional analyses is associated with expected generation

– Capacity value is associated with the “insurance value” of the turbine (Important to dealing with hydro uncertainty)

• Price responsiveness of demand– Value depends on the number of hours of market prices

over some relatively high threshold– Exact duration of future market prices at various levels

is unknown

Background


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Existing Tools

• Aurora– Can test sensitivity to loads, hydro

• Genesys– Detailed analysis of reliability risks associate with hydro

generation, loads, power plant availability

• But,– Both are computationally intensive– Neither is a generalized risk model– Neither can perform cost minimization subject to risk

constraints

Background


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Portfolio Analysis

• Stock portfolio– Collection of risky assets with differing maturities

– Correlation is important, risks can offset or reinforce each other

– Maximize return, subject to a constraint on risk

• Analogous– Plants, conservation, and contracts are assets

– Sources of risk are correlated

– Roughly same objective

• But different. For example …– More than just price risk: also requirements and cost risk

– Quantity of asset can vary with market prices

Background


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What is a “resource portfolio,” exactly?

Hourly demand

Coal

Buy in Market

Buy in Market

Sell in Market

Gas Fired

Price-driven generation

Hydro

Contracts

Hydro

Total Resources

Year 1Summer Winter

Year 2Summer Winter

Background


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How do we determine risk for our “portfolio?”

• Choose a set of correlated values for monthly hydro, monthly loads, monthly fuel prices, monthly market prices for electricity, etc., etc.

• Calculate the cost of our portfolio• Return to step 1 as many times as necessary to

obtain a sample distribution that adequately describes the underlying distribution of costs

Background


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Portfolio Analysis

• Possible measures (“goal”, “objective”)– Minimize expected annual cost, given a fixed

dollar constraint on 10 percent “worst outcomes,” or

– Minimize the variance of rates over a given time frame, or

– Minimize the likelihood of large rate increases, or ….

Background

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Portfolio Analysis, Example

• Before resource additions: We want minimum expected annual cost, such that the chance of exceeding $25,700 M is less than 10 percent

Frequency Chart ofAnnual System Cost Simulations

$M

Mean = 17500

.000

.011

.022

.033

.044

0

5.5

11

16.5

22

-10000 3750 17500 31250 45000

33%

Good Bad

Risk limit = 25700

…but portfolio analysis tells us we have a problem ….

Background

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Portfolio Analysis, Example

• After addition of CCCTs, renewables, conservation, price-responsive demand programs, and so forth, our objective is achieved, but at some increase in expected cost

Frequency Chart ofAnnual System Cost Simulations

$M

Mean = 18300

.000

.011

.022

.033

.044

0

5.5

11

16.5

22

-10000 3750 17500 31250 45000

10%

Risk limit = 25700

Good Bad

Background


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Portfolio Analysis

• Initial questions– Which measures make sense for the region in the short

and long-term?

– What kinds of portfolios would benefit, in terms of risk management, from which measures in the short and long term? How much benefit is there?

• This information provides the rationale for the policy choices necessary to achieve implementation of preferred portfolios

Background


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Portfolio Analysis

• Prototype model– Excel workbook running crystal ball– Sixty months of on- and off-peak data for

(dynamic) thermal plant dispatch, hydro, loads, contracts, conservation, market purchases

– Single 200 x 100 cell worksheet– 500 simulations in one and a half minutes

Background


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Background Summary

• Reviewed the approach and tools we have used in the past to prepare power plans

• Described what has changed• Described the new approach

Background


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Agenda



Objectives


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Objectives of SAAC

• Peer review of assumptions, data, and technique• Sharing of techniques and information

– What has worked; what has not

– Which portfolios benefit from particular measures; which do not, and why

– Operational or administrative rules for risk management

• Evaluate the specific Issues• Developing a portfolio assessment tool for

practitioners

Objectives


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Principles

• Significant to strategic risk issues– “Does this threaten our extinction?”

• Transparency

• Quantitative analysis, but in the service of insight and communication

Objectives


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Agenda



Terms and Concepts


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The Language of Decision Analysis

• Frequency-based vs subjective probabilities• Volatility vs uncertainty• Variation over time vs volatility• Risk and risk metrics• Operational vs financial risk• Long and short positions• Volume, price, and cost risk• Markets: energy, capacity, operating and spinning

reserve, load following, regulation; spot and forward

Terms and Concepts


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Agenda



The Portfolio Model


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What is a “resource portfolio,” exactly?

Hourly demand

Coal

Buy in Market

Buy in Market

Sell in Market

Gas Fired

Price-driven generation

Hydro

Contracts

Hydro

Total Resources

Year 1Summer Winter

Year 2Summer Winter

The Portfolio Model


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Flow diagram: Monte Carlo

The Portfolio Model

Draw, for this "game"Fuel prices by monthEmission tax by yearOn- and off-peak, by month:

Monthly hydro generationMonthly loadsElectric market prices

for each month of a six-year planning window

Compute generation g(p_m, p_fi) of dispatchabletechnologies from monthly on- and off-peak fuel prices(p_fi) and market prices (p_m)

Buy requirements from market or sell into the market,by month, on- and off-peak

Subtract net generation and contract purchases from net load and contract sells, and sell surplus (buy requirements) from the market

If purchases are constrained and requirements can not bemet, record a "loss of load' event and compute unservedenergy.

Compute and record, by year and for the study, totalvariable cost of operation and any fixed cost associatedwith capacity additions. Compute and record, by year andfor the study, LOLP and UE

Finished withgames?

Calculate metricdistributions and

statistics

Basic input variables:

For the studyCapacity, heatrate, fuel loading factor, annual

fixed cost, FOR, fuel for thermal units, certain dispatch parameters

Interest rate, simplified hrs/monthMonthly volatilities and correlations for

Hydro generationLoadFuel pricesMarket price of electricity

Probability distributions for hydro, load, fuel prices, market price, emission taxes,chronological structures

Chronological structure of uncertainty ("paths")Supply curve for conservation and development

policy

By monthNumber of units of supply (e.g., thermal units)

By month, on- and off-peakExpected fuel pricesExpected load forecastExpected market priceExpected gen for non-dispatchable suppliesContracted energy


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Flow diagram: Optimization

The Portfolio Model

Basic input variables:

For the studyCapacity, heatrate, fuel loading factor, annual

fixed cost, FOR, fuel for thermal units, certain dispatch parameters

Interest rate, simplified hrs/monthMonthly volatilities and correlations for

Hydro generationLoadFuel pricesMarket price of electricity

Probability distributions for hydro, load, fuel prices, market price, emission taxes,chronological structures

Chronological structure of uncertainty ("paths")Supply curve for conservation and development

policy

By monthNumber of units of supply (e.g., thermal units)

By month, on- and off-peakExpected fuel pricesExpected load forecastExpected market priceExpected gen for non-dispatchable suppliesContracted energy

Draw, for this "game"Fuel prices by monthEmission tax by yearOn- and off-peak, by month:

Monthly hydro generationMonthly loadsElectric market prices

for each month of a six-year planning window

Compute generation of dispatchable technologies frommonthly on- and off-peak fuel prices and market prices

Buy requirements from market or sell into the market,by month, on- and off-peak

Subtract net generation and contract purchases from net load and contract sells, and sell surplus (buy requirements) from the market

If purchases are constrained and requirements can not bemet, record a "loss of load' event and compute unservedenergy.

Compute and record, by year and for the study, totalvariable cost of operation and any fixed cost associatedwith capacity additions. Compute and record, by year andfor the study, LOLP and UE

Finished withgames?


statistics

Met optimizationconstraints and

objectives?


statisticsModify resources


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The 40,000 Foot View

The Portfolio Model

correlations and volatilities

decision variables

interest rate, hours per period

chronological structure of uncertainty

conservation assumptions

conservation calculations


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The Portfolio Model

input input

calculation calculation

on-peak off-peakrandom

variables


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The Portfolio Model

annual and study cost calculations and metrics


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The Excel Model

• A Tour

• A Demonstration

• Some Results

• Tour of Crystal Ball

The Portfolio Model


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Agenda



Using Tools to Make Decisions


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Study Flow

Using Tools to Make Decisions

Expected values;relationships,

where historicaldata is lacking

Aurora

Genesys

Regionaloptimization

Explore situationsand measures

SAACvolatilities,

correlations,metrics

Sensitivities for"stress analysis,"scenario analysis


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Agenda



Outcomes and Milestones


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SAAC Meetings

• Friday, Oct 4

• Thursday, Oct 24

• Friday, Nov 22

• Thursday, Dec 19

• Thursday, Jan 16

• Thursday, Feb 20

• Thursday, March 20



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Thursday, Oct 24

• Metrics– Candidates and their application

• Representations in the portfolio model– ** Plan Issues **– thermal generation– hydro generation– conservation and renewables– loads– contracts– reliability



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Friday, Nov 22

• Natural gas prices– Sumas, AECO, Rocky mountains– historical and monthly forwards and volatilities– correlations with other variables– subjective forwards

• Hydro generation– historical and monthly forwards and volatilities– correlations with other variables



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Thursday, Dec 19

• Electricity prices– Mid-C, COB– historical and monthly forwards and volatilities– correlations with other variables– subjective forwards

• Loads– historical and monthly forwards and volatilities– correlations with other variables



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Thursday, Jan 16

• Review of risk management problems of 2000-2001– What worked and what did not

• Initial optimization for Region, using all mechanisms



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Thursday, Feb 20

• Identifying “ideal” portfolios for each measure

price responsiveness of demand

traditional supply-side alternatives

sustained investment in efficiency

transmission and reliability

resource diversity and distributed generation



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Thursday, March 20

• Wrap up!!



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Agenda



Action Items


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Action Items for Oct 24

• Be prepared to discuss items

• Locate sources for the kinds of information we will be needing at future meetings

Action Items


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See you on the 24th!

Fine


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Probability

• Frequency-based Probabilities– Volatility– A sample of the distribution is manifest

• hydro variability, load, gas price variability within the relevant time period (year); mean is relevant

– Normal variation in gas prices within a year

• Subjective Probabilities– Uncertainty– Only one outcome will manifest– Example: carbon tax, economic recession

Terms and Concepts


48

Risk and Risk Metrics

• Risk is probability of unfavorable outcomes– This definition is not universal

• Risk metrics are how we measure outcomes to determine whether one is worse than the other– Example of one-tailed test

Terms and Concepts


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Operational and Financial

• Operational Risk

• Financial Risk

Terms and Concepts


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Price and Volumetric Risk

• Long and Short Positions• Price risk

– potential exposure to unfavorable prices

• Volumetric risk– potential exposure to unfavorable quantities or

positions

• Cost risk– potential exposure to unfavorable costs

Terms and Concepts


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Electricity Markets

• By its nature, distinct markets for electricity exists for different locations and times

Terms and Concepts

Mid C Heavy Load Hour Firm Prices

1

10

100

1000

10000

1/1/

01

3/1/

01

5/1/

01

7/1/

01

9/1/

01

11/1

/01

$/M

WH

r'96

'97

'98

'99

2000

• Variation vs Volatility

• The prices in the figure at the right have NO volatility


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Electricity Markets

• Different markets exist for– Regulation– Load following– Spinning Reserve– Operating Reserve– other Capacity

Terms and Concepts


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Electricity Markets

• Different markets exist for– Forwards– Spot (hour-ahead forward?)

Terms and Concepts

10/4/20021 Systems Analysis Advisory Committee (SAAC) Friday, October 4, 2002 Michael Schilmoeller...

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