How Idaho Power Company uses AURORAxmp

Integrated Resource Planning:

- An Engineer’s Perspective

October 25, 2013

J. James Peterson

Road Map

• Who we are

– Company overview

– Modeled system

– Unique modeling challenges

• Plan on uncertainty

– Stochastic modeling techniques

– Interfacing with SQL Server

• Lessons learned

Idaho Power Service Area and Resources

Modeled Region

Major Power Plants

Modeling Hydro in AURORA

• 17 Hydro plants

– Of these plants Brownlee, Oxbow and Hells Canyon are used to meet hourly

load variations (load following)

– All other facilities are modeled as “run of river” and are not used to meet

hourly load variations

• Stream flow forecasts

– Reflect declining stream flows in the Snake River

• Generation forecast from PDR580

– Monthly generation forecasts for each plant

2013 IRP: Brownlee Total Inflow

Forecasted Flows 2013 - 2032

7.00

7.50

8.00

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9.00

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Mill

ion

s o

f Acr

e-F

eet

Brownlee Total Inflow

Forecasted Flows 2013 - 2032

50% Exceedance

70% Exceedance

90% Exceedance

Seasonal Reservoir Cycle

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10/1/2011 11/1/2011 12/1/2011 1/1/2012 2/1/2012 3/1/2012 4/1/2012 5/1/2012 6/1/2012 7/1/2012 8/1/2012 9/1/2012

BR

N e

lev

ati

on

( f

eet)

flo

w (

cfs)

OUTFLOW INFLOW BRN ELEVATION

AURORA HCC vs Observed HCC

AURORA Output

2009 HCC Observed

Nameplate Capacity

Environmental Stewardship

Integrated with Operations

Above: Fall Chinook Salmon Redds

Fall Chinook Salmon Redd

On-line Wind Capacity

Divided Public Opinion

Modeling Wind in AURORA

• Monthly Generation Trends

• Hourly Generation Trends

The inherent variability of wind provides modeling challenges. For the 2013 IRP, wind is

modeled based on 2009 2011 historical data for southern Idaho. Hourly generation data is

scaled to better represent recently installed wind projects. Modeled PURPA wind projects have

a combined nameplate capacity of approximately 576 MW.

Upon analysis, trends were observed in the wind generation data. These hourly and monthly

trends were used in the development of a12 month x 24 hour matrix of capacity factors.

Capacity factors were then applied to the combined nameplate rating to produce wind

generation values that reflect monthly and hourly variation in the wind. The AURORA

simulation applies the generation values derived from the 12 month x 24 hour matrix.

The same analysis was applied to the Elkhorn Valley wind project.

0

50

100

150

200

250

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

PU

RPA

Win

d G

ener

atio

n (M

W)

Hours in Day

January February March April

May June July August

September October November December

PURPA Wind by Month 2009 2011

Alternative Modeling: Pumped Storage

0

500

1000

1500

2000

2500

3000

3500

4000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Syst

em G

ener

atio

n (M

Wh)

Hours in Day

July 25, 2018: Peak Load = 3,437 MW

On-Peak Hours 3-7 PM

Total Existing System: Generation, PPA & Market Purchases

LL Wind Pumped Storage

LL Wind Generation Removed (System Pumping)


Hour January February March April May June July August September October November December

1 204,607 188,028 219,546 203,301 198,432 171,303 149,050 148,926 138,962 161,723 241,367 210,947

2 200,920 185,203 222,091 198,633 195,611 166,243 154,315 146,589 140,919 151,499 244,069 209,825

3 197,646 180,659 219,381 201,089 187,641 160,597 152,576 145,124 147,152 149,608 250,588 210,981

4 189,748 180,006 218,994 206,429 186,469 158,665 150,599 145,432 148,072 154,694 251,301 214,801

5 184,688 174,210 220,274 206,407 186,912 155,561 160,262 144,026 151,406 149,831 259,590 212,458

6 176,752 170,978 225,934 207,044 185,497 159,837 161,153 139,793 156,557 155,409 266,097 213,593

7 0 0 0 0 0 0 0 0 0 0 0 0

8 200,000 200,000 200,000 109,186 0 0 0 0 0 200,000 200,000 152,142

9 200,000 200,000 200,000 200,000 0 0 0 0 0 200,000 200,000 200,000

10 200,000 200,000 200,000 200,000 0 0 0 0 0 200,000 200,000 200,000

11 47,529 200,000 200,000 200,000 45,092 0 0 0 0 47,378 198,426 0

12 0 0 200,000 200,000 0 0 0 0 0 0 0 0

13 0 0 23,105 0 0 0 0 0 0 0 0 0

14 0 0 0 0 0 0 0 0 0 0 0 0

15 0 0 0 0 0 0 0 0 0 0 0 0

16 0 0 0 0 0 200,000 180,232 135,723 0 0 0 0

17 0 0 0 0 200,000 200,000 200,000 200,000 148,435 0 0 0

18 0 0 0 0 200,000 200,000 200,000 200,000 200,000 0 200,000 200,000

19 200,000 156,254 0 0 200,000 200,000 200,000 200,000 200,000 0 200,000 200,000

20 200,000 200,000 0 0 200,000 200,000 200,000 200,000 200,000 200,000 200,000 200,000

21 200,000 0 200,000 200,000 200,000 52,897 0 0 200,000 200,000 200,000 200,000

22 0 0 200,000 200,000 200,000 0 0 0 0 0 0 0

23 199,164 178,015 226,635 205,732 209,456 172,054 146,787 146,077 155,051 201,488 244,720 209,525

24 205,886 188,219 226,025 207,847 206,346 171,862 150,548 153,685 147,425 184,969 240,300 208,046

LL Wind Generation (kW)

*System Pumping*

HH Pumped Storage

Generation (kW)

*System Generating*

LL Wind Generation (kW)

*System Pumping*

Represents wind energy being stored during off-

peak hours

Represents stored energy being discharged

during on-peak hours (3-7 pm)

Combined Wind Projects to Pumped Storage


January February March April May June July August September October November December

19 9 9 11 22 19 19 19 21 20 19 19

9 8 10 9 19 18 18 18 19 21 20 20

20 10 8 10 18 17 17 17 18 9 8 18

10 20 11 21 21 16 20 20 20 10 9 21

8 11 21 12 20 20 16 16 17 8 21 9

21 19 12 22 17 21 21 21 16 11 10 10

11 21 22 8 11 15 15 15 22 22 18 8

18 12 13 20 12 22 22 22 15 12 11 11

12 22 7 13 16 14 14 14 14 19 22 22

22 7 20 19 13 13 13 13 13 13 12 12

13 13 14 18 14 12 23 12 12 14 13 13

7 18 19 14 15 23 12 23 11 18 7 17

Peak Load Hours (2013 IRP Forecast): Ranked Hours From Highest Load

• Generation hours were selected based on hourly load ranking (highest to lowest)

•Pumped storage plant generated at full capacity on these identified hours

Ho

ur

End

ing


• Reduce need for additional peak-hour capacity

•Convert intermittent product into a firm product

•Help integrate wind energy (possibly reduce wind integration charge)

Mid-C Market Prices

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Mid

-C M

arke

t P

rice

s ($

/MW

h)

AURORA Mid-C LL

Forwards (Nov 28, 2012) Mid-C LL

2018 2032 Carbon Adder

Increases Market Prices in

AURORA Mid-C LL

Peak-Hour Deficits: 2013 IRP

Applying Modeling Results

• Alternative portfolios are layered over existing system

configuration

– Total portfolio costs are derived and compared

– 20 year IRP planning period analyzed

• AURORA Portfolio Costs

Total portfolio cost = Resource cost + Contract purchases (ie. PURPA & PPA) +

Market Purchases – Market Sales

Stephen Hawking:

A Brief History of Time




Hawking noted that an editor warned him

that for every equation in the book the

readership would be halved.

Stephen Hawking:

A Brief History of Time




Hawking noted that an editor warned him

that for every equation in the book the

readership would be halved DOUBLED.

AURORA Stochastic Overview

• Better Insight

– The basic relationships of the electricity system

are nonlinear. A stochastic analysis can lead to

insights that might not otherwise be understood.

These observed relationships change over time.

• Uncertainty Analysis of Fundamental Drivers

– Variability in the following drivers were studied:

* Natural gas

* Customer load

* Hydroelectric variability

* CO2 adder

• Uses Common and Well-Known Techniques

– The 2013 IRP stochastic studies were done using

Latin-Hypercube sampling. While not used, the

Monte Carlo method is also an option.

26

Two Methods For Examining

Uncertainty in AURORA

• Endogenous

– AURORAxmp has the internal capability to specify distributions for select drivers/variables, and will generate samples from the statistical distributions using Monte Carlo or Latin Hypercube sampling.

– It will also tabulate the input variables and specified results by iteration.

• Exogenous

– You can use an external Monte Carlo sampling application to generate input data for use in AURORAxmp.

– The external source of data can be used to create samples for multiple studies where AURORAxmp is used as the electric market pricing engine.

– AURORAxmp scripting or computational dataset capabilities can be used to modify the input data.

Slide courtesy of EPIS

27

Portfolios Analyzed

• Portfolio 1

– B2H_DR_SCCT

• Portfolio 2

– B2H_DR

• Portfolio 6

– ICL_BSU: Coal Retirement

• Portfolio 7

– Coal Conversion to NG

• Portfolio 3

– DR_CCCT_SCCT

• Portfolio 4

– CCCT_DR_CCCT

• Portfolio 5

– CCCT_CCCT_DR

* Refer to 2013 IRP for complete portfolio descriptions

• Portfolio 8 (Valmy closure)

– B2H_DR_CCCT

• Portfolio 9 (Valmy closure)

– CCCT_CCCT_SCCT

Risk Factors Sampled

• Customer Load (regional & local)

– Normal Distribution

– 50% Regional Correlation

• Henry Hub Natural Gas Price

– Log-normal Distribution

– 65% Serial correlation

• Hydro Generation (local and regional)

– Normal Distribution

– 50% Serial Correlation, 70% Regional Correlation

• Carbon Adder

– Low, Planning & High Scenarios

– Stratified Sample

Approach

• Random draws performed on an annual basis

• Each risk factor simultaneously employed

• 102 Iterations performed for each of the 7 portfolios

• NPV for 20 year period

Stochastic Dispersion:

P_0

P_1

P_2

P_3

P_4

P_5

P_6

P_7

$2,000,000 $4,000,000 $6,000,000 $8,000,000 $10,000,000 $12,000,000 $14,000,000

Po

rtfo

lio N

um

be

r

Total Portfolio Cost [2013 through 2032] (NPV 000s $)

Stochastics: 1 Iteration

Planning Carbon


P_0

P_1

P_2

P_3

P_4

P_5

P_6

P_7

$2,000,000 $4,000,000 $6,000,000 $8,000,000 $10,000,000 $12,000,000 $14,000,000

Po

rtfo

lio N

um

be

r


Stochastics: 3 Iiterations

High Carbon

Planning Carbon

Low Carbon


P_0

P_1

P_2

P_3

P_4

P_5

P_6

P_7

$2,000,000 $4,000,000 $6,000,000 $8,000,000 $10,000,000 $12,000,000 $14,000,000

Po

rtfo

lio N

um

be

r


Stochastics: 12 Iiterations

Low Carbon

Planning Carbon

High Carbon

Stochastic Results

0%

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$0 $2,500,000 $5,000,000 $7,500,000 $10,000,000

Exce

ed

ance

Pro

bab

ilit

y

Total Portfolio Costs (2013 NPV $000s)

Portfolio #1: B2H_DR_SCCT

Portfolio #2: B2H_DR

Portfolio #3: DR_CCCT_SCCT

Portfolio #4: CCCT_DR_CCCT

Portfolio #5: CCCT_CCCT_DR

Portfolio #6: ICL_BSU

Portfolio #7: Coal to NG Conversion

Portfolio #8:B2H_DR_CCCT

Portfolio #9:DR_CCCT_CCCT_SCCT

Stochastic Results

0%

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$0 $2,500,000 $5,000,000 $7,500,000 $10,000,000

Ex

cee

da

nce

Pro

ba

bil

ity

Total Portfolio Costs (2013 NPV $000s)

Portfolio #1: B2H_DR_SCCT

Portfolio #2: B2H_DR

Portfolio #3: DR_CCCT_SCCT

Portfolio #4: CCCT_DR_CCCT

Portfolio #5: CCCT_CCCT_DR

Portfolio #6: ICL_BSU

Portfolio #7: Coal to NG Conversion

Portfolio #8:B2H_DR_CCCT

Portfolio #9:DR_CCCT_CCCT_SCCT

Another Stochastic Example

$3,000,000

$4,000,000

$5,000,000

$6,000,000

$7,000,000

$8,000,000

$9,000,000

$10,000,000

-$5 $5 $15 $25 $35 $45 $55

Tota

l Po

rtfo

lio C

ost

(N

PV

201

3 $0

00s)

2018 Carbon Adder (Nominal $s)

Stochastic Based - Carbon Adder Tipping Point

Low Carbon

Planning Carbon

High CarbonPortfolio #6 (ICL_BSU)

Portfolio #2 (B2H_DR)

Low Carbon

Planning Carbon

High Carbon

Another Stochastic Example

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$0 $2 $4 $6 $8 $10 $12 $14 $16 $18 $20 $22

Mid

-C M

arke

t P

rice

s ($

/MW

h)

Henry Hub Natural Gas Price ($/MMBtu)

High Carbon

Planning Carbon

Low Carbon

Preferred Portfolio

• AURORA

– Latin Hyper-cube performed well

– Flexibility for resource analysis

– Serial and regional correlations of risk variables

– Wide range of stochastic futures sampled in short time period

• Mid-C market has quantifiable, non-linear trends

– Stochastic modeling assists in the identification of trends

Lessons Learned

• SQL Server

– Fast

– Multiple users

– Multiple instances of AURORA

– Huge DB size capacity

– Flexibility for queries

Questions?

1981 Flashback

How Idaho Power Company uses AURORAxmp

Technology

Transcript of How Idaho Power Company uses AURORAxmp