This document summarizes modeling work done for an integrated resource plan. It discusses modeling Idaho Power's service area and resources using the AURORA modeling software. Specific topics covered include modeling Idaho Power's hydroelectric resources considering declining stream flows, modeling wind generation based on historical data, and modeling a potential pumped storage facility to help integrate intermittent wind energy. The document also discusses using AURORA to analyze portfolio costs and risks over 20 years considering uncertainties in load, natural gas prices, hydro generation, and carbon costs.

1. Integrated Resource Planning:
- An Engineer’s Perspective
October 25, 2013
J. James Peterson

2. Road Map
• Who we are
– Company overview
– Modeled system
– Unique modeling challenges
• Plan on uncertainty
– Stochastic modeling techniques
– Interfacing with SQL Server
• Lessons learned

3. Idaho Power Service Area and Resources

4. Modeled Region

5. Major Power Plants

6. 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

7. 2013 IRP: Brownlee Total Inflow
Forecasted Flows 2013 - 2032
7.00
7.50
8.00
8.50
9.00
9.50
10.00
10.50
11.00
11.50
12.00
MillionsofAcre-Feet
Brownlee Total Inflow
Forecasted Flows 2013 - 2032
50% Exceedance
70% Exceedance
90% Exceedance

8. Seasonal Reservoir Cycle
1860
1880
1900
1920
1940
1960
1980
2000
2020
2040
2060
2080
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
55000
60000
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
BRNelevation(feet)
flow(cfs)
OUTFLOW INFLOW BRN ELEVATION

9. AURORA HCC vs Observed HCC
AURORA Output
2009 HCC Observed
Nameplate Capacity

10. Environmental Stewardship
Integrated with Operations
Above: Fall Chinook Salmon Redds

11. Fall Chinook Salmon Redd

12. On-line Wind Capacity

13. Divided Public Opinion

14. Divided Public Opinion

15. 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.

16. 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
PURPAWindGeneration(MW)
Hours in Day
January February March April
May June July August
September October November December
PURPA Wind by Month 2009  2011

17. 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
SystemGeneration(MWh)
Hours in Day
July 25, 2018: Peak Load = 3,437 MW
On-PeakHours3-7PM
Total Existing System: Generation,
PPA & Market Purchases
LL Wind
Pumped
Storage
LL Wind Generation Removed
(System Pumping)

18. Alternative Modeling: Pumped Storage
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

19. Alternative Modeling: 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
HourEnding

20. Alternative Modeling: Pumped Storage
• Reduce need for additional peak-hour capacity
•Convert intermittent product into a firm product
•Help integrate wind energy (possibly reduce wind integration charge)

21. Mid-C Market Prices
$-
$20
$40
$60
$80
$100
$120
$140
2013_01
2013_06
2013_11
2014_04
2014_09
2015_02
2015_07
2015_12
2016_05
2016_10
2017_03
2017_08
2018_01
2018_06
2018_11
2019_04
2019_09
2020_02
2020_07
2020_12
2021_05
2021_10
2022_03
2022_08
2023_01
2023_06
2023_11
2024_04
2024_09
2025_02
2025_07
2025_12
2026_05
2026_10
2027_03
2027_08
2028_01
2028_04
2028_11
2029_04
2029_09
2030_02
2030_05
2030_12
2031_05
2031_10
2032_03
2032_08
Mid-CMarketPrices($/MWh)
AURORA Mid-C LL
Forwards (Nov 28, 2012) Mid-C LL
2018  2032 Carbon Adder
Increases Market Prices in
AURORA Mid-C LL

22. Peak-Hour Deficits: 2013 IRP

23. 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

24. Stephen Hawking:
A Brief History of Time
• AURORA Portfolio Costs
Total portfolio cost = Resource cost + Contract purchases (ie. PURPA & PPA) +
Market Purchases – Market Sales
Hawking noted that an editor warned him
that for every equation in the book the
readership would be halved.

25. Stephen Hawking:
A Brief History of Time
• AURORA Portfolio Costs
Total portfolio cost = Resource cost + Contract purchases (ie. PURPA & PPA) +
Market Purchases – Market Sales
Hawking noted that an editor warned him
that for every equation in the book the
readership would be halved DOUBLED.

26. 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

27. 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

28. 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

29. 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

30. 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

31. 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
PortfolioNumber
Total Portfolio Cost [2013 through 2032] (NPV 000s $)
Stochastics: 1 Iteration
Planning Carbon

32. 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
PortfolioNumber
Total Portfolio Cost [2013 through 2032] (NPV 000s $)
Stochastics: 3 Iiterations
High Carbon
Planning Carbon
Low Carbon

33. 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
PortfolioNumber
Total Portfolio Cost [2013 through 2032] (NPV 000s $)
Stochastics: 12 Iiterations
Low Carbon
Planning Carbon
High Carbon

34. Stochastic Dispersion:

35. Stochastic Results
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
$0 $2,500,000 $5,000,000 $7,500,000 $10,000,000
ExceedanceProbability
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

36. Stochastic Results
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
$0 $2,500,000 $5,000,000 $7,500,000 $10,000,000
ExceedanceProbability
Total PortfolioCosts (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

37. 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
TotalPortfolioCost(NPV2013$000s)
2018 Carbon Adder (Nominal$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

38. Another Stochastic Example
$0
$20
$40
$60
$80
$100
$120
$140
$160
$180
$200
$0 $2 $4 $6 $8 $10 $12 $14 $16 $18 $20 $22
Mid-CMarketPrices($/MWh)
Henry Hub Natural Gas Price ($/MMBtu)
High Carbon
Planning Carbon
Low Carbon

39. Preferred Portfolio

40. • 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

41. Questions?

42. 1981 Flashback

43. 1981 Flashback