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.
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
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
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
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)
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
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
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