NorthWestern Energy Group, Inc. · NASDAQ:NWE
NorthWestern Energy — Technical Presentation (November 2013)
40 slides from NorthWestern Energy's technical presentation, Nov 13, 2013
Slide 1: Portfolio Valuation of PPL Hydro Asset Acquisition
Portfolio Valuation of PPL Hydro Asset Acquisition Gary Dorris, Ph.D. Sean Burrows, Ph.D. Mark Dyson Understanding the Latent Value of Hydro Generation Alex Lauderbaugh Brock Mosovsky, Ph.D. November 13, 2013 gdorris@ascendanalytics.com 303.415.0311 www.ascendanalytics.com
Slide 2: Outline
Outline • Portfolio Characteristics and Valuation Results • Quantifying the Value of Risk • Market Inputs • Simulation Validation • Operating Dynamics and Detailed Results Preliminary Data 2
Slide 3: Preliminary Results of Hydro Acquisition Analysis
Preliminary Results of Hydro Acquisition Analysis Preliminary Data 3
Slide 4: Best Practices in Resource Analysis
Best Practices in Resource Analysis • Portfolio perspective Fuel diversity Reliability and intermittent resources Uncertainty in fuel, emissions, and power • Use of market for fuel and power • Analyze generation resource options with respect to market dynamics • Capture new dynamics Weather
Slide 5: Key Points of Analysis
Key Points of Analysis • Supply Cost and Risk = costs and uncertainty in energy supply costs (fixed and variable) • Risk Premium = monetized value of risk—uncertainty in supply costs • Net Position Exposure = balance of NWE resources relative to load obligations • Supply portfolios to be assessed: o
Slide 6: Tools of Supply Planning Model Attributes
Tools of Supply Planning Model Attributes Uncertainty Factor Production Cost Models Integrated Risk Based Planning Models Load Growth Fixed Simulated Uncertainty Load Patterns “Typical” profile Uncertainty in Profile and Usage Pattern Conservation Fixed Weather Based Demand Response Weather Fixed We
Slide 7: Net Position Report
Net Position Report 2000 NWE net position 0 -2000 GWh/yr -4000 -6000 -8000 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 Base-mean Base-p5 Base-p95 Base plus hydro-mean Base plus hydro-p5 Base plus hydro-p9
Slide 8: PPL Hydro Assets
PPL Hydro Assets Capacity Storage Generator MW Net Factor (%) (A/F) Thompson 94 60 14,970 Falls Mystic Lake 12 50 Kerr* 194 64 200,000 Madison 8 85 27,200 Hebgen - - 386,000 Hauser Lake 19 81 64,253 Holter 48 75 82,000 Black Eagle 21 73 1,820 Rainbow 60 60 1,050 Cochrane 69 50 2,700 Ryan 60 80 5,000
Slide 9: Quantifying the Value of Risk
Quantifying the Value of Risk Preliminary Data 9
Slide 10: Deterministic vs. Stochastic Models
Deterministic vs. Stochastic Models • Heavy dependence on deterministic results with scenarios Excessive focus over input forecasts The likelihood of result is not understood Model inputs are variable and interdependent • What’s the impact of unused information? Inaccurate forecasting Asse
Slide 11: Definition of Common Planning Risk Metrics
Definition of Common Planning Risk Metrics Cost at Risk (CaR) for Rev Req = CaR = 95th percent costs – mean costs How do you decide the value of risk? Avista IRP 2011 • Risk metrics are useful but need to be translated into a monetary value by quantifying the value of uncertainty Preliminary Data 11
Slide 12: Risk Premium (RP)
Risk Premium (RP) How to calculate the RP in discrete time? • Calculate the area under the cost distribution curve. f (x ) • The orange dotted section has been overlaid with rectangles to reflect this discretized area. • RP calculation is the same calculation used to value options or assess actuaria
Slide 13: Simulation Validation
Simulation Validation Preliminary Data 13
Slide 14: Integrating Physical and Financial Uncertainty
Integrating Physical and Financial Uncertainty Dispatch Detail • Hourly chronological dispatch • Forced Outages modeled as random events • Seasonal and peak period generating characteristics Optimal Resource Selection Unified simulation framework reflecting joint financial and physical uncertainty o
Slide 15: Confidence Intervals and Simulation Spread
Confidence Intervals and Simulation Spread Forward Price Simulations Mid-C Heavy price simulations spread from the mean • Simulated forward prices are plotted over time for the mean, 5th, and 95th percentiles • Expect that uncertainty will grow with time F0=E(Ft)=E(FT)=E(St) Confidence Intervals for
Slide 16: Forward Price Validation History and Simulations: Natural Gas
Forward Price Validation History and Simulations: Natural Gas Forward Price Validation Historic prompt month prices for Henry Hub Price Paths for Final Evolved Forward Curve Simulation • Large price spikes followed by mean reversion • 2 to five year cycle AECO Natural Gas • Large volatility in price
Slide 17: Volatilities
Volatilities Volatility in Forward Contracts by Delivery Date • Calculated from recent forward market transactions • Controls the amount of uncertainty in simulated forward prices • Decreases exponentially through time • Displays seasonal trends • Distinct patterns observed by commodity Preliminary
Slide 18: Fuel Price Simulations
Fuel Price Simulations • Simulated forward prices are plotted over time for the mean, 5th, and 95th percentiles PRB Coal • Seasonal changes in price and uncertainty AECO Gas • Uncertainty grows with time • Average price increases to account for inflation Preliminary Data 18
Slide 19: Scenario Analysis
Scenario Analysis Common Scenarios: High, Med, Low • Which forecast are the best? Gas Price Scenario Analysis • Scenarios with prices monotonically increasing 14.00 over time. 12.00 • Are these prices consistent 10.00 with observed patterns? Scenario 1=$4 • What’s the probability $/MBtu 8.00 Scenari
Slide 20: Gas Price Simulation
Gas Price Simulation 2013 Resource Procurement Plan 9.5 Natural Gas Price Comparisons (all forecasts include transportation costs to & through MT) 8.5 7.5 6.5 $/MMBtu 5.5 4.5 3.5 2011 RPP Base Case : Levelized $6.43 2013 EIA AEO : Levelized $5.21 2013 NPCC Medium Case : Levelized $5.32 2013 RPP Asce
Slide 21: Forward Price Validation
Forward Price Validation Risk Bands for Implied On-Peak Heat Rates Maintaining Correlations • Simulations maintain implied heat rate distributions • Correctly value generation assets • Preserve proper value of portfolio risks • Enables proper value of cross hedges 120 12 100 10 80 8 60 6 Simulated f
Slide 22: Carbon Penalty Effect
Carbon Penalty Effect Forecasting Carbon Costs • In 2021, a carbon penalty is priced in to both generation and forward curves. Carbon Market Price Projections $100 Avista (2013 IRP) $90 PacifiCorp (2013 $80 IRP) PGE (2012 IRP) Dollars per Ton ($/ton) $70 PSE (2013 IRP) $60 NWE (Carbon $50 Forecast)
Slide 23: Weather, Hydro Flows, Load, Price Simulations
Weather Hydro Flows Load Price Simulations Hydro Flows Intermittent Generation Gas Electric Weather Load Price Preliminary Data 23
Slide 24: Weather-Load-Price Paradigm
Weather-Load-Price Paradigm Weather Load Price • Weather is a • Load is driven • Electricity price stochastic primarily by is input–so an weather—high predominantly appropriate and low driven by load, range of future temperatures though other states must be increase the factors simulated based deman
Slide 25: Weather Overview
Weather Overview • Weather data taken from the National Climatic Data Center Publicly available at http://ncdc.noaa.gov 10+ years of historical data More than 30 weather stations across Montana Preliminary Data 25
Slide 26: Weather Load Relationship
Weather Load Relationship Weather-Load Validation Maintaining Correlations Simulated vs. Historical • Incorporating weather into the load model maintains integrity in the weather – load relationship • Simulations nicely smooth out “bumps” of historical weather record • Simulations provide for new ex
Slide 27: Load – Evaluating Difference 5th, Mean, 95th
Load – Evaluating Difference 5th, Mean, 95th Load Simulation Validation Results • Demonstrate a robust model to simulate future load • Capture uncertainty in load growth with extreme weather scenarios validated prior to a load simulation • Validated monthly load values • Validated hourly load values
Slide 28: Load Validation
Load Validation Load Confidence Intervals August • Confidence intervals for hourly load by month at the mean, 5th, and 95th percentiles • Alignment of simulations with historical data January Daily Load Shape • Morning and evening peak during cold months • Single afternoon peak during warm months Pr
Slide 29: Spot Price Simulations
Spot Price Simulations Daily historical market prices for Mid-C Heavy Mid-C February Confidence Intervals compared to NWE load • Low correlation between NWE load and power prices. Hourly P10-Mean-P90 Confidence Intervals for February Mid-C power prices • Historical in red, simulation in blue • Good
Slide 30: Comparisons of Hydro Generation, Load, Prices, & Weather
Comparisons of Hydro Generation, Load, Prices, & Weather Daily market prices for Mid-C heavy compared to AECO gas • Power shows a positive correlation with gas • Extreme weather days (below 10th percentile of average temp) highlighted in orange AECO Gas confidence Intervals • Historical in red, simu
Slide 31: Comparisons of Hydro Generation, Load, Prices, & Weather
Comparisons of Hydro Generation, Load, Prices, & Weather Daily historical Mid-C vs. Hydro generation • Meaningful negative correlation between power Simulated Confidence Intervals vs. Historical Hydro Generation prices and hydro generation. 450000 • Extreme weather days (below 10th percentile of 400
Slide 32: Historical River Flows
Historical River Flows River Discharge • June river discharge for years 1957-2012. • Notice negligible correlation between river systems. River Discharge 700 600 Columbia River (1000 ft3/s) 500 400 300 200 100 0 0 5 10 15 20 25 30 35 Missouri River (1000 ft3/s) Preliminary Data 32
Slide 33: Wind Generation Validation
Wind Generation Validation • Simulated wind production matches the historical behavior of NWE wind assets • Wind is scaled to meet 15% of load in 2015 onwards 80% 70% Wind Monthly Capacity Factor 60% 50% 40% 30% 20% 10% 0% 1 2 3 4 5 6 7 8 9 10 11 12 Month Historical data Sim P5 Sim Mean Sim P95 Prel
Slide 34: Site A: Hourly Wind and Load
Site A: Hourly Wind and Load Average of hourly load and wind generation 100% 800 90% 700 80% Wind Capacity Factor 600 70% 60% 500 Load MW 50% 400 40% 300 30% 200 20% 10% 100 0% - 3/31 0:00 3/31 6:00 3/31 12:00 3/31 18:00 Gordon Butte Wind NWE Load • Wind generation declines as system load peaks • Wi
Slide 35: Site B: Hourly Wind and Load
Site B: Hourly Wind and Load Average of hourly load and wind generation 100% 800 90% 700 80% 600 Wind Capacity Factor 70% 60% 500 Load MW 50% 400 40% 300 30% 200 20% 10% 100 0% - 3/31 0:00 3/31 6:00 3/31 12:00 3/31 18:00 Judith Gap Wind NWE Load • Diversifying wind portfolio can “make the wind blow
Slide 36: Hourly Portfolio Attributes
Hourly Portfolio Attributes Preliminary Data 36
Slide 37: Net Energy Position
Net Energy Position Current supply portfolio: 2020 31% 47% 53% 69% Hydro added to portfolio: 2020 Owned Market purchases 13% 87% Preliminary Data 37
Slide 38: Resource Integration: Ancillary Service Implications
Resource Integration Ancillary Service Implications • Perception that increasing RPS creates value for flexible resources Negative power prices Large potential swings in supply greater demand for ancillary services and flexibility 1,000 15% of Load Supplied from Renewables 900 800 700 600 Wind
Slide 39: Generation Stations Report
Generation Stations Report • Net Position Report Portfolios o Generation o Load • Stations Report Capacity factor Fuel consumed Generation Emissions (CO2) Preliminary Data 39
Slide 40: Contact Information
Contact Information U.S. Offices Headquarters: Gary Dorris | 303.415.0311 1877 Broadway St President Suite 706 gdorris@ascendanalytics.com Boulder, CO 80302 (303) 415-1400 Other Offices: Berkeley, CA Bozeman, MT
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