NorthWestern Energy Group, Inc. · NASDAQ:NWE
NorthWestern Energy — Technical Presentation (August 2017)
28 slides from NorthWestern Energy's technical presentation, Aug 16, 2017
Slide 1: PowerSimm for Applications of Resource Valuation
PowerSimm for Applications of Resource Valuation Presented by: Sean Burrows, PhD Alankar Sharma/Kristina Wagner [email protected] 303.415.1400 August 16, 2017
Slide 2: Outline
Outline I. Renewables are proliferating throughout the WECC (partly due to QF contracts) II. Renewables’ impact on (1) flexible requirements and (2) market prices III. Analysis of ancillary services with NorthWestern’s current thermal fleet vs. additional ICEs and batteries IV. Simulating how future
Slide 3: As renewables add zero variable cost energy, average market price declines
As renewables add zero variable cost energy, average market price declines Renewable energy is being deployed aggressively in the WECC RPS Standards in WECC California – 100% RPS by 2045 Arizona – 15% by 2025 Oregon – 50% by 2040 New Mexico – 20% by 2020 Washington – 15% by 2020 Colorado 30% by 2020
Slide 4: Effect of increasing renewable penetration on WECC load
Effect of increasing renewable penetration on WECC load • The duck curves demonstrate how power prices are sensitive to renewables • Solar saturation: carves out load carved-out heat rates, higher price volatility, increased flexible generation requirements Shifts curves downwards overall lower heat
Slide 5: Ancillary Services Modeled
Ancillary Services Modeled Ancillary services modeled are specific to Northwestern Energy region; not tied to NERC standards Contingency Reserves: Resources that address outages of major units Spinning Online resources that can quickly address major outages; must respond within requirements 10 minut
Slide 6: Renewables increase NorthWestern's flexible requirements
Renewables increase NorthWestern’s flexible requirements Flexible generation = 15 minute ramp (INC) + 1 minute ramp (Regulation) REGULATION TOTAL FLEXIBLE SCENARIO INC CAPACITY CAPACITY REQUIREMENTS Base 252 44 296 +100 MW Solar 262 (4%) 50 (1%) 312 (5%) +200 MW Wind 375 (49%) 60 (63%) 435 (45%) +10
Slide 7: How are ancillary contributions fixed?
How are ancillary contributions fixed? Regulation is the most expensive ancillary service Fast ramping units provide regulation When economics of energy dispatch are “in the money” and unit is online, it can commit to Regulation and Spin • Hourly unit commitment performed on co-optimization between
Slide 8: How are ancillary contributions fixed?
How are ancillary contributions fixed? When economics of energy dispatch are not “in the money”, the unit can provide NonSpin if it has the appropriate ramp rate (able to come online in 10 minutes) • Maximum amount of unit contribution can be fixed via UI 8 ascend analytics
Slide 9: How is current fleet doing in providing ancillary services?
How is current fleet doing in providing ancillary services? Base portfolio 100% $60 90% Cost of generation ($/MWh) $50 80% % of hours in a year 70% $40 60% 50% $30 40% $20 30% 20% $10 10% 0% $- Basin Creek Colstrip 3 Colstrip 4 DGGS 1-1 DGGS 1-2 DGGS 2-1 DGGS 2-2 DGGS 3-1 DGGS 3-2 Spin Non-Spin Regu
Slide 10: Ancillary contributions with competing ICE unit
Ancillary contributions with competing ICE unit 100% $50 90% $45 Cost of generation ($/MWh) 80% $40 % of hours in a year 70% $35 60% $30 50% $25 40% $20 30% $15 20% $10 10% $5 0% $- Basin Creek Colstrip 3 Colstrip 4 DGGS 1-1 DGGS 1-2 DGGS 2-1 DGGS 2-2 DGGS 3-1 DGGS 3-2 Generic ICE Spin Non-Spin Regu
Slide 11: Ancillary contributions with competing battery
Ancillary contributions with competing battery 100% $60 90% Cost of generation ($/MWh) $50 80% % of hours in a year 70% $40 60% 50% $30 40% $20 30% 20% $10 10% 0% $- Basin Creek Colstrip 3 Colstrip 4 DGGS 1-1 DGGS 1-2 DGGS 2-1 DGGS 2-2 DGGS 3-1 DGGS 3-2 Battery Spin Non-Spin RegulationUp RegulationD
Slide 12: Need for flexible resources
Need for flexible resources • NorthWestern’s current fleet is unable to adequately fulfill ancillary requirements due to capacity characteristics • Violations are reported across all ancillary types • Being a BA, for reliability reasons, NorthWestern Energy should look to expand existing fleet with
Slide 13: Forward fuel prices
Forward fuel prices Prices peak in winter months (January) Gas prices are forecasted to escalate over time, with higher fuel prices in the colder months. Source: Canadian Gas Price Reporter 13 ascend analytics
Slide 14: Long-term implied heat rates (annual)
Long-term implied heat rates (annual) Note: Ascend’s work only 14 Market quotes for forwards till 2020 for long term valuation 12 10 Implied Heat Rate (MMBTU/MWh) 8 6 4 2 0 Heavy Load HRs Light Load HRs • Due to the increasing penetration of zero variable cost renewable generation in WECC, the impli
Slide 15: Long-term implied heat rates (monthly)
Long-term implied heat rates (monthly) Late summer (August-September) peaks in implied heat rates slowly decline over time due to increasing solar and wind generation. By 2025, with market 18 maturity, the peaks in late summer and late winter (February) normalize to the same extent 16 14 12 Implied
Slide 16: Understanding box plots for spot simulations
Understanding box plots for spot simulations 16 ascend analytics
Slide 17: Effects of increasing spot volatilities on prices
Effects of increasing spot volatilities on prices Increasing spot volatility leads to a larger range of simulated prices 17 ascend analytics
Slide 18: Effects of increasing spot volatilities on prices
Effects of increasing spot volatilities on prices Increasing volatility = larger inter-quartile range Higher volatilities imply higher upper limit of simulated prices 18 ascend analytics
Slide 19: Effects of increasing spot volatilities on prices
Effects of increasing spot volatilities on prices • Simulations are effectively unbounded but have a soft lower limit on negative prices according to economics of the marginal thermal unit (~ -$25) • As hourly volatility increases: Greater density of prices in upper bounds of simulations Greater int
Slide 20: Simulated forward curves and 2X forward volatility
Simulated forward curves and 2X forward volatility $70 Original Forward Curve Volatilities $60 $50 $40 $30 $20 $10 $- 2X Forward Curve Volatilities $70 $60 $50 $40 $30 $20 $10 $- 20 ascend analytics
Slide 21: Same spot volatility, higher forward volatility
Same spot volatility, higher forward volatility 1X FC Vol 4X Spot Vol 2X FC Vol 4X Spot Vol • Prices are more volatile when high spot volatility is combined with increasing forward volatility • Greater inter quantile range for 2X forward volatility case 21 ascend analytics
Slide 22: Geometric Brownian motion simulation of forwards
Geometric Brownian motion simulation of forwards Forward Simulations under Geometric Brownian Motion – ARMA(1,1) $120 $100 $80 $60 $40 $20 $- MEAN P5 P95 • Geometric Brownian motion is a stochastic simulation where price simulations become non- mean reverting and do not follow volatility structures
Slide 23: Thermal returns under base case price scenario
Thermal returns under base case price scenario Net returns 1X FC Vol 1X Spot Vol $60 $50 Net return ($/kW) $40 $30 $20 $10 $- CC CT ICE Colstrip • All gas units lose money due to declining implied heat rates (lower power prices) and increasing gas prices • ICEs lose less value than CCs due to their
Slide 24: Thermal returns under 4X spot volatility price scenario
Thermal returns under 4X spot volatility price scenario Net returns 1X FC Vol 4X Spot Vol $70 $60 $50 Net return ($/kW) $40 $30 $20 $10 $- CC CT ICE Colstrip • ICEs do not lose value as drastically with higher spot volatilities because they are more flexible than CCs and can quickly adapt to price f
Slide 25: Thermal returns under 2X forward volatility price scenario
Thermal returns under 2X forward volatility price scenario Net returns 2X FC Vol 1X Spot Vol $80 $70 $60 Net return ($/kW) $50 $40 $30 $20 $10 $- CC CT ICE Colstrip • Relative value of CCs, ICEs, and Colstrip closely resemble base case returns • Total returns for all units are higher, consistent wit
Slide 26: Thermal returns under GBM price scenario
Thermal returns under GBM price scenario Net returns GBM Forwards $60 $50 Net return ($/kW) $40 $30 $20 $10 $- CC CT ICE Colstrip • GBM case (drastic monthly volatility): Relative value of CCs, ICEs, and Colstrip closely resemble returns under 2X forward volatility, but all units have larger returns
Slide 27: Impact of price scenarios on thermal net returns
Impact of price scenarios on thermal net returns • Monthly volatility has less effect on the relative value of each unit • Hourly volatility has greater effect on the relative value of each unit Flexible units like CCs are most valuable because they can quickly dispatch and adapt to intra day price
Slide 28: Key takeaways
Key takeaways • Renewables are proliferating throughout the WECC • Increasing renewables lead to 1. Greater regulation requirements 2. Lower heat rates and volatile prices • NorthWestern’s current fleet is having a hard time providing ancillary services • In the future, heat rates (power prices) wil
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