Best Practices in Risk-Based Resource Planning A Case Study of NorthWestern s Acquisition of Hydros

Transcription

1 Best Practices in Risk-Based Resource Planning A Case Study of NorthWestern s Acquisition of Hydros Presenters: Gary Dorris, PhD August 4, 2015

2 Overview of Ascend Short, Intermediate, & Long-Tem: operating and planning analytics Founded in 2002, continuing steady growth, currently with over 30 employees Offices in Boulder CO, Oakland CA, and Bozeman MT Decision Analysis Timeframe One Model Through Time Operational Strategy (PowerSimm OPS) Intermediate Analytics (PowerSimm Portfolio Manager) Long-term Planning (PowerSimm Planner) Today 10 days 1 month to ~5 years ~6 years to 30+ years Optimal short-term dispatch Short-term load & price forecasting Decision support for trading Portfolio management Energy purchases and sales CFaR, GMaR, EaR Power Planning, IRP Asset valuation Cost versus risk tradeoff resource analysis Renewable integration studies 2 ascend analytics

3 Case Study in Resource Planning NorthWestern Energy 3 ascend analytics

4 Ascend Planning $1 billion utility hydro acquisition in 2014 for NorthWestern Energy Ascend provided economic and physical modeling of Hydros In Evergreen s opinion, NorthWestern s efforts are fully consistent with industry best practices 4 ascend analytics

5 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 Load Wind generation Market conditions Forward markets for fuel and power Spot price dynamics Resource adequacy Reliable service at cost effective rates Market interactions 5 ascend analytics

6 Need for New Tools to Incorporate Uncertainty Deterministic vs. Stochastic Models Heavy dependence on deterministic results with scenarios The likelihood of result is not understood Model inputs are variable and interdependent Deterministic modeling misses critical scenarios producing an inconsistent value What s the impact of unused information? Inaccurate forecasting Assessing risk becomes difficult One outcome for deterministi c 6 ascend analytics

7 PowerSimm use in NorthWestern Energy IRP 7 ascend analytics

8 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: Market purchases Purchase of PPL hydro (494 MW) Combined cycle (239 MW) Optional Analysis Reliability of supply Flexibility function 8 ascend analytics

9 Acquisition of PPL Hydro Assets Generator MW Net Capacity Factor (%) Storage (A/F) Thompson Falls ,970 Mystic Lake Kerr* ,000 Madison ,200 Hebgen ,000 Hauser Lake ,253 Holter ,000 Black Eagle ,820 Rainbow ,050 Cochrane ,700 Ryan ,000 Morony ,700 Total ,693 *Kerr generation rights end in ascend analytics

10 Net Position Report MWh/year 2,000,000 1,000,000 - (1,000,000) (2,000,000) (3,000,000) (4,000,000) (5,000,000) (6,000,000) Annual Net Position Current Current + CC Current + Hydro 10 ascend analytics

11 Main results: Range of likely costs through time Over the 30 year study horizon, the hydro asset reduces the exposure of NWE ratepayers to price spikes and narrows the range of potential costs 1,400 Annual Supply Costs 1,200 1,000 $Millions Current P5 P95 Current + CC P5 P95 Current + Hydro P5 P95 $455M $403M $289M 11 ascend analytics

12 CO2 price distribution In this analysis, CO2 prices are modeled beginning in year 2021 CO2 prices in $/tonnes are modeled using a triangular distribution with a miniumum of 0, a mode of 21, and a maximum of 42 The mode is consistent with the EIA forecast for CO2 prices As shown in the figure below, these CO2 price assumptions are conservative relative to other plans 12 ascend analytics

13 Distribution of Costs How do we measure the difference in risk between the portfolios? RP measures the dollar value of risk. Effective cost = Risk premium (RP) + cost Consider: Base = has higher costs and more risks Base+CC = adding CC slightly reduces risk Base+Hydro = reduces costs and substantially reduces risk to market prices of fuel, power, and potentially CO 2 13 ascend analytics

14 NPV Total Cost of Supply 30 Year Risk Premium adds to the cost advantage of Current + Hydro Cost of Supply = Fuel Cost + Variable O&M + Emission Costs + Market Purchases Market Sales + New Gen Capital Costs 14 ascend analytics

15 Aggregation & Monetization of Risk 15 ascend analytics

16 Risk Premium Definition Risk Premium captures the expected value of the upper tail of the cost distribution for each portfolio Similar to established means of valuing a financial option, or an insurance policy c = Expected cost Probability p 1 Risk premium is the probabilityweighted average of costs exceeding the mean Risk premium = i=1 (c i c ) p i p i : Probability of cost c i k p k c 1 Total portfolio costs $M c k 16 ascend analytics

17 Risk Premium Calculation Risk Premium (RP) monetizes the portfolio risks Risk Reduction Value = RP(Current of $451M) RP(Current + Hydro of $247M) = $204M NPV of risk premium $Millions $500 $450 $400 $350 $300 $250 $200 $150 $100 $50 $- $451 $380 $247 Current Current + CC Current + Hydro 17 ascend analytics

18 NPV Total Cost of Supply 30 Year Risk Premium adds to the cost advantage of Current + Hydro Cost of Supply = Fuel Cost + Variable O&M + Emission Costs + Market Purchases Market Sales + New Gen Capital Costs 18 ascend analytics

19 Definition of Common Planning Risk Metrics Cost at Risk (CaR) for Rev Req = CaR = 95 th percent costs mean costs Standard deviation (Std) of Ann Rev Req (ARR) = Std = E[AAA 2 ] (E AAA ) 2 How do you decide where to be on the frontier? Avista IRP 2011 Timing: Avoid roll-up effect over long time horizons CaR of Rev Requirements (standard risk metric) Ave annual over study period Max annual over study period Reflect 1 in 20 events Standard deviation (66 th percentile) year over year change in Rev Requirements Used by Avista, PacifiCorp Reflects 1 in 3 events 19 ascend analytics

20 Simulations Realizing Meaningful Uncertainty 20 ascend analytics

21 Integrating Physical and Financial Uncertainty Unified simulation framework reflecting joint financial and physical uncertainty o o Rigorous validation Capture of critical causal effects During delivery simulations Wx Sim Load Sim Renewables Spot Price Sim Calibrated Spot Prices Optimal Dispatch (Thermal, Hydro) Forward & forecast Prices Forecast Price Sim Power, Gas, Coal, Oil, Emissions,,Dalles, Supply & Transmission Portfolio Summarization Portfolio Selection Seasonal Hydro Sim 21 ascend analytics

22 Validation Criteria Simulated Risk Factor Validation Activities Forecast/ Forward Prices Spread of prices 5th, mean, 95th; rate of reversion, correlations, seasonality, implied heat rate Weather Match historic 5th, mean, 95th temps, patterns and time-series pattern Load Match 5th, mean, 95th hourly and monthly load of customer system Hydro Flows Match 5th, mean, 95th flows, pattern, incorporate current year forecasts Spot Prices Gas Match uncertainty of 5th, mean, 95th and preserve relationship with temperature, load, renewables Spot Prices Power Match uncertainty of 5th, mean, 95th with gas, price spikes, key explantory variables of ERCOT load, gas prices, and renewables 22 ascend analytics

23 Forward Price Validation History and Simulations: Natural Gas Simulated forward market prices for Henry Hub Large price spikes followed by mean reversion 2 to five year cycle Large volatility in prices followed by quiescent periods Forward Price Validation- Price Paths for Final Evolved Forward Curve Simulation Historic prompt month prices for Henry Hub Large price spikes followed by mean reversion 2 to five year cycle Large volatility in prices followed by relative quiescent periods 23 ascend analytics

24 Confidence Intervals and Simulation Speed Forward Price Simulations Simulated forward prices are plotted over time for the mean, 5 th, and 95 th percentiles Expect that uncertainty will grow with time Power price simulations spread from the mean F 0 =E(F t )=E(F T )=E(S t ) Confidence Intervals for Power Price simulations 5 th, mean, 95 th Forward Price Validation Tests uncertainty in the distribution of simulated forward prices Uncertainty grows over time Ranges of prices are consistent with market expectations and historic perspectives of forward price uncertainty 24 ascend analytics

25 Forecast Fuel Price Simulations Simulated forward prices are plotted over time for the mean, 5 th, and 95 th percentiles Seasonal changes in price and uncertainty Coal Gas Uncertainty grows with time Average price increases to account for inflation 25 ascend analytics

26 Weather Renewables Load Price Simulations Hydro Flows Intermittent Generation Gas Weather Load Electric Price 26 ascend analytics

27 Weather Overview Weather data taken from the National Climatic Data Center Publicly available at years of historical data More than 30 weather stations across Montana 27 ascend analytics

28 Weather Load Relationship Weather-Load Validation Simulated vs. Historical Maintaining Correlations 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 extreme values to exceed historic record Validating Relationship Validate by capturing the weather load relationship in the historical period and simulated backcast The structural state space modeling captures the changes in shape with changes in load 28 ascend analytics

29 Load Validation Load Confidence Intervals Confidence intervals for hourly load by month at the mean, 5 th, and 95 th percentiles Alignment of simulations with historical data August January Daily Load Shape Morning and evening peak during cold months Single afternoon peak during warm months 29 ascend analytics

30 Spot Price Simulations Spot Price Simulations Daily historical market prices for Mid-C Heavy compared to NWE load Low correlation between NWE load and power prices. Mid-C February Confidence Intervals Hourly P10-Mean-P90 Confidence Intervals for February Mid-C power prices Historical in red, simulation in blue Good distributional agreement between simulated and historical data 30 ascend analytics

31 Site A: Hourly wind and load Wind generation declines as system load peaks Wind doesn t blow when you need it to Average of hourly load and wind generation Wind Capacityy Factor 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 3/31 0:00 3/31 6:00 3/31 12:00 3/31 18: Load MW Gordon Butte Wind NWE Load 31 ascend analytics

32 Site B: Hourly wind and load Diversifying wind portfolio can make the wind blow when you need it to Adding more wind farms in the vicinity of wind rather than just one wind farm in the place where wind is most likely Average of hourly load and wind generation Wind Capacityy Factor 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 3/31 0:00 3/31 6:00 3/31 12:00 3/31 18:00 Judith Gap Wind NWE Load Load MW 32 ascend analytics

33 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% Monthly capacity factor 60% 50% 40% 30% 20% 10% 0% Month Historical data Mean P5 P95 33 ascend analytics

34 Solar Generation, Load, and Market Conditions Simulation of load, solar and market prices Position exposure Cost of supply exposure Solar Validation PowerSimm Modeling of Palo Verde Prices 34 ascend analytics

35 Hourly Portfolio Attributes PowerSimm simulated all assets for each hour of the 30 year study horizon, for each simulation iteration 35 ascend analytics

36 What s Next? Renewable Integration Studies Portfolio Integration of Hydros Optimal Capacity Expansion Planning 36 ascend analytics

37 Renewable Integration Examine Impacts of Renewable Additional regulation and load following requirements Cost of renewable integration Calculate CPS2 scores Regulation Used to meet the minute-by-minute system imbalances Correct for area control errors (ACE) Contingency Reserves Comprised of multiple products based on response time Spinning Reserves Non-Spinning Reserves 37 ascend analytics

38 Joint Thermal & Hydro Optimization of Energy & Ancillaries 38 ascend analytics

39 Deterministic capacity expansion optimization: Best athlete The best expansion plan for each scenario is akin to the best athlete for each sport. Which is the best plan for overall scenarios? Different conditions yield different optimal plans Objective: Find the cheapest capacity expansion plan that satisfies the constraints Cheapest is often defined as the net present value of the revenue requirements Constraints : Reliability, RPS requirements, ancillary services Result: Optimal capacity expansion of generation resources and conservation options to minimize revenue requirements subject constraints. Best swimmer/runner/cyclist is a sports analogy referring to the best expansion plan from a deterministic run (single forecast) of simulated weather, load, and renewables. Michael Phelps Best Swimm er Ryan Hall Best Runner Chris Froome Matt Biondi Alberto Salazar Tejay Van Garderen Ryan Lochte Steve Prefontaine Taylor Phinney Gary Hall, Jr. Galen Rupp Chris Horner Best Cyclist 39 ascend analytics

40 Stochastic capacity expansion optimization: Best triathlete PowerSimm Planner Result Best Expansion Plan for All Future Simulated States Requirements Robust simulations Advanced optimization of energy and ancillary services Advantages Simplifies decision choices Captures full tail of cost distribution Accounts for multiple future conditions Dave Scott Michael Phelps Ryan Hall Chris Froome Best Triathlete 40 ascend analytics

41 Thank You! U.S. Offices Headquarters: Boulder, CO 1877 Broadway Street Suite 706 Boulder, CO (303) Other Offices: Oakland, CA Providence, MA Bozeman, MT Gary Dorris President O: ascend analytics