PowerSimm for Applications of Resource Valuation

Transcription

1 PowerSimm for Applications of Resource Valuation Presented by: Sean Burrows, PhD Alankar Sharma/Kristina Wagner June 15, 2017

2 Agenda Defining Model Objectives for Resource Evaluation Principal Model Drivers for Asset Valuation Validation Results Evaluation of Flexible Generation Technologies PowerSimm Configurations & Operations 2 ascend analytics

3 Key Questions Addressed Resource Requirement Addressed in April ETAC 1) How does PowerSimm model the need for flexible generation? 2) How does PowerSimm model the need for capacity? Resource Valuation Today s ETAC 1) How does PowerSimm validate inputs of market conditions for modeling? a) Forecast monthly prices i. Regional modeling ii. NorthWestern market price inputs b) Daily market and system conditions 2) How does PowerSimm value flexible capacity? a) How does PowerSimmco-optimize between energy, ancillary services, and flexibility requirements? b) How does PowerSimm distinguish in value between ICE and CT c) How does PowerSimm model thermal generation vs storage? d) How does PowerSimm model renewables + storage? 3 ascend analytics

4 Agenda Defining Model Objectives for Resource Evaluation Principal Model Drivers for Asset Valuation Validation Results Evaluation of Flexible Generation Technologies PowerSimm Configurations & Operations 4 ascend analytics

5 PowerSimm Compared to Other Solutions Capability Competitors Ascend Detailed sub hourly dispatch optimization Flexible requirements as function of renewables X Integrated ability to calculate peak resource (MW) X Simulated weather, load, renewables, price X Substantiate meaningful uncertainty X Match market vols, heat rates, and prices X Optimal expansion planning under uncertainty X Transmission modeling Statistical summarization of output variables X Rapid response parallel summarization process X 5 ascend analytics

6 Keys to Future Supply Portfolios Need to manage cost uncertainty and not position uncertainty Recognize new exposure is toprice volatilitynot cost of energy Declining prices limit exposure of average open positions Increased volatility in prices increase exposure to future cost uncertainty Need supply portfolio to address renewable integration Need to incorporate uncertainty in decision analysis Increased renewable generation causes increased market price volatility May have long-term impact of pricing itself out of market Need to maintain key structural relationships WX Renewables & Load Transmission Market Prices Consistency with forward market and fundamentals 6 ascend analytics

7 Resource Valuation: Objective Functions Min Expected Value of Total Cost E[NPV of Total Costs] = E 1 Fixed Costs follow revenue requirements Depreciation, Amortization, Current Taxes, Deferred Taxes, Insurance, Property Taxes, On-going capital improvements, return on equity and debt Variable Operating Costs comes from hourly dispatch aggregated up to monthly totals including: Start up costs, min uptime, and min downtime constraints, emissions & variable heat rates Subject To: Reserve Margin Constraints =1 Ancillary Service Requirements ` =1 where γis the required reserve margin Energy Constraint =1 Renewable Constraint =1 7 ascend analytics

8 Calculation of Loss of Load Probability Weather drives load and renewable generation Simulated renewables generation & load Simulated generation outages Hydro Gen Simulated Weather Renewables + Generation outages = Capability to serve peak load PowerSimmcaptures the interactions of weather renewables, hydro, and load and generation Load Note: Competing models to PowerSimm don t simulate full generation outages with expected duration and variance in outage duration. Traditional models use an expected outage rate times available capacity in a deterministic construct. Thus, they can t calculate probability of outages. 8 ascend analytics

9 Resource Adequacy PowerSimm ssimulation framework captures the structural relation of weather with renewables and load over a wide range of future conditions MW 0% Reserve Margin MW Short Hours (Load > Capacity) MEAN P5 P95 Hours Short by Year 9 ascend analytics

10 Minutely Dispatch and Regulation 2020 Baseline MW Wind MW Solar Solar 1 MW ~ 0.2 MW flexible resources Wind 1 MW ~ 0.6 MW of flexible resources Changes in gradient of net load Base load 3 s in gradient Net load 7 s in gradient 10 ascend analytics

11 Total Flexibility Requirements in NWE Minimizing Fast Ramp Flexible requirement = 15 minute ramp (Inc) + 1 minute ramp (Regulation) Illustration of Flexible Capacity Requirement Year Scenario Inc Capacity Regulation Capacity Flexible Requirements 2016 Base Base Base Base Base MW Solar MW Wind MW Solar + 200MW Wind Increasing renewable penetration causes growth in system flexibility requirements 11 ascend analytics

12 Agenda Defining Model Objectives for Resource Evaluation Principal Model Drivers for Asset Valuation Validation Results Evaluation of Flexible Generation Technologies PowerSimm Configurations & Operations 12 ascend analytics

13 Implications of Weather During Delivery Conditions 13 ascend analytics

14 Simulation Engine: Integrating Physical and Financial Uncertainty Ascend s PowerSimm is able to capture meaningful uncertainty, through: Producing realistic simulations of physical system and market conditions Capturing the core structural relationship between weather, load, renewables and prices Simulating over a wide range of conditions physical variables that drive market prices The flow chart outlines PowerSimm s modeling framework. PowerSimmstarts by simulating weather, which influences both the simulation of load and renewable generation. Forward prices are also simulated, and all of these simulations contribute to the generation of spot prices, which then feed into the dispatch optimization process. 14 ascend analytics

15 PowerSimm Mirrors NWE s Weather-Load Relationship Weather is a key driver during delivery conditions Space cooling rapidly drives up energy usage Space heating drives a slower load response 15 ascend analytics

16 PowerSimm Simulates Realistic Price-Load Relationships Red plus signs are historical data Blue circles are simulated data A high degree of overlap shows the model is effectively capturing the underlying dynamics 16 ascend analytics

17 Renewables vs Temperature Wind generation has a structural relationship with temperature differences but is weakly correlated with average temperatures Solar Generation (MWh) Wind Generation (MWh) Average Temp F Solar generation exhibits an upward trend as temperature increases Average Temp F 17 ascend analytics

18 Seasonal Energy Production of NorthWestern Hydros Generation peaks with spring snow melt in April & May Generation MWh Hydro assets are largely run-of-river, but have pondage to provide capacity and some flexible generation 18 ascend analytics

19 Fundamental Determinants of Volatility in Power Prices 19 ascend analytics

20 Market Potential for Capacity Energy Flows throughout the WECC PowerSimmperforms fundamental modeling of the WECC region inclusive of transmission congestion and losses determined through an integrated generation dispatch and DC optimal power flow model. Major interties support seasonal flow of power N to S (summer) and S to N (winter) California imports about 20% of energy During the solar peak, CA exports to the neighboring states. Wind and hydro are predominant in the Northwest Solar dominates CA, but also significant penetration in AZ, UT, NM, NV, NM, and CO 20 ascend analytics

21 Price Volatility in CAISO over time 22% 7% 42% 60% Peaks and Valleys in Renewables (as % of load) correspond to peaks & valleys in pricing volatility. Increase in wind/solar increase volatility in LMPs. 50% 100% 21 ascend analytics

22 Over-generation due to increasing renewables is already here EIM Real Time Price Map. March 27, 2017 at 11:00 AM kkkk Energy Imbalance Market in the Northwest 22 ascend analytics

23 California needs to export excess solar to the WECC immediately CA is ahead of expectations in terms of renewable export due to 5 GW of rooftop solar plus utility scale solar. March GWhof renewables were curtailed due to over-supply. The Western Energy Imbalance Market is expected to provide a mechanism to find off takers of renewable energy. Montana PSC strongly encourages NWE to evaluate possibility of joining EIM. See slide 68 for more discussion of EIM. 23 ascend analytics

24 Changing Market Dynamics as a Function of Renewables Hour 24 ascend analytics

25 Observed and Forecasted Increases in Price Volatility through Time WECC 10% 7% Mid-C Volatility of DA LMP 25% 11% 25 ascend analytics

26 WECC Market Analysis: Renewables and Market Volatility Recorded Forecast Recorded Forecast Market price volatility is expected to increase as renewable generation increases Greater premium on highly flexible capacity By 2027 the growth in price volatility diminishes Volatility peaks during the summer months 26 ascend analytics

27 Forward Prices and Volatilities 27 ascend analytics

28 Forward Fuel Prices Prices peak in winter months (January) Source: Canadian Gas Price Reporter 28 ascend analytics

29 Long-term Implied Heat Rates (Annual) Implied Heat Rates - Annual Market quotes for forwards till 2020; liquid forward curves post-2020 Implied Heat Rate (MMBTU/MWh) Heavy Load HRs Light Load HRs With the increasing penetration of zero variable cost renewable generation in WECC, the implied heat-rate is expected to decrease steadily over time. 29 ascend analytics

30 Long-term Implied Heat Rates (Monthly) Implied HRs - 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 maturity, the peaks in late summer and late winter (February) normalize to the same extent Implied Heat Rate (MMBTU/MWh) Implied heat rates dip in June Heavy Load HRs Light Load HRs 30 ascend analytics

31 Forward Price Volatility Inputs Forward Volatilities 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Forward Price Volatility Inputs Gas Volatilities Heavy Load Power Volatilites Light Load Power Volatilities Forward price volatilities are used to accurately model correlations between monthly gas and power prices Monthly forward volatilities are higher closer to delivery date of the forward Higher volatilities closer to transaction date of forward (i.e. day of sourcing data 05/11/2017) Source: Bloomberg 31 ascend analytics

32 Spot Price Volatility Inputs Spot price volatilities account for increasing variability in intra-month power prices Spot price volatilities for power are calculated as = (Standard Deviation of prices / Mean of prices) on monthly time steps Volatility forecasts are developed based on relationship between existing generation type and historical power price volatilities. These were forecasted based on announced power project types and capacities in the WECC region. Greater number of solar power projects contribute to increasing volatilities in summer months and greater number of wind projects contribute to volatility during shoulder and winter months. Weighted averages based on location and project type was applied to WECC volatilities to obtain final volatility inputs for NWE s modeling Relative standard deviation method is used to account for negative power prices because using conventional log of lagged price definition (Vol = log (P t /P t-1 )) under predicts hourly volatilities as log(-ve number) = _NULL_ 3 Spot Price Volatility Inputs Volatilities ascend analytics

33 Demonstrate PowerSimm UI Forward Curve Editor, Forward Curve Constraints Editor (Corr, Vol), Spot Price Editor 33 ascend analytics

34 Raw Spot Price Simulations Raw spot price data shows simulations consistent with market dynamics Higher spot volatilities signified by increasingly larger price fluctuations in the future Frequency of prices being >$100 increase over time consistent with observations in WECC region Negative future prices are simulated to accurately mirror historical negative price observations With increasing spot volatilities and decreasing implied heat rates, the region will continue observing higher negative prices ( ) until conditions stabilize with renewable saturation ( ) 34 ascend analytics

35 Long-Run Equilibrium Evolution to High Renewable Penetration Rates 35 ascend analytics

36 100% New Thermal Units Capacity Factors With decreasing power price forward marks, the monthly capacity factors for each generation type decreases. CCs suffer marginally more than ICEs with more volatile power prices and incurred startup costs 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Thermal Assets - Capacity Factors CC CT ICE 36 ascend analytics

37 Net return ($/kw) Long-run Equilibrium: Thermal Generation Planning traditionally assumes long-run equilibrium conditions are satisfied through wholesale price conditions supporting new unit entry of a CC or CT. Due to the lowering of implied heat rates and increasing volatility caused by renewables, Montana s market does not support construction of new CC, CT and ICE units. Long-run assumptions use new CT construction fixed costs are between $80/kW-yr to $110/kWyr, while fixed construction costs for ICEs are $135/kW-yr to $190/kW-yr. $50 $40 $30 $20 $10 $- Historically, CCs produced greater returns than ICEs with market price not being too volatile Net Returns over Installed Thermal Capacity Flexible ICEs generate greater returns than inflexible CCs with more volatile prices in long run CC CT ICE 37 ascend analytics

38 ColStrip Capacity Factors ColStripwithout must run component shows declining capacity factors with longtermprice conditions If modeled as must run, the must run component incurs annual losses owing to power prices being unfavorable (negative power prices) to keep the unit on 100% ColStrip Capacity Factor 90% 80% 70% 60% 50% 40% ColStrip (non must run) 38 ascend analytics

39 ColStrip Net Revenue Returns ($/kw year) With cheap coal prices, ColStripplants will continue to generate revenues with increasing volatile prices Revenues gained from higher positive prices will be greater than losses incurred from operating during negative price hours due to inflexibility With low operating costs, ColStrip does not show declining net returns on revenues $70 ColStrip- Net Revenue Returns Net revenue return ($/kw) $60 $50 $40 $30 $20 $10 $- ColStrip (non must run) 39 ascend analytics

40 Agenda Defining Model Objectives for Resource Evaluation Principal Model Drivers for Asset Valuation Validation Results Evaluation of Flexible Generation Technologies PowerSimm Configurations & Operations 40 ascend analytics

41 Forward Price Simulations PowerSimmsimulates monthly forward curves for power for both on-peak (heavy) and off-peak (light) periods PowerSimmsimulations closely represent volatility conditions with higher price spreads closer to today s transaction date and lower spreads further out For peak power prices, price spreads (p95-p5) decreases from approximately $25/MWh in 2020 to $15/MWh in 2040 Power prices during off-peak (light) periods show a similar decrease in monthly price volatility patterns over time, though the distribution is tighter owing to lower prices 41 ascend analytics

42 Forward Price Simulations Figures represent 5 simulated forward curve price paths for Heavy and Light load hours 42 ascend analytics

43 Load Validation 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 for hours 1-24 Load Simulation Modeling Developed through a structural state-space model integrating weather as a dependent variable Weather effects modeled through a fourth order polynomial that reflects load-temperature relationships Calendar structure in terms of month, weekday and hour Autocorrelation affects where extreme load conditions are measured Mean, 5 th and 95 th of historical and simulated closely match 43 ascend analytics

44 Temperature Validation PowerSimm simulates temperature at multiple sites BILLINGS MISSOULA PowerSimm simulates temperature at multiple sites 44 ascend analytics

45 Weather Simulations: WECC Regional Weather In addition to simulating Northwestern s weather driven customer load, PowerSimmalso simulates weather and load conditions for WECC region The structural relationship between WECC weather patterns and historical prices drive simulations of future price patterns to capture broader regional dynamics Graphs below validate simulations of weather in Tucson, AZ and Riverside, CA 45 ascend analytics

46 Demonstrate PowerSimm UI Main Market Model (WECC System Load) 46 ascend analytics

47 Agenda Defining Model Objectives for Resource Evaluation Principal Model Drivers for Asset Valuation Validation Results Evaluation of Flexible Generation Technologies PowerSimm Configurations & Operations 47 ascend analytics

48 NWE System Optimization Objective Function Minimizing the cost to meet load min System Level Constraints Meet load: + Maximum and minimum generation levels Maintain sufficient flexibility reserves: Ramp rate limits = Availability for energy and regulation Minimum up and down times Unit Level Constraints Availability for energy and regulation Maximum and minimum generation levels Ramp rate limits Minimum up and down times 48 ascend analytics

49 Summary of Technologies and Valuation Approach Technology Valuation Summary Standalone battery storage Batteries generate system capacity value when they free up thermal resources from the task of regulation. Combined cycle units can therefore move from the mid-point to operating at full capacity with greater efficiency. Otherwise, batteries need to provide four hours of operating capacity. Renewables plus battery storage Co-locating wind or solar generation with battery storage provides a value whereby the sum of the whole is no greater than the parts. The energy value is derived relative to the market. Capacity value for the battery is valued as stated above. Pumped hydro storage Valueis derived through co-optimization between energy and ancillary services subject to the physical constraints of generation. Compressed air energy storage See above. CAES operates like pumped storage with slower switching capability (CAES) between generation and charging. Hydro with storage (minimum Valueis derived through co-optimization of energy and ancillary services subject to the must-take with limited range for physical constraints of generation. dispatch) Demand Response resources Energy value derived by avoiding marginal peaking unit. Reciprocating internal Valuated through co-optimization of energy and ancillary services subject to physical combustion engine (RICE) units constraints of generation. Combustionturbine (CT)thermal Valuated through co-optimization of energy and ancillary services subject to physical units constraints of generation. Other thermal technologies Valuated through co-optimization of energy and thermal generation. Market products (PPA) not tied to specific resources User enters PPA contract terms, which are compared against energy market prices. PPAs include the cost of firm transmission capacity. 49 ascend analytics

50 Economics of Batteries 50 ascend analytics

51 Three Uses of Batteries Three Uses of Batteries Regulation Ramping Balancing out minute-to-minute fluctuations in load and generation Respond to sub-hourly and hourly ramps (e.g. evening ramp as solar generation tails off) Loadshifting Store excess generation that would otherwise be dump energy and discharge energy at a later time (e.g. excess day-time solar generation stored and released at night) 51 ascend analytics

52 Battery Benefits: Avoided Costs Battery benefits consist of two key components: 1.) The avoided fuel cost from batteries providing regulation services Thermal units have a declining heat rate (i.e. operate more efficiently) as they run closer to full load capacity. When thermal units serve regulation, they operate below their full load capacity, at a heat rate close to their midpoint. Utilizing batteries enables thermal generators to avoid operating at these inefficient levels. Batteries also can eliminate costly start-ups from thermal generators. E.g. Save $8,000 on a start-up of a CT Typical heat rates for combined cycle and coal generators 52 ascend analytics

53 Battery Benefits: Capacity Value 2.) Batteries create capacity by relieving thermal plants of regulation responsibilities and allowing them to instead commit energy during peak load conditions Capacity Value vs Battery Size 160 Capacity value of the 10, 25, 50 and 100 MW ESS over time MW MW 25 MW 50 MW 100 MW The largest battery sizes provide the most capacity value Capacity value increases over time due to higher renewable penetration (and in turn higher system flexibility requirements) 53 ascend analytics

54 Production (Fuel) Cost Savings from Batteries The value of the batteries is measured as the difference in net revenue with and without batteries. Batteries primarily derive value from furnishing regulation, enabling thermal generation to operate more efficiently. $8,000,000 $7,000,000 $6,000,000 $5,000,000 $4,000,000 $3,000,000 $2,000,000 $1,000,000 $0 Production Cost Savings vs. Battery Size (10 MW, 25 MW, 50 MW, 100 MW ESS) 10 MW 25 MW 50 MW 100 MW 10 MW 25 MW 50 MW 100 MW 20 minute duration 1 hour duration ascend analytics

55 Economics of Energy-serving Batteries $ per MWh $10,000 $9,000 $8,000 $7,000 $6,000 $5,000 $4,000 $3,000 $2,000 $1,000 $0 Annual Battery Revenues Small Battery (300 MWh) Medium Battery (1000 MWh) Large Battery (5000 MWh) In an economic dispatch scenario, batteries serving energy are valuable in smaller capacities Shorter duration of storage yield more valuable cycles dispatching during higher market prices. Higher capacity batteries do not provide proportional increase in revenues and are not able to recover capital expenditures 55 ascend analytics

56 Battery Charge and Discharge Relative to Power Price Battery Charge/Discharge (MWh) Medium Battery (1000 MWh) -Market Price vs Battery Net Position for Oct 22 and Oct 23 As price spikes, battery discharges As price drops, battery charges Battery doesn t cycle when there are no major price fluctuations due to 10% cycle losses $40 $35 $30 $25 $20 $15 $10 $5 MID-C market Price ($) -50 0:00 6:00 12:00 18:00 0:00 6:00 12:00 18:00 Hour $0 Battery Net Position MID-C Price 56 ascend analytics

57 600 Medium Battery (1000 MWh) State of Charge for Oct 22 and Oct 23 $40 MWh Battery charges between 2:00am and 6:00am $35 $30 $25 $20 $15 $10 $5 MID-C Market Price 0 0:00 6:00 12:00 18:00 0:00 6:00 12:00 18:00 Hour Battery State of Charge (MWh) MID-C Price Medium Battery (1000 MWh) Resource Interaction for Oct 22 and Oct 23 $ Battery charges in early morning when load and prices decrease Battery discharges in evening when load and prices increase MWh :00 6:00 12:00 18:00 0:00 6:00 12:00 18:00 Hour Renewable+Hydro Generation Thermal Generation Battery Net Position Market Purchases Energy Contracts Load 57 ascend analytics

58 Demonstrate Hourly Reports for Batteries (scrollbar charts) 58 ascend analytics

59 Example Valuation Strategies by Technology 59 ascend analytics

60 Stand-Alone Battery Provides capacity and more efficient energy to the system by substituting for conventional power plants in providing for ancillary services The conventional power plants no longer have to back down from their most efficient operating point to hold capacity in reserve for flexibility MW Charges at half power when energy is cheap Constraints for optimization: Battery Net Charge Position Minutely Time-Scale Track energy stored in battery: = + Power Limit:, Energy within capacity: Discharges at half power for peak load 100 MW battery does 100 MW Regulation through the rest of the day with incremental charging 60 ascend analytics

61 Pumped Hydro Storage Co-optimized operation between energy and ancillary services and provide capacity. Economics driven by the spread between low and high prices throughout the day Perform some regulation all day Hydro Net Position Discharge in morning and evening ramps MW Minutely Time-Scale Pump when extra energy is available from midday solar Constraints for optimization: Ramp rate limit: Track water stored in reservoir: = + Power and energy within system capacity:, 61 ascend analytics

62 Internal Combustion Engine (ICE) Co-optimized operation between energy and ancillary services Net Position 100 MW plant does 50 MW of regulation and 50 MW of energy MW Minutely Time-Scale Constraints for optimization: Ramp rate limit: Power within capacity: Regulation bounded by operating power:, 62 ascend analytics

63 Agenda Defining Model Objectives for Resource Evaluation Principal Model Drivers for Asset Valuation Validation Results Evaluation of Flexible Generation Technologies PowerSimm Configurations & Operations 63 ascend analytics

64 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 requirements Online resources that can quickly address major outages; must respond within 10 minutes Non-spinning requirements Offline resources that can quickly address outages; must respond within 10 minutes Flexible Reserves: Resources that address system variability outside of contingency events. Regulation up requirements Online resources that can quickly ramp up to meet rapid changes in system requirements; must respond within 1 minute Regulation down requirements Online resources that can quickly ramp down to meet rapid changes in system requirements; must respond within 1 minute INC requirements System balancing reserves that provide additional capability; must respond within 10 minutes DEC requirements System balancing reserves that backoff generation; non-binding 64 ascend analytics

65 Demonstrate PowerSimm UI -Portfolio, Generation (allowed ancillary contributions tab), Ancillary Reserve Editor, etc. 65 ascend analytics

66 Unit Behavior Base Portfolio - Basin Creek and ColStrip Basin Creek CT unit primarily serves Non Spin and INC requirements ColStrip supper section (dispatchable, non-must-run) acts as a flexible generation resource serving regulation services and spin, non spin as required Must run portion of ColStrip serves energy (capacity) 66 ascend analytics

67 Unit Behavior Base Portfolio - David Gates David Gates - fast ramping resource but expensive to run with high operating cost With 6 total units modeled under David Gates plant, it can provide all types of ancillary services primarily providing regulation owing to high flexible capabilities 67 ascend analytics

68 Unit Behavior Generic ICE Portfolio ICE vs David Gates New ICE units are cheaper to operate compared to existing flexible David Gates unit A new ICE introduced in NWE s portfolio takes over providing regulation and freeing David gates to be available for Non Spin and INC incidences because it costs less to keep the ICE unit ON 68 ascend analytics

69 Unit Behavior Generic Battery Portfolio Regulation Serving Battery Regulation serving batteries primarily serve flexible ramp requirements i.e. regulation Batteries are cheaper to operate compared to thermal assets and are always ON causing batteries to primarily serve regulation requirements but also being available for contingency requirements (Spin, Non-Spin) 69 ascend analytics

70 Unit Behavior Hydros Hydro units are modeled as energy only components and ancillary only components Energy only hydro components are modeled on weather driven generation levels Ancillary serving Hydro components are modeled as battery storage items providing corresponding ancillary service 70 ascend analytics

71 Demonstrate Hourly Cube Reports for Ancillary Contributions 71 ascend analytics