Portland General Electric Co · NYSE:POR

Portland General Electric — Technical Presentation (August 2015)

167 slides from Portland General Electric's technical presentation, Aug 13, 2015

1. Slide 1: Integrated Resource Plan 2016 Public Meeting #3

   INTEGRATED RESOURCE PLAN 2016 Public Meeting #3 Thursday, August 13, 2015 © 2015 Portland General Electric. All rights reserved.

2. Slide 2: Welcome: Meeting Logistics

   Welcome: Meeting Logistics August 13, 2015 Slide 2  Local Participants:  DoubleTree facility  Virtual Participants:  Ask questions via ‘chat’ or ‘raise hand’ feature  Meeting will stay open during breaks, but will be muted

3. Slide 3: Welcome: Today's Topics

   Welcome: Today’s Topics August 13, 2015 Slide 3  Safety moment  Public process  Capacity update  Flexibility update  Demand Response update  Load Forecast  Natural Gas Forecast  Portfolio and Future Ideation

4. Slide 4: Safety Moment: Concussions

   Safety Moment: Concussions August 13, 2015 Slide 4 Determine if a person has a concussion 1. Assess consciousness 2. Assess the person 3. Check for physical symptoms 4. Check for cognitive symptoms 5. Watch the person DON’T DO

5. Slide 5: Public Process Update

   Public Process Update

6. Slide 6: 2016 IRP Timeline

   2016 IRP Timeline August 13, 2015 Slide 6 August 13, 2015 2015 Q1 2015 Q2 2015 Q3 2015 Q4 2016 Q1 2016 Q2 2016 Q3 2016 Q4 2016 IRP 1st Public Complete Draft File Final Kick‐off Meeting 2016 IRP 2016 IRP PGE Preparation Public Meetings and PGE Analysis Draft IRP Draft IRP Development Distribution Pub

7. Slide 7: 2016 IRP: Meeting Schedule And Planned Topics

   2016 IRP: Meeting Schedule And Planned Topics August 13, 2015 Slide 7 Q2/Q3 2015 July 15 Workshop #1 Technical • Load Forecast Methodology Implementation July 6 April 2 Meeting #1 Workshop #1 • Load Forecast Public Commission (Salem) Results • Welcome • EIM Study Update • Load Forecast • 111(d) Repr

8. Slide 8: 2016 IRP: Meeting Schedule And Planned Topics

   2016 IRP: Meeting Schedule And Planned Topics August 13, 2015 Slide 8 Q3 2015 (Tentative) August 13 October 5 September 25 Meeting #3 Workshop #2 Workshop #2 Public Technical Commission (Salem) • Development • Development • Development • Demand Response • 111(d) Rule update • Portfolios and Futures

9. Slide 9: 2016 IRP: Meeting Schedule And Planned Topics

   2016 IRP: Meeting Schedule And Planned Topics August 13, 2015 Slide 9 Q4 2015 (Tentative) December 3 December 4 Date TBD Workshop #3 Meeting #4 Workshop #3 Technical Public Commission (Salem) • Analysis • Development • Results • 111(d) Demonstration • EIM Study • Portfolios and Futures • Analysis •

10. Slide 10: 2016 IRP: Meeting Schedule And Planned Topics

    2016 IRP: Meeting Schedule And Planned Topics August 13, 2015 Slide 10 Q1 2016 (Tentative) February 10, 2016 Date TBD Date TBD Meeting #5 Additional Additional Public Workshops Meetings • Results • As Required • As Required • Colstrip Portfolios • Variable Resource Integration • Trigger Points • Pre

11. Slide 11: Clean Power Plan: Preliminary takeaways

    Clean Power Plan: Preliminary takeaways August 13, 2015 Slide 11 • Final rule focuses on fossil units; proposal focused on states. • 2020 compliance date extended to 2022 – aligns with Boardman timing. • Renewables count toward compliance if built after 2012. • Hydro averaged over 1990-2012, instead

12. Slide 12: PGE Summer Peak Load

    PGE Summer Peak Load August 13, 2015 Slide 12 • Portland area experienced extreme hot weather the week of July 27 • 103oF high; four hours at or above 100oF • Demand response programs were called upon during this time – est. 26 MW reduction • 3,965 MW – Net system peak (July 30, ~6pm) • 3,949 MW – P

13. Slide 13: 2016 IRP: Status

    2016 IRP: Status August 13, 2015 Slide 13 Item Status Meetings 6 Total (2 Complete, 4 Scheduled) Workshops 4 Total (2 Complete, 2 Scheduled) Feedback Forms 1 Received 2013 IRP Action Plan 5 Actions (OPUC Order No. 14-415) Supply Side In progress (Hydro contracts, portfolios, no major resources) Dema

14. Slide 14: Capacity and Flexibility Update

    Capacity and Flexibility Update

15. Slide 15: Capacity and Flexibility Update

    Capacity and Flexibility Update August 13, 2015 Slide 15  PGE’s resource portfolio is undergoing significant changes  Loss of longstanding hydro and coal assets  Increasing penetration of variable renewable generation  Increasing need for resource flexibility  PGE is seeking a rigorous method f

16. Slide 16: Capacity and Flexibility Update

    Capacity and Flexibility Update August 13, 2015 Slide 16  PGE retained E3 to study capacity and flexibility needs of PGE’s system under a range of future conditions considered in this IRP  Calculate a Planning Reserve Margin that is sufficient to meet a 1-day- in-10 year reliability standard  Pro

17. Slide 17: Capacity and Flexibility Needs under Higher Renewables

    Capacity and Flexibility Needs under Higher Renewables Portland General Electric IRP Public Meeting #3 August 13, 2015 Portland, Oregon Arne Olson, Partner Elaine Hart, Managing Consultant Ana Mileva, Senior Consultant

18. Slide 18: E3's expertise at the nexus of planning, policy and markets

    E3’s expertise has placed us at the nexus of planning, policy and markets San Francisco-based company with 40+ professionals Foremost North American consultancy in electricity sector economics, regulation, planning and technical analysis Consultant to many of the world’s largest utilities and renewa

19. Slide 19: Defining today's planning problem

    Defining today’s planning problem Introduction of variable renewables has shifted the planning paradigm • No longer sufficient to plan for adequate capacity Today’s planning problem consists of two related questions: 1. How many MW of dispatchable resources are needed to (a) meet load, and (b) meet

20. Slide 20: Problem is stochastic in nature

    Problem is stochastic in nature Load is variable and uncertain • Often characterized as “1-in-2” or “1-in-10” • Subject to forecast error Renewable output is variable and uncertain Conventional generation can also be stochastic • Hydro endowment varies from year to year • Generator forced outages ar

21. Slide 21: E3 Approach

    E3 Approach E3 has developed stochastic planning techniques to estimate capacity and flexibility needs under high renewables within a consistent analytical framework 1. RECAP: Loss-of-Load Probability study completed first to ensure the system has sufficient “pure capacity” to meet a defined reliabi

22. Slide 22: Planning Reserve Margin Investigation Using E3's RECAP Model

    Planning Reserve Margin Investigation Using E3’s Renewable Energy Capacity Planning Model Arne Olson, Partner

23. Slide 23: PGE currently utilizes a 12% PRM

    PGE currently utilizes a 12% PRM In the past, PGE has used a 12% planning reserve margin (PRM) for establishing resource adequacy: 1 1 2 • Standard is based on a heuristic: 6% for operating reserves + 3% for more extreme weather + 3% for forced outages • This approach was adequate when most resource

24. Slide 24: Current method needs updating

    Current method needs updating December reliable capacity method may no longer be appropriate given fast-growing summer peak Current method does not lend itself well to developing a rigorous measure of the capacity contribution of dispatch-limited resources such as wind and solar • Current method is

25. Slide 25: E3 investigated experience & methods in other jurisdictions

    E3 investigated experience & methods in other jurisdictions E3 investigated reliability criteria, planning reserve margins, and PRM accounting methodologies for several utilities • Other utilities in the West and similarly-sized utilities throughout the country High-level findings: • No industry-sta

26. Slide 26: Planning criteria used by other utilities

    Planning criteria used by other utilities Peak Demand in 2021 Planning Criterion PRM Peak Season (MW) Puget Sound Energy 7,000 MW LOLP: 5%* 16% (2023 ‐ 2024) Winter Summer: 1,700 MW; 22% (14% + Avista LOLP: 5%* Both Winter: 1,900 MW operating reserves) PacifiCorp 10,876 MW LOLE: 2.4 hrs/ year 13% Su

27. Slide 27: RECAP Methodology

    ©) Energy+Environmental Economics RECAP METHODOLOGY

28. Slide 28: E3's Renewable Energy Capacity Planning Model (RECAP)

    E3’s Renewable Energy Capacity Planning Model (RECAP) E3 has developed an open- source model for evaluating power system reliability and resource capacity value within high penetration renewable scenarios Based on extensive reliability modeling literature Used by a number of utilities and state agen

29. Slide 29: RECAP Model overview

    RECAP Model overview RECAP Model assesses reliability performance of a power system using the following metrics: • Loss of Load Probability (LOLP): probability of capacity shortfall in a given hour • Loss of Load Expectation (LOLE): expected hours of capacity shortfall in a given year • Expected Uns

30. Slide 30: Step 1: Create load distributions

    Step 1: Create load distributions Create probability Load Probability Distribution distribution of hourly Weekday ‐ September HE 13 EST load for each month/ hour/weekday-weekend 10% Low Load Hours 9% combination Probability at Load (%) 8% High Load Hours (12x24x2=576 total 7% distributions) 6% 5% So

31. Slide 31: Step 2: Calculate available dispatchable generation

    Step 2: Calculate available dispatchable generation Generator 1 (50 MW) Generator 2 (500 MW) Generator 3 (1,000 MW) Probability of Availability Equivalent Forced Outage Rate = 10% Equivalent Forced Outage Rate = 10% Equivalent Forced Outage Rate = 10% 100% 80% 60% 40% 20% 0% 0 10 20 30 40 50 0 100 2

32. Slide 32: Step 3: Calculate LOLP

    Step 3: Calculate LOLP Combination of load and resource distributions determines Loss-of-Load Probability for a given hour Load is most likely to exceed generation during hours with high load, high generator outages, or both Generation distribution Load Generation Probability Load distribution Load

33. Slide 33: Step 4: Sum across all simulated years to get LOLE

    Step 4: Sum across all simulated years to get LOLE LOLP is the probability of lost load in a given hour. LOLE is the annualized sum of LOLP across all hours (h) and simulated years (n) PGE has selected a LOLE standard of 24 hours in 10 years, or 2.4 hours/year PGE defines “loss of load” during a giv

34. Slide 34: LOLE converted into Target PRM for planning and procurement

    LOLE converted into Target PRM for planning and procurement LOLE is an accurate estimate of a system’s reliability, however it can be cumbersome to use directly in planning and procurement • It is more convenient to convert result into a Target PRM to translate LOLE (hrs./yr.) into need (MW) • Targe

35. Slide 35: Example Results

    ©) Energy+Environmental Economics EXAMPLE RESULTS

36. Slide 36: Key inputs and assumptions for PGE system

    Key inputs and assumptions for PGE system Thermal resources • Reliable capacities for each month, forced outage rates Hydro resources • Monthly dependable capacities for PGE units • Historical distribution of water availability for Mid-C contracts Renewables • 2004-2006 simulated production profiles

37. Slide 37: PGE has higher capacity gap in summer than winter

    PGE has higher capacity gap in summer than winter Load is higher in winter, with secondary peak in July/August Available resources lower in summer due to thermal de-rates, lower hydro output, and unavailability of imports Peak Capacity Need 1-in-2 Peak Load in July in December 37

38. Slide 38: LOLP on PGE system is highest on summer afternoon, winter evening

    LOLP on PGE system is highest on summer afternoon, winter evening Chart shows hours of LOLP by month/hour time slice Sum of time slices is test year LOLE: 334 hours per year before adding resources Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 0.006 0.004 0.000 0.000 0.000 0.000 0.001 0.018 0.00

39. Slide 39: Preliminary PRM is 15.1% for 2021 test year

    Preliminary PRM is 15.1% for 2021 test year Unit MW A 1-annual-event-in-10- Natural Gas 1,821 years standard Colstrip 296 (LOLE=2.4) implies an Hydro Projects 575 annual capacity shortage Mid‐C Hydro Agreements 123 Other Contracts 9 of 932 MW in 2021 DSM 142 Renewables 98 Equivalent to a 15.1% Impor

40. Slide 40: Seasonal LOLE

    Seasonal LOLE PGE system is dual peaking, with non-zero LOLP in both summer and winter seasons E3 and PGE have developed a three-part test that ensures PGE system is resource adequate in both seasons while meeting annual LOLE target of 2.4 hours per/yr. PGE’s system is defined to be resource adequat

41. Slide 41: Independent seasonal and annual resource adequacy tests

    Independent seasonal and annual resource adequacy tests Winter need calculated using winter-only RECAP run Winter test intended to ensure no more than one winter loss-of-load event in 10 years Winter Standard: LOLE less Winter Winter Winter than 2.4 winter hours per Standard Capacity Shortage year R

42. Slide 42: Independent seasonal and annual resource adequacy tests

    Independent seasonal and annual resource adequacy tests Summer need calculated independently using summer-only RECAP run Summer test intended to ensure no more than one summer loss-of-load event in 10 years Winter Standard: LOLE less Winter Winter Winter than 2.4 winter hours per Standard Capacity S

43. Slide 43: Independent seasonal and annual resource adequacy tests

    Independent seasonal and annual resource adequacy tests Annual need calculated independently using year- round RECAP run Annual test intended to ensure no more than one loss- of-load event in 10 years (any time of year) Winter Standard: LOLE less Winter Winter Winter than 2.4 winter hours per Standa

44. Slide 44: Calculating Annual and Seasonal Planning Reserve Margins

    Calculating Annual and Seasonal Planning Reserve Margins Annual, winter and summer capacity requirements can be translated into annual, winter and summer PRMs Definitions: • Winter PRM: Winter reliable MW divided by 1-in-2 winter peak load • Summer PRM: Summer reliable MW divided by 1-in-2 summer pe

45. Slide 45: Preliminary Target PRM is 14.3% for Winter Test

    Preliminary Target PRM is 14.3% for Winter Test Unit MW A 1-winter-event-in-10- Natural Gas 1,870 years standard implies a Colstrip 296 winter capacity shortage Hydro Projects 624 of 630 MW in 2021 Mid‐C Hydro Agreements 127 Other Contracts 9 DSM 142 Equivalent to a 14.3% Renewables 130 PRM Imports

46. Slide 46: Preliminary Target PRM is 14.6% for Summer Test

    Preliminary Target PRM is 14.6% for Summer Test Unit MW A 1-summer-event-in-10- Natural Gas 1,772 years standard implies a Colstrip 296 summer capacity shortage Hydro Projects 525 of 915 MW in 2021 Mid‐C Hydro Agreements 119 Other Contracts 9 DSM 142 Equivalent to a 14.6% Renewables 92 PRM Imports 0

47. Slide 47: Preliminary Target PRM is 15.1% for Annual Test

    Preliminary Target PRM is 15.1% for Annual Test Unit MW A 1-annual-event-in-10- Natural Gas 1,821 years standard Colstrip 296 (LOLE=2.4) implies an Hydro Projects 575 annual capacity shortage Mid‐C Hydro Agreements 123 Other Contracts 9 of 932 MW in 2021 DSM 142 Renewables 98 Equivalent to a 15.1% I

48. Slide 48: Summary

    Summary PGE has selected a resource adequacy standard of 1-day-in-10 years • This is interpreted as 2.4 hours/year within the context of E3’s RECAP model E3 and PGE have developed independent winter, summer, and annual capacity requirements based on 1-day-in-10 years 1. No more than 2.4 winter hours

49. Slide 49: Capacity Contribution of Dispatch-Limited Resources

    CAPACITY CONTRIBUTION OF DISPATCH-LIMITED RESOURCES

50. Slide 50: Renewable resources can contribute to system reliability

    Renewable resources can contribute to system reliability Illustrative Capacity Values No resource is (not based on PGE system) perfectly available to help reduce LOLP 95 MW Capacity Value By convention, dispatchable resources rated at 60 MW 100 MW nameplate and Capacity nameplate forced outages Valu

51. Slide 51: Renewables subtracted from load in LOLP calculations

    Renewables subtracted from load in LOLP calculations Renewable production is subtracted from gross load to yield “net load”, which is always lower LOLP decreases in every hour Net thermal generation distribution Gross load Probability Net load Gross load distribution distribution with Thermal Renewa

52. Slide 52: Calculating ELCC

    Calculating ELCC Since LOLE has decreased with the addition of renewables, adding pure load will return the system to the original LOLE The amount of load that can be added to the system is the Effective Load-Carrying Capability (ELCC) Original system LOLE Additional load to return to original syste

53. Slide 53: Capacity value in applications

    Capacity value in applications The portfolio capacity value is the Individual most relevant calculation to Solar consider in resource planning Capacity Combined Value Capacity Value • Due to the complementarity of different Individual resources the portfolio value will be Wind Capacity higher than t

54. Slide 54: Factors that affect the capacity value of variable generation

    Factors that affect the capacity value of variable generation Coincidence with load • Locations with better resources and better correlation with high load periods will have higher ELCC values Coincidence with existing variable generation • Common resource types show diminishing marginal returns; ea

55. Slide 55: Marginal capacity value declines as penetration increases

    Marginal capacity value declines as penetration increases A resource’s contribution towards reliability depends on the other resources on the system The diminishing marginal peak load impact of solar PV is illustrative of this concept • While the first increment of solar PV has a relatively large im

56. Slide 56: Example Draw: High Load Weekday in August

    Example Draw: High Load Weekday in August Day-Type Bins - Load Day-Type Bins - Wind Day-Type Bins - Solar Low High Low High Low High Load Load Load Load Load Load Weekends/Holidays Weekdays Jan Jan Jan Feb Feb Feb Mar Mar Mar Apr Apr Apr May May May Jun Jun Jun Jul Jul Jul Aug Aug Aug Sep Sep Sep Oc

57. Slide 57: Example Draw: High Load Weekday in August

    Example Draw: High Load Weekday in August Within each bin, choose each (load, wind, and solar) daily profile randomly, and independent of other daily profiles Load Bin Wind Bin Solar Bin 10000 5000 80000 Solar Power (MW) Wind Power (MW) 8000 4000 60000 Load (MW) 6000 3000 40000 4000 2000 20000 1000

58. Slide 58: Gorge wind has low output during hours with high LOLP

    Gorge wind has low output during hours with high LOLP Coincidence of high renewable output and high system LOLE results in a higher ELCC • System LOLE is concentrated in • Sample Gorge wind site has summer afternoon hours relative low output on summer afternoons, resulting in low ELCC Average Normal

59. Slide 59: Montana wind output is higher during hours with high LOLP

    Montana wind output is higher during hours with high LOLP Coincidence of high renewable output and high system LOLE results in a higher ELCC • System LOLE is concentrated in • Sample Montana wind site has summer afternoon hours higher relative output on summer afternoons, resulting in higher ELCC Av

60. Slide 60: Solar output is high during summer peak hours

    Solar output is high during summer peak hours Coincidence of high renewable output and high system LOLE results in a higher ELCC • System LOLE is concentrated in • Solar PV has high output on summer afternoon hours summer afternoons, resulting in high ELCC Average Normalized Solar Output System LOLE

61. Slide 61: Gorge wind is negatively correlated with load during summer peak hours

    Gorge wind is negatively correlated with load during summer peak hours Correlation between 80th load percentile August load and renewable Weekday, HE17 output may exist even within each month-hour- day type • E.g. decrease in wind output in high load hours, as both are correlated to high temperature

62. Slide 62: Gorge wind is negatively correlated with load during summer peak hours

    Gorge wind is negatively correlated with load during summer peak hours Correlation between 80th load percentile August load and renewable Weekday, HE17 output may exist even within each month-hour- day type • E.g. decrease in wind output in high load hours, as both are correlated to high temperature

63. Slide 63: Preliminary ELCC for PGE's current renewable portfolio is 11.4%

    Preliminary ELCC for PGE’s current renewable portfolio is 11.4% Winter Summer Annual Nameplate rating MW 861 861 861 Portfolio ELCC (MW) 130 92 98 Portfolio ELCC (% of nameplate MW) 15.1% 10.7% 11.4% Preliminary results – do not cite PGE portfolio currently has 861 MW of renewables • Most is wind ca

64. Slide 64: Preliminary marginal ELCC of incremental resources

    Preliminary marginal ELCC of incremental resources Marginal ELCC measures the additional ELCC provided by adding new resources to the portfolio Sample portfolio includes two Gorge sites and PV • The Gorge sites add little diversity to the existing portfolio and have relatively low ELCCs • Incrementa

65. Slide 65: Preliminary marginal ELCC of incremental resources by season

    Preliminary marginal ELCC of incremental resources by season Gorge wind resources have higher ELCC in winter than in the summer Solar PV has high summer value due to coincidence of output with peak needs, but very low winter value due to nighttime peak loads Portfolio effects result in similar total

66. Slide 66: Flexibility Assessment Using E3's Renewable Energy Flexibility Model

    Flexibility Assessment Using E3’s Renewable Energy Flexibility Model Elaine Hart, Managing Consultant

67. Slide 67: Background

    Background Introduction of variable renewables has shifted the capacity planning paradigm PGE has been directed by the Oregon PUC to provide an “Evaluation of new analytical tools for optimizing flexible resource mix to integrate load and variable resources” The new planning problem consists of two

68. Slide 68: Flexibility Planning Challenges

    Flexibility Planning Challenges 1. Downward ramping capability 3. Upward ramping capability Thermal & hydro resources operating to serve Thermal & hydro resources must ramp up loads at night must be ramped downward and quickly and new units may be required to start potentially shut down to make room

69. Slide 69: Many Resource Characteristics Can Be Important for Flexibility

    Many Resource Characteristics Can Be Important for Flexibility Characteristic How it helps with system flexibility Upward ramping capability on multiple Helps meet upward ramping demands time scales:  1 minute, 5 minutes, 20 minutes, 1 hour, 3 hours, 5 hours Downward ramping capability on multiple

70. Slide 70: Flexibility and Economics

    Flexibility and Economics Renewable integration can be framed as an economic operating decision Flexibility violations in upward and downward directions are substitutes for one another • Upward ramping shortages can be solved using renewable curtailment Strategy to Minimize Downward Violations Strat

71. Slide 71: Cost-Effective Flexibility Investment

    Cost-Effective Flexibility Investment Curtailment can be difficult if relied on as a long-term grid Least‐cost flexible capacity flexibility solution Reliability and procurement Flexibility Costs Total System Cost • Must compensate curtailed generator • Requires systems in place to calculate Cost of

72. Slide 72: Scope of this project

    Scope of this project Estimate expected flexibility violations • REFLEX: Adapted production simulation methodology designed to assess system flexibility Identify and assess candidate portfolios of flexibility solutions • Renewable portfolio diversity • Energy storage • Peaking thermal resources 72

73. Slide 73: REFLEX Methodology

    ©) Energy+Environmental Economics REFLEX METHODOLOGY

74. Slide 74: Renewable Energy Flexibility (REFLEX) Model

    Renewable Energy Flexibility (REFLEX) Model REFLEX answers critical questions about flexibility need through adapted production simulation • Captures wide distribution of operating conditions through Monte Carlo draws of operating days • Illuminates the significance of the operational challenges by

75. Slide 75: REFLEX Has Features of Reliability and Production Simulation Models

    REFLEX Has Features of Reliability and Production Simulation Models LOLP Model Production Simulation Reliability/Resource Adequacy Production simulation E.g., RECAP, GE-MARS, SERVM E.g., GridView, PLEXOS Determines quantity of Calculates least-cost dispatch resources needed to meet load subject to g

76. Slide 76: Flexibility Metrics

    Flexibility Metrics Flexibility violations occur when the power system cannot meet all changes in net load over all time scales REFLEX reports two categories of flexibility violations: • EUE: Expected Unserved Energy • EOG: Expected Overgeneration, aka renewable curtailment • Hourly and within-hour

77. Slide 77: Stochastic Sampling of Load, Wind, and Solar

    Stochastic Sampling of Load, Wind, and Solar Day-Type Bins - Load Day-Type Bins - Wind Day-Type Bins - Solar Low High Low High Low High Load Load Load Load Load Load Weekends/Holidays Weekdays Jan Jan Jan Feb Feb Feb Mar Mar Mar Apr Apr Apr May May May Jun Jun Jun Jul Jul Jul Aug Aug Aug Sep Sep Sep

78. Slide 78: Example Draw: High Load Weekday in August

    Example Draw: High Load Weekday in August Within each bin, choose each (load, wind, and solar) daily profile randomly, and independent of other daily profiles • 24 hour spin-up and spin-down periods included in the optimization Load Bin Wind Bin Solar Bin 10000 5000 80000 Solar Power (MW) Wind Power

79. Slide 79: Stochastic Sampling of Hydro Conditions

    Stochastic Sampling of Hydro Conditions Traditional production simulation analysis typically relies on a single year of hydro conditions REFLEX samples energy budgets from a wide range of historical hydro conditions (1928-2008) • Northwest Power and Conservation Council (NWPCC) simulated monthly out

80. Slide 80: Flexibility Challenges in the PGE System

    FLEXIBILITY CHALLENGES IN THE PGE SYSTEM

81. Slide 81: Minimum Generation Challenges

    Minimum Generation Challenges Low net load conditions • May increase cycling of thermal plants • May require renewable curtailment to ensure system reliability Curtailment will occur when the minimum thermal generation required for reliability exceeds the net load 2021 Load 2021 Load minus existing

82. Slide 82: Ramping Challenges

    Ramping Challenges Continued wind development increases the tails of ramping distributions • Existing renewables increase magnitude of most extreme ramp events by factor of 1.3 – 1.5 relative to no renewables • Candidate portfolios increase magnitude of extreme ramp events by factor of ~2.5 relative

83. Slide 83: Example scheduling and dispatch – Existing renewables

    Example scheduling and dispatch – Existing renewables REFLEX models real-time (5-minute) dispatch and day-ahead and hour-ahead unit commitment based on imperfect forecasts Example dispatch shown below meets all 2021 capacity needs with entirely inflexible “Block Capacity” resource Early morning day-

84. Slide 84: Curtailment patterns at higher wind & solar penetrations

    Curtailment patterns at higher wind & solar penetrations Average renewable curtailment by month‐hour in 2021 Hour of Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 55 75 93 110 116 86 36 8 1 ‐ ‐ ‐ ‐ ‐ ‐ ‐ 0 ‐ ‐ ‐ ‐ ‐ 2 24 2 23 47 68 73 63 46 19 4 0 ‐ ‐ ‐ ‐ ‐ 0 0 ‐ 0 ‐ ‐ ‐ ‐ 1 1

85. Slide 85: Completed Work and Next Steps

    Completed Work and Next Steps Develop REFLEX cases for several renewable portfolios  PGE loads and resources  PGE hydro conditions  Colstrip dispatch behavior  On-peak/off-peak import treatment Quantify flexibility challenges  Simulate dispatch and quantify curtailment with inflexible “Block Ca

86. Slide 86: Incorporation into PGE IRP process

    Incorporation into PGE IRP process Example (not to scale below): Metrics from REFLEX can be Value adder in AURORA = used to supplement outputs [Unit value in REFLEX w/ all constraints] – from AURORA [Unit value in REFLEX w/o flexibility constraints] • Example: REFLEX models constraints related to st

87. Slide 87: Thank You!

    Thank You! Energy and Environmental Economics, Inc. (E3) 101 Montgomery Street, Suite 1600 San Francisco, CA 94104 Tel 415-391-5100 Web http://www.ethree.com Arne Olson, Partner ([email protected]) Elaine Hart, Managing Consultant ([email protected]) Ana Mileva, Senior Consultant (ana.mileva@ethr

88. Slide 88: UM 1719 – Capacity Contribution of VER

    UM 1719 – Capacity Contribution of VER August 13, 2015 Slide 88  August 17th workshop with Commissioners that will include presentations from three noted experts in renewable capacity contribution studies.  Andrew Mills – Lawrence Berkeley National Laboratory  Findings from "An Evaluation of Sola

89. Slide 89: Demand Response Update

    Demand Response Update

90. Slide 90: Purpose of Today's Discussion

    Purpose of Today’s Discussion August 13, 2015 Slide 90 Start a dialogue about how PGE will consider Demand Response and Dynamic Pricing in the 2016 IRP:  Discuss projects in the field  Share pilots in development  Share the results of the 2015 DR Potential Study

91. Slide 91: Dynamic Pricing & Demand Response Efforts

    Dynamic Pricing & Demand Response Efforts April 2, 2015 Slide 91 Ongoing: Completed:  Time of Use  Flex PriceSM Critical Peak Pricing  Demand Buyback Pilot (Sch. 12)  Schedule 77 Load Curtailment  Transactive Node Water Heater Demand Response Pilot  Energy PartnerSM Automated Demand Response P

92. Slide 92: Pricing Pilot Overview

    Pricing Pilot Overview August 13, 2015 Slide 92  Two-year Behavioral Demand Response and Dynamic Pricing pilot  Winter and summer programs  Pilot tests two approaches  New time-of-use (TOU) rates  Peak time rebates (PTR) event based incentives Control Schedule 7 Day and Night Peak only Revised

93. Slide 93: 2015 Smart Thermostat Pilot Overview

    2015 Smart Thermostat Pilot Overview August 13, 2015 Slide 93  Uses residential, programmable, communicating (“smart”) thermostats for automated demand response under a bring-your-own-thermostat structure  Participants receive an incentive payment for each event season  Two-year pilot will enroll

94. Slide 94: Smart Water Heater Pilot

    Smart Water Heater Pilot August 13, 2015 Slide 94  Investigates the market readiness and potential value of CEA-2045 enabled “smart” water heaters.  BPA funded three-year project Under Cover  600 water heaters spread across multiple NW utilities  2017-2020  Can help unlock opportunities for use

95. Slide 95: Questions

    Questions August 13, 2015 Slide 95

96. Slide 96: Portland General Electric's Demand Response Potential

    Portland General Electric’s Demand Response Potential August 2015 Stakeholder Presentation PREPARED FOR PREPARED BY Ryan Hledik Ahmad Faruqui Lucas Bressan August 2015 Copyright © 2015 The Brattle Group, Inc.

97. Slide 97: Acknowledgements

    Acknowledgements We would like to acknowledge the contributions of Ingrid Rohmund, Dave Costenaro, Sharon Yoshida, and Bridget Kester of Applied Energy Group. They led the market data collection and program cost development in this study. We would also like to thank the PGE team including Josh Keeli

98. Slide 98: In this presentation…

    In this presentation… We estimate the peak reduction capability that could be achieved through the deployment of demand response (DR) programs in PGE’s service territory We also assess the cost‐effectiveness of each DR option based on a comparison of program costs to avoided resource costs The findi

99. Slide 99: The study analyzes "maximum achievable potential"

    The study analyzes “maximum achievable potential” Assumes enrollment rates reach levels of successful DR programs around the country Several factors suggest that PGE’s customer base could reach these levels of participation ▀ Success with energy efficiency programs ▀ Environmentally conscious custom

100. Slide 100: 28 different options are analyzed

     28 different options are analyzed Not all customer segments are eligible for each DR option 100 | brattle.com

101. Slide 101: Participation rates

     Participation rates Pricing options ▀ Based on review of market research studies and full‐scale deployments ▀ Opt‐in participation ranges from 13% to 28% ▀ Opt‐out participation ranges from 63% to 92% ▀ Varies by rate option and customer class Conventional non‐pricing options ▀ Largely based on 75th

102. Slide 102: Multi-year transition to steady state enrollment levels

     We account for a multi-year transition to the steady state enrollment levels Illustration of S‐Shaped Diffusion Curve Comments ▀ Changes in participation are assumed to happen over a 5‐year timeframe once the new programs are offered ▀ The ramp up to steady state participation follows an “S‐shaped”

103. Slide 103: Load impacts per participant

     Load impacts per participant Pricing options ▀ Based on impacts observed in 225 pricing tests in past 12 years ▀ Accounts for differences across rate design, season, and offering ▀ Response is a function of peak‐to‐off‐peak price ratio ▀ Price ratios based on PGE designs: TOU is 2:1, CPP is 4:1, PTR

104. Slide 104: Adjustments to benefit and cost assumptions

     Important adjustments are made to the benefit and cost assumptions in the cost-effectiveness analysis Avoided costs are derated ▀ Avoided capacity costs are derated by between 19% and 47% ▀ Accounts for operational limitations of the DR programs ▀ For example, limitations on number of events per yea

105. Slide 105: Important caveats

     Important caveats ▀ The load reduction potential and cost‐effectiveness of each DR option are evaluated in isolation from the other options; the potential estimates are not additive and economics may change when the DR options are offered as part of a portfolio ▀ Our analysis is based on “typical” p

106. Slide 106: Organized around 9 key findings

     The remainder of this presentation is organized around 9 key findings 1. The most attractive DR opportunities are in the residential and large C&I customer segments 2. Residential pricing programs present a large and cost‐effective opportunity to leverage the value of PGE’s AMI investment 3. The inc

107. Slide 107: Finding #1: Residential and large C&I opportunities

     Finding #1: The most attractive DR opportunities are in the residential and large C&I customer segments 107 | brattle.com

108. Slide 108: The top 10 measures in terms of potential

     The top 10 measures in terms of potential Winter Potential Summer Potential ▀ The largest programs are in the industrial and residential sectors ▀ Opt‐out dynamic pricing generally provides the largest aggregate impacts due to high expected enrollment rates 108 | brattle.com

109. Slide 109: The top 10 measures in terms of cost-effectiveness

     The top 10 measures in terms of cost-effectiveness Benefit‐Cost Ratios Comments ▀ Several large C&I and residential programs are highly cost‐effective ▀ The most cost‐effective programs tend to be pricing programs and curtailable tariffs 109 | brattle.com

110. Slide 110: Programs with the biggest bang for the buck

     The programs with the biggest “bang for the buck” are in the residential and large C&I classes Winter Potential vs. B‐C Ratio by Measure The highlighted programs provide large, highly cost‐ effective DR potential 110 | brattle.com

111. Slide 111: Finding #2: Residential pricing programs

     Finding #2: Residential pricing programs present a large and cost-effective opportunity to leverage the value of PGE’s AMI investment 111 | brattle.com

112. Slide 112: Residential pricing programs have higher potential if opt-out

     Residential pricing programs have significantly higher potential if deployed on an opt-out basis Winter Potential – Residential Pricing & BDR Comments ▀ Opt‐out deployment leads to aggregate peak reduction capability that is between 90% and 300% higher than an opt‐in deployment ▀ While PTR is likely

113. Slide 113: Residential pricing programs are cost-effective

     Residential pricing programs are cost-effective Cost‐Effectiveness – Residential Pricing & BDR Comments ▀ For conventional pricing programs the opt‐in offering has a slightly higher benefit‐ cost ratio than the opt‐out offering due to marketing and education costs that are lower on a dollars‐per‐kW

114. Slide 114: Finding #3: Enabling technology with pricing

     Finding #3: The incremental benefits of coupling enabling technology with residential pricing options are modest and perhaps best realized through a BYOT program 114 | brattle.com

115. Slide 115: Enabling technology modestly increases price response

     The provision of enabling technology modestly increases price response in the aggregate Winter Potential – Residential Pricing with Tech Comments ▀ The incremental impact of enabling technology provides a 90% boost over the impact of price alone among those equipped with the technology ▀ We have ass

116. Slide 116: Cost-effectiveness of enabling technology coupled with price

     The cost-effectiveness of enabling technology coupled with price is questionable Cost‐effectiveness – Residential Pricing with Tech Comments ▀ Assuming there is already a plan to roll out dynamic pricing, the incremental impact of enabling technology, above and beyond the impact that would be achiev

117. Slide 117: Finding #4: BYOT programs

     Finding #4: BYOT programs offer better economics than conventional DLC programs but lower potential in the short- to medium-term 117 | brattle.com

118. Slide 118: Residential DLC is a potentially large summer resource

     Residential DLC is a potentially large summer resource Seasonal Potential – Residential DLC Comments ▀ DLC produces larger aggregate impacts than BYOT because more customers are eligible to participate ▀ A/C load control has by far the largest demand reduction potential N/A N/A 118 | brattle.com

119. Slide 119: All DLC options are cost-effective under peaking conditions

     Under expected system peaking conditions, all DLC options are cost-effective Cost‐effectiveness – Residential DLC Comments ▀ BYOT programs offer better cost savings than conventional DLC because there is no associated equipment cost ▀ Conventional air‐ conditioning DLC will become increasingly cost‐

120. Slide 120: DLC package passes the cost-effectiveness screen

     When offered as a package targeting multiple end- uses, DLC passes the cost-effectiveness screen Cost‐effectiveness – Combined DLC Program Comments ▀ Since the DLC program would likely be offered to target multiple end uses, it makes sense to consider the cost‐effectiveness of the program in the agg

121. Slide 121: Finding #5: Residential water heating load control

     Finding #5: Residential water heating load control is an attractive opportunity with a broad range of potential benefits 121 | brattle.com

122. Slide 122: Two types of water heating load control programs modeled

     Two types of water heating load control programs were modeled Conventional water heating DLC ▀ Control technology retrofit on existing or new electric water heaters ▀ Equipment + installation = $300 per participant “Smart” water heating DLC ▀ Assumes “DR‐ready” electric water heaters gain growing ma

123. Slide 123: Water heating load control benefits beyond system peak

     Water heating load control can provide benefits beyond reductions in the system peak Benefits will vary depending on the load control strategy and the characteristics of the electric water heater Ancillary services ▀ If equipped with the appropriate control technology, electric resistance water heat

124. Slide 124: Water heating load control is cost-effective

     Both forms of water heating load control are cost- effective based on avoided peak capacity costs Cost‐effectiveness – Water Heating Load Control Comments ▀ DR‐ready water heaters offer a number of cost saving opportunities relative to conventional DLC, primarily in the form of reduced equipment and

125. Slide 125: Finding #6: Small C&I DLC

     Finding #6: Small C&I DLC has a small amount of cost- effective potential 125 | brattle.com

126. Slide 126: Only space heating DLC is cost-effective for small C&I

     Only space heating DLC is cost-effective for the small C&I segment and its potential is small Cost‐effectiveness – All Small C&I DR Options Comments ▀ Space heating DLC is cost‐effective for the small C&I segment; winter potential is around 6 MW ▀ Small C&I customers tend to be unresponsive to time‐

127. Slide 127: Finding #7: DR for large and medium C&I

     Finding #7: DR is highly cost-effective for large and medium C&I customers and the potential can be realized through a variety of programs 127 | brattle.com

128. Slide 128: All DR measures are cost-effective for medium and large C&I

     All DR measures are cost-effective for medium and large C&I customers Cost‐effectiveness – Medium and Large C&I Comments ▀ Customer acquisition costs tend to be lower on a dollars‐per‐kilowatt basis for these segments, leading to attractive economics for DR ▀ The large C&I segment accounts for the m

129. Slide 129: Large/medium C&I programs have large potential

     In addition to being highly cost-effective, several large/medium C&I programs have large potential Winter Potential – Large and Medium C&I Comments ▀ There is significant untapped potential in a curtailable tariff and a third‐party DLC program ▀ CPP provides similarly large peak impacts ▀ These prog

130. Slide 130: Finding #8: Agricultural DR programs

     Finding #8: Agricultural DR programs are small and uneconomic 130 | brattle.com

131. Slide 131: Agricultural DR programs are small and uneconomic

     Agricultural DR programs are small and uneconomic Summer Potential vs. B‐C Ratio, All Measures Comments ▀ PGE has little irrigation pumping load, making it an unattractive target for DR programs ▀ Relative to other options, programs focused on agricultural customers are small and not cost‐effective

132. Slide 132: Finding #9: Ancillary services economics

     Finding #9: The economics of some programs improve when accounting for their ability to provide ancillary services 132 | brattle.com

133. Slide 133: "Fast" DR provides additional value

     “Fast” DR provides additional value Cost‐effectiveness for measures with “fast” load Comments decrease and increase capability ▀ Mass market water heating load control and medium and large C&I load control could provide fast ramping capability in the form of load increases and decreases ▀ With a rec

134. Slide 134: Key Considerations for the Future

     Key Considerations for the Future 134 | brattle.com

135. Slide 135: Considerations

     Considerations Run a dynamic pricing / behavioral DR pilot ▀ A new pilot could provide insight about relatively untested issues such as the impact of a PTR in PGE’s service territory, persistence in behavioral DR impacts, and the relative difference in seasonal impacts of these programs (an under‐re

136. Slide 136: Considerations (continued)

     Considerations (continued) Continue to pursue opportunities in the large and medium C&I sectors ▀ The large C&I potential can be achieved through curtailable tariffs, third‐ party programs, and pricing options; which of these to pursue is largely a strategic question, as each have their advantages a

137. Slide 137: Considerations (continued)

     Considerations (continued) Develop a long‐term rates strategy enabled by PGE’s AMI investment ▀ The strategy should address important considerations such as whether to offer new rates on an opt‐in versus opt‐out basis, the advantages and disadvantages of CPP versus PTR, whether a demand charge or in

138. Slide 138: Considerations (continued)

     Considerations (continued) Develop a “supply curve” approach to integrating DR into the IRP modeling process ▀ DR options can be represented in resource planning models essentially as the equivalent of supply‐side resources and dispatched against new generation options to determine the economically

139. Slide 139: Presenter information

     Presenter information RYAN HLEDIK Principal │ San Francisco, CA [email protected] +1.415.217.1018 Mr. Hledik specializes in the economics of policies and technologies that are focused on the energy consumer. He assists clients confronting complex issues related to the recent slowdown in electr

140. Slide 140: Load Forecast

     Load Forecast

141. Slide 141: Technical Workshop – Highlights

     Technical Workshop – Highlights August 13, 2015 Slide 141  Topics  Overview of Modeling Initiatives  Long Term Energy Models  Peak Demand Model  Treatment of Programmatic Energy Efficiency  Q&A Discussion Items  Impacts of Climate Change  Flexible models allow for scenario analysis  Impact

142. Slide 142: Forecast Before and After Energy Efficiency

     Forecast Before and After Energy Efficiency August 13, 2015 Slide 142 3,500 Average Annual Growth Rate 1.7% 1.2% 3,000 1.1% 2,500 Average MW 2,000 1,500 Gross (before Energy Efficiency) 1,000 Base Forecast Net of Energy Efficiency 500 - 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 202

143. Slide 143: Load Forecast Action Item (LC 56)

     Load Forecast Action Item (LC 56) August 13, 2015 Slide 143  Order No. 14-415: “…require PGE to convene a series of workshops with interested parties to examine PGE’s load forecast methodology in detail.”  Public Meeting #1 (4/2/2015) • PGE load forecasters presented the underlying fundamentals of

144. Slide 144: Natural Gas Forecast

     Natural Gas Forecast

145. Slide 145: Summary

     Summary August 13, 2015 Slide 145  Historically, natural gas has been one of North America’s most volatile commodities  Shale revolution dramatically increased recoverable supply, lowered prices and diminished expected volatility  Improvements in extraction process have continued to lower prices

146. Slide 146: Vintaged Wood Mackenzie Long Term Forecasts

     $/MMBtu (2015$) 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 Jan‐97 Jan‐98 2005 Jan‐99 August 13, 2015 Jan‐00 2006 Jan‐01 Jan‐02 Slide 146 Jan‐03 2007 Jan‐04 Jan‐05 Jan‐06 2008 Jan‐07 Jan‐08 Jan‐09 2009 Historical Forecasts Jan‐10 Jan‐11 Jan‐12 2010 Jan‐13 Jan‐14 Jan‐15 2011 Jan‐16 Jan‐17

147. Slide 147: Supply and Demand Updates

     Supply and Demand Updates August 13, 2015 Slide 147 • LNG Exports Prices Up • Electricity Generation Demand – Carbon Regulation • Drilling and Recovery Regulation • Low Oil Prices – Domestic Supply Contraction • Methanol and Fertilizer Manufacturing • Mexican Exports Prices Down • Expanded Supply •

148. Slide 148: Gas Forecast Sensitivities

     Gas Forecast Sensitivities August 13, 2015 Slide 153 9 8 7 Henry Hub Price 2015$ ($/MMBTU) 6 5 4 3 2 1 0 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 EIA High Oil EIA Reference WM High WM Reference EIA High Oil and Gas WM Low

149. Slide 149: Gas Forecast Sensitivities: EIA AEO Forecast Range

     Gas Forecast Sensitivities August 13, 2015 Slide 154 EIA 2015 AEO Forecast Range 9 8 7 Henry Hub Price 2015$ ($/MMBTU) 6 5 4 3 2 1 0 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 EIA High Oil EIA Reference WM High WM Reference EIA High Oil and Gas WM Low

150. Slide 150: Gas Forecast Sensitivities: Wood Mackenzie Forecast Range

     Gas Forecast Sensitivities August 13, 2015 Slide 155 9 Wood Mackenzie 2015 Forecast Range 8 7 Henry Hub Price 2015$ ($/MMBTU) 6 5 4 3 2 1 0 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 EIA High Oil EIA Reference WM High WM Reference EIA High Oil and Gas WM Low

151. Slide 151: Summary and Recap

     Summary and Recap August 13, 2015 Slide 156  Natural gas growth in US power sector anticipated following Clean Power Plan.  LNG exports anticipated to increase US gas demand by over 25%.  Dramatic expansion in supply and continued lowering of breakeven price keeps prices low despite increased dem

152. Slide 152: Portfolios and Futures

     Portfolios and Futures

153. Slide 153: Portfolios and Futures: Objective

     Portfolios and Futures: Objective August 13, 2015 Slide 158 Balance cost and risk to provide opportunity to obtain the best resource portfolio in a constantly changing environment • Efficiency • Compliance Technology Policy • Diversity • Corporate responsibility METRICS IRP • Contingency • Cost effe

154. Slide 154: Portfolios and Futures: IRP analytical process

     Portfolios and Futures: IRP analytical process August 13, 2015 Slide 159 Action + Plan 1. 2. 3. 4. 5. Futures Portfolios Modeling Scoring Results Potential uncertainties The broad world of AURORAxmp Assess objectives Selection of the affecting the resource resource alternatives production cost (cost

155. Slide 155: Portfolios and Futures: IRP Guidelines

     Portfolios and Futures: IRP Guidelines August 13, 2015 Slide 160 Reference Select IRP Requirements – Futures 07-002 (1b) Load requirements (1b, 4g) Fuel prices (1b) Hydroelectric generation (1b) Electricity prices (1b) Forced outage rates (1b, 4g) Cost of compliance with GHG regulation (4b) High and

156. Slide 156: Portfolios and Futures: Potential risk factors

     Portfolios and Futures: Potential risk factors August 13, 2015 Slide 161 Policy Technology Reliability Price Carbon Resources Load Power • 111(d) • EE/DR • Forecast • Market • CO2 • Wind/Solar • ESS • Hydro • Gas • Distributed Renewable Capital Reserves* Fuel** • RPS • Wind/Solar • PRM • Wind/Solar

157. Slide 157: Portfolios and Futures: 2013 IRP impacts

     Portfolios and Futures: 2013 IRP impacts August 13, 2015 Slide 162 2013 IRP Preferred Portfolio Performance Futures vs. Reference Case NPVRR (2013$ millions) Low/High Carbon ($856) $2,484 High/Low Hydro ($326) $147 Low/High PGE Load (2 Std Dev) ($1,960) $2,069 Low/High PGE Load (1 Std Dev) ($964) $9

158. Slide 158: Portfolios and Futures: Potential Futures

     Portfolios and Futures: Potential Futures August 13, 2015 Slide 163 111(d) On Off CO2 Zero Synapse Ref Synapse High Gas Wood Mac Ref EIA High Load Low Reference High Western ISO CA 50% RPS Capital Cost CO2 Trigger Heavy DG Solar Low water Climate load Mass Based CE Inflation Mass Based AE Renewable

159. Slide 159: Portfolios and Futures: Futures feedback

     Portfolios and Futures: Futures feedback August 13, 2015 Slide 164  Use the feedback form on the PGE IRP website  www.PortlandGeneral.com/IRP

160. Slide 160: Portfolios and Futures: IRP Guidelines

     Portfolios and Futures: IRP Guidelines August 13, 2015 Slide 165 Reference Select IRP Requirements – Resource Alternatives 07-002 (1a) All known resources for meeting the utility's load (1a, 4h) Resource fuel types, technologies, lead times, in-service dates, durations and locations (4c) Existing an

161. Slide 161: Portfolios and Futures: Resource alternatives

     Portfolios and Futures: Resource alternatives August 13, 2015 Slide 166 Central Distributed Demand Integration Hydro Solar Energy Storage Efficiency Wind Smart Grid Solar CHP Demand Other Response Geothermal Market Position Biomass DSG CVR Existing Gas Resource

162. Slide 162: Portfolios and Futures: Portfolio design

     Portfolios and Futures: Portfolio design August 13, 2015 Slide 167  Preliminary portfolio design to target capacity need  Capacity need defined by load, contingency reserves (spin/non-spin), planning reserves, and reliable resource capacity Load + Reserves  Studying: Load  planning reserve margi

163. Slide 163: Portfolios and Futures: Portfolio examples

     Portfolios and Futures: Portfolio examples August 13, 2015 Slide 168  Given resource alternatives from Slide 40 and seasonal targets = many possible combinations  Two examples to meet approximately 800 MW winter need: Capacity Capacity Resource Nominal Reliable Reliable Resource Nominal Reliable R

164. Slide 164: Appendix

     Appendix

165. Slide 165: 2016 IRP: Feedback Status

     2016 IRP: Feedback Status August 13, 2015 Slide 170 Topic Feedback Received Resolution Completed For stakeholder questions, provide a General Passing the mic was cumbersome. stationary microphone at a podium or 4/13/2015 mics at each table. Update schedule slides to account for automation. Plan to r

166. Slide 166: 2016 IRP: Feedback Status

     2016 IRP: Feedback Status August 13, 2015 Slide 171 Topic Feedback Received Resolution Completed PGE to addressed questions about EE Load Forecast For future discussion, how is the ETO 7/15/2015 and during 7/15/2015 load forecast technical Methodology forecast in later years developed? 7/16/2015 wor

167. Slide 167: 2016 IRP: Feedback Status

     2016 IRP: Feedback Status August 13, 2015 Slide 172 Topic Feedback Received Resolution Completed Sage grouse habitat based on the WECC Geospatial data viewer: DG Study What is the data source for sage grouse? 8/13/2015 http://184.169.179.203/flexviewers/WECC 3/index.html DG Study What QF rate was us

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