Overview of EPA’s Platform v6 using IPM® and Scenario Suite

Serpil KayinClean Air Markets Division

EPA Power Sector Modeling Platform v6 Using IPMJune 2018

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What’s New on EPA’s Website?EPA’s Power Sector Modeling website https://www.epa.gov/airmarkets/clean-air-markets-power-sector-modeling

► Power Sector Modeling Resources• NEEDS v6 - Latest National Electric Energy Data System• EPA's Power Sector Modeling Platform v6 using IPM - Documentation and outputs from a suite of

runs.– Documentation for EPA's Power Sector Modeling Platform v6– Results using EPA's Power Sector Modeling Platform v6– IPM Peer Review– Retail Price Model - Documentation describing how EPA estimates the difference in average

retail electricity prices between projection scenarios.– Power Sector Labor Analysis Methodology - Documentation describing how EPA estimates the

difference in power sector labor demand between projection scenarios.– Previous versions of EPA Platforms

• Results Viewer - A spreadsheet tool to compare different IPM runs in conjunction with historical data• Retrofit Cost Analyzer - A spreadsheet tool to estimate the cost of building and operating pollution

controls.• Energy Resources for State, Local, and Tribal Governments - Includes tools that estimate emission,

health, and economic benefits of clean energy policies and programs (such as AVERT and COBRA).• Clean Air Markets Data Resources - Includes historical power sector data from AMPD and eGRID

► Power Sector Modeling Regulatory Applications

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What’s New in v6?

► IPM v6 positions EPA to more effectively address newly emerging power sector drivers, including:► Significant penetration of variable renewable energy (VRE)► Greater value of — and demand for — energy storage► Building capability to model electrification scenarios

► In addition to architectural improvements, we have worked on improving the experience of interacting with IPM► Reworked documentation► Improved run output files► Multiple scenarios► IPM Results Viewer

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National Electric Energy Data System (NEEDS v6)

► NEEDS is the unit-level database of all generating units that is used to construct the model plants in IPM

► NEEDS v6 represents a “snapshot” of the generating fleet in the first model run year (2021)► Includes all operational capacity (existing units) plus units that are

not currently operating, but have either broken ground and/or secured financing (“planned-committed”)

► Planned retirements are removed from NEEDS when there is a high degree of certainty

► Contains plant characteristics, including:► Location, type, capacity, age, heat rate► Existing/planned pollution controls► Fuel access► NOX emissions rates

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NEEDS Representation

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What’s New in v6? Updates & Data Sources

See Table 1-1 in Documentation► Modeling Framework: Greater Detail and Granularity

► Incorporation of three seasons► Increasing the number of load segments from 12 to 72 per year (accounting for time-

of-day)• Hourly generation profiles for wind and solar

► Adjustment of model region boundaries to reflect current state of power markets► Model output years: 2021, 2023, 2025, 2030, 2035, 2040, 2045, 2050

► Power System Operation: Latest Available Data► Updates based on recent data from EIA, NERC, FERC► AEO 2017 NEMS region level electricity demand, disaggregated to IPM model region

level. ► IPM model region level peak load projection is based on the future load factors from

NERC 2017 ES&D and AEO 2017 (peakier in early years)► Updated transmission capabilities and regional reserve margins (2015-2016 ISO/RTO

NERC Reports)► Updated inventory of state emission regulations; NSR, state and citizen settlements ► CSAPR, MATS, BART and 111b are reflected

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What’s New in v6? Updates & Data Sources

► Generating Resources► NEEDS, the database of existing and planned-committed units and their emission

control configurations (EIA 860 2016 annual, EIA 860m October 2017, AEO 2017, EPA ETS 2017)

► Cost and performance characteristics for potential (new) conventional, and nuclear generating units (AEO 2017) and renewable units (NREL ATB 2017)

► Wind and solar technologies have revised cost and resource base estimates, capacity credit calculation methodology, hourly generation profiles and time of day based load segments to improve curtailment modeling (NREL 2017)

► Life extension costs allowing nuclear units to continue operation over the extended 80 year life (Sargent and Lundy 2017)

► Emission Control Technologies► Cost and performance assumptions for SO2, NOx, Hg, HCl and CO2 emission controls

based on engineering studies by S&L (2017); updated cost and performance for coal to gas conversion and HRI option

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What’s New in v6? (continued)

► Coal► Complete update of coal supply curves and transportation matrix (Wood Mackenzie

2016 and Hellerworx 2016)► Coal switching costs for units burning both Bituminous and Subbituminous coals ► Updated VOM cost methodology for existing coal units

► Natural gas► Modeled through annual gas supply curves and IPM region level seasonal basis

differentials (ICF 2017)

► Other Fuels► Biomass supply curves at a state and IPM region level (DOE 2016)► Update of price assumptions for fuel oil, nuclear fuel and waste fuel (AEO 2017)

► Financial Assumptions► Update of discount and capital charge rate assumptions based on a hybrid capital cost

model of utility and merchant finance structures for new units► Use of separate capital charge rates for retrofits based on utility and merchant finance

structures► Capital cost adder for uncontrolled new coal, new coal with 30% CCS and CC units ► Additional run reflecting December 2017 Tax Law

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EPA’s Power Sector Modeling Platform v6► What is the initial run? What does it represent?

► IPM produces projections, not predictions.► What are the key drivers making a substantial impact on outcomes of interest (e.g., fleet

composition and emissions)?

► Suite of scenarios with respect to the initial run► High Demand Case► Low Demand Case► Higher NG Cost Case► High RE Technology Cost Case► Low RE Technology Cost Case► December 2017 Tax Law Update

► Near Future Updates► Final ELG cost implementation+45Q+AEO2018 demand► NOx rates in small units in CA► A minimum oil burn rule for NYC based on average seasonal capacity factors

► Area of Active Development► Storage Technology (Current Projects and Mandates)► Illustrative Electrification Case (Starting with LD EV Penetration)► RE Commitments (beyond RPS)

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Platform v6 Scenario Suite

►Input assumptions in the “initial run”►AEO 2017 demand►Mid RE Technology costs from NREL ATB 2017►Financial assumptions reflecting pre-2017 Tax Reform

Bill►Gas supply is based on 20% estimated ultimate recovery

(EUR) learning-curve growth for every doubling of well completions (yields projected national average delivered price range of $3.30-4.30/MMBTu during the modeling horizon between 2021 and 2040)

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Platform v6 Scenario Suite

Parameters modified in the scenario suite with respect to the initial run► High Demand Case (2% higher by 2030; 5% higher by 2040)

► Electricity demand from the AEO 2018 High Economic Growth case

► Low Demand Case (4% lower by 2030; 5% lower by 2040)► Electricity demand from the AEO 2018 with CPP Case

► Higher NG Cost Case (~16% and 28% higher by 2030 and 2040–projected average delivered price range of $3.30-5.23/MMBTU between 2021 and 2040) ► Halved the recovery growth rate for every doubling of U.S. and Canada total well completions.► Higher LNG exports

► High RE Technology Cost Case (higher cost, lower capacity factor)► High LCOE (pessimistic) assumptions for new solar PV, new solar thermal and new onshore wind

units from NREL ATB 2017

► Low RE Technology Cost Case (lower cost, higher capacity factor)► Low LCOE (optimistic) assumptions for new solar PV, new solar thermal and new onshore wind

units from NREL ATB 2017

► 2017 Tax Reform Bill (financing costs are lower allowing more room for new investments; lower CCR, higher DR)► Reflecting the IDC, life extension costs, wind PTC and solar tariffs based on the revised discount

rate

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Model Plant Aggregation

► Section 4.2.6 of documentation► Model Region► Unit Technology Type► Cogen► Fuel Demand Region► Applicable Environmental Regulations► State► Facility (ORIS) for fossil units► Unit Configuration► Heat Rates► Fuel► Size

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Treatment of Cogeneration Units

► Listed in NEEDS v6 (about ~1300). ► Identified from EIA Form 860 and 923 based on their net energy

export to the grid. ► Section 4.2.7 of documentation

• The dispatch decisions are only based on the benefits obtained from the electric portion of a cogeneration unit.

• A cogeneration unit uses a net heat rate, which is calculated by dividing heat content of fuel consumed for power generation by electricity generated from this fuel.

• To capture the total emissions from the cogeneration unit, a multiplier is applied to the power only emissions. The multiplier is calculated as a ratio between the total heat rate and the net heat rate where the total heat rate is calculated by dividing the heat content of fuel consumed for power and steam generation by electricity generated from this fuel.

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Treatment of Cogeneration Units

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Generation and NOx Emissions from US Cogen Units

YearAnnual

Generation (GWh)

Summer NOx Emissions (MTons)

Annual NOx Emissions (MTons)

Capacity (MW)

2021 225,104 33.2 73.9 54,383 2023 233,293 37.2 83.8 54,383 2025 237,730 37.9 88.1 54,383 2030 221,913 32.7 72.2 54,383 2035 225,156 32.7 73.4 54,383 2040 218,169 32.6 72.0 54,383 2045 203,013 30.8 68.1 54,383 2050 190,393 29.2 64.8 54,383

Treatment of Cogeneration Units

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Generation and NOx Emissions from US Geothermal, Landfill Gas and MSW Units

Plant Type YearAnnual

Generation (GWh)

Summer NOx Emissions (MTons)

Annual NOx Emissions (MTons)

Capacity (MW)

Geothermal 2021 19,321 - - 2,784 2023 21,308 - - 3,036 2025 21,589 - - 3,072 2030 21,589 - - 3,072 2035 21,589 - - 3,072 2040 21,589 - - 3,072 2045 21,589 - - 3,072 2050 21,589 - - 3,072

Landfill Gas 2021 11,889 13.1 30.6 2,020 2023 11,889 13.1 30.6 2,020 2025 11,889 13.1 30.6 2,020 2030 10,518 11.7 26.9 2,020 2035 10,728 12.1 27.4 2,020 2040 10,824 12.2 27.7 2,020 2045 11,066 12.8 28.4 2,020 2050 10,873 12.3 27.8 2,020

Municipal Solid Waste

2021 13,815 21.4 51.7 2,133

2023 13,813 21.4 51.7 2,133 2025 13,813 21.4 51.7 2,133 2030 13,494 21.2 51.1 2,133 2035 13,488 21.3 51.1 2,133 2040 13,490 21.3 51.1 2,133 2045 13,528 21.4 51.2 2,133 2050 13,494 21.3 51.1 2,133

Demand projection method in IPM

16Source: http://www.epa.gov/sites/production/files/2015-07/documents/chapter_2_modeling_framework_0.pdf

The historical 2011 load curve used in IPM is modified such that it matches the average and the peak demand from AEO.► Below is 2011 historical load and this data modified for IPM.► 2011 base and peak demand are “squeezed” closer together reflecting less variation. ► FY value = FY average + (FY peak-FY average)*((2011 value – 2011 average)/(2011 peak-2011 average))

2011 historical load IPM load curve

Average

Minimum

Peak

Average

Minimum

Peak

Load Duration Curves in IPM► For each region, an hourly chronological load curve

is divided into seasons and re-ordered by hourly demand (gross load) to produce a load duration curve (LDC).

► Below is an illustrative example of this process carried out on 2015 CAISO load data:

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5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

Load

(MW

)

HourWinter (MW) Shoulder (MW)

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

Load

(MW

)

HourWinter (MW) Shoulder (MW)

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Representing Load in Time Segments (continued)► Seasonal load duration curves are divided into segments of

similar demand hours.► The model is tasked with solving for the average demand level in

each segment; the price received by each contributing resource is the variable cost of the marginal unit.► The solution for the average hour is applied to all hours of that

segment.► Below is the CAISO summer LDC, grouped in traditional time

segment sizes by gross load:

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5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

Load

HourSegment 1 Segment 2 Segment 3

- 5,000

10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000

Segment 1 Segment 2 Segment 3

Segment 4 Segment 5 Segment 6

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Applying Time-of-Day Categorization► Applying four time-of-day

categories to each hour in the segment, we can observe extensive diversity.

► For example, below is CAISO segment 4 from the summer season by the categories:► Morning (6am – 1pm)► Afternoon (1pm – 5pm)

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10,000

20,000

30,000

40,000

50,000

Load

HourSegment 1 Segment 2 Segment 3Segment 4 Segment 5 Segment 6

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10,000

20,000

30,000

40,000

Load

(MW

)

Hour

CAISO, Summer Segment 4 by Time-of-Day Categorization

Night Morning Afternoon Evening

Evening (5pm – 10pm)

Night (10pm – 6am)

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Quantifying the Impact of Time-of-Day Load

► Running an IPM test case with time-of-day load treatment makes a significant impact.

► Cumulative solar additions dropped by 50% in 2030; wind and new gas builds increased by 40% and 20%, respectively.► An additional 4 GW of coal and 3 GW of nuclear

remained in service► These projected changes led to a national-level

increase in emissions of 3-5% across all pollutant types for all run years

-

10,000

20,000

30,000

40,000

Load

(MW

)

Night Morning -

10,000

20,000

30,000

40,000

Load

(MW

)

Hour

Status Quo Time-of-Day

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Dispatch in IPM

► IPM organizes dispatch in the same basic way as system operators, by lowest variable cost.► Available resources are organized by lowest

cost, then the model calls upon each resource until the demand level in that particular time period is met.

IPM builds dispatch curves in each model region for each time segment.

A time segment is a group of hours that share important characteristics (season, demand level, time of day). IPM solves for the average hourly demand in each segment.

A time segment has one energy price (the variable cost of the marginal unit); this is the price all participating resources receive for all hours contained in that segment.

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Retirements in IPM► The retirement logic in IPM is simple – if a facility

cannot cover its going-forward costs with its going-forward revenues, it retires.► IPM will not retire a facility simply for losing money in a

given year.► Facilities typically receive the bulk of revenues from

energy sales and contributing to reserve requirements (seasonal peak load + reserve margin).► The capacity price compensates facilities for contributing

to reserve requirement. Consequently, the shape of the demand curve is important

IPM uses load duration curves, which take each hour in a season, re-order them by demand level and then group them by similar characteristics into time segments.

A load curve with higher peaks typically provides base load capacity with greater margins and a higher capacity price

The methodology for producing the shape and time segments for load duration curves was significantly reworked for v6.

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Cost Methodologies in IPM

► Resources with higher fixed costs, such as coal-fired EGUs and nuclear plants, are dependent on high utilization and large energy margins.

► While drivers of revenue (fuel prices, demand levels, new entrants) attract a lot of attention, the development and application of cost methodologies to existing units is highly influential in projections of capacity.

► IPM’s granular representation of the existing fleet allows us to ask detailed questions of the cost data:► How can we eliminate the noise in reported cost data but maintain the

natural heterogeneity across facilities?► How do costs change over time? Is it a linear relationship?► What is the useful life of a facility and what does it cost to extend it?

► Currently, there is a significant amount of operating capacity losing money – why is this capacity still operating? How long can it continue to do so? Is IPM capturing this dynamic appropriately?

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Natural Gas Price Projections Over the Years

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• Projections have been adjusting downwards for last several versions of AEO and EPA’s IPM• Latest version includes lowest price forecast yet, reflecting improved resource

fundamentals and pipeline build out of takeaway capacity. Price remain below $4.00/mmBtu up to 2040.

Natural Gas Consumption Projections

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• Projected power sector consumption of natural gas is up 20% or more from historical maximum levels of 10 Tcf, reflecting significant planned (15% increase in NGCC capacity alone from under construction capacity) and projected new builds.

• Also reflects higher projected capacity factors at NGCCs (in the 60% range)

Perfect Foresight v. Uncertain Futures

Perfect foresight modeling does not account for real-world risk evaluation and hedging strategiesEPA examined whether coal retirements change in a delayed

action (hedging) scenario where gas supply doesn’t change but owners make the retirement decision at a later point in time

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Projected retirements decrease (but not by much) the longer coal-steam owners delay retirement decisions These results help us consider the degree of

uncertainty affecting retirement projections

Gas Supply Uncertainties and Analysis

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• EPA’s initial case assumes increased well productivity.

• Recovery rates increase 20% for every doubling of wells

• Improved exploratory success• Improved well recovery• Cost reductions of platform,

drilling, and other components

• EPA conducted a sensitivity with more conservative well productivity assumptions and more aggressive LNG export:

• Recovery rate increases of 10% (not 20%)

• LNG export at 21 Bcf/d by 2035 (not 13 Bcf/d)

• 2030 national cross-price elasticity for generation to gas price:

• Coal gen : 1.08• Gas gen : -1.21

Coal/Gas Competition (Environmental Implications)

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Idled generation-based reductions sensitive to fuel price fluctuations

* Reflects Emissions from EPA Part 75 Sources

Coal Production (current markets)

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• Coal production at lowest levels since late ‘70s

• Over 90% of coal production is thermal coal, used in electricity generation

• Steep recent reduction largely driven by coal-to-gas generation shifting

• 2017 slight rebound mainly driven by uptick in exports. Not matched by domestic consumption.

• Decline projected to resume through near-term (2021) before stabilizing

WY 28%WV 51%KY 68%PA 35%TX 22%MT 37%IN 13%IL -29%OH 53%ND -3%

Top 10 Coal Producing States - % Reduction in Coal

Production Between 2010