Results of MISO’s Analysis of EPA’s Clean Power Plan CPP Final Rule Study... · 2019-01-14 · Results of MISO’s Analysis of EPA’s Clean Power Plan October 2016 The results

Results of MISO’s Analysis of

EPA’s Clean Power Plan

October 2016

The results of MISO’s analysis are not recommendations. Instead, they are intended to help policymakers understand

impacts of the CPP on the MISO system. As a reminder, the scope of this analysis was developed with stakeholder input

before the U.S. Supreme Court decided to stay the CPP while it is being litigated. MISO respects that some states have

scaled back or halted work on CPP-related matters in light of the court’s decision.

Environmental / Regulatory

• Mercury & Air Toxics Standards (MATS)

• Air-quality standards for ozone, SO2, etc.

• Potential greenhouse gas regulations

Economics

• Low-cost natural gas

• Economic recovery

• Demand growth shift

• Infrastructure investment

State & Federal Policy

• Renewable portfolio standards

• Energy efficiency/demand-side management programs

• Tax credits

• FERC orders addressing demand response participation in wholesale energy markets

Evolving Technologies

Electric Industry

While EPA’s Clean Power Plan (or other carbon rules) may affect the electric

industry in the future, many other forces are already having major impacts

• Wind power • Energy storage • Load-modifying resources

• Solar energy • Distributed

generation

2 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)

3

Study started with a resource forecasting screening analysis to determine

effective compliance strategies and a range of compliance costs

*Compliance costs are the difference between production and supply/demand side resource costs from reference case costs.

This does not include carbon costs or transmission costs.

Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)

4

Costs of compliance strategies are greatly influenced by natural gas prices

Gas Prices: *Compliance costs are the difference between production and supply/demand side resource costs from reference case costs.

This does not include carbon costs or transmission costs.


The production cost model produces optimal hourly economic

dispatch considering generation, transmission, and environmental

constraints for the following fixed capacity expansion scenarios

Business-as-

Usual

(BAU)

CPP

Constraints

(CPP)

Coal-to-Gas

Conversions

(C2G)

Gas

Build-Out

(GBO)

Gas, Wind,

Solar Build-Out

(GWS)

High EE, Wind,

Solar Build-Out

(EWS)

25% of coal

capacity per

region is

incrementally

converted to run

on natural gas

25% of coal

capacity per

region is

incrementally

retired

New gas-fired

generators are

built to

compensate for

retired capacity

30% of coal

capacity per

region is

incrementally

retired

13% of the

retired capacity

is replaced by

new gas units

17% by wind +

solar

EE at 1.5% of

energy sales

beginning in

2020 with 1.5%

year-over-year

growth

15% footprint-

wide RPS

Assumptions

consistent with

MTEP15 BAU

economic

planning

model

12.6 GW of

MATS-related

coal

retirements in

MISO

CPP

constraints

applied

CPP constraints applied

Assumptions applied across all scenarios


As new non-CO2 emitting resource penetration increases, rate-

based compliance becomes less expensive

Each scenario includes a resource mix that is assumed to have been built due to

economic or policy drivers other than the CPP, and compliance impacts are

measured using this resource mix.

Increasing change in system build-out from current trends

$-

$20

$40

$60

$80

$100

$120

$140

$160

20

30

EI C

O2 P

rice

(N

om

ina

l $

/sh

ort

to

n) Rate

Mass

6

CPP

Constraints

(CPP)

Coal-to-Gas

Conversions

(C2G)

Gas

Build-Out

(GBO)

Gas, Wind,

Solar Build-Out

(GWS)

High EE, Wind,

Solar Build-Out

(EWS)


Under rate-based compliance, continued investment in non-CO2 emitting

resources is necessary to mitigate CO2 price increases

• Less stringent initial compliance targets lead to lower CO2 prices in early years

• Early deployment of renewables drives down CO2 prices under rate-based compliance

• Continued deployment of renewables is needed to sustain these lower prices

• Coal retirements have a bigger impact on CO2 prices under mass-based compliance

$-

$20

$40

$60

$80

$100

$120

$140

2022 2025 2030

CO

2 P

rice (

$/s

ho

rt t

on

)

CPP GBO C2G GWS EWSCPP EWS GBO C2G GWS

Rate-based

compliance

Rate-based

compliance

Mass-based

compliance


System dispatch faces relatively less change under mass-based

compliance

0

100

200

300

400

500

600

0

100

200

300

400

500

600

700

800

BAU CPP C2G GBO GWS EWS CPP C2G GBO GWS EWS

2030 E

mis

sio

ns i

n M

ISO

(m

illio

n s

ho

rt t

on

s C

O2)

2030 G

en

era

tio

n i

n M

ISO

by F

uel

Typ

e (

TW

h)

Nuclear Other Coal Old CC New CC CT Renewable EE (Incremental) Emissions

BAU Sub-category Rate Mass


2022 2025 2030

Blue indicates lower production costs under sub-category rate compliance.

Green indicates lower production costs under mass compliance.

If all states move towards non-CO2 emitting resources the rate/mass advantage holds, but if a small

number of states move towards non-CO2 emitting resources they will see a rate advantage.

Most states see a mass-based compliance advantage unless a

regional heavy penetration of renewables and energy efficiency is

achieved

Gradient charts show the relative difference between rate-

based production costs and mass-based production costs,

when all states use the same compliance mechanism.

State CPP C2G GBO GWS EWS

IN

SD

IL

IA

MO

MN

LA

TX

MI

MS

KY

WI

AR

ND


IN

SD

IL

IA

MO

MN

LA

TX

MI

MS

KY

WI

AR

ND


IN

SD

IL

IA

MO

MN

LA

TX

MI

MS

KY

WI

AR

ND

Increasing change in system build-out from current state


Under a ‘patchwork’ mix of both rate & mass compliance, states

with a rate advantage may lose that benefit if other states go mass

As the process of creating patchwork model is iterated, individual states without a strong

advantage between rate and mass will tend toward the regional compliance advantage.

1st Mixed

mass/rate run

50/50 split

All states choose

mass based

compliance

All states choose

rate based

compliance

5 MISO states see

rate advantage

(SD, LA, MI, MS, ND).

17 EI states total

2nd Mixed

mass/rate run

9 MISO states see

mass advantage

(IN, IL, IA, MO, MN,

TX, KY, WI, AR).

17 EI states total

0 MISO states see

rate advantage.

8 EI states total

14 MISO states see

mass advantage.

26 EI states total

Sort states

by cost

advantage

55

% le

ss e

xp

en

siv

e

60% less expensive than all states choose rate

70% less expensive than all states choose rate

Patchwork models use the 2030 CPP scenario.


-

100

200

300

400

500

600

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033

Mil

lio

n s

ho

rt t

on

s

Accelerated CPP Partial CPP Final CPP Reference Case

11

43%

34%

17%

Reductions by 2030 from 2005 levels

Partial CPP models a 17% emission reduction

Final CPP models a 34% emission reduction

Accelerated CPP models a 43% emissions reduction

Reference case does not include CPP constraints.

Retirement analysis identifies coal capacity that could

retire under various levels of CO2 reduction


12

A range of coal capacity retirement levels was modeled for each

CO2 reduction scenario

$240

$245

$250

$255

$260

$265

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 30 35 40

Bill

ion

s

Coal retirements (GW)

Final CPP

Total System Costs* ($B)

$250

$255

$260

$265

$270

$275

$280

$285

15 16 17 18 19 20 21 22 23 24 25 30 25 40

Bill

ion

s


Accelerated CPP

Total System Costs* ($B)

$237

$238

$239

$240

$241

$242

$243

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15


Partial CPP

Total System Costs* ($B) Retirement levels that produce a

minimum range of total system costs are

identified for each scenario.

Final CPP 16 – 21 GW

Accelerated CPP 24 – 30 GW

Partial CPP 8 – 11 GW

* Dollar figures are 2016 USD in billions and include capital and production costs


200

250

300

350

400

450

500

550

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035

CO

2 e

mis

sio

ns

fro

m a

ffe

cte

d E

GU

s (

millio

n s

ho

rt t

on

s)

13

A single retirement level was then selected for each scenario

based on resultant emissions reduction and system cost*

Dotted lines = emission targets

Solid lines = emissions resulting

from modeled retirements

Reference Case

Scenario Coal Retirement Level

Partial CPP 8 GW

Final CPP 16 GW

Accelerated CPP 24 GW

* System costs include capital and production costs


MISO’s Analysis of the final Clean Power Plan

MISO’s CPP Workshop – What is in the final rule? https://www.misoenergy.org/_layouts/miso/ecm/redirect.aspx?id=211452

CPP Analysis Scope: https://www.misoenergy.org/_layouts/MISO/ECM/Redirect.aspx?ID=211503

Regional near-term modeling results: https://www.misoenergy.org/_layouts/MISO/ECM/Redirect.aspx?ID=216573

State impacts from regional results of near-term modeling: https://www.misoenergy.org/_layouts/MISO/ECM/Redirect.aspx?ID=218325

Mid-term modeling results: https://www.misoenergy.org/_layouts/MISO/ECM/Redirect.aspx?ID=220225

Final Report on MISO’s CPP Analysis: https://www.misoenergy.org/_layouts/MISO/ECM/Redirect.aspx?ID=229189

EPA regulations webpage https://www.misoenergy.org/WhatWeDo/EPARegulations/Pages/111(d).aspx

Additional questions? Please contact:

Jordan Bakke at [email protected]

Maire Waight at [email protected]


https://www.misoenergy.org/_layouts/miso/ecm/redirect.aspx?id=211452

https://www.misoenergy.org/_layouts/MISO/ECM/Redirect.aspx?ID=211503







https://www.misoenergy.org/WhatWeDo/EPARegulations/Pages/111(d).aspx

mailto:[email protected]

mailto:[email protected]

Appendix 1:

Study structure

The final rule study evaluates CPP compliance pathways

and will inform the transmission planning process

Near-Term Modeling (Understanding compliance

pathways)

Mid-Term Modeling (Preparing for transmission

overlay development)

• Rate/mass comparison

• Rate/mass interaction

• State/regional compliance

• Trading options

• Compliance sensitivities

• Relative compliance costs

• Potential generation

retirements

• Optimal resource

expansion

• Renewables penetration

• Renewables mix

• Renewables siting

Long-Term Modeling (Developing transmission

overlay)

• Will be informed by state

compliance plans

• Will use futures formulated

through MTEP17 process

• Updates to assumptions as

needed over MTEP18 and

‘19 cycles

MISO’s CPP Final Rule Study

MISO’s near-term analysis does not attempt to recommend compliance pathways,

optimize the resource mix, identify optimal electric transmission expansion, or

identify optimal gas pipeline expansion.


Capital

- Resource Forecasting Models

Production

- Production Cost Models

Transmission

- Production Cost Models

- Reliability Models

Gas Pipeline

- Resource Forecasting Models

- Gas Pipeline Models

Other

Different cost categories for CPP implementation require

different models


What tools can we use to study impacts of the CPP and

other drivers of industry transition?

18

Resource Forecasting

Models

Reliability Models

Production Cost Models

Tell us what transmission upgrades are

needed to maintain system reliability; quantify potential voltage, thermal

and stability impacts.

Tell us how resources are

dispatched and where there is

transmission system congestion;

simulates market behavior.

_____

Energy production costs are factored

into the model.

Tell us the type, timing and amount of new resources needed to serve

future load. _____

Resource capital costs and energy

production costs are factored into the

model.

Gas Pipeline Models

Tell us impacts on gas pipeline flows and gas storage

usage. _____

Economics of

producing and shipping gas are factored into the

model.

19

Production

cost models

Resource

forecasting

models

Resource

forecasting

models

Production

cost models

Gas pipeline

models

Production

cost models

Resource

forecasting

models

Resource

forecasting

models

Reliability

models

CPP Final Rule

Study

CPP MISO-PJM

Study

MTEP (Planning

for a wide range

of futures)

CPP State

Reliability

Assessment

CPP Draft Rule Study

Reliability

models

Ad-hoc Studies

Base Business Studies

What is MISO doing to understand and plan for the CPP?


20

Final Clean Power Plan Building Blocks

1. Heat rate improvement at

existing coal-fired EGUs

(assuming best practices

and equipment upgrades)

2. Increased usage of

natural gas combined cycle

units to 75% capacity factor

(based on net summer

capacity)

3. Increase in cleaner

generation sources

Best System of Emission Reduction (BSER)

1: Heat rate

improvement

2: Max NGCC

energy potential

3: Max renewable

energy potential

Eastern

Interconnection 4.3% 988 TWh 438 TWh

Western


Texas


The EPA altered the building blocks in the final rule

and switched to defining BSER on a regional level


Appendix 2:

Additional Model Inputs and

Outputs

Notes

The results of MISO’s analysis are not recommendations. Instead, they are intended to help

policymakers understand impacts of the CPP on the MISO system. As a reminder, the scope of this

analysis was developed with stakeholder input before the U.S. Supreme Court decided to stay the

CPP while it is being litigated. MISO respects that some states have scaled back or halted work on

CPP-related matters in light of the court’s decision.

• All models assume reliability is maintained through the addition of new resources

• Models reflect current generation, assumed retirements and resource expansion, including

– Units with signed Generator Interconnection Agreements (GIA)

– Resources forecasted as part of the MTEP15 7-step process to meet planning reserve margins and

renewable portfolio standards

• Additional scenarios look at other possible resource changes beyond current trends with

the assumption that the changes would occur regardless of the CPP

• Results in this presentation model:

– Trading ready sub-category rate and mass based compliance

– Interstate energy and emissions trading across the Eastern Interconnect

• Generators are counted for compliance in the state in which they are physically located


23

Modeled 77 cases to reflect a range of potential compliance actions and pathways

• Business-as-usual (BAU) model includes known and forecasted resource plans

• 3 years (2022, 2025, 2030)

Reference case (BAU) (3 runs)

• No change in capacity (MW) from BAU

• CPP constraints applied at state, regional and Eastern Interconnection levels

• Average rate, sub-category rate, mass, mass/NSC*, mixed mass**

BAU + CPP constraints (39 runs)

• Change in capacity (MW) from BAU

• CPP constraints applied at the Eastern Interconnection level

• Sub-category rate, mixed mass**

Alternative resource scenarios + CPP constraints (24 runs)

• Patchwork mix of Rate and Mass

• Gas prices

• w/wo Fermi 3

Sensitivities

* NSC = New Source Complement

** Mixed mass = MISO states comply under mass target and non-MISO regions comply under mass + NSC targets


In the CPP scenario, coal unit capacity factors decrease greatly over time

under the CPP, more dramatically with a rate-based implementation

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Co

al

Un

it C

ap

acit

y F

acto

rs

% of Available Coal Generators

2030 BAU 2030 Rate 2030 Mass

2022 BAU 2022 Rate 2022 Mass

Each point on the graph

represents a single coal

unit’s capacity factor. For

example, 40% of the coal

units in the 2030 rate

scenario have a capacity

factor greater than ~10%.

Low capacity

factors indicate

units may not

be economically

viable.

Coal units run more in the near term under rate-based compliance and in the long term

under mass-based compliance.


Coal unit capacity factors with coal retirements and significant

penetration of renewables

25

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Co

al U

nit

Cap

acit

y Fa

cto

rs

% of Available Coal Generators

2030 BAU 2030 GWS Mass 2030 EWS Rate 2030 GWS Rate 2030 EWS Mass

Low capacity

factors indicate

units may not

be economically

viable.

Each point on the graph

represents a single coal

unit’s capacity factor. For

example, 45% of the coal

units in the 2030 rate

scenario have a capacity

factor greater than ~60%.


Coal units face increased risks under CPP compliance

• In 2030, both compliance pathways increase coal cycling, ramping, hours offline and units idled

compared to the BAU.

• As the stringency of compliance increases, coal units move from dispatching as baseload to

intermediate to peaking units.

• Intermediate units tend to see the most operational performance impacts.

• Coal units cycle and ramp less in rate-based compliance because they are running less often.

PLEXOS modeling includes certain coal unit operating constraints: minimum up time, minimum down time,

ramp rates, start costs, min/max capacity, heat rate curves, variable O&M, maintenance and outages.

Definition 2022 Mixed

Mass/NSC

2022 Sub-

category Rate

2030 Mixed

Mass/NSC

2030 Sub-

category Rate

Cycling* Number of unit starts 58% -29% 71% 55%

Ramping* Total MW traveled (ramp up

+ ramp down) 11% 2% 30% 7%

Hours offline* # of hours of zero generation 68% 3% 157% 246%

Total MWh* Total generation -10% -2% -36% -68%

Units idled # of units offline all year 0 0 6 9

*Percent change from BAU scenario.


27

States

selling

ERCs /

allowances

States

buying

ERCs /

allowances

Mass Rate

Modeling includes Fermi 3 in Michigan.

Vertical lines show range of emission trading over all scenarios.

Resource forecast siting assumptions influence the outcome of rate/mass advantage.

-30

-20

-10

0

10

20

30

MI LA IL SD MT MS ND TX MN IA WI AR MO IN KY

Mill

ions o

f A

llow

ance

s/E

RC

s T

rade

d in

20

30

States selling ERCs see more

value under rate-based

compliance.

Mass-based compliance produces a more balanced mix of buyers

and sellers within MISO


Results of MISO's Analysis of EPA's Clean Power Plan (October 2016) 28

(30)

(20)

(10)

-

10

20

30

MI LA IL ND SD MT MS MN TX IA WI AR KY MO IN

Millio

ns

of

ER

Cs

/allo

wa

nc

es b

ou

gh

t/s

old

Modeling includes Fermi 3 in Michigan.

Resource forecasting siting assumptions influence the outcome of rate/mass advantage.

Mass-based compliance produces a more balanced mix of buyers

and sellers within MISO

Buyers of ERCs/allowances

Sellers of ERCs/allowances

29

Generation will rise/fall in similar locations under both rate & mass, so

transmission expansion, if needed, will be similar under both M

ass +

Mix

ed

NS

C

Su

b-c

ate

go

ry R

ate

2022 2025 2030

Maps shown

result from

the CPP

scenario.

While the magnitude and location of impacts on generation change with varying capacity expansion

scenarios, within each scenario the impact of rate and mass compliance are similar.


Under current capacity trends, all MISO states have a mass based

compliance advantage

State CPP CPP 1st Mixed CPP 2nd Mixed

IN (M) (M)

SD (R) (M)

IL (M) (M)

IA (M) (M)

MO (M) (M)

MN (M) (M)

LA (R) (M)

TX (M) (M)

MI (R) (M)

MS (R) (M)

KY (M) (M)

WI (M) (M)

AR (M) (M)

ND (R) (M)

(R) indicates a state is modeled under rate compliance,

(M) indicates a state is modeled under mass compliance

A dark blue box

indicates that rate

costs are less

expensive.

A dark green box

indicates that

mass costs are

less expensive. A change in cell color

across columns indicates a

change in compliance

advantage.

An (R) in a green box

indicates that although the

state previously saw an

advantage with rate, that

advantage is lost when a

group of other states

choose mass compliance.

The CPP 2nd mixed rate/mass model results show that all input

advantages match the output advantages, indicating the system has

reached an equilibrium.


31

BAU Sub-Category Rate Mixed Mass/NSC

Gas generation & emissions under current resource trends

0

10

20

30

40

50

60

70

80

0

20

40

60

80

100

120

140

160

180

2022 2025 2030 2022 2025 2030 2022 2025 2030

CO

2 E

mis

sio

ns

in

MIS

O (

Mil

lio

ns

of

Sh

ort

To

ns

)

Ga

s G

en

era

tio

n in

MIS

O b

y T

yp

e (

TW

h)

Old CC - Generation New CC - Generation Old CC - Emissions New CC - Emissions* NSC = New Source Complement

** Mixed mass = MISO states comply under mass target and non-MISO regions comply under mass + NSC targets

2022 2025 2030 2022 2025 2030 2022 2025 2030


32

MISO LMPs

$0.00

$10.00

$20.00

$30.00

$40.00

$50.00

$60.00

$70.00

$80.00

$90.00

2022 2025 2030 2022 2025 2030 2022 2025 2030 2022 2025 2030

$/M

Wh

BAU CPP GWS EWS


33

Peak Day – GWS

0

100

200

300

400

500

600

EI

Gen

era

tio

n (

GW

)

0

20

40

60

80

100

120

140

160

180

200

EI

Gen

era

tio

n (

GW

)

Rate BAU Mass


34

Peak Day – EWS

0

100

200

300

400

500

600

EI

Gen

era

tio

n (

GW

)

0

20

40

60

80

100

120

140

160

180

200

EI

Gen

era

tio

n (

GW

)

Rate BAU Mass


35

Shoulder Day – GWS

0

50

100

150

200

250

300

350

400

EI

Gen

era

tio

n (

GW

)

0

20

40

60

80

100

120

140

160

180

EI

Gen

era

tio

n (

GW

)

Rate BAU Mass


36

Shoulder Day – EWS

0

50

100

150

200

250

300

350

400

EI

Gen

era

tio

n (

GW

)

0

20

40

60

80

100

120

140

160

180

EI

Gen

era

tio

n (

GW

)

Rate BAU Mass


37

Sunniest Day – GWS

0

50

100

150

200

250

300

350

400

EI

Gen

era

tio

n (

GW

)

0

20

40

60

80

100

120

140

160

EI

Gen

era

tio

n (

GW

)

Rate BAU Mass


38

Sunniest Day – EWS

0

50

100

150

200

250

300

350

400

EI

Gen

era

tio

n (

GW

)

0

20

40

60

80

100

120

140

160

EI

Gen

era

tio

n (

GW

)

Rate BAU Mass


0

500

1000

1500

2000

2500

3000

3500

4000

4500

EI

Gen

era

tio

n (

GW

)

39

2030 Yearly Trends – GWS

0

500

1000

1500

2000

2500

3000

3500

4000

4500

EI

Gen

era

tio

n (

GW

)

Poly. (Nuclear)

Poly. (ST Coal)

Poly. (CC)

Poly. (Wind)

Poly. (Solar PV)

Poly. (CT Gas)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

EI

Gen

era

tio

n (

GW

)

BAU

Rate Mass


40

2030 Yearly Trends – EWS

0

500

1000

1500

2000

2500

3000

3500

4000

4500

EI

Gen

era

tio

n (

GW

)

Poly. (Nuclear)

Poly. (ST Coal)

Poly. (CC)

Poly. (Wind)

Poly. (Solar PV)

Poly. (CT Gas)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

EI

Ge

ne

rati

on

(G

W)

BAU

Rate Mass

0

500

1000

1500

2000

2500

3000

3500

4000

4500

EI

Ge

ne

rati

on

(G

W)


Results of MISO's Analysis of EPA's Clean Power Plan (October 2016) 41

Appendix 3:

Capacity Retirements and

Expansion Maps

BAU and CPP expansion sites for 2013-2030


Coal-to-Gas (C2G) conversion sites


Gas Build-Out (GBO)

Retirement Sites Expansion Sites


Gas/Wind/Solar (GWS)

Retirement Sites Expansion Sites


EE/Wind/Solar (EWS) expansion sites


Appendix 3:

State Example Slides

49

State Summary - Arkansas

Steam Turbine CC CT Renewable EE Nuclear Other



CPP C2G GBO GWS EWS

2022

2025

2030

The model

assumes a

regional heavy

penetration of

renewables and

EE as an input to

the EWS scenario.

As a result, EWS

rate compliance

would likely be

less expensive

than EWS mass.

+644 MW

+900 MW

• In 2025 and 2030, the increasingly stringent targets lead Arkansas’ coal- and gas-heavy fleet to have

a cost advantage under mass-based compliance.

• In the capacity scenarios studied, Arkansas needs to buy ERCs or allowances to maintain

compliance, except when a large amount of coal retirements positions it as an allowance seller.

0

2

4

6

8

10

12

14

16

18

2030 C

ap

acit

y (

GW

)

-5,082 MW

+524 MW

-524 MW -524 MW



50

State Summary - Kentucky



The model assumes a

regional heavy

penetration of

renewables and EE

as an input to the

EWS scenario.

As a result, EWS rate

compliance would

likely be less

expensive than EWS

mass.

0

5

10

15

20

25

2030 C

ap

acit

y (

GW

)

-1415 MW -1540 MW

+1200 MW

+600 MW +3300 MW

+1262 MW


CPP C2G GBO GWS EWS

2022

2025

2030

+1415 MW +1500 MW


• In 2025 and 2030, the increasingly stringent targets lead Kentucky’s coal-heavy fleet to see a cost advantage under

mass-based compliance except when a heavy penetration of renewables and EE leads to a convergence in rate and

mass costs.

• In all of the alternative capacity scenarios studied, Kentucky needs to buy ERCs or allowances to maintain

compliance.


51

State Summary - Louisiana



The model assumes a

regional heavy

penetration of

renewables and EE

as an input to the

EWS scenario.


compliance would

likely be less

expensive than EWS

mass.

• Under rate-based compliance, Louisiana’s steam turbine gas units are regulated under the same rate

as steam turbine coal, and can therefore produce high-priced ERCs in 2030, creating additional value.

• Louisiana sees a cost advantage under rate-based compliance unless industry trends lead to

replacement of coal units with natural gas.

0

5

10

15

20

25

30

2030 C

ap

acit

y (

GW

)

+194 MW

-194 MW

-3526 MW

+1500 MW

+1126 MW


-194 MW

CPP C2G GBO GWS EWS

2022

2025

2030



52

State Summary - Illinois



The model assumes a

regional heavy

penetration of

renewables and EE

as an input to the

EWS scenario.


compliance would

likely be less

expensive than EWS

mass.

0

10

20

30

40

50

60

70

2030 C

ap

acit

y (

GW

)


CPP C2G GBO GWS EWS

2022

2025

2030


-5684 MW

+5684 MW

-5684 MW

+6600 MW

+1800 MW

-2510 MW

+600 MW +3375 MW

+2080 MW +2100 MW

• In 2025 and 2030, the increasingly stringent targets lead Illinois to see a cost advantage under mass-based

compliance except when a heavy penetration of renewables and EE leads to a rate-based cost advantage.

• In most of the alternative capacity scenarios studied, Illinois is a net seller of ERCs and allowances, except when a

regionally-heavy penetration of renewables and EE allows more other states to compete in the emissions market.


53

State Summary - Indiana




The model assumes a

regional heavy

penetration of

renewables and EE

as an input to the

EWS scenario.


compliance would

likely be less

expensive than EWS

mass.

• In 2025 and 2030, the increasingly stringent targets lead Indiana’s coal-heavy fleet to see a cost

advantage under mass-based compliance except when a heavy penetration of renewables and EE leads

to a rate-based cost advantage.

• In the capacity scenarios studied, Indiana would need to buy allowances or ERCs to maintain

compliance, except when a heavy penetration of gas or renewables positions it as a seller (GBO, GWS).

Increasing change in system build-out from current state CPP C2G GBO GWS EWS

2022

2025

2030

0

5

10

15

20

25

30

35

40

2030 C

ap

acit

y (

GW

)

-4175 MW

+4175 MW

-4175 MW

+4175 MW

+2700 MW +1408 MW +1600 MW

+1600 MW


54

State Summary - Iowa

Blue indicates lower production costs under

sub-category rate compliance.

Green indicates lower production costs under

mass compliance.

• Although the resource mix changed significantly on a regional level, Iowa’s resource mix was not impacted under most

scenarios. With no incremental renewables to produce ERCs under rate-based compliance, mass-based compliance is

less expensive.

• Only about 40% of Iowa’s renewable capacity in the CPP scenario is eligible to generate ERCs due to restrictions on

installation dates.

• The increase in renewables and energy efficiency in the EWS scenario leads rate-based compliance to be less

expensive than mass-based compliance.

Year CPP C2G GBO GWS EWS

2022

2025

2030

0

5

10

15

20

25

CPP EWS

2030 C

ap

acit

y (

GW

)


+1800 MW +676 MW

Scenario Inputs Scenario Outputs


55

State Summary - Michigan

0

5

10

15

20

25

30

35

40

45

2030 C

ap

acit

y (

GW

) -2886 MW

+2886 MW

-2886 MW

+2400 MW

+2400 MW

-3837 MW +1538 MW

+2100 MW

CPP w/o Fermi 3 CPP C2G GBO GWS EWS

2022

2025

2030

Scenarios include Fermi 3.


The model assumes a

regional heavy

penetration of

renewables and EE

as an input to the

EWS scenario.


compliance would

likely be less

expensive than EWS

mass.

• In 2025 and 2030, Michigan has a strong rate-based compliance advantage in most scenarios due to the ERC-

producing Fermi 3, scheduled to go in service in 2025. Conversely, without Fermi 3, mass-based compliance is shown

to be less expensive.

• In the capacity scenarios studied, Michigan consistently is a net seller of ERCs and allowances, with or without the

installation of Fermi 3.




56

State Summary - Minnesota



The model assumes a

regional heavy

penetration of

renewables and EE

as an input to the

EWS scenario.


compliance would

likely be less

expensive than EWS

mass.

0

5

10

15

20

25

30

2030 C

ap

acit

y (

GW

)


-907 MW -850 MW +2400 MW +600 MW

-2493 MW

+300 MW

CPP C2G GBO GWS EWS

2022

2025

2030


+907 MW +1029 MW +2100 MW

• In 2025 and 2030, the increasingly stringent targets lead Minnesota to see a cost advantage under mass-based

compliance except when a heavy penetration of renewables and EE leads to a rate-based cost advantage.

• In the capacity scenarios studied, Minnesota is a net buyer of allowances and ERCs, except when increased coal

retirements position it as an allowance seller or increased renewable and EE penetration positions it as an ERC seller.


57

State Summary - Mississippi



The model assumes a

regional heavy

penetration of

renewables and EE

as an input to the

EWS scenario.


compliance would

likely be less

expensive than EWS

mass.

• In 2030, the increasingly stringent targets lead Mississippi’s coal- and gas-heavy fleet to see a cost

advantage under mass-based compliance except when a heavy penetration of renewables and new

gas leads to a rate-based cost advantage.

• In the capacity scenarios studied, Mississippi is a net seller of allowances and a net buyer of ERCs

unless a regionally-heavy penetration of renewables and EE positions it as an ERC seller.

0

5

10

15

20

25

2030 C

ap

acit

y (

GW

) +1200 MW +600 MW

+658 MW

+1050 MW

CPP C2G GBO GWS EWS

2022

2025

2030


+1550 MW



58

State Summary - Missouri



The model assumes a

regional heavy

penetration of

renewables and EE

as an input to the

EWS scenario.


compliance would

likely be less

expensive than EWS

mass.

• In 2025 and 2030, the increasingly stringent targets lead Missouri’s coal-heavy fleet to see a cost

advantage under mass-based compliance, except when a heavy penetration of renewables and EE

leads to a convergence in rate and mass costs.

• In the capacity scenarios studied, Missouri is a net buyer of ERCs and allowances, except when a

large fleet transition from coal to gas in the state positions it as an allowance seller.

0

5

10

15

20

25

30

35

2030 C

ap

acit

y (

GW

) +6849 MW

-4718 MW

+6000 MW

+1800 MW

+1081 MW

+1800 MW


CPP C2G GBO GWS EWS

2022

2025

2030


-6849 MW

+4450 MW


59

State Summary - North Dakota



The model assumes a

regional heavy

penetration of

renewables and EE

as an input to the

EWS scenario.


compliance would

likely be less

expensive than EWS

mass.

• In the alternate capacity scenarios, the increasingly stringent targets lead North Dakota’s coal-heavy

fleet to see a cost advantage under mass-based compliance, in 2030 except when a heavy

penetration of new renewables and EE leads to a rate-based cost advantage.

• North Dakota sees a rate-based cost advantage under current capacity trends as well, because 63%

of the state’s renewable units qualify to produce ERCs due to their installation dates.

0

2

4

6

8

10

12

2030 C

ap

acit

y (

GW

)

+645 MW -702 MW

+600 MW

+2400 MW

+192 MW


CPP C2G GBO GWS EWS

2022

2025

2030

-645 MW



60

State Summary - South Dakota



The model assumes a

regional heavy

penetration of

renewables and EE

as an input to the

EWS scenario.


compliance would

likely be less

expensive than EWS

mass.

• In 2025 and 2030, the increasingly stringent targets lead South Dakota to see a cost advantage under

mass-based compliance.

• In the capacity scenarios studied, South Dakota is a net seller of ERCs and a net buyer of allowances

unless a regionally-heavy penetration of renewables and EE positions it as a seller.

0

1

2

3

4

5

6

2030 C

ap

acit

y (

GW

)


+156 MW

CPP C2G GBO GWS EWS

2022

2025

2030


+600 MW


61

State Summary - Wisconsin



The model assumes a

regional heavy

penetration of

renewables and EE

as an input to the

EWS scenario.


compliance would

likely be less

expensive than EWS

mass.

• In 2025 and 2030, the increasingly stringent targets lead Wisconsin’s coal- and gas-heavy fleet to see

a cost advantage under mass-based compliance, except when a regionally-heavy penetration of

renewables and EE lead to a rate-based cost advantage.

• In the capacity scenarios studied, Wisconsin is a net buyer of both ERCs and allowances.

0

5

10

15

20

25

30

2030 C

ap

acit

y (

GW

) -1477 MW -1043 MW -2448 MW

+2800 MW +900 MW +1029 MW

+ 1200 MW


CPP C2G GBO GWS EWS

2022

2025

2030


+1477 MW


Documents

Results of MISO’s Analysis of EPA’s Clean Power Plan CPP Final Rule Study... · 2019-01-14 · Results of MISO’s Analysis of EPA’s Clean Power Plan October 2016 The results