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.
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
5 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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)
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
7 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
8 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
State CPP C2G GBO GWS EWS
IN
SD
IL
IA
MO
MN
LA
TX
MI
MS
KY
WI
AR
ND
State CPP C2G GBO GWS EWS
IN
SD
IL
IA
MO
MN
LA
TX
MI
MS
KY
WI
AR
ND
Increasing change in system build-out from current state
9 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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.
10 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
-
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
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
$285
15 16 17 18 19 20 21 22 23 24 25 30 25 40
Bill
ion
s
Coal retirements (GW)
Accelerated CPP
Total System Costs* ($B)
$237
$238
$239
$240
$241
$242
$243
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Coal retirements (GW)
Partial CPP
Total System Costs* ($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
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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]
14 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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.
16 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
17 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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?
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
Interconnection 2.1% 306 TWh 161 TWh
Texas
Interconnection 2.3% 204 TWh 107 TWh
The EPA altered the building blocks in the final rule
and switched to defining BSER on a regional level
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
22 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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.
24 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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%.
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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.
26 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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)
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.
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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.
30 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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)
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
43 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
Coal-to-Gas (C2G) conversion sites
44 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
Gas Build-Out (GBO)
Retirement Sites Expansion Sites
45 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
Gas/Wind/Solar (GWS)
Retirement Sites Expansion Sites
46 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
EE/Wind/Solar (EWS) expansion sites
47 Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
Appendix 3:
State Example Slides
49
State Summary - Arkansas
Steam Turbine CC CT Renewable EE Nuclear Other
Blue indicates lower production costs under sub-category rate compliance.
Green indicates lower production costs under mass compliance.
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
Increasing change in system build-out from current state
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
50
State Summary - Kentucky
Blue indicates lower production costs under sub-category rate compliance.
Green indicates lower production costs under mass compliance.
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
Steam Turbine CC CT Renewable EE Nuclear Other
CPP C2G GBO GWS EWS
2022
2025
2030
+1415 MW +1500 MW
Increasing change in system build-out from current state
• 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.
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
51
State Summary - Louisiana
Blue indicates lower production costs under sub-category rate compliance.
Green indicates lower production costs under mass compliance.
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.
• 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
Steam Turbine CC CT Renewable EE Nuclear Other
-194 MW
CPP C2G GBO GWS EWS
2022
2025
2030
Increasing change in system build-out from current state
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
52
State Summary - Illinois
Blue indicates lower production costs under sub-category rate compliance.
Green indicates lower production costs under mass compliance.
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
10
20
30
40
50
60
70
2030 C
ap
acit
y (
GW
)
Steam Turbine CC CT Renewable EE Nuclear Other
CPP C2G GBO GWS EWS
2022
2025
2030
Increasing change in system build-out from current state
-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.
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
53
State Summary - Indiana
Steam Turbine CC CT Renewable EE Nuclear Other
Blue indicates lower production costs under sub-category rate compliance.
Green indicates lower production costs under mass compliance.
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.
• 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
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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
)
Steam Turbine CC CT Renewable EE Nuclear Other
+1800 MW +676 MW
Scenario Inputs Scenario Outputs
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
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.
Increasing change in system build-out from current state
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.
• 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.
Blue indicates lower production costs under sub-category rate compliance.
Green indicates lower production costs under mass compliance.
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
56
State Summary - Minnesota
Blue indicates lower production costs under sub-category rate compliance.
Green indicates lower production costs under mass compliance.
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
30
2030 C
ap
acit
y (
GW
)
Steam Turbine CC CT Renewable EE Nuclear Other
-907 MW -850 MW +2400 MW +600 MW
-2493 MW
+300 MW
CPP C2G GBO GWS EWS
2022
2025
2030
Increasing change in system build-out from current state
+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.
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
57
State Summary - Mississippi
Blue indicates lower production costs under sub-category rate compliance.
Green indicates lower production costs under mass compliance.
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.
• 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
Increasing change in system build-out from current state
+1550 MW
Steam Turbine CC CT Renewable EE Nuclear Other
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
58
State Summary - Missouri
Blue indicates lower production costs under sub-category rate compliance.
Green indicates lower production costs under mass compliance.
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.
• 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
Steam Turbine CC CT Renewable EE Nuclear Other
CPP C2G GBO GWS EWS
2022
2025
2030
Increasing change in system build-out from current state
-6849 MW
+4450 MW
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
59
State Summary - North Dakota
Blue indicates lower production costs under sub-category rate compliance.
Green indicates lower production costs under mass compliance.
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.
• 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
Steam Turbine CC CT Renewable EE Nuclear Other
CPP C2G GBO GWS EWS
2022
2025
2030
-645 MW
Increasing change in system build-out from current state
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
60
State Summary - South Dakota
Blue indicates lower production costs under sub-category rate compliance.
Green indicates lower production costs under mass compliance.
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.
• 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
)
Steam Turbine CC CT Renewable EE Nuclear Other
+156 MW
CPP C2G GBO GWS EWS
2022
2025
2030
Increasing change in system build-out from current state
+600 MW
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)
61
State Summary - Wisconsin
Blue indicates lower production costs under sub-category rate compliance.
Green indicates lower production costs under mass compliance.
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.
• 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
Steam Turbine CC CT Renewable EE Nuclear Other
CPP C2G GBO GWS EWS
2022
2025
2030
Increasing change in system build-out from current state
+1477 MW
Results of MISO's Analysis of EPA's Clean Power Plan (October 2016)