Emissions from Coal in High Wind Scenarios
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Emissions from Coal in High Wind Scenarios
David Luke OatesRenewElec Project
Department of Engineering and Public PolicyCarnegie Mellon UniversityAdvisor: Paulina Jaramillo
USAEE Concurrent SessionOctober 11, 2011
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A baseloaded coal unit
Source: CEMS 2008
A Texas Coal Unit
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A coal unit being extensively cycled…
Source: CEMS 2008
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Wind power is intermittent and we can expect more of it
Source: (J Apt 2007)
Aggregated output from 6 turbines • 29 States have RPS• Wind is most likely
source to meet RPS• NREL: 30% energy
from wind is feasible
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PartLoad:Ramping:
What is ‘cycling’?“increased start-ups, ramping and periods of operation at low load levels” (Troy et al. 2010)
Startup:
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Two reasons we should care about cycling:
Costs• Cycling increases cost of
operating a coal unit:– Increased O&M– Increased forced outage
replacement power– Extra fuel costs
• Challenging to quantify costs• One estimate: $15-$225
thousand/cycle
Emissions• Coal units incur emissions
penalties for part-load operation, ramping, and startups
• One estimate for starting up coal suggests a penalty of 100% for CO2, 300% for NOX and 250% for SO2
Sources: (Bentek 2008; Lefton&Besuner 2006; D Lew 2011)
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Research context and objectives
Context• NREL estimated coal cycling
dependence on wind penetration– 1 measure of cycling– no sensitivity based on power
system, fuel mix, etc. • Most studies use very
simplistic emissions models
Objectives• Determine effect of wind
penetration on coal:– Startups– Ramping– Part load operation
• Calculate system-level economic benefits of coal cycling
• Determine emissions implications of coal cycling
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Two Important Data Sources
CEMS• Allows us to determine
emissions impact of cycling• EPA’s Continuous Emissions
Monitoring System• Hourly power output and
CO2, SO2, NOX for electricity units > 25 MW
EWITS• Allows us to model different
wind penetration levels• Eastern Wind Integration
and Transmission Study model results
• Modeled wind power output for thousands of sites across the country
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Three Big Questions:
1. How much coal cycling can we expect to see as wind penetration increases?
2. What is the value to the power system of coal cycling in high wind scenarios?– Determine the system-level economic benefit to
compare with the private cost3. How much does cycling affect emissions of
CO2, NOX, and SO2 from coal units?
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Part 1: Regression Analysis
Overview Independent Variables• Coal output time series• Wind energy
• Goal: What is correlation between wind penetration and cycling?
• Focus on ERCOT• Years: 2007-2010• Weekly Analysis
Cycling Measure Coefficient ± Std. Error R2
Startups (startups/wk/%wind) 1.2 ± 0.2 0.52
Part Load (%Capacity Factor/%wind) -0.3 ± 0.1 0.78
Ramping (%COV coal/%wind) 0.18 ± 0.05 0.81
ConclusionsNeed more sophisticated model to examine emissions
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Part 2: Model Overview“What are the capacities
of each unit and demand for electricity?”
“How much power does each unit produce every hour?”
“How much CO2, NOX and SO2 are produced?”
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UCED model capacity mix is comparable to PJM
Source: NEEDS 2006
PJM 06167 GW
Model168 GW
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
OTHERHYDRONUCWINDNGSTEAMNGCTNGCCSUBPCBIT
Capa
city
Frac
tion
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Unit Commitment and Economic Dispatch (UCED) Characteristics
Costs• Fuel• Startup• Shutdown• Reserve commitment• Wind curtailment
Constraints• Supply = Demand• Minimum Generation• Capacity• Ramp rate• Min up / Min down• Reserve requirement
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UCED Characteristics Continued
• Minimizes total cost of meeting demand• Hourly Dispatch• Perfect Information• Daily dispatch, iterated through 1 year• Solved using Mixed Integer (Linear)
Programming
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Emissions Model Rationale
• Many analyses assume constant emissions factors
• This method does not adequately account for emissions associated with startup/shutdowns or ramping
• Coal units incur emissions penalties for part-load operation, ramping, and startup
Sources: (Katzenstein& Apt 2009; D Lew 2011; Bennett &McBee 2011)
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Higher emissions rate during startup
Source: CEMS
NOx emissions from a coal unit
Power output (MW)
NO
x em
issio
ns (k
g/m
in)
Note: I’m going to eliminate the coloring
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Base Case: Validation
PJM 2006 UCED Jan 06 UCED Jun 060%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
OtherWindHydroGasCoalNuclear
Perc
enta
ge o
f tot
al re
sour
ce
Reference Model January Model June
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Base Case: PJM Resource Use
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Next Steps: Comparative Scenario Analysis
No Cycling Constraints
Low Cycling Constraints
Moderate Cycling Constraints
…
No Added Wind
Base Case $ / CO2 / NOX / SO2
$ / CO2 / NOX / SO2
$ / CO2 / NOX / SO2
5% Wind $ / CO2 / NOX / SO2
$ / CO2 / NOX / SO2
$ / CO2 / NOX / SO2
$ / CO2 / NOX / SO2
10% Wind $ / CO2 / NOX / SO2
$ / CO2 / NOX / SO2
$ / CO2 / NOX / SO2
$ / CO2 / NOX / SO2
…
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Policy Implications
Gives a clearer picture of how coal units behave with renewables
• Renewable Portfolio Standards– Given that coal cycling occurs, how much emissions benefit do we see
from RPS?• Electricity Market Structure
– Given that cycling produces private costs and (maybe) social benefits, do we need to change the way we pay operators of coal units?
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Acknowledgments
• CEIC, RenewElec for financial support• Paulina Jaramillo for a great deal of guidance• Todd Ryan, Allison Weis for countless
discussions• Bri Matthias-Hodge and NREL Wind
Technology Center for help building my UCED model
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References• Apt, J, 2007. The spectrum of power from wind turbines. Journal of Power
Sources.• Bennett, P. &McBee, B., 2011. The Wind Power Paradox. pp.1–58.• D Lew, G.B.A.M.M.N., 2011. Does Wind Affect Coal? Cycling, Emissions, and
Costs (Presentation), National Renewable Energy Laboratory (NREL). pp.1–21.• Energy, B., 2008. How Less Became More: Wind, Power, and Unintended
Consequences in the Colorado Energy Market,• Katzenstein, W. & Apt, Jay, 2009. Air Emissions Due To Wind And Solar Power.
Environmental Science & Technology, 43(2), pp.253–258.• Lefton, S.A. &Besuner, P., 2006. The Cost of Cycling Coal Fired Power Plants.
Coal Power Magazine, (Winter 2006), pp.16–19.• Troy, N., Denny, E. & O'Malley, M., 2010. Base-Load Cycling on a System With
Significant Wind Penetration. Power Systems, IEEE Transactions on, 25(2), pp.1088–1097.
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End
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EWITS Data Use
• Average power available: 43 GW
• 30% of PJM peak demand
Locations of EWITS Sites near PJM region
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EWITS data doesn’t match Kolmogorov below 3 h
Source: Todd Ryan
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Fleet Selector
• DESCRIBE OPTIMIZATION
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NREL suggests cycling increases with wind penetration
Source: Western Wind and Solar Integration Study, NREL, 2010, p. 153
Operating Level:
No Wind 10% Wind 20% Wind30% Wind
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Timeline
Accomplished• Integrate data sources• Select representative fleets
from available sources• Build CO2 emissions models• Implemented working UCED
Before January• Implement forced/unforced
outages• Perform sensitivity on
reserve requirements, fuel mix
• Build NOX and SO2 emissions models
• Run scenarios varying wind penetration and cycling constraints
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Use of Hourly Data
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Reserve Requirements
3+1• Carry 1% of peak load for regulation is
reasonable for modest penetration• Or Regulation equal to stddev of 10-min net
load changes
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Startups and Wind Penetration
0 5 10 15 20 25 30 350
5
10
15
20
25
30
35
40
45
50
Wind Energy Penetration [%]
Incr
ease
in C
oal S
tart
ups [
%]
NOT ACTUAL DATA
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System Costs and Startups
40 50 60 70 80 90 100 1100
5
10
15
20
25
30
35
# of Coal Startups
Incr
ease
in S
yste
m C
ost [
%]
Slope: $/startup
NOT ACTUAL DATA
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Anticipated Sensitive Parameters
• Reserve rules– Contingency reserve margin– Regulating reserve margin
• Fuel Mix– Amount of coal
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Ramp Rate Definition
• Output of each unit approximated by discontinuous stepwise curve
• Ramp rate approximated by change in power output from hour to hour (MW/h)
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DETAIL ON OPTIMIZATION METHOD
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Base Costs and Constraints
• Show all costs and constraints
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Fleet Selector
• Problem 1: data on every unit in power systems not available
• Problem 2: want to be able to test different fuel mixes
• Problem 3: many differences between units of the same fuel type
• Solution: program to match distributions of size and heat rate to reference system
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Results summary for a particular wind penetration
Cost CO2 NOX SO2
Startups (200±50) $/start (10±3) t/start (10±3) t/start (10±3) t/start
Energy produced below 50% capacity
(100±20) $/MWh (15±4)t/MWh (15±4)t/MWh (15±4)t/MWh
Ramping (10±3) $/MW (5±2) t/MW (5±2) t/MW (5±2) t/MW
NOT ACTUAL DATA
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Average vs. incremental HR
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Base Case: Validation ContinuedPJM 06 UCED
Mean CO2 output emissions rate [t/MWh]
0.94 0.92
Startups / month 24 22
Ramping distance[MW/month]
3000 3250
Energy produced below 50%[MWh/month]
1000 1250
*Note that uncertainties are not yet available and will be included in final results
NOT ACTUAL DATA