Emissions from Coal in High Wind Scenarios David Luke Oates RenewElec Project Department of Engineering and Public Policy Carnegie Mellon University Advisor: Paulina Jaramillo USAEE Concurrent Session October 11, 2011

A baseloaded coal unit 2 Source: CEMS 2008 A Texas Coal Unit

A coal unit being extensively cycled… 3 Source: CEMS 2008

Wind power is intermittent and we can expect more of it 4 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

Part Load: Ramping: What is ‘cycling’? “increased start-ups, ramping and periods of operation at low load levels” (Troy et al. 2010) 5 Startup:

Two reasons we should care about cycling: 6 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 CO 2, 300% for NO X and 250% for SO 2 Sources: (Bentek 2008; Lefton&Besuner 2006; D Lew 2011)

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 7

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 8

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 cost 3.How much does cycling affect emissions of CO 2, NO X, and SO 2 from coal units? 9

Part 1: Regression Analysis OverviewIndependent Variables Coal output time series Wind energy 10 Goal: What is correlation between wind penetration and cycling? Focus on ERCOT Years: Weekly Analysis Cycling MeasureCoefficient ± Std. ErrorR2R2 Startups (startups/wk/%wind)1.2 ± Part Load (%Capacity Factor/%wind)-0.3 ± Ramping (%COV coal/%wind)0.18 ± Conclusions Need more sophisticated model to examine emissions

Part 2: Model Overview 11 System Data Unit capacity, etc. Hourly Demand Hourly Wind UCED Model Optimization Model Determine Schedule Emissions Model Many Regression Models Determine emissions “What are the capacities of each unit and demand for electricity?” “How much power does each unit produce every hour?” “How much CO 2, NO X and SO 2 are produced?”

UCED model capacity mix is comparable to PJM 12 Source: NEEDS 2006

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 13

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 14

Emissions Model Rationale 15 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)

Higher emissions rate during startup 16 Source: CEMS NOx emissions from a coal unit Power output (MW) NOx emissions (kg/min) Note: I’m going to eliminate the coloring

Base Case: Validation 17 ReferenceModel JanuaryModel June

Base Case: PJM Resource Use 18

Next Steps: Comparative Scenario Analysis No Cycling Constraints Low Cycling Constraints Moderate Cycling Constraints … No Added Wind Base Case$ / CO 2 / NO X / SO 2 5% Wind$ / CO 2 / NO X / SO 2 10% Wind$ / CO 2 / NO X / SO 2 … 19

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? 20

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 21

References Apt, J, The spectrum of power from wind turbines. Journal of Power Sources. Bennett, P. &McBee, B., The Wind Power Paradox. pp.1–58. D Lew, G.B.A.M.M.N., Does Wind Affect Coal? Cycling, Emissions, and Costs (Presentation), National Renewable Energy Laboratory (NREL). pp.1–21. Energy, B., How Less Became More: Wind, Power, and Unintended Consequences in the Colorado Energy Market, Katzenstein, W. & Apt, Jay, Air Emissions Due To Wind And Solar Power. Environmental Science & Technology, 43(2), pp.253–258. Lefton, S.A. &Besuner, P., The Cost of Cycling Coal Fired Power Plants. Coal Power Magazine, (Winter 2006), pp.16–19. Troy, N., Denny, E. & O'Malley, M., Base-Load Cycling on a System With Significant Wind Penetration. Power Systems, IEEE Transactions on, 25(2), pp.1088–

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EWITS Data Use Average power available: 43 GW 30% of PJM peak demand 24 Locations of EWITS Sites near PJM region

EWITS data doesn’t match Kolmogorov below 3 h 25 Source: Todd Ryan

Fleet Selector DESCRIBE OPTIMIZATION 26

NREL suggests cycling increases with wind penetration 27 Source: Western Wind and Solar Integration Study, NREL, 2010, p. 153 Operating Level: No Wind10% Wind20% Wind30% Wind

Timeline Accomplished Integrate data sources Select representative fleets from available sources Build CO 2 emissions models Implemented working UCED Before January Implement forced/unforced outages Perform sensitivity on reserve requirements, fuel mix Build NO X and SO 2 emissions models Run scenarios varying wind penetration and cycling constraints 28

Use of Hourly Data 29

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 30

Startups and Wind Penetration 31 NOT ACTUAL DATA

System Costs and Startups 32 Slope: $/startup NOT ACTUAL DATA

Anticipated Sensitive Parameters Reserve rules – Contingency reserve margin – Regulating reserve margin Fuel Mix – Amount of coal 33

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) 34

DETAIL ON OPTIMIZATION METHOD 35

Base Costs and Constraints Show all costs and constraints 36

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 37

Results summary for a particular wind penetration CostCO 2 NO X SO 2 Startups(200±50) $/start(10±3) t/start Energy produced below 50% capacity (100±20) $/MWh(15±4)t/MWh Ramping(10±3) $/MW(5±2) t/MW 38 NOT ACTUAL DATA

Average vs. incremental HR 39

Base Case: Validation Continued 40 PJM 06UCED Mean CO2 output emissions rate [t/MWh] Startups / month2422 Ramping distance [MW/month] Energy produced below 50%[MWh/month] *Note that uncertainties are not yet available and will be included in final results NOT ACTUAL DATA