Clean Power Plan Insights for Pennsylvania Jeffrey Anderson, Paul Fischbeck, Haibo Zhai, David Rode Department of Engineering and Public Policy Carnegie Mellon University Pittsburgh, PA CEDM Annual Meeting Carnegie Mellon University Pittsburgh, PA 24 May 2016

Clean Power Plan (CPP) Building Blocks— Final Regulation Increase coal boiler heat rate efficiency Re-dispatch to lower CO 2 emitting sources Create low/zero carbon generating sources Block 1 Block 2 Block 3 2.1% to 4.3% improvement Increase dispatch to 75% summertime peak capacity Credits to offset CO 2 National average of 32% reduction in 2005 CO 2 net emission intensity (lbs/MWh) by

3 Setting the Two Clean Power Plan Source Targets Rate-based Emission rate based upon technology NGCC: 771 lbs/MWh net Steam: 1,305 lbs/MWh net State goal Generation Mix Mass-based Existing sources only state goal times 2012 fossil-fuel generation New sources allowed state goal times extra renewable generation Trading Mechanisms: Emission Reduction Credits (ERCs) Mass Allowances (MAs)

4 Pennsylvania Assessment Framework

5 Optimization Parameters Objective function: Minimize fleet LCOE Decision variable: Capacity Subject to: Emission targets Retirement of existing capacity Limits on new capacity Sufficient ERCs or MAs Demand

6 Historical Fleet can be CPP Compliant for Mass-based Approaches Mass allowance price set to $0/short ton.

7 Block 1 and 2 Application Yields Compliance Can achieve mass-compliance with existing fleet

8 Emissions and LCOE further reduced for Mass- based Approach with New Sources

9 Allowing for New NGCC further reduces Coal Emissions Existing and new wind generation not required

10 Emissions and LCOE further reduced for Mass- based Approach with New Sources 205 TWh92% of 2012 total net generation

11 Approaches are Nonlinear When Demand Increases 205 TWh92% of 2012 total net generation

12 Approaches are Nonlinear When Demand Increases

13 Approaches are Nonlinear When Demand Increases

14 Approaches are Nonlinear When Demand Increases CCS with bypass

15 Mass-based with Existing Sources dependent upon CCS

16 Mass-based with New Sources mostly dependent upon NGCC

17 Initial Key Findings CPP compliance achieved without new generation sources CPP building blocks 1 and 2 Increase nuclear capacity factor Nuclear fleet is important for Pennsylvania compliance NGCC is preferred over new renewables Fuel price is important Capacity limits influences choices Rate-based approach makes larger emission reductions, but LCOE is higher Uncertainty in future demand is an important consideration Tradeoffs required to meet future demand: fleet LCOE, total emissions Increases in demand can lead to CCS solutions

18 Key Policy Implications State decisions are complicated because Uncertainty in prices Uncertainty in demand Uncertainty in nuclear reliability Uncertainty in renewable incentives Uncertainty in Washington DC Requirement for early approach lock-in for states Difficult decisions may need to be made Trading and Real Options could be important

19 Acknowledgements This work was supported by the Engineering and Public Policy Department at Carnegie Mellon University.

Clean Power Plan Formulation 20 Final limits 1,400 lbs/MWh gross (new coal-fired) 1,000 lbs/MWh gross (new gas-fired) Current Coal Plant Emissions 1,800 lbs/MWh net (advanced coal-fired)* 2,249 lbs/MWh net (existing coal fleet)** *Source: IECM **Source: Clean Air Act (1970) Section 111(b)—New Standards of Performance Section 111(d)—Existing Standards of Performance

21 Pennsylvania Fossil Fuel State Targets EPA CPP Rate: 1,642 lbs/MWh 1,095 lbs/MWh -33% Mass: 120 million tons 89.8 million tons-25% Reduction Total Fossil-Fuel Rate: 1,713 lbs/MWh 1,095 lbs/MWh -36% Mass: 112 million tons 89.8 million tons-20%

22 Pennsylvania Fossil Fuel State Targets Affected Sources Rate: 1,642 lbs/MWh 1,095 lbs/MWh -33% Mass: 120 million tons 89.8 million tons-25% Reduction Study Sources Rate: 1,620 lbs/MWh 1,095 lbs/MWh -32% Mass: 102 million tons 89.8 million tons-12% All Inclusive Rate: 1,713 lbs/MWh 1,095 lbs/MWh -36% Mass: 112 million tons 89.8 million tons-20%

23 Methodology—Essay 3 Affected PC EGUs and NGCC plants Cluster Regression for PC EGUs Generation-weighted averages for operational parameters Simulated in the Integrated Environmental Control Model (IECM) for power plant performance and cost assessments Simulation profile based upon 2010/2012 National Electric Energy Data System (NEEDS) Emissions & Generation Resource Integrated Database (eGRID) Energy Information Administration (EIA) form EIA-923 (2012 PA average) National Oceanic and Atmosphere Administration (NOAA) Quality Controlled Local Climatological Date (2012—PIT) 2030 simulation basis EIA projection of fuel price changes due to Clean Power Plan proposal* 2012 average weather conditions *Energy Information Administration. Analysis of the Impacts of the Clean Power Plan. n.d. 22 May Web. 18 October

24 Methodology—Essay 3 Utility Renewable Energy (RE) Existing profile based upon 2010/2012 National Electric Energy Data System (NEEDS) Emissions & Generation Resource Integrated Database (eGRID) Future RE profiles National Renewable Energy Laboratory (NREL): Renewable Electricity Scenario Viewer* 90% RE-Incremental Technology Improvement Scenario (RE-ITI) 90% RE-Evolutionary Technology Improvement Scenario (RE-ETI) The Integrated Planning Model (IPM) RE capital and operation and maintenance costs (O&M) Wind EIA: Assumptions to the Annual Energy Outlook 2014 U.S. Department of Energy (DOE): 2013 Wind Technologies Market Report Solar Lawrence Berkeley National Laboratory and SunShot DOE: Tracking the Sun VII Lawrence Berkeley National Laboratory: LBNL *“Renewable Electricity Futures Scenario Viewer.” n.d. n.p. Web. 20 August

25 EIA Projection for Impact of CPP Proposal on Henry Hub Natural Gas Price Predicted increase price from 2012: 81.25% *Energy Information Administration. Analysis of the Impacts of the Clean Power Plan. n.d. 22 May Web. 18 October

26 Historical Utility Photovoltaic Capital Costs Barbose, G., S. Weaver, and N. Darghouth. "Tracking the Sun VII: An Historical Summary of the Installed Price of Photovoltaics in the United States from 1998 to 2013." Lawrence Berkeley National Laboratory, Berkeley, CA (2014)..

27 Projections for Future Utility Photovoltaic Capital Costs Feldman, David, et al. "Photovoltaic System Pricing Trends: Historical, Recent, and Near-Term Projections–2014 Edition." Golden, CO: National Renewable Energy Laboratory, PR-6A Accessed August 20 (2015): 2014.

28 Historical and Projections for Future Utility Photovoltaic Operation & Maintenance Costs Bolinger, M., Weaver, S., & Zuboy, J. (2015). Is $50/MWh Solar for Real? Falling Project Prices and Rising Capacity Factors Drive Utility-Scale PV Toward Economic Competitiveness. Progress in Photovoltaics: Research and Applications. doi: /pip Report Number: LBNL

29 Historical Wind Turbine Capital Costs Wiser, Ryan, and Mark Bolinger. "2013 Wind Technologies Market Report." (2014).

30 Historical Wind Turbine Operation & Maintenance Costs Wiser, Ryan, and Mark Bolinger. "2013 Wind Technologies Market Report." (2014).

31 Fossil Fuel Optimization Parameters

32 Results for Cluster Regression on PC EGUs

33 Generation Source Optimization Parameters

34 Generation Source Capacity Limits for 2030

35 Fuel Price Influences Technology Choice for Mass-based CPP Compliance Strategies Mass allowance price set to $0/short ton.

36 Application of Block 1 and 2 Yields Compliance Can achieve mass-compliance with existing fleet

37 Fuel Price Influences Technology Choice for Mass-based CPP Compliance Strategies

38 Allowing for New NGCC further Reduces Coal Emissions Existing and new wind generation not required

39 Fuel Price Influences Technology Choice for Mass-based CPP Compliance Strategies

40 Higher Natural Gas Price Reduces New NGCC Capacity in 2030 Existing and new wind generation not required

41 Fuel Price Influences Technology Choice for Mass-based CPP Compliance Strategies

42 Application of Block 1 and 2 Yields Compliance New wind generation not required

Historical Lumped Generation

44 Optimization Strategies for Rate-based Approach

45 Fossil Fuel Optimization Parameters

46 Rate-based Approach for Technology Option Uses More New NGCC

47 Approaches are Nonlinear When Demand Increases in Strategy 8

48 Approaches are Nonlinear When Demand Increases in Strategy 8

49 Mass-based with New Sources mostly dependent upon NGCC

50 Mass-based Approach with Existing Sources Needs NGCC with CCS

51 Reduction in CO 2 Approach Objectives is Proportional for Strategy 8