Fuel Switching from Coal to Gas: The Impact of Coal Stockpiling at U.S. Coal-fired Plants

Transcription

1 Fuel Switching from Coal to Gas: The Impact of Coal Stockpiling at U.S. Coal-fired Plants Sul-Ki Lee Colorado School of Mines 35th USAEE/IAEE North American Conference November 13, 2017

2 Motivation Research question Do coal stockpiles of coal-fired plants influence the generation decision of power plant operators?

3 Motivation Research question Do coal stockpiles of coal-fired plants influence the generation decision of power plant operators? Fuel switching from coal-to-gas... Tightening environmental regulations Fracking boom

4 Motivation Research question Do coal stockpiles of coal-fired plants influence the generation decision of power plant operators? Fuel switching from coal-to-gas... Tightening environmental regulations Fracking boom... is restricted by Min-take contracts Coal stockpiling

5 Motivation Research question Do coal stockpiles of coal-fired plants influence the generation decision of power plant operators? Fuel switching from coal-to-gas... Tightening environmental regulations Fracking boom... is restricted by Min-take contracts Coal stockpiling Notice of Proposed Rulemaking (NOPR) Power plants that have a 90-day fuel stockpile would be eligible for full recovery of costs To preserve the diversity of the fuel mix

6 Contribution Coal generation, fuel switching, and GHG emissions respond to fuel costs Nonlinear impact of the relative fuel prices (Cullen and Mansur, 2015) Natural experiment for a carbon tax (Lu et al., 2012) Marginal emissions rate (Holladay and LaRiviere, 2015) Renewable energy (Fell and Kaffine, 2017) Inventory management impacts operation decision Chen et al. (2013); Hall and Rust (2007); Scarf (1959); Jha (2014) The interaction effect is missing

7 Outline Theoretical model Min-take contracts and coal storage constraints reduce the sensitivity of power plants to changes in relative input prices Empirical analysis Coal plants are less responsive to fuel price fluctuations when coal stockpile levels are higher Counterfactual experiment 18% larger impacts of a $20 carbon tax if no such effects of stockpiling

8 Conceptual Model low p Regimes t high p t (high p g t ) (low pg t ) Coal plants are inframarginal producers (Some) coal plants are marginal producers Model 1 Model 2 (Q t is fixed) (Q t is a choice variable) Extended period of high p t leads to higher coal stockpiles Fuel price elasticity of coal generation is smaller when coal storage constraint is binding

9 Data Form EIA-906 ( ) and EIA-923 ( ) Monthly plant-level data Coal stockpiles, capacity, generation, etc. Form FERC-423 ( ) and EIA-923 ( ) Order-level data, aggregated to monthly plant-level Fuel prices 325 Utility-owned coal plants are examined (31,855 observations)

10 Summary Statistics (1) (2) (3) (4) (5) stats p c /p g Coal Stockpile Coal stockpile, Coal-fired Generation deviation from generation capacity MOY avg. (1,000 metric tons) (GWh) (MW) Panel A: The whole period ( ) mean sd Panel B: Period 1 ( ) mean sd Panel C: Period 2 ( ) mean sd

11 Econometric Model log Q it =β 0 + β 1 g(log p it, log z i,t 1 ) + β 2 log Cap it + θ i + η m + ξ y + µ NERC,m + ν it where p it : relative fuel costs (p c it /pg it ) z i,t 1 : a measure of coal stockpile at the beginning of month Cap it : generation capacity θ i, η m, ξ y and µ NERC,m : Fixed effects

12 Policy Variable z i,t 1 : policy variable. A measure of coal stockpiles A: Coal stockpile levels B: A minus plant-level MOY average stockpile levels C: Binning

13 Model Specifications Model A (Coal stockpile levels) β 1 g( ) =β 11 log p it log z i,t 1 + β 12 log p it (log z i,t 1 ) 2 + β 13 (log p it ) 2 log z i,t 1 + β 14 (log p it ) 2 (log z i,t 1 ) 2 + β 15 log p it + β 16 (log p it ) 2 + β 17 log z i,t 1 + β 18 (log z i,t 1 ) 2. Model B (deviation from within-plant MOY average) β 1 g( ) =β + 1 [I(z i,t 1 > 0) g(log p it, log z i,t 1 )] +β 1 [I(z i,t 1 0) g(log p it, log( z i,t 1 ))]

14 Model Specifications (continued) Model C (Binning) 5 5 log Q it = β 0 + β 1,j BIN j,t 1 log p it + β 2,j BIN j,t 1 (log p it ) 2 j=1 j=1 5 + β 3 BIN j,t 1 + β 4 log Cap it j=1 + θ i + η m + ξ y + µ NERC,m + ν it. Bin1 Bin2 Bin3 Bin4 Bin Percentile

15 Hypothesis Argument Fuel price elasticity of coal-based generation is decreasing in z Null hypothesis log Q it log p it is non-decreasing in z log Q it log p it is non-increasing in z 2 log Q it H 0 : 0 log p it log z i,t 1 2 log Q it H A : > 0 log p it log z i,t 1

16 Results: Model A With demand controlled

17 Results: Model B With demand controlled

18 Results: Model C ɛ j = log Q it / log p it j = β 1,j + 2β 2,j log p it Null hypothesis (1) (2) (3) (4) (5) H 0 : ɛ 1 ɛ (0.101) (0.097) (0.098) (0.122) (0.090) H 0 : ɛ 2 ɛ * (0.111) (0.106) (0.106) (0.122) (0.073) H 0 : ɛ 3 ɛ * * * *** (0.096) (0.094) (0.093) (0.122) (0.041) H 0 : ɛ 4 ɛ (0.079) (0.077) (0.077) (0.078) (0.035) Time FE N Y Y Y Y Nerc month FE N N Y Y Y Standard errors clustered at Plant-level Plant-level Plant-level State-level NERC-level Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

19 Counterfactual Experiment Carbon tax drives up relative fuel costs Then we would expect coal stockpiling to hinder fuel price elasticity of coal-based generation E = CI ( ) ( ton MMBtu HR Btu ) ( ) kw h 10 6 MMBtu Btu Q (kw h) E = E coal E gas Results E = 55MMTCO2 E (3.34% reduction) in the benchmark E = 65MMTCO 2 E (3.95% reduction) in the counterfactual percent larger carbon abatement without binding coal storage constraint

20 Conclusion Coal stockpiling and the min-take contracts are important components in forecasting the fuel mix when relative fuel prices change Without binding coal storage constraint, we would have observed larger degree of fuel switching from coal to gas

21 Questions?

22 Go back

23 Go back

24 References Chen, Y., W. Xue, and J. Yang (2013). Technical note optimal inventory policy in the presence of a long-term supplier and a spot market. Operations Research 61(1), Cullen, J. and E. T. Mansur (2015). Inferring carbon abatement costs in electricity markets: A revealed preference approach using the shale revolution. Working Paper. Fell, H. and D. T. Kaffine (2017). The fall of coal: Joint impacts of fuel prices and renewables on generation and emissions. American Economic Journal: Economic Policy. Hall, G. and J. Rust (2007). The (S, s) policy is an optimal trading strategy in a class of commodity price speculation problems. Economic Theory 30(3), Holladay, J. S. and J. LaRiviere (2015). The impact of cheap natural gas on marginal emissions from electricity generation and implications for energy. Working Paper , University of Tennessee. Jha, A. (2014). Dynamic regulatory distortions: coal procurement at US power plants. Lu, X., J. Salovaara, and M. B. McElroy (2012). Implications of the recent reductions in natural gas prices for emissions of CO 2 from the US power sector. Environmental science & technology 46(5), Scarf, H. E. (1959). The optimality of (S,s) policies in the dynamic inventory problem. Mathematical methods in the social sciences.