Measuring the Effects of Natural Gas Pipeline Constraints on Regional Pricing and Market Integration

Transcription

1 Measuring the Effects of Natural Gas Pipeline Constraints on Regional Pricing and Market Integration Roger Avalos, Timothy Fitzgerald, and Randal Rucker Montana State University 6 November 2012 USAEE/IAEE North American Conference Austin, TX

2 The Big Picture Motivation U.S. natural gas supply and demand have changed We need pipelines to move it around

3 Research Questions To what extent does pipeline capacity affect the price fluctuations that we observe in wholesale prices? Can we improve understanding of what it means to have an integrated natural gas market?

4 Empirical Study Motivation

5 Preview of U.S. natural gas markets are largely integrated as demonstrated by cointegration tests concordant with recent literature and regulatory expectations

6 Preview of U.S. natural gas markets are largely integrated as demonstrated by cointegration tests concordant with recent literature and regulatory expectations In cases where pipeline capacity binds, price spikes result Florida (simple network): $2.50 /MMBtu S. California (more complicated network): $0.17 /MMBtu This makes it look like engineering constraints, not market power

7 Previous Work Motivation Did deregulation work? DeVany & Walls (1993, 1996) 46% market pairs cointegrated in % market pairs cointegrated in 1991 most markets integrated by 1996, except for where no arbitrage paths or constrained capacity S. California is an unusual market because local market shocks persist Serletis & Rangel-Ruiz (2004), Cuddington & Wang (2006) deregulation has helped integrate gas markets

8 Previous Work Changes over time Marmer et al. (2007): prices in CA, Rockies, Midwest not cointegrated with Henry Hub Murry & Zhu (2008): asymmetric market response in some hubs indicates market power Brown & Yücel (2008): market links weakened over time What is the cause of the weakening of the market links (breakdown in cointegration)?

9 Graphical Representation of P S p C p U D 0 D 1 Q

10 Price Series Over Time

11 Reduced Form Estimates If prices are cointegrated, so that the risk of spurious regression is low, can estimate effect directly Our data allow us to do this for the first time historically regulated structure would have prevented this Identify when breakdown in integration is due to pipeline capacity binding leaving open the possibility that it is due to something else P cg t =π 0 + π 1 BindingCap t + π 2 HDD t + π 3 CDD t + π 4 P o + π 5 P mfg t + π 6 Y t + π 7 Pt hh + π 8 Shutins t + π 9 + e t

12 Florida Southern California Table: Florida Pipeline Capacity Figures Mt.Vernon Gulf St.200 Total Import Days at Capacity Factors Capacity Factor Days Days Days 100% >99% >98% >97% >96% >95% >90% 303 1, Pipeline Capacities As Of MMCFD MMCFD MMCFD 10/1/06 2,250 1,114 3,364 1/7/09 2,250 1,118 3,368 1/12/2009 2,250 1,253 3,503 4/4/2011 2,950 1,253 4,203 7/2/2011 3,050 1,253 4,303

13 Florida Southern California Figure: FGT Mt. Vernon Throughput Capacity and Flows

14 Florida Southern California Table: Southern California Pipeline Capacity Figures Kern River El Paso El Paso Transwestern Total Cap Veyo North TX-NM West SoCal Days at Capacity Factors Cap Factor Days Days Days Days Days 100% >99% >98% >97% >96% >95% 1, >90% 1, Pipeline Capacities As Of MMCFD MMCFD MMCFD MMCFD MMCFD 10/1/06 2,042 2,190 2, ,732 4/9/10 2,197 2,190 2, ,887

15 Florida Southern California Figure: Kern River Veyo Station Throughput Capacity and Flows

16 Florida Southern California Table: Florida: All Pipelines (1) (2) (3) (4) VARIABLES Real FL Price Real FL Price Real FL Price Real FL Price Total Capacity Factor 100% 2.503*** 2.498*** 2.479*** 2.511*** (0.870) (0.886) (0.885) (0.873) Real HH Price 1.015*** 1.003*** 1.063*** 1.008*** (0.0579) (0.0588) (0.0229) (0.0571) Real Ind. Price Index FL (0.0243) (0.0268) (0.0252) Heating Degree Days 0.185*** 0.189*** 0.187*** 0.186*** (0.0370) (0.0396) (0.0392) (0.0381) Cooling Degree Days 0.109*** 0.119*** 0.121*** 0.110*** (0.0157) (0.0200) (0.0201) (0.0175) Hurricane Shutins, Bcfd (0.105) (0.110) (0.103) (0.116) Constant * (7.741) (9.181) (0.538) (9.127) Month FE NO YES YES NO Quarter FE NO NO NO YES Observations R Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

17 Florida Southern California Table: California: All Pipelines Capacity Factor 95% (1) (2) (3) (4) VARIABLES Real SoCal Price Real SoCal Price Real SoCal Price Real SoCal Price Total Capacity Factor >95% 0.165*** 0.173*** 0.173*** 0.164*** (0.0622) (0.0598) (0.0568) (0.0616) Real HH Price 0.865*** 0.860*** 0.925*** 0.871*** (0.0278) (0.0286) (0.0173) (0.0284) Real Coal Rockies Price *** *** *** (0.265) (0.286) (0.248) Real Industrial Price *** *** *** ( ) ( ) ( ) Heating Degree Days *** *** *** *** ( ) ( ) ( ) ( ) Cooling Degree Days ** *** *** ** ( ) ( ) ( ) ( ) Hurricane Shutins *** *** *** *** (0.0479) (0.0485) (0.0506) (0.0482) Constant (1.380) (1.230) (0.129) (1.348) Month FE NO YES YES NO Quarter FE NO NO NO YES Observations R Avalos, Fitzgerald, and Standard Ruckererrors ineffects parentheses of Pipeline Constraints

18 Implications Restructuring has offered broad benefits in terms of market integration Constraints are constraints Need to consider where and when to invest upstream downstream Capacity is first step towards deliverability

19 Extensions Understanding the pipeline network is an important part of this study, and fits in with broader studies of economics of networks Building pipelines is fraught with engineering, legal, political, and economic issues: e.g., Keystone XL. Construct welfare estimates of price spikes have to know something about pricing structure into retail market