1. Center for Global Trade Analysis Department of Agricultural Economics, Purdue University 403 West State Street, West Lafayette, IN 47907-2056 USA contactgtap@purdue.edu http://www.gtap.agecon.purdue.edu Global Trade Analysis Project Capacity utilization and expansion in the dynamic energy landscape Jeffrey C. Peters PhD Candidate (Dec 2015), Center for Global Trade Analysis, Purdue University Thomas W. Hertel Executive Director, Center for Global Trade Analysis, Purdue University 33 rd USAEE/IAEE North American Conference (2015)

2. US shale oil and gas boom and fall in gas prices Decreasingly relative price for electricity generation from gas Opportunity for oil exports Opportunity for LNG exports Increasing efficiency of renewable technologies Increasing efficiency of end-use electricity Plug-in electric vehicles Clean Power Plan and other environmental policies Economy-wide factors may have important consequences on the electricity sector Examples of the dynamic energy landscape 2

3. “Bottom-up” models Partial equilibrium or simulation-based Can be technologically-rich Exogenous projections of input costs and electricity demand drive endogenous outcomes in the electricity sector Typically, capacity factors for technologies and fuel prices are fixed “Top-down” models – computational equilibrium Economy-wide equilibrium captures inter-industry and inter- regional linkages Endogenous input prices and electricity demand – “feedbacks” Limited sector-level detail Rarely validated against observations Electric power and economy-wide modeling 3 Electricity sector Rest of economy Electricity sector Rest of economy

4. Computational equilibrium models (e.g. CGE) are well-suited for the economy-wide linkages in the dynamic energy landscape How can we overcome aforementioned limitations? Advances in economically-consistent databases GTAP-Power expands “electricity” to T&D and 11 generating technologies (Peters, 2015) Matrix balancing specific to electric power (Peters and Hertel, forthcoming) Balancing methodology shown to influence modeling results (Peters and Hertel, in review) Advances in representing electric power Capacity factor utilization – i.e. adjustments to economic conditions with existing capacity Capacity expansion – i.e. additional and retiring capacity Validated against observations Increasing technological detail 4

5. Capacity utilization and expansion 5

6. Flexible technologies substitute O&M for capital Increase labor Increase regularly scheduled maintenance Increasingly costly Inflexible technologies cannot substitute (fixed short-run capacity) Utilization: flexible vs inflexible 6

7. Substitution of flexible technologies Imperfect substitution Represent base and peak load Impacts returns to capital Decrease in gas prices leads to: substitution to gas power, increasing returns substitution away from coal power, decreasing returns decreasing returns for inflexible technologies due to lower overall cost Utilization: substitution 7

8. Utilization: validation 8 Exogenous shocks Input prices O&M Gas Oil Coal Income Population Total electricity demand Capacity expansion

9. Expansion: a multinomial logit model 9

10. Expansion: controlling for total capacity 10 Control for total capacity “Perfect foresight” of service year fuel prices

11. Expansion: controlling for total capacity 11 Service year prices Planning year prices Reality somewhere in between Model fails in an expected way Foresight of decline in gas prices

12. Exogenous projections of generation needs from rolling average of EIA Annual Energy Outlooks Again, fails in expected way Highlights the importance of economic linkages Expansion: projecting total capacity 12 AEO overestimated actual generation needs Not all 2017 and 2018 planned yet

13. Limited sector-level detail Capacity factor utilization Capacity expansion Their interdependency Rarely validated against observations Capacity factor utilization is highly correlated with observations 2002--2012 Total capacity expansion is highly correlated using EIA AEO demand projections Contributions to expansion for each technology are also highly correlated The validation exercises fail in expected ways Overcoming the limitations 13

14. US Clean Power Plan Improved plant-level efficiency (exogenous) Switching from coal to gas power with existing plants (utilization) Constructing more renewable power (expansion) Two strategies Carbon tax (economically efficient) Investment subsidy for wind and solar (a more tractable policy?) How does the US electric power sector evolve in the response to these two strategies? Carbon tax versus investment subsidy 14

15. Preliminary results: shocks to 2030 15 BaselineCarbon TaxWind and solar subs. 2014 fuel prices Population Income Labor cost Total generation with endogenous TFP -13.6% total CO 2 Baseline shocks Swap total generation with TFP Carbon tax of $34/metric ton CO 2 -23.6% total CO 2 Baseline shocks Swap total generation with TFP Capital subsidy for wind and solar -70% -23.6% total CO 2

16. Results: utilization and returns 16 1 Declining capacity factor 2 "Hurt" more under carbon tax 3 "Loses" with renewable subsidy

17. Results: generation 17

18. Important economic and operational insight can be captured Linkage between capacity utilization and returns to capacity Investment subsidies picks winners (and losers) The computational equilibrium here overcomes methodological limitations Detailed representation of electricity Validated against observations The next step is to incorporate stronger inter-industry and inter- regional linkages in CGE framework Welfare impacts – total and distributional Trade – LNG, coal opportunities and impacts domestically and abroad Conclusions and future work 18

19. Center for Global Trade Analysis Department of Agricultural Economics, Purdue University 403 West State Street, West Lafayette, IN 47907-2056 USA contactgtap@purdue.edu http://www.gtap.agecon.purdue.edu Global Trade Analysis Project Thank you Jeffrey C. Peters and Thomas W. Hertel peters83@purdue.edu

20. Many researchers have independently disaggregated the electricity sector into specific technologies Technology-specific policies (renewable subsidies) Refined operational considerations (generation mixes) Electricity disaggregation 20 Tech 1Tech …Tech T Capital O&M Coal Gas Oil GTAP ‘ely’ Capital O&M Coal Gas Oil

21. Termed the matrix-balancing problem: “Given a rectangular matrix Z 0, determine a matrix Z that is close to Z 0 and satisfies a given set of linear restrictions on its entries.” (Schneider and Zenios, 1990) The disaggregation problem 21 Tech 1Tech …Tech T Capital O&M Coal Z0Z0 Gas Oil Tech 1Tech …Tech T Capital O&M Coal Z0Z0 Gas Oil ‘ely’ Capital O&M Coal Gas Oil

22. The “bottom-up” data to create Z 0 : total input employment in aggregate sector (e.g. GTAP ‘ely’) total generation (GWh) by each new technology levelized/annual costs of capital, O&M, and fuels Many researchers use the same or similar data However, the matrix-balancing methodologies to convert Z 0 to Z differ Resulting in fundamentally different baselines for modeling Remain largely undocumented Constructing the target matrix, Z 0 22

23. Share preserving cross entropy 23

24. Correlations 24

25. Utilization: validation 25 Exogenous shocks Input prices O&M Gas Oil Coal Income Population Total electricity demand Capacity expansion

26. Utilization: policy-adjusted validation 26 Includes non-economic considerations EPA mercury regulations Increased base load substitution due to shortening of coal contracts Gains in correlation Illustrates the joint importance of qualitative information