Center for Global Trade AnalysisDepartment of Agricultural Economics, Purdue University403 West State Street, West Lafayette, IN 47907-2056 USA

contactgtap@purdue.eduhttp://www.gtap.agecon.purdue.edu

Global Trade Analysis Project

Capacity utilization and expansion in the dynamic energy landscape

Jeffrey C. PetersPhD Candidate (Dec 2015), Center for Global Trade Analysis , Purdue University

Thomas W. HertelExecutive Director, Center for Global Trade Analysis, Purdue University

33rd USAEE/IAEE North American Conference (2015)

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• 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

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• “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

Electricity sector

Rest of economy

Electricity sector

Rest of economy

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• 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

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• Explicitly and endogenously determine capacity utilization, expansion, and their interdependency

• Increased utilization drives up returns to capital, drives expansion• Increased expansion can crowd-out utilization• (percent change)

Capacity utilization and expansion

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• 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

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• 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

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Utilization: validation• Exogenous shocks

• Input prices• O&M• Gas• Oil• Coal

• Income• Population• Total electricity

demand• Capacity expansion

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• The MNL is a choice model where• Utility of the choice is given by

• With probability of choosing

• Which is also the share of new capacity allocated to a certain technology• -

• Need to validate • Total capacity • Contributions from each technology

Expansion: a multinomial logit model

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Expansion: controlling for total capacity• Control for total

capacity• “Perfect foresight”

of service year fuel prices

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Expansion: controlling for total capacity• Service year prices

• Planning year prices

• Reality somewhere in between

• Model fails in an expected way

Foresight of decline in gas prices

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• 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

AEO overestimated actual generation needs

Not all 2017 and 2018 planned yet

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• 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

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• 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

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Preliminary results: shocks to 2030Baseline Carbon Tax Wind and solar subs.2014 fuel prices

PopulationIncomeLabor cost

Total generation with endogenous TFP

-13.6% total CO2

Baseline shocks

Swap total generation with TFP

Carbon tax of $34/metric ton CO2

-23.6% total CO2

Baseline shocks

Swap total generation with TFP

Capital subsidy for wind and solar -70%

-23.6% total CO2

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Results: utilization and returns

Nuclear Coal GasBL Wind Hydro Other GasP Oil Solar

-60-40-20

020406080

Baseline

Carbon Tax

W+S Subsidy

Perc

enta

ge c

hang

e in

ca

paci

ty u

tiliz

atio

n

1 Declining capacity factor

2 "Hurt" more under carbon tax

3 "Loses" with renewable subsidy

Nuclear Coal GasBL Wind Hydro Other GasP Oil Solar

-60

-40

-20

0

20

40

60

Baseline

Carbon Tax

W+S Subsidy

Perc

enta

ge c

hang

e in

re-

turn

s to

capa

city

4 Returns hit mainly by tax 5 High relative rates of return

6 Other tech loses big by picking winners

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Results: generation

Baseline Carbon Tax W+S Subsidy -

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

841 863 843

230 243 228 79 109 62

1,210 800 900

971

734 516

573

507 492

817

1,356 1,623

127 192 214

SolarWindOilGasPGasBLCoalOtherHydroNuclear

TW

h

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• 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

Center for Global Trade AnalysisDepartment of Agricultural Economics, Purdue University403 West State Street, West Lafayette, IN 47907-2056 USA

contactgtap@purdue.eduhttp://www.gtap.agecon.purdue.edu

Global Trade Analysis Project

Thank you

Jeffrey C. Peters and Thomas W. Hertelpeters83@purdue.edu

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• Many researchers have independently disaggregated the electricity sector into specific technologies

• Technology-specific policies (renewable subsidies)• Refined operational considerations (generation mixes)

Electricity disaggregation

Tech 1 Tech … Tech TCapitalO&MCoal

GasOil

GTAP ‘ely’CapitalO&MCoalGasOil

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• Termed the matrix-balancing problem:• “Given a rectangular matrix Z0, determine a matrix Z that is

close to Z0 and satisfies a given set of linear restrictions on its entries.” (Schneider and Zenios, 1990)

The disaggregation problem

Tech 1 Tech … Tech T

Capital

O&MCoal Z0

GasOil

Tech 1 Tech … Tech T

Capital

O&MCoal Z0

GasOil

‘ely’

Capital

O&MCoalGasOil

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• The “bottom-up” data to create Z0 :• 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 Z0 to Z differ

• Resulting in fundamentally different baselines for modeling• Remain largely undocumented

Constructing the target matrix, Z0

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(16) 

(17)

Share preserving cross entropy

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Correlations

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Utilization: validation• Exogenous shocks

• Input prices• O&M• Gas• Oil• Coal

• Income• Population• Total electricity

demand• Capacity expansion

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Utilization: policy-adjusted validation• 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