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Capacity utilization and expansion in the dynamic energy la Capacity utilization and expansion in the dynamic energy la

Capacity utilization and expansion in the dynamic energy la - PowerPoint Presentation

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Capacity utilization and expansion in the dynamic energy la - PPT Presentation

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 ID: 536322

capacity total expansion utilization total capacity utilization expansion electricity power amp gas generation tech prices sector capital coal matrix

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Slide1

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)Slide2

US shale oil and gas boom and fall in gas pricesDecreasingly relative price for electricity generation from gas

Opportunity for oil exports

Opportunity for LNG exportsIncreasing efficiency of renewable technologiesIncreasing efficiency of end-use electricityPlug-in electric vehiclesClean Power Plan and other environmental policiesEconomy-wide factors may have important consequences on the electricity sector

Examples of the dynamic energy landscape

2Slide3

“Bottom-up” modelsPartial equilibrium or simulation-based

Can be technologically-rich

Exogenous projections of input costs and electricity demand drive endogenous outcomes in the electricity sectorTypically, capacity factors for technologies and fuel prices are fixed“Top-down” models – computational equilibriumEconomy-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 economySlide4

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 databasesGTAP-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 observationsIncreasing technological detail

4Slide5

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

5Slide6

Flexible technologies substitute O&M for capitalIncrease labor

Increase regularly scheduled maintenance

Increasingly costlyInflexible technologies cannot substitute (fixed short-run capacity)Utilization: flexible vs inflexible

6Slide7

Substitution of flexible technologies

Imperfect

substitutionRepresent base and peak loadImpacts returns to capital Decrease in gas prices leads to:substitution to gas power, increasing returnssubstitution away from coal power, decreasing returns

decreasing returns for inflexible technologies due to lower overall cost

Utilization: substitution

7Slide8

Utilization: validation

8

Exogenous shocks

Input pricesO&M

Gas

Oil

CoalIncomePopulation

Total electricity demandCapacity expansionSlide9

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

9Slide10

Expansion: controlling for total capacity

10

Control for total capacity“Perfect foresight” of service year fuel pricesSlide11

Expansion: controlling for total capacity

11

Service year pricesPlanning year pricesReality somewhere in between

Model fails in an expected way

Foresight of decline in gas pricesSlide12

Exogenous projections of generation needs from rolling average of EIA Annual Energy Outlooks

Again, fails in expected way

Highlights the importance of economic linkagesExpansion: projecting total capacity

12

AEO overestimated actual generation needs

Not all 2017 and 2018 planned yetSlide13

Limited sector-level detailCapacity factor utilization

Capacity expansion

Their interdependencyRarely validated against observationsCapacity factor utilization is highly correlated with observations 2002--2012 Total capacity expansion is highly correlated using EIA AEO demand projectionsContributions to expansion for each technology are also highly correlated

The validation exercises fail in expected ways

Overcoming the limitations

13Slide14

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 subsidy14Slide15

Preliminary results: shocks to 2030

15

Baseline

Carbon

Tax

Wind and

solar subs.

2014 fuel prices

PopulationIncomeLabor cost

Total generation with endogenous TFP-13.6% total CO2

Baseline shocksSwap total generation with TFP

Carbon tax of $34/metric ton CO2

-23.6% total CO2

Baseline shocksSwap

total generation with TFP

Capital subsidy for wind and solar -70%

-23.6% total

CO

2Slide16

Results: utilization and returns

16

1 Declining

capacity factor

2 "Hurt"

more under carbon tax

3 "Loses"

with renewable subsidySlide17

Results: generation

17Slide18

Important economic and operational insight can be capturedLinkage between capacity utilization and returns to capacity

Investment subsidies picks winners (and losers)

The computational equilibrium here overcomes methodological limitationsDetailed representation of electricityValidated against observationsThe next step is to incorporate stronger inter-industry and inter-regional linkages in CGE frameworkWelfare impacts – total and distributionalTrade – LNG, coal opportunities and impacts domestically and abroad

Conclusions and future work

18Slide19

Thank you

Jeffrey C. Peters and Thomas W. Hertel

peters83@purdue.eduSlide20

Many researchers have independently disaggregated the electricity sector into specific technologies

Technology-specific policies (renewable subsidies)

Refined operational considerations (generation mixes)Electricity disaggregation20

Tech 1

Tech …

Tech T

Capital

O&M

Coal

Gas

Oil

GTAP ‘

ely

Capital

O&M

Coal

Gas

OilSlide21

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

21

Tech 1

Tech …

Tech T

Capital

O&M

Coal

Z

0

Gas

Oil

Tech 1

Tech …

Tech T

Capital

O&M

Coal

Z

0

Gas

Oil

ely

Capital

O&M

Coal

Gas

OilSlide22

The “bottom-up” data to create

Z

0 :total input employment in aggregate sector (e.g. GTAP ‘ely’)total generation (GWh) by each new technologylevelized/annual costs of capital, O&M, and

fuelsMany researchers use the same or similar dataHowever, the matrix-balancing methodologies to convert

Z

0

to Z differ

Resulting in fundamentally different baselines for modelingRemain largely undocumented

Constructing the target matrix, Z0

22Slide23

(16)

 

(17)

 

Share preserving cross entropy

23Slide24

Correlations

24Slide25

Utilization: validation

25

Exogenous shocks

Input pricesO&M

Gas

Oil

CoalIncomePopulation

Total electricity demandCapacity expansionSlide26

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