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
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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