Center for Global Trade AnalysisDepartment of Agricultural Economics, Purdue University403 West State Street, West Lafayette, IN 47907-2056 USA
[email protected]://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)
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
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
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
5
• 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
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
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
8
Utilization: validation• Exogenous shocks
• Input prices• O&M• Gas• Oil• Coal
• Income• Population• Total electricity
demand• Capacity expansion
9
• 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
10
Expansion: controlling for total capacity• Control for total
capacity• “Perfect foresight”
of service year fuel prices
11
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
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
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
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
15
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
16
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
17
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
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
Center for Global Trade AnalysisDepartment of Agricultural Economics, Purdue University403 West State Street, West Lafayette, IN 47907-2056 USA
[email protected]://www.gtap.agecon.purdue.edu
Global Trade Analysis Project
Thank you
Jeffrey C. Peters and Thomas W. [email protected]
20
• 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
21
• 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
22
• 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
23
(16)
(17)
Share preserving cross entropy
24
Correlations
25
Utilization: validation• Exogenous shocks
• Input prices• O&M• Gas• Oil• Coal
• Income• Population• Total electricity
demand• Capacity expansion
26
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