Post on 19-Jan-2016
THE EFFECT OF CLEAN ELECTRICITY ON CO2 EMISSIONS FROM
PLUG-IN ELECTRIC VEHICLES
Samaneh Babaee*and Joseph DeCarolisNorth Carolina State University
Department of Civil, Construction, & Environmental Engineering
33rd USAEE/IAEE North American ConferenceOctober 2015 - Pittsburgh
* This work was performed while I was a doctoral student and postdoctoral fellow at NC State University. I am currently ORISE fellow at U.S. EPA, Research Triangle Park, NC.
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Motivation
1. U.S. oil security and availability, anthropogenic climate change, and urban air quality
2. Policy or technological change in the electric sector electricity prices plug-in electric vehicle (PEV) deployment 3. Effect of clean electric sector scenarios that could reduce emissions over time and the efficacy of using PEVs to further reduce CO2 emissions 4. Several plausible options for targeted electric sector policy (e.g., the recent U.S. EPA proposed emission guidelines for CO2
emissions from fossil fuel-fired power plants)
Electric
Plug-in Hybrid
Hybrid Electric
Plug-in Hybrid
Electric
Electric
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Objective
Under assumptions favorable to PEV deployment (high oil prices and low battery cost), quantify the effect of clean electricity scenarios on:
Technology deployment in the electric and light duty vehicle sectors
Electricity price Electric sector CO2 intensity and system-wide CO2 emissions
Electric
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Overview on TIMES and NUSTD
Utilize a technology-rich energy system model comprised of: Model generator: The Integrated MARKAL-EFOM System (TIMES)
Energy-economy optimization framework to identify the least-cost way to satisfy end-use demands over the model time horizon
Inputs: National U.S. TIMES Dataset (NUSTD) Represents the U.S. energy system at the national scale and over the next 40 years Data include:
Model outputs Optimal installed capacity and utilization by technology, equilibrium energy prices, and emissions
Fuel prices Technology cost and performance estimates End-use demands
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Sector Representation in NUSTD
NUSTD is publicly available at http://www.energy-modeling.org/
Model time horizon = 2010 to 2050 Model time periods = 5 years 3 seasons and 4 diurnal time segments A single region Social discount rate = 5%
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Scenario Description
5 scenarios examined with and without PEV availability:
1. Base Case Scenario2. Renewable Portfolio Standard (RPS) Scenario3. EPA CO2 Rules Scenario4. Clean Energy Standard (CES) Scenario5. Low Wind and Solar Cost Scenario
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Base Scenario Focused on High PEV Deployment
Highest PEV deployment scenario without new policy:High oil prices, Reference natural gas prices, Low battery cost, No RPS, No CO2
.
.
.
.
Scenario Nat Gas Price Oil Price RPS CO2 Policy Battery Cost EDV (% LDV
market)
1 High High Yes Yes Low 0.42
2 High High Yes Yes Ref 0.29
3 High High Yes Yes High 0.27
4 High High Yes No Low 0.42
5 High High Yes No Ref 0.27
6 High High Yes No High 0.24
7 High High No Yes Low 0.42
8 High High No Yes Ref 0.29
9 High High No Yes High 0.27
10 High High No No Low 0.42
11 High High No No Ref 0.28
12 High High No No High 0.24
13 High Ref Yes Yes Low 0.42
14 High Ref Yes Yes Ref 0.25
15 High Ref Yes Yes High 0.22
45 Ref High No Yes High 0.27
46 Ref High No No Low 0.42
47 Ref High No No Ref 0.28
48 Ref High No No High 0.24
49 Ref Ref Yes Yes Low 0.42
Base scenario:Highest PEV deployment without new policy (34% of LDVs).
.
.
.
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Low Carbon Electricity Scenarios
Renewable Portfolio Standard (RPS) Scenario High oil prices, Reference natural gas prices, Low battery cost, RPS, No CO2
Based on the Title I of the American Clean Energy and Security Act of 2009 (H.R. 2454)
The minimum requirement for renewable energy generation is 9.5% in 2015, which gradually increases to 20% by 2020 and is held constant from 2020 to 2050.
Wind, solar PV and concentrating thermal, biomass gasification, and
incineration of municipal solid waste
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Low Carbon Electricity Scenarios
The EPA CO2 Rules Scenario(High oil prices, Reference natural gas prices, Low battery cost, No RPS, No CO2) + EPA CO2 Rules in the electric sector
Based on the U.S. EPA proposed new source performance standard for CO2 emissions from new fossil fuel-fired electric generating units: 1100 lbs/MWh for new fuel-fired boilers, IGCC, and small gas-fired combustion turbines 1000 lbs/MWh for large gas-fired combustion turbines Emissions rate limits are applied to applicable new capacity in model year 2015 and remain in place through 2050. Based on the U.S. EPA proposed emission guidelines for existing fossil fuel-fired power plants (Option 1) which requires: A reduction in electric sector CO2 emissions below 2005 levels of
26% in 2020, 29% in 2025, and 30% in 2030 The 30% upper bound constraint on total CO2 emissions is
extended from 2030 to 2050.
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Low Carbon Electricity Scenarios
Clean Energy Standard (CES) Scenario Based on Clean Energy Standard Act of 2012 (S. 2146) A minimum requirement for electricity purchase from clean power plants including solar, wind, geothermal, municipal solid waste, biomass, new nuclear, coal-based IGCC-CCS, and NGCC-CCS
Year Percent Clean Energy
CES 2012 This study
2015 24.0 NA
2020 39.0 24.0
2025 54.0 39.0
2030 69.0 54.0
2035 84.0 69.0
2040 NA 84.0
2045 NA 84.0
2050 NA 84.0
(High oil prices, Reference natural gas prices, Low battery cost, No RPS, No CO2) + Clean Energy Standard in the electric sector
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Low Carbon Electricity Scenarios
Low Wind and Solar Cost Scenario(High oil prices, Reference natural gas prices, Low battery cost, No RPS, No CO2) + Low Wind and Solar Cost
Based on Azevedo et al. (2013), historical mean learning rate for wind is 16%, for solar PV and thermal is 22%.
Assuming four-fold increase in wind and solar, the capacity capital cost reduction is 30% for wind and 40% for solar PV and thermal by 2050 relative to 2015.
The capacity constraint requiring one unit backup capacity of natural gas turbine for every capacity unit of wind, solar thermal, and solar PV was removed.
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Effect of Electricity Scenario on LDV Market Share
No PEVs With PEVs
Base and 4 Low Carbon Electricity Scenarios
0
1000
2000
3000
4000
5000
6000
7000
2010 2015 2020 2025 2030 2035 2040 2045 2050
LDV
Dis
tanc
e Tr
avel
ed (1
09km
)
Year
GaolineE85XDieselDiesel Hybrid
0
1000
2000
3000
4000
5000
6000
7000
2010 2015 2020 2025 2030 2035 2040 2045 2050
LDV
Dis
tanc
e Tr
avel
ed (1
09km
)
Year
GasolineE85XDieselDiesel HybridPHEV60BEV160
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Average Annual Electricity Price
With PEVs
0
20
40
60
80
100
120
140
2010 2015 2020 2025 2030 2035 2040 2045 2050
Elec
tric
ity P
rice
($/M
Wh)
Year
Base
RPS
CES
EPA Rules
Low Wind/Solar Cost
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Electricity Production
0
1000
2000
3000
4000
5000
6000
Elec
ltric
ity G
ener
ation
(TW
h/yr
)
Scenario with PEVs
NGCC-CCS
New solar
Existing solar
New geothermal
Existing hydro
New wind
Existing wind
New nuclear
Existing nuclear
New natural gas
Existing natural gas
New coal
Existing coal
2010 2030 2050
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Electric Sector CO2 Intensity
0
100
200
300
400
500
600
700
2010 2015 2020 2025 2030 2035 2040 2045 2050
CO2
Inte
nsit
y in
Ele
ctri
c Sec
tor
(kg
CO2/
MW
h)
Year
Base: No PEVS BaseRPS: No PEVS RPSCES: No PEVS CESEPA Rules: No PEVS EPA RulesLow Wind/Solar Cost: No PEVS Low Wind/Solar Cost
HICC, MRO
RFCSERCTRE,FRCC
ASCC
WECC
NPCC
SPP
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National U.S. CO2 Emissions
0
1000
2000
3000
4000
5000
6000
7000
2010 2015 2020 2025 2030 2035 2040 2045 2050
Tota
l sys
tem
-wid
e CO
2em
issi
ons (
mill
ion
tonn
es)
Base: no PEVS BaseRPS: no PEVS RPSCES: no PEVS CESEPA Rules: no PEVS EPA RulesLow Wind/Solar Cost: no PEVS Low Wind/Solar Cost
-4%; +0.5%
-9%; -6%
-20%; -3%
-36%; -5%
Incremental PEV Change
0%; +0.6%
Scenario Change
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No difference in LDV market share across the various electricity scenarios. The direct effect of electric sector policies reducing national CO2 emissions is much larger than the effect produced by PEV deployment. The EPA Rules and Clean Energy Standard scenarios produce the largest incremental reduction in CO2 emissions across low carbon scenarios due to
PEV deployment. Wind and solar compete favorably against other low carbon options. In the absence of policy constraints, coal remains a viable generation option, particularly as natural gas prices increase over time. Reducing electric sector CO2 emissions will increase the efficacy of using PEVs to further reduce emissions without producing a significant effect on PEV cost-effectiveness. Policymakers must be attentive to electric sector developments when considering policy related to PEV deployment.
Insights
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Questions and Comments …?
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Overview on TIMES
Utilize a technology rich energy system model comprised of: Model generator: The Integrated MARKAL-EFOM System (TIMES)
Energy-economy optimization framework to identify the least-cost way to satisfy end-use demands over the model time horizon
Natural Gas Electricity
Combined-Cycle Gas Turbine
Capital CostVariable & Fixed O&M CostThermal EfficiencyEmissions Coefficients
.
.
.
Size of the box represents installed capacity
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The TIMES Model Generator
The Integrated MARKAL (MARket ALlocation)-EFOM (Energy Flow Optimization Model) System (TIMES): Developed under the auspices of the International Energy Agency by the Energy Technology Systems Analysis Program (ETSAP1):
A widely used bottom-up energy system model Represents energy system as a network of technologies linked
together via flows of energy commodities Performs linear optimization to minimize the discounted cost of
energy over the model time horizon, represented by present value, subject to user-imposed constraints such as emissions limits. Decision variables are technology capacity and commodity flows over time(1) http://www.iea-etsap.org/web/index.asp
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Reference Energy System
Gargiulo et al. (2011)
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Scenario Description
Scenario development is focused on five factors likely to affect the cost-effectiveness of EDVs relative to other vehicle technologies:
Natural gas price Crude oil price EDV battery cost A federal cap on CO2 emissions A federal renewable portfolio standard (RPS)
Scenarios
TIMES+NUSTD(Bottom-Up
Energy System Model)
Fuel Prices Battery Cost CO2 Policy, RPS
Tech Deployment
Emissions
108
Factor Assumption 1 Assumption 2 Assumption 3
Natural gas price Reference Low High
Crude oil price Reference Low High
Federal CO2 cap No Yes
Federal RPS No Yes
Battery development Reference Low High
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Baseline Assumptions
In the electric sector:
Constraints on SO2 and NOx emissions based on Annual Energy Outlook (AEO) results, Mercury and Air Toxics Standards (MATS), and the Cross-State Air Pollution Rule (CSAPR)
The overall minimum share of renewable energy for all states (including state-level RPSs): 2% in 2010 and 13% by 2025.
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Baseline Assumptions
In the transportation sector:
New CAFE standard and the corresponding GHG emissions rate limit for LDVs: (49.6 mpg and 163 grams CO2 per mile in model year 2025) Renewable fuel requirements based on the Energy Independence and Security Act of 2007 and AEO2012
The effect of existing fuel subsidies and tax credits for new vehicles is included in the baseline cost assumptions (AEO).
NUSTD: Fuel Supply Sector
A set of exogenous and period-specific fuel prices for oil, coal, natural gas (NG), biomass, and nuclear
Coal supply: maintain distinction between coal types and sulfur level Oil, natural gas, nuclear, and biomass supply: include exogenously specified prices of refined oil, natural gas, nuclear, and biomass feedstocks based on the projected AEO1 fuel price
Emissions factors associated with the fuel production process (EPA2)
1 Annual Energy Outlook 2012 with projections to 2035, U.S. Energy Information Administration
2 U.S. Environmental Protection Agency, National MARKAL Database Documentation
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NUSTD: End-Use Demand Sectors
Commercial, Industrial, and Residential sectors are comprised of: Single aggregate energy demand (AEO) Fuel share constraints (AEO) Emission factors associated with in-sector fossil fuel combustion (EPA, AEO)
0
2000
4000
6000
8000
10000
12000
14000
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2010 2015 2020 2025 2030 2035 2040 2045 2050
Tota
l Ene
rgy
Dem
and
(PJ)
Fuel
Sha
re (%
)
Electricity
Natural Gas
Distillate
LPG
Commercial sector
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NUSTD: Electric Sector
32 electric generation technologies (coal, nuclear, natural gas, oil, hydro, wind, solar) 71 emission retrofit technologies to capture CO2, NOX, and SO2
emissions (EPA) Cost (capital, variable and fixed O&M), availability factor, efficiency, start year, lifetime, peak fraction, existing capacity (EPA and AEO) The price of electricity is determined endogenously.
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NUSTD: Transportation Sector
Transportation sector: light duty vehicles, heavy duty vehicles, and off-highway technologies Light Duty Transportation Sector: 85 Light Duty Vehicles (LDVs)
7 vehicle size classes (Mini-compact, Compact, Full, Minivan, Pick-up, Small SUV, Large SUV)
6 fuels types (E10, E85X, Diesel, Electric, CNG, H2) 13 vehicle types Vehicles cost and performance data is driven from EPA, but is updated based on AEO. The total demand for vehicle miles is drawn from AEO. PEVs: plug-in hybrid (PHEV20 and PHEV60) and electric (BEV160) PEVs performance data is based on GREET model. Hurdle rates of 10% (hybrid, plug-in hybrid, electric, diesel, CNG, and
hydrogen fuel cell vehicles)
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Technology Deployment in Two Extreme Scenarios
Lowest EDV deployment (Low oil, base NG & bat, no RPS & CO2) Highest EDV deployment (High oil, Low NG & bat, RPS & CO2)
0
1000
2000
3000
4000
5000
6000
7000
2010 2015 2020 2025 2030 2035 2040 2045 2050
LDV
Dis
tanc
e Tr
avel
ed (1
09km
)
Year
GSL E85X Diesel
0
1000
2000
3000
4000
5000
6000
7000
2010 2015 2020 2025 2030 2035 2040 2045 2050
Elec
tric
Gen
erati
on (T
Wh)
Year
Existing coal Existing natural gas New natural gas Existing nuclearNew nuclear Existing windNew wind Existing hydroNew geothermal New solar
0
1000
2000
3000
4000
5000
6000
7000
2010 2015 2020 2025 2030 2035 2040 2045 2050
LDV
Dis
tanc
e Tr
avel
ed (1
09km
)
Year
GSL E85XDiesel Diesel HybridPHEV60 BEV160
0
1000
2000
3000
4000
5000
6000
7000
2010 2015 2020 2025 2030 2035 2040 2045 2050
Elec
tric
Gen
erati
on (T
Wh)
Year
Existing coal New coalExisting natural gas New natural gasExisting nuclear New nuclearExisting wind New windExisting hydro New geothermalNew solar
Lowest EDV deployment (Low oil, High NG & bat, no RPS & CO2) Highest EDV deployment (High oil, Low NG & bat, RPS & CO2)
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Effect of Scenario Drivers on EDV Deployment
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Effect of EDV Deployment & Scenario Drivers on Emissions
Larger bubbles: higher oil price
0
1000
2000
3000
4000
5000
6000
7000
0% 10% 20% 30% 40% 50%
Estim
ated
205
0 CO
2 Em
issi
ons i
n (M
tonn
es)
2050 Light Duty Market Share of Electric Drive Vehicles
Battery Cost: LowBattery Cost: ReferenceBattery Cost: High
CO2
Scenarios with CO2 policy
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Effect of EDV Deployment & Scenario Drivers on Emissions
• Larger bubbles indicate higher oil price• Scenarios in the dashed boxes are with CO2 policy
0
1000
2000
3000
4000
5000
6000
7000
0% 10% 20% 30% 40% 50%
Estim
ated
205
0 SO
2Em
issi
ons (
kton
nes)
Battery Cost: LowBattery Cost: ReferenceBattery Cost: High
SO2
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Effect of EDV Deployment & Scenario Drivers on Emissions
• Larger bubbles indicate higher oil price• Scenarios in the dashed boxes are with CO2 policy
0100020003000400050006000700080009000
100001100012000
0% 10% 20% 30% 40% 50%
Estim
ated
205
0 N
Ox
Emis
sion
s (kt
onne
s)
Battery Cost: LowBattery Cost: ReferenceBattery Cost: High
NOx
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The Sector-Specific Differences in 2050 Emissions
-400
-200
0
200
400
Supply Electricity LDV HDV OH End-Use Net
Emis
sion
s Ch
ange
(E
DV H
igh−
ED
V Lo
w)
Sector
SO2 (ktonnes) NOX (ktonnes) CO2 (Mtonnes)
-400
0
400
800
1200
Supply Electricity LDV HDV OH End-Use Net
Emis
sion
s Ch
ange
(E
DV H
igh−
ED
V Lo
w)
Sector
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Research Contributions
Previous modeling work on PEVs My modeling work on PEVs
Employs sector-specific models that ignore the interaction of PEVs with the rest of the energy system.
Utilizes an energy system model, which accounts for interactions across all energy sectors.
Considers a single point in time. Incorporates a model time horizon extending from 2010 to 2050 with stage-wise decisions every 5 years.
Utilizes a complex energy system model to study a limited number of scenarios that are not specifically focused on the effect of PEV deployment.
Examines several factors likely to affect PEV deployment to create a large number of scenarios, which are used to quantify emissions impacts.
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High oil prices and low EDV battery costs are the strongest drivers of EDV deployment. Across the 108 scenarios studied, there is no clear and consistent trend towards lower system-wide emissions of CO2, SO2, and NOX in the U.S. as EDV deployment increases. The incremental CO2 emissions benefit associated with PEV deployment largely depends on the evolving electricity generation mix and to a lesser extent changes across the broader energy system. Time-of-day PEV charging does not produce a significant impact on electricity prices, PEV deployment, or total system-wide CO2 emissions in the U.S. through 2050. The net effect of PEVs over time on national emissions will depend on a variety of factors beyond vehicle deployment numbers (e.g., new energy and environmental policies, prevailing fuel prices, and technology innovation). Incentivizing the purchase of PEVs will not automatically lead to emissions reductions.
Thesis Summary
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Future Work
Develop a U.S. regional energy system database
Quantify the potential local air quality benefits associated with PEV deployment
Examine the effects of large scale EDV deployment on crude oil consumption, imports, and overall energy security
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PHEV Representation in NUSTD
PHEV20 and PHEV60: E10-Electric and E85x-Electric:
Compact/full Minivan/Small SUV Large SUV/Pickup truck
Fuel economy improves from 2010 to 2020 based on GREET2012 Constant efficiency and fuel ratio from 2020 to 2050 Fractional distance traveled annually in CD mode for PHEV20=28%
and PHEV60=47% (Michalek et al., 2011)
Ratio (gasoline/electricity) =
Total gasoline consumption in CD CS modes
Total electricity consumption in CD CS modes
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PHEV Representation in NUSTD
CD Efficiency Electricity CS (109 mile/PJ) Gasoline Compact/full E10-Electric (2010) PHEV20 0.28 0.326 6.35
0.72 93.65• All electric range: 20 km• Blended control strategy
PHEV60
0.47 0.352 17.5 0.53 82.5• All electric range: 60 km• All electric control strategy
CD/CS (Michalek, 2011), Fuel economy (GREET2012)
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Hurdle rate
Compare the year-by-year cumulative discounted cost (estimated fuel cost and the annualized capital cost) for the alternative vehicle with the conventional gasoline vehicle The vehicle-specific hurdle rate is set such that the year-by-year cumulative discounted cost of the alternative vehicle exceeds that of the gasoline vehicle for 3-5 years past the 15-year lifetime of the vehicle. 2 categories of the alternative vehicles based on the current availability of required refueling infrastructure The highest vehicle-specific hurdle rate in each group is assigned to all the vehicles within the group Hybrid, plug-in hybrid, Electric, CNG, hydrogen fuel cell, and diesel vehicles = 10%
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Time Slice Representation in NUSTD
Model time horizon = 2010 to 2050 (with 5-year time periods) Each year breaks down to 3 seasons: summer, winter, and intermediate (Fall + Spring) and 4 diurnal time segments: morning, afternoon/evening, peak, and night Use sub-annual timeslices to describe the changing electricity load within a year, which may affect the required electricity generation capacity and other commodity flows 2010 2015 2020 2025 2030 2035 2040 2045 2050
Summer Winter Intermediate
Morning Afternoon Night Peak
SM SA SN SP WM WA WN WP IM IA IN IP
Years
Seasons
Times of Day
Time Slices
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Assumptions to the Annual Energy Outlook 2010 , page 91*Electricity Market ModuleTable 8.2. Cost and Performance Characteristics of New Central Station Electricity Generating Technologies
Samaneh:I updated these costs based on AEO2012 data
Technology
Online
YearSize (MW)
Leadtimes (Year
s)
Base Overnight Costs in
2010 ($2009/k
Project
Contingency Factor
Technologic
al Optimi
sm
Total Overnight
Cost in 2010 (2009
Variable O&M
($2010 mills/kWh
)Fixed O&M ($2010/kW)
Heatrate in 2011
(Btu/kWhr)
Heatrate nth-of-a-
kind (Btu/kWr)
efficiency, %
efficiency, %
Scrubbed Coal New 2013 600 4 2,658 1.07 1 2,844 4.25 29.67 8,800 8,740 39% 39%Integrated Coal-Gasification Combined-Cycle 2013 550 4 3,010 1.07 1 3,220 6.87 48.9 8,700 7,450 39% 46%IGCC with Carbon Sequestration 2016 380 4 4,852 1.07 1.03 5,348 8.04 69.3 10,700 8,307 32% 41% 3412/10700 3412/8307Conv Gas/Oil Comb Cycle 2012 250 3 931 1.05 1 977 3.43 14.39 7,050 6,800 48% 50% 0.3188785 0.410738Adv Gas/Oil Comb Cycle (CC) 2012 400 3 929 1.08 1 1003 3.11 14.62 6,430 6,333 53% 54%ADV CC with Carbon Sequestration 2016 400 3 1,834 1.08 1.04 2,060 6.45 30.25 7,525 7,493 45% 46%Conv Combustion Turbine 2011 160 2 927 1.05 1 974 14.7 6.98 10,745 10,450 32% 33% 3.14917937 3.06272Adv Combustion Turbine 2011 230 2 634 1.05 1 666 9.87 6.7 9,750 8,550 35% 40% 2.85756155 2.505862Fuel Cells 2012 10 3 5,918 1.05 1.1 6,836 0 350 9,500 6,960 36% 49%Advanced Nuclear 2016 1350 6 4,619 1.1 1.05 5,335 2.04 88.75 10,460 10,460 33% 33%Distributed Generation -Base 2012 2 3 1366 1.05 1 1,434 7.46 16.78 9,050 8,900 38% 38%Distributed Generation -Peak 2011 1 2 1,640 1.05 1 1,722 7.46 16.78 10,056 9,880 34% 35%Biomass 2013 80 4 3,519 1.07 1.05 3,859 5 100.55 13,500 13,500 25% 25%MSW - Landfill Gas 2010 30 3 7,694 1.07 1 8,233 8.33 378.76 13,648 13,648 25% 25%Geothermal 2010 50 4 2,393 1.05 1 2,513 9.64 108.62 9,760 9,760 35% 35%Conventional Hydropower 2013 500 4 2,134 1.1 1 2,347 2.55 14.27 9,760 9,760 35% 35%Wind 2009 50 3 2,278 1.07 1 2,437 0 28.07 9,760 9,760 35% 35%Wind Offshare 2013 100 4 4,345 1.1 1.02 5,974 0 53.33 9,760 9,760 35% 35%Solar Thermal 2012 100 3 4,384 1.07 1 4,691 0 64 9,760 9,760 35% 35%Photovoltaic 2011 5 2 4,528 1.05 1 4,755 0 16.7 9,760 9,760 35% 35%
http://www.eia.gov/forecasts/aeo/assumptions/pdf/electricity.pdf