National Renewable Energy Laboratory 

Innovation for Our Energy Future 

A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 

A Preliminary Assessment of 

Plug-In Hybrid Electric Vehicles 

on Wind Energy Markets 

W. Short and P. Denholm

Technical Report 

NREL/TP-620-39729 

April 2006 
A Preliminary Assessment of 

Plug-In Hybrid Electric Vehicles 

on Wind Energy Markets 

W. Short and P. Denholm

Prepared under Task No. WUA7.1000 

Technical Report 

NREL/TP-620-39729 

April 2006 

National Renewable Energy Laboratory 

1617 Cole Boulevard, Golden, Colorado 80401-3393 

303-275-3000 • www.nrel.gov 

Operated for the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337
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TABLE OF CONTENTS 

Introduction....................................................................................................................... 1 

Technology Status............................................................................................................. 1 

Description of the WinDS Model and Base Case ........................................................... 4 

Addition of PHEVs to the WinDS Model ....................................................................... 7 

Conclusions...................................................................................................................... 18 

Future Work.................................................................................................................... 18 

Appendix 1: Effects of Increasing PHEV Reserve Capacity via “Engine-On” 

Capability................................................................................................................ 20 

Appendix 2: Summary of Data Used in WinDS........................................................... 21 

iii
1

Introduction 

The United States currently faces some of its most daunting energy challenges in recent 

history. And the challenge with the largest visibility and immediate consumer impact is 

the high price of gasoline and natural gas. A little less visible, but still strong in the 

national consciousness, is the issue of oil availability. The nation’s dependence on 

foreign imports (especially from the unstable Middle East), along with vulnerabilities 

from international terrorism, heightens the immediacy of the oil availability issue. 

Finally, on top of these oil supply/price issues, the United States faces the strong 

possibility that its use of fossil fuels is damaging the Earth’s climate. 

This report examines a measure that may potentially reduce oil use and also more than 

proportionately reduce carbon emissions from vehicles. The authors present a very 

preliminary analysis of plug-in hybrid electric vehicles (PHEVs) that can be charged 

from or discharged to the grid. These vehicles have the potential to reduce gasoline 

consumption and carbon emissions from vehicles, as well as improve the viability of 

renewable energy technologies with variable resource availability. This paper is an 

assessment of the synergisms between plug-in hybrid electric vehicles and wind energy. 

The authors examine two bounding cases that illuminate this potential synergism. 

Technology Status 

The following discusses the issues associated with wind energy and PHEV technologies. 

Wind 

The use of wind energy for electricity generation has grown dramatically with decreasing 

costs and improved performance of wind turbines, increasing fossil fuel costs, and 

growing environmental concerns. The United States has a large wind power resource 

base, exceeding the current installed electricity generation capacity from all sources. The 

development and use of this power resource is limited by a number of factors, including 

the location of high-value wind resources, the resource variability of wind energy, and 

the relatively low availability (measured as amount of electricity actually 

generated/maximum electricity available, if operated continuously at full-rated power) of 

this generation source, compared to conventional alternatives. 

Variability in wind output implies limited predictability; high natural ramp rates; and, 

often, limited coincidence with peak demand. These factors can restrict the ultimate 

penetration of wind power into traditional electric power systems. The high reliability 

required by such systems dictates that ample capacity is always available and that 

conventional generators are able to follow the variations in loads, forced outages, and 

variable supplies like wind. Where wind power adds to these capacity requirements, it 

usually incurs additional costs. 
One possible solution to the problem of variable wind output is energy storage—the 

application of any of several technologies that can store electricity when it is not needed 

—and that can deliver stored electricity when demand is high, or renewable output is low. 

The United States currently has about 20 GW of pumped hydroelectric storage in place, 

with further expansion restricted by lack of available sites, environmental issues and high 

cost. While there are a few other options, such as compressed air energy storage,1

 these 

technologies all add significantly to the cost of electricity to be stored. 

The optimal solution for wind would be coupling it with a low-cost source of energy 

storage (or dispatchable load) that is perhaps already in existence for some other purpose. 

The emergence of “plug-in” hybrid electric vehicles may provide this significant 

opportunity. 

PHEVs 

The emergence of hybrid-electric vehicles (HEVs) provides a potentially significant 

enabling technology for variable-generation sources such as wind energy. As currently 

offered by several major auto manufacturers, hybrid-electric vehicles add a battery and 

electric motor to an internal-combustion (IC) engine. This combination increases fuel 

economy by allowing the IC engine to operate more efficiently, shutting off the engine 

during stops, and recapturing otherwise discarded kinetic energy through regenerative 

braking. While the overall efficiency is increased, all of the energy is still derived from 

petroleum. However, some of the drive energy could be derived from grid electricity by 

increasing the size of the HEV’s battery and by adding external charging capability. 

A “plug-in” HEV (PHEV) may also be designated by its effective “all-electric” range, 

such as PHEV-20, referring to a vehicle that may be driven 20 miles from its batteries. 

Beyond this range, the vehicle operates as a conventional HEV. For the average driver, 

the use of a relatively small battery delivers much of the benefits of a pure electric 

vehicle, without the disadvantages of prohibitive cost or limited range. 

The economic incentive for drivers to use electricity as fuel is the comparatively low 

cost. Assuming a vehicle efficiency of 3.4 mile/kWh, and 85% charging efficiency, a 

PHEV would need about 9-10 kWh to drive the 25 to 30 miles provided by a gallon of 

 

1 Denholm, P, 2006. "Improving the Technical, Environmental, and Social Performance of Wind Energy 

Systems Using Biomass-Based Energy Storage" Renewable Energy. 31, 1355-1370. 

2
gasoline.2

 However, unlike the $2/gallon cost of gasoline, today’s cost of this electricity 

would be less than $1 in most locations, and could be less than 50 cents when using offpeak power at preferential rates.

The large-scale deployment of PHEVs may be possible with continued improvements 

and decreasing costs of existing HEV technology, as well as advanced batteries. A study 

by the Electric Power Research Institute (EPRI) found a significant potential market for 

PHEVs, depending on vehicle cost and the future cost of petroleum.3 In this paper, the 

authors do not directly address the economics of PHEVs. Rather, they examine the 

implications of an assumed high level of penetration of PHEVs into the light-duty vehicle 

(LDV) market. 

Vehicle to Grid (V2G) Capability 

Many researchers have noted that the maximum economic benefit of PHEVs may be 

derived by adding “vehicle-to-grid” (V2G) capability, where the vehicle can discharge, as 

well as charge.4 This capability adds potentially significant revenue to the owner by 

providing high-value electric system services to the grid, such as regulation and spinning 

reserve. These services are described in more detail in the next section. 

To maximize the economic value of the PHEV to the consumer, it is almost certain that 

the charging and discharging vehicle will be controlled directly or indirectly by the utility 

system. External control allows the vehicle to be charged with the lowest-cost electricity, 

and also allows the vehicle to provide high-value ancillary services. With direct control, 

the utility would send a signal to an individual vehicle or a group of vehicles. Such a 

concept is already in use through other load-control programs in place for water heaters, 

air conditioners, etc. The direct control could also be established through an aggregator 

that sells the aggregated demand of many individual vehicles to a utility, regional system 

operator, or a regional wholesale electricity market. 

Under the second option—indirect control—the vehicle would respond intelligently to 

real-time price signals or some other price schedule to buy or sell electricity at the 

appropriate time. In either control scheme, the vehicles would be effectively “dispatched” 

to provide the most economical charging and discharging. 

 

2

 This efficiency is probably higher than a U.S. fleet of PHEVs, resembling the current fleet of new lightduty vehicles. A study by EPRI found an electric-drive efficiency of 2.3, 2.7, and 4.0 miles/kWh for three 

reference vehicles characterized as full-sized SUVs, mid-size SUVs, and compact cars (see Reference 3). 

The same study cites the fuel economy of a reference (conventional IC engine) mid-sized SUV at 22.2 

MPG. Given the current new-car fleet average fuel economy of about 25 mpg (AEO 2006, Table A7), the 

equivalent PHEV fleet electric drive efficiency would be closer to the mid-size SUV fuel economy, perhaps 

around 2.9 miles/kWh. 

3

 Electric Power Research Institute, 2002. " Comparing the Benefits and Impacts of Hybrid Electric Vehicle 

Options for Compact Sedan and Sport Utility Vehicles " EPRI, Palo Alto, Calif,, 1006891 4

 Kempton, W. and J. Tomic, 2005. "Vehicle-to-grid power fundamentals: Calculating capacity and net 

revenue." Journal of Power Sources, 144(1): 268-279. 

3
Description of the WinDS Model and Base Case 

To assess the benefits and impacts of PHEVs on the electric grid, the authors used the 

Wind Deployment System (WinDS) model. The (WinDS) model is a computer model 

originally designed to evaluate the potential for wind energy generation in the United 

States. To do this, the model optimizes the regional expansion of electric generation and 

transmission capacity in the continental United States during the next 50 years. The 

model “competes” wind and conventional alternatives (fossil, nuclear), considering the 

requirements of the electric power systems, as well as the economic and technical 

characteristics of each technology. The model includes region-specific data for wind, and 

also considers, in detail, the statistical impacts of wind-resource variability. 

WinDS minimizes system-wide costs of meeting electric loads, reserve requirements, and 

emission constraints by building and operating new generators and transmission in 26 

two-year periods from 2000 to 2050. The primary outputs of WinDS are the amount of 

capacity and generation of each type of prime mover—coal, gas combined cycle, gas 

combustion turbine, nuclear, wind, etc.—in each year of each 2-year period. Additional 

documentation about the model structure and treatment of wind and conventional 

generation resources is available at www.nrel.gov/analysis/winds. Electricity demand 

forecasts, as well as forecasted costs of conventional generation and fuels used in the 

WinDS model, are based largely on the U.S. Energy Information Administration’s 

Annual Energy Outlook (AEO), which is updated annually. Aside from the assumptions 

regarding market penetration of plug-in hybrids, the results presented in this document 

are based on the 2005 AEO, summarized in Appendix 2. 

Figures 1 and 2 present the results from a base case WinDS run through 2050 that does 

not include PHEVs. This base case represents a “business-as-usual” scenario for U.S. 

energy policies in effect in spring 2005 (i.e. no carbon constraints, etc.) 

4
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Figure 1: Base Case Projection of U.S. Electric System Capacity from WinDS 

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Figure 2: Base Case Projection of U.S. Electric System Generation from WinDS 

5
Figure 1 provides total installed capacity by type, while Figure 2 provides generation by 

type. The base case projects that there will be significant growth in wind capacity—more 

than 200 GW by 2050. However, this value is much less than the technically exploitable 

wind resources in the United States of more than 7,000 GW (includes Class 3 wind),5 and 

provides less than 10% of the nation’s electricity. Wind deployment in this business-asusual case is constrained by a range of factors including environmental, land-use, and 

siting issues; transmission constraints; low conventional fuel costs; and the resource 

variability of wind. The addition of PHEVs to the model did not relax any of the basic 

constraints on wind energy development, with the exception of reducing the impact of 

wind-resource variability via the capacity available in PHEV batteries. 

The variability in wind generation precludes wind from contributing fully to the reserve 

margins required by utilities to ensure continuous system reliability. Within WinDS, grid 

reliability is captured by two constraints on planning reserves and operating reserves. 

Planning reserves ensure adequate capacity during all hours of the year. Typical systems 

require a “peak reserve margin” of 10%-18%. This means a utility must have in place 

10%-18% more capacity than their projected peak power demand for the year. This 

ensures reliability against generator or transmission failure, underestimates of peak 

demand, or extreme weather events. WinDS estimates planning reserves directly through 

a system constraint—the aggregated installed capacity multiplied by a reliability factor 

must exceed the peak demand multiplied by the peak reserve margin for each North 

American Electric Reliability Council (NERC) region. Due to the resource variability of 

wind generation, only a small fraction of a wind farm’s nameplate capacity is usually 

counted toward the planning reserve margin requirement. In fact, as wind penetrates 

further into an electric grid, this “capacity credit” for wind generally declines, especially 

if the wind farms are developed near each other, i.e. if their output is well correlated. 

With its 358 wind-supply regions, WinDS can assess the correlation between the outputs 

of different wind farms and, therefore, more accurately calculate the capacity credit for 

each addition of wind capacity installed. 

Operation reserves include several types of reserves in place to respond to short-term 

unscheduled demand fluctuations, or generator/other system failure. Operating reserve 

represents generators that can be started or ramped up quickly. There are several 

categories of operating reserves, often referred to as ancillary services. WinDS does not 

model each of these individual services directly, but instead aggregates them into a single 

operating-reserve constraint. This constraint requires the system to have a certain amount 

of “quick-start” and “spinning” capacity in the system. Quick-start capacity includes 

combustion turbines and hydroelectricity, while spinning capacity represents other partly 

loaded fossil and/or hydroelectric plants. The introduction of wind power into a grid can 

increase these operation-reserve requirements, due to the variability in wind generation. 

While WinDS simulates many other factors that influence the market potential of wind 

power, e.g., transmission requirements, this report focuses on the impacts of wind output 

variability, because these can be partly mitigated by PHEVs connected to the grid. 

 

5

 Denholm, P. and W. Short, 2006. “Documentation of WinDS Base Case Data” Available at: 

http://www.nrel.gov/analysis/winds/pdfs/winds_data.pdf (see Appendix A, Wind Resource Dataset) 

6
PHEVs can supply some of the planning and operating reserves required by the grid, 

which relieves some of the burden on wind. 

To assess the impact of PHEVs on the market potential of wind power, it was necessary 

to modify the WinDS model to allow PHEVs to contribute both planning and operatingreserve capacity. This was done by adding the capacity available from the stock of 

PHEVs that have penetrated the market (see Figure 1) to the reserves in the planning and 

operating-reserve constraints of WinDS. 

PHEVs can also supply regulation reserve to the electric grid. This ancillary service 

assists the grid in following the second-by-second variations in load. Regulation reserve 

can be a fairly expensive form of reserve with costs that regularly exceed $35 per hour 

for each MW made available.6

 Regulation reserve could be easily provided by PHEV 

batteries, because energy draws are minuscule with charging and discharging reversing 

every few seconds as loads fluctuate up and down. Studies have shown that PHEVs 

provide significantly more value in the form of regulation reserve than they do in the 

form of planning reserves or operating reserves.7,8

 When evaluating the economics of 

PHEVs, consideration of regulation reserve value is critical. The authors do not consider 

it here for two reasons: 1) the authors aren’t considering the economics of PHEVs (they 

simply assume a PHEV penetration scenario), and 2) wind power does not significantly 

impact regulation reserve requirements and, therefore, is not impacted by the regulation 

reserve available through PHEVs. 

Addition of PHEVs to the WinDS Model 

PHEV Market Penetration

In this preliminary study, the authors assumed PHEVs penetrate the market under the 

following scenario: 

It is assumed that the total light-duty vehicle fleet in 2050 is 448 million vehicles— based 

on Energy Information Administration (EIA) projections of 309 million vehicles in 

20259

—and a 1.5% annual growth rate from 2025 to 2050. It is assumed that PHEVs are 

introduced in 2008, and achieve a 50% market share of the light-duty vehicle stock by 

 

6

 Kirby, B., 2004. “Frequency Regulation Basics and Trends,” ORNL/TM-2004/291. Available at

www.ornl.gov/~webworks/cppr/y2001/rpt/122302.pdf

7

 Brooks, A. and T. Gage, 2001. “Integration of Electric Drive Vehicles with the Electric Power Grid -- a 

New Value Stream” 18th International Electric Vehicle Symposium (EVS-18), Berlin, World Electric 

Vehicle Association (WEVA). Available at www.acpropulsion.com/EVS18/ACP_V2G_EVS18.pdf 8

 Kempton & Tomic, 2005. 

9

 Energy Information Administration, 2005. “Supplement Tables to the Annual Energy Outlook 2005” 

Available at http://www.eia.doe.gov/oiaf/archive/aeo05/supplement/index.html (Table 48) 

7
2050 as shown in Figure 1. The market penetration shape in Figure 3 is based on a 

market-diffusion “S-curve”10. 

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Fleet)

Figure 3: PHEV Total Market-Share Assumption 

The actual penetration of PHEVs will depend on the relative economics of PHEVs and 

other vehicle alternatives. This preliminary study does not address these economics. It is 

our intent simply to show the potential benefits of a relatively high level of PHEV market 

penetration on the use of wind power in the electric sector. In the near future, the authors 

hope to expand this analysis to assess the feasibility of the market penetration shown in 

Figure 3 by evaluating the relative economics of PHEVs. 

PHEV Technical Characteristics

The analysis assumed that the average PHEV achieves an electric-drive efficiency of 3.4 

miles/kWh (0.29 kWh/mile), and a charging efficiency of 85%.11 This corresponds to a 

“plug efficiency” of 2.9 miles/kWh, a value which can be compared directly to a 

conventional fuel efficiency typically measured in miles/gallon. This value is based on 

estimates by EPRI for a mid-sized passenger vehicle that corresponds to a conventional 

vehicle with an average fuel economy of about 25 mpg.12 The authors also assumed that 

two different sizes of PHEVs are available: a PHEV-20 with a 5.9 kWh battery, or a 

PHEV-60 with a 17.7 kWh battery. In all cases in this document, battery capacity is 

 

10 Bass model: 

Tqp

Tqp

e

p

q

e

•+−

•+−

•+

−

)(

)(

1

1

 where T is the year, p and q are values that characterize the S-curve 

shape. 

11 Electric-drive efficiency (miles/kWh) is similar to “conventional” fuel efficiency, typically measured in 

miles/gallon. 

12 Electric Power Research Institute, 2001. "Comparing the Benefits and Impact of Hybrid Electric Vehicle 

Options," EPRI, Palo Alto, Calif,, 10003496892. 

8
considered useful capacity, meaning the battery can be cycled over its full-rated capacity 

(5.9 or 17.7 kWh) without affecting useful life. Finally, the authors assumed that all 

PHEVs have V2G capability. 

[The analysis assumed that PHEVs operate in “blended mode,” where the vehicle 

operates in “EV-only” mode at low speeds, but requires some operation of the IC engine 

at high speeds. This reduces the size of the EV components and reduces overall cost, at 

the expense of reduced “electric-only” miles. Combining this assumption with typical 

driving patterns produces an average “electric miles traveled” of about 15 miles per day 

for a PHEV-20, and about 25 miles per day for a PHEV-60.13] 

As a result of these assumptions, the overall reduction in petroleum use is about 50% for 

the PHEV-20 and about 80% for the PHEV-60, compared to a conventional IC engine 

vehicle. It should be noted that a significant fraction of the fuel use benefits are derived 

from the hybridization of the vehicle. Converting the “average” vehicle to a HEV-0 

(nonplug-in hybrid) may improve the fuel economy by about 25%.14

PHEV Capacity Credit

Several assumptions were made to establish the total capacity credit that utilities might 

apply to PHEVs that provide operating and planning reserve capacity. Estimating the 

effective “capacity” provided by a fleet of PHEVs is somewhat challenging, given the 

important time-sensitive (how many cars are plugged in and when) nature of PHEV use. 

The power capacity of an individual PHEV is a function of many factors: whether or not 

it is plugged in, the capacity of the plug circuit, the amount of time vehicle discharge is 

required, the vehicle useful-battery capacity, the state of charge of the battery at the 

initiation of discharge, and whether or not the IC engine may be turned on to provide 

electricity. For a reasonable number of vehicles deployed, each of these factors can be 

expressed as a distribution, or average, which may or may not vary over time. 

PHEV plug-in factor: Data from the U.S. Department of Transportation15 indicates that 

only a small fraction of vehicles (fewer than 20%) are on the road at any one time. While 

it is likely that the fraction of vehicles on the road will vary significantly during the 

course of a day, the most important value is the fraction plugged in during peak periods, 

because the planning reserve constraint is based on capacity required at peak. The 

authors chose a plug-in factor of 50%. Because the vehicle-to-grid services are required 

at all times—including times when a high percentage of vehicles will be away from their 

home base—this high plug-in factor may imply that charging facilities will have to be 

made available at workplaces and perhaps shopping locations. 

 

13 EPRI, 2002. 

14 Ibid. 

15 U.S. Department of Transportation, 2004. “2001 National Household Travel Survey.” Available at 

http://nhts.ornl.gov/2001/index.shtml 

9
Maximum circuit capacity: A PHEV could have an internal electric system capacity 

that exceeds 100 kW.16 However, not all of this capacity will be accessible to the grid for 

planning and operating reserves. Nearly all PHEVs will be plugged in to conventional 

residential and commercial electric circuits at 120V or 240V. At these voltages, the line 

capacity is the bottleneck on power flow to and from the grid. However, many customers 

(such as fleet owners) may choose to utilize much higher capacity circuits for maximum 

economic benefits. The overall range of likely circuits is 2.4 kW (120V @ 20A) to 

perhaps 24 kW (240V @ 100A). The authors chose 9.6 kW (e.g., 240 V @ 40 A) as their 

“average” value for PHEV grid connection.17 

Energy constraint: The continuous power rating of a PHEV is limited by the stored 

energy in its batteries. A fully charged PHEV-20 with a 5.9 kWh (useful capacity) battery 

could provide 9.6 kW for 0.6 hours. Operating-reserve events are typically shorter than 

this period; however, for planning reserves, utilities will likely require dependable 

discharge times of several hours or more. The amount of discharge time required (and the 

state of charge of the battery) heavily influences how much capacity credit may be given 

to the battery fleet as a whole, particularly if generation from the IC engine is restricted. 

Figure 4 illustrates the potential capacity credit for a single PHEV, as a function of the 

required discharge time. In this case, the vehicle is assumed to have a fully charged 

battery, and is connected to a 9.6 kW circuit. For short-term events (30 minutes or less 

for a PHEV-20), the vehicle is line-limited, illustrated by the flat line at the upper lefthand side of the vehicle capacity curve. For longer-term events requiring hours of 

continuous discharge, the size of the battery limits the capacity credit that may be applied 

to an individual vehicle. 

 

16 2006 Toyota Motor Sales, U.S.A., Inc. Available at: http://toyota.com/highlander/specs_hybrid.html 17 New high-capacity 240V circuits for parking lot and fleet-charging stations would cost the same as new 

120 V circuits see EPRI, 2002. 

10
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PHEV Discharge Capacity Credit 

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PHEV-60 (17.7 kWh)

PHEV-20 (5.9 kWh)

Figure 4: Capacity of a PHEV as a Function of Discharge Time Required (fully charged 

battery, 9.6 kW plug circuit assumed) 

The analysis assumed a discharge requirement of 30 minutes for operating reserves18and 

4 hours for planning reserves. 

The final capacity value, representing the total capacity for the average PHEV, can be 

calculated according to the formula: 

Vehicle Capacity = The minimum of 

Line capacity 

 OR (1) 

Battery energy (kWh) * SOC * % plugged in / Discharge Time Required (hours) 

The authors established two cases where the fleet is comprised only of either PHEV-20s 

or PHEV-60s. They assumed that the PHEV-60, with its larger useful battery capacity, 

would generally have a higher state of charge at the beginning of a reserve need. In both 

cases, the vehicles must provide energy only from the battery—the IC engine cannot be 

used as a stationary generator. Table 1 provides the PHEV capacity assumptions for the 

two cases. 

 

18 Kirby, B., 2003. “Spinning Reserve From Responsive Loads” ORNL/TM 2003/19. Available at: 

http://certs.lbl.gov/PDF/Spinning_Reserves.pdf 

11
Table 1: Effective Capacity of a PHEV for Two Evaluated Cases 

Parameter PHEV-20 Case PHEV-60 Case 

Line Capacity (kW) 9.6 9.6 

% Plugged in 50 50 

Battery Size (useful capacity - 

kWh) 

5.9 17.7 

Battery SOC (%) 60 70 

Operating Reserve Capacity 

(kW per PHEV)

3.5 5.8 

Planning Reserve Capacity (kW 

per PHEV) 

0.4 1.5 

The effective system-wide capacity provided by a fleet of PHEVs can be calculated by 

multiplying the per-vehicle capacity calculated in Equation 1 by the number of vehicles. 

The assumptions for vehicle penetration in Figure 3 and the assumptions in Table 1

provide the effective PHEV reserve capacity, illustrated in Figure 5 as a function of time. 

 

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Figure 5: PHEV Capacity Assumptions 

PHEV Charging Requirements

Based on the stated PHEV performance assumptions, the daily charging requirement of 

the PHEV-20 is 5.2 kWh per day, while the PHEV-60 requires 8.6 kWh per day. The 

analysis assumes that the majority of the PHEVs are charged in the evening off-peak 

(60%), with some additional charging during the shoulder periods (30% morning 

shoulder – 7 a.m. to 1 p.m.; and 10% evening shoulder – 6 p.m. to 10 p.m.). This 

12
distribution was based roughly on the results from the “V2G-load” tool developed by the 

NREL Energy Analysis Office to assess the impacts of utility dispatchable load.19

Results 

PHEV Impacts on the Electric Sector 

As discussed previously, in the base case that does not consider PHEVs, WinDS 

estimates cost-effective wind installations to be about 208 GW by 2050. Wind 

installations increase with the addition of PHEVs and the reserve capacity they bring to 

the grid. In the PHEV-20 case, WinDS projects an increase in wind installations to 235 

GW by 2050; while, in the PHEV-60 case, the WinDS model produced a final installed 

wind capacity of 443 GW. The wind installations in the PHEV-60 case represent a 

~110% increase over the base case installations, with wind providing about 16% of the 

total U.S. electric generation (1554 TWh out of 10082 TWh total electric generation). 

Figures 6 and 7 show the capacity and generation of all generator types in the PHEV-60 

Case. 

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2038

2042

2046

2050

GW

nuclear

oil-gas-steam

Coal-IGCC

Coal-new

Coal-old-no

scrub

Coal-oldw/scrub

Gas-CC

Gas-CT

Hydro

Wind

Figure 6: Capacity Expansion in the WinDS PHEV-60 Case 

 

19 Denholm, P. and W. Short, 2006. “An Evaluation of Utility System Impacts and Benefits of Plug-In 

Hybrid Electric Vehicles”, NREL (forthcoming) 

13
0

2000

4000

6000

8000

10000

12000

2000

2004

2008

2012

2016

2020

2024

2028

2032

2036

2040

2044

2048

TWh

nuclear

o-g-s

Coal-IGCC

Coal-new

Coal-old-no

scrub

Coal-oldw/scrub

Gas-CC

Gas-CT

Hydro

Total Wind

Figure 7: Electricity Generation in the WinDS PHEV-60 Case 

The large amount of reserve capacity provided by PHEVs in the PHEV-60 case 

eliminates the need for much of the conventional capacity formerly required to stabilize 

the electric grid. This allows wind to compete more on a “cost of energy” basis. In other 

words, a nondispatchable kWh of energy from wind can compete more directly with a 

dispatchable kWh from conventional sources. Because a significant amount of wind 

generation is projected to be at or below the cost of conventional alternatives on a purely 

energy basis, the deployment of PHEVs results in vastly increased use of wind. 

Figure 8 shows the change over time in generation from the base case to the PHEV-60 

case. After a period in which both wind and coal increase due to the increased load of 

PHEVs, the reserve capacity offered by PHEVs allows wind to both replace coal that 

would have been otherwise built to meet normal load, as well as satisfy the increased 

electricity demand due to PHEVs. 

14
-400

-200

0

200

400

600

800

1000

2000 2010 2020 2030 2040 2050

Generation (PHEV-60 - Base) (TWh) . 

Wind

Coal

Figure 8: Comparison of Generation by Wind and Coal in the Base and PHEV-60 Cases 

The possibility of wind effectively providing the entire electric demands of a PHEV-60 

fleet is illustrated in Table 2. In this case, the additional annual PHEV load in 2050 is 

690 TWh, while the additional wind generation created by the addition of PHEVs is 797 

TWh. This means that the additional wind can meet the entire additional PHEV demand, 

with 107 TWh of wind generation “left over” to decrease the amount of coal generation 

needed for normal (non-PHEV) demand. 

 

Table 2: Summary of 2050 WinDS/PHEV Results 

2050 Projected Values Base Case (no 

PHEVs) 

PHEV-20 Case PHEV-60 Case 

Wind Capacity (GW) 208 235 

(13% increase) 

443 

(113% increase)

Wind Generation (TWh/year) 757 853 

(13% increase) 

1554 

(105% increase) 

Total Load (TWh/year) 9392 9808 

(4.4% increase 

due to PHEV 

load) 

10082 

(7.3% increase 

due to PHEV 

load) 

% Of Electricity from Wind 8.1 8.7 15.6 

Total Installed Generation 

Capacity (GW) 

2161 2092 1972 

Generation from Coal 

(TWh/year) 

8272 8597 

(3.9% increase) 

8169 

(1% decrease) 

Electric Sector CO2 Emissions 

(Million Tons CO2/year) 

7273 7538 

(3.6% increase) 

7220 

(1% decrease) 

Not only do PHEVs increase the amount of cost-effective wind, they also reduce the need 

for peaking combustion turbines—which, by 2050, amount to about 500 GW in the base 

15
case and less than 90 GW in the PHEV-60 case.20 Nonetheless, the total installed electric 

capacity is similar in all three cases. The additional wind enabled by PHEVs has a lower 

capacity factor than the fossil plants displaced, requiring more capacity per unit of 

generation. Hence, the reduction in combustion turbine capacity is offset by additional 

wind capacity. 

The above discussion has emphasized the dramatic impacts of the PHEV-60 case in the 

electric sector. The PHEV impact in the electric sector under the PHEV-20 case is less 

impressive. The PHEV-20 electric energy requirements increase the total electric load 

4.4% over the base case. This additional load is met by new generation from wind and 

other generators, primarily coal. Wind generation increases 13% over the base case, 

while coal generation increases by 4% (the 96 TWh/year increase in wind generation is 

insufficient to meet the 416 TWh/year increase in electric demand associated with 

PHEVs). The net effect is a 3.6% increase in carbon emissions from the electric sector 

(The next two sections will show, however, that total carbon emissions from both the 

combined electric and transportation sectors decrease) 

It is important to keep in mind that many of the best wind sites used in the PHEV-60 case 

are still available in the PHEV-20 case, which further decreases carbon emissions in the 

electric sector. These wind sites could be developed if more storage were available 

through PHEVs or other storage technologies—or if the cost of wind improved relative to 

other generation options. 

In both the PHEV-20 and the PHEV-60 cases, the ability of the PHEVs to provide 

reserve capacity to the grid was constrained by the energy available in their batteries. 

This constraint would be largely removed if it were possible to operate the IC engines of 

these vehicles while parked and connected to the grid. This possibility has significant 

safety and control issues associated with it. The authors examine its impact on the grid 

and the penetration of wind in Appendix 1. 

Impacts on the Transportation Sector 

The impact of PHEVs on gasoline use and mobile source emissions in 2050 depends 

partly on the assumed fleet characteristics in 2050 in the various cases. Table 3 provides 

estimates of the LDV gasoline consumption and CO2 emissions under two cases, where 

the fleet average for non-PHEVs is 22 mpg and 35 mpg. In both cases, it is assumed that 

the average PHEV has an efficiency of 35 mpg when operating in HEV mode. 

 

20 The impact on generation from CT’s is minimal, since these generators, built primarily to meet reserve 

requirements, are idle most of the time 

16
Table 3: Summary of 2050 Results for LDV Fleet Gasoline Consumption and Emissions 

2050 Projected LDV Transportation 

Sector 

Base Case 

(no PHEVs) 

PHEV-20 

Case 

PHEV-60 

Case 

Gasoline Use (Billion gal/year) 

 Conventional Fleet (22 mpg avg) 

 HEV Fleet (35 mpg avg) 

226 

142 

148 

107 

127 

85 

CO2 Emissions (Mil. Tons/year) 

 Conventional Fleet (22 mpg avg) 

 HEV Fleet (35 mpg avg) 

2261 

1422 

1486 

1066 

1273 

853 

Simple arithmetic shows that the assumption of a 50% penetration of PHEV-20s by 2050, 

with 50% of their drive energy provided by electricity, results in at least a 25% (0.5 * 0.5) 

reduction in base oil consumption for the fleet of U.S. light-duty vehicles (see the “HEV 

Fleet” line under “Gasoline Use” in Table 3). As shown in Table 3, this 25% reduction 

in oil use would produce a proportional 25% reduction in carbon emissions from LDVs 

(see the “HEV Fleet” line under “CO2 Emissions” in Table 3). If the fleet is dominated 

by PHEV-60s (last column of Table 3), or the remaining vehicles are dominated by 

conventional low-efficiency vehicles (see “Conventional Fleet” lines in Table 3), the 

reduction in petroleum use could be even greater. 

Combined Impacts 

Table 4 provides a summary of the combined impacts of PHEVs in the electric and LDV 

sectors. 

Table 4: Summary of 2050 Results for LDV Fleet Gasoline Consumption and Emissions 

2050 Projected CO2 Emissions 

from the combined electric/LDV 

sector (Million Tons/year) 

Base Case 

(no PHEVs) 

PHEV-20 

Case 

PHEV-60 

Case 

Conventional Fleet (22 mpg avg.) 9534 9024 8493 

HEV Fleet (35 mpg avg.) 8695 8604 8073 

The somewhat-limited reduction in overall carbon emissions results partly from the fact 

that half of the vehicle fleet is still 100% petroleum-fueled. An alternative viewpoint is 

to examine the net change in emissions associated with the PHEV fleet. In the PHEV-60 

case, the fleet results in a carbon reduction of 622 or 1,041 million tons/year of CO2, 

depending on the base fuel economy. This can be compared to the emissions of 50% of 

the vehicle fleet (711 or 1,131 million tons/year). The net reduction in carbon emissions 

due to the introduction of PHEV-60s is nearly equal to the emissions of these vehicles in 

the base case. In other words, all the electricity from PHEV-60s is derived from carbonfree wind enabled by the vehicles; and the additional carbon reduction in the electric 

sector of 53 million tons/year due to the introduction of PHEVs is a large fraction of the 

PHEV fleets’ 142 million tons of IC engine emissions. As a result, the PHEV-60 fleet is 

nearly carbon neutral. 

17
Conclusions 

The results in this paper are limited by the fact that the authors have not considered the 

economics of PHEVs. In addition, they have examined only two cases (and the 

additional IC engine-on case in Appendix 1). These cases represent fixed points with 

many assumptions regarding fleet size, vehicle efficiency, driving patterns, and plug-in 

rates. 

Nonetheless, these cases do allow several provisional conclusions to be drawn. First, it 

does appear that PHEVs could be a significant enabling factor for increased penetration 

of wind energy. However, this will likely require greater storage capacity than the 

PHEV-20 case presented in this report. Increasing wind penetration through the use of 

PHEVs would require any combination of the following: increasing the size of the PHEV 

fleet, increasing the PHEV plug-in-rate, increasing the PHEV battery size, or allowing 

the IC engine to run to provide greater capacity. The more aggressive PHEV-60 case 

resulted in more than doubling installed wind capacity, as well as decreasing electricsector carbon emissions, even considering the increased electric load resulting from the 

replacement of 40% of the nation’s LDV gasoline use with electricity. 

It should also be noted that the potentially conservative battery size in the PHEV-20 case 

may have a significant impact on these provisional results. The 5.9 kWh battery size 

used in this report is based on a electric drive efficiency of 3.4 miles/kWh, which 

assumes that the average new vehicle sold in the United States in the future will be 

significantly smaller (and/or lighter) than the current average new vehicle, which is 

heavily influenced by low-efficiency SUVs and light-duty trucks. If the average vehicle 

sold in the United States continues to be relatively large in size, then the average electricdrive efficiency of PHEVs will be lower, requiring larger batteries. These larger batteries 

will result in more per-vehicle reserve capacity, which could increase the amount of wind 

enabled by a PHEV fleet. 

PHEVs present a significant opportunity to directly address two of the most prominent 

energy issues faced by the United States today—oil imports/energy security and climate 

change. In addition, the reductions in oil use possible through the introduction of PHEVs 

worldwide could reduce pressures on international oil supplies, decreasing the price of 

oil. 

Future Work 

This report presents a scoping study that estimates the potential benefit of PHEVs by 

enabling increased wind generation in the electric sector. However, it’s still necessary to 

estimate the costs of PHEVs and their competitive position in the marketplace. This task 

is complicated by the fact that PHEVs have value both as a means of transportation and 

as a means of providing reserve capacity to the electric sector. Future efforts will 

examine these costs and the full set of PHEV benefits, analytically estimating the 

penetration of PHEVs into the market (as opposed to the simple assumptions on 

penetration made for this analysis). This will require a better representation of driving 

18
profiles for LDVs, implications of those driving profiles for PHEV charging profiles, 

estimation of regulation reserve benefits provided by PHEVs, and the marriage of a 

vehicle-choice model with our WinDS model. 

19
Appendix 1: Effects of Increasing PHEV Reserve Capacity via “Engine-On” 

Capability. 

The increase in wind penetration in the PHEV-60 case depends on the capacity offered by 

a relatively large battery. Given the relatively high cost of batteries and typical driving 

patterns, a PHEV-20 may be more suited to most consumers. Given the modest 

improvements in wind penetration in the PHEV-20 case—and the PHEV-20’s potentially 

more likely market penetration—the authors decided to investigate an alternative PHEV20 scenario that allows the vehicle’s IC engine to run. Allowing the IC engine to run 

substantially increases the per-vehicle planning capacity. Because most peaking 

generators are run for a very small number of hours per year, it may be acceptable for 

some fraction of vehicles to run their engines on rare occasions to provide firm capacity. 

However, the fraction of vehicles capable of running the IC engine would be limited by a 

number of issues such as safety, security, owner concerns, etc. For example, ventilation 

in open parking garages may be inadequate, and costly safety interlocks would be 

required to eliminate dangers associated with vehicle operation in closed garages. In 

addition, the cooling systems of vehicles may have to be upgraded to reject the heat that 

would be produced by a stationary vehicle generating more than a few kW, especially 

because the planning capacity of the vehicles in the electric system most likely would be 

rated at their capacity on hot summer days. 

Allowing a utility to remotely start and control privately owned vehicles raises many 

concerns that are not trivial nor easily dismissed. However, these issues may not be 

insurmountable, at least for some fraction of the PHEV fleet (perhaps including large 

corporate fleets and government-owned vehicles). 

Actual operation of the engines of these PHEVs while parked will be a relatively rare 

occurrence, because much of the use of operating and planning reserves is for extreme 

scenarios—such as a generator failure during peak demand. Furthermore, since capacity 

reserves typically provide a very small amount of energy, the overall fuel use and 

resulting pollution (even though greater with engine start-up than is indicated by averageEPA-cycle estimates) could be relatively small. (These emissions would have to be 

compared to alternative peaking generation units that include older less efficient thermal 

plants, IC engines or simple-cycle CTs that may have relatively high emission rates.) 

To consider this possibility, the authors created a PHEV-20 “engine run” case, where 

they allowed up to 30% of all PHEVs to run their engines while parked. In this case, the 

planning capacity credit was 1.8 kW/vehicle, and the operating capacity was 3.9 

kW/vehicle. 

The result of the WinDS run in this case was a total wind installation of 483 GW of wind, 

about 10% more than the PHEV-60 case. This demonstrates that smaller PHEVs may 

still enable large increases in wind energy, if the engine is allowed to run during extreme 

events. The authors believe the actual amount of engine run to be relatively small (much 

less than 100 hours per year), but further work will be needed to provide a better estimate 

of both the numbers of hours per year and the number of engine-on events that might be 

required for each vehicle.

20
Appendix 2: Summary of Data Used in WinDS 

NOTE: A more comprehensive data set and documentation is available in 

“Documentation of WinDS Base Case Data” available at 

http://www.nrel.gov/analysis/winds/pdfs/winds_data.pdf 

1) Introduction 

The Wind Deployment System (WinDS) model is a computer model that optimizes the 

regional expansion of electric generation and transmission capacity in the continental 

United States over the next 50 years. WinDS competes many different generation types to 

design a “least-cost” electric power system under a number of technical, reliability, and 

environmental constraints. Detailed documentation of the WinDS model formulation is 

available at http://www.nrel.gov/analysis/winds/. 

This document summarizes the key data inputs to the Base Case of the WinDS model. 

The Base Case was developed simply as a point of departure for other analyses to be 

conducted using the WinDS model. It does not represent a forecast of the future, but 

rather is a consensus scenario whose inputs depend strongly on others’ results and 

forecasts. For example, WinDS derives many of its inputs from the EIA’s Annual Energy 

Outlook (EIA 2005)—particularly its conventional technology cost and performance 

parameters, its current and future fossil fuel prices, and its electric-sector loads. 

The following sections present the parameters and input values used for the WinDS Base 

Case. Unless specifically stated otherwise, all cost data are expressed in year 2004 

dollars. 

2) Financials 

WinDS optimizes the electric power system “build,” based on the projected life-cycle 

costs, including capital costs and cumulative discounted operating costs over a fixed 

evaluation period. The “overnight” capital costs are adjusted to reflect the actual total 

cost of construction, including tax effects, interest during construction, and financing 

mechanisms. Table A2.1 provides a summary of the financial values used to produce the 

net capital and operating costs. 

Table A2.1: Base Case Financial Assumptions 

Name Value Notes & Source 

Inflation Rate 3% Based on recent historical inflation rates 

Real Discount Rate 8.5% Equivalent to weighted cost of capital. Based on EIA 

assumptions (U.S. DOE 2005b) 

21
Debt/Equity Ratio 0 Consistent with the use of a weighted cost of capital for the 

real discount rate 

Real interest rate 0 Consistent with the use of a weighted cost of capital for the 

real discount rate 

Marginal Income 

Tax Rate 

40% Combined Federal/State Corporate Income Tax Rate 

Evaluation Period 20 Years Base Case Assumption 

Depreciation 

Schedule 

 Conventionals 

 Wind 

15 Year 

5 Year

MACRS 

MACRS 

Nominal Interest 

rate during 

construction 

10% Base Case Assumption 

Dollar year 2004 All costs are expressed in year 2004 dollars 

3) Power System Characteristics 

3.1 Electric System Loads 

Loads are defined by region and by time. WinDS meets both the energy requirement and 

the power requirement for each of 136 Power Control Area (PCA) regions. Energy is met 

for each PCA in each of 16 time slices within each year modeled by WinDS. Time slices 

are defined in Table A2.2. 

Table A2.2: WinDs Demand Time-Slice Definitions 

Slice 

Name 

Number 

of Hours 

Per Year 

Season Time Period 

H1 1152 Summer Weekends plus 11PM-6AM weekdays 

H2 462 Summer Weekdays 7AM-1PM 

H3 264 Summer Weekdays 2PM-5PM 

H4 330 Summer Weekdays 6PM-10PM 

H5 792 Fall Weekends plus 11PM-6AM weekdays 

H6 315 Fall Weekdays 7AM-1PM 

H7 180 Fall Weekdays 2PM-5PM 

H8 225 Fall Weekdays 6PM-10PM 

H9 1496 Winter Weekends plus 11PM-6AM weekdays 

H10 595 Winter Weekdays 7AM-1PM 

H11 340 Winter Weekdays 2PM-5PM 

H12 425 Winter Weekdays 6PM-10PM 

H13 1144 Spring Weekends plus 11PM-6AM weekdays 

H14 455 Spring Weekdays 7AM-1PM 

H15 260 Spring Weekdays 2PM-5PM 

H16 325 Spring Weekdays 6PM-10PM 

The seasons in Table A2.2 are defined as: 

 Summer: June, July, August 

 Fall: September, October 

 Winter: November, December, January, February 

 Spring: March, April, May 

22
The electric load in 2000 for each PCA and time slice is derived from a RDI/Platts 

database (http://www.platts.com/Analytic%20Solutions/BaseCase/). Figure A2.1 is the 

WinDS load-duration curve for the entire United States for the base year, illustrating the 

16 load time slices. As a reference, the actual U.S. coincident load-duration curve is 

illustrated as well (also derived from the Platts database). This aggregated data for the 

United States shown in Figure A2.1 is not used directly in WinDS, as the energy 

requirement is met in each PCA. However, this curve does give a general idea of the 

WinDS energy requirement. It should be noted that the load-duration curve does not 

include the “super peak,” which occurs in most systems for a few hours per year. 

0

100

200

300

400

500

600

700

0 2000 4000 6000 8000

Hour

Power (GW)

WinDS Simplified

LDC

U.S. Aggregate

LDC

Figure A2.1: National Load-Duration Curve for Base Year in WinDS 

3.2 Growth Rate and Capacity Requirements 

Load growth is defined at the NERC Region level. It is assumed that the load in all 

PCAs within each NERC region grows at the same rate. Table A2.3 provides the annual 

growth rates for each NERC region. The load growth rates are assumed to be uniform in 

each PCA, based on NERC Region. 

WinDS assumes that the growth rate in each time slice is constant; i.e. the load shape 

remains the same for all regions. 

There are two basic categories of capacity requirements in WinDS: 

1) Capacity for Peak Energy –This is the amount of reliable capacity actually 

delivering power during the peak time slice shown in Table A2.3. This total 

capacity includes all plant types (including wind) and considers the forced outage 

rate for each type. 

2) Capacity for Peak Demand and Peak Reserve – This is the total system 

capacity, equal to the peak load plus an additional fraction, determined by the 

23
reserve margin. The peak load is somewhat greater than the peak energy time 

slice, and represents the “super peak,” which occurs for a few hours per year. The 

peak reserve represents additional capacity to cover contingencies caused by 

generator or transmission system failure or unexpected peak demand. The peak 

demand and reserve are combined into a single capacity requirement in WinDS. 

The capacity constructed to meet the peak reserve margin in WinDS is not 

required to actually deliver energy. The small amount of energy that is typically 

delivered by this “super peak” capacity in real systems is delivered in the form of 

the peak time-slice generation in WinDS. 

Table A2.3 provides the growth rates and reserve-margin requirements for each NERC 

region. 

Table A2.3: Growth Rates and Required Reserve Margins 

NERC Region Abbreviation Annual Load 

Growth 

Required 

Reserve Margin 

1 ECAR 1.019 0.12 

2 ERCOT 1.021 0.15 

3 MAAC 1.016 0.15 

4 MAIN 1.018 0.12 

5 MAPP 1.017 0.12 

6 NY 1.017 0.18 

7 NE 1.017 0.15 

8 FL 1.019 0.15 

9 STV/SERC 1.017 0.13 

10 SPP 1.012 0.12 

11 NWP 1.025 0.08 

12 RA 1.026 0.14 

13 CNV 1.021 0.13 

Notes: 

1) NERC Regions defined by http://www.nerc.com/regional/

2) Reserve margin is ramped from initial value in 2000 to the 2010 requirement and maintained 

thereafter. Source: Energy Observer Issue No. 2, July 2004, PA Consulting Group 

3) Reserve margin may be met by any generator type, or by interruptible load. 

4) Growth Rate from: U.S. DOE. 2005b (Tables 60 through 72 "Total Net Energy For Load") 

Figure A2.2 illustrates the capacity requirements in 2000 and 2050. As noted previously, 

the peak reserve and peak demand are combined as a single constraint in the WinDS 

model. 

24
0.0

0.5

1.0

1.5

2.0

2.5

2000 2050

Capacity Requirement (TW)

Peak Reserve

Peak Demand

Peak Energy (w/o

FOR)

Figure A2.2: National Capacity Requirement in WinDS 

4) Wind 

4.1 Wind Resource Definition 

Wind power classes are defined as follows: 

Table A2.4. Classes of Wind Power Density 

Wind 

Power 

Class 

Wind Power 

Density, W/m2

Speed 

m/s (mph) 

3 300-400 6.4-7.0 

4 400-500 7.0-7.5 

5 500-600 7.5-8.0 

6 600-800 8.0-8.8 

7 >800 >8.8 

Note: Wind speed measured at 50 meters above ground level 

Source: Elliott and M.N. Schwartz 1993 

The wind power density and speed are not used explicitly in WinDS. The different 

classes of wind power are distinguished in WinDS through the resource levels, capacity 

factors, turbine costs, etc., all of which are discussed in the following paragraphs. 

4.2 Resources 

The wind resource dataset for the WinDS model is based on a “supply curve” for 

onshore, shallow offshore, and deep offshore. Each is expressed in the following format: 

25
Wind Region Class 3 

Resource 

(MW) 

Class 4 

Resource 

(MW) 

Class 5 

Resource 

(MW) 

Class 6 

Resource 

(MW) 

Class 7 

Resource 

(MW)) 

1 926.5 286.4 82.3 41.5 18.8 

2 214.9 69 43.7 43.7 16.7 

3 467.1 248.3 127.7 120.3 66.9 

4 3265.6 2100.9 501.1 86.8 6.1 

This regional wind resource dataset is generated by multiplying the total available area of 

a particular wind resource by an assumed wind farm density of 5 MW/km2

. 

(http://www.nrel.gov/wind/uppermidwestanalysis.html). 

The amount of land available for each class is based on a dataset for each of the 358 wind 

regions for onshore, shallow offshore, and deep offshore. 

 

4.3 Basic Wind Cost and Performance 

The following tables provide the projected cost and performance for land-based (onshore) 

and offshore (shallow and deep) wind turbines. The onshore values were derived from 

data obtained from personal communication between the authors and Joseph Cohen at 

Princeton Energy Resources International (2003). Cohen’s data was manipulated to 

divide the cost/performance improvements into that due to industry learning-by-doing 

and that due to R&D. The improvements shown in Table A2.5 represent that due to only 

R&D. The learning-by-doing improvements are calculated endogenously within WinDS 

using an 8% learning rate (McDonald, 2001), based on both U.S. and world production 

estimates. 

Table A2.5: Onshore Turbines (values constant after 2020) 

Resource 

Class Install Year Capacity Factor 

Capital cost 

($/kW)* 

Fixed O&M

($/kW-yr) 

Variable 

O&M 

($/MWh) 

3 2000 0.2 942.60 7.54 4.71 

3 2005 0.25 929.24 7.54 3.77 

3 2010 0.275 922.57 7.54 3.71 

3 2020 0.3 915.89 7.54 3.64 

4 2000 0.251 942.60 7.54 4.71 

4 2005 0.2885 915.89 7.54 3.77 

4 2010 0.349 914.32 7.54 3.71 

4 2020 0.361 898.61 7.54 3.64 

5 2000 0.3225 942.60 7.54 4.71 

5 2005 0.3535 897.82 7.54 3.77 

5 2010 0.397 897.04 7.54 3.71 

5 2020 0.4135 881.33 7.54 3.64 

6 2000 0.394 942.60 7.54 4.71 

6 2005 0.4185 879.76 7.54 3.77 

6 2010 0.445 879.76 7.54 3.71 

6 2020 0.466 864.05 7.54 3.64 

26
7 2000 0.414 942.60 7.54 4.71 

7 2005 0.4385 879.76 7.54 3.77 

7 2010 0.465 879.76 7.54 3.71 

7 2020 0.486 864.05 7.54 3.64 

 * Overnight capital cost 

Table A2.6: Shallow Offshore Turbines (values constant after 2025) 

Resource 

Class 

Install 

Year 

Capacity 

Factor 

Capital cost 

($/kW) 

Fixed O&M 

($/kW-yr) 

Variable O&M 

($/MWh) 

3 2000 0.33 1194.00 10.00 15.00 

3 2005 0.33 1194.00 10.00 15.00 

3 2010 0.345 1143.83 10.00 14.13 

3 2020 0.35 1073.67 10.00 13.07 

3 2025 0.35 1064.33 10.00 12.77 

4 2000 0.33 1194.00 10.00 15.00 

4 2005 0.33 1194.00 10.00 15.00 

4 2010 0.345 1143.83 10.00 14.13 

4 2020 0.35 1073.67 10.00 13.07 

4 2025 0.35 1064.33 10.00 12.77 

5 2000 0.37 1194.00 10.00 15.00 

5 2005 0.37 1194.00 10.00 15.00 

5 2010 0.365 1143.83 10.00 14.13 

5 2020 0.395 1073.67 10.00 13.07 

5 2025 0.395 1064.33 10.00 12.77 

6 2000 0.42 1194.00 10.00 15.00 

6 2005 0.42 1194.00 10.00 15.00 

6 2010 0.4375 1143.83 10.00 14.13 

6 2020 0.445 1073.67 10.00 13.07 

6 2025 0.445 1064.33 10.00 12.77 

7 2000 0.44 1194.00 10.00 15.00 

7 2005 0.44 1194.00 10.00 15.00 

7 2010 0.4575 1143.83 10.00 14.13 

7 2020 0.465 1073.67 10.00 13.07 

7 2025 0.465 1064.33 10.00 12.77 

Table A2.7: Deep Offshore (cost and performance constant after 2025) 

Resource

Class Install Year Capacity Factor 

Capital cost 

($/kW) 

Fixed O&M 

($/kW-yr) 

Variable O&M 

($/MWh) 

3 2000 0.33 2004.00 10.00 18.00 

3 2005 0.33 2004.00 10.00 18.00 

3 2010 0.345 1887.50 10.00 16.57 

3 2020 0.35 1735.67 10.00 15.40 

3 2025 0.35 1696.67 10.00 15.30 

4 2000 0.33 2004.00 10.00 18.00 

4 2005 0.33 2004.00 10.00 18.00 

4 2010 0.345 1887.50 10.00 16.57 

4 2020 0.35 1735.67 10.00 15.40 

27
4 2025 0.35 1696.67 10.00 15.30 

5 2000 0.37 2004.00 10.00 18.00 

5 2005 0.37 2004.00 10.00 18.00 

5 2010 0.365 1887.50 10.00 16.57 

5 2020 0.395 1735.67 10.00 15.40 

5 2025 0.395 1696.67 10.00 15.30 

6 2000 0.42 2004.00 10.00 18.00 

6 2005 0.42 2004.00 10.00 18.00 

6 2010 0.4375 1887.50 10.00 16.57 

6 2020 0.445 1735.67 10.00 15.40 

6 2025 0.445 1696.67 10.00 15.30 

7 2000 0.44 2004.00 10.00 18.00 

7 2005 0.44 2004.00 10.00 18.00 

7 2010 0.4575 1887.50 10.00 16.57 

7 2020 0.465 1735.67 10.00 15.40 

7 2025 0.465 1696.67 10.00 15.30 

5) Conventional Generation 

5.1 Generator Types 

Available generator types that may be built are based on the most likely types, as 

determined by the DOE Energy Information Administration (U.S. DOE 2005c). 

Table A2.8: Conventional (Non-Wind) Generation Types Considered by WinDS 

Generator Type Existing 

Capacity?

May Be Built New In WinDS? 

Conventional pulverized coal steam plant 

(No SO2 Scrubber) 

Y No – Scrubbers may be added to meet 

SO2 constraints. Existing plants may 

also switch to low-sulfur coal 

Conventional pulverized coal steam 

plants (With SO2 Scrubber) 

Y No 

Advanced supercritical coal steam plant 

(With SO2 and NOx controls) 

Y Y 

Integrated Coal Gasification Combined 

Cycle (IGCC) 

Y Y – sequestration may be added 

Oil/Gas Steam Turbine (OGS) Y N – Assumes CT or CCGT will be built 

instead 

Combined Cycle Gas Turbine Y Y – sequestration may be added 

Gas Combustion Turbine Y Y 

Fuel Cell None Not in base case – Generic Fuel Cell 

with H2 fuel is an optional technology 

Nuclear Y Y 

Conventional Hydropower - Hydraulic 

Turbine 

Y No – small hydro to be added 

Municipal Solid Waste / Landfill Gas Y No – to be added 

Biomass (as thermal steam generation) Y No – to be added in the form of cofiring, thermal steam, and/or 

gasification 

28
5.2 Cost and Basic Performance (capital cost, fixed O&M, variable O&M, heat rate) 

Values for capital cost, heat rate (efficiency), fixed O&M, and variable O&M for 

conventional technologies are provided in Table A2.9. 

Table A2.9: Basic Cost and Performance Characteristics for Conventional Generation 

Type Install Date 

Capital Cost

$/kW* 

Fixed O&M 

$/MW-yr 

Var O&M 

$/MWh 

Heat rate 

MMBTU/MWh

Gas-CT 2000 504 8,415 3.16 10.82 

2005 407 10,315 3.07 10.03 

2010 386 10,315 3.07 9.50 

2020 358 10,315 3.07 9.50 

2030 351 10,315 3.07 9.50 

2040 351 10,315 3.07 9.50 

2050 351 10,315 3.07 9.50 

Gas-CC 2000 626 10,527 2.10 7.20 

2005 584 11,021 1.85 6.97 

2010 568 11,021 1.85 6.57 

2020 537 11,021 1.85 6.57 

2030 527 11,021 1.85 6.57 

2040 527 11,021 1.85 6.57 

2050 527 11,021 1.85 6.57 

Existing Coal 2000 N/A 23,410 3.40 10 

(Scrubbed) 2005 N/A 25,847 3.75 10 

(see note # 7) 2010 204 28,537 4.14 10 

2020 204 34,786 5.05 10 

2030 204 42,404 6.16 10 

2040 204 51,690 7.51 10 

2050 204 63,010 9.15 10 

Existing Coal 2000 N/A 27,156 3.94 10 

(Unscrubbed) 2005 N/A 29,982 4.35 10 

2010 N/A 33,103 4.81 10 

2020 N/A 40,352 5.86 10 

2030 N/A 49,189 7.14 10 

2040 N/A 59,961 8.71 10 

2050 N/A 73,092 10.62 10 

Coal-new 2000 1,249 25,091 4.18 8.84 

2005 1,249 25,091 4.18 8.84 

2010 1,232 25,091 4.18 8.67 

2020 1,193 25,091 4.18 8.6 

2030 1,176 25,091 4.18 8.6 

2040 1,176 25,091 4.18 8.6 

2050 1,176 25,091 4.18 8.6 

Coal-IGCC 2000 1,444 25,091 2.58 8.31 

2005 1,444 25,091 2.58 8.31 

2010 1,406 25,091 2.58 7.52 

2020 1,305 25,091 2.58 7.2 

2030 1,182 25,091 2.58 7.2 

2040 1,182 25,091 2.58 7.2 

29
2050 1,182 25,091 2.58 7.2 

Oil/gas/steam 2000 N/A 25,256 3.16 9 

2005 N/A 27,884 3.49 9.23 

2010 N/A 30,786 3.85 9.46 

2020 N/A 37,528 4.70 9.94 

2030 N/A 45,747 5.73 10.45 

2040 N/A 55,765 6.98 10.99 

2050 N/A 67,978 8.51 11.55 

Nuclear 2000 2016 61,862 0.45 10.4 

2005 2016 61,862 0.45 10.4 

2010 1958 61,862 0.45 10.4 

2020 1862 61,862 0.45 10.4 

2030 1814 61,862 0.45 10.4 

2040 1814 61,862 0.45 10.4 

2050 1814 61,862 0.45 10.4 

Source: U.S. DOE. 2005c. 

Fossil capital costs: Table 48 (Reference Case) 

Fossil heat Rates: Table 48 (Reference Case) 

Nuclear capital cost: Table 49 (Reference Case) 

Fixed and Variable O&M: Table 38 

Notes: 

1) Capital costs are “overnight” costs, not including interest during construction 

2) The current AEO projects costs every 5 years to 2025. Values in WinDS are interpolated 

linearly to derive the even year values. Values beyond 2025 are assumed to be constant. 

3) Cost values and heat rates for the Gas-CT and Gas-CC are based on the average of the 

“conventional” and “advanced” cases in the AEO. 

4) New nuclear may not be constructed before 2010. 

5) Old coal and oil/gas/steam may not be constructed in WinDS. 

6) Costs are adjusted to $2004. 

7) This value represents the cost of converting unscrubbed to scrubbed coal. 

8) O&M = operation and maintenance. O&M costs do not include fuel. 

9) Heat rate is net heat rate (including internal plant loads). 

10) Values are interpolated for intermediate years in WinDS. 

5.3 Capital Cost Adjustment Factors 

Interest during construction can increase the effective capital cost for each technology. 

Table A2.10 indicates the construction time and schedule for each conventional 

technology. 

30
Table A2.10: Capital Cost Adjustment Factors 

Construction 

Time 

Schedule 

Hydro NA NA 

Gas-CT 3 A 

Gas-Combined Cycle (CC) 3 C 

Coal-old-1 (Scrubbed) 3 (add scrubber 

to existing) 

A 

Coal-old-2 (Uncrubbed) NA NA 

Coal-new 4 D 

Coal-IGCC 4 D 

Oil/gas/steam (o-g-s) NA NA 

Nuclear 6 B 

Geothermal NA NA 

Biopower NA NA 

Concentrating Solar (CSP) 3 C 

Landfill Gas (lfill-gas) NA NA 

Notes: 

1) Construction time source: U.S. DOE. 2005a. Table 38. 

2) Schedule refers to the fraction of capital cost that must be committed each year during 

construction. This is used to calculate interest, and the total capital cost required to build 

each plant. Assumed schedules are in Table A2.11. 

Table A2.11: Generator Construction Schedules 

Fraction of Cost in Each Year 

Schedule 1 2 3 4 5 6 

A 80% 10% 10% 

B 10% 20% 20% 20% 20% 10% 

C 50% 40% 10% 

D 40% 30% 20% 10% 

 

5.4 Outage Rates (Forced Outage and Planned Outage) 

WinDS considers the outage rate when determining the net capacity available for energy 

generation and in determining the capacity value of each technology. Planned outages are 

assumed to occur in all seasons except the summer. Table A2.12 provides the outage 

rate for each conventional technology. 

31
Table A2.12: Conventional Generator Outage Rates 

Generator Type Forced 

Outage Rate 

(%) 

Planned 

Outage Rate 

(%) 

Hydro 2.0 5.0 

Gas-CT 10.7 6.4 

Gas-Combined Cycle (CC) 5.0 7.0 

Existing Coal (Scrubbed) 7.9 9.8 

Existing Coal (Unscrubbed) 7.9 9.8 

Coal-new 7.9 9.8 

Coal-IGCC 7.9 9.8 

Oil/gas/steam (o-g-s) 7.9 9.8 

Nuclear 5.0 5.0 

Source: National Electric Reliability Council’s (NERC) Generating Availability Data System 

(GADS) http://www.nerc.com/~gads/ Planned outage rate based on SOF (scheduled outage 

hours divided by total hours). Forced outage rate based on EFORd (Equivalent Forced Outage 

Rate demand). Derived from a data run by Mike Curley of NERC (3/12/03). 

5.5 Emission Rates 

Emission rates are estimated for SO2, NOx, Mercury (Hg), and CO2. Table A2.13 

provides the input emission rates for (lbs/MMBTU of input fuel) plants that use 

combustible fuel. Output emission rates (lb/MWh) may be calculated by multiplying 

input emission rate by heat rate. 

Table A2.13: Input Emissions Rates (lbs/MMBTU fuel input) 

 SO2 NOx 

Mercury 

(Hg) CO2 

Hydro 0 0 0 0 

Gas-CT 0.0034 0.08 0 122.1 

Gas-Combined Cycle (CC) 0.0034 0.02 0 122.1 

Existing Coal (Scrubbed) 0.26 0.448 0.0000046 207.3 

Existing Coal (Uncrubbed) 1.7 0.448 0.0000046 207.3 

Coal-new 0.09 0.02 0.0000046 207.3 

Coal-IGCC 0.04 0.02 0.0000046 122.1 

Oil/gas/steam (o-g-s) 0 0.1 0 122.1 

Nuclear 0 0 0 0 

Biopower 0 0 0 0 

Concentrating Solar (CSP) 0.0065 0.02 0 30.5 

Landfill Gas (lfill-gas) 0.045 0 0 0 

Sources and Notes: 

1) SO2: SO2 emissions result from the oxidization of sulfur contained in the fuel. Natural gas rate 

source: U.S. EPA. 1996, (AP-42 Section 3.1 Stationary Gas Turbines). SO2 input emissions rate 

for coal is based on the fuel content of the fuel and the use of post-combustion controls. The 

“base” emissions rate for existing and new conventional coal plants is based on a national 

average sulfur content of 0.9 lbs/MMBTU (1.8 lb SO2/mmBTU). WinDS assumes the national 

average for “low sulfur” coal is 0.5 lbs SO2/MMBTU. Values based on national averages from 

AEO Assumptions (2005), Table 73. Scrubber removal efficiency is assumed to be 85% for 

retrofits, 95% for new plants (U.S. EPA. 1996 AP-42 Section 1.1.4 Controls). 

32
2) CO2: CO2 emissions result from the oxidization of carbon in the fuel and emissions rate is 

based solely on fuel type, and therefore constant for all plants burning the same fuel type. All 

emissions are point-source emissions from the plant only (not “life-cycle” emissions). Natural gas 

emission rate are from U.S. EPA 1996, (AP-42 Section 3.1 Stationary Gas Turbines). CO2

content for coal is based on the national average from AEO Assumptions (U.S. DOE 2005c), 

Table 73. Biofuels are assumed to be carbon neutral. Landfill gas is assumed to have zero 

carbon emissions, because the gas would be flared otherwise. CSP plants burn a small amount 

of natural gas, resulting in CO2 emissions. CO2 emissions are not constrained in the WinDS base 

case. 

 

5.6 Fuel Prices

Fuel prices for natural gas and coal are derived from projections from the AEO 2005 

(U.S. DOE. 2005b -Energy Prices by Sector and Source). These tables provide the prices 

in each census region, which are then assigned to a NERC subregion used in WinDS. 

Prices in the AEO are projected to 2025. Beyond 2025, WinDS increases fuel prices at 

the same national annual average rate as projected by the AEO between 2015 and 2025. 

Figure A2.3 illustrates the projected fossil fuel prices in constant 2004$. The prices are 

projected out to 2070, because WinDS attempts to design a cost-optimal system over an 

evaluation horizon of 20 years. Values to the right of the vertical line in Figure A2.3 (at 

2025) are extrapolation of EIA fuel price projections. (Averages shown on graph do not 

reflect the “real” average, which would be weighted by sales, but are the average 

weighted by region.) 

0

2

4

6

8

10

12

14

2000 2010 2020 2030 2040 2050 2060 2070

Year

Fuel Price ($/MMBTU)

Higest Gas

Price (PAC)

Average U.S.

Gas Price

Lowest Gas

Price (WSC)

Highest Coal

Price (NE)

Average U.S.

Coal Price

Lowest Coal

Price (WNC)

Figure A2.3: Projected Coal and Natural Gas Prices in WinDS to 2080 

Uranium fuel price in WinDS is constant at $0.4/MMBTU (U.S. DOE. 2003, pg 68). 

33
6) Federal and State Energy Policy 

6.1 Federal Emission Standards 

CO2: WinDS has the ability to add a national cap on CO2 emissions from electricity, or a 

CO2 emission charge (tax). Neither a carbon cap nor charge is implemented in the base 

case. 

SO2: Emissions of SO2 are capped at the national level. WinDS uses a cap established 

by the 1990 Clean Air Act Amendments. 

6.2 Federal Energy Incentives 

There are several classes of incentives applied at the federal level. These incentives 

generally have the effect of reducing the cost of providing energy from an incentivized 

source. A production tax credit (PTC) provides an offset to the tax liability of 

companies, based on the production of energy from an incentivized source; an investment 

tax credit provides an offset to tax liability, based on investment in an incentivized 

source. 

Table A2.14: Federal Renewable Energy Incentives 

Name Value Notes & Source 

Renewable Energy 

PTC 

$18/MWh Applies to wind. No limit to the aggregated amount 

of incentive. Value is adjusted for inflation. Expires 

end of 2007 

Source: U.S. Congress 2005 

6.3 State Energy Incentives 

Several states also have production and investment incentives for renewable energy 

sources. The values used in WinDS are listed in Table A2.15. 

Table A2.15: State Renewable Energy Incentives 

State PTC 

$/MWh 

ITC Assumed State 

Corporate Tax 

Rate 

IA 5.00% 10.0% 

ID 5.00% 7.60 

MN 6.50% 9.8% 

NJ 6.00% 9.0% 

NM 10 7.0% 

OK 2.5 6.0% 

UT 4.75% 5.0% 

WY 4.00% 9.0% 

 Tax rates from: http://www.taxadmin.org/fta/rate/corp_inc.html 

34
6.4 Federal Renewable Portfolio Standards 

A renewable portfolio standard (RPS) requires that a certain fraction of a region’s energy 

be derived from renewable energy. While there is no federal RPS in place (as of March 

2006), WinDS can accommodate a national RPS, with input values for fraction of energy 

to be provided by renewables, RPS start year, duration, and shortfall penalty. 

6.5 State Renewable Portfolio Standards 

There are several states that currently have RPS policies. States may have capacity 

mandates as an alternative or supplement to an RPS. A capacity mandate requires a 

utility to install a certain fixed capacity of renewable energy generation. 

Table A2.16: State Renewable Portfolio Standard (RPS) 

State RPS Start 

Year1

Penalty in 

$/MWh 

RPS 

Fraction2

Load 

Fraction3

AZ 2025 50 0.079 1 

CA 2017 5 0.034 0.63 

CO 2015 50 0.044 0.69 

CT 2010 55 0.013 0.94 

DE 2019 25 0.056 0.75 

IL 2013 10 0.062 0.92 

MA 2009 50 0.026 0.85 

MD 2019 20 0.045 0.8 

MN 2015 10 0.071789 1 

MT 2015 10 0.075 0.9 

NJ 2008 50 0.029 1 

NM 2011 10 0.026 0.53 

NV 2015 10 0.133 0.89 

NY 2013 5 0.035 0.84 

OK 2016 50 0.05 1 

OR 2020 5 0.078 1 

PA 2020 45 0.014 0.98 

RI 2019 55 0.069 0.99 

TX 2009 50 0.01 1 

VT 2012 10 0.05 1 

WI 2011 10 0.006 0.75 

Notes: 

1) RPS Start Year is the year that the "fraction" or mandate must be met 

2) RPS Fraction is the fraction of state demand that must be met by renewables by the start year. 

This value is adjusted to estimate the fraction actually provided by wind, because WinDS does 

not currently include other renewables such as biomass cofiring, certain hydro projects, etc. 

3) Load fraction is the fraction of the total state load that must meet the RPS. In certain locations, 

municipal or cooperative power systems may be exempt from the RPS. 

35
7) References for Appendix 2 

Elliott, D.L. and M.N. Schwartz, 1993. “Wind Energy Potential in the United States.” 

PNL-SA-23109. Richland, WA: Pacific Northwest Laboratory. NTIS No. DE94001667. 

McDonald, Alan and L. Schrattenholzer, 2001. “Learning rates for energy technologies,” 

Energy Policy, Vol 29, pp 255-261. 

U.S. Department of Energy (DOE), 2003. “Annual Energy Outlook, With Projections to 

2025”, Energy Information Administration, DOE/EIA-0383(2003), 

www.eia.doe.gov/oiaf/aeo/, February. 

U.S. DOE, 2003b. “Analysis of S. 485, the Clear Skies Act of 2003, and S. 843, the 

Clean Air Planning Act of 2003,” Energy Information Administration, SR/OIAF2003-

03(2003), September.

U.S. DOE, 2005a. “Annual Energy Outlook, With Projections to 2025,” Energy 

Information Administration, DOE/EIA-0383(2005), www.eia.doe.gov/oiaf/aeo/, 

February. 

U.S. DOE, 2005b. “Supplemental Tables to the Annual Energy Outlook,” Energy 

Information Administration, http://www.eia.doe.gov/oiaf/aeo/supplement/index.html 

U.S. DOE, 2005c. “Assumptions to the Annual Energy Outlook,” Energy Information 

Administration, www.eia.doe.gov/oiaf/aeo/assumption/pdf/0554{2005}.pdf

U.S. Environmental Protection Agency (EPA), 1996. “Compilation of Air Pollutant 

Emission Factors, AP-42,” Fifth Edition, Volume I: Stationary Point and Area Sources. 

U.S. EPA. http://www.epa.gov/ttn/chief/ap42/

U.S. EPA, 2005. “eGRID Emissions & Generation Resource Integrated Database,” U.S. 

EPA Office of Atmospheric Programs,

http://www.epa.gov/cleanenergy/egrid/index.htm

U.S. Congress, 2005. “Domenici-Barton Energy Policy Act of 2005,” Washington, D.C. 

109th Congress, July 2005. 

36
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4. TITLE AND SUBTITLE

A Preliminary Assessment of Plug-in Hybrid Electric Vehicles on 

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14. ABSTRACT (Maximum 200 Words)

This report examines a measure that may potentially reduce oil use and also more than proportionately reduce 

carbon emissions from vehicles. The authors present a very preliminary analysis of plug-in hybrid electric vehicles 

(PHEVs) that can be charged from or discharged to the grid. These vehicles have the potential to reduce gasoline 

consumption and carbon emissions from vehicles, as well as improve the viability of renewable energy technologies 

with variable resource availability. This paper is an assessment of the synergisms between plug-in hybrid electric 

vehicles and wind energy. The authors examine two bounding cases that illuminate this potential synergism. 

15. SUBJECT TERMS

plug-in hybrid electric vehicle; PHEV; analysis; transportation; carbon emissions; energy; oil; gasoline; fuel; fuelefficient vehicles; wind energy; Wind Deployment System model; WinDS; Walter Short; Paul Denholm 

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