|
Items in cart [1] |
Questions? Call 888-624-8373 |
| Copyright © 2009. National Academy of Sciences. All rights reserved. Terms of Use and Privacy Statement |
Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 93
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
4
Economics of Renewable Electricity
Having established the availability of renewable resources and outlined the
technology options for converting those resources into electricity, this chapter explores
the challenges and opportunities for bringing substantial renewable electricity generation
to market to serve future U.S. electricity needs. Given the experience with renewables
over the past 20-30 years, there is an inherent understanding that the economics of
renewables have not been favorable. The economics of renewables is about profitability,
and profitability depends on three drivers: (1) the market price or value of renewable
electricity; (2) the costs of renewables relative to those of other energy resources; and (3),
importantly, policies to promote renewables and environmental goals (particularly
climate and energy security policies) that raise costs of using fossil fuels and/or subsidize
costs of renewables.
The economic future for renewables depends on how market price, costs, and
policy evolve. This chapter examines these drivers, the factors that underlie them, and
issues associated with making predictions about them and their effects on the success of
renewables in the marketplace. It sets out the fundamentals of the electricity market,
explores technical and regional issues that affect renewables economics, and outlines the
many entities engaged in renewable generation and what they bring to the table. The
chapter concludes by summarizing and analyzing cost estimates for the renewable
technologies with the greatest likelihood of contributing significantly to electricity
generation in the next decade. The goal is not only to compare the costs of various
technology options and how they will evolve over time, but also to clarify how markets
and government actions can affect the near-term deployment of renewables.
This chapter focuses on the renewable technologies that are closest to market and
for which assessments of current and future costs are thus more readily available. These
include biomass, wind, concentrating solar power, solar photovoltaics, and geothermal
(hydrothermal), but exclude traditional hydropower, because the potential for future
extraction of this resource is limited as noted in Chapter 2. The chapter also excludes
hydrokinetics and enhanced geothermal technologies, which are still in the early stages of
technological development. The costs presented here come from a wealth of data
obtained from projects built in the recent past.
THE VALUE OF RENEWABLES
Predicting the economics of future renewable generation involves predicting the
cost of generation from alternative sources and the value of electricity delivered to the
93
OCR for page 94
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
marketplace. The competitive value would be the wholesale price of electricity for grid
scale resources and something close to the retail price of electricity for distributed
renewable resources.1 These prices define the value of adding renewables to the mix.
The ability to predict electricity price is key to making predictions about future market
penetration of renewable sources of electricity.
The value of generation from renewables will vary geographically and by time of
day, because the marginal generator,2 which sets the electricity price, varies with location
and over the course of the day with fluctuations in total electricity demand and available
supply. Construction of more transmission facilities will increase the value of
renewables by reducing transmission constraints between regions with abundant
renewable resources and those with abundant load (Vajjhala et al., 2008).
The importance of relative costs means that efforts to understand how future
expected declines in renewables cost are likely to affect renewables penetration will
depend on future predictions of the market price of electricity. An analysis of the
accuracy with which past studies from the 1970s and 1980s of several different
renewable technologies, including wind, solar photovoltaics (PV), concentrating solar
power (CSP), geothermal, and biomass, predicted future costs and future penetrations
finds that these past studies performed reasonably well at predicting future cost declines
but did not accurately predict market penetrations (McVeigh et al., 2000). McVeigh’s
analysis shows that predictions consistently overestimated the expected retail price of
electricity in future years. The renewable technologies included in the study for the most
part had large reductions in cost over time, but these reductions were matched or
exceeded by declines in the real cost of supplying electricity with fossil fuels and thus
renewables did not achieve predicted increases in penetration. This suggests that the
challenge of predicting future costs of renewables may be exceeded by the challenge of
predicting future market conditions that will confront those technologies, which will be
equally if not more important in determining the ability of renewables to penetrate the
market.
In addition to selling electric energy, most wholesale electricity markets also have
an additional source of revenue from capacity payments. Capacity payments are made to
encourage some generation to be readily available to meet changes in demand and ensure
a high level of reliability in delivered electricity despite unforeseen outages.
Requirements for the amount of capacity required vary regionally, but the value directly
correlates to the expected performance of the unit when needed for generation. For
dispatchable fossil generation and renewables, the capacity value is the highest, usually
based on close to 100 percent of the unit’s rated capacity. For other renewables, the
capacity value is typically lower to reflect the intermittent availability of the resource.
The capacity value of a given renewable technology is regionally specific, due to how the
1
In his analysis of the value of electricity produced by solar PV installations on household and
business rooftops, Severin Borenstein (2008b) points out that while the value to a consumer of not having
to purchase electricity may be the retail price of the purchases avoided, the avoided cost to society from
installing PV on one’s rooftop is less than the full retail price, which includes payments for recovery of past
costs, including the California Energy crisis, and sunk costs of past high-priced electricity contracts.
2
To meet electricity demand at lowest cost, system operators tend to dispatch electricity generators in
the order of their variable cost of generation, which includes fuel and operating and maintenance costs.
The marginal generator is the last generator, and therefore typically the highest-cost generator, that is
dispatched to meet electricity demand at any point in time.
94
OCR for page 95
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
capacity value is determined and the relative alignment between resource and load.
Although intermittent, the capacity value of grid-scale solar would typically be higher
than that of wind, because there is often better correlation between electricity demand and
when the sun is shining. Solar resource availability is more predictable than wind, though
clouds do have a serious impact on solar flux. In a region where the wind resource
availability does not correlate well with periods of system load, the capacity value may
be as low as 8 to 10 percent of the rated capacity of the unit (ERCOT, 2007; GE Energy
Consulting, 2005). In areas where resource or transmission availability allows for better
correlations with load, renewables will qualify for higher capacity payments. Capacity
payments do not lower costs, but they affect the economics of renewables, because they
provide an additional incentive to increase dispatchability.
Another source of value for most renewables3 is that their operation typically does
not contribute to air pollution through emissions of NOx and SO2, and greenhouse gas
emissions (GHG), particularly emissions of CO2.4 Substituting renewable generation for
fossil fuel generation could reduce air pollution and greenhouse gas emissions. These
benefits would depend on the type of fossil generation displaced, the emission controls on
the fossil generation, the resulting emissions rate of that fossil generation, and the form of
environmental regulation governing pollutants.5 For pollutants subject to an emissions
cap, as is the case for SO2 nationally or CO2 in states participating in the Regional
Greenhouse Gas Initiative, there will not be reduced emissions or environmental benefits.
Emissions caps are both a ceiling and a floor on the level of emissions, as emissions
reductions at one facility will be made up by increases at another facility, unless the cap
is reduced or is no longer binding, which could occur with a dramatic increase in
renewable generation.
If emissions are capped and emission trading is allowed, there could be an
important effect on emission allowance markets and thus on the costs of electricity
production from fossil fuels with greater penetration of renewables. Greater use of
renewables could reduce demand for emission allowances for SO2 and NOx and other
capped pollutants, which could reduce their allowance price. To the extent that
renewables displace natural gas, at least initially, this effect is likely to be small for
pollutants like SO2 and NOx. However, the effect could be larger for pollutants like CO2
if they were capped, though it is a value that would accrue to everyone who has to
purchase allowances and not just to the utility that is adopting more renewables.
Most emissions of CO2 from electricity generators in the United States are not
capped. Increasing renewables generation to replace fossil fuel generation would reduce
CO2 emissions, at least relative to business-as-usual emissions. Identifying the extent of
those reductions requires some caution. The effects would vary by location, based on the
composition of the existing generation fleet and the types of new non-renewable
generators and fuels that might otherwise be put in place to meet future electricity needs.
These reductions in CO2 emissions would have value to society, and renewable
3
With the exception of hydrothermal, which emits SO2 and CO2, and biopower, which emits NOx and
CO2.
4
There are emissions associated with the manufacture of different renewable technologies. These life
cycle effects are discussed in Chapter 5.
5
Greater reliance on intermittent renewables like wind or solar could increase the need for spinning
reserves from fossil generators, and increased operation of these generators in spinning mode or at less than
full capacity could reduce the CO2 and NOx emissions benefits.
95
OCR for page 96
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
generators might be able to capture some of that value if they could identify consumers
willing to pay a premium for CO2-free electricity or green power.
COSTS AND ECONOMICS OF RENEWABLE ELECTRICITY
Cost is the principal barrier to the widespread adoption of renewable technologies.
Generating electricity using renewable energy technologies is more costly than
generating with fossil fuels, especially coal, which supplies about half of the electricity
generated in the United States each year. More transmission infrastructure in key
locations would also be required for a dramatic increase in power supplied by
renewables. Recent increases in renewables market penetration, particularly new wind
power, have largely been in response to policies like the federal renewable energy
production tax credit and state renewable portfolio standards. These policies seek to close
the cost gap in the short-term by subsidizing renewable generation. By encouraging
greater market penetration, these policies enable reductions in long-term costs through
increased scale and learning in manufacturing and in the use of the technology.
To achieve greater market penetration, renewables would have to undergo cost
reductions at a rate greater than the rate of cost improvement by technologies that set the
market price of electricity, including natural gas and coal fired generation. These
reductions might result from major breakthroughs in technology, improvements in
manufacturing, or improved operating performance of equipment, such as higher capacity
factors for wind turbines. Likewise, increases in the costs of fossil generation could have
an impact on the relative competitiveness of renewables, though the magnitude might not
be as great if cost increases also improved the competitiveness of energy efficiency
options and nuclear generation.
Estimates abound of present and future costs for particular types of renewables
and other sources of generation. Comparability of these estimates depends on the
underlying assumptions and the types of costs captured in summary measures. The next
sections discuss the types of costs associated with constructing and operating renewable
generating facilities, important assumptions underlying those costs, and how they can be
used to construct summary measures of the cost of supplying energy.
Cost Concepts and the Levelized Cost of Energy
Developing a particular technology to generate electricity incurs costs for the
capital equipment, such as the wind turbine and its tower, photovoltaics, or solar panels;
the land or property, if necessary for installation; and operating and maintaining the
equipment. Some costs vary with the amount of electricity generated, and some costs are
fixed. When a technology requires a fuel, such as biomass generation (biopower), the
cost of the fuel would be a part of the variable operating and maintenance cost.
Capital costs do not vary with the amount of electricity generated by the facility
and are typically stated in dollars per kW ($/kW). Capital costs generally vary with the
size of the facility or installation, with economies of scale or volume discounts on
equipment orders favoring larger enterprises. Coal-fired and nuclear generating facilities
96
OCR for page 97
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
exhibit economies of scale, and larger plants tend to have lower average cost of
generation than smaller plants. For renewables such as wind and solar PV, economies of
scale can be greater at the equipment manufacturing stage than at the electricity
generating site, and increased capacity does not decrease the average cost of generation
as much as it does for fossil and nuclear plants. Capital costs can also vary across sites,
depending on land cost and the costs of installation or construction of the facility.
Fixed operating and maintenance (O&M) costs are also stated in $/kW, but unlike
capital costs, they are an ongoing expense associated with some unit of time ($/kW-year).
Typically technologies are characterized by their annual fixed O&M costs. This category
includes costs such as wages, materials, and land lease payments.
Variable O&M costs are typically expressed as dollars per MWh ($/MWh). Fuel
costs can be expressed as dollars per unit of mass of the fuel ($/ton), dollars per unit of
heat content of the fuel ($/Btu), or $/MWh. The last formula takes into account the
efficiency of the technology in converting Btu of heat input into MWh of electricity.
In comparing the costs of generating electricity for different renewable
technologies and for fossil fuels and nuclear technologies, cost estimates are typically
converted into a levelized cost of energy (LCOE), which is expressed in $/MWh. The
initial cost of the capital equipment and installation constitutes a large portion of the cost
of generating electricity, particularly for renewables, which have no fuel costs, with the
exception of biopower generation. Converting this large up-front cost to cost per MWh
requires making assumptions about the lifetime and capacity factor of the equipment,6 as
well as the discount rate and the timing of returns on that capital. For intermittent
technologies such as concentrated solar power (CSP), solar photovoltaics (PV), and wind
power, the capacity factor can vary considerably, depending on the location and the
quality of the resource (e.g., wind speed and constancy for wind turbines, and hours of
sunlight with no cloud cover for CSP and PV); likewise, the levelized cost of energy will
vary depending on the capacity factor at a particular installation and location.
The cost of fuel plays an important role in calculating levelized cost for biopower.
Biopower is typically a baseload technology with a high capacity factor. On an annual
basis its fixed equipment costs could be recovered over many hours of operation.
However, the hours of operation and the amount of electricity generated by biopower
would depend on the cost of fuel, which accounts for about 1/3 of the total levelized cost
of energy from biopower (Venkataraman et al., 2007). The cost of biomass fuel is
uncertain and would depend on competing demands for crops and other agricultural
inputs, including demands from the transportation sector.
Costs Beyond Generator Costs
The costs of purchasing, installing and operating a specific power plant might not
be the total costs to the system and to electricity consumers of deploying a new
renewable generation facility. Costs that might be missing from the traditional levelized
cost measure could be the costs of new infrastructure necessary to connect the renewable
generator to the grid and to ensure continued quality of power supply. Other costs
6
The capacity factor of the equipment is the percentage of the 8,760 hours in a typical year that the
equipment will be generating electricity at the power level relative to its full capacity.
97
OCR for page 98
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
include upfront costs for approval of siting the new facility and costs for appraising the
resource at the site, as well as costs of obtaining financing and environmental permits.7
Transmission
While fossil fuels may be transported from the mine or the wellhead to an electric
generation facility, renewable generating plants must be located at or near the resource.
There might be some degree of greater flexibility in location for biopower, but not much.
It can be costly to ship biomass fuels, given the relatively low energy density compared
to fossil fuels. Thus, biopower facilities are typically located close to sources of fuel.
Wind and some solar resources often are located at some distance from the
existing transmission grid, and would require new transmission lines to transport the
power to the centers of electricity demand or load. As with any new generation, the cost
of constructing additional transmission lines should be included in the cost of supplying
electricity from renewable resources. A recent report looked at 40 transmission studies
covering a broad geographic area on the costs associated with the transmission
requirements of wind (Mills and Wiser, 2009). The transmission costs associated with
wind ranged from $0 to $1,500 per kW, and the majority were less than or equal to $500
per kW, with a median of $300 per kW. These numbers correspond to $0 to $79 per
MWh, with the majority below $25 per MWh, and a median of $15 per MWh.
Intermittent renewables generation requires an additional consideration. Because of low
capacity factors, dedicated transmission lines sized to transmit the full amount of power
produced during peak generation hours would be unused or underused some of the time.
Siting additional peaking capacity along a new transmission corridor could potentially
leverage the available capacity during periods of underuse by the renewables.
A caveat to the preceding discussion is that distributed renewables, such as
distributed PV, might end up closer to the load than conventional generation and could
lead to less need for investment in transmission. To really achieve substantial benefits in
terms of avoiding investment in transmission infrastructure may require substantial
amounts of distributed renewables investment in particular locations.
Intermittency
At sufficiently high capacities of solar and wind generation, the costs of
intermittency could extend beyond costs associated with dedicated transmission facilities
to affect the operation of the interconnected transmission grid. More generation from
intermittent resources will require additional or alternative resources to help track load,
provide voltage support, and meet needs for capacity reserves. These include demand for
second-by-second electricity load balancing service, or regulation; load following within
the hour; and unit commitment of generators to be available at particular times of the day
or week. Renewable electricity must be used when generated because the electricity can
7
Levelized cost estimates also typically exclude the costs of the ultimate disposal of the generation
equipment at the end of its useful life. Disposal may be complicated and costly for some types of
equipment that contain hazardous chemicals that require special disposal procedures.
98
OCR for page 99
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
only be generated when the resource is available. Typically, fossil fuel generators that are
easily dispatchable such as natural gas combustion turbines supply these ancillary
services. As renewables generation increases and fossil generators are curtailed,
renewable generation technologies themselves or additional system assets, such as
storage, will be needed to meet the increased need for ancillary services, at some
additional cost. When system managers have improved tools and technology for
predicting resource availability, it will be easier to determine the need for additional
generation resources to back up intermittent renewables. Smart Grid technologies, which
allows system managers to manage supply and demand in real time, could also mitigate
some of the costs of renewable intermittency. An upgrade and expansion of the grid will
be necessary no matter what happens with renewables, given the age of the grid and the
anticipated growth in electricity demand.
Studies in the past five years looked at the costs of integrating wind into the grid,
as summarized in Figure 4-1 (Smith, 2007; Wiser and Bolinger, 2007). These studies
examined the costs of regulation service, load following, unit commitment, and natural
gas and found that the incremental costs per MWh range from about $1.50 to almost
$5.00. A study on using wind to serve 50 percent of demand showed that the incremental
costs are $10 to $20 per MWh, including transmission, storage, and backup generation
(DeCarolis and Keith, 2004). The European Wind Energy Association conducted a study
of over 180 sources and determined that additional costs range from $1.50 to $10.20 per
MWh for market penetration levels of 10 percent and from $2.80 to $11.50 for higher
penetration levels (EWEA, 2005). Typically the predicted costs are higher in studies that
focus on higher market penetration of wind. In the studies on different levels of
penetration, the costs were higher with the higher levels of penetration, but the
incremental effect of increased penetration varied across studies. Generally, where the
average cost of wind generation would be about $80 per MWh, the impact of grid
integration costs appeared to be less than 15 percent where wind produced 20 percent or
less of total electricity generation.
Energy Storage
Energy storage could mitigate the impact of intermittent renewables. Today there
is very little storage in the United States, as high costs, low efficiencies, and
technological uncertainty precluded storage from becoming economically viable.8 Costs
for battery and other storage technologies are generally about two to five times higher
than the cost target that would make them competitive (less than about $200/kWh for a 4-
hour system) (Rastler, 2008). However, technologies might be called on in the future to
store electricity generated from renewable resources if their combined market penetration
would rise to 20 percent and beyond.
Efficient, cost-effective energy storage could promote grid-scale renewable
electricity. Wind and solar system operators have limited control over the amount and
8
The exception is pumped hydroelectric storage, of which there was 21,461 MW of capacity
nationwide in 2006 (EIA, 2007a). However, it is widely acknowledged that there is little chance for
additional pumped storage as most of the viable pumped hydro opportunities have been exhausted. For this
reason, this section omits pumped storage.
99
OCR for page 100
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
timing of power generation, and their production does not line up well with demand
requirements. Storage would allow a grid operator to align the dispatch curve with the
demand curve, a process referred to as load shifting. In addition to generating revenue
when the wholesale market is at its peak, the ability to draw on storage would obviate the
need for some peaker generators at the margin. Storage would also alleviate the
reliability concerns associated with wind and solar. When renewables provide less than
forecasted output, the operator has to turn to the spot market or bring on idle combined
cycle natural gas generators to make up the difference. Conversely, when renewables
provide too much power, those holding day-ahead gas contracts might not realize the
value of their contracts. The market penalizes renewables for this uncertainty and, while
recent studies have shown that this might not matter until renewables reach penetration
levels in excess of 20 percent, this uncertainty may have to be addressed if they are to
extend any further (DOE, 2008).
Storage would also mitigate some site limitations of renewable electricity and
help reduce the size and increase the utilization of transmission lines installed for
renewable sources. Small scale domestic storage could also change the economics of
distributed wind and solar generation, providing homes with energy security, while
perhaps making it possible to sell stored energy or capacity back to the grid. As plug-in
hybrid electric vehicles (PHEVs) become a reality, households could store the energy
they generate right on their vehicles. The National Renewable Energy Laboratory
(NREL) found that PHEVs could enable increased penetration of wind energy (Short and
Denholm, 2006).
Figure 4-2 displays how some of these storage technologies compare in terms of
cost of energy and cost of power. At the grid-scale level (greater than 10 MW),
compressed air energy storage (CAES) appears to be the most economical now, though
the practicality of CAES also depends on the availability of suitable sites. Iowa Energy
Storage Park (IESP), a 268 MW system, is scheduled to come online in Iowa in 2011.
Projected costs for IESP are $200 million to $250 million, or $746 to $933 per kW, and
the system is designed to go from idle to full output in under 15 minutes. The Texas
State Energy Conservation Office estimated total overnight capital costs of a new CAES
system at $605/kW. Development and fixed O&M costs were listed at $28.00/kW and
$14.07/kW respectively, and variable O&M costs were estimated to be $1.50/MWh
(Ridge Energy Storage, 2005). Batteries are modular and non-site specific, which makes
them ideal for distributed generation. The quick, cheap response time also makes
batteries ideal for providing backup power, or uninterruptible power supply (UPS). Yet
despite broad application in other sectors, batteries are still very expensive, as shown in
Figure 4-2.
Financing Costs
Another element of cost that should be included when evaluating the overall
competitiveness of any generation project is the cost of money. Because electricity
generation projects are capital-intensive and have long lifetimes, access to capital and the
rates at which it is paid back are key components of project cost. The magnitude of these
costs differs, depending on the type of generation financed. For example, a renewable
100
OCR for page 101
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
project that does not require fuel has a much larger portion of its costs associated with the
initial capital expense of the plant than a gas-fired power project that will have greater
operating expenses throughout its lifetime, even if both have similar LCOEs. These costs
are project-specific, based on circumstances related to the project’s financing strategy,
the maturity of the technology, and risk factors discussed in Chapter 6.
Financing renewable projects differs from that of fossil-fueled plants. While the
total magnitude of capital may be smaller for a renewable project than for a fossil project,
the capital intensity relative to operating costs is much higher for renewables without
fuel, such as wind and solar. There is more upfront risk in the renewables project's
financial model. Further, the tax incentives that could subsidize some renewables might
not be directly available to the project developer and could require more complex
financing structures to access the benefit. The project financing structure, such as the
debt to equity ratio; the types and costs of loans, depending on the risk profile; and the
magnitude and timing of returns to financing entities can have an impact of as much as
$15/MWh on a wind project’s levelized cost of energy (Harper et al., 2007). Regional
variability in the costs of land and logistical support and time variability in the selling
price of electricity due to market forces complicate financing for renewables projects.
For wind projects before the 2008-2009 economic crisis, the cost of tax equity appeared
to decline by approximately 3 percent and interest rate margins on debt transactions by
approximately 0.5 percent (Wiser and Bolinger, 2008). This trend toward cheaper capital
resulted directly from reduced project risks as the wind power industry matured and the
resulting increase in available capital for wind projects. Economic events dramatically
reversed this trend for all forms of power generation, but have affected renewables to a
greater degree, due to the reliance on investor tax capacity in order to realize the
economic benefit of the production tax credit (PTC). The American Recovery and
Reinvestment Act of 2009 attempted to address this issue by allowing non-solar
renewable electricity facilities to elect a 30 percent investment tax credit in lieu of the
PTC.
Because of their small scale and modularity, advantages that wind and solar PV
projects have over fossil projects is the shorter time between purchase of the equipment
and placing it on-line and the ability to start up the first few generators while others are
under construction (Bierden, 2007; Royal Academy of Engineering, 2004; Sheehan and
Hetznecker, 2008). These two features reduce the magnitude of draws on cash flow and
accelerate the repayment of debt.
Methodologies for Projecting Costs
An overview of the different approaches to projecting future costs places the cost
projections in this report in context. The panel identified three methods for predicting
future costs of renewables.
The first methodology predicts the levelized cost of energy that must be achieved
from a particular renewable generating source to be competitive with other sources of
electricity by some date in the future. This method requires estimating the future
wholesale market price of electricity with which renewable resources must compete.
These predictions omit consideration of uncertainties, the relationship between
101
OCR for page 102
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
government policy and expenditures, and changes in the costs of using renewables to
supply electricity. The Western Governors’ Association (WGA) Solar Task Force took
this approach and developed a series of referent market prices that depend on the
assumed price of natural gas, the energy source typically setting the market price of
electricity in the western states (WGA, 2006b). The higher the predicted price of natural
gas, the lower the cost reduction hurdle for renewable technologies.
A second approach that is similar to the first involves the enunciation of cost and
technical performance goals, such as availability factors, that those researching future
developments of the technology, such as the Office of Energy Efficiency and Renewable
Energy (EERE) at the DOE, expect the technology to achieve as a result of their research
program. The idea behind this approach is to establish goals for an research and
development (R&D) program and also to provide some benchmark expectations about
technological improvement that could be used later to judge the performance of the
research program after the fact. This approach is used in NREL’s projections that they
develop for DOE (NREL, 2007).
EIA took a third approach to cost prediction in constructing a more formal model
of how technical costs might evolve over time. Models can be based on a projection of
past trends or a formal learning or technological improvement function. Cost reductions
through learning are greater for new technologies than for mature technologies. EIA took
the learning curve approach to predict how costs would evolve as greater amounts of a
particular technology penetrate the market in response to a combination of policies and
electricity demand growth (EIA, 2007a). This is the approach underlying the costs
estimates for EIA and the Electric Power Research Institute (EPRI) shown in Tables 4-A-
1 and 4-A-2 in the annex at the end of this chapter.
Some might argue that present day costs would be the best predictor of future
costs, particularly in the short-term. This approach might suffice for forecasting future
costs for more mature renewable technologies such as wind, but might be less appropriate
for nascent technologies. Another problem with this approach is that factors contributing
to short-term cost increases, such as the recent increases in the cost of wind turbines and
solar cells due to material shortages, might not be sustained into the future, as entry into
the industry, greater availability of materials, and innovations might bring costs down.
POLICIES AND PRACTICES THAT AFFECT THE ECONOMICS OF
RENEWABLE ELECTRICITY GENERATION
The United States and other nations have implemented policies to increase the
market penetration of renewables. Typically these policies work by either subsidizing the
cost of renewable generation (and thereby decreasing their relative costs) or by increasing
the demand for renewable generation. Some policies provide for additional sources of
revenue for renewable generators, such as through the sale of renewable energy credits.
The form of the policy and the amount of the payment or subsidy for renewables are both
determinants of the policy’s effect on renewables penetration.
The panel considers three classes of policies in this section: (1) policies and
practices targeted at renewable technologies; (2) environmental policies that raise the cost
of using conventional technologies, thereby improving the relative cost competitiveness
102
OCR for page 103
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
of renewables; and (3) other electricity market policies that could affect the economics of
using renewables and their ability to penetrate the future market.
Policies to Promote Renewables
Both the federal government and a majority of the states have policies to promote the use
of renewable technologies to supply electricity. Most of these policies are described in
Chapter 1. Here, the panel focuses on a few policies and describes how they appear to
affect the economics of renewable generation. Some policies target large central station
facilities, while others focus on distributed renewables intended for personal
consumption. The following review focuses on the major policies in terms of their
potential capability and their relevance for renewables market penetration.9
Production Tax Credits
A renewable energy production tax credit (PTC) policy allows firms that generate
electricity with eligible renewable technologies to offset their income tax liability by the
amount of the tax credit times the number of kWh generated. The federal PTC applies to
a range of renewable technologies, with some technologies, including wind, solar, closed-
loop biomass, and geothermal, eligible for a larger tax credit than others, such as open-
loop biomass, small hydroelectric, landfill gas, and municipal solid waste.10 Generators
are eligible for the tax credit for every kWh of electricity generated during their first 10
years of operation. The federal renewables PTC policy was recently extended until 2012
and beyond, as described in Chapter 1. Typically this policy, initially passed in 1992,
had only been approved for one to two years into the future and lapsed three times since
its inception. As shown in Figure 4-3, the intermittency of this policy led to large
fluctuations in demand for wind turbines as project developers raced to beat the deadline
and then lost interest in new projects when the policy lapsed (Wiser, 2008).
In addition to the federal PTC, five states (Florida, Iowa, Maryland, Nebraska,
and New Mexico) also offer PTCs that provide a tax credit for every kWh of electricity
generated. Another seven states offer direct payments for each kWh of electricity
generated by certain renewable technologies.
Both an investment tax credit (ITC) and a renewable PTC reduce the cost of
generating electricity using renewables. The PTC is arguably more effective at getting
performance out of a generator, because the level of the PTC subsidy depends directly on
how much electricity the generator produces while the ITC does not differentiate between
a renewable that is productive and one that does not generate much electricity. However,
9
In addition to the policies identified below, some states offer low-interest loans for renewables.
However, generators that avail themselves of this type of state support may make a particular project
ineligible for the renewable energy production tax credit discussed in the next section.
10
Several renewable technologies (wind, solar, geothermal, and small biomass generators) are also
eligible for accelerated depreciation, which allows them to depreciate their capital over 5 years instead of
the 20- year lifetime for most fossil generators (15 years for new nuclear). In addition, renewables may be
eligible for a method of depreciation within the 5-year time period that allows more than half of the
investment value to be depreciated in the first 2 years of use.
103
OCR for page 122
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
the most accessible sites in the highest wind class areas. Development of these prime
resources will thus entail significant resource cost shifts as markets adjust to exploit next-
tier resources. At present, onshore wind is an economically favored option relative to
other (non-hydroelectric) renewable resources, and hence wind power is expected to
continue to grow rapidly if recent policy initiatives continue into the future.
Although some forecasts show that biopower will play an important role in
meeting future renewable portfolio standards targets, the degree of competition with and
recent mandates for use of liquid biofuels for providing transportation fuel and, of course,
the use of biomass for food, agricultural feed, and other uses will impact the prospects for
greater use of biomass in the electricity market. The future of distributed renewable
electricity generation from sources such as residential photovoltaics will depend on how
its costs compare to the retail price of power delivered to end users, on whether prices
fully reflect variations in cost over the course of the day, and on whether the external
costs of fossil-based electricity generation are increasingly incorporated into its price.
Formulation of robust predictions about whether the price of electricity will
meet or exceed the price required for renewable sources to be profitable and what
their resulting level of market penetration will be remain a difficult proposition.
Comparisons between past forecasts of renewable electricity penetration and actual
data show that, while renewable technologies generally have met forecasts of cost
reductions, they have fallen short of deployment projections. Further, the profitability
and penetration of electricity generated from renewable resources may be sensitive to
investments in energy efficiency, especially if efficiency improvements are sufficient to
meet growth in the demand for electricity or lower the market-clearing price of
electricity. If the financial operating environment for fossil fuel and other in-place
sources of electricity remains unchanged, then the competitiveness of renewable
electricity may be affected more than that of other electricity sources. However, at this
time, the deployment of renewable electricity is being driven by tax policies, in particular
by the renewable production tax credit, and by renewable portfolio standards.
In particular, the panel finds that:
• Onshore wind is an economic option that could scale to a material penetration
by 2020, and will likely see rapid growth if recent policy initiatives continue into the
future. Biopower is also forecast to play an important role in meeting future renewable
portfolio standards (RPS) targets, but greater use in the future electricity market will
depend on competition from demand for liquid biofuels for transportation.
• Some kind of incentive (RPS with high REC price, PTC, or feed-in tariff) will
be needed to increase renewables’ use as long as external costs of fossil generation,
particularly those associated with greenhouse gas emissions, are not incorporated into the
costs of using those technologies and fuels.
• Renewables policies will be more effective if they are stable and predictable
than if they cycle on and off or have a highly uncertain future, as has been the case with
the federal PTC.
• Intermittency of wind is manageable using current technology, and storage is
not required for levels of market penetration expected within the 2020 timeframe.
• The evolution of renewables’ costs will depend on technological
breakthroughs, the potential for policies to achieve greater market penetration and
122
OCR for page 123
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
technological learning, and the rapidity with which low-cost resources, such as the most
accessible sites in the highest wind class areas, are exhausted.
• Investment in energy efficiency, which can lower the market-clearing price of
electricity, could diminish the future profitability of renewable electricity generation.
• A key determinant of the future success of renewables in penetrating the
market is the value that renewables suppliers will obtain for their generation in the
electricity marketplace, which is largely determined by the wholesale market price of
electricity for grid-scale renewables and the retail price of electricity for distributed
renewables. Predicting that price and the resulting level of renewables market
penetration has been and continues to be a difficult proposition.
• Projections of levelized costs of energy in 2020 for wind across all data
sources are generally no higher than EIA projections of levelized costs for coal integrated
gasification combined cycle (IGCC) or natural gas combined cycle with carbon capture
and sequestration. However, the full costs of integrating wind power into the grid, which
are not typically reflected in levelized cost projections, could lead to a change in the
relative cost ranking of these technologies.
• Projections of the levelized cost of energy from wind, concentrating solar
power, and solar PV technologies in the 2020 timeframe vary substantially across
different studies and data sources, with the most optimistic projections putting levelized
costs at between 20 percent and 33 percent of the most pessimistic projections.
REFERENCES
ASES (American Solar Energy Society). 2007. Tackling Climate Change in the U.S.:
Potential Carbon Emission Reductions from Energy Efficiency and Renewable
Energy by 2030. Washington, D.C.
Bierden, P. 2007. The Process of Developing Wind Power Generators. Presentation at the
Second Meeting of the Panel on Electricity from Renewables. December 6, 2008.
Washington, D.C.
Bird, L., and B. Swezey. 2006. Green Power Marketing in the United States: A Status
Report. 9th Edition. Technical Report NREL/TP-640-40904. National Renewable
Energy Laboratory, Golden, Colo. November.
Bird, L., L. Dagler, and B. Swezey. 2007. Green Power Marketing in the United States:
A Status Report. 10th Edition. Technical Report NREL/TP-670-42502. National
Renewable Energy Laboratory, Golden, Colo. December.
Bird, L., and E. Lockey. 2007. Interaction of Compliance and Voluntary Renewable
Energy Markets. NREL/ TP-670-42096. National Renewable Energy Laboratory,
Golden, Colo. October.
Black and Veatch. 2007. 20 Percent Wind Energy Penetration in the United States⎯A
Technical Analysis of the Energy Resource. Black & Veatch Project #144864,
prepared for the America Wind Energy Association, Walnut Creek, Calif.
Borenstein, S. 2007. Electricity Rate Structures and the Economics of Solar PV: Could
Mandatory Time-of-Use Rates Undermine California’s Solar Photovoltaic
Subsidies? Center for the Study of Energy Markets Working Paper 172.
123
OCR for page 124
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
University of California, Berkeley, Calif. September. Available at
http://www.ucei.berkeley.edu/PDF/csemwp172.pdf.
Borenstein, S. 2008a. The Market Value and Cost of Solar Photovoltaic Electricity
Production. Center for the Study of Energy Markets Working Paper 176.
University of California, Berkeley, Calif. January.
Borenstein, S. 2008b. Response to Critics of “The Market Value and Cost of Solar
Photovoltaic Electricity Production.” Available at
http://faculty.haas.berkeley.edu/borenste/SolarResponse.pdf.
CEC (California Energy Commission). 2008. Energy Renewable Programs. Available at
http://www.energy.ca.gov/renewables/emerging_renewables/index.html.
Cornelius, C. 2007. DOE Solar Energy Technologies Program. Presentation at the First
Meeting of the Panel on Electricity from Renewables, September 18, 2007,
Washington, DC.
Cory, K.S., and B.G. Swezey. 2007. Renewable Portfolio Standards in the States:
Balancing Goals and Implementation Strategies. Technical Report NREL/TP-
670-41409. National Renewable Energy Laboratory, Golden, Colo. December.
DeCarolis, J.F., and D.W. Keith. 2004. The economics of large-scale wind power in a
carbon constrained world. Energy Policy 34:395-410.
Denholm, P., and R. Margolis. 2008. Supply Curves for Rooftop Solar PV-Generated
Electricity for the United States. Technical Report, NREL/TP-6A0-44073.
National Renewable Energy Laboratory, Golden, Colo. November.
DOE (U.S. Department of Energy). 2008. 20-Percent Wind Energy by 2030: Increasing
Wind Energy’s Contribution to U.S. Electricity Supply. Energy Efficiency and
Renewable Energy, DOE, Washington, D.C.
EIA (Energy Information Administration). 2004. Analysis of S. 1844, the Clear Skies Act
of 2003; S. 843, the Clean Air Planning Act of 2003; and S. 366, the Clean Power
Act of 2003. SR/OIAF/2004-05. DOE, Washington, D.C. May.
EIA. 2005. Production Tax Credit for Renewable Energy Generation, Issues in Focus,
AEO2005. DOE, Washington, D.C.
EIA. 2007a. Assumptions to the Annual Energy Outlook for 2007. DOE/EIA-
0554(2007). DOE, Washington, D.C.
EIA. 2007b. Analysis of Alternative Extensions of the Existing Production Tax Credit for
Wind Generators. Memorandum to the Committee on Ways and Means, U.S.
House of Representatives. DOE, Washington, D.C.
EIA. 2007c. Impacts of a 15-Percent Renewable Portfolio Standard. SR/OIAF/2007-03.
DOE, Washington, D.C.
EIA. 2008a. Energy Market and Economic Impacts of S. 1766, The Low Carbon
Economy Act of 2007. SR/OIAF/2007-06. DOE, Washington, D.C.
EIA. 2008b. Energy and Economic Impacts of Implementing Both a 25-Percent
Renewable Portfolio Standard and a 25-Percent Renewable Fuel Standard by
2025. SR/OIAF/2007-05. DOE, Washington, D.C.
EIA. 2008c. Annual Energy Outlook 2008 with Projections to 2030. DOE/EIA-
0383(2008). DOE, Washington, D.C.
EIA. 2008d. Annual Energy Outlook 2009, Early Release. DOE/EIA-0383(2009). DOE,
Washington, D.C.
124
OCR for page 125
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
EPA (U.S. Environmental Protection Agency). 2008. EPA Analysis of the Lieberman-
Warner Climate Security Act of 2008, S. 2191 in the 110th Congress.
Washington, D.C.
EPRI (Electric Power Research Institute). 2007a. Renewable Energy Technical
Assessment Guide. Palo Alto, Calif.
EPRI. 2007b. The Power to Reduce CO2 Emissions: The Full Portfolio. Palo Alto, Calif.
ERCOT (Electric Reliability Counsel of Texas). 2007. ERCOT Target Reserve Margin
Analysis. Global Energy Decisions, Inc., Sacramento, Calif.
Economist. 2008. German lessons: An ambitious cross-subsidy scheme has given rise to a
new industry. Vol. 387, Iss. 8574, pp. 67-68, April 5.
EWEA (European Wind Energy Association). 2005. Large Scale Integration of Wind
Energy in the European Power Supply: Analysis, Issues and Recommendations.
Brussels, Belgium.
FERC (Federal Energy Regulatory Commission). 2006. Assessment of Demand Response
and Advanced Metering Staff Report. Docket AD06-2-000. Washington, D.C.
GE Energy Consulting. 2005. The Effects Of Integrating Wind Power On Transmission
System Planning, Reliability, and Operations. Prepared for New York State
Energy Research and Development Authority, Albany, N.Y.
GeothermEx Inc. 2004. New Geothermal Site Identification and Qualification. Report to
the California Energy Commission, Public Interest Energy Research Program,
Sacramento, Calif.
German Federal Ministry for the Environment, Nature Conservation, and Nuclear Safety.
2007. Renewable Energy Sources in Figures⎯National and International
Development. Bonn, Germany.
Green, M.A. 2004. Third Generation Photovoltaics: Advanced Solar Energy Conversion.
Springer, Berlin, Germany.
Harper, J.P., M.D. Karcher, and M. Bolinger. 2007. Wind Project Financing Structures:
A Review and Comparative Analysis. Technical Report LBNL-63434. Lawrence
Berkeley National Laboratory, Berkeley, Calif. September.
IEA (International Energy Agency). 2006. Renewables Database. Renewable Energy
Feed-in Tariffs (III). Paris, France. Available at
http://www.iea.org/textbase/pm/?mode=re&id=3846&action=detail.
Klein, A., A. Held, M. Ragwitz, G. Resch, and T. Faber. 2006. Evaluation of Different
Feed-In Tariff Design Options⎯Best Practice for the International Feed-In
Cooperation. Fraunhofer Institute Systems and Innovation Research, Economics
Group, Karlsruhe, Germany.
McElroy, A.K. 2008. Lukewarm on co-firing. Biomass Magazine February. Available at
http://www.biomassmagazine.com/article.jsp?article_id=1429&q=&page=all.
McVeigh, J., D. Burtraw, J. Darmstadter, and K. Palmer. 2000. Winner, loser, or innocent
victim: Has renewable energy performed as expected? Solar Energy 68(3):237-
255.
Mills, A., and R. Wiser. 2009. The Cost of Transmission for Wind Energy: A Review of
Transmission Planning Studies. Lawrence Berkley National Laboratories,
Berkeley, Calif.
125
OCR for page 126
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
NREL (National Renewable Energy Laboratory). 2006. Power Technologies Energy
Data Book. NREL/TP-620-39728. Golden, Colo. August. Available at
http://www.nrel.gov/analysis/power_databook/.
NREL. 2007. Projected Benefits of Federal Energy Efficiency and Renewable Energy
Programs. NREL/TP-640-41347. Golden, Colo. March. Available at
http://www1.eere.energy.gov/ba/pba/pdfs/41347.pdf.
Palmer, K., and D. Burtraw. 2005. Cost effectiveness of renewable energy policies.
Energy Economics 27:873-894.
Palmer, K., D. Burtraw, and J.S. Shih. 2007. The benefits and costs of reducing emissions
from the electricity sector. Journal of Environmental Management 83:1.
Patel, S. 2009. PV sales in the U.S. soar as solar panel prices plummet. Power Magazine
March 1.
Rastler, D. 2008. Electric Energy Storage Briefing. Presentation at the Fourth Meeting of
the Panel on Electricity from Renewables, March 11, Washington, DC.
Ridge Energy Storage and Grid Services, L.P. 2005. The Economic Impact of CAES of
Wind in TX, OK, and NM. Final Report for Texas State Energy Conservation
Office, Austin, Tex. June 27.
Royal Academy of Engineering. 2004. The Costs of Generating Electricity. London, U.K.
SEIA (Solar Energy Industries Association). 2004. Our Solar Power Future—The U.S.
Photovoltaic Industry Roadmap Through 2030 and Beyond. Washington, D.C.
Sheehan, G., and S. Hetznecker. 2008. Utility Scale Solar Power. Power and Energy
Magazine 5, October.
Short, W., and P. Denholm. 2006. A Preliminary Assessment of Plug-In Hybrid Electric
Vehicles on Wind Energy Markets. NREL Technical Report. NREL, Golden,
Colo. April.
Smith, J.C. 2007. Integrating Wind Into the Grid. Presentation to the National
Academies of Sciences Panel on the Future of Renewables for Electricity Supply,
Washington, DC. December 7.
Smith, R.K. 2006. EIA Geothermal Supply Curve. Memorandum. September 15.
Surek, T. 2005. Crystal growth and materials research in photovoltaics: Progress and
challenges. Journal of Crystal Growth 275:292-304.
Swezey, B., J. Aabakken, and L. Bird. 2007. A Preliminary Examination of the Supply
and Demand Balance for Renewable Electricity. NREL/TP-670-42266. NREL,
Golden, Colo. October.
Vajjhala, S., A. Paul, R. Sweeney, and K. Palmer. 2008. Green corridors: Linking
interregional transmission expansion and renewable energy policies. Discussion
Paper 08-06. Resources for the Future, Inc., Washington, D.C. March.
Venkataraman, S., D. Nikas, and T. Pratt. 2007. Which Power Generation Technologies
Will Take the Lead in Response to Carbon Controls? S&P Credit Research,
Standard and Poor’s, New York, N.Y. May 11.
WGA (Western Governors’ Association). 2006a. Clean and Diversified Energy
Initiative: Geothermal Task Force Report. Denver, Colo.
WGA. 2006b. Clean and Diversified Energy Initiative: Solar Task Force Report. Denver,
Colo.
126
OCR for page 127
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
Wiser, R. 2008. The Development, Deployment, and Policy Context of Renewable
Electricity: A Focus on Wind. Presentation at the Fourth Meeting of the Panel on
Electricity from Renewables, March 11, 2008, Washington, DC.
Wiser, R., and G. Barbose. 2008. Renewables Portfolio Standards in the United States: A
Status Report with Data Through 2007. Lawrence Berkeley National Laboratory.
Berkeley, Calif.
Wiser, R., and M. Bolinger. 2008. Annual Report on U.S. Wind Power Installation, Cost
and Performance Trends: 2007. DOE/GO-102008-2590. DOE, Washington, D.C.
127
OCR for page 128
ANNEX
TABLE 4-A-1 Current Cost Assumptions for Renewable Technologies ($2007)
Overnight Variable O&M Levelized Cost of
Capital Cost Capacity (+Fuel Costs ) Fixed O&M Energy
Technology Source Case/Scenario ($ per KW)* ($ per MWh) ($ per KWh)+
Factor ($ per KW)
Biopower
Biopower–IGCC EIA AEO2009 Input table 3,766 83% 6.71 (+$15)^ 64.45 $0.080
#
Biopower–Stoker EPRI TAG 2007 3,520 85% 3.74 (+$35) 91.79 $0.0977&
Biopower–50 MW EPRI TAG 2007 3,629 85% 4.26 (+$35)# 94.49 $0.101&
Fluidized Bed
Biopower S&P 2007 2,596 85% 7.27 (+$28)# 166.13 $0.090
Concentrating Solar Power
Concentrating Solar EERE 2008 Program 3,645 65% 8.10 0.00 $0.071**
Concentrating Solar EIA AEO2009 Reference 5,021 31% 0.00 56.7 $0.200
128
Concentrating Solar EPRI 2007 Summer Study Limited and full portfolio 34% $0.170
Concentrating Solar– EPRI TAG 2007 3,271 34% 0.00 60.2@ $0.130
Trough
Concentrating Solar S&P 2007 4,153 43% 31.20 34.3 $0.170
Concentrating Solar– ASES $0.160-$0.190
Trough
Photovoltaic
Photovoltaic EERE 2008 Program 4,050 21% 0.00 10.4 $0.220**
Photovoltaic–Distributed EPRI 2007 Summer Study Limited and full portfolio $0.260
Photovoltaic Flat Plate EPRI TAG 2007 5,487 25% 0.00 19.5 $0.251&
Photovoltaic 2-Axis EPRI TAG 2007 8,876 32% 0.00 46.6 $0.330&
Photovoltaic–Distributed SEIA2004 Baseline $0.150
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
Photovoltaic–Distributed SEIA2004 Roadmap $0.080
OCR for page 129
Overnight Variable O&M Levelized Cost of
Capital Cost (+Fuel Costs ) Energy
Capacity Fixed O&M
Technology Source Case/Scenario ($ per KW)* Factor ($ per MWh) ($ per KW) ($ per KWh)+
Photovoltaic–Central EIA AEO2009 Input table 6,038 22% 0.00 11.7 $0.320
Wind
Onshore Wind EIA AEO2009 Input table 1,923 36% 0.00 30.3 $0.069+
Onshore Wind EERE 2008 Baseline 1,052 45% 0.00 26.2 $0.033**
Onshore Wind EERE 2008 Program 927 46% 0.00 25.3 $0.029**
Onshore Wind EPRI 2007 Summer Study Limited and full portfolio 32.5% $0.100
Onshore Wind EPRI TAG 2007 Class 6, 100 MW 1,820 42% 0.00 72.7 $0.068&
Onshore Wind S&P 2007 1,765 33% 0.00 26.0 $0.073+
†
Onshore Wind Black & Veatch 20% Wind Study Energy 1,713 35% to 50% 5.70 11.9 $0.064 to $0.047+,†
by 2030
Onshore Wind Midwest ISO MTEP 2008 Reference 1,983 34% 0.00 16.5 $0.071+
129
Offshore Wind EIA AEO2009 Input table 3,851 34% 0.00 89.5 $0.157+
Offshore Wind Black & Veatch 20% Wind Study Energy 2,388 37% to 52% † 15.60 18.7 $0.094 to $0.071†,+
by 2030
Conventional
Pulverized Coal EIA AEO2009 Input table 2,058 85% 4.64 (+$16.7)^ 27.53 $0.050
Conventional Gas EIA AEO2009 Input table 962 87% 2.09 (+$45.1) 12.48 $0.060
Combined Cycle
Conventional EIA AEO2009 Input table 670 30% 3.60 (+$69.3) 12.11 $0.100
Combustion Turbine
&
Calculated from inputs based on 20 year economic life and real cost of capital of 7.5%.
+
Levelized costs here are generic and do not include site specific development costs or cost of facilitating delivery.
#
Fuel cost per MWh imputed from EPRI summer study levelized cost and TAG specifications for CFB biomass plant.
* Fuel cost per MWh reported by source.
^
Fuel cost imputed from AEO2009 Early Release model solution
^
Fuel cost imputed from AEO2009 Early Release model solution. AEO2009 Energy Prices (2007$/mmBtu) in 2012 are $1.91 for coal, $6.63 for natural gas, and $1.96 for biomass.
** EERE numbers are for 2010.
@
This estimate comes from a personal communication with Steve Gehl of EPRI.
†
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
Depending on wind class.
OCR for page 130
TABLE 4-A-2 2020 Cost Projections and Comparisons in $2007
Total Variable Fixed Levelized
Capital O&M/ Fuel O&M Cost of
Overnight Levelized Transmission
Cost Capacity Cost Cost Costs (per Cost Energy
Factor (per MWh) (per MWh) MWh) (per kWh)+
Technology Source Case/ Scenario (per kW)* (per kW) (per MWh)
Conventional Sources
Pulverized Coal AEO2009 Reference 1,985 85% 195 52.30 23.06 3.70 3.61 $0.083
[$0.079]
IGCC AEO2009 Reference 2,233 85% 219 60.64 18.59 5.19 3.61 $0.088
[$0.084]
IGCC with AEO2009 Reference 3,171 85% 311 69.54 23.26 6.19 4.01 $0.103
Sequestration [$0.099]
Combined Cycle AEO2009 Reference 928 87% 91 18.63 59.21 1.64 3.88 $0.083
[$0.079]
Advanced AEO2009 Reference 892 87% 88 17.98 55.46 1.54 3.88 $0.079
Combined Cycle [$0.075]
130
Advanced AEO2009 Reference 1,729 87% 170 34.64 68.84 2.61 3.93 $0.110
Combined Cycle [$0.106]
with Sequestration
Combustion Turbine AEO2009 Reference 647 30% 63 33.55 88.49 4.61 11.41 $0.138
[$0.127]
Advanced AEO2009 Reference 587 30% 58 30.71 75.21 4.01 11.41 $0.121
Combustion [$0.110]
Turbine
Renewables
Biopower
Biopower AEO2009 Reference 3,390 83% 333 61.62 22.81 8.86 4.14 $0.097
[$0.093]
Biopower - EPRI 2007 Full Port. 85% $0.096
Stoker
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
OCR for page 131
Total Variable Fixed Levelized
Capital O&M/ Fuel O&M Cost of
Overnight Levelized Transmission
Cost Capacity Cost Cost Costs (per Cost Energy
Factor (per MWh) (per MWh) MWh) (per kWh)+
Technology Source Case/ Scenario (per kW)* (per kW) (per MWh)
Biopower – EPRI 2007 Limited P. 85% $0.101
Stoker
Biopower ASES Report WGA Biomass 90% ~ $0.080**
Task Force
Geothermal
Geothermal AEO2009 Reference 1,585 90% 156 75.44 0.00 22.22 5.00 $0.103
[$0.098]
Concentrating Solar
Concentrating EERE 2008 Program Case 2,860 72% 4.47 0.00 $0.050
Solar
Concentrating AEO2009 Reference 4,130 31% 405 180.02 0.00 21.30 11.00 $0.212
Solar [$0.201]
131
Concentrating EPRI 2007 Limited P. 34% $0.170
Solar
Concentrating EPRI 2007 Full Port. 34% < $0.083**
Solar
Photovoltaic
Photovoltaic EERE 2008 Program 2,547 21% 250 135.81 0.00 5.59 $0.141
Photovoltaic EPRI 2007 Full Port. $0.220
Photovoltaic EPRI 2007 Limited P. $0.260
Photovoltaic AEO2009 Reference 5,185 22% 509 292.84 0.00 6.21 13.69 $0.313
[$0.299]
Photovoltaic – SEIA2004 Baseline $0.110
Distributed
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
Photovoltaic – SEIA2004 Roadmap $0.050
OCR for page 132
Total Variable Fixed Levelized
Capital O&M/ Fuel O&M Cost of
Overnight Levelized Transmission
Cost Capacity Cost Cost Costs (per Cost Energy
Factor (per MWh) (per MWh) MWh) (per kWh)+
Technology Source Case/ Scenario (per kW)* (per kW) (per MWh)
Distributed
Photovoltaic - ACES Report DOE’s Solar $2.50/Wp $0.075-
Distributed America installed cost $0.010&
Initiative
Wind
Onshore Wind AEO2009 Reference 1,896 35% 186 81.38 0.00 9.95 8.66 $0.100
[$0.091]
Onshore Wind EERE 2008 Baseline 1,076 46% 0.00 27.10 $0.033
Onshore Wind EERE 2008 Program 916 49% 0.00 23.40 $0.027
Onshore Wind EPRI 2007 Full Port. 42% $0.078
Onshore Wind EPRI 2007 Limited P. 33% $0.097
132
Onshore Wind Black and DOE 20% Wind 1,630 38%-52% 160 $0.05-$0.043
Veatch/DOE Study (depending
on wind
class)
Offshore Wind AEO2009 Reference 3,552 33% 348 154.36 0.00 26.72 9.31 $0.191
[$0.181]
Offshore Wind Black and DOE 20% Wind 2,232 38%-52% 219 $0.074-
Veatch/DOE Study (depending $0.053
on wind
class)
NOTES: Reflects the base capital cost from AEO2009 table 39, adjusted for learning. This figure does not reflect taxes and depreciation, which are included in the total capital
cost.
+
[ ] contain AEO estimates of busbar levelized cost of energy, i.e., without transmission related costs
* The overnight cost includes the effects of technological learning but does not include other project costs, which are reflected in the levelized cost estimated.
** Cost estimate is for 2015.
&
Interpolated between reported targets for 2015 and 2030.
SOURCE: Based on data in ASES (2007); Black and Veatch (2007); EIA (2008d); EPRI (2007b); and SEIA (2004).
PREPUBLICATION COPY—SUBJECT TO FURTHER EDITORIAL CORRECTION
