Distributed Generation System 

Characteristics and Costs in the 

Buildings Sector

August 2013

Independent Statistics & Analysis

www.eia.gov

U.S. Department of Energy

Washington, DC 20585
U.S. Energy Information Administration | Distributed Generation System Characteristics and Costs in the Buildings Sector i

This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and 

analytical agency within the U.S. Department of Energy. By law, EIA’s data, analyses, and forecasts are 

independent of approval by any other officer or employee of the United States Government. The views 

in this report therefore should not be construed as representing those of the U.S. Department of Energy 

or other Federal agencies.
June 2013

U.S. Energy Information Administration | Distributed Generation System Characteristics and Costs in the Buildings Sector 1

Distributed Generation System Characteristics and Costs in the 

Buildings Sector

Distributed generation in the residential and commercial buildings sectors refers to the on-site 

generation of energy, often electricity from renewable energy systems such as solar photovoltaics (PV) 

and small wind turbines. Many factors influence the market for distributed generation, including 

government policies at the local, state, and federal level, and project costs, which vary significantly 

depending on time, location, size, and application. 

As relatively new technologies on the globalized production market, PV and small wind are experiencing 

significant cost changes through technological progress and economies of scale. The current and future 

equipment costs of renewable distributed generation are subject to uncertainty. As part of the Annual 

Energy Outlook (AEO), EIA updates its projections to reflect the most current publicly-available historical 

cost data and utilizes multiple third-party estimates of future costs in the near and long terms. 

Performance data is likewise based on currently available technology and expert projections of future 

technologies. 

During the AEO2011 reporting cycle, EIA contracted with an external consultant to develop cost and 

performance characterizations of PV and small wind installations in the building sector.1

 Rather than 

develop two separate paths for residential and commercial, the contract provided cost and performance 

data for systems of various sizes at five-year increments beginning in 2010 and terminating in 2035. 

Two levels of future technology optimism were offered, a base case and an advanced case, with the 

advanced case including lower equipment costs, higher efficiency, or both. 

From this information, EIA used annual weighted-average costs for a typical system size in each sector. 

Abbreviated tables of these system sizes and costs are presented in the residential and commercial

chapters of the AEO Assumptions Report in Tables 4.3 and 5.3, respectively. Additional information in 

the contracted report, such as equipment degradation rates, system life, annual maintenance costs, 

inverter costs, and conversion efficiency, were adapted for input in the Distributed Generation 

Submodules of the buildings sectors modules of the National Energy Modeling System. 

As described in the assumptions reports, other information not included in the report, such as resource 

availability, avoided electricity cost, interconnection limitations, incentive amounts, installed capacitybased cost reductions, and other factors, ultimately affect the capacity of renewable distributed 

generation added within a given sector, year, and Census division.

For editions after AEO2011, certain assumptions (mainly system costs) have been updated based on 

reports from the National Renewable Energy Laboratory and Lawrence Berkeley National Laboratory. 

Table 1 shows the cost and efficiency assumptions for residential and commercial solar photovoltaic and 

small wind systems used in the AEO2010 (published prior to the contract reports), the AEO2011 

(published after the contract reports), and the AEO2013.

 1 Distributed generation systems often cost more per unit of capacity than utility-scale systems. Another, separate analysis 

involves assumptions for electric power generation plant costs for various technologies, including utility-scale photovoltaics and 

both on-shore and off-shore wind turbines used in the Electricity Market Module. http://www.eia.gov/forecasts/capitalcost/
June 2013

U.S. Energy Information Administration | Distributed Generation System Characteristics and Costs in the Buildings Sector 2

The solar photovoltaic report, Photovoltaic (PV) Cost and Performance Characteristics for Residential 

and Commercial Applications, is available in Appendix A while the small wind report, The Cost and 

Performance of Distributed Wind Turbines, 2010-2035, is available in Appendix B. When referencing 

these reports they should be cited as reports by ICF International prepared for the U.S. Energy 

Information Administration.
June 2013

U.S. Energy Information Administration | Distributed Generation System Characteristics and Costs in the Buildings Sector 3

Table 1: Efficiency and Capital Cost Assumptions for Selected Years

Year

Representative 

System Size 

(kW)

Electrical 

Efficiency

Installed 

Capital Cost 

($2009/kW

DC)

Electrical 

Efficiency

Installed 

Capital Cost 

($2009/kW

DC)

Electrical 

Efficiency

Installed 

Capital Cost 

($2009/kW

DC)

2010 3.5 0.18 $9,315 0.15 $7,183 0.15 $7,200 

2015 4 0.2 $8,042 0.175 $5,346 0.175 $4,965 

2020 5 0.22 $6,770 0.192 $4,549 0.192 $3,890 

2025 5 0.22 $5,498 0.197 $4,284 0.197 $3,664 

2030 5 0.25 $4,225 0.2 $4,102 0.2 $3,508 

2035 5 0.25 $4,225 0.2 $4,048 0.2 $3,462 

2010 32 0.18 $6,684 0.15 $6,889 0.15 $6,410 

2015 35 0.2 $5,893 0.175 $5,109 0.175 $4,475 

2020 40 0.22 $5,102 0.192 $4,332 0.192 $3,558 

2025 40 0.22 $4,312 0.197 $4,067 0.197 $3,340 

2030 45 0.25 $3,521 0.2 $3,890 0.2 $3,195 

2035 45 0.25 $3,521 0.2 $3,837 0.2 $3,151 

2010 2 0.13 $7,472 0.13 $7,802 0.13 $7,802 

2015 3 0.13 $7,106 0.13 $6,983 0.13 $6,983 

2020 3 0.13 $6,758 0.13 $6,604 0.13 $6,604 

2025 3 0.13 $6,427 0.13 $6,234 0.13 $6,234 

2030 4 0.13 $6,111 0.13 $6,051 0.13 $6,051 

2035 4 0.13 $6,111 0.13 $5,903 0.13 $5,903 

2010 32 0.13 $4,270 0.13 $5,243 0.13 $5,243 

2015 35 0.13 $4,061 0.13 $4,715 0.13 $4,715 

2020 40 0.13 $3,862 0.13 $4,287 0.13 $4,287 

2025 40 0.13 $3,672 0.13 $3,973 0.13 $3,973 

2030 50 0.13 $3,492 0.13 $3,717 0.13 $3,717 

2035 50 0.13 $3,492 0.13 $3,627 0.13 $3,627 

Note: kWDC = kilowatts of direct current

Solar 

Photovoltaic

Residential

Commercial

Small Wind

Residential

Commercial

AEO2010 AEO2011 AEO2013
June 2013

U.S. Energy Information Administration | Distributed Generation System Characteristics and Costs in the Buildings Sector 

APPENDIX A
 

EIA Task Order No. DE-DT0000804, Subtask 3 

Photovoltaic (PV) Cost and 

Performance 

Characteristics for 

Residential and 

Commercial Applications 

Final Report 

August 2010 

Prepared for: 

Office of Integrated Analysis and Forecasting 

U.S. Energy Information Administration Prepared by: 

ICF International 

Contact: 

Robert Kwartin 

T: (703) 934-3586 

E: rkwartin@icfi.com 
 

ii 

Table of Contents 

Executive Summary ...................................................................................................................... v

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

1.1 Objective....................................................................................................................1

1.2 Approach....................................................................................................................1

1.3 Report Organization...................................................................................................2

2. Technologies.........................................................................................................................3

2.1 PV Cell Technology ...................................................................................................3

2.2 Modules & Arrays.......................................................................................................6

2.3 Tracking Technology..................................................................................................7

2.4 Inverters.....................................................................................................................8

2.5 System Efficiency.......................................................................................................9

3. Markets ...............................................................................................................................11

3.1 U.S. Market Perspective ..........................................................................................11

3.2 Installation and Financing ........................................................................................11

3.3 International Market Volatility...................................................................................12

4. Historical Costs ...................................................................................................................13

4.1 Installed PV System Costs.......................................................................................13

4.2 Component Costs ....................................................................................................18

5. Forecast of PV Characteristics – Reference Case..............................................................20

5.1 Technical Performance............................................................................................20

5.2 Cost..........................................................................................................................27

6. Forecast of PV Characteristics – Advanced Case ..............................................................38

References..................................................................................................................................40

Appendix A. Recommended Characteristics, Crystalline PV, Reference Case.......................46

Appendix B. Recommended Characteristics, Thin-film PV, Reference Case..........................47

Appendix C. GDP Implicit Price Deflator Index........................................................................48
 

iii 

Tables 

Table 1. PV Prototypes............................................................................................................ v

Table 2. PV Prototypes............................................................................................................1

Table 3. Report Organization...................................................................................................2

Table 4. PV Technologies........................................................................................................4

Table 5. Impact of Azimuth and Tilt on Solar Energy ..............................................................8

Table 6. Derate Factors Used in PVWATTS..........................................................................10

Table 7. Relationship of PVWATTS Derate Factors to Efficiency Values..............................10

Table 8. Installed PV in U.S. through 2008............................................................................13

Table 9. Grid Connected PV Coverage in Tracking the Sun II ..............................................13

Table 10. Grid Connected PV Coverage in Tracking the Sun II ..............................................15

Table 11. Rack Mounted Systems Installed in 2008................................................................17

Table 12. Forecast Parameters, Module Efficiency .................................................................21

Table 13. Forecast Parameters, System Efficiency.................................................................23

Table 14. Forecast Parameters, Degradation (% per yr) .........................................................24

Table 15. Forecast Parameters, Module and Inverter Lifetime (yrs)........................................25

Table 16. Starting Point Inverter Costs (2008$/kWDC) .............................................................27

Table 17. Starting Point Costs for Module Plus Other Components (2008$/kWDC).................27

Table 18. Forecast Parameters, O&M Costs, (2008$ / kWDC / yr) ...........................................37

Table 19. Crystalline Costs, Reference and Advanced Cases ................................................39

Table 20. Thin-film Costs, Reference and Advanced Cases ...................................................39
 

iv 

Figures 

Figure 1. Illustration of Grid-connected PV System..................................................................3

Figure 2. Relationship of PV Cells, Modules, and Arrays.........................................................4

Figure 3. Historical Laboratory Cell Efficiencies – Best Research. ..........................................6

Figure 4. Installed Capacity by State......................................................................................14

Figure 5. Number of Sites by State ........................................................................................15

Figure 6. PV Installed Cost Trends.........................................................................................16

Figure 7. PV Installed Cost Trends by System Size...............................................................17

Figure 8. PV Installed Costs for Crystalline and Thin-film Technologies................................18

Figure 9. Component Costs (systems installed in 2008) ........................................................19

Figure 10. Forecast, Module Efficiency, Reference Case ....................................................22

Figure 11. Forecast, System Efficiency, Reference Case ....................................................23

Figure 12. Forecast, Degradation, Reference Case.............................................................24

Figure 13. Forecast, Module and Inverter Life, Reference Case..........................................26

Figure 14. Normalized Cost Trend for PV Modules and Other Components........................28

Figure 15. Cost Projection for 5 kWDC Crystalline System....................................................29

Figure 16. Recommended Crystalline Installed Costs, Reference Case..............................30

Figure 17. Recommended Thin-film Installed Costs, Reference Case.................................31

Figure 18. Residential Installed Capital Costs, Reference Case..........................................32

Figure 19. Historical and Forecast Residential Capital Costs...............................................33

Figure 20. Commercial Installed Capital Costs, Reference Case.........................................34

Figure 21. Historical and Forecast Commercial Capital Costs .............................................35

Figure 22. Recommended O&M Costs, Reference Case.....................................................37

Figure 23. Cost Trends for Reference Case and Advanced Case .......................................38
 

v 

Executive Summary 

Technical performance and cost characteristics were developed for residential and 

commercial photovoltaic (PV) systems for a time horizon extending to 2035. 

Characteristics were developed for six typical PV systems shown in Table 1. As 

indicated, crystalline and thin-film PV technologies were evaluated in three sizes – 5, 

25, and 250 kWDC. The 5 kWDC size is representative of residential applications, and 

the 25 and 250 kWDC sizes are representative of commercial installations. 

Table 1. PV Prototypes 

Application Technology Size (kWDC) 

Residential Crystalline 5 

Thin-film 5 

Crystalline 25 

250 

25 

Commercial 

Thin-film 

250 

Based on a comprehensive literature search, discussions with PV stakeholders, and 

ICF in-house data, the following characteristics were developed: 

 Module Efficiency1

 

 System Efficiency2

 Degradation 

 Life 

 Installed Capital Costs 

 O&M Costs 

Key results and observations from this study include: 

Module Efficiency. Module efficiencies for crystalline technologies operating in the 

field are estimated to range from 14% in 2008 to 20% in 2035. For thin-film 

technologies, module efficiencies are anticipated to range from 10% to 14% over this 

same time span (2008 to 2035). 

System Efficiency. System efficiencies (DC to AC power) for crystalline technologies 

are expected to increase from levels in the range of 78% to 82% in 2008, to levels in the 

range of 86% to 90% in 2035. For thin-film technologies, system efficiencies are 

forecast to increase from a range of 77% to 81% in 2008, to a range of 86% to 90% in 

2035. 

 

1

 In this report, module efficiency refers to the conversion of sunlight to direct current (DC) power. 2

 System efficiency refers to the conversion of DC to AC power. 
 

vi 

Degradation. Forecast degradation rates for crystalline technologies start at 0.60%/yr 

in 2008, and decline to 0.33%/yr in 2035. Forecast degradation rates for thin-film 

technologies are higher, ranging from 1.00%/yr in 2008 and falling to 0.73%/yr in 2035. 

Lifetime. Crystalline PV modules and balance of plant components (except the 

inverter) are forecast to have an expected lifetime of 25 years in 2008. Thin-film 

modules and balance of plant components (except the inverter) are forecast to have a 

lifetime of 20 years in 2008. Both technologies are forecast to have a lifetime of 30 

years by 2035. Inverters, which are assumed to be identical for both crystalline and 

thin-film technologies, are forecast to have lifetime of 10 years in 2008, rising to 15 

years by 2035. 

Residential Installed Capital Costs (expressed in 2008 dollars). For residential 

systems, crystalline technologies are forecast to have lower costs compared to thin-film 

technologies. Forecast costs for installed residential PV systems are approximately 

$7,100/kWDC (crystalline) and $7,300/kWDC (thin-film) in 2010. These costs fall to 

approximately $4,000/kWDC (crystalline) and $4,100/kWDC (thin-film) by 2035. 

Commercial Installed Capital Costs (expressed in 2008 dollars). For commercial 

applications, thin-film technologies are forecast to have lower costs compared to 

crystalline systems (reverse situation compared to residential systems). In 2010, 

forecast costs for installed commercial PV systems are in the range of $5,500/kWDC

(thin-film, 25 kWDC) to $6,800 (crystalline, 25 kWDC). By 2035, forecast costs are 

estimated to decline to an approximate range of $3,200/kWDC (thin-film, 25 kWDC and 

250 kWDC) to $3,800/kWDC (crystalline, 25 kWDC) 

O&M Costs. O&M consists of periodic system inspection and solar panel cleaning. For 

forecasting purposes, it is assumed that both commercial and residential PV system 

owners will properly maintain their systems. Residential homeowners will likely take a 

“do it yourself” approach, while commercial sites will use a maintenance contract. In the 

case of a DIY approach, a cost is still incurred in terms of time required to complete the 

maintenance. O&M is assumed to scale in direct proportion to panel size, which 

decreases as module efficiency increases, and with overall system capacity (decreases 

as capacity increases). Crystalline O&M costs are forecast to decline 30% between 

2008 and 2035, reaching levels in the range of $11.20/ kWDC to $$16.80/kWDC by 2035. 

For thin-film, forecast costs decline 29%, reaching levels in the range of $16.00/kWDC to 

$24.80/kWDC by 2035. 

The recommended characteristics described above correspond to a reference case, or 

business-as-usual, scenario. In addition to a reference case analysis, an advanced 

case was developed based on more aggressive assumptions concerning technology 

advancements and market penetration. The primary difference between the reference 

case and the advanced case is that installed capital costs decline more quickly over 

time in the advanced case as a result of accelerated R&D investments. 
 

1 

1. Introduction 

The Energy Information Administration (EIA) produces a wide range of analyses and 

reports, including forecasts for energy supply and demand, and the diffusion of 

technologies in the marketplace. To develop forecasts, EIA uses the National Energy 

Modeling System (NEMS), which is a robust model that describes energy markets in the 

United States. Each year, EIA produces the Annual Energy Outlook (AEO), which 

includes projections generated with NEMS. The AEO report covers a time horizon of 25 

to 30 years, and includes market penetration estimates for a wide range of 

technologies, including residential and commercial photovoltaic (PV) systems. 

To develop reliable projections using NEMS, it is important to have accurate technical 

performance and cost characteristics describing supply side and demand side 

technologies. Regarding demand side technologies, the residential and commercial PV 

characteristics that EIA has previously used to support NEMS are based on a solar 

roadmap baseline projection prepared in 2004.3

1.1 Objective 

The objective of this project was to develop a recommended set of technical 

performance and cost characteristics for residential and commercial PV technologies for 

the time period extending from 2010 to 2035. 

1.2 Approach 

ICF conducted a comprehensive literature review and talked with solar experts at 

manufacturing organizations, national laboratories, and academic institutions. This 

information was analyzed and used to shape a forecast of PV characteristics through 

2035. Recommended characteristics were developed for six PV system prototypes as 

shown in Table 2. The 5 kWDC size is intended to be representative of residential 

applications, and the 25 and 250 kWDC capacities are consistent with commercial 

installations (25 kWDC at the low end, and 250 kWDC at the high end). 

Table 2. PV Prototypes 

Capacity (kWDC)

4 Technology 

5, 25, 250 Crystalline 

5, 25, 250 Thin-film 

As indicated in Table 2, the prototypes are based on crystalline and thin-film solar cell 

technology. Multi-junction technologies were also evaluated. However, multi-junction 

technologies are not expected to have significant market penetration in residential and 

 

3

 Our Solar Power Future, The U.S. Photovoltaics Industry Roadmap Through 2030 and Beyond, 

September 2004. 

4

 Unless noted otherwise, all PV power ratings (kWDC) in this report are based on direct current (DC) at 

standard test conditions (STC). Standard test conditions are 1,000 W/m2

 of solar irradiance, cell 

temperature of 25 o

C, and air mass (AM) of 1.5. 
 

2 

commercial applications in the foreseeable future, and prototypes were therefore based 

only on crystalline and thin-film systems. 

Using the prototypes shown in Table 2, a set of recommended PV characteristics was 

developed that is consistent with a reference case scenario. The reference case 

scenario is intended to reflect a business-as-usual outcome, assuming that the current 

pace of R&D investments and policy drivers will prevail over the forecast time horizon. 

In addition to the reference case scenario, a set of recommended PV characteristics 

was also developed for an advanced case. The advanced case is based on a scenario 

that includes higher levels of R&D investments that may accelerate the adoption of 

residential and commercial PV. 

1.3 Report Organization 

This report is organized as shown in Table 3. An overview of PV technologies is 

provided in Section 2, followed by a discussion of markets in Section 3. Historical cost 

trends from 1998 through 2008 are covered in Section 4. In Section 5, PV 

characteristics used in the AEO 2010 report are discussed. Results from discussions 

with PV experts and the literature search are presented in Section 6. In Section 7, 

recommended PV characteristics for a reference case are presented, and in Section 8 

characteristics for an advanced case are described. 

Table 3. Report Organization 

Section Title 

1 Introduction 

2 Technologies 

3 Markets 

4 Historical Costs 

5 Forecast of PV Characteristics – Reference Case 

6 Forecast of PV Characteristics – Advanced Case 
 

3 

2. Technologies 

For residential and commercial PV applications, the two main components are a PV 

array (also called solar array) and an inverter. A third component in many PV 

installations is bank of batteries for energy storage. The PV array produces direct 

current (DC) from sunlight, and the inverter converts the direct current to alternating 

(AC) current. The AC power is then used on site or exported to the grid. A simplified 

schematic for a residential PV installation is shown in Figure 1 (no battery backup). 

Source: Homepower magazine 

Figure 1. Illustration of Grid-connected PV System 

Residential and commercial PV systems can be connected to the grid or configured as 

an off-grid system. Off-grid installations are typically only used in remote locations, and 

have little or no impact on the national energy forecast; as a result, this report is focused 

on grid-connected PV only. 

In this section, key components of a PV system are discussed, including the current 

status and development trends. The discussion is organized into the following sections: 

 PV Cells 

 Modules & Arrays 

 Tracking Systems 

 Inverters 

 System Efficiency 

2.1 PV Cell Technology 

The building block for a PV system is a PV cell (or solar cell). Multiple PV cells are 

interconnected and assembled in a support structure, or frame, to form a PV module (or 

solar panel). Multiple modules are then combined to form a PV array (see Figure 2). 

DC Power AC Power 

Sunlight 
 

4 

Source: NASA 

Figure 2. Relationship of PV Cells, Modules, and Arrays 

Photovoltaic (PV) technologies are constructed using semiconductor materials that have 

the ability to convert sunlight into electricity. PV technologies are typically divided into 

three categories – crystalline silicon, thin-film, and multi-junction (see Table 4). 

Table 4. PV Technologies 

Category Semiconductor Material 

Crystalline Silicon --- 

Cadmium Telluride (CdTe) 

Gallium Arsenide (GaAS) 

Copper Indium Gallium Diselenide (CIGS) 

Thin-film 

Amorphous Silicon (a-SI) 

Multi-junction --- 

Of the three categories, crystalline technologies are the oldest, and were 

commercialized by Bell Labs in the 1950s. Crystalline PV cells are manufactured by 

slicing silicon into thin wafers, with state-of-the-art technology near 170 microns (Shah 

2009). There are two types of crystalline cells – monocrystalline and polycrystalline. 

Compared to polycrystalline cells, monocrystalline cells offer higher efficiencies, but are 

more expensive to manufacture. Polycrystalline cells have lower efficiencies, but are 

lest expensive to manufacture. 

Thin-film PV cells are produced by depositing very thin layers of a semiconductor 

material on an inexpensive substrate, such as glass, plastic, or metal. Table 4 shows 

four common types of semiconductor materials that are used in thin-film PV cells. 

Compared to crystalline technologies, thin-film cells are typically less expensive to 

manufacture, but tend to have lower efficiencies. 
 

5 

Multi-junction cells are fabricated using thin-film techniques, but have two or more 

different semiconductor materials. The semiconductor materials in a multi-junction cell 

capture solar energy from different ranges of the solar spectrum, thereby optimizing the 

conversion of solar energy to electricity. Compared to crystalline and thin-film 

technologies, multi-junction cells are significantly more expensive to manufacture. Due 

to the high cost, multi-junction cells do not currently compete in residential and 

commercial markets. 

Crystalline Technology – Trends and Observations 

Crystalline modules have dominated residential and commercial PV markets. 

Crystalline cell efficiencies in the field have improved from approximately 11% to over 

14% over the past five years (Shah 2009, Barnett 2009). , Efficiencies in the lab, which 

are higher than efficiencies in the field, have reached 26% under standard test 

conditions (STC) (Green 2009).

In recent years, the silicon wafer thickness has been reduced from approximately 300 to 

170 microns, and manufacturers have generally increased warranty times from 20 to 25 

years. Crystalline cell research is currently focused on reducing material costs, 

increasing efficiencies, improving the manufacturing processes, and improving reliability 

of modules (DOE 2008). 

Thin-film Technology – Observations and Trends 

Over the past five years, thin-film efficiencies have increased from the range of 5-8% to 

approximately 10% (Barnett 2009). The thin-film market is currently dominated by 

modules using cadmium telluride (CdTe) as a semiconductor (Maycock and Bradford 

2007; Ullal and von Roedern 2007; Venkataraman 2009). In the lab, CdTe modules 

have reached efficiencies greater than 16% (Green 2009). Two emerging thin-film 

technologies are copper indium diselenide (CIS) and copper indium gallium diselenide 

(CIGS). These two cell technologies have shown lab efficiencies of approximately 19% 

(Green 2009). Another thin-film technology is based on the deposition of amorphous 

silicon (a-Si) less than a micron thick (Maycock and Bradford 2007). One advantage of 

a-Si is that these cells can be manufactured in long continuous rolls rather than by 

batch production (Maycock and Bradford 2007). 

Thin-film technologies continue to undergo advancements. CdTe manufacturers are 

working to standardize film growth equipment, achieve higher efficiencies, and prevent 

moisture ingress (Ullal 2007). CIGS manufacturers are developing standardized layer 

deposition equipment and working to achieve higher efficiencies and reduced layer 

thicknesses (Ullal 2007). 

Cell Efficiency – Observations and Trends 

As indicated in Figure 3, solar cell efficiencies have increased at a steady rate over the 

last several decades (DOE 2006). Efficiencies for advanced multi-junction technologies 

have approached 40% in laboratory settings at STC conditions. However, efficiencies 
 

6 

for practical cells, such as crystalline and thin film technologies, are well below these 

levels in the field. 

Source: NREL (2010) 

Figure 3. Historical Laboratory Cell Efficiencies – Best Research. 

2.2 Modules & Arrays 

Optimizing Performance 

Several environmental factors contribute to system output losses, including sub-optimal 

orientation with respect to the sun, soiling, shading, and seasonal snow cover. The 

soiling factor is the percent of output lost by dirt or any other film that obscures the 

module surface, and ranges from slightly under 1% to 4% (Xantrex 2009). The amount 

of soiling depends on factors such as physical location (proximity to dusty roads, etc.), 

type of dust or film, and length of time since the last rainfall. Regular cleaning 

minimizes the impact of soiling (Xantrex 2009). 

PV modules are connected in series, and a mismatch in electrical output between 

modules will decrease the electrical production of the PV array. Electrical mismatch can 

occur due to shading from buildings, trees, or other obstacles that interfere with direct 

sunlight striking the solar array. The magnitude of the mismatch depends on the array 

area affected, length of time, and time of day (Xantrex 2009). In colder climates, 

seasonal snow cover also shades systems and leads to mismatch. All of these factors 

need to be taken into account when estimating conversion losses of a PV system. 
 

7 

The electrical efficiency of a solar cell in the lab under Standard Test Conditions (STC) 

is almost always higher than the field efficiency, in part due to temperature differences. 

For STC measurements, the solar cell is held at 25 o

C. The efficiency of a solar cell 

decreases with increasing temperature, and the field temperature of a solar cell is 

almost always higher than 25 o

C. Roof mounted arrays can reach temperatures of 70-

80o

C (Wiles 2009). For rooftop conditions, the California Energy Commission 

recommends a de-rating factor of 89% from STC lab conditions to expected field power 

(Xantrex 2009). 

Building Integrated PV (BIPV) 

This report is primarily focused on PV panels that are rack mounted. However, an 

interesting development is the growth of building integrated PV (BIPV). BIPV 

technologies are currently more expensive than rack mounted systems, but BIPV 

breakthroughs could push down PV costs in residential and commercial applications 

(Chiras 2009). 

2.3 Tracking Technology 

Maximum PV output occurs when a solar panel is oriented perpendicular to incoming 

sunlight. The optimum orientation changes through the day as the sun moves across 

the sky, and on a seasonal basis as the height of the sun above the horizon changes. 

Tracking systems can be added to PV arrays to optimize electrical output. 

In most residential applications, PV panels are placed on roof tops in fixed frames (also 

called “racks”), and tracking systems are not utilized. When the panels are located 

directly on the roof top, they are referred to as “flat racked” systems. Unless the roof is 

pitched at the local latitude angle, the system’s power output can be increased by tilting 

the racking to be closer to the latitude angle to capture more sunlight (see Table 5). 

This type of tilting is referred to as “latitude racking.” 

Latitude racking is more expensive than flat-racking for both the residential and 

commercial sectors. Compared to flat-racked systems, significantly more hardware, 

assembly, and labor is involved in latitude racked systems. However, there is a 

financial trade off to consider. Even though flat-racking costs less, the modules are 20-

30% less efficient than latitude racked systems (Focusing on Energy 2008). 

In addition to static latitude racking, more sophisticated dynamic tracking systems can 

be used. Dynamic tracking systems can be either single-axis or dual-axis designs. A 

single axis design follows the daily east-west arc of the sun. With a dual axis system, 

hourly tracking (east-west) is achieved as well as seasonal tracking (north-south). 
 

8 

Table 5. Impact of Azimuth and Tilt on Solar Energy 5

2.4 Inverters 

PV arrays produce direct current (DC) from sunlight; and this DC current is converted to 

alternating (AC) current with an inverter. Inverters are not 100% efficient, and energy is 

lost during this conversion process. 

Today, the highest inverter conversion efficiency of DC to AC power is 96-97%, 

compared to approximately 94% in the 2004 time frame (Waiter 2009). In practice, 

typical inverter efficiencies in the field range from 92% to over 94% (Shah 2009). 

Residential inverters are smaller and slightly less efficient than larger scale commercial 

inverters, which leads to larger conversion losses in the residential sector (Shah 2009). 

For a PV system that includes battery energy storage, there are additional energy 

losses that occur as batteries are charged and discharged. The battery efficiency, 

which is often referred to as the “roundtrip” efficiency, depends on several factors, 

including the type of battery (e.g., lead acid or nickel cadmium) and the state of charge 

 

5

 This table is derived from the NREL Surface Orientation Factor charts in: Christensen, Craig B. 

and Greg M. Barker. Effects of Tilt and Azimuth Angle on Annual Incident Solar Radiation for 

United States Locations. Washington, DC: Proceedings of Solar Forum 2001 – Solar Energy: 

The Power to Choose, April 21-25, 2001. The table is presented for a latitude of approximately 

32o

N.
 

9 

(i.e., near full charge or at some lower charge level). Deep discharge lead acid 

batteries are frequently used for PV applications, and these batteries have a roundtrip 

efficiency level typically near 80% (i.e., 80% of the energy used to charge the battery is 

available for discharge). 

A common configuration for residential and commercial PV systems is to use a single 

inverter (see Figure 1) located near the electrical service panel for the building. PV 

systems that use multiple inverters – referred to as microinverters – are entering the 

market. Microinverters convert DC to AC power in a unit attached directly to each PV 

module, instead of through a single stand-alone inverter that serves the entire PV array. 

Microinverters are an emerging technology, and there is limited data available to assess 

actual performance and costs. However, potential advantages of microinverters may 

include: 

 Increased reliability. A separate inverter for each module means there is no 

single point of failure. If one microinverter fails, other modules continue to 

operate. 

 Longer life. Enphase, a manufacturer of microinverters, reports that their 

microinverters are designed for a service life greater than 20 years.6

 

 Improved performance of each module. A separate microinverter on each 

module maximizes performance of that module. 

 Lower installation costs. Simplified installation with no wiring required for a 

central inverter. 

2.5 System Efficiency 

Inverters are just one source of power loss when converting from DC to AC power. An 

example of other factors that contribute to power losses in PV systems is shown in 

Table 6. This table, which is taken from NREL data used in the PVWATTS tool, shows 

that there are 10 factors in addition to the inverter that may contribute to power losses. 

For the default values in the PVWATTS tool, the inverter derate factor is 0.92 and the 

overall derate factor is 0.77. 

In this report, a detailed analysis and forecast of derate factors, or efficiency losses, by 

component, was not conducted. Rather, the analysis and forecast was divided into two 

categories: 

 System efficiency (includes all factors that contribute to DC to AC power with 

the exception of age) 

 Degradation (accounts for power losses that occur due to the age of the 

system) 

 

6

 Enphase web site, 

http://www.enphaseenergy.net/downloads/Enphase_WhitePaper_Reliability_of_Enphase_Microinverters.pdf , accessed March 2010. 
 

10 

A cross map of PVWATTS derate factors and efficiency factors used in this report is 

show in Table 7. 

Table 6. Derate Factors Used in PVWATTS 

Component Derate Factors PVWATTS Default Range 

PV module nameplate DC rating 0.95 0.80 - 1.05 

Inverter and Transformer 0.92 0.88 - 0.98 

Mismatch 0.98 0.97 – 0.995 

Diodes and connections 1.00 0.99 – 0.997 

DC wiring 0.98 0.97 - 0.99 

AC wiring 0.99 0.98 – 0.993 

Soiling 0.95 0.30 – 0.995 

System availability 0.98 0.00 – 0.995 

Shading 1.00 0.00 - 1.00 

Sun-tracking 1.00 0.95 - 1.00 

Age 1.00 0.70 - 1.00 

Overall DC-to-AC derate factor 0.77 --- 

Source: NREL PVWATTS, http://rredc.nrel.gov/solar/calculators/PVWATTS/system.html 

Table 7. Relationship of PVWATTS Derate Factors to Efficiency Values 

Derate Component in PVWATTS Efficiency Component in this Report 

PV module nameplate DC rating 

Inverter and Transformer 

Mismatch 

Diodes and connections 

DC wiring 

AC wiring 

Soiling 

System availability 

Shading 

Sun-tracking 

System Efficiency (changes by year, 

cell material, and capacity) 

Age Degradation Rate (changes by year and 

PV cell material) 
 

11 

3. Markets 

3.1 U.S. Market Perspective 

Federal, state, and utility incentives provide strong drivers that push the adoption of PV 

systems. At the Federal level, there is an investment tax credit (ITC), which provides an 

income tax credit for residential and commercial PV installations. The ITC was revised 

in 2009 as part of the American Recovery and Reinvestment Act (ARRA). ITC 

provisions in ARRA that specifically relate to PV include: 

 30% ITC extended through end of 2016 for both residential and commercial 

solar installations 

 $2,000 cap eliminated for residential PV 

 Utilities allowed to benefit from credit (utilities were previously excluded) 

 Tax payers (both individuals and businesses) that are required to file 

Alternative Minimum Tax (AMT) are allowed to claim credit (previously 

excluded) 

PV market size, maturity, and total installed costs vary widely from state to state. The 

growth of residential and commercial PV markets within a state has been driven almost 

entirely by state-based incentive programs (Venkataraman 2009). The overwhelming 

majority of residential and commercial PV installations have occurred in just two states 

– California and New Jersey (Wiser 2009). Both of these states have well developed 

incentive programs that have stimulated PV adoption. 

In addition to capacity based incentives and performance based incentives, states have 

used a variety of other tools to encourage the installation of PV, including sales and 

property tax exemptions, net metering laws, feed-in tariffs, solar access laws, 

standardized and liberalized interconnection procedures, etc. The incentive mix 

changes continuously; refer to the Database of State Incentives for Renewables and 

Efficiency (DSIRE) for the most recent information (DSIRE 2009). 

3.2 Installation and Financing 

Historically, the installation of PV systems has been performed by companies that 

specialize in PV. However, the drop in demand for new construction and building 

retrofit work, coupled with growing demand for end-use PV, has motivated construction 

companies, roofing contractors, and electrical contractors to enter the PV installation 

business (Shah 2009). With their project management and business experience, these 

companies are streamlining the installation process and increasing competition within 

the industry. 

In addition, new financing methods have begun to emerge that are encouraging the 

adoption of PV systems. The financial factors that influence a consumer’s decision to 

purchase include upfront costs, financial incentives, utility bill savings, and maintenance 

costs. Due to the current weak economic conditions, residential homeowners and 
 

12 

commercial building owners/developers are reluctant to make expensive capital 

investments such as PV (Coughlin 2009). New financing methods, such as the 

commercial solar power purchase agreement (SPPA) and the residential solar lease, 

seek to overcome these financial barriers by significantly reducing or eliminating the 

upfront cost to commercial and residential customers (Coughlin 2009). 

3.3 International Market Volatility 

The U.S. PV industry is influenced by the volatility of the larger international solar 

market. Manufacturers focus their attention, and their sales, on the fastest growing and 

most profitable markets. For example, Spain’s feed-in tariff motivated rapid growth and 

made Spain the largest PV market in the world in 2008. Unprecedented demand in 

Spain put a strain on global supply that kept equipment costs high in the U.S. and 

elsewhere in the world (Tarbell 2009). Growth in Spain has slowed recently, but growth 

in other markets has picked up. For example, Germany installed 3.8 GW of PV in 2009, 

and 1.45 GW in December alone.7

 

7 http://www.pv-tech.org/lib/printable/8828, accessed May 2010.
 

13 

4. Historical Costs 

Historical costs for PV systems are discussed in this section, which is organized as 

follows: 

 Installed PV System Costs 

 Component Costs 

4.1 Installed PV System Costs 

Technological developments across the PV supply chain, from commodities to 

efficiencies, have pushed total installed costs downward. An increase in silicon 

manufacturing has increased supply and lowered the price of silicon in crystalline PV 

modules (Hasan 2009). Improved manufacturing processes have increased the 

production output of facilities, while decreasing the costs of production (GT Solar 2009). 

In recent years, streamlined manufacturing has led to decreased manufacturing costs.

Machine manufacturers have begun to offer turn-key production lines which are 

complete manufacturing system packages. Turn-key solutions are sold for every stage 

of the supply chain, from wafer fabrication to module fabrication (GT Solar 2009). These 

automated turn-key production lines have helped increase productivity, quality, and 

yields, while lowering manufacturing costs. Automated systems have also made it 

easier for new firms to enter the manufacturing arena, thereby increasing competition 

and putting downward pressure on prices. 

A recent report titled “Tracking the Sun II” by Lawrence Berkeley National Laboratory 

summarizes the installed cost of PV systems in the United State from 1998 through 

2008. Costs in this report cover approximately 52,000 residential and non-residential 

systems, with a total capacity of 566 MW (71% of grid connected capacity in the United 

States at the end of 2008). PV installed capacity and coverage as reported in Tracking 

the Sun II are shown in Table 8 and Table 9, respectively. 

Table 8. Installed PV in U.S. through 2008 

Type of Installation Capacity 

(MW) (%) 

Grid Connected 798 88% 

Off Grid 109 12% 

Total 906 100% 

Table 9. Grid Connected PV Coverage in Tracking the Sun II 

Capacity 

(MW) (% of all grid 

connected) 

(% of all PV) 

Covered in TS II 566 71% 62% 

Not Covered in TS II 231 29% 26% 

Total Grid Connected 798 100% 88% 
 

14 

PV system data reported in Tracking the Sun II was collected from 16 states. A 

comparison of the PV installed capacity across the 16 states is shown in Figure 4, and 

a comparison of the number of PV installations by state is shown in Figure 5. As 

indicated, California has the highest representation in the sample, followed by New 

Jersey. 

AZ

1% MA

1%

CT

2%

NY

1% Other 10 States

2%

NJ

11%

CA

82%

Figure 4. Installed Capacity by State 
 

15 

NY

AZ 2%

MA 2%

2%

CT

1%

NJ

6%

Other 10 States

5%

CA

82%

Figure 5. Number of Sites by State 

Average annual costs for all PV systems in the LBNL sample are shown in Table 10 

and graphed in Figure 6. These data are based on all system types in the data sample 

(e.g., rack-mounted, building integrated, tracking, non-tracking, crystalline, thin-film, 

etc.). As indicated in the table, the simple average of PV installed costs has declined 

from $12,260/kWDC in 1998 to $8,243 in 2008 (a total decrease of 33%, or 3.9% per 

year). 

Table 10. Grid Connected PV Coverage in Tracking the Sun II 

Year Number of Installed Cost (2008$ / kWDC) 

Systems 

Capacity (MW)

Capacity Weighted Simple Average 

1998 39 0.2 10,849 12,260 

1999 180 0.8 10,600 11,611 

2000 217 0.9 9,485 10,900 

2001 1,308 5.4 9,768 10,492 

2002 2,489 15.0 9,754 10,455 

2003 3,526 34.0 8,370 9,308 

2004 5,527 44.0 8,287 8,566 

2005 5,193 57.0 7,770 8,264 

2006 8,677 90.0 7,838 8,385 

2007 12,103 122.0 7,837 8,474 

2008 13,097 197.0 7,480 8,243 

52,356 566.3 
 

16 

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Year

Installed Cost (2008$ / kWDC)

Capacity Weighted

Simple Average

Figure 6. PV Installed Cost Trends 

Figure 7 shows a breakout of historical PV costs by size range. Systems less than 100 

kWDC showed a steady decline from 1998 through about 2005, and then remained 

generally flat from 2005 through 2007, followed by a decline in installed costs for 2008. 

Compared to systems under 100 kWDC, there are far fewer systems with capacities 

above 100 kWDC, and the data are somewhat more scattered for these larger systems. 

However, based on Figure 7, it is clear that there are economies of scale, with larger 

systems consistently showing lower costs. 
 

17 

$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Installation Year

Average Installed Cost (2008$ / kWDC)

<5 kW

5-10 kW

10-100 kW

100-500 kW

>500 kW

Figure 7. PV Installed Cost Trends by System Size 

Table 11 shows average costs for rack mounted PV technologies in 2008 with a 

breakdown for crystalline and thin-film technologies in three size categories. In 2008, 

similar to other years, the majority of PV installations have used rack mounted 

crystalline technology. In 2008, over 10,500 rack mounted crystalline systems were 

installed, representing 80% of the total PV installations tracked by LBNL in 2008 

(13,097 total systems in 2008). 

Table 11. Rack Mounted Systems Installed in 2008 

Technology 

Crystalline Thin-film 

Size 

Number of 

Systems 

Capacity 

(MW) 

Cost 

(2008$/kWDC) 

Number of 

Systems 

Capacity 

(MW) 

Cost 

(2008$/kWDC) 

< 10 kWDC 9,179 43 8,200 22 0.1 8,500 

10-100 kWDC 1,098 24 7,900 16 0.7 6,400 

>100 kWDC 242 86 7,200 6 2.4 6,700 

10,519 153 44 3.2 

Compared to the population of crystalline systems, LBNL identified far fewer rack 

mounted thin-film installations. As indicated in Table 11, there are only 44 total thin film 

systems identified in all three size categories, with a combined capacity of 3.2 MW. 
 

18 

While the cost numbers for the thin-film systems seem reasonable (range from 

$6,400/kWDC to $8,500/kWDC), these results should be viewed with caution given the 

small sample size. The cost numbers for thin-film technologies could change 

significantly as the sample size grows and becomes more statistically relevant. 

Figure 8 compares the average costs for crystalline and thin-film technologies by the 

three size categories. For systems <10 kWDC, crystalline technologies show slightly 

lower costs -- $8,200/kWDC for crystalline compared to $8,500 for thin-film. However, 

for systems >10 kWDC, thin-film technologies have lower costs. In the 10-100 kWDC size 

range, the cost differential is $1,500/kWDC ($7,900/kWDC compared to $6,400/kWDC), 

and in the >100 kWDC size range the differential is $500/kWDC ($7,200/kWDC compared 

to $6,700/kWDC). 

8,200

7,900

7,200

8,500

6,400

6,700

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

< 10 kW 10-100 kW >100 kW

Size 

Cost (2008$/kWDC)

Crystalline

Thin Film

Figure 8. PV Installed Costs for Crystalline and Thin-film Technologies 

4.2 Component Costs 

Component level cost data are scarce. However, in Tracking the Sun II component 

costs are reported for a single year (2008) as shown in Figure 9. The costs in this 

figure are average costs for crystalline and thin-film technologies combined. The costs 
 

19 

are separated into three different size ranges – 1) under 10 kWDC, 2) 10-100 kWDC, and 

3) >100 kWDC. 

$4,600 $4,500

$3,900

$2,900

$2,600

$2,800

$500

$600

$700

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

$8,000

$9,000

<10 kW 10-100 kW >100 kW

Size

Average Cost (2008$ / kWDC)

Other

Inverter

Module

(56%)

(9%)

(35%)

(58%)

(8%)

(34%)

(54%)

(7%)

(39%)

Figure 9. Component Costs (systems installed in 2008) 

As indicated in Figure 9, module costs account for the largest fraction of PV installed 

costs, ranging from 54% to 58% of the average total installed cost. Module costs are 

$600 to $700/kWDC lower for systems over 100 kWDC compared to the two small size 

bins, which suggests that there are may be bulk purchasing discounts that help reduce 

module costs for large systems. 

Based on 2008 system data in the LBNL Tracking the Sun II report, inverters account 

for 7% to 9% of the total installed cost. As system sizes increase, inverter costs show 

declining costs (decline from $700/kWDC in smallest size bin to $500/kWDC in largest 

size bin). A report prepared by Navigant for NREL (NREL 2006) offers additional 

insights into inverter costs. In this report, which is based on data from 2006, inverters 

are estimated to account for 10-20% of the initial PV system installed cost (higher than 

the 7-9% reported by LBNL in 2008) 

In Figure 9, the “other” category includes costs associated with design, engineering, 

installation labor, and regulatory compliance. These other expenses account for a third 

or more of total installed costs (range from 34% to 39% depending on PV size). 
 

20 

5. Forecast of PV Characteristics – Reference Case 

While there is ample research and analysis on the size and scope of the PV market, 

there are few detailed forecasts regarding PV costs and technical performance in the 

public domain. To develop a PV forecast, ICF collected information from several 

sources, including interviews with industry PV stakeholders, publicly available literature, 

and in-house ICF data. The data were grouped into three capacities (5, 25, and 250 

kWDC) and two technology types (crystalline and thin-film), resulting in six unique PV 

technology categories. 

No rigid formula was used to develop a composite industry forecast of PV technical 

performance and cost characteristics. Rather, all data were examined, and data that 

appeared to lie well outside norms were excluded. The remaining data were further 

examined and ICF forecasts were developed. 

The characteristics described in this section correspond to a reference case scenario 

consistent with the assumptions used for the reference case described in the AEO 2010 

report. The discussion of recommended reference case characteristics is organized as 

follows: 

 Technical Performance 

o Module Efficiency 

o System Efficiency 

o Degradation 

o Lifetime 

 Cost 

o Component Costs (including inverter) 

o Installed Capital 

o O&M 

For reference, tables with selected results for the reference case are shown in 

Appendix A (crystalline technologies) and Appendix B (thin-film technologies). In 

these tables, and elsewhere in this report, costs are reported in 2008 dollars unless 

noted otherwise. Conversions between dollar years, if necessary, have been calculated 

using a gross domestic product (GDP) index shown in Appendix C. 

5.1 Technical Performance 

5.1.1 Module Efficiency 

Module efficiencies are primarily dependent on the type of solar cell, and no significant 

efficiency differences are expected for different capacities. However, different efficiency 

curves are expected for crystalline and thin-film technologies. 
 

21 

To develop a forecast, ICF estimated values for module efficiency when installed in the 

year 2008, and then looked at potential upper limits. A range of module efficiencies 

were examined from manufacturers, industry experts, and research reports. Based on 

this review, ICF selected an average crystalline module efficiency in 2008 of 14%, and 

an average thin-film module efficiency of 10%. The analysis also suggested that a 

reasonable upper limit for crystalline modules is 20%, and a reasonable upper limit for 

thin-film is 14%. 

Note that these module efficiencies are based on field performance, and not laboratory 

measurements. Laboratory measurements conducted at standard test conditions 

almost always exceed average field performance values. 

Linear improvement rates were then developed to connect the starting values and end 

points. The improvement rates were adjusted by “eye” to achieve a smooth transition 

over time. The module efficiency forecast parameters are shown in Table 12, and the 

resulting values are shown in Figure 10. 

Table 12. Forecast Parameters, Module Efficiency 

PV Cell Technology 

Crystalline Thin-film 

Starting Value (2008) 14% (0.140) 10% (0.100) 

Annual Change +0.005 thru 2018 +0.002 thru 2028 

 +0.001 thru 2028 --- 

 no change after 2028 no change after 2028 

Value in 2035 20% 14% 
 

22 

0%

5%

10%

15%

20%

25%

2005 2010 2015 2020 2025 2030 2035

Year

Module Efficiency (%)

Crystalline

Thin-film

 

Figure 10. Forecast, Module Efficiency, Reference Case 

5.1.2 System Efficiency 

The overall efficiency of a PV system is determined by several factors, including inverter 

losses, resistance of wires and connectors, soiling, and module mismatch. While these 

factors affect all types of PV systems, there are also differences between PV system 

types. Residential inverters are smaller and therefore less efficient than commercial 

inverters, leading to generally lower system efficiencies in residential PV technologies 

(all other factors being equal). 

Similar to module efficiencies, linear improvement rates were developed to connect 

starting values and end points. The improvement rates were adjusted to achieve a 

smooth transition over time. The system efficiency forecast parameters are shown in 

Table 13, and the resulting system efficiency curves are shown in Figure 11. 
 

23 

Table 13. Forecast Parameters, System Efficiency 

PV Cell Technology 

Crystalline Thin-film 

5 kW 25 kW 250 kW 5 kW 25 kW 250 kW 

Starting 

Value (2008) 

78% 80% 82% 77% 79% 81% 

+0.01 thru 2012 +0.01 thru 2012 

+0.005 thru 2020 +0.005 thru 2022 

Annual 

Change 

no change after 2020 no change after 2022 

Value in 

2035 

86% 88% 90% 86% 88% 90% 

As indicated, crystalline system efficiencies are expected to increase from levels in the 

range of 78% to 82% in 2008, to levels in the range of 86% to 90% in 2035. For thin-film 

technologies, the efficiencies increase from the range of 77% to 81% in 2008, to 86%to 

90% by 2035 (same end point for thin film as crystalline). 

75%

80%

85%

90%

95%

100%

0 5 10 15 20 25 30

Year

System Efficiency (%)

Crystalline, 5 kW

Crystalline, 25 kW

Crystalline, 250 kW

Thin-film, 5 kW

Thin-film, 25 kW

Thin-film, 250 kW

Figure 11. Forecast, System Efficiency, Reference Case 
 

24 

5.1.3 Degradation 

PV modules typically lose capacity over time as a result of UV effects on construction 

materials and other aging factors. The rate at which modules lose capacity is 

debatable. However, based on sources consulted for this report ICF selected the 

starting values and annual change rates shown in Table 14. Based on these 

parameters, the resulting degradation curves are shown in Figure 12. 

Table 14. Forecast Parameters, Degradation (% per yr) 

PV Cell Technology 

Crystalline Thin-film 

Starting Value (2008) 0.60% (0.0060) 1.00% (0.1000) 

Annual Change -0.0001 thru 2035 

Value in 2035 0.33% 0.73% 

0.0%

0.1%

0.2%

0.3%

0.4%

0.5%

0.6%

0.7%

0.8%

0.9%

1.0%

2005 2010 2015 2020 2025 2030 2035 2040

Year

Degradation (% / yr)

Crysalline

Thin-film

Figure 12. Forecast, Degradation, Reference Case 

In the ICF forecast, the degradation rate for crystalline technologies declines by about 

45% between 2008 and 2035 (0.60% to 0.33%), and by about 27% for thin film 

technologies (1.00% to 0.73%). Over the forecast horizon, thin-film degradation rates 

are held higher than crystalline technologies. 
 

25 

Thin-film technologies have a higher surface area than crystalline systems for 

equivalent rated capacity, and a higher surface area could contribute to higher 

degradation rates. However, in general, there are no fundamental reasons that thin-film 

systems should have higher degradation rates than crystalline systems. However, 

compared to crystalline technologies, there are fewer thin-film technologies currently 

being used in residential and commercial applications. The higher degradation rate for 

thin-film technologies is a conservative value based on a smaller data set with 

potentially unknown or not-well characterized degradation factors. 

5.1.4 Lifetime 

Thin-film technologies are relatively new, and there is little field experience data 

available to support lifetime projections. However, for forecasting purposes, ICF 

assumed that thin-film systems would follow similar lifetime trends as more mature 

crystalline technologies, but lag behind in terms of the time required to achieve these 

lifetime estimates. For crystalline technologies, ICF developed the forecasting 

parameters shown in Table 15. This table also shows the forecasting parameters 

developed for thin-film technologies and inverters. 

Table 15. Forecast Parameters, Module and Inverter Lifetime (yrs) 

PV Cell Technology 

Crystalline Thin-film 

Inverter 

Starting Value (2008) 25 yrs 20 yrs 10 yrs 

Annual Change + 0.5 yrs thru 2018 + 0.5 yrs thru 2018 + 0.5 yrs thru 2018 

 + 0.5 yrs thru 2018 + 0.5 yrs thru 2018 + 0.5 yrs thru 2018

 no change after 2018 no change after 

2028 

no change after 

2018 

Value in 2035 30 yrs 30 yrs 15 yrs 

Lifetime forecasts are shown in Figure 13. As indicated, the lifetime of thin-film 

modules is forecast to lag crystalline modules through 2028. From 2028 onward, the 

lifetime for both technologies is assumed to be 30 years. For forecasting purposes, 

ICF is estimating that average inverter lifetimes will start at 10 years in 2008, and 

increase to 15 years by 2018. 
 

26 

0

5

10

15

20

25

30

35

2005 2010 2015 2020 2025 2030 2035 2040

Year

Lifetime (yrs)

Crystalline

Thin-film

Inverter

Figure 13. Forecast, Module and Inverter Life, Reference Case 
 

27 

5.2 Cost 

Long term cost projections for residential and commercial PV installations are scarce in 

the literature. However, industry stakeholders did provide opinions on long term cost 

trends. These opinions were combined with ICF in-house data to develop cost 

projections, which are provided in the following subsections: 

 Component costs (including inverters) 

 Installed capital costs 

 O&M costs 

5.2.1 Component Costs 

For forecasting purposes, PV components were divided into three categories 

 Module 

 Inverter 

 Other (installation labor, regulatory compliance, and overhead) 

ICF set the starting point costs for inverters to be consistent with data reported in the 

LBNL Tracking the Sun II report (see Figure 9). These inverter starting point costs are 

shown in Table 16. 

Table 16. Starting Point Inverter Costs (2008$/kWDC) 

Cost by System Size ($/kWDC) – same for crystalline and thin-film 

5 kWDC 25 kWDC 250 kWDC

$700 $600 $500 

Starting point costs for modules and other components (less the inverter) were 

combined and calculated by subtracting inverter costs from the total costs reported by 

LBNL for crystalline and thin-film technologies. These costs are shown in Table 17. 

Table 17. Starting Point Costs for Module Plus Other Components 

(2008$/kWDC) 

` Cost by System Size ($/kWDC) 

5 kWDC 25 kWDC 250 kWDC

Crystalline $7,500 $7,300 $6,700 

Thin-film $7,800 $5,800 $6,200 

Cost trends over time were then developed for modules and other components based 

on input from PV stakeholders and ICF in-house data. Module and other costs (less the 

inverter) were assumed to follow the same cost trend, which is shown in Figure 14. 
 

28 

0%

20%

40%

60%

80%

100%

2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034

Year

Cost Relative to 2008

Figure 14. Normalized Cost Trend for PV Modules and Other Components 

(does not apply to inverter) 

Inverter costs presented somewhat of a dilemma. The technical performance of 

inverters is evolving (e.g., development of microinverters), but there is mixed 

information on whether inverter costs are declining or remaining steady. Some PV 

stakeholders suggested that inverter costs are remaining steady, although price 

declines have occurred in recent months.8

 ICF weighed the limited information 

available for inverter costs, and chose to forecast inverter costs as remaining 

unchanged in future years (same costs for crystalline and thin-film technologies). It is 

expected that inverter performance features will continue to advance, but for forecasting 

purposes ICF assumed that manufacturers will hold inverter prices relatively constant as 

inverter performance improves (i.e., inverter value will increase, but prices will remain 

steady). 

An example of how the forecast costs for modules, inverters, and other components 

changes over time is shown in Figure 15. This figure corresponds to a 5 kWDC

crystalline PV system. As indicated, costs start at $8,200/kWDC ($700 inverter plus 

$7,500 for module and other components) in 2008, with inverters accounting for 9% of 

the installed cost. Inverters remain flat over the forecast horizon, while module and 

 

8

 Solarbuzz provides an index of monthly inverter and PV module costs 

(http://www.solarbuzz.com/Inverterprices.htm ). 
 

29 

other components decline. By 2035, the total installed cost is forecast to decline to 

$4,000/kWDC with inverters accounting for 18% of the total installed cost. Similar 

behavior is forecast for thin film technologies and larger sizes (25 kWDC and 250 kWDC). 

Inverter

Module & Other 

Components

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034

Year

Cost (2008$/kW)

Figure 15. Cost Projection for 5 kWDC Crystalline System 

5.2.2 Installed Capital Costs 

Using the methodology described in the previous section, installed capital cost forecasts 

were developed for crystalline and thin-film technologies. Reference case cost 

projections for three sizes (5, 25, and 250 kWDC) of crystalline technologies are shown 

in Figure 16, and cost projections for the same three sizes of thin-film technologies are 

shown in Figure 17. Unless noted otherwise, all costs are reported in 2008 dollars. 

As indicated, installed capital costs for all three crystalline sizes start in the range of 

$7,000 to slightly greater than $8,000/kWDC in 2008, and then decline to a range 

between $3,500 and $4,000/kWDC in 2035. For thin film technologies, the 5 kWDC size 

starts at approximately $8,500/kWDC in 2008 and declines to slightly above 

$4,000/kWDC in 2035. The larger 25 and 250 kWDC sizes start near $6,500/kWDC in 

2008, and decline to slightly above $3,000/kWDC in 2035. 
 

30 

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

2005 2010 2015 2020 2025 2030 2035

Year

Cost (2008$/kW)

5 kW

25 kW

250 kW

Figure 16. Recommended Crystalline Installed Costs, Reference Case 

One unexpected result shown in Figure 17 is that costs for a 250 kWDC thin-film system 

are forecast to be slightly higher than a 25 kWDC system. Based on economy of scale 

considerations, one would expect costs for a 250 kWDC system to be lower than a 25 

kWDC system. However, these forecasts are consistent with historical costs, which do 

show an up turn in costs for large thin film PV systems. Historical PV costs are 

discussed in Section 4.1, and this discussion includes an important note concerning 

thin-film costs. As mentioned in Section 4.1, historical thin-film costs should be viewed 

with caution because these costs are based on a small sample size. It would not be 

surprising if future thin-film costs follow economy of scale considerations (i.e., costs 

decline as capacities increase). 
 

31 

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

2005 2010 2015 2020 2025 2030 2035

Year

Cost (2008$/kW)

5 kW

25 kW

250 kW

Figure 17. Recommended Thin-film Installed Costs, Reference Case 

Figure 18 offers a perspective of how the forecast PV costs correspond to residential 

applications. In this figure, 5 kWDC crystalline and 5 kWDC thin-film costs are shown, 

which are representative of the residential market. As indicated, costs start in the range 

of $8,000 to $8,500/kWDC in 2008, and then decline to approximately $4,000/kWDC by 

2035. More specifically, costs are approximately $7,100 (crystalline) and $7,300 (thinfilm) in 2010. These costs fall to approximately $4,000/kWDC (crystalline) and 

$4,100/kWDC (thin-film) by 2035. 
 

32 

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

2000 2005 2010 2015 2020 2025 2030 2035

Year

Cost (2008$/kW)

Crystalline 5 kW

Thin-film 5 kW

Figure 18. Residential Installed Capital Costs, Reference Case 

Figure 19 includes both historical residential PV installed costs along with 

recommended residential costs. Historical costs have fluctuated, but the trend over the 

next 5-10 years in recommended PV costs is generally consistent with historical cost 

trends over the past decade. As the market matures, PV costs begin to stabilize and 

decline at lower rates. 
 

33 

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

11,000

2000 2005 2010 2015 2020 2025 2030 2035

Year

Cost (2008$/kW)

Historical, < 10 kW

Crystalline, 5 kW

Thin-film, 5 kW

Figure 19. Historical and Forecast Residential Capital Costs 

Recommended commercial capital costs are shown in Figure 20. This figure includes 

recommended forecast values for four technologies (crystalline 25 kWDC, crystalline 250 

kWDC, thin-film 25 kWDC, and thin-film 250 kWDC). As indicated, installed costs for 

commercial PV installations range from approximately $6,500 to $8,000/kWDC in 2008, 

and then decline to a range between $3,000 and $4,000/kWDC in 2035. More 

specifically, commercial costs in 2010 are approximately $5,500/kWDC (thin-film, 

25kWDC), $5,800/kWDC (thin-film, 250 kWDC), $6,200/kWDC (crystalline, 250 kWDC), and 

$6,800 (crystalline, 25 kWDC). In 2035, the costs are approximately $3,200/kWDC (thinfilm, 25 kWDC and 250 kWDC), $3,500/kWDC (crystalline, 250 kWDC), and $3,800/kWDC

(crystalline, 25 kWDC) 

For perspective, historical and forecast trends are shown in Figure 21. Similar to the 

residential results, historical cost trends have fluctuated, but the recommended cost 

trends over the next 5-10 years are generally consistent with historical trends. Similar 

to residential prices, the commercial prices begin to stabilize as the market matures. 
 

34 

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

2000 2005 2010 2015 2020 2025 2030 2035

Year

Cost (2008$/kW)

Crystalline, 25 kW

Crystallline, 250 kW

Thin-film, 25 kW

Thin-film, 250 kW

Figure 20. Commercial Installed Capital Costs, Reference Case 
 

35 

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

2000 2005 2010 2015 2020 2025 2030 2035

Year

Cost (2008$/kW)

Crystalline, 25 kW

Crystalline 250 kW

Thin-film, 25 kW

Thin-film, 250 kW

Historical, >10 kW

Figure 21. Historical and Forecast Commercial Capital Costs 

The forecast of installed capital costs presented in this report was developed based on 

opinions from PV stakeholders and other data sources concerning how costs may 

change over the next couple decades. The forecast was not developed in conjunction 

with a detailed demand forecast. While demand was not formerly considered, it is 

interesting to consider how the installed capital costs projected in this report might be 

correlated with a demand forecast. 

Concerning the relationship of demand and PV costs, a recent EPRI report (EPRI 2009) 

presented data showing the global average sales price of PV modules as a function of 

cumulative sales between 1976 and 2008. During this time period, the market size 

grew by approximately a factor of 100,000 and prices fell by more than 90%. Based on 

the historical data, the EPRI report authors concluded that prices have been declining 

by about 20% in recent years for each doubling of market size (i.e., learning rate of 

20%). The authors also concluded that the PV market has been growing by about 20% 

per year in recent years. 

The ICF forecast of installed capital costs turns out to be more conservative compared 

to the results reported by EPRI. In rough terms, the ICF forecast shows a reduction of 

installed capital costs of approximately 50% between 2008 and 2035. This installed 

cost behavior is consistent with a learning rate of 12%, and an annual growth rate of 
 

36 

15%. The EPRI estimates suggest that learning rates and annual growth rates may 

both be closer to 20% over the next two to three decades. 

5.2.3 O&M 

For the purposes of this report, operation and maintenance (O&M) costs are assumed 

to include regular inspection and cleaning. Major maintenance requirements, such as 

replacing an inverter, are not included. 

For PV systems, routine O&M consists primarily of washing the solar panels to ensure 

that electricity production is maximized. In both residential and commercial applications, 

it is possible that systems will not be properly maintained, including periodic washing of 

PV panels, in which case degradation rates will likely exceed the values reported 

previously in this report. However, for forecasting purposes, it is assumed that both 

residential and commercial PV installations will be properly maintained. 

In the case of a commercial PV installation, it is reasonable to assume that a 

maintenance contract will be used to cover O&M. Maintenance contracts for basic 

service of commercial systems have been reported by SunEdison and others to be in 

the range of $15/kW to $25/kW (costs generally declining as system size increases). 

For a residential PV installation, it is likely that a homeowner will take a “do it yourself” 

(DIY) approach for system inspection and cleaning. Even though a cash expense is not 

incurred for a DIY approach, the homeowner does incur an expense in terms of time 

required to complete PV system O&M. In some residential applications, it is possible 

that homeowners will choose to pay for routine PV inspection and cleaning, rather than 

undertaking these chores. In the forecast presented in this report, an O&M cost is 

assigned to residential PV installations to reflect the value of time for a DIY approach, or 

the cost of an O&M contract. 

The O&M forecast was developed by starting with a $20/kW O&M cost for a 25 kW 

crystalline system. Costs were scaled by +/- 20% based on size (5 kW more expensive, 

250 kW less expensive). It was further assumed that O&M will scale with surface area, 

since O&M is primarily associated with panel cleaning. O&M costs were therefore 

scaled using the module efficiencies discussed previously. A summary of the forecast 

parameters is shown in Table 18, and a graph of the O&M costs over time is shown in 

Figure 22. 
 

37 

Table 18. Forecast Parameters, O&M Costs, (2008$ / kWDC / yr) 

PV Cell Technology 

Crystalline Thin-film 

5 kWDC 25 kWDC 250 kWDC 5 kWDC 25 kWDC 250 kWDC

Starting $24.00 $20.00 $16.00 $33.60 $28.00 $22.40 

Value (2008) (20% more 

than 25 kW) 

(20% less 

than 25 kW) 

 

Annual 

Change 

Adjust based on module efficiency (see Table 12 and Figure 10) 

Value in 

2035 

$16.80 $14.00 $11.20 $24.00 $20.00 $16.00 

As indicated, the forecast is for crystalline O&M costs to decline 30% between 2008 and 

2035, reaching levels in the range of $11.20/ kWDC to $$16.80/kWDC (costs decline as 

size increases). For thin-film, costs decline 29%, reaching levels in the range of 

$16.00/kWDC to $24.80/kWDC by 2035. 

0

5

10

15

20

25

30

35

2005 2010 2015 2020 2025 2030 2035 2040

Year

O&M (2008$ / kWDC / yr)

Crystalline, 5 kW

Crystalline, 25 kW

Crystalline, 250 kW

Thin-film, 5 kW

Thin-film, 25 kW

Thin-film, 250 kW

Figure 22. Recommended O&M Costs, Reference Case 
 

38 

6. Forecast of PV Characteristics – Advanced Case 

In this section, PV characteristics for an advanced scenario are described. The 

rationale for the advanced case is that additional R&D investments will drive a high 

degree of technology innovation and accelerated cost improvements. In an advanced 

scenario, both technical characteristics and cost would be expected to improve 

compared with the reference case. However, as an initial step, the advanced case 

discussion in this section is focused on accelerated reductions in capital costs. 

As a first step, a cost trend curve was developed for an advanced case. This cost trend 

is shown in Figure 23. 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

2005 2010 2015 2020 2025 2030 2035

Year

Cost Trends (2008 = 1)

Forecast, Reference Case

Forecast, Advanced Case

Figure 23. Cost Trends for Reference Case and Advanced Case 

Using the advanced case cost trend, the expected costs for crystalline and thin film 

technologies were computed. The crystalline costs are shown in Table 19, and the thinfilm costs are shown in Table 20. As discussed in greater detail in Section 5.2.2, thinfilm costs appear to show a dis-economy of scale between 25 and 250 kW, which may 

be an artifact of a small sample size. 
 

39 

Table 19. Crystalline Costs, Reference and Advanced Cases 

Cost (2008$ / kWDC) 

5 kWDC 25 kWDC 250 kWDC

Year 

Ref Case Adv 

Case 

Change Ref Case Adv 

Case 

Change Ref Case Adv 

Case 

Change 

2010 $7,075 $7,075 0.0% $6,805 $6,805 0.0% $6,195 $6,195 0.0% 

2015 $5,275 $4,773 9.5% $5,053 $4,573 9.5% $4,587 $4,151 9.5% 

2020 $4,495 $3,820 15.0% $4,294 $3,649 15.0% $3,890 $3,306 15.0% 

2025 $4,233 $3,415 19.3% $4,038 $3,258 19.3% $3,656 $2,949 19.3% 

2030 $4,054 $3,095 23.6% $3,864 $2,951 23.6% $3,496 $2,669 23.6% 

2035 $4,000 $2,909 27.3% $3,812 $2,772 27.3% $3,448 $2,508 27.3% 

Table 20. Thin-film Costs, Reference and Advanced Cases 

Cost (2008$ / kWDC) 

5 kWDC 25 kWDC 250 kWDC

Year 

Ref Case Adv 

Case 

Change Ref Case Adv 

Case 

Change Ref Case Adv 

Case 

Change

2010 $7,330 $7,330 0.0% $5,530 $5,530 0.0% $5,770 $5,770 0.0% 

2015 $5,458 $4,939 9.5% $4,138 $3,745 9.5% $4,282 $3,875 9.5% 

2020 $4,647 $3,949 15.0% $3,535 $3,004 15.0% $3,637 $3,091 15.0% 

2025 $4,374 $3,529 19.3% $3,332 $2,688 19.3% $3,420 $2,759 19.3% 

2030 $4,188 $3,198 23.6% $3,193 $2,438 23.6% $3,272 $2,499 23.6% 

2035 $4,132 $3,005 27.3% $3,152 $2,292 27.3% $3,228 $2,348 27.3% 

In the advanced scenario, installed capital costs begin diverging from the reference 

case in 2011, and fall approximately 27% below reference case values by 2035. In the 

advanced scenario, costs for all components, including the inverter, are reduced at the 

same rate relative to the reference case. 

 
 

40 

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46 

Appendix A. Recommended Characteristics, Crystalline 

Reference Case 

5 kWDC

Year Module 

Efficiency (%) 

System Efficiency 

Degradation System 

Life 

Inverter Life 

Installed Capital Costs (2008$/kWDC) 

Inverter Cost (2008$/kWDC)

O(2y

2010 15.0% 80% 0.58% 26 11 $7,075 $700 2015 17.5% 84% 0.53% 29 14 $5,275 $700 2020 19.2% 86% 0.48% 30 15 $4,495 $700 2025 19.7% 86% 0.43% 30 15 $4,233 $700 2030 20.0% 86% 0.38% 30 15 $4,054 $700 2035 20.0% 86% 0.33% 30 15 $4,000 $700 

25 kWDC

Year Module 

Efficiency (%) 

System Efficiency 

Degradation System 

Life 

Inverter Life 

Installed Capital Costs (2008$/kWDC) 

Inverter Cost (2008$/kWDC)

O(2y

2010 15.0% 82% 0.58% 26 11 $6,805 $600 2015 17.5% 86% 0.53% 29 14 $5,053 $600 2020 19.2% 88% 0.48% 30 15 $4,294 $600 2025 19.7% 88% 0.43% 30 15 $4,038 $600 2030 20.0% 88% 0.38% 30 15 $3,864 $600 2035 20.0% 88% 0.33% 30 15 $3,812 $600 

250 kWDC

Year Module 

Efficiency (%) 

System Efficiency 

Degradation System 

Life 

Inverter Life 

Installed Capital Costs (2008$/kWDC) 

Inverter Cost (2008$/kWDC)

O(2y

2010 15.0% 84% 0.58% 26 11 $6,195 $500 2015 17.5% 88% 0.53% 29 14 $4,587 $500 2020 19.2% 90% 0.48% 30 15 $3,890 $500 2025 19.7% 90% 0.43% 30 15 $3,656 $500 2030 20.0% 90% 0.38% 30 15 $3,496 $500 2035 20.0% 90% 0.33% 30 15 $3,448 $500 
47 

Appendix B. Recommended Characteristics, Thin-film 

Reference Case 

5 kWDC

Year Module 

Efficiency (%) 

System Efficiency 

Degradation System 

Life 

Inverter Life 

Installed Capital Costs (2008$/kWDC) 

Inverter Cost (2008$/kWDC)

O(2y

2010 10.4% 79% 0.98% 26 11 $7,330 $700 2015 11.4% 83% 0.93% 29 14 $5,458 $700 2020 12.4% 85% 0.88% 30 15 $4,647 $700 2025 13.4% 86% 0.83% 30 15 $4,374 $700 2030 14.0% 86% 0.78% 30 15 $4,188 $700 2035 14.0% 86% 0.73% 30 15 $4,132 $700 

25 kWDC

Year Module 

Efficiency (%) 

System Efficiency 

Degradation System 

Life 

Inverter Life 

Installed Capital Costs (2008$/kWDC) 

Inverter Cost (2008$/kWDC)

O(2y

2010 10.4% 81% 0.98% 26 11 $5,530 $600 2015 11.4% 85% 0.93% 29 14 $4,138 $600 2020 12.4% 87% 0.88% 30 15 $3,535 $600 2025 13.4% 88% 0.83% 30 15 $3,332 $600 2030 14.0% 88% 0.78% 30 15 $3,193 $600 2035 14.0% 88% 0.73% 30 15 $3,152 $600 

250 kWDC

Year Module 

Efficiency (%) 

System Efficiency 

Degradation System 

Life 

Inverter Life 

Installed Capital Costs (2008$/kWDC) 

Inverter Cost (2008$/kWDC)

O(2y

2010 10.4% 83% 0.98% 26 11 $5,770 $500 2015 11.4% 87% 0.93% 29 14 $4,282 $500 2020 12.4% 89% 0.88% 30 15 $3,637 $500 2025 13.4% 90% 0.83% 30 15 $3,420 $500 2030 14.0% 90% 0.78% 30 15 $3,272 $500 2035 14.0% 90% 0.73% 30 15 $3,228 $500 
 

48 

Appendix C. GDP Implicit Price Deflator Index 

(Year 2005 = 1) 

Year GDP Index 

1990 0.72201 

1991 0.74760 

1992 0.76533 

1993 0.78224 

1994 0.79872 

1995 0.81536 

1996 0.83088 

1997 0.84555 

1998 0.85511 

1999 0.86768 

2000 0.88647 

2001 0.90650 

2002 0.92118 

2003 0.94100 

2004 0.96770 

2005 1.00000 

2006 1.03257 

2007 1.06214 

2008 1.08483 

2009 1.09777 

2010 1.11100 

Source: 1990 through 2009 data from U.S. Department of Commerce, Bureau of Economic 

Analysis. The 2010 value is from EIA’s January 2010 Short-term Energy Outlook. 

The GDP Implicit Price Deflator index is used in this report to convert costs in constant 

dollars between different basis years. For example, to convert costs that are expressed 

in 2005 dollars to 2008 dollars, multiply the 2005 values by 1.08483 (1.08483 / 

1.00000). 
Headquarters

ICF International 

9300 Lee Highway 

Fairfax, Virginia 22031 

www.icfi.com 
June 2013

U.S. Energy Information Administration | Distributed Generation System Characteristics and Costs in the Buildings Sector 

APPENDIX B 
 

EIA Task Order No. DE-DT0000804, Subtask 3 

The Cost and Performance 

of Distributed Wind 

Turbines, 2010-35 

Final Report 

 

June 2010 

Prepared for: 

Office of Integrated Analysis & Forecasting 

U.S. Energy Information Administration Prepared by: 

ICF International 

Contact: 

Robert Kwartin 

T: (703) 934-3586 

E: RKwartin@icfi.com 
 ii 

Table of Contents 

Executive Summary ..................................................................................................................... iv

Introduction ................................................................................................................................... v

1. Technology Overview............................................................................................................1

2. Market Overview ...................................................................................................................2

3. Potential Market Size ............................................................................................................4

3.1 Technical Factors.......................................................................................................4

3.2 Economic Factors - Benefits......................................................................................6

3.3 Economic Factors - Costs..........................................................................................7

3.4 Market Potential - Conclusions ..................................................................................7

4. Performance Objectives........................................................................................................8

5. Interpreting Performance Data for Distributed Wind Technology........................................10

6. Data Issues – Implications for NEMS..................................................................................12

7. Technology Baseline...........................................................................................................13

8. Sources for Improvement....................................................................................................14

8.1 Technological...........................................................................................................14

8.2 Cost..........................................................................................................................15

9. Projection Methodology.......................................................................................................17

Endnotes.....................................................................................................................................19

Bibliography ................................................................................................................................21
 

iii

Tables 

Table 1: The U.S. Distributed Wind Market ..................................................................................3

Table 2: Market Projections of Grid-Connected Domestically Installed Wind Turbines................8

Table 3: Percentage Difference between Manufacturer Literature and NREL Test Data ...........12

Table 4: Dimensional Data for Selected Distributed Wind Turbines ...........................................13

Table 5: Cost and Performance Data for Selected Distributed Wind Turbines, 2009.................14

Table 6: Improvement in the GE 1.5 MW Turbine ......................................................................14

Table 7: Annual Operation and Maintenance Expenses.............................................................16

Table 8: Assumptions for the Base and Advanced Cases..........................................................18

Figures 

Figure 1: Main Components of a Wind Turbine ............................................................................1

Figure 2: Growth of the Small Wind Market in the U.S. ................................................................4

Figure 3: National Wind Resource Map ........................................................................................5

Cover photos courtesy of the American Wind Energy Association. 
 

iv

Executive Summary 

This report and its accompanying data tables provide cost and performance projections 

for distributed wind turbines in the 1-100 kW nominal size range over the 2010-35 

period. These factors were developed by compiling manufacturer-provided cost and 

performance of popular present-day turbines in this size range; adjusting these 2009 

data to reflect independent test results and to conform to new data definitions that will 

apply in 2010 and beyond; and then developing cost and performance trajectories for 

the forecast period. These trajectories are based on interviews with market participants, 

particularly manufacturers, distributed wind project developers, and researchers. 

Projections were developed for both a reference case and an advanced case. The 

advanced case is distinguished from the reference case by an assumption of much 

higher private sector research and development investment, resulting in more rapid and 

more substantial improvements in cost and performance over the projection period. 

Specific parameters include: 

Performance: Turbine productivity measured in kWh generated per year is projected 

to increase by 28% in the base case and 36% in the advanced case over the forecast 

horizon compared with present-day turbine productivity. 

Cost: Distributed wind installed costs, in constant dollars, are projected to fall by 20% 

in the base case and 24% in the advanced case through 2035. 

Economic Viability: The combination of improving performance and falling cost is 

projected to yield a 37% and 44% reduction in installed cost per annual kWh produced 

in the base case and the advanced case respectively over the 2010-35 period. 

Operation and Maintenance (O&M) Expenses: O&M expenses are projected to fall 

by 10% in the base case and by 12% in the advanced case by 2035 compared with 

current-day costs. 

Availability: Turbine availability was assumed to be 98% under both scenarios. 

Equipment Life: Turbines were assumed to have a 25-year lifetime under both 

scenarios. 
 

v

Introduction 

The purpose of this paper is to provide cost and performance projections for residential 

and commercial scale distributed wind turbines over the period 2010-35. These 

projections will be used as inputs for the U.S. Energy Information Administration (EIA) 

National Energy Modeling System (NEMS), the principal modeling platform used by EIA 

to develop its Annual Energy Outlook. The paper will provide an overview of existing 

technology, briefly explain its applicability in the market, and discuss potential changes 

in the cost and performance of the technology over the projection period. 

This paper is not intended to be an exhaustive review of distributed wind technology. 

Rather, it is intended to provide a conceptual framework for analyzing the projected 

evolution of distributed wind technologies, and to provide a credible basis for projecting 

cost and performance characteristics. 
 

1 

1. Technology Overview 

This section will discuss wind technology generally, and explain why residential and 

commercial customers adopt wind turbines at the lower end of the available size range. 

The discussion will continue with a description of the factors that influence turbine 

energy productivity and the components of system cost. 

 

Figure 1: Main Components of a Wind Turbine1

Modern wind turbines capture the kinetic energy of moving air and convert it into shaft 

power to drive an electrical generator/alternator. The turbine is typically comprised of 

three basic parts: the rotor, the nacelle and the tower. 

The rotor includes the turbine’s blades (most often 3 in horizontal wind axis turbines) 

and the nose cone/hub. The nacelle contains the driveshaft, transmissiona, the unit’s 

generator/alternator, the electronic controls to convert the generator’s or alternator’s 

electrical output to quality suitable for use, and the tail vane or yaw drive that keeps the 

turbine oriented to the wind, either upwind or downwind depending on the turbine’s 

design. 

Because wind speed generally increases and turbulence decreases with height, a tower 

helps the system increase its energy production and reduces turbulence-induced 

mechanical stresses, thus enhancing its economic benefit. 

The ability of a turbine to produce energy from the wind fundamentally depends on the 

wind resource and the swept area of the turbine. Simplifying somewhat, the power 

output of a turbine is proportional to the cube of the wind velocity and the square of the 

blade length. A doubling of the wind speed thus yields an eight-fold increase in wind 

power while a doubling of a turbine’s blade length yields a four-fold increase in energy 

capture (all other things kept constant). 

Larger turbines with longer blades not only produce more energy for a given wind 

resource, they are also more capital cost-effective as well, as a result of inherent 

 

a

 Many small turbines are direct drive. Larger turbines more often use gearboxes to step up the rotor’s 

rotational speed to a rotational speed suitable for the generator/alternator. 
 

2 

economies of scale as well as inefficiencies in the market for smaller turbines. As 

shown in Table 5, the installed cost/kW for a small turbine is twice that for a mid-scale 

turbine and can be several times as expensive as that for a utility-scale turbine. 

While these factors argue for choosing the windiest sites and installing the largest 

turbines on the highest towers that are cost-effective for the site – an argument 

understood by wind farm developers – residential and commercial site hosts cannot 

follow this logic to its conclusion. It is a rare home or business owner that is going to 

move their establishment simply to take advantage of a windier site. And several 

practical constraints prevent home and business owners from using the giant turbines 

typically found in utility-scale wind farms. Neighbors might object to the presence of a 

turbine hundreds of feet tall because of safety, noise and visual concerns. The turbine’s 

capital costs are an additional consideration: even if a site host has the space for a giant 

turbine, the multi-million dollar capital cost can be difficult to finance for someone not in 

the wind industry. As a result of these constraints, most distributed wind turbine 

installations are sized roughly equivalent to the site host’s electrical load and use 

turbines much smaller than those found in current-day wind farms. This analysis 

therefore assumes that residential customers will install turbines with nominal capacity 

ratings of 1-9 kW, while commercial customers will install turbines with nominal capacity 

ratings of 10-100 kW. 

2. Market Overview 

Distributed wind technology is used to power homes, small businesses, farms and 

ranches, schools and colleges, county and state facilities, and many other site hosts. 

Buyers are motivated by a variety of factors, typically a blend of the following: 

 a distributed wind turbine may simply be a good investment; 

 buyers may be seeking to moderate the volatility in the prices they pay for 

electricity; 

 buyers may want to reduce their environmental impact by generating 

electricity without fossil fuel combustion; 

 some buyers may be motivated by economic development concerns: a 

distributed wind turbine creates employment during installation and for 

ongoing operation, and onsite electricity generation can keep funds in the 

community; and 

 particularly for public sector and educational institutions, there is a 

corollary goal of demonstrating a new technology and educating citizens 

and students about renewable energy-generation possibilities. 

Whatever the balance between these motivations, more turbines will be purchased as 

project economics improve; few buyers can afford to ignore cost and economic return 
 

3 

entirely, no matter how strongly they might otherwise be motivated.b Project economics 

are discussed in greater detail below. 

The small wind market grew rapidly in the U.S. in 2008. The American Wind Energy 

Association’s survey2 indicates that over 10,000 small (100 kW and smaller) turbines 

were sold in the U.S. in 2008 with an aggregate nameplate capacity of 17.3 MW. This 

represented an increase of 14% in unit sales and 78% in nameplate capacity sales 

compared with 2007. The distribution of sales by size is shown in Table 1 and the 

growth in sales is shown in Figure 2 below: 

 Table 1: The U.S. Distributed Wind Marketc

0-0.9 kW 1-10 kW 11-20 kW 21-100 kW Total 

Units Sold 6,706 3,521 72 87 10,386 

Capacity (kW) 2,784 7,599 1,331 5,660 17,374 

 

b

 The author is not aware of public-domain literature providing a rigorous analysis of buyer motivation for 

installing distributed wind turbines. (Informal pre-purchase surveys have been conducted by Home 

Power magazine, for example, and other informal surveys have assessed barriers to purchase.) 

Anecdotally, it is clear that many buyers are motivated by non-economic factors, but the extent of this 

motivation and its relative importance in different buying segments is not clear. Such a study would 

increase the realism of market penetration studies by public and private analysts. c

 The majority of turbines with nameplate capacity of 1 kW and below are purchased for off-grid 

applications, such as powering remote loads, off-grid cabins, boats, etc. AWEA estimates that 7,402 

turbines with a nameplate capacity of 3,764 kW were sold for off-grid uses in 2008. These applications 

are not represented in NEMS and are not further treated in this paper. 
 

4 

Figure 2: Growth of the Small Wind Market in the U.S.3

3. Potential Market Size 

The potential market for distributed wind is constrained by technical and economic 

factors. 

3.1 Technical Factors 

A proposed distributed wind project can be impaired by a number of different technical 

considerations. The most important is the availability of wind. A site with poor wind 

resources cannot support an economically viable wind project. Figure 2 below provides 

a coarse-scale representation of the country’s wind resources at a 50-meter hub height; 

the map displays Wind Power Classes, which are based on wind power density (Watts 

of wind power per square meter of rotor cross section). 

Although it is not an ironclad rule, in general, if a site has a wind resource below Wind 

Power Class 3, it is unlikely to be economically successful. Almost 70% of the land 

surface in the Lower 48 states is in Wind Power Classes 1 and 2.4 At the lower hub 

heights used for the small to mid-scale turbines evaluated in this paper, the percentage 

of low-wind surface area is even higher. 
 

5 

 

Figure 3: National Wind Resource Map

Land availability and usability is the second most important technical factor. The land 

parcel on which the turbine will be sited needs to be of sufficient size to satisfy any local 

zoning codes related to set-back as well as safety, noise and visual considerations. In 

addition, the parcel must have sufficient room so that the turbine will not be in close 

proximity to trees, structures or other features that can slow the wind or create 

turbulence. Smaller residential turbines mounted on towers of appropriate height 

typically require a parcel of half an acre or a full acre5

. Northern Power Systems 

recommends 500 feet of clearance around one of its 100 kW turbines, equivalent to an 

18 acre parcel.6 Thus, it is difficult to implement distributed wind turbines in cities and 

heavily developed suburbsd

. 

Several other technical issues constrain the implementation of distributed wind: steep 

terrain; high elevations; zoning restrictions on tower heights; availability of 3 phase 

power (generally for machines > 30 kW), concerns about bird and bat kills; etc. 

 

d

 Vertical axis wind turbines (VAWT) have some potential to fit into smaller land parcels, as they can be 

installed on lower towers or even mounted on buildings, and thus require less set-back. Doing so, 

however, reduces the available wind resource and increases mechanical stress arising from turbulence. 

VAWTs have generally suffered from lower energy productivity compared with horizontal axis turbines 

and have struggled to demonstrate their commercial viability. 
 

6 

3.2 Economic Factors - Benefits 

The financial analysis of a wind project includes the following factors: 

 Revenue from electricity generation. In most cases, the majority of the 

“revenue” from a distributed wind turbine’s electricity generation is actually the 

displacement of electric power deliveries by the electric utility. This displacement 

of electricity sales at relatively high retail rates is usually the largest single 

revenue source for a distributed wind turbine.e

 Any excess electricity above the 

site’s consumption can be sold the local electric utility, but the value of these 

sales varies dramatically. At a minimum, utilities are obliged to pay at least some 

proxy for wholesale electricity prices for electricity purchased from a 

customer/generator. In many states, excess generation of electricity above the 

site’s consumption can be sold back to the distribution utility at the full retail rate 

and excess generation from one month may be carried over to net against 

electricity purchases in subsequent months, often for up to a year. 7 

 Benefits from policy support. A variety of public policies provide additional 

benefits to distributed wind project owners: 

o Federal tax benefits. At the Federal level, tax benefits are available to 

distributed wind turbine owners. The most important is the recentlyenhanced Investment Tax Credit (ITC). This credit is valued at 30% of the 

project’s installed cost, without any upper limit on the credit amount, and is 

available through December 31, 2016. Under Section 1603 of the 

American Recovery and Reinvestment Act, this tax credit can also be 

converted into an outright cash grant from the Treasury, which is 

particularly favorable for taxable entities that do not anticipate sufficient 

taxable income to take full advantage of the ITC and for entities that prefer 

the certainty of a cash grant in the near term to a tax credit taken during 

one or more subsequent tax years. This conversion option is available 

only if significant project efforts (5% of project costs) are made by 

December 31, 2010. Alternatively, a project may take advantage of the 

Production Tax Credit (PTC), worth approximately 2 cents per kWh when 

output is sold to an unrelated third party over a 10-year period. (For the 

majority of distributed wind projects, the ITC is more valuable than the 

PTC.) In addition, wind turbines are eligible for accelerated depreciation 

under the Modified Accelerated Cost Recovery System.8

o Direct spending benefits. Federal and State agencies provide 

incentives for distributed wind projects through a variety of programs. For 

example, the U.S. Department of Agriculture’s Rural Energy for America 

 

e

 The valuation of displaced electricity requires some analysis. For residential customers with simple kWh 

meters, displaced electricity will be worth the full retail rate (less any fixed customer charge). For 

commercial/industrial customers whose tariffs include both capacity (kW) and energy (kWh) based 

charges, consideration needs to be given to the uncertainty as to whether the wind turbine will reliably 

reduce the kW-based charges, for example, by comparing the facility’s load profile against the likely 

power production profile of the turbine. Unlike photovoltaic technology in hot climates, distributed wind 

generation cannot be assumed to be peak-coincident. In reality, a distributed wind turbine may not 

reliably avoid the peak capacity charge at all, in which case its displacement value is limited to the energy 

component of the tariff. 
 

7 

Program offers grants for feasibility studies and renewable energy 

installations. Many states offer direct grants for distributed wind projects 

or production-based incentives. 

o Renewable Energy Certificates (RECs). RECs can be understood to 

represent the positive environmental and fuel diversity attributes arising 

from the generation of each MWh of renewable electricity. RECs can be 

marketed separately from the electric commodity and are purchased by 

entities subject to state Renewable Portfolio Standards to satisfy their 

obligations under those programs (“mandatory RECs”). In addition, many 

electricity customers purchase RECs voluntarily to “green up” their 

electricity supply (“voluntary RECs”). A distributed wind project owner can 

choose to retain the RECs created by their project (to keep their own 

electricity supply “green”), or sell the RECs created by their project, or 

some combination of the two. REC sales can be a significant additional 

revenue stream for a distributed wind project. 

o Other policy support. A variety of other policy tools are used to enhance 

the financial viability of distributed wind projects: government-sponsored 

low-interest loans, sales tax abatements, property tax abatements, state 

income tax credits and deductions, preferential feed-in/buy-back tariffs, 

etc. These policy tools evolve rapidly; refer to the Database of State 

Incentives for Renewables and Efficiency (DSIRE) for additional details. 

3.3 Economic Factors - Costs 

The most important cost component for a distributed wind project (60-80%) is the cost 

of the hardware: rotor, nacelle and tower. Transportation and installation costs 

(including labor, equipment rental, concrete, wiring, metering, interconnecting with the 

distribution utility, etc.) can be considerable (10-35%), particularly with taller towers, 

remoter sites and more difficult terrain. Pre-construction costs – feasibility analyses, 

project design, permitting, zoning, etc. -- may be modest for a rural residential project 

confronting limited zoning and permitting challenges, but run to tens of thousands of 

dollars for a commercial-scale project with more complex design and engineering 

requirements, and can account for 5-15% of initial project costs. In addition, turbine 

owners need to plan for annual operation and maintenance (O&M) costs, warranty 

expenses, as well as costs related to insurance, incremental property taxes (if any), and 

eventual decommissioning of the turbine when it reaches the end of its useful life. 

Example capital costs for present-day distributed wind turbines are shown in Table 5; 

O&M costs are show in Table 7. 

3.4 Market Potential - Conclusions 

A recent analysis evaluated the technical and economic potential of existing mid-scale 

distributed wind projects in the 10-5000 kW size rangef. The analysis found that 

 

f

 The study evaluated individual turbines up to 1000 kW as well as small community wind projects 

consisting of five 1000 kW turbines. 
 

8 

commercial, industrial and institutional buyers motivated purely by economics would 

purchase over 2700 10 kW turbines, about 10,000 50 kW turbines, approximately 200 

100 kW turbines, and about 3500 250 kW turbines if 2008 incentive levels were 

assumed to remain unchanged for 10 years. 9 

This study had some important limitations. It did not evaluate residential buyers in the 

1-10 kW range. It also did not attempt to quantify market penetration driven by noneconomic factors, such as the desire to reduce greenhouse gas emissions, even though 

anecdotal information suggests that non-economic factors are important drivers of 

distributed wind projects. In addition, the study was completed prior to the 

implementation of the uncapped 30% ITC, which can be expected to dramatically 

increase the number of viable projects. 

This study also evaluated the impact of improving today’s mid-scale distributed wind 

turbines. Longer blades, taller towers, greater productivity (particularly at low wind 

speeds), and lower costs combined to increase the potential market for 250 and 500 kW 

turbines by a factor of 25 or more.10 

A second study provides market potential estimates based on technology application: 

Table 2: Market Projections of Grid-Connected Domestically Installed Wind Turbines11

Cumulative Units 

Installed 

Residential (1-25 kW) Farm, Business, 

Industrial (10-400 kW) 

2005 1,800 20 

2010 6,250 1,270 

2015 14,000 4,270 

2020 36,500 7,395 

4. Performance Objectives 

Ideally, a wind turbine would extract the maximum amount of kinetic energy from the 

wind at the lowest possible cost. However, wind turbines, like all other goods, represent 

a set of compromises to satisfy multiple goals. These include: 

1. Energy productivity: Turbines vary in their ability to extract energy from the 

wind, and all other things being equal, a turbine that produces more energy from 

a given wind resource is more valuable than a turbine that produces less. This 

can be achieved by using longer blades, more efficient blade design, more 

efficient transmission (or direct drive), a more efficient generator/alternator, better 

yaw control, etc. The turbine’s behavior over a dynamic range is also a critical 

factor: turbines can be designed to start spinning at lower speeds, to produce 

more energy at the most frequent wind speeds, or to continue producing at 

higher wind speeds, but it is difficult to design a turbine that can do all three. 
 

9 

Because wind is variable at a given site and even more variable across many 

sites, no one turbine model is optimal for every site. 

2. Project Cost: Turbines are expensive, and manufacturers seek out ways to 

reduce costs. This can include using less-expensive materials, improving 

manufacturing techniques, sourcing less-expensive components, and improving 

the efficiency of distribution and installation. Volume is an important determinant: 

many turbine models in the 1-250 kW range are produced in limited quantities, 

which drives up unit costs. Larger production runs give the manufacturer more 

leverage in negotiating with upstream suppliers, and permits more investment in 

production tooling. 

3. Overspeed Control: Once the turbine is producing at its maximum output, any 

further increase in wind speed is essentially “wasted” from a power generation 

perspective and at very high speed can cause structural damage to the turbine. 

Turbines use a variety of methods to regulate turbine loading and rotational 

speed: by furling the rotor towards the tail vane (thereby reducing the rotor crosssection presented to the wind), deploying various types of blade-mounted brakes, 

changing the blade pitch, stalling, and/or by electrical braking. 

4. Tower Height and Cost: In most terrains, wind speed increases with height, at 

least for tens of meters. Tall towers improve cost-effectiveness in most areas. 

Some turbines are available on tilt-up towers, while others can only be mounted 

on fixed towers. For fixed towers, erection costs increase as tower height 

increases. Taller towers also make maintenance visits more hazardous and 

time-consuming, driving up O&M costs. 

5. Reliability/Durability: Utility-scale wind farms can afford to have on-site 

technicians to provide regular maintenance and repair services. A single 

distributed wind turbine at a home or business cannot be expected to receive the 

same level of attention, and therefore should be designed and built to minimize 

maintenance requirements. (Some level of maintenance will always be 

required.) Although wind turbines have few moving parts, they are subjected to 

significant stresses and vibration from winds that vary in speed and direction. 

The rotor and drivetrain is expected to spin hundreds of times per minute with 

blade tip speeds of over 100 mph, and this performance is expected to last for 

decades. To reduce maintenance costs, production degradation and downtime, 

the turbine should be built with well-engineered components fabricated from 

long-lasting materials. However, this drives up costs. Lower rotor speeds can 

increase longevity, but at the cost of reduced energy production. 

6. Noise: Several design choices that increase energy productivity (longer blades, 

higher rotational speed, and certain overspeed controls, such as furling) increase 

the sound pressure produced by the turbine. This tradeoff can be particularly 

objectionable for distributed wind turbines as they are ordinarily sited close to 

homes and businesses. 

NEMS “builds” distributed generation (DG) in future years based on how cost-effective 

the technologies are. NEMS incorporates a 30-year discounted cash flow model that 

assesses the internal rate of return (IRR) of various DG technologies; NEMS then uses 

the IRR and a learning function model to forecast the technology’s penetration in the 
 

10 

marketplace. Other thing being equal, if a DG technology’s IRR increases in a future 

year, it will penetrate the market further than if its IRR remains unchanged or falls.12 

Of the key performance objectives listed above, only the turbine’s cost and performance 

figure into the IRR calculation. Overspeed control, reliability/durability and noise are not 

considered. In the real world, however, these parameters are part of the turbine design 

process, and manufacturers make necessary compromises to ensure that turbines 

perform safely and reliably over the long run. The cost and performance projections 

presented later in this paper were developed under the assumption that manufacturers 

would continue to balance multiple objectives in the future as they have done in the 

past. 

It is also important to note that NEMS only considers decisions made on economic 

grounds; the model does not endogenously account for turbines installed for reasons 

other than economics. 

5. Interpreting Performance Data for Distributed Wind 

Technology 

Distributed wind technology performance data needs to be interpreted with care. The 

challenge arises partly from a lack of industry standardization, partly from the highly 

site-specific performance of wind turbines, and partly because some manufacturers 

provide inaccurate or incomplete data to their buyers. 

Industry standardization. Until very recently, the distributed wind industry lacked 

standardized terminology, test methods or product certification processes. Rated 

capacity, for example, is a less meaningful metric than it may seem. Manufacturers 

choose the wind speed at which they rate their turbines’ capacity, and as noted in Table 

4, the values can vary considerably. This makes it difficult to make an “apples to 

apples” comparison of two turbines even of the same nominal rated capacity; one may 

be rated based on an 11 m/s wind, for example, while another is rated at 14 m/s. 

Lack of industry standardization should begin to recede as an issue in the near future. 

The American Wind Energy Association (AWEA) published a performance and safety 

standard for small wind turbinesg

 in late 2009 that established specific test methods for 

various performance parameters, including rated capacity, annual energy production, 

and noise.13 For example, the standard established that a turbine’s capacity should be 

rated at a wind speed of 11 m/s. 

In addition, 2010 will see the Small Wind Certification Council commence operations. 

The SWCC is an independent organization that will certify turbine testing conducted in 

accordance with the AWEA small wind turbine standard, provide an SWCC label to 

certified turbines, and provide test results on its web site.14 The combination of 

standardized test methods and independent certification of testing results will make a 

material contribution to improving the usefulness of turbine performance data. 

 

g

 The standard applies to turbines with swept areas of 200 square meters, or a rotor diameter of about 

16m. This translates to a nominal capacity of about 65 kW. 
 

11 

Site-specific data. Although a turbine’s rated capacity is often the first point of 

reference, in fact, the most important metric for a wind project is the turbine’s estimated 

annual energy production (AEP) for the project site. This metric drives the project’s 

potential revenue much more directly than rated capacity does. To estimate AEP, it is 

necessary to know the following information: 

 The project’s hub height; 

 The wind resource distribution: how many hours per year the wind blows 

within specific speed ranges (bins) at the project’s hub height; 

 Turbulence at the project’s hub height due to nearby trees, buildings and 

other obstructions; 

 The turbine’s production curve: how many kWh the turbine produces for 

each specific wind speed bin. 

 The turbine’s expected availability and losses (e.g., line losses) 

For large, utility scale projects, it is typical to measure the wind resource at hub height 

at multiple points across the development area for at least a year-long period, and then 

to use sophisticated software to estimate the effects of terrain and of the turbine array 

itself on AEP for each turbine. Distributed wind projects in the 1-100 kW range rarely 

utilize such a data-intensive approach, usually only at the upper end of the range. 

Instead, homeowners, turbine dealers and project developers rely on a combination of 

coarse-scale wind maps (themselves derived from extensive modeling) and 

manufacturer-supplied production curve data. 

It is not uncommon for this approach to result in significant mis-estimation of AEP. To 

start with, the typical state-level coarse-scale wind map is merely a starting point for 

wind estimation. Actual wind conditions within each of the map’s raster cells can vary 

dramatically. Second, even assuming that the map provides an accurate estimate of 

the wind resource at a specific site, the map may be estimating wind resources at one 

height (e.g., a 50-meter hub height), but the turbine may be mounted at a different 

height (e.g., 30 meters, where wind power is substantially less). Or turbulence caused 

by site conditions (e.g., nearby buildings or large trees) may be ignored, although it can 

have a powerful effect on turbine performance. 

A third factor is incorrect or incomplete information supplied by manufacturers, 

particularly the turbine’s power curve. (See Table 3 below.) For example, one widelyused distributed wind turbine (Turbine A) has recently undergone independent testing at 

NREL. Comparing NREL’s measured data with manufacturer literature indicates that 

the manufacturer overstates AEP by 71% at the AWEA reference speed of 5 m/s. The 

differences between NREL’s measured data and manufacturer literature data were 

somewhat lower at higher wind speeds, falling to a 25% difference at an 8 m/s annual 

average wind speed for this turbine. Note, however, that the literature associated with 

Turbine B goes to some lengths to qualify potential turbulence factors at various wind 

speeds (e.g., hedges, windbreaks, buildings) and appears to have adjusted its AEP 

values accordingly.15 
 

12 

Table 3: Percentage Difference between Manufacturer Literature and NREL Test Datah

Average Annual 

Wind Speed Wind Turbine A Wind Turbine B Wind Turbine C 

4 m/s NA NA 46% 

5 m/s 71% -23% 26% 

6 m/s 43% -24% 9% 

7 m/s 31% NA NA 

8 m/s 25% NA NA 

Manufacturers and dealers may also deemphasize noise considerations or fail to 

educate the buyer on the desirability of (or need for) a tall tower. The advent of the 

AWEA small wind standard and the SWCC will make it easier for buyers to obtain 

accurate and meaningful information, but only education will ensure that buyers get the 

most value from their significant investment in distributed wind. 

6. Data Issues – Implications for NEMS 

NEMS “builds” distributed wind turbines in Census divisions, which are then overlaid 

with an NREL wind map. NEMS refers to the geographical overlay of a Census division 

and wind map polygon as a “niche”. For each niche, NEMS estimates the distribution of 

wind speeds using a Weibull k of 2 and a wind shear exponent of 0.2. 

The model then estimates AEP by applying a turbine’s rated capacity across a cubic 

power equation. 

This method is vulnerable to the following sources of error: 

 The wind resource map may not be appropriate for the turbine’s hub 

height; 

 The wind speed distribution may not be appropriate in light of the 

topography of the niche; 

 The varying altitude across the niches may not be appropriately 

incorporated into the computations; 

 Turbulence may not be appropriately represented; 

 The nominal power curve may not be appropriate for other specific 

turbines; 

 Scaling the AEP based on ratios of rated capacity may not be appropriate 

in view of the inconsistency of capacity rating methods. 

 

h

 Values represent the difference between the manufacturer’s data and NREL’s data, divided by the 

NREL data. Positive differences indicate that manufacturer AEP data are higher than NREL AEP data for 

the same average annual wind speed. Manufacturer data for Turbines B and C were not available for unit 

values of m/s and were interpolated from values expressed in half meters per second (e.g., 4.5 m/s, 5.5 

m/s, etc.) using a simple average. 
 

13 

Going forward, the author recommends that EIA take the following steps to increase the 

realism of its modeling efforts: 

1. Verify that the national wind map corresponds to the hub heights of the typical 

distributed wind turbine, for example 30-40 meters. 

2. Consider whether to vary the Weibull k and wind shear exponent based on the 

land surface and altitude within the niche. 

3. Altitude and turbulence factors could also be more directly represented (e.g. the 

decrease in air pressure at higher altitudes will decrease the energy output for a 

wind turbine at a lower altitude, all other things being equal). 

4. As soon as it becomes available in quantity, use AWEA-standard AEP data that 

have been certified by the SWCC for the base year, and use these data to 

interpolate the AEP for other turbine sizes that have not yet been certified by 

SWCC. 

5. Until SWCC data become available: 

a. use manufacturer-supplied AEP data for the base year, but derate these 

data to reflect the discrepancies described in Table 3 above. 

b. apply the adjusted AEP data to specific sites, and further modify the data 

to reflect local wind resources, altitude, Weibull k, and wind shear 

exponent. 

7. Technology Baseline 

Cost and dimensional data for baseline distributed wind technology are represented in 

Tables 4 and 5 below. The turbines chosen for this table were selected to represent a 

range of project capacities and are popular models within their respective size classes. 

Note that each turbine’s rated capacity is derived from a different reference wind speed. 

Table 4: Dimensional Data for Selected Distributed Wind Turbines

Typical

Rated Rated Wind Rotor Tower

Capacity Speed Diameter Height

Manufacturer Model (kW) (m/s) (m) (m)

Southwest Windpower Skystream 3.7 2.4 13 3.7 26

Bergey BWC XL-S 10 14 7 37

Entegrity EW50 50 11.3 15 37

Northern Power Systems Northwind 100 100 14.5 21 37
 

14 

Table 5: Cost and Performance Data for Selected Distributed Wind Turbines, 200916

AEP @ 

Typical 5 m/s

Rated Rated Wind Rotor Tower Installed Installed average annual

Capacity Speed Diameter Height Cost Cost/kW wind speed

Model (kW) (m/s) (m) (m) ($) ($) (kWh)

Skystream 3.7 2.4 13 3.7 26 19,000 $ 7,917 $ 3,600 

BWC XL-S 10 14 7 37 62,000 $ 6,200 $ 13,200 

EW50 50 11.3 15 37 230,000 $ 4,600 $ 72,000 

Northwind 100 100 14.5 21 37 435,000 $ 4,350 $ 145,000 

8. Sources for Improvement 

8.1 Technological 

Advances in modeling, materials science, fabrication techniques, blade design and 

electronics have allowed utility-scale turbines to grow steadily and to harvest the scale 

economies of that growth for almost 3 decades. Technological improvement has 

translated into lower installed cost per kW, greater energy production, and higher 

reliability. 

However, as discussed above, it is not practical for the typical residential or business 

customer to enjoy these benefits by following the modern turbine’s increase in size and 

hub height. The distributed wind turbine purchased by these customers has seen less 

rapid improvement, but particularly in recent years, significant improvement has been 

achieved. In some instances, small turbines have been earlier adopters of advanced 

technologies compared with larger turbines. 

A utility-scale example offers the first demonstration that technological improvement is 

possible without an increase in size. As shown in Table 6 below, the GE 1.5 MW 

turbine has improved steadily in just the past seven years – without a capacity uprating 

– through incremental application of an improved generator and main bearing design, a 

better blade pitch mechanism, longer blades, and an improved gearbox. 17 

Table 6: Improvement in the GE 1.5 MW Turbine 

 2002 2009 

Rotor Diameter (m) 70 82.5 

Capacity Factor (%) 39 52 

Reliability (%) 85 98 

A second example falls in the distributed wind size range. First produced in 1983, the 

Bergey Excel has undergone significant development over the years. The original airfoil 

has been succeeded by two new generations, as has the inverter, most recently in 

2008. 2008 also saw the introduction of a new neodymium-based alternator. The 

cumulative effect of these changes is a 30% increase in energy production, a reduction 

in noise, and no increase in price.18
 

15 

Distributed wind turbines still have substantial performance improvement potential; the 

representative of one distributed wind manufacturer believes that a 10-20% 

improvement in cost and productivity for this category over the next 5 year period will be 

“easy”.19 Improvement may be found in several areas: 

 Blades and rotor: Improved blade designs, lighter-weight and stronger 

materials, and improved manufacturing techniques may allow for lower 

cut-in speeds, greater low-speed energy production, lower-noise 

overspeed control, and greater AEP per 

square meter of rotor cross-section. 

Current day blades are estimated to be 

about 32% efficient; an industry 

workshop set a goal of 42-45% 

efficiency. 

 Generators: Many distributed wind 

turbines are now equipped with rareearth permanent magnet generators, 

which are smaller, lighter, and more 

efficient than ferrite or wound-rotor generators. 

 Inverters: Many small distributed wind turbines use inverters optimized for 

photovoltaic systems. Inverters optimized for small wind turbines would 

have a larger voltage range, and, potentially, greater efficiency. 

 Drivetrain: Most distributed wind turbines use direct drive whereby the 

rotor directly drives the generator, without the use of a gearbox to step up 

the rotational speed. Direct drive increases efficiency by eliminating gear 

losses, and also eliminates a frequent point of failure. 

 Control electronics: At the larger end of the distributed turbine range, it 

may be possible to incorporate more advanced sensors and pitch controls 

to mitigate blade and tower loading, and thus enable the use of longer 

blades with greater energy capture. 

8.2 Cost 

Anecdotal evidence suggests that initial cost and long-term investment rate of return are 

the two most important factors in whether a distributed wind turbine is purchased and 

installed. There are several opportunities to significantly reduce the first cost of the 

turbine, the cost of installation, and ongoing cost of operation and maintenance: 

 Volume: Many distributed wind turbine models have limited production 

volumes. As the market matures, higher volumes can drive down unit 

costs through more efficient operation of manufacturing plant, lower input 

costs, and better amortization of fixed costs. In addition, higher volumes 

(and revenue) can justify greater investment in more advanced tooling and 

Physical Performance Limits 

A turbine cannot extract 100% of the power 

available in a stream of wind. If it did, the wind 

would stop, and so would the turbine. The upper 

limit in practice is 59%, known as the Betz limit 

after its discoverer, Albert Betz. Modern utility 

scale turbines extract about 50% of the wind 

energy at wind speeds below their rated wind 

speed.a
 

16 

manufacturing capacity. In 2008, over $160 million was invested in small 

wind manufacturers worldwide, with about half the funds invested in the 

United States.20

 Greater competition: In certain market segments, only one or a few 

manufacturers offer a product and have the dealer network available to 

support a project in a specific region. Some turbines are in short supply or 

only built-to-order. As more companies enter the market, customers will 

enjoy a greater choice of technology, shorter lead times, and a more 

competitive service environment. 

 Industry consolidation: While industry diversity will benefit certain 

segments compared with today’s baseline, other segments may benefit 

from some consolidation, which would allow greater scale economies in 

manufacturing, distribution and after-market service. 

 Outsourcing: The U.S. is currently a leading area of distributed wind 

turbine manufacturing; U.S. manufacturers account for about half of global 

small wind sales, and about 95% of the U.S. market.21 However, other 

countries, particularly China, are clearly bidding to enter the renewable 

energy market generally and the distributed wind market specifically. 

Imports from regions with lower manufacturing costs may put pressure on 

U.S. distributed wind turbine prices. 

 Component reduction: Some of the performance improvements 

discussed above may increase costs, but others, such as eliminating the 

gearbox using direct drive, can serve to reduce turbine costs. 

 Tower: Tower costs and crane rental can be a substantial fraction of total 

installed costs for a distributed wind turbine. Greater use of tilt-up and 

self-erecting towers, as well as lighter weight towers could reduce project 

costs.22

 Operation and maintenance costs: O&M costs could potentially be 

reduced through hardware and software improvements. Hardware 

improvements include the elimination of the gearbox, better lubrication, 

and more durable blade materials which are also more resistant to fouling. 

Software improvements include design strategies that reduce rotor and 

tower loading, better yaw and overspeed controls, and better monitoring 

technology to minimize the need for site visits and to provide early warning 

of emerging problems. The baseline (2010) O&M costs are shown in 

Table 7 below. 

Table 7: Annual Operation and Maintenance Expenses23

Assumed Annual Expenses Unit Expense 

Operations & Maintenance $/kWh $0.0100/kWh 

Operations & Maintenance 

Contingency Fund $/kWh $0.0030/kWh 

Insurance $/kW $6.70/kW 

Property Tax $/kW $4.70/kW 
 

17 

Admin/Financial/Legal Management $/kW $0.30/kW 

Warranty Expense $/kW $7.70/kW 

Decomm. Fund Post-Warranty 

Expiration $/kW $1.00/kW 

Other Expense $/kW $1.30/kW 

9. Projection Methodology 

For the purposes of the NEMS projections, it was assumed that the 2010 baseline was 

represented by the turbines listed in Tables 4 and 5 above. Essentially, the four 

turbines were assumed to become the prototypical turbines for the next 25 years. For 

the base year, each turbine’s AEP was derated to some degree to reflect the findings 

shown in Table 3. The derating was not constant across turbines. Maintenance costs 

were taken from Table 7. 

The future is represented by two scenarios: a base, or reference case; and an 

advanced case. 

Under the base case, it is assumed that present-day policies will continue in force until 

their legislated expiration (if any); that present-day research and development 

investment flows will continue; and that the trend of technology and cost improvement 

will continue in the future much as it has in the recent past. 

The advanced case is similar to the base case, except that it assumes a much higher 

level of private sector R&D investment, and thus more rapid and more extensive 

improvements in technology performance and more rapid and deeper reductions in 

cost. The advanced case does not assume any changes to the policy environment 

compared with the base case.i

 

For the period 2010-35, three improvement trajectories were developed: 

 Cumulative AEP Improvement: This trajectory describes the increase in 

kWh produced by a turbine compared with its 2010 baseline. 

 Cost Improvement Factor: This trajectory describes the reduction in a 

turbine’s installed costs, in constant dollars, compared with its 2010 

baseline. 

 

i

 The implementation of the uncapped 30% ITC in February 2009 is perhaps the most important policy 

initiative in favor of distributed wind in several decades. This policy will only begin to have full impact in 

2010 and beyond; in effect, the base case does not fully reflect this new policy. This policy could lead to 

larger market volumes, greater private sector investment, etc., producing a scenario more consistent with 

the advanced case. 
 

18 

 O&M Factor: This trajectory describes the reduction in annual O&M costs 

compared with the 2010 baseline. 

Table 8: Assumptions for the Base and Advanced Cases 

Cumulative AEP 

Improvement 

Factor vs. 2010 

Cost 

Improvement 

Factor vs. 2010 

O&M Factor vs. 

2010 

Base Advanced Base Advanced Base Advanced 

2015 10% 12% -8% -10% 

 

0.98 0.97 

2020 18% 21% -13% -14% 

 

0.96 0.94 

2025 23% 28% -16% -18% 

 

0.94 0.92 

2030 26% 33% -18% -21% 

 

0.92 0.90 

2035 28% 36% -20% -24% 

 

0.90 0.88 

Other assumptions include: 

 Capacity factor: As noted above, the authors recommend using AEP as 

the key metric of energy performance. However, the accompanying data 

table provides calculated capacity factors for different turbines over the 

projection horizon by turbine size, year and scenario. 

 Equipment life: A 25 year life was assumed for both scenarios. 

 Availability: We assume 98% availability in both scenarios. 

 O&M Costs: Summing the values in Table 7 yields an annual O&M factor 

based partly on capacity ($21.70/kW-year) and partly on energy 

production ($0.013/kWh).
 

19 

Endnotes 

 

1 http://www.industry.nsw.gov.au/energy/sustainable/renewable/wind

2

 AWEA, Small Wind Turbine Global Market Study: Year Ending 2008. 

3 Ibid, p. 5.

4

 Kwartin et al., An Analysis of the Technical and Economic Potential for Mid-Scale 

Distributed Wind, NREL, December 2008, p. 41. 

5

 Southwest Windpower, “Skystream 3.7: 2.4 kW Residential Power Appliance”. 

6 http://www.northernpower.com/wind-powerbasics/faq.php#WhatMakesAGoodWindSite

7 http://dsireusa.org/summarytables/rrpre.cfm 

8 Ibid. 

9

 Kwartin et al., p. 54 

10 Kwartin et al., p. 61 

11 Forsyth, T., and Baring-Gould, I., “Distributed Wind Market Applications,” NREL, 

November 2007, p. 5. 

12 DOE, Commercial Sector Demand Module of the National Energy Modeling System: 

Model Documentation 2009, DOE/EIA-M066(2009), May 2009, pp 40-44. 

13 American Wind Energy Association, AWEA Small Wind Turbine Performance and 

Safety Standard, (AWEA Standard 9.1 – 2009) © The American Wind Energy 

Association, 2009.

14 http://www.smallwindcertification.org/index.html 

15 Smith, Joe, “NREL Small Wind Technology Update,” AWEA Small & Community 

Wind Conference & Exhibition, November 5, 2009, Detroit, MI, and manufacturer 

literature. 

16 Sagrillo, Mick, “Size Matters!” Windletter, 28(3); AEP data: 

a. Bergey Skystream 3.7, 3-CMLT-1338-01 REV F 1-09 

b. “Power Your Dream With the Wind”, Bergey Windpower, accessed May 2010. 
 

20 

 

c. “Entegrity Wind Systems: Commercial Wind Energy Provider and 

Manufacturer of the EW50”, 2009. 

d. “Northwind 100: Community Scale Wind Turbine”, Northern Power Systems, 

2009. 

17 Frick, Bob, untitled presentation, AWEA Small & Community Wind Conference & 

Exhibition, November 5, 2009, Detroit, MI. 

18 Wilke, Steve, “The Evolution of the Bergey Excel wind turbine,” AWEA Small & 

Community Wind Conference & Exhibition, November 5, 2009, Detroit, MI. 

19 Kruse, Andy, personal communication, AWEA Small & Community Wind Conference 

& Exhibition, November 5, 2009, Detroit, MI. 

20 AWEA, Small Wind Turbine Global Market Study: Year Ending 2008, p. 10. 

21 Ibid, p. 15. 

22 U.S. Department of Energy, 20% Wind Energy by 2030: Meeting the Challenges, 

Proceedings of the Workshop, October 6-7, 2008, pp. 13-25. 

23 Kwartin et al., pp. 33-34, for commercial/industrial turbines and unpublished data for 

residential turbines. Values in Table 7 represent a 2/3 weighting for residential scale 

turbines and a 1/3 weighting for commercial/industrial scale turbines. 
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