National Renewable Energy Laboratory 

Innovation for Our Energy Future 

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

Office of Energy Efficiency & Renewable Energy

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

Concentrating Solar 

Deployment System (CSDS) – 

A New Model for Estimating 

U.S. Concentrating Solar Power 

(CSP) Market Potential 

Preprint 

N. Blair, M. Mehos, W. Short, and D. Heimiller 

To be presented at Solar 2006 

Denver, Colorado 

July 8–13, 2006 

Conference Paper 

NREL/CP-620-39682 

April 2006 
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Concentrating Solar Deployment System (CSDS) – A New Model for Estimating U.S. 

Concentrating Solar Power (CSP) Market Potential 

Nathan Blair 

Mark Mehos 

Walter Short 

Donna Heimiller 

National Renewable Energy Laboratory 

1617 Cole Boulevard 

Golden, Colorado 80401-3393 

ABSTRACT

This paper presents the Concentrating Solar Deployment 

System Model (CSDS). CSDS is a multiregional, multitimeperiod, Geographic Information System (GIS), and linear 

programming model of capacity expansion in the electric 

sector of the U.S. CSDS is designed to address the principal 

market and policy issues related to the penetration of 

concentrating solar power (CSP) electric sector 

technologies. This paper discusses the current structure, 

capabilities, and assumptions of the model. Additionally, 

results are presented for the impact of continued research 

and development (R&D) spending, an extension to the 

investment tax credit (ITC), and use of a production tax 

credit (PTC). 

CSDS is an extension of the Wind Deployment System 

(WinDS) model created at the National Renewable Energy 

Laboratory (NREL). While WinDS examines issues related 

to wind, CSDS is an extension to analyze similar issues for 

CSP applications. Specifically, a detailed representation of 

parabolic trough systems with thermal storage has been 

developed within the existing structure. 

1. BACKGROUND and MODEL OVERVIEW

CSDS is a computer model of expansion of generation and 

transmission capacity in the U.S. electric sector spanning 

the next 50 years. It minimizes system-wide costs of 

meeting loads, reserve requirements, and emission 

constraints by building and operating new generators and 

transmission in each of 26 two-year periods from 2000 to 

2050. CSDS is focused on addressing the market issues of 

greatest significance to renewables – specifically issues of 

transmission and resource variability. 

CSDS attempts to examine these issues, primarily by using 

a much higher level of geographic disaggregation than other 

models. Other models – such as the National Energy 

Modeling System (NEMS) model used by the U.S. Energy 

Information Agency – have only a few regions in the U.S. 

(13 in the case of NEMS). Because of this, these models 

have to make assumptions about the cost of transmission 

and resource variability on the electric grid. With a high 

level of geographic disaggregation, we can model these 

geographic impacts more directly within the model. CSDS 

uses 358 different regions in the entire United States. Much 

of the data inputs to CSDS are tied to these regions and 

derived from a detailed GIS model/database of the 

renewable resources, transmission grid, and existing plant 

data. The geographic disaggregation of solar resources 

allows CSDS to calculate transmission distances and the 

benefits of dispersed solar plants supplying power to a 

demand region. 

For CSP, a certain level of average radiation is needed 

before the resource can be considered to be viable. In the 

United States, those viable resource areas are located 

primarily within the southwestern states. Therefore, in the 

CSDS model, this subset of regions is the area in which CSP 

solar plants are allowed (Fig. 1). This reduction in the 

number of regions significantly reduces the run-time 

requirements of the CSDS additions to WinDS, as well as 

the amount of solar GIS inputs. The entire United States is 

still modeled, and wind power and conventional generation 

are built as needed throughout the country. 

Similar to the model’s existing breakdown of wind resource 

into five standard classes, the solar resource appropriate for 

CSP systems has also been divided into five classes that are 

defined by the annual average direct normal radiation. The 

breakdown by class is: 

• Class 1 is 6.75 - 6.99 kW/m2/day 

• Class 2 is 7.00 - 7.24 kW/m2/day 

• Class 3 is 7.25 - 7.49 kW/m2/day 

• Class 4 is 7.50 - 7.74 kW/m2/day 

• Class 5 is 7.75 - 8.06 kW/m2/day 

1
Fig. 1: CSDS Regions are in the southwestern United States 

Additionally, there are a variety of exclusions applied to the 

solar resource if the slope exceeds 1%, average annual 

radiation is less than 6.75 kWh/m2/day, the area is a major 

urban or wetland area or a protected federal land. If the 

remaining resource lands are less than 5 contiguous sq. km, 

they are excluded. 

Fig. 2 maps the location of the solar resource that is used 

within CSDS, and the CSDS regions are shown by the light 

gray boundaries. 

Fig. 2: Solar Resource in CSDS1

Linear programs, such as WinDS and CSDS, work by 

minimizing an objective function. The CSDS objective 

function is a minimization of all the costs of the U.S. 

electric sector including: 

• the present value of the installation cost and anticipated 

operating and maintenance (O&M) costs of both 

generation and transmission capacity (conventional and 

renewable) installed in each period (each period 

represents 2 years), plus 

• the cost of using the existing transmission grid as 

represented in the model, plus 

• the cost of operating that capacity during the current 

period (fuel costs) to meet load, plus 

• the cost of reserve capacity. 

By minimizing these costs while meeting the system 

constraints (discussed below), the linear program determines 

(A) at which level to operate the currently installed capacity 

and (B) which types of new capacity and amount are the 

most economical to add in each period for each region in the 

country. Therefore, the capacity factor for each 

dispatchable technology in each region is an output of the 

model, and not exogenously defined. Note that hydropower, 

wind, and CSP are typically operated as much as possible, 

due to negligible variable cost. Therefore, these 

technologies operate at the maximum capacity factor 

possible, based on the resource class or hydro availability. 

The cost minimization that occurs within CSDS is subject to 

more than 70 types of constraints, which result in thousands 

of equations in the model (due primarily to the large number 

of regions). These constraints fall into several main 

categories, including: 

• Resources: The total amount of CSP installed in each 

region must be less than the solar resource potentially 

available – by region and resource class – as estimated 

by the NREL Resource and GIS teams. 

• Access to existing transmission lines: The amount of 

new solar capacity that can be transmitted on existing 

lines is limited by the transmission capacity available 

on nearby lines. The GIS portion of CSDS determines 

which solar resources would be most cost-effective in 

using existing transmission capacity. 

• Load constraints: The primary load constraint is that 

the electric load in each power control area (PCA) – 

there are 136 power control areas in CSDS for the 

entire country – must be met in each time slice (of 

which there are 16) throughout a year. The load and its 

rate of growth in each North American Electric 

Reliability Council (NERC) region are derived from the 

Reference Case of the U.S. Energy Information 

Agency’s Annual Energy Outlook3

. 

• Reserve margin constraint: There are two types of 

reserves constraints – planning reserve margin and 

operating reserve. For CSP, in the current version, the 

amount of storage is assumed to be adequate to provide 

capacity equal to nameplate capacity during the peak 

period. In other words, CSP is assumed to be 

dispatchable. Of course, the cost inputs reflect the 

larger cost associated with storage (discussed below). 

For comparison, capacity value for wind power is 

handled statistically. In each period, CSDS updates the 

estimate of the marginal capacity value of the next wind 

farm built in each region using a detailed statistical 

approach. The approach takes into account the 

2
dispersion of the wind sites contributing to the load and 

the correlation in the output of those sites. When the 

ability to model solar-only CSP plants is added to 

CSDS, an approach similar to wind power’s method of 

estimating capacity value will be used. 

• Operating reserve constraint: Unlike wind, CSP is 

currently assumed to be fully dispatchable and, 

therefore, requires no additional operating reserve. 

2. CURRENT CSP SYSTEM ASSUMPTIONS

This paper reports on the first stage of, hopefully, several in 

the implementation of CSP within the WinDS/CSDS 

framework. Ideally, the model would compete solar-only, 

trough with storage, dishes, solar towers, etc. in an 

economically optimum portfolio. However, this first stage 

of development limits the situation to a single technology 

(parabolic trough Rankine cycle, similar to the SEGS plants 

installed in California) with a preselected thermal storage 

level (six hours of thermal storage). These factors, 

combined with an assumed scale of 100 MW plant size, 

determine the initial cost and performance characteristics. 

The NREL CSP analysis tool, Excelergy2

, is a Microsoft 

Excel-based performance and financing tool for parabolic 

trough systems, with current costs and performance 

assumptions. 

The storage assumption greatly simplifies the treatment of 

resource variability. Because the plant is assumed to be 

dispatchable, the capacity value for the plant is assumed to 

be equal to the capacity factor during the summer peak 

period, which is essentially the nameplate capacity. 

Additionally, no operating reserve is necessary for this 

plant, and surplus is assumed to be negligible due to the 

alignment of the solar resource and load. 

TABLE 1: TIMESLICE PLANT CAPACITY FACTORS

Timeslice

solar 

class 1

solar 

class 2

solar 

class 3

solar 

class 4

solar 

class 5 Hours

H1 0.384 0.367 0.381 0.395 0.446 1152

H2 0.642 0.769 0.821 0.872 0.855 462

H3 0.989 0.974 1.020 1.065 1.066 264

H4 0.831 0.732 0.794 0.856 0.920 330

H5 0.274 0.199 0.229 0.259 0.276 792

H6 0.535 0.661 0.677 0.694 0.740 315

H7 0.900 0.894 0.890 0.887 0.978 180

H8 0.405 0.402 0.401 0.399 0.558 225

H9 0.115 0.111 0.106 0.102 0.122 1496

H10 0.259 0.326 0.326 0.326 0.327 595

H11 0.502 0.581 0.560 0.540 0.542 340

H12 0.035 0.047 0.040 0.034 0.031 425

H13 0.330 0.313 0.328 0.343 0.327 1144

H14 0.640 0.716 0.730 0.745 0.708 455

H15 0.947 0.935 0.973 1.010 0.950 260

H16 0.742 0.684 0.703 0.722 0.736 325

Average 0.409 0.413 0.427 0.442 0.457

Excelergy was also used outside of CSDS to determine the 

performance of the assumed system for a variety of 

locations, representing all five solar classes. For each 

location, the hourly output of Excelergy was aggregated into 

the 16 timeslices within CSDS to determine the average 

capacity factor for each timeslice of the year, for each solar 

class (Table 1). For this analysis, it was conservatively 

assumed that these capacity factors (i.e. solar plant 

performance) were unchanged in the future. In reality, it is 

expected that these would improve through R&D and shared 

operational improvements. 

3. BASE-CASE ASSUMPTIONS AND RESULTS

In this analysis, the Base Case is a business-as-usual case 

that relies heavily on the Reference Case scenario of the 

U.S. Energy Information Agency Annual Energy Outlook 

(AEO) for 2005 for inputs that fall outside the scope of 

CSDS. The single deviation from the AEO 2005 is the price 

of natural gas going forward; which, for CSDS, is based 

instead on the California Market Price Reference (CA 

MPR).4

 Although partly based on the EIA projections, the 

CA MPR reference is significantly higher. It was 

determined that this reference was more reflective of the 

market in the southwestern United States. As with the U.S. 

EIA’s AEO, this price trajectory will change each year. 

Based on the 2005 DOE Solar Program Multiyear 

Technology Plan,5

 we assume that 54% of the cost 

improvements projected by DOE will occur through R&D 

(Table 2). In addition to the improvements over time shown 

in Table 2, CSDS also allows for “learning” improvements 

in the cost values. For each doubling of installed worldwide 

CSP capacity (a scenario of CSP installations outside the 

United States reaching 120 GW by 2040 is input), there is 

an 8% reduction in costs. 

TABLE 2: R & D-DRIVEN COST AND PERFORMANCE

Solar class Year

Capacity 

Factor Capital cost

Fixed 

O&M Var O&M

 - 1000$/MW 1000$/MW-$/Mwh

1 2005 0.409 4865 5.83 2.54

1 2030 0.409 2832 5.83 0.55

1 2050 0.409 2554 5.83 0.50

2 2005 0.413 4865 5.83 2.52

2 2030 0.413 2832 5.83 0.55

2 2050 0.413 2554 5.83 0.50

3 2005 0.427 4865 5.84 2.46

3 2030 0.427 2832 5.84 0.53

3 2050 0.427 2554 5.84 0.48

4 2005 0.442 4865 5.84 2.39

4 2030 0.442 2832 5.84 0.52

4 2050 0.442 2554 5.84 0.47

5 2005 0.457 4865 5.84 2.33

5 2030 0.457 2832 5.84 0.51

5 2050 0.457 2554 5.84 0.46

3
With these Base Case inputs, CSDS projects that solar 

power will provide about 55 GW of capacity in 2050, far 

larger than today’s 350 MW (see top slice of graph in Fig. 

3). Although this growth is largely attributable to 

improvements in the cost and performance of solar power 

plants, there are many other drivers. By about 2014, the 

increase in natural gas prices (as forecast by the CA MPR) 

stalls the recent growth in new installations of combinedcycle, natural gas-fired power plants (fourth slice from the 

bottom). At about the same time, installations of advanced 

coal plants surge and dominate the new-build market until 

2050 (seventh slice from the bottom – “Coal-new”). 

0

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Conc. Solar

nuclear

oil-gas-steam

Coal-IGCC

Coal-new

Coal-no scrub

Coal-w/scrub

Gas-CC

Gas-CT

Hydro

Total Wind

Fig. 3: National Capacity Projections for the Base Case 

The dominance of new coal plants in the new capacity 

market of the second quarter of the 21st century is dramatic. 

Unlike natural gas power plants, the fuel for these coal 

plants is expected to remain relatively inexpensive, leaving 

no room for renewable energy to penetrate the market as a 

fuel saver. Likewise, sulfur dioxide and NOx emissions 

from new advanced coal plants are almost an order-ofmagnitude less than today’s average existing coal plant, 

limiting the advantage of renewable energy for emission 

caps. However, a carbon tax or CO2 limit would have a 

dramatic impact on the growth of renewable energy and 

energy efficiency. 

Fig. 4 depicts the modeled growth in future CSP capacity by 

solar class. As expected, the best solar resource is used first 

and used the most by the model. However, the solar Class 5 

resource is not completely deployed before solar Classes 4 

and 3 (to a much lesser extent) begin to be deployed in the 

model. This is primarily due to transmission distance and 

load growth within the local region in which the solar 

resource exists. In other words, a lower-class solar resource 

close to the load is more economic, in some cases, than a 

better solar resource much farther from the load. 

0

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GW

Class 5 (7.75 - 8.06 kW/m2/day)

Class 4 (7.50 - 7.74 kW/m2/day)

Class 3 (7.25 - 7.49 kW/m2/day)

Class 2 (7.00 - 7.24 kW/m2/day)

Class 1 (6.75 - 6.99 kW/m2/day)

Fig. 4: CSP Base Case Penetration by Solar Class 

Fig. 5 depicts the CSP capacity growth by the method of 

transmission. “Existing Grid between Regions” indicates 

that the model pays some price to build a line to the existing 

grid and uses some fraction of the existing grid capacity to 

get out of the region to satisfy load in another region or 

PCA. “Used for In-region Load” indicates that the power 

produced by the solar plant is used to satisfy load within the 

same region as the solar capacity, and only local distribution 

transmission is necessary. Finally, “New Grid Lines 

between Regions” indicates that a dedicated line only for 

that CSP capacity is built from one region to another (which 

might be quite some distance away). For the CSDS Base 

Case, the CSP solar power initially is used to satisfy load 

within the region. This is typically the cheapest method of 

transmission. Second, the existing grid is used to transmit 

the power. Often, this is just a short distance to adjoining 

regions with much larger load. For example, the solar region 

east of Los Angeles transmits power to be used in the Los 

Angeles region itself. 

0

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Used For In-region Load

New Grid Lines Between Regions

Existing Grid between Regions

Fig. 5: CSP Capacity Growth by Transmission Type 

4
Another interesting question of deployment, which can be 

answered by CSDS, is where the future CSP capacity will 

be located. Based on the GIS inputs to CSDS, transmission, 

siting issues, and load location and load growth, the model 

selects the economically best sites for each period to add 

new capacity. Fig. 6 shows the location of CSP capacity in 

2050. As expected, the regions with the greatest deployment 

level have excellent solar resource or are close to large 

electric-load growth (such as in southern California). 

Fig. 6: CSP Capacity deployment by region in 2050 

4. SENSITIVITY CASES FOR FEDERAL POLICIES

We examined three federal policies including continued 

research and development (R&D), extension of the current 

30% investment tax credit and switching to use of a 

production tax credit. We present results from each below. 

4.1 Continued R&D for Concentrating Solar Power

As stated above, the primary reason for the surge in CSP 

capacity in the Base Case is the anticipated improvements in 

costs and performance. Fig. 7 compares the projected 

deployment of CSP in the Base Case with a case in which 

there are no R&D-driven improvements in solar plant costs 

– the costs are the same costs as today throughout time. By 

2050, the total CSP deployed in this no-R&D case is only 

831 MW, or less than 2% of the Base Case installations. 

Although both cases include improvements due to learning, 

the absence of the R&D improvements precludes the 

installations that could lead to significant learning-based 

cost/performance improvements. 

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20002004

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CSP Capacity (GW)

Base Case

No R & D Improvements

Fig. 7: CSP Capacity Expansion with and without 

technological improvements 

Research and development improvements are just one 

method for lowering the cost of CSP. There are other policy 

and engineering methods for lowering these costs. Instead of 

trying to model each of these cost-reduction techniques, 

another methodology is to simply lower the total “cost 

trajectory” through time. In other words, we can take the 

Base Case solar cost trajectory and lower it by a certain 

percentage. Fig. 8 demonstrates the impact of lowering the 

cost incrementally up to 40% of the Base Case cost 

trajectory. This shows that a reduction in cost of 50% 

throughout time causes the initial penetration of CSP to 

move forward in time by roughly 10 years. If the cost 

trajectory is reduced by 50%, roughly 32 GW of CSP 

capacity are installed by 2020 vs. less than 1 GW for the 

Base Case. 

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CSP Capacity (GW)

100%

90%

80%

70%

60%

50%

40%

Cheaper Capital and O&M Costs

Fig. 8: Cost Reduction Impact on Capacity Penetration 

4.2 Extension of the Investment Tax Credit

The Energy Policy Act of 2005 added a 30% investment tax 

credit until 2007 on top of the existing permanent 10% 

investment tax credit for solar power. There are several 

reasons for extending the 30% ITC far past the current 2007 

expiration date. The lead time for a CSP plant is long 

enough that a 2-year window on the ITC does not allow 

5
enough project development time to take full advantage of 

the ITC. Therefore, to provide financial stability in the 

industry and adequate development time to keep costs low 

and the supply chain efficient, we examined what the impact 

would be on extending the investment tax credit to a 

timeframe commensurate with utility-scale power. 

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Capacity (GW)

Base Case (30% ITC to 2007, 10% thereafter)

Extend 30% ITC to 2012 (10% thereafter)

Extend 30% ITC to 2017 (10% thereafter)

Fig. 9: Impact of Investment Tax Credit Extension 

As shown in Fig. 9, continuing the ITC to 2017, as 

recommended by the Western Governors’ Association solar 

taskforce, will significantly increase CSP capacity through 

the end of the simulation period. However, as might be 

expected, with continued R&D-driven improvements in 

solar power plants and increasing fossil fuel prices, after 30 

years, the amount of CSP capacity installed is less 

dependent on the ITC extension, and that effect starts to be 

damped out. Two counteracting forces lead to this result. 

The first, and more obvious, is that increased deployment 

leads to improved CSP cost/performance through learning. 

The second is that the best solar sites are used quickly in the 

ITC extension cases, leaving only lower-quality sites once 

the ITC has expired. However, in the Base Case, the better 

sites are not used until later, allowing the installations in the 

Base Case to start bridging the gap with those of the ITCextension cases. 

These results might lead one to question the rate of growth 

in the CSP industry during the ITC period. These growth 

rates seem extreme in comparison to the current capacity. 

The CSDS model does contain a growth penalty such that if 

the growth is greater than 20% in any year from the prior 

year, a penalty is paid by the model to build this capacity. 

However, this level of capacity growth is not without 

precedent. The growth in the recent upsurge in natural gascombustion turbine power plants was more extreme with the 

addition of 62 GW in 2002 alone, and significantly less 

since that year. 

4.3 Impact of a Production Tax Credit 

Another federal policy that is available to CSP is the 

production tax credit (PTC). This policy tool has been a 

major driver for wind industry growth in the United States 

in recent years. In WinDS, a PTC extension greatly 

increases the amount of wind deployment during the 

intervening years. However, as Fig. 10 shows, this is not the 

case with CSP – at least at the current federal PTC level of 

1.8 cents/kWh. Because the current costs of CSP are 

considerably higher than wind, this PTC is inadequate to 

generate significant growth in excess of the Base Case in the 

near term. In fact, having a PTC only – and losing the 

permanent 10% ITC – significantly reduces the amount of 

CSP throughout time. If the PTC is extended to the end of 

the simulation period (2050), the amount of installed 

capacity is initially lower than the Base Case, but then 

higher than the Base Case after 2038. This increase is 

because the costs for CSP have decreased significantly 

through R&D and learning, such that the PTC is more 

valuable in those years than the ITC. 

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CSP Capacity (GW)

Base Case (30% ITC to 2007, 10% thereafter)

PTC to 2012 (18 $/MWH, no ITC)

PTC to 2050 (18 $/MWH, no ITC)

Fig. 10: Impact of a Production Tax Credit instead of an 

Investment Tax Credit 

5. CONCLUSIONS

• A tool has been developed to model the future capacity 

growth of CSP trough systems that can be used to 

examine various cost and policy scenarios, while more 

accurately accounting for transmission needs. 

• CSP will contribute a significant share of future U.S. 

electric generation in our Base Case scenario. 

• Increased research and development leading to further 

reductions in cost are vital to the eventual penetration 

of CSP into the electric sector. 

• CSP deployment is very sensitive to cost, because the 

resource is concentrated and relatively close to load 

centers. Appropriate incentives are necessary to help 

ensure a more sustained technology expansion. 

6
• Extending the investment tax credit past 2007 will 

dramatically increase the generation from solar power, 

and should help to ensure a growing U.S. solar power 

industry, as well as support local economies and job 

markets. 

• Implementing a production tax credit for CSP similar to 

the PTC for wind power has a minimal or negative 

impact on CSP deployment, until costs have come 

down to make the PTC a major portion of the cost. 

6. REFERENCES

1. NREL CSP maps http://www.nrel.gov/csp/maps.html 

2. Price, H. (2003). “Parabolic Trough Solar Power Plant 

Simulation Model,” preprint, 12 pp., NREL Report No. 

CP-550-33209. 

3. United States Department of Energy, Energy 

Information Administration, Annual Energy Outlook 

2005, January 2005, DOE/EIA-0383(2005)

4. http://www.cpuc.ca.gov/PUBLISHED/RULINGS/4382

5.htm

5. United States Department of Energy, Solar Energy 

Technologies Program, Multi-Year Program Plan 2007-

2011, 

http://www1.eere.energy.gov/solar/pdfs/set_myp_2007-

2011_proof_2.pdf, page 67, 2005 

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5b. GRANT NUMBER

4. TITLE AND SUBTITLE

Concentrating Solar Deployment System (CSDS) -- A New Model 

for Estimating U.S. Concentrating Solar Power (CSP) Market 

Potential: Preprint 

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

NREL/CP-620-39682 

5e. TASK NUMBER

ASA5.1387 

6. AUTHOR(S)

N. Blair, M. Mehos, W. Short, and D. Heimiller 

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401-3393 

8. PERFORMING ORGANIZATION 

REPORT NUMBER

NREL/CP-620-39682 

10. SPONSOR/MONITOR'S ACRONYM(S)

NREL 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

11. SPONSORING/MONITORING 

AGENCY REPORT NUMBER

12. DISTRIBUTION AVAILABILITY STATEMENT

National Technical Information Service 

U.S. Department of Commerce 

5285 Port Royal Road 

Springfield, VA 22161 

13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

This paper presents the Concentrating Solar Deployment System Model (CSDS). CSDS is a multiregional, multitimeperiod, Geographic Information System (GIS), and linear programming model of capacity expansion in the electric 

sector of the United States. CSDS is designed to address the principal market and policy issues related to the 

penetration of concentrating solar power (CSP) electric-sector technologies. This paper discusses the current 

structure, capabilities, and assumptions of the model. Additionally, results are presented for the impact of continued 

research and development (R&D) spending, an extension to the investment tax credit (ITC), and use of a production 

tax credit (PTC). CSDS is an extension of the Wind Deployment System (WinDS) model created at the National 

Renewable Energy Laboratory (NREL). While WinDS examines issues related to wind, CSDS is an extension to 

analyze similar issues for CSP applications. Specifically, a detailed representation of parabolic trough systems with 

thermal storage has been developed within the existing structure. 

15. SUBJECT TERMS

Concentrating Solar Deployment System Model; CSDS; Wind Deployment System Model; WinDS; GIS; 

concentrating solar power; CSP; wind; analysis models; wind power; investment tax credit; ITC; load constraints; 

Walter Short; Nate Blair; Donna Heimiller; Mark Mehos 

16. SECURITY CLASSIFICATION OF: 19a. NAME OF RESPONSIBLE PERSON 

a. REPORT 

Unclassified 

b. ABSTRACT 

Unclassified

c. THIS PAGE 

Unclassified

17. LIMITATION 

OF ABSTRACT

UL 

18. NUMBER 

OF PAGES 

19b. TELEPONE NUMBER (Include area code)

Standard Form 298 (Rev. 8/98) 

Prescribed by ANSI Std. Z39.18