Technical Report

NREL/TP-6A2-45832

September 2009

The Solar Deployment System 

(SolarDS) Model: Documentation 

and Sample Results 

Paul Denholm, Easan Drury, and Robert Margolis
National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393

303-275-3000 • www.nrel.gov

NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency and Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

Contract No. DE-AC36-08-GO28308

Technical Report

NREL/TP-6A2-45832

September 2009

The Solar Deployment System 

(SolarDS) Model: Documentation 

and Sample Results 

Paul Denholm, Easan Drury, and Robert Margolis

Prepared under Task No. PVD9.1210
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iii

Executive Summary

The Solar Deployment System (SolarDS) model is a geospatially rich, bottom-up, 

market-penetration model that simulates the potential adoption of photovoltaics (PV) on 

residential and commercial rooftops in the continental United States through 2030. 

SolarDS was developed by the National Renewable Energy Laboratory (NREL) to 

examine the market competitiveness of PV based on regional solar resources, capital 

costs, electricity prices, utility rate structures, and federal and local incentives. SolarDS 

calculations are run at a high level of disaggregation by calculating PV generation in 216 

solar resource regions, shown in Figure ES-1, and combining PV output with state-based 

electricity rate distributions from 3000 utilities. Regional PV financial performance is 

used to simulate PV adoption rates for each customer type and building type. SolarDS

then aggregates regional PV adoption to the state and national level. This document 

introduces the SolarDS model, describes how it works, and shows model results for a 

series of PV penetration scenarios. 

Figure ES-1. The 216 solar resource regions used in SolarDS with observation stations 

shown as red triangles. 

SolarDS Components

Figure ES-2 shows the main components of the SolarDS model, which are described in 

detail below. 

Figure ES-2. SolarDS model structure
iv

1. PV Performance Simulator. The PV performance simulator estimates the 

amount of electricity that a PV module of a given size will generate each hour, for 

hundreds of locations in the continental United States and for a variety of module 

orientations. Hourly PV performance is simulated at 216 locations in the using 

solar insolation and weather data from Typical Meteorological Year (TMY) 

stations. PV output is calculated for multiple module orientations to characterize a 

range of roof types and orientations. 

2. PV Annual Revenue Calculator. The annual revenue calculator combines the 

PV technical performance simulations with electricity rates, electricity rate 

structures, and building load simulations to calculate the expected annual savings 

for a multiple PV systems in each location. PV revenue is defined as the avoided 

cost of electricity and is calculated from the combination of hourly PV output and 

regional electricity rates. For residential buildings, annual PV revenues are

calculated for both standard flat rates and time-of-use rates. For commercial 

buildings, annual revenues are calculated for standard flat rates, time-of-use rates, 

and demand-based rates.

3. PV Financial Performance Calculator. The financial performance calculator 

combines the annual revenue generated by a PV system with PV costs and 

financing assumptions to generate financial performance metrics for individual 

PV systems. PV costs are based on current price data and price projections from 

the Energy Information Agency (EIA) or the U.S. Department of Energy (DOE) 

Solar Energy Technologies Program (SETP) targets. Users can also specify their 

own PV cost projections or set PV learning rates (which characterize the decrease 

in cost with each doubling of cumulative installed capacity). Federal and state 

incentives are applied, reducing the up-front cost of PV systems. A distribution of 

financing parameters is used including cash payments, home-equity type loans, 

and conventional loans. For residential PV systems, the financial performance 

module calculates a “time-to-net-positive” cash flow as the base financial metric. 

For commercial systems, the internal rate of return (IRR) is calculated as the base 

financial metric.

4. PV Market Share Calculator. The PV market share calculator uses financial 

performance metrics (generated in the financial performance calculator) to 

simulate PV purchasing probabilities that are unique to each solar resource region, 

local utility electricity rate and rate structure, customer type, customer financing, 

panel orientations, building size, and building age. Financial performance metrics 

are used to calculate both the total potential PV market share (using marketpenetration curves) and the adoption rate (using Bass diffusion). Different PV 

market-penetration curves are used to characterize residential and commercial 

customers in new and retrofit markets.

5. Regional Aggregator. The regional aggregator module combines the hundreds of 

thousands of PV adoption probabilities with the number of buildings associated 

with each unique system type, and aggregates PV adoption statistics to the state 

and national level. The number of residential and commercial buildings suitable 

for PV is generated using census data and projected into the future using 

population growth estimates. The total number of buildings that adopt PV is 
v 

calculated by combining PV purchasing probabilities with the total number of 

buildings suitable for PV and then aggregates over each system type and region to 

generate state and national PV adoption statistics. The model outputs the 

cumulative and annual installed PV capacity, the number of buildings with PV, 

fractional PV market share, and PV payback times at the state and national levels 

for each time period.

SolarDS Results

SolarDS simulates a wide range of installed PV capacity for a wide range of userspecified input parameters. In preliminary model runs, we have simulated PV marketpenetration levels from 15 to 193 GW by 2030, as shown in Figure ES-3. SolarDS results 

are primarily driven by three model assumptions: (1) future PV cost reductions, (2) the 

maximum PV market share assumed for systems with a given financial performance, and 

(3) PV financing parameters and policy-driven assumptions, such as the possible future 

cost of carbon emissions.

Cumulative Installed PV Capacity

0

25

50

75

100

125

150

175

200

2005 2010 2015 2020 2025 2030

PV Capacity (GW)

PV Cost / Market Penetration / Finance & Policy

DOE SETP / Navigant / Aggressive

DOE SETP / NEMS / Aggressive

DOE SETP / Navigant / Base

DOE SETP / NEMS / Base

EIA / Navigant / Base

EIA / NEMS / Base

Figure ES-3. Cumulative installed PV capacity through 2030 for a range of model input 

parameters, including PV costs (EIA and DOE SETP), PV market adoption rates (NEMS and 

Navigant), and PV financing and policy assumptions (base case and aggressive case)

The lower range of PV penetration represents higher PV costs, based on EIA cost 

projections, where PV reaches $4.23/W for residential systems and $2.85/W for 

commercial systems by 2030.

1

 

1 Here and elsewhere, PV costs and revenues are given in nominal $2008 U.S. dollars. All model interest 

and escalation rates are real, not nominal.

The higher range of PV penetration represents lower PV 

costs, based on cost projections from the SETP targets, where residential and commercial 

PV reaches $2.10/W and $1.66/W, respectively, by 2030. The highest PV penetration 

scenarios include attractive PV financing parameters (e.g. attractive loan rates, increased 
vi 

loan terms, lower down payment fractions), an annual increase in electricity rates of 1%, 

and the value added for avoided carbon emissions. The large range in SolarDS results 

illustrates the model’s sensitivity to key parameters, where simulated PV adoption is 

primarily driven by PV costs, financing parameters and the assumption relating PV 

financial performance to PV market share. Results are discussed in detail in section 4. 
vii 

Table of Contents

List of Figures...........................................................................................................................................viii

List of Tables...............................................................................................................................................ix

1 Background and Introduction.............................................................................................................. 1

2 PV Annual Value Preprocessor........................................................................................................... 2

3 PV Market Share Module ....................................................................................................................10

4 SolarDS Results ..................................................................................................................................25

References .................................................................................................................................................31

Bibliography...............................................................................................................................................35

Appendix A: Model Regions and Electricity Rates ................................................................................36

Appendix B: PV Performance and Cost Reductions .............................................................................47

Appendix C: Building Assumptions ........................................................................................................49

Appendix D: PV Financing Assumptions................................................................................................51

Appendix E: PV Market-Penetration Curves...........................................................................................52
viii

List of Figures

Figure ES-1. The 216 solar resource regions used in SolarDS with observation stations 

shown as red triangles. ..................................................................................... iii

Figure ES-2. SolarDS model structure ............................................................................... iii

Figure ES-3. Cumulative installed PV capacity through 2030 for a range of model input 

parameters, including PV costs (EIA and DOE SETP), PV market adoption 

rates (NEMS and Navigant), and PV financing and policy assumptions (base 

case and aggressive case) .................................................................................. v

Figure 1. SolarDS components ........................................................................................... 2

Figure 2. PV annual value preprocessor ............................................................................. 2

Figure 3. PV energy output calculator ................................................................................ 3

Figure 4. The 216 TMY3 sites used in SolarDS and associated solar resource regions ..... 4

Figure 5. SolarDS regions in New York ............................................................................. 5

Figure 6. Annual PV revenue submodule ........................................................................... 5

Figure 7. Distribution of residential flat electricity rates in five states ............................... 7

Figure 8. Commercial demand reduction calculator ........................................................... 9

Figure 9. Monthly building demand reduction for various locations in the United States . 9

Figure 10. Overview of computational steps involved in the PV market share module .. 10

Figure 11. Time-to-net positive cash flow calculation ..................................................... 15

Figure 12. Market share as a function of payback period used in NEMS ........................ 17

Figure 13. Computational steps involved in the market share module ............................. 18

Figure 14. Maximum residential (a) and commercial (b) PV market share expressed as a 

function of payback time ................................................................................ 19

Figure 15. PV adoption rates based on PV payback times and the amount of time PV has 

been in the market ........................................................................................... 20

Figure 16. Aggregating buildings with PV ....................................................................... 24

Figure 17. Cumulative installed PV capacity through 2030 for a range of model input 

parameters, including PV costs (EIA and DOE SETP), PV market adoption 

rates (NEMS and Navigant), and PV financing and policy assumptions 

(base case and aggressive case) ...................................................................... 28

Figure 18. Cumulative installed residential PV capacity .................................................. 28

Figure 19. Cumulative installed commercial PV capacity ................................................ 29

Figure A-1. EIA Census + 4 Regions ............................................................................... 36

Figure B-1. PV Cost reductions for two PV learning rates (LR) derived in recent 

studies. ............................................................................................................ 48
ix

List of Tables

Table 1. SolarDS Input Parameters ................................................................................... 27

Table 2. Reference Case SolarDS Cumulative Installed PV Capacity ............................. 30

Table A-1. SolarDS Electricity Price Regions .................................................................. 36

Table A-2. AEO 2009 Rate Escalation by Region: Residential ....................................... 37

Table A-3. AEO 2009 Rate Escalation by Region: Commercial ...................................... 38

Table A-4. SolarDS Regions and Population Allocation .................................................. 38

Table B-1. PV Derate Factor by Component .................................................................... 47

Table B-2. PV Cost Reductions ........................................................................................ 47

Table C-1. Residential Roof Orientations ......................................................................... 49

Table C-2. Regional Roof Shading ................................................................................... 49

Table C-3. Commercial Roof Orientations ....................................................................... 49

Table C-4. Commercial PV Capacity by Building Type .................................................. 50

Table D-1. Residential Financing Inputs .......................................................................... 51

Table D-2. Distribution of Residential Financing ............................................................. 51

Table D-3. Commercial Financing Parameters ................................................................. 51

Table D-4. Commercial Capital Depreciation Schedule (MACRS) ................................. 51
1

1 Background and Introduction

1.1 Existing DOE PV Market-Penetration Models

The primary market-penetration model used by the Energy Information Administration 

(EIA) is the National Energy Modeling System (NEMS). NEMS is a very large model 

that covers all sectors of the U.S. energy economy with sufficient detail to analyze many 

energy issues (LaCommare et al. 2003, EIA 2008a, 2008b, 2008c). NEMS models the 

market penetration of rooftop solar PV within two building modules: the Residential 

Sector Demand Module (EIA 2008b) and the Commercial Sector Demand Module (EIA 

2008a). NEMS is a complex model designed to evaluate the entire U.S. energy system–

not individual technologies–in detail. As a result, it is difficult to simulate the complexity 

of the distributed PV market using NEMS (Margolis and Wood 2004). Other U.S. PV 

market-penetration models include a spreadsheet model developed by Navigant 

Consulting, Inc. (NCI) for the DOE’s Renewable System Interconnection study in 2007 

(Paidipati et al. 2008).

Because NEMS and other models do not consider either the large range of building and 

customer types or the geographical variation in PV performance and electricity rates, the 

Solar Deployment System (SolarDS) model was developed as a stand-alone tool to 

evaluate the potential market penetration of residential and commercial PV.

1.2 SolarDS Design Goals

SolarDS was developed as an easy-to-use alternative to the NEMS model that can 

provide a more detailed simulation of market-penetration dynamics for distributed PV 

technologies under a range of assumptions. Other design goals included short model run 

times (less than one hour) and reasonably transparent model assumptions and 

formulation.

SolarDS uses many of the same input parameters as NEMS and employs a method for 

calculating PV market penetration that is similar to the method used by NEMS. Inputs to 

SolarDS include regional solar insolation data, which are combined with a distribution of

current electricity rates and rate structures, to calculate the value of PV in multiple 

regions.

1.3 SolarDS Model Formulation

SolarDS was written as a collection of Visual Basic for Applications (VBA) modules 

with a Microsoft Excel interface. The front end was designed to allow users to easily 

modify key input parameters, and thus simulate PV market penetration under a variety of 

assumptions.

Figure 1 illustrates the basic structure of the SolarDS model, which consists of an input 

database, a PV Annual Value Preprocessor, and the main SolarDS module. The PV 

Annual Value Preprocessor simulates the annual revenue generated by a PV system in 

each geographical location. The preprocessor also simulates module orientation, 

electricity price region, and utility rate structure. The main SolarDS module combines the 

annual revenue and the cost of the PV system (including various incentives and financing

scenarios) to produce a financial performance metric. This performance metric is then 
2

used to calculate a market share, which is aggregated over all regions to estimate state 

and national PV market share. SolarDS forecasts PV market share in two-year increments 

from 2008 to 2030.2

Figure 1. SolarDS components

Section 2 describes the PV annual value module in more detail, and section 3 describes 

the financial performance calculations used to determine PV market share.

2 PV Annual Value Preprocessor

The Annual Value Preprocessor calculates the annual revenue generated by residential 

and commercial PV systems with a variety of system orientations, electricity rates, and 

utility rate structures. Figure 2 shows an overview of the major components of SolarDS, 

including the PV Annual Value Preprocessor, which calculates the revenue generated by 

PV systems for thousands of combinations of input parameters including location, PV 

orientation, electricity rate structures, and customer types.

Figure 2. PV annual value preprocessor

 2 SolarDS has the capability to run through 2050, but this option requires users to extrapolate PV cost 

reductions, electricity rates, and rate structures from 2030 through 2050. 

PV Annual 

Revenue 

Preprocessor

Model Input Data Base

SolarDS

Submodules

PV Annual 

Revenue 

Preprocessor

Model Input Data Base

SolarDS

Submodules
3

2.1 PV Performance Simulator

The PV technical performance simulator estimates the amount of electricity that a PV 

module of a given size will generate each hour, at hundreds of locations in the continental 

United States, and for a variety of module orientations (multiple tilt and orientation 

angles). Figure 3 is a simplified diagram of the simulation process for generating PV 

performance libraries.

Figure 3. PV energy output calculator

The amount of electricity generated per 1 kW of PV capacity is calculated using hourly 

solar insolation data from 216 locations in the Typical Meteorological Year 3 (TMY3) 

data set3 from the National Solar Radiation Database (NSRDB) (NREL 2007, Wilcox 

and Marion 2008). The amount of AC electricity generated by a given amount of PV 

capacity at each location and module orientation is calculated using the 

PVFORM/PVWATTS model,

4

 3 The 216 locations chosen for this analysis are, with a few exceptions, the stations in the original 1961-

1990 NSRDB. Although the updated (1991-2005) NSRDB contains several hundred additional sites, the 

216 original sites provide adequate coverage to capture the variation in solar resource within each state. For 

additional detail about the NSDRB, refer to NREL (2007).

which includes direct current (DC) to alternating current 

(AC) derate factor and a temperature-based parameterization of PV efficiency (Marion et 

al. 2005). TMY3 stations are associated with adjacent census blocks, which are defined 

as solar resource regions in SolarDS as shown in Figure 4. Further information on PV 

module derate factors, orientations and the location of TMY3 sites is provided in 

Appendix A.

4 PVFORM is the PV performance model used in the PVWatts tool. PVFORM accounts for changes in 

module efficiency with temperature and the variation in inverter efficiency as a function of load. Additional 

details are available at http://www.nrel.gov/rredc/pvwatts/.

TMY3 Solar 

Insolation 

Database

PV Technical 

Characteristics 

and Orientations

Binned PV Output

Binning 

Tool

Hourly PV Output

(8760 hours)

Hourly PV 

Output PVFORM
4

Figure 4. The 216 TMY3 sites used in SolarDS and associated solar

resource regions

The hourly PV simulations are archived in a PV performance library that is used by the 

Annual Revenue Calculator.

2.2 PV Annual Revenue Calculator 

The PV Annual Revenue Calculator combines the PV technical performance, electricity 

rates and rate structures, and building load simulations to create the expected annual 

savings associated with a specific PV system.

Annual revenue is calculated for each unique combination of TMY site, electricity price 

region, rate structure, and orientation. The combination of TMY site and electricity price 

region is defined as a SolarDS region. There are 435 unique SolarDS regions in the 

United States. The definition of a SolarDS region is demonstrated in Figure 5, which 

shows TMY3 sites for New York state.
5

Figure 5. SolarDS regions in New York

The most appropriate TMY site is not always located within a state’s borders. For 

example, a consumer evaluating potential PV system performance in far northeastern 

New York would likely use the insolation data for Burlington, Vermont. To calculate the 

financial performance of a PV system in northeastern New York, a combination of New 

York electricity rates and a TMY site in Vermont would be used. For the state of New 

York, 14 TMY sites (7 internal and 7 external) are combined with the state’s electricity 

rates, resulting in 14 SolarDS regions. At the national level, the unique combinations of 

TMY sites and state-based rate structures results in 435 SolarDS regions.

Figure 6 illustrates how the SolarDS calculates the annual PV revenue for each simulated 

PV system. Annual PV revenue is calculated as the avoided cost of electricity, or the 

amount the customer would have paid the utility without the PV system.

Figure 6. Annual PV revenue submodule
6

The revenue generated by a PV system is calculated by multiplying the hourly PV output 

by the hourly cost of electricity at each location. The cost of electricity is determined by 

the rate structure, which may be a flat, time-of-use (TOU) rate or a demand rate 

(discussed in more detail in the next section).

In SolarDS, the escalation in electricity rates through 2030 is estimated using either the 

Energy Information Agency’s Annual Energy Outlook (AEO) 2009 forecast (EIA 2009a) 

or a user-supplied escalation scenario. The end product is a database containing the 

annual revenue generated by a PV system in each unique combination of geographic 

location, module orientation, rate region, rate structure, and rate escalation scenario.

2.2.1 Treatment of Basic Rate Structures

The large variation in electricity rates and rate structures used by U.S. utilities is 

challenging to capture in national end-use models. The average retail price of electricity 

can vary significantly both within and among states. Regional variation in electricity 

prices can significantly impact the simulated adoption rate of PV. For example, using the 

average of electricity rates for each state would underestimate PV adoption because of 

the non-linear relationship assumed between PV revenue/cost and customer adoption 

rates (sections 3.2-3.3). Capturing early adopters in this manner is especially important 

when calculating learning based cost reduction5

Most residential customers in the United States are billed based on flat or seasonal flat 

rates. However, time-of-use (TOU) rates are offered for an increasing number of 

customers, and TOU rates may increase the value of PV to many consumers (Hoff and 

Margolis, 2004; Mills et al. 2008). TOU rates establish two or three billing periods within 

each day (on-peak, off-peak, and “shoulder”) and may include two or three demand 

“seasons” (summer, winter, and spring/fall), leading to a total of four to nine rate periods.

(discussed later in section 3.1 and 

appendix B). 

Another common rate structure combines one of three basic rate structures (flat, seasonal 

flat, or TOU) with “block” or “tiered” rate structures. Combinations of structures like 

these charge a different amount for electricity based on the quantity used. Increasing 

block/tiered rates are most common in California’s investor owned utilities.

SolarDS simulates the availability of several rate types as well as the regional variation in 

electricity costs. SolarDS establishes a base rate, flat rate, and TOU rate for 53 price 

regions, based on the current tariff sheet from the largest utility in that region. The price 

regions include all 48 continental U.S. states and D.C., with California and Nevada split 

into two regions (north and south), and New York separated into three regions (New 

York City, Long Island, and the rest of New York). Appendix A provides a list of the 

SolarDS utility regions and representative utilities. Once the base rates are determined, a 

distribution of electricity rates for residential and commercial customers in each region is 

generated.

 5 To characterize the cost reduction of a manufactured good with cumulative amount produced, we use a 

learning- or experience-based parameterization. Historic PV prices show a range of price decreases from 17 

to 26% with each doubling in manufactured PV capacity (Nemet 2006). The NEMS model uses a learning 

rate that is approximately 13% (EIA 2008a). 
7

This distribution consists of five rate bins, representing the range of electricity prices in 

each region and the fraction of customers in each rate bin. This range is derived from the 

EIA form 861 data set, which provides total revenue and total sales for each electricity 

service provider in the United States.6 Creating a distribution based on average revenue 

assumes that the rate structure for the remaining utilities is essentially the same as the 

largest utility, which roughly captures the price difference for utilities in the state.7

Modeling tiered rates is difficult because PV installations reduce electricity use at the 

marginal tier. Tiered rates can exceed 30 cents/kWh for residential customers in the 

highest tier, but only a fraction of customers will consume a substantial amount of energy 

at that tier. Many customers in California pay for electricity using tiered rates that are 

roughly captured by the wide spread in the electricity rate distributions in California.

Figure 7 illustrates the distribution of residential electricity rates within each state and the 

differences among states.

Distribution of Residential Flat Rates

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

6-8 8-10 10-12 12-14 14-16 16-18

Cents/kWh

Fraction of Customers

Colorado

Mississippi

Massachusetts

Pennsylvania

Washington

Figure 7. Distribution of residential flat electricity rates in five states

SolarDS offers several options for treating net metering. The user can choose a full net 

metering option where surplus PV generation (output that exceeds building load) is sold 

back to the utility at retail rates. Alternately, the user can choose a no net metering option 

where a fraction of the PV electricity is assumed to be used by the customer when it is 

produced and valued at retail electricity rates (as it directly offsets electricity purchases), 

and the remaining PV generation is exported to the grid and sold at “avoided costs.” 

 6 Ideally, utility-level data for 2008 would be used. Because they were not available, the state average 2008 

escalation was applied to 2007 utility data.

7 This method could not be used to evaluate a single PV system, but it does capture general trends.
8

Avoided costs are generally equal to the cost of avoided fuel use and can be set at the 

prevailing cost of natural gas generation (which is typically at the margin during hours of 

peak PV output) or equal to the cost of the fuel for the average generation mix for each 

state (EIA, 2009b). 

The escalation in electricity prices from 2008 through 2030 is approximated using 

residential and commercial rates escalations from the Annual Energy Outlook 2009 (EIA 

2009a), which are estimated at the census level. Alternatively, SolarDS users can specify 

different rate escalations at the state level to capture different future scenarios. 

2.2.2 Simulation of Demand Rates

Small commercial customers are often charged on flat and TOU rates and are captured in 

SolarDS using the same method as residential customers. However, many large 

commercial customers are billed based on demand rates which charge for both the 

amount of energy use ($/kWh) and the amount of peak power use ($/kW peak demand). 

The value of PV in a demand rate is the sum of two components: the value of reducing 

peak demand each month and the value of electrical energy generated by the PV system

(Mills et al. 2008). The demand reduction value is calculated for each building type by 

multiplying the building-specific reduction in peak load demand by the demand charge of 

a representative utility for each state. The PV energy value is calculated for flat or TOU 

rates, depending on the representative utility rate structure.

PV demand reductions are calculated by simulating hourly load profiles for a range of 

commercial buildings types. First, a commercial building load library was generated 

based on 22 “benchmark” commercial building types that were simulated using 

EnergyPlus8

The end product of the PVFORM and EnergyPlus simulations is a library of peak 

demand reductions for commercial buildings. Figure 8 illustrates the process of 

generating the demand reduction library.

(Griffith et al. 2008). EnergyPlus models energy use in buildings, including 

heating, cooling, ventilation and lighting loads. The load profiles for these benchmark 

building types are associated with the 14 building types in the EIA’s Commercial 

Buildings Energy Consumption Survey (CBECS) database (EIA 2003), which are 

described in detail in Appendix C. Hourly load profiles for these 14 building types are 

simulated in each of the 216 solar resource regions to derive a “normal” building load 

profile for a total of 3024 unique building type/location combinations. To evaluate the 

potential demand reduction from PV, the hourly building load simulations were 

combined with hourly PV output. Both the EnergyPlus building simulations and PV 

simulations use the same weather data, which allows us to capture the coincidence of 

weather driven electricity demand and solar resource availability. To increase simulation 

speed and limit data requirements, hourly data were used in all simulations. As a result, 

the SolarDS model does not capture short-term fluctuations in PV output (e.g. passing 

clouds) that could significantly impact demand charges. Demand-responsive controls 

(e.g. solar load controls) could mitigate most of these impacts, even if they are only 

installed on HVAC components (Hoff et al 2007).

 8

EnergyPlus is a publically available model that can be downloaded at http://www.energyplus.gov/.
9

Figure 8. Commercial demand reduction calculator

Figure 9 demonstrates the demand reduction output for four building types. The general 

pattern of PV demand reduction is similar for most buildings and locations. Summer 

building peak loads are primarily driven by air conditioning demand, and PV reduces 

peak demand by 20%-60% of its installed capacity. Demand reduction is not 100% 

because of the 2-4 hour difference between solar peak (around 1 p.m. Daylight Saving 

Time) and thermal peak (around 3-6 p.m. DST). As a result, a 10-kW PV system can be 

expected to reduce peak demand by 2-6 kW. Winter building loads are primarily driven 

by heating and lighting that are not coincident with peak PV output. During winter 

months, PV frequently reduces peak demand by less than 10% of its installed capacity.

0%

10%

20%

30%

40%

50%

60%

70%

80%

Jan

FebMar

Apr

June

July

Aug

Sept

Oct

Nov

Dec

Month

Demand Reduction (% of PV Size)

Fresno School Chicago Large Office

Sioux City Hospital Minot, ND Medium Office

Figure 9. Monthly building demand reduction for various locations in the United States

TMY3 

Database

EnergyPlus

Commercial 

Building 

Database

Commercial 

Building 

Performance 

Library

Commercial 

Demand 

Reduction 

Hourly PV Library

Output
10

2.2.3 Value of Avoided Carbon Emissions 

SolarDS allows for the potential impact of a carbon constraint and corresponding increase 

in the price of retail electricity.9 Since the future price of carbon is highly uncertain, the 

reference SolarDS model does not include a price for carbon emissions. However, users

can specify the price of carbon emissions ($/ton CO2) at each two-year model time step. 

Users have three options for estimating the carbon intensity of displaced electricity. They 

can: (1) set a carbon intensity based on the local mix of fuels used to generate 

electricity,10

3 PV Market Share Module

(2) select a carbon intensity based on natural gas generation, which is 

frequently at the margin when PV output is highest (Denholm et al. 2009), or (3) set the 

carbon intensity for each state based on other criteria. These options allow users to 

quantify the sensitivity of PV adoption to multiple carbon price scenarios.

The main SolarDS module simulates the market adoption of PV in each SolarDS region, 

based on the price of PV systems, the revenue generated by PV systems, and system 

financing parameters. SolarDS first generates financial metrics of PV performance from 

PV cash flows over a fixed analysis period. SolarDS then simulates the decision-making 

process used by potential PV customers. The time-to-net positive cash flow metric is used 

for residential customers, and internal rate of return (IRR) is used for commercial 

customers. These financial metrics are used to estimate the maximum potential PV 

market share and the rate of PV adoption. Figure 10 illustrates the three submodules that

calculate PV cash flows, simulate PV market share and adoption rates for each region, 

and aggregate the regional results to state and national PV market adoption rates.

Figure 10. Overview of computational steps involved in the PV market share module

 9 A price for carbon will increase the price of electricity generated using fossil fuels, which will increase 

the value of avoided electricity purchases. 

10 Carbon intensity for each state is adapted from Paidipati et al. (2008) based on the EIA AEO 2007. 

PV Revenue

(1) Value of Avoided Electricity 

(2) Federal and State Incentives

(3) Value of Avoided Carbon 

PV System Cost

PV and Building 

Aggregator Residential & Commercial 

Building Stock

Submodules

Market Share

(1) Maximum Market share curves

(2) Market Adoption rate 

PV Market Share 

Calculator

PV Cash Flow Calculator

PV Revenue

(1) Value of Avoided Electricity 

(2) Federal and State Incentives

(3) Value of Avoided Carbon 

PV System Cost

PV and Building 

Aggregator Residential & Commercial 

Building Stock

Submodules

Market Share

(1) Maximum Market share curves

(2) Market Adoption rate 

PV Market Share 

Calculator

PV Cash Flow Calculator
11

3.1 PV Costs 

The total cost of a PV system is the sum of the initial cost of the unit (module, inverter 

and installation fees) plus the variable cost incurred for operation and maintenance 

(O&M). Substantial PV cost reductions are expected in the future. However, estimates 

for these cost reductions are highly uncertain. SolarDS users can characterize PV costs 

over time in three ways. They can: (1) choose PV cost reductions based on the EIA 

projections (EIA 2009a) or the SETP targets (DOE 2008); (2) specify their own PV cost 

trajectory; or (3) choose learning-based cost reductions where the cost of PV is reduced 

by the user-specified rate for each doubling of cumulative installed PV capacity. In the 

literature, historical PV learning rates range from 17% to 26% (Nemet 2006). SolarDS 

users can specify different learning rates for each 2-year model time step to characterize 

evolving market dynamics as the PV industry matures. PV cost projections (EIA and 

SETP targets) and learning-based cost reductions are discussed in more detail in 

Appendix B.

Larger PV systems typically have lower costs per unit of capacity (Wiser et al 2007), and 

SolarDS simulates this decrease in PV cost with system size for both commercial and 

residential PV systems. Residential PV costs ($/kW) are based on 4-kW PV systems and 

adjusted by 2% per kW for larger or smaller systems (a 3-kW system is 2% more 

expensive and a 5-kW system is 2% less expensive than a base 4-kW system per unit of 

capacity (Wiser et al. 2007)). SolarDS assumes a distribution of residential PV 

installation sizes ranging from 1 to 6 kW, with mean PV installation sizes of 3.7 kW and 

3.0 kW for pre-existing detached and attached single-family homes and 4.25 and 3.2 kW 

for new detached and attached single-family homes. Mobile homes are assumed to have 

an average PV installation size of 1.7 kW. For commercial buildings, the base PV cost 

represents a 300-kW PV installation and the price is adjusted by 0.02% per kW11

3.2 Federal and State PV Incentives 

for 

larger and smaller installations. Since PV systems installed on new buildings typically 

cost less than systems installed on existing buildings, SolarDS allows the user to set a 

cost premium for PV on new buildings. In the reference case, PV is assumed to be 10% 

less expensive on new buildings.

Many PV incentives are available, including the federal investment tax credit (ITC), state 

incentives and local incentives at the municipal and utility levels. Incentives can be 

broadly categorized as capacity-based and production-based. Capacity-based incentives 

are based on the size or cost of the PV system. Production-based incentives are based on 

the amount of electrical energy (kWh) a PV system generates. SolarDS includes both 

capacity-based and production-based incentives at the federal and state levels. In a few

states (e.g. Colorado), state-level incentives are not offered, but utility incentives are 

available to more than 50% of the population. These utility-level incentives are included 

as state-level incentives in SolarDS, but they are capped by their budgets and expiration 

dates.

PV incentives are implemented in SolarDS to achieve two design goals: (1) to accurately 

characterize the current federal and state PV incentives for reference case simulations, 

 11 For example, a 200-kW PV system will be 2% more expensive per kW than a 300-kW PV system.
12

and (2) to allow users to simply and intuitively adjust incentive amounts, durations and 

budgets to quantify the impact of a wide range of potential incentive scenarios. SolarDS 

includes federal and state PV tax credits, state rebates, state production-based incentives, 

and the option to include federal production-based incentives, as discussed below.

1. Federal tax credits. The federal investment tax credit (ITC)12

2. Federal production-based incentive. Although there are no federal productionbased PV incentives, the user can add a federal production based incentive to 

quantify the impact of various incentives. A federal solar production tax credit 

(PTC) would be analogous to the incentives currently for wind generation. 

SolarDS users can set the incentive amount ($/kWh), duration, and expiration 

date.

is included in 

SolarDS. This 30% tax credit has no residential or commercial cap and expires in 

2016. After 2016, we assume a 10% commercial tax credit but no residential tax 

credit. Users can either run SolarDS with the current ITC or adjust the incentive 

amount, expiration date, or incentive cap.

3. State tax credits. State PV tax credits range from 15% to 100% of installed 

system costs. Incentive caps vary widely by state and the type of installation 

(residential or commercial). State-level incentives were characterized using the 

DSIREUSA database13

4. State rebates. State rebates are capacity-based incentives similar to state tax 

credits. As with state tax credits, we include the most current existing state 

rebates, but users are allowed to adjust incentive parameters as specified above.

and the information provided by each state. In the 

reference case, state tax credits are applied until their legislated expiration dates if 

they are specified or until 2014 if they are not specified. As with the other 

incentives, user can adjust incentive parameters, including whether the state 

budget allocation limits the incentives, or whether budgets will be expanded.

5. State production-based incentive. Production-based incentives are based on the 

amount of electrical energy generated. This production-based revenue is added to 

the net revenue of the PV system in the year it is generated.

The cost of both federal and state PV incentives is calculated for residential and 

commercial installations at each time step. The amount of money spent on incentives is 

tracked and compared with the budgets allocated for PV incentives at the state level. 

Tracking the money spent also enables users to quantify the relative impact per dollar 

spent for different incentives.

 12 The Energy Improvement and Extension Act of 2008 (EIEA), passed in October 2008, extended the 30% 

PV Investment Tax Credit through 2016, lifted the cap on residential PV installations, allowed utilities to 

use the ITC, and allowed customers to apply the tax credit against the alternative minimum tax (AMT). 13 Incentives and incentive caps are adapted from http://www.dsireusa.org/.
13

3.3 Cash Flow Calculator

PV installations require a large initial capital investment that is followed by operation and 

maintenance costs. The initial capital expenditure is generally financed, and the SolarDS 

model projects multiyear cash flows to generate financial performance metrics.

Many financial performance metrics are available, including payback period (simple or 

discounted), net present value, cost to benefit ratio, and the levelized cost of electricity. 

The metric used in a given analysis depends largely on consumer types. Residential 

customers may use relatively simple metrics, such as simple payback period, to decide 

whether to invest in a PV system. Commercial customers may use more sophisticated 

metrics to account for capital depreciation.

SolarDS model users can specify the loan rate, loan term, and down payment fraction for 

financing PV systems. SolarDS uses the combination of PV cost and revenue to: (1) 

generate different financial performance metrics for residential and commercial systems, 

both of which were adopted from the NEMS model, and (2) simulate the different 

decision-making process used by the different customer types. Residential systems use 

the time-to-net positive cash flow metric (LaCommare et al. 2003), which represents the 

first year that the revenue generated by the PV system exceeds the costs of ownership. 

Commercial systems use the internal rate of return (IRR) (EIA 2008a), which is 

equivalent to the discount rate that makes the net present value of PV cash flows equal to 

zero. The IRR metric is likely used to analyze the cost effectiveness of PV on commercial 

buildings. The IRR metric also better captures the value of PV with oscillating positive 

and negative cash flows, based on the accelerated capital depreciation schedule for 

renewable energy technologies.14

The method for calculating time-to-net positive cash flow for residential systems is based 

on an annual cash flow analysis. The initial cash flow is given by the down payment on 

the PV system. The loan amount is given by the PV system cost minus relevant state 

rebates and federal and state tax credits. The annual loan payment is calculated from the 

loan amount using the uniform capital recovery factor (UCRF) given by

The details of these calculations are outlined below.

UCRF = (1 ) 1

(1 )

+ −

+

n

n

r

r r

, where r is the interest rate and n is the loan term in years. The 

following summarizes the cash flows in the initial year. Loan amounts and loan payments 

are calculated using the following equations:

Loan Amount = PV System cost - Down payment

- Federal and State Tax Credits & Rebates (1) 

Loan Payment = Loan Amount * UCRF (2)

Residential systems are financed using a distribution of down payment fractions 

(Appendix D) to capture a range of financing options. Users can modify both residential 

 14 Capital depreciation for renewable energy systems follows the five-year Modified Accelerated Cost 

Recovery System (MACRS) schedule.
14

and commercial down payment fractions. With these options, users can characterize both 

systems that are not financed (100% down payment) and the impact of new financing 

options (Fuller et al., 2009). For financed PV installations, the annual loan payment is 

calculated following equations 1 and 2 above.

Fixed incentives are subtracted from the initial PV system costs. Fixed incentives are 

generally onetime payments, such as federal or state investment tax credits or system 

buydowns (rebates). Buydowns, which are available in the form of state or utility credits, 

are subtracted from the initial purchase price. The base case assumes that state-level tax 

credits are federally taxable;

15

Annual cash flows for PV systems are used to calculate both the time-to-net positive cash 

flow and IRR metrics. We set the PV cash flow at year zero to be the down payment 

assumed for the system. In subsequent years, the annual cash flow is calculated from the 

sum of avoided electricity costs, tax savings on loan interest, state production incentives, 

avoided carbon costs (if there is a charge for carbon emissions in the future) minus the 

loan payment and annual operation and maintenance fees:

however, state and utility-level buy downs are not 

considered taxable income.

Year 0:

Annual Cash flow(t=0) = - Down Payment (3)

Years 1-30:

Annual Cash flow(t) = Avoided Electricity Costs

+ tax savings on the loan interest

+ State production-based incentives

+ avoided cost of carbon emissions

- Loan Payment

- Operations and Maintenance costs (4)

The tax savings on the loan interest is given by: 

Interest deductiony = Marginal Tax Rate * r * Loan Principaly (5)

where r is the interest rate. Production-based incentives are often available at the state 

and utility level and are added to the PV cash flow in the years they are earned. The 

Operations and Maintenance costs are primarily based on inverter replacement. Inverter 

costs and lifetimes are based on SETP targets and Wiser et al. (2009) and inverter 

replacement costs are subtracted from PV cash flows in the year they are replaced.

Residential cash flows are calculated for 30 years. If the net cash flow becomes positive, 

the payback period is calculated by interpolating over the last time period to find the 

fractional number of years to reach net positive cash flow. If the net cash flow remains 

negative through the 30-year analysis, the payback period is set to 30 years, which results 

in zero market penetration. Figure 11 illustrates the process of calculating the time-to-net 

positive cash flow metric for residential systems.

 15 State buydowns reduce customers’ state tax burdens, which are tax deductable at the federal level. 
15

Figure 11. Time-to-net positive cash flow calculation

To calculate the payback period for commercial PV installations, we first separate 

potential customers into for-profit and not-for-profit categories. With not-for-profit 

buildings, where cash flows are similar to residential buildings with tax considerations 

removed, a similar time-to-net positive cash flow simulation was run. With for-profit 

buildings, customers have more complicated tax structures that include capital both 

depreciation of PV systems and tax deductable energy and O&M costs. These additional 

tax considerations often lead to net cash flows that oscillate between positive and 

negative values over the analysis period, making the time-to-net positive cash flow metric 

difficult to interpret. We account for the following four tax impacts for commercial PV 

customers:

• PV system costs are depreciated, decreasing the tax burden according to the 

depreciation schedule. SolarDS uses the Modified Accelerated Cost Recovery 

System (MACRS) schedule.

• Since electricity expenses are tax deductible, the net annual savings from a PV 

system is reduced by the marginal tax rate.

• The interest on a PV system loan is tax deductable.

• Operation and maintenance (O&M) expenses are tax deductible.

To calculate the payback period for PV installations, we use the internal rate of return 

(IRR) metric for commercial for-profit customers. IRR is the annualized effective rate of 

return that can be earned by investing capital in a PV system, and is calculated from the 

annual cash flow generated by a PV system. At year zero, the cash flow is negative and 

equal to the down payment for the PV system. In each subsequent year, cash flow is 

calculated by the sum of PV revenue, loan payments, and tax credits. This calculation is 

similar to the calculation in equations 1 to 5 but with added tax considerations. IRR is 

calculated by finding the equivalent rate of return necessary for the PV system to have a 

net present value (NPV) of zero:

∑=

= + =

N

t t

t

IRR

C

NPV

0

0

(1 ) (6)
16

where Ct is the annual net revenue of the PV system in year t. Following the methodology 

used in the NEMS commercial distributed generation module, we derive a simple 

payback period (T) for a PV system by calculating the time required for capital to double 

in value while appreciating at the annualized IRR (EIA 2008b).

(1+ ) = 2 T IRR (7)

log(1 )

log(2)

IRR

T

+ = (8)

IRR is translated into payback time because the market adoption curves used by NEMS 

and many other studies are “calibrated” to payback time (Kastovich et al. 1982, Paidipati 

et al., 2008; R.W. Beck, 2009). We cap the maximum payback time (T) at 30 years, 

which results in zero PV market penetration.

While IRR is likely a better metric for simulating the decision process for installing 

commercial PV systems, the IRR of a system is often difficult to interpret. Since PV 

incentives are highest in the first few years of ownership (e.g. tax rebates, buydowns and 

accelerated depreciation), a financed system will frequently have positive cash flows in 

the first five years followed by negative cash flows. This can lead to a circumstance with 

very high IRRs that significantly discount the negative cash flows following the 

accelerated depreciation schedule.

Both payback calculations require a number of inputs, including PV capital costs, O&M 

costs, and various policy-based incentives. The base case assumptions for these values 

are described in section 4, along with user-defined options for adapting the model to 

additional run scenarios.

3.4 Market Share Calculator

Forecasting PV adoption by end users based on the financial performance of a PV 

investment is perhaps the most subjective aspect of any market-penetration model. The 

model must simulate both the ultimate fraction of customers16

Most market-penetration models use a “market diffusion” or “S-curve” model that 

simulates the rate of market adoption over time. The general characteristics of an S-curve 

model are shown in Figure 12, which illustrates the annual adoption rate as a function of 

payback time and diffusion rate used in the NEMS model for PV systems on new 

buildings (EIA 2008a).

that will adopt PV 

technology and the rate of customer adoption. Considerable literature exists on this 

subject; however, solar PV has unique characteristics that differentiate it from many of 

the products and services examined in market-penetration studies. As a result, there is 

considerable uncertainty about which consumer behavior studies are applicable to solar 

PV adoption.

 16 A ‘customer’ refers to the occupant of a building that is suitable for PV adoption as described in section 

3.5.
17

Source: Adopted from LaCommare et al., 2003

Figure 12. Market share as a function of payback period used in NEMS
18

The curves in Figure 12 represent different payback periods, where products with a 

quicker payback time achieve a higher market share and have faster adoption rates. 

Market-penetration is simulated in the NEMS model by the product of the maximum 

market share and the adoption rate, expressed as follows:

PV Market Share(PV payback time, time) =

Max Market Share(PV payback time) * Adoption Rate(time) (9)

SolarDS uses a similar dynamic market share calculator that is based on an S-Curve 

market diffusion model. PV market share is calculated by the product of the maximum 

market share (which is approximated using an empirical relationship to payback time) 

and the rate of adoption (which is approximated using a Bass diffusion model [Bass 

1969, Mahajan, et al. 1990]). Figure 13 illustrates the computational steps in the market 

share module.

Figure 13. Computational steps involved in the market share module

SolarDS users have three options for characterizing the maximum market share as a 

function of payback time: (1) use the NEMS maximum market share curves (EIA 2004), 

(2) use the Navigant Consulting, Inc. curves (Paidipati et al. 2008), or (3) supply userdefined maximum market share curves. Each method has different maximum adoption 

curves to capture the different market dynamics for new and existing buildings. Userdefined curves are calculated using an exponential fit (R.W. Beck 2009), defined as 

follows:

 * e Payback Sensitivity Payback Time Maximum Market Fraction − = (10)
19

The Payback Sensitivity variable determines the shape of the maximum market fraction 

curve and can be set by the user to characterize different PV market dynamics and 

conduct sensitivity analysis. A value of 0.3 has been used previously (R.W. Beck 2009)

to fit the mean of two market adoption curves (Kastovich et al. 1982, Paidipati et al.

2008). Figure 14 shows the maximum market fraction curves modified from NEMS and 

Navigant, and a user-defined market adoption curves with Payback Sensitivity = 0.3 for 

commercial and residential customers.

(a)

Maximum PV Market Share: Residential

0

10

20

30

40

50

60

70

80

90

0 5 10 15 20 25 30

Payback Time

Max Market Share (%)

Navigant Retrofit

NEMS Retrofit

Navigant New

NEMS New

Exp(-0.3*Payback)

(b)

Maximum PV Market Share: Commercial

0

10

20

30

40

50

60

70

80

90

0 5 10 15 20 25 30

Payback Time

Max Market Share (%)

Navigant Retrofit

Navigant New

NEMS New & Retrofit

Exp(-0.3*Payback)

Figure 14. Maximum residential (a) and commercial (b) PV market share expressed as

a function of payback time17

 17 The minimum payback time allowed in SolarDS is one year.
20

The rate of PV adoption (S-Curve) is calculated using the Bass-Diffusion model, 

expressed as follows:

Adoption Rate (t) = 

p q T

p q T

e

p

q

e

− + •

− + •

















+

−

( )

( )

1

1

(11)

where T is the time from the initial year the product was introduced, p represents the 

“coefficient of innovation” characterizing early adopters of a technology, and q

represents the “coefficient of imitation” characterizing late adopters of a technology. We

simulate increasing adoption rates with decreasing payback times, as indicated by the 

three S-curves used in SolarDS that are shown in Figure 15. Additionally, users can 

modify the p and q parameters to simulate different PV diffusion rates.

Figure 15. PV adoption rates based on PV payback times and the amount of time PV has 

been in the market

One complication of using multiple S-curves is transitioning from one curve to another 

when PV payback periods decrease with PV cost reductions and/or increasing electricity 

rates. Since the market-penetration fraction scales the total building stock, this transition 

would lead to a step increase in PV adoption in the year that the model switched from one 

curve to another. To smooth this transition, we calculate the position on the new S-curve 

by solving for the “equivalent year” that represents the previous year’s market share, 

shown in Equation 12, which is equivalent to equation 11 solved for T using the previous 

year’s market share:

0%

20%

40%

60%

80%

100%

0 5 10 15 20 25 30

Years the Technology has been in the Market

 Cumulative Adopters

payback < 3 years

3 < payback < 10 years

payback > 10 years
21

 

Max Market Share(t -1) 1-

Max Market Share(t) ln 

Max Market Share(t -1) q 1+ *

Max Market Share(t) p Equivalent Year = -(p+q)

         

        (12)

After the equivalent year is calculated, the model steps forward in time by two years and 

solves for the total market share using the p and q parameters from the new S-curve. The 

initial year for the diffusion curves is set to 2001, when the total U.S. installations 

exceeded 25 MW. The initial year is set to 1999 in California to characterize the quicker 

adoption rates (Paidipati et al. 2008).

3.5 Calculating building stock and aggregating PV installations 

The market share calculator produces an array containing hundreds of thousands of PV 

purchasing probabilities that are unique to each building type, size, age, utility rate 

structure, and local solar insolation. To estimate the number of buildings that will have 

PV installed, PV adoption probabilities must be multiplied by the actual number of 

buildings corresponding to the combination of input parameters. The number of buildings 

is calculated in a building stock database that consists of multidimensional arrays that 

characterize the range of unique buildings types for residential and commercial buildings. 

This building stock database is populated from a variety of sources.

Residential Building Array

The residential buildings stock for attached and detached single-family homes, mobile 

homes, and rental units is estimated from the 2000 U.S. Census. The fraction of each 

building type that is occupied by an owner or renter is estimated using the EIA’s 2005 

Residential Energy Consumption Survey (RECS) (EIA 2005). The base building stock is 

scaled down to remove homes that are unsuitable for PV, primarily due to shading. 

Shading estimates are applied at the EIA’s “Census + 4” regions, which includes the 

census regions plus the four largest states. Data on shading are limited, and the base 

SolarDS case uses a previous national estimate that is adjusted to increase shading in the 

eastern United States and decrease shading in the Southwest. Shading fraction 

assumptions are listed in Appendix C.

The growth rate of residential building stock is forecasted in two-year increments from 

2008 to 2030, based on census population projections at the state level. We assume that: 

(1) the building stock will grow in proportion to the state population, (2) the regional 

distribution of the population will stay fixed, and (3) the distribution of building types 

will remain constant.

After simulating the base number of homes suitable for PV, the building stock library 

categorizes these homes, based on the following seven array dimensions: SolarDS region, 

building size, roof orientation, finance type, rate type, rate bin, and building vintage.

1. SolarDS Region. Census data provide the numbers of detached single-family 

homes, attached single-family homes, and mobile homes in each state. The states 
22

are then divided into SolarDS regions, based on census blocks. Each census block 

is assigned to a specific TMY meteorological site. The number of buildings in 

each SolarDS region is calculated from the fraction of the state population that 

resides in the associated census blocks.

2. Building PV Size. SolarDS calculates a range of residential PV system sizes 

based on building types, where the “average” size for a residential PV installation 

is 4.3 kW and 3.8 kW for new and existing detached single-family homes, 3.2 kW 

and 3.0 kW for new and existing attached single-family homes, and 2.0 kW for all 

mobile homes. The size distribution is assumed to be uniform in all geographical 

regions.

3. Roof Orientation. We assume a distribution of solar orientations in the SolarDS 

model. For residential homes, we assume that 10% of buildings have flat roofs 

and the remaining 90% have pitched roofs characterized by seven solar 

orientations that are uniformly distributed around 360°. Roof characteristics are 

assumed to be identical for all geographical regions. A table of roof orientations 

and distributions is provided in Appendix C.

4. Finance Type. SolarDS uses a distribution of down payment fractions and 

marginal tax rates to characterize the variety of financed residential PV systems 

(see Appendix D). Users can modify customer down payment fractions, marginal 

tax rates, loan rates, and loan terms to quantify the sensitivity of PV adoption to 

various scenarios.

5. Rate Type. SolarDS allows users to select from three rate types: all flat, all timeof-use (TOU), and a combination of flat and TOU rates that changes over time to 

reflect the gradual adoption of TOU rates. The “all flat” and “all TOU” options 

are provided to test the sensitivity of PV deployment using the two rate structure 

bounds.

6. Rate Bin. SolarDS splits residential customers into five rate bins in each region to 

characterize the distribution of customer rates, as discussed in section 2.2.

7. Vintage. The cost of PV installations on pre-existing buildings is higher than the 

cost of PV installations on new construction. SolarDS allows users to specify the 

relative cost reduction for new buildings, which in the reference case is set to 

10%. New and existing buildings also use different maximum market-penetration 

curves, reflecting the different decision process made by the builder and 

homeowner. Existing buildings are calculated from the census data, as described 

above. New construction is primarily driven by the growth in building stock but 

also from rebuilds.

Rental properties represent a large fraction of commercial and residential buildings. This 

significantly limits the use of solar PV. Building owners, who do not pay the electric bill, 

have no incentive to install PV. Renters have limited incentive to make long-term capital 

investments on property they do not own. The fraction of homes that are rented for 

attached single-family, detached single-family, and mobile homes are 12%, 34%, and 

17%, respectively, as taken from the 2001 Residential Energy Consumption Survey 
23

(RECS) at the EIA’s “Census + 4” regions. PV adoption on rental units is simulated 

separately using decreased market-penetration curves that can be modified by the user.

Commercial Building Array

Creation of the commercial building array begins with the “base” number of commercial 

buildings in the EIA’s 2003 Commercial Buildings Energy Consumption Survey 

(CBECS) (EIA 2003), allocated for the “Census + 4” regions. To account for roof 

shading, the amount of roof area is decreased by a regional shading fraction using a 

similar methodology as was used for residential buildings. Leased units are estimated 

using a uniform rent/own fraction for each region, based on the CBECS tax status in 

owned buildings (taxable/non-taxable). PV adoption on leased buildings is simulated 

using decreased market-penetration curves. This establishes the “base” number 

commercial buildings suitable for PV, which the building stock library then categorizes 

based on six array dimensions: SolarDS region, roof orientation, occupant class, building 

type, rate type, rate bin, and age.

1. SolarDS Region. Commercial buildings are allocated to SolarDS regions, based 

on the fraction of the state population in census blocks associated with TMY 

stations, as described previously.

2. Roof Orientation. Commercial PV orientations are based on the type on the 

“predominant roof material” of each building type, as reported in the CBECS 

database. If the reported roof material is shingle, wood, or slate, the roof is 

assumed to be pitched. For pitched roofs, we assume the same roof slope as 

residential buildings and the azimuth orientations listed in Appendix C. For the 

remaining roof types, we assume a flat roof surface filled with equal parts flat 

oriented PV and southerly facing PV, tilted at 25°. Fewer commercial roof 

orientations are evaluated than residential roof orientations because of the much 

larger size of the commercial building stock array.

3. Occupant Class. There are four classes of building occupants: for-profit owner 

occupied, non-profit owner occupied, leased, and government owned. These 

distinctions are made for three reasons. First, non-profit and government buildings 

are not taxable. Second, non-profit and government buildings may have a 

different decision-making process for evaluating PV investment. Finally, leased 

buildings likely have either a significantly lower market adoption rate or a thirdparty owner of the PV systems. Ownership data are derived from the CBECS tax 

status in owned buildings (taxable/non-taxable).

4. Building Type. SolarDS uses 14 building types from the Commercial Buildings 

Energy Consumption Survey database (EIA, 2003). Available roof area for PV is 

calculated by building type. PV installation sizes are calculated for each building 

type by scaling the available roof space by the module efficiency (W/ft2

). The 

types of buildings and associated roof areas are listed in Appendix C.

5. Utility Rate Class. SolarDS allows users to select from four rate types: all flat 

rates, all time-of-use (TOU) rates, all demand-based rates, and mixed rates, which 

represent a combination of flat, TOU, and demand-based rates. The all flat, all 
24

TOU, and all demand options are provided as benchmarks to test the sensitivity of 

PV adoption to commercial rate structures.

6. Vintage. As with residential buildings, users are given the option to set how much 

less expensive PV will be on new commercial buildings than on existing 

buildings. Also, new and existing buildings use different maximum marketpenetration curves, reflecting the different decision-making processes followed by 

builders and owners. New and existing commercial building stock is calculated 

from census projections, similar to the way in which building stock is calculated 

for residential buildings.

Based on these assumptions, the total potential PV capacity (technical potential) in 

SolarDS by 2030 is 583 GW, including 271 GW of residential and 312 GW of 

commercial PV capacity, which is in line with previous estimates of U.S. rooftop PV 

capacity (Denholm and Margolis 2008). Using the Navigant Consulting maximum 

market-penetration curves, the maximum obtainable rooftop PV capacity18

Buildings Aggregator

by 2030 is 

425 GW (182 GW residential, 243 GW commercial). Using the NEMS marketpenetration curves, the maximum obtainable rooftop PV capacity by 2030 is 225 GW (95 

GW residential, 130 GW commercial).

Once the residential and commercial buildings databases are established, the number of 

buildings adopting PV is calculated by multiplying the number of buildings in each class 

by the adoption fraction associated with each class. Figure 16 illustrates general approach 

for aggregating the new and existing buildings with PV. This process is run separately for 

residential and commercial buildings.

Figure 16. Aggregating buildings with PV

The submodule begins by calculating the existing building stock available for PV for 

each unique building type. The number of existing buildings with PV is calculated from 

 18 Minimum PV payback times are capped at one year in SolarDS due to model resolution. 

Building Stock Aggregator

Initial building 

stock database

Existing Buildings 

Suitable for PV

New Buildings 

Suitable for PV

Home 

Rebuilds

No PV installed at the end of year

Existing Buildings 

with PV

Cumulative PV 

installations by State

Building growth 

projections 

database
25

the product of the fractional market share and the total number buildings in that particular 

building class.

The model similarly calculates the fraction of new buildings that install PV. The number 

of new buildings is calculated from the sum of (1) building growth calculated from 

census population projections at the state level, and (2) building rebuilds, which are 

assumed to be 1% of the existing buildings per year. The adoption of PV on new 

buildings is calculated from the product of the number of new buildings and the current 

adoption rate, calculated by the market share calculator. New buildings that do not adopt 

PV are then placed into the existing building stock.

The number of buildings with PV is summed over each building type to produce the 

amount of installed PV capacity (MW), and the fraction of buildings with PV for each 

state and time period. Total PV capacity (MW) is calculated from the product of the 

number of buildings that install PV and the size of the PV installation by building type. 

The state results are further aggregated to give national PV statistics for each time period.

4 SolarDS Results

SolarDS outputs the results of each simulation to a new Microsoft Excel workbook that 

includes user-defined run parameters saved in a main worksheet and the model output (at 

the state and national level) in a series of worksheets that include:

1. Annual PV Capacity

2. Cumulative PV Capacity

3. Annual Residential PV Capacity

4. Cumulative Residential PV Capacity

5. Annual Commercial PV Capacity

6. Cumulative Commercial PV Capacity

7. Annual # of Residential Buildings that Installed PV

8. Cumulative # of Residential Buildings that Installed PV

9. Annual # of Commercial Buildings that Installed PV

10. Cumulative # of Commercial Buildings that Installed PV

11. Fraction of Residential Buildings with PV

12. Fraction of Commercial Buildings with PV

13. Annual Cost of Residential Incentives

14. Annual Cost of Commercial Incentives

15. Cumulative Cost of Residential and Commercial Incentives

16. Residential Payback Times (aggregated to the state level)

17. Commercial Payback Times (aggregated to the state level)
26

The output file also contains an automated plotting tool to help the user quickly visualize

SolarDS results.

4.1 SolarDS Example Results

The amount of PV penetration simulated by SolarDS is highly dependent on model input 

parameters, primarily (1) future PV cost reductions, (2) the assumed maximum PV 

market-penetration curves as a function of PV financial performance, and (3) PV 

financing and policy-based assumptions. We illustrate the range of SolarDS output using 

six PV penetration scenarios. In the first four scenarios, we simulate PV adoption for 

combinations of high and low PV cost reductions and two maximum market share curves. 

In the last two scenarios, we illustrate the upper bound on PV penetration using high PV 

cost reductions in addition to aggressive financing and policy-based parameters. These 

parameters are defined by (1) decreasing the loan rate from 6% (real) to 4% (real), (2) 

increasing the loan term from 15 to 30 years for residential retrofit and commercial 

systems, (3) changing the residential loan structure so that 30% of customers pay no 

down payment on PV loans and 70% of customers pay a 20% down payment, (4) 

increasing the electricity rate escalations from AEO 2009 estimates to a 1% annual 

increase, and (5) adding a cost to future carbon emissions. The input parameters for the 

six model scenarios are summarized in Table 1.
27

Table 1. SolarDS Input Parameters

Scenario ( PV Cost / Market-Penetration Curve / Finance & Policy )

EIA/

NEMS/

Base

EIA/

Navigant/

Base

DOE 

SETP/

NEMS/

Base

DOE 

SETP/

Navigant/

Base

DOE SETP/

NEMS/

Aggressive

DOE SETP/

Navigant/

Aggressive

PV Cost EIA EIA DOESETP

DOESETPDOE-SETP DOE-SETP

Max PV Market 

Share

NEMS Navigant NEMS Navigant NEMS Navigant

Rate Escalation AEO 2009 AEO 2009 AEO 2009 AEO 2009 1% Annual 1% Annual

Rate Structures Base Mix Base Mix Base Mix Base Mix Base Mix Base Mix

Carbon Pricea - - - -

$20/ton CO2

2010 - 2020

$30/ton CO2

2021 - 2030

$20/ton CO2

2010 - 2020

$30/ton CO2

2021-2030

Federal ITC 30% to 

2016

com: 10% 

after

30% to 

2016

com: 10% 

after

30% to 

2016

com: 10% 

after

30% to 

2016

com: 10% 

after

30% to 2016

res: 10% 

after

com: 10% 

after

30% to 2016

res: 10% 

after

com: 10% 

after

State Incentives Current 

Incentives

Current 

Incentives

Current 

Incentives

Current 

Incentives

Current 

Incentives

Current 

Incentives

Loan Rate 6% (real) 6% (real) 6% (real) 6% (real) 5% (real) 5% (real)

Loan Term

(years)

Com: 15b

Resnew: 30

Resexisting: 

15

Com: 15 

Resnew: 30 

Resexisting: 

15

Com: 15 

Resnew: 30 

Resexisting: 

15

Com: 15 

Resnew: 30 

Resexisting: 

15

Com: 30 

Resnew: 30 

Resexisting: 30

Com: 30 

Resnew: 30 

Resexisting: 30

Down 

Payment

(%)

Com 20% 20% 20% 20% 20% 20%

20% of 

Res 

Homes

100% 100% 100% 100% 0% 0%

80% of 

Res 

Homes

20% 20% 20% 20% 20% 20%

a U.S. $2008 dollars 

b Bolinger (2009)

Simulated PV penetration is shown in Figures 17 through 19. It ranges from 15 to 193 

GW of PV capacity installed by 2030. The amount of installed PV capacity is highly 

dependent on future PV cost reductions. Modeled cumulative installed PV capacity 

reaches approximately 12 GW if future cost reductions follow EIA estimates (PV reaches 

$4.23/W and $2.85/W by 2030 for residential and commercial systems). Simulated PV 

capacity is approximately five times higher if PV cost reductions follow the DOE SETP

targets (PV reaches $2.09/W and $1.66/W for residential and commercial systems by 

2030). PV penetration is also highly sensitive to financing parameters, where a 

combination of the attractive financing parameters and a cost associated with carbon 

emissions (summarized in Table 1) leads to 158-193 GW of PV capacity by 2030.

The different PV maximum adoption fractions based on PV financial performance 

(NEMS and Navigant, see Figure 14) lead to small differences in the lower penetration 
28

scenarios, but significant differences (> 25 GW) in the high penetration scenario. This 

reflects similar adoption estimates for PV systems with higher payback times but a 

fundamental difference in estimated PV adoption for systems with very short payback 

periods. Additionally, the Navigant maximum market share curves lead to a significantly 

higher fraction of commercial to residential PV installations.

Cumulative Installed PV Capacity

0

25

50

75

100

125

150

175

200

2005 2010 2015 2020 2025 2030

PV Capacity (GW)

PV Cost / Market Penetration / Finance & Policy

DOE SETP / Navigant / Aggressive

DOE SETP / NEMS / Aggressive

DOE SETP / Navigant / Base

DOE SETP / NEMS / Base

EIA / Navigant / Base

EIA / NEMS / Base

Figure 17. Cumulative installed PV capacity through 2030 for a range of model input 

parameters, including PV costs (EIA and DOE SETP), PV market adoption rates (NEMS and 

Navigant), and PV financing and policy assumptions (base case and aggressive case)

Cumulative Residential PV Capacity

0

10

20

30

40

50

60

70

2005 2010 2015 2020 2025 2030

PV Capacity (GW)

PV Cost / Market Penetration / Finance & Policy

DOE SETP / Navigant / Aggressive

DOE SETP / NEMS / Aggressive

DOE SETP / NEMS / Base

DOE SETP / Navigant / Base

EIA / NEMS / Base

EIA / Navigant / Base

Figure 18. Cumulative installed residential PV capacity
29

Cumulative Commercial PV Capacity

0

20

40

60

80

100

120

140

2005 2010 2015 2020 2025 2030

PV Capacity (GW)

PV Cost / Market Penetration / Finance & Policy

DOE SETP / Navigant / Aggressive

DOE SETP / NEMS / Aggressive

DOE SETP / Navigant / Base

DOE SETP / NEMS / Base

EIA / Navigant / Base

EIA / NEMS / Base

Figure 19. Cumulative installed commercial PV capacity
30

Table 2. Reference Case SolarDS Cumulative Installed PV Capacity

Scenario

PV 

Cost

Market 

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

Reference 

Casea

EIA NEMS

Total 0.8 1.9 3.5 5.7 6.5 7.8 8.7 10.5 11.2 12.1 13.3 14.9

Residential 0.4 0.7 1.0 1.4 1.8 2.3 2.6 2.7 2.9 3.1 3.5 3.9

Commercial 0.4 1.2 2.5 4.3 4.8 5.5 6.1 7.8 8.3 9.0 9.8 10.9

EIA Navigant

Total 0.8 2.3 4.7 7.5 8.4 9.4 10.3 13.1 14.0 14.8 16.0 17.7

Residential 0.4 0.6 0.8 1.1 1.1 1.2 1.3 1.3 1.3 1.3 1.4 1.4

Commercial 0.4 1.7 3.9 6.4 7.2 8.2 9.0 11.8 12.7 13.5 14.6 16.3

DOE 

SETP

NEMS

Total 0.8 1.9 3.6 6.4 9.5 14.8 19.3 26.7 33.6 42.3 52.4 62.7

Residential 0.4 0.9 1.5 2.7 4.5 7.4 9.2 10.7 12.6 15.1 18.0 21.5

Commercial 0.4 1.0 2.1 3.6 5.0 7.5 10.0 16.0 21.0 27.3 34.3 41.2

DOE 

SETP

Navigant

Total 0.8 2.4 5.2 9.2 12.5 17.7 22.9 33.3 41.3 51.2 62.8 74.7

Residential 0.4 0.6 1.0 1.6 2.4 3.7 5.0 5.2 5.6 6.2 7.1 8.4

Commercial 0.4 1.8 4.3 7.6 10.1 14.0 17.9 28.1 35.7 45.0 55.7 66.3

Attractive 

Caseb

DOE 

SETP

NEMS

Total 0.8 3.9 8.7 15.1 24.9 37.9 50.9 75.2 95.8 118.0 139.0 157.7

Residential 0.4 1.7 3.1 5.7 9.5 14.7 19.8 25.5 32.6 40.7 49.5 58.5

Commercial 0.4 2.2 5.6 9.4 15.4 23.3 31.2 49.7 63.3 77.2 89.5 99.2

DOE 

SETP

Navigant

Total 0.8 5.0 11.2 19.6 32.0 47.5 63.7 95.0 120.3 146.3 170.6 192.5

Residential 0.4 1.8 3.2 5.9 9.9 15.3 21.0 26.8 34.2 42.8 52.4 63.1

Commercial 0.4 3.2 8.0 13.7 22.1 32.2 42.8 68.2 86.1 103.5 118.2 129.5 a Reference case PV financing simulation, EIA electricity rate escalations, no carbon tax.

b Attractive financing simulation, 1% annual electricity rate escalations and a $20-30/TonC cost for carbon emissions.
31

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36

Appendix A: Model Regions and Electricity Rates

Figure A-1. EIA Census + 4 Regions

Table A-1. SolarDS Electricity Price Regions

SolarDS Price 

Region

Representative Utility Residential Electricity 

Prices

Commercial Electricity 

Prices

Mean Min Max Mean Min Max

Alabama Alabama Power Co 9.32 6.40 13.70 8.70 5.58 13.87

Arizona Arizona Public Service Co 9.66 5.31 13.19 8.27 2.05 12.06

Arkansas Entergy Arkansas Inc 8.73 5.43 12.59 6.91 4.10 10.73

California (North) Pacific Gas & Electric Co 14.41 8.40 23.42 13.23 6.11 14.21

California (South) Southern California Edison 14.41 5.88 24.21 11.86 2.55 21.44

Colorado Colorado Springs 9.25 6.06 13.80 7.62 2.24 18.55

Connecticut United Illuminating Co 19.08 11.68 20.62 13.60 10.81 17.42

Delaware Delmarva Power & Light Co. 13.15 10.78 15.70 10.59 8.55 15.29

District of Columbia Potomac Electric Power Co 11.15 11.16 14.26 9.72 10.98 14.01

Florida Florida Power Corp (Progress 

Energy)

11.22 7.63 14.32 9.75 6.79 14.41

Georgia Georgia Power Co 9.10 5.47 13.63 8.07 5.79 12.50

Idaho Idaho Power Co 6.36 4.91 9.81 5.14 4.09 8.19

Illinois Commonwealth Edison Co 10.12 4.51 13.35 7.57 4.92 11.00

Indiana PSI Energy Inc (Cinergy) 8.26 4.80 10.98 7.29 4.21 20.71

Iowa Interstate Power (Alliant) 9.45 4.95 14.31 7.15 3.10 14.73

Kansas Kansas City Power & Light 8.19 4.85 14.42 6.84 2.74 34.12

Kentucky Kentucky Utilities Co 7.34 5.47 9.48 6.76 5.29 11.11

Louisiana Entergy Louisiana Inc 9.37 3.10 12.36 9.13 2.94 33.33

Maine Central Maine 9.62 9.37 34.64 8.27 9.23 23.82

Maryland Baltimore Gas & Electric Co 11.83 7.37 14.42 10.23 7.06 18.11

Massachusetts Boston Edison (Nstar) 15.53 9.21 18.65 12.56 10.06 19.87

Michigan Detroit Edison Co 10.21 6.21 18.31 8.69 6.38 17.01

Minnesota Northern States Power Co 9.18 5.36 14.58 7.48 1.88 13.59

Mississippi Entergy Mississippi Inc 9.36 7.22 12.01 8.92 6.94 12.70
37

Missouri Union Electric Co (Ameren) 7.69 4.67 13.48 6.34 3.96 13.52

Montana NorthWestern Energy LLC 8.77 4.67 11.16 8.18 1.57 10.58

Nebraska Omaha Public Power District 7.59 4.90 12.87 6.39 1.85 15.12

Nevada (North) Sierra Pacific Power Co 8.40 5.64 13.04 6.09 4.15 12.56

Nevada (South) Nevada Power Company 11.92 5.50 13.26 10.76 0.87 14.06

New Hampshire Public Service Co of NH 14.88 10.18 31.70 12.65 10.20 16.37

New Jersey Jersey Central Power & Lt Co 14.14 9.11 15.30 11.75 9.18 15.98

New Mexico Public Service Co of NM 9.12 7.59 16.65 7.66 3.86 41.97

New York (Long 

Island)

Long Island Power Authority 20.25 18.49 22.67 16.11 16.81 20.99

New York (NYC) Consolidated Edison-NYC 19.08 19.08 30.62 17.50 17.18 19.75

New York (Remain) Niagara Mohawk Power Corp 13.30 2.64 19.36 10.40 3.37 18.42

North Carolina Carolina Power & Light 

(Progress) 

9.40 6.70 13.86 7.43 5.94 16.67

North Dakota Northern States Power Co 7.30 4.93 9.73 6.58 1.90 9.32

Ohio Ohio Power Co (AEP) 9.35 5.56 12.85 8.22 4.71 13.24

Oklahoma Oklahoma Gas & Electric Co 8.58 5.37 16.40 7.33 4.48 28.99

Oregon Portland General Electric 

Company

8.19 4.22 10.70 7.11 3.40 10.96

Pennsylvania PECO Energy Co 10.86 6.99 20.45 8.98 5.09 17.94

Rhode island Narragansett Electric Co 14.04 13.85 40.12 11.89 12.15 42.02

South Carolina South Carolina Electric & Gas 9.19 7.50 15.63 7.74 4.61 11.32

South Dakota Black Hills Power Inc 8.07 4.85 11.49 6.61 1.98 17.16

Tennessee Memphis City of 7.84 1.37 11.51 8.09 5.61 12.97

Texas TXU Energy Retail Co LP 12.34 7.32 18.16 9.87 4.10 16.24

Utah PacifiCorp 8.15 2.64 11.90 6.54 2.08 13.25

Vermont Central Vermont Pub Service 14.15 8.16 16.91 12.29 9.63 18.51

Virginia Virginia Electric & Power 

(Dominion)

8.74 6.26 12.99 6.38 5.51 13.14

Washington Puget Sound Energy Inc 7.26 2.27 9.92 6.56 2.28 10.00

West Virginia Appalachian Power Co 6.73 6.15 13.15 5.85 5.58 13.20

Wisconsin Wisconsin Electric Power Co 10.87 6.17 15.45 8.71 5.65 13.87

Wyoming PacifiCorp 7.75 2.94 13.06 6.25 1.68 13.12

Because load patterns may vary for different climate patterns in northern and southern 

California, the state is separated into two regions to allow for different TOU rates. 

Nevada is split into two regions to allow for escalation based on the NEMS EMM 

regions. New York is separated into three regions. Generation and transmission into New 

York City and Long Island is severely constrained, resulting in much higher than average 

prices.

Table A-2. AEO 200919

Regiona

Rate Escalation by Region: Residential

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

NE 1.00 0.93 0.97 0.97 0.97 0.98 0.99 0.98 0.99 1.01 1.02 1.03 0.1

MA 1.00 0.89 0.93 0.97 0.98 0.99 1.00 1.00 1.03 1.06 1.08 1.09 0.7

ENC 1.00 0.95 0.97 0.99 1.00 1.01 1.02 1.04 1.05 1.08 1.10 1.12 0.5

WNC 1.00 1.01 1.02 1.03 1.02 1.02 1.02 1.02 1.02 1.03 1.04 1.06 0.3

SA 1.00 0.96 0.99 1.00 1.01 1.02 1.03 1.04 1.06 1.10 1.13 1.14 0.8

ESC 1.00 0.98 0.98 0.98 0.98 0.99 0.99 1.00 1.02 1.05 1.08 1.10 0.8

WSC 1.00 0.93 0.95 0.97 0.98 1.00 1.03 1.04 1.06 1.10 1.13 1.14 0.7

MTN 1.00 1.02 1.02 1.03 1.05 1.09 1.11 1.12 1.17 1.22 1.26 1.30 1.4

PAC 1.00 0.96 0.94 0.93 0.93 0.94 0.94 0.94 0.95 0.98 1.00 1.01 0.1 a Regions are shown in Figure A-1.

 19 Real, not nominal, electricity rate escalations (EIA 2009).
38

Table A-3. AEO 2009 Rate Escalation by Region: Commercial

Regiona 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 %

NE 1.00 0.89 0.89 0.87 0.87 0.89 0.91 0.90 0.92 0.94 0.97 0.99 -0.1

MA 1.00 0.85 0.86 0.89 0.92 0.94 0.95 0.96 0.99 1.03 1.06 1.08 0.6

ENC 1.00 0.92 0.91 0.91 0.93 0.95 0.97 0.99 1.00 1.04 1.07 1.10 0.7

WNC 1.00 0.99 0.99 1.00 0.99 1.00 1.00 0.99 1.00 1.01 1.03 1.06 0.8

SA 1.00 0.92 0.94 0.94 0.94 0.95 0.96 0.96 0.98 1.02 1.05 1.06 0.7

ESC 1.00 0.95 0.94 0.92 0.92 0.93 0.93 0.94 0.96 0.99 1.02 1.04 0.3

WSC 1.00 0.90 0.90 0.92 0.92 0.96 0.99 1.00 1.03 1.08 1.12 1.14 0.5

MTN 1.00 0.97 0.94 0.93 0.94 0.97 0.98 0.98 1.02 1.06 1.10 1.13 0.9

PAC 1.00 0.97 0.92 0.89 0.89 0.90 0.90 0.89 0.90 0.93 0.95 0.97 -0.1 a Regions are shown in Figure A-1.

Table A-4. SolarDS Regions and Population Allocation

SolarDS Site 

Number

SolarDS 

Electricity

Rate Region

TMY Site

(State & City)

Allocated 

Population

(yr 2000)

Fraction of 

State 

Population

1 AL AL Birmingham 1,572,515 35.36%

2 AL AL Huntsville 907,369 20.40%

3 AL AL Montgomery 759,750 17.08%

4 AL AL Mobile 620,977 13.96%

5 AL GA Columbus 302,428 6.80%

6 AL MS Meridian 102,777 2.31%

7 AL

TN 

Chattanooga 97,596 2.19%

8 AL FL Tallahassee 69,559 1.56%

9 AL GA Atlanta 14,129 0.32%

10 AR AR Little Rock 1,329,509 49.73%

11 AR AR Fort Smith 628,182 23.50%

12 AR TN Memphis 395,581 14.80%

13 AR LA Shreveport 170,909 6.39%

14 AR MO Springfield 139,097 5.20%

15 AR MS Jackson 10,122 0.38%

16 AZ AZ Phoenix 3,275,528 63.84%

17 AZ AZ Tucson 1,094,445 21.33%

18 AZ AZ Flagstaff 296,989 5.79%

19 AZ AZ Prescott 179,906 3.51%

20 AZ NV Las Vegas 140,581 2.74%

21 AZ CA San Diego 122,405 2.39%

22 AZ UT Cedar City 9,379 0.18%

23 AZ

NM 

Albuquerque 7,672 0.15%

24 AZ

CO Grand 

Junction 3,727 0.07%

25 CA So CA Long Beach 8,706,405 25.70%

26 CA No

CA San 

Francisco 7,101,835 20.97%

27 CA So

CA Los 

Angeles 6,245,807 18.44%

28 CA No CA Sacramento 3,859,322 11.39%

29 CA So CA San Diego 3,285,362 9.70%

30 CA So CA Fresno 1,568,991 4.63%

31 CA So CA Daggett 1,103,365 3.26%

32 CA So CA Bakersfield 750,676 2.22%

33 CA So CA Santa Maria 658,686 1.94%

34 CA No CA Arcata 348,186 1.03%

35 CA No NV Reno 136,149 0.40%

36 CA No OR Medford 74,816 0.22%

37 CA So AZ Prescott 14,411 0.04%

38 CA So NV Tonopah 7,501 0.02%
39

39 CA So NV Las Vegas 7,002 0.02%

40 CA No OR Burns 1,594 0.00%

41 CA So AZ Phoenix 1,540 0.00%

42 CO CO Boulder 2,773,036 64.47%

43 CO

CO Colorado 

Springs 667,357 15.52%

44 CO

CO Grand 

Junction 251,827 5.85%

45 CO CO Pueblo 205,112 4.77%

46 CO CO Eagle 178,849 4.16%

47 CO CO Alamosa 87,957 2.04%

48 CO WY Cheyenne 65,940 1.53%

49 CO KS Goodland 46,235 1.07%

50 CO NE Scottsbluff 23,387 0.54%

51 CT CT Bridgeport 1,681,647 49.38%

52 CT CT Hartford 1,415,748 41.57%

53 CT RI Providence 204,546 6.01%

54 CT MA Worchester 54,689 1.61%

55 CT

NY New York 

City 48,935 1.44%

56 DC VA Sterling 308,382 53.91%

57 DC MD Baltimore 263,677 46.09%

58 DE DE Wilmington 715,639 91.33%

59 DE NJ Atlantic City 65,930 8.41%

60 DE PA Philadelphia 2,031 0.26%

61 FL FL Tampa 4,380,438 27.41%

62 FL FL Miami 3,871,126 24.22%

63 FL

FL Daytona 

Beach 2,863,036 17.91%

64 FL

FL West Palm 

Beach 2,004,991 12.55%

65 FL FL Jacksonville 1,493,393 9.34%

66 FL FL Tallahassee 677,509 4.24%

67 FL AL Mobile 526,559 3.29%

68 FL AL Montgomery 89,085 0.56%

69 FL FL Key West 76,241 0.48%

70 GA GA Atlanta 4,094,795 50.02%

71 GA GA Macon 824,151 10.07%

72 GA GA Athens 754,462 9.22%

73 GA GA Savannah 545,545 6.66%

74 GA

TN 

Chattanooga 455,212 5.56%

75 GA GA Columbus 435,625 5.32%

76 GA GA Augusta 430,861 5.26%

77 GA FL Tallahassee 386,233 4.72%

78 GA FL Jacksonville 223,874 2.73%

79 GA TN Knoxville 30,782 0.38%

80 GA NC Asheville 4,913 0.06%

81 KS IA Des Moines 879,236 30.05%

82 KS IL Moline 654,336 22.36%

83 KS IA Waterloo 603,638 20.63%

84 KS IA Sioux City 252,352 8.62%

85 KS IA Mason City 226,679 7.75%

86 KS NE Omaha 213,387 7.29%

87 KS WI La Crosse 43,869 1.50%

88 KS SD Sioux Falls 37,013 1.26%

89 KS MN Rochester 14,291 0.49%

90 KS

MO Kansas 

City 1,523 0.05%

91 ID ID Boise 584,342 45.16%

92 ID ID Pocatello 425,334 32.87%

93 ID WA Spokane 267,814 20.70%
40

94 ID MT Missoula 14,240 1.10%

95 ID NV Elko 1,297 0.10%

96 IL IL Chicago 7,930,677 63.86%

97 IL IL Springfield 886,011 7.13%

98 IL MO St. Louis 872,763 7.03%

99 IL IL Rockford 793,140 6.39%

100 IL IL Peoria 696,099 5.60%

101 IL IN Evansville 384,252 3.09%

102 IL IL Moline 380,800 3.07%

103 IL WI Milwaukee 325,274 2.62%

104 IL IN Indianapolis 92,878 0.75%

105 IL MO Columbia 55,277 0.45%

106 IL WI Madison 2,122 0.02%

107 IN IN Indianapolis 2,563,710 42.16%

108 IN IN Fort Wayne 939,718 15.45%

109 IN IN South Bend 811,154 13.34%

110 IN IL Chicago 639,617 10.52%

111 ININ Evansville 484,918 7.97%

112 IN KY Louisville 383,808 6.31%

113 IN KY Covington 176,230 2.90%

114 IN OH Dayton 81,330 1.34%

115 KS KS Wichita 854,287 31.78%

116 KS

MO Kansas 

City 742,639 27.62%

117 KS KS Topeka 602,412 22.41%

118 KS KS Dodge City 247,028 9.19%

119 KS OK Tulsa 85,965 3.20%

120 KS MO Springfield 60,854 2.26%

121 KS

NE Grand 

Island 44,366 1.65%

122 KS KS Goodland 42,533 1.58%

123 KS NE North Platte 5,953 0.22%

124 KS TX Amarillo 2,381 0.09%

125 KY KY Louisville 1,179,255 29.18%

126 KY KY Lexington 1,024,225 25.34%

127 KY IN Evansville 521,506 12.90%

128 KY KY Covington 401,353 9.93%

129 KY WV Huntington 343,734 8.50%

130 KY TN Nashville 295,411 7.31%

131 KY TN Bristol 138,437 3.43%

132 KY TN Knoxville 123,850 3.06%

133KY TN Memphis 13,046 0.32%

134 LA

LA New 

Orleans 1,650,111 36.92%

135 LA

LA Baton 

Rouge 1,342,161 30.03%

136 LA LA Shreveport 789,351 17.66%

137 LA

LA Lake 

Charles 581,675 13.02%

138 LA MS Jackson 101,030 2.26%

139 LA TX Port Arthur 2,323 0.05%

140LA TX Lufkin 1,241 0.03%

141 LA AR Little Rock 1,084 0.02%

142 MA MA Boston 3,942,239 62.09%

143 MA MA Worchester 942,773 14.85%

144 MA RI Providence 681,104 10.73%

145 MA CT Hartford 667,208 10.51%

146 MA NY Albany 112,944 1.78%

147 MA NH Concord 2,829 0.04%

148 MD MD Baltimore 3,812,860 71.99%

149 MD VA Sterling 1,074,159 20.28%

150 MD DE Wilmington 225,657 4.26%
41

151 MD WV Elkins 101,035 1.91%

152 MD NJ Atlantic City 33,529 0.63%

153 MD VA Norfolk 24,184 0.46%

154 MD PA Harrisburg 22,844 0.43%

155 MD VA Richmond 2,218 0.04%

156 ME ME Portland 1,106,971 86.83%

157 ME ME Caribou 161,071 12.63%

158 ME NH Concord 6,879 0.54%

159 MI MI Detroit 4,119,864 41.45%

160 MI MI Flint 1,521,185 15.31%

161 MI

MI Grand 

Rapids 1,268,740 12.77%

162 MIMI Lansing 916,640 9.22%

163 MI MI Muskegon 466,710 4.70%

164 MI OH Toledo 403,237 4.06%

165 MI

MI Traverse 

City 402,807 4.05%

166 MI IN South Bend 312,555 3.14%

167 MI MI Alpena 161,440 1.62%

168 MI MI Houghton 159,262 1.60%

169 MI

MI Sault Ste. 

Marie 83,312 0.84%

170 MI WI Green Bay 82,885 0.83%

171 MI IN Fort Wayne 39,807 0.40%

172 MN

MN 

Minneapolis 2,925,313 59.46%

173 MN MN Saint Cloud 750,930 15.26%

174 MN MN Rochester 290,122 5.90%

175 MN ND Fargo 277,098 5.63%

176 MN MN Duluth 275,3105.60%

177 MN SD Sioux Falls 128,968 2.62%

178 MN MN Int. Falls 102,273 2.08%

179 MN IA Mason City 100,571 2.04%

180 MN WI La Crosse 66,761 1.36%

181 MN MI Houghton 2,133 0.04%

182 MO MO St. Louis 2,305,473 41.20%

183 MO

MO Kansas 

City 1,339,611 23.94%

184 MO MO Springfield 928,348 16.59%

185 MO MO Columbia 789,478 14.11%

186 MO TN Memphis 183,023 3.27%

187 MO IA Des Moines 25,526 0.46%

188 MO NE Omaha 7,522 0.13%

189 MO IL Moline 6,541 0.12%

190 MO AR Fort Smith 5,576 0.10%

191 MO AR Little Rock 3,290 0.06%

192 MS MS Jackson 987,829 34.73%

193 MS TN Memphis 640,766 22.53%

194 MS MS Meridian 566,672 19.92%

195 MS AL Mobile 392,699 13.80%

196 MS

LA Baton 

Rouge 107,514 3.78%

197 MS

LA New 

Orleans 106,861 3.76%

198 MS AL Huntsville 35,500 1.25%

199 MS AL Birmingham 4,255 0.15%

200 MS AR Little Rock 2,562 0.09%

201 MT MT Helena 215,795 23.92%

202 MT MT Billings 160,099 17.75%

203 MT MT Missoula 156,726 17.37%

204 MT MT Kalispell 114,474 12.69%

205 MT MT Great Falls 92,603 10.26%
42

206 MT MT Lewistown 48,210 5.34%

207 MT MT Miles City 46,451 5.15%

208 MT MT Glasgow 30,864 3.42%

209 MT MT Cut Bank 26,874 2.98%

210 MT WY Sheridan 9,725 1.08%

211 NC NC Raleigh 2,415,001 30.00%

212 NC NC Charlotte 1,937,866 24.07%

213 NC NC Greensboro1,565,781 19.45%

214 NC NC Wilmington 968,937 12.04%

215 NC NC Asheville 553,844 6.88%

216 NC VA Norfolk 186,361 2.32%

217 NC TN Bristol 117,225 1.46%

218 NC

NC Cape 

Hatteras 99,890 1.24%

219 NC SC Greenville 77,647 0.96%

220 NC TN Knoxville 72,0090.89%

221 NC VA Richmond 54,752 0.68%

222 ND ND Fargo 299,370 46.62%

223 ND ND Bismarck 169,768 26.44%

224 ND ND Minot 161,314 25.12%

225 ND SD Rapid City 5,255 0.82%

226 ND SD Huron 3,288 0.51%

227 ND MT Miles City 3,205 0.50%

228 NE NE Omaha 1,003,230 58.63%

229 NE

NE Grand 

Island 255,129 14.91%

230 NE NE Norfolk 170,039 9.94%

231 NE NE North Platte 123,185 7.20%

232 NE NE Scottsbluff 87,861 5.13%

233 NE IA Sioux City 44,784 2.62%

234 NE KS Topeka 14,250 0.83%

235 NE SD Pierre 4,749 0.28% 236 NE KS Goodland 3,696 0.22%

237 NE SD Rapid City 3,378 0.20%

238 NH NH Concord 1,036,495 83.87%

239 NH MA Boston 90,888 7.35%

240 NH MA Worchester 67,484 5.46%

241 NH ME Portland 40,919 3.31%

242 NJ NJ Newark 4,737,665 56.30%

243 NJ PA Philadelphia 1,393,310 16.56%

244 NJ

NY New York 

City 1,183,079 14.06%

245 NJ NJ Atlantic City 802,935 9.54%

246 NJ PA Allentown 218,664 2.60%

247 NJ DE Wilmington 78,697 0.94%

248 NM

NM 

Albuquerque 1,160,120 63.78%

249 NM TX El Paso 330,045 18.14%

250 NM NM Tucumcari 150,391 8.27%

251 NM TX Midland 88,442 4.86%

252 NM CO Alamosa 53,246 2.93%

253 NM

CO Grand 

Junction 22,789 1.25%

254 NM CO Pueblo 7,509 0.41%

255 NM AZ Tucson 4,324 0.24%

256 NM TX Lubbock 2,180 0.12%

257 NV South NV Las Vegas 1,402,358 70.18%

258 NV North NV Reno 498,607 24.95%

259 NV North NV Elko 46,363 2.32%

260 NV North

NV 

Winnemucca 29,454 1.47%

261 NV North NV Ely 10,799 0.54%
43

262 NV North NV Tonopah 8,231 0.41%

263 NV North UT Cedar City 2,445 0.12%

264 NYC

NY New York 

City10,444,179 55.04%

265 NY Long Is. CT Bridgeport 1,609,017 8.48%

266 NY Remain NY Albany 1,363,859 7.19%

267 NY Remain NY Buffalo 1,301,957 6.86%

268 NY Remain NY Syracuse 1,194,945 6.30%

269 NY Remain NY Rochester 1,167,876 6.15%

270 NY Remain NJ Newark 683,302 3.60%

271 NY Remain NY Binghamton 600,670 3.17%

272 NY Remain NY Massena 177,545 0.94%

273 NY Remain PA Bradford 162,687 0.86%

274 NY Remain VT Burlington 123,234 0.65%

275 NY Remain PA Williamsport 65,309 0.34%

276 NY Remain

PA Wilkes-

Barre 43,752 0.23%

277 NY Remain PA Erie 30,434 0.16%

278 NY Long Is. RI Providence 7,253 0.04%

279 OH OH Cleveland 2,081,112 18.33%

280 OH OH Columbus 1,971,782 17.37%

281 OH OH Dayton 1,575,838 13.88%

282 OH OH Akron 1,430,359 12.60%

283 OH KY Covington 1,401,769 12.35%

284 OH OH Toledo 950,637 8.37%

285 OH

OH 

Youngstown 706,904 6.23%

286 OH OH Mansfield 584,256 5.15%

287 OH WV Huntington 264,509 2.33%

288 OH PA Pittsburgh 187,418 1.65%

289 OH WV Charleston 89,023 0.78%

290 OH IN Fort Wayne 86,071 0.76%

291 OH PA Erie 22,699 0.20%

292 OK

OK Oklahoma 

City 1,532,614 44.42%

293 OK OK Tulsa 1,205,649 34.94%

294 OK

TX Wichita 

Falls 258,003 7.48%

295 OK AR Fort Smith 245,796 7.12%

296 OK TX Fort Worth 79,327 2.30%

297 OK KS Wichita 64,081 1.86%

298 OK KS Dodge City 39,340 1.14%

299 OK TX Amarillo 22,345 0.65%

300 OK MO Springfield 2,625 0.08%

301 OR OR Portland 1,496,864 43.75%

302 OR OR Salem 600,743 17.56%

303 OR OR Eugene 416,059 12.16%

304 OR OR Medford 339,927 9.94%

305 OR OR Redmond 165,561 4.84%

306 OR OR Pendleton 129,504 3.79%

307 OR OR North Bend 124,757 3.65%

308 OR OR Astoria 63,423 1.85%

309 OR ID Boise 33,681 0.98%

310 OR OR Burns 19,947 0.58%

311 OR WA Yakima 19,267 0.56%

312 OR CA Arcata 11,269 0.33%

313 PA PA Philadelphia 3,234,722 26.34%

314 PA PA Pittsburgh 2,725,698 22.19%

315 PA PA Harrisburg 1,770,423 14.42%

316 PA PA Allentown 1,447,170 11.78%

317 PA

PA Wilkes-

Barre 763,011 6.21%
44

318 PA PA Williamsport 566,515 4.61%

319 PA PA Bradford 524,237 4.27%

320 PA DE Wilmington 429,600 3.50%

321 PA PA Erie 417,499 3.40%

322 PA

OH 

Youngstown 207,952 1.69%

323 PA VA Sterling 87,401 0.71%

324 PA NY Binghamton 67,329 0.55%

325 PA WV Elkins 27,144 0.22%

326 PA MD Baltimore 6,507 0.05%

327 PA NJ Newark 5,846 0.05%

328 RI RI Providence 1,043,056 99.50%

329 RI MA Worchester 5,263 0.50%

330 SC SC Greenville 1,146,765 28.58%

331 SC SC Columbia 989,877 24.67%

332 SC SC Charleston 895,614 22.32%

333 SC NC Charlotte 340,746 8.49%

334 SC GA Augusta 205,513 5.12%

335 SC NC Wilmington 203,937 5.08%

336 SC GA Savannah 161,436 4.02%

337 SC GA Athens 40,482 1.01%

338 SC NC Asheville 17,608 0.44%

339 SC NC Raleigh 9,391 0.23%

340 SD SD Sioux Falls 261,762 34.68%

341 SD SD Rapid City 180,019 23.85%

342 SD SD Huron 170,861 22.64%

343 SD SD Pierre 74,640 9.89%

344 SD NE Norfolk 23,383 3.10%

345 SD IA Sioux City 21,031 2.79%

346 SD ND Fargo 14,552 1.93%

347 SD ND Bismarck 8,596 1.14%

348 TN TN Nashville 1,838,670 32.32%

349 TN TN Memphis 1,388,930 24.41%

350 TN TN Knoxville 1,085,595 19.08%

351 TN

TN 

Chattanooga 691,610 12.16%

352 TN TN Bristol 486,175 8.55%

353 TN AL Huntsville 186,174 3.27%

354 TN NC Asheville 12,129 0.21%

355 TX TX Fort Worth 5,608,872 26.90%

356 TX TX Houston 4,955,756 23.77%

357 TX TX San Antonio 1,916,970 9.19%

358 TX TX Austin 1,434,729 6.88%

359 TX TX Brownsville 978,369 4.69%

360 TX

TX Corpus 

Christi 750,132 3.60%

361 TX TX Waco 748,004 3.59%

362 TX TX El Paso 690,669 3.31%

363 TX TX Lufkin 637,769 3.06%

364 TX LA Shreveport 494,013 2.37%

365 TX TX Port Arthur 431,235 2.07%

366 TX TX Midland 390,808 1.87%

367 TX TX Lubbock 389,346 1.87%

368 TX TX Amarillo 382,332 1.83%

369 TX TX Abilene 309,567 1.48%

370 TX TX Victoria 273,952 1.31%

371 TX

TX Wichita 

Falls 224,3721.08%

372 TX TX San Angelo 208,457 1.00%

373 TX NM Tucumcari 12,779 0.06%

374 TX

LA Lake 

Charles 6,917 0.03%
45

375 TX AR Fort Smith 3,715 0.02%

376 TX KS Dodge City 3,057 0.01%

377 UT

UT Salt Lake 

City 1,999,247 89.53%

378 UT UT Cedar City 167,106 7.48%

379 UT

WY Rock 

Springs 34,848 1.56%

380 UT

CO Grand 

Junction 27,207 1.22%

381 UT ID Pocatello 2,774 0.12%

382 UT NV Ely 1,503 0.07%

383 VA VA Sterling 2,331,222 32.93%

384 VA VA Norfolk 1,596,461 22.55%

385 VA VA Richmond 1,298,552 18.34%

386 VA VA Lynchburg 686,010 9.69%

387 VA VA Roanoke 598,542 8.46%

388 VA TN Bristol 308,708 4.36%

389 VA NC Greensboro 114,750 1.62%

390 VA WV Elkins 100,092 1.41%

391 VA NC Raleigh 26,430 0.37%

392 VA WV Charleston 14,425 0.20%

393 VA TN Knoxville 3,3230.05%

394 VT VT Burlington 469,834 77.17%

395 VT NH Concord 60,899 10.00%

396 VT NY Albany 49,152 8.07%

397 VT MA Worchester 16,013 2.63%

398 VT CT Hartford 12,929 2.12%

399 WA WA Seattle 3,616,970 61.37%

400 WA WA Spokane 572,474 9.71%

401 WA WA Yakima 476,574 8.09%

402 WA OR Portland 440,819 7.48%

403 WA WA Olympia 427,598 7.25%

404 WA OR Pendleton 253,513 4.30%

405 WA OR Astoria 63,216 1.07%

406 WA WA Quillayute 42,957 0.73%

407 WI WI Milwaukee 1,981,735 36.95%

408 WI WI Green Bay 1,203,284 22.43%

409 WI WI Madison 997,047 18.59%

410 WI WI Eau Claire 446,623 8.33%

411 WI WI La Crosse 297,580 5.55%

412 WI IL Rockford 157,144 2.93%

413 WI

MN 

Minneapolis 117,746 2.20%

414 WI MN Duluth 96,387 1.80%

415 WI MI Houghton 46,879 0.87%

416 WI IL Moline 11,857 0.22%

417 WI MN Rochester 6,733 0.13%

418 WV WV Charleston 778,188 43.03%

419 WV WV Elkins 450,975 24.94%

420 WV WV Huntington 190,247 10.52%

421 WV PA Pittsburgh 166,590 9.21%

422 WV VA Sterling 147,523 8.16%

423 WV VA Roanoke 69,468 3.84%

424 WV TN Bristol 5,353 0.30%

425 WY WY Cheyenne 122,428 24.79%

426 WY WY Casper 94,348 19.11%

427 WY WY Sheridan 76,994 15.59%

428 WY WY Lander 52,669 10.67%

429 WY

WY Rock 

Springs 49,670 10.06%

430 WY MT Billings 29,315 5.94%

431 WY ID Pocatello 25,100 5.08%
46

432 WY NE Scottsbluff 14,945 3.03%

433 WY

UT Salt Lake 

City 14,695 2.98%

434 WY SD Rapid City 12,531 2.54%

435 WY CO Eagle 1,087 0.22%
47

Appendix B: PV Performance and Cost Reductions

Table B-1. PV Derate Factor by Component

Component Derate Factor

PV module nameplate DC rating 0.95

Inverter and transformer 0.92

Mismatch 0.98

Diodes and connections 0.995

DC wiring 0.98

AC wiring 0.99

Soiling 0.95

System availability 0.98

Overall DC-to-AC derate Factor 0.77a

a This corresponds to the derate factor of a new module. We also assume that PV output 

degrades over time, resulting in a 10% reduction in 20 years.

Table B-2. PV Cost Reductionsa

Year

Residential

($/kW)

Commercial 

($/kW)

EIA DOE - SPT EIA DOE - SPT

PV Inverterb PV Inverter PV Inverter PV Inverter

2008 8000 729 8000 500 6800 527 6800 500

2010 6603 729 5159 310 5515 519 4128 200

2012 6438 729 4458 290 5024 519 3389 190

2014 6214 721 3756 280 4533 512 2650 180

2016 5704 717 3303 260 4192 512 2212 170

2018 5403 713 3099 240 4000 508 2075 160

2020 5101 709 2894 220 3808 504 1938 165

2022 4928 705 2721 210 3616 501 1861 150

2024 4754 705 2547 200 3424 497 1783 140

2026 4580 697 2386 190 3232 497 1727 130

2028 4406 693 2239 190 3040 493 1692 130

2030 4233 689 2091 190 2848 489 1657 130 a In nominal 2008 U.S. dollars

b Inverter replacement is scheduled every 10-20 years, with inverter lifetime increasing over time.

Learning-based Cost Reductions

The idea of learning-based cost reductions stems from the observation that workers in 

manufacturing plants become more efficient as they produce more units. This idea has 

been extended to describe the cost reductions and quality improvements in a wide variety 

of manufactured products. One reason for its widespread use is the availability of data on 

unit production and unit cost. The learning rate, LR, is derived from cost data, Ct, and 

cumulative manufactured data, qt, using the following relationship:

ln(1 )

ln(2)

1

1

LRt

t

t t

t

q C C

q

−

+

+

  =     (B-1)
48

The global cumulative installed PV amount/rate is inferred from the cumulative amount 

of PV installed in a given time period using the fraction of PV installed in the US, fUS,t, 

as follows:

,

,

US t

t

US t

q

q f = (B-2)

Recent surveys of PV learning rates suggest values ranging from 17% to 26% (Maycock

2002; Strategies Unlimited 2003; Nemet 2006). The NEMS model uses a constant PV 

learning rate of approximately 13%. SolarDS allows users to specify both the learning 

rate and the fraction of U.S. to global cumulative installed PV capacity for each time 

period in SolarDS. Figure B-1 shows the PV cost reductions for fixed learning rates of 

17% and 26%, assuming a fixed U.S. to global cumulative installed PV capacity fraction.

Relative Cost of PV

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50

Installed PV (GW)

Relative Cost

LR = 0.26 (Maycock, 2002) LR = 0.17 (Strat. Unlim, 2003)

Figure B-1. PV Cost reductions for two PV learning rates (LR) derived in recent studies.

Although PV prices can be reasonably expected to decrease with cumulative production, 

users should keep certain factors in mind when using learning rates. Extrapolating 

learning rates from historical PV price and production data may not reflect the current 

and future reductions in PV costs. Additionally, learning rates are based on the 

cumulative global PV market, of which the U.S. share has declined steadily from more 

than 30% in the mid-1990s to 11% in 2006. SolarDS does not forecast the global PV 

market, and the user must specify the U.S. fraction of the global PV market from 2008 to 

2030.
49

Appendix C: Building Assumptions

Table C-1. Residential Roof Orientations

Orientation Tilt Azimuth Flat

4-sided 

Roof

2-sided 

Roof Rooftop 

10% 45% 45% Fraction

1 0° 0° 100% - - 10%

2 25° -90° - - 14% 6%

3 25° -60° - - 14% 6%

4 25° -30° - 33% 14% 21%

5 25° 0° - 33% 14% 21%

6 25° 30° - 33% 14% 21%

7 25° 60° - - 14% 6%

8 25° 90° - - 14% 6%

Table C-2. Regional Roof Shading 

Region (EIA Census + 4) Shaded Fraction (%)

California 35

East North Central 55

East South Central 55

Florida 35

Mid Atlantic 55

Mountain 40

New England 60

New York 55

Pacific 40

South Atlantic 55

Texas 35

West North Central 45

West South Central 55

Table C-3. Commercial Roof Orientations

Orientation Tilt Azimuth Rooftop 

Fractiona

1 0° 0° 10-50%

2 25° 0° 40-50%

3 25° -30° 0-25%

4 25° 30° 0-25% a Commercial roof orientation fractions vary by building type within the range specified.
50

Table C-4. Commercial PV Capacity by Building Typea

Building Type

Roof Area

(1000 

ft2/building)

Unshaded 

Fraction (%)

Potential Roof 

Capacity

( kW / building )

Education 15.59 80 112

Food Sales 4.88 80 35

Food Service 4.10 80 30

Inpatient 46.00 80 331

Outpatient 6.28 80 45

Lodging 12.65 80 91

Retail (other than mall) 7.66 80 55

Enclosed and Strip Malls 26.90 80 194

Office 6.02 80 43

Public Assembly 8.69 80 63

Public Order and Safety 9.20 80 66

Religious Worship 6.32 80 45

Service 5.35 80 38

Warehouse and Storage 13.79 80 99 a Represents unshaded commercial PV capacity by building type. This capacity is further reduced 

by the regional shading assumptions in Table C.2.
51

Appendix D: PV Financing Assumptions 

The residential and commercial financing parameters are user input options in the 

SolarDS model. The reference case financing assumptions and the distribution of 

residential financing options are given below.

Table D-1. Residential Financing Inputs

Financing Parameters New Construction Retrofit Construction

Loan Rate 6.0% (real) 6.0% (real)

Loan Term 30 years 15 years

Table D-2. Distribution of Residential Financing

Down Payment Fraction Tax Rate Fraction of Households

20% 25% 40%

20% 33% 40%

100% 25% 10%

100% 33% 10%

Table D-3. Commercial Financing Parameters

Commercial Inputs Value

Loan Term 15 years

Loan Rate 6.0% (real)

Down Payment Fraction 20%

IRR analysis period 25 years

Table D-4. Commercial Capital Depreciation Schedule (MACRS)

Year Depreciation Fraction

1 20.00%

2 32.00%

3 19.20%

4 11.52%

5 11.52%

6 5.76%
52

Appendix E: PV Market-Penetration Curves

NEMS

The maximum annual adoption of PV by residential and commercial customers is 

calculated in NEMS as follows (EIA 2008a, 2008b):

0.30 0.75, ()

Maximum PenetrationNew Construction Min

Payback Time years =

(E-1)

Existing Buildings

0.30 0.005, 40* ( )

Maximum Penetration Min

Payback Time years =

(E-2)

Navigant, Inc.

The maximum PV market share from Navigant (Paidipati et al. 2008) was based on a 

customer survey of the potential residential adoption of a theoretical heat pump 

technology that is 20% more efficient than existing technology (Kastovich et al. 1982). 

The heat pump curve was decreased slightly, based on industry interviews. The Navigant 

study decreased the adoption rate on new construction for payback periods greater than 

three years because builders may be hesitant to include a technology with longer payback 

periods. Navigant curves result in significantly higher PV penetration than result from 

using NEMS curves for shorter payback periods.

R.W. Beck, Inc.

The maximum PV market share from R.W. Beck (2009) was calculated using an 

exponential increase in PV adoption with decreasing payback time:

0.3* ( ) Payback Time years Maximum Penetration e All Buildings− =

(E-3)

This parameterization approximates the mean between the maximum PV market share 

curves assumed in the Navigant study and the original Kastovich et al. (1982) curve.
F1147-E(10/2008) 

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The Solar Deployment System (SolarDS) Model: Documentation 

and Sample Results

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DE-AC36-08-GO28308

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14. ABSTRACT (Maximum 200 Words)

The Solar Deployment System (SolarDS) model is a bottom-up, market-penetration model that simulates the potential adoption of 

photovoltaics (PV) on residential and commercial rooftops in the continental United States through 2030. NREL developed 

SolarDS to examine the market competitiveness of PV based on regional solar resources, capital costs, electricity prices, utility 

rate structures, and federal and local incentives. This report provices details on the model, which uses the projected financial 

performance of PV systems to simulate PV adoption for building types and regions then aggregates adoption to state and national 

levels. The main components of SolarDS include a PV performance simulator, a PV annual revenue calculator, a PV financial 

performance calculator, a PV market share calculator, and a regional aggregator. The model simulates a variety of installed PV 

capacity for a range of user-specified input parameters. PV market-penetration levels from 15 to 193 GW by 2030 were simulated 

in preliminary model runs. SolarDS results are primarily driven by three model assumptions: (1) future PV cost reductions, (2) the 

maximum PV market share assumed for systems with given financial performance, and (3) PV financing parameters and policydriven assumptions, such as the possible future cost of carbon emissions.

15. SUBJECT TERMS

SolarDS; photovoltaics; PV; PV capacity; PV systems; solar deployment system; adoption; residential; commercial; buildings; 

state; national; United States; model; model results; market model; market penetration; market competitiveness; regional solar 

resources; solar capital costs; electricity prices; utility rate structures; federal incentives; local incentives; annual revenue; financial 

performance calculator; market share; cost reductions; financing parameters

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