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

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Breakeven Prices for 

Photovoltaics on Supermarkets 

in the United States

Sean Ong, Nathan Clark, Paul Denholm, 

and Robert Margolis

Technical Report

NREL/TP-6A20-57276

March 2013 
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Breakeven Prices for 

Photovoltaics on Supermarkets 

in the United States 

Sean Ong, Nathan Clark, Paul Denholm, 

and Robert Margolis

Prepared under Task No. SS12.2230

Technical Report

NREL/TP-6A20-57276

March 2013 
NOTICE

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Acknowledgments

This work was made possible by the Solar Energy Technologies Program at the U.S. 

Department of Energy (DOE). The authors wish to thank Clinton Campbell, Jarett Zuboy, 

Billy Roberts, Easan Drury, David Kline, Trieu Mai, Nate Blair, and Robin Newmark of 

the National Renewable Energy Laboratory (NREL) for reviewing various versions of the 

document, as well as Justin Baca (SEIA) for his review. The authors also thank Mary 

Lukkonen of NREL’s Communications Office for a thorough technical edit of 

the document. 
iv

Executive Summary

The photovoltaic (PV) breakeven price is the PV system price at which the cost of PVgenerated electricity equals the cost of electricity purchased from the grid. This point is 

also called “grid parity” and can be expressed as dollars per watt ($/W) of installed PV 

system capacity. Achieving the PV breakeven price depends on many factors, including 

the solar resource, local electricity prices and rate structure, customer load profile, PV 

incentives, and financing. In the United States, where these factors vary substantially 

across regions, breakeven prices vary substantially across regions as well. 

In this study, we estimate current and future breakeven prices and drivers for PV systems 

installed on U.S. supermarkets. As of 2012, 85,988 supermarkets exist in the United 

States that use about 36,000 gigawatt-hours (GWh) of electricity per year. This analysis 

suggests that more than 17% of these supermarkets are in utility territories where PV 

breakeven conditions exist under today’s market and policy conditions. PV systems are 

installed on less than 0.3%1

 of supermarkets in the United States, as of the end of 2012. 

This analysis is a first step in examining the breakeven price for PV systems across the 

larger category of commercial “big-box” retail stores. Current and future (2020) 

breakeven prices are calculated. We compare breakeven prices under default electricityrate assumptions versus optional rate assumptions (typically time-of-use rates) that 

increase PV’s value, and we compare these breakeven prices with current and potential 

future commercial PV prices. We also analyze the impacts of incentives on breakeven 

prices in each state. Finally, we analyze the sensitivity of breakeven price to electricity 

prices and financial, technical, and policy factors. 

The results suggest that breakeven prices for PV systems installed on supermarkets vary 

by more than a factor of 30 across the United States, even though the solar resource 

varies by less than a factor of 2. Non-technical factors—including electricity rates, rate 

structures, and incentives—drive breakeven prices more than technical factors such as 

solar resource or system orientation. Additional key results of this analysis include: 

• Under base-case assumptions, about 17% of supermarkets nationwide were in 

utility territories where breakeven conditions existed at a PV system price of 

$5/W in 2011 (the U.S. average installed price of commercial PV systems in 

2011). Using the estimated 2012 installed price of commercial PV systems 

($3.43/W), 40% of supermarkets were in utility territories where breakeven 

conditions existed. These percentages increase to 33% and 53%, respectively, 

when rate structures favorable to PV (time-of-use, tiered rates) are used. 

• In 2020 (where we assume higher electricity prices and lower PV incentives), 

under base-case assumptions, we estimate that about 17% of supermarkets will be

in utility territories where breakeven conditions exist at a PV system price of 

 

1 The percentage of supermarkets with PV systems is calculated by dividing the estimated number of 

supermarkets with PV systems by the total number of supermarkets in the United States (85,988). The 

number of supermarkets with PV systems (183) is estimated from SEIA 2012. 
v 

$3/W; this increases to 79% at $1.25/W (the DOE SunShot Initiative’s 

commercial PV price target for 2020) (DOE 2012a). These percentages increase 

to 26% and 91%, respectively, when rate structures favorable to PV are used.

• In general, the areas with the highest PV breakeven prices (i.e., those most 

favorable for PV) are located in areas with high electricity prices and favorable 

incentives.

• Breakeven price is most sensitive to variations in electricity rate and then, 

generally, to variations in policy, technical performance, and financing factors. 
vi 

Table of Contents

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

2 Data and Methodology.......................................................................................................................... 3

3 Near-Term PV Breakeven Prices ......................................................................................................... 8

4 Future Market Sensitivities of Breakeven Prices.............................................................................13

5 Conclusions.........................................................................................................................................19

References .................................................................................................................................................20

Appendix A: Calculation of Breakeven Price .........................................................................................22

Appendix B: Incentives Used in this Study ............................................................................................23

Appendix C: Complete Listing of All Building Locations and Associated ASHRAE Zone................26

Appendix D: Grid Parity and Installed PV Prices by State as of August 8, 2012 ................................30
vii 

List of Figures

Figure 1. U.S. map depicting locations with high-fidelity utility rate data and annual 

average utility rate data from EIA-861 ......................................................................................6

Figure 2. PV breakeven price ($/W) for supermarkets in 2012 using the default rate 

structure and all incentives .........................................................................................................8

Figure 3. PV breakeven price ($/W) for supermarkets in 2012 using an optional rate 

structure (where offered) and all incentives .............................................................................10

Figure 4. Components of the breakeven value for the largest utility in each state and 

Washington, D.C. and range in breakeven value for all utilities in each state ........................11

Figure 5. Grid parity for supermarkets at the 2012 PV price ($3.43/W) using an optional 

rate structure with incentives (where applicable) ....................................................................12

Figure 6. PV breakeven prices ($/W) for supermarkets in 2020 using the base-case 

assumptions in Table 1 and the default rate structure ..............................................................14

Figure 7. PV breakeven prices ($/W) for supermarkets in 2020 using the base-case 

assumptions in Table 1 and an optional rate structure .............................................................15

Figure 8. Range of PV breakeven prices in the 2020 scenarios: Top 26 states and 

Washington, D.C. .....................................................................................................................17

Figure 9. Range of PV breakeven prices in the 2020 scenarios: Bottom 25 states ........................18

Figure D-1. Grid parity for supermarkets at current state average PV prices using an 

optional rate structure with incentives (where applicable) ......................................................30

List of Tables

Table 1. PV Base and Sensitivity Cases in 2020 ...........................................................................16

Table B-1. Statewide Incentives (as of May 2012)........................................................................23

Table B-2. Utility Incentives in the Form of Tax Credits or Rebates as of May 2012 ..................24

Table B-3. Statewide Feed-In Tariffs as of May 2012 ..................................................................25

Table B-4. Utility Feed-In Tariffs as of May 2012 ........................................................................25

Table C-1. Complete Listing of All Building Locations, Associated ASHRAE Zone, and 

Electric Load Summary ...........................................................................................................26

Table D-1. Installed Prices for 50-kW to 500-kW PV Systems Installed Between January 

1 and August 8, 2012 ...............................................................................................................31
1 

1 Introduction

The photovoltaic (PV) breakeven price is the PV system price at which the cost of PVgenerated electricity equals the cost of electricity purchased from the grid. This point is 

also called “grid parity” and can be expressed as dollars per watt ($/W) of installed PV 

system capacity.2 Achieving the PV breakeven price depends on many factors, including 

the solar resource, local electricity prices, PV incentives, and financing. In the United 

States, where these factors vary substantially across regions, breakeven prices vary 

substantially across regions as well.

The National Renewable Energy Laboratory (NREL) previously estimated breakeven 

prices for residential rooftop PV systems in the United States (Denholm et al. 2009).3

This work expands on the previous study by using similar methods to estimate current 

and future breakeven prices for PV systems installed on U.S. supermarkets, a subset of 

commercial buildings within the larger category of “big-box” retail stores. We also 

evaluate key drivers of current and future PV breakeven prices by region. 

Our analysis begins by defining the characteristics of a hypothetical supermarket PV 

system and then models the financial performance of this system nationwide using the 

System Advisor Model (SAM) with meteorological, utility-rate, and building-load data 

inputs. We calculate breakeven price as the point at which the net present cost (NPC) of a 

PV system equals the net present benefit (NPB) realized to its owner in the form of 

reduced electricity bills, which is also the point at which the PV system’s net present 

value (NPV) equals zero. Current and future (2020) breakeven prices are calculated. For 

each timeframe, we compare breakeven prices under default electricity-rate assumptions 

versus optional rate assumptions (typically time-of-use rates) that increase PV’s value, 

and we compare these breakeven prices with current and potential future commercial PV 

prices. We also analyze the contribution of various cost components (e.g., electricity rates

and incentives) to breakeven prices in each state. Finally, we analyze the sensitivity of 

breakeven price to electricity prices and financial, technical, and policy factors.

Note that the presence of breakeven conditions in an area does not imply that all potential 

supermarkets in that area could achieve PV breakeven. It is likely that only a fraction of 

customers could meet the location-specific criteria necessary for breakeven. Caps on PV 

incentive and net-metering programs could also limit the prevalence of breakeven 

conditions in practice. Further, the presence of breakeven conditions does not necessarily 

equate to widespread adoption of PV systems. Finally, large-scale adoption of PV would 

change electricity demand and price patterns and could decrease the value of PV under 

optional rate structures. Thus, this study of PV breakeven prices is not a market-depth 

analysis or an estimate of PV adoption, but it does provide new insights about the 

potential viability of one important segment of the rooftop commercial PV market.

 

2 This price refers to $/DC-Watt, which is the system’s rating before conversion to AC. This nomenclature 

differs from that generally applied to traditional power plants, which are typically stated in terms of their 

price per AC-Watt of capacity. 3 Additional studies have estimated the breakeven cost of PV, including Herig et al. (2002) and Herig et al. 

(2003). 
2 

The remainder of this report details the study’s data and methodology (Section 2), current 

PV breakeven price results (Section 3), and future PV breakeven price results and 

sensitivity analysis (Section 4). Section 5 presents conclusions and directions for future 

research.
3 

2 Data and Methodology

Understanding rooftop solar economic performance requires an analysis of the interaction 

between the building load, PV generation, rate structure, and a variety of financial and 

policy assumptions. The complex interaction between building load, solar production, 

and electricity rate structure requires a model that can simultaneously address all 

elements involved. SAM (Section 2.1) is used to generate solar production and economic 

performance results from a variety of inputs, including solar resource data (Section 2.2), 

utility rate data (Section 2.3), and hourly building load data (Section 2.4). The following 

sections provide details on the data and methodology used in this analysis. 

2.1 System Advisor Model and Calculations

Analysis in this report was performed using SAM, which was developed by NREL in 

collaboration with Sandia National Laboratories and the U.S. Department of Energy

(DOE). SAM is a performance and economic model designed to facilitate decision 

making and analysis for renewable energy projects (Gilman and Dobos 2012). SAM uses 

meteorological data, a PV performance model, and user-defined assumptions to simulate 

hourly PV generation data. In this analysis, a reference system was modeled using the 

following assumptions4 to generate PV performance data: 

• 250 kW (DC)5

• 15-degree tilt

• South facing (180-degree azimuth) 

• A derate factor of 80% 

• Annual degradation of 0.5%. 

PV breakeven prices were evaluated for both near-term and future (2020) scenarios. We 

define the breakeven price of PV as the point at which the NPC of the PV system equals 

the NPB realized to its owner. This may also be expressed as the point at which the NPV 

is equal to zero. The breakeven system price ($/W) was calculated by iteratively varying 

the price of PV until the NPC equaled the NPB.6 A review of the methods used to 

calculate NPC and NPB is provided in Appendix A. 

The NPC of the system includes all financing and incentives, while the NPB is the 

cumulative discounted benefits of reduced electric bills. All financing assumptions used 

in this study are from DOE’s SunShot Vision Study (DOE 2012a). The NPC in our base 

 

4 Note that SAM contains many input fields. SAM default inputs were used for any assumption not 

specifically called out in this report.

5 For context, a commercial building with a rooftop area of 45,000 ft2 could support a 300 DC-kW PV 

system, assuming a module efficiency of 15% and 50% rooftop availability due to shading and 

obstructions. The DOE supermarket building model used in this analysis has a single-story floor area of 

45,000 ft2

, from which we assume a rooftop area of approximately 45,000 ft2. 6 Often, a linear relationship between the breakeven price and NPV was observed. Whenever a linear 

relationship was observed, the calculation was performed by solving for the breakeven price at NPV = 0 to 

reduce simulation time. 
4 

scenario assumes a system financed with a loan (with tax-deductible interest and a 35% 

marginal federal tax rate), a 40% down payment, a real interest rate and discount rate of 

4.5%, and a loan term of 20 years.7

 The evaluation period for the analysis is 30 years.8 

Near-term analysis considered several incentive programs, including the 30% federal 

investment tax credit (ITC), as well as known state, local, and utility incentives derived 

from the DSIRE database.9 Tax credits were applied at the end of year one in the NPC 

calculation.10 The taxability of rebates and their effect on the federal ITC must be 

considered. In our base assumption, we assume that the rebate is paid to the installer 

rather than the building owner. This effectively reduces the installation price to the 

building owner by the amount of the rebate and also reduces the basis for the federal ITC. 

A list of the state and local incentives used in this analysis is provided in Appendix B. 

Near-term analysis also assumed the federal 5-year Modified Accelerated Cost Recovery 

System (MACRS) depreciation schedule, an annual operation and maintenance (O&M) 

cost of $23.50/kW, and inverter replacement at 15 years.

Sensitivities to these assumptions are evaluated in Section 4. 

2.2 Solar Resource Data

The PV production data used in this analysis were simulated using the Typical 

Meteorological Year 3 (TMY3) dataset of the National Solar Radiation Database (Wilcox 

and Marion 2008). The TMY3 dataset is intended to represent a typical year’s weather 

and solar resource patterns, although the dataset does not consist of an actual 

representative year. Rather, TMY3 was created by combining data from multiple years.11

The meteorological dataset was used as an input for SAM, which simulated hourly PV 

production for use in the financial calculations. 

2.3 Utility Rate Data 

The breakeven price for PV was calculated for 3,143 utilities in the United States, which 

represents about 98% of the total commercial load (based on annual energy 

consumption). We evaluated breakeven prices under two electricity rate categories: one 

based on the default rate (typically demand rates and/or tiered rate structures) and one 

 

7 Here and elsewhere, we use real interest rates as opposed to nominal interest rates. The relationship is real 

interest rate = nominal interest rate – inflation rate. 8 This implies an expected 30-year life of system. 9 Database of State Incentives for Renewables and Efficiency (DSIRE), http://www.dsireusa.org/. All 

incentives are as of May 2012.

10 This analysis makes two assumptions regarding sales taxes and property taxes. First, it assumes that PV 

systems are exempt from sales tax, which is true in some but not all states. In states where PV systems are 

taxed, the breakeven price would be reduced by a percentage roughly equal to the sales tax rate. Second, 

the analysis assumes that PV systems are exempt from property tax, which is also true for many but not all 

regions and states. For a list of states and localities that exempt PV systems from sales and property tax, see 

DSIRE (http://www.dsireusa.org/). 11 For example, the month of January may be from one year (e.g., 1989), while February may be from 

another year (e.g., 1994). Each TMY3 file may contain data from up to 12 different years. Data were 

intentionally selected to represent typical meteorological conditions. 
5 

based on an optional rate (typically time-of-use rate structures or rates with lower 

demand charges and higher energy charges than the default rate). A default rate refers to 

a rate that a building would be subject to by default, based on applicability requirements 

such as peak demand, voltage requirements, or energy consumption. An optional rate 

refers to a rate that customers may choose in lieu of the standard rate option. Utilities 

offer various commercial rate structures for different load sizes and types. We considered 

applicability requirements when collecting rates for the load data used in this analysis.

12

Net metering is characterized as follows: PV energy production is compensated at the 

retail electricity rate for all energy produced, up to 100% of the building’s annual 

electricity use.13 

A combination of tariff sheet data and Energy Information Administration (EIA) utility 

data is used in this study. Form EIA-861 data provide the total revenue and total energy 

sales for all utilities in the United States.14 This dataset formed the basis for an “average” 

cost of electricity to commercial customers. However, these data do not provide insight 

into the actual rate structure because they represent an annual average and include fixed 

billing charges and other components that would not be offset by customer-sited 

PV generation. 

To establish the relative difference in value between the annual average cost of electricity 

for each utility and the actual value of PV, we used rate data obtained from the current 

tariff sheet for the largest utility in each state, here referred to as “high-fidelity” rate data. 

The value of PV in a specific utility territory was calculated twice: once using the highfidelity rate data and again using the annual average cost of electricity. The relative 

difference in value between these two calculations established a scale factor. This scale 

factor was then applied to the remaining utilities in each state in order to approximate the 

value of PV under actual electricity tariffs as well as removing fixed billing components. 

A total of 104 rates from 52 utility companies were evaluated. Figure 1 shows the utilities 

with high-fidelity rate data available. These rates were obtained from the online Utility 

Rate Database (URDB) on the OpenEI platform15 and were current as of late 2011 and 

early 2012. 

 

12 Rates ineligible to be used with supermarkets (or any building with similar demand requirements as the 

supermarket load data used) are not evaluated in this analysis.

13 Net metering may not be available in all states or utilities. For a complete list of utilities participating in 

net-metering arrangements, see DSIRE at http://www.dsireusa.org/. In this analysis, PV production never 

exceeds 100% of the building’s annual electricity use.

14 Because 2010 was the most recent year available at the time of this report, we scaled each utility to 2011 

values using the state average value for 2011 derived from the EIA (EIA 2012). 15 http://en.openei.org/wiki/Gateway:Utilities 
6 

Figure 1. U.S. map depicting locations with high-fidelity utility rate data (dark blue) and 

annual average utility rate data from EIA-861 (light blue)

2.4 Hourly Building Load Data

Obtaining or simulating building load data is an important component of any analysis that 

includes demand charges and tiered rates. Demand charges are usually based on the peak 

monthly power demand of a building; consequently, quantifying the demand reduction 

value of a PV system requires a detailed load profile for a building. Load profiles are also 

required when evaluating tiered rates and demand charges, where rates vary depending 

on monthly energy use. For the analysis presented here, we generated a set of regionspecific simulated hourly load profiles for supermarkets. These load profiles are based on 

the DOE commercial reference building models for supermarkets (Deru et al. 2011) and 

were simulated using DOE’s EnergyPlus software.16 In order to ensure that the simulated 

supermarket load and PV production profiles were properly aligned, we used the same 

TMY3 datasets to simulate both sets of data.

Building construction and model assumptions were varied throughout the United States 

with 16 unique supermarket models used [i.e., one for each of the official climate zones 

recognized by the American Society of Heating, Refrigeration, and Air-Conditioning 

Engineers (ASHRAE)]. These region-specific building models account for factors such 

as region-specific building codes, characteristics, major loads and plug loads. The 16 

 

16 For more information on the EnergyPlus model, see http://apps1.eere.energy.gov/buildings/energyplus/. 
7 

region-specific building models were then simulated at 79 TMY3 locations throughout 

the United States (there are a total of 1,020 TMY3 locations) in order to generate a more 

geographically diverse set of load profiles that could be matched to TMY-specific solargeneration profiles. Appendix C contains a complete listing of all building locations and 

associated ASHRAE zones and TMY sites. The total hourly electrical load of each 

building location was then entered into SAM. 

As of 2012, DOE estimates there are 85,988 supermarkets17 in the United States, 

representing 4.8% of commercial buildings and 6.3% of all commercial building 

electricity consumption (DOE 2012b). The average simulated building has a floor area of 

45,000 ft2, an annual electricity consumption of 1,687 MWh, and a peak demand of 

367 kW. While supermarkets represent an important segment of the commercial building 

sector, a more comprehensive analysis of PV economic performance in the commercial 

building sector would require simulating load profiles across multiple building types. 

 

17 Building statistics are obtained from the Energy Index 

(http://buildingsdatabook.eren.doe.gov/CBECS.aspx). The 85,988 buildings are representative of the 

modeled supermarket and not smaller (e.g., convenience stores) or larger (e.g., mixed-use stores) buildings. 
8 

3 Near-Term PV Breakeven Prices

We begin by evaluating the breakeven price of PV ($/W) in the base scenario. This 

scenario uses the default rate structure, which generally consists of demand and/or tiered

rates and includes all federal and local incentives. Figure 2 shows the base-case 

breakeven price of PV for each utility service territory evaluated. For the areas in each 

state where utility data is unavailable (consisting of about 2% of total U.S. commercial

electricity sales), we assume the PV performance from the largest utility in that state 

combined with the average electricity price from the smallest utilities in that state. All 

other assumptions are identical to those of the base case. 

Figure 2. PV breakeven price ($/W) for supermarkets in 2012 using the default rate 

structure and all incentives

The average installed cost of commercial PV systems (250–500 kW) in the United States 

was $4.90/W in 2011 (Barbose et al. 2012) and $3.43/W in 201218 (Feldman et al. 2012). 

At $5/W, 17% of supermarkets are in utilities where breakeven conditions exist. At 

$3.43/W, 40% of supermarkets are in utility territories where breakeven conditions exist. 

In practice, only a fraction of customers in these utility service territories are likely to 

 

18 Benchmarked based on fourth quarter 2011 data.
9 

meet all the criteria (full retail net metering, good solar exposure, and access to financing) 

to be at breakeven, and the presence of breakeven conditions does not necessarily equate 

to large consumer adoption. Furthermore, there are budget caps for most current incentive 

programs and typically limits or caps on the number of net-metered systems that can be 

connected to the grid in a specific utility service territory. Also, in Figure 2 and 

elsewhere, this analysis represents a single point in time. Because incentives and 

electricity prices are constantly changing, results for any single area may be substantially 

different when evaluated later.

The methodology used to generate Figure 2 was repeated for the optional rate scenario, 

using the tariff sheets for the largest utility in each state to estimate the change in PV 

value associated with optional rates. Optional rates typically consist of time-of-use rate 

structures or rates that have lower demand charges and higher energy charges than the 

default rate option. In states without an optional rate, the default rate was evaluated, and 

the breakeven price did not change from the default rate scenario (Figure 2). In each state 

where the largest utility offers optional rates, we assumed that a similar optional rate 

structure would be applied to other utilities within that state and that the value of PV 

would be scaled proportionally across the state. The results of this analysis are shown in 

Figure 3. 

Optional rates do not always result in a net benefit to a customer. About 26% of the 

optional rates evaluated showed a decrease in PV value when shifting the customer from 

the default to the optional rate. In addition, even with an optional rate that increases PV 

value, some customers may opt not to choose optional rates because their “base” use 

would result in increased bills relative to a default rate. In this analysis, we assumed that 

customers chose optional rates only when those rates increased PV value. For a complete 

discussion of commercial rate impacts on PV value and bill savings, see Ong et 

al. (2012). 
10 

Figure 3. PV breakeven price ($/W) for supermarkets in 2012 using an optional rate 

structure (where offered) and all incentives

The results of this scenario are similar to those in the default scenario (Figure 2) but with 

higher breakeven prices in several northeastern and central states. In the optional rate 

scenario, the fraction of supermarkets in utility areas where breakeven conditions exist 

increases to 33% and 53% for installed prices of $5/W and $3.43/W, respectively. The 

lowest breakeven prices occur in Louisiana, where electricity prices are low and 

primarily driven by demand charges.19 Actual adoption will be restricted by consumer 

adoption behavior and limits on incentives, and large-scale adoption of PV will change 

demand and price patterns, decreasing the value of PV on optional rates (Darghouth et 

al. 2013).20

Figure 4 illustrates the breakeven price for the largest utility in each state along with a 

distribution of the breakeven price components, including the default electricity rate, 

optional rate “adder” (where available21), the effect of tax-deductible interest on loans, 

 

19 Rates that are driven by demand charges have been shown to reduce the value of PV generation (Ong et 

al. 2010).

20 At large-scale deployment, PV would suppress the mid-day demand for electricity and thus the electricity 

price. This would decrease the value of PV and reduce the likelihood of receiving full retail net metering. 

Also see Denholm and Margolis (2007a and 2007b) for a discussion of the grid impacts of large-scale PV 

deployment. 21 In this analysis, we assumed that customers chose optional rates in all cases in which those rates 

increased PV value.
11 

and federal and local incentives. Also included is the range in breakeven values for all 

utilities evaluated. 

Figure 4. Components of the breakeven value for the largest utility in each state and 

Washington, D.C. (colored bars) and range in breakeven value for all utilities in each state

(whiskers)

Note: For clarity, breakeven prices above $8/W are not displayed.

As shown in Figure 4, each state shows considerable variability. In some cases, the 

breakeven value for the most attractive utility is several dollars per watt more than the 
12 

largest utility. However, these more attractive utilities tend to be significantly smaller 

than the largest utility, often providing less than a few percent of the state’s sales.22 We 

also see breakeven prices above $4/W in only a few places without local incentives. 

Without local incentives or the federal ITC, half of the largest utilities in each state have 

a breakeven price below $2.50/W. 

Figure 5 repeats the analysis in Figure 3 but illustrates the breakeven price relative to the 

2012 average installed price ($3.43/W, based on Q4 2011 data) data for commercial PV 

systems from Feldman et al. (2012). We identify locations at or below grid parity, within 

25% of parity (the current average installed price is within 25% of being at grid parity), 

and beyond 25% of parity. At an installed price of $3.43/W, the fraction of supermarkets 

that are at or below grid parity is 53%, while an additional 20% are within 25% of grid 

parity. Note again that only a fraction of customers in these utility service territories are 

likely to meet all our assumptions to be at breakeven, and the presence of breakeven

conditions does not necessarily lead to large consumer adoption. 

Figure 5. Grid parity for supermarkets at the 2012 PV price ($3.43/W) using an optional rate 

structure with incentives (where applicable) 

 

22 As an example, at the time of this analysis, the base-case breakeven price in the largest utility in Arizona 

(serving 32% of the state’s commercial load) is about $3.60/W, while the maximum breakeven price in the 

state is nearly $7.40/W. However, this higher price applies only to a small utility serving about 5% of the 

state’s commercial load.
13 

4 Future Market Sensitivities of Breakeven Prices

The high breakeven prices in many states that were noted in the previous section are 

driven primarily by state, utility, and federal incentive programs. These programs are 

designed primarily to encourage the development of PV markets; however, over time 

they are expected to be phased down as the price of PV systems decreases and PV 

markets become self-sustaining. In this section, we examine the projected breakeven

prices of PV systems installed on supermarkets in 2020, and we consider the sensitivity 

of breakeven prices to a number of factors. 

We begin by establishing a base scenario for 2020 with a uniform set of assumptions, 

including system performance, electricity price escalation, financing, and incentives. This 

is similar to the previous scenario but includes an annual real electricity price escalation 

of 0.5% that results in an overall increase in electricity prices of 4% by 2020. State and 

local incentives are eliminated, and the federal ITC is reduced to 10%.23 

Figure 6 (default electricity rate structure) and Figure 7 (optional rate structure) provide 

the results for the 2020 base case. Note that the color scales in these figures are different 

from those in Figures 2 and 3. In Figures 6 and 7, the presence of any color other than 

white indicates a breakeven price above DOE’s SunShot commercial PV price target of 

$1.25/W (DOE 2012a). 

 

23 Under the current Internal Revenue Service (IRS) tax code, the 30% federal ITC will revert to a 10% ITC 

for commercial and utility systems after 2016 (see http://www.dsireusa.org/).
14 

Figure 6. PV breakeven prices ($/W) for supermarkets in 2020 using the base-case

assumptions in Table 1 and the default rate structure

Note: The color scale is different from the scale used in Figures 2 and 3. 

The 2020 case shows a dramatic reduction in the breakeven price of PV where local 

incentives existed in early 2012. Breakeven prices are also reduced in locations where no 

incentives existed in early 2012 owing to the reduction of the ITC from 30% to 10%. In 

this case, 17% of supermarkets are in utility areas where breakeven conditions exist at a 

PV system price of $3/W. At a price of $1.25/W, 79% of supermarkets are in utility areas

where breakeven conditions exist. 

Figure 7 repeats the analysis for an optional rate structure. Again, the dramatic reduction 

in breakeven prices from 2012 is due to the elimination of state and local incentives and a 

reduction in the ITC. Here, 26% of supermarkets are in utility areas where breakeven 

conditions exist at a PV system price of $3/W. At $1.25/W, 91% of supermarkets are in 

utility areas where breakeven conditions exist. 
15 

Figure 7. PV breakeven prices ($/W) for supermarkets in 2020 using the base-case

assumptions in Table 1 and an optional rate structure

Note: The color scale is different from the scale used in Figures 2 and 3. 

The base 2020 scenario represents only a single scenario among many possible scenarios 

for future breakeven prices. The PV market of the future will have a variety of customers 

with different financing options, buildings with non-optimal orientations, and changes in 

electricity prices and rate structures. Many of these drivers, such as escalation of 

electricity prices and future net-metering policies, are highly uncertain. As a result, it is 

important to consider the sensitivity of the breakeven price to a variety of drivers. 

We examined the sensitivity of the breakeven price for each state to a set of four classes 

of impacts: technical performance, electricity cost, financing, and policies. Table 1 lists 

the base case and the four sensitivity cases evaluated. 
16 

Table 1. PV Base and Sensitivity Cases in 2020a

Base Case

Finance Technical Electricity Policy

Low High Low High Low High Low High

Down 

Payment 40% 60% 20%

Base

Base

Base

Federal

Tax 

Bracket

35% 33% Base

Discount 

Rate 5% 7% 4%

Interest 

Rate 5% 7% 4%

Financing 

Type 20-yr Loan 15 yr

Base Evaluation 

Period 30 yr 25 yr

Solar 

Resource 

Location

Largest 

Utility

Base

low high

Orientation

South 

Facing - 15 

Deg - Fixed

Flat 25 Deg

Derate 80% 77% 82%

O&Mb $7.50/kW-yr $10/kWyr

$5/kWyr

Rate Type

Default 

(Demand 

Based)

Base

Base

Time 

of 

Use/ 

Tier

Electricity 

Price 

Escalation

0.5% per 

year

0% 2%

Electric 

Cost 

Location

Largest 

Utility low high

Incentives Federal ITC 

(10%) Base 0% 30% 

ITC

a The values used in Table 1 are not intended to represent all possible scenarios but provide a reasonable 

range of values for each parameter. 

b O&M values are based on DOE SunShot targets. Inverter replacements occur at year 15 (2035), with a 

cost of $0.11/W. 

Figures 8 and 9 show the results. For each state, the gray bar indicates the base-case 

breakeven price based on the largest utility in the state, and four error bars show the 

range of breakeven prices for the sensitivity cases. Each of the four drivers has a low case 

and a high case. The low case, which decreases the economic performance of PV and 

moves the error bar to the left, represents a lower breakeven price. Examples include 

lower PV output from non-optimal orientation or a total elimination of the federal ITC. 

The high case represents improved economic performance, which increases the 
17 

breakeven price and moves the error bar to the right. Examples include an improved 

derate factor (perhaps resulting from improved inverter efficiency) or a more effective 

system orientation. 

The scenarios and error bars in the figures are partially additive. For example, both an 

extension of the 30% ITC and improved derate factors could occur, increasing the 

breakeven price more than these factors individually. However, these factors are not 

completely additive; for example, the highest solar resource location in each state may 

not correspond to the highest electricity price region. 

Figure 8. Range of PV breakeven prices in the 2020 scenarios: Top 26 states and 

Washington, D.C. The gray bars indicate the base case from the largest utility in the state, 

and the four error bars show the range of breakeven prices for the sensitivity cases. 

Note: For clarity, breakeven prices above $8/W are not displayed.

0 1 2 3 4 5 6 7 8

WA

OH

MA

MO

NY

OK

VT

TN

ND

MT

PA

AZ

NV

NJ

NM

MD

IL

WI

DC

NH

GA

SD

SC

CA

CT

HI

Finance Technical Electric Policy

Breakeven Price ($/W)
18 

Figure 9. Range of PV breakeven prices in the 2020 scenarios: Bottom 25 states. The gray 

bars indicate the base case from the largest utility in the state, and the four error bars 

show the range of breakeven prices for the sensitivity cases. 

Note: For clarity, breakeven prices above $8/W are not displayed.

As shown in Figures 8 and 9, the base-case breakeven price in 2020 is between $0.38/W 

and $7.27/W. Electricity price is the biggest driver of breakeven price variation and is 

followed generally by policy (availability of the ITC), technical performance, and finance 

factors. The variation in the electricity prices is due more to the spread between utilities 

within a state than the variation in the price escalation assumed. 

0 1 2 3 4 5 6 7 8

LA

ID

WY

KY

MS

ME

NE

IN

MI

TX

AR

KS

WV

MN

CO

FL

AL

IA

RI

VA

NC

DE

UT

OR

Finance Technical Electric Policy

Breakeven Price ($/W)
19 

5 Conclusions

We evaluated breakeven prices for rooftop PV installed on U.S. supermarkets. Our 

results suggest that breakeven prices vary by more than a factor of 30 nationwide, even 

though the solar resource varies by less than a factor of 2. Non-technical factors—

including electricity rates, rate structures, and incentives—drive breakeven prices more 

than technical factors like solar resource or system orientation. 

Under base-case assumptions, about 17% of supermarkets nationwide were in utility 

territories where breakeven conditions existed at a PV system price of $5/W in 2011. 

Using the estimated 2012 installed price of commercial PV systems ($3.43/W), 40% of 

supermarkets were in utility territories where breakeven conditions existed. These 

percentages increase to 33% and 53%, respectively, when rate structures favorable to PV 

(time-of-use, tiered rates) are used. In 2020 (where we assume higher electricity prices 

and lower PV incentives), up to 26% of supermarkets are in utility territories where 

breakeven conditions exist at a PV system price of $3/W; this increases to 91% at 

$1.25/W (the DOE SunShot Initiative’s commercial PV price target). 

The general trend observed in this analysis is that breakeven conditions appear first in 

parts of the East Coast, California, and Hawaii, where they are driven by high electricity 

prices. As PV system prices continue to decline, breakeven conditions begin to occur in 

parts of the central United States. Very low electricity prices could preclude breakeven

conditions in certain areas even with PV prices approaching $1.25/W.

Overall, the scenarios evaluated represent a market entry point for PV. However, the 

scenarios do not examine the potential for a deep, sustained market. Therefore, caution 

must be used when considering this analysis. PV breakeven does not imply that 

customers will necessarily adopt PV. In reality, only a fraction of supermarkets in each 

utility are currently likely to have access to the range of factors that make PV an 

attractive and viable option. A more detailed depth-of-market analysis is required to 

determine a “demand curve” for PV at various price points. This type of depth-of-market 

analysis could be combined with analysis of various commercial building types to 

provide a more robust estimate of the market potential of commercial rooftop PV. In 

future work we will examine breakeven in a broader set of commercial building types and 

explore how breakeven relates to market depth and market evolution. 
20 

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Summary of the Installed Price of Photovoltaics in the United States from 1998 to 2011.” 

LBNL-5919E. Berkeley, CA: Lawrenece Berekely National Laboratory. 

Darghouth, N.; Barbose, G.; Wiser, R. (2013). ” Electricity Bill Savings from Residential 

Photovoltaic Systems: Sensitivities to Changes in Future Electricity Market Conditions.” 

LBNL-6017E. Berkeley, CA: Lawrenece Berekely National Laboratory.

Denholm, P.; Margolis. R. M. (2007a). “Evaluating the Limits of Solar Photovoltaics 

(PV) in Traditional Electric Power Systems.” Energy Policy (35); pp. 2852–2861. 

Denholm, P.; Margolis. R. M. (2007b) “Evaluating the Limits of Solar Photovoltaics 

(PV) in Electric Power Systems Utilizing Energy Storage and Other Enabling 

Technologies.” Energy Policy (35); pp. 4424–4433. 

Denholm, P.; Margolis, R.; Ong, S.; Roberts, B. (2009). “Breakeven Cost for Residential 

Photovoltaics in the United States: Key Drivers and Sensitivities.” NREL/TP-6A2-46909. 

Golden, CO: National Renewable Energy Laboratory. 

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Department of Energy Commercial Reference Building Models of the National Building 

Stock. NREL/TP-5500-46861. Golden, CO: National Renewable Energy Laboratory. 

U.S. Department of Energy (DOE). (2012a). SunShot Vision Study. DOE/GO-102012-

3037. Accessed July 23, 2012: http://www1.eere.energy.gov/solar/pdfs/47927.pdf. 

DOE. (2012b). “2011 Buildings Energy Databook.” Washington, DC: Office of Energy 

Efficiency and Renewable Energy.

U.S. Energy Information Administration (EIA). (2012). Monthly Electric Sales and 

Revenue Report with State Distributions Report: November 2011. EIA-826. Washington, 

DC: Energy Information Administration. 

Feldman, D.; Barbose, G.; Margolis, R.; Wiser, R.; Darghouth, N.; Goodrich, A. (2012). 

Photovoltaic (PV) Pricing Trends: Historical, Recent, and Near-Term Projections. 

NREL/TP-6A20-56776. Golden, CO: National Renewable Energy Laboratory. 

Gilman, P.; Dobos, A. (2012). System Advisor Model, SAM 2011.12.2: General 

Description. NREL/TP-6A20-53437. Golden, CO: National Renewable Energy 

Laboratory.

Herig, C.; Gouchoe, S.; Haynes, R; Perez, R.; Hoff, T. (2002). “Customer Sited PV:

A State Market Analysis.” Proceedings of ASES Annual Meeting, Reno, NV. 
21 

Herig, C.; Gouchoe, S.; Perez, R.; Hoff, T. (2003). “PV in Commercial Buildings - 

Mapping the Breakeven Turn-Key Value of State Markets.” Proceedings of ASES 

Annual Meeting, Austin, TX. 

Ong, S.; Denholm, P.; Doris, E. (2010). “The Impacts of Commercial Electric Utility 

Rate Structure Elements on the Economics of Photovoltaic Systems.” NREL/TP-6A2-

46782. Golden, CO: National Renewable Energy Laboratory.

Ong, S.; Campbell, C.; Clark, N. (2012). “Impacts of Regional Electricity Prices and 

Building Type on the Economics of Commercial Photovoltaic Systems.” NREL/TP6A20-56461. Golden, CO: National Renewable Energy Laboratory. 

Solar Energy Industries Association (SEIA). (2012). Solar Means Business: Top 

Commercial Solar Customers in the U.S. 

Wilcox, S.; Marion, W. (2008). Users Manual for TMY3 Data Sets. NREL/TP-581-

43156. Golden, CO: National Renewable Energy Laboratory. 
22 

Appendix A: Calculation of Breakeven Price

The breakeven price of a PV system is defined as the point where the net present cost 

(NPC) of the system equals the net present benefit (NPB) to its owner. 

The NPC is the cumulative discounted cost of the system, including initial cost, 

financing, tax impacts, incentives, and O&M, equal to the sum of the cost in each year 

multiplied by the discount factor in that year.

The discount factor in year y = y d)1(

1

+

Where d is the discount rate. 

The cost in each year is based on the system financing. At the beginning of the financing 

period, a down payment and then a loan amount are established by: 

Loan Amount = PV System Cost - Down Payment - Initial Rebates

The annual loan payment is then calculated by multiplying the load amount by the capital 

recovery factor: 

Loan Payment = Loan Amount * 

1)1(

)1(

−+

+

n

n

i

ii

Where i is the interest rate and n is the loan term in years. The tax savings on the loan 

interest in each year is given by: 

Interest Deductiony = Marginal Federal Tax Rate * i * Current System Balancey

Some incentives (such as tax incentives) may not occur until a year or so after 

installation. These incentives are discounted by one year. 

Tax savings from 5-year federal MACRS depreciation is also considered in this analysis.

The NPB is the discounted cumulative benefits of reduced electricity bills over the 

evaluated period or the sum of the benefits in each year multiplied by the discount factor. 
23 

Appendix B: Incentives Used in this Study

Table B-1. Statewide Incentives (as of May 2012) 

State Incentive ($/W)

AZ $0.10

CA $0.42

CO $0.10

DC $0.07 

DE $0.10

FL $0.40

GA $1.76

HI $2.00

IA $0.18

KS $0.11

KY $0.01

MA $0.02

MD $0.05

ME $1.99

MT $0.80

NC $1.03

ND $0.25

NE $0.01

NH $0.40

NJ $0.04

NM $0.67

NV $0.46

NY $1.75

OK $0.07

PA $0.21

SC $0.14

TN $1.44

UT $0.17

VT $0.29

WA $4.92

WI $0.06

Note: All incentives from http://www.dsireusa.org/. All incentives expressed in dollars per watt.
24 

Table B-2. Utility Incentives in the Form of Tax Credits or Rebates as of May 2012

State Utility Name

Incentive

($/W)

AZ Mohave Electric Coop Inc $0.20

AZ Tucson Electric Power Co $2.20

CA Azusa, CA (City of) $0.50

CA Corona, CA (City of) $0.19

CA Glendale Water & Power $2.00

CA Gridley, CA (City of) $0.02

CA Healdsburg, CA (City of) $0.09

CA Imperial Irrigation District $1.91

CA Lodi, CA (City of) $1.50

CA Lompoc, CA (City of) $3.00

CA Los Angeles Dept of Water & Power $2.56

CA Merced Irrigation District $0.28

CA Moreno Valley Public Utilities $2.80

CA PacifiCorp $2.00

CA Redding Electric Utility $4.78

CA Riverside Public Utilities $2.50

CA Shasta Lake, CA (City of) $0.77

CA Truckee Donner Public Utility District $3.65

CA Ukiah, CA (City of) $0.06

CO Highline Electric Association $0.02

CO Holy Cross Electric Association Inc $1.50

CO Public Service Co of Colorado $1.60

DE Delaware Electric Coop Inc $0.08

DE Dover, DE (City of) $0.06

FL Florida Power & Light Co $0.17

FL Orlando Utilities Commission $0.31

MA Concord, MA (Town of) $0.01

MI Detroit Edison Co $0.18

MT NorthWestern Corp $0.02

NM El Paso Electric Co $0.09

NM Public Service Co of New Mexico $0.08

NM Southwestern Public Service Co $0.10

NY Long Island Power Authority $1.75

OR Ashland, OR (City of) $0.03

TX Austin Energy $0.14

TX El Paso Electric Co $0.17

TX Entergy Texas Inc $0.35

TX Southwestern Electric Power Co $0.07

UT PacifiCorp $0.09

VT Green Mountain Power Corp $1.28
25 

State Utility Name

Incentive

($/W)

WA PUD No 1 of Chelan County $0.74

WA PUD No 1 of Klickitat County $0.01

WA PUD No 1 of Okanogan County $1.34

Note: All incentives from http://www.dsireusa.org/. 

Table B-3. Statewide Feed-In Tariffs as of May 2012

State Incentive

HI $0.189/kWh

OR $0.432/kWh

VT $0.240/kWh

Note: All incentives from http://www.dsireusa.org/. 

Table B-4. Utility Feed-In Tariffs as of May 2012

State Utility Name Incentive

(Multiple) Tennessee Valley Authority24 $0.22/kWh

TX San Antonio, TX (City of) $0.27/kWh

VA Bristol Virginia Utilities $0.22/kWh

WI River Falls, WI (City of) $0.30/kWh

WI Madison Gas & Electric Co $0.25/kWh

Note: All incentives from http://www.dsireusa.org/. 

 

24 http://www.tva.gov/ee/in_home_eval_dist.htm
26 

Appendix C: Complete Listing of All Building Locations and Associated 

ASHRAE Zone

Table C-1. Complete Listing of All Building Locations, Associated ASHRAE Zone, and Electric Load Summary 

TMY3 Station Name State ASHRAE 

Zone

Peak

Demand

(kW)

Peak

Demand

Month

Annual Load 

(kWh) 

MONTGOMERY DANNELLY FIELD AL 3A 395 Jun 1,822,494 

LITTLE ROCK ADAMS FIELD AR 3A 396 Jul 1,795,596 

FLAGSTAFF PULLIAM ARPT AZ 5B 319 Jul 1,556,093 

PHOENIX SKY HARBOR INTL ARPT AZ 2B 403 Jul 1,830,044 

TUCSON INTL ARPT AZ 2B 378 Jul 1,747,086 

ARCATA ARPT CA 4B 303 Oct 1,588,265 

LOS ANGELES INTL ARPT CA 3B 315 Sep 1,706,465 

SAN FRANCISCO INTL ARPT CA 3C 309 Jun 1,618,194 

BROOMFIELD/JEFFCO [BOULDER - SURFRAD] CO 5B 337 Jun 1,587,414 

EAGLE COUNTY AP CO 6B 332 Jul 1,642,215 

PUEBLO MEMORIAL AP CO 5B 343 Aug 1,629,719 

BRIDGEPORT SIKORSKY MEMORIAL CT 5A 355 Aug 1,653,340 

ANDREWS AFB MD 4A 379 Jul 1,692,667 

WILMINGTON NEW CASTLE CNTY AP DE 4A 371 Jul 1,683,104 

MIAMI INTL AP FL 1A 373 Jul 2,008,354 

TAMPA INTERNATIONAL AP FL 2A 386 Jul 1,982,531 

ATLANTA HARTSFIELD INTL AP GA 3A 375 Jul 1,771,016 

SAVANNAH INTL AP GA 2A 392 Jul 1,868,763 

NASA SHUTTLE FCLTY HI 2A 358 Aug 2,011,748 

DES MOINES INTL AP IA 5A 385 Jul 1,671,076 
27 

TMY3 Station Name State ASHRAE 

Zone

Peak

Demand

(kW)

Peak

Demand

Month

Annual Load 

(kWh) 

MASON CITY MUNICIPAL ARPT IA 6A 391 Jul 1,659,628 

BOISE AIR TERMINAL [UO] ID 5B 340 Jul 1,573,929 

CHICAGO O’HARE INTL AP IL 5A 377 Jul 1,661,322 

SPRINGFIELD CAPITAL AP IL 5A 388 Jul 1,698,913 

INDIANAPOLIS INTL AP IN 5A 394 Jul 1,687,539 

GOODLAND RENNER FIELD KS 5A 371 Jul 1,653,292 

WICHITA MID-CONTINENT AP KS 4A 381 Jul 1,723,956 

LEXINGTON BLUEGRASS AP KY 4A 372 Jul 1,712,492 

NEW IBERIA NAAS LA 2A 392 Jul 1,919,480 

SHREVEPORT REGIONAL ARPT LA 3A 384 Aug 1,836,323 

BOSTON LOGAN INTL ARPT MA 5A 369 Jul 1,636,845 

BALTIMORE BLT-WASHINGTON INTL MD 4A 381 Jul 1,695,038 

CARIBOU MUNICIPAL ARPT ME 7A 346 Aug 1,597,984 

PORTLAND INTL JETPORT ME 6A 383 Jul 1,607,226 

DETROIT METROPOLITAN ARPT MI 5A 383 Aug 1,644,386 

HOUGHTON LAKE ROSCOMMON CNTY AR MI 6A 351 Aug 1,616,760 

TRAVERSE CITY CHERRY CAPITAL MI 6A 369 Jul 1,629,565 

INTERNATIONAL FALLS INTL AP MN 7A 359 Jun 1,617,660 

MINNEAPOLIS-ST PAUL INTL ARP MN 6A 380 Jul 1,638,514 

KANSAS CITY INTL ARPT MO 4A 382 Jul 1,707,772 

JACKSON INTERNATIONAL AP MS 3A 392 Aug 1,824,543 

BILLINGS LOGAN INTL ARPT MT 6B 345 Jul 1,592,177 

GREENSBORO PIEDMONT TRIAD INT NC 4A 381 Jul 1,738,573 

WILMINGTON INTERNATIONAL ARPT NC 3A 388 Jul 1,804,250 

BISMARCK MUNICIPAL ARPT [ISIS] ND 6A 375 Jul 1,616,434 
28 

TMY3 Station Name State ASHRAE 

Zone

Peak

Demand

(kW)

Peak

Demand

Month

Annual Load 

(kWh) 

MINOT AFB ND 7A 371 Jul 1,618,827 

NORTH PLATTE REGIONAL AP NE 5A 395 Jul 1,684,894 

CONCORD MUNICIPAL ARPT NH 6A 364 Aug 1,628,888 

ATLANTIC CITY INTL AP NJ 4A 384 Aug 1,681,071 

ALBUQUERQUE INTL ARPT [ISIS] NM 4B 337 Jun 1,609,145 

LAS VEGAS MCCARRAN INTL AP NV 3B 379 Jul 1,675,363 

RENO TAHOE INTERNATIONAL AP NV 5B 327 Aug 1,573,274 

NEW YORK CENTRAL PARK OBS BELV NY 4A 380 Aug 1,676,682 

ROCHESTER GREATER ROCHESTER I NY 5A 371 Jul 1,648,672 

SYRACUSE HANCOCK INTL ARPT NY 5A 349 Jun 1,648,827 

CLEVELAND HOPKINS INTL AP OH 5A 373 Jul 1,659,179 

OKLAHOMA CITY WILL ROGERS WOR OK 3A 384 Aug 1,766,035 

BURNS MUNICIPAL ARPT [UO] OR 5B 324 Jun 1,543,472 

PENDLETON E OR REGIONAL AP OR 5B 357 Jul 1,613,894 

LANCASTER PA 5A 382 Jul 1,687,642 

PHILADELPHIA INTERNATIONAL AP PA 4A 366 Jul 1,652,704 

PROVIDENCE T F GREEN STATE AR RI 5A 378 Aug 1,644,430 

CHARLESTON INTL ARPT SC 3A 400 Jul 1,842,480 

PIERRE MUNICIPAL AP SD 6A 393 Jul 1,648,259 

MEMPHIS INTERNATIONAL AP TN 3A 391 Aug 1,789,899 

NASHVILLE INTERNATIONAL AP TN 4A 386 Jun 1,762,733 

AMARILLO INTERNATIONAL AP [CANYON - UT] TX 4B 351 Jun 1,665,680 

EL PASO INTERNATIONAL AP [UT] TX 3B 347 Jul 1,685,767 

HOUSTON BUSH INTERCONTINENTAL TX 2A 400 Aug 1,915,653 

CEDAR CITY MUNICIPAL AP UT 5B 344 Jul 1,638,632 
29 

TMY3 Station Name State ASHRAE 

Zone

Peak

Demand

(kW)

Peak

Demand

Month

Annual Load 

(kWh) 

SALT LAKE CITY INTL ARPT [ISIS] UT 5B 337 Jul 1,603,261 

RICHMOND INTERNATIONAL AP VA 4A 390 Jul 1,739,577 

BURLINGTON INTERNATIONAL AP VT 6A 377 Jul 1,615,115 

SEATTLE SEATTLE-TACOMA INTL A WA 4C 315 Aug 1,582,913 

YAKIMA AIR TERMINAL WA 5B 360 Jul 1,579,183 

GREEN BAY AUSTIN STRAUBEL INT WI 6A 378 Jun 1,636,747 

CHARLESTON YEAGER ARPT WV 4A 370 Jul 1,704,164 

ELKINS ELKINS-RANDOLPH CO ARP WV 5A 353 Jun 1,655,137 

CHEYENNE MUNICIPAL ARPT WY 6B 326 Jul 1,576,865 
30 

Appendix D: Grid Parity and Installed PV Prices 

by State as of August 8, 2012 

The analysis in the main report assumes a uniform price for PV installations. Preliminary 

data indicate a wide range in installed prices for commercial systems, especially in 

locations with limited PV markets. This is indicated in Table D-1, which provides 

installed prices for 50-kW to 500-kW PV systems installed between January 1 and 

August 8, 2012. These prices were derived from the OpenPV database 

(https://openpv.nrel.gov/). This dataset has not been validated, and the results in this 

section are useful primarily to indicate the general relationship between installed prices

and breakeven conditions. Figure D-1 applies these prices in addition to the other factors 

described previously and identifies locations at or below grid parity, within 25% of parity 

(where current installed prices are within 25% of being at grid parity), and beyond 25% 

of parity. 

Figure D-1. Grid parity for supermarkets at current state average PV prices using an 

optional rate structure with incentives (where applicable) 
31 

Table D-1. Installed Prices for 50-kW to 500-kW PV Systems Installed Between January 1

and August 8, 2012

State Installed Price

($/W)

AL 8.00

AR 5.42

AZ 4.50

CA 4.40

CO 4.41

CT 5.12

DC 5.29

DE 4.90

FL 5.77

GA 6.00

HI 4.84

IA 4.79

ID 5.00

IL 7.32

IN 5.93

KS 4.84

KY 7.80

LA 5.40

MA 4.92

MD 4.92

ME 6.59

MI 5.63

MN 3.40

MO 6.13

MS 7.10

MT 4.84

NC 4.59

ND 4.84

NE 9.70

NH 4.53

NJ 4.88

NM 4.84

NV 5.98

NY 5.13

OH 4.38

OK 6.27
32 

State Installed Price

($/W)

OR 5.85

PA 4.70

RI 4.48

SC 4.84

SD 4.84

TN 5.45

TX 5.99

UT 3.45

VA 4.84

VT 6.44

WA 6.78

WI 7.10

WV 4.84

WY 4.84