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

Impacts of Regional Electricity 

Prices and Building Type on the 

Economics of Commercial 

Photovoltaic Systems

Sean Ong, Clinton Campbell, and Nathan Clark

Technical Report

NREL/TP-6A20-56461

December 2012
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

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Contract No. DE-AC36-08GO28308

Impacts of Regional Electricity 

Prices and Building Type on the 

Economics of Commercial 

Photovoltaic Systems

Sean Ong, Clinton Campbell, and Nathan Clark

Prepared under Task No. SM12.1031

Technical Report

NREL/TP-6A20-56461

December 2012
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iii

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 Paul Denholm, Elizabeth Doris, Trieu Mai, Nate 

Blair, and Robin Newmark of the National Renewable Energy Laboratory (NREL) for reviewing 

various versions of the document, as well as Henrietta de Veer (Prime Solutions, Inc.), Nicholas 

Bowden (Illinois State University), David Arfin (DOE), and Ammar Qusaibaty (DOE) for their 

thoughtful reviews. The authors also thank Mary Lukkonen of NREL’s Communications Office 

for a thorough technical edit of the document. Finally, and naturally, any remaining errors are the 

fault of the authors. 
iv

Executive Summary

Business owners often have a choice between multiple electricity rate options. For businesses 

with photovoltaic (PV) installations, choosing the right rate is essential to maximize the value of 

PV generation. The complex interaction between PV generation, building load, and rate structure 

makes determining the best rate a challenging task. We evaluated 207 rate structures across 77 

locations and 16 commercial building types and identified the impacts of regional electricity 

prices and building type on the economics of solar PV systems. Results for expected solar value 

that result from bill savings are reported for all locations, building types, and rate structures

evaluated. Aggregated results are also reported, showing general trends across various impact 

categories. Key findings include:

• Regional electricity price differences are more important than building type when 

considering PV economics. On average, system economic performance varies 30% by 

building type. Buildings with relatively large rooftop area and lower energy consumption 

tend to yield higher-than-average solar value. Regionally, PV system economics vary by 

a factor of 10, with the most economically attractive locations having the highest average 

electricity prices. 

• The best electricity rate for a business depends on the amount of PV capacity 

installed relative to the building’s electricity load. The rate structure that minimizes 

the business’s electricity expenses prior to a PV installation still remains the best rate 

after a PV system is installed, as long as the system is small compared to the business’s 

electric load. Other rates provide greater value than the initial rate for larger PV system 

sizes (see Figure ES-1 for an example).

• Rate structure elements impact PV economic performance, with energy-only rates 

being most favorable. On average, energy-only rates were found to increase solar value 

by 13%, versus rates with demand charges. Flat energy-only rates were found to be the 

most favorable, while rates that combined demand charges with tiered rates were found to 

be the least favorable.
v

Figure ES-1. Value of PV generation under various rate structures and penetration levels for 

warehouses in the Wisconsin Electric Power territory

Notes: Additional details for each rate type is provided in Appendix A. Dashed lines represent nonapplicable rates for this building type.

Rate Name
vi

Table of Contents

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

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

2.1 Data and Methodology Overview.................................................................................................... 2

2.2 Load Data......................................................................................................................................... 2

2.3 Rate Data.......................................................................................................................................... 6

2.4 Solar Resource Data......................................................................................................................... 8

2.5 System Advisor Model and Technical Calculations........................................................................ 8

2.6 Bill Savings and Solar Value Calculations...................................................................................... 9

3 Results................................................................................................................................................. 12

3.1 Net Value of Solar ......................................................................................................................... 12

3.2 Impact of PV Building Penetration................................................................................................ 16

4 Conclusion .......................................................................................................................................... 21

References................................................................................................................................................. 22

Appendix A ................................................................................................................................................ 23

Appendix B ................................................................................................................................................ 30

Appendix C: Digital Appendix ................................................................................................................. 33
vii

List of Figures

Figure 1. Locations of simulated building load data, shown within ASHRAE climate zones........3

Figure 2. Simulated hourly load data for medium-sized office buildings during the last week of 

March .........................................................................................................................................5

Figure 3. Simualted hourly load data for medium-sized office buildings during the second week 

of July.........................................................................................................................................5

Figure 4. Example of applicability of electricity rates for commercial facilities in three utility 

service territories in California ..................................................................................................7

Figure 5. Value of PV generation under various rate structures and penetration levels for a school 

in the PG&E service territory ..................................................................................................10

Figure 6. Net value of PV generation under various rate structures and penetration levels for a 

school in the PG&E service territory.......................................................................................11

Figure 7. Distribution of electricity prices and calculated solar values........................................12

Figure 8. Average solar value for each building type....................................................................13

Figure 9a. Average solar value for the top 39 locations................................................................14

Figure 9b. Average solar value for the next 38 locations..............................................................15

Figure 10. Solar value by rate type, averaged across all locations and building types evaluated .16

Figure 11. Net solar value with respect to penetration for all Entergy Texas rates evaluated.......18

Figure 12. Net solar value with respect to penetration for all PG&E rates evaluated ...................18

Figure 13. Distribution of optimal penetration levels for all locations and building types 

evaluated ..................................................................................................................................19

Figure 14. Comparison of solar values relative to the highest solar value, averaged for all 

locations and building types.....................................................................................................20

List of Tables

Table 1. DOE Reference Buildings With Average Energy Consumption and Peak Power............4

Table 2. Summary of Rate Types Used in This Study.....................................................................8

Table 3. Average PV Penetration Values by Building Type .........................................................17

Table A-1. List of Utilities and Rates Evaluated With Information About Their Rate Type, 

Average Price Without Solar, Average Maximum Solar Value, and Applicable Range ........23

Table B-1. Maximum Solar Value Found for Each Building Type in Each Location ..................30
1

1 Introduction

Businesses are increasingly considering solar technologies as a way to help offset a portion of 

their annual energy expenditures. Many commercial buildings have large, flat roofs that could 

allow for solar photovoltaic (PV) systems capable of generating a significant portion of their 

annual electricity needs. However, the value of this generation is highly dependent on the 

business’s electricity rate. Utilities often offer a choice between multiple rate options. 

Understanding conditions for optimal solar value requires an analysis of the interaction between 

the building electric load, the amount of PV generation, and rate structure. The availability of 

high resolution data (i.e., hourly or sub-hourly resolution) is essential when determining the 

impacts of time-of-use (TOU) rates and demand charges. These considerations may present a 

challenging task for businesses that are trying to determine whether or not solar makes economic 

sense for their building. In this study, we evaluate the impacts of regional electricity prices and 

building type on the economics of solar PV systems.

A variety of analyses on the impacts of rate structures on the economics of PV systems have 

been conducted, including the evaluation of the residential sector (Darghouth et al. 2010) and 

commercial sector (Wiser et al. 2007) in California. Commercial rate structures have also been 

evaluated for specific building categories, such as office buildings (Ong et al. 2010) and schools

(Ong and Denholm 2011). This report adds to the literature by providing a national-scale 

evaluation of the impacts of commercial rate structures across a variety of building categories.

In this study, 207 rate structures from 52 electric utilities are evaluated. These rate structures

were used to assess solar value and annual savings for 16 different building types. Rate impacts 

are dependent on individual building load profiles, which vary from one location to another. PV 

performance trends and relative bill savings are reported across all rates, building types, and 

locations evaluated. These results are not intended to represent any specific customer. Businesses

considering a solar installation should evaluate their facility’s unique load profile and use this 

report as a guide to analyze the potential impacts of a PV system. 
2

2 Data and Methodology

2.1 Data and Methodology Overview

The complex interaction between building load, solar production, and electricity rate structure 

requires a model that can simultaneously process all elements involved. The System Advisor 

Model (Section 2.5) is used to generate solar production and bill savings results from a variety of 

inputs, including: hourly building load data (Section 2.2), utility rate data (Section 2.3), and 

meteorological data (Section 2.4). The following sections provide details on the data and 

methodology used in this analysis.

2.2 Load Data

Building load datasets are important components in any rate structure analysis that includes 

demand charges and tiered rates. Demand charges (defined in Section 2.3) are usually based on 

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

value of a PV system requires a load profile. Load profiles are also required when evaluating 

tiered rates and demand charges, where rates vary depending on monthly energy usage. This 

analysis uses load profile data for 16 building categories from the U.S. Department of Energy 

(DOE) commercial reference building models (Deru et al. 2011), which were simulated using the 

EnergyPlus simulation software.1 All loads and buildings for the benchmark models were 

simulated under typical meteorological year 3 (TMY3) conditions. TMY3 is a dataset of the 

National Solar Radiation Database (Wilcox 2007; Wilcox and Marion 2008). For consistency, 

TMY3 conditions were also used when simulating PV performance. Section 2.4 contains more 

information about the TMY3 weather data. Load data were simulated for 77 locations throughout 

the United States. Locations were selected from the largest utilities in each state and chosen to 

represent all climate zones in the contiguous United States. Figure 1 shows the locations 

analyzed and official climate zones recognized by the American Society of Heating, 

Refrigeration, and Air Conditioning Engineers (ASHRAE). Sixteen building categories were 

simulated for each of the 77 locations, resulting in 1,232 unique load profiles used in this 

analysis. Table 1 summarizes each building type used in this study.

 1 The reference buildings used in this study represent are based on current building codes and are not intended to 

represent older building construction. For more information on the EnergyPlus model, see 

http://apps1.eere.energy.gov/buildings/energyplus/.
3

Figure 1. Locations of simulated building load data, shown within ASHRAE climate zones
4

Table 1. DOE Reference Buildings With Average Energy Consumption and Peak Power

The simulated building data includes aggregated hourly load profiles for all electrical loads 

associated with each building and includes smaller loads such as plug loads. The total hourly 

electrical load of each building was entered into the System Advisor Model (SAM).2 See 

Section 2.5 for SAM details. Figure 2 and Figure 3 show select examples for simulated hourly 

office load data during the last week of March and the second week of July, respectively.

 2 Demand charges are usually measured and billed according to 15- or 30-minute time increments. The lack of 15-

minute data resolution for this analysis may present an overestimation of a PV system’s ability to offset demand 

charges. This could occur if the hourly data masks or smoothes sub-hourly spikes and dips in demand and 

production. 

Building Type

Floor Area 

(ft2)

Number of 

Floors

Annual 

Energy 

(MWh)

Peak Power 

(kW)

Full-Service Restaurant 5,500 1 322 68

Hospital 241,351 5 9,287 1,510

Large Hotel 122,120 6 2,842 553

Large Office 498,588 12 6,244 1,580

Medium Office 53,628 3 742 318

Mid-Rise Apartment 33,740 4 242 67

Outpatient 40,946 3 1,388 321

Primary School 73,960 1 888 328

Quick-Service

Restaurant 2,500 1 194 39

Secondary School 210,887 2 3,193 1,178

Small Hotel 43,200 4 600 133

Small Office 5,500 1 66 19

Standalone Retail 24,962 1 327 104

Strip Mall 22,500 1 297 93

Supermarket 45,000 1 1,687 367

Warehouse 52,045 1 269 96
5

Figure 2. Simulated hourly load data for medium-sized office buildings during the last week of 

March

Figure 3. Simualted hourly load data for medium-sized office buildings during the second week of 

July
6

2.3 Rate Data

A total of 207 rates from 52 utility companies were evaluated.

3 These rates were obtained from 

the online Utility Rate Database (URDB) on the OpenEI platform4 and from utility tariff sheets. 

This study assumes that PV energy production is compensated at the retail electricity rate for all 

energy produced, up to 100% of the building’s annual electricity usage.

5 The utilities offer 

various commercial rate structures for different load sizes and types. In this analysis, we consider 

standard rates and optional rates. A standard 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. Smaller loads typically have more rate choices than larger loads because 

smaller users may sometimes choose to be on rates designed and made mandatory for larger 

loads. In some cases, larger facilities with solar installations have the option to use rates designed 

for smaller facilities. Eligibility criteria were obtained for each rate evaluated and were 

considered when calculating utility bills for each building type. Figure 4 illustrates the eligibility 

range for 22 utility rates in California, based on building demand in kilowatts (kW). Some rates 

are not applicable to all buildings, and they were not considered in the rate and cost impact 

calculations, though they were still analyzed for reference purposes. For a complete list of utility 

rates evaluated and their respective eligibility requirements, see Appendix A.

 3 Although we evaluated 77 unique locations for the building load profiles, there are instances when two or more 

locations are served by the same utility company. Each of the 77 locations is served by one of the 52 utilities.

4 Open Energy Information (OpenEI) is a knowledge-sharing online community dedicated to connecting people with 

the latest information and data on energy resources from around the world (http://www.OpenEI.org). OpenEI was 

created in partnership with the DOE and federal laboratories across the nation. OpenEI’s URDB

(http://en.openei.org/wiki/Gateway:Utilities) contains downloadable rate structure information from electric utilities 

around the United States. 

5 This arrangement is also known as net metering, which may not be available in all states or utilities. For a complete 

list of utilities participating in net-metering arrangements, see the Database of State Incentives for Renewables and 

Efficiency (DSIRE) (http://www.dsireusa.org/). In this analysis, PV production never exceeds 100% of the 

building’s annual electricity usage.
7

Figure 4. Example of applicability of electricity rates for commercial facilities in three utility 

service territories in California

Various utility rate elements are used throughout the United States. The most common rate 

elements (Ong et al. 2010) include the following:

• Customer Charge. A fixed monthly charge that is independent of energy use. Customer 

charges typically range from $10 for small businesses to over $1,000 for large facilities. 

• Energy Charges. Energy charges are rates based on energy consumption, usually in 

dollars per kilowatt-hour or cents per kilowatt-hour.
8

• Demand charges. Normally included with energy charges in applicable rate structures, 

demand charges charge customers for their peak power (kW) usage. Demand charges can 

also be fixed or vary by season or hour. 

• Flat rates. Fixed cost of electricity that does not vary except for fuel cost adjustments 

and other fees.

• Seasonal rates. Rates that vary by season. A typical seasonal rate structure has a lower 

rate for winter months and a higher rate for summer months. 

• Time-of-use rates. TOU or time-of-day rate structures usually vary 2–4 times a day. A 

typical TOU rate has a lower cost at night, a higher cost during the late afternoon, and an 

intermediate cost during the mornings and evenings. The term “on-peak” or “peak” is 

generally used to describe hours with higher prices, while “off-peak” is used to describe 

hours with lower prices. 

• Tiered or block rates. Tiered rates typically refer to rates that increase with increasing 

electricity usage, while block rates typically refer to rates that decrease with increasing 

electricity usage. Block rates are most common in the form of energy charges; however, 

tiered demand charges are also used. 

Utilities may combine multiple rate elements within a single rate structure. Demand charges are 

typically combined with other rate elements for commercial tariffs. Table 2 provides a summary 

of rate elements from the rates used in this report. For a complete list of utility rates evaluated 

and their respective rate type, see Appendix A.

Table 2. Summary of Rate Types Used in This Study

Rate Type Flat/Seasonal Demand Charge TOU Tiered

Number of 

Ratesa 97 141 55 61 a Due to combinations of various rate elements, the sum of rates across all categories is greater than the number of 

rates we evaluated (207). 

2.4 Solar Resource Data

The PV production data used in this analysis were simulated using the 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, though the dataset does not consist 

of an actual representative year. Rather, TMY3 was created by combining data from multiple 

years.6 The meteorological dataset was used as an input for SAM, which simulated hourly PV 

production for use in the financial calculations.

2.5 System Advisor Model and Technical Calculations

Developed by the National Renewable Energy Laboratory (NREL) in collaboration with Sandia 

National Laboratories and DOE, SAM is a performance and economic model designed to 

facilitate decision making and analysis for renewable energy projects (Gilman and Dobos 2012). 

 6 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 was intentionally selected to be 

representative of typical meteorological conditions. 
9

The TMY3 meteorological data was provided as an input for SAM, which uses a performance 

model and user-defined assumptions to simulate hourly PV generation data. The following 

assumptions were used when generating the PV performance data:

• Tilt of 15 degrees

• South facing (180-degree azimuth)

• A derate factor of 85%

• Annual degradation of 0.5%.

In addition to the meteorological data, hourly building load data and utility rate data were given 

as inputs for SAM. A rooftop PV system was simulated for various penetration levels ranging 

from 0% (no PV system) to 100% (PV system generates the same amount of energy as each 

building’s annual electrical energy consumption7

) in increments of 5%. PV penetration is 

defined as the percentage of a facility’s annual electrical energy consumption that is met by a PV 

system. The value of the PV system’s generation under various penetration levels and rate 

structures was evaluated by comparing the buildings’ annual electricity costs both with and 

without the PV system in each scenario. Any resulting difference from the comparison was 

attributed to the PV system. 

2.6 Bill Savings and Solar Value Calculations

Solar value provides insight into the value of the energy the system is generating. Solar value is 

calculated as follows:

Figure 5 illustrates the value of a rooftop PV system on a secondary school building using 

Pacific Gas & Electric (PG&E) rates under different penetration levels. Rate A-6 yields the 

greatest solar value at $0.23/kWh, far above the other rate structures. Rate A-6 is a very 

expensive rate, with summer afternoon prices approaching $0.45/kWh. Although this gives high 

value to a PV system, a school switching to this rate from a less-expensive rate experiences an 

increase in total electricity cost, causing net losses rather than savings. Evaluating a rate structure 

in isolation without considering net bill impacts or other rate structure options is insufficient 

when conducting a rate analysis. 

 7 Although the PV system generates the equivalent of 100% of the building’s annual electricity consumption, there 

will be times that the PV system exports energy to the grid (afternoons) and times that the building imports energy 

(nights). Existing net-metering policies typically allow excess generation to be credited toward the following 

month’s bill, effectively allowing the generation to be compensated, up to 100% of annual consumption, at retail 

rates. 
10

Figure 5. Value of PV generation under various rate structures and penetration levels for a school 

in the PG&E service territory

In order to accurately assess the value of PV under each rate structure, it is necessary to compare 

the building’s annual electricity costs without PV using the least-cost rate. The least-cost rate is 

the rate that minimizes annual electricity expense. This allows for the proper assessment of solar 

value in relation to the building’s lowest cost option prior to the PV installation. This calculation 

can be expressed as the following equation:

Figure 6 shows how the solar value chart changes once we employ the net solar value calculation 

method. This is a significant change from the previous chart, showing that rate A-6 is no longer 

the most attractive rate at all penetration levels. Many rates yield a negative value when PV 

penetration is small. This is because switching to these rates from rate A-10 (the best or leastexpensive rate option without PV) increases the building’s annual energy cost, despite having a 

small rooftop PV system. Though the PV system is still providing value to the building, it is not 

enough to overcome the increase in cost associated with switching to a more expensive rate. The 

result is a net annual loss for the building. At higher solar penetrations, the increase in solar 

value (under rates with high energy charges and high daytime rates) is enough to offset the cost 

increases from switching rates, yielding a net savings. All subsequent mentions of solar value or 

PV value in this paper refer to the net solar value metric above. 
11

Figure 6. Net value of PV generation under various rate structures and penetration levels for a 

school in the PG&E service territory

It is important to note that we do not consider impacts of system cost in this analysis. Businesses 

may purchase the system with cash, finance the system through a loan, or use third-party 

financing such as a power purchase agreement (PPA) or solar lease. In order to assess the 

economic feasibility of a solar project, it is necessary to evaluate both the costs and benefits that 

the PV system provides over time. Several metrics that are used to evaluate economic 

performance of PV systems include, but are not limited to: break-even cost, internal rate of 

return, simple payback period, and net annual bill savings. Several reports have described or 

used these metrics to evaluate rooftop PV systems (Denholm et al. 2009; Ong and Denholm 

2011; Short et al. 1995). 
12

3 Results

The impact of rates, building types, solar penetration, and location was evaluated across 1,232 

unique datasets. The aggregated results show trends across major categories. Furthermore, the 

aggregated results only consider the best rate choices: reported averages do not include results 

from sub-optimal rate options. Detailed results for each building type, location, and rate

(including sub-optimal rate choices) are provided in Appendix A and Appendix C. 

3.1 Net Value of Solar

We examine the distribution of the net solar values calculated for PV penetration levels that yield 

the highest net solar value. Figure 7 compares the net solar value distribution with the 

distribution of electricity prices evaluated. On average, the solar value at each location is $0.03

lower than the least-cost retail electricity rate without solar. The lower solar value is a result of 

the prevalence of demand charges in the rates used for this study. Seventy percent of all rates 

evaluated utilized demand charges, which typically reduce solar value (Ong et al. 2010). 

Figure 7. Distribution of electricity prices and calculated solar values

Figure 8 shows the average solar value for all building types evaluated. Although solar value

varies up to 30% by building type, the location is a bigger driver of solar value differences. 

Figure 9 shows the average solar value by location, with error bars designating differences due to 

building type. The primary driver of solar value by location is regional electricity price, and not 
13

solar resource. For example, New Orleans’s solar resource is 13% greater than Green Bay, 

Wisconsin. Yet, the net solar value in Green Bay is 440% greater than in New Orleans.

8 Solar 

resource in the United States varies by less than a factor of 2, while our results indicate that solar 

value vary by more than a factor of 10.

Figure 8. Average solar value for each building type 

 8 New Orleans is served by Entergy Louisiana, which utilizes commercial rates with high levels of demand charges. 

This causes a reduction in PV value. The high PV value in Green Bay is driven by Wisconsin Electric Power’s rate 

schedule Cg 6, which includes very high TOU energy rates.
14

Figure 9a. Average solar value for the top 39 locations 

Note: Error bars designate the range of values resulting from building type differences.
15

Figure 9b. Average solar value for the next 38 locations

Note: Error bars designate the range of values resulting from building type differences.
16

Figure 10 shows the average solar value by six of the most common commercial rate structure 

combinations evaluated. On average, flat energy rates produce the highest solar value. Flat rate 

structures with demand charges reduce the average value by 17%. Tiered rates with demand 

charges result in a 21% reduction in value compared with energy-only tiered rates. TOU rates 

with demand charges, however, demonstrate a 6% increase in solar value compared with energyonly TOU rates. 

Figure 10. Solar value by rate type, averaged across all locations and building types evaluated

3.2 Impact of PV Building Penetration

Building load profile, utility rate structure, and solar generation interact such that the value 

(per kilowatt-hour) that a PV system provides is dependent on PV penetration. PV penetration is 

defined as the percentage of a facility’s annual electrical energy consumption that is met by a PV 

system. Table 3 shows the PV system size required to reach 100% penetration for each building 

type, averaged over all locations. Regions with higher solar insolation will require slightly 

smaller system capacities, and those with less sunlight will require larger capacities. For 

reference, the maximum rooftop penetration was estimated for each building type9 and provides 

an approximate solar penetration value, should the PV installation be limited to the building’s 

rooftop area. This analysis evaluates penetration levels from 0% to 100% because a building’s 

PV system location is not limited to the rooftop area but may be installed on awnings, facades,

parking garages, and other structures.

10

 9 An estimate for maximum rooftop solar capacity is calculated by assuming a 14% module efficiency (140 Watts 

per square meter) and utilization of 50% of the total rooftop area (to account for potential obstructions due to HVAC 

equipment and other infrastructure.) Each building’s rooftop area is assumed to be equivalent to the floor space area. 

For multi-story structures, the total rooftop area is assumed to be equivalent to the area of a single floor. 10 Note that placing PV systems on facades and other structures with sub-optimal orientation are likely to have lower 

PV performance (James et al. 2011).
17

Table 3. Average PV Penetration Values by Building Type

Building Type Roof Area 

(m2)

Peak 

Power 

(kW)

Maximum

Rooftop 

Solara (kW)

100% 

Penetration 

Solar Size 

(kW)

Maximum

Solar 

Energy 

Penetration

Full-Service 

Restaurant 511 68 36 237 15.2%

Hospital 3,737 1,510 262 6,861 4.6%

Large Hotel 1,621 553 113 2,095 6.3%

Large Office 3,563 1,580 249 4,600 5.9%

Medium Office 1,661 318 116 549 21.1%

Mid-Rise Apartment 784 67 55 178 30.8%

Outpatient 1,268 321 89 1,023 8.7%

Primary School 6,871 328 481 654 73.6%

Quick-Service 

Restaurant 232 39 16 143 11.2%

Secondary School 9,796 1,178 686 2,341 29.3%

Small Hotel 1,003 133 70 443 15.8%

Small Office 511 19 36 49 73.6%

Standalone Retail 2,319 104 162 241 67.2%

Strip Mall 2,090 93 146 219 66.8%

Supermarket 4,181 367 293 1,245 23.5%

Warehouse 4,835 96 338 199 170.1% a An estimate for maximum rooftop solar capacity is calculated assuming a 14% module efficiency and utilization of 

50% of the total rooftop area (to avoid obstructions such as HVAC equipment.)

Our results indicate that in many cases, a single rate structure option has the highest solar value

for all penetration levels. An example of this is shown in Figure 11 (for a strip mall in Houston, 

Texas), where rate GS provides the highest net solar value. The declining solar value with 

increasing penetration is due to the inability of solar generation to offset demand charges that 

occur during non-sunlight hours (Wiser et al. 2007). Figure 12 shows an example of one rate 

structure providing the highest solar value for low penetrations (rate A-10 TOU), while another 

rate structure becomes more valuable at higher penetrations (rate A-6). The scenario in Figure 11

shows that the highest solar value occurs at the smallest penetration, while Figure 12 shows the 

highest solar value at the maximum penetration. 
18

Figure 11. Net solar value with respect to penetration for all Entergy Texas rates evaluated

Note: Dotted lines represent rates that are not applicable to this building and are included for reference 

purposes only.

Figure 12. Net solar value with respect to penetration for all PG&E rates evaluated 

Note: Dotted lines represent rates that are not applicable to this building and are included for reference 

purposes only.
19

Figure 13 shows the distribution of optimal penetration levels for all locations and building types 

evaluated. The highest solar values occur at very small penetrations in 63% of cases, while they 

occur at maximum penetration 26% of the time. This pattern occurs because under most demandbased rates, smaller PV penetrations yield greater value (Wiser et al. 2007), while TOU rates 

favor large PV penetrations due to the coincidence of high rates and solar resource (Ong et al.

2010). 

Figure 13. Distribution of optimal penetration levels for all locations and building types evaluated

It is important to note that the data in Figure 13 represent shallow optimums. Figure 14 compares 

all solar values relative to the highest solar value, averaged for all locations and buildings. 

Results indicate that even “least-optimal” penetration levels are still within 85% of the highest 

solar value at the optimum penetration.
20

Figure 14. Comparison of solar values relative to the highest solar value, averaged for all 

locations and building types
21

4 Conclusion

An evaluation of 16 different building types across 77 locations reveals that PV system value is

highly dependent on the host building’s rate structure. Although PV system economic 

performance may vary up to 30% by building type, regional electricity price differences are the 

primary driver of solar economics across the United States, causing solar value to vary by a 

factor of 10. System economics—under current net-metering rules—favor TOU rates and rates

with little or no demand charges. This analysis found that there is often no single best rate. 

Rather, the most economical rate depends on PV penetration level with reference to the building 

electricity load. Optimal penetration levels are most often either very small (5%) or very large 

(100%), although penetration level effects are shown to exhibit shallow optimums. 

These results identify general relationships between rate structures and PV installations on 

commercial buildings. This rate analysis applies to simulated load profiles (see Section 2.2) and 

is not intended to represent all buildings in the United States. Recommendations for future 

studies include identifying the impacts of potential changes in net-metering rules and evaluating 

additional sensitivities that have an impact on system economics, such as financing options and 

PV array orientation.
22

References

Denholm, P.; Margolis, M.; Ong, S.; Roberts, B. (2009). Break-Even Cost for Residential 

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

CO: National Renewable Energy Laboratory.

Wilcox, S.; Marion, W. (2008). Users Manual for TMY3 Data Sets. NREL/TP-581-43156. 

Golden, CO: National Renewable Energy Laboratory. 

Darghouth, N.; Barbose, G.; Wiser, R. (2010). The Impact of Rate Design and Net Metering on 

the Bill Savings from Distributed PV for Residential Customers in California. LBNL-3276E. 

Berkeley, CA: Ernest Orlando Lawrence Berkeley National 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.

James, T.; Goodrich, A.; Woodhouse, M.; Margolis, R.; Ong, S. (2011) Building-Integrated 

Photovoltaics (BIPV) in the Residential Sector: An Analysis of Installed Rooftop System Prices. 

NREL/TP-6A20-53103. Golden, CO: National Renewable Energy Laboratory.

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.; Denholm, P. (2011). Maximizing the Value of Photovoltaic Installations on Schools in 

California: Choosing the Best Electricity Rates. NREL/TP-6A2-51694. Golden, CO: National 

Renewable Energy Laboratory. 

Short, W.; Packey, D.; Holt, T. (1995). A Manual for the Economic Evaluation of Energy 

Efficeicny and Renewable Energy Technologies. NREL/TP-462-5173. Golden, CO: National 

Renewable Energy Laboratory.

Wilcox, S. (2007). National Solar Radiation Database 1991 – 2005 Update: User’s Manual.

NREL/TP-581-41364. Golden, CO: National Renewable Energy Laboratory.

Wiser, R.; Mills, A.; Barbose, G.; Golve, W. (2007). The Impact of Retail Rate Structures on the 

Economics of Commercial Photovoltaic Systems in California. LBNL-63019. Berkeley, CA: 

Ernest Orlando Lawrence Berkeley National Laboratory.

Deru, M.; Field, K.; Studer, D.; Benne, K.; Griffith, B.; Torcellini, P. (2011). U.S. Department of 

Energy Commercial Reference Building Models of the National Building Stock. NREL/TP-5500-

46861. Golden, CO: National Renewable Energy Laboratory. 
23

Appendix A 

Table A-1. List of Utilities and Rates Evaluated With Information About Their Rate Type, Average 

Price Without Solar, Average Maximum Solar Value, and Applicable Range

Utility Rate

Flat SeasonalDemandTOUTiered

Average 

Price

Average 

Solar 

Value

Applicability 

(kW)

Alabama Power 

Co

LPSE   $0.126 $0.043 0-50

LPME   $0.082 $0.057 50 and Up

LPL   $0.077 $0.070 0-20,000

LPLE

  $0.074 $0.051

1,200-

20,000

BTA   $0.095 $0.034 0 and Up

LPM   $0.100 $0.054 0 and Up

SCH   $0.077 $0.069 0 and Up

Entergy Arkansas 

Inc

SGS   $0.069 $0.064 6-100

LGS   $0.070 $0.063 100-1,000

LGS-TOU   $0.079 $0.019 100-1,000

LPS

  $0.062 $0.057

1,000 and 

Up

LPS-TOU

  $0.059 $0.033

1,000 and 

Up

Salt River Project

E-32   $0.073 $0.016 0 and Up

E-34  $0.082 $0.013 0 and Up

E-36   $0.060 $0.058 0 and Up

E-61   $0.053 $0.021 417 and Up

E-63  $0.055 $0.041 417 and Up

E-65

 $0.050 $0.033

1,000 and 

Up

Arizona Public 

Service Co

E-32 TOU (ES)  0-20

E-32 TOU (S)   $0.116 $0.101 21-100

E-32 TOU M   $0.124 $0.106 101-400

E-32 (L)   $0.086 $0.079 400 and Up

E-32 TOU (L)   $0.102 $0.040 400 and Up

SOLAR-3  $0.158 $0.115 0 and Up

Pacific Gas & 

Electric Co

A-1  $0.176 $0.181 0-200

A-1TOU  $0.167 $0.160 0-200

A-6  $0.185 $0.145 200 and Up

A-10 TOU   $0.113 $0.129 200-499

A-10   $0.104 $0.109 200-500

E-19   $0.109 $0.119 500-1,000
24

Utility Rate

Flat SeasonalDemandTOUTiered

Average 

Price

Average 

Solar 

Value

Applicability 

(kW)

E-20

 $0.151 $0.172

1,000 and 

Up

City of Los 

Angeles 

California (Utility 

Company)

A-SGS

 $0.154 $0.178

0-30

B-SGS

 $0.171 $0.178

0-30

A-2

 $0.127 $0.098 30 and Up

A-2 TOU

 $0.131 $0.100 30 and Up

A-SUB

 $0.126 $0.081 30 and Up

B-SUB

 $0.116 $0.107 30 and Up

Southern 

California Edison 

Co

GS-1

 $0.159 $0.142

0-20

GS-2-TOU-R

 $0.200 $0.206 20

-200

GS-1-TOU

 $0.177 $0.134 20 and Up

GS-3-TOU-R

 $0.140 $0.107 200

-500

TOU-8-R

 $0.123 $0.073 500 and Up

Public Service Co 

of Colorado

C

 $0.095 $0.099

0-25

SG

 $0.083 $0.032 0 and Up

STOU

 $0.078 $0.086 0 and Up

Connecticut Light 

& Power Co

27

 $0.173 $0.196

0-350

37

 $0.152 $0.166 200

-350

56

 $0.161 $0.135 350

-1,000

58

 $0.102 $0.079

1,000 and 

Up

Delmarva Power 

& Light Co

SGS-S



0-15

MGS-S

 $0.079 $0.070 15

-300

LGS-S

 $0.120 $0.098 300 and Up

Florida Power & 

Light Co.

GS-1



0-20

GST-1



0-20

GSD-1

 $0.087 $0.080 20

-500

GSDT-1

 $0.080 $0.057 20

-500

GSLD-1

 $0.075 $0.065 500

-2,000

GSLDT-1

 $0.084 $0.100 500

-2,000

Georgia Power 

Co

PLS-7

 $0.138 $0.132

0-30

TOU-EO-6

 $0.168 $0.112

0-30

PLM-7

 $0.119 $0.101 30

-500

TOU-GSD-6

 $0.104 $0.094 30

-500

PLL-7

 $0.112 $0.083 500 and Up

TOU-SSD-6

 $0.093 $0.091 500 and Up
25

Utility Rate

Flat SeasonalDemandTOUTiered

Average 

Price

Average 

Solar 

Value

Applicability 

(kW)

MidAmerican 

Energy Co (Iowa)

GBS

 $0.079 $0.069

0-200

GUS

 $0.069 $0.074

0-200

LLS

 $0.043 $0.057 200 and Up

LNS

 $0.075 $0.034 200 and Up

Idaho Power Co

sc7 SGS



0-3

sc9 LGS

 $0.053 $0.050

3-1,000

sc19 LPS

 $0.050 $0.045

1,000 and 

Up

Commonwealth 

Edison Co

GSSL



$0.108 $0.0690-100

GSML

 $0.107 $0.075 100

-400

GSLL

 $0.091 $0.071 400

-1,000

GSVL

 $0.090 $0.070

1,000-

10,000

WHDC

 $0.094 $0.096 0 and Up

Duke Energy 

Indiana Inc

LLF

 $0.098 $0.094 0 and Up

LLF-TOU

 $0.109 $0.080 0 and Up

Westar Energy 

Inc

SGS south

 $0.070 $0.075 0 and Up

MGS south

 $0.081 $0.055 200 and Up

HLF

 $0.081 $0.044

1,000 and 

Up

Kentucky Utilities 

Co

GS

 $0.078 $0.093

0-50

PS

 $0.085 $0.072 50

-250

TODS

 $0.059 $0.058 250

-5,000

Entergy 

Louisiana Inc

LGS-21

 $0.045 $0.051

0-3,000

GS-1W

 $0.068 $0.039 0 and Up

MMRAS

 $0.063 $0.020 0 and Up

Massachusetts 

Electric Co

G-1

 $0.075 $0.079 0 and Up

G-2

 $0.095 $0.065

0-200

G-3

 $0.094 $0.065 180 and Up

Baltimore Gas & 

Electric Co

SG

 $0.101 $0.060 0 and Up

SGS

 $0.068 $0.061 2.8 and Up

SGL

 $0.104 $0.065 60 and Up

Bangor Hydro-

Electric Co

GS

 $0.076 $0.074

0-25

MP

 $0.118 $0.097 25 and U

p

Detroit Edison Co D3.4 TOU

 $0.107 $0.080 0 and Up

D4 LGS

 $0.090 $0.067 0 and Up

Northern States 

A-12

 $0.071 $0.055

0-25
26

Utility Rate

Flat SeasonalDemandTOUTiered

Average 

Price

Average 

Solar 

Value

Applicability 

(kW)

Power Co 

(Minnesota) 

Excel Energy

A-16

 $0.087 $0.049

0-25

A-10

 $0.090 $0.069

0-25

A-14

 $0.083 $0.072

0-1,000

A-15

 $0.087 $0.055 0 and Up

A-23

 $0.088 $0.046

0-1,000

A-24

 $0.086 $0.075 0 and Up

Union Electric Co

2(M)

 $0.091 $0.070

0-100

2(M)-TOD

 $0.078 $0.075

0-100

3(M)

 $0.093 $0.087 100 and Up

3(M)-TOD

 $0.095 $0.083 100 and Up

Entergy 

Mississippi Inc

GS-295

 $0.066 $0.086 0 and Up

B-31

 $0.068 $0.090 200 and Up

HLF-4

 $0.065 $0.076 200 and Up

C-26

 $0.061 $0.065

1,000 and 

Up

ALGS-7



5,000 and

Up

North Western 

Corporation

GSEDS-1

 $0.036 $0.035 0 and Up

GS-1

 $0.087 $0.050 0 and Up

Duke Energy 

Carolinas LLC

SGS

 $0.093 $0.042

0-75

LGS

 $0.081 $0.067 75 and Up

OPT-G (NC)

 $0.076 $0.074 0 and Up

Montana-Dakota 

Utilities Co (North 

Dakota)

SGES-20

 $0.078 $0.069

0-20

SGTOD-25

 $0.063 $0.065

0-20

GES-30

 $0.095 $0.055 0 and Up

GESTOD-31

 $0.090 $0.061 0 and Up

Omaha Public 

Power District

GS-230

 $0.036 $0.024

0-50

GS-231

 $0.052 $0.039 50

-1,000

GS-232

 $0.030 $0.019

1,000 and 

Up

Public Service Co 

of NH

G-OTOD

 $0.130 $0.048 0 and Up

G

 $0.128 $0.090 0 and Up

GV

 $0.133 $0.088 0 and Up

LG

 $0.131 $0.080 0 and Up

Public Service 

Elec & Gas Co

BGS-FP

 $0.105 $0.082

0-750

LPL

 $0.106 $0.084 150 and Up

Public Service Co 2A

 $0.094 $0.070

0-50
27

Utility Rate

Flat SeasonalDemandTOUTiered

Average 

Price

Average 

Solar 

Value

Applicability 

(kW)

of NM 2B  $0.166 $0.136 0-50

3B   $0.132 $0.096 50 and Up

3C   $0.132 $0.116 50 and Up

4B   $0.104 $0.114 500 and Up

Nevada Power 

Co

GS  0-5

OGS-TOU  0-5

LGS-1   $0.089 $0.083 5-300

LGS-2   $0.073 $0.072 300-1,000

LGS-3

  $0.081 $0.085

1,000 and 

Up

LGS-X

  25,000 and 

Up

Consolidated 

Edison Co-NY Inc

SC2-I  0-10

SC2-II  0-10

SC9-I   $0.238 $0.107 10-1,500

SC9-III   $0.163 $0.092 10-1,500

SC9-II

  $0.089 $0.054

1,500 and 

Up

Ohio Power Co

GS-1  0-10

GS-2   $0.096 $0.062 10-8,000

GS-TOD  $0.085 $0.082 0-500

Oklahoma Gas & 

Electric Co

GS-1  $0.077 $0.066 10-400

GS-TOU  $0.067 $0.069 10-400

PL   $0.064 $0.074 400 and Up

PL-TOU   $0.058 $0.054 400 and Up

PacifiCorp 

(Oregon)

23-200  $0.074 $0.042 0-30

23-210  $0.052 $0.057 0-30

28-200   $0.078 $0.068 31-200

28-210    $0.093 $0.066 31-200

30-200    $0.092 $0.068 201-999

47-3,999 Self 

Generating

  $0.079 $0.056 0-4,000

47-4,000 Self 

Generating

  4,000 and 

Up

PECO Energy Co

GS-2    $0.088 $0.076 0-100

GS-3   $0.086 $0.076 101-500

GS-4   $0.091 $0.085 500 and Up

The Narragansett C-06   $0.125 $0.028 0-200
28

Utility Rate

Flat SeasonalDemandTOUTiered

Average 

Price

Average 

Solar 

Value

Applicability 

(kW)

Electric Co

G-02

 $0.056 $0.041 10

-200

G-32

 $0.063 $0.020 200 and Up

South Carolina 

Electric & Gas Co

9

 $0.155 $0.123 0 and Up

20

 $0.124 $0.095 75 and Up

21

 $0.130 $0.077 50

-1,000

Black Hills Power 

Inc

GS

 $0.112 $0.077 0 and Up

GS (TE)

 $0.102 $0.081 0 and Up

GL

 $0.080 $0.077 125 and Up

City of Memphis 

Tennessee 

(Utility Company)

GSA

 $0.100 $0.099

0-5,000

TDGSA

 $0.158 $0.045

1,000-5,000

TGSA

 $0.098 $0.061

1,000-5,000

CPS Energy

PL

 $0.063 $0.068 0 and Up

LLP

 $0.079 $0.038 0 and Up

ELP

 $0.060 $0.04

2

1,000 and 

Up

Entergy Texas 

Inc.

SGS



0-20

GS

 $0.078 $0.080

5-2,500

GS-TOD

 $0.108 $0.034

5-2,500

LGS

 $0.065 $0.057 300

-2,500

LGS-TOD

 $0.064 $0.053 300

-2,500

LIPS



2,500 and 

Up

LIPS-TOD



2,500 and 

Up

Moon Lake 

Electric Assn Inc 

(Utah)

GS-3

 $0.085 $0.101

0-49

LP

 $0.072 $0.069 50 and Up

Virginia Electric & 

Power Co

GS-1

 $0.084 $0.105

0-30

GS-2

 $0.104 $0.063 30

-500

DP-2

 $0.066 $0.087 30

-500

GS-2



$0.072 $0.079 500 and Up

Central Vermont 

Pub Serv Corp

Rate 10

 $0.150 $0.084 0 and Up

Rate 2

 $0.133 $0.123 0 and Up

Puget Sound 

Energy Inc

24

 $0.099 $0.102

0-50

25

 $0.102 $0.086 50

-350

26

 $0.094 $0.083 350 and Up

Wisconsin 

Electric Power Co

Cg 1



0-14

Cg 2-A

 $0.116 $0.105 14 and Up
29

Utility Rate

Flat SeasonalDemandTOUTiered

Average 

Price

Average 

Solar 

Value

Applicability 

(kW)

Cg 2-B   $0.101 $0.100 14-42

Cg 3   $0.131 $0.069 42 and Up

Cg 6-A  $0.135 $0.144 0 and Up

Cg 6-B  $0.138 $0.133 0 and Up

Harrison Rural 

Elec Assn Inc

Sch B  $0.125 $0.094 0-50

C&1   $0.075 $0.068 50 and Up

Powder River 

Energy 

Corporation

GS  $0.070 $0.063 0-50

LP   $0.058 $0.050 50 and Up
30

Appendix B

Table B-1. Maximum Solar Value Found for Each Building Type in Each Location

Location

Full-Service RestaurantHospitalLarge HotelLarge OfficeMedium OfficeMid-Rise ApartmentOut PatientPrimary SchoolQuick-Service RestaurantSecondary SchoolSmall HotelSmall OfficeStandalone RetailStrip MallSupermarketWarehouse

Montgomery, AL 0.07 0.07 0.06 0.07 0.07 0.06 0.07 0.08 0.07 0.08 0.06 0.08 0.07 0.07 0.08 0.08

Little Rock, AR 0.06 0.05 0.04 0.05 0.06 0.06 0.06 0.08 0.06 0.07 0.04 0.07 0.07 0.07 0.07 0.06

Flagstaff, AZ 0.05 0.06 0.05 0.06 0.06 0.05 0.06 0.06 0.05 0.06 0.05 0.06 0.06 0.06 0.06 0.06

Phoenix, AZ 0.11 0.08 0.08 0.09 0.13 0.12 0.12 0.11 0.11 0.10 0.11 0.14 0.13 0.13 0.09 0.14

Tucson, AZ 0.11 0.08 0.08 0.10 0.13 0.12 0.12 0.11 0.11 0.11 0.11 0.13 0.13 0.13 0.11 0.14

Arcata, CA 0.19 0.18 0.19 0.19 0.20 0.19 0.18 0.19 0.19 0.21 0.19 0.19 0.18 0.18 0.19 0.19

Los Angeles, CA 0.11 0.09 0.09 0.09 0.12 0.09 0.11 0.13 0.11 0.13 0.09 0.12 0.11 0.12 0.12 0.10

San Francisco, CA 0.23 0.22 0.22 0.22 0.23 0.24 0.22 0.22 0.24 0.23 0.23 0.16 0.24 0.24 0.21 0.24

Boulder, CO 0.06 0.06 0.05 0.07 0.07 0.06 0.07 0.08 0.06 0.08 0.05 0.07 0.06 0.07 0.08 0.07

Eagle County, CO 0.06 0.06 0.06 0.07 0.07 0.06 0.07 0.08 0.06 0.08 0.06 0.07 0.07 0.07 0.07 0.07

Pueblo, CO 0.06 0.06 0.06 0.06 0.07 0.06 0.07 0.08 0.06 0.08 0.06 0.07 0.07 0.07 0.08 0.07

Bridgeport, CT 0.11 0.08 0.10 0.08 0.12 0.11 0.12 0.14 0.12 0.10 0.11 0.14 0.13 0.13 0.12 0.13

Wilmington, DE 0.06 0.09 0.07 0.09 0.11 0.06 0.10 0.13 0.06 0.13 0.05 0.08 0.08 0.08 0.12 0.08

Miami, FL 0.07 0.06 0.06 0.06 0.09 0.06 0.08 0.10 0.07 0.08 0.07 0.09 0.08 0.08 0.08 0.11

Tampa, FL 0.07 0.06 0.06 0.07 0.09 0.07 0.08 0.10 0.08 0.09 0.06 0.09 0.08 0.09 0.09 0.09

Atlanta, GA 0.09 0.08 0.10 0.10 0.11 0.10 0.09 0.12 0.10 0.12 0.09 0.14 0.11 0.11 0.10 0.11

Savannah, GA 0.09 0.08 0.09 0.10 0.11 0.10 0.09 0.11 0.10 0.11 0.09 0.13 0.11 0.11 0.10 0.11

Des Moines, IA 0.07 0.03 0.02 0.03 0.03 0.07 0.03 0.05 0.07 0.04 0.07 0.07 0.07 0.07 0.04 0.07

Mason, IA 0.07 0.03 0.02 0.03 0.03 0.07 0.03 0.04 0.07 0.04 0.07 0.07 0.07 0.07 0.04 0.07

Boise, ID 0.05 0.04 0.04 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.04 0.06 0.05 0.05 0.04 0.05

Chicago, IL 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10

Springfield, IL 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10

Indianapolis, IN 0.09 0.08 0.07 0.09 0.09 0.08 0.09 0.11 0.08 0.11 0.07 0.10 0.10 0.10 0.10 0.10

Goodland, KS 0.06 0.05 0.06 0.06 0.06 0.06 0.06 0.07 0.06 0.07 0.06 0.07 0.07 0.07 0.07 0.07

Wichita, KS 0.06 0.05 0.06 0.06 0.07 0.06 0.07 0.07 0.06 0.07 0.06 0.07 0.07 0.07 0.07 0.07

Lexington, KY 0.06 0.04 0.04 0.04 0.05 0.05 0.05 0.07 0.09 0.06 0.03 0.09 0.08 0.08 0.06 0.08

New Orleans, LA 0.04 0.02 0.02 0.02 0.03 0.04 0.02 0.03 0.04 0.02 0.03 0.08 0.04 0.04 0.02 0.04

Shreveport, LA 0.04 0.02 0.02 0.02 0.03 0.04 0.02 0.03 0.04 0.03 0.03 0.08 0.04 0.04 0.02 0.04

Boston, MA 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08
31

Location

Full-Service RestaurantHospitalLarge HotelLarge OfficeMedium OfficeMid-Rise ApartmentOut PatientPrimary SchoolQuick-Service RestaurantSecondary SchoolSmall HotelSmall OfficeStandalone RetailStrip MallSupermarketWarehouse

Baltimore, MD 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08

Caribou, ME 0.09 0.09 0.08 0.10 0.10 0.09 0.10 0.12 0.09 0.12 0.09 0.07 0.10 0.11 0.12 0.10

Portland, ME 0.11 0.10 0.09 0.10 0.11 0.09 0.11 0.13 0.11 0.13 0.09 0.07 0.12 0.12 0.14 0.11

Detroit, MI 0.0

8 0.08 0.08 0.09 0.12 0.09 0.08 0.11 0.08 0.10 0.08 0.09 0.09 0.09 0.09 0.10

Houghton-Lake, MI 0.08 0.08 0.08 0.09 0.12 0.09 0.08 0.10 0.08 0.10 0.08 0.09 0.09 0.09 0.08 0.10

Traverse City, MI 0.08 0.08 0.08 0.09 0.12 0.09 0.08 0.09 0.08 0.10 0.08 0.09 0.09 0.09 0.09 0.10

International Falls, 

MN 0.07 0.07 0.06 0.07 0.09 0.06 0.07 0.09 0.07 0.09 0.06 0.09 0.08 0.08 0.08 0.08

Minneapolis, MN 0.07 0.07 0.06 0.07 0.09 0.06 0.08 0.09 0.07 0.08 0.06 0.09 0.08 0.07 0.08 0.08

Kansas City, MO 0.09 0.08 0.0

8 0.08 0.09 0.09 0.08 0.09 0.09 0.09 0.09 0.09 0.10 0.10 0.09 0.09

Jackson, MS 0.06 0.05 0.05 0.05 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.07 0.06 0.06 0.06 0.06

Billings, MT 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04

Greensboro, NC 0.07 0.06 0.06 0.07 0.07 0.06 0.08 0.10 0.06 0.10 0.06 0.09 0.08 0.09 0.08 0.08

Wilmington, NC 0.07 0.06 0.06 0.07 0.08 0.06 0.07 0.09 0.06 0.09 0.06 0.08 0.09 0.08 0.08 0.08

Bismarck, ND 0.07 0.07 0.06 0.08 0.09 0.07 0.09 0.10 0.08 0.11 0.06 0.09 0.10 0.10 0.10 0.09

Minot, ND 0.08 0.08 0.06 0.08 0.09 0.08 0.09 0.11 0.08 0.11 0.06 0.09 0.10 0.10 0.10 0.10

Omaha, NE 0.05 0.04 0.05 0.05 0.06 0.05 0.06 0.06 0.08 0.07 0.05 0.09 0.06 0.06 0.06 0.06

Concord, NH 0.11 0.11 0.10 0.11 0.12 0.11 0.12 0.13 0.12 0.13 0.11 0.13 0.12 0.12 0.13 0.12

Atlantic City, NJ 0.10 0.11 0.10 0.11 0.12 0.09 0.11 0.13 0.11 0.12 0.09 0.12 0.12 0.12 0.12 0.11

Albuquerque, NM 0.12 0.09 0.12 0.09 0.13 0.12 0.13 0.14 0.17 0.11 0.12 0.16 0.13 0.13 0.13 0.13

Las Vegas, NV 0.07 0.07 0.06 0.07 0.08 0.07 0.07 0.08 0.07 0.08 0.07 0.08 0.08 0.08 0.08 0.08

Reno, NV 0.07 0.07 0.06 0.07 0.08 0.07 0.08 0.09 0.07 0.09 0.07 0.09 0.08 0.08 0.08 0.08

New York, NY 0.09 0.05 0.09 0.06 0.12 0.11 0.09 0.16 0.08 0.16 0.09 0.11 0.12 0.12 0.15 0.13

Rochester, NY 0.09 0.06 0.09 0.05 0.12 0.11 0.09 0.20 0.08 0.17 0.09 0.14 0.12 0.12 0.15 0.12

Syracuse, NY 0.09 0.05 0.09 0.06 0.12 0.11 0.09 0.17 0.08 0.16 0.09 0.14 0.12 0.12 0.14 0.12

Cleveland, OH 0.08 0.09 0.07 0.09 0.08

0.08 0.08 0.08 0.08 0.11 0.08 0.08 0.08 0.08 0.08 0.08

Oklahoma City, OK 0.07 0.05 0.05 0.05 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07

Burns, OR 0.06 0.06 0.05 0.06 0.06 0.06 0.06 0.07 0.07 0.07 0.06 0.02 0.07 0.07 0.06 0.07

Portland, OR 0.07 0.05 0.05 0.06 0.07 0.06 0.07 0.08 0.06 0.08 0.06 0.02 0.07 0.07 0.07 0.07

Philadelphia, PA 0.08 0.09 0.08 0.09 0.09 0.08 0.09 0.10 0.08 0.10 0.08 0.09 0.09 0.09 0.09 0.09

Pittsburgh, PA 0.09 0.09 0.08 0.09 0.09 0.08 0.09 0.10 0.08 0.11 0.08 0.10 0.09 0.09 0.10 0.09

Providence, RI 0.05 0.07 0.07 0.07 0.07 0.05 0.08 0.08 0.05 0.08 0.05 0.06 0.05 0.05 0.08 0.05

Charleston, SC 0.14 0.11 0.12 0.13 0.14 0.15 0.13 0.16 0.16 0.14 0.13 0.16 0.14 0.14 0.14 0.15

Pierre, SD 0.10 0.08 0.08 0.08 0.08 0.09 0.08 0.08 0.10 0.08 0.09 0.11 0.10 0.10 0.08 0.10
32

Location

Full-Service RestaurantHospitalLarge HotelLarge OfficeMedium OfficeMid-Rise ApartmentOut PatientPrimary SchoolQuick-Service RestaurantSecondary SchoolSmall HotelSmall OfficeStandalone RetailStrip MallSupermarketWarehouse

Memphis, TN 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10

Nashville, TN 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10

Amarillo, TX 0.06 0.05 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.07 0.06 0.06 0.06 0.06

El Paso, TX 0.06 0.05 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.07 0.06 0.06 0.06 0.06

Houston, TX 0.07 0.05 0.04 0.05 0.08 0.07 0.06 0.07 0.07 0.07 0.06 0.08 0.07 0.07 0.07 0.08

Cedar City, UT 0.05 0.05 0.04 0.06 0.06 0.04 0.06 0.08 0.06 0.07 0.04 0.06 0.06 0.06 0.06 0.06

Salt Lake City, UT 0.05 0.05 0.04 0.05 0.06 0.04 0.06 0.07 0.06 0.07 0.04 0.06 0.06 0.06 0.06 0.06

Richmond, VA 0.07 0.03 0.01 0.03 0.07 0.07 0.07 0.08 0.07 0.07 0.07 0.06 0.07 0.07 0.07 0.07

Burlington, VT 0.11 0.10 0.09 0.11 0.11 0.12 0.10 0.13 0.12 0.13 0.10 0.15 0.11 0.12 0.12 0.12

Seattle, WA 0.09 0.08 0.07 0.09 0.08 0.09 0.08 0.09 0.10 0.10 0.08 0.10 0.09 0.09 0.08 0.09

Yakima, WA 0.09 0.08 0.07 0.08 0.08 0.09 0.08 0.09 0.10 0.09 0.08 0.10 0.09 0.09 0.09 0.09

Green Bay, WI 0.15 0.15 0.15 0.14 0.15 0.15 0.14 0.14 0.15 0.14 0.15 0.15 0.13 0.13 0.14 0.15

Charleston, WV 0.06 0.06 0.06 0.06 0.07 0.06 0.07 0.08 0.14 0.08 0.06 0.14 0.07 0.07 0.07 0.07

Elkins, WV 0.06 0.06 0.06 0.06 0.07 0.06 0.07 0.08 0.14 0.08 0.06 0.14 0.07 0.07 0.07 0.07

Cheyenne, WY 0.05 0.05 0.04 0.05 0.05 0.04 0.05 0.06 0.06 0.06 0.04 0.06 0.05 0.05 0.06 0.05
33 

Appendix C: Digital Appendix

The digital appendix includes detailed charts and tables for each location, building type, and rate 

structure evaluated. 

http://en.openei.org/wiki/Impact_of_Utility_Rates_on_PV_Economics_-_Digital_Appendix