Technical Report

NREL/TP-6A2-46782

June 2010

The Impacts of Commercial 

Electric Utility Rate Structure 

Elements on the Economics of 

Photovoltaic Systems

Sean Ong, Paul Denholm, and Elizabeth Doris
National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393

303-275-3000 • www.nrel.gov

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Contract No. DE-AC36-08-GO28308

Technical Report

NREL/TP-6A2-46782

June 2010

The Impacts of Commercial 

Electric Utility Rate Structure 

Elements on the Economics of 

Photovoltaic Systems

Sean Ong, Paul Denholm, and Elizabeth Doris

Prepared under Task No. PVC7.9P02
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iii

Acknowledgements 

This work was made possible by the Solar America Cities program of the Solar Energy Technologies 

Program at the U.S. Department of Energy (DOE). The authors wish to thank Mike Coddington, 

Jason Coughlin, and Robin Newmark of the National Renewable Energy Laboratory (NREL) for 

reviewing various versions of the document, as well as Charlie Hemmeline (DOE), Kevin Lynn 

(SENTECH, Inc.), and Colin Murchie (SolarCity) for their thoughtful reviews. We also thank Mike 

Meshek, 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

This analysis uses simulated building data, simulated solar photovoltaic (PV) data, and actual electric 

utility tariff data from 25 cities to understand better the impacts of different commercial rate 

structures on the value of solar PV systems. By analyzing and comparing 55 unique rate structures 

across the United States, this study seeks to identify the rate components that have the greatest effect 

on the value of PV systems. Understanding the beneficial components of utility tariffs can both assist 

decision makers in choosing appropriate rate structures and influence the development of rates that 

favor the deployment of PV systems. Results from this analysis show that a PV system’s value 

decreases with increasing demand charges. Findings also indicate that time-of-use rate structures 

with peaks coincident with PV production and wide ranges between on- and off-peak prices most 

benefit the types of buildings and PV systems simulated. By analyzing a broad set of rate structures 

from across the United States, this analysis provides an insight into the range of impacts that current 

U.S. rate structures have on PV systems. 
v 

Table of Contents

List of Figures ............................................................................................................................................ vi

List of Tables .............................................................................................................................................. vi

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

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

2.1 PVrate Tool ..........................................................................................................................2

2.2 Rate Data ..............................................................................................................................2

2.3 Load Data .............................................................................................................................3

2.4 Solar Data.............................................................................................................................6

2.5 PV Savings Metric ...............................................................................................................6

3 Results and Discussion....................................................................................................................... 7

3.1 PV Value and PV Savings ...................................................................................................7

3.2 Demand Charges and PV systems .......................................................................................8

3.3 Impact of TOU and Seasonal Rate Structures ...................................................................11

4 Conclusions ........................................................................................................................................ 18

References................................................................................................................................................. 19

Appendix .................................................................................................................................................... 20
vi 

List of Figures

Figure 1. Rate structures collected and their impacts relative to PV systems. ................................7

Figure 2. PV peak demand reduction in July, climate zone 5A .....................................................10

Figure 3. PV savings versus demand charges ................................................................................11

Figure 4. The rate structure associated with each data point in the analysis .................................12

Figure 5. TOU rate structure that correlates well with PV production ..........................................13

Figure 6. TOU rate structure showing poor correlation with PV production ................................13

Figure 7. PV savings by correlation between TOU pricing and PV production ............................14

Figure 8. Rate structure with wider range in peak to off-peak energy prices. ...............................15

Figure 9. Rate structure with narrower range in peak to off-peak energy prices. ..........................16

Figure 10. PV savings by TOU peak to off-peak price range ........................................................17

Figure 11. PV savings by ratio of summer prices to winter prices in seasonal flat rates. .............17

List of Tables

Table 1. Number of Rate Structures Studied under each Rate Structure Type ................................3

Table 2. Climate Zone Associated with each Solar America City ..................................................4

Table 3. Building and PV System Characteristics Associated with each Climate Zone. ................5

Table 4. Average Capacity Values and Peak-Solar to Peak-Demand Difference for all Buildings 

and PV Systems Studied across the Ten Climate Zones Used in this Analysis. ................9

Table A-1. Actual Utility Rates with Selected Results. .................................................................20
1 

1 Introduction

Compensation for commercial net-metered PV systems is dictated primarily by the utility rate 

structure under which the solar photovoltaic (PV) system and building operates. Electric utility tariffs 

across the United States consist of many different rate components, all of which have an impact on 

PV system economics. Identifying the effects of rate structures on system economics can help 

individuals and entities make informed choices on available rate structures in order to maximize their 

investment returns. A greater understanding of these impacts can also aid rate setters who seek to 

design tariffs that encourage PV adoption. 

A growing body of literature addresses the impact of rate structures on the economic performance of 

both residential and commercial PV systems. Recent literature on rate structure impacts focuses on 

case studies of simulated (e.g, Borenstein 2008) and actual (Wiser et al. 2007) solar energy system 

production and building load data. Results from these studies show the relative impacts of various 

rate structure types on PV systems. For example, it was shown that PV systems under demand-based 

rates lost value on a $/kWh basis with increasing PV penetration, that TOU rates were generally 

more favorable than flat rates, and that customers with PV systems benefit from a choice in various 

rate structures (Wiser et al. 2007). 

This report adds to the literature by identifying common trends in utility rate structures that impact 

the value of PV to commercial customers in locations across the United States. This work also 

identifies the fundamental relationships between rate structure components (such as demand and 

energy charges) and the value of PV. Investigating the mechanisms behind the impacts, rather than 

just the impacts of rate structures, provides a deeper understanding of these relationships. 

In this study, the sample population consists of the 25 Solar America Cities,

1

2

 which represent a broad 

range of the geographic regions of the United States and span a range of city sizes and demographics. 

This analysis presents results from actual rate structures in the 25 cities, as they were applied to a 

simulated office building in each city. These rate structures include demand charges, flat rates, timeof-use (TOU) rates, and seasonally varying rates. The rate structures used are applicable to the 

commercial sector. Details of the rate structures used in this analysis are discussed in Section .2. 

While generalizations about rates are challenging because of the complex nature of rate design and 

system variation, the results of this analysis show that applications for PV are greatly assisted by 

rates that focus on energy charges rather than demand charges and have temporal features that 

coincide with PV production. 

 

1 Solar America Cities is a program of the US Department of Energy’s (USDOE) Energy Efficiency and Renewable 

Energy Office (EERE) Solar Energy Technology Program (SETP). The Solar America Cities program, begun in 

2007, contributes to the SETP goal of bringing solar electricity into cost-competitiveness with grid electricity by 

2015. The 25 selected cities receive grant funding partnered with technical assistance from the DOE to overcome the 

barriers to solar electricity production within their jurisdictions, develop replicable methodologies for similar cities, 

and accomplish the stated program goals. The cities that participate in this program are the following: Ann Arbor, 

MI, Austin, TX, Berkeley, CA, Boston, MA, Denver, CO, Houston, TX, Knoxville, TN, Madison, WI, Milwaukee, 

WI, Minneapolis-Saint Paul, MN, New Orleans, LA, New York City, NY, Orlando, FL, Philadelphia, PA, 

Pittsburgh, PA, Portland, OR, Sacramento, CA, Salt Lake City, UT, San Antonio, TX, San Diego, CA, San 

Francisco, CA, San Jose, CA, Santa Rosa, CA, Seattle, WA, Tucson, AZ.
2 

2 Methodology and Data Sources

2.1 PVrate Tool

This analysis uses PVrate, a Microsoft Excel-based tool to evaluate the impact of rate structures on 

PV systems throughout the United States. By analyzing both energy and demand charges, the tool 

can assess the financial benefits of stand-alone PV systems and PV systems integrated with specific 

buildings or loads. PVrate has been used in previous NREL rate analysis work including a case study 

in San Diego (Doris et al. 2008) and a national residential PV break-even study (Denholm et al. 

2009). 

PVrate uses 15-minute or hourly data on building load and system production, as well as energy and 

demand charge information from a utility tariff sheet (usually available either online or by request). 

The building load data are chronologically aligned with the PV production data to determine the 

reduction in demand for each time segment. The tool then matches each time segment to the 

electricity rate that is applicable during that time to determine the energy and demand charges. The 

tool summarizes the energy, demand, and cost savings for each time segment, allowing for the 

identification of the beneficial components in each rate structure with regard to the PV system. 

2.2 Rate Data

A total of 55 individual rate structures were collected from electric utilities in each of the 25 cities in 

the spring of 2009; tariffs from the largest utilities serving each of the 25 cities were collected 

through the utilities’ Web sites. Rate structures collected were those that are applicable to mediumsized loads from the commercial sector, corresponding to buildings with a peak load in the range of 

124 kW to 200 kW. More information on the loads used can be found in Section 2.3. Various types 

of rate structures were collected, including flat rates, seasonally varying rates, time-of-use rates, and

demand-based rates (Table 1). Some rates that were applicable only to load classes outside our load 

range were also included in order to capture a broader range of rate structure types and elements.2

This allows for a more comprehensive comparison of rate structures, not only among the 25 cities but 

also within each city. The scope of this analysis includes only the ratepayer owning the system but 

does not consider other models of ownership, such as power purchase agreements.3

 

2 Tariffs for larger loads typically have a lower rate level than tariffs for smaller loads. Since our methods focus on a 

percent savings metric, rate level differences are irrelevant to the results. Rates for different building classes can be 

assessed without distorting our results. 

 

3 An ownership model in which a third party owns and maintains the PV system—as with a power purchase 

agreements (PPA)—often consist of an exclusive rate negotiated with the utility.
3 

Table 1. Number of Rate Structures Studied under each Rate Structure Type

Rate Type Rate Structures Studied

Flat 19

Seasonal Flat 11

Time-of-Use 25

Demand 27

Based on the tariff sheets collected from each of the 25 cities (Table 1), and despite the regional 

variations, just a few distinct types of rate structures were identified. The most common rate types 

identified in the data set used were as follows4

• Flat Rates—Fixed cost of energy that does not vary except for fuel cost adjustments and 

other fees. 

: 

• Seasonal Flat Rates—Flat rates that vary by season. A typical seasonal flat rate structure has 

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

• Time-of-Use (TOU)—Time-of-use or time-of-day rate structures usually vary two to four 

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. 

• Demand Charges—Demand charges, which are included with energy charges in applicable 

rate structures, charge customers for their power demand (usually in maximum kW

demanded over a specific interval). Demand charges can also be fixed or vary by season or 

hour. More information on demand charges is given in Section 3. 

In addition to the categories above, rate structures often included a combination of the rate types. For 

example, a rate structure that varies by time during summer months but remains flat during the winter 

months would be a combination of the first three categories. A complete list of the rate structures 

collected for this analysis is available in the appendix. 

2.3 Load Data

Building load data are an important component in any rate structure analysis including demand based 

rates. Demand charges are usually based on the peak monthly demand of a building; consequently, 

quantifying a PV system’s demand reduction value requires a load profile. The load profile data used 

in this analysis were initially created in part for the DOE commercial building benchmark models 

(Torcellini et al. 2008) simulated using the EnergyPlus simulation software.5

 

4

 These definitions were derived in part from Alt (2006.) More detailed definitions and explanations of these items 

can be found in this reference.

 All loads and buildings 

for the benchmark models were simulated under typical meteorological year 2 (TMY2) conditions. 

TMY2 is a dataset of the National Solar Radiation Database (NSRDB) (Marion and Urban 1995). 

TMY2 conditions were also used when simulating PV performance for consistency (Section 2.4). 

Although the benchmark models consists of a variety of different commercial building types across 

5 For more information on the EnergyPlus model, see http://apps1.eere.energy.gov/buildings/energyplus/. 
4 

sixteen climate zones, the data used for this analysis consist of only simulated medium-sized office 

buildings across ten climate zones (Table 2).6 The medium-sized office building was assumed to 

have a total floor area of 53,630 ft2 on three floors. The building simulation data included hourly load 

profiles for individual loads within the building, such as lighting, cooling, and ventilation. Peak 

building loads ranged from 124 kW to 202 kW with an average annual energy consumption of 493 

MWh (Table 3). The simulated PV system associated with each building was sized to meet 20% of 

the buildings annual energy consumption (20% penetration). Due to regional differences in building 

consumption and solar resource across the United States, system capacities ranged from 61 kW to 91 

kW (Table 3). The total hourly electrical load of each building was used as an input in PVrate.7

Table 2. Climate Zone Associated with each Solar America City 

 

Solar America City ASHRAE Climate 

Zone 

Austin, TX 2A

Houston, TX 2A

New Orleans, LA 2A

Orlando, FL 2A

San Antonio, TX 2A

Tucson, AZ 2B

Sacramento, CA 3B

San Diego, CA 3B-Coast

Berkeley, CA 3C

San Francisco, CA 3C

San Jose, CA 3C

Santa Rosa, CA 3C

Knoxville, TN 4A

New York City, NY 4A

Philadelphia, PA 4A

Portland, OR 4C

Seattle, WA 4C

Ann Arbor, MI 5A

Boston, MA 5A

Pittsburgh, PA 5A

 

6 The climate zones used are 2A, 2B, 3B, 3B-c, 3C, 4A, 4C, 5A, 5B and 6A. These zones are a subset of the climate 

zones officially recognized by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers 

(ASHRAE). 

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

data resolution for this analysis may present an over estimation 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. 

Despite a potential for overestimation, our results still show that demand charges are poorly suited for PV systems.
5 

Solar America City ASHRAE Climate 

Zone 

Denver, CO 5B

Salt Lake City, UT 5B

Madison, WI 6A

Milwaukee, WI 6A

Minneapolis – Saint Paul, MN 6A

Table 3. Building and PV System Characteristics Associated with Each Climate Zone.

ASHRAE 

Climate Zone

Annual 

Building Load 

(MWh)

Peak Annual 

Load (kW)

20% Solar 

Penetration 

(MWh)

PV Nameplate 

Capacity (DC kW)

2A 556 182 111 91

2B 544 202 109 68

3B 511 189 102 62

3B-Coast 474 149 95 65

3C 439 124 88 61

4A 518 176 104 85

4C 442 144 88 90

5A 483 170 97 82

5B 471 162 94 67

6A 488 202 98 78

Average 493 170 99 75
6 

2.4 Solar Data

The PV production data used in this analysis were simulated using the typical meteorological year 28

(TMY2) dataset of the National Solar Radiation Database (NSRDB) (Marion and Urban 1995). The 

hourly solar data from the TMY2 dataset were converted into hourly PV production data using the 

PVWATTS model (Marion et al. 2005). A 77% AC-DC derate factor9

2.5 PV Savings Metric

was used for a system facing 

south at a 25° tilt. 

Accurately capturing the impacts of various rate structure elements on a PV system’s value can be 

difficult because electricity prices differ widely across the United States. The average retail price of 

electricity for the commercial sector in 2009 ranged from 6.5 cents/kWh (Idaho) to 18.0 cents/kWh 

(Connecticut).10 Price differences occur for a variety of reasons including but not limited to a 

region’s fuel mix, regulations, and transmission constraints.11

PV savings = (electric bill without PV – electric bill with PV)/(electric bill without PV)*100%.

 This analysis focuses on the relative 

savings impact due to the utility rate structure, as opposed to the absolute savings (or value) of a PV 

system. The primary metric used in this analysis is the PV savings metric, defined as the fraction of 

the annual electricity cost saved by the PV system:

This metric allows us to abstract from rate levels, thereby isolating the effects of the rate structure as 

opposed to regional differences in energy prices. A further example of this metric is to consider two 

different flat rates—one at 5 cents/kWh and another at 10 cents/kWh. In both cases, the PV system 

analyzed would offset 20% of the annual energy and thus 20% of the annual electricity charges. 

Because the rate structures are exactly the same (flat), they have the same PV benefit of offsetting 

20% of annual costs. 

 

8 Although TMY3 data were available at the time of this analysis, the TMY2 data were used because the DOE 

Benchmark buildings simulation data were also simulated using the TMY2 data. This allows for a more consistent 

treatment of building demand reduction and demand charge benefits. 9 A 77% derate factor is used primarily for residential PV systems and is considered conservative for commercial 

PV applications.

10 http://www.eia.doe.gov/cneaf/electricity/epm/table5_6_b.html. Last accessed February 3, 2010. 11 For more information on regional electricity price variations, see 

http://tonto.eia.doe.gov/energyexplained/index.cfm?page=electricity_factors_affecting_prices. Last accessed 

February 10, 2010.
7 

3 Results and Discussion

3.1 PV Value and PV Savings

Figure 1 plots all the rate structures collected, demonstrating the PV savings metric as a function of 

the total annual electricity cost savings. This image illustrates the wide range of effects for both rate 

levels and rate structures. The percent of bill savings or PV savings (y-axis) are entirely due to 

differences between rate structures studied, while the annual PV value (x-axis) is primarily due to 

differences in rate levels (the local cost of electricity). As seen in the chart, there is a distinctive 

alignment of data points along the 20% savings mark. This is typical for flat, energy only rate 

structures where a 20% PV penetration yields a 20% annual savings. Some flat rates are also seen 

well below the 20% savings line. These are due to demand charges, which will be discussed in the 

next section. Since consumers may ultimately seek to lower their electricity costs, it is important to 

consider both the effects of rate structure and rate level when choosing between various rates. 

Though rate levels are generally consistent within a region, they may differ amongst customers 

within each region.12 

Figure 1. Rate structures collected and their impacts relative to PV systems

This figure plots all the rate structures studied in this analysis. PV savings is plotted against 

annual value for a medium-sized office building and a PV system with 20% penetration.

 

12 Most utilities in the United States offer rates that need to be approved by a regulatory agency. These agencies 

ensure that rates enable utilities to make a reasonable rate of return on their infrastructure investment while 

protecting ratepayers from excessively high rates. Flat rates result when utilities spread this cost evenly over all kWh 

sold. Utilities may also decide to offer TOU rates, which better reflect the temporal cost of generation. Customers 

with high loads during the peak hours will notice a much higher rate level than those with high loads during off-peak 

hours. The aggregate effects of these rate level differences, however, must be revenue neutral to the utility due to 

regulatory requirements. 

4%

6%

8%

10%

12%

14%

16%

18%

20%

22%

24%

26%

28%

$0 $2,000 $4,000 $6,000 $8,000 $10,000 $12,000 $14,000 $16,000 $18,000 $20,000

% Bill savings because of PV

Total Annual Electricity Cost Savings

TOU Flat Seasonal Flat
8 

3.2 Demand Charges and PV systems

Demand charges are billed to customers based on how much power they demand during a particular 

interval. This interval is commonly 15 minutes; however, some utilities may use other timeframes. 

Utilities usually charge for peak monthly demands (Alt 2006), and these charges are usually billed in 

addition to energy charges. The following example illustrates the difference between energy and 

demand charges: a 100-Watt light bulb running for 10 hours uses the same amount of energy as a 

1000-Watt heater running for 1 hour (1 kWh of energy). Although the customer will be charged 

equal amounts for the energy consumed by the bulb and the energy consumed by the heater, the 

heater will cost the customer more because it requires more power during a given period, driving up 

the demand charges.13

One of the more attractive features of solar technologies is their general correlation with peak 

demand (Denholm and Margolis 2007). In most parts of the United States, peak demand occurs 

during the afternoon of summer weekdays. Because of the high cost of peak power generation, 

common TOU electric rate structures charge users higher rates for use at peak times. As a result, PV 

becomes very attractive because it can provide a peak-shaving impact during the first few hours of 

the afternoon peak,

 

14

PV systems may offset a customer’s load by providing electricity during high demand hours. 

However, because demand is often measured in 15-minute intervals, if a PV system’s output is 

reduced as a result of clouds or maintenance during this peak load period, the actual benefit of PV on 

demand reduction can be substantially reduced. For buildings under a demand ratchet, this effect may 

be amplified.

 offsetting expensive electricity from the grid. 

15

 

13 This assumes the heater is used during the interval in which the peak demand is measured.

 Additionally, peak building demand often occurs later in the afternoon, whereas PV 

output generally peaks at noon (standard time), depending on longitude. In these cases, PV systems 

cannot reduce peak loads by their full rated capacity, but rather a percentage of their capacity. 

Table 4 lists the capacity values of all PV systems studied. Here, the capacity value is defined as the 

total monthly peak demand reduction as a percentage of the PV systems’ rated capacity. The capacity 

value allows us to assess the actual impact on the monthly demand charge bill. Capacity values are 

highest during the summer months, averaging 38% between June and August compared to 6% 

between December and February. On average, peak PV production and peak demand occurs three 

hours apart for all months. On a clear day, PV systems can provide between 44% and 69% of rated 

capacity three hours from solar peak in December and June respectively. This sets a fundamental 

limit on the capacity value, which is dependent on the peak demand coincidence. Due in part to this 

limit as well as cloud cover and other hours of high demand, the capacity values approach a 

maximum of 40% during the summer and averages 21% for all months. Figure 2 illustrates several 

factors that affect PV capacity value. Here, the building’s normal peak load of 162 kW (without PV) 

occurs at 4 p.m. The PV output during this hour is equal to 38 kW, which is equal to a capacity value 

14 The afternoon summer peak usually occurs between 1 p.m. and 7 p.m., while PV generation peaks between

10 a.m. and 3 p.m., depending on the longitude and latitude. The maximum system peak occurs around 3 p.m. to

4 p.m. local time (Denholm 2007)

15 Companies that operate under demand ratchets (usually larger loads) are billed for a percentage of their peak 

annual demand regardless of their actual monthly consumption. For example, a company that uses 1 MW during any 

hour in the previous 12 months is billed for 750 kW (if under a 75% ratchet) for all other months whether it uses any 

power the rest of the year. Such customers may have little incentive to install PV systems, given their variability in 

demand reduction.
9 

of 46% (the PV capacity is 82 kW), reducing the demand at 4 p.m. to 124 kW. However, this demand 

reduction shifts the building’s peak demand to 8 a.m. where the normal demand of 151 kW is 

reduced to 131 kW because the PV output during this hour is only 20 kW. As a result, the total 

effective demand reduction of the PV system is only 31 kW (31%) 

Table 4. Average Capacity Values and Peak-Solar to Peak-Demand Difference for all Buildings and PV 

Systems Studied across the Ten Climate Zones Used in this Analysis. 

Month PV Capacity Values Hours between Peak Solar

and Peak Demand

January 4% 3 

February 10% 3 

March 15% 3 

April 21% 3 

May 30% 3 

June 40% 3 

July 38% 3 

August 35% 3 

September 30% 4 

October 13% 3 

November 8% 2 

December 3% 3 

Average 21% 3 
10 

Figure 2. PV peak demand reduction in July, climate zone 5A 

Although the PV system reduced demand by 53 kW during the peak solar output and reduced 

demand by 38 kW during the peak demand hour, the effective demand reduction is only 31 kW 

due to other high loads during low solar output.

An average capacity value of only 21% suggests that PV systems may not be suitable to operate 

under demand-based rates. Results show that the simulated PV systems realize approximately 5%–

10% savings under rate structures with heavy demand charges (>60% of electric bill) while 

approximately 20%–25% savings are realized under energy-only rate structures. Figure 3 illustrates 

the correlation between increasing demand charges and the decreasing value from PV systems. The 

heaviest demand charges (>60% of electric bill) occur under tariffs applicable in Boston (68%, 74%), 

Denver (73%), and Minneapolis (67%). These four rate structures16 have a relatively low energy 

charge compared to other rates of the same class while maintaining a relatively high demand charge. 

Such rate characteristics are usually seen in rate structures that are applicable to loads that are much 

larger than the simulated buildings studied in this analysis.

17

 

16 Massachusetts Electric Company Rate G-2, Massachusetts Electric Company Rate G-3, Xcel Energy (Public 

Service Company of Colorado) Schedule SG, Xcel Energy (Northern States Power Company) Rate A15.

Similar analyses also show that PV 

systems with high demand charges tend to lose value on a $/kWh basis with increasing penetration 

(Wiser et al. 2007). 

17 Boston’s rates were applicable to a load size that fell outside the simulated building load range. Boston’s rates had 

a minimum peak load requirement of 180 kW. The simulated building for Boston’s climate zone had an annual peak 

load of 170 kW. Though not applicable to our building class, this rate was still analyzed in order to capture a wide 

range of rate structures. The rates for Denver and Minneapolis were applicable to the simulated loads for those 

climate zones. 

0

20

40

60

80

100

120

140

160

180

4 

AM

5 

AM

6 

AM

7 

AM

8 

AM

9 

AM

10 

AM

11 

AM

12 

PM

1 

PM

2 

PM

3 

PM

4 

PM

5 

PM

6 

PM

7 

PM

8 

PM

9 

PM

10 

PM

KW

Load Without PV Net Load PV Peak Load 

before PV

Peak Load 

after PV
11 

Figure 3. PV savings versus demand charges

Each point represents an individual rate structure from each of the 25 cities.

From the utility’s perspective, these demand charge effects are justified because of PV’s lack of 

capacity value. The utility must be able to provide reliable power when needed. Because solar energy 

is inherently variable, it cannot reliably meet demand at every instant. PV may become more 

valuable to the utility if its output is smoothed or controllable. One way this is achieved is when PV 

systems are coupled to solar load controllers that temporarily reduce building HVAC loads during 

cloud transients (Perez et al. 2003). Another option is to re-orient PV systems to maximize output for 

capacity, as opposed to energy. Further analysis is necessary to evaluate the economic benefits of 

these options.

3.3 Impact of TOU and Seasonal Rate Structures

In addition to demand charges, other rate structure elements also have a notable effect on PV system 

value. Figure 4, a modified version of Figure 3, illustrates the impacts of rate structure types. Within 

the overall pattern of declining PV savings that are due to increasing demand charges, another, more 

subtle pattern reveals that TOU rates give PV systems more value than flat rates. The TOU rates 

studied resulted in savings that were over 10% higher than the flat rates. This is primarily because 

TOU rates are more expensive during the day, coinciding with PV production. A complete listing of 

all rate structures collected is provided in the appendix. 

R2 = 0.897

5%

7%

9%

11%

13%

15%

17%

19%

21%

23%

25%

0% 20% 40% 60% 80%

% Bill savings because of PV

Demand Charge as Percentage of Total Bill
12 

Figure 4. Rate structure associated with each data point in the analysis 

While the data indicate that TOU rates are more beneficial to PV system economics than flat rate 

structures, it can also be seen in Figure 4 that not all TOU rates are beneficial.18

 

18 This can be seen most easily in the portion of Figure 4 where there are no demand charges (0%). The lowest data 

point is a TOU rate.

 Figures 5 and 6 show 

that some TOU structures may be correlated well with PV output while others might not have a good 

coincidence. Pacific Gas and Electric’s A-6 tariff (Figure 5) peaks between noon and 6 p.m. during 

the summer while Portland General Electric’s Schedule 32 TOU rate (Figure 6) peaks between 3 p.m. 

and 8 p.m. during the summer. TOU rates that are poorly correlated with PV production may result in 

a situation in which the PV system helps offset energy under a low rate while other portions of 

building loads are still subject to high prices. This can reduce the effectiveness of the PV system’s 

ability to offset costs. 

4%

9%

14%

19%

24%

29%

0% 20% 40% 60% 80%

% Bill savings because of PV

Demand Charge as percentage of bill

Flat Rate TOU Seasonal Flat
13 

Figure 5. TOU rate structure that correlates well with PV production

Rate structure and PV production during two days in August in Berkeley, California. Rate shown 

corresponds to Pacific Gas and Electric’s A-6 tariff.

Figure 6. TOU rate structure showing poor correlation with PV production

Rate structure and PV production during two days in August in Portland, Oregon. Rate shown 

corresponds to Portland General Electric’s Schedule 32-TOU tariff.

5

10

15

20

25

30

35

40

45

0

5

10

15

20

25

30

35

40

0 6 12 18 24 6 12 18

Cents/kWh

kWh

Hour

Berkeley, CA (PG&E Sch A-6)

PV Production

Rate

3

4

5

6

7

8

9

10

11

12

0

5

10

15

20

25

30

35

40

45

0 6 12 18 24 6 12 18

Cents/kWh

kWh

Hour

Portland, OR (Schedule 32 TOU)

PV Production

Rate
14 

Figure 7 plots the PV energy charge savings against the correlation19 between PV production and 

TOU pricing for all TOU tariffs studied. This illustrates the strong relationship between wellcorrelated TOU rates and increased benefit from PV systems. 

Figure 7. PV savings by correlation between TOU pricing and PV production

In some cases, PV system owners may actually decrease their PV savings if switching from a flat rate 

to a TOU rate because of poor correlation in TOU rates. This happens when high rates occur when a 

PV system does not produce as much electricity but the building’s load remains high. For example, a 

building under a TOU rate that peaks late in the evening will most likely have an increased energy 

bill if that building’s load also peaks late in the evening. The building’s PV system will not help 

much because of reduced output in the evening hours. TOU rates are usually designed with the 

regional demand in mind. For example, Portland General Electric’s (Figure 6) peak rate occurs 

during the evening hours, because that is when the utility’s demand peaks (PGE 2010).

Because electricity demand typically peaks later in the afternoon, fixed PV systems can potentially 

be more valuable to utility systems if they are oriented slightly west of due south. This would shift 

PV production later in the day, increasing its coincidence with the utility peak. Under certain TOU 

rates, this increase in energy value may more than offset the decrease in total energy production from 

the system resulting from the re-oriented system. However, additional analysis is required to examine 

the impacts of azimuth on the value of PV systems over a large range of locations and rates. 

 

19 The correlation coefficient is 1 when all data points are fully correlated and it is -1 when the data are anticorrelated. 
15 

PV system owners may also realize greater savings from TOU rates if the off-peak to peak pricing 

range is large. Figures 8 and 9 show two TOU rate structures based on San Diego Gas and Electric’s 

A6-TOU tariff during two days in August. One of these rates (Figure 8) was scaled from the original 

rate structure while keeping the same hours for the peak, mid-peak, and off-peak periods. Both rates 

are revenue neutral; that is, without a PV system, both rate structures would result in the same energy 

charge based on the simulated building for San Diego’s climate zone. The rate that ranges from 

1 cent/kWh to 15 cents/kWh (Figure 8) yields a bigger savings (savings are 19% larger) than the rate 

that ranges from 7 cents/kWh to 11 cents/kWh (Figure 9). Also, by weighting the price of electricity 

from the evening and night hours to the daytime, a customer would enjoy low prices at night and 

their PV system would contribute to the load during the daytime when rates are more expensive. 

Figure 8. Rate structure with wider range in peak to off-peak energy prices

0

2

4

6

8

10

12

14

16

0

5

10

15

20

25

30

35

40

45

0 6 12 18 24 6 12 18

Cents/kWh

kWh

Hypothetical rate with high peak/off-peak spread

PV Production

Rate
16 

Figure 9. Rate structure with narrower range in peak to off-peak energy prices

Figure 10 plots the PV energy savings against the ratio of peak to off-peak TOU prices for all rate 

structures studied. In general greater peak to off-peak price differences result in greater savings. 

However, note that two TOU rate structures evaluated result in saving less than 20% despite having a 

ratio of near 2.5 and 3. It is interesting that these two rates drop below the 20% mark because in a 

20% penetration scenario, PV systems under flat rates will always result in energy charge savings of 

20%. This suggests that switching from a flat rate to one of these two TOU rates will actually 

decrease the value of PV. This effect is due to peak pricing which occurs late in the evening. Summer 

peak rates started at 2 p.m. and 3 p.m. and ended at 8 p.m. for these two rates.20

 

20 Portland General Electric Schedule 32 TOU and Sacramento Municipal Utility District Schedule GS-TOU3

Typical summer 

TOU rates of the tariffs collected typically begin at or before noon and end at around 6 p.m. This 

shows that poor correlation between TOU pricing and PV production can actually cause a TOU tariff 

to be less beneficial than flat rates. Flat rates that vary seasonally also exhibit a slight increase in 

value with increasing ratio between summer months and winter months, though this trend is very 

small (Figure 11). 

0

5

10

15

20

25

30

35

40

45

0

5

10

15

20

25

30

35

40

45

0 6 12 18 24 6 12 18

Cents/kWh

kWh

Rate with low peak/off-peak spread

PV Production

Rate
17 

Figure 10. PV savings by TOU peak to off-peak price range 

Figure 11. PV savings by ratio of summer prices to winter prices in seasonal flat rates 

18%

19%

20%

21%

22%

23%

24%

25%

26%

1 2 3 4 5 6 7

Percent Savings in Energy Charges from PV

Ratio of TOU Peak Price to Off Peak Price

18%

19%

20%

21%

22%

23%

24%

1 1.1 1.2 1.3 1.4 1.5

Percent Savings in Energy Charges

Ratio of Seasonal Flat Rate Summer Price to Winter Price
18 

4 Conclusions

This effort gathered and analyzed 55 unique rate structures from 25 cities across ten U.S. climate 

zones. By using a fixed PV penetration across all locations, we were able to identify the impacts of 

individual rate structure components on the value of PV generation. Common rate structure elements 

that appear to increase the value of PV include: 

• TOU tariffs with peak pricing that is well correlated with PV production 

• TOU tariffs that have a wide range between off-peak prices and peak prices

• Seasonal flat tariffs that have a relatively higher price in the summer than winter.

Alternately, tariffs with demand charges tend to decrease the value of PV production. It was shown 

that PV systems, on average, have a relatively low capacity value, making them less attractive under 

rates with heavy demand charges. The results also indicate that although TOU rates are generally 

more beneficial to buildings with PV systems, some TOU rates are less beneficial than others are due 

to undesirable correlation between peak pricing and PV production. 

Follow-up analyses could further clarify the relationship between additional rate structures, such as 

demand ratchets, block rates, and inverted block rates. Exploring how these findings would change in 

a scenario involving various PV penetration levels and various building types might yield significant 

insights into the impacts of rate structures. Finally, further studies could explore the concept that 

minor alterations in PV system design (such as facing the system slightly west) could maximize 

system economics under available rate structures. 
19 

References 

Alt, L. E. (2006). “Energy Utility Rate Setting: A Practical Guide to the Retail Rate-Setting 

Process for Regulated Electric and Natural Gas Utilities.” Raleigh, NC: LuLu. 

Borenstein, S. (2008). “The Market Value and Cost of Solar Photovoltaic Electricity 

Production.” CSEM WP 176. Berkeley, CA: Center for the Study of Energy Markets (CSEM). 

http://www.ucei.berkeley.edu/PDF/csemwp176.pdf

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

Traditional Electric Power Systems.” Energy Policy (35:5); pp. 2853-2861. 

Denholm, P.; R.M. Margolis; S. Ong; B. Roberts. (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. 

Doris E.; S. Ong, S.; Van Geet, O. (2009). “Rate Analysis of Two Photovoltaic Systems in San 

Diego.” NREL/TP-6A2-43537. Golden, CO: National Renewable Energy Laboratory. 

Marion, B.; Anderberg. M.; Gray-Hann, P. (2005). “Recent Revisions to PVWATTS.” 

NREL/CP-520-38975. Golden, CO: National Renewable Energy Laboratory. 

Marion, W.; Urban, K. (1995). “Users Manual for TMY2s Typical Meteorological Years.” 

Golden, CO: National Renewable Energy Laboratory. 

Perez, R; Hoff, T; Herig, C; Shah J. (2003). “Maximizing PV Peak Shaving with Solar Load 

Control: Validation of a Web-based Economic Evaluation Tool.” Solar Energy (74); pp. 409-

415. 

PGE (Portland General Electric). (2010). Time of Use rate description Web page. 

http://www.portlandgeneral.com/residential/your_account/billing_payment/time_of_use/default.

aspx. Accessed February 2010. 

Solar Alliance. (2009). Utility Rates and Revenue Policies. http://www.solaralliance.org/four

-pillars/utility-rates-revenue-policies.html. Accessed July 2, 2009. 

Torcellini, P.; Deru, M.; Griffith, B.; Benne, K. ; Halverson, M.; Winiarski D. (2008). DOE 

“Commercial Building Benchmark Models.” NREL/CP-550-43291. 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. 
20 

Appendix 

Table A-1 lists the actual rate structures collected for this analysis. Each rate structure was entered into PVrate, which calculated various 

results such as the annual electric bill, PV value, and percent savings from the PV system. A selected set of results are shown in the table. 

Table A-1. Actual Utility Rates with Selected Results. 

City, State, Utility, Rate Structure Type of 

utility

Rate Type Annual 

Electric Bill

Total Bill 

savings (%)

Demand 

Charge as % 

of Bill

Seattle, WA, Seattle City Lights, SMC Muni Flat $19,500 20% 0%

San Diego, CA, SDGE, Sch A IOU Flat $37,679 20% 0%

San Antonio, TX, CPS, Rate PL IOU Flat $48,452 18% 11%

Sacramento, CA, SMUD, Sch GS Muni Flat $55,586 17% 19%

Portland, PGE, Sch 32 IOU Flat $32,375 20% 0%

Pittsburgh, PA, WPPC, Sch 20 IOU Flat $53,241 18% 16%

Philadelphia, PA, PECO, General Rate GS IOU Flat $72,349 18% 14%

Orlando, FL, OUC, Small Commercial Muni Flat $45,686 20% 0%

Orlando, FL, OUC, General Option A Muni Flat $43,176 15% 28%

New Orleans, LA, Entergy, SE22 IOU Flat $57,925 16% 23%

Milwaukee, WI, Wisconsin EP, Schedule Cg1 IOU Flat $46,640 20% 0%

Milwaukee, WI, Wisconsin EP, Schedule Cg1 (demand) IOU Flat $47,052 17% 19%

Madison, WI, MGE, Clean Power Partner IOU Flat $102,209 19% 7%

Knoxville, TN, KUB, Schedule GSB Muni Flat $47,168 11% 53%

Knoxville, TN, KUB, Schedule GSA Muni Flat $41,980 20% 0%

Houston, TX, Centerpoint Energy, Sheet 6.3 IOU Flat $29,904 20% 0%

Boston, MA, Nstar, G2 (demand) IOU Flat $64,320 8% 68%

Austin, TX, Austin Energy, E06 Muni Flat $49,553 11% 51%

Ann Arbor, MI, Detroit Edison, Schedule D3 IOU Flat $39,850 20% 0%
21 

City, State, Utility, Rate Structure Type of 

utility

Rate Type Annual 

Electric Bill

Total Bill 

savings (%)

Demand 

Charge as % 

of Bill

Tucson, AZ, Tucson Electric Power, GS-10 IOU Seasonal Flat $41,293 20% 0%

San Jose; San Francisco; Santa Rosa; CA, PGE, A-1 IOU Seasonal Flat $57,168 21% 0%

Sacramento, CA, SMUD, Sch GS (Demand) Muni Seasonal Flat $43,694 20% 0%

NYC, NY, ConEd, General Small, Rate I IOU Seasonal Flat $77,295 20% 0%

Minneapolis, MN, Xcel, Small General IOU Seasonal Flat $23,273 20% 0%

Madison, WI, MGE, Schedule Cg-5 IOU Seasonal Flat $48,894 20% 0%

Denver, CO, PSCO, SG (demand) IOU Seasonal Flat $35,289 7% 73%

Denver, CO, PSCO, C IOU Seasonal Flat $29,453 20% 0%

Boston, MA, Nstar, G1 IOU Seasonal Flat $55,418 20% 0%

Berkeley, CA, PGE, Rate A-1 IOU Seasonal Flat $57,168 21% 0%

Austin, TX, Austin Energy, E02 Muni Seasonal Flat $41,199 20% 0%

Tucson, AZ, Tucson Electric Power,GS-76F (TOU) IOU TOU $51,900 22% 0%

San Jose; San Francisco; Santa Rosa; PGE, A-6 (TOU) IOU TOU $58,241 24% 0%

San Jose; San Francisco; Santa Rosa; PGE, A-10 (Demand) IOU TOU $50,387 17% 22%

San Jose; San Francisco; Santa Rosa; PGE, A-10 (TOU) IOU TOU $50,585 17% 22%

San Diego, CA, SDGE, PA-T-1 F IOU TOU $55,082 15% 36%

San Diego, CA, SDGE, PA-T-1 E IOU TOU $54,804 16% 36%

San Diego, CA, SDGE, PA-T-1 D IOU TOU $54,447 17% 36%

San Diego, CA, SDGE, PA-T-1 C IOU TOU $54,709 16% 36%

San Diego, CA, SDGE, AL-TOU IOU TOU $62,411 15% 42%

San Diego, CA, SDGE, A6-TOU IOU TOU $63,485 14% 46%

Salt Lake City, UT, RMP, Sch 23 (Demand) IOU TOU $43,940 16% 25%

Sacramento, CA, SMUD, Sch GS-TOU3 (TOU) Muni TOU $39,413 18% 10%

Portland, OR, PGE, Sch 32 (TOU) IOU TOU $22,467 20% 0%
22 

City, State, Utility, Rate Structure Type of 

utility

Rate Type Annual 

Electric Bill

Total Bill 

savings (%)

Demand 

Charge as % 

of Bill

Orlando, FL, OUC, General Option B (TOU) IOU TOU $44,067 17% 28%

NYC, NY, ConEd, General Small, Rate II (TOU) IOU TOU $74,835 22% 0%

Minneapolis, MN, Xcel, Small General (TOU) IOU TOU $23,342 25% 0%

Minneapolis, MN, Xcel, General (TOU) IOU TOU $26,124 10% 67%

Milwaukee, WI, Wisconsin EP, Schedule Cg6 (TOU -B) IOU TOU $47,836 23% 0%

Milwaukee, WI, Wisconsin EP, Schedule Cg6 (TOU-A) IOU TOU $52,864 24% 0%

Milwaukee, WI, Wisconsin EP, Schedule Cg3 (TOU) IOU TOU $44,661 14% 40%

Madison, WI, MGE, Schedule Cg-3 (TOU) Muni TOU $50,777 24% 0%

Boston, MA, Nstar, G3 (TOU) IOU TOU $51,036 7% 74%

Berkeley, CA, PGE, Rate A-6 (TOU) IOU TOU $58,241 24% 0%

Berkeley, CA, PGE, Rate A-10 IOU TOU $50,596 18% 22%

Ann Arbor, MI, Detroit Edison, Schedule D3.4 IOU TOU $45,032 22% 0%
F1147-E(10/2008)

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4. TITLE AND SUBTITLE

Impacts of Commercial Electric Utility Rate Structure Elements on 

the Economics of Photovoltaic Systems

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DE-AC36-08-GO28308

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6. AUTHOR(S)

S. Ong, P. Denholm, and E. Doris

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This analysis uses simulated building data, simulated solar photovoltaic (PV) data, and actual electric utility tariff data 

from 25 cities to understand better the impacts of different commercial rate structures on the value of solar PV 

systems. By analyzing and comparing 55 unique rate structures across the United States, this study seeks to identify 

the rate components that have the greatest effect on the value of PV systems. Understanding the beneficial 

components of utility tariffs can both assist decision makers in choosing appropriate rate structures and influence the 

development of rates that favor the deployment of PV systems. Results from this analysis show that a PV system’s 

value decreases with increasing demand charges. Findings also indicate that time-of-use rate structures with peaks 

coincident with PV production and wide ranges between on- and off-peak prices most benefit the types of buildings 

and PV systems simulated. By analyzing a broad set of rate structures from across the United States, this analysis 

provides an insight into the range of impacts that current U.S. rate structures have on PV systems. 

15. SUBJECT TERMS

commercial electric utility rate structures; utility rate structure; rate structure comparison; utility tariffs; solar photovoltaic 

systems; PV; PV system value; economics; Solar America Cities; time-of-use rates

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