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

Maximizing the Value of 

Photovoltaic Installations on 

Schools in California: Choosing 

the Best Electricity Rates 

Sean Ong and Paul Denholm

Technical Report

NREL/TP-6A20-51694

July 2011
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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303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Maximizing the Value of 

Photovoltaic Installations on 

Schools in California: Choosing 

the Best Electricity Rates 

Sean Ong and Paul Denholm

Prepared under Task No. PVC9. 93J1

Technical Report

NREL/TP-6A20-51694

July 2011
NOTICE

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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 Alicen Kandt, Walter Short, and Robin Newmark of 

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

document, as well as Tom Kelly (KyotoUSA) and Chris Nordstrom (San Diego Unified School 

District) for their thoughtful reviews. The authors also thank Mary Lukkonen and Scott Gossett 

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

Schools in California often have a choice between multiple electricity rate options. For schools 

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

PV generation. The rate option that minimizes a school’s electricity expenses often does not 

remain the most economical choice after the school installs a PV system. The complex 

interaction between PV generation, building load, and rate structure makes determining the best 

rate a challenging task. Twenty-two rate structures across three of California’s largest electric 

utilities—Pacific Gas and Electric Co. (PG&E), Southern California Edison (SCE), and San 

Diego Gas and Electric (SDG&E)—were evaluated in order to identify common rate structure 

attributes that are favorable to PV installations. Key findings include: 

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

installed. The rate structure that minimizes the school’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 school’s electric load. Other rates become more economical than 

the initial rate for larger PV system sizes (see Figure ES-1).

• When a school’s PV installation is large, rates with high daytime prices are 

favorable. The best rates for schools with relatively large PV systems, or high 

penetrations, are those with very high afternoon energy prices and little or no demand 

charges. However, when the PV installation is small, these expensive rates increase the 

school’s annual electricity expenses, even with the PV system helping to offset costs.

• The best size for a school’s PV system depends on the available rate options. When 

evaluating the economics of a PV system, bigger is not always better. In San Diego, the 

best PV system is one that is sized to meet about 10% of a school’s annual electric load. 

For PG&E customers, maximizing the size1

• With the best rates considered, power purchase agreements (PPAs) may be a better 

option for schools than cash purchases. A school purchasing a PV system up front will 

break even

of the PV installation is best.

2

 at a PV cost of about $3–$5/W. This is below the average installed cost of 

$6/W,3 as determined at the time of this report. Because public schools cannot take 

advantage of tax incentives, purchasing the system up front may not be in the school’s 

best economic interest. The break-even PPA prices, however, are in the range of $0.16–

$0.22/kWh, making the PPA option economically attractive. 

 

1 It is important, however, to stay within the net-metering limits. Exported PV that is not compensated at retail rates 

will cause the value to decrease sharply. The limit at the time of this report is 100% of net annual consumption. 2 See Section 2.4 on the definition for break-even and how it is calculated. 3 See Section 2.4.
v 

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

schools in the PG&E service territory
vi 

Table of Contents

List of Figures ........................................................................................................................................... vii

List of Tables ............................................................................................................................................ viii

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

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

2.1 Load Data .............................................................................................................................2

2.2 Rate Data ..............................................................................................................................4

2.3 Solar Data.............................................................................................................................7

2.4 System Advisor Model and Calculations .............................................................................7

3 Results................................................................................................................................................. 11

3.1 Net Value of Photovoltaic Generation ...............................................................................11

3.2 Impacts of Cost ..................................................................................................................14

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

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

Appendix A. Case Study: Berkeley High School ................................................................................... 23

Introduction and Summary of Findings ...................................................................................23

Data and Methodology .............................................................................................................26

Load Data ...........................................................................................................................26

Rate Data ............................................................................................................................27

Solar Data...........................................................................................................................29

System Advisor Model and Calculations ...........................................................................29

Conclusion for Berkeley High School Case Study ..................................................................29

Appendix B. Case Study: Lewis Middle School..................................................................................... 30

Introduction and Summary of Findings ...................................................................................30

Data and Methodology .............................................................................................................33

Load and Solar Data ...........................................................................................................33

Rate Data ............................................................................................................................34

System Advisor Model and Calculations ...........................................................................35

Conclusion for Lewis Middle School Case Study ..... .............................................................35
vii 

List of Figures

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

schools in the PG&E service territory ................................................................................ v

Figure 1. Climate zone map of the United States ........................................................................... 2

Figure 2. Hourly school load profiles during the third week of February ...................................... 3

Figure 3. Hourly school load profiles for the last week of September ........................................... 4

Figure 4. Applicability of electricity rates for commercial facilities in each of the three utility 

service territories studied .................................................................................................... 5

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

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

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

schools in the PG&E service territory .............................................................................. 12

Figure 7. Net value of PV generation under various rate structures and penetration levels for 

schools in the SCE service territory .................................................................................. 13

Figure 8. Net value of PV generation under various rate structures and penetration levels for 

schools in the SDG&E service territory ............................................................................ 13

Figure 9. Simple payback for school PV system in PG&E service territory ................................ 14

Figure 10. Simple payback for school PV system in SCE service territory ................................. 15

Figure 11. Simple payback for school PV system in SDG&E service territory ........................... 15

Figure 12. Break-even PV cost for each utility ............................................................................. 16

Figure 13. Break-even PPA price for each utility ......................................................................... 17

Figure 14. Annual electricity bill savings under various PPA prices and penetration levels for 

school in the PG&E service territory ................................................................................ 18

Figure 15. Annual electricity bill savings under various PPA prices and penetration levels for 

school in the SCE service territory ................................................................................... 18

Figure 16. Annual electricity bill savings under various PPA prices and penetration levels for 

school in the SDG&E service territory ............................................................................. 19

Figure 17. Annual electricity bill savings under the GO bond scenario ....................................... 20

Figure A-1. Berkeley High School campus with locations favorable for rooftop PV identified 

(A–D) ................................................................................................................................ 23

Figure A-2. Net value of PV generation under various rate structures and penetration levels ..... 24

Figure A-3. Payback period for Berkeley High School under various PV system costs and 

penetration levels .............................................................................................................. 24

Figure A-4. Annual electricity bill savings for Berkeley High School under various PPA prices 

and penetration levels ....................................................................................................... 25

Figure A-5. Annual electricity bill savings for Berkeley High School under the GO bond option

........................................................................................................................................... 25

Figure A-6. Berkeley High School electricity load data during 2010—measured monthly energy 

data is compared with measured sub-hourly data and scaled sub-hourly data ................. 27

Figure A-7. Applicability of PG&E rates for commercial facilities up to 1,000 kW ................... 28

Figure B-1. Lewis Middle School with existing rooftop PV installation ..................................... 30

Figure B-2. Net value of PV generation under various rate structures and penetration levels ..... 31

Figure B-3. Payback period for LMS under various PV system costs and penetration levels ..... 32

Figure B-4. Annual electricity bill savings for LMS under various PPA prices and penetration 

levels ................................................................................................................................. 32
viii

Figure B-5. Annual electricity bill savings for LMS under the GO bond option ......................... 33

Figure B-6. 2010 daily load profile and PV generation for Lewis Middle School ....................... 34

Figure B-7. Applicability of SDG&E rates for commercial facilities up to 1,000 kW ................ 34

List of Tables

Table 1. Climate Zone and Building Characteristics Associated with Each Utility Service 

Territory .............................................................................................................................. 3

Table 2. Summary of Applicable Categories and Price Levels for the Rates Evaluated ................ 6

Table A-1. 2010 Energy Consumption of Berkeley High School ................................................ 26

Table A-2. Summary of Applicable Categories and Price Levels for the Rates Evaluated .......... 28

Table B-2. Summary of Applicable Categories and Price Levels for the Rates Evaluated .......... 35
1 

1 Introduction

In California, schools are increasingly considering solar technologies as a way to help offset a 

portion of their annual energy expenditures. School buildings in the state typically have 

relatively large 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 school’s electricity rate. Schools often have a choice between multiple 

rate options. Understanding what rate structure optimizes savings requires an analysis of the 

interaction between the building load, PV generation, and rate structure. High resolution data 

(hourly or sub-hourly resolution) is essential when determining the impacts of time-of-use 

(TOU) charges and demand charges. The cost of the PV installation must also be factored into 

the analysis in order to determine the net effect on the school’s annual expenses. These 

considerations may present a challenging task for schools that are trying to determine whether or 

not solar makes economic sense for their campus or for schools deciding whether or not to 

switch rates in order to maximize existing PV system value. This report identifies the rate 

structure elements that are beneficial to schools and the conditions under which various rate 

structures should be considered. 

In this study, 22 rate structures from the top three electric utilities in California were evaluated. 

These utilities are Pacific Gas and Electric (PG&E), Southern California Edison (SCE), and San 

Diego Gas and Electric (SDG&E). These rate structures were used to assess PV value and annual 

savings for schools in each of the three utility service territories. Two case studies were also 

conducted for actual schools in Berkeley (Berkeley High School) and San Diego (Lewis Middle 

School). These case studies can be found in Appendix A and Appendix B. Rate impacts are 

dependent on individual school load profiles, which vary from one school to another. These 

results are not intended to represent all schools in California. Schools 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. The report results are intended to explore rate 

structure elements that are typically beneficial for PV installations and to help identify general 

trends for the impacts of rate structures on schools with solar systems. 
2 

2 Data and Methodology

2.1 Load Data

Building load data are an important component in any rate structure analysis that includes 

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

demand of a building; consequently, quantifying the demand reduction value of a PV system 

requires a load profile. Load profiles are also required when evaluating tiered rates, where rates 

vary depending on monthly energy usage. This analysis uses load profile data created in part for 

the U.S. Department of Energy (DOE) commercial building benchmark models (Torcellini et al. 

2008), which were simulated using the EnergyPlus simulation software.4

 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 (Marion and Urban 

1995; Wilcox 2007). For consistency, TMY2 conditions were used when simulating PV 

performance. Although the benchmark models consist of a variety of different commercial 

building types across 16 climate zones, the data used for this analysis consist of only simulated 

high school buildings across two climate zones.5 Figure 1 shows the locations of each climate 

zone in the United States. Table 1 summarizes the climate zones associated with each utility 

service territory covered in this analysis along with the schools’ annual electricity usage patterns.

Figure 1. Climate zone map of the United States 

Source: DOE 2011

 

4 For more information on the EnergyPlus model, see http://apps1.eere.energy.gov/buildings/energyplus/. 5 The climate zones used in Figure 1 are 3B-Coast and 3C. These zones are a subset of the climate zones officially 

recognized by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). 
3 

Table 1. Climate Zone and Building Characteristics Associated with Each Utility Service Territory 

Utility Service Territory ASHRAE 

Climate 

Zone 

Annual 

Building Load 

(MWh)

Peak Annual 

Load (kW)

Pacific Gas and Electric 3C 2,682 1,150

Southern California Edison 3B-Coast 2,632 941

San Diego Gas and Electric 3B-Coast 2,632 941

The high school buildings are modeled to have three floors with a total floor area of 210,890 ft2. 

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

associated with each school building and includes smaller loads such as plug loads. EnergyPlus 

simulated this data using the 2003 Commercial Building Energy Consumption Survey6 (CBECS)

results as guidance on the various load types within each facility. The total hourly electrical load 

of each building was entered into the System Advisor Model (SAM).7 See Section 2.4 for SAM 

details. Figures 2 and 3 show the hourly load profile for each of the three simulated school 

buildings. 

Figure 2. Hourly school load profiles during the third week of February 

 

6 For more information on CBECS, visit 

http://www.eia.doe.gov/emeu/cbecs/cbecs2003/detailed_tables_2003/detailed_tables_2003.html. 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 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. 

Despite a potential for overestimation, previous studies still show that rates with demand charges are poorly suited 

for PV systems (Ong et al. 2010).
4 

Figure 3. Hourly school load profiles for the last week of September 

2.2 Rate Data

A total of 22 utility rates in the three utility service territories were evaluated. These rates were 

obtained from the online Utility Rate Database (URDB) on the OpenEI platform8

 

8 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 (

 and verified 

with the official utility tariff sheets to ensure accuracy. The utilities offer various commercial 

rate structures for different load sizes and types. Smaller loads typically have more rate choices 

than larger loads since 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. In some cases, large schools may divide electricity 

consumption across multiple meters. Each meter is treated independently and measures only a 

portion of the campus’s total load, thereby allowing for greater flexibility in rate choice. Figure 4 

illustrates the eligibility range for each of the 22 utility rates. Note that rates A in SDG&E and 

rates GS-1 and GS-1-TOU in SCE are only available to customers with a maximum demand of 

20 kW or less. Because this limit is very low compared to typical school campus loads (even 

with split meters), they were not considered in the rate and cost impact calculations, though they 

were still analyzed for reference purposes. 

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 hundreds of 

electric utilities around the United States. 
5 

Figure 4. Applicability of electricity rates for commercial facilities in each of the three utility 

service territories studied

Various types of utility rates are used throughout the United States. The most common rate types 

(Ong et al. 2010) include the following: 

• Flat rates. Fixed cost of energy 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. 
6 

• Time-of-use rates. Time-of-use (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 “onpeak” or “peak” is generally used to describe hours with higher prices while “off-peak” is 

used to describe hours with lower prices. 

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

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

charges can also be fixed or vary by season or hour. 

• 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. These rates are most common in the form of energy charges; however, 

tiered demand charges are also used. 

Table 2 summarizes the various categories represented by the 22 rates used for this analysis. 

Tiered or flat rates were not evaluated. There was a good representation of seasonal rates, TOU 

rates, and demand charges; 13 of the 22 rates combined all three of those categories.

Table 2. Summary of Applicable Categories and Price Levels for the Rates Evaluated

Utility

Rate 

Name Flat Seasonal TOU Demand Tiered Relative Price Level

SCE

GS-1 Moderate-to-high energy prices

GS-1 

TOU

Very high energy prices during 

summer afternoons

GS-2

Moderate energy prices; high 

summer demand charges.

GS-2-

TOU-A

High energy prices; moderate 

demand charges

GS-2-

TOU-B

Moderate energy prices; high 

demand charges.

GS-2-

TOU-R

Very high energy prices; low 

demand charges

TOUGS-3-AModerate-to-high energy prices;

moderate demand charges

TOUGS-3-BLow energy prices; moderate-tohigh demand charges.

TOUGS-3-RHigh energy prices; low demand 

charges

TOU-8-

A

High summer afternoon energy 

charges

TOU-8-

B

Low energy prices; high summer 

afternoon demand charges

TOU-8-

R

High energy prices; low demand 

charges

SDG&E

A Very high energy prices

A6-TOU

Low energy prices; high demand 

charges

DG-R High energy prices; low demand 
7 

Utility

Rate 

Name Flat Seasonal TOU Demand Tiered Relative Price Level

charges

AL-TOU 

Intermediate energy prices; high 

demand charges

PG&E

A-1 Moderate-to-high energy prices

A-1 

TOU

High energy prices during 

summer afternoons; moderate 

prices otherwise

A-10

Moderate energy prices; high 

demand charges

A-10 

TOU

Low off-peak energy prices;

moderate peak energy prices; 

high demand charges

A-6

Very high energy prices during 

summer afternoons; low-tomoderate prices otherwise

E-19

Moderate energy prices during 

summer afternoons; lower energy 

prices otherwise; very high 

demand charges during summer 

afternoons; moderate demand 

charges otherwise

2.3 Solar Data

The PV production data used in this analysis were simulated using the TMY29 dataset of the 

National Solar Radiation Database (Marion and Urban 1995; Wilcox 2007). The TMY2 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, TMY2 was created by combining data 

from multiple years.

10

2.4 System Advisor Model and Calculations

The meteorological dataset was used as an input for the SAM, which 

simulated hourly PV production for use in the financial 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 (NREL 2011). The TMY2 

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:

• 15-degree tilt

 

9 Although TMY3 data was available at the time of this analysis, the TMY2 data was used because the DOE 

benchmark buildings simulation data was also simulated using the TMY2 data. This allows for a more consistent 

treatment of building demand reduction and demand charge benefits.

10 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 TMY2 file may contain data from up to 12 different years. Data was intentionally selected to be 

representative of typical meteorological conditions. 
8 

• South facing (180-degree azimuth) 

• A de-rate factor of 85% 

• Annual degradation of 0.5%. 

In addition to the meteorological data, hourly building load data and utility rate data11 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 

school’s annual electrical energy consumption12

The impacts of system costs were also considered in the analysis. Schools may choose various 

ways to finance a rooftop solar installation. For schools in California, typical choices include: 

) 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 schools’ 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. The combination of scenarios requires hundreds of unique simulations, from which the 

model can determine the PV penetration and rate structures that are likely optimal.

• Third-party ownership/power purchase agreements. This arrangement consists of a 

third party owning and maintaining the PV system installed on campus. The third party 

charges the school for the energy generated by the PV system, usually based on a prenegotiated price (in cents per kilowatt-hour). The school, in turn, will realize savings 

from a reduced electricity bill because of the energy offset by the PV production. Since 

schools are non-profit entities, they cannot take advantage of tax incentives such as the 

30% federal investment tax credit. However, the system owner can take advantage of the 

tax incentives, which may result in the solar system being economically beneficial to both 

the third party and the school.13

• Cash purchase. The school district directly pays for the PV system with general funds. 

Schools may be less attracted to this option for large installations due to high upfront 

costs and ineligibility for tax incentives.

Third-party ownership may also be in the form of a lease 

agreement, where the school pays a fixed monthly lease payment for the solar equipment 

instead of a price per generated kilowatt-hour. 

• Publically funded/general obligation bond. Schools paying for solar systems under a 

general obligation (GO) bond will have little or no upfront costs. This option is favorable 

to schools because they can realize the benefits of a reduced electricity bill while having 

little or no costs associated with the system. Public financing arrangements are not 

always available to schools and must first be approved by local governments or voters. 

 

11 SAM communicates directly with OpenEI’s online URDB to obtain the latest rate information available on 

OpenEI. For more information about the rate data and the online rate database, see Section 2.2 and 

http://apps1.eere.energy.gov/buildings/energyplus/. 12 Although the PV system generates the equivalent of 100% of the school’s annual electricity consumption, there 

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

(nights). Existing net-metering policies 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. 

13 Being able to take advantage of the 30% investment tax credit allows the system owner to pass their savings on to 

the school (in the form of a lower PPA rate) while still making a reasonable return on their investment. 
9 

Four metrics are used to evaluate PV system economics under the three ownership models 

described above. For power purchase agreements (PPA) and GO bonds, the bill impacts metric is 

used, which quantifies the percentage increase or decrease in the schools’ annual electricity 

expenses. The bill impact metric is calculated as follows: 

ݏݐ݉݁݊ݕܽ݌ ܣܲܲ െ ܸܲ ݄ݐ݅ݓ ܾ݈݈݅ ݐݏ݋ܿ ݐݏ݁ݓ݋ܮ െ ܸܲ ݐݑ݋݄ݐ݅ݓ ܾ݈݈݅ ݐݏ݋ܿ ݐݏ݁ݓ݋ܮ = ሻ%ሺ ݏ݅݊݃ݒܽݏ ܾ݈݈݅ ݈ܽݑ݊݊ܣ

ܸܲ ݐݑ݋݄ݐ݅ݓ ܾ݈݈݅ ݐݏ݋ܿ ݐݏ݁ݓ݋ܮ

For GO bonds, the equation above still applies but without a PPA payment cost. It is important to 

note that many PPA prices include an annual escalation factor (including inflation). In this 

analysis, it is assumed that annual electricity escalation (including inflation) is equivalent to the 

PPA price escalation. This simplifying assumption allows any annual escalation factors to be 

cancelled out of the bill impacts equation. 

In the analysis, PPA prices were also evaluated on a break-even basis. The break-even PPA price 

is the PPA price at which the schools’ annual electricity expenses neither increase nor decrease. 

Essentially, the break-even PPA price is the point at which the PPA price equals the net PV 

value. The break-even PPA prices help to determine if the schools will be saving or losing 

money annually. If the PPA price is above break-even, then the school will be losing money 

(annual expenses are increased). However, if the PPA price is below break-even, then the school 

will be saving money (annual expenses are decreased). 

The third metric considered is the break-even PV cost, which is the point at which the lifetime 

costs associated with a PV system are equivalent to the lifetime benefits (Denholm et al. 2009). 

The break-even cost was calculated by varying the installed PV cost until the net present cost 

equaled the net present benefits. The following assumptions were used in the break-even cost 

calculations: 

• Upfront cash payment 

• 30-year system lifetime, 30-year analysis period 

• Real discount rate of 5% 

• No federal, state, or local incentives 

• Annual PV degradation of 0.5% 

• Inverter replacements at year 10 and year 20 ($500/kW each time). 

When evaluating a cash purchase scenario, the simple payback metric is used, which roughly 

quantifies the number of years required to “pay back” the upfront investment using the savings 

from a PV system. Simple payback is calculated as follows: 

ݐݏ݋ܿ ݐ݊݋ݎ݂݌ݑ ݈݅ܽݐ݊݅ܫ = ሻݏݎ݁ܽݕሺ ܾܽܿ݇ݕܽ݌ ݈݁݌ܵ݅݉

݁ݐܽݎ ݐݏܾ݁ ݎ݊݀݁ݑ ݁݉ݐݏݕݏ ܸܲ ݉݋ݎ݂ ݐܾ݂݁݊݁݅ ݈ܽݑ݊݊ܣ

 

According to the California Solar Initiative database (CSI 2011), government and non-profit 

solar installation costs in California range from less than $3/W to well over $10/W. The 
10 

weighted average cost for government/non-profit solar installations is $6/W.14 In this analysis, 

upfront purchases were evaluated using $3/W, $4/W, $5/W, and $6/W. 

 

14 Evaluated for systems with nameplate capacities ranging from 20 kW to 800 kW.
11 

3 Results 

3.1 Net Value of Photovoltaic Generation

In order to compare the PV value across various penetration levels, it is important to focus more 

on value per unit of energy than absolute PV value—in this case, dollars per kilowatt-hour. 

Figure 5 illustrates the value of a rooftop PV system on a school building using PG&E rates

under different penetration levels. PV value under rates A-6, A-1, and A-1 TOU do not vary with 

penetration level, while the remaining rates decrease with increasing penetration. This is because 

the first three rates in question do not have any demand charge components, but the latter rates 

do (see Table 2 in Section 2.2). Studies have shown that PV value under rates with demand 

charge components tend to lose value with increasing PV penetration (Wiser et al. 2007).15 Rate 

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

very expensive rate, with summer afternoon rates approaching $0.45/kWh. Although this gives 

high value to a PV system, results show that a school switching to this rate from a less expensive 

rate experiences an increase in total electricity cost, rendering any PV savings useless. 

Evaluating a rate structure in isolation without considering net bill impacts or other rate structure 

options is insufficient when conducting a rate analysis. 

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

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 schools’ annual electricity costs without PV using the least cost rate. The least cost rate is the 

 

15 This is because PV generation is limited to the afternoon hours, and increasing PV production simply shifts the 

facility’s peak demand to hours when the sun is not shining. 
12 

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

in relation to the schools’ lowest cost option prior to the PV installation. This calculation can be 

expressed as the following equation: 

Net PV value = Energy cost with PV under rate in question – Energy cost without PV under least expensive rate

Rate A-10 turns out to be the least cost rate for the school load profile used in PG&E before 

installing PV. Figure 6 shows how the PV value changes once A-10 is set as the rate against 

which all other rates are compared. 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 increases the school’s annual energy cost, despite having a small rooftop PV system.16 At 

higher solar penetrations, the increase in PV 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. For PG&E, rate A-10 is the most economical rate until a 45% PV penetration, at which 

time rate A-1-TOU briefly becomes the best rate. After a 50% penetration, rate A-6 becomes and 

remains the most economical rate. The net PV value under various penetrations is also shown for 

schools in SCE and SDG&E (see Figures 7 and 8). The dotted lines denote rates that were only 

eligible for loads with peak demands of 20 kW or less. These rates were included in Figures 7 

and 8 for comparison. Since typical school loads are much larger, these rates were not used in the 

bill savings and payback calculations in Section 3.2. See Section 2.2 for details on the applicable 

load levels for each rate.

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

schools in the PG&E service territory

 

16 Though the PV system is still providing value to the school, 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 to the school.
13 

Figure 7. Net value of PV generation under various rate structures and penetration levels for 

schools in the SCE service territory

Figure 8. Net value of PV generation under various rate structures and penetration levels for 

schools in the SDG&E service territory
14 

3.2 Impacts of Cost

Identifying the best rates under various PV penetration levels is important; however, in order to 

make a decision about installing a PV system, costs have to be factored into the analysis. Four 

metrics were used to evaluate PV system economics under the three ownership models described 

in Section 2.4: 

• Simple payback

• Break-even PV cost 

• Break-even PPA price

• Annual bill savings. 

The simple payback period was calculated under the best rate option for each cost and PV 

penetration scenario. Payback periods for PG&E, SCE, and SDG&E are shown in Figures 9, 10, 

and 11, respectively. Payback periods at PG&E peak at a 45% PV penetration (13–27 years) and 

are shortest when approaching 100% penetration (10–21 years). Payback periods at SCE are 

shortest when PV penetration is under 30% and longest with 60% penetration. PV installations at 

SDG&E have a unique payback period curve due to the DG-R rate being applicable only with a 

10% or greater PV penetration.

17 This causes abrupt minimum payback periods at a 10% 

penetration. At $6/W, the payback period for a PV system with a 5% penetration level is 21 

years and quickly drops to 12 years with a 10% penetration. Payback periods continue to 

increase as penetration increases, with the longest periods occurring at 100% penetration. 

Figure 9. Simple payback for school PV system in PG&E service territory

 

17 DG-R requires at least a 10% capacity penetration rather than a 10% energy penetration. It was discovered that a 

7.5% energy penetration is sufficient to provide 10% of peak annual load. Because the PV penetration resolution is 

limited to 5% increments, DG-R was chosen to become effective at a 10% energy penetration.
15 

Figure 10. Simple payback for school PV system in SCE service territory

Figure 11. Simple payback for school PV system in SDG&E service territory
16 

The simple payback metric is useful when trying to roughly determine if an investment is 

reasonable, but the break-even cost metric provides a more thorough economic analysis. 

Figure 12 shows the break-even PV costs for the three utility service territories. Break-even PV 

cost in PG&E ranged from $2.86/W to $3.78/W. These prices are well below the $6/W average 

installed cost determined at the time of this report (see Section 2.4). Break-even PV costs will be 

lower for schools compared to other commercial buildings in California, partly because public 

schools are ineligible to take advantage of the 30% federal tax credit. Break-even costs for SCE 

are slightly higher, ranging from $4.05/W to $4.58/W, while SDG&E has the highest break-even 

cost of $6.58/W at a 10% penetration. 

Figure 12. Break-even PV cost for each utility

When evaluating PV systems under a PPA, it is important to look at the net effect on the school’s 

annual electricity expense. If the PV value is greater than the PPA price, then the school will 

realize a net savings on annual energy expenses. If the PV value is less than the PPA price, then 

the school will realize a net loss. The break-even PPA price (PPA price at which the school 

neither saves nor loses money) is shown in Figure 13. The highest break-even PPA price is seen 

at SDG&E, where prices exceed $0.20/kWh for PV penetrations of 10%–30%, with a peak 

above $0.30 at a 10% penetration. Break-even price is also above $0.20/kWh at SCE until a 30% 

penetration, after which prices level between $0.19 and $0.20. PG&E’s minimum break-even 

PPA price occurs at a 45% penetration with $0.146/kWh, after increasing to nearly $0.19/kWh

when approaching a 100% penetration. 
17 

Figure 13. Break-even PPA price for each utility

In addition to the break-even PPA price, it is also useful to understand how annual electricity 

expenses will be impacted at various PPA price levels. Figures 14, 15, and 16 show the annual 

bill savings (as a percentage) under various PPA prices and penetration levels for schools in each 

of the three utility service territories. A change in PG&E’s most economical rate, from A-10 to 

A-6, causes the elbow seen at the 45%–50% penetration level (see Figure 9). This chart shows 

that PPA prices of $0.15/kWh and below will result in a net savings to the school’s annual 

electricity bill under most penetration levels. PPA prices of $0.20/kWh and above will always 

result in a net increase in the school’s expenditures. 

Schools in the SCE service area will always realize a net savings with PPA prices of $0.15/kWh

and below. Very little change in annual electricity expenses will result under a PPA price of 

$0.20. Above that price, schools in the SCE service area will likely see an increase in annual 

electricity expenses. 

Schools in the SDG&E service area can realize an annual savings under any of the evaluated 

PPA prices with a 10% PV penetration. This is because of the DG-R rate. Switching to this rate 

results in notable savings even when a PV system is not installed. However, SDG&E offers the

DG-R rate only to buildings with at least 10% capacity penetration18

 

18 The DG-R rate requires at least a 10% capacity penetration rather than a 10% energy penetration. This analysis 

found that a 7.5% energy penetration is sufficient to provide 10% of peak annual load. Because the PV penetration 

resolution is limited to 5% increments, DG-R was chosen to become effective at a 10% energy penetration.

from an eligible distributed 

generation installation. When PV penetration is small (10%), and even when the PPA price is 

high, switching to the DG-R rate may cover the PPA price and yield enough savings. 
18 

Figure 14. Annual electricity bill savings under various PPA prices and penetration levels for 

school in the PG&E service territory 

Figure 15. Annual electricity bill savings under various PPA prices and penetration levels for 

school in the SCE service territory 
19 

Figure 16. Annual electricity bill savings under various PPA prices and penetration levels for 

school in the SDG&E service territory 

PV systems purchased under a GO bond require little or no upfront or recurring costs for the 

school. Figure 17 shows the potential annual savings that can be realized by the schools in each 

of the three utility service territories. Note that schools in the PG&E service area may actually 

realize a net revenue (even though the penetration is limited to 100%) by taking advantage of 

California’s net-metering rules.

19

 

19 This is because the net-metering rules allow on-peak generation to be compensated at retail electricity prices even 

if electricity is exported during the on-peak hours as long as there are no net exports (all hours considered) at the end 

of each year (CPUC 2010). PG&E’s A-6 rate has very high on-peak energy charges while having low-to-moderate 

prices during other hours, allowing for the PV system to benefit from this net-metering rule. 
20 

Figure 17. Annual electricity bill savings under the GO bond scenario
21 

4 Conclusion

PV system economics are highly dependent on the host building’s rate structure. System 

economics—under current net-metering rules—favor rates with high on-peak energy prices and 

low-to-moderate prices at other times. Rates with little or no demand charges are also favorable. 

This analysis found that there was no single best rate in any of the three utility service territories 

evaluated. Rather, the most economical rate depended on PV penetration. The rate that 

minimizes electricity expenses without PV was found to remain the rate of choice for low PV 

penetrations. For high PV penetration, rates with low demand charges and high on-peak energy 

prices became the most cost-effective option. 

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

schools. It is important to reiterate that the rate analysis applies to specific school load profiles 

(see Section 2.1) and is not intended to represent all schools in California. Results in case studies 

conducted for PG&E and SDG&E (see Appendix A and Appendix B) using actual school load 

profiles differ from those reached when using the simulated load profiles, showing that the 

results are sensitive to individual school load profiles. Recommendations for future studies 

include identifying the impacts of various school load variations, such as schools with summer or 

evening classes; assessing the impacts of critical-peak pricing; and evaluating the impacts of 

potential changes in net-metering rules. 
22 

References 

California Public Utilities Commission (CPUC). (2010). “Introduction to the Net Energy 

Metering Cost Effectiveness Evaluation.” CPUC energy division. 

California Solar Initiative (CSI). (2011). “California Solar Statistics.” 

http://www.californiasolarstatistics.ca.gov/. Accessed March 2011.

Clean Power Research. (2011). “SolarAnywhere.” http://www.cleanpower.com/SolarAnywhere.

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. 

Accessed March 2011.

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

CO: National Renewable Energy Laboratory. 

National Renewable Energy Laboratory (NREL). (2011). “System Advisor Model.” 

https://www.nrel.gov/analysis/sam/. Accessed April 2011. 

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. 

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. 

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.

U.S. Department of Energy (DOE). (2011). “Fuel Cell Power Model Case Study Data.” 

http://www.hydrogen.energy.gov/cf/fc_power_analysis_model_data.cfm. Accessed May 2, 2011. 
23 

Appendix A. Case Study: Berkeley High School

Introduction and Summary of Findings

Berkeley High School (BHS) in Berkeley, California, serves approximately 3,000 students and 

has a large campus that consists of several buildings, some of which have been identified as 

favorable for rooftop PV placement (denoted as A through D in Figure A-1). These areas total 

approximately 47,000 ft2—or enough to support 400 kW of PV capacity. The remaining roof 

space may support additional PV capacity. 

Figure A-1. Berkeley High School campus with locations favorable for rooftop PV identified (A–D)

Source: SunPower 2009

The aggregated annual electricity consumption for all BHS facilities exceeds 3 million kWh. The 

school qualifies for a total of six PG&E rates on the four meters that measure the school’s load. 

These rates were evaluated to determine likely conditions for maximizing value and savings in 

annual energy expenses. Figure A-2 illustrates the PV value under different penetration levels 

and rate structures. The evaluation found that rate E-19 is the optimal option for PV penetrations 

up to 35%. At higher penetrations, rate A-6 becomes and remains the most economical rate 

option. This is because rate A-6 consists of very high daytime energy rates, which makes it too 

expensive under lower PV penetrations but more attractive with higher PV penetrations (see 

Section 3.1). Figure A-3 shows how system costs impact simple payback under optimal utility 

rates. Payback periods are longest at a 35% PV penetration and shortest when penetration levels 

approach 100%. Figure A-4 shows the impacts of various PPA prices on annual electricity 

expenses. System costs below $0.20/kWh are necessary in order to realize a positive impact on 

the school’s annual energy costs. Figure A-5 shows the annual electricity bill savings under a 

general obligation bond scenario. Since little or no upfront or recurring costs are required of the 

school and annual savings are very high and exceed 100% for penetrations above 80%. This is 

possible under net-metering rules that are applicable at the time of this report (see Section 3.2). 
24 

Figure A-2. Net value of PV generation under various rate structures and penetration levels

Figure A-3. Payback period for Berkeley High School under various PV system costs and 

penetration levels
25 

Figure A-4. Annual electricity bill savings for Berkeley High School under various PPA prices and 

penetration levels

Figure A-5. Annual electricity bill savings for Berkeley High School under the GO bond option
26 

Data and Methodology

Load Data

Due to the size of BHS and its energy use, PG&E has four separate meters that measure 

electricity usage from the school’s various buildings and sections. Table A-1 shows the annual 

energy consumption of BHS during 2010, grouped by each of the four PG&E meters. 

Table A-1. 2010 Energy Consumption of Berkeley High School 

2010 Energy Use 

(kWh)

Meter 1 788,915

Meter 2 2,074,653

Meter 3 315,200

Meter 4 44,160

Total 3,222,928

Monthly billing and energy data were available for each of the four meters. For detailed 

analyses, however, hourly or sub-hourly data are preferred.

20

 

20 By using hourly or sub-hourly data, the impacts of TOU rates and demand charges on PV system economics can 

be determined. High resolution data also helps identify hours that the PV system is exporting energy to the grid, 

which may significantly impact system economics depending on the net-metering policy in place. 

 Of the four meters, only meter #2 

met the threshold for PG&E to make sub-hourly measurements. In order to conduct analyses, it 

was assumed that the sub-hourly measurements for meter #2, recorded in 30-minute intervals,

reflect the hourly pattern for the entire campus. The data from meter #2 was scaled to match the 

annual load from all four BHS meters. Figure A-6 illustrates that, on a monthly basis, meter #2 

represents the total BHS campus consumption pattern adequately. The scaled sub-hourly data 

(light blue) has a seasonal variation similar to that of the actual monthly measured energy data 

for the entire campus (dark blue). 
27 

Figure A-6. Berkeley High School electricity load data during 2010—measured monthly energy 

data is compared with measured sub-hourly data and scaled sub-hourly data

Rate Data

BHS qualifies for a total of six PG&E utility rates. These rates were obtained from the online 

URDB on the OpenEI platform and verified them with the PG&E tariff sheets to ensure 

accuracy. PG&E offers various commercial rate structures for different load sizes and types. 

Smaller loads have more rate choices than larger loads since smaller users may optionally be on 

rates designed and made mandatory for larger loads. Larger facilities with solar installations may 

also be on rates designed for smaller facilities. At BHS, each of the four separate meters is 

treated independently and can utilize any of the eligible rate structures. Figure A-7 illustrates the 

eligible facility demand range for each of the six utility rates. Except for meter #2, all meters 

qualified for all six rates. Due to its monthly peak demand of approximately 400 kW, meter #2 

qualified only for rates E-19, A-6, and A-10 TOU. 
28 

Figure A-7. Applicability of PG&E rates for commercial facilities up to 1,000 kW

Table A-2 categorizes the six PG&E rates used for this analysis. BHS did not qualify for any 

tiered or flat rates but did qualify for a good representation of seasonal rates, TOU rates, and 

demand charges. Three of the six rates fell into two or more categories.

Table A-2. Summary of Applicable Categories and Price Levels for the Rates Evaluated

Rate 

Name Flat

Seasonal 

Flat TOU Demand Tiered Relative Price Level

A-1 Moderate-to-high energy prices

A-1 TOU

High energy prices during summer 

afternoons; moderate prices otherwise

A-10

Moderate energy prices; high demand 

charges

A-10 

TOU

Low off-peak energy prices; moderate 

peak energy prices; high demand 

charges

A-6

Very high energy prices during summer 

afternoons; low-to-moderate prices 

otherwise

E-19

Moderate energy prices during summer 

afternoons; lower energy prices 

otherwise; very high demand charges 

during summer afternoons; moderate 

demand charges otherwise
29 

Solar Data

The PV production data used for BHS was simulated using hourly meteorological data from the 

SolarAnywhere® database (Clean Power Research 2011). The SolarAnywhere dataset is similar 

to the National Solar Radiation Database (Wilcox 2007); however, it contains more recent data.

21

System Advisor Model and Calculations

Hourly meteorological data was obtained for the year 2010 from a 10 km-by-10 km grid cell that 

contained the BHS campus. The meteorological dataset was used as an input for SAM, which 

simulated hourly PV production for use in the financial calculations. 

Using SAM, PV performance data was generated using the meteorological data obtained from 

SolarAnywhere and the following assumptions: 

• 15-degree tilt

• South facing (180-degree azimuth) 

• A de-rate factor of 85% 

• An annual degradation of 0.5%. 

In addition to the meteorological data, hourly building load data22

Conclusion for Berkeley High School Case Study

and utility rate data were given 

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

ranging from 0% (no PV system) to 100% (PV system produces 100% of the school’s annual 

electrical energy needs) in increments of 5%. The value of the PV system’s generation under 

various penetration levels and rate structures was evaluated by comparing the school’s annual 

electricity costs both with and without the PV system. Any resulting difference was attributed to 

the PV system. The combination of scenarios required 240 unique simulations, from which the 

model determined the economically optimal PV penetration and rate structure.

Under the conditions of this analysis, two rates maximize PV value at BHS. Rate E-19 

maximizes savings for lower PV penetrations (35% and under), and rate A-6 maximizes savings 

for penetrations above 35%. This assessment assumes that there are no significant changes in the 

school’s load profile. Changes in the size or shape of the school’s electricity usage pattern will 

likely impact the results. 

 

21 The most recent National Solar Radiation Database update contains data through 2005. The SolarAnywhere 

dataset is continuously updated and contains data through the present time. Since the BHS load data is from 2010, it 

is important to use solar and meteorological data from the same time period to accurately capture TOU and demand 

charge impacts. 

22 Although sub-hourly (30-minute) resolution load data were obtained from PG&E, this data was converted to 

hourly resolution because SAM is currently an hourly performance model. The meteorological data obtained was 

also limited to hourly resolution.
30 

Appendix B. Case Study: Lewis Middle School

Introduction and Summary of Findings

Lewis Middle School (LMS), located in San Diego, California, is a moderately sized school with 

an existing 200 kW PV installation. The installation provided approximately 98% of the school’s 

electricity consumption in 2010 (98% penetration). Figure B-1 shows the LMS campus with the 

PV installation (dark rectangles) covering a significant portion of the available rooftop area. 

After obtaining detailed, 15-minute resolution data for the campus electricity consumption and 

PV generation, the data was scaled to evaluate a range of PV penetration scenarios. The results 

from this case study are intended to inform other similar schools that are exploring their options 

for solar generation. Since the LMS PV installation and financing is already complete, the 

options and recommendations given are for reference only. 

LMS installed the rooftop PV system as part of a re-roofing effort, hence the high utilization of 

available rooftop area. The added value of combining the re-roofing and PV installation was not 

taken into consideration in this analysis and may significantly increase the overall economics for 

the school.23 

Figure B-1. Lewis Middle School with existing rooftop PV installation

Source: Google Maps, 2011

With an aggregated annual electricity consumption exceeding 300,000 kWh, LMS qualifies for 

two SDG&E rates. These rates were evaluated to determine optimal conditions for maximizing 

 

23 LMS re-roofed its buildings using new roofing material with flexible solar panels bonded to it. The school was 

guaranteed maintenance-free roofs for 20 years. 
31 

value and savings in annual electricity expenses. Figure B-2 illustrates the PV value under 

different penetration levels and rate structures. The evaluation found that rate AL-TOU is the 

optimal option for PV penetrations up to 10%. At higher penetrations, rate DG-R becomes and 

remains the most economical rate option. Rates A and A-6 TOU, denoted by the dotted lines, are 

not applicable to be used at LMS and were included in the chart for comparison only. Rate DG-R 

is available only to buildings that have an eligible distributed generation technology with a 

capacity that is 10% or more of their peak annual load.24 

Figure B-2. Net value of PV generation under various rate structures and penetration levels

Figure B-3 shows how system costs impact simple payback under optimal utility rates. Payback 

periods are longest when approaching 100% PV penetration and shortest when penetration is at 

10%. The abrupt dip in payback period at the 10% mark is due to the effect of rate DG-R 

becoming available for use after a 10% penetration.

25

 

24 A 7.5% PV penetration is sufficient to provide 10% of the LMS peak annual load. Because the PV penetration 

resolution is limited to 5% increments, the DG-R rate was chosen to become effective at a 10% PV penetration.

 Figure B-4 shows the impacts of various 

PPA prices on annual electricity expenses. With all PPA prices evaluated, LMS will always 

realize a net savings on annual electricity expenses when PV penetration is at 10% due to the 

DG-R rate. Below a PPA price of $0.15/kWh, LMS will always realize a net savings on annual 

electricity expenses, regardless of penetration level. Figure B-5 shows the annual electricity bill 

savings under a GO bond scenario. Since little or no upfront or recurring costs are required of the 

school, annual savings are very high and exceed 90% for penetrations approaching 100%. 

25 Ibid.
32 

Figure B-3. Payback period for LMS under various PV system costs and penetration levels

Figure B-4. Annual electricity bill savings for LMS under various PPA prices and penetration 

levels
33 

Figure B-5. Annual electricity bill savings for LMS under the GO bond option

Data and Methodology

Load and Solar Data

SDG&E measures and records LMS’s energy data, including actual building energy use and PV 

production, in 15-minute increments. The detailed records eliminated the need to simulate data 

for this case study. Figure B-6 illustrates the daily school energy consumption and PV generation 

data for the year 2010. The PV system met approximately 98% of the LMS load in 2010. The 

data were entered into SAM in order to determine the impact of available rate structures on the 

economics of the PV system. 
34 

Figure B-6. 2010 daily load profile and PV generation for Lewis Middle School

Rate Data

LMS qualifies for two SDG&E utility rates. A total of four rates were evaluated for comparison, 

but used only the LMS-eligible rates to calculate bill savings and payback periods. All rates were 

obtained from the online URDB and verified with the SDG&E tariff sheets to ensure accuracy. 

Figure B-7 illustrates the eligibility range for each of the four utility rates. Since LMS had a peak 

annual load of 130 kW, the only rates applicable are AL-TOU and DG-R. 

Figure B-7. Applicability of SDG&E rates for commercial facilities up to 1,000 kW
35 

Table B-2 summarizes the various categories and features of the four SDG&E rates used for this 

analysis. There were no tiered or flat rates in the four SDG&E rates evaluated. 

Table B-2. Summary of Applicable Categories and Price Levels for the Rates Evaluated 

 Rate 

Name Flat 

Seasonal 

Flat TOU Demand Tiered Relative Price Level 

A Very high energy prices 

A6-TOU 

Low energy prices; high demand 

charges 

DG-R 

High energy prices; low demand 

charges 

AL-TOU 

Intermediate energy prices; high 

demand charges 

System Advisor Model and Calculations 

Hourly PV generation data, hourly building load data,26 and utility rate data27 were entered into 

SAM. The PV generation data were analyzed for various penetration levels ranging from 0% (no 

PV system) to 100% (PV system produces 100% of school’s annual electrical energy 

consumption) in increments of 5%. The value of the PV system’s generation was evaluated 

under various penetration levels and rate structures by comparing the school’s annual electricity 

costs both with and without the PV system. Any resulting difference was attributed to the PV 

system. 

Conclusion for Lewis Middle School Case Study 

LMS boasts a PV system that meets nearly 100% of the school’s annual electricity consumption. 

Under a PPA, results show that large PV penetrations are ideal for PPA prices at or below 

$0.10/kWh, where increasing PV penetrations yield increased bill savings. For PPA prices 

$0.15/kWh and above, the optimal penetration level is 10%, or the lowest penetration level for 

rate DG-R to be used. A 10% penetration is also the point that minimizes the payback period for 

systems purchased up front. This case study did not look at the California Solar Initiative 

incentives, which were not available at the time of this report but may have been available when 

the PV system was installed at LMS. Including the incentives will alter these results and increase 

PV value. The LMS PV system was also installed as part of a re-roofing effort, which may also 

increase the overall economics for the school. 

 

26 Although sub-hourly (15-minute) resolution load data were obtained from SDG&E, the data were converted to 

hourly resolution because SAM is currently an hourly performance model. 

27 SAM communicates directly with OpenEI’s online URDB to obtain the latest rate information available on 

OpenEI.