A national laboratory of the U.S. Department of Energy

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National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Solar San Diego: The Impact 

of Binomial Rate Structures 

on Real PV Systems 

Preprint 

Otto VanGeet and Elizabeth Brown 

National Renewable Energy Laboratory 

Tom Blair 

City of San Diego 

Andrew McAllister 

Center for Sustainable Energy California 

Presented at SOLAR 2008 - American Solar Energy Society (ASES) 

San Diego, California 

May 3–8, 2008 

Conference Paper 

NREL/CP-670-42923 

May 2008 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
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Solar San Diego: The Impact of Binomial Rate Structures on Real PV Systems 

Otto VanGeet 

Elizabeth Brown 

1617 Cole Blvd, Golden, Co 80401 

otto_vangeet@nrel.gov 

Elizabeth_brown@nrel.gov 

Tom Blair 

9601 Ridgehaven Ct, Ste 210 

San Diego, CA 92123 

tblair@sandiego.gov

Andrew McAllister 

Center for Sustainable Energy California 

8690 Balboa Avenue, Ste 100 

San Diego, CA 92123 

Andrew.mcallister@energycenter.org 

ABSTRACT

There is confusion in the marketplace regarding the impact 

of solar photovoltaics (PV) on the user’s actual electricity 

bill under California Net Energy Metering, particularly with 

binomial tariffs (those that include both demand and energy 

charges) and time-of-use (TOU) rate structures. The City of 

San Diego has extensive real-time electrical metering on 

most of its buildings and PV systems, with interval data for 

overall consumption and PV electrical production available 

for multiple years. 

This paper uses 2007 PV-system data from two city 

facilities to illustrate the impacts of binomial rate designs. 

The analysis will determine the energy and demand savings 

that the PV systems are achieving relative to the absence of 

systems. A financial analysis of PV-system performance 

under various rates structures is presented. 

The data revealed that actual demand and energy use 

benefits of bionomial tariffs increase in summer months, 

when solar resources allow for maximized electricity 

production. In a binomial tariff system, varying on- and 

semi-peak times can result in approximately $1,100 change 

in demand charges per month over not having a PV system 

in place, an approximate 30% cost savings. The PV systems 

are also shown to have a 30%-50% reduction in facility 

energy charges in 2007. 

Future work will include combining demand and electricity 

charges and increasing the breadth of rate structures tested, 

including the impacts of non-coincident demand charges. 

1. INTRODUCTION

Solar photovoltaic systems are touted as beneficial in certain 

geographic areas because of both their financial and external 

benefits (1). However, recent evidence in California 

suggests that even with all financial benefits incorporated, 

the high capital cost of equipment and installation is 

prohibitive to project completion (2). PV-system installation 

costs, productivity, and cost-effectiveness are highly 

variable based on geography, available incentives, and 

actual maintenance practices. Indeed, the value of 

externalities may be the primary economic driver for many 

systems, and that is determined specific to the project in the 

absence of carbon and other environmental consideration 

markets. 

The City of San Diego has been a regional leader in PV 

implementation on its facilities, with more than 1 MW of 

PV installed since 2005. In San Diego, there is increasing 

emphasis on solar power as a an important source of inregion generation as well as for its potential to improve 

reliability, hedge future rate increases, decrease regional 

carbon emissions, and diversify the resource base. The city 

has invested in multiple solar PV systems and measurement 

equipment to measure PV-system effectiveness at both a 

cost and energy reliability level. Understanding the impacts 

of those systems on the City’s cost of energy, as well as 

greater impacts on the grid, is important to determining the 

viability of future investments. 

Under net energy metering, the electric customer avoids 

consumption from the grid with each kWh produced on-site, 

thus is implicitly credited with the retail value of that 

energy. Current literature suggests that the viability of solar 

electricity production depends largely on the rate structure 

that it is associated with (3). A detailed understanding of 

each rate - combined with load data from particular facilities 

- provides a solid basis for analysis that is tailored to enable 

sound energy management decision making by the 

1
customer. The wealth of data collected on the San Diego 

systems allows for analysis of the relative impact of 

different rates on the specific solar-PV systems and their 

associated electricity loads - vital information for City 

officials going forward. 

This paper uses the first year of data on the systems to 

identify and determine how rates impact system costeffectiveness. Note that the systems both came into use in 

February 2007, so the savings will be less than a full year. 

The analysis reflects the nature of binomial rates, and so it is 

divided into two portions: the peak demand charge, a charge 

that is applied to the peak monthly demand of the load; and 

the energy charge, based on consumption. 

Future analyses will include analyses of the value of 

exported energy (momentary net over production), a broader 

array of rate possibilities, an incorporation of the demand 

and energy charges’ combined impact on system costeffectiveness and an estimation of the impacts of other 

charges within rates (specifically non-coincident rate 

charges). 

2. METHODOLOGY

This analysis uses collected 15-minute interval data from 

two solar photovoltaic systems in San Diego to estimate the 

impacts of different rate structures on the system. Both 

systems are sloped 15 degrees and have the following 

characteristics (more information on the systems can be 

found at: http://www.sandiego.gov/environmentalservices/energy/programsprojects/saving/renewable.shtml): 

• System A: a 495 kW AC system atop water 

treatment reservoirs that provides power for the 

water testing lab building and pumping station. 

This system faces 45 degrees west of south. 

• System B: a 450 kW AC system also atop water 

treatment reservoirs that provides power for a 

drinking water pumping and pressurization system. 

This system faces south. 

The rate analysis is completed in two parts. First, the impact 

of different peak and off-peak timing on the demand costs 

of the two systems is illustrated by calculating the kilowatt 

peak demand in the on-peak and semi-peak time segments. 

Second, the real solar production data is shown under 

different rate structures to illustrate the impact of different 

rates to illustrate the impacts on energy usage costs. 

Table 1 summarizes the different structures applied to real 

solar production. Because the systems have different load 

profiles for the produced electricity, the difference between 

the demand savings will vary. The benefit of analyzing both 

buildings is to show the variation and the broad impacts of 

rate structures. 

TABLE 1: ON-PEAK AND SEMI-PEAK TIME 

AND DEMAND RATES USED IN ANALYSIS 

Rate Season On-Peak (pm) Semi-Peak 

Summer 11 am-6 pm 

6-11 am, 

6-10 pm 

0 Winter 5-8 

6 am-5 pm 

8-10 pm 

Summer 12-3 

6 am-12 pm 

3-10 pm 

1 Winter 5-8 

6 am-5 pm 

8-10 pm 

Summer 12- 4 

6 am-12 pm 

4-10 pm 

2 Winter 5-8 

6 am-5 pm 

8-10 pm 

Summer 1-3 

6 am -1 pm 

3-10 pm 

3 Winter 5-8 

6 am-5 pm 

8-10 pm 

Summer 1-4 

6 am-1pm 

4-10 pm 

4 Winter 5-8 

6 am-5 pm 

9-10 pm 

Notes: The rates 1-4 are based approximately on 

options B-E of the PAT-1 Rate Schedule available 

through SDG&E respectively. Rate 0 peak times are 

based on PAT-1-Option A (and AL-TOU) are not 

included in the demand section of this analysis due to 

lack of non-coincident charge data. Summer demand 

charge: $5.49/kW and Winter demand charge: 

$4.94/kW. 

2.1 Demand Cost Impacts of Different Rates

The demand cost analysis is completed for rates 1-4 in 

Table 1. Rate zero is omitted for this analysis because the 

sample rate that it is based on has a demand cost complexity 

that is beyond the scope of this paper. The peak demand for 

each time period described is applied to the actual output 

data from both city PV systems to identify the peak energy 

import time in a billing period. The baseline scenario is 

calculated from the actual system tracking and utility import 

meter data and assumes that building load will be the same 

in the absence of the PV system. The demand charges are 

calculated by multiplying the peak demand by the actual 

demand charges associated with peak charges on the utility 

bills for the systems in 2007. Those charges are as follows: 

• System A: summer $5.65/kW, winter $5.51/kW 

• System B: summer $5.59/kW, winter $5.46/kW. 

2
2.2 Rate Impacts on Energy Consumption Charges for 

Building with a PV System

In the second analysis, two types of energy charge rates are 

analyzed for their impacts on PV systems. 

• Flat Rate. Based on average $/kWh for energy charges in 

2007, this hypothetical rate is included to illustrate nontime-of-use rates impacts on facilities with PV systems. 

This value is $0.10/kWh for both systems. 

• Energy Charges from Binomial Tariffs. The on- and semi 

peak times for these rates are described in Table 1. The 

energy charge rates for all these time of use options are 

the same: 

o On-Peak: summer $0.21/kWh, winter $0.13/kWh 

o Semi-Peak: summer $0.15/kWh, winter $0.07/kW 

Analysis of the PV-system data for both facilities intends to 

illustrate the energy charge value of the systems over the 

course of activity in 2007. For each type of tariff, import, 

production, and export data are used to determine: 

• Avoided Consumption. This calculation is estimated by 

subtracting the exported system energy from the overall 

system production. 

• Avoided Costs. This is the value of the energy produced 

by the PV system as avoided energy costs. This 

calculation involves aggregating system data seasonally 

and according to peak and multiplying the kWh produced 

in each category by the appropriate charge for the rate 

type. 

3. RESULTS AND DISCUSSION

3.1 Demand Charge Variation Impacts

Tables 2 and 3 present the monthly on-peak demand savings 

in 2007 by month for each of the PV systems using the 

actual rate structure for the systems. System A has a 7%-

79% kW reduction with an average of a 35% reduction. 

System B has a larger range of kW reductions at 0%-95% 

kW and an average of 46%. For both systems, as expected, 

increased resource intensity in the summer months increases 

PV-system impact on peak-demand reduction. Despite the 

systems size and load differences, both systems resulted in 

demand reduction savings in the thousands of dollars for 

2007 relative to a non-existent system. 

Tables 4 and 5 illustrate the impacts of various rate 

structures on the demand impacts for each system including 

both on-peak and semi-peak charges. The rates analyzed are 

described in the methodology. As expected, the monthly 

demand savings are greatly impacted by the available 

resources and are larger in the summer months. Although 

the demand savings vary widely, the average monthly onpeak savings is about $1,100 for both systems. For System 

A, savings represents an approximate 30%-40% reduction in 

on-peak demand charges over the course of the year. For 

System B, that means a 2007 on-peak demand savings of 

approximately 30%. (Note that demand charges vary 

widely.) The shaded column in the graphic represents the 

approximation of the actual demand rate structure and 

indicates that the on- and semi-peak times are optimized for 

the peak demand of these system loads. 

TABLE 2: “SYSTEM A” PV-SYSTEM 

ESTIMATED DEMAND SAVINGS

On-Peak 

Demand (kW) 

Billing 

Cycle 

End 

Date 

W/O 

PV W/PV 

Savings 

(kW) 

On-Peak 

Demand 

Cost Savings 

($) 

03/15/07 290 244 47 $258 

04/15/07 267 215 52 $285 

05/14/07 373 212 160 $884 

06/13/07 330 155 175 $963 

07/15/07 394 83 311 $1,715 

08/15/07 417 310 107 $606 

09/13/07 432 215 217 $1,225 

10/14/07 360 271 89 $501 

11/12/07 284 265 20 $111 

12/13/07 264 264 0 $0 

Estimated Peak Demand $ Savings: $6,550 

Notes: a) Billing cycles begin with 3/15 because the 

system came on-line in February 2007 b) Demand with 

PV is calculated from building meter systems, not 

utility bills. All values are matched to utility bill 

charges except billing cycles ending 4/15 and 10/14. 

These were billed higher than the calculated meter 

data, so the estimates are conservative. c) The demand 

charges are calculated using the on-peak demand 

charge of $5.51/kW for 3/15 - 7/15 and $5.65/kW for 

8/15 - 12/13. This is a conservative peak charge 

estimate because a portion of the bill ending 7/15 was 

charged to the higher rate by the utility. 

3
TABLE 3: “SYSTEM B” PV-SYSTEM 

ESTIMATED SAVINGS

On-Peak 

Demand (kW) 

Billing 

Cycle 

End 

Date 

W/O 

PV W/ PV 

Savings 

(kW) 

On-Peak 

Demand 

Cost 

Savings ($) 

03/15/07 536 536 0 $0 

04/15/07 436 328 108 $590 

05/14/07 333 320 13 $72 

06/13/07 334 76 258 $1,409 

07/15/07 432 20 412 $2,250 

08/15/07 333 180 153 $856 

09/13/07 660 280 380 $2,124 

10/14/07 434 168 266 $1,489 

11/12/07 326 324 2 $9 

Estimated Peak Demand Savings: $8,800 

Notes: a) Demand with PV is calculated from building 

meter systems, not utility bills. B)All values are 

matched to utility bill charges except billing cycles 

ending 7/15 c) The demand charges are calculated using 

the on peak demand charge of $5.46/kW for 3/15 - 7/15 

and $5.59/kW for 8/15 - 11/12. 

TABLE 4: “SYSTEM A” PV-SYSTEM RELATED 

DEMAND SAVINGS FOR VARYING PEAK AND 

SEMI-PEAK TIME RATE STRUCTURES (2007)

Bill End Rate (See Table 1 for description) 

Date 1 2 3 4 

3/15/07 592.81 592.81 780.57 592.81 

4/15/07 501.60 501.60 660.81 501.60 

5/14/07 1,470.55 1,439.92 1,510.94 1,510.94 

6/13/07 1,290.59 1,289.61 1,477.36 1,477.36 

7/15/07 1,756.49 1,751.55 2,212.37 2,106.96 

8/15/07 1,046.96 1,020.28 1,132.92 1,132.92 

9/13/07 1,660.83 1,380.84 1,746.48 1,380.84 

10/14/07 845.06 845.06 933.98 933.98 

11/12/07 378.62 378.62 626.30 378.62 

12/13/07 249.96 249.96 249.96 249.96 

Total 

2007 

PVsystem 

Demand 

Savings 9,793 9,450 11,332 10,266 

Note: Rate specifics can be found in Table 1. Demand 

Charges: On-Peak: $5.49/kW, Semi-peak $4.94/kW. 

Numbers differ from Table 2 because this table 

includes semi-peak demand savings 

TABLE 5: “BUILDING B” PV-SYSTEM 

RELATED DEMAND SAVINGS FOR VARYING 

PEAK AND SEMI-PEAK TIME RATE 

STRUCTURES (2007)

Bill End Rate

Date 1 2 3 4

3/15/07 0.00 0.00 0.00 0.00

4/15/07 1,086.80 1,086.80 1,086.80 1,086.80

5/14/07 110.01 110.01 111.99 111.99

6/13/07 1,461.42 1,324.18 1,437.71 1,330.11

7/15/07 2,267.81 1,301.57 2,279.66 1,313.42

8/15/07 848.97 848.97 848.97 848.97

9/13/07 1,355.81 1,355.81 2,129.67 1,778.31

10/14/07 1,482.30 1,478.34 1,482.30 1,482.30

11/12/07 19.98 19.98 26.57 19.98

12/13/07 359.63 359.63 359.63 359.63

Total 

2007 

PVsystem 

Demand 

Savings 8,993 7,885 9,763 8,332

Note: Rate specifics can be found in Table 1. Demand 

Charges: On-Peak:$5.49/kW, Semi-peak $4.94/kW. 

Numbers differ from Table 3 because this table 

includes semi-peak demand savings

3.2 Energy Use Consumption Charge Impacts of Rate 

Structures

Graphically, Figures 1 and 2 illustrate the general impact of 

time of use tariff systems as applied to a facility with a PV 

system. In the case of Figures 1 and 2, the building load and 

PV production data are real data taken from the 2007 dataset 

for System A in San Diego, California (the rate applied is 

Rate 3 as described in Table 1). You can see that the 

demand of the facility peaks at midday, as does solar 

production in both seasons. The winter load and production 

are both reduced relative to the summer months due 

primarily to reduced heating and cooling loads and 

insolation, respectively. 

Both figures illustrate the benefits of time of use rates for 

solar production facilities in this case. Because the PV 

system produces the most electricity at summer peak and 

semi-peak times for the grid system, the electricity produced 

there is of high value. In the case of Rate 3, the avoided 

electricity costs are substantial relative to the total daily 

costs of energy without the system. 

Tables 6 and 7 illustrate the impacts of different time-of-use 

energy charge rates, using the rate schedules from Table 1. 

These illustrate the varying impacts of different peak timing 

scenarios on the PV systems. The impact of the time-of-use 

4
TABLE 6. “SYSTEM A” 2007 CONTRIBUTION 

TO FACILITY LOAD (KWH)

Peak Time Summer Winter Total 

Rate 0 

On-Peak 207,623 9,352 216,975 

Semi-Peak 68,093 202,496 270,590 

Off Peak 97,097 69,633 166,730 

Rate 1 

On-Peak 98,276 9,352 107,628 

Semi-Peak 177,441 202,496 379,937 

Off Peak 97,097 69,633 166,730 

Rate 2 

On-Peak 129,738 9,352 139,090 

Semi-Peak 145,978 202,496 348,474 

Off Peak 97,097 69,633 166,730 

Rate 3 

On-Peak 65,829 9,352 75,181 

Semi-Peak 209,887 202,496 412,384 

Off Peak 97,097 69,633 166,730 

Rate 4 

On-Peak 97,292 9,352 106,644 

Semi-Peak 178,425 202,496 380,921 

Off Peak 97,097 69,633 166,730 

Flat Rate 

Total 372,814 281,481 654,295 

TABLE 7. “SYSTEM B” 2007 CONTRIBUTION 

TO FACILITY LOAD (KWH)

Peak Time Summer Winter Total 

Rate 0 

On-Peak 133,555 4,693 138,247 

Semi-Peak 54,347 96,612 150,959 

Off Peak 58,551 29,610 88,161 

Rate 1 

On-Peak 62,539 4,693 67,231 

Semi-Peak 125,363 96,612 221,975 

Off Peak 58,551 29,610 88,161 

Rate 2 

On-Peak 82,423 4,693 87,116 

Semi-Peak 105,479 96,612 202,090 

Off Peak 58,551 29,610 88,161 

Rate 3 

On-Peak 41,636 4,693 46,329 

Semi-Peak 146,266 96,612 242,877 

Off Peak 58,551 29,610 88,161 

Rate 4 

On-Peak 61,521 4,693 66,214 

Semi-Peak 126,381 96,612 222,993 

Off Peak 58,551 29,610 88,161 

Flat Rate 

Total 246,453 130,914 377,367 

peak definitions can be very large for these actual systems. 

Depending on the timing, the summer peak production - the 

most financially valuable production - can vary from 65-207 

kWh. The flat production is the total amount of electricity 

produced by each system in 2007. 

Tables 8 and 9 illustrate the financial impacts of the 

different structures. The flat rate results in the least avoided 

energy cost, reflecting the benefit of solar under time-of-use 

tariffs because the production coincides with facility peaks, 

as is a typical touted benefit of solar PV systems. 

Preliminary sensitivity testing, however, illustrates that 

small variations in the flat rate (from 10-12 cents, for 

example) quickly increase the competitiveness of this 

schedule among the time-of-use schedules. Among the timeof-use energy charges, Rate 0 (with the 11 am to 6 pm onpeak time) proved the most beneficial to solar systems. The 

lease effective rates for maximizing system impact are those 

that have summer peaks that start after noon. Regardless of 

the rates, average monthly savings of approximately $5,000 

for System A and $3,000 for System B reduced the actual 

monthly energy charges for the facilities from 30%-50% in 

2007. Note that monthly charges vary widely, and the 

largest savings are when the PV systems are most 

productive in the summer months. 

5
TABLE 8. “SYSTEM A” 2007 AVOIDED 

ENERGY PURCHASES ($)

Rate Summer Winter Total 

Rate 0 

On-Peak 44,315 1,229 45,544 

Semi-Peak 10,558 14,715 25,273 

Off Peak 12,652 5,030 17,681 

Total 88,499 

Rate 1 

On-Peak 20,976 1,229 22,205 

Semi-Peak 27,512 14,715 42,228 

Off Peak 12,652 5,030 17,681 

Total 82,114 

Rate 2 

On-Peak 27,691 1,229 28,921 

Semi-Peak 22,634 14,715 37,349 

Off Peak 12,652 5,030 17,681 

Total 83,951 

Rate 3 

On-Peak 14,051 1,229 15,280 

Semi-Peak 32,543 14,715 47,258 

Off Peak 12,652 5,030 17,681 

Total 80,220 

Rate 4 

On-Peak 20,766 1,229 21,995 

Semi-Peak 27,665 14,715 42,380 

Off Peak 12,652 5,030 17,681 

Total 82,057 

Flat Rate 

Total 37,281 28,148 65,429 

Note: For all rate schedules, the Summer on peak 

electricity charge for electricity is $0.21/kWh and 

semi peak is $0.13/kWh. The winter charges are 

$0.15/kWh and $0.07/kWh respectively. The flat 

rate charge is $0.10/kWh.

TABLE 9. “SYSTEM B” 2007 AVOIDED 

ENERGY PURCHASES ($)

Rate Summer Winter Total 

Rate 0 

On-Peak 28,506 617 29,123 

Semi-Peak 8,427 7,021 15,447 

Off Peak 7,629 2,139 9,768 

Total 54,338 

Rate 1 

On-Peak 13,348 617 13,965 

Semi-Peak 19,438 7,021 26,458 

Off Peak 7,629 2,139 9,768 

Total 50,191 

Rate 2 

On-Peak 17,592 617 18,209 

Semi-Peak 16,354 7,021 23,375 

Off Peak 7,629 2,139 9,768 

Total 51,352 

Rate 3 

On-Peak 8,887 617 9,504 

Semi-Peak 22,678 7,021 29,699 

Off Peak 7,629 2,139 9,768 

Total 48,971 

Rate 4 

On-Peak 13,131 617 13,748 

Semi-Peak 19,595 7,021 26,616 

Off Peak 7,629 2,139 9,768 

Total 50,132 

Flat Rate 

Total 24,654 13,091 37,737 

Note: For all rate schedules, the Summer on peak 

electricity charge for electricity is $0.21/kWh and 

semi peak is $0.13/kWh. The winter charges are 

$0.15/kWh and $0.07/kWh respectively. The flat 

rate charge is $0.10/kWh. 

4 SUMMARY AND NEXT STEPS

The analysis completed in this report uses actual 2007 PVsystem data from two facilities in San Diego California to 

illustrate the impacts of different rate structures. The data 

show that the demand savings for the 10 month period for 

which data was collected (from the February date that the 

systems came online through the end of 2007) could range 

from about $9,800-11,300 for System A and about $8,300-

9,800 for System B, depending on the rate structure. 

In addition to demand charges, avoided energy costs present 

a larger area of impact on the economics of a solar PV 

system. For System A, the savings amounted to between 

$65,000 and $88,000 in 2007; and for System B, the range 

was between $37,000 and $55,000 for the year. Both 

systems came online in February 2007, so the savings is 

6
over 10 months. Without the PV systems in place, the 5 REFERENCES

energy charges would have been 30%-50% higher than 

those in the 2007 actual bills. (1) Smeloff, 2005. Quantifying the Benefits of Solar 

Power in California. Prepared for the Vote Solar 

Initiative. URL: 

http://www.votesolar.org/tools_QuantifyingSolar's

Benefits.pdf) 

In summary, this analysis shows that rate structures, 

particularly those with time-of-use energy charges, have an 

impact on the overall economics of a PV system, and that 

the impact may be large. Demand charges can also vary 

widely depending on when the peak time is set. 

(2) Borenstein, 2008. The Market Value and Cost of 

Solar Photovoltaic Electricity Production. Prepared 

for the Center for the Study of Energy Markets at 

the University of California Energy Institute. URL: 

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

This analysis is a first step in a multi-analyses process that 

will estimate the financial impacts of many different tariff 

types on actual systems. Next steps in the analysis include 

combining the impacts of the demand charge and energy 

charges, the impact of exported energy from the PV-system 

on economics, as well as expanding the variety and 

increasing the complexity of tariffs, such as those with noncoincident demand charges applied. 

(3) Wiser, Mills, Barbose, and Golove, 2007 The 

Impact of Retail Rate Structures on the Economics 

of Commercial Photovoltaic Systems in California. 

Lawrence Berkeley National Laboratory. URL: 

http://repositories.cdlib.org/cgi/viewcontent.cgi?art

icle=5510&context=lbnl 

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Imported from 

utility: 13 

c/kwh

PV meeting 

semi-peak 

load: 15 c/kwh 

avoided

13:45

14:30

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On Peak

Exported

On-Peak 

Avoided:

21 c/kwh

Figure 1: Sample Summer Weekday Profile with Rate 3 Overlay 

7
Figure 2: Sample Winter Weekday Profile with Rate 3 Overlay 

8

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11:30

12:1513:0013:4514:30

15:15

16:0016:4517:3018:15

19:00

19:45

20:3021:1522:00

22:45

23:30

Time

15

7:00

7:45

8:309:15

10:0010:4511:30

12:1513:00

13:45

14:30

15:15

16:0016:45

17:30

Time

Semi-Peak

5

20:3021:15

22:0022:45

Semi-Peak

Imported from 

utility: 7 c/kwh

PV meeting

semi-peak 

load: 7 c/kwh 

avoided

30

18:15

19:0019:45

2

On Peak

On-Peak 

Avoided:

13 c/kwh

Exported
F1146-E(12/2004) 

REPORT DOCUMENTATION PAGE Form Approved 

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PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION.

1. REPORT DATE (DD-MM-YYYY)

May 2008 

2. REPORT TYPE

Conference Paper 

3. DATES COVERED (From - To)

 

5a. CONTRACT NUMBER

DE-AC36-99-GO10337 

5b. GRANT NUMBER

4. TITLE AND SUBTITLE

Solar San Diego: The Impact of Binomial Rate Structures on Real 

PV Systems; Preprint 

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

NREL/CP-670-42923 

5e. TASK NUMBER

6700.1040 

6. AUTHOR(S)

E. Brown, O. VanGeet, T. Blair, and A. McAllister 

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401-3393 

8. PERFORMING ORGANIZATION 

REPORT NUMBER

NREL/CP-670-42923 

10. SPONSOR/MONITOR'S ACRONYM(S)

NREL 

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13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

There is confusion in the marketplace regarding the impact of solar photovoltaics (PV) on the user’s actual electricity 

bill under California Net Energy Metering, particularly with binomial tariffs (those that include both demand and 

energy charges) and time-of-use (TOU) rate structures. The City of San Diego has extensive real-time electrical 

metering on most of its buildings and PV systems, with interval data for overall consumption and PV electrical 

production available for multiple years. This paper uses 2007 PV-system data from two city facilities to illustrate the 

impacts of binomial rate designs. The analysis will determine the energy and demand savings that the PV systems 

are achieving relative to the absence of systems. A financial analysis of PV-system performance under various rate 

structures is presented. The data revealed that actual demand and energy use benefits of binomial tariffs increase in 

summer months, when solar resources allow for maximized electricity production. In a binomial tariff system, varying 

on- and semi-peak times can result in approximately $1,100 change in demand charges per month over not having a 

PV system in place, an approximate 30% cost savings. The PV systems are also shown to have a 30%-50% 

reduction in facility energy charges in 2007. 

15. SUBJECT TERMS

NREL; California; Elizabeth Brown; Otto Van Geet; solar photovoltaics; PV; electricity; binomial rate structure; San 

Diego; PV system; binomial tariffs; energy savings 

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F1146-E(12/2004) 

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