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

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Photovoltaic Incentive 

Design Handbook 

T.E. Hoff 

Clean Power Research 

Napa, California 

Subcontract Report 

NREL/SR-640-40845 

December 2006 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
Photovoltaic Incentive 

Design Handbook 

T.E. Hoff 

Clean Power Research 

Napa, California 

NREL Technical Monitor: R. Margolis 

Prepared under Subcontract No. ADC-6-66286-01 

Subcontract Report 

NREL/SR-640-40845 

December 2006 

National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393 

303-275-3000 • www.nrel.gov 

Operated for the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337
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Executive Summary 

Investments in customer-owned grid-connected photovoltaic (PV) energy systems are 

growing at a steady pace. This is due, in part, to the availability of attractive economic 

incentives offered by public state agencies and utilities. In the United States, these 

incentives have largely been upfront lump payments tied to the system capacity rating. 

While capacity-based “buydowns” have stimulated the domestic PV market, they have 

been criticized for subsidizing systems with potentially poor energy performance. As a 

result, the industry has been forced to consider alternative incentive structures, 

particularly ones that pay based on long-term measured performance. The industry, 

however, lacks consensus in the debate over the tradeoffs between upfront incentive 

payments versus longer-term payments for energy delivery. 

Objective 

This handbook is designed for agencies and utilities that offer or intend to offer incentive 

programs for customer-owned PV systems. Its purpose is to help select, design, and 

implement incentive programs that best meet programmatic goals. The handbook begins 

with a discussion of the various available incentive structures and then provides 

qualitative and quantitative tools necessary to design the most appropriate incentive 

structure. It concludes with program administration considerations. 

Results 

Two structures emerge as being particularly attractive. One is a performance based 

incentive (PBI) and the other is an expected performance based buydown (EPBB). A 

PBI makes multiple payments over a longer period of time, based on actual, measured 

energy production by the system. An EPBB makes a single upfront payment based on 

expected system performance. 

Both a PBI and an EPBB can address one-time issues that have the potential to affect 

system output. These one-time issues include system rating (based on equipment and 

installation), system design (based on orientation, shading), and geographical location. 

Each of these structures, however, has strengths and weaknesses. 

PBI exceeds EPBB’s ability to guarantee energy production because it captures the effect 

of recurring issues, such as dirt accumulation, module degradation, and inverter failure as 

well as year-to-year weather variations. The PBI, however, is likely to have higher 

administrative costs due to its longer duration, and it does not address the initial capital 

investment barrier as well as the EPBB. 

The EPBB exceeds the PBI’s ability to address the initial capital investment barrier 

without relying on external financial resources, and it is likely to have simpler program 

administration. Unlike the PBI, however, the EPBB cannot guarantee long-term system 

output because it can only address one-time performance factors. 

 iii
A clean energy program’s selection of the incentive structure will depend upon the 

program’s goals, constraints, and beliefs about where the potential performance problems 

will occur and program administration costs. Depending upon the size of the program, it 

may be desirable for a program to implement multiple structures simultaneously. 

 iv
Acknowledgments 

This handbook represents the efforts by people at a number of agencies, companies, and 

utilities. Some have performed or sponsored research and analysis while others have 

been involved in commenting on results at various stages of the work. They include 

Americans for Solar Power, Arizona Corporation Commission, BP Solar, BEW 

Engineering, California PV Utility Program Managers, California Energy Commission, 

California Public Utilities Commission, Cal SEIA, Energy Ideas, Kyocera Solar, 

Lawrence Berkeley Labs, National Renewable Energy Laboratory, Northwest Solar 

Center, Prometheus Institute, PV Now, The Rahus Institute, Renewable Ventures, Salt 

River Project, Sacramento Municipal Utility District, SEIA, Segue Consulting, Sharp 

Solar, Spectrum Energy, Solar Electric Power Association, SPG Solar, SunEdison, Vote 

Solar, and others. 

While many people provided comments on all or portions of this Handbook, very detailed 

review and other comments were provided by Ben Norris (Norris Energy Consulting), 

Sue Gouchoe (North Carolina Solar Center), Karlynn Cory (National Renewable Energy 

Laboratory), and Karl Knapp (City of Palo Alto). Any errors or omissions are the 

responsibility of the author. 

This Handbook was prepared with support from DOE/NREL Subcontract No. ADC-6-

66286-01.

 v
Table of Contents 

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

Objective ......................................................................................................................... 3 

Overview of Incentive Structures ....................................................................................... 4 

Introduction..................................................................................................................... 4 

Incentive Framework ...................................................................................................... 4 

PBI Subtypes................................................................................................................... 5 

Hybrid Incentives............................................................................................................ 8 

Application to the California Solar Initiative.................................................................. 8 

Qualitative Evaluation ...................................................................................................... 10 

Introduction................................................................................................................... 10 

Incentive Design Principles .......................................................................................... 10 

Identify Evaluation Perspectives............................................................................... 10 

Identify Program Goals and Constraints................................................................... 10 

Incentive Structure Screening....................................................................................... 12 

Budgetary Example................................................................................................... 12 

Ratepayer Funds Example ........................................................................................ 13 

Detailed Qualitative Evaluation.................................................................................... 13 

PBI Example ................................................................................................................. 16 

Utility Perspective..................................................................................................... 16 

Customer Perspective................................................................................................ 17 

Combined Perspectives............................................................................................. 18 

Quantitative Evaluation Using Verification...................................................................... 19 

Introduction................................................................................................................... 19 

Performance Verification under Incentive Structures................................................... 19 

Performance Factors ..................................................................................................... 20 

System Rating ........................................................................................................... 20 

Design Factor............................................................................................................ 20 

Location Factor......................................................................................................... 21 

Availability Factor .................................................................................................... 21 

Weather Factor.......................................................................................................... 21 

Comparison of Various Structures................................................................................ 21 

Incentive Design ............................................................................................................... 23 

Introduction................................................................................................................... 23 

Maintain Constant Cost-Effectiveness.......................................................................... 23 

Shape Market Demand.................................................................................................. 24 

Program Administration and Other Factors...................................................................... 27 

Administrative Tools .................................................................................................... 27 

Incentive Trigger........................................................................................................... 27 

Incentive Calculation .................................................................................................... 28 

EPBB Calculation ..................................................................................................... 28 

Issues Not Given Detailed Consideration..................................................................... 29 

Billing and Administrative Costs.............................................................................. 29 

Net Metering ............................................................................................................. 29 

Green Tag Ownership............................................................................................... 29 

 vi
Conclusions and Future Work .......................................................................................... 30 

Summary....................................................................................................................... 30 

Conclusions................................................................................................................... 30 

Next Steps..................................................................................................................... 30 

References......................................................................................................................... 32 

Appendix A: Performance-Based Elements in PV Incentive and REC Programs........... 34 

Appendix B: Verification Provided by Incentive Structures............................................ 39 

Appendix C: PV System Rating Conventions.................................................................. 44 

Appendix D: Incentive Design That Maintains Constant Cost-Effectiveness.................. 46 

Appendix E: Incentive Design That Shapes Market Demand .......................................... 58 

Appendix F: EPBB Incentive Calculation ........................................................................ 64 

 vii
List of Figures 

Figure 1. Sample of PBI structures in the U.S................................................................... 2 

Figure 2. Incentive framework and structure classifications. ............................................ 5 

Figure 3. Variations on PBI structures............................................................................... 7 

Figure 4. Expanded incentive framework and structure classifications. ........................... 8 

Figure 5. California Solar Initiative proposals................................................................... 9 

Figure 6. Annual budget expenditures for hypothetical program.................................... 13 

Figure 7. PBI structure establishes the interaction between utility and the customer. .... 16 

Figure 8. Utility perspective: PV system performance is a critical uncertainty. ............. 17 

Figure 9. Customer perspective: customer makes decision to buy now or to wait.......... 18 

Figure 10. Incentive level using calendar-based trigger. ................................................. 25 

Figure 11. Incentive level using volume-based trigger.................................................... 26 

Figure 12. Implied market demand.................................................................................. 26 

Figure 13. Total value versus year................................................................................... 56 

Figure 14. Incentive administered on a volume basis for model and Joint Filing results.62 

List of Tables 

Table 1. Incentive structures. ............................................................................................. 5 

Table 2. Qualitative assessment of attributes for various structures:............................... 15 

Table 3. Ability of structures to account for factors that affect system performance...... 22 

Table 4. Proposed CEC new homes incentive structure. ................................................. 25 

Table 5. Performance-based elements in PV incentive and REC programs.................... 35 

Table 6. Cash flows associated with two investment alternatives................................... 48 

Table 7. Difference between “Buy Now” versus “Wait 1 Year” cash flows................... 48 

Table 8. Assumptions....................................................................................................... 55 

Table 9. PBI calculations. ................................................................................................ 56 

Table 10. Verification of customer timing indifference. ................................................. 57 

Table 11. Incentive schedule............................................................................................ 62 

Table 12. Incentive comparisons (higher Incentive Rate). .............................................. 67 

Table 13. Capacity factor and design factor .................................................................... 68 

Table 14. Incentive comparisons (Reference System is horizontal w/ shading). ............ 68 

 viii
Introduction 

Investments in customer-owned grid-connected photovoltaics (PV) have been growing at 

a steady pace for several years. PV delivers clean, renewable electricity to the customer 

and the power grid, and provides a number of benefits to utilities and electric ratepayers. 

PV offsets the costs of power generation and T&D infrastructure, lowers domestic fuel 

consumption, has environmental benefits, and provides a hedge against future fuel price 

volatility. The technology is highly reliable with an exceptionally long service life, and it 

is virtually maintenance-free. PV has the potential to dramatically change the way 

electric power is generated and distributed throughout the world. 

The principal barrier to widespread adoption of PV is the high capital cost. While prices 

are on a downward trend, most commercial and residential customers are not able to 

justify the cost through utility bill savings alone. Many public state agencies and utilities 

in the U.S. recognize that prices will drop as production and installation experience is 

increased and have established monetary incentive programs to make systems cost 

competitive for the customer. 

Most of these incentive programs pay upfront incentives based on system capacity. This 

Handbook refers to these incentives as capacity-based buydowns (CBB). A criticism 

directed at CBB incentives is that they do not motivate effective system design, efficient 

component selection, or the owner to establish regular maintenance once the project is 

installed. As a result, there has been a growing interest in performance based incentives 

(PBI). Unlike CBBs, PBIs reward system performance by paying on the basis of actual 

energy produced. 

A range of PBI structures have been implemented in the U.S. Figure 1 presents a sample 

of the structures. For example: 

• California Energy Commission’s pilot program pays a constant rate of 50¢ per 

kWh over the duration of 3 years, 

• We Energies (Wisconsin) pays a constant rate of 22.5¢ per kWh for a duration of 

10 years, and 

• New Jersey’s Clean Energy Program (as well as other programs) offers a 

capacity-based incentive plus the payment of market-based Renewable Energy 

Credits that do not have a fixed duration or rate. 

Appendix A provides details for additional performance-based programs and references 

[2], [3], [5], and [6] contain results from other studies. A detailed review of programs is 

available in [17]. 

 1
Mass

Energy

WI Focus on Energy

PA SDF

CEC PBI Pilot WE Energies

Solar Buy-Back The Energy

Coop (PA) TVA Green

Power Switch

WA REC

Incentive

PNM Customer

Solar PV Program

Bonneville EF

$0.00

$0.25

$0.50

$0.75

$1.00

$1.25

$1.50

2006 2008 2010 2012 2014 2016 2018 2020

PBI Rate ($/kWh)

Mass

Energy

WI Focus on Energy

PA SDF

CEC PBI Pilot WE Energies

Solar Buy-Back The Energy

Coop (PA) TVA Green

Power Switch

WA REC

Incentive

PNM Customer

Solar PV Program

Bonneville EF

Mass

Energy

WI Focus on Energy

PA SDF

CEC PBI Pilot WE Energies

Solar Buy-Back The Energy

Coop (PA) TVA Green

Power Switch

WA REC

Incentive

PNM Customer

Solar PV Program

Bonneville EF

$0.00

$0.25

$0.50

$0.75

$1.00

$1.25

$1.50

2006 2008 2010 2012 2014 2016 2018 2020

PBI Rate ($/kWh)

$0.00

$0.25

$0.50

$0.75

$1.00

$1.25

$1.50

2006 2008 2010 2012 2014 2016 2018 2020

PBI Rate ($/kWh)

Figure 1. Sample of PBI structures in the U.S.1

The lack of consistency across these programs and studies suggest that there is little 

industry consensus in addressing “best practice” questions such as: 

• What should the PBI rate be to provide customers with an adequate return? 

• Should the PBI be paid out over 1 year? 3 years? 5 years? 10 years? 20 years? 

• Should the PBI be constant or vary over time? 

• Should the PBI structure change for new customers in subsequent years? 

• How should the PBI vary for different regions with different resources? 

One of the motivations to move to a PBI structure is that the German feed-in tariff2

 (a 

form of a PBI) has successfully stimulated the growth of the world’s largest PV market. 

There is a difference, however, between the German-style PBI and the PBIs that are 

typically being implemented in the U.S. The feed-in tariff is the sole means of revenue 

for PV owners in Germany while PV owners in the U.S. typically receive the combined 

economic benefits of utility bill savings and incentive payments. Under the U.S. model, 

the utility bill savings already provides a performance-based benefit. 

 

1

 The WA REC incentive can be significantly higher by carefully selecting locally produced equipment. 

2

 The German "Feed-in Law" (2004) compensates solar generation for ground installations at 45.7 euro 

cents per kWh ($0.585) and building mounted systems at 57.4 euro cents per kWh ($0.735). See 

www.solarbuzz.com/FastFactsGermany.htm for more details. 

 2
As a result of these and other factors, there is an industry debate as to the tradeoffs 

between upfront incentives and incentives tied to production. This debate is occurring at 

small programs and large incentive programs, including the California Solar Initiative 

(CSI). 

While the CBB provides capital to lower investment cost, manage capital risk, and lower 

financing requirements, it does not promote performance. While the PBI promotes 

performance, it does not relieve the capital cost barriers. In response to this dilemma, a 

hybrid structure has been proposed called the expected performance based buydown 

(EPBB). The EPBB is an upfront payment based on expected system performance (using 

such inputs as component efficiencies, design orientation, location, and weather data). 

These structures will be discussed in more detail, with their respective advantages and 

disadvantages to help decision-makers choose the system that base aligns with their 

programmatic objectives. 

Objective 

The objective of this Handbook is to assist agencies and utilities that offer or intend to 

offer incentive programs for customer-owned grid-connected PV systems in selecting, 

designing, and implementing incentives to best meet their programmatic goals. 

The Handbook addresses the following topics: 

• Overview of incentive structures 

• Selection of incentive structures using qualitative approaches 

• Selection of incentive structures using a quantitative verification approach 

• Incentive design 

• Program administration 

Analytical models and other details are presented in the appendices. 

 3
Overview of Incentive Structures 

Introduction 

Selecting an incentive structure that best satisfies the overall goals and constraints of an 

incentive program requires knowledge of the available types of incentives. This chapter 

presents a high level framework by which the various incentive structures can be 

classified. It demonstrates how the framework can be applied by categorizing proposals 

submitted by parties involved in the California Solar Initiative (CSI). 

Incentive Framework 

Incentive structures may be classified in terms of Payment Basis and Payment Timing.3 

Payment Basis refers to the technical attribute that is rewarded, and can include 

equipment rating, expected system performance, or actual performance. Payment Timing 

refers to the timing of incentive payments, either a single upfront payment or multiple 

payments over time. That is, incentives are categorized within the framework as follows: 

• Payment Basis 

o Equipment rating (system capacity, or kW) 

o Expected performance (derived from a combination of system capacity, 

installation, orientation, location, and weather assumptions) 

o Actual performance (system output, or kWh) 

• Payment Timing 

o One upfront payment 

o Multiple payments over time 

In this Handbook, each incentive type is described as a combination of the Payment Basis 

and the Payment Timing. In terms of Payment Basis, incentives based exclusively on 

system capacity include the word “Capacity”, incentives based on expected performance 

include the words “Expected Performance”, and incentives based on actual performance 

include the word “Performance”. In terms of Payment Timing, incentives paid out as one 

upfront payment include the word “Buydown” and incentives paid out over time include 

the word “Incentive”.4

The Handbook combines these options together to result in the five incentive structures.5

 

These structures are described in Table 1 and are placed within the framework as shown 

in Figure 2. Note that combinations of these structures is also possible, such as having a 

portion of the payment made upfront and another portion made over time. 

 

3

 The initial phases of this work categorized the incentives in terms of Payment Basis and Payment Rate 

[10]. 4

 The industry currently lacks consistent standards with respect to incentive terminology. 

5

 While a sixth incentive type is feasible – Expected Performance Based Incentive (EPBI) – it is not 

included in this report because it has not been encountered in practice and it is unlikely to be implemented, 

because payments over time can be easily based on actual performance (not just expected performance). 

 4
Table 1. Incentive structures. 

Incentive Structure Identifier Description 

Capacity Based 

Buydown 

CBB A single upfront payment based on manufacturer 

system rating such as DC module rating or AC rating. 

Expected 

Performance Based 

Buydown 

EPBB A single upfront payment based on estimated, 

expected long-term performance using such inputs as 

component efficiencies, design orientation, location, 

and weather data 

Performance Based 

Buydown 

PBB A single upfront payment based on expected longterm performance with periodic adjustments over 

time based on measured system output. 

Capacity Based 

Incentive 

CBI Multiple payments over time based on manufacturer 

system rating (kW - AC or DC). The CBI is similar 

to the CBB except that several payments are made 

rather than a single payment. 

Performance Based 

Incentive 

PBI Multiple payments over time based on measured 

system output (kWh). 

One Upfront Payment

Payment Timing

Multiple Payments

CBB

Capacity

Based

Buydown

PBB

Performance

Based

Buydown

EPBB

Expected Performance

Based

Buydown

CBI

Capacity

Based

Incentive

Payment Basis

PBI

Performance

Based

Incentive

Equipment

Rating

Actual

Performance

Expected

Performance

One Upfront Payment

Payment Timing

Multiple Payments

CBB

Capacity

Based

Buydown

PBB

Performance

Based

Buydown

EPBB

Expected Performance

Based

Buydown

CBI

Capacity

Based

Incentive

Payment Basis

PBI

Performance

Based

Incentive

Equipment

Rating

Actual

Performance

Expected

Performance

Figure 2. Incentive framework and structure classifications. 

PBI Subtypes 

The PBI structure has several possible subtypes. Subtypes account for differences in 

payment durations and payment rates for existing or new customers. For example, the 

terms of a PBI may specify a fixed duration where payments are made for a fixed number 

of years, independent of the year in which the customer invests. Alternatively it may 

 5
specify a variable duration where all payments for a program would stop in a given year 

so that early investors benefit from a longer stream of payments relative to customers 

who invest later. 

Variations of rate are possible in addition to variations of duration. A fixed rate PBI 

specifies a constant incentive rate across all years for a particular customer upon 

investment.6 A variable rate PBI specifies changes in rate over time (usually according 

to a predefined schedule) for a given customer. 

The four possible PBI subtypes are: 

• Fixed duration, fixed rate 

• Fixed duration, variable rate 

• Variable duration, fixed rate 

• Variable duration, variable rate 

These subtypes are illustrated in Figure 3 using a set of hypothetical payment rates. The 

black lines of each figure show the incentive, based on investment year – either 2007 

(top) or 2012 (bottom). The solid yellow portions illustrate the total incentive payments 

to customers, over the entire incentive period in the program based on investment year.7

PBI subtypes are added to the framework as illustrated in Figure 4.

 

6

 The incentive rate for new customers, however, can still change. 

7

 The Variable Duration, Variable Rate structure has only one line because it is the one structure that can be 

engineered to have a constant NPV with a rate that is independent of investment date. 

 6
2007 Investment Date Highlighted 

Fixed Duration, Fixed Rate 

2007

2009

2012

2015

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

2007 2017 2

Year

Rate

Investment 

 Year

027

2007

2009

2012

2015

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

2007 2017 2

Year

Rate

Investment 

 Year

027

Fixed Duration, Variable Rate 

2007

2009

2012

2015

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

2007 2017 2

Year

Rate

027

2007

2009

2012

2015

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

2007 2017 2

Year

Rate

027

Variable Duration, Fixed Rate 

2007

2009

2012

2015

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

2007 2017 2

Year

Rate

Investment Year

027

2007

2009

2012

2015

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

2007 2017 2

Year

Rate

Investment Year

027

Variable Duration, Variable Rate 

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

2007 2017 2

Year

Rate

027

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

2007 2017 2

Year

Rate

027

2012 Investment Date Highlighted 

Fixed Duration, Fixed Rate 

2007

2009

2012

2015

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

2007 2017 2

Year

Rate

Investment 

 Year

027

2007

2009

2012

2015

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

2007 2017 2

Year

Rate

Investment 

 Year

027

Fixed Duration, Variable Rate 

2007

2009

2012

2015

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

2007 2017 2

Year

Rate

027

2007

2009

2012

2015

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

2007 2017 2

Year

Rate

027

Variable Duration, Fixed Rate 

2007

2009

2012

2015

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

2007 2017 2

Year

Rate

Investment Year

027

2007

2009

2012

2015

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

2007 2017 2

Year

Rate

Investment Year

027

Variable Duration, Variable Rate 

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

2007 2017 2

Year

Rate

027

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

2007 2017 2

Year

Rate

027

Figure 3. Variations on PBI structures 

(Investment payouts are shown in yellow). 

 7
Fixed 

Duration &

Fixed Rate

Fixed

Duration &

Variable Rate

Variable

Duration &

Fixed Rate

Variable

Duration &

Variable Rate

One Upfront Payment

Payment Timing

Multiple Payments

CBB

Capacity

Based

Buydown

PBB

Performance

Based

Buydown

Equipment

Rating

EPBB

Expected Performance

Based

Buydown

CBI

Capacity

Based

Incentive

Payment Basis

PBI

Actual

Performance

Expected

Performance

Fixed 

Duration &

Fixed Rate

Fixed

Duration &

Variable Rate

Variable

Duration &

Fixed Rate

Variable

Duration &

Variable Rate

One Upfront Payment

Payment Timing

Multiple Payments

CBB

Capacity

Based

Buydown

PBB

Performance

Based

Buydown

Equipment

Rating

EPBB

Expected Performance

Based

Buydown

CBI

Capacity

Based

Incentive

Payment Basis

PBI

Actual

Performance

Expected

Performance

Figure 4. Expanded incentive framework and structure classifications. 

Hybrid Incentives 

It is also possible to combine upfront and ongoing incentive payments as a hybrid 

incentive. For example, the program could use a combination of an EPBB and PBI. The 

goal of such a program would be to capture the benefits of some upfront payments to 

defray capital costs, while also providing longer-term incentives that encourage proper 

maintenance to insure optimal system performance. 

Application to the California Solar Initiative 

In March 2006, a number of interested parties individually submitted proposed incentive 

structures to the California Public Utilities Commission for consideration under the 

California Solar Initiative (CSI) proceedings. Figure 5 illustrates how these proposals 

may be classified under the incentive framework for non-residential (top) and residential 

(bottom) customers.8

 This illustration suggests that the framework is robust enough to 

capture a wide variety of the incentive structures [10]. 

 

8

 The dashed lines indicate hybrid proposals. For example, the PV Now proposal for non-residential 

customers had 75 percent CBB and 25 percent fixed duration fixed rate PBI. The SCE residential proposal 

is 50 percent CBB and 50 percent CBI. 

 8
Non-Residential Customers 

One Upfront Payment

Payment Timing

Multiple Payments

CBB

Fixed 

Duration &

Fixed Rate

Fixed

Duration &

Variable Rate

Variable

Duration &

Fixed Rate

Variable

Duration &

Variable Rate

PBB

EPBB

CBI

Payment Basis

PBI

PG&E, SCE

ASPv (>100 kW)

PV Now (>30 kW)

DRA (New)

DRA (Retrofit)

ASPv (<100 kW)

SCG/

SDG&E

GSP

Equipment

Rating

Actual

Performance

Expected

Performance

One Upfront Payment

Payment Timing

Multiple Payments

CBB

Fixed 

Duration &

Fixed Rate

Fixed

Duration &

Variable Rate

Variable

Duration &

Fixed Rate

Variable

Duration &

Variable Rate

PBB

EPBB

CBI

Payment Basis

PBI

PG&E, SCE

ASPv (>100 kW)

PV Now (>30 kW)

DRA (New)

DRA (Retrofit)

ASPv (<100 kW)

SCG/

SDG&E

GSP

Equipment

Rating

Actual

Performance

Expected

Performance

Residential Customers 

One Upfront Payment

Payment Timing

Multiple Payments

CBB

Fixed 

Duration &

Fixed Rate

Fixed

Duration &

Variable Rate

Variable

Duration &

Fixed Rate

Variable

Duration &

Variable Rate

PBB

EPBB

CBI

Payment Basis

PBI

DRA (New)

PG&E

PV Now

SCE

DRA (Retrofit) ASPv

SCG/

SDG&E

GSP

Equipment

Rating

Actual

Performance

Expected

Performance

One Upfront Payment

Payment Timing

Multiple Payments

CBB

Fixed 

Duration &

Fixed Rate

Fixed

Duration &

Variable Rate

Variable

Duration &

Fixed Rate

Variable

Duration &

Variable Rate

PBB

EPBB

CBI

Payment Basis

PBI

DRA (New)

PG&E

PV Now

SCE

DRA (Retrofit) ASPv

SCG/

SDG&E

GSP

Equipment

Rating

Actual

Performance

Expected

Performance

Figure 5. California Solar Initiative proposals. 

 9
Qualitative Evaluation 

Introduction 

The previous chapter described five incentive structures that are available to an incentive 

program. They included CBB (Capacity Based Buydown), CBI (Capacity Based 

Incentive), EPBB (Expected Performance Based Buydown), PBB (Performance Based 

Buydown), and PBI (Performance Based Incentive). The structures differ in how the 

payments are calculated and the time frame over which the payments are made. 

In considering which of the five structures would be most advantageous, the incentive 

agency may find that a preliminary screening will quickly rule out some of these 

structures due to programmatic goals and constraints. Then, a more detailed qualitative 

evaluation could be performed for the remaining candidate structures. 

This chapter describes how to establish a set of design principals and how to perform a 

preliminary screening and a more detailed evaluation. Examples are provided. 

Incentive Design Principles 

The design of an effective incentive structure must take into account goals and 

constraints from the perspectives of key participating stakeholders. The collection of 

these different perspectives is referred to as the evaluation perspectives. This section 

discusses these evaluation perspectives and their associated goals and constraints. 

Identify Evaluation Perspectives

The evaluation perspectives of most importance to program design need to be identified 

at the outset of the analysis. These perspectives might include: 

• Incentive program designer (incentive agency or utility) 

• Purchasing customer 

• PV industry (including manufacturer and reseller) 

Any or all of these evaluation perspectives might be given consideration as part of the 

incentive design, depending upon the focus of the incentive program. 

Identify Program Goals and Constraints

Once the high priority evaluation perspectives are selected, the goals and constraints from 

each of the perspectives must be determined. 

Incentive Agency or Utility 

Consider possible goals and constraints from the incentive agency’s or utility’s 

perspective. 

 10
Goals might include: 

• Maximize the power rating (MW), energy output (kWh), or value (electrical 

system benefits, such as reducing congestion) of the systems installed 

• Obtain specific types of benefits as a result of the program (new/retained jobs, 

environmental benefits, etc.) 

• Foster an environment of product innovation 

• Maximize effectiveness of ratepayer funds 

• Install systems early in program period 

Constraints might include: 

• Available budget over the life of the program 

• Uncertain budgets from year to year 

• Limitations on the ability to transfer funds from one year to the next 

• Limited program duration 

• Limits on administration portion of budget 

• The need to maintain consistency with an established incentive structure 

Purchasing Customer 

Next, consider possible goals and constraints from the purchasing customer’s perspective. 

Goals might include: 

• Ensure that the system is “cost-effective”9

• Improve the environment 

• Offset total customer energy consumption using solar 

Constraints might include: 

• Limited borrowing power and/or access to financing to cover capital costs 

PV Industry 

Finally, consider possible goals and constraints from the perspective of the PV 

manufacturers and resellers. 

Goals might include: 

• Maximize short-term product sales 

• Maximize product development (and thus long-term sales) 

Constraints might include: 

• Manufacturing capacity 

• Limitations on the rate of company growth and/or access to capital 

• Production costs 

 

9

 Cost-effectiveness might be defined, for example, as a positive net present value over the life of the 

system, a payback in a specified number of years, or a minimum internal rate of return. 

 11
Incentive Structure Screening 

The screening step focuses on identifying major problems that may prevent a given 

structure from being implemented by a particular program. A starting point for the list of 

screening criteria is the list of goals and constraints developed above. Two screening 

examples are presented. 

Budgetary Example

First, suppose that a utility has an incentive program funded through its public benefits 

charge and that the program is unable to carry over funds from one fiscal year to the next. 

Furthermore, suppose that the program has a fixed 10-year duration. Timing of 

expenditures is a critical constraint under this scenario and the various structures should 

be screened under this constraint. 

Figure 6 presents hypothetical annual budget expenditures for a 10-year program that 

begins in 2007 and ends in 2017 and assumes a consistently growing stream of PV 

investments. The figure includes the annual expenditures for the EPBB, PBB, and four 

variant PBI structures.10

The figure suggests that the fixed duration PBI structures require payments that last 10 

years after the end of the program for project awards made at the end of the program, 

ruling them out. The variable duration PBI structures meet the 10-year program 

constraint, but require budgets that are substantially increasing over time to pay for the 

cumulative number of projects throughout the life of the program (assuming additional 

projects are approved each year). In this case, only the EPBB or PBB structures appear 

to satisfy the program constraints – therefore these are the two programs that should be 

evaluated in more depth. 

 

10 While there is some difference in the present value expenditures for the various incentive structures due 

to differences in discount rates, the key point of the figure is the shape of the curves relative to each other. 

 12
$0

$500

$1,000

$1,500

2007 2017 2027

Annual Expenditure ($M)

EPBB

PBB

2017 Expiration

(Variable Rate) PBI

Fixed 10-Year

(Variable Rate) PBI

Fixed 10-Year

(Fixed Rate) PBI

2017 Expiration

(Fixed Rate) PBI

$0

$500

$1,000

$1,500

2007 2017 2027

Annual Expenditure ($M)

EPBB

PBB

2017 Expiration

(Variable Rate) PBI

Fixed 10-Year

(Variable Rate) PBI

Fixed 10-Year

(Fixed Rate) PBI

2017 Expiration

(Fixed Rate) PBI

Figure 6. Annual budget expenditures for hypothetical program. 

Ratepayer Funds Example

Next, suppose that a primary goal of a program is to maximize effectiveness of ratepayer 

funds. This rules out the capacity incentives (CBB and CBI) because they do not hold 

the customer accountable for actual energy production. The EPBB, PBB, and PBI would 

be candidates for further analysis, because they encourage optimal system performance. 

Detailed Qualitative Evaluation 

A more rigorous qualitative analysis may be performed for the two or three incentive 

structures remaining from the screening step. Table 2 presents a set of attributes that 

program designers might consider at this stage. The table summarizes the relative 

strengths (+) and weaknesses (–) for each of the five incentive structures. 

The table identifies Program’s Use of Ratepayer Money as being critical from the 

program’s perspective. Each of these is subsequently divided into several subcategories. 

For example, the table divides the program’s use of ratepayer funds into direct program 

cost, indirect program cost, and the assurance of energy production. Under each of these 

subcategories, the table also goes one level deeper to demonstrate that the direct program 

cost is a function of other criteria. 

Consider a few examples of how the five incentive structures fare under specific 

criterion. First, consider how the incentive capitalizes on the agency’s lower discount 

rate. In general, agencies and utilities have a lower cost of capital than consumers and 

businesses. As a result, structures that pay the incentive earlier in time (Buydowns) will 

cost the program less due to the difference in the time value of money for the 

agency/utility versus the consumer. Second, consider the criterion that protects against 

 13
poor long-term system performance. This criterion encourages optimal system 

performance; the PBI structure excels in this area, because the incentive payments are 

directly linked to system output. 

While there are many factors that are identified in this table, there may be others that the 

agency would develop for the qualitative assessment. The agency should choose the 

factors it feels are most relevant in providing it with an understanding of how to 

qualitatively evaluate the structures. 

 14
Table 2. Qualitative assessment of attributes for various structures: 

Strength (+), weakness (–), depends (+/–), and not determined (?). 

 

CBB CBI EPBB PBB PBI 

Program Perspective (Incentive Agency or Utility) 

Program’s Use of Ratepayer Money ($ per kWh)

 Direct Program Cost

 Capitalizes on agency’s lower discount rate + – + + – 

 Reduces production risk premium required by customers + + + – – 

 Promotes product innovation – – +/– + + 

 Maximizes state and federal tax benefits ? ? ? ? ? 

 Indirect & Administrative Cost

 Minimizes number of payments to make to customer + – + +/– – 

 Eliminates requirement of separate metering + + + – – 

 Reduces need to verify system performance for payment + + +/– – – 

 Minimizes number of customer interactions11 + – + – – 

 Assurance of Energy Production

 Protects against poor system design and installation – – +/– + + 

 Protects against poor long-term system performance – – – +/– 12 + 

Other Factors

Smoothes annual expenditures when used w/o escrow 

account 

+ – + + – 

Payments correspond to value being obtained over time – + – + + 

Promotes systems that maximize hourly value to utility + + + + – 

Industry Perspective (Manufacturer and System Reseller) 

Product Sale

Promotes ease of sale + +/– + +/– +/– 

Represents transition from status quo + – +/– – – 

Reduces need for additional warranties + + + – – 

Product Innovation

Rewards products w/ high kWh production per kW capacity – – +/– + + 

Rewards products (e.g. inverters) with long life times – – – + + 

Lessens need to for agency to certify equipment – – – + + 

Customer Perspective 

Customer Economics

Minimizes payment risk + + + – – 

Reduces initial capital/loan amount required by customer + – + + – 

May improve long-term system performance – – – + + 

Can be engineered to provide various cash flow streams – + – – + 

 

11 Administrative cost is likely to be higher with PBI because there is likely to be more times where 

consumers are not paid due to non-performance. 

12 There is a default risk associated with PBB where customers may have underperforming systems but the 

program may not be able to collect money back from customers due to non-performance. 

 15
PBI Example 

The following example illustrates how a utility with a clean energy program might 

combine the goals and constraints of the program from two perspectives (utility and 

customer), in a detailed qualitative analysis. Suppose that a utility wants to design a 

customer-owned PV incentive program that has the primary goal of maximizing PV 

energy production. The PBI structure is selected since it encourages optimal energy 

production. 

The analysis begins with an examination of the perspectives of two key parties affected 

by the PBI structure: the utility that is making the incentive payments (and receiving part 

of the benefits of the PV system energy production) and the customer that is receiving the 

payments. 

As shown in Figure 7, the utility uses a PBI to influence customer behavior: (i) the utility 

establishes the PBI structure (rate and duration), (ii) the customer determines whether or 

not to purchase, what sort of system to purchase, and how to install and maintain the PV 

system, and (iii) the utility obtains some of the benefits from the energy produced by the 

PV system. 

Utility Customer PBI 

Rate and duration of PBI ($) 

Potential benefits of energy production. 

Figure 7. PBI structure establishes the interaction between utility and the customer. 

There are two fundamental decisions that these parties face with regard to the incentive. 

First, the utility needs to establish the PBI structure. Second, the customer must decide 

whether (and when) to invest. 

Utility Perspective

The value provided to the utility is a function of the PV system performance (kWh) and 

the corresponding avoided utility costs of service, such as generation, transmission, and 

distribution. The added cost to the utility and its ratepayers includes the incentive 

payments plus administrative costs to run the program. 

 16
The utility needs to establish a PBI structure that ensures that systems are installed and 

maintained in a way that most cost-effectively allocates ratepayer-collected funds. The 

utility seeks to maximize the benefits of its investment while keeping program spending 

within budget. In addition, the utility wants to be protected against funding systems that 

have poor performance (or no performance due to system failure, equipment removal, or 

some other reason) over the program duration. 

A number of uncertainties affect the PV system’s ultimate ability to deliver energy to the 

grid: timely installation, equipment ratings and efficiencies, orientation and shading, the 

solar resource at the customer’s location, and ongoing maintenance practices. The PBI 

structure shifts the responsibility of all these factors to the customer-investor, and the 

utility’s uncertainty is reduced to the quantity of energy production. This is illustrated by 

circles in Figure 8. The system could have good or poor performance for each year of 

the PBI program. If the system performs poorly in a given year, the PBI structure may 

need to be adjusted to stimulate new customers to properly maintain their systems in 

order to provide the desired amount of energy production. 

First Year Second Year 

Good Performance 

Poor

Performance

Good Performance 

Poor

Performance

Good Performance 

Poor

Performance

First Year Second Year Third Year 

Good Performance 

Poor

Performance

Good Performance 

Poor

Performance

Good Performance 

Poor

Performance

Good Performance 

Poor

Performance

Good Performance 

Poor

Performance

Good Performance 

Poor

Performance

Third Year 

Figure 8. Utility perspective: PV system performance is a critical uncertainty. 

Customer Perspective

In considering whether and when to invest in PV, the customer considers all of the 

economic benefits and costs, taking into account the continuing decline in PV system 

prices13 over time and the increase in utility rates over the same period of time. Benefits 

depend on the PBI structure, the utility rate structure, tax impacts, and system 

performance. Costs include capital and operation and maintenance costs. 

Suppose that, from the customer’s perspective, two basic criteria must be satisfied. First, 

the investment must be cost-effective. Second, as shown by the decisions represented by 

squares in Figure 9, the timing of the investment should result in the best economic 

advantage. The customer could invest in a given year or wait in anticipation of declining 

PV prices. Since the utility intends to maximize production using PV generators, the PBI 

should be designed to remove the benefit of waiting by offering a declining incentive rate 

commensurate with declining PV prices. 

 

13 PV prices can have short-term increases when the demand for modules is high. While modeling efforts 

typically assume that prices decline over the long term, presents an example of how an 

analysis should be applied if PV prices are increasing over the near term. 

Appendix D

 17
Wait 

Buy

Wait 

Buy

Wait 

Buy

First Year Second Year Third Year 

Wait 

Buy

Wait 

Buy

Wait 

Buy

Wait 

Buy

Wait 

Buy

Wait 

Buy

First Year Second Year Third Year 

 

Figure 9. Customer perspective: Customer makes decision to buy now or to wait. 

Combined Perspectives

The PBI structure flows out of the combined perspectives. The utility wants to create a 

PBI structure that provides customers with a sufficient incentive to invest sooner while 

protecting itself against poorly performing systems. It accomplishes this by combining 

the customer’s investment decision with PV system performance uncertainty. Once the 

customer invests and the PV performs well, the utility pays the incentive. If, however, 

the system fails to perform as expected, the utility will make a reduced payment to the 

current customer. If the system fails altogether, this will leave the utility with a sufficient 

level of resources to provide future customers with an incentive program so that there 

will be more investment in PV. 

 18
Quantitative Evaluation Using Verification 

Introduction 

The previous chapter’s qualitative assessment is designed to screen out incentive 

structures that fail to satisfy critical program constraints and to qualitatively assess 

structures that best satisfy a program’s goals. This chapter takes a more quantitative 

evaluation to achieve the same goals, which will be helpful to quantify the benefits and 

costs of particular incentive programs. 

In order to conduct a quantitative analysis, PV output verification becomes critical. Each 

incentive structure, however, has an inherent level of verification that it is capable of 

providing. This section explores the inherent verification capabilities of the structures 

and examines how to conduct a quantitative evaluation for each. Appendix B provides 

the detailed analysis upon which this chapter is based. 

Performance Verification under Incentive Structures 

The U.S. PV industry is moving toward incentive structures that are tied to performance. 

Such structures, by verifying performance either through one-time power measurements 

or monthly energy metering, assure the granting agency and ratepayers that the PV 

systems are encouraged to perform as expected, that incentive funds are well spent, and 

that the agency or utility receives most, if not all of the expected cost savings. 

The only structure that provides complete verification of energy production is a PBI that 

pays for as-delivered energy over the entire life (25 to 30 years) of the PV investment. 

Any incentive paid out over a shorter time period falls short of this ideal. All three 

capital buydown structures and any short-duration PBI structure (less than 25 or 30 years) 

fail to ensure complete verification. 

While a long-term PBI represents the highest standard of verification and the best 

guarantee of performance, it comes with additional costs and requires long program 

durations. Meter reading, billing, and program administration costs all increase when 

verification requirements are more stringent over a longer period of time. 

The agency therefore needs to consider the tradeoff between the benefits of verification 

(i.e., guaranteed energy production) versus the costs of implementation. It should also 

consider whether sufficient accuracy is provided by other structures (or a shorter PBI) 

with more limited verification by considering individual factors that account for longterm performance captured by the PBI. 

 19
Performance Factors 

An important observation from Appendix B is that the performance factors include both 

“one-time” design and installation decisions and “recurring” practices. Performance 

factors can be classified as follows:14

• One-Time Issues 

o System Rating (equipment ratings) 

o Design Factor (orientation, shading) 

o Location Factor (geographical location) 

• Recurring Issues 

o Availability Factor (dirt accumulation, increased shading from tree 

growth, module degradation, inverter failure, and maintenance) 

o Weather Factor (variation from typical year) 

Incentive structures may be designed to include any or all of these performance factors, 

depending upon the desired level of verification and the implementation cost. For 

example, the CBB only includes the System Rating, while the PBI effectively includes all 

of the factors. These factors are described more fully below. 

System Rating

System Rating (kW) can account for equipment ratings and installation effects and may 

be expressed in either units of DC or AC. The CBB incentive structure defines incentive 

payments based on System Rating, typically without independent field verification. 

An alternative is to use a system rating that can be verified based on field measurements. 

While most incentive structures have not been stated in system AC units and thus could 

not be verified in the field, nothing prevents the industry from using a system AC rating. 

See Appendix C for a more detailed discussion of the rating issue. As a result, all of the 

incentive structures have the potential to provide for system rating verification.

Design Factor

The Design Factor quantifies the effects of system design, including shading and system 

orientation (horizontal, tilted, tracking, etc.) or multiple system orientations if the PV 

array is split in various directions. It is defined as the ratio of two simulated outputs 

using a common weather data set representing the location of the system. The numerator 

uses actual design orientation and shading, while the denominator uses a reference 

system design at the same location. 

Incentives that include a Design Factor will reward system installations that are oriented 

for optimal energy production and where shading does not adversely affect output. Such 

an incentive structure provides partial assurance of performance by promoting effective 

system designs. 

 

14 See Reference [14] for a slightly different categorization. 

 20
Location Factor

The Location Factor quantifies the effect of geographical location. It is the ratio of two 

simulated system outputs based on long-term weather data. The numerator uses weather 

data from the actual location while the denominator uses data from a reference location. 

The Location Factor will promote installations at the most favorable locations in the 

region where the incentive is available. 

Availability Factor

The Availability Factor captures the effects of system degradation and outages related to 

maintenance and equipment life. PV panels that are not regularly cleaned deliver reduced 

energy. Modules degrade naturally over time, which also results in reduced output. 

Inverter failures from damaged switching devices or blown fuses will prevent energy 

delivery until the problems are rectified. All of these long-term issues are reflected in the 

Availability Factor. 

Availability Factor equals the ratio of two quantities based on the actual design and 

location. The numerator is based on measured production per unit of actual capacity 

while the denominator is based on simulated production per unit of reported capacity 

using the actual weather data that corresponds to the same time and location as the 

measured data. 

Incentives that include the Availability Factor encourage proper maintenance practices, 

such as cleaning and repairs, to ensure ongoing production. It may also spur 

manufacturing innovation in developing products with long service lives. 

Weather Factor

The Weather Factor establishes the difference between actual and ideal (long-term 

predicted) weather patterns. It equals the ratio of two quantities that are based on actual 

design and actual location. The numerator is based on actual weather data while the 

denominator is based on predicted long-term weather data. As long as realistic data is 

used to model long-term behavior, the Weather Factor represents the transfer of weather 

viability risk from the granting agency to the investor-owner.15

Comparison of Various Structures 

The five incentive structures are compared in relation to the above performance factors in 

Table 3. The table indicates that all of the structures have the potential to provide for 

system rating verification (so long as they include some sort of field verification). The 

EPBB, PBB, and PBI structures can be designed to verify the one-time performance 

effects of system design and geographical location while the CBB and CBI structures 

cannot. The PBB and PBI structures may be designed to take into account the full range 

of performance factors and provide the highest assurance of energy delivery. Note that 

while some form of verification is possible for all incentive structures, this has not 

 

15 New York State Energy Research and Development Authority (NYSERDA) is one agency that has 

implemented a system that compares long-term average weather data to actual weather data using satellite 

data. This site is available at www.iedat.com/sirs-ny/sirs-ny.php3. This satellite data has been linked to the 

Clean Power Estimator so that New York PV system owners can verify their system performance. 

 21
typically been done in practice because the focus has been on component-based ratings 

rather than system-based ratings. Component-based ratings are very difficult to verify 

after a system has been installed in the field. 

Table 3. Ability of structures to account for factors that affect system performance. 

Factor CBB CBI EPBB PBB PBI 

One-Time System Rating X X X X X 

Design X X X 

Location X X X 

Recurring Availability X X 

Weather X X 

 

 22
Incentive Design 

Introduction 

The purpose of any PV incentive program is to provide a temporary pathway for 

investment until such time as prices decline to the point of cost-effectiveness in the 

marketplace (“market transformation”). The program therefore presumes a decline in 

prices that will follow from ongoing industry experience, increased volumes of 

production and other forces, in part driven by the incentives themselves. An effective 

incentive design, therefore, should take into account this anticipated price decline. This 

chapter describes two strategies for addressing this in incentive structure design. 

One strategy is to design the incentive to meet a fixed cost-effectiveness test each year 

during the program. The incentive is intended to provide the necessary funding so that 

the consumer considers the investment to be cost-effective each year. In an environment 

of declining prices, the incentive rates would likewise decline and the program would 

neither encourage the consumer to invest early nor encourage the consumer to delay 

investment. 

A second strategy is to establish an incentive that increases the relative cost-effectiveness 

over time, thus shaping market demand and encouraging an acceleration of participation. 

With such a program, designers would therefore expect that market penetration would 

increase over the duration of the program in relation to the increasing economic 

advantage to the consumer. While this approach would cause some consumers to wait in 

order to achieve better economic return, it is more closely tied to traditional market 

economics where the quantity of sales is related to the cost-effectiveness of the 

investment. 

Maintain Constant Cost-Effectiveness 

In the first strategy, the goal of incentive structure design is to make the PV investment 

equally cost effective today as in the future. Under such a structure, the customer has no 

advantage or disadvantage of delaying investment because the relative cost-effectiveness 

is held constant even though PV prices are declining. A detailed derivation of the 

mathematical formulas using this approach as applied to a PBI structure is presented in 

Appendix D. 

The appendix demonstrates several things. First, a variable duration and variable rate 

PBI structure (refer back to Figure 3 for an illustration of the structure) would apply for 

all customers independent of when the investment is made. Second, the year-by-year 

incentive is decreasing over time (as PV prices decline and the level of incentive to make 

the system cost-effective declines as well).16 Third, the incentive rate is calculated based 

on the difference between the added cost of investing early (because one loses the benefit 

of declining prices) minus the additional utility bill savings of investing early (because 

one gains the benefit of obtaining utility bill savings earlier rather than later). 

 

16 See for a discussion of the situ Appendix D ation where prices are expected to increase. 

 23
Shape Market Demand 

The second strategy for dealing with declining PV prices is to design an incentive 

structure that is consistent with program goals and then to assess the reasonableness of 

the implied market demand. Under such a program, payments are not necessarily defined 

to meet cost-effectiveness. Economic return may increase over the duration of the 

program, leading to higher participation rates year by year according to market demand. 

A detailed derivation of the mathematical formulas using this approach is presented in 

Appendix E. 

This is the approach that was used to help support the design of the California Energy 

Commission (CEC) New Solar Homes Partnership (NSHP) incentive [15]. Key points of 

that analysis are repeated here for the benefit of the reader and the results are summarized 

in Table 4. As explained below, increased economic return can still mean that the 

incentive level provided to consumers decreases over time, just not as rapidly as they 

would under the first strategy for dealing with declining PV prices. 

The first column in Table 4 presents the incentive rate that is consistent with the 

following program goals: 

• 400 MW of PV installations 

• $2.25 per Watt starting incentive rate for smooth transition from Emerging 

Renewables Program (ERP) to the NSHP 

• Declining incentives to zero by 2017 

• 35% market growth rate 

• $300 Million incentive budget 

The declining incentive level, which can be either calendar-based (Figure 10) or volumebased (Figure 11), is combined with other variables (rate structures, tax effects, etc.) to 

perform a cost-effectiveness test from the purchasing customer’s perspective for each 

year of the analysis. Possible tests include simple payback, net present value, internal 

rate of return, and net cash flow. The first year net cash flow was used as part of the 

NSHP analysis and the results are presented in the third column of Table 4. The annual 

volume (second column of Table 4) is combined with the market size and average PV 

system size to determine the market sales (last column of Table 4). 

The market sales are plotted versus the cost-effectiveness results in order to determine the 

implied market demand (Figure 12). The policy maker’s job is to assess whether or not 

this implied market demand is realistic. If it is deemed to be unrealistic, then one or more 

of the program goals (MW goal, market growth rate, budget, or incentive structure) 

should be revised. 

 24
Table 4. Proposed CEC new homes incentive structure. 

Incentive 

($/WAC-CEC)

Volume 

(MWAC-CEC)

Net Savings 

($/kWh) Market Sales

2007 $2.25 7 ($0.02) 3.1%

2008 $2.03 10 ($0.01) 3.9%

2009 $1.80 13 $0.00 5.0%

2010 $1.58 18 $0.01 6.5%

2011 $1.35 24 $0.03 8.3%

2012 $1.13 33 $0.04 10.7%

2013 $0.90 44 $0.05 13.8%

2014 $0.68 60 $0.06 17.7%

2015 $0.45 81 $0.07 22.8%

2016 $0.23 109 $0.08 29.3%

Current ERP

$0.00

$0.50

$1.00

$1.50

$2.00

$2.50

$3.00

Jan-07 Jan-09 Jan-11 Jan-13 Jan-15 Jan-17

Cumulative PV (MW-AC CEC)

Incentive ($/Watt-AC CEC)

Figure 10. Incentive level using calendar-based trigger. 

 25
Current ERP

$0.00

$0.50

$1.00

$1.50

$2.00

$2.50

$3.00

0 100 200 300 400

Cumulative PV (MW-AC CEC)

Incentive ($/Watt-AC CEC)

Figure 11. Incentive level using volume-based trigger. 

2007 2008 2009 

2010 

2011 

2012

2013 

2014

2015 

2016 

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

($0.04) ($0.02) $0.00 $0.02 $0.04 $0.06 $0.08 $0.10

Net Savings ($/kWh)

New Homes with 2 kW PV (%)

Figure 12. Implied market demand. 

 26
Program Administration and Other Factors 

The incentive program needs to be implemented once the incentive structure has been 

selected and designed. Program implementation is a broad topic that goes well beyond 

the scope of this study. It is useful, however, to discuss a few key areas as they directly 

apply to incentive design. This chapter discusses some issues related to program 

administration and other factors that relate to incentive design. 

Administrative Tools 

One issue that deserves consideration is the software tool(s) that will be used to manage 

the program. There are basically two options that are available. The first option is to 

internally develop tools using spreadsheet or database software. The second option is to 

use software programs that have been developed specifically for clean energy program 

management. 

When evaluating the costs and benefits of the various alternatives, consideration should 

be given to all aspects of program management. Building an internal software tool will 

require resources (time and money) to develop, whereas buying software will cost money 

to purchase and may require training. Implementation concerns include assessing the 

costs of incentive application processing, internal reporting, external reporting, program 

analysis, and program management after systems have been installed (including the 

analysis of verified system performance). The broader data collection requirements of 

the program (including model validation and system output recording) should also be 

given consideration in making the decision. 

Incentive Trigger 

Another administrative issue is what event will trigger a change in the incentive rate. 

Two methods that have been proposed are calendar-based and volume-based triggers. A 

calendar-based trigger means that the rate changes on a specific, pre-determined date. A 

volume-based trigger means that the rate changes at the point in time when a specific 

volume target has been reached. 

Appendix E shows that when the market is growing at an exponential rate and the 

incentive rate decreases linearly each year, the following results are true: 

• The incentive trigger mechanism can be either calendar-based or volume-based. 

• A calendar-based trigger results in greater certainty as to how long the program 

will last. 

• A volume-based trigger results in greater certainty as to the amount of PV that 

will ultimately be installed under the program and the total cost of the program. 

 27
Incentive Calculation 

Another administrative issue is how to calculate the incentive payment. 

• CBB or CBI payments are based on equipment capacity. To date, most U.S. 

incentive programs reference the equipment listed by the CEC to perform these 

calculations.17

• PBI payments are based on measured system performance. While measured 

system performance does not rely on simulation models or verified equipment to 

make the payment, it is likely that the agency will need to perform an expected 

incentive payment calculation in order to properly budget the program and to 

manage funds and cash flow. 

• EPBB payments are based on estimated system performance as determined by 

modeling and possibly field verification procedures. 

• PBB payments are based on estimated system performance with a true up 

occurring after the system begins operation based on actual, measured system 

performance. 

EPBB Calculation

Several issues need to be addressed for the EPBB incentive calculation: (a) which models 

will be used to estimate the performance, (b) which performance attributes will be 

estimated and (c) which attributes will be verified by measurement. 

For estimating performance, two internet-accessible models are available: PVWATTS18

and Clean Power Estimator.19 If the design incorporates shading into the analysis, 

PVWATTS will need to be combined with the output from a Solar Pathfinder20 analysis 

or some other shading analysis; Clean Power Estimator has an integrated shading 

analysis.21 A third model is currently under development by the California Energy 

Commission [16]. 

Once the model is chosen, the formula that will be used to perform the calculation must 

be selected. Appendix B demonstrates that the PBI calculation could be expressed in 

either of two mathematically equivalent ways for the purpose of incentive structure 

comparison. Similarly, the EPBB calculation can be expressed in two mathematically 

equivalent ways. They include: 

• Incentive Energy Rate ($/kWh) x Expected Output (kWh) 

• Incentive Capacity Rate ($/kW) x System Capacity (kW) x Design Factor (%) 

Appendix B suggests that, while multiplying an energy rate by expected output is more 

intuitive, the second method is analytically superior because it reduces the incentive 

 

17 www.consumerenergycenter.org/erprebate/equipment.html. 

18 http://rredc.nrel.gov/solar/codes_algs/PVWATTS 

19 http://www.clean-power.com. 

20 http://www.solarpathfinder.com. 

21 Clean Power Estimator has also been integrated into PowerClerk (www.powerclerk.com) to 

automatically perform the necessary EPBB calculations. PVWatts has been used to calculate the EPBB for 

a variety of programs. 

 28
payment’s sensitivity to data and modeling inaccuracies, and incorporates the system’s 

rating. 

Issues Not Given Detailed Consideration 

A number of issues were not given detailed consideration in this report. They are listed 

below. 

Billing and Administrative Costs

Billing and administrative costs associated with running an incentive program under 

different incentive structures have not been given detailed consideration in this report. 

The agency should consider the costs to implement the various programs, including onetime setup costs, incremental per-customer costs, and the costs of integrating a payment 

mechanism into the existing utility billing system. 

Net Metering

Net metering is currently offered in over 35 states, usually to customers of investorowned utilities. Net metering, establishes retail value for production during all hours, 

even when wholesale electricity rates are lower. This Handbook considered net metering 

as part of the utility bill savings available from PV systems. 

Green Tag Ownership

Some states mandate that utilities procure a percentage of energy from renewable energy 

sources under a “renewable portfolio standard (RPS),” while some utilities procure 

renewables on a voluntary basis to claim the environmental attributes. In either case, PV 

installations have value to utilities by either fulfilling RPS requirements or contributing to 

environmental goals. Three regions (New England, the Pennsylvania-New JerseyMaryland interconnect, and Texas) currently track renewable energy certificates and 

three other regions (New York, the West and the Mid-West) are currently constructing 

green tag tracking systems. In these systems, the renewable attributes of PV may be 

certified, a single owner of the attribute must be identified and the owner of the “green 

tag” may sell it in various markets, either in-state or out-of-state. This Handbook did not 

address this issue of Green Tag Ownership. 

 29
Conclusions and Future Work 

Summary 

This Handbook was designed for agencies and utilities that operate or intend to operate 

incentive programs for customer-owned PV systems. Its purpose is to help these 

agencies select and implement an incentive design that best meets the needs of their 

program. The Handbook began with a discussion of the various incentive structures that 

are available and then provided qualitative and quantitative methods to select and then 

design the most appropriate structure given programmatic goals and constraints. It 

concluded with some considerations related to program administration. 

Conclusions 

Based on the goal of focusing on structures that promote optimal performance, two 

structures emerge as being particularly attractive. One is a PBI and the other is an EPBB. 

A PBI makes multiple payments over a longer period of time, based on actual, measured 

energy production by the system. An EPBB makes a single upfront payment based on 

expected system performance. 

Both a PBI and an EPBB can address one-time issues that have the potential to affect 

system output. These one-time issues include system rating (based on equipment and 

installation), system design (based on orientation, shading), and geographical location. 

Each of these structures, however, has strengths and weaknesses. 

PBI exceeds EPBB’s ability to guarantee energy production because it captures the effect 

of recurring issues, such as dirt accumulation, module degradation, and inverter failure as 

well as year-to-year weather variations. The PBI, however, is likely to have higher 

administrative costs due to its longer duration and it does not address the initial capital 

investment barrier as well as the EPBB. 

The EPBB exceeds the PBI’s ability to address the initial capital investment barrier 

without relying on external financial resources and it is likely to have simpler program 

administration. Unlike the PBI, however, the EPBB cannot guarantee long-term system 

output because it can only address one-time performance factors. 

A clean energy program’s selection of the incentive structure will depend upon the 

program’s goals, constraints, and beliefs about where the potential performance problems 

will occur and program administration costs. Depending upon the size of the program, it 

may be desirable for a program to implement multiple structures simultaneously. 

Next Steps 

There are several issues that should be addressed in future work to make the results of 

this Handbook more useful. 

First, this Handbook embodies a substantial level of modeling. The models should be 

verified using incentive program data. This can be accomplished by carefully designing 

 30
and incorporating data collection efforts into the incentive processing system. 

Verification should include the cost-effectiveness tests used by consumers to make 

investment decisions, implied market demand associated with these cost-effectiveness 

tests, and PV price projection models within the context of world demand. In addition, 

programs should collect a sufficient level of field data (verified kW and kWh) to 

numerically address the issue of which portions of the PV system (equipment, design, 

and operation) are most likely to affect long-term system performance. 

Second, this Handbook evaluated incentive structures of increasing verification levels, up 

to and including the PBI. The next generation incentive structure may be a value-based 

(or an expected-value based) incentive [16]. This structure would pay based on the timeand location-specific value of the output rather than on single rate per kWh. For 

example, PV systems that are oriented to maximize production during high demand, or 

systems that are located in transmission-constrained areas could be eligible for higher 

incentive amounts. This structure has analytical appeal since it would provide the highest 

cost savings to the utility. Prior to implementation, however, the incentive program 

should perform a PV value analysis to determine if the added computational and 

implementation complexity of implementing a value-based incentive justifies the added 

benefit.22

Third, it may be useful to create an electronic screening tool to assist in incentive 

program design. The tool would cover both the qualitative aspects (by asking the user 

questions about their priorities) and the quantitative aspects of incentive design. 

Fourth, further consideration could be given to how to expand the analysis to include 

other perspectives such as non-participating ratepayers, local, state, and federal 

government, and the PV industry. While some consideration was given to this area, it 

would be beneficial to expand these efforts [4]. 

 

22 An example of a recent value analysis is one that was performed for Austin Energy [11]. That study 

considered five fixed configurations (horizontal, South-30o, SW-30o, West-30o, West-45o) and two 1-axis 

tracking configurations (horizontal and 30o tilted). It found that the per kWh value ranged by plus or minus 

5 percent depending upon system configuration.

 31
References 

[1]. Robert M. Margolis, “Photovoltaic Technology Experience Curves and Markets,” 

Paper Presented at the NCPV and Solar Program Review Meeting, Denver Colorado, 

March 24, 2003. 

[2]. Thomas E. Hoff and Robert M. Margolis, “Economic Benefits of PerformanceBased Incentives,” Working Paper, July 9, 2004, www.clean-power.com. 

[3]. Thomas J. Starrs, “Designing a Performance-Based Incentive for Photovoltaic 

Markets,” American Solar Energy Society’s Solar 2004 Conference, Portland, OR, July 

2004. 

[4]. Thomas E. Hoff and Robert M. Margolis, “Moving Toward a More Comprehensive 

Framework to Evaluate Distributed Photovoltaics,” Internal NREL Report, June 2005, 

http://www.clean-power.com/research/customerPV/EvaluationFramework.pdf. 

[5]. Kelly Sackheim, “Photovoltaic Performance Based Incentives Program 

Development,”,Final Report, SMUD RFP No. 40512.CJB, September 21, 2005. 

[6]. “Motion of the Americans for Solar Power for Adoption of Performance-Based 

Incentives for Large Commercial Customers in the California Solar Initiative,” 

November 10, 2005. 

[7]. Nellie Tong, “Emerging Renewables Program Systems Verification Report 2004-

2005,” December 2005. 

[8]. Commissioner Peevey and ALJ Malcolm, “Interim Order Adopting Policies and 

Funding for the California Solar Initiative,” January 12, 2006, 

www.cpuc.ca.gov/word_pdf/AGENDA_DECISION/52719.pdf

[9]. California Public Utilities Performance Based Solar Incentives Workshop, March 

16, 2006, http://www.cpuc.ca.gov/static/energy/solar/060316_pbiworkshop.htm

[10]. Thomas E. Hoff, “Overview of PBI Design Issues,” Presented at the California 

Public Utilities Performance Based Solar Incentives Workshop on March 16, 2006, 

http://www.clean-power.com/research/customerPV/CPUC_PBI_Workshop.pdf and 

http://www.cpuc.ca.gov/static/energy/solar/060316_pbipresentations.htm. 

[11]. Thomas E. Hoff, et. al., “The Value of Distributed Photovoltaics to Austin Energy 

and the City of Austin,” March 17, 2006, 

http://www.austinenergy.com/About%20Us/Newsroom/Reports/PV-ValueReport.pdf

[12]. “CPUC Energy Division Staff Proposal for California Solar Initiative Design and 

Administration 2007-2016, Rulemaking 06-03-004 (Filed March 2, 2006),” April 24, 

2006, http://www.cpuc.ca.gov/PUBLISHED/RULINGS/55786.htm. 

 32
[13]. “Comments of the California Solar Energy Industries Association, PV Now, and 

the Vote Solar Initiative Regarding the CPUC Energy Division Staff Proposal for 

California Solar Initiative Design and Administration 2007-2016,” May 16, 2006, 

http://www.cpuc.ca.gov/static/energy/solar/comments%20ca%20solar%20ind%20(x7780

1).pdf. 

[14]. Tim Tutt, “New Solar Homes Partnership,” June 12, 2006, 

http://www.energy.ca.gov/renewables/06-NSHP-1/documents/2006-06-

12_workshop/2006-06-12_TUTT_TIM_OVERVIEW.PDF. 

[15]. Thomas E. Hoff, Ryan Wiser, and Mark Bolinger, “Incentive Level Analysis,” June 

12, 2006, http://www.energy.ca.gov/renewables/06-NSHP-1/documents/2006-06-

12_workshop/2006-06-12_CLEAN_POWER_INCENTIVE.PDF. 

[16]. Bill Pennington, “Technical Procedures, Eligible Systems and Specifications, 

Expected Performance-Based Incentives,” June 12, 2006, 

http://www.energy.ca.gov/renewables/06-NSHP-1/documents/2006-06-

12_workshop/2006-06-12_PENNINGTON_BILL.PDF. 

[17]. Galen Barbose, Ryan Wiser, and Mark Bolinger, “Designing PV Incentive 

Programs to Promote Performance: A Review of Current Practice,” Draft Report, August 

2006. 

 33
Appendix A: Performance-Based Elements in PV Incentive and 

REC Programs23

Introduction 

There is a wide range of performance-based approaches and program durations currently 

used across the U.S. As shown in Table 5, there are four main types of incentive 

programs: (1) state incentive programs, (2) Renewable Energy Credit (REC) purchase 

programs developed for compliance with renewable portfolio standards, (3) voluntary 

green power programs, and (4) utility PV incentive programs. Each program is described 

in more detail in the subsections below. 

State PV Performance Incentive Programs 

States are showing increased interest in implementing a performance-based approach to 

PV incentive programs to ensure quality installations that operate well. Performancebased approaches typically involve either payments over time based on direct metering 

(PBI) or upfront payments based on expected performance using production estimation 

software, shading analysis and other site-specific details (EPBB). The list below 

highlights some of the state PV performance incentive programs described in Table 5.

• The Pennsylvania Sustainable Development Fund (SDF)’s PV program mixes a 

$4/W rebate with a one-time PBI of $1/kWh for the first year’s generation. 

Unique among incentive programs, SDF also offers installers a PBI of $0.10/kWh 

for the first year’s production. (Note: this program is currently closed.) 

• The Emerging Renewables Rebate Program in California offers an option for 

consumers to participate in a PBI pilot program, paying out 50¢/kWh for three 

years in lieu of the traditional $/kW buydown – currently at $2.60/kW for PV. 

• Wisconsin offers an EPBB calculated using the PVWATTS production estimate 

that incorporates shading and other site-specific conditions. The incentive is paid 

after installation similar to capacity-based rebates. 

• Connecticut recently transitioned its previous capacity-based program to an EPBB 

structure calculated using Clean Power Estimator through PowerClerk. 

• In 2005, Washington State enacted a long-term PBI of $0.15/kWh (capped at 

$2,000 per year) through June 30, 2014, for individuals, businesses, or local 

governments that generate electricity from PV and other renewables. Using a set 

of multipliers, an even greater incentive is paid if the modules, inverter, or other 

components are manufactured in Washington State. The state's utilities, whose 

participation is voluntary, will pay the incentives and earn a tax credit equal to the 

cost of those payments. 

 

23 This appendix was assembled by Sue Gouchoe, North Carolina Solar Center. 

 34
Table 5. Performance-based elements in PV incentive and REC programs. 

State Program Name Amount Payment Duration

State PV Incentive Programs 

California CEC Emerging Renewables Program 

PBI Pilot 

$0.50/kWh 3 years 

Pennsylvania Sustainable Development Fund Solar 

PV Grant Program 

$4/W capacity-based incentive plus 

$1/kWh produced in first year 

1 year 

Washington Renewable Energy Production 

Incentives 

$0.15/kWh, multiplied by a factor 

dependent on technology type and 

where equipment was manufactured 

Through mid-2014 

Wisconsin Focus on Energy Incentives/Grants $1.00 - $2.00/kWh depending on 

installer and building type; 

$1.50/kWh for systems > 20 kW 

Upfront (EPBB) 

Connecticut Clean Energy Fund Small PV Rebate $5,165/W-AC, adjusted based on 

expected performance 

Upfront (EPBB) 

REC Programs for RPS Compliance 

Nevada Renewable Energy Credit Program Market based undefined 

New Jersey Solar Renewable Energy Certificate 

Program 

Market based; ~ $200 per MWh 

($0.20 per kWh) 

undefined 

New Mexico PNM Customer Solar PV Program $0.13/kWh Through 2018 

Arizona SRP EarthWise Solar Energy Program $2.50/W-DC, adjusted based on 

expected performance 

Upfront (EPBB) 

Green Power Programs 

Massachusetts Mass Energy 

REC Incentive Program 

$0.06/kWh 3 years 

North Carolina NC GreenPower 

Production Incentive 

$0.18/kWh + avoided cost Undefined 

Pennsylvania The Energy Cooperative 

Solar Energy Buy-Back Program 

$0.28/kWh for ECAP customers 

$0.10/kWh for PECO customers 

2 years 

Tennessee 

Kentucky 

Georgia 

Mississippi 

North Carolina 

TVA 

Green Power Switch Generation 

Partners Program 

$500 (residential only) plus 

$0.15/kWh (residential/smallcommercial) or $0.20/kWh 

(commercial) 

10 years 

Washington 

Oregon 

Bonneville Environmental Foundation 

Solar Starters – REC purchase 

$0.10/kWh 5 years 

Washington Chelan County PUD 

SNAP Program 

$0.47/kWh + avoided cost Undefined 

Wisconsin We Energies – 

Solar Buy-Back Rate 

$0.225/kWh 10 years 

Utility PV Incentive Programs 

Arizona SRP Earthwise Solar PV Program $3.25/W-AC (commercial), adjusted 

for performance & system rating 

Upfront (EPBB) 

California SMUD Solar PV Program $2.80/W-AC (residential), $3/W-AC 

(commercial), adjusted for expected 

performance 

Upfront (EPBB) 

California LADWP Solar PV Program $0.04/kWh - $0.16/kWh depending 

on system size, customer type, etc. 

Upfront (EPBB 

based on 20 yrs. of 

production) 

 35
Nearly all state PV programs, whether capacity-based or performance-based, allow the 

project owner to retain ownership of the renewable energy certificates (RECs)24 and to 

sell them separately. For example, PV owners in Washington State can combine the state 

production incentive with the REC purchase program offered by Bonneville 

Environmental Foundation, which provides two separate revenue sources. In New 

Jersey, participants in the state rebate program can sell their RECs via an online trading 

system to energy providers who must comply with the solar set-aside in the state’s 

renewables portfolio standard (RPS). The New Jersey solar REC price is market based 

and therefore will fluctuate based on market conditions; currently NJ solar RECs are 

selling around $265/MWh according to Evolution Markets.25

Utility Compliance with RPS Solar Set-asides 

A growing number of state RPS programs contain provisions which require energy 

suppliers to provide specific percentages of retail energy from solar electric resources. 

Programs are still under development in some states, but the following utility and statewide performance-based programs have emerged. State solar RPS information was taken 

from the Database of State Incentives for Renewable Energy (www.dsireusa.org). 

• The New Jersey RPS calls for at least 2.12% of retail sales (about 1,500 MW of 

PV capacity) to be met with solar electric generation by 2021. To demonstrate 

compliance with the solar set-aside, electric suppliers must use an on-line solar 

REC tracking system established by the NJ Board of Public Utilities. PV system 

owners register with the program and use the online market to sell S-RECs (in 1 

megawatt-hour denominations). The S-REC program is anticipated to 

compensate system owners an average rate of around $200 per MWh ($0.20 per 

kWh). An engineering estimate is used to calculate the monthly S-REC 

generation for systems under 10 kilowatts. The program web site allows owners 

of systems 10 kW and larger to upload monthly meter readings and/or production 

information. 

• Nevada also has a 5% solar set-aside by 2015 as part of its RPS. Each kWh of 

electricity generated from customer-maintained PV systems is equivalent to 2.4 

RECs for RPS compliance purposes. Therefore, the solar set-aside will result in 

the equivalent of approx. 600 MW of PV capacity or approx. 600 MW of 

concentrated solar power; most likely it will be some combination of the two 

technologies. The two investor-owned utilities offer a capacity-based rebate for 

PV systems up to 30 kW and retain ownership of the RECs for RPS compliance. 

However, larger PV and other renewable energy installations register through the 

Nevada Public Utilities Commission and can sell RECs on a performance-basis. 

The REC price is market-based. 

 

24 Renewable energy certificates are a tradable commodity that capture the environmental attributes of 

electricity generated by a renewable facility and allow it to be traded separately from the energy. They may 

also be called RECs, tradable renewable credits (TRCs), green tags, attributes, or environmental attributes. 25 Evolution Markets REC Monthly Market Update, June 2006, 

http://www.evomarkets.com/assets/mmu/mmu_rec_jun_06.pdf

 36
• PNM, an investor-owned utility in New Mexico, offers a PBI program as part of 

its plan to comply with the state’s RPS. PNM purchases RECs from customers 

who install solar PV systems (up to 10 kW) at a rate of $0.13/kWh through 2018. 

The electricity output of the PV system may be used on-site, and customers retain 

their net-metering benefit. 

Green Power Programs 

In order to include solar in their green power product mix, several utilities and green tag 

marketers offer production-based incentives for PV energy and their associated RECs, or 

for REC-only transaction. The payments range from $0.06/kWh to $0.47/kWh with 

contract periods ranging from two years to 10 years. Some programs have not defined 

the payment duration. 

• The Tennessee Valley Authority and participating distributors in Georgia, 

Kentucky, Alabama, Tennessee, North Carolina, Mississippi, and Virginia offer a 

$0.15/kWh incentive for a minimum of 10 years for PV or wind power (plus a 

one-time $500 incentive to residents). Large commercial customers receive 

$0.20/kWh for 10 years. The output from these systems serves as renewable 

resources for TVA's Green Power Switch Program. 

• We Energies offers to purchase electricity and associated RECs generated by PV 

systems owned by its Wisconsin customers to supply a portion of the energy sold 

under the "Energy for Tomorrow" green power program. The payment is 

$0.225/kWh for a 10-year period. 

• Chelan PUD (WA) offers up to $1.50/kWh plus an avoided cost payment for the 

combined RECs and electricity produced by PV and other renewables as part of 

the Sustainable Natural Alternative Power (SNAP) Program, the utility’s green 

power program. The 2005 rate was $0.47/kWh. Under the SNAP program, the 

PV system owner sells 100 percent of the PV generation. Payments are made to 

SNAP Producers annually, with the actual incentive rate based on the total 

amount contributed by Chelan customers to the SNAP program and the total kWh 

generated by SNAP producers. The duration of incentive payments is undefined. 

Several utilities in Minnesota and one in Alaska offer similar programs. 

• NC GreenPower, a statewide green-power program in North Carolina, offers REC 

payments for grid-tied PV and other renewables. PV systems less than 10 kW 

generally receive $0.18/kWh for RECs plus approximately $0.04/kWh under a 

power-purchase agreement for the electricity value. The time period is undefined. 

• The Bonneville Environmental Foundation, a nonprofit Green Tag marketer based 

in Portland, Oregon, has teamed with the Northwest Solar Cooperative to market 

RECs from small solar installations throughout Oregon and Washington. The 

cooperative pays PV system owners $0.10/kWh for a 5-year contract period for 

RECs. BEF offers the Green Tags for resale to its wholesale and retail customers. 

(Note: this program is currently closed.)

 37
• Nonprofit programs in Massachusetts (Mass Energy Consumers Alliance) and 

Rhode Island (People's Power & Light) purchase RECs from PV systems at 

$0.06/kWh for three years. The RECs are packaged together with wind, small 

hydro and biomass renewable energy certificates and sold as New England 

GreenStartSM, a green power product offered by National Grid. 

• The Energy Cooperative Association of Pennsylvania (ECAP), a nonprofit, 

Philadelphia-based competitive energy supplier, pays PV system owners 

$0.28/kWh for a 2-year contract who are ECAP customers for the combined 

RECs and electricity. These customers must purchase 100% green power through 

ECAP. PECO customers may also participate, receiving $0.10/kWh for 2 years. 

Output from participating systems provides the solar component of ECAP’s 

Green-e certified product, “EcoChoice100”. 

Utility PV Incentive Programs 

Salt River Project (SRP), Sacramento Municipal Utility District (SMUD) and the Los 

Angeles Department of Water & Power have developed EPBB programs. SMUD offers 

a capacity-based rebate of $3/W, adjusted for expected performance. LADWP’s recently 

launched EPBB is structured as an expected production-based incentive. The incentive 

rate ranges from $0.04/kWh to $0.16/kWh depending on system size, tax status, customer 

type, and other factors. Ownership of the RECs associated with PV production is granted 

to the utility. SRP is one of the first programs to implement field verification with their 

EPBB incentive. 

 38
Appendix B: Verification Provided by Incentive Structures 

Introduction 

This appendix presents the typical method used to calculate a PBI payment and then 

expands upon this calculation by adding a series of interim steps to explicitly identify 

performance-related issues. The first subsection presents how a PBI rate is established 

and then uses that rate to calculate the total incentive under the PBI structure. The 

second subsection expands the PBI incentive calculation to include a set of adjustment 

factors. 

For illustration purposes, it is assumed that the PBI has a fixed rate and one year duration 

so that payment is made in full at the end of first year of operation. An extension to this 

derivation would take into account multiple-year programs. 

Variable Definitions 

It is useful to begin with a specification of the terms that will be used in analysis. 

The terms “actual” and “ideal” occur multiple times in the variables specified below. 

“Actual” refers to the actual system that is installed. “Ideal” refers to the following: 

• Ideal capacity is the reported capacity used in the PBI rate calculation. 

• Ideal system design is the system with the configuration that maximizes energy 

production for a given location (e.g., if only fixed systems are considered, this is a 

system oriented to maximize energy production with no shading). 

• Ideal location is the geographical location within the applicable incentive territory 

that has the highest potential PV energy production. 

• Ideal weather data is the long-term data set used for performance modeling, such 

as NREL’s TMY data. 

Now consider each of the variables. 

R = Incentive revenue required by customer ($ per kW for ideal system) 

R represents the capacity-based incentive revenue ($ per kW) that the granting agency 

will pay for an ideal system in the ideal location based on an ideal weather data set. R is 

the result of a separate analysis and needs to consider the effect of non-ideal conditions 

(rating, design, location, etc.) under which most customers will invest. It is expressed in 

units of $ per kW of ideal capacity. The measured data will adjust for non-ideal 

conditions. 

CI = Ideal capacity (kW) to produce ideal output EI,I,S,I – defined below 

(kWh) 

CA = Actual capacity (kW) to produce actual output EA,A,M – defined 

below (kWh) 

 39
C refers to system capacity. The capacity can be based on the ideal capacity (CI) required 

to produce an ideal amount of energy or the actual capacity (CA) required to produce an 

actual amount of energy. 

Design Location Method Weather 

Data 

Capacity 

EA,A,M = Output (kWh) Actual Actual Measured N/A CA

EI,I,S,I = Output (kWh) Ideal Ideal Simulated Ideal CI

EA,A,S,I = Output (kWh) Actual Actual Simulated Ideal CI

EI,A,S,I = Output (kWh) Ideal Actual Simulated Ideal CI

EA,A,S,A = Output (kWh) Actual Actual Simulated Actual CI

E is the amount of energy produced. There are five different ways that E can be 

estimated/calculated, which are represented by the term E with various subscripts. The 

subscripts correspond to design, geographical location, calculation method, and weather 

data set. The first term is a measured value and the remaining four terms are simulated 

outputs. 

PBI Incentive 

Calculating an incentive payment under a PBI structure is accomplished in three steps. 

First, the granting agency determines how much it will pay for an ideal system. Second, 

the PBI rate is set by dividing the incentive by the amount of energy that an ideal system 

is expected to produce. Third, the PBI rate is multiplied by measured energy production 

to determine the total incentive payment. Notice that, while the output is measured, the 

PBI rate is established through the use of a set of assumptions and simulated data 

designed to encourage optimal performance. 

The first step is to determine how much the granting agency is willing to pay (R) for an 

ideal system ($/kW). An ideal system is perfectly rated, has the optimal orientation with 

no shading, is located in the geographical location with best solar resource, and operates 

with perfect reliability. The ideal payment amount is determined by a separate analysis 

and must take into account the fact that customers will typically receive lower payments 

due to non-ideal conditions. The amount needs to be set high enough to attract a 

sufficient number of customers to participate in the program. 

Second, the specified incentive is divided by the amount of energy that an ideal system 

(with an ideal capacity) is expected to produce (kWh per kW) in order to determine the 

PBI rate. Finally, after the system has been in operation for some period of time defined 

by the granting agency, the PBI rate is multiplied by the measured energy produced by 

the system. The result is the payment amount to the system owner. 

[ ][ ] ( ) ( ) ( ) 876 64 744 4 844 Production

Measured

E

Rate

PBI

C

E

Incentive RatePBI Measured Production R I MA,A,

ISII

× ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = =

,,,

1 (1)

 40
As described above, EI,I,S,I is the output for a system with capacity C I based on an ideal 

design (first I subscript), at the ideal location (second I subscript), simulated with a model 

(S subscript), using ideal weather data (last I subscript). 

Expand PBI Calculation Equation 

The PBI calculation can be expanded to provide a better understanding of the 

performance issues that it mitigates. This is accomplished by multiplying Equation (1)

by the following ratios26 and then rearranging. 

• CI/CI

• CA/CA

• EA,A,S,I/EA,A,S,I

• EI,A,S,I/EI,A,S,I

• EA,A,S,A/EA,A,S,A

The result presented in Equation (2) is that the total PBI-based incentive equals an 

Incentive Revenue ($/kW) times ideal system capacity (kW) times five adjustment 

factors. The details of each of the terms are described below, as well as how to apply this 

equation in different situations. 

S Simulated

M Measured

IdealI

ActualA

Location I),or (A Design WeatherS),or (M MethodI),or (A I)or (A Data Energy E

Ior A C Capacity

Where

Issues Recurring

Factor

Weather

E

Factor

tyAvailabili

/E

/E

Issues time-One

Factor

Location

Factor

Design

Factor

Rating

CBB Similar to

Capacity

System

Revenue

Incentive

,,,

AS,A,A,

AS,A,A,

MA,A,

,,,

,,,

,,,

,,,

=

=

=

=

=

=

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

×⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ ×

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

×⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

× ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ × ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

× ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

=

14 2444 4 3444

64 74 4 8 64 4748

14 24444 4 34444

876 647 648 4748

14 244 4 344

876876

I ISAA

A

ISII

ISAI

ISAI

ISAA

I

A

I EC

C

E

E

E

E

C

C

CR

Incentive

(2) 

One-Time Issues 

Rating Factor

The Rating Factor accounts for inaccuracies in system rating methods, rating 

conventions, and the effects of varying system installation practices. One way to 

 

26 Multiplying by a series of 1s does not change the value of the equation. 

 41
measure the actual system rating is to dispatch an independent inspector to the site to 

measure the actual rating once the system is commissioned. Another approach is to base 

the system rating on measured output and weather conditions over some period of time. 

The utility could collect measured data for this period and compare it to a simulation 

model using actual time-correlated weather data corresponding to the location of the 

system. 

If the second approach is taken, there are some practical difficulties that need to be 

addressed in order to perform this calculation. The selection of PV simulation model and 

weather data set (measured on site, satellite, other?) would have to be determined and 

explained in the program terms. Also, if the PV system were to become unavailable 

during the test period, there must be some means to determine this and repeat the test.27 

Note that a separate meter for the PV system would be required to measure energy over 

the test period. 

Design and Location Factors

The Design and Location factors are based solely on simulated data using ideal (longterm) weather data for a system with the same ideal capacity. Since these are simulated 

quantities, as long as the same model and weather data are used, the factor calculations 

do not require high precision in the PV simulation model or weather data set; 

inaccuracies in the simulation will tend to cancel each other out. 

Thus, it should be sufficient to use an industry accepted model that can account for 

location, orientation, and shading28 and a long-term data set such as NREL’s typical 

meteorological year (TMY)29 or similar data set, as determined by the agency. In 

addition, performing a one-year simulation based on TMY data should be sufficient to 

determine the impact of system design on output over the life of the system (again, the 

factor takes the ratio of two simulations). 

Recurring Issues 

Availability Factor

The Availability Factor accounts for long-term system availability relative to a defined 

standard. All parts of the system must be in working order and the overall system must 

be well maintained over the long-term in order for the owner to maximize the incentive 

payments. This factor requires measured system output and the corresponding measured 

weather data over the long-term, and the method of simulating output must be defined in 

advance of program implementation. Note that this factor cannot be estimated using a 

short-term data set. 

 

27 The agency might, for example, give the incentive applicant the right to request up to two additional 

measurements of the Rating Factor. To avoid extra administrative burden, the final value that is calculated 

is the one that is used to determine the rebate. 

28 A model such as the Clean Power Estimator should be sufficient because it accounts for both orientation 

and shading for multiple sub-system orientations. An alternative would be PVWATTS (combined with a 

suitable shading program). 

29 http://www.eere.energy.gov/buildings/energyplus/weatherdata_sources.html#TMY2

 42
Weather Factor

The Weather Factor accounts for variations in weather trends over the long-term. This 

factor requires simulated system performance based on actual weather data versus 

simulated performance based on ideal (long-term) weather data. As with the Availability 

Factor, the Weather Factor cannot be estimated using a short-term data set. 

Application to Incentive Design 

The PBI structure is the one that guarantees that a granting agency only pays for the 

energy it receives. It places the burden for one-time issues (system rating, design, and 

location) and recurring issues (availability and weather variability) on the PV system 

owner. 

There may be some circumstances when a granting agency does not want to transfer full 

responsibility for all issues to the system owner. For example, it might prefer that a 

homeowner not bear the risk of weather variability. In this case, the PBI rate would be 

adjusted to eliminate the Weather Factor. As another example, an agency may want to 

offer an upfront payment to avoid the administrative burden of metering and billing under 

a PBI program. It may elect to adjust only for one-time performance issues but not for 

the recurring issues. In this case, only the one-time factors would be used. Each agency 

must asses which combination of factors makes the most sense for their program. 

 43
Appendix C: PV System Rating Conventions 

Incentive programs that use rated capacity as part of the payment basis must define a 

system power rating convention or select one from existing conventions. Most programs 

currently use component-based ratings. Some use a component-based DC system rating 

(the nameplate rating of the PV modules times the number of modules). Others use a 

component-based AC rating defined by the California Energy Commission (AC-CEC) – 

this rating equals the PV module rating under PVUSA Test Conditions30 times the 

number of modules times the weighted average inverter efficiency and is referred to as 

the AC-CEC rating. 

Component-based ratings may provide accurate standards but they are difficult to 

validate after systems have been installed. For example, in the case of the AC-CEC 

rating, it is difficult to separately measure the PVUSA Test Conditions rating of the 

modules and then to measure the efficiency of the inverter. The situation is analogous to 

evaluating the fuel efficiency of a car. While each of the components within a car has an 

efficiency rating (engine, transmission, etc.), a car’s miles per gallon is based on the 

complete system. In order to verify a car’s mileage, one would take the car out under the 

established rating conditions and actually test the car. It is difficult to determine the 

efficiency of just the engine once it has been installed in a vehicle. 

In order to establish an incentive based on performance, an incentive program should 

consider using a rating that can be physically verified after the system has been installed. 

If this approach is taken, the AC rating would be based on the rating of the entire system, 

not just the components. Such a rating is referred to as the system AC rating. If it so 

desired, the incentive program could then base the incentive payment on the verified 

rating, thus rewarding manufacturers and installers of high quality systems. Consumers 

could also be equipped with a tool to assess instantaneous system performance. 

Implementing a system AC rating requires the acceptance of a verification protocol. 

While specification of a protocol is beyond the scope of this Handbook, some protocols 

exist. The protocol needs to identify the required on-site measurements (e.g., irradiance, 

module temperature, power output), testing period (instantaneous versus some time 

period), and how to translate those measurements into a system AC rating. 

While the verified rating could be used to pay the incentive, all incentive structures need 

an estimated system rating in order to reserve the appropriate amount of incentive funds, 

once an application has been submitted. A system AC rating convention was proposed 

by the California Public Utilities Commission (CPUC) staff [12] in calculating the 

Expected Performance Based Buydown for systems under 100 kW.31 The System Rating 

is the AC rating of the entire installed system as defined under PVUSA Test Conditions. 

 

30 PV USA Test Conditions are at 1,000 Watt/m2

 solar irradiance, 20o Celsius ambient temperature, and 1 

meter per second wind speed. 

31 Subsequent recommendations by the Staff have been to use the AC-CEC rating until a verification 

protocol has been accepted. 

 44
The Estimated Rating is calculated as follows: 

Estimated Rating = Number Modules x PV PTC Module Rating 

x Inverter Efficiency x Other Loss Factor (Assumed to be 90%) 

For reference purposes, the derating from DC to the AC-CEC rating is typically about 

85.5 percent and the derating from DC to system AC (under PV USA Test Conditions) is 

about 77 percent. The derating from AC-CEC to AC is about 90 percent, thus accounting 

for the Other Loss Factor. 

 45
Appendix D: Incentive Design That Maintains Constant CostEffectiveness 

This appendix describes how to design an incentive that maintains a constant level of 

cost-effectiveness for all customers entering the program at any point over its duration. 

This approach would be taken by incentive agencies with the goal to subsidize PV only to 

the extent of its defined cost-effectiveness test. In an environment of declining PV 

prices, the incentive would likewise decline. This approach differs from one in which 

incentive agencies seek to increase market demand to correspond with increasing costeffectiveness. 

While there are different measures of cost-effectiveness, the present analysis uses a zero 

net present value of cash flows over the life of the system. It is further simplified by 

considering only the capital cost, utility bill savings and incentive amounts. That is, the 

analysis assumes that the incentive amount is calculated such that the customer “breaks 

even” when considering the present value of these cash flows. 

For purposes of illustration, the derivation assumes a variable duration, variable rate PBI 

structure. This simplifies the problem because the incentive structure is independent of 

the year in which the customer invests; customers who wait to invest forgo the PBI 

payments associated with the previous years. 

The appendix begins with a simple example, then presents the detailed mathematical 

formulation, and then concludes with hypothetical numerical results. 

Optimization Problem 

The incentive design is determined by formulating a constrained optimization problem. 

The incentive agency wants to maximize its benefits minus cost, subject to the following 

goals and constraints: 

• Incentive program cost does not exceed program budget at any point in time 

• The incentive rate is sufficient for customers to invest immediately 

• The PBI structure compensates for predicted lower PV prices thus eliminating the 

motivation for customers to wait before investing 

• The agency is indifferent as to whether systems perform as predicted (or are even 

removed entirely) 

Rather than formulating the general optimization problem, this appendix focuses on the 

constraints since they are critical in designing the PBI structure. 

 46
Example 

Consider an example PBI design problem. Suppose that the incentive agency desires 

165,000 kWh of PV-generated electricity per year and that a customer is considering 

investing in a PV system. Assume: 

• The current year is 2007 

• A 100 kW system will produce 165,000 kWh per year 

• A 100 kW system costs $600,000 in 2007 and will cost $550,000 in 2008 

• The only other benefit a customer gains from investing in PV is the utility bill 

savings; that is, all tax effects are excluded 

• A 100 kW system reduces the current annual utility bill by $20,000 and these 

savings escalate at a rate of 3 percent per year 

• PV system life is 30 years 

• The PBI payments will last some currently unknown period (but less than 30 

years) 

• The discount rate is 10 percent 

The agency wants to determine what the PBI rate should be for 2007. 

Table 6 presents the cash flows associated with the two investment alternatives that are 

available to the customer under a given PBI set of rules. The top part of the table is the 

“Buy Now” alternative and the bottom part of the table is the “Wait 1 Year” alternative. 

If the customer purchases immediately, the utility bill savings will be $20K in 2007, 

$20.6K in 2008, etc., the customer will receive the PBI for some number of years, and the 

customer will incur an immediate cost of $600K. If the customer delays investment for 

one year, the utility bill will be reduced by $20.6K in 2008, $21.2K in 2009, etc., the 

customer will receive the (now slightly lower) PBI, and for one less year, and the 

customer will incur a cost of $550K in 2008. 

Notice that there is very little difference between the cash flows for the two alternatives. 

The only differences are that a customer choosing the “Buy Now” alternative (i) saves on 

the utility bill immediately, (ii) receives a PBI payment in 2007, and (iii) incurs the 

higher system cost in 2007 while a customer choosing the “Wait 1 Year” alternative (i) 

adds a year of utility bill savings after 30 years (ii) forfeits the 2007 PBI payment, and 

(iii) incurs a lower system cost one year later in 2008. 

In order to provide the customer with the proper investment incentive in 2007, the agency 

needs to set the incentive amounts so that there is no economic benefit for customers 

between buying now and waiting. This would occur when the two cash flow streams are 

equal in net present value terms. 

The difference between the cash flows for the two alternatives is shown in the top of 

Table 7 and the discounted difference is shown in the bottom part of the table. An 

examination of the bottom part of the table indicates that the only way to make the sum 

of the net discounted cash flows equal to $0 (so that there is no economic benefit in 

waiting to invest) is to set the 2007 incentive at $82.8K, so that it satisfies the equation 0 

 47
= I2007 -$580k + $500k - $2.8k. As a 100 kW system produces 165,000 kWh, per year, 

this corresponds to a PBI payment of 50¢ per kWh in 2007. 

The next section will demonstrate how to establish the full PBI structure. 

Table 6. Cash flows associated with two investment alternatives. 

2007 2008 2009 ... 2036 2037 

Buy Now 

Bill Savings $20K $20.6K $21.2K ... $47.1K 

Incentive I2007 I2008 I2009 ... 

System Cost -$600K

Wait 1 Year 

Bill Savings $20.6K $21.2K ... $47.1K $48.5K 

Incentive I2008 I2009 ... 

System Cost -$550K 

Table 7. Difference between “Buy Now” versus “Wait 1 Year” cash flows. 

2007 2008 2009 ... 2036 2037 

Difference: Buy Now Minus Wait 1 Year 

Net I2007 -

$580K

$550K 0 ... 0 -$48.5K 

Net 

(Discounted) 

I2007 -

$580K

$500K 0 ... 0 -$2.8K 

 48
Constrained Optimization Problem 

The simple example above produced the incentive amount in 2007 in order to provide 

customers with no economic benefit in waiting until 2008 to invest. Several questions 

still remain. First, what should the PBI rate be after 2007? Second, how many years 

should the PBI payments be made? This section formalizes the concepts presented above 

to derive an expression for the incentive payment in any given year. 

The PBI structure needs to be designed to satisfy two criteria. First, for every year of the 

program, a customer should be economically indifferent between investing and waiting. 

Second, the PBI payments should continue until a defined cost-effectiveness test has been 

satisfied. 

Value to Customer 

Consider the value of a PV investment that occurs either now or at some time in the 

future. The value (expressed in $/kW) of waiting W years to invest (neglecting tax 

effects and O&M costs) equals the present value of the utility bill savings over the life of 

the PV system plus the present value of the PBI payments minus the system price. That 

is, 

( ) ( ) ( )

6474 484 64748 647Price48

r

P

Performanc Incentives Based e

r

EI

 SavingsBillUtility 

r

EU

W Year

in Investing

of Value

W

W

D

Wt

t

tt

WL

Wt

t

tt

+ − +

+

+ = ∑ ∑

−

=

+−

= 1 1 1

1 1 (3) 

where: 

• t is the year 

• Ut is the effective utility bill savings ($ per kWh) including the impacts of 

demand charge savings, net metering, and net production 

• It is the performance based incentive ($ per kWh) 

• Et is the energy produced by the PV system (kWh per kW per year) 

• L is the life of the PV system (years) 

• W is the number of years that the customer waits before investing (years) 

• r is the customer’s discount rate (percent) 

• D is the PBI program duration (years) 

• PW is the price of the PV system in year W ($ per kW)32

This analysis also assumes that, with the declining cost of PV installations and the 

increasing utility costs, PV will become cost effective without further subsidies within 

the service life of PV systems installed today. That is, the PBI program duration (D) is 

shorter than PV system life (L). 

 

32 This analysis does not include tax effects. The fundamental conclusions, however, will still be valid 

when those effects are included. 

 49
First Constraint: No Benefit in Waiting

The first criterion is that a customer needs to be economically indifferent to the year in 

which the investment is made over the duration of the PBI. This is satisfied when the 

value to the customer given by Equation (3) is the same whether the customer invests 

now (W=0) or the customer invests later (W>0). If it is assumed that the energy 

production E is constant over time, the customer is indifferent when: 

( ) ( ) { ( ) ( ) ( )

64 74444444 4 84444444

1 4342 14243 14243

64 7444444 4 8444444

14243 14243

Years W in Invest of Value

Price

W

W

Performanc Incentives Based e

D

Wt

t

t

 SavingsBillUtility 

WL

Wt

t

t

Now Invest of Value

Price Performanc Incentives Based e

D

t

t

t

 SavingsBillUtility 

L

t

t

t

r

P

r

EI

r

EU P

r

EI

r

EU

+ − +

+

+ =− +

+

+ ∑ ∑ ∑ ∑

−

=

+−

=

−

=

−

= 1 1 1 1 1

1 1

0

1

0

1

0

(4) 

Many of the terms are the same on both sides of Equation (4) and the terms cancel. The 

result is that 

( ) ( ) ( ) ( ) ⎭

⎬

⎫

⎩

⎨

⎧

+ − + − + −= + ∑ ∑∑

−+

=

−

=

−

=

1 1

0

0

1

0 1 1 1 1

WL

Lt

t

t

W

t

t

t

W

w

W

t

t

t

r

EU

r

EU

r

P

P

r

EI

(5) 

This can be rewritten by expanding the price terms to create a summation and by 

modifying the second utility bill savings summation in order to have all of the summation 

terms begin at 0 and end at W-1 years. 

( ) ( ) ( ) ( ) ( ) ⎭

⎬

⎫

⎩

⎨

⎧

+ − +

⎥ −

⎦

⎤ ⎢

⎣

⎡

+ − + = + ∑∑ ∑∑

−

=

+

+

−

=

−

=

+

+

−

=

1

0

1

0

1

0

1

1

1

0 1 11 1 1

W

t

tL

tL

W

t

t

t

W

t

t

t

t

t

W

t

t

t

r

EU

r

EU

r

P

r

P

r

EI

(6) 

The incentive for each year is calculated by varying W from 1 to the PBI duration D and 

then substituting back into Equation (6) for each previously defined It. The result is that 

the PBI for any particular year is the cost premium of not waiting for the following year’s 

price reduction less the added upfront utility savings benefits, or: 

( )

64 744 4 844 64 74 4 84 Benefit Added

r

U U

Premium Cost

Er

P

PI L

tL

t

t

t t ⎥

⎦

⎤ ⎢

⎣

⎡

+ −−⎥

⎦

⎤ ⎢

⎣

⎡ ⎟

⎠

⎞ ⎜

⎝

⎛

⎟

⎠

⎞ ⎜

⎝

⎛

+ −= + +

1

1

1

1

(7) 

Second Constraint: Cost-Effectiveness Test

The second criterion is that the PBI payments should continue until some costeffectiveness test has been met based upon forecasted prices and market conditions. This 

test could be based on the net present value, simple payback, rate of return, or some other 

test in an unsubsidized market. The incentive rate is calculated using Equation (7) for 

each year t until the year in which the investment is expected to be cost-effective and then 

it is equals 0 after that point. 

 50
Incentive Result

When these two criteria are satisfied, the result is that the PBI rate for any particular year 

equals the cost premium of not waiting for the following year’s price reduction less the 

added upfront utility savings benefits. The payments continue until the year that the costeffectiveness test is satisfied in an unsubsidized market. The result is as follows: 

( )

⎪

⎪

⎩

⎪

⎪

⎨

⎧

>=

⎥ <

⎦

⎤ ⎢

⎣

⎡

+ −−⎥

⎦

⎤ ⎢

⎣

⎡ ⎟

⎠

⎞ ⎜

⎝

⎛

⎟

⎠

⎞ ⎜

⎝

⎛

+ = − + +

D t for 

D t for

Benefit Added

r

U U

Premium Cost

Er

P I P L

tL

t

t

t t

0

1

1

1

1

64 744 4 844 64 74 4 84

(8) 

In this relationship, the “Cost Premium” is the premium the customer would have to pay 

to invest in year t rather than wait for a lower (discounted) price the following year t+1. 

The “Added Benefit” is the utility bill savings gained from investing in year t versus 

waiting for the following year. As desired, the optimum PBI payment It provides only 

the incremental additional incentive the customer would require to in order to invest in 

the given year. 

Suppose, for example, that the current price of a PV system is $6,000 per kW and the 

price next year is expected to be $5,500 per kW. Assume that the system produces 1,650 

kWh per kW per year, the utility bill savings rate is 12¢ per kWh currently and 30¢ per 

kWh in 30 years, and the discount rate is 10 percent. Equation (8) suggests that the “Cost 

Premium” is 60¢ per kWh, the “Added Benefit” is 10¢ per kWh, and the PBI rate is the 

difference between the two and equals 50¢ per kWh. 

In order to have a positive incentive, the “Cost Premium” due to waiting in Equation (8)

must exceed the “Added Benefit” due to investing immediately. There are cases, 

however, where the PV price is actually increasing. For example, suppose that rather 

than decreasing, the price of PV is expected to increase by 5 percent from $8,000 per kW 

to $8,400 per kW over the course of a year. Assume that the discount rate is 10 percent 

and the system produces 1,650 kWh per kW per year. The “Cost Premium” equals 22¢ 

per kWh.33 If the current utility rate is 10¢ per kWh and is expected to be 25¢ per kWh 

in 30 years, the “Added Benefit” equals 8¢ per kWh34 and the incentive rate should be set 

at 14¢ per kWh. Conversely, if the current utility rate is 30¢ per kWh and is expected to 

be 60¢ per kWh in 30 years, the “Added Benefit” equals 26¢ per kWh35 and the incentive 

rate should be set at 0¢ because the “Added Benefit” exceeds the “Cost Premium”. 

Verification 

The PBI payments are intended to provide a customer with the incentive to purchase PV, 

an otherwise uneconomic investment, until such time as the price of PV drops to a costcompetitive level and subsidies are no longer required. To minimize the cost to the utility 

 

33 ($8,000 - $8,400/1.1)/1,650 = 22¢. 

34 10¢ - 25¢/1.1^30 = 8¢. 

35 30¢ - 60¢/1.1^30 = 26¢. 

 51
(and its ratepayers), the value of these payments should equal, but should not exceed, the 

economic shortfall. This section demonstrates that the PBI structure provides the correct 

value to achieve this balance. 

Under such a balanced PBI structure, the customer would be considered to “break even” 

provided that the “Wait to Invest” alternative (in which the investment is made in a year 

W) resulted in the same value as a future, cost-competitive, unsubsidized PV system. By 

definition, this would first occur in year D, the year following the conclusion of the PBI 

program. Thus, it needs to be demonstrated that the customer investing in year W is 

made whole, deriving the same value as the customer investing in year D. 

The value of the “Wait to Invest” alternative is given by Equation (3) for any year W less 

than or equal to D with the assumption that the energy production E for any year is the 

same. The PBI structure from Equation (8) is substituted into Equation (3) (where t<D). 

As shown below, the result is that the value of the “Wait to Invest” alternative is equal to 

the value of an investment in year D, discounted to the current year. The conclusion is 

that PBI structure provides the correct level of subsidy to ensure that the early investment 

for any year between 0 and D is economically equivalent to the future, unsubsidized 

investment. 

That is, the value of investing at any time over the duration of the PBI program equals the 

value of the investment in the year after the PBI payments cease (year D), discounted to 

the current year. 

 52
( ) ( ) ( )

( )

( )

( ) ( )

( ) ( ) ( ) ( ) ( ) ( )

( ) ( ) ( )

( ) ( )

( ) ( )

64 744 4 8 644 4748Year Current

 to Discount

r

D Year in Investment

 of Value Present

P

r

EU

r

P

r

EU

r

P

r

EU

r

EU

r

P

r

EU

r

EU

r

P

r

P

r

EU

r

P

r

E

r

U U

Er

P

P

r

EU

r

P

r

EI

r

EU

W Year

in Investing

of Value

D D

L

t

t

Dt

D

D

DL

Dt

t

t

D

D

DL

WLt

t

t

WL

Dt

t

t

W

W

D

Wt

tL

tL

t

t

D

Wt

t

t

t

t

WL

Wt

t

t

W

W

D

Wt

t

L

tL

t

t

t WL

Wt

t

t

W

W

D

Wt

t

t

WL

Wt

t

t

⎥

⎦

⎤ ⎢

⎣

⎡

+

⎥

⎦

⎤ ⎢

⎣

⎡ − + =

+ − + =

+ − +

+

+ =

+

⎥ −

⎦

⎤ ⎢

⎣

⎡

+ − +

⎥ −

⎦

⎤ ⎢

⎣

⎡

+ − +

+

+ =

+ − +

⎪⎭

⎪

⎬

⎫

⎪⎩

⎪

⎨

⎧

⎥

⎦

⎤ ⎢

⎣

⎡

+ −−⎥

⎦

⎤ ⎢

⎣

⎡ ⎟

⎠

⎞ ⎜

⎝

⎛

⎟

⎠

⎞ ⎜

⎝

⎛

+ −

+

+ =

+ − +

+

+ =

∑

∑

∑∑

∑∑ ∑

∑∑

∑∑

−

=

+

−+

=

−+

+=

+−

=

−

=

+

+

−

=

+

+

+−

=

−

=

+ +

+−

=

−

=

+−

=

1

1

1

11

1 11

1 11 11 1

1 1

1

1

1

1

1 11

1

0

1

1 1

1 1

1

1

1

1

1

1

1 1

 53
Variable Specification 

The PBI rate structure can be completely specified using Equation (8). There are, 

however, several variables that are unknown in the current year. These variables include 

future utility bill savings and PV system prices. 

Future Utility Bill Savings

It will be assumed that utility bills savings will escalate at a consistent rate (e) over time 

so that the utility bill savings at time t is a function of the utility bill savings at time 0. 

( )t

t 0 1+= eUU (9) 

PV System Price

While PV system prices could be assumed to decline from time 0 at a fixed percentage, a 

more accurate approach would be to use a learning curve price model. A good review of 

learning curve price models can be found in [1]. The author, Margolis, provides a brief 

review of the literature that examines learning by doing, learning by using, and 

experience curves. The general form of the experience curve as applied to price 

estimates is: 

( )

( ) 2ln

ln

0

0

PR

t

t Q

Q PP ⎥

⎦

⎤ ⎢

⎣

⎡ = (10) 

where Qt is the cumulative quantity of installed PV capacity at time t and PR is the 

progress ratio. The Progress Ratio for crystalline PV modules is currently estimated to be 

0.82. A 0.82 progress ratio indicates that the price is reduced by 18 percent for a 

doubling of cumulative PV capacity. It is important to note that the price is sensitive to 

this progress ratio assumption. 

Using this formula, future PV capital costs are estimated based upon today’s cost and an 

estimate of future production.36

 

36 While the progress ratio presents the price in real terms, it will be used for the nominal price projection 

due to uncertainty in what is the correct progress ratio for a complete PV system rather than PV modules 

alone (see [1] for a presentation of the range of progress ratios for PV prices). 

 54
Sample PBI Results 

The PBI structure can be calculated based on the assumptions presented in Table 8 using 

Equations (8), (9), and (10).

37 While other cost-effectiveness tests could be used, it is 

assumed that the PV investment is cost-effective (and thus the point at which a PBI is no 

longer paid) when the PV Price is less than or equal to 10 times the corresponding year’s 

utility bill savings (i.e., the investment has a 10 year simple payback). The results are 

summarized in Table 9 and Figure 13. For this particular set of assumptions, the result 

is an optimized 13-year PBI that starts at 47¢, and declines to 10¢ in the final year. 

Table 8. Assumptions. 

Utility Savings

Average Utility Savings (U 0 ) $0.12 per kWh

Savings Escalation (e) 3% per year

PV Is Cost Effective When yrs savings > cost 10

PV System

Effective Price (P 0 ) $6,000 per kW

Energy Production (E) per kW per year 1,650

System Life (L ) years 30

PV Market Estimate

Installed PV Capacity (Q 0 ) GW 5.00

Annual PV Sales GW per year 1.50

Growth in Sales 20% per year

Progress Ratio (PR) 82%

General

Discount Rate (r) 10%

 

37 The price is assumed to be the price a commercial customer would pay after a 30 percent federal tax 

credit. 

 55
Table 9. PBI calculations. 

Cumulative PV 

Capacity (GW)

Annual PV 

Sales (GW)

Effective PV 

System Price

Utility 

Savings PBI Rate

2007 5.0 1.5 $6,000 $0.12 $0.47

2008 6.5 1.8 $5,566 $0.12 $0.41

2009 8.3 2.2 $5,190 $0.13 $0.36

2010 10.5 2.6 $4,857 $0.13 $0.32

2011 13.1 3.1 $4,559 $0.14 $0.28

2012 16.2 3.7 $4,288 $0.14 $0.25

2013 19.9 4.5 $4,040 $0.14 $0.22

2014 24.4 5.4 $3,812 $0.15 $0.20

2015 29.7 6.4 $3,601 $0.15 $0.18

2016 36.2 7.7 $3,404 $0.16 $0.15

2017 43.9 9.3 $3,220 $0.16 $0.13

2018 53.2 11.1 $3,048 $0.17 $0.11

2019 64.4 13.4 $2,887 $0.17 $0.10

2020 77.7 16.0 $2,735 $0.18 $0.00

2021 93.8 19.3 $2,592 $0.18 $0.00

2022 113.1 23.1 $2,457 $0.19 $0.00

2023 136.2 27.7 $2,330 $0.19 $0.00

2024 163.9 33.3 $2,209 $0.20 $0.00

2025 197.2 39.9 $2,095 $0.20 $0.00

2026 237.1 47.9 $1,988 $0.21 $0.00

$0.00

$0.10

$0.20

$0.30

$0.40

$0.50

$0.60

2007

2009

2011

2013

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

Value ($ per kWh)

PBI Rate

Utility Savings

Figure 13. Total value versus year. 

The results of this analysis can be verified by demonstrating that the customer is 

economically indifferent between investing and waiting during each year using the PBI 

 56
structure specified in Table 9. In order to do this, the utility bill savings are added to the 

PBI rate and then multiplied by the PV system output to give the annual benefits. The 

results are shown in Table 10. 

Table 10. Verification of customer timing indifference. 

Annual 

Benefit

30-yr NPV 

Savings

Effective PV 

System Price

Savings - 

Cost

Discounted 

Savings - Cost

2007 $968 $6,347 $6,000 $347 $347

2008 $876 $5,948 $5,566 $382 $347

2009 $803 $5,610 $5,190 $420 $347

2010 $743 $5,319 $4,857 $462 $347

2011 $691 $5,067 $4,559 $508 $347

2012 $647 $4,847 $4,288 $559 $347

2013 $608 $4,656 $4,040 $615 $347

2014 $573 $4,489 $3,812 $677 $347

2015 $541 $4,345 $3,601 $744 $347

2016 $512 $4,223 $3,404 $819 $347

2017 $486 $4,121 $3,220 $901 $347

2018 $462 $4,039 $3,048 $991 $347

2019 $440 $3,977 $2,887 $1,090 $347

2020 $291 $3,934 $2,735 $1,199 $347

2021 $299 $4,052 $2,592 $1,460 $384

2022 $308 $4,173 $2,457 $1,716 $411

2023 $318 $4,298 $2,330 $1,969 $428

2024 $327 $4,427 $2,209 $2,218 $439

2025 $337 $4,560 $2,095 $2,465 $443

2026 $347 $4,697 $1,988 $2,709 $443

For example, the benefits equal $968 in 2007 when the sum of the utility savings (12¢ per 

kWh) plus the PBI rate (47¢ per kWh) is multiplied by the annual output (1,650 kWh per 

installed kW). This is repeated for each year as shown in the first column. The second 

column presents the 30-year present value savings. The net present value to the customer 

in any particular year is the difference between the 30-year present value savings (second 

column) and the price (third column) and is presented in the fourth column. The final 

step is to discount future years to 2007 (fifth column). 

The table shows that the net present value of the investment from the perspective of 2007 

is constant during all years that the PBI is offered. This confirms that the customer is 

economically indifferent between investing immediately and investing at any time during 

the next 12 years when the PBI is offered. 

Notice also that the discounted savings minus cost is constant until after year 13, the 

point at which the cost-effectiveness test is satisfied. 

 57
Appendix E: Incentive Design That Shapes Market Demand 

Appendix D derived an incentive design that maintains a constant level of costeffectiveness for all customers entering the program at any point over its duration. This 

appendix derives an incentive design that maintains a steady incentive decline based on 

program goals. It uses the CSI program as an example of how this is applied (note that 

results are presented in units of system-AC). 

Analysis 

One approach to incentive design is to assume that the incentive is going to decline at a 

steady rate. This appendix begins with two assumptions: (1) the incentive rate declines 

linearly each year and (2) the market is growing at an exponential rate. Suppose that the 

year in which the PV is cost-competitive without an incentive is estimated to occur in 

year T. Mathematically, these two assumptions mean that at any time t less that T, the 

incentive equals: 

⎥

⎦

⎤ ⎢

⎣

⎡ −=

T

t IIt 1 0 (11)

The volume at time t equals: 

( )t

t 0 1+= gVV (12)

where g is the annual market growth rate (%) 

Total Volume

Since the market volume is growing at exponential rate as defined in Equation (12), the 

total volume installed over the life of the program can be determined. Let V0 represent 

the volume that will be installed in year 0, V1 represent the volume that will be installed 

in year 1, etc. The total volume installed over the life of the program equals the sum of 

each annual value until the year before systems become cost-effective without any 

incentive. 

∑

−

=

=

1

0

T

t

Total Volume Vt

(13)

Substituting Equation (12) into Equation (13) and solving, it can be shown that the total 

volume equals 

( ) 0 11

0 ⎥ ≠

⎦

⎤ ⎢

⎣

⎡ −+ = gfor

g

g Total VVolume

T

(14)

Equation (14) can be solved for the first year volume so that the total volume in 

conjunction with the growth rate is used to determine what the first year volume must be. 

 58
[ ] ( ) ⎥

⎦

⎤ ⎢

⎣

⎡

−+ = 11 0 T

g

g TotalV Volume (15)

Incentive Budget

Most incentive programs are constrained by the annual budgets they have available to 

spend over the life of the program. The total budget is calculated as the sum of the 

incentive rate times the annual volume summed over the life of the program. 

∑

−

=

=

1

0

T

t

Total Budget VI tt

(16)

Substituting for the definition of incentive at time t in Equation (11) and volume at time t 

in Equation (12), and then simplifying, the total budget equals 

( ) ( ) ⎥

⎦

⎤ ⎢

⎣

⎡ ⎟ + ⎠

⎞ ⎜

⎝

⎛ = ∑ −

−

=

1

0

00 11

T

t

t g T

t Total VIBudget (17)

Then, solving for the first year’s incentive: 

( ) ⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎣

⎡

⎟ + ⎠

⎞ ⎜

⎝

⎛ −

⎥

⎦

⎤ ⎢

⎣

⎡ =

∑

−

=

1

0

0

0

11

1 1

T

t

t g T

V t

TotalI Budget (18)

Substituting for the first year volume in Equation (15), the first year incentive equals 

( )

( ) ⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎣

⎡

⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛ ⎟ + ⎠

⎞ ⎜

⎝

⎛ −

⎥

⎦

⎤ ⎢

⎣

⎡ −+ ⎥

⎦

⎤ ⎢

⎣

⎡ =

∑

−

=

1

0

0

11

11 1

T

t

t

T

g T

g t

g

Total Volume

Total Budget I (19)

Summary

The key equations that have been developed include the volume over time, the incentive 

over time, the first year volume, and the first year incentive. These equations are 

summarized below. 

 59
( )

[ ] ( )

( )

( )

Effectiven-Cost to YearsT incentive no withess

Annualg (%) Rate Growth Market

g T

g t

g

Total Volume

Total Budget I

g

g TotalV Volume

where

elseTtfor T

t II

gVV

T

t

t

T

T

t

t

t

=

=

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎣

⎡

⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛ ⎟ + ⎠

⎞ ⎜

⎝

⎛ −

⎥

⎦

⎤ ⎢

⎣

⎡ −+ ⎥

⎦

⎤ ⎢

⎣

⎡ =

⎥

⎦

⎤ ⎢

⎣

⎡

−+ =

< ⎥

⎦

⎤ ⎢

⎣

⎡ −=

+=

∑

−

=

1

0

0

0

0

0

11

11 1

11

:

1 0,

1

(20)

One of the most important things to notice about Equation (20) is that the entire incentive 

structure is specified by four inputs: total budget, total volume, market growth rate, and 

years to cost effectiveness. 

Equivalence of Calendar and Volume-Based Incentive Triggers 

The previous subsection assumed that the incentive rate declines linearly each year. 

There has been the recommendation in the industry, however, that a change in the 

incentive should be triggered on a volume basis rather than a calendar basis. 

This subsection demonstrates that the results presented in Equation (20) are applicable to 

a volume-based trigger when the incentive rate is linearly related to the natural log of the 

market volume, a recommendation that has been implicitly made by a number of 

parties.38 That is, the incentive rate at time t (It) equals some slope (m) times the natural 

log of the volume at that same time (Vt) plus a y-intercept (b). 

( ) bVmIt ln t += (21)

Two sets of points are required to solve the equation for a line. Assume that one set is 

based on the start of the program (time t = 0) and the other set is at time T at the end of 

the program when the incentive rate is 0. The solution is: 

( ) () [ ] () ( ) 0

0

0

0 lnln

lnln

0 VV

VV

I

II t

T

t ⎥ −

⎦

⎤ ⎢

⎣

⎡

−

− +=

(22)

 

38 The linearly relationship between the incentive rate and the logarithm of the market volume is an implicit 

recommendation that has been made by a number of parties, one of which is the Joint Solar Parties filing in 

the CSI program [13]. 

 60
Equation (12) can be substituted into Equation (22) and simplified. 

[ ] ( ) ( ) { } [ ] ( ) ( )

() ( ) [ ] ( ) { } () ( ) [ ] ( )

[ ] ( )

[ ] ( )

( )

( )

⎥

⎦

⎤ ⎢

⎣

⎡ −=

⎭

⎬

⎫

⎩

⎨

⎧

+

+ −=

⎭

⎬

⎫

⎩

⎨

⎧

+

+ −=

−++ ⎪⎭

⎪

⎬

⎫

⎪⎩

⎪

⎨

⎧

−++

− +=

−+ ⎪⎭

⎪

⎬

⎫

⎪⎩

⎪

⎨

⎧

−+

− +=

T

t I

gT

gt II

g

g II

V Vg V Vg

I

I

VgV VgV

I II

T

t

t

T

t

t T

1

1ln

1ln

1ln

1ln

ln1lnln

ln1lnln

ln1ln

ln1ln

0

00

00

0 0

0 0

0

0

0 0

0 0

0

0

(23)

The result is that Equation (23) is identical to Equation (11), the assumption that the 

incentive is declining linearly over time. That is, assuming that the incentive is linearly 

related to the natural log of the market volume is analogous to assuming that the 

incentive is declining linearly over time when the market is growing at an exponential 

rate. 

Results 

The full incentive structure can be specified with the analytical approach summarized in 

Equation (20). Consider an example using the CSI program and the Joint Parties filing. 

Suppose that the only things known about the program are as follows: 

• Systems will be cost effective in 10 years without incentives (T = 10) 

• The market will grow at a rate of 35 percent per year (g = 0.35) 

• The program goal is to install 2,295 MWAC of PV (Cumulative Volume = 2295 – 

this should produce the equivalent amount of energy as 2,550 MWAC-CEC) 

• The total incentive budget is 85% of $2,500 Million or $2,125 Million (Budget = 

2125) 

Table 11 presents the incentive structure calculated using Equation (20) and Figure 14

presents the incentive administered on a volume basis as compared to the Joint Filing 

proposed incentive structure. While not exact, the results that are generated through the 

use of four input variables are comparable to the incentive structure proposed by the Joint 

Parties. 

 61
Table 11. Incentive schedule. 

Incentive ($/Watt) Volume (MW) Growth Rate Budget ($M)

2007 $2.99 42 $117

2008 $2.69 57 35% $142

2009 $2.39 77 35% $170

2010 $2.09 103 35% $201

2011 $1.79 140 35% $233

2012 $1.49 189 35% $262

2013 $1.19 254 35% $283

2014 $0.90 344 35% $286

2015 $0.60 464 35% $258

2016 $0.30 626 35% $174

Total 2,295 $2,125

Incentive Structures

$0.00

$0.50

$1.00

$1.50

$2.00

$2.50

$3.00

$3.50

0 500 1,000 1,500 2,000 2,500

Cumulative Volume (MWAC w/ 93% Design Factor)

Incentive Level ($/Watt)

Joint Parties Incentive

Modeled Incentive

Figure 14. Incentive administered on a volume basis for model and Joint Filing 

results. 

(Note: results are presented in system AC units, not AC-CEC units). 

 62
Conclusions 

This appendix derived an analytical model to design an incentive structure.39 The model 

is based on two assumptions: incentives are declining linearly (or incentive rates should 

be linearly related to the natural log of the annual sales) and annual PV sales grow at an 

exponential rate. 

Several results can be derived based on these assumptions. 

1. The incentive trigger mechanism can be either calendar-based or volume-based. 

2. The complete incentive structure and trigger mechanisms are specified by four 

input parameters: 

a. Total PV installations (MW) over the life of the program. 

b. Total program budget ($M). 

c. Years until cost-effectiveness is reached without incentives. 

d. Annual growth of program participation. 

 

39 This model can be applied to other technology incentive programs as well. 

 63
Appendix F: EPBB Incentive Calculation 

Introduction 

The EPBB incentive is one of the structures that has been identified in this Handbook. 

Two different formulas can be used to calculate the EPBB. This appendix reviews the 

strengths and weaknesses of the two formulas. It also provides some cautionary notes 

that need to be observed when a program is transitioning from a CBB to an EPBB 

incentive. 

Analysis 

A straightforward way to calculate an EPBB incentive is to define a baseline energy 

production incentive rate ($ per kWh) and multiply it by the simulated output of the 

designed system over some period of time. 

Incentive( ) = ( ) kWh for Output Simulated x /$RateEnergy $ ( ) kWh System Designed (24)

While this approach has intuitive appeal because of its simplicity, it has limitations 

because performance simulations are inherently subject to error, making it difficult to 

validate results without extended field testing. For this reason, it is desirable to derive a 

form of Equation (24) that minimizes simulation error and provides for direct field 

verification over a short period of time. 

First, consider how one would establish the Energy Rate ($ per kWh) presented in 

Equation (24). One needs to set an Incentive Rate ($ per kWAC), multiply it by the 

System Rating (kWAC), and divide the result by the simulated output (kWh) for some 

Reference System over a given time period (typically a year should be sufficient). 

That is, 

( ) ( ) ( )

for Output Simulated ( ) kWh System Reference

kWRating System x $/kWRate Incentive /$ AC AC RateEnergy kWh =

Substituting this back in to Equation (24), the Incentive equals the Energy Rate times the 

Simulated Output for Designed System (kWh). 

( ) ( ) ( )

( ) for Output Simulated ( ) kWh System Designed for Ouptut Simulated kWh System Reference

kWRating System x $/kWRate Incentive $ AC AC Incentive = x

The terms in this equation, however, can be rearranged as 

( ) ( ) ( ) ( )

for Output Simulated ( ) kWh System Reference

for Output Simulated kWh System Designed $ kWRating System x $/kWRate Incentive AC AC Incentive = x

This can be written as 

 64
( ) $ Incentive ( ) ( ) Factor esignDxkWRating Systemx $/kWRate Incentive = AC AC

where 

( )

for Output Simulated ( ) kWh System Reference

kWh System Actualfor Output Simulated Design Factor =

(25)

Equation (25) is identical to the CPUC’s proposed EPBB calculation when the Reference 

System is Fixed 30o South-facing with no shading [12]. 

Discussion 

While Equation (25) is more complex than Equation (24), it overcomes the key 

limitations of Equation (24) and offers a number of advantages as discussed below. 

Potential Performance Issues Are Disaggregated

All of the performance factors and sources of error are lumped into a single term (the 

Simulated Output for Designed System) in Equation (24). Equation (25), on the other 

hand, disaggregates the performance factors into two terms. Performance due to system 

rating issues is captured by the System Rating term; performance due to system 

orientation and shading issues (and location if the program chooses to include this) are 

captured by the Design Factor term. 

System Rating can be Verified

With Equation (25), the System Rating has the potential to be directly verifiable through 

field measurements. This is a fundamental feature that has been lacking throughout most 

capacity based incentive structures: most are operated using rating conventions that rely 

on calculated values but cannot be directly verified using field measurements. 

The System Rating in Equation (25) captures all of the losses and inefficiencies that make 

up the AC rating of the system. Rating inaccuracies for PV modules and inverters, 

internal wiring losses and other losses are captured by the System Rating. If desired, the 

incentive granting agency can specify test procedures to directly measure the System 

Rating. 

Design Factor can be Verified

The parameters used in the Design Factor can be verified by a simple visual inspection of 

the system. The inspection would confirm that it is installed with the planned orientation 

and with the shading factors as specified by the applicant. 

High Performance Systems are Rewarded

Since the EPBB incentive is tied to the System Rating and Design Factor, investors 

would take care to ensure that these factors are given due consideration. The incentive 

would help to ensure that efficient modules and inverters are used, that internal wiring 

losses are minimized, that design orientations more closely match the reference system, 

 65
and shading is minimized. These factors are likely to lead manufacturers to market more 

efficient equipment and to encourage system installers to design more effective systems. 

The Calculation is Less Susceptible to Modeling Error

In Equation (24), it is critical that both the model and weather data used in the incentive 

calculation be highly accurate. Paying an incentive that is highly dependent on model or 

weather data accuracy leads to uncertainty among investors who may challenge model 

and data accuracy. 

This situation is much more manageable with EPBB programs using Equation (25). 

Since the Design Factor is the ratio of two simulated quantities, relative model and data 

accuracy is of importance, not absolute accuracy. The Design Factor determines what 

percent of annual energy production the actual design should have relative to the 

Reference System. Since the Design Factor uses the same model and same weather data 

for both the numerator and the denominator, modeling and data inaccuracies are less 

critical.40

The form of the Design Factor in Equation (25) normalizes the results relative to the 

Reference System. Bias in any element of the modeling would be present in both the 

numerator and the denominator, tending to cancel out in the ratio. For example, if the 

simulation were based on an optimistic weather data set, the estimated performance of 

both the actual System and Reference System would be increased, and the individual 

errors would tend to cancel in the ratio. 

Transitioning from CBB to EPBB 

If a program is transitioning from a CBB to the EPBB incentive structure, two major 

factors need to be accounted for. First, most CBB incentives use a component-based 

rating while it is recommended that the EPBB calculation use a system AC rating. 

Second, the EPBB uses a Design Factor that introduces a penalty for all systems that have 

lower energy production than the Reference System. 

These two factors can be address by either (1) increasing the Incentive Rate or (2) 

defining a sub-optimal Reference System. In order to illustrate how this can be done, an 

example is presented using a program that is transitioning from a CBB to an EPBB. 

Adjustment Option: Increase Incentive

The first option is to increase the incentive. Suppose that the average PV system installed 

in the program produces 94 percent as much energy as the Reference System. Suppose 

that a customer installs a 100 kWAC-CEC and the program wants to maintain economic 

parity with the existing incentive of $2.50/WattAC-CEC. 

As presented in Table 12, the customer would receive $250,000 under the current CBB 

program. What would it require for a customer to be equally well off under the EPBB 

structure? A 100 kWAC-CEC is equivalent to 90 kWAC and the Design Factor is 94 percent. 

 

40 This is true when the Design Factor does not incorporate a location element. 

 66
As a result, the EPBB Incentive Rate needs to increase by 18 percent to $2.96/WattAC to 

provide the customer with the same economic benefit as the CBB incentive. 

Table 12. Incentive comparisons (higher Incentive Rate). 

SGIP Program

EPBB Staff Proposal w/ 

Higher Incentive Rate

Intentive Rate ($/Watt) $2.50 $2.96

Rating Calculation

Number of Modules 1,000 1,000

PV PTC Module Rating (W) 105.2 105.2

Inverter Efficiency 95% 95%

Other Losses - 90%

Estimated Rating (kW) 99.9 89.9

Design Factor - 94%

Incentive Amount ($K) $250 $250

Adjustment Option: Use Different Reference System

The second option is to use a different reference system. An analysis was performed 

using the Clean Power Estimator for a system in San Jose, CA.41 A fixed 30o southfacing system with no shading is estimated to have a DC-based capacity factor of 16 

percent. A recent report by the California Energy Commission, however, found that the 

average DC-based capacity factor for systems including the effect of orientation and 

shading was 15 percent [7]. Thus, based on the CEC report, it appears that systems have 

an average of 6 percent design losses. When the 6 percent design losses are combined 

with the 10 percent rating losses, the result is a combined loss of 15 percent. 

In order to compensate for this loss through the Design Factor, the Reference System 

needs to be chosen to have an expected output that is 85 percent of a fixed 30o southfacing system with no shading.42 Analysis using the Clean Power Estimator suggests that 

one system that fits this description is a horizontal system with 5 percent shading losses. 

The capacity factors for various system configurations are presented in the top part of 

Table 13. The Design Factors using a horizontal system with 5 percent shading losses as 

the Reference System are presented in the bottom part of Table 13. 

 

41 PV Watts is another on-line simulation tool. It does not, however, have the capability of performing a 

shading analysis as is incorporated into the Clean Power Estimator 

(http://www.njcep.com/html/estimator_f.html). The Clean Power Estimator was run with 10 percent PV 

Output Adjustment to be consistent with PV Watts 0.77 derating factor. 

42 1/0.85 = 1.18. 

 67
Table 13. Capacity factor and design factor 

(San Jose, CA using Clean Power Estimator). 

Capacity Factor (Based on DC Rating)

Degrees of Shading

Tilt 0 5 10 15 20

Horizontal 14.2% 14.2% 14.1% 14.0% 13.5%

10 15.2% 15.2% 15.0% 14.8% 14.3%

20 15.8% 15.8% 15.6% 15.3% 14.7%

30 16.0% 15.9% 15.7% 15.4% 14.8%

Design Factor (Reference: Horizontal System, 5% or 20o Shading)

Degrees of Shading

Tilt 0 5 10 15 20

Horizontal 105% 105% 104% 103% 100%

10 112% 112% 111% 109% 106%

20 117% 116% 115% 113% 109%

30 118% 118% 116% 114% 109%

To illustrate how the calculations work, assume that a customer installs a fixed 10o southfacing system with minor shading (i.e., a system with a 15 percent DC capacity factor). 

As presented in Table 13, the Design Factor for this system is 111 percent. Assume that 

the Incentive Rate is $2.50 per WattAC-CEC under the CBB incentive and remains at $2.50 

per WattAC under the EPBB incentive program. Table 14 demonstrates that the total 

incentive is $250K for both structures. 

Table 14. Incentive comparisons (Reference System is horizontal w/ shading). 

SGIP Program

EPBB Staff Proposal w/ 

Modified Design Factor

Intentive Rate ($/Watt) $2.50 $2.50

Rating Calculation

Number of Modules 1,000 1,000

PV PTC Module Rating (W) 105.2 105.2

Inverter Efficiency 95% 95%

Other Losses - 90%

Estimated Rating (kW) 99.9 89.9

Design Factor - 111%

Incentive Amount ($K) $250 $250

 68
Conclusions 

The EPBB incentive allows the market to transition to an incentive that is performance 

based. The EPBB structure creates an incentive calculation that has the potential to 

provide some (but not all) of the benefits of a PBI structure. In particular, 

1. Short duration field testing (as yet to be fully specified) and visual inspection can 

verify the accuracy of critical factors that affect energy production 

2. The incentive can be adjusted for the expected energy production of the system by 

using a verified system rating (thus promoting efficient components and good 

installations) 

3. The incentive is adjusted for expected energy production of the system due to 

orientation and shading (thus promoting effective system design) 

4. The incentive calculation procedure is not highly sensitive to modeling and data 

accuracy (thus resulting in greater program objectivity) 

Transitioning from a CBB structure to the EPBB incentive structure may result in a 

reduction in the incentive for fixed PV systems when compared to a CBB program. If the 

goal is to retain a total incentive amount that is unchanged compared to existing CBB 

incentive levels, an adjustment needs to be made to the EPBB incentive calculation. This 

can be accomplished by modifying either the Incentive Rate or the Reference System. 

 69
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Photovoltaic Incentive Design Handbook 

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NREL/SR-640-40845 

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PVC6.1301 

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T.E. Hoff 

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Clean Power Research 

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NREL Technical Monitor: Robert Margolis 

14. ABSTRACT (Maximum 200 Words)

Investments in customer-owned grid-connected photovoltaic (PV) energy systems are growing at a steady pace. 

This is due, in part, to the availability of attractive economic incentives offered by public state agencies and utilities. 

In the United States, these incentives have largely been upfront lump payments tied to the system capacity rating. 

While capacity-based “buydowns” have stimulated the domestic PV market, they have been criticized for subsidizing 

systems with potentially poor energy performance. As a result, the industry has been forced to consider alternative 

incentive structures, particularly ones that pay based on long-term measured performance. The industry, however, 

lacks consensus in the debate over the tradeoffs between upfront incentive payments versus longer-term payments 

for energy delivery.This handbook is designed for agencies and utilities that offer or intend to offer incentive 

programs for customer-owned PV systems. Its purpose is to help select, design, and implement incentive programs 

that best meet programmatic goals. The handbook begins with a discussion of the various available incentive 

structures and then provides qualitative and quantitative tools necessary to design the most appropriate incentive 

structure. It concludes with program administration considerations. 

15. SUBJECT TERMS

NREL; Clean Power Research; Solar Energy Technologeis Program; solar; PV; photovoltaics; performance based 

incentive; PBI; expected performance based buydown; EPBB; PV incentives; utilities; incentive programs; customers; 

California Solar Initiative; market demand; green tags; net metering; Robert Margolis. 

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