NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Renewable Energy Cost 

Modeling: A Toolkit for 

Establishing Cost-Based 

Incentives in the United States 

March 2010 — March 2011

Jason S. Gifford and Robert C. Grace

Sustainable Energy Advantage, LLC

Framingham, Massachusetts

Wilson H. Rickerson

Meister Consultants Group, Inc.

Boston, Massachusetts

Subcontract Report

NREL/SR-6A20-51093

May 2011
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

1617 Cole Boulevard

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Renewable Energy Cost 

Modeling: A Toolkit for 

Establishing Cost-Based 

Incentives in the United States 

March 2010 — March 2011

Jason S. Gifford and Robert C. Grace

Sustainable Energy Advantage, LLC

Framingham, Massachusetts

Wilson H. Rickerson

Meister Consultants Group, Inc.

Boston, Massachusetts

NREL Technical Monitor: Karlynn Cory

Prepared under Subcontract No. LEU-9-99277-01 

Subcontract Report

NREL/SR-6A20-51093

May 2011
This publication received minimal editorial review at NREL.

NOTICE

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Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately 

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trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors 

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iii

Acknowledgments

This work was funded by three programs of the U.S. Department of Energy’s (DOE) Energy 

Efficiency and Renewable Energy (EERE) Division. The authors wish to thank participating 

staff—Jennifer DeCesaro from the EERE Solar Program, Carla Frisch and Cody Taylor from the 

EERE Corporate Analysis Program, Arlene Anderson from the EERE Geothermal Program, 

Kate Young and Chad Augustine from the National Renewable Energy Laboratory, and Kevin 

Porter of Exeter Associates—for providing useful insights and overall direction for this project. 

This work was performed in conjunction with the National Association of Regulatory Utility 

Commissioners (NARUC). The authors are very grateful to Julie Taylor and Bevan Flansburg 

(former NARUC staff) for their participation. The authors are also grateful for the guidance and 

helpful input of the project managers—Karlynn Cory, Lori Bird, Mike Mendelsohn, and Claire 

Kreycik of NREL. Additionally, the authors appreciate the input of Robert Margolis, Gian Porro, 

Thomas Schneider, David Kline, and Jim Newcomb of NREL. 

We are grateful for—and wish to thank—our external peer reviewers—Matt Karcher of Deacon 

Harbor Financial and Ryan Pletka, Mon Hong, and Carrie Bellamy of Black & Veatch Corp. for 

their expert counsel during the final development stages of this report and the associated models. 

We also thank the individuals who provided invaluable feedback during the research and 

development phases of this report—particularly Tom Stanton (National Regulatory Research 

Institute); Matt Baker and Rich Mignogna (Colorado Public Utilities Commission); John Cole 

and Stacy Kawasaki (Hawaii Public Utilities Commission); Jesse Harlow and Katherine Trachsel

(Michigan Public Services Commission); Steve Johnson (Washington Utilities and 

Transportation Commission); John Crider (Gainesville Regional Utilities); Jim MacDougall 

(Ontario Power Authority); Maike Schmidt (Zentrum für Sonnenenergie- und WasserstoffForschung); Hans Cleijne, Jitske Burgers, and Caroline Faasen (KEMA BV); Toby Couture (E3 

Analytics); David Jacobs (Freie Universität Berlin); Tyler Leeds and Silas Bauer (Sustainable 

Energy Advantage); and Andy Belden (Meister Consultants Group). We offer our appreciation to 

Kathryn Craddock (Sustainable Energy Advantage) for providing detailed editorial support.
iv

List of Acronyms

Act 45 2009 Vermont Energy Act 

ADEME French Environment and Energy Management Agency 

(Agence de l’Environnement et de la Maîtrise de l’Energie)

COE cost of energy 

CREB clean renewable energy bond 

CREST Cost of Renewable Energy Spreadsheet Tool

CRF capital recovery factor

DCF discounted cash flow 

DOE U.S. Department of Energy 

DSIRE Database of State Incentives for Renewable Energy

ECCR economic carrying-charge rate

ECN The Energy Research Centre of the Netherlands

(Energieonderzoek Centrum Nederland) 

FCR fixed-charge rate

FIT feed-in tariff

GETEM Geothermal Electricity Technology Evaluation Model 

GRU Gainesville Regional Utilities

H2A DOE Hydrogen Analysis 

IRR internal rate of return

ITC investment tax credit

kWh kilowatt-hour 

LCOE levelized cost of energy

NARUC National Association of Regulatory Utility Commissioners

NPV net present value 

NREL National Renewable Energy Laboratory 

O&M operations and maintenance 

PI profitability index

PIM profitability index method

PPA power purchase agreement

PSB Vermont Public Service Board

PTC production tax credit

PV photovoltaic 

REPI Renewable Energy Production Incentive

RESOP Renewable Energy Standard Offer Program 

RETI Renewable Energy Transmission Initiative

ROE return on equity

SAM Solar Advisor Model 

SPEED Sustainably Priced Energy Enterprise Development Program 

(Vermont)

T&D transmission and distribution infrastructure

ZSW Zentrum für Sonnenenergie- und Wasserstoff-Forschung 
v 

Executive Summary

Policymakers across the United States continue to explore and consider a wide range of 

renewable energy (RE) incentive structures. Consideration of these policy alternatives is 

typically driven by either proposed or adopted legislative or regulatory mandates and often draws 

on input from diverse stakeholder groups. One of the key policy questions is the approach to 

setting the incentive level. Different approaches to setting incentive levels include: competitive 

policies (such as requests for proposals and auctions), value-based policies (in which incentive 

payments may differ by time of production or location), and cost-based policies (which may be 

performance- or capacity-based and are sized relative to the cost to install and operate a 

particular subcategory of renewable energy assets). 

The advantages and disadvantages of each type of incentive must be considered with the 

applicable jurisdiction’s policymakers’ objectives in mind. In this context, cost-based policies 

have been the garnering increased attention and are currently implemented widely throughout 

Europe and North America. As a result, this report focuses on renewable energy cost modeling 

and possible techniques, tools, and approaches for establishing cost-based incentives in the 

United States.

From an historic and international perspective, the feed-in tariff (FIT) is one of the most 

commonly used cost-based incentive tools. A FIT is a policy that typically provides eligible 

renewable generators with guaranteed access to a predictable, long-term revenue stream from a 

creditworthy buyer. FITs are generally cost-based in design and performance-based in execution 

(i.e., the FIT payment is sized to cover both installation and operating costs, but the tariff is only 

paid for actual energy production). FITs and other cost-based incentives are currently offered in 

over 50 countries worldwide. Similar policies have recently been implemented in Florida1

This report is intended to serve as a resource for policymakers who wish to learn more about 

establishing cost-based incentives. The report will identify key renewable energy cost modeling 

options, highlight the policy implications of choosing one approach over the other, and present 

recommendations on the optimal characteristics of a model to calculate rates for cost-based 

incentives, FITs, or similar policies. These recommendations will be utilized in designing the 

Cost of Renewable Energy Spreadsheet Tool (CREST). Three CREST models will be publicly 

available and capable of analyzing the cost of energy (COE) associated with solar, wind, and 

geothermal electricity generators. (The CREST models and user manual are now available for 

download at 

 and 

Vermont. 

http://financere.nrel.gov/finance/content/CREST-model.) 

The CREST models will be developed for use by state policymakers, regulators, utilities, 

developers, and other stakeholders to assist them in current and future rate-setting processes for 

both FIT and other renewable energy incentive payment structures and policy analyses.

Taxonomy of Calculation Methodologies

The report defines and qualitatively compares different methodologies for calculating the COE in 

order to identify a preferred methodology for the CREST model. COE methodologies can be 

 

1 In the Gainesville Regional Utility service territory.
vi 

broadly grouped into cash flow forecasts and recovery factor analyses. The discounted cash flow 

(DCF) method is the primary example of a cash flow forecast. The recovery factor analysis 

methods include the capital recovery factor, fixed-charge rate, and economic carrying-charge 

rate. Other investment analysis tools include simple payback and the profitability index method 

(PIM), which is used to compare projects based on the net present value created per dollar of 

investment. 

The report finds that the DCF approach is the preferred methodology for the CREST 

model. A DCF model is the most versatile tool for calculating after-tax cash flows and can take 

into account U.S. federal tax incentives, which play a significant role in renewable energy 

finance and market development. A DCF model also provides a transparent year-by-year 

analysis that can foster stakeholder participation and support. 

Survey of Existing Renewable Energy Cost Modeling Tools

The report reviews existing cost modeling tools in order to identify relevant industry best 

practices and enable the CREST model to build upon previous work. Included in the survey are: 

• California Renewable Energy Transmission Initiative (RETI) model (Black & 

Veatch Corp.) 

• The State of Vermont, Standard Offer models 

• The RETScreen model (Canadian government) 

• The Gainesville Regional Utilities Feed-In Tariff model 

• The System Advisor Model (SAM) (National Renewable Energy Laboratory) 

• The European Union’s Photovoltaic (PV) Technology Platform 

• The Vote Solar Initiative model 

• The Geothermal Electricity Technology Evaluation Model (GETEM) (U.S. DOE).

No single publicly available analytical tool was found to be both simple to use and robust 

enough to take into account the wide array of existing incentives when designing cost-based 

FITs or similar incentive payment structures. However, a variety of useful features were 

found in these models and subsequently integrated into the Cost of Renewable Energy 

Spreadsheet Tool (CREST) model. These features include a “quick-start guide,” “notes” in the 

spreadsheet cells to guide users, user-driven selection of different levels of analysis detail, inputs 

for the most common federal and state tax incentives, and graphic representation of results. A 

table summarizing the survey of existing models can be found in Appendix A. 

Industry Experience with Feed-In Tariff Calculation Methodology 

Because FITs are one of the most common cost-based renewable energy incentives in use today, 

this report reviews the FIT rate-setting methodologies and processes in five jurisdictions that 

designed their own FIT calculation models. These markets include Germany; the Netherlands; 

Ontario, Canada; Gainesville, Florida; and Vermont. The report finds that there are common 

approaches to FIT rate-setting methodologies internationally, even if the inputs and assumptions 

used in models vary across the jurisdictions surveyed. With few exceptions, the models are 

characterized by comparative simplicity, DCF after-tax analysis, assumed private commercial 
vii 

ownership, the use of IRR as an evaluation metric, an assumption that other incentives are taken 

into account when calculating the rate, and the aggregation of cost inputs. These commonalities, 

as well as areas of divergence, are a useful benchmark for the development of the CREST model. 

A table summarizing the survey of international rate setting approaches can be found in 

Appendix B.

Model Inputs

The report discusses different approaches to structuring COE model inputs and the types of 

inputs that are typically employed in COE models. The report highlights that models need to 

balance simplicity and ease of use; accuracy, precision, and representativeness; and data 

granularity. The report finds that the preferred design for the CREST model would accommodate 

a wide range of inputs and levels of complexity. Specifically, the preferred design for the 

CREST model would: 

• Allow for simple, intermediate, or complex installed cost inputs so that the model is 

useful in the full range of potential policymaking processes 

• Enable the analysis of different ownership and financing structures 

• Enable recognition of the full range of typical financing and development costs 

• Focus on after-tax returns, assuming that investors can monetize tax incentives

• Enable consideration of the most common federal and state incentives.

Policy Decisions

The report highlights the fact that both the approach to building a COE model and the approach 

to modeling itself involve policy decisions that can have important implications for renewable 

energy incentive development and implementation. These choices include, for example, the type 

and detail of input data gathered, the degree of technology differentiation, and the comparative 

aggressiveness or conservativeness of the approach used to set rates. The report also discusses 

factors that could shape the modeling process, such as legislative constraints on the rate-setting 

process, whether the process is adjudicatory or based on stakeholder engagement, the familiarity 

of the regulators or stakeholders with cost methodologies, and the availability of accepted tools. 

Conclusion

The report concludes by outlining the preferred design criteria for the CREST model. In order to 

create a flexible, easy-to-use, and robust rate-setting tool, the CREST model would: 

• Be designed to accommodate a range of policymaking processes 

• Utilize a DCF approach with the COE calculated based on after-tax returns and take 

into account the availability of available federal and state incentives 

• Include key features observed in other models:

o An introduction worksheet embedded in the model, which serves as a “quickstart guide” 

o Descriptive, embedded notes providing an explanation of the appropriate use 

of many input cells, as well as the typical range of values for some inputs 
viii

o Frequent use of “check” cells, which provide visual cues to help ensure that 

the user populates all cells required to operate the model

• Provide users the option to enter inputs at one of three levels of detail

• Allow tariff differentiation based on size, resource quality, or other factors

• Model both private and public ownership scenarios 

• Allow a range of capital structures

• Promote graphic representation of results

• Promote transparency and simplicity in the rate-setting process wherever possible.

The preferred CREST model design would utilize the after-tax DCF approach with the internal 

rate of return (IRR) as a target input in order to calculate the COE. This COE would be used to 

inform the rate-setting process. In contrast to the models used in other jurisdictions, however, 

the CREST model design would support a greater degree of cost input detail, enable analysis of 

both publicly and privately owned projects, and allow the inclusion of all or a portion of various 

incentives. The preferred design would not be so complex as to render the CREST model 

difficult to use across different regulatory decision-making contexts in numerous states. 
ix

Table of Contents

List of Figures ........................................................................................................................................... xii

List of Tables ............................................................................................................................................. xii

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

1.1 Background ..........................................................................................................................1

1.2 Report Purpose and Design of CREST Model ....................................................................2

1.3 Orientation of This Report: Cost-Based Incentive Mechanisms .........................................3

1.3.1 Differentiated Cost of Eligible Resources ..................................................................4

1.3.2 A “Fixed-Price” Payment Schedule Measured in Nominal Dollars ...........................4

1.3.3 An All-In “Bundled” Payment Structure ....................................................................4

1.3.4 Long-Term Payments..................................................................................................4

1.3.5 100% of Output Sold to the Interconnecting Utility ...................................................5

1.4 Structure of this Report ........................................................................................................5

2 Taxonomy of Calculation Methodologies .......................................................................................... 6

2.1 Overview of Calculation Methodologies .............................................................................6

2.2 Discounted Cash Flow Analysis ..........................................................................................7

2.3 Recovery Factor Analyses ...................................................................................................8

2.3.1 Capital Recovery Factor .............................................................................................8

2.3.2 Fixed-Charge Rate ......................................................................................................8

2.3.3 Economic Carrying-Charge Rate ................................................................................8

2.4 Other Investment Analysis Tools .........................................................................................9

2.4.1 Simple Payback ...........................................................................................................9

2.4.2 Profitability Index Method ..........................................................................................9

2.5 Conclusion: Applying the Discounted Cash Flow Model .................................................10

3 Survey of Existing Renewable Energy Cost Modeling Tools ........................................................ 12

3.1 Survey of Existing Financial Models .................................................................................12

3.2 Renewable Energy Transmission Initiative Cost of Generation Model ............................13

3.3 Vermont Standard Offer Models .......................................................................................13

3.4 RETScreen .........................................................................................................................14

3.5 Gainesville Regional Utilities FIT Model ..........................................................................14

3.6 System Advisor Model ......................................................................................................14

3.7 European Union Photovoltaic Technology Platform .........................................................15

3.8 Vote Solar Incentive Comparison Model ..........................................................................15

3.9 Geothermal Electricity Technology Evaluation Model .....................................................16

3.10 Additional Models ............................................................................................................16

3.10.1 ECN Financial Gap Calculation Model ..................................................................16

3.10.2 Hydrogen Analysis Production Cash Flow Analysis Tool .....................................17

3.11 Conclusion .......................................................................................................................17

4 Industry Experience with FIT Calculation Methodology................................................................. 19

4.1 Survey Methodology ..........................................................................................................19

4.2 Comparing the Spreadsheet Models ..................................................................................20

4.2.1 Calculation Methodology..........................................................................................21

4.2.2 Tax Treatment ...........................................................................................................21

4.2.3 Assumed Ownership Structure .................................................................................21

4.2.4 Evaluation Metrics for Return on Invested Capital ..................................................22

4.2.5 Assumed Capital Structure .......................................................................................22
x 

4.2.6 Treatment of Incentives ............................................................................................23

4.2.7 Tariff Rate Escalation ...............................................................................................24

4.2.8 Accounting for Operating Cost Inflation ..................................................................24

4.2.9 Granularity of Cost Input Detail ...............................................................................24

4.2.10 Key Observations from Comparison of Spreadsheet Models .................................25

4.3 Comparing the Rate-Setting Process .................................................................................25

4.3.1 Data Sources .............................................................................................................25

4.3.2 Process Transparency and Stakeholder Involvement ...............................................26

4.4 Approval Process ...............................................................................................................26

4.5 Conclusion .........................................................................................................................26

5 CREST Model Inputs .......................................................................................................................... 28

5.1 Model Inputs and Their Context ........................................................................................28

5.1.1 Implications on Cost Modeling Dictated by the Process ..........................................28

5.1.2 Implications of Policy Objectives on Model Characteristics ....................................28

5.1.3 Implications of Model Use ........................................................................................28

5.1.4 Finding the Right Balance Among Trade-Offs in Model Design .............................29

5.2 Total Installed Cost ............................................................................................................30

5.3 Operating Costs ..................................................................................................................32

5.4 Government Incentives ......................................................................................................33

5.5 Ownership and Capital Structure .......................................................................................35

5.5.1 Ownership Structure .................................................................................................35

5.5.2 Modeling Options for Determining Capital Structure ..............................................37

5.5.3 Sources of Capital .....................................................................................................38

5.5.4 Cost of Capital ..........................................................................................................40

5.5.5 Term ..........................................................................................................................41

5.6 Conclusion .........................................................................................................................41

6 Policy Decisions ................................................................................................................................. 43

6.1 Cost-Modeling Factors Dictated by Data Sources .............................................................43

6.2 Cost-Modeling Factors Dictated by the Policymaking Process .........................................45

6.3 Cost-Modeling Factors Dictated by Policy Objectives ......................................................45

6.4 Range of Cost of Energy Variance Associated with Technology-Specific Input 

Assumptions .......................................................................................................................45

6.4.1 Feed-In Tariff Rate Differentiation ...........................................................................46

6.4.2 Aggressive Versus Conservative FIT Rate Setting ...................................................47

6.5 How Will Model Results be Used to Set FIT Rates? .........................................................50

6.6 Modeling Rate Changes Over Time ..................................................................................50

7 Conclusion: The Cost-Based Model Methodology and Spreadsheet Tool Design 

Characteristics.................................................................................................................................... 51

7.1 Lessons Learned from Survey of Existing Models ............................................................51

7.2 Lessons Learned from Industry Experience with FIT Calculation Methodology .............52

7.3 Cost Modeling Methodology: Discounted Cash Flow .......................................................52

7.4 Output Metrics and Constraints .........................................................................................53

7.5 Data Inputs and Level of Data Granularity ........................................................................53

7.6 Model Data Flow Chart .....................................................................................................54

7.7 CREST Models ..................................................................................................................56

References................................................................................................................................................. 57

Resources.................................................................................................................................................. 62

Appendix A. Summary of Survey of Existing Models ........................................................................... 63
xi 

Appendix B. Survey of Industry Experience with FIT Calculation Methodology ............................... 66

The Netherlands’ Tariff Calculation Case Study .....................................................................69

Gainesville Regional Utilities’ Tariff Calculation Case Study ................................................70

Vermont’s Tariff Calculation Case Study ................................................................................72

Ontario’s Tariff Calculation Case Study ..................................................................................73

Germany’s Tariff Calculation Case Study ...............................................................................75
xii 

List of Figures

Figure 1. Adapted from a Competitive Renewable Energy Zone in a study of California’s 

Renewable Energy Transmission Initiative ................................................................ 50

Figure 2. CREST model schematic data flow chart ...................................................................... 55

List of Tables

Table 1. Summary of Calculation Methodologies .......................................................................... 7

Table 2. Summary of Total Installed Cost Inputs ......................................................................... 31

Table 3. Summary of Operating Inputs Options ........................................................................... 32

Table 4. Summary of Modeling Options for Federal, State, and Local Incentives ...................... 33

Table 5. Summary of Eligibility for Federal Incentives ............................................................... 35

Table 6. Summary of Ownership Structures and Financial Modeling Implications ..................... 36

Table 7. Summary of Capital Structure Options ........................................................................... 37

Table 8. Summary of Capital Structure Components ................................................................... 38

Table 9. Summary and Categorization of Sources of Capital Available for Project Financing ... 39

Table 10. Summary of Cost of Capital Modeling Options ........................................................... 41

Table 11. Summary of Modeling Complexity Recommended for FIT Rate-Setting Tool ........... 54

Table A-1. Summary of Limitations and Useful Features of Existing Models ............................ 64

Table B-1. Case Studies Highlighting FIT Rate-Setting Models and Processes in Five 

Jurisdictions ................................................................................................................ 66
1 

1 Introduction

1.1 Background

From an historic and international perspective, the feed-in tariff (FIT) is the most commonly 

used cost-based incentive tool. A FIT is a policy that typically provides eligible renewable 

generators with guaranteed access to a predictable, long-term revenue stream from a 

creditworthy buyer. While FITs are generally cost-based in design, they are regularly 

performance-based in execution (i.e., the size of the FIT payment is based on the development 

and operating costs, but the FIT is only paid in response to energy production). 

Cost-based tariff policies have spread rapidly throughout the world during the past several years, 

and the number of countries with national FIT policies increased from 37 in 2007 to more than 

50 in 2009 (DB Climate Change Advisors 2010; Martinot 2008; Martinot and Sawin 2009).Costbased incentives, such as FITs, have been slow to emerge in the United States. Unlike Europe, 

the United States historically has used tax incentives to spur renewable energy development; 

however, there has been a sharp increase in interest in FIT policy development during the past 

few years. At the beginning of 2006, FIT policy activity was limited to exploratory regulatory 

proceedings and workshops in a handful of states (Rickerson and Grace 2007). As of October 

2009, however, FITs were being considered at the federal, state, local, and utility levels (Couture 

and Cory 2009). 

At the regional and local levels, some cities and utilities have been exploring cost-based 

incentives using FITs. Gainesville Regional Utilities (GRU) (GRU 2009) established the nation’s 

first municipal FIT in February 2009 and provides photovoltaic (PV) generators a 20-year 

payment of $0.32/kWh. The Sacramento Municipal Utility District in August 2010 announced a 

FIT for up to 100 MW of either renewable energy or combined-heat-and-power generators 

(SMUD 2010), and CPS Energy in San Antonio, Texas, in September 2009 announced a solar 

FIT similar to Gainesville’s (CPS 2010). Several other cities also have been exploring municipal 

FITs.2

In Michigan, Consumers Energy launched a pilot FIT for solar in 2009 (Consumers Energy 

2009),3

 and the Indiana Utility Regulatory Commission approved a FIT rate for Indianapolis 

Power and Light service territory in 2010 (Indiana Utility Regulatory Commission 2010).4

In response to the sharp increase in U.S. FIT development, a number of recent studies have 

sought to catalog international FIT policy best practices and discuss potential U.S. FIT designs 

(Ahn et al. 2009; Burgie and Crandall 2009; Couture et al. 2010; DB Climate Change Advisors 

2009; Grace et al. 2009; Grace et al. 2008; KEMA 2008; Pollock and McNamara 2010; 

 

2 Both Santa Monica, California, and Palm Desert, California, announced plans to pursue FITs, but state legislation 

submitted to develop FITs for each city [S.B. 523, 2009–2010 Sess. (Ca. 2009) and A.B. 432, 2009–2010 Sess. (Ca. 

2009), respectively] has not been passed. The City of Los Angeles also has proposed to develop FITs to deploy 

150 MW of PV. 3 The program had a cap of 2 MW, and the queue for the incentive was filled quickly.

4 Several utilities around the country have established programs under which they purchase electricity and renewable 

energy certificates from their customers under long-term contracts to supply their voluntary green power programs. 

These include several Wisconsin utilities (i.e., WE Energies, Alliant, and Madison Gas and Electric) and the 

Tennessee Valley Authority. These policies are similar to FITs but tend to be limited in size and are tied to voluntary 

green power demand.
2 

Rickerson et al. 2007). A common theme to most of these studies is the importance of setting the

“correct” FIT rate. As stated in a recent National Renewable Energy Laboratory (NREL) report 

(Cory et al. 2009, p. 11): 

Detailed analysis is required to properly set the payment level at the outset. The 

payment level must ensure revenues will be adequate to cover project costs. If the 

FIT payments are set too low, then little new RE development will result. And if 

set too high, the FIT could provide unwarranted profits to developers. 

FIT rates set and maintained at too high a level can lead to overpayment by ratepayers and 

taxpayers. In turn, this results in abrupt changes in the policy, undermining the very investor 

security and market growth that the policy was intended to encourage. 

Although some U.S. industry commentators have expressed anxiety about the lack of established 

FIT rate-setting methodologies,5

This report is intended to serve as a resource for policymakers who wish to learn more about 

establishing cost-based incentives. This report provides insights into the process, analytical 

tools, and policy and modeling decisions that state policymakers must undertake if they wish to 

apply such cost-based incentives to support renewable energy development. 

 policymakers might find just the opposite—there are too many 

policy options and rate-setting methodologies to choose from. During the past several decades in 

the United States and abroad, a wide range of formal analytical approaches has been employed to 

estimate the cost of electricity and set incentive levels for both renewable and fossil-fueled 

generators. As a result, policymakers can choose different approaches to discovering generation 

data, building quantitative models, and engaging industry stakeholders—approaches that vary 

considerably in complexity and transparency. 

1.2 Report Purpose and Design of CREST Model

State utility commissions, in conjunction with the National Association of Regulatory Utility 

Commissioners (NARUC) and the U.S. Department of Energy (DOE) Solar Energy 

Technologies Program, have asked NREL for assistance in devising a calculation methodology 

that helps determine cost-based tariff or incentive levels that might be used to support expended 

adoption of a variety of renewable energy technologies. The agencies seek a framework for how 

to set payment levels for FITs or other incentive payment structures, as well as a model and 

instruction manual to assist the states and participating stakeholders in current and future ratesetting processes. 

The purpose of this report is to identify key renewable energy cost modeling methodological 

options, highlight the policy implications of choosing one approach over the other, and present 

recommendations on characteristics of a model to be made available for use by a broad target 

audience. This report catalogs and analyzes several approaches to cost-based tariff rate setting 

and discusses the approaches most likely to satisfy the objectives of U.S. policymakers. 

The analysis contained in this report has informed the design of a model that can be used to 

calculate FIT rates or other incentive payment structures—the Cost of Renewable Energy 

Spreadsheet Tool (CREST). The CREST models are a series of publicly available spreadsheet 

 

5 See, e.g., Barclay (2009, p. iii), “Feed-In Tariffs: Are They Right for Michigan?” 
3 

tools capable of analyzing the cost of energy (COE) from solar, wind, and geothermal electricity 

generating facilities. 

The primary output of CREST will be the modeled project’s COE—the revenue (in cents per 

kilowatt-hour) necessary for the project to cover all expenses, debt (if applicable), and its equity 

investors’ required after-tax rate of return. This COE can be used to establish—for example—the 

FIT rate in the project’s first year of commercial operation. It is recommended that the CREST 

model include the option to specify a tariff escalation rate, as well as the opportunity to define 

what percentage of the tariff is subject to such escalation. The FIT rate in subsequent contract 

years is the year-one COE adjusted by these two additional factors, if applicable. If no escalation 

is specified, then the CREST model would produce a levelized6

The CREST models are intended as tools for use by state policymakers, regulators, utilities, and 

stakeholders to assist them in rate-setting processes for implementing cost-based incentives and 

other renewable energy policies. The CREST models will be available for download from the 

NREL renewable energy project finance website.

 cost of energy (LCOE) value, 

which is the same for each FIT contract year.

7

 The CREST models will be accompanied by 

an instruction manual that provides an overview of how the models can be utilized, interpreted, 

and modified. The instruction manual will also be available for download from the NREL 

CREST Web page. (The CREST models and user manual are now available for download at 

http://financere.nrel.gov/finance/content/CREST-model.) 

1.3 Orientation of This Report: Cost-Based Incentive Mechanisms

FITs are the most common example of cost-based renewable energy incentives, although many 

of the modeling and implementation issues associated with other cost-based incentives (such as 

gap payments and targeted production incentives) are similar to the analytical requirements of 

FIT design. Renewable energy incentive mechanisms can be structured in many ways, including: 

• As a function of the fixed and operating costs of eligible renewable energy 

technologies (a cost-based tariff)

• As a function of the value of the commodities produced by eligible generators, akin to 

an avoided cost benchmark (a value-based tariff) (Grace et al. 2008). 

Incentives can also be structured as fixed payments, premiums greater than conventional energy 

prices, or as variable payments that cover the gap between a target incentive rate and the price of 

conventional energy (also called a “spot market gap” payment) (Cory et al. 2009; Couture and 

Gagnon 2010). 

For purposes of this analysis, we evaluate only cost-based incentive mechanisms. Specifically, 

this analysis focuses on incentives that are paid on a cents-per-kilowatt-hour basis, called 

production-based incentives. It is also possible, however, for cost-based incentives to be 

implemented on a per-kilowatt basis, called capacity-based incentives. It is not the goal of this

 

6 The LCOE is the single value, expressed in cents per kilowatt-hour, that has the equivalent economic effect over 

the analysis term as the year-one rate with an escalation factor. 7 See http://financere.nrel.gov/finance/content/CREST-model. 
4 

report to define a model incentive mechanism, nor is it the goal to opine about the relative merits 

of one policy design over another. However, cost-based incentive structures are currently under 

significant consideration by a number of state and municipal entities. Therefore, this report 

focuses on the factors relevant to cost-based incentives.

1.3.1 Differentiated Cost of Eligible Resources

Different FITs, or other cost-based incentives, are assumed to be made available to generators 

falling into different categories of eligibility, which can be defined by such factors as technology, 

size, resource quality, and location. 

1.3.2 A “Fixed-Price” Payment Schedule Measured in Nominal Dollars

The payments under the FIT are assumed to be fixed at a constant rate in nominal dollars (often 

referred to as “nominal levelized”) or with a fixed initial rate in the first year of operation and 

annual escalation thereafter according to an agreed-upon index (such as an inflation index) 

applied to all or part of the initial rate in subsequent years. 

1.3.3 An All-In “Bundled” Payment Structure

In return for the payment from the interconnecting utility, the FIT is assumed to require the 

delivery of all commodities produced (i.e., energy, capacity, and renewable energy certificates). 

This, by far, has been the predominant model of FIT established or proposed in the United 

States. The model therefore focuses on establishing estimates of the all-in COE or revenue 

requirement, including an assumed required return to investors. Accordingly, the generator is not 

expected to require an alternative revenue stream to cover its costs and return to investors. 

1.3.4 Long-Term Payments

FITs for fixed-price resources are assumed to have long-term contracts of between 15 and 25 

years. This is the most common contract range internationally (e.g., Klein et al. 2008) and also is 

1. Applying Model Results to Different Feed-In Tariff Structures

Although a cost model will, by its nature, calculate the total revenue requirement of a generator (equivalent 

to an all-in fixed FIT rate), policymakers might choose to adopt a cost-based FIT structure that is not fixedprice or does not convey all commodities. Examples include:

• A premium payment approach (the payment is meant to convey an amount greater than forecasted 

energy and capacity revenues)

• A spot-market-gap or contract-for-differences payment structure (the payment represents the 

difference between the cost-based all-in rate and the actual spot market energy revenues available 

to a generator)

• Unbundled (e.g., energy only or REC only, assuming that the generator sells all commodities not 

covered under the tariff elsewhere).

If an alternative FIT structure is adopted by policymakers, then estimating the appropriate FIT payment 

requires first assessing the total cost of renewable generation (as under a cost-based FIT payment). The 

actual payment under these alternative approaches then can be calculated as the difference between the 

calculated total revenue requirement and the applicable actual or estimated alternative revenue streams. It is 

important to note, however, that these decisions can have implications on the availability of financing for 

projects receiving FIT payments.
5 

the most common contract range of the current and proposed FITs in the United States. Longer 

terms are favorable for renewable electricity generators such as wind and solar because they 

reduce re-contracting risk (Corfee et al. 2010) and enable lower FIT rates by decreasing levelized 

generation costs. It should be noted that the spreadsheet models will be capable of being 

modified to accommodate both shorter-term and longer-term contracts (up to 30 years) as well.8

1.3.5 100% of Output Sold to the Interconnecting Utility

This will enable policymakers to explore the trade-offs between tariff duration and incentive 

level.

The conventional concept of a FIT reflects payments for energy (and associated commodities, 

such as renewable energy certificates) delivered into the local utility distribution system—with 

which interconnection is guaranteed. This report therefore focuses on the sale of 100% of 

generator output to the interconnecting utility.9

1.4 Structure of this Report

 This denotes a direct physical interconnection to 

the utility distribution system. FITs theoretically could be applied to generators whose output is 

partially consumed on-site by a retail host and partially sold to the interconnecting utility. There 

are complexities associated with reconciling this model with rate-setting approaches based on 

generation cost as well as interaction with net-metering policies, both of which are beyond the 

scope of this report. The CREST model associated with this report, therefore, should not be 

constructed to value or otherwise differentiate production that can be consumed on a retail 

customer’s side of the meter.

Section 2 provides a cost-modeling taxonomy, characterizing the range of possible modeling 

approaches and assessing their strengths and limitations. Section 3 presents the results of a 

survey of existing renewable energy cost modeling tools in widespread use in the United States. 

Section 4 summarizes a representative sampling of industry experience with FIT cost-modeling 

approaches to date and compares different FIT rate-setting processes. The purpose of the survey 

was to identify relevant industry best practices and to build on existing models rather than 

duplicating previous efforts. Section 5 reviews the types of input assumptions required for FIT 

cost modeling and discusses the range of options, particularly with respect to level of complexity 

and associated trade-offs. Section 6 explores the policy objectives, processes, and decisions that 

influence the choice of modeling approaches, methodologies, inputs, and output uses. Section 7 

lays out the characteristics of the CREST models based on the information and analysis in the 

previous sections. This methodology is applied to the development of FIT spreadsheet models 

for wind, solar, and geothermal electricity and used to calculate the COE from each generating 

technology. The appendices include: 

• Appendix A. Summary of Survey of Existing Models 

• Appendix B. Survey of Industry Experience with FIT Calculation Methodology. 

 

8 Although it is rare for FIT contracts to be more than 25 years, there are exceptions such as the 40-year FIT for 

hydropower in Ontario (Ontario Power Authority 2009). 

9 Although a material portion of the U.S. electricity marketplace operates under a competitive retail model, FITs

require a one-to-one correspondence of generator to buyer, which is not present if the competitive retail electricity 

supplier were to be designated as the buyer (Grace et al. 2008).
6 

2 Taxonomy of Calculation Methodologies

To set cost-based FIT rates or other cost-based incentives, first the COE for the target 

technologies must be calculated. The purpose of this section is to identify, in a clear and 

organized manner, the most commonly used methodologies for calculating the COE from a 

renewable energy facility. The intention is to create a taxonomy of methodologies: naming, 

defining, and classifying the approaches in common use for establishing a COE estimate for 

renewable energy generators and drawing clear distinctions among the different approaches. 

These methodologies vary significantly in detail and complexity, and their usefulness greatly 

depends not only on whether they adequately capture the key economic drivers of renewable 

energy projects, but also whether they reflect the goals and objectives of policymakers. As a 

result, there might not be a single FIT rate calculation methodology that is ideally suited to all 

situations. The descriptions that follow are intended to be detailed enough to provide an

understanding and comparison of each calculation methodology and to determine its usefulness 

to a specific context. 

2.1 Overview of Calculation Methodologies

The range of methodologies used to calculate the COE from a power generating asset can be 

placed into one of two broad categories, cash-flow forecasts or recovery factor analyses. The first 

category provides for the annual estimation of all project revenues, expenses, tax obligations or 

benefits, and repayments to all capital providers. These individual annual net cash flows 

subsequently are discounted to a single net present value (NPV) and internal rate of return (IRR). 

This generally is referred to as a discounted cash flow analysis. The FIT rate can be established 

by calculating the revenue per kilowatt-hour and escalation factor (if applicable), which results in 

a zero NPV using the project’s assumed cost of equity and satisfies other applicable cash-flow 

constraints10

The second COE methodology relies upon a single factor (i.e., a multiplier) to convert capital 

investment-related costs to an annual figure that estimates tax benefits or obligations and 

repayments to all capital providers over the life of the project. Using this latter methodology, the 

COE (whether nominal or real levelized) is calculated by multiplying the initial investment by 

the recovery factor, dividing this value by the estimated annual production, and adding a 

simplified estimate of operating expenses in a representative year. A number of alternative 

methodologies fall into the recovery factor analysis category. 

or an IRR, which meets or exceeds the equity investors’ requirements for a 

specified period. 

Table 1 summarizes several 

different cash flow forecast and recovery factor methodologies. This table also includes two 

additional investment analysis tools—the simple payback method and the profitability index 

method (PIM). Although not suited to the estimation of an appropriate FIT rate on their own, the 

simple payback and PIM can be used in conjunction with detailed COE analyses. Detailed 

descriptions of each of the methodologies follow the table. 

 

10 The NPV might have to be slightly greater than zero to both meet the required return and satisfy cash-flow 

constraints (such as minimum debt service coverage ratios).
7 

Table 1. Summary of Calculation Methodologies 

Method Summary Description Calculation Cost of Energy 

Cash-Flow Forecast Methodology

Discounted 

Cash Flow

Discounts to present value the 

estimated annual cash flows to 

equity investors; provides 

either before-tax or after-tax

results

Initial equity investment plus

NPV of free cash flow to 

equity over the life of the 

project; internal rate of return 

of investment and cash flows 

for a specified period

Cents per kilowatt-hour

revenue assumption yielding 

target weighted average cost of 

capital; can be in nominal 

levelized dollars, real levelized 

dollars, or increasing from an 

initial value at a defined 

escalation rate

Recovery Factor Analyses

Capital 

Recovery 

Factor

Amortizes an investment into 

a stream of equal annual 

payments; provides pretax

results; also called “annuity 

method”

Sums weighted average cost 

of capital and depreciation 

annuity

((CRF * Total Installed Cost) + 

Total Annual Variable Cost) / 

Annual Production; the CRF 

method typically calculates 

COE in nominal dollars

Fixed-Charge 

Rate

Calculates the percent of a 

project’s year-one revenue 

requirement attributable to 

fixed costs; provides after-tax

results

Sums annual weighted 

average cost of capital, tax, 

depreciation, and fixed 

overhead

((FCR * Total Installed Cost) + 

Total Variable Cost) / Annual 

Production; the FCR method 

typically calculates COE in 

nominal dollars

Economic 

CarryingCharge Rate

Amortizes all fixed costs to 

produce a stream of annual 

payments that increase at a 

constant rate; provides aftertax results

Sums year-one weighted 

average cost of capital, tax, 

depreciation, and fixed 

overhead to derive year-one 

COE

((ECCR * Total Installed Cost) 

+ Total Variable Cost) / 

Annual Production; the ECCR 

method typically calculates 

COE in real dollars;this value 

is escalated annually

Other Investment Analysis Tools

Simple Payback Estimates the number of years 

necessary to recover an initial 

equity investment; provides 

before-tax results

Initial equity 

investment/annualized cash 

flow to equity

Cents per kilowatt-hour 

revenue assumption yielding 

payback in targeted year

Profitability 

Index Method

Indicates the efficiency of 

invested capital; used to rank 

projects based on NPV per 

dollar invested

NPV/total installed cost PIM does not set a FIT rate; it 

is a universal scale for comparing profitability of investments

2.2 Discounted Cash Flow Analysis

A discounted cash flow (DCF) analysis is a method of calculating the NPV and IRR on a 

potential renewable energy investment by estimating future free cash flows to equity on a 

periodic (i.e., annual, quarterly, or monthly) basis, taking into consideration the time value of 

money. According to Corporate Finance, “conventional NPV analysis discounts a project’s cash 

flows estimated for a certain project life” (Ross et al. 2002, p. 213). A DCF analysis can be used 

to calculate a project’s IRR both before and after tax. Cash flows are estimated using projectspecific revenue and expense forecasts, debt service obligations, depreciation schedules, and 

income tax assumptions (as applicable). A DCF analysis takes a project’s operational and 

financing milestones—including evolving tax obligations—into account when estimating its 

NPV and IRR. The DCF method also is capable of recognizing constraints on project financing, 

such as minimum debt service coverage ratios, and time-sensitive operational events, such as 

major equipment repairs or replacements (e.g., inverter for solar and gearbox for wind). It is the 

most detailed methodology discussed here. 
8 

2.3 Recovery Factor Analyses

Recovery factor analyses replace the year-by-year free cash flow estimates of the DCF method 

with a simplifying formula. The simplification combines the expected levelized annual operating 

expenses11

2.3.1 Capital Recovery Factor

(or year-one expense plus escalation) with the product of (i) the estimated installed 

costs and (ii) a constant percentage factor intended to represent the portion of a project’s initial 

investment and fixed costs recovered each year. Due to the tax-oriented nature of renewable 

energy investments, however, the simplifying assumptions of a recovery factor analysis result in 

less precision compared to a year-by-year after-tax DCF analysis. Several of these 

methodologies are identified and described below. 

The capital recovery factor (CRF) is a value between 0 and 1 designed to calculate the annual 

dollar amount required to fully amortize an investment over a specified period. The CRF 

“converts a present value into a stream of equal annual payments over a specified time, at a 

specified discount rate (interest)” (Khatib 2008, p. 39). The CRF is derived by combining a 

project’s weighted average cost of capital with the depreciation annuity. The depreciation “is 

based on the concept that funds must be accrued during the lifetime of a project that will equal 

the original cost of the plant, thereby allowing for its replacement” (Kahn 1991, p. 44). The CRF 

approach also sometimes is referred to as the equivalent annual cost or the annuity method 

because, when multiplied by the total capital costs of the project, it yields the amount of money 

that must be collected annually to recover the initial investment at a specified rate and over a 

specified number of years.

2.3.2 Fixed-Charge Rate

The fixed-charge rate (FCR) is “a fraction between 0 and 1 which expresses the sum of annual 

requirements for return, taxes, depreciation, and sometimes other fixed overhead costs” (Kahn 

1991, p. 42). In this approach the annual return requirements consolidate debt and equity returns 

into a single figure, such as a weighted average cost of capital. Other fixed overhead costs can 

include insurance, project management, and debt principal. To calculate the portion of a project’s 

year-one revenue requirement attributable to these fixed costs, the FCR is multiplied by the 

project’s total installed cost. 

2.3.3 Economic Carrying-Charge Rate

Like the FCR method, the economic carrying-charge rate (ECCR) (sometimes referred to as the 

real carrying charge) “represents all of the costs associated with a new unit, including the 

depreciation of and return on the initial investment, and property taxes and fixed operating and 

maintenance costs over the life of the unit” (GE Power Systems Energy Consulting 2003, pp. 2–

3). The ECCR differs from the FCR in that it is used specifically to calculate a project’s cost of 

energy in the first year. This year-one value then is escalated by an assumed inflation rate to 

approximate the cost of market entry over time. Therefore, the ECCR “method of allocating 

capital costs over time produces a stream of payments that increase at a constant rate” (Kahn 

1991, p. 168). The present value of the escalating payment stream calculated via the ECCR is 

equal to the present value of the constant stream calculated in the FCR methods. According to 

 

11 Depending on the recovery factor method selected and the detail orientation of the individual performing the 

analysis, the estimated annual operating expenses might or might not account for major equipment repairs and 

replacements.
9 

Edward Kahn’s Electric Utility Planning & Regulation, “this approach is still an approximation 

to a detailed multi-year optimization” (Kahn 1991, p. 171). 

2.4 Other Investment Analysis Tools

2.4.1 Simple Payback

Simple payback is a commonly used yet often misunderstood method of estimating how long it 

takes to recover an investment. Applied to the electric-generator context, the simple payback 

period is calculated by dividing the initial equity investment by the estimated annual cash flow to 

equity. The value of simple payback is a conceptually easy-to-grasp description of the return of 

invested capital and typically is utilized by entities that plan to finance a project using internal 

funds and not incur project-level debt. The simple payback method is limited in its applicability 

and insight, as it ignores the cash flow benefits to the equity investor after the initial investment 

is recovered and provides no means to measure project profitability. Furthermore, simple 

payback analyses can be misleading for several reasons. One is that they do not take into account 

the time-value of money. Another reason is that payback analyses either ignore or over-simplify 

the annual variability in cash flow due to depreciation expense and other tax-related 

considerations. These factors dramatically influence annual cash flows and project 

profitability—particularly for renewable energy projects, which can offer investors important tax 

advantages. For these reasons, payback targets and payback analyses are more communication 

tools than financial analysis. They are not recommended for use in establishing FIT rates and are 

not discussed further in the present report. 

2.4.2 Profitability Index Method

The PIM creates a metric that “indicates the efficiency of invested capital” (Chabot 2009, p. 12) 

and is a scale on which a range of projects can be equitably compared. The profitability index 

(PI) is calculated by dividing a project’s expected NPV by the initial equity investment. 

The numerator in the PIM is the project’s NPV, which can be calculated using an underlying 

DCF, a fixed-charge rate, or a CRF analysis. The PIM pioneer, Bernard Chabot, uses the CRF 

approach. Therefore, the PIM is not an alternative to the DCF, FCR, or CRF analyses described 

above but can be used in conjunction with one of these analyses to help the participants in a 

regulatory process set FIT rates that provide for a similar level of profitability across a range of 

project sizes and technology types. For example, a project with an NPV of zero will generate a 

PI of zero because NPV is the numerator of the PI formula. This result denotes a project rate of 

return that is expected to meet—but not exceed—the investor’s weighted average cost of capital

and, implicitly, its threshold equity-return target. 

The PI is intended to be a “universal profitability scale based on investor strategies” (Chabot 

2009, p. 8), whereas NPV and IRR are profitability metrics that vary based on term. According 

to Chabot, FIT rates that result in a project profitability index between 0.1 and 0.3 are 

appropriate if the policy objective is to promote “fair and efficient tariffs” (Chabot and Saulnier 

2001, p. 4). If scaled technology deployment and industry growth are the objective, Chabot 

opines that the target PI range should then be between 0.3 and 0.6. To this end, industry 

regulators and participants could elect to include a PI calculation in the quantitative modeling 

process and could target a PI range for each technology (or technology subcategory) that is 

commensurate with their policy objectives. Although policymakers intend to encourage all 

eligible projects equally, a single PI score could be targeted for all tariffs. If the objective is to 
10 

increase technology diversity or promote adoption of specified technologies, for example, then 

different PI scores could be targeted for each category. 

The PIM’s creators conceived the method as a simplified analysis to quickly measure a project’s 

potential profitability. To this end, a CRF is used to calculate a pretax NPV. Although simplified, 

this approach nonetheless is reasonable in European or other markets in which returns are 

predominantly cash-based and the tax implications are relatively uniform for investors. In the 

United States, however, where renewable energy investing is defined by complex structures to 

maximize the use of a multitude of tax incentives, a PI calculated based on a pretax NPV (or any 

other pretax analysis) is insufficient to draw actionable conclusions about project profitability.

To apply the PIM concept in a useful manner in the United States, the NPV used in the 

numerator must be calculated based on a detailed after-tax DCF or FCR analysis. Admittedly, 

this impairs the simplicity objective of the PIM; however, it provides a mechanism for making 

the PIM a potentially useful tool for comparing the profitability of a range of renewable energy 

projects.

2.5 Conclusion: Applying the Discounted Cash Flow Model

It is recommended that the CREST models utilize the DCF method of calculating the NPV and 

after-tax IRR on a renewable energy investment. This is based on several factors. One is that any 

FIT rate calculation should incorporate all broadly available tax incentives. In the United States, 

tax incentives are an important component of investing in renewable energy projects, and the 

efficient monetization of all tax incentives is important even for smaller projects. Although the 

2009 American Reinvestment and Recovery Act created a program that converts a portion of 

these tax benefits into cash, these provisions are set to expire for projects not under construction 

by the end of 2010. Unless there is permanent migration to cash-focused returns, renewable 

energy investing in the United States will remain strongly motivated by tax benefits. The DCF 

models easily can be structured to accommodate (and switch between) scenarios that involve tax 

impacts and those that do not. In other words, the DCF model provided in this report is a tool

that can adapt with U.S. renewable energy policy over time. 

Importantly, a successful, stakeholder-driven FIT design process requires a substantial degree of 

transparency. This is especially important with respect to the assumptions that drive the ratesetting modeling process. The year-by-year analysis in the DCF method provides for 

transparency regarding the impact of these assumptions throughout the life of the project. 

The FIT rate-setting process also should be flexible and should allow for a range of complexity 

in modeling assumptions and calculations. The DCF method allows for a range of complexity in 

the level of input detail and modeling calculations that either surpasses the other methodologies 

available or would otherwise necessitate a separate modeling effort to inform the applicable 

recovery factor analysis. 

A DCF model also supports the analysis of project returns for different types of investors. It is 

problematic to assume that all investors will be able to fully monetize the available tax 

incentives, therefore it is helpful to be able to assess the range of FIT rates required for different 

types of investors to achieve a defined rate of return (e.g., monetizing only a portion of the tax 

benefits). The DCF model accommodates a range of tax appetites and can approximate the 

utilization of tax benefits on an annual basis. This also is an important consideration for 
11 

government entities or non-profit agencies that do not have a tax basis to which to apply the tax 

credits.

Additionally, state legislators and regulators also might wish to develop FIT rate models that

recognize the strengths and limitations of local investors. The DCF method is flexible and can be 

customized to suit a range of state-specific conditions. This enables policymakers to adapt their 

programs over time to account for changing federal incentives as well as overall conditions in the 

renewable energy market. Lastly, as noted in Section 4, a survey of cost-modeling methodologies 

in other jurisdictions revealed that the DCF method is used in many jurisdictions with FITs.
12 

3 Survey of Existing Renewable Energy Cost Modeling Tools

3.1 Survey of Existing Financial Models

This section surveys publicly available financial models in common use for estimating the COE

from renewable energy generators and identifies the features most useful and applicable to a 

state-by-state cost-based incentive rate-setting process. Some of these models also are identified 

during the discussion of industry experience in Section 4. The most useful features are

summarized in Appendix A and, as appropriate, are incorporated into the CREST models 

provided with this analysis. 

The calculation methodologies and features—as well as the summaries of inputs and outputs—

used in renewable energy project financial models vary widely in detail and complexity. The 

level of granularity of inputs and modeling calculations should take into account the intended use 

of the model. For example, a project-specific economic feasibility analysis could estimate tax 

benefits as a percentage of total project costs and assume complete monetization of all 

incentives. By comparison, a detailed project finance model intended for use with potential 

lenders and equity investors likely would determine the eligibility of project costs for the 

investment tax credit (ITC) (if applicable) on a line-by-line basis and calculate each partner’s tax 

benefits based on individual tax appetite and capital account balances. In other words, model 

developers can match the complexity of inputs, calculations, and outputs to stakeholders’ needs 

and preferences. In this case, the survey of models was conducted to identify the types of input 

and output summaries, calculation methodologies, and other features or details that could be 

useful to consider. 

All of the models surveyed are publicly available. Each model is used to estimate a project’s 

COE per kilowatt-hour based on estimated (or actual) project costs and financial parameters. 

Some of these models are in widespread use and others have been applied more narrowly. An 

informal but broadly cast survey of available models yielded the following prospects for 

examination, and each is discussed in detail below. 

• The California Renewable Energy Transmission Initiative (RETI) model (Black & 

Veatch Corp.) 

• The State of Vermont’s Standard Offer models 

• The RETScreen model (Canadian government) 

• The Gainesville Regional Utilities Feed-In Tariff model 

• The Solar Advisor Model (SAM) (NREL)

• The European Union’s Photovoltaic Technology Platform 

• The Vote Solar Initiative model 

• The Geothermal Electricity Technology Evaluation Model (GETEM) (U.S. DOE).
13 

3.2 Renewable Energy Transmission Initiative Cost of Generation Model12

The RETI cost of generation spreadsheet model13

3.3 Vermont Standard Offer Models

—designed by Black & Veatch Corp. for the 

State of California—is a simple yet versatile DCF model. The purpose of the RETI model is to 

calculate the cost of generation for multiple renewable energy technologies. It has been used for 

developing a supply curve of renewable energy generation resources in both the California RETI 

and the Western Renewable Energy Zones to explore policies relating to construction of new 

transmission to reach these zones. The model’s simplified level of input detail might not be 

sufficient for some users, but the model includes all inputs necessary to conduct an introductory 

COE analysis for a variety of fuel-based and non-fuel-based technologies, making RETI a useful 

and effective tool. The RETI model can calculate a project’s minimum year-one COE subject to 

user-defined equity return requirements, debt parameters, and other inputs. If escalation 

assumptions are provided, then the model calculates the LCOE. The user-friendly interface 

allows users to compute these values without being required to understand and operate Microsoft 

Excel’s manual “goal seek” or “solver” optimization functions. The RETI model presents the 

strongest candidacy for using a publicly available model for exploring FIT rate calculations. 

Based on this report’s analysis of the policymaking process in other jurisdictions, however, a 

model with greater input detail and project design flexibility is necessary to support the 

stakeholder processes associated with most FIT policy development.

14

In late 2009, the Vermont Public Service Board (PSB) opened two related, non-contested 

dockets15

Due to a number of instances in which inputs have been embedded in formulas, however, the 

Vermont models are not user-friendly enough to recommend for widespread use in evaluating 

to examine the current COE from various renewable energy technologies and inform 

the board’s decision on standard offer rates under the Sustainably Priced Energy Enterprise 

Development Program (SPEED). The model used throughout the stakeholder process followed a 

standard DCF format and initially was developed by Green Mountain Power Corporation, a 

Vermont utility. Although the original model was not designed to calculate FIT rates, the model 

provided a starting point for stakeholders to estimate the COE from a range of renewable energy 

technologies. Separate models ultimately were established for solar, wind, landfill gas, hydro, 

and farm-methane projects to support the board’s consideration of technology-differentiated 

standard offer contracts. Complete transparency with respect to the model formulae fostered a 

collaborative process in which both developers and regulators gained comfort with the 

calculations and were able to focus their energy on trying to achieve consensus on inputs. The 

model can be used to calculate the input power purchase agreement (PPA) price that satisfies all 

cost estimates and financing conditions by using Microsoft Excel’s “goal seek” or “solver” 

function. Useful features in this model include the estimated funding of reserve accounts, state 

tax incentives (in addition to federal), and the ability to refinance project debt after an initial 

five- to seven-year debt tenor. This last feature reflects the limitations in the debt market as of 

late 2009 and early 2010. 

 

12 The RETI cost of generation spreadsheet is available at http://www.energy.ca.gov/reti/documents/index.html. 

Accessed August 17, 2010.

13 RETI materials are available at: http://www.energy.ca.gov/reti/documents/index.html. 14 The Vermont Standard Offer Models are available at:

http://psb.vermont.gov/docketsandprojects/electric/7523/finalprice. Accessed April 7, 2011. 15 Docket No. 7523; Docket No. 7533.
14 

cost-based FITs. Additionally, a COE model intended for use in many different states and under 

a variety of circumstances might need additional flexibility and functionality with respect to the 

level of detail of cost and operating inputs. 

3.4 RETScreen

The Canadian government has widely distributed its RETScreen clean energy project analysis 

software. The purpose of the software is to evaluate energy production, costs, and financial 

viability—among others elements—for various types of renewable energy technologies. 

According to the RETScreen International website, the model has been used by more than 

240,000 people in 222 countries and is part of the curriculum at more than 250 universities 

worldwide.16

3.5 Gainesville Regional Utilities FIT Model

 It has been used not only in support of renewable electricity projects (e.g., wind, 

solar, hydro, and geothermal) but also to calculate the potential savings associated with 

renewable heating and cooling, energy efficiency, and combined heat and power projects. 

Although the model’s well-designed user interface might facilitate operation by users who do not 

possess detailed knowledge of renewable energy finance, the model’s lack of transparency could 

pose a problem for developers who must ensure the proper treatment of all project attributes or 

for regulators with rate-setting responsibility. A limitation on utilizing RETScreen for the 

purposes of a general U.S. cost-based FIT model is the fact that it does not easily incorporate the 

U.S. tax incentives, which, as noted, are material to the finances of most U.S.-based renewable 

energy projects. The RETScreen software has several other useful features of note, including the 

option to select alternative levels of input detail in several categories, the sensitivity and risk 

analysis features, and a built-in help function for each model input. 

17

The Gainesville FIT model is a DCF model used to establish this Florida utility’s solar FIT rate. 

As was the case in Vermont, the model inputs were informed through a stakeholder process. 

Compared to other publicly available models, the inputs are greatly aggregated and might not 

provide sufficient detail and flexibility for a tool to be applied across a wide variety of 

circumstances. The model’s formatting also does not clearly define the inputs or the project-level 

and system-level outputs. The model’s useful features include a calculation of the estimated 

impact on the utility’s annual fuel cost adjustment to ratepayers.

3.6 System Advisor Model18

SAM is the result of a partnership between NREL, Sandia National Laboratory, and the DOE 

Solar Energy Technologies Program (NREL 2010). SAM is robust, offering many detailed sets 

of input options to evaluate multiple solar electric technologies. The tool is available for 

download on the NREL website. This model has an advanced graphic interface and offers a 

comprehensive set of output metrics and sensitivity analyses but can be daunting for entities such 

as policymakers who are unfamiliar with complex renewable energy systems. Among SAM’s 

outputs are cash flow statements, which can be exported to Excel and reveal SAM to be a DCF

model. The calculations associated with the cash flow statements exported from this version of 

SAM are neither transparent nor available to the user for review. Functional, Excel-based SAM 

cash flow models are available in draft form and without the graphic user interface from NREL’s 

 

16 The RETScreen model is available at http://www.retscreen.net/ang/home.php. Accessed August 17, 2010. 17 The Gainesville FIT Model is available from Gainseville Regional Utilities. 18 SAM is available at https://www.nrel.gov/analysis/sam/. Accessed August 17, 2010.
15 

website.19

Among its many output options, SAM calculates the year-one PPA price, the LCOE in nominal 

dollars, and the LCOE in real dollars. Depending on policymakers’ preferences regarding 

inflation of the revenue stream, one or more of these metrics would provide information useful to 

establishing FIT rates. Other useful features include SAM’s ability to generate an array of 

sensitivity and optimization analyses, as well as graphical representations of many of the model’s 

outputs. Graphics and sensitivity analyses can facilitate more efficient communications in a 

consensus-driven stakeholder process.

 This is a limiting factor in SAM’s potential application in a rate-setting process where 

a premium is placed on transparency. The ability to view all calculations is a likely requirement 

to get both policymaker and developer buy-in during the regulatory process. SAM, while highly 

robust, is also very complex and requires a high level of knowledge with respect to weather and 

resource data, system costs and function, and economic evaluation. Further, SAM currently 

offers no option to incorporate reserve requirements for debt, working capital, and replacement 

components such as inverters. These reserve accounts are very regularly required of renewable 

energy developers and represent a real cost of renewable energy production. 

3.7 European Union Photovoltaic Technology Platform20

The European Union’s Photovoltaic (PV) Technology Platform was designed specifically as a 

FIT rate calculator by the French Environment and Energy Management Agency (ADEME). 

The model is based on the DCF method, simplified by using the project owner’s assumed 

weighted average cost of capital rather than project-specific debt service assumptions. Although 

well-suited to the European markets, the PV platform does not accommodate the accelerated 

depreciation and tax-credit benefits that drive renewable energy investing in the United States. 

Nonetheless, this model offers several useful features, including the clear definition of output 

metrics and a worksheet dedicated to the explanation of each input parameter—and often 

including a range of potential values to help new users get started. If inputs are dependent on 

other project-specific decisions, then an explanation of the process to determine the input is 

provided. One caveat is that these sample inputs must be updated periodically to remain useful. 

Further, a geographic area as diverse as the United States requires location-specific sample 

inputs. 

3.8 Vote Solar Incentive Comparison Model21

The Vote Solar Initiative is a California-based group that has the goal of fostering the economies 

of scale necessary to reduce the installed cost of new solar installations.22

 

19 Excel-based SAM cash flow models are available at 

 Among its online 

resources, Vote Solar offers an Excel-based incentive comparison model—developed by 

Crossborder Energy—that calculates the incentives necessary to provide the same project-based 

economic results under three policy scenarios: an upfront incentive, a performance-based 

incentive, and a FIT. This DCF model’s useful features include a description of the model’s 

purpose, key inputs, key outputs, and model operations. Acting as a “quick-start guide,” this 

feature can provide substantial value to policymakers or stakeholders who do not work with 

financial models on a regular basis. Additionally, the Vote Solar model calculates the cumulative 

https://www.nrel.gov/analysis/sam/support.html#fin_spread. 

Accessed August 17, 2010.

20 The European Union PV Platform model is available at http://www.eupvplatform.org/. Accessed August 17, 2010. 21 The Vote Solar model is available at http://votesolar.org/resources/incentive-model/. Accessed August 17, 2010. 22 http://votesolar.org/who-we-are/. Accessed August 17, 2010.
16 

program incentives paid based on estimates of total capacity installed under—or electricity 

generated as a result of—the program. The level of detail in the model, however, is insufficient 

for widespread use. For example, the model is limited to the use of a single dollar-per-watt input 

for the estimation of total capital costs.

3.9 Geothermal Electricity Technology Evaluation Model23

GETEM is “a detailed model of the estimated performance and costs of currently available U.S. 

geothermal power systems” (Entingh 2006, p. 1). The GETEM model “was developed to aid the 

[DOE] Geothermal Technologies Program in understanding the performance and the cost of the 

technologies it is seeking to improve” (Entingh 2006, p. i). Whereas most of the models surveyed 

rely on external resource assessments and power-conversion analyses (in the case of solar and 

wind resource estimates, for example), an evaluation of the efficiency and cost of different 

geothermal resource capture technologies is one of the core features of GETEM.24

By comparison, the CREST model seeks to support a FIT rate-setting discussion among 

stakeholders—many of whom might not possess the engineering background necessary to fully 

comprehend the GETEM model. For geothermal resources to be effectively included in the FIT

rate-setting dialogue, such a model must succeed at bringing regulators, developers, and 

investors together in a discussion of what it takes to create successful geothermal electricity 

projects. This report and its associated model are intended to support the rate-setting process and 

are not designed to rank or otherwise compare the “pros and cons” of different geothermal 

technologies. If the COE from different geothermal technologies varies widely, then participants 

to the FIT process could explore whether it makes sense to differentiate geothermal FIT rates by 

technology, size, or other criteria. 

 The GETEM 

model was built to help geothermal technology researchers gain insights into how particular 

improvements in performance, changes in technology, or reductions in the cost of 

subcomponents impact the facility’s total cost of power.

3.10 Additional Models

The existing models surveyed in this report were collected by NREL and the authors via a 

renewable energy industry peer group that had expertise in the analysis and application of FITs

worldwide. In addition to the models individually described above, the survey resulted in the 

identification of two more models, which—although related to FITs in some instances—take 

different approaches to the calculation of FIT rates. These models are described briefly below. 

3.10.1 ECN Financial Gap Calculation Model

The Energy Research Centre of the Netherlands (ECN) model is used to establish FITs in the 

Netherlands. Similar to many of the other models surveyed in this analysis, the ECN model uses 

the DCF methodology to determine the COE. Rather than setting the tariff rate based solely on 

this COE analysis, however, the ECN model also incorporates a long-term electricity price 

forecast to calculate what the model refers to as the “financial gap.” The financial gap is the 

difference between the initial equity contribution and the NPV of after-tax cash flow to equity, 

divided by the discounted electricity production. The resulting per-kilowatt-hour value sets the 

 

23 The GETEM model is available at http://www1.eere.energy.gov/geothermal/getem.html. Accessed August 

17, 2010. 24 One notable exception is that SAM’s automation includes the integration of production estimates generated by 

PVWatts. See http://rredc.nrel.gov/solar/calculators/PVWATTS/version1/. Accessed August 17, 2010.
17 

FIT. The Dutch FIT assumes that eligible facilities deliver all electricity to the grid, are paid the 

market-based value of production, and then collect an additional FIT payment that reconciles the 

total revenue per kilowatt-hour with the COE. This analysis and the associated CREST models 

take the approach that the FIT is set equal to the COE in its entirety. Although the CREST 

models take into account the market-based revenues that are available to the project after the FIT 

contract expires and before the end of the project’s useful life, the CREST models do not 

endeavor to calculate a financial gap similar to the ECN model. Nonetheless, policymakers still 

have the option to calculate the expected market value of production25

3.10.2 Hydrogen Analysis Production Cash Flow Analysis Tool

from different types of 

renewable energy generators and subtract this value from the COE calculated with CREST and 

then use this gap to set a FIT rate in the Dutch style.

The DOE Hydrogen Analysis (H2A) model is a DCF analysis tool intended to calculate the cost 

of hydrogen produced (by a variety of different renewable energy technologies) over the analysis 

period at a specified after-tax equity cost of capital. The model is well conceived and 

constructed, however its focus on the cost of hydrogen as the output rather than the cost of 

electricity prevents further consideration of its use as a model to develop a cost-based FIT. The 

H2A model nonetheless was reviewed and its input and output formatting taken into account 

when considering design options for the CREST models that will be developed in association 

with this whitepaper. 

3.11 Conclusion

This survey of existing financial models suggests that there is no single publicly available and 

recognized tool in widespread use that can be readily and easily applied to the task of calculating 

cost-based FITs for a variety of generation technologies in the United States. The survey, 

however, revealed a number of useful models from which best practices and features can be 

borrowed to create a spreadsheet financial model for use by renewable energy policymakers and 

stakeholders across the country. Such a model must be straightforward enough to be used 

effectively by a diverse set of participants, flexible enough to accommodate a consensus-driven 

process for identifying inputs that can vary in detail from state to state, and transparent enough to 

be useful in a public process. The key features observed in other models and proposed for 

incorporation into the CREST models include: 

• An “Introduction” worksheet embedded in the model that serves as a quick-start 

guide 

• Descriptive, embedded “notes” providing an explanation of the appropriate use of 

many input cells as well as the typical range of values for selected inputs 

• Frequent use of “check” cells, which provide visual cues to help ensure that the user 

populates all cells required to operate the model

• The option to provide inputs at one of three potential levels of detail, chosen by the 

user and based on need 

• Inputs for major federal and state tax incentives

 

25 The wholesale market clearing prices are not transparent in all U.S. electricity markets; therefore, the financial gap 

approach might be easier to implement in some regions than in others.
18 

• Graphic representation of results.

The survey also identified important limitations of publicly available models. In general, these 

limitations relate to:

• The level and flexibility of input detail 

• Transparency with respect to model formulae (calculations are hidden) 

• Consideration of the financial impact of major federal and state tax incentives. 

A summary of the key characteristics of each surveyed model, as well as its applicability to FIT 

rate setting, is provided in Appendix A. The lessons learned from this review of existing models 

are applied to the development of the model accompanying this report and are described further 

in Section 7. 
19 

4 Industry Experience with FIT Calculation Methodology

Different approaches to setting FIT rates based on generation cost have been employed by 

jurisdictions both within the United States and abroad. Previous studies and reports have either 

described FIT rate-setting methodologies for specific countries (BMU 2007), compared FIT ratesetting processes across different countries (KEMA 2008), or compared general modeling 

approaches and advocated for a certain approach over others (Chabot et al. 2002; Gipe 2008). To 

date, however, there has not been a detailed, comprehensive comparison of FIT rate-setting 

approaches internationally. This section reviews and compares a representative sampling of costbased FIT rate-setting approaches used in the United States and abroad. The goal is to establish a 

set of benchmarks for use in developing a general-purpose cost-modeling methodology for use in 

the United States. Specifically, the section identifies the type of model utilized in each 

jurisdiction, the type and complexity of model inputs, and other key assumptions that shaped the 

model structure. By surveying the model design options currently used and identifying designs 

that are common across jurisdictions, this section provides a point of comparison for the specific 

design recommendations discussed in Section 5. 

Additionally, this section reviews and compares

• The rate-setting processes (e.g., how data is gathered, who gathers it, how data is 

verified, and how stakeholders are involved in the process)

• The rate-setting approval process (e.g., by regulatory order and by law). 

Although these processes and procedures are not as directly relevant to the CREST model as the 

review of comparative model designs, they are included to give policymakers a better sense of 

how FIT rates are developed and codified.

4.1 Survey Methodology

A survey of FIT rate-setting methodologies was conducted, and key criteria for comparing FIT 

rate-setting methodologies were identified. The goals of this survey were to develop a common 

language and framework for comparing different rate-setting approaches and to identify common 

or best-practice approaches against which to benchmark current and future U.S. FIT rate-setting 

efforts. Another key goal of the survey was to directly inform the development of generalpurpose CREST models that could be used by stakeholders in the United States to set FIT rates. 

After surveying different FITs around the world, five jurisdictions were chosen for in-depth 

analysis and comparison: Germany; the Netherlands; Ontario, Canada; Vermont; and 

Gainesville, Florida. These jurisdictions were chosen because they represent a diverse sample of 

different generation cost-based FITs. They include a mix of well-established and relatively new 

policies, tariffs from both Europe and North America, and policies established at the national, 

state, and local levels. Moreover, each of the jurisdictions used a different approach to building 

customized spreadsheet models rather than using any of the pre-existing tools. The customized 

Vermont and Gainesville models were included in Section 3. Other jurisdictions did not use 

publicly available tools, however, and either are not yet willing to make the customized models 

available or have not yet released an English language version of the model. 
20 

The comparison between the jurisdictions was conducted through a literature review, 

comparisons of the modeling spreadsheets, and interviews with government officials, utilities, or 

consultants involved with design. Interviews for each jurisdiction were conducted with the 

following individuals during the period from fall 2009 to spring 2010: 

• Germany: Maike Schmidt, Zentrum für Sonnenenergie- und WasserstoffForschung (ZSW)

• Netherlands: Hans Cleijne, Principal Consultant, KEMA B.V. 

• Ontario, Canada: Jim MacDougall, Manager, FIT Design, Ontario Power Authority 

• Vermont26: Matt Karcher, Principal, Deacon Harbor Financial27

• Gainesville, Florida: John Crider, Planning Engineer, GRU. 

As described below, some of the spreadsheet models are available online, whereas others are 

confidential and not available to the general public. Detailed reviews were conducted of the 

available spreadsheets, supplemented by in-depth interviews with the models’ designers. In cases 

where the models were not available for direct review, the analysis was completed through the 

use of a detailed questionnaire and follow-up interviews with program managers. 

Broadly, the comparison revealed that there are significant differences in the approaches that the 

selected jurisdictions have used for FIT rate setting. These differences primarily derive from the 

different regulatory and political systems present across the jurisdictions and the processes

established to gather cost data for renewable energy generation technologies. The comparison 

also revealed, however, that there are clear commonalities among the rate-setting processes, 

particularly among the spreadsheet models that were developed and employed. 

4.2 Comparing the Spreadsheet Models

In each of the case study jurisdictions, the FIT designers relied on spreadsheet-based financial 

models to calculate the FIT rates for target technologies. As discussed in greater detail in Section 

5, the spreadsheets employed can range from simple financial models with only a few inputs to 

highly complex models that require hundreds of inputs and assumptions. There are trade-offs

inherent in the relative complexity of spreadsheet modeling, and different levels of complexity 

might be preferred depending on the spreadsheets’ intended purpose or audience. Project 

developers, for example, could find that more complex spreadsheets are required by potential 

project financiers to predict project cash flows to a degree sufficient to evaluate investments. 

Policymakers, on the other hand, could prefer to use less complex, more generalized models for 

policy-design purposes. Simpler models can be more transparent and accessible to stakeholders 

and can be designed to be representative of a broad range of potential projects (rather than 

tailored exactly to match a specific project).

Generally, the models used to set the rates for the case study FITs tended to be less complex than 

the full financial pro formas required by prospective capital providers. For the models used both 

in the United States and abroad, inputs tended to be aggregated (e.g., one or a few inputs for 

 

26 The authors of this report also were directly involved in the Vermont rate-setting proceedings. 27Karcher served as an expert witness for Renewable Energy Vermont in the proceedings and supported the 

development of a model that was adopted by the parties.
21 

installed cost instead of 20), and the models tended to be limited to one or two worksheets. This 

section compares the spreadsheet models used in the case study jurisdictions in greater detail but 

does not attempt to discuss the drawbacks and benefits of different approaches. More in-depth 

discussion of pros, cons, and policy implications of different modeling approaches, levels of 

detail (i.e., granularity), and other factors can be found in Section 2, Section 3, and Section 5. 

More detailed descriptions of each jurisdiction’s approach to rate setting are included in 

Appendix B.

4.2.1 Calculation Methodology

As described in Section 2, several standard types of calculation methodologies can be employed. 

Most of the jurisdictions studied utilized a DCF methodology (see Section 2.2). The one 

exception was Germany, which used a type of FCR methodology (see Section 2.3.2) for most 

resource types. A FCR model initially was proposed for use in Vermont (Vermont Public Service 

Board 2009a). This methodology was replaced by a DCF model early in the proceedings, 

however, after stakeholders suggested that a model that included a detailed pro forma would be 

more appropriate for the decision-making process (Vermont Public Service Board 2009a). 

4.2.2 Tax Treatment

Tax treatment refers to whether potential tax impacts on project economics (i.e., income taxes on 

power sales, income tax credits for installation or production, and accelerated depreciation) are 

taken into account by the model. Most of the models analyzed use after-tax data, meaning that 

the impacts of tax benefits and losses are taken into account when calculating the COE. The 

exception to this is Germany, where the FIT rate typically is based on project economics before 

taxes are applied. The German government opted for a pretax calculation because tax benefits 

available to renewable energy generators are not significant and because tax liabilities vary 

across ownership types and are difficult to generalize.

Although most of the case study jurisdictions employed after-tax analysis in their models, some 

important differences were noted in their approaches. Most of the jurisdictions assumed that 

generators could and would depreciate their eligible property according to the available 

schedules.28

4.2.3 Assumed Ownership Structure

 Vermont’s model added an extra layer of complexity, however, by assuming that 

future capital expenses (e.g., replacing the PV inverter) also would be depreciated. Another 

difference between the after-tax models was the treatment of tax credits. The United States relies 

more heavily on tax incentives at the federal, state, and local levels than do European countries; 

as a result, the treatment of tax credits in the U.S. models has important implications. Not all of 

the individual costs that comprise an aggregate “installed cost” figure, for example, are 

necessarily eligible for depreciation or tax credits (Bolinger 2009), but individual models 

approach this differently. The Vermont model allows for the percentage of costs eligible for 

depreciation to be specified, whereas the Gainesville model does not provide this level of detail.

Closely related to the issue of tax treatment is the issue of assumed ownership structure. 

Ownership structure can have important implications for tax incentives and liabilities and the 

 

28 Ontario, for example, assumes that 60%–80% of project costs will be depreciated under the Class 43.2 Capital 

Cost Allowance, with the remainder of capital costs depreciated according to an 8% declining balance schedule 

(Ontario Power Authority 2009). 
22 

availability of additional incentives. As discussed further in Section 5, FITs can be established 

based on an assumed ownership structure. A conservatively set tariff might assume the most 

efficient (lowest-cost) structure and a more aggressive tariff model might assume a less 

optimized ownership structure, thereby allowing a broader range of ownership structures to 

thrive.

Another option to account for the differences is to differentiate the FIT payment levels based on 

ownership. Due to the differences in applicability of tax-based incentives, as well as the ability 

for government bodies to utilize tax-free bonding or similarly advantaged financing mechanisms, 

the assumed ownership structure can have a material impact on the revenue needs, or COE, for a 

renewable energy project. This observation suggests that calculation methodologies and their 

corresponding spreadsheet models might be built to accommodate both taxable and tax-free 

ownership structures (Green Mountain Power 2009; Tregilus 2010). None of the models 

surveyed, however, were specifically built to model ownership by non-taxpaying entities. 

All of the case study jurisdictions used models that assume commercial, private-sector 

ownership, except the Netherlands’ model, which assumes that small-scale PV is owned by a 

residential homeowner. Ontario’s FIT rate setting differentiates between three different corporate 

tax schedules (i.e., different project sizes are assumed to be owned by different corporation 

types29

4.2.4 Evaluation Metrics for Return on Invested Capital

) but does not assume residential ownership of any system type or size (Ontario Power 

Authority 2009). 

Each of the models is constructed to solve for one or more evaluation metrics. These metrics are 

not merely model “outputs”; however, they represent minimum thresholds or constraints that 

must be met for the renewable energy project to achieve the financing necessary for construction 

and operation. The models then adjust the FIT level until each constraint is met and the 

evaluation metrics demonstrate an economically feasible project—keeping all other inputs 

constant. The most common evaluation metrics in the jurisdictions studied include the IRR 

(which can be calculated on either before- or after-tax cash flows to equity providers) and the 

NPV. The IRR metric reports the percentage return on equity for the analysis period (likely to be 

the length of the proposed FIT contract in this case). By comparison, the NPV assumes a target 

percentage return on equity requirement and uses this target to calculate the present dollar value

of the investment. A resulting NPV that is equal to or greater than zero denotes an investment 

that meets or exceeds the investor’s minimum required return. An NPV of less than zero 

represents an investment that fails to meet the investors required return. 

4.2.5 Assumed Capital Structure

Sources of capital to finance generation projects generally consist of equity and debt and also 

could include grants or other incentives. As a practical matter, not all projects use the same 

financing vehicles. For example, individual project financings could differ with respect to the 

obligations of, and rights granted to, equity providers. If leverage is used, then project owners 

must select one or more lenders, establish payment priorities, and determine whether the lender 

has recourse beyond the generator assets if the project company is unable to service the loan. In 

 

29 For example, General Corporation—General Corporate Income Rate; Canadian-Controlled Private Corporation—

Small Business Rate; Canadian-Controlled Private Corporation—General Active Business Rate. 
23 

some cases, more complex capital structures are desired; for example, to facilitate changes in 

ownership allocations at various times during the project’s useful life (Harper et al. 2007) or to 

make use of more sophisticated financing instruments that enable the conversion of debt into 

equity. Spreadsheet models used for policy analysis typically utilize capital structures on the 

simple end of the spectrum, assuming a single source of equity and a single source of debt. In 

these cases policymakers must identify the equity investor’s target return on equity (typically 

after-tax dollars) and, if debt is used, the total loan amount, tenor, and interest rate. 

Based on assumptions about market conditions and the type of debt applied to the project (e.g., 

with or without recourse beyond the assets of the project),30 different debt terms were assumed 

by different jurisdictions. These terms include the amount of debt available to the project as a 

percentage of total capital contributed, the tenor, the interest rate, and the required average and 

minimum annual debt service coverage ratio.31 Almost all of the models allow for modeling of a 

single conventional debt instrument and assume that projects will take advantage of some degree 

of leverage. The exception to this is the Gainesville model, which assumes that projects will be 

paid for with 100% equity. Both Ontario and the Netherlands generally assumed that the same 

debt-to-equity ratio applied to all technology types (i.e., 80/20 in the Netherlands;32 70/30 in 

Ontario). In both Germany and Vermont, each of the different technologies was assumed to have 

different debt-to-equity ratios, which generally reflected their different risk profiles and type of 

debt. Most jurisdictions assumed that the debt term was equal to the FIT term.33

4.2.6 Treatment of Incentives

Incentives for renewable energy generation can take many forms, including tax credits and 

accelerated depreciation deductions, production incentives, rebates, and grants (both taxable and 

non-taxable). Applicability can depend on availability or the developer’s tax liabilities. Such 

incentives typically fall into two broad categories, “generally available” or “of limited 

availability” (e.g., budget limited, set to expire, or accessed only by winners of competitive 

processes). Additionally, the full value of generally available incentives that depend on tax 

appetite—the presence of sufficient tax liabilities to monetize tax incentives—might not be 

realized. Generally speaking, each of the jurisdictions took other available incentives into 

account in determining the FIT rate. The most significant variance was the degree to which 

incentives were presumed to be available or monetized. 

The Netherlands model explicitly incorporates the national energy investment allowance 

incentive34

 

30 In Vermont, the PSB assumed that most project debt was recourse except for PV, which was assumed to be 

project financed (Vermont Public Service Board 2010). 

where available. In the United States, both Gainesville and Vermont took the 30% 

31 A debt service coverage ratio is a fraction that uses earnings before interest, taxes, depreciation and amortization

(EBITDA) (operating cash flow) as the numerator and the sum of principal and interest owed for the same period as 

the denominator. The debt service coverage ratio is one of several tools used by lending institutions to assess the risk 

that a project will not be able to repay its loan.

32 The exceptions to this are the Dutch small PV tariff, which is assumed to be 100% debt financed through a 

residential mortgage and the Dutch waste-to-energy tariff, which is set assuming corporate finance. 33 The exceptions to this were for hydropower in the Netherlands, which assumes a 30-year debt term but only a 15-

year FIT life; in Vermont, where the PSB found that the model should assume a “loan tail” for each generator—that 

is, assume that the loan term would be shorter than the contract term.

34 Known in Dutch as the Energie-investeringsaftrek, or EIA.
24 

federal income tax credit into account for eligible generators.35

4.2.7 Tariff Rate Escalation

There was controversy in 

Vermont over whether state incentives—which included a state grant and state tax credits—

should be taken into account. The law states that “reasonably available” incentives should be 

included in the FIT calculation. Some stakeholders argued that the limited availability of the 

state grant and the limited state tax equity available to monetize the tax credits meant that those 

incentives did not pass the “reasonably available” test. Additional guidance from the state 

clarified that generators could not take both the state grant and the FIT, but it was ultimately 

assumed that the state tax credits could be claimed against a portion of the capital costs for most 

generators (Vermont Public Service Board 2010). In Ontario, the FIT rates do not assume that 

generators can take advantage of the federal ecoEnergy incentive because it is available on a first

come, first served basis and is already oversubscribed. 

Another important FIT policy design decision is whether to allow all or a portion of the tariff rate 

to escalate over time. The options for this policy issue range from establishing a fixed FIT rate 

that remains constant for the duration of the contract to setting a rate that automatically adjusts 

each year—by multiplying either with a fixed escalation factor or with a designated inflation 

index such as consumer price index or gross domestic product implicit price deflator. The 

models examined vary in their approach to escalation. In Germany, the Netherlands, and 

Gainesville, Florida, the FIT rate remains fixed over the term of the policy. Ontario escalates 

20% of the tariff rate each year at an assumed 2.25% inflation rate, and Vermont escalates 30% 

of the FIT rates annually for inflation.36 Where applied, tariff-rate escalation is intended to serve 

as a risk-mitigating tool,37

4.2.8 Accounting for Operating Cost Inflation

at least partially protecting the project investor from the uncertainty 

associated with the future cost of owning and operating the renewable energy facility. 

Separate from (but not unrelated to) tariff-rate escalation, operating-cost inflation represents 

another decision point in the FIT rate-setting process. The question of whether to inflate 

operating costs typically is encountered at the cost-modeling stage. Some models assume a rate 

of inflation that is applied to all annual operating expenses. Some models apply inflation rates or 

indices to only a subset of annual operating costs. Some models assume no inflation at all, setting 

the FIT rate based on a static assessment of project capital and operating costs. Both Germany 

and the Netherlands assume a fixed inflation rate in their respective modeling of operating costs. 

By contrast, the Gainesville model does not include inflation in its calculation of the FIT rate,

nor does it escalate its FIT rate. 

4.2.9 Granularity of Cost Input Detail

Each of the models included comparatively aggregated inputs for costs, except for Germany’s 

model, which included detailed capital and operating cost inputs. All of the other models 

included more simplified cost inputs, with the Netherlands, Gainesville, and Ontario each 

aggregating capital costs as a single input. Vermont, by contrast, distinguished initial financing 

 

35 The exception to this was farm methane generators in Vermont. For farms, neither the federal nor the state tax 

credit was taken into account when calculating the FIT rate because it was assumed that farms did not have the tax 

appetite to take advantage of either incentive.

36 In both Ontario and Vermont, the PV rate remains fixed and is not adjusted for inflation. 37 The Ontario Power Authority (2009), for example, stated that “20% is generally consistent with the proportion of 

project costs that vary with inflation and provides protection against changes in the rate of inflation” (p. 32).
25 

costs, such as debt and operating reserves, from hard capital costs. Most of the jurisdictions’ 

models also require operating cost inputs,38

4.2.10 Key Observations from Comparison of Spreadsheet Models

 which tend to be more differentiated than the capital 

cost inputs (e.g., three to five inputs, divided between fixed and variable operating costs). 

The case study jurisdictions have employed similar approaches to structuring their rate-setting 

models, even if the exact inputs and assumptions employed differed. With a few exceptions, the 

models generally are characterized by:

• Comparatively simple inputs (see Section 5) 

• DCF, after-tax analysis 

• Assumed private sector, commercial ownership

• The use of IRR as an evaluation metric

• An assumption that other available incentives are taken into account before the rate is 

calculated

• Aggregated cost inputs. 

4.3 Comparing the Rate-Setting Process

Although there is commonality among the spreadsheet models employed, there are significant 

differences in how the case study jurisdictions structured and managed their rate-setting 

processes—including differences in data gathering, stakeholder engagement, and approval 

processes. Although these processes and procedures are not as directly relevant to the CREST 

model as the review of comparative model designs, they are included to give policymakers a 

better sense of how FIT rates are developed and codified. Sections 4.3.1 and 4.3.2 discuss the 

extent to which the data gathering and stakeholder engagement shape model design. Section 4.4 

on the approval process highlights the fact that the outcomes of a rate-setting analysis could be 

exposed to additional amendments by policymakers. 

4.3.1 Data Sources

Different jurisdictions used different sources of data to develop assumptions for their models. A 

significant differentiating factor among the case studies is that the European countries generally 

have more data on recent and local project costs to inform rate setting. This largely is a result of 

Europe’s lengthier experience with FIT policies and because of requirements that generators 

receiving FITs must provide cost and performance data to subsequent rate-setting processes. As a 

result, European data-gathering processes tend to be more robust. Germany employs eight 

different research institutes to analyze bottom-up cost data from both published industry studies 

and from an analysis of costs reported by generators receiving FITs. The Netherlands employs 

both private sector and public sector research consultancies to create initial estimates, which are 

cross-referenced with industry cost data through a transparent, public engagement process 

(KEMA 2008). Each of the North American data-gathering processes was focused primarily on 

surveys of top-down data (i.e., average installed costs) rather than on bottom-up research 

processes (i.e., researching the costs of individual components). Ontario relied on published cost 

 

38 The exception to this was Gainesville, which automatically populated O&M cost assumptions as a function of the 

installed cost input into the cash flow model.
26 

estimates from recent consulting studies as well as experience from the 2006 Renewable Energy 

Standard Offer Program (RESOP). Gainesville and Vermont both relied on publicly available 

data gathered from regional programs, published studies, and input from industry. 

4.3.2 Process Transparency and Stakeholder Involvement

The process by which cost data were gathered and ultimately integrated into the spreadsheet 

models shaped the amount and type of stakeholder input permitted in the rate-setting process. 

Germany and Ontario published descriptions of their calculation methodologies, rate-setting 

approaches, and data sources and also permitted some stakeholder comments and participation. 

The rate-setting models themselves, however, are not publicly available. 

Both the Netherlands and Vermont utilized highly participatory processes featuring transparent 

spreadsheet models posted online. Stakeholders were encouraged to submit their own datasets to 

inform the model. Gainesville’s process also provided an opportunity for stakeholder input 

through city commission workshops, but the process was not as extensive as those used in the 

Netherlands or in Vermont. The spreadsheet model is not available online from GRU but is 

available upon request. In some instances the nature of the stakeholder process informed the 

development of the model. In both the Netherlands and in Gainesville, for example, the ratesetting models were developed with simplicity in mind to accommodate and focus stakeholder 

input on key assumptions. As discussed in greater detail in Section 6, there are trade-offs in 

administrative costs, process length, and process complexity inherent in the amount and type of 

data gathered, the transparency of the rate-setting approach, and the degree to which stakeholders 

are involved. 

4.4 Approval Process

The approval process determines the degree to which policymakers can influence or shape the 

rate-setting process subsequent to cost modeling. A key question here is whether the modeling 

exercise is the final step in the rate-setting process or whether the rates are developed as data 

points for use by policymakers and therefore are subject to further change. Across the case study 

jurisdictions there was little commonality among the processes by which the FITs were 

approved. This largely is a function of the different legal systems and regulatory processes of 

each jurisdiction. In all cases, however, the outputs informed policymakers, who then had 

authority to make the final decision to adopt the rates or alter them. In Germany and the 

Netherlands, for example, the FIT rate development process is managed by the responsible 

government ministry, but the final rates are proposed to the national legislature for adoption. 

During the parliamentary process, there are opportunities for the proposed rates to be amended 

before they are passed into law. In 2008, for example, the German legislature increased the wind 

rates rather than adopting them as proposed. In Vermont, by contrast, the FIT rate-setting process 

was initiated by a law that directed the PSB to examine what reasonable rates should be. The 

board used its authority to set the final FIT rates, following a formal regulatory proceeding. In 

Gainesville, the utility managed the development of the rates, which were then formally adopted 

in an ordinance approved by the city commission. 

4.5 Conclusion

As described in Section 4.2.10, the comparison of different jurisdictions’ spreadsheet models 

reveals that there are clear commonalities between the rate-setting approaches employed both in 

the United States and abroad. Each of the spreadsheets also contains distinct design features that 
27 

reflect the different regulatory frameworks, analytical philosophies, and policy objectives present 

in the different jurisdictions. These findings provide a frame of reference for the recommended 

design of the CREST model described in Section 5. Some of the common design decisions, for 

example, could be appropriate for direct integration into CREST, whereas some might not enable 

the full range of function and flexibility envisioned for the CREST model. 
28 

5 CREST Model Inputs

This section provides a clear pathway to determining the set of inputs that must be defined to 

calculate a FIT rate for each technology-differentiated tariff and can be used to inform the 

structure of the CREST model. 

5.1 Model Inputs and Their Context

In determining the ideal characteristics of a spreadsheet model to be used in support of FIT 

policy development and implementation context matters, the three categories of issues that 

should be considered are the policymaking process, policy objectives, and trade-offs among 

competing objectives. 

5.1.1 Implications on Cost Modeling Dictated by the Process

As discussed in Section 6.1, the process of deriving and collecting inputs has implications for 

modeling choices. If the policymaking process relies on surveys of installers or developers to 

determine the “top-down” all-inclusive installed costs or total operating costs of a particular 

renewable energy technology, then few input cells would be required. Some FIT development 

processes have sought to develop detailed “bottom-up” cost estimates based on the costs of 

individual components. In such instances, a more detailed set of inputs could be required in the 

model.39

5.1.2 Implications of Policy Objectives on Model Characteristics

A model intended to accommodate a range of processes should be designed to accommodate a greater variety and granularity of inputs than one using only greatly aggregated, topdown inputs. Conversely, a model with numerous minutely detailed input cells (i.e., high-input 

granularity) could prove confusing and cumbersome for users and could be prone to user errors 

such as leaving costs in detailed input cells that are already incorporated into aggregate numbers 

entered into another cell. A spreadsheet model able to accommodate both highly granular inputs 

and highly aggregated inputs is most useful within a wide range of policymaking processes. 

Policy objectives also influence FIT cost model design. As discussed further in Section 6, there 

are numerous policy decisions to be made in FIT design, many of which have modeling 

implications. For example, is a single rate calculated for each technology or are costs further 

differentiated by project size, location, or another characteristic? Do policymakers wish to 

encourage a diversity of ownership structures or locations? Are policymakers seeking to develop 

an aggressive price point that might support a wide range of projects or a conservative price that 

might only support the most cost-effective project sizes or configurations? These choices could 

influence whether the costs of individual components (which might be sensitive to scale) can be 

varied in the model, whether the model can accommodate varying tax consequences and 

incentive eligibility, and whether the model is able to consider a variety of ownership structures. 

5.1.3 Implications of Model Use

Another factor to consider is how the model results will be used. Will the result be used directly 

to set the FIT rate, for instance, or be used to guide a process of rate setting informed by the 

modeling? A model able to produce results for a range of inputs enables good consideration of 

the range of potential costs for a particular technology, enabling policymakers to make more 

 

39 Although these inputs could be aggregated external to the model, doing so has the potential to introduce some 

inaccuracy. 
29 

informed decisions regarding where to place the FIT rate on the aggressive-conservative 

continuum (discussed further in Section 6).

5.1.4 Finding the Right Balance Among Trade-Offs in Model Design

There are many trade-offs to be considered in the art of modeling. These usually fall along the 

spectra of simplicity versus complexity and of perceived accuracy versus ease of use.

• Simplicity and ease of use. For a model to be used within a policymaking context, it 

must be both readily understood by users who do not create financial models 

professionally and difficult to “break” or cause to malfunction. The more complex the 

model, the more likely it is that it will violate these criteria. 

• The trade-off between high-level and detailed analysis. There will be a natural 

inclination among some users to seek greater detail and resolution in the name of 

accuracy. Clearly, the more features and options included, the more case-, 

technology-, or site-specific analysis a model can handle. Conversely, some users 

might seek a simplified model to estimate project costs. For policy-analysis purposes, 

it might be overkill to require the same level of detail as a model designed to support 

project financing.

• Accuracy, precision, and representativeness. Allowing for detailed cost inputs 

might provide a more accurate estimate of COE by increasing resolution. A more 

detailed set of model inputs, however, also can lead to false or “illusory” precision. 

This can occur if many of the inputs must be estimated because data are not readily 

available at that level of detail or if the range of cost variations among installations of 

the same technology is greater than the degree of precision gained.40

On the other hand, common methods used to simplify financial calculations—for 

example the “recovery factor” methodologies described in Section 2—can introduce 

imprecision (in this example, the inability to accurately model depreciation, 

applicability of tax incentives, and cash flow constraints). When it comes to model 

inputs, the right level of detail for a spreadsheet tool is influenced by such factors as 

the availability of reliable input data, the representativeness of that data for the 

applicable technology as a whole, and the policymaker’s inclination to differentiate 

tariffs. As a result, a flexible model design might be more amenable to the range of 

anticipated CREST model uses than a one-size-fits-all set of model inputs. 

Sometimes a 

representative estimate is sufficient.

o Level of data granularity. As discussed, there exists a wide range of 

potential granularity of input data. A project’s total installed cost, for example, 

could be either (1) input as a single value that implicitly includes items such 

as construction interest, permitting costs, developer’s fees, and financing 

expenses, or (2) input as multiple values with equipment- and installationspecific detail. The process for collecting and compiling data, the 

 

40 For example, even for a single technology, costs can vary widely based on factors such as scale economies, 

resource quality, distance from transmission, whether or not a substation is required, and other site-specific factors. 

Further, changes in costs over time (before a FIT rate might be reset) can also outstrip the precision of a specific 

model result.
30 

sophistication of the users, and the desire to vary specific components to 

explore tariff differentiation or the aggressive-conservative decision all 

influence the decision about this trade-off.

o Ease of model use. Depending on the timeline for the rate-setting process, a 

complex model with a significant degree of granularity could be less practical 

to utilize, as learning, explaining, populating, and debating it might take more 

time than is available.

Given the range of policy objectives and policymaking processes throughout U.S. electricity 

markets, the analysis in this section takes the above factors into account in developing 

recommended inputs for a spreadsheet modeling tool that is both easy to use and flexible enough 

to account for a variety of policy-design choices.

5.2 Total Installed Cost

Renewable energy installations are complex and capital-intensive projects. A diverse 

combination of equipment, labor, and service providers is required to bring each project to 

completion. Therefore, a project’s true installed cost—those costs collectively aggregated for 

permanent financing—includes not only the cost of equipment and installation labor but also 

those associated with arranging financing, construction interest paid, conducting investor due 

diligence, establishing necessary operating reserve accounts, negotiating PPAs, designing and 

implementing utility interconnection, completing a resource assessment, and a plethora of 

development and permitting tasks. Equipment and associated installation expenses typically are 

referred to as “hard costs.” The costs of financing and due diligence are referred to as “soft 

costs.” All work related to studying project feasibility, engineering and design, obtaining 

permits, and negotiating power sales are referred to as “development costs.” The treatment (for 

modeling purposes) of each of these cost categories was studied during the survey of modeling 

tools described in Section 3 and the review of industry experience with FIT modeling found in 

Section 4. The recommendations for modeling total installed costs in the CREST model are 

based on these analyses. 

Total installed costs can be modeled many different ways. Some project stakeholders tend to 

speak of and analyze installed costs in aggregate dollars per kilowatt installed, and others require 

detailed lists of equipment and service costs. For some purposes, the component-level detail is 

necessary; for example, regarding the eligibility of costs for the ITC or for depreciation 

classification. The level of installed-cost detail included in a financial analysis helps to determine 

the level of complexity in other areas of the model. If installed costs are broken down in great 

detail, for example, then assignments of depreciation schedules and incentive eligibility could be 

done on a line-by-line basis. In the absence of such detail, depreciation allocations and incentive 

eligibility can be assigned for the purpose of estimating the COE from the proposed facility, 

using rules of thumb based on the composition of project costs. Table 2 summarizes the range of 

complexity in installed-cost modeling options. 
31 

Table 2. Summary of Total Installed Cost Inputs

Range of Complexity in Total Installed Cost Inputs

Simple Intermediate Complex

Total Installed 

Cost Inputs

 Total dollar

 Dollar per kilowatt

 Generation equipment

 Balance of plant

 Interconnection

 Development costs

 Financing costs

 Each piece of 

equipment and 

service provided has 

its own input

The list of installed cost inputs is the primary example of an area in which policymakers can 

elect to have calculations made either within or external to the cost model. The financial model 

must have a reliable estimate of total installed cost, but it need not include a detailed breakdown 

to be able to calculate a defensible estimate of the COE for a specified project.

A financial analysis also can be constructed to allow the user to select the single input (simple), a 

categorical approach (intermediate), or a detailed level of granularity (complex). With such 

functionality, policymakers and stakeholders together can determine how much installed-cost 

transparency is desired. The specified line-items vary from project to project in the most detailed 

analysis, so it is impractical to attempt to develop a model that predicts all of the necessary 

inputs. Therefore, for the complex approach to be useful, its structure must allow for a user to 

specify a custom line-item detail. By definition, FITs are rates intended to be representative of a 

category of resources, and not a single project or the simple or intermediate approaches appear to 

be better tools to accomplish state objectives. Nonetheless, the complex structure can provide for 

those situations where highly granular cost detail is available or desirable. 

Assessment. For the purposes of this project, an intermediate level of complexity for modeling 

the total installed cost of renewable energy projects appears to be most appropriate. This level of 

granularity allows for a reasonable amount of cost allocation among major categories yet 

maintains focus on ease of operation, which is a critical component to its value to policymakers. 

As discussed in Section 6, however, the specific policymaking process and the nature of the data 

sources can dictate whether a more highly aggregated or more detailed set of cost inputs is 

desired. For a model with the goal of being useful in a wide range of contexts, including a userdefined option to select simple, intermediate, or complex cost-input granularity is ideal.

2. The Intersection of Modeling Inputs and Consensus-driven Stakeholder Processes

Although not essential to the modeling process, a detailed discussion of the composition of installed costs

nonetheless could be helpful to the stakeholder consensus process. In this case, policymakers might wish to 

derive a detailed list of installed cost inputs (and arrive at a final representative installed cost number) through 

a collaborative process but only enter a single input—or defined set of aggregated inputs—into the rate-setting 

model. Doing so allows a more transparent consideration of some of the underlying assumptions or policy 

preferences. For instance, explicitly considering how much cost to include for such items as interconnection, 

line extension, and substation costs aligns with decisions on whether a FIT is intended to encourage project 

location at the most efficient locations to minimize these costs or over a more geographically diverse footprint. 

Conversely, such detail requires a level of data maturity and depth not necessarily available in the United 

States, as well as sufficient time for stakeholder debate on installed-cost components.
32 

5.3 Operating Costs

Although the cost of many renewable energy projects is dominated by initial capital expenditures 

and not by operations, the annual fixed and variable costs also impact a project’s economics. 

Annual charges often are casually referred to as “operations and maintenance” (O&M); however, 

this term paints an incomplete picture of a facility’s annual cost obligation. The full annual cost 

to operate a renewable energy facility also includes project management, insurance, property 

taxes (or payments in lieu thereof), permit maintenance, site maintenance, land lease or royalty 

payments, and other potential fixed and variable costs. A minimum level of specificity that 

accounts for these costs must be achieved to set FIT rates appropriately. As with total installed 

costs, operating costs can be estimated either individually or in aggregate. The presence of fixed 

operating costs that are independent of production, however, makes it difficult to accurately 

model all operating costs as a single value (i.e., in cents per kilowatt-hour) when production is 

expected to vary from year to year. As a result, the CREST model at minimum should include 

inputs for both fixed operating expenses (in dollars per kilowatt per year) and variable operating 

expenses (in cents per kilowatt-hour). The recommendations for modeling operating costs in the 

CREST model are based on the survey of modeling tools discussed in Section 3 and the review 

of industry experience with FIT modeling found in Section 4. Table 3 summarizes the range of 

complexity in modeling operating cost inputs. 

Table 3. Summary of Operating Inputs Options

Range of Complexity in Operating Cost Inputs

Simple Intermediate Complex

Operating Cost 

Inputs

 Variable operating costs 

(¢/kWh)

 Fixed operating costs 

($/kW-year)

Fixed Costs

 Fixed O&M41

 Insurance

 Project management

 Property taxes

Variable Costs

 Variable O&M

 Royalties

 Fuel or other consumables 

(if applicable)

 Line-item inputs for 

individual non-capital 

cost components

Assessment. Depending on the situation, either the simple level or the intermediate level of 

operating cost detail could be appropriate. As a result, it is recommended that the CREST model 

provide the flexibility to employ either the simple or intermediate approach. At a minimum, the 

user will define both fixed and variable operating expenses. The intermediate option allows for 

further specificity in the categories shown in Table 3. The intermediate approach is intended to 

offer enough detail to address important issues raised during stakeholder processes yet maintain 

focus on ease of use. If further granularity in operating cost inputs is desired, policymakers and 

stakeholders can elect to compile a more detailed list external to the model. Once complete, the 

detail list can be partially aggregated to fit into the limited number of fixed- and variable-cost 

categories offered within the model. Because FITs are intended to be representative of a category 

of resources and not a single project, the categorical approach appears to be a better tool to 

accomplish state objectives. 

 

41 This input could be used to model a long-term service agreement.
33 

Additionally, it is recommended that CREST also take into account major equipment repairs or 

replacements likely to occur during the project’s expected useful life. This would include, for 

example, inverter replacements for solar generators, gearbox repairs, replacements for wind 

projects, and make-up wells for geothermal facilities. Although these costs could be thought of 

categorically as ongoing maintenance, from a modeling perspective they should be treated as 

capital expenditures and depreciated accordingly. 

5.4 Government Incentives

In this report, the term “government incentives” refers to the range of financial support 

mechanisms available to electricity-generating projects from federal, state, and (potentially) local 

governmental bodies. Due to the substantial and continually evolving variety of incentive 

programs available to renewable energy generation across the country, it is impractical to design 

and construct a rate-setting tool capable of modeling every conceivable state and local incentive. 

It is both important and feasible, however, to establish FIT rates that take into account both the 

broadly available federal incentives and the most common structures for both state and federal 

incentives and assume their efficient utilization. The recommendations for modeling government 

incentives in the CREST model are based on the survey of modeling tools undertaken in Section

3 and the review of industry experience with FIT modeling found in Section 4. Table 4 identifies 

each major category of government incentive and describes three levels of complexity at which 

each incentive can be modeled. 

Table 4. Summary of Modeling Options for Federal, State, and Local Incentives

Modeling Options 

for Federal, State, 

& Local Incentives Range of Input Complexity in Modeling Renewable Energy Incentives

Federal Production 

Tax Credit (PTC)

Federal Incentives

 PTC value

 Duration (10 years)

 Eligibility: Y/N

Simple Intermediate Complex

Federal ITC  Single input

 Percent of total 

installed cost

 Cost eligibility modeled 

categorically

 Applicability 

determined line by

line for detailed 

capital costs inputs

Utilization of PTC 

or ITC42

 Full

 Single input at 100%

 Partial

 Single input between 0% 

and 100%

 Partial

 Based on investorspecific tax 

obligations

Accelerated 

Depreciation

(Federal)

 Total project cost is 

allocated across 

several depreciation 

categories using % 

inputs

 Total project cost is 

divided into categories 

and then allocated across 

depreciation schedules

 Assignments made 

for each cost line

item

 

42 Although many models assume that the full value of such incentives can be utilized, in practice that often is not 

the case. For example, in bringing in third-party equity investors, developers often have to give up some of the value 

in the deal. In other cases, the value of tax credits is fully realized only when the owner has sufficient tax liability to 

offset with the credits. Over the course of a project life it often is the case that, in some years, owner tax liabilities 

are insufficient to monetize the full value of the tax credits. The recent recession, for example, has resulted in 

shrinking profits and corresponding federal income tax liabilities for many of the traditional tax equity investors.
34 

Modeling Options 

for Federal, State, 

& Local Incentives Range of Input Complexity in Modeling Renewable Energy Incentives

Utilization of 

Annual Net 

Operating Losses 

(Federal)

 All tax losses 

monetized as generated

 User defines whether tax 

losses are used as 

generated or carried 

forward

 Tax losses allocated 

across partnership

State ITC

State & Local Incentives

 Single input

 Percent of total 

installed cost

 Cost eligibility model 

categorically

 Applicability 

determined line by

line for detailed 

capital costs inputs

Utilization of State 

ITC

 Full

 Single input at 100%

 Partial

 Single input between 0% 

and 100%

 Partial

 Based on investorspecific tax 

obligations

State Grant, 

Rebate, or 

Capacity-Based 

Incentive 

 Single, dollar-value 

input

 Dollar value calculated 

external to model

 Dollar value calculated 

internally

 Look up table of 

available incentives 

by state

Production-Based 

Incentives

 Dollar per megawatthour

 Duration

 Escalation

 Dollar per megawatt-hour

 Duration

 Escalation

 Funding cap, if applicable

 Look up table of 

available incentives 

by state

State Property Tax 

Exemption

 Ignore property taxes  Create entry for fixed 

annual payments in lieu 

of taxes (PILOT);

43

 Model using tax rate 

and basis; populate as 

necessary

populate as necessary

State Sales Tax 

Exemption

 Reflect exemption 

through lower

installed-cost estimate

 Break-out sales tax 

calculation categorically; 

zero out tax rate as 

applicable

 Apply sales tax 

calculation line by

line; zero out tax rate 

as applicable

It is important to note that a project’s eligibility for any individual incentive could depend on the 

technology deployed as well as on the project’s ownership composition, capital structure, offtake arrangements, and other factors. Table 5 provides a high-level summary of eligibility for 

federal incentives by technology and ownership structure. The presence and applicability of state 

incentives are too diverse to include in a single table. A good source for information on state 

incentives is the Database of State Incentives for Renewable Energy (DSIRE) website, located at 

http://www.dsireusa.org. 

 

43 If PILOT is not applicable, the user can estimate the equivalent average annual property tax payment and enter it 

in the cell for the PILOT input. 
35 

Table 5. Summary of Eligibility for Federal Incentives

Selected Eligibility Criteria for Federal Renewable Energy Incentives

By Technology By Ownership

Solar Wind Geothermal Private Public

PTC Through 12/31/12 Through 12/31/13 √

ITC Through 12/31/16 Through 12/31/12 Through 12/31/13 √

U.S. Treasury Grant44 √ √ √ √

Accelerated Depreciation √ √ √ √

Clean Renewable Energy

Bonds (CREBs) √ √ √ √

USDA Grants & Loans √ √ √ √

Renewable Energy Production Incentive (REPI)45 √ √ √ √

Notwithstanding the general rules described and summarized in Table 5, the ability to make 

efficient use of the tax-oriented incentives depends on the tax status and appetite of project 

investors. As a result, the impact of incentives on any FIT rate-setting exercise is representative 

and not specific to the individual investors in a specific renewable energy project.

Assessment. It is recommended that the CREST model include the most widely available and 

commonly used federal and state incentive programs. The survey of models and review of FIT 

experience in other jurisdictions is used to inform the list of incentives included in the CREST 

model. 

5.5 Ownership and Capital Structure

Ownership and capitalization structures also can influence a project’s COE or revenue 

requirements. This section defines a range of potential ownership and capital-structure options 

that could be assumed when modeling FIT rates. Policymakers’ ultimate FIT design choices will 

have a significant impact on the availability and terms of financing for eligible renewable energy 

generators. The recommendations for modeling ownership and capital-structure options in the 

CREST model are based on the survey of modeling tools in Section 3, and the review of industry 

experience with FITs is found in Section 4. 

5.5.1 Ownership Structure

A project’s owners are responsible for both providing the initial investment and meeting any 

ongoing cash obligations. The owners also gain from a project’s cash flow benefits. Ownership 

structure refers to the types of entities with a defined percentage ownership interest in the 

project, as well as their respective tax status. For example, a project described as a private jointventure denotes ownership by two or more taxable entities. As a practical matter, one of these 

owners likely is without the tax liability necessary to utilize all of the project’s available benefits. 

This circumstance is the basis for most private partnerships and represents the most commonly 

used structure in today’s market for utility-scale project financing. In contrast, a public project 

refers to ownership by a non-taxable government entity. Public-private partnerships are a hybrid 

of the two types of structure. The selected ownership structure has important implications for 

 

44 Available to projects under construction by December 31, 2010, and meeting all PTC or ITC eligibility criteria. 45 REPI is subject to annual appropriation and historically has been underfunded as compared to outstanding REPI 

commitments.
36 

taxation and the project’s eligibility for federal and state incentives. Table 6 summarizes these 

issues and their impact on FIT rate setting.

Table 6. Summary of Ownership Structures and Financial Modeling Implications

Tax Status Issue

Private Ownership

(Income Taxable)

Public Ownership

(Not Income Taxable) Implications

Income Taxes Yes No Due to the importance of tax incentives in 

the United States, FIT rates should be set 

based on after-tax returns

Depreciation Yes No Can provide up to one-third of NPV 

benefit

Federal PTC, 

ITC, ITC Cash 

Grant

Yes No Can provide up to one-third of NPV 

benefit

Other Incentives

State Rebates

Other Grants

Yes

Yes

Yes

Yes

Depreciation basis likely reduced by 50% 

of rebate or grant amount; basis could be 

reduced 100% if incentives are not taxable

PerformanceBased Incentives

PTC 

REPI 

State 

PerformanceBased Incentives

Yes

No

State specific

No

Yes

State specific

PTC available to eligible private-sector 

owner during first 10 years of operation;

REPI subject to appropriation

Property Tax Yes No Can be a fixed or variable amount

Sales Tax State specific No Impacts total installed cost

Assessment. For the purposes of this project, enabling analysis of projects utilizing different 

ownership (public and private) and financing structures (combinations of debt and equity) 

recognizes the variety of ownership structures—and associated range of COE—that 

policymakers and stakeholders might wish to consider. The CREST model should set limits on 

the complexity of ownership structures by assuming in the private ownership case that a single 

equity investor will provide both cash and tax equity investment and that the assumed equity 

return requirement is the combined IRR requirement for both the cash and tax equity. 

3. Which Ownership Structure “FITs?”

Another question likely to confront policymakers is determining what ownership structure, or structures, to 

assume when setting FIT rates. The answer depends—once again—on each jurisdiction’s policy objectives. 

If the policy is to prioritize least cost ahead of the pace and diversity of development, regulators will be 

driven to find what is believed to be the most cost-effective ownership and financing design. If this were 

universal and known ahead of time, then the spreadsheet model would have no need for flexibility in this 

area and would offer only one financing option. By comparison, if having a diversity of project owners 

(including cooperatives and government entities, for example, in addition to the private sector) is equally 

important, regulators are more likely to set FIT rates in a manner that accommodates a broader range of 

capital structures. As described in Section 5.5, ownership and financing structures impact the COE. One 

reason is that an owner’s tax status impacts the availability of significant federal and state incentives (Table 

6). Although arguably incentives are available for both private (e.g., ITC/PTC) and public (e.g., tax-free 

debt) project owners, there are many possible financing configurations, and these incentives should not be 

thought to universally level the playing field.
37 

5.5.2 Modeling Options for Determining Capital Structure

Capital structure refers to the proportions and sources of funds invested in a project and can 

include a variety of equity and debt instruments. In some cases projects are funded entirely by 

equity investors. This is more common among the largest developers with balance sheets that can 

support an all-equity46

Table 7. Summary of Capital Structure Options

project financing but is not an option available to many of the smaller 

(and less well-capitalized) market participants also likely to participate in a FIT program. In 

other cases, debt is incurred at the project level as a way of reducing the amount of equity at risk 

for a single project and concurrently reducing the project’s weighted average cost of capital. 

Some (typically public) entities even might pursue projects funded mostly—or entirely—with 

debt. A project’s capital structure can remain constant for the life of the project or it can evolve 

with changes in ownership. Table 7 summarizes the range of complexity in capital structure 

options. For the purposes of this project, input assumptions allowing for a capital structure, 

which includes both equity and debt investments and which remains constant until the project 

debt is fully repaid, appear most practical. 

Range of Complexity in Capital Structure

Simple Intermediate Complex

Capital Structures 

Employed by 

Renewable Energy 

Projects

 100% equity  Both equity and project-level debt

 Debt-to-total-capital ratio either 

fixed or determined by available 

cash flow and a constant debt

service coverage ratio

 Capital structure changes 

during project life due to 

refinancing, change in 

project ownership 

allocation, or sale of 

project

Table 8 summarizes a capital structure’s key components and provides a brief explanation of the 

industry’s standard practices for quantifying the values and constraints associated with each type 

of capital.

 

46 Although it is common for large developers to fund projects using cash on hand (sometimes termed “using its 

balance sheet”), it also is relatively common for these same entities to group multiple projects funded in this manner 

and then seek financing to leverage the entire portfolio. The practice is referred to as “back-leverage financing” or 

corporate-level financing.
38 

Table 8. Summary of Capital Structure Components

Source 

of Funds

Percent of Total 

Project Cost Cost of Capital Term Constraints

Debt Sized relative to 

expected 

operating cash 

flows

Based on the risk-free 

rate for a similar tenor, 

plus the lender’s 

margin—based on the 

project’s perceived risk 

profile

The length of the 

loan is no more than 

the length of the 

PPA and usually is a 

year or two less than 

the term of the PPA

Debt payments are sized to be 

less than the total available cash 

flow; the ratio of PV-adjusted 

earnings before interest, taxes, 

depreciation, and amortization

to debt service is the debt 

service coverage ratio; lender 

sets a minimum debt service 

coverage ratio

Equity Balance of capital 

required beyond 

loan

Based on alternative 

investment opportunities

Return sought by 

target year

Need for long-term revenue 

certainty; use of proven 

technology; project’s overall 

financial outlook

Other Grants might be available to buy-down the total installed cost and 

reduce the burden on other capital providers

Can restrict power sales or 

impose other requirements

If project owners seek to finance using a combination of debt and equity, then the amount of debt 

modeled can be either defined by the user with a single percentage value or derived through a 

formula that calculates the maximum sustainable debt subject to available cash flow (specifically 

EBITDA) and a constant debt service coverage ratio. The defined capital structure method (a 

single fixed input) results in a “mortgage-type” amortization. The maximum sustainable debt 

method results in debt service obligations that vary with the project’s expected operating cash 

flows.

Assessment. Although commercial banks typically favor a detailed calculation of sustainable 

debt, such complexity might not be helpful, necessary, or appropriate for the type of projects 

affected by the FIT rate-setting process. By comparison, setting the debt-to-total-capital ratio 

with a single fixed input is likely to be intuitive to policymakers and other users, making it a 

practical solution for the need to model debt as an important financing tool for FIT projects. The 

recommended approach utilizes the fixed-input method to define capital structure. Model users, 

however, must be aware that the defined capital-structure method requires the user to monitor the 

resulting minimum annual and average debt service coverage ratios to ensure that the project’s 

variable cash flow is sufficient to meet its ongoing debt service obligation. These ratios serve as 

modeling constraints. For users who understand and favor the maximum sustainable debt 

method, the total loan amount can be calculated external to the model and then entered as a 

single equivalent percentage value. Both methods require the maintenance of a debt service 

reserve account.

5.5.3 Sources of Capital

The specific sources of capital available depend on factors including, but not limited to, the 

project owner’s tax status and credit rating, the terms of the PPA, the operating history of the 

selected technology, and the availability of federal and state incentives at project completion. 

The type and availability of capital also depends on whether the project is seeking development 

capital or permanent capital. Development capital is cash equity provided by entities with the 

appetite to take on permitting, power sales, and other development risks. For the purpose of cost 

modeling for establishing FITs, it is reasonable to include the return on development capital 
39 

(which also can be referred to as a “development fee”) in the build-up to total project cost. 

Investors of development capital are assumed to be repaid at the closing of the project’s 

permanent financing. Permanent capital (also referred to as project capital) can be obtained from 

a much broader range of equity investors and lenders. 

Table 9 summarizes the types of capital regularly available for renewable energy projects and 

categorizes them into simple, intermediate, and complex project financings. Although raising 

equity for a significant portion of project costs is commonplace, equity does not always mean 

simplicity. The tax-motivated nature of renewable energy investing is responsible for the 

market’s development of multiple complex project finance structures, which include shared 

ownership and combinations of cash and tax equity. 

Although the recent recession has restricted project-level (as well as corporate-level) debt 

financing (Schwabe et al. 2009), project-level debt historically has been a component of the 

financial structure of many independent power projects. Trends and innovations, however, have 

resulted in a reduced role of project-level debt financing over time. Federal loan guarantees and 

economic recovery are expected to stimulate debt financing and, as lenders begin to reengage the 

market, one or more layers of project debt also can be included in certain circumstances. The 

nature of FITs as a long-term guaranteed revenue stream with known access is highly compatible 

with debt financing as a tool to create leverage for more projects to be viable under a fixed rate.

Additionally, policymakers (whose objectives could include supporting renewable energy but 

minimizing the ratepayer costs) might expect the use of leverage to minimize project revenue 

requirements. If so, allowing CREST to reflect the benefits of such leverage supports these 

policymaker expectations regardless of whether debt is applied at the project or corporate level.

Table 9 lists several types of equity and debt. In general, equity refers to the capital invested by a 

project’s owner that the owner seeks to recover—plus a targeted return—over the lifetime of the 

project. A tax equity investor earns its return by offsetting its income tax liability from other 

business activities using the tax benefits generated by the project. Such benefits could include 

either the PTC or the ITC, depending on the project’s ability to meet IRS-established eligibility 

criteria and accelerated depreciation.

Table 9. Summary and Categorization of Sources of Capital Available for Project Financing

Range of Complexity in Sources of Capital

Simple Intermediate Complex

Types of Capital 

Available to 

Renewable Energy 

Projects

Potential Sources of Capital

 100% equity (“unleveraged”); assumes single 

owner able to efficiently 

monetize all tax benefits

Potential Sources of Capital

 Cash equity

 Tax equity

 Term debt

Potential Sources of Capital

 Cash equity

 Tax equity

 Development sponsor 

equity stays in project

 Senior debt

 Subordinated debt

In contrast, debt refers to invested capital, which must be repaid in calculable amounts 

(regardless of, and prior to, returns to equity investors). The amounts typically include both 

principal and interest and are paid at specified intervals over a predetermined period. Different 

debt instruments are used at different stages in a project’s life cycle. Construction debt generally 

applies to the relatively short period from ground-breaking to commissioning but also can be 
40 

used to finance long-lead-time items like equipment deposits. Term debt is the vehicle used to 

finance the project’s total installed cost and is repaid out of operations. When debt is used in a 

project’s capital structure, term debt often is used to refinance construction debt. If multiple term 

loans exist, one loan assumes senior status and the rest receive subordinate status. This senior 

and subordinate relationship refers to the priority with which the cash flows available for debt 

service are directed to various lenders and the security interest each has in the project (i.e., senior 

lenders have a first lien on the property). Project loans can have fixed or variable repayment 

structures, including balloon repayments. For example, during the recovery period following the 

recent financial crisis, the availability of debt generally was limited to mini-perm structures. A 

mini-perm sizes the loan amount and first several years of payments based on the same 

amortization as a term loan (e.g., 15 years) but requires the amount outstanding at approximately 

the fifth to seventh year to be repaid in full at that time. This lump-sum repayment is referred to 

as a balloon payment. 

Assessment. The recommended approach assumes a single provider of both cash equity and tax 

equity and includes inputs that enable the recognition of a development fee, interest on a 

construction loan, and the utilization of permanent project debt. 

5.5.4 Cost of Capital

The cost of capital is the required return on the funds spent—in this case to construct a 

renewable energy project. This concept applies to both debt and equity. The cost of debt capital 

is the interest rate. The cost of equity capital is the investor’s targeted rate of return. Returns can 

be calculated both before and after tax. Pretax returns are a useful comparison metric for an 

investor considering multiple potential uses for the same capital. This is especially true when 

each investment has a different tax consequence. For investments that are within a single 

industry and subject to the same tax obligations and incentives, an after-tax return provides 

insight that is more useful for the project’s cash flow implications.

Assessment. Given the tax-oriented nature of renewable energy investing in the United States, 

this project focuses on after-tax, project-level returns assuming that investors have sufficient tax 

liability to efficiently monetize all (or a specified fraction of) available federal and state tax 

incentives. There also is the secondary option for the project to monetize the tax incentives based 

on the cash flows in the project itself over a longer period. 

The cost of capital inputs is another example of an area in which policymakers can elect to have 

calculations made either internal or external to the model. If calculations are made internally, 

then the inputs section of the model must capture the percentage and cost of equity as well as the 

percentage and cost of debt. These inputs are used to calculate the project’s repayment 

obligations and determine whether the rates meet investor requirements. The alternative is to 

calculate the project’s weighted average cost of capital externally and use this single simplifying 

input to model the portion of the FIT rate necessary to cover the project’s cost of financing. 

Assessment. The CREST models enable a user to define the key terms of a project’s equity and 

debt (if applicable) investments. This method, shown in the “Intermediate” column of Table 10, 

is intended to provide a level of detail sufficient to approximate the cost and terms of capital 

most likely to be available to projects subject to the tariff.
41 

Table 10 summarizes the modeling inputs required by each approach. 

Table 10. Summary of Cost of Capital Modeling Options

Range of Complexity in Modeling the Cost of Capital

Simple Intermediate Complex

Cost of Capital 

Modeling 

Options

Cost of Capital Inputs

 Weighted average cost of 

capital

 Calculated external to the FIT 

rate-setting model

Cost of Capital Inputs

 Cost of debt

 Percentage of debt

 Cost of equity

 Percentage of equity

 All used within the FIT 

rate-setting model

The cost of capital 

inputs do not become 

more complex but 

might become more 

numerous if a complex 

capital structure is used

5.5.5 Term

In the context of a discussion on capital structure, term refers to the period over which a return 

on investment is realized. For equity investments, the term either can be the project’s expected 

useful life or be only as long as it takes for the investor to realize the targeted rate of return. 

Investors seeking the tax benefits generated by a renewable energy project, for example, are less 

motivated to remain invested after these benefits are fully realized than is a cash-based investor 

that is dependent on the continuing cash flows to earn its return. It is important to note that the 

United States IRS requires tax equity investors to meet minimum investment-duration 

requirements before it can exit an investment without triggering the recapture of some of its tax 

benefits. Once these requirements are met, the tax equity investor can sell its ownership share in 

the project without incurring a penalty.

For debt investments, the term is the number of years over which the loan must be repaid. Like 

the overall amount of debt, the tenor available is based on the length of a project’s PPA and the 

creditworthiness of the buyer. The tenor typically is a year or two shorter than the term of the 

PPA. To this end, policymakers contemplating FIT design (including the tariff duration) have a 

great deal of influence over the terms—and ultimate feasibility—of renewable energy project 

financing through their ability to establish a stable, long-term source of revenue. FIT programs 

are under consideration in many jurisdictions as a way to meet the market needs for stability and 

creditworthy long-term contracts. The long-term revenue certainty associated with a FIT also 

could lead to longer debt tenors than typically seen in other contracting circumstances. 

Financing structures for FIT projects are not yet well defined in the United States. In the near

term, project owners are expected to adopt structures that make effective use of existing federal 

and state tax incentives. In most cases, this requires the integration of a tax equity investor. Some 

projects might be able to monetize enough of the tax benefits internally (e.g., at the project level) 

to use either a simple all-equity or a traditional cash-equity plus term debt project finance 

approach. 

5.6 Conclusion

The research and analysis performed in support of this section demonstrates that the selection 

and organization of modeling inputs for any cost-based tariff calculation is inextricably linked to 

the identification and prioritization of clear policy objectives. When this defined set of inputs is 

deployed within a functional model, the architects also must consider the end-user of the model 

and the purpose for the model’s use. Based on the answers to these questions, the model’s 
42 

architects then must build a model that balances ease of use and the presence of complex 

functionality. In the end, any spreadsheet tool will be successful only if it enables policymakers 

to be well informed during the rate-setting process without exerting undue effort. 
43 

6 Policy Decisions

In many cases, the objectives of the users and the policymaking process can dictate the model 

methodology and structure, the nature of the inputs, and how those models and their outputs are 

used. The purposes of this section are to articulate the connection between policy-driven 

decisions and modeling choices, help the model user understand the decisions required to utilize 

the model, and discuss how to integrate the model into the FIT policymaking and rate-setting 

processes. The text box examples in this section illustrate the connection between policy 

objectives and modeling approach and provide some guidance for model users. 

The CREST models are targeted for use by state policymakers, utilities, and developers or other 

stakeholders participating in the regulatory process. The need to make the CREST models 

useable by state policymakers such as public utility commissions or legislative staffers drives 

some degree of simplification relative to SAM or the highly detailed spreadsheet models used by 

developers, lending institutions, and tax equity investors to support the financing of specific 

projects. The need to make the CREST models useful for utilities and developers who participate 

in the rate-setting process, however, requires that the model have sufficient depth and accuracy

to both gain acceptance and to accommodate material factors that influence renewable energy 

project cost. The nature of the policymaking process, then, suggests the following ideal 

characteristics of a spreadsheet cost model.

• Usability—For example, limited number of “levers” to pull; well-documented; easy 

to use; inputs and choices clearly identified; robust (i.e., checks for errors in inputs) 

• Transparency—“Black boxes” do not engender trust and stakeholder buy-in

• Credibility across the range of stakeholders—Familiarity with respect to approach, 

look, and feel; lack of inherent bias; limited degree of error introduced when 

simplifications are used

• Adaptability—Functions in a range of policymaking and rate-setting processes.

Section 5.1 introduced a number of policy considerations that could impact the modeling 

approach, the model’s level of detail (or input granularity) and how this affects precision, and the 

model’s comparative complexity or simplicity. Policy goals also can influence the choice of 

input assumptions and how the results are used to set FIT rates. This section explores how policy 

decisions related to data sources, the policymaking process, and policy objectives can influence 

cost-modeling decisions. 

6.1 Cost-Modeling Factors Dictated by Data Sources

As noted in Section 5.1, the policymaking process can dictate a number of modeling choices. If 

the policymaking process relies on gathering top-down47 installed and operating cost 

information, a more simplified set of inputs could be required than if the process relied on a 

bottom-up48

 

47 A top-down approach utilizes data and assumptions about the aggregated cost of a project; for example, wind 

projects cost $2,400/kW installed.

 survey of individual component or subsystem costs. A top-down process does not 

48 A bottom-up approach utilizes data and assumptions about the cost of components of a project; for example, 

identifying the cost of a wind turbine tower, nacelle, and blades.
44 

require a model with extremely detailed cost inputs. A process that gathers detailed bottom-up 

information will benefit from a model that can accommodate such data directly. Such model 

features are not mandatory, however, as component-level costs can be aggregated prior to being 

entered into the model. Attention should be paid to the fact that ITC and accelerated depreciation 

could apply differently to cost subcomponents. If aggregation includes a weighted averaging of 

component-level details on ITC and depreciation method distinctions, then such preprocessing 

need not compromise accuracy. Alternatively, a sufficiently accurate “rule of thumb” for this 

weighted averaging also can bypass the need for a more detailed set of inputs. 

Data source options can be constrained by the rate-setting timetable. For instance, a tight and 

mandatory legislatively driven timetable could preclude a detailed analytical approach to 

gathering representative cost data, forcing reliance on existing studies or stakeholder surveys. 

Policymakers also should consider how the data gathered might need to be filtered, adjusted, or 

interpreted prior to being inserted into a cost model. Some key questions that are likely to arise 

include the following:

• What if cost data gathered are described as generic, representative, or national 

average? In such cases, it could be necessary for state policymakers to adjust these 

figures to reflect their location-specific cost profile. It is common for the cost of 

doing business to vary in the different parts of the country. This results from factors 

such as prevailing labor costs, land costs, permitting environments, and state or local 

taxation. Any generic data available for these factors should be used to make regionspecific adjustments to generic costs. For example, the cost of doing business is 

greater in the northeast than in the central plains region. Cost differences also can 

result from differences in resource quality, landscape characteristics, or site 

accessibility. Care should be taken to understand how these factors compare between 

the data sources and the local environment. Policymakers also should identify the 

degree of interconnection-related and transmission-related costs included in such 

figures so that appropriate adjustments for the local context can be made as needed. If 

policymakers fail to take the steps necessary to ensure that the data used is applicable 

to the local context, then the probability of FIT rates being set either too low or too 

high increases. 

• How will costs be collected or estimated for emerging technologies? In the case of 

emerging technologies, there is a smaller base of experience and directly applicable 

data to rely upon. Consider, for example, the difficulty of estimating the cost of 

offshore wind projects in the United States when, to date, not a single project has 

been built in the country and the cost data from Europe reflects mature installation 

and maintenance infrastructure that is not in place in the United States. In the absence 

of actual project data, there is little choice but to more heavily rely on studies, 

engineering estimates, and attempts to translate information on the few sample 

European installations to the local context. The risk increases that the price set based 

on cost modeling could be too high or too low. In such cases, FIT rate-setting policy 

decisions—such as choosing a point along the aggressive-conservative spectrum and 

determining the applicability of degression—are of heightened importance. These 

issues are discussed further in Section 6.3. 
45 

6.2 Cost-Modeling Factors Dictated by the Policymaking Process

Other aspects of the policymaking process influence the choice of modeling methodology as well 

as input granularity, including the nature of the rate-setting process, the participants in the 

process, and the participants’ defined roles. Experiences to date in FIT development (as 

discussed in Section 3) and similar policy processes suggest that the starting point for asserting 

the modeling methodology is likely to be influenced by a number of factors such as those listed 

below. 

• Whether there are legislative constraints on the rate-setting process, such as 

establishing how cost data will be collected and from where

• Whether the process is a consensus stakeholder process or an adjudicatory process

• Who is driving the process and what that entity puts forth as a proposed or mandated 

process, level of data granularity, or modeling methodology 

• Whether the process requires, or a tight timeline dictates, that a proposal be put forth

(for instance, by commission staff), or alternatively, whether all options are open for 

consideration (which might include offering dueling models) 

• Familiarity among regulators or stakeholders with cost methodologies or precedents 

set in similar settings

• The availability of widely used and accepted tools (the objective of this project). 

Finally, public policymaking processes benefit from model transparency. Even if a user cannot 

change all the formulas, being able to see how the data flows from input to result is more likely 

to be accepted than a model perceived as a “black box.”

6.3 Cost-Modeling Factors Dictated by Policy Objectives

Many of the factors described in the previous two sections have implications for the model 

structure, methodology, or level of granularity, but specific policy objectives can shape the 

choice of inputs to use in the model and how the modeling results are used to set FIT rates. 

6.4 Range of Cost of Energy Variance Associated with Technology-Specific 

Input Assumptions

Among other factors, a renewable energy project’s COE is sensitive to project size (due to scale 

economies), resource quality (due to capacity factor and production profile), and infrastructure 

availability (e.g., proximity to transmission, need for step-up transformer or substation, roads to a 

mountain ridge, and repower versus greenfield project). As a result of these factors and others, 

the COE for projects using the same technology at two different locations can vary widely. This 

can lead to overpayment or underpayment if the two projects are eligible for the same 

(undifferentiated) FIT payment level. Text Box 4 uses a basic wind LCOE model49

 

49 Developed by Sustainable Energy Advantage, LLC. Such sensitivities can be explored using the CREST models 

once completed.

to illustrate 

hypothetical examples of wind power installations and the sensitivity of COE to a variety of 

factors.
46 

6.4.1 Feed-In Tariff Rate Differentiation

As noted in Section 4 and in Appendix B, FIT rates for the same generation type (e.g., wind and 

solar) often are differentiated, meaning that FIT rates are defined for different subcategories of 

generators based on such distinguishing factors as technology (e.g., solar PV versus hightemperature geothermal), project size, resource quality, location (e.g., onshore versus offshore 

wind), or ownership type (e.g., public versus private). If policymakers have started with the 

presumption or legislative imperative to differentiate tariffs, then the key design questions could 

include the following.

• How many subcategories should be used? 

• How are the subcategories defined?

4. Simplified Example of Cost of Energy Range for Illustrative Wind Projects

This example shows the range of potential values within a given technology and is used to illustrate how 

policy decisions can influence the inputs used to estimate COE and how such results can be used to 

establish FIT rates.

• Sample Plant #1: Single 1.8 MW turbine, 26% capacity factor, interconnecting to local 

distribution system without the need for a new substation.

• Sample Plant #2: 12 MW, 31% capacity factor, with a new mountain-access road, multi-mile 

generator lead, and a new substation.

• Sample Plant #3: 150 MW, 35% capacity factor, existing roads need only modest upgrades, 

need new multi-mile generator lead, and a new substation.

Modeled LCOE uses consistent financing and other key assumptions. These figures are for illustrative 

purposes; the absolute value of the costs should not be used for any particular purpose, as it might not be 

illustrative of local and current conditions.

• Sample Plant #1: $160/MWh

• Sample Plant #2: $130/MWh

• Sample Plant #3: $90/MWh

What is the “correct” FIT rate for wind? The answer depends on the project objectives.

0

20

40

60

80

100

120

140

160

180

0 20 40 60 80 100 120 140 160

Cost of Energy ($/MWh)

Capacity (MW)

Variation in Wind Cost of Energy 

with Change in Project Size

($/MWh)
47 

• What payment level should apply within each subcategory? 

Policymakers could also decide to differentiate tariffs if modeling results indicate a wide range 

of COE for a particular technology.50

5. Examples of Policy Objective Implications for Tariff Differentiation Within Resource Type

 This might be the case for any eligible technology with a 

wide range of project sizes, a steep scale-economy function, or a wide range of resource 

intensities. Without differentiation, projects can experience a wide range of return on investment. 

Differentiation enables policymakers to target more similar returns to investors in projects with 

different characteristics. The answer to these questions regarding how to differentiate a tariff 

largely will be dictated by policy objectives. 

If the Policy Objective is to Encourage . . . Then the Tariff should be Designed to...

 Variety of technologies (e.g., PV and solar thermal, 

enhanced and low-temperature geothermal, and 

commercial-scale and small wind turbines) or 

applications (e.g., building integrated PV or 

biomass CHP) 

 Best (lowest cost) ownership structure, technology, 

application, resource quality, and scale

 Wide variety of ownership structures, technologies, 

applications, resource qualities, and locations, with 

less concern about cost containment

 Development in a variety of locations 

 Different ownership structures or investor types

 Create differentiated tariffs open to the specific

technologies or applications to be encouraged

 No differentiation, so long as the rate is set towards 

the conservative end of the spectrum

 No differentiation, with rate set at the more 

aggressive end of the spectrum

 Create tariffs differentiated by resource (i.e., wind 

or solar resource), by geographical region, or by 

availability of transmission 

 Create differentiated tariffs that align with the 

ownership structure the policymakers wish to 

encourage

A critical observation is that even if policymakers wish to differentiate by such factors as project 

or equipment size or resource quality, they must make an aggressive/conservative decision 

within each subcategory—a concept discussed in more detail in the next section.

6.4.2 Aggressive Versus Conservative FIT Rate Setting

A conservatively set FIT is one that is designed to create a market for, and encourage, only the 

most efficient (low-cost) projects.51

 

50 For a detailed discussion of FIT differentiation, see “A Policymaker’s Guide to Feed-in Tariff Policy Design” 

(Couture et al. 2010) at 

Its rate would be based on the generation costs of the most 

competitive developers or the most competitive project scale or resource quality. In Ontario, 

Canada, for example, the 2006 RESOP PV tariff level was too low (roughly equivalent to 

$0.41/kWh) for all but the largest systems effectively excluding small and mid-size projects. A 

more aggressive FIT would establish rates sufficiently high to accommodate a broader range of 

systems of different sizes, types, resources, and ownership structures. In Germany, for example, 

biogas FITs are set relatively high to encourage small farm digesters (Grace et al. 2008). Given 

the infinite variety of specific project-cost factors, the conservative versus aggressive options 

form a continuum, and where a FIT rate is established along the conservative-aggressive 

spectrum is (or should be) a function of the policymaker’s objectives. 

http://www.nrel.gov/docs/fy10osti/44849.pdf. 51 In this context, the terms conservative and aggressive are used from the perspective of the policymakers 

establishing the FIT. “Aggressive” connotes a tariff intended to aggressively promote renewable energy 

development, and “conservative” connotes a more measured approach tilted towards supporting the best, most costeffective projects.
48 

Furthermore, setting a FIT rate to achieve policy objectives can be challenging. If the price is set 

too high, there is the risk of overpaying individual projects, overstimulating the market, and 

greatly impacting ratepayers—particularly when a tariff is open to large projects where ample 

resource potential is present.52

As discussed in Section 

Conversely, if the price is inadequate to attract investment, a FIT 

might be ineffectual. The policy choice of setting a FIT rate aggressively or conservatively alone 

or in combination with tariff-rate differentiation also can manage the trade-off between a FIT’s

effectiveness at increasing the quantity of renewable energy and its ratepayer impact.

6.4.1, FIT differentiation often is used when there is a wide range of 

COE for a particular technology. The more subcategories into which a technology is 

differentiated, the more accurate a price can be determined—meaning the smaller the variance in 

cost of the generators eligible for the tariff. Nonetheless, even within a differentiated tariff—for 

example, different prices established for larger and smaller generators—policymakers still must

decide where to set the tariff along the aggressive-conservative spectrum. A key consideration is 

how “representative project” costs are determined for each subcategory. Options might include 

selecting an average project within the technology/size subcategory, selecting a project from the 

greater end of the bracket to encourage cost effectiveness and economies of scale, or choosing a 

project of the 75th percentile of project costs for that technology/size bracket to support a wider 

range of projects. Text Box 6 describes the aggressive versus conservative decision-making 

process, examining both tariffs that are differentiated by size, location, or other factors and tariffs 

that are not differentiated other than by technology. 

 

52 As described in Section 2, the profitability index method (PIM)—a technique used to create a universal 

profitability scale on which to compare projects with different characteristics—could be utilized to help

policymakers select and substantiate a FIT rate at a level along the conservative-to-aggressive continuum, which is 

most consistent with the jurisdiction’s stated policy objectives. Recall that a profitability index (PI) of zero denotes a 

project that meets, but does not exceed, the assumed cost of capital. To this end, how the PIM is used depends on 

whether the target rate of return applied in the rate-setting process is the minimum value that the market is assumed 

to be willing to bear or is something greater. In the modeling context where the target after-tax IRR is set at the 

absolute minimum, FIT rates that result in a PI of 0.0 to 0.3 comprise the conservative spectrum, and rates resulting 

in a PI of 0.3 to 0.6 represent more aggressive tariffs. Based on this approach, a PI greater than 0.6 represents an 

overstimulation of the market.
49 

6. Considerations in Aggressive Versus Conservative FIT Rate Setting—

Single (Undifferentiated) Tariff

If policymakers are focused on encouraging the most cost-effective projects or are otherwise concerned about 

the risk of overpayment, then they can establish a conservative (lower-priced) FIT rate based on a modeled COE 

consistent with a low-cost proxy plant. For example, this could be represented by a wind project with scale 

economies, near transmission, in a location with strong winds, and with a relatively simple, low-cost financial 

structure. Under a single wind FIT rate that was not differentiated by such factors as project size, resource 

quality, or ownership, the most cost-effective projects are the most likely to get built. Large projects with good 

wind resources that are able to take advantage of scale economies or greater capital-cost projects, with the 

exceptional wind resources, might be able to attract financing and be viable under this type of FIT.

A more aggressive policymaker perspective might be to establish a FIT rate based on the modeled COE of less 

competitive proxy projects. (Policymakers might pursue such an approach if they desire to encourage, for 

example, a variety of project ownership structures, sizes, or locations.) Projects might have some combination of 

smaller-scale slower wind, a greater distance from transmission, or a less-than-optimal ownership structure—and 

therefore a greater COE—than the more conservative FIT rate described above. In establishing the FIT rate, 

policymakers either must model an idealized higher-cost single proxy plant or be informed by modeled COEs 

associated with the range of potential conditions they seek to encourage. A more aggressive FIT rate would be 

made available to projects that have a lesser calculated COE, thus encouraging scale economies but implicitly 

permitting those projects that are more cost effective than the selected proxy plants to realize higher profits. 

Based on the wind COE graph in Text Box 4, a conservative single-payment tariff rate could be $85–$90/MWh, 

and a more aggressive tariff might be from $100–$150/MWh, depending on the nature of tariff differentiation 

and other policymaker decisions.

Considerations in Aggressive Versus Conservative FIT Rate Setting—Differentiated Tariff

If a resource type can be developed over a wide range of size, technology, resource strength, or other factor that 

results in either a steep scale economy or steep supply curve (a steep curve suggests scarcity of the “best” sites 

and a wide variety of costs among the resources plotted, with a limited number of lower-cost sites and diminishing economics for subsequent developments), and policymakers wish to encourage some degree of diversity 

among project size, location, or ownership type without offering above-market returns to the lowest-cost projects, then a differentiated tariff can be used. Differentiating a tariff requires decisions on the number of differentiated rates (subcategories), the eligibility characteristics of those subcategories, and the FIT rate that applies

to each subcategory. There is no precise formula for determining the number of differentiated rates. The choice 

is influenced by both the policy objectives and the shape of the supply curve or scale-economy functions. 

Consider the illustrative wind supply curve in Figure 1 (the data was received in spreadsheet form corresponding 

to the data used in the Phase 1B study) and the COE versus project scale curve shown in Text Box 4.

If policymakers establish two subcategories differentiated by project size, then Subcategory A (projects less than 

40 MW nameplate capacity) receives one price, and Subcategory B (projects exceeding 40 MW) receives a 

reduced price. The COE data suggests that the Subcategory A price could fall in the range of $110/MWh to 

$150/MWh, and the Subcategory B price could fall in the range of $82/MWh to $110/MWh. The supply curve, 

although it captures more variables than just the sensitivity to project size, suggests that a FIT rate of $110 

would support most of the available supply and overpay the majority of that supply by $10/MWh to $15/MWh. 

It also reveals that there is little to no wind likely to take advantage of a FIT rate of less than $95/MWh (because 

the resource analysis shows $95/MWh to be the lowest price on the supply curve). 

One additional caution for policymakers is to consider how market participants are likely to react to a differentiated tariff. In the case that differentiates tariffs by nameplate capacity (provided above), for example, a 

significant difference in prices tends to encourage projects to be built up to (but not exceeding) 40 MW and thus 

forego the benefits of scale economies or encourage the division of larger projects into projects that are less than 

40 MW each. It is critical to take into account the definition of a “project” to avoid such manipulations.
50 

Figure 1. Adapted from a Competitive Renewable Energy Zone in a study of California’s 

Renewable Energy Transmission Initiative (Black and Veatch 2009) 

6.5 How Will Model Results be Used to Set FIT Rates?

An individual “run” of a model provides only a single perspective on COE, based on a unique set 

of inputs (unless the model is designed to generate sensitivity of COE to varying key input 

variables). As can be seen from the preceding examples, to inform regulators and stakeholders in 

the selection of a FIT rate, a model result can be used directly as the FIT rate or one or more 

model runs can be utilized in combination with a consideration of policy objective, resource 

potential, or other factors. If policy objectives include a conservative tariff rate and stakeholders 

can agree on cost inputs that are achievable for the most cost-effective projects, a single model 

run could suffice. A more robust and predictable outcome, however, will utilize multiple model 

runs to consider the range of possible COE figures and select the appropriate point within that 

range based on policy objectives and other factors. Such an exercise informs the process on 

expectations regarding what portion of the range of eligible resources meets investors’ assumed 

threshold returns at that FIT rate schedule.

6.6 Modeling Rate Changes Over Time

Discussions of FIT rate setting often include discussion regarding how such rates could change 

over time. The FIT changes over time are neither an input nor an output of a COE model. Such 

models can be used to demonstrate the drivers of COE to all stakeholders, however, and to assess 

the appropriateness of changes to FIT rates over time to reflect current or anticipated changes in 

equipment or financing costs. Proposed changes to FIT rates over time also can stem from 

changes in policy objectives or could be a response to the market’s experience with FITs. 

Degression, for example, is a schedule of automatic rate declines by which the FIT payment rate 

decreases over time. It typically is based on each technology’s projected experience curve (Grace 

et al. 2009). Policymakers seeking to maintain downward cost pressure on FIT rates—

particularly if experience (scale economies and technological process) is expected to reduce costs 

over time—might wish to periodically repeat or update a modeling exercise to adapt FIT rates 

for the assumed experience curve (in the case of degression) or to incorporate changes in cost of 

new installations over time (in the case of periodic review and tariff revision). Using a cost 

model to inform a degression rate or periodic rate reset involves changing cost and performance 

inputs over time, as well as monitoring and evaluating projected cost and production data trends. 

A B
51 

7 Conclusion: The Cost-Based Model Methodology and 

Spreadsheet Tool Design Characteristics 

This section summarizes the key features recommended for a FIT or other cost-based incentive 

cost-modeling methodology and the design of CREST spreadsheet cost models for use in 

establishing cost-based payment rates for three renewable energy generation technologies: solar, 

wind, and geothermal. The selection of these design features has been informed by the analyses 

and discussion provided in Section 2 through Section 6 of this report. Also provided is a flow 

chart (Figure 2) depicting the overall modeling methodology to be incorporated in the CREST 

models. 

One of the key attributes of the CREST model is its flexibility to support a range of 

policymaking processes, as well as accommodate a range of data sources and levels of input 

granularity. This flexibility enables policymakers to consider tariff differentiation and aggressive 

versus conservative rate-setting, which is consistent with underlying policy objectives. 

7.1 Lessons Learned from Survey of Existing Models

The survey of existing financial models described in Section 3 concluded that there is no single 

publicly available and recognized tool in widespread use that can be readily applied to the task of 

calculating cost-based FITs for a variety of generation technologies in the United States. The 

survey, however, revealed a number of useful models from which the best practices and features 

that influence the design of the CREST models can be drawn. These best practices reveal that the 

model must be: 

• Straightforward enough to be used effectively by a diverse set of participants

• Flexible enough to accommodate a consensus-driven process for identifying inputs 

(which can vary in detail from state to state)

• Transparent enough to satisfy regulatory requirements. 

The key features observed in other models that are recommended for inclusion into the CREST 

model include: 

• “Introduction” tab that is embedded in the model and serves as a “quick-start guide”

• “Notes” providing a descriptive explanation of the appropriate use of many input 

cells, as well as the typical range of values for each input, where applicable 

• “Check” cells used frequently to provide visual cues to ensure that the user populates 

all cells required to operate the model

• Option for user to provide inputs at one of three levels of detail 

• Inputs for major federal and state tax incentives

• Sensitivity analyses of modeling results

• Graphic representation of results.
52 

7.2 Lessons Learned from Industry Experience with FIT Calculation 

Methodology

The case study jurisdictions reviewed in Section 4, with a few exceptions, generally are 

characterized by:

• Comparative simplicity

• DCF, after-tax analysis 

• Assumed private sector, commercial ownership

• Use of IRR as an evaluation metric

• Assumption that other available incentives are taken into account before the rate is 

calculated

• Aggregated cost inputs.

Additionally, the case studies identified interplay between stakeholder engagement and model 

structure and how different jurisdiction’s legal and regulatory processes can shape modeling 

decisions or define how modeling outputs are used. 

To be as useful as the other FIT models, it is recommended that the CREST models utilize the 

after-tax DCF approach and the IRR as an evaluation metric. Due to the desire to make the tool 

useful in a wide range of circumstances, however, the CREST model should have flexibility that 

supports a greater degree of cost detail, a wider range of ownership, and the ability to reflect all 

or a portion of various incentives in FIT rate determination. 

7.3 Cost Modeling Methodology: Discounted Cash Flow

Based on the taxonomy of modeling methodologies described in Section 2, as well as lessons 

learned from industry experience with FIT calculation methodology described in Section 4, it is 

recommended that the CREST model utilize the DCF method of calculating the NPV and aftertax IRR on a renewable energy investment. This recommendation was based on several factors

including the following: 

• The DCF approach’s ability to incorporate all broadly available tax incentives, 

including accelerated depreciation schedules. This feature is critical in the United 

States, where tax incentives historically have played an important role in renewable 

energy development and where the efficient monetization of all tax incentives can be 

important, even for smaller projects. The DCF models easily are made detailed and 

versatile enough to accommodate a range of tax incentives, cash incentives, or the 

migration from one to the other. At present, the American Reinvestment and 

Recovery Act has—at least temporarily—shifted the U.S. structure away from tax 

benefits to cash grants. The ability to handle both tax-based and other policy 

incentives, however, makes the DCF a durable tool that can adapt with U.S. 

renewable energy policy over time. 

• The requirement for a substantial degree of transparency, necessitated by the likely 

use of stakeholder-driven FIT design processes. Transparency especially is important 

with respect to the assumptions that drive the rate-setting modeling process. The year-
53 

by-year analysis in the DCF method provides for transparency regarding the impact 

of various key assumptions throughout the life of the project. 

• The DCF method provides the flexibility necessary to support a wide range of 

circumstances and policy objectives. It allows a range of complexity in the level of 

input detail and modeling calculations. It also supports the analysis of project returns 

for different types of investors and accommodates a range of tax appetites. Lastly, it 

can aid the adaptation of FIT programs over time to account for changing federal 

incentives and overall conditions in the renewable energy market. 

• Most FIT models that were investigated used the DCF approach as well, likely for the 

reasons listed here.

7.4 Output Metrics and Constraints

Consistent with the DCF methodology, the CREST model is recommended to use an after-tax 

IRR as its primary evaluation metric. An acceptable after-tax IRR is one that meets or exceeds 

the equity investor’s required rate of return. The model, however, also must enable the user to 

ensure that any cash flow constraints, such as the debt service coverage ratio (if the project has 

taken on debt), are met. Lastly, the model should be designed to solve for the COE and meet the 

equity IRR and cash-flow constraints. The COE output of the model, therefore, is the desired 

result and can be used to either represent or inform the FIT payment rate. 

The COE models sometimes provide results in real or constant dollars expressed in specific 

calendar years (e.g., constant 2012 dollars per megawatt-hour). This approach is attractive to 

economists as such a revenue requirement metric takes the general rate of inflation out of the 

equation. A result in real dollars, however, requires an ongoing adjustment for it to be useful in 

determining annual payments. As such, it is recommended that the CREST model specify 

outputs in nominal dollars. It is expected that policymakers and project financiers will be far 

more comfortable with rates specified in nominal dollars that correspond to actual payments 

under FIT rates. To address the range of potential FIT rate structures that can be considered by 

model users, the COE should be calculable as either: 

• A nominal levelized rate in dollars per megawatt-hour (e.g., a fixed, flat rate for the 

life of the FIT contract)

• A year-one nominal rate in dollars per megawatt-hour (it is recommended that the 

user also be able to define an escalation rate, as well as the portion of the tariff to 

which it applies).

7.5 Data Inputs and Level of Data Granularity

The CREST model will be designed to include inputs at a level of detail sufficient to accurately 

capture each of the key economic drivers of renewable energy projects but not so complex as to 

render the model difficult to use in the generalized, state-by-state, regulatory decision-making 

context. The analysis involved with each design choice is discussed in detail in Section 5. 

Table 11 summarizes the level of input detail recommended for this analysis. The CREST model 

provides for user-defined selection of data input granularity for the “Total Installed Cost” and 

“Operating Cost” categories. Using selections made from a drop-down menu, different sets of 

inputs are shown (and the input cells associated with alternative levels of granularity are disabled).
54 

The simple option provides for a single input, and the intermediate option provides for a discrete 

set of five inputs. Users selecting the complex option are guided to utilize a larger free-form set of 

line-item inputs that are rolled into the subcategories corresponding with the intermediate option. 

For each installed cost input, eligibility for the ITC and various depreciation schedules can be 

specified. The resulting CREST model includes more detail than a simple economic feasibility 

study but less detail than would be necessary to satisfy a lender or (tax) equity investor for projectfinancing purposes or for analyses in which users attempt to understand the allocation of costs and 

benefits among a multi-member project-specific partnership.

Table 11. Summary of Modeling Complexity Recommended for FIT Rate-Setting Tool

Input Category

Recommended Input Complexity

Simple Intermediate Complex

Total Installed Cost

Select via drop-down 

menu

Select via drop-down 

menu

Select via drop-down 

menu

Operating Costs

Select via drop-down 

menu

Select via drop-down 

menu

Ownership & Capital Structure

Cost of Capital √

Term √

Incentives

Federal PTC √

Federal ITC √

Utilization of Federal PTC/ITC √

State ITC √

Utilization of State ITC √

Grant, Rebate, or Capacity-Based Incentive √

Production-Based Incentives √

Property Tax Exemption √

Sales Tax Exemption √

7.6 Model Data Flow Chart

Figure 2 provides a schematic representation of the proposed flow of model data—from inputs to 

outputs and depicting the key internal calculation steps. It is recommended that the worksheets 

for the CREST model be ordered in the same sequence as the order in which they are to be used. 

As such, it is proposed that the user first be presented with a worksheet on which all inputs are 

aggregated and are arranged by category. Although the CREST model initially might be 

populated with illustrative values, the user must research and enter all inputs prior to reviewing 

or interpreting any model outputs. These inputs would flow into the recommended DCF analysis. 

The modeled cash flows then can be summarized on an annual basis. As described in Section 1, 

it is recommended that the CREST models solve for the COE. This enables the project to meet 

all expenses as well as the equity investor’s required after-tax rate of return—and maintain 

minimum debt service coverage, if applicable. These steps are demonstrated graphically in 

Figure 2. 
55 

Figure 2. CREST model schematic data flow chart
56 

7.7 CREST Models

Based on the decisions documented in this report, the next step in this project is to develop the 

CREST models for each of three illustrative technologies: 

• Solar (model can be used for PV or solar thermal electric applications)

• Wind

• Geothermal.

The CREST models will have a user’s manual, and each will be populated with illustrative data. 
57 

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Report (RETI-1000-2008-003-F).” San Francisco, CA: RETI Coordinating Committee and RETI 

Stakeholder Steering Committee. http://www.energy.ca.gov/2008publications/RETI-1000-2008-

003/RETI-1000-2008-003-F.PDF. Accessed August 19, 2010. 

Gainesville Regional Utilities. (2008). “Proposal to Replace Non-Residential Solar Photovoltaic 

Rebate and Net Metering Financial Incentives with a Solar Feed-In Tariff.” Gainesville, FL: 

Gainesville Regional Utilities. 

http://www.gru.com/Pdf/futurePower/Solar%20Tariff%20Doc.pdf. Accessed August 22, 2010. 

Los Angeles Department of Water and Power. (2008). “Solar LA: The Los Angeles Solar Energy 

Plan.” Los Angeles, CA: City of Los Angeles. 

http://www.ci.la.ca.us/Mayor/stellent/groups/electedofficials/@myr_ch_contributor/documents/c

ontributor_web_content/lacity_004982.pdf Accessed August 19, 2010. 
63 

Appendix A. Summary of Survey of Existing Models

A survey of publicly available financial models was conducted in an effort to identify either an 

existing tool for industry regulators and stakeholders to use in estimating the COE from various 

renewable energy technologies and help inform FIT rate-setting discussions or a series of useful 

model features that could be incorporated into a new model for this same purpose. Although a 

single tool was not identified, the slate of available models revealed a number of useful features.

A summary of these useful features, as well as the model’s applicability to FIT rate setting, is 

provided in Table A-1. The lessons learned from this review of existing models are applied to the 

development of the model accompanying this document. 
64 

Table A-1. Summary of Limitations and Useful Features of Existing Models

Model Name, Web URL, and 

Date of Access Created By

Key Characteristics Applicability to FIT Rate Setting

Purpose and

Objectives Taxonomy Limitations Useful Features

RETI Cost of Generation Spreadsheet; available 

at 

http://www.energy.ca.gov/reti/documents/index.

html (accessed August 17, 2010)

Black & Veatch Corp.

(as part of RETI 

Phase 1B) 

Calculate the cost of 

generation for 

multiple 

technologies

DCF Inputs might not be 

sufficiently 

detailed for some 

users

Minimizes year-one 

COE subject to equity 

return requirement, 

then calculates LCOE

Vermont Standard Offer Models; 

http://psb.vermont.gov/docketsandprojects/elect

ric/7523/finalprice (accessed August 17, 2010)

Green Mountain 

Power Corp., adapted 

by stakeholders in 

regulatory process

Estimate and inform 

rate setting for 

Vermont Standard 

Offer Program

DCF Some inputs 

embedded in 

formulas; cost 

inputs highly 

aggregated

Mini-perm53

refinancing; reserve 

accounts; federal and 

state tax incentives

RETScreen; available at 

http://www.retscreen.net/ang/home.php

(accessed August 17, 2010)

Canadian government Evaluate economic 

feasibility of 

multiple technology 

types

Unknown Does not 

adequately address 

U.S. tax incentives; insufficient 

transparency

Alternate levels of 

input detail

Gainesville FIT model; 

http://psb.vermont.gov/sites/psb/files/docket/75

23/CostAnalysis/Attachment_-

_Gainesville_FIT_model.xls (accessed August 

17, 2010)

Gainesville Regional 

Utilities

Set solar FIT rates DCF Inputs might not be 

sufficiently 

detailed for some 

users

Shows summary of 

capacity factor 

degradation;

calculates estimated 

impact on annual fuel 

cost adjustment to 

ratepayers

Solar Advisor Model (SAM); 

https://www.nrel.gov/analysis/sam/

(accessed August 17, 2010)

National Renewable 

Energy Laboratory;

Sandia National 

Laboratory;

Department of Energy

Consistent 

methodology for 

analysis across all 

solar technologies

DCF Calculations are 

not available in 

Web-based version 

of model

Range of input 

options; sensitivity 

and optimization 

analyses; output 

graphics

 

53 The term “mini-perm” refers to a type of debt with a shorter repayment schedule (e.g., 5 to 7 years) than a typical project loan but with a similar underlying 

amortization schedule (e.g., 15 to 18 years). The result is a project with standard debt service obligations for the first 5 to7 years but which must refinance the 

outstanding balance on the loan when it comes due in full at the end of the mini-perm because project cash flows alone could never support such repayment. The 

outstanding balance due at the end of the mini-perm often is called a “balloon payment.”
65 

Model Name, Web URL, and 

Date of Access Created By

Key Characteristics Applicability to FIT Rate Setting

Purpose and

Objectives Taxonomy Limitations Useful Features

EU PV Platform; http://www.eupvplatform.org/

(accessed August 17, 2010)

ADEME (French 

Environment & 

Energy Management 

Agency)

FIT calculator Simplified DCF, 

using weighted 

average cost of

capital

Depreciation 

schedule tied to 

life of project; will 

not accommodate 

U.S. tax incentives

Key metrics are 

defined; provides 

sample inputs to get 

started

Vote Solar Incentive Comparison Model; 

http://votesolar.org/resources/incentive-model/

(accessed August 17, 2010)

Crossborder Energy Calculate incentives

necessary to provide 

the same project 

economics under 

three different 

policy scenarios

DCF Inputs might not be 

sufficiently 

detailed for some 

users

Tab dedicated to 

description of model;

calculates not only 

project incentives but 

also total program 

incentives paid

Geothermal Electricity Technology Evaluation 

Model (GETEM); 

http://www1.eere.energy.gov/geothermal/getem

.html (accessed August 17, 2010)

Princeton Energy 

Resources 

International; National 

Renewable Energy 

Laboratory; Sandia 

National Laboratory; 

Idaho National Lab

Evaluate the 

economic feasibility 

of Enhanced 

Geothermal 

Systems

FCR (GETEM is 

more a cost 

comparison 

model than a 

project finance 

model)

Substantial detail 

in system design 

and performance 

parameters makes 

model difficult for 

use by wide range 

of stakeholders

Detailed list of cost 

inputs; calculates 

LCOE for multiple 

technology types
66 

Appendix B. Survey of Industry Experience with FIT Calculation 

Methodology

This appendix provides case studies of the different jurisdictions’ FIT rate-setting models and 

processes described in Section 4. Table B-1 summarizes these case studies and compares the 

different jurisdictions according to the key design options and rate-setting approaches. The table 

is followed by detailed profiles of each jurisdiction. 

Table B-1. Case Studies Highlighting FIT Rate-Setting Models and Processes in Five Jurisdictions 

Germany Netherlands Ontario Vermont

Gainesville, 

FL

Degree of Tariff 

Differentiation

High

(e.g., technology 

and size 

differentiation)

Medium 

(e.g., technology 

differentiation; 

limited size 

differentiation)

High 

(e.g., 

technology and 

size 

differentiation)

Medium

(e.g., 

technology 

differentiation; 

limited size 

differentiation)

Low (e.g., PV)

Methodology FCR DCF DCF DCF DCF

Payment Structure Nominal fixed Nominal fixed 20% of tariff 

price adjusted 

at 2.25% 

inflation for all 

technologies

except PV; PV 

fixed nominal

30% of tariff 

price adjusted 

at 1.6% 

inflation for all 

technologies

except PV; PV 

fixed nominal

Fixed; inflation 

not taken into 

account in the 

model

Tax Treatment Pretax After tax After tax After tax After tax

Ownership 

Structure Assumed

Private Private Private Private Private
67 

Germany Netherlands Ontario Vermont

Gainesville, 

FL

Data Granularity 

(Model Inputs) 

A detailed listing 

of capital and 

operating costs is 

included in the 

model*

Capital Costs

- One input for 

installed cost

Operating Costs

- Fixed 

maintenance

- Variable 

maintenance

- Other 

operating costs

- Fuel costs

Capital Costs

- One input for 

installed costs

Operating 

Costs

- Variable 

O&M

- Fixed O&M

- Other costs

- Fuel costs

- Property tax

Capital Costs

- Debt reserve

- Maintenance 

reserve

- Working 

capital

- Financing 

costs

- Installation 

costs

Operating 

Costs

- Varies by 

technology 

-10 to 12 input 

cells 

available in 

the model 

(e.g., labor, 

insurance, 

and staffing) 

but most 

models used 

one input for 

maintenance 

with 1 to 2 

other 

technologyspecific 

inputs

Capital Costs

- One input for 

installed 

costs

Operating 

Costs

 - No inputs 

(installed 

cost input is 

multiplied 

automatically by 

a dollar-perkilowatthour 

assumption 

to calculate 

operating 

costs, and 

this value is 

embedded in 

the pro 

forma)

Capital Structure Debt/equity 

varies by tariff

80/20 

debt/equity; 

100% debt for 

small PV; 66/33 

for waste-toenergy

70/30 

debt/equity; 

non-recourse 

project 

financing

Varies by 

technology. 

Ranges from 

75/25 

debt/equity for 

farm methane 

to 30/70 for 

100 kW wind

100% equity

Debt Term Relative 

to Economic Life

Same (20 years), 

except for large 

hydro

Same (15 years), 

except for hydro

Same

(20 years)

Differs by 

technology; 

although all 

tariffs assume 

“loan tail”

(i.e., loan is 

shorter than 

contract)

No debt
68 

Germany Netherlands Ontario Vermont

Gainesville, 

FL

Other Incentives None Energy 

investment 

deduction tax 

credit

(if applicable)

None - 30% federal 

ITC for all 

but farm 

methane

- Portion** of 

state tax 

credit 

assumed for 

all but farm 

methane

30% federal 

ITC 

Interconnection/T&D 

Costs

- Interconnection 

built into rates

- Transmission 

and 

distribution

upgrades 

assumed 

socialized

-Interconnection 

built into FIT 

rate

- Cost 

assumptions 

for lines and 

transformers 

built into FIT 

rate;

- Connection 

charges 

recovered 

through 

transmission 

surcharge

- Built into 

capital cost 

assumptions

-T&D 

upgrades 

assumed to be 

paid by the 

system 

operator and 

recovered from 

ratepayers 

(when 

justified)

-

Interconnect

ion costs 

built into 

rate as part 

of installed 

cost

- No T&D 

upgrades 

assumed

-

Interconnect

ion costs 

built into 

rate as part 

of installed 

cost

- No T&D 

upgrades 

assumed

Data Sources - Market 

research

- Data from feedin tariff 

generators

- Industry 

surveys

- Market 

research

- Stakeholder 

process 

involving 

publicly 

available 

models

- Market 

research

- Publicly 

available 

data

- Industry 

polling

- Workshops

- Regulatory 

process 

using expert 

testimony

- Publicly 

available 

data and 

models

- Data 

submitted by 

industry

- Publicly 

available 

data

- Workshops

Granularity of Data 

Sources 

Bottom-up Top-down Top-down Top-down

Approval Process Rates are 

recommended to 

policymakers. 

Research 

recommends 

rates to Ministry, 

Ministry 

recommends 

rates to 

Parliament, 

Parliament 

codifies in law

Rates are 

recommended to 

policymakers. 

Research 

recommends 

rates to 

Ministry, 

Ministry 

recommends 

rates to 

Parliament, 

Parliament 

codifies in law

Law set final 

rates, informed 

by a parallel

Ontario Power 

Authority feedin tariff design 

process

Rates are 

recommended 

to 

policymakers. 

Law requires 

regulator to set 

rates. PSB 

approves rates 

after regulatory 

proceeding

Rates are 

recommended 

to 

policymakers. 

Ordinance 

approved by 

the Gainesville 

City 

Commission 

following ratesetting process
69 

Germany Netherlands Ontario Vermont

Gainesville, 

FL

Model Available to 

the Public?

No Online No Online Upon request

Process 

Transparency

- Some 

stakeholder 

engagement

- Published 

description of 

methodology

- High degree of 

process 

transparency

- Methodology 

subject to 

public 

comment

- Some 

stakeholder 

engagement

- Published 

description 

of 

methodolog

y

- High degree 

of process 

transparency

- Methodology 

subject to 

public 

comment

- High degree 

of process 

transparency

- Methodology 

subject to 

public 

comment

Evaluation Metric IRR After-tax return 

on equity (ROE)

After-tax ROE After-tax ROE After-tax IRR

Target Return on 

Equity 

5% to 9% 12% for waste to 

energy

10% for PV

15% for all 

others

11% 12.13% 

(interim 

rates)***

9.75% (final 

rates)****

4.99%

* Maike Schmidt, ZSW, personal communication, May 2010 (Germany’s model is not publicly available).

**The rates were set assuming that 50% of the Vermont ITC could be utilized in the first year, with the remaining 

50% providing no tax benefit to the project because it is assumed that generators would not have sufficient instate tax appetite. For farm methane, the rates were assuming no tax credits because “the assumed tax appetite 

or taxable income of . . . projects is inadequate to apply either the Federal or State Investment Tax Credits” 

(Vermont Public Service Board 2010, p. 42). 

***Established September 15, 2009, through Docket 7523.

**** Established January 15, 2010, through Docket 7533.

The Netherlands’ Tariff Calculation Case Study

• Tariff overview: The FIT in the Netherlands is based on a spot market gap model, in 

which generators are paid an incentive equal to the difference between the market 

price for electricity and the generation cost-based FIT payment level (Cory et al. 

2009; Couture and Gagnon 2010). If the FIT rate for landfill gas, for example, was 

8.3 Euro cents, and the spot market price for electricity was 5.3 Euro cents, then the 

FIT payment would be equal to 3 Euro cents.

• Degree of differentiation: The FIT is differentiated into 10 different technology 

categories with limited additional differentiation by size. For PV and biomass, size is 

differentiated according to capacity, and for hydropower, size is differentiated by 

height. 

• Calculation methodology: DCF model. 

• Payment structure: The FIT payment is set as a nominal fixed value and is not 

adjusted for inflation. Instead, an assumed inflation of 2% is built into the model. 

• Ownership structure and tax treatment: The FIT assumes that the generation is 

privately owned by commercial entities, and the rate is calculated on an after-tax 

basis, taking into account the corporate income tax rate and depreciation. For PV 
70 

systems of less than15 kW, the FIT is calculated assuming residential ownership, and 

no income tax on FIT revenues is assumed.

• Granularity of cost inputs: Both the capital and operating costs inputs are highly 

aggregated. Capital costs are aggregated into one cell, and operating costs are divided 

into fixed, variable and “other” operating costs, and fuel costs. 

• Evaluation metric: The evaluation metric for the FIT rate is a 15% return on equity 

for most resources, except for a 12% return on equity for waste-to-energy plants.

• Assumed capital structure: The model assumes a leveraged structure of 80% debt 

and 20% equity for almost all of the technology categories. The two exceptions are 

waste-to-energy, for which a 67/33 debt-to-equity ratio is used to reflect the 

assumption that the plant is corporate financed, and PV of less than15 kW, which is 

assumed to be financed through a residential mortgage (i.e., 100% debt). In each case, 

the model assumes that the FIT term (which is either 12 or 15 years, depending on the 

technology) is the same as both the loan term and the depreciation period. The one 

exception to this is hydropower, which assumes a 30-year loan term and depreciation 

period but only a 15-year FIT term.

• Interaction with other incentives: The model assumes that some technologies—

such as onshore wind and PV systems larger than 15 kW—are eligible for the energy 

investment deduction tax credit. The value of the energy investment allowance is

taken into account when calculating the FIT rate 

• Interconnection and transmission costs: Cost assumptions for additional wiring and 

transformers are built into the capital cost assumptions for the model.

• Data-gathering and rate-setting processes: The data-gathering and rate-setting 

processes are highly transparent. Initial assumptions for the model are collected 

through a joint research effort undertaken by a non-profit research institution and a 

for-profit energy consulting company. The results of the research effort then are 

entered into a public spreadsheet, which is made available for stakeholder comment 

online. Stakeholders can comment on the models’ assumptions and lobby to have the 

cost figures changed, provided they can support their arguments with proof from real 

project-development efforts. 

• Approval process: After the research and stakeholder processes, the Ministry of 

Economic Affairs recommends a set of final rates to the Parliament, which then 

establishes rates through legislation.

Gainesville Regional Utilities’ Tariff Calculation Case Study

In March 2009, the GRU became the first municipal utility in the United States to offer a solar 

electricity FIT. The City of Gainesville previously offered utility customers a $1.50/kW rebate 

along with net metering. The FIT was designed as an alternative policy to this suite of incentives.

• Tariff overview: The Gainesville FIT provides a fixed, 20-year payment for solar 

electricity, renewable energy certificates, and any other environmental attributes that 

can accrue to the generation. The tariff is capped at 4 MW of PV capacity annually 

and is available each year through 2016. The available tariff declines each year 

starting in 2011.
71 

• Degree of differentiation: The FIT is available only to PV generators and is 

differentiated into two rates. The higher rate is available to systems installed on 

buildings, on pavement, and to ground-mounted systems that are smaller than 25 kW. 

The lower rate is available to free-standing systems that are not pavement-mounted 

and are larger than 25 kW. 

• Calculation methodology: DCF model. 

• Payment structure: The FIT payment is set as a nominal fixed value and is not 

adjusted for inflation. The spreadsheet model also does not account for inflation. 

• Ownership structure and tax treatment: The FIT assumes that the generation is 

privately owned by commercial entities, and the rate is calculated on an after-tax 

basis, taking into account the corporate income tax rate and depreciation. 

• Granularity of for cost inputs: The capital cost input is aggregated into one cell. 

O&M cost assumptions are embedded in the cash flow model as $25/kW/year O&M 

and $1,000/kW for an inverter replacement in year 10.

• Evaluation metric: The evaluation metric for the FIT rate is 4.99% return on equity. 

• Assumed capital structure: The model assumes that the PV project is 100% equity 

financed with no debt. 

• Interaction with other incentives: The model assumes that PV generators claim the 

30% federal tax incentive as well as the MACRS-accelerated depreciation schedule. 

• Interconnection and transmission costs: Interconnection costs are not built into the 

model separately. They are assumed to be included in installed cost data that were 

gathered during the rate-setting process and in the installed cost assumptions that 

ultimately were included in the model.

• Data-gathering and rate-setting processes: The GRU rate-setting model required 

inputs for system costs, capacity factor, O&M costs, federal corporate tax rate, and 

system degradation rates. These values were collecting from existing sources and 

price surveys. Aggregated system costs (in dollars per watt) were determined using 

top-down data from the state rebate program and a survey of local installations. 

Capacity factor inputs were determined using NREL’s PV Watts calculator.

54

 

54 See: 

A PV 

output degradation factor was drawn from analyses conducted by the California 

Public Utilities Commission and the Energy Information Administration. The policydevelopment process included a series of public meetings during which draft tariff 

designs and input assumptions were presented and discussed. The rate-setting model 

was designed to be less complex to make it an accessible tool for the stakeholders. 

The staff of the GRU managed the FIT rate analysis and rate-setting outcomes. The 

cost-setting model was refined during the course of the policy process based on 

stakeholder input.

http://www.nrel.gov/rredc/pvwatts/. Accessed May 12, 2011.
72 

• Approval process: The FIT policy was adopted through a city ordinance55

Vermont’s Tariff Calculation Case Study

 passed by 

the Gainesville Regional Commission and signed by the mayor. The commission has 

the authority to set the rates at whatever levels it deems appropriate, and it included 

the rates recommended by the GRU when it adopted the ordinance. 

The 2009 Vermont Energy Act (Act 45)56

• Tariff overview: The SPEED program is a long-term, fixed-price tariff for 

electricity, renewable energy certificates, and other associated environmental 

attributes.

established the Standard Offer for Qualifying SPEED. 

The authorizing legislation included preliminary tariff rates as well as guidance that the PSB was 

to establish final tariff levels. An interim adjusted tariff rate went into effect in September 2009 

(Docket 7523), and final tariff rates were established in January 2010 (Docket 7533). 

57

• Degree of differentiation: The FIT is differentiated to target six technologies (PV, 

hydro, farm methane, landfill gas, wind, and biomass). The wind rates are further 

differentiated into wind smaller than 100 kW and wind larger than 100 kW. 

The tariff is capped at 50 MW statewide, which is subdivided into 12.5 

MW caps for each of the targeted technologies. 

• Calculation methodology: DCF model. 

• Payment structure: The FIT payment for PV is set as a nominal fixed value and is 

not adjusted for inflation. For the remaining resources, 30% of the payment level 

adjusts for inflation each year. Inflation is set at 1.6% rather than at the actual 

inflation level each year. 

• Ownership structure and tax treatment: The FIT assumes that the generation is 

privately owned by commercial entities, and the rate is calculated on an after-tax 

basis, taking into account the corporate income tax rate and depreciation. 

• Granularity of cost inputs: Hard capital costs are aggregated into one input cell, but 

financing (i.e., soft) costs are broken out by debt reserve, maintenance reserve, and 

working capital. With regard to annual costs, maintenance is aggregated into one 

input cell, but there are additional entry cells for property tax, insurance, and lease 

payments (where applicable).

• Evaluation metric: Act 45 requires the PSB to set the FIT such that project rate of 

return on equity is not less than the highest return on equity allowed for any of the 

state’s investor-owned utilities. The PSB initially determined that all tariffs would be 

set to provide a 12.13% return on equity but subsequently lowered the return on 

equity of 9.75% when establishing the final rates in January 2010. 

• Assumed capital structure: The models assumed all projects would use some level 

of debt financing, although the debt-to-equity ratio differed for each generation type. 

 

55 See http://gainesville.legistar.com/View.ashx?M=F&ID=593626&GUID=B99E78E7-2DBD-42C5-921D41D9B65F8C64. Accessed August 17, 2010. 56 Available at http://www.leg.state.vt.us/docs/2010/Acts/ACT045.pdf. Accessed August 17, 2010. 57 The exception is farm methane generators, which retain ownership of their renewable energy credits per the 

statute.
73 

The PSB assumed that PV systems would use project financing and other 

technologies would use more traditional recourse financing. For modeling purposes, 

debt-to-equity ratios were set to assure that debt service coverage ratios averaged 1.5 

throughout the life of the project. Debt levels for the PV model also were set to assure 

that debt service coverage ratios never were below 1.2. 

• Interaction with other incentives: The authorizing legislation tasked the PSB with 

taking into account all “reasonably available” incentives in setting its tariff rates. For 

applicable technologies, this included both the federal ITC and five-year MACRS 

depreciation. The PSB did not assume that all project costs were eligible for the ITC 

and five-year MACRS. The models also partially account for state tax credits but do 

not assume the availability of funds from the state’s Clean Energy Development Fund 

grant program. 

• Interconnection and transmission costs: The PSB’s final order noted that utility 

interconnection requirements generally are site-specific and can vary by the size of 

the generation project and location. Ultimately, “[p]arties agreed that the costs of 

[i]nterconnection were generally integrated into the cost modeling as part of the 

installed capital costs and no additional interconnection costs needed to be added to 

the project costs” (Vermont Public Service Board 2010, p.39). 

• Data-gathering and rate-setting processes: The PSB established final tariff rates 

through a non-contested public docket that featured expert witness testimony from a 

range of market participants. Witness testimony informed the initial creation of the 

tariff-setting model and several iterations of the model were created over the course 

of the process to reflect new expert testimony. All tariff models were made public on 

the PSB website (http://psb.vermont.gov/docketsandprojects/electric/7523/finalprice, 

accessed August 22, 2010). Although initial cost surveys focused on some granular 

data points (Vermont Public Service Board 2009c), the data gathered for the final 

rates was primarily top-down, attained through surveys of different technology’s total 

installed costs, rather than bottom-up data focusing on the prices of technology 

components (Vermont Public Service Board 2009b, p. 30). 

• Approval process: Act 45 set interim rates and directed the PSB to determine 

whether the rates were a “reasonable approximation” of generation cost-based rates 

and then set those as interim rates. Act 45 also directed the PSB to develop final rates 

by January 2010. The PSB set interim rates in Docket 7523 and final rates in Docket 

7533. 

Ontario’s Tariff Calculation Case Study

• Tariff overview: Ontario’s FIT is paid as a fixed, long-term power purchase price. 

Different prices are offered for different technologies and for different project sizes. 

Tariffs are offered for 20 years for all technologies with the exception of hydro 

plants, which are offered 40-year contracts. 

• Degree of differentiation: The FIT is differentiated into seven different technology 

categories with a high degree of project-size differentiation within each technology. 

Both biogas and solar PV systems have five different size categories.

58

 

58 For specific rates in Ontario, see: 

 Onshore wind 

http://fit.powerauthority.on.ca/. Accessed May 12, 2011.
74 

is distinguished from offshore wind, and two different size categories are offered for 

landfill gas, biomass, and hydro-power projects with different FIT prices awarded to 

each. Additionally, a sliding-scale bonus payment of up to $0.015/kWh is offered for 

community-owned and aboriginal-owned projects, based on the degree of local or 

aboriginal participation.

• Calculation method: DCF model. 

• Payment structure: The FIT payment is a nominal fixed value. For all technologies 

other than solar PV, it indexes 20% of the initial tariff price to changes in the 

consumer price index, meaning that the price is adjusted upward annually. The 

assumed annual rate of inflation is 2.25%. 

• Ownership structure and tax treatment: The assumed ownership structure in 

Ontario’s FIT is private and commercial. Due to the different tax structures that apply 

to different types of private investors, three different corporate ownership structures 

were modeled. Calculations are made on an after-tax basis, taking into account the 

different corporate income tax rates and applicable depreciation. 

• Granularity for cost inputs: There is one input for capital cost. Operating costs are 

broken out by fixed O&M, variable O&M, fuel costs, and property tax payments.

• Evaluation metric: The target rate of return is set at an 11% IRR. The cost of debt is 

assumed to be 7%. 

• Assumed capital structure: The model used in Ontario assumes a 70% debt, 30% 

equity capital structure for all technologies and project sizes. It assumes a nonrecourse project financing structure.

• Interaction with other incentives: The FIT payments are offered without an explicit 

reference to other supplementary incentives. Incentives offered under the federal EcoEnergy program are not assumed within the tariff calculation model, due in part to 

their unreliability and to the different abilities of market participants to capitalize 

upon them. 

• Interconnection and transmission costs: Unspecified assumptions for 

interconnection costs implicitly were incorporated within the set of survey-based cost 

assumptions relating to a standard case. The costs of connection to the grid are 

assumed to be borne by the project developer, but the costs of necessary upgrades to 

the transmission and distribution infrastructure are passed on to the transmission 

authority and, ultimately, are passed on to ratepayers. Each upgrade is subjected to an 

economic connection test, which assesses the economic justification for the additional 

transmission investment in relation to similar investments in other areas of the grid, as 

well as their priority over others based on the date that the project proposals are 

submitted. 

• Data-gathering and rate-setting processes: The data gathering was done privately 

by consultants, in conjunction with the Ontario Power Authority, which drew on 

previous bids submitted from the last round of competitive solicitations. The results 

of the analysis then were submitted in the context of an open public consultation 

process, and individual stakeholders could offer comments and suggest modifications 
75 

to the proposed tariff amounts. The final tariffs can be adjusted based on this open 

consultation to reflect legitimate arguments raised by stakeholders. 

• Approval process: After a detailed stakeholder engagement process, and after 

receiving Royal Assent in the provincial legislature, the Ministry of Energy and 

Infrastructure announced the launch of the Green Energy and Green Economy Act, of 

which a central component is the FIT program. 

Germany’s Tariff Calculation Case Study

• Tariff overview: Germany’s FIT is paid as a fixed, long-term power purchase price. 

Different prices are offered for different technologies, as well as for different project 

sizes, different fuels, and different technology applications. Tariffs are offered for 20 

years for all technologies.

• Degree of differentiation: Germany’s FIT is one of the world’s most highly 

differentiated. 

• Calculation method: FCR, which the German Environment Ministry refers to as the 

annuity method. 

• Payment structure: The tariff prices are fixed nominal values and are offered within 

purchase guarantees that extend over 20 years (15 years for biomass). The tariff 

calculation assumes a 2% inflation rate per year. All costs for renewable electricity 

generation therefore are calculated on a real basis, adjusting them to inflation based 

on a specific reference year.

• Ownership structure and tax treatment: The FIT rate calculations are conducted 

on a pretax basis, although the FIT assumes that the systems are owned by private 

sector, commercial entities.59

• Granularity for cost inputs: Capital and operating cost inputs are aggregated in the 

spreadsheet model. The data gathered to support the aggregated cost inputs, however, 

are granular compared to some of the other jurisdictions. The German government 

collects and analyzes data on annual electricity production, hard costs (including 

machinery, electrical systems, construction, grid connection, and transformers), 

consultant and legal fees, interest incurred during the construction phase, and 

operating costs (fuel costs, labor, service, insurance, administrative, and land-lease 

costs).

• Evaluation metric: The German FIT assumes different returns on equity for different 

technologies, depending on their risk profile and assumed financing structure. 

• Assumed capital structure: The assumed capital structure varies by generation type 

and project size. 

• Interaction with other incentives: No other incentives are assumed in the setting of 

the FIT rate.

 

59 In previous rounds of the rate-setting process, the ownership structure of small PV systems was assumed to be 

residential, and both taxes and tax credits were taken into account in the FIT calculation.
76 

• Interconnection and transmission costs: Interconnection costs are borne by the 

generator and built into the FIT rate. The FITs are calculated based on typical 

“model” projects, and therefore, average values (i.e., based on average 

interconnection distances) are assumed as part of the cost model. Transmission and 

distribution system upgrades are socialized among national ratepayers. 

• Data-gathering and rate-setting processes: Germany relies on a network of 

research institutes to carry out comprehensive surveys of plant developer/operator 

data, which are subsequently compared to published data and other relevant case 

studies. The data is derived both from market studies and from data reported by FIT 

generators. The data is used to define a “reference” generator for each tariff level that 

embodies system performance under standard operating conditions. This reference 

generator then is used as the basis for rate setting.

• Approval process: Similar to the Netherlands, the German FIT rates are adapted by 

the national legislature (the Bundestag), following recommendations from the 

Ministry of Environment.