LBNL-61580 

Weighing the Costs and Benefits of 

State Renewables Portfolio Standards: 

A Comparative Analysis of StateLevel Policy Impact Projections

Cliff Chen, Ryan Wiser, and Mark Bolinger

Environmental Energy 

Technologies Division 

 

March 2007

The work described in this report was funded by the Office of Electricity 

Delivery and Energy Reliability (Permitting, Siting and Analysis Division) and 

the Office of Energy Efficiency and Renewable Energy (Wind & Hydropower 

Technologies Program) of the U.S. Department of Energy under Contract No. 

DE-AC02-05CH11231. The authors are solely responsible for any omissions or 

errors contained herein. 

ERNEST ORLANDO LAWRENCE

BERKELEY NATIONAL LABORATORY
Disclaimer

This document was prepared as an account of work sponsored by the United States 

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the United States Government nor any agency thereof, nor The Regents of the 

University of California, nor any of their employees, makes any warranty, express or 

implied, or assumes any legal responsibility for the accuracy, completeness, or 

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

or favoring by the United States Government or any agency thereof, or The Regents of 

the University of California. The views and opinions of authors expressed herein do 

not necessarily state or reflect those of the United States Government or any agency 

thereof, or The Regents of the University of California. 

Ernest Orlando Lawrence Berkeley National Laboratory is an equal opportunity 

employer. 
LBNL-61580 

Weighing the Costs and Benefits of State Renewables 

Portfolio Standards: A Comparative Analysis of State-Level 

Policy Impact Projections

Prepared for the 

Permitting, Siting and Analysis Division 

Office of Electricity Delivery and Energy Reliability 

U.S. Department of Energy 

and the 

Wind & Hydropower Technologies Program 

Office of Energy Efficiency and Renewable Energy 

U.S. Department of Energy 

Principal Authors: 

Cliff Chen, Ryan Wiser, and Mark Bolinger 

Ernest Orlando Lawrence Berkeley National Laboratory 

1 Cyclotron Road, MS 90R4000 

Berkeley CA 94720-8136 

March 2007 

The work described in this report was funded by the Office of Electricity Delivery and Energy 

Reliability (Permitting, Siting and Analysis Division) and the Office of Energy Efficiency and 

Renewable Energy (Wind & Hydropower Technologies Program) of the U.S. Department of 

Energy under Contract No. DE-AC02-05CH11231. The authors are solely responsible for any 

omissions or errors contained herein. 
Acknowledgements 

The work described in this report was funded by the Office of Electricity Delivery and Energy 

Reliability (Permitting, Siting and Analysis Division) and the Office of Energy Efficiency and 

Renewable Energy (Wind & Hydropower Technologies Program) of the U.S. Department of 

Energy under Contract No. DE-AC02-05CH11231. The authors are solely responsible for any 

omissions or errors contained herein. We appreciate the funding and continued support of our 

U.S. Department of Energy colleagues, especially Larry Mansueti, Jack Cadogan, Steve 

Lindenberg, and Alejandro Moreno. For openly providing the data on which this report is based, 

we also thank the authors of the state renewables portfolio standard cost-impact studies that are 

reviewed in the pages that follow, including: Jason Abiecunas, Alison Bailie, Ron Binz, Steve 

Clemmer, Pat DeLaquil, Jeff Deyette, Marshall Goldberg, Bob Grace, Kristen Graf, Michael 

Lazarus, Travis Madsen, Jennifer Martin, Ryan Pletka, Jake Schmidt, Brian Smith, Dick 

Spellman, Howard Wenger, and Tim Woolf. Many of these cost study authors also diligently 

reviewed earlier drafts of the report and provided invaluable feedback. In addition to those 

mentioned above, we also thank Bernadette Del Chiaro, Karlynn Cory, Ray Dracker, Steve Fine, 

Gail Mosey, and Blair Swezey for their helpful comments. Of course, any remaining omissions 

or inaccuracies are our own. 
Table of Contents 

Executive Summary......................................................................................................................... i 

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

2. Overview of State RPS Cost Analyses........................................................................................4

2.1 Study Identification..............................................................................................................4 

2.2 Design of State RPS Policies in Study Sample....................................................................6 

2.3 Primary Authorship and Funding Source of Studies in Sample ..........................................8 

3. Projected Renewable Resource Mix and Direct Costs..............................................................10 

3.1 Methodology for Comparing Results from Multiple Studies ............................................10 

3.2 Projected Renewable Resource Mix: Base-Case Results .................................................11 

3.3 Direct Cost Impacts: Base-Case Results...........................................................................13 

4. Scenario Analysis......................................................................................................................18 

5. Projected Benefits......................................................................................................................22 

5.1 Macroeconomic Impacts....................................................................................................23

5.2 Risk Mitigation Benefits....................................................................................................25 

5.2.1 Suppression of Electricity and Natural Gas Prices ....................................................25 

5.2.2 Hedging Energy Price Uncertainty ............................................................................28 

5.3 Environmental Benefits .....................................................................................................31 

6. Comparison of Study Methodologies and Assumptions ...........................................................35 

6.1 General Modeling Approaches ..........................................................................................35 

6.2 State RPS Market Structure ...............................................................................................39

7. Modeling Renewable Technologies, Resources, and Costs......................................................43 

7.1 Technologies Modeled.......................................................................................................43 

7.2 Renewable Resource Characterization ..............................................................................45 

7.3 Renewable Energy Cost Characterization .........................................................................47 

7.3.1 Sources and Methods for Estimating Busbar Costs...................................................47 

7.3.2 Wind Capital Cost Assumptions................................................................................49 

7.3.3 Federal Production Tax Credit Availability...............................................................50 

7.3.4 Treatment of Secondary Costs...................................................................................51 

7.4 Renewable Demand Characterization................................................................................53 

8. Modeling Avoided Cost ............................................................................................................54 

8.1 General Methodologies......................................................................................................54 

8.2 Temporal and Geographic Variations in Avoided Cost.....................................................56 

8.3 Natural Gas Price Forecasts...............................................................................................56 

9. Conclusions ...............................................................................................................................58 

References......................................................................................................................................62 

Appendix A. Bibliography of RPS Cost Studies.....................................................................65 

Appendix B. Base-Case Scenarios Chosen for State RPS Cost Studies .................................69 

Appendix C. Methodology for Converting Cost Results ........................................................70 
List of Figures and Tables 

Figure 1. State RPS Policies Currently in Place........................................................................... 1 

Figure 2. State RPS Cost-Impact Studies Included in Report Scope ........................................... 4 

Figure 3. Primary Authorship and Funding Source of State RPS Cost Studies........................... 8 

Figure 4. Incremental Renewable Energy Deployment by Study and Technology ................... 12 

Figure 5. Mix of Incremental Renewable Generation from All Studies Combined (GWh, %). 13 

Figure 6. Distribution of Base-Case Impacts on Average Retail Electricity Rates.................... 14 

Figure 7. Projected Electricity Rate Impacts by State RPS Cost Study..................................... 15 

Figure 8. Relationship Between Incremental State RPS Targets and Retail Electricity Rate 

Impacts........................................................................................................................ 16 

Figure 9. Typical Residential Electricity Bill Impacts Projected by State RPS Cost Studies.... 17 

Figure 10. Sensitivity Scenarios Modeled by State RPS Cost Studies........................................ 18 

Figure 11. Changes to Base-Case Residential Monthly Electricity Bill Impacts by Individual 

Driver (Cost Decreasing Scenarios)............................................................................ 21 

Figure 12. Changes to Base-Case Residential Monthly Electricity Bill Impacts by Individual 

Driver (Cost Increasing Scenarios)............................................................................. 21 

Figure 13. Potential Benefits Modeled by State RPS Cost Studies ............................................. 22 

Figure 14. Employment Impacts Predicted by State RPS Cost Studies ...................................... 24 

Figure 15. Wholesale Energy and Capacity Price Changes Projected by State RPS Cost 

Studies......................................................................................................................... 26 

Figure 16. Delivered Natural Gas Price Savings Projected by State RPS Cost Studies, Averaged 

Over Each Study’s Timeframe.................................................................................... 27 

Figure 17. Sensitivity of State RPS Bill Impacts to Alternative Natural Gas Price Scenarios.... 30 

Figure 18. Sensitivity of State RPS Bill Impacts to Alternative Wholesale Electric Price 

Scenarios..................................................................................................................... 30 

Figure 19. Projected CO2 Emissions Displaced by State RPS Policies in Initial Peak Year of 

RPS ............................................................................................................................. 32 

Figure 20. Projected CO2 Abatement Costs in Initial Peak Year of State RPS........................... 34 

Figure 21. General Modeling Approaches Used for Estimating State RPS Costs....................... 39 

Figure 22. Graphical Representation of Market-Clearing State RPS Compliance Model .......... 40 

Figure 23. Graphical Representation of Average-Cost State RPS Compliance Model ............... 41 

Figure 24. Approaches for Characterizing State RPS Market Structure ..................................... 42 

Figure 25. Principal Sources of Renewable Resource Data ........................................................ 46 

Figure 26. Principal Sources of Renewable Energy Busbar Cost Data ....................................... 47 

Figure 27. Wind Power Capital Cost Assumptions (Base-Case Analysis).................................. 50 

Figure 28. Duration of PTC Availability Assumed by State RPS Cost Studies (Base-Case 

Analysis) ..................................................................................................................... 51 

Figure 29. State RPS Cost Study Methodologies for Estimating Avoided Cost ......................... 55 

Figure 30. Base-Case Natural Gas Price Assumptions of State RPS Cost Studies ..................... 57 

Table 1. List of Reviewed State RPS Cost-Impact Studies ........................................................ 5 

Table 2. State RPS Policies as Modeled by RPS Cost Studies................................................... 7 

Table 3. Employment and Gross State Product Impacts Predicted by State RPS Cost Studies 24 
Table 4. Effect of Wholesale Market Price Reductions on State RPS Rate Impacts in 1st

 

Year of Peak State RPS Target ................................................................................... 26 

Table 5. Summary of Basic Modeling Approaches .................................................................. 38 

Table 6. Technologies Modeled by Each State RPS Cost Study.............................................. 44 

Table 7. Secondary Costs of Renewable Generation Considered by State RPS Cost Studies . 52 

i

Executive Summary 

Introduction 

State renewables portfolio standards (RPS) have emerged as one of the most important policy 

drivers of renewable energy capacity expansion in the U.S. Collectively, these policies now 

apply to roughly 40% of U.S. electricity load, and may have substantial impacts on electricity 

markets, ratepayers, and local economies. As RPS policies have been proposed or adopted in an 

increasing number of states, a growing number of studies have attempted to quantify the 

potential impacts of these policies, focusing primarily on projecting cost impacts, but sometimes 

also estimating macroeconomic and environmental effects. 

This report synthesizes and analyzes the results and methodologies of 28 distinct state or utilitylevel RPS cost impact analyses completed since 1998.1

 Together, these studies model proposed 

or adopted RPS policies in 18 different states. We highlight the key findings of these studies on 

the costs and benefits of state RPS policies, examine the sensitivity of projected costs to model 

assumptions, assess the attributes of different modeling approaches, and suggest possible areas of 

improvement for future state RPS analysis. 

Key Findings 

Projected rate impacts are generally modest. Seventy percent of the state RPS cost studies in 

our sample project base-case retail electricity rate increases of no greater than one percent in the 

year that each modeled RPS policy reaches its peak percentage target.2

 In six of those studies, 

electricity consumers are expected to experience cost savings as a result of the state RPS policies 

being modeled. On the other extreme, nine studies predict rate increases above 1%, and two of 

these studies predict rate increases of more than 5%. Though most of the studies project 

relatively limited impacts on retail electricity rates, the wide range of impacts shown in Figure 

ES - 1 underscores the large variability among the studies’ results. When translated to monthly 

electricity bill impacts for a typical residential customer, these impacts range from a savings of 

over five dollars per month to an increase of over seven dollars per month.3

 However, the 

median bill impact across all of the studies in our sample is an increase of only $0.38 per month. 

 

1

 Since out analysis was conducted, two additional otherwise-relevant RPS cost studies have been published: 

LaCapra et al. (2006) [North Carolina] and Gittell and Magnusson (2007) [New Hampshire]. These two studies are 

not included in the analysis that follows. 

2

 We use the term “base case” to refer to the baseline state RPS scenario, while we use the term “reference case” to 

refer to the business-as-usual, non-RPS scenario. We use data from the “peak target year” (e.g., 7% in 2012 for 

Massachusetts, 9% in 2010 for Minnesota, etc.) to compare most of the studies’ projections because we believe it to 

be the most tractable and consistent method for comparing the long-term state RPS impacts of studies that provide 

projected impacts in widely varying formats and timeframes. The direct cost impacts referred to here account for 

any reductions in wholesale electricity market prices that the studies may have modeled, but do not include any 

potential reductions in consumer natural gas bills. 

3

 All cost figures in this report have been converted to 2003 dollars. 
ii

-6%

-3%

0%

3%

6%

9%

TX (UCS) - 3%

CA (Tellus) - 11%

HI (GDS) - 4%

WA (UCS) - 12%

CA (UCS) - 13%

OR (Tellus) - 11%

WI (UCS) - 7%

PA (B&V) - 7%

CO (PPC) - 9%

CA (CRS) - 17%

WA (Lazarus) - 15%

NY (DPS) - 8%

CO (UCS) - 6%

NE (UCS) - 10%

IA (WUC) - 9%

NY (Potomac) - 7%MD (Synapse) - 8%

VT (Synapse) - 10%

VA (CEC) - 17%

MN (WUC) - 9%

AZ (PEG) - 1%

CA LADWP (EC) - 20%

MA (SEA) - 7%

WA (Tellus) - 17%

NY (CCAP) - 5%

NJ (Rutgers) - 14%

IN (EEA) - 10%

RI (Tellus) - 18%

NY (ICF) - 8%

AZ (PIRG) - 20%

Study - Incremental RPS Target %

-0.50

-0.25

0.00

0.25

0.50

0.75

 Average retail (% - left axis)

 Average retail (¢/kWh - right axis)

Median retail rate increase = 0.7%

Median change in retail rates = 0.04¢

Change in Avg. Retail Elec. Rates in 1st Peak Target Yr.

Change in Avg. Retail Elec. Rates in 1st Peak Target Yr. 

(2003 ¢/kWh)

 

Figure ES - 1. Projected State RPS Electricity Rate Impacts by Cost Study 

Wind is expected to be the dominant technology in meeting state RPS requirements. Figure ES 

- 2 presents the projected mix of new renewable generation used to meet the modeled state RPS 

policies (for the 23 studies that forecast the renewable technology mix). The renewable 

generation mix is an input assumption to some studies and a model output to others. Perhaps not 

surprisingly, wind is expected to be the dominant technology, representing in aggregate 62% of 

incremental state RPS generation across all of these studies combined. Projected wind 

development is particularly prevalent in the Midwest and Texas, accounting for 94% of expected 

incremental RPS generation in those states. Geothermal, which accounts for 18% of projected 

incremental generation across the studies, is a distant second, and almost all of the expected 

geothermal additions are from three California studies. Biomass co-firing and direct combustion 

account for approximately 8% of expected incremental generation, while hydro, landfill gas, and 

solar each comprises less than 4%.4

 

 

4

 These percentages are purely intended for illustrative purposes. They do not represent the overall state RPS mix 

that would be developed if RPS policies were adopted in all of the states for which cost studies have been 

performed. Renewable energy deployment data are not available for all states, and multiple cost studies exist in 

some states, thereby “double counting” the impacts of those states’ RPS policies on these percentage figures. 
iii

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

VT (Synapse)

RI (Tellus)MA (SEA)

NY (CCAP)

PA (B&V)

NY (DPS)

NY (Potomac)

VA (CEC)

NE (UCS)IA (WUC)

WI (UCS)

MN (WUC)

TX (UCS)

IN (EEA)

HI (GDS)

AZ (PEG)

CO (UCS)

OR (Tellus)

CO (PPC)

WA (Tellus)

WA (Lazarus)

AZ (PIRG)

CA (Tellus)

CA (UCS)CA (CRS)

Generation (GWh)

Other

Geothermal

Solar

Hydro

Landfill Gas

Biomass Co-Fire

Biomass

Wind

East Midwest West

 

Figure ES - 2. Incremental Renewable Energy Deployment by Study and Technology 

Scenario analyses reveal significant cost sensitivity to input parameters. The majority of the 

studies we reviewed include some form of scenario analysis using input assumptions that differ 

from those used in the base case. The most commonly modeled scenarios focus on the 

availability of the federal production tax credit, varying projections of renewable technology 

cost, fossil fuel price uncertainty, and wholesale market price uncertainty. The prevalence of 

these scenarios implies – but does not prove – that projected state RPS costs are more likely to be 

sensitive to these particular factors than to others. Due to the wide range of scenarios modeled 

and the different assumptions used within each type of scenario, it is difficult to draw definitive 

conclusions about the relative impact of different cost drivers. In most cases, the residential 

electricity bill impacts of the scenarios analyzed by the studies – as measured by changes from 

the base case state RPS – are less than one dollar per month. Though such changes are not 

overwhelming, it is important to recognize that the median base-case residential electricity bill 

impact among the studies in our sample is just $0.38 per month. Therefore, even a one dollar per 

month change from this base case is sizable in percentage terms, and demonstrates significant 

cost sensitivity to input parameters. 

Some of the public benefits of state RPS policies are still not well understood. An increasing 

number of studies are modeling macroeconomic or public benefits of state RPS policies. Almost 

a third of the studies in our sample model the macroeconomic effects of state RPS policies.5

 All 

of these studies predict some level of net employment gain, but the magnitude of this impact 

 

5

 However, our sample does not include state RPS analyses that have focused exclusively on macroeconomic 

benefits. 
iv

varies widely and appears to depend more strongly on the assumptions of the studies than on the 

amount of incremental renewable generation required to meet the modeled state RPS policies. 

These assumptions include the different mixes of renewable technologies developed, the 

proportion of in-state versus out-of-state renewable project development and manufacturing, and 

the incorporation (or lack thereof) of energy bill impacts into the macroeconomic analysis. 

About a quarter of the studies in our sample also model the risk mitigation benefits of state RPS 

generation, estimating a broad range of reductions in wholesale electricity and natural gas prices; 

still other studies evaluate the sensitivity of the projected cost of state RPS policies to variations 

in the projected price of natural gas. Half of the studies we reviewed quantify potential 

environmental benefits, most commonly carbon dioxide (CO2) emissions reductions. Most of 

these studies indicate that RPS generation is expected to displace CO2 emissions at a rate that is, 

on average, slightly higher than that of a natural gas plant. Although the spread of projected CO2

abatement costs across the studies is extremely broad, a majority of these studies project CO2

reduction costs that fall within the range of the U.S. Energy Information Administration’s (EIA) 

projections of carbon reduction costs under various regulatory regimes (Wiser and Bolinger 

2004). 

Analysis assumptions are likely as or more important than the choice of model. In the absence 

of a universally accepted methodology for analyzing state RPS cost impacts, the studies in our 

sample employ a diverse array of modeling approaches, ranging from simple spreadsheet models 

to highly sophisticated integrated energy models. This diversity in modeling approaches may be 

due in part to regional differences in state RPS policies and electricity markets, as different 

situations call for different modeling approaches. However, the limited budgets and short 

timeframes that typically apply to state RPS cost studies are probably the more important 

determinants of the modeling approach chosen, as the sophistication and detail of the analysis is 

likely to be constrained by these limiting factors. Though more sophisticated models can 

account for interesting and potentially significant price feedbacks and may be better received by 

policymakers and state RPS stakeholders, it is not entirely clear that such models necessarily 

improve predictive accuracy. Given the significant uncertainty surrounding numerous state RPS 

cost factors, it is likely that the assumptions governing these factors, such as the natural gas price 

forecast and the presumed availability of the production tax credit, are as or more important than 

the type of model used. 

Studies appear to have underestimated both renewable technology costs and avoided fuel 

costs. The vast majority of studies we reviewed appear to have underestimated two major state 

RPS cost factors: wind power capital costs and natural gas prices. Since wind is expected to be 

the dominant contributor to state RPS generation requirements, wind cost assumptions are 

critically important for estimating the cost impacts of state RPS policies. Since the studies did 

not anticipate the sudden leap in wind costs over the past several years, the wind capital cost 

assumptions in most of the studies, which typically fall between $800-1300/kW in the 2005-2010 

timeframe, are significantly below current costs (which are reportedly in the $1400-2000/kW 

range). This disparity between study expectations and current market reality suggests that (all 

else being equal) the actual cost impacts of state RPS policies may significantly exceed those 

estimated in our sample of studies, especially if higher wind costs persist. However, most, if not 

all, of the studies appear to have also substantially underestimated natural gas prices, which are 
v

perhaps the most important input to the avoided cost estimates of several studies.6

 Current 

natural gas prices (and near-term price expectations) are much higher than those assumed by the 

studies, as most of the studies rely on dated Energy Information Administration natural gas price 

forecasts projecting prices that are far lower than current price expectations. It is uncertain to 

what degree this apparent underestimate of natural gas prices will negate the effects of 

underestimating current wind costs; the uncertainties involved with predicting these two inputs 

highlight the importance of performing scenario analysis.

Conclusions 

With few exceptions, the long-term electricity rate impacts of state RPS policies are projected to 

be relatively modest. When these electricity cost impacts are combined with possible state RPSinduced natural gas price reductions and corresponding gas bill savings, the overall cost impacts 

are even smaller. 

The large diversity of modeling methodologies and assumptions used to estimate state RPS costs 

demonstrates that state RPS cost analysis is still an evolving process, and that a standard 

template has not yet emerged. Moreover, like most prospective analyses of electricity markets, 

state RPS cost analysis is an inherently uncertain practice, highlighting the importance of 

evaluating the sensitivity of projected state RPS costs to uncertain input parameters. Though this 

report focuses most heavily on RPS-induced rate impacts, an increasing number of studies are 

modeling the macroeconomic or other public benefits of state RPS policies, either in addition to 

or exclusive of rate impacts. 

State RPS cost studies are becoming more sophisticated, but improvements are still possible. We 

identify a number of areas of possible improvement for future state RPS cost studies: 

• Improved treatment of transmission costs, integration costs, and capacity values: 

Transmission availability and transmission expansion costs have become among the most 

important barriers to renewable energy in many states, but these costs are often poorly 

understood and imprecisely modeled in state RPS cost studies.7

 The capacity value of 

renewable energy (wind, in particular), as well as the cost of integrating renewable energy 

into larger electricity systems, are likewise emerging as potentially important variables, and 

studies analyzing state RPS policies with relatively high incremental targets must be careful 

to properly account for these potential costs and impacts. 

• More rigorous estimates of the future cost and performance of renewable technologies. As 

the renewable energy market continues to rapidly evolve and expand, the need for accurate, 

rigorous, and up-to-date estimates of renewable resource cost, performance, and potential is 

as acute as ever. Unfortunately, some of the most commonly used data sources for the cost 

and potential of renewable generation technologies are somewhat dated and arguably not up 

 

6

 This is not true of studies that assume that avoided costs will be effectively determined by the cost of non-naturalgas generators (i.e., coal-fired generators), but most of the studies in our sample have explicitly or implicitly 

assumed that avoided costs will be primarily determined by the cost of natural gas-fired generation. 7

 The same criticism also often applies to some extent to cost evaluations of transmission expansion needed for 

conventional generation. 
vi

to the task. Developing better estimates of future renewable technology cost and 

performance would require time and resources that are beyond the scope of many state RPS 

cost studies, and would probably be best managed by a government agency. The availability 

of such information would improve the credibility of state RPS cost analysis and lend more 

weight to economic analysis of renewable technologies in general. 

• Consideration of competing state RPS requirements: As the number of states that have 

adopted RPS policies continues to grow, the available supply of renewable energy in regions 

with limited renewable potential (e.g., New England) may become more costly due to 

increased demand. Future cost studies would be well served to consider renewable demand 

from existing and potentially new RPS policies in neighboring states and regions and 

evaluate the potential effect of this demand on state RPS rate impacts.

• Estimating the future price of natural gas: Where possible, base-case natural gas price 

forecasts should (arguably) be benchmarked to then-current NYMEX futures prices 

(Bolinger et al. 2006). Furthermore, given fundamental uncertainty in future gas prices, a 

healthy range of alternative price forecasts should be considered through sensitivity analysis. 

To calculate the potential secondary impacts of increased renewable energy deployment on 

natural gas prices, either an integrated energy model or the simplified tool developed by 

Wiser et al. (2005) might be used. 

• Evaluation of coal as the marginal price setter: With high natural gas prices, some states 

are shifting away from natural gas towards other resources, especially coal. A few of the 

state RPS cost studies already assume that coal is the marginal fuel type that is offset by 

increased renewable generation, but most of the studies assume that natural gas will be the 

primary source of displaced electricity generation. New studies should more closely 

investigate the possibility that RPS generation may increasingly displace coal-fired and other 

non-gas-fired generation. Such a shift would likely reduce the importance of natural gas bill 

savings, but could also increase the importance of carbon emissions reductions. 

• Greater use of scenario analysis: The inaccuracy of long-term fundamental gas price 

forecasts from the EIA and other private sector firms in recent years underscores the 

importance of using scenario analysis to bound possible outcomes. Not only is the future 

cost of conventional generation unknowable, renewable technologies themselves are 

experiencing rapid changes, both of which render the long-term impacts of RPS policies 

highly uncertain. Such uncertainty can be evaluated, to a degree, through greater use of 

scenario analysis. Some of the variables that may be most appropriate for scenario analysis 

include renewable technology potential and costs, future natural gas prices, the period of 

PTC extension, and the potential impact of future carbon regulations. 

• Consideration of future carbon regulations: As some states and regions begin to implement 

carbon regulations, renewable generators may stand to benefit. It is also possible that federal 

carbon regulations will be developed within the time horizon of state RPS policies. Although 

these trends may significantly reduce the incremental cost of renewable generation required 

by state RPS policies, the risk of future carbon regulation has only been modeled by four of 
vii

the studies in our sample. In future studies, we recommend that the risk of future carbon 

regulations be explicitly considered, at a minimum through scenario analysis. 

• Accurate representation of state RPS market structure: In some regions of the country, 

state RPS compliance strategies based on short-term markets for renewable energy credits 

(RECs) have led to unexpected cost impacts. For example, in Massachusetts, a lack of longterm contracts to support new renewable development (coupled with high demand for RECs 

and difficulties in siting and permitting) has resulted in ratepayer costs that are substantially 

higher than anticipated. State RPS cost studies should seek to adopt modeling approaches 

that are consistent with probable state RPS market structures. 

• More robust treatment of public benefits: Though an increasing number of studies have 

modeled macroeconomic benefits, the assumptions driving these analyses are often 

inconsistent, and the wide range of results may detract from the credibility of such studies. 

More work is needed to identify the most feasible and defensible assumptions governing the 

public benefits of renewable energy, including the fossil fuel hedge value of renewable 

energy and the benefits of reduced carbon emissions, in addition to employment and 

economic development impacts. 

Actual state RPS costs may differ from those estimated in the state RPS cost studies. The 

improvements listed above, if adopted, should lead to more accurate and realistic projections of 

the costs and benefits of state RPS policies in the future. In the meantime, it is difficult to assess 

whether the state RPS impact studies reviewed in this report present overly optimistic or overly 

conservative estimates of future costs. Some of the assumptions in the state RPS cost studies 

that may result in an underestimation of actual RPS costs include: 

• Wind capital cost assumptions that appear too low in many cases, given recent increases 

in wind costs; 

• Transmission and integration costs that are not fully considered in some instances; 

• Use of an “average cost” approach to estimate incremental renewable generation costs in 

some situations when a marginal-cost-based approach may be more appropriate; 

• Lack of full consideration for the potential demand for renewable energy from other 

sources (such as demand from other state RPS policies); 

• Increased likelihood that coal-fired generation will set wholesale market prices in some 

regions which, in the absence of carbon regulations, may make renewable generation less 

economic than when renewable energy is presumed to compete with natural gas; and, 

• Expectations in some cases that the federal production tax credit (PTC) will be available 

indefinitely, which may be overly optimistic given the political uncertainty affecting PTC 

extension. 

Conversely, a number of other cost study assumptions may result in an overestimation of actual 

state RPS costs, including: 

• Reliance on natural gas price forecasts that are almost universally substantially below 

current price expectations; 
viii

• Secondary natural gas and/or wholesale electric price reductions that have not been 

modeled in many of the studies; 

• The potential for future carbon regulations, which are ignored in most of the studies in 

our sample; and 

• Expectations in many cases that the PTC will only be available for either a very limited 

period or not at all, which may be overly conservative given the recent two-year 

extension of the PTC and the possibility for longer-term extension. 

As states accumulate more empirical experience with actual state RPS policies, future analyses 

should benchmark the cost projections from state RPS cost studies against actual realized cost 

impacts as a way to both inform future state RPS modeling efforts and better weigh the potential 

costs and benefits of state RPS policies. 
1

1. Introduction 

Renewables portfolio standards (RPS) require that a minimum amount of renewable energy is 

included in each retail electricity supplier’s portfolio of electricity resources. They do so by 

establishing numeric targets for renewable energy supply, which generally increase over time. 

To date, 21 states in the U.S., along with the District of Columbia, have adopted such standards 

(Figure 1). Additional states, such as Illinois and Vermont, have established voluntary standards, 

while still others are considering enacting obligatory RPS policies. RPS policies have also been 

developed in several other countries, and have been considered (but not adopted) by the U.S. 

Congress. 

Figure 1. State RPS Policies Currently in Place (March 2007)8

A well-designed RPS should generally encourage competition among renewable developers and 

provide incentives to electricity suppliers to meet their renewable purchase obligations in a leastcost fashion. In part to accommodate diverse goals and regional differences, however, state RPS 

policies differ in their design. The definition of eligible renewable projects and the amount of 

renewable energy that is required varies. In many – but not all – jurisdictions, electricity 

suppliers can meet their RPS obligations through the use of tradable renewable energy 

certificates (RECs); in theory, the use of RECs increases compliance flexibility and may 

therefore reduce overall compliance costs. RPS policies in some states provide for resource tiers 

or credit multipliers, which are designed to promote diversity among renewable technologies.9

 

State RPS policies also vary in their scope of application (e.g., whether publicly owned utilities 

 

8

 Illinois and Vermont have established voluntary state RPS policies. Maine has also recently adopted a goal that 

“new” renewable energy comprise 10% of the state’s electricity supply on a capacity basis. 

9

 With the resource tier approach, higher cost or higher priority technologies are grouped together in a compliance 

tier, so that they are not competing with lower cost or lower priority technologies, which comprise a second tier. 

Credit multipliers provide additional state RPS compliance “credit” for certain types of renewable generation, e.g. 

PV technology or renewable energy generated in-state. 

WI: 10% by 2015

NV: 20% by 2015

TX: 5880 MW by 2015

PA: 8% by 2020

NJ: 22.5% by 2021

CT: 10% by 2010

MA: 4% by 2009

ME: 30% by 2000

NM: 20% by 2020

CA: 20% by 2010 

MN: 25-30% by 2020-25

IA: 105 aMW

MD: 7.5% by 2019

RI: 16% by 2019

HI: 20% by 2020

AZ: 15% by 2025 

NY: 24% by 2013

CO: 10% by 2015

MT: 15% by 2015

DE: 10% by 2019

DC: 11% by 2022

WA: 15% by 2020

WI: 10% by 2015

NV: 20% by 2015

TX: 5880 MW by 2015

PA: 8% by 2020

NJ: 22.5% by 2021

CT: 10% by 2010

MA: 4% by 2009

ME: 30% by 2000

NM: 20% by 2020

CA: 20% by 2010 

MN: 25-30% by 2020-25

IA: 105 aMW

MD: 7.5% by 2019

RI: 16% by 2019

HI: 20% by 2020

AZ: 15% by 2025 

NY: 24% by 2013

CO: 10% by 2015

MT: 15% by 2015

DE: 10% by 2019

DC: 11% by 2022

WA: 15% by 2020
2

are required to comply), and in their use of compliance flexibility and non-compliance 

penalties.10 

Opponents of state RPS policies frequently claim that these policies are not worth implementing 

because the incremental costs of renewable energy may lead to substantial increases in electricity 

prices. State RPS proponents often counter these claims by presenting evidence of the modest 

cost of renewable energy resources and touting the macroeconomic and social benefits of state 

RPS policies. In many states, RPS stakeholders – often proponents or neutral parties, but 

possibly opponents as well – have authored or commissioned studies to analyze the potential 

costs and benefits of such policies. 

This report summarizes the results and methodologies of 28 state RPS cost-impact analyses 

completed since 1998 in the United States. Though a number of additional national- and 

regional-level cost-impact studies have also been performed, we limit our survey to state- or 

utility-level analyses to reflect the present reality in which no national or regional-level RPS 

policy exists in the United States. Because our primary aim is to compare studies that report the 

projected impacts of state RPS policies on retail electricity rates, we also exclude state RPS 

analyses that do not report such impacts but that instead focus exclusively or primarily on 

projections of macroeconomic effects (e.g., effects on employment and gross state output).11 We 

similarly exclude studies that model state RPS policies as part of a larger portfolio of climate 

change or clean energy policies, unless RPS-specific costs are provided.12 Finally, we exclude 

two otherwise-relevant cost studies that were published after our analysis had already begun.13 

The primary purpose of this report is to summarize, in as consistent a fashion as possible, the 

results of these 28 cost-impact analyses, including both the projected costs and benefits of stateand utility-level RPS programs. In so doing, we hope to illustrate the expected bounds of likely 

impacts. We also highlight and, in some cases, critique the various methods used by these 

studies, with a goal of identifying possible areas of improvement for future state RPS analyses.14 

The remainder of this report is organized as follows: 

• Section 2 presents a general overview of the 28 state-level RPS cost studies included in our 

analysis. 

• Section 3 provides a summary and comparison of the renewable resource mix and direct cost 

impacts projected by the state RPS cost studies. 

 

10 For an international review of early experience with RPS policies, see van der Linden et al. (2005). For a 

somewhat dated review of U.S. experience, see Wiser et al. (2004). See Rader and Hempling (2001) for a detailed 

but also dated discussion of RPS design issues. 

11 Examples of such analyses include Bournakis et al. (2005), Madsen et al. (2007), Perryman (2005), and Virtus 

(2002). 

12 For example, we exclude the Connecticut Climate Change Action Plan, which models a state RPS as one of 

several climate change mitigation policies but does not identify RPS-specific costs, and we exclude a recent study 

for Michigan that looks at the impacts of renewable energy and energy efficiency investments in combination. 13 These are LaCapra et al. (2006) [North Carolina] and Gittell and Magnusson (2007) [New Hampshire]. 14 We do not compare the projected costs and benefits of state RPS policies with the realized costs and benefits of 

those state RPS policies that are now operating. We leave that important comparison for future work. 
3

• Section 4 identifies any alternative scenarios that are analyzed by the state RPS cost studies, 

and presents the anticipated costs associated with those scenarios. 

• Section 5 summarizes the projected benefits of the state RPS policies, and compares the 

expected employment impacts, risk mitigation benefits, and CO2 emissions reductions that 

are quantified in the studies. 

• Section 6 compares the general modeling approaches used by the state RPS cost studies and 

includes a discussion of how the studies have represented state RPS market structure. 

• Section 7 describes the methodologies and assumptions that the state RPS cost studies have 

used in modeling renewable resource potential and cost. 

• Section 8 describes the methodologies and assumptions that the state RPS cost studies have 

used in modeling avoided cost. 

• Section 9 summarizes our key findings and highlights some possible areas of improvement 

for future state RPS cost studies. 
4

2. Overview of State RPS Cost Analyses 

2.1 Study Identification 

Eighteen states, covering most regions of the country, are represented in the 28 state RPS cost 

studies we surveyed (see Figure 2 below and Table 1 on the following page). A complete 

bibliography of the studies is provided in Appendix A. Not surprisingly, most of these costimpact studies analyze state RPS policies in states that now have RPS programs in place. Only 

five of the reviewed studies (in Indiana, Oregon, Nebraska, Virginia, and Vermont) apply to 

states that have not yet adopted a mandatory RPS. 

 

Figure 2. State RPS Cost-Impact Studies Included in Report Scope15

 

15 Tellus (2004) is a single study that models RPS policies for Washington, Oregon, and California. Because results 

are presented for each state individually, we include this study in our sample. 

Synapse 

2003 

Synapse 2003

Rutgers 2004

Tellus 2002

SEA 2002

GDS 2001 

EC 2004 

(LADWP) 

Lazarus 2003 

Tellus 2004 

UCS 2006 

PPC 2004 

UCS 2004 

PEG 1998 

AZ PIRG 2005 

UCS 2005

UCS 

2006 

WUC 

2000 

WUC 

2001 

B&V 2004

UCS 

2001 UCS 

2001 

CCAP/ICF 2003

ICF 2003 

DPS 2004 

Potomac 2005

Tellus 2004 

Tellus 

2004 

CRS 

2005 

CEC 2006

EEA 

2006 
5

Table 1. List of Reviewed State RPS Cost-Impact Studies

State Principal Author(s) Year Title

AZ AZ PIRG Education Fund 

(AZ PIRG) 

2005 Renewing Arizona’s Economy: The Clean Path to Jobs and Economic 

Growth 

AZ Pacific Energy Group 

(PEG) 

1998 Solar Portfolio Standard Analysis 

CA Union of Concerned 

Scientists (UCS) 

2001 Powering Ahead: A New Standard for Clean Energy and Stable Prices 

in California 

CA/OR/ 

WA 

Tellus 2004 Turning the Corner on Global Warming Emissions: An Analysis of 

Ten Strategies for California, Oregon, and Washington 

CA 

(LADWP) 

Environment California 

(EC) 

2004 Clean and Affordable Power: Updated Cost Analysis for Meeting a 

20% Renewables Portfolio Standard by 2017 at LADWP 

CA Center for Resource 

Solutions 

2005 Achieving a 33% Renewable Energy Target 

CO Public Policy Consulting 

(PPC) 

2004 The Impact of the Renewable Energy Standard in Amendment 37 on 

Electric Rates in Colorado 

CO UCS 2004 The Colorado Renewable Energy Standard Ballot Initiative: Impact on 

Jobs and the Economy 

HI GDS Associates (GDS) 2001 Analysis of Renewable Portfolio Standard Options for Hawaii 

IA Wind Utility Consulting 

(WUC) 

2000 Projected Impact of a Renewable Portfolio Standard on Iowa’s 

Electricity Prices 

IN Engineering Economic 

Associates (EEA) 

2006 Rate Impact of a Renewable Electricity Standard in Indiana 

MA Sustainable Energy 

Advantage (SEA) & La 

Capra 

2002 Massachusetts RPS: 2002 Cost Analysis Update – Sensitivity 

Analysis 

MD Synapse Energy 

Economics 

2003 The Maryland Renewable Portfolio Standard: An Assessment of 

Potential Cost Impacts 

MN Wind Utility Consulting 

(WUC) 

2001 Projected Impact of a Renewable Portfolio Standard on Minnesota’s 

Electricity Prices 

NE UCS 2001 Strong Winds: Opportunities for Rural Economic Development Blow 

Across Nebraska 

NJ Rutgers CEEEP 2004 Economic Impact Analysis of New Jersey’s Proposed 20% Renewable 

Portfolio Standard 

NY Center for Clean Air 

Policy (CCAP)/ICF 

2003 Recommendations to Governor Pataki for Reducing New York State 

Greenhouse Gas Emissions 

NY ICF Consulting 2003 Report of Initial Analysis of Proposed New York RPS 

NY NY Department of Public 

Service (DPS) 

2004 Renewables Portfolio Standard Order Cost Analysis 

NY Potomac 2005 Estimated Market Effects of the New York Renewable Portfolio 

Standard 

PA Black & Veatch (B&V) 2004 Economic Impact of Renewable Energy in Pennsylvania 

RI Tellus 2002 Rhode Island RPS Modeling 

TX UCS 2005 Increasing the Texas Renewable Energy Standard: Economic and 

Employment Benefits 

VA Clean Energy 

Commercialization (CEC) 

2005 A Portfolio-Risk Analysis of Electricity Supply Options in the 

Commonwealth of Virginia 

VT Synapse 2003 Potential Cost Impacts of a Vermont Renewable Energy Portfolio 

Standard 

WA Lazarus, Lazar, 

Hammerschlag 

2003 Economics of a Washington Energy Portfolio Standard: Effects on 

Ratepayers 

WA UCS 2006 The Washington Clean Energy Initiative: Effects of I-937 on 

Consumers, Jobs and the Economy 

WI UCS 2006 A Study to Evaluate the Impacts of Increasing Wisconsin’s RPS 
6

2.2 Design of State RPS Policies in Study Sample 

The publication of most of these studies was timed to coincide with state RPS legislation that had 

been proposed or implemented, and many studies evaluate state RPS policies designed as 

proposed or implemented through that legislation. Less frequently, some studies advance their 

own proposals for state RPS legislation. Twenty-one of the 28 studies have been published since 

2003, reflecting the recent surge in state RPS adoption. Because some of the studies analyze 

state RPS proposals that were later substantially modified or never adopted, the RPS design that 

each study analyzes does not necessarily reflect the policy that was eventually adopted (if a 

policy was adopted at all). A few of the studies have updated their original analysis to more 

accurately reflect the state RPS design that was ultimately adopted; in these instances, we 

include only the quantitative results from the updated analysis, though we sometimes describe 

qualitative aspects of the original studies. 

As one might expect, the state RPS policies modeled by these studies differ substantially with 

respect to structure, design, and quantitative target level. Table 2 briefly summarizes some of the 

most pertinent details of the state RPS policy designs that are modeled by the cost studies in our 

review.16 Table 2 primarily identifies the “base-case” state RPS policies analyzed in each study; 

many of the studies evaluate multiple RPS designs as alternative cases, and these are discussed in 

Section 4. A number of the studies do not explicitly identify a “base-case” scenario; Appendix B 

identifies these studies and our rationale for choosing a base-case scenario in each instance. 

For the purposes of the table, and subsequent analysis, we define the “incremental target” as the 

incremental amount of new renewable generation needed to achieve the “overall RPS target,” 

taking into consideration the fact that in some cases existing renewable generation is eligible to 

help meet the overall state RPS target.17 In other words, the incremental target is our estimate of 

the difference between the overall state RPS target and the existing baseline renewable 

generation level.18 A few studies project some level of new renewable generation in the 

reference case scenario without the application of the state RPS (i.e., CA/OR/WA (Tellus), 

Texas (UCS), Virginia (CEC), and Washington (UCS)).19 In these cases, we allocate this new 

renewable generation to the existing baseline renewable generation level when estimating the 

incremental target, thereby “depressing” the incremental target.20 This approach is used because 

in evaluating the costs and benefits of state RPS policies, these few studies compare the 

modeling output of the state RPS scenario relative to the reference-case scenario, which also 

includes some level of renewables development. Because many of the other studies do not take a 

 

16 For more information on the state RPS design modeled by each individual study, readers should refer to the 

original cost-impact studies cited in Table 1. Appendix A contains URLs for those studies that are available online. 17 A “new” renewable resource is typically defined as a facility that comes online after a specific date. This 

date is generally set to be a few months to a few years prior to when state RPS requirements go into effect. 18 In states that do not allow for state RPS participation by existing resources, the incremental RPS target is equal to 

the overall target. We also assume the two target levels to be equal when existing resources are eligible, but the 

baseline level of existing renewable generation is negligible. 

19 In addition, Rhode Island (Tellus) includes a negligible amount of increased generation from existing renewable 

plants in the study’s reference case. 

20 Were we not to allocate this new renewable generation to the baseline, then the incremental targets would be 6.3% 

for Texas (UCS), 21.4% for CA (Tellus), 15.7% for OR (Tellus), and 18.5% for WA (Tellus). In the case of CA 

(Tellus), most of the new renewable generation in the reference case is due to an existing 20% by 2017 RPS (as 

compared to the policy case state RPS, which calls for 33% renewable generation by 2020). 
7

similar approach – effectively assuming that no renewable generation will be developed absent 

the state RPS – this does create some minor inconsistency in study comparisons. 

Table 2. State RPS Policies as Modeled by RPS Cost Studies 

Study Overall State 

RPS Target

Incremental 

RPS Target 

Year Target is 

Reached

Additional Notes

AZ (PIRG) 20% 20% 2020 

AZ (PEG) 1% 1% 2002 Only eligible technology is solar 

CA (CRS) 33% 16.7% 2020 Target percentages are measured with respect to the 

load of investor-owned utilities 

CA (UCS) 20% 13.2% 2010 

CA (Tellus) 33% 11.2% 2020 Incremental to existing 20% state RPS 

CA LADWP 

(EC) 

20% 20% 2017 2004 update to original 2003 study; RPS applies only 

to the Los Angeles Department of Water and Power 

(LADWP), which represents approx. 10% of statewide 

load 

CO (PPC) 10% 6.5% 2015 Update to earlier study; includes credit multiplier for 

in-state resources and 0.4% set-aside for solar 

CO (UCS) 10% 6.3% 2015 Includes credit multiplier for in-state resources and 

0.4% set-aside for solar 

HI (GDS) 9.5% 3.8% 2010 Also models a 10.5% state RPS target 

IA (WUC) 10% 8.6% 2015 

IN (EEA) 10% 10% 2017 

MA (SEA) 7% 7% 2012 2002 Update to original 2000 study 

MD (Synapse) 7.5% 7.5% 2013 

MN (WUC) 9% 9% 2010 

NE (UCS) 10% 10% 2012 

NJ (Rutgers) 20% 13.5% 2020 Incremental to existing 6.5% state RPS; includes 

incremental solar tier of 0.64%. 

NY (CCAP) 8% 5.2% 2012 

NY (ICF) 25% 8% 2013 Resource tiers: at least 0.4% fuel cells and 0.4% solar 

PV 

NY (DPS) 25% 7.7% 2013 2004 update to original 2003 and 2004 studies; 

includes 0.15% customer-sited tier 

NY (Potomac) 25% 6.9% 2013 Includes 0.15% customer-site resource tier 

OR (Tellus) 20% 10.6% 2020 

PA (B&V) 10% 7.2% 2020 Update to earlier study; two-tiered portfolio standard, 

but we only include results from Tier I: the renewable 

energy tier 

RI (Tellus) 20% 18.4% 2020 Also models 10% and 15% targets 

TX (UCS) 10,000 MW 2.7% 2025 Also models 20% by 2020 target 

VA (CEC) 20% 16.9% 2015 Also models 15% target 

VT (Synapse) 10% 10% 2015 Also models 5% and 20% targets 

WA (Lazarus) 15% 15% 2023 State RPS includes efficiency, but 15% targets 

identified here only reflect renewables 

WA (UCS) 15% 11.9% 2020 RPS includes efficiency, but we only include results 

attributable to the renewable additions 

WA (Tellus) 20% 16.6% 2020 

WI (UCS) 10% 7.2% 2015 2006 update to original 2003 study 
8

A few of the studies evaluate state RPS policies with multiple resource tiers. These resource 

tiers may include energy efficiency measures, as well as resources that are not generally 

considered to be renewable. For example, Tier II of Pennsylvania’s “Advanced Energy Portfolio 

Standard” includes waste coal facilities, integrated gasification combined cycle plants, and 

measures that reduce greenhouse gas emissions. In this case, we only report and evaluate the 

impacts of Tier I of the energy standard, which includes renewable resources. Similarly, when 

energy efficiency is eligible, we isolate the cost impacts of the renewable resources to facilitate 

comparability with the other state RPS studies. In several other cases, state RPS policy designs 

include solar or customer-sited distributed generation tiers; studies that include such tiers are 

identified in Table 2. 

2.3 Primary Authorship and Funding Source of Studies in Sample 

Figure 3 identifies the types of organizations that have served as the primary authors and funding 

sources of the state RPS cost-impact studies that we reviewed. The vast majority of studies have 

been authored by consultants (over 55%) and non-governmental organizations (NGOs, roughly 

35%). Funding has predominantly come from non-profit foundations and interest groups 

(representing over 55% of primary funding sources) and state utility commissions or energy 

agencies (representing roughly 25% of primary funding sources).21 

Consultant

(16)

Academic

(1)

State agency

(1)

Nongovernmental 

organization

(10) State 

commission/ 

agency

(7)

Trade 

organization

(1)

Utility group

(1)

National 

agency/department

(2)

ISO

(1)

Foundation/

NGO

(16)

Figure 3. Primary Authorship and Funding Source of State RPS Cost Studies 

Some of these studies were conducted as part of an extended public process. These reports 

typically involved the participation and input of diverse stakeholder groups, and in some cases 

were part of a larger, state-sponsored regulatory proceeding that allowed for public comments on 

 

21 When studies have been funded by multiple entities, we only identify the primary funding source. 
9

draft versions of the study. Most of the studies in our sample, however, were not distributed for 

broad public review prior to publication. It is also noteworthy that many of the reviewed studies 

have been produced by organizations and authors that are strongly supportive of state RPS 

policies, whereas few of the studies have been funded or conducted by state RPS opponents. 

This report does not attempt to account for any potential bias that might result from the type of 

study author or funding source, though it does scrutinize the studies’ methods and assumptions 

more generally. 
10

3. Projected Renewable Resource Mix and Direct Costs 

This section summarizes two of the most important outputs of the state RPS cost-impact studies: 

the projected impacts of state RPS policies on renewable energy deployment by technology and 

on the direct costs of that deployment. In the former case, we present the expected amount of 

generation from each renewable technology used to meet the state RPS policies. In the latter 

case, we define direct costs to include the impact of renewables deployment on retail electricity 

rates and bills. These direct costs do not include the effect of increased renewable generation on 

the price of input fuels such as natural gas, which may then generate consumer savings outside of 

the electricity sector (this potential impact is covered in Section 5). Direct costs do, however, 

include wholesale electricity price reductions, including any electricity price reduction caused by 

lower natural gas prices; these impacts are included as direct effects because they influence 

consumer electricity bills. We focus here on impacts in the base-case state RPS scenario; the 

results of alternative scenarios are identified here, but are discussed in more depth in Section 4.22 

Here, and elsewhere in the report, we ignore the social costs of state RPS policies and focus 

exclusively on retail cost impact projections, since the studies themselves are focused on 

consumer, and not social, costs.23 

3.1 Methodology for Comparing Results from Multiple Studies 

The studies in our sample present projected state RPS costs in many different ways. Though 

most studies report expected retail rate impacts, some studies only report changes in electricity 

sector generation (i.e. utility) costs. In addition, the studies use different units to convey cost 

results, including percentage change in costs (either on a retail- or generation-cost basis), total 

incremental system costs in dollars, changes to retail rates in cents per kilowatt-hour (kWh), 

changes to monthly electricity bills, and renewable energy certificate (REC) prices. 

Developing a consistent set of metrics for comparing cost projections across studies is therefore 

necessary. To do so, we compare cost projections using two metrics that are easily understood 

and, where necessary, are readily converted from other data: (1) percentage changes in retail 

electricity rates, and (2) monthly electricity bill impacts for a typical residential household. The 

specific approaches that we used to convert cost data to these metrics are described in Appendix 

B. To further facilitate comparisons, all cost data have been converted to real 2003 dollars. 

It is also difficult to create a method for comparing results from different time periods. Each 

study uses a different timeframe for its analysis.24 The studies also report expected costs using a 

 

22 We use the term “base case” to represent the baseline state RPS scenario (as compared to the alternative RPS 

scenarios described in more depth in Section 4), while we use the term “reference case” to refer to the business-asusual, non-RPS scenario. 

23 Though social costs may be more important from a strictly economic perspective, electricity market analyses often 

emphasize consumer cost impacts. Though these cost impacts may sometimes represent wealth transfers rather than 

true social costs, consumer cost projections are likely to be more relevant to most RPS stakeholders than are 

expected costs to society that ignore wealth transfers. 24 For instance, the New York (DPS) study reports cost and renewable generation results for the 2006-2013 period, 

which coincides with the time interval during which the New York RPS requirements take effect. In contrast, the 

Colorado (PPC) study reports cost and renewable generation results for the 2005-2024 period, which is longer than 

the state RPS implementation period of 2006-2015. 
11

variety of different time horizons; they may report annual costs, costs averaged over a given 

timeframe, and/or the present or net present value of RPS-induced costs. The use of averaged 

data or individual “snapshot” years also complicates comparisons. More generally, comparing 

results from studies that themselves have been conducted over a span of several years is 

potentially problematic because underlying conditions may have changed over this period. 

Perhaps most obviously, natural gas prices (and price expectations) are much higher today than 

they were in years past, so a state RPS study conducted several years ago would naturally yield 

different results than one conducted in the same manner today. 

Given these challenges, complete comparability across all of the studies in our sample is simply 

not possible. Nonetheless, we temporally normalize the results from the different studies by 

presenting results from the first year that each state RPS reaches its ultimate target level. For 

New York, we present results for 2013, when the RPS first reaches its ultimate percentage target 

level of 25%. For Colorado, we document results for 2015, when that state’s RPS first reaches 

its ultimate percentage target of 10%.25 (Note that the absolute amount of renewable energy may 

increase somewhat after the state RPS target initially reaches its percentage peak due to load 

growth, even if the percentage itself then remains constant). 

Though an imperfect metric for characterizing the full trajectory of cost impacts and renewable 

resource projections within each study, using the results from the initial peak year is a tractable 

and consistent method for comparing the projected impacts across studies with very different 

timeframes, especially considering the data limitations of the reviewed studies. The projected 

costs of state RPS policies in these initial peak target years tend to be the highest or close-tohighest of the cost impacts from all of the years that are modeled, allowing us to be conservative 

in reporting expected costs (i.e., to avoid under-representing the potential long-term costs of state 

RPS policies). 26 Presenting data from the initial peak target year is also advantageous because 

the majority of the state RPS cost studies provide data for that year.27 

3.2 Projected Renewable Resource Mix: Base-Case Results 

Though most of the studies in our sample are focused on cost impacts, the majority (23 of 28 

studies) also forecast the mix of renewable technologies most likely to be used to meet state RPS 

requirements (typically assuming that the least-cost renewable resources are selected before the 

more expensive ones). Figure 4 and Figure 5 present the projected mix of new renewable 

generation used to meet the modeled state RPS policies.28 (For a complete list of technologies 

modeled by the cost studies, please see Section 7.1.) 

 

25 Due to data limitations, we were required to allow a few exceptions to this rule. Because Arizona (PEG) does not 

provide annual cost data, we use average 1998-2030 data as a proxy for long-term rate impacts. Iowa (WUC) 

provides only averaged data, so we use data averaged over 2005-2014 We interpolate between 2010 and 2015 data 

from New York (CCAP) to approximate estimates for 2012 (the initial peak target year of the state RPS policy 

modeled in the study). 

26 Most cost projections indicate that state-RPS-induced rate impacts will decrease following the initial peak target 

year, sometimes by a substantial amount. Several factors may cause this result, but perhaps of most importance is 

the fact that the studies assume continued upward movement in the expected cost of fossil generation. 27 It would have been far more difficult, for instance, to compare average cost impacts from the reviewed studies, 

because many of the studies do not include sufficient data to enable such a comparison. 28 Again, for consistency the data are taken from the first year in which each modeled RPS reaches its ultimate 

target. Here and elsewhere in this report, results from Rhode Island (Tellus) reflect the impacts of state RPS policies 
12

Perhaps not surprisingly, wind is expected to be the dominant technology, representing 62% of 

incremental state RPS generation across all of the studies combined. Projected wind deployment 

is particularly prevalent in the Midwest and Texas, accounting for 94% of projected incremental 

RPS generation in those states. Geothermal, which accounts for 18% of projected incremental 

generation across the studies, is a distant second, and almost all of the expected geothermal 

additions are from the two California studies. Biomass co-firing and direct combustion account 

for approximately 8% of expected incremental state RPS generation, while hydro, landfill gas, 

and solar each comprise less than 4%.29 

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

VT (Synapse)

RI (Tellus)MA (SEA)

NY (CCAP)

PA (B&V)

NY (DPS)

NY (Potomac)

VA (CEC)

NE (UCS)IA (WUC)

WI (UCS)

MN (WUC)

TX (UCS)

IN (EEA)

HI (GDS)

AZ (PEG)

CO (UCS)

OR (Tellus)

CO (PPC)

WA (Tellus)

WA (Lazarus)

AZ (PIRG)

CA (Tellus)

CA (UCS)CA (CRS)

Generation (GWh)

Other

Geothermal

Solar

Hydro

Landfill Gas

Biomass Co-Fire

Biomass

Wind

East Midwest West

Figure 4. Incremental Renewable Energy Deployment by Study and Technology 

 in Connecticut and Massachusetts as well as in Rhode Island. These impacts are apportioned to Rhode Island based 

on the state’s contribution to demand for new renewable generation in the region. Please refer to the study for more 

detail on its modeling assumptions. 

29 These percentages are purely intended for illustrative purposes. They do not represent the overall RPS mix that 

would be developed if RPS policies were adopted in all of the states for which cost studies have been performed. 

Renewable energy deployment data are not available for all states, and multiple cost studies exist in some states, 

thereby “double counting” the impacts of those states’ RPS policies on these percentage figures. 
13

Other

 13,278 

 4%

Geo

 51,155 

18%

LFG

 5,866 

2%

Hydro

 9,459 

3%

Solar

 10,063 

3%

Wind

178,627 

62%

Bio Cofire

 7,703 

3% Biomass 

Direct

 16,311 

6%

Figure 5. Mix of Incremental Renewable Generation from All Studies Combined (GWh, %) 

Although not evident by the figures, it is worth mentioning that some studies treat the specific 

mix of renewable energy resources as an input to their cost estimation model, whereas in other 

studies the mix of renewable resources is a model output. When treated as an input, the 

renewable energy mix is typically crudely estimated according to the cost study author’s 

knowledge of the situation in the state(s) being modeled. When treated as an output, renewable 

energy deployment is usually estimated by constructing an aggregate renewable resource supply 

curve, with the state RPS target level determining which generators in the supply curve are 

“selected” in a given year. The methodologies and assumptions governing renewable resource 

estimates are discussed in greater detail in Section 7.2. 

3.3 Direct Cost Impacts: Base-Case Results 

Figure 6 presents the distribution of expected retail electricity rate impacts from the studies in 

our sample, again focusing on the initial peak target year of each study.30 On the whole, stateRPS-induced rate impacts are typically projected to be relatively modest. More than half of the 

reviewed studies report base-case rate increases of between 0% and 1%. Six studies project that 

electricity consumers will experience cost savings as a result of the state RPS policies being 

modeled, at least in the base-case scenario. On the other extreme, nine studies predict rate 

increases above 1%, and two of these studies predict rate increases of more than 5%. 

 

30 The number of studies in Figure 6 is higher than 26 (the number of studies in our review) because the individual 

state results from CA/OR/WA (Tellus) are shown separately. 
14

0 2 4 6 8 10 12 14 16

<-1%

-1%-0

0-1%

1%-2%

2%-3%

3%-4%

4%-5%

>5%

Change in Avg. Retail Elec. Rates in 

1st Peak Target Yr. 

Number of Studies

Figure 6. Distribution of Base-Case Impacts on Average Retail Electricity Rates 

 

Figure 7 summarizes projected electricity rate impacts in percentage and ¢/kWh terms, for each 

individual state RPS cost study (again, focusing on the base-case scenario). Among our sample, 

the median projected increase in retail electricity rates is 0.7%, or 0.04 ¢/kWh. Relatively few 

studies predict increases in retail electricity rates that exceed 0.25 ¢/kWh. The largest cost 

savings are reported in the Texas (UCS) study, which estimates that the modeled Texas RPS 

could reduce consumer electricity costs by 5.2% (-0.4 ¢/kWh) compared to the business-as-usual 

reference case. The largest rate increase is predicted by the Arizona (PIRG) study, which 

estimates that electricity rates in the state could increase by 8.8% (0.7 ¢/kWh) compared to the 

reference case. 

These outlying rate projections are a function of the assumptions used in each study. The Texas 

(UCS) study assumed that the large amount of wind development resulting from the Texas RPS 

would have ripple effects on the national level. Specifically, the model assumed that the 

significant amount of Texas wind capacity additions would stimulate wind technology cost 

reductions on the national level, which would lead to increased wind development and greater 

natural gas price savings nationwide. In the case of the Arizona (PIRG) study, the high rate 

impact projections are in large part due to the study’s assumption that 20% of the required state 

RPS generation would be produced by relatively high-cost solar technologies (for reference, the 

average contribution of solar technologies to state RPS generation across all of the studies that 

modeled RPS resource mix is less than 4%). 
15

-6%

-3%

0%

3%

6%

9%

TX (UCS) - 3%

CA (Tellus) - 11%

HI (GDS) - 4%

WA (UCS) - 12%

CA (UCS) - 13%

OR (Tellus) - 11%

WI (UCS) - 7%

PA (B&V) - 7%

CO (PPC) - 9%

CA (CRS) - 17%

WA (Lazarus) - 15%

NY (DPS) - 8%

CO (UCS) - 6%

NE (UCS) - 10%

IA (WUC) - 9%

NY (Potomac) - 7%MD (Synapse) - 8%

VT (Synapse) - 10%

VA (CEC) - 17%

MN (WUC) - 9%

AZ (PEG) - 1%

CA LADWP (EC) - 20%

MA (SEA) - 7%

WA (Tellus) - 17%

NY (CCAP) - 5%

NJ (Rutgers) - 14%

IN (EEA) - 10%

RI (Tellus) - 18%

NY (ICF) - 8%

AZ (PIRG) - 20%

Study - Incremental RPS Target %

-0.50

-0.25

0.00

0.25

0.50

0.75

 Average retail (% - left axis)

 Average retail (¢/kWh - right axis)

Median retail rate increase = 0.7%

Median change in retail rates = 0.04¢

Change in Avg. Retail Elec. Rates in 1st Peak Target Yr.

Change in Avg. Retail Elec. Rates in 1st Peak Target Yr. 

(2003 ¢/kWh)

Figure 7. Projected Electricity Rate Impacts by State RPS Cost Study 

Though most of the studies project relatively limited impacts on retail electricity rates, the wide 

range of impacts shown in Figure 6 and Figure 7 underscores the large variability among the 

studies’ cost results. In fact, cost results can vary widely even within a single state. For 

instance, two of the three cost studies that analyze essentially the same RPS design in New York 

estimate retail rate increases of less than one percent (DPS and Potomac), but the third (the ICF 

study) projects the second highest cost increase of any study in our sample.31 

Figure 8 presents a scatter plot of projected impacts on electric rates against the incremental state 

RPS target as modeled by each cost study. There is a faint correlation between state RPS target 

levels and incremental costs, but the R-squared of the linear regression is a modest 0.19. Clearly, 

factors beyond the state RPS targets are driving expected costs. 

The vertical error bars shown in Figure 8 represent the high- and low-cost estimates for each 

study that conducted scenario analysis around the base-case results. In some instances, these 

ranges can be extremely large. For example, the high estimate of the New Jersey Rutgers study, 

which applies to a scenario in which renewable technology fails to achieve expected future cost 

reductions, corresponds to a retail electricity rate increase of almost 23%. The results of this 

New Jersey study, as well as the other studies that conduct scenario analysis, reveal the 

sensitivity of projected state-RPS-induced costs to key input parameters. Section 4 contains a 

more detailed description of these scenario analyses. 

 

31 The fourth New York RPS study, which was written by CCAP, analyzes an RPS policy that is substantially 

different from the policy that was ultimately adopted. 
16

-10%

-5%

0%

5%

10%

15%

20%

25%

0% 5% 10% 15% 20%

Incremental RPS Target

Change in Avg. Retail Elec. Rates in 1st 

Year of Peak RPS Target

n = 30

R2 = 0.19

Diamonds indicate basecase rate impacts. 

Vertical error bars 

represent rate impacts of 

alternate scenarios. 

Figure 8. Relationship Between Incremental State RPS Targets and Retail Electricity Rate Impacts 

Direct costs can also be presented as the expected increase in an average residential consumer’s 

monthly electricity bill. Figure 9 presents projected cost impacts in this form, along with error 

bars for those studies that include scenario analyses. As shown in these figures, cost studies of 

state RPS policies in Eastern states (and, more specifically, in Northeastern states) generally 

forecast higher cost impacts than studies of state RPS policies in other parts of the country. Four 

of the six highest projected RPS-induced cost impacts are from studies of Eastern states. The 

higher expected costs in the East are most-likely attributable to the region’s lower renewable 

resource potential compared to elsewhere in the country and the higher costs of developing 

renewable projects in the Northeast. Though the predicted costs of state RPS policies in the East 

may be relatively high compared to those in the rest of the country, the median monthly 

residential bill impact among the Northeastern studies is still modest, at $0.82/month. Among 

the other (non-Eastern) states, the median monthly bill impact for an average household is 

$0.13/month. All but three of these studies forecasts monthly bill increases of less than $1.00 for 

an average household. The most noteworthy exception is the Arizona PIRG study, which 

projects a bill increase of over $7.00 per month. 
17

$(6)

$(4)

$(2)

$-

$2

$4

$6

$8

$10

PA (B&V) - 7%

NY (DPS) - 8%

NY (Potomac) - 7%

VT (Synapse) - 10%

VA (CEC) - 17%

MD (Synapse) - 8%

MA (SEA) - 7%

NY (CCAP) - 5%RI (Tellus) - 18%

NJ (Rutgers) - 14%

NY (ICF) - 8%

TX (UCS) - 3%

WI (UCS) - 7%

NE (UCS) - 10%

IA (WUC) - 9%

MN (WUC) - 9%

IN (EEA) - 10%

HI (GDS) - 4%

WA (UCS) - 12%

CA (Tellus) - 11%

CA (UCS) - 13%

OR (Tellus) - 11%

CO (PPC) - 9%

CA (CRS) - 17%

CO (UCS) - 6%

WA (Lazarus) - 15%

CA LADWP (EC) - 20%

AZ (PEG) - 1%

WA (Tellus) - 17%

AZ (PIRG) - 20%

Study - Incremental RPS Target %

Avg. Bill Impact in 1st Peak Target Yr. (2003$/mo)

$16.8 

East Midwest West

Base case

Alternate scenarios

Figure 9. Typical Residential Electricity Bill Impacts Projected by State RPS Cost Studies
18

4. Scenario Analysis 

Twenty-one of the 28 studies we reviewed include some form of scenario analysis using input 

assumptions that differ from those used in the base case. No single type of scenario is dominant. 

This diversity is not surprising, and reflects differences among the state RPS policies and the 

electricity market conditions in each state.32 

Among the studies we reviewed, the scenarios that are most commonly modeled are the 

availability of the federal production tax credit, varying projections of renewable technology 

cost, fossil fuel price uncertainty, and wholesale market price uncertainty (Figure 10).33 The 

prevalence of these scenarios perhaps implies – but does not prove – that projected state RPS 

costs are more likely to be sensitive to these particular factors than to others. State RPS cost 

sensitivity may be caused by a scenario’s potentially large effect on electricity rates, by a high 

probability that a scenario will occur, or by a combination of a scenario’s rate impact and 

probability of occurrence. 

1

1

2

2

3

4

4

5

6

7

9

10

11

0 1 2 3 4 5 6 7 8 9 10 11 12

Portfolio Risk

Load Grow th

Maximum Compliance Penalty Cost

Demand for RE from Other Sources

Resource Eligiblity

Carbon Credit Value

Availability of Imports

Financing/Contract Assumptions

Alternate RPS Target Levels

Wholesale Market Price Uncertainty

Fossil Fuel Price Uncertainty

Renew able Technology Cost

Production Tax Credit Availability

Number of Studies Considering Each Scenario

Figure 10. Sensitivity Scenarios Modeled by State RPS Cost Studies

The sensitivity scenarios modeled by the state RPS cost studies are briefly described below, in 

qualitative terms (as before, we use the term “base case” to represent the baseline RPS scenario, 

while we use the term “reference case” to refer to the business-as-usual, non-RPS scenario.): 

 

32 As an example, cost studies of states that rely heavily on natural gas for their electricity supply are more likely to 

model the sensitivity of RPS rate impacts to changes in natural gas prices than are cost studies of states that are more 

reliant on coal. 

33 Two of the cost studies that we reviewed, New York (DPS) and Pennsylvania (B&V), include scenario analysis 

in earlier versions of their rate impact analysis, but did not re-model those scenarios in updated versions of that 

analysis. We do not include the results of these earlier analyses in this report, but we do count the scenario 

categories in Figure 10. 
19

• Production Tax Credit availability: Reflects changes to the assumed duration of federal 

production tax credit (PTC) availability. Extended availability of the PTC results in lower 

state RPS rate impacts. 

• Renewable technology cost: Reflects changes to base-case renewable technology cost, fuel, 

and performance assumptions. Higher expected renewable technology costs result in higher 

state RPS rate impacts.

• Fossil fuel price uncertainty: Reflects changes to reference-case fossil fuel (typically 

natural gas) prices. Higher fossil fuel prices result in lower expected state RPS rate impacts.

• Wholesale market price uncertainty: Reflects changes to reference-case wholesale 

electricity market prices. Higher wholesale market prices result in lower state RPS rate 

impacts.

• Alternate State RPS target levels: Reflects variations in the state RPS percentage target. 

Higher targets tend to increase expected state RPS rate impacts, either positively or 

negatively depending on the sign of the rate increase in the base case. 

• Financing/contract assumptions: Reflects changes to base-case renewable financing terms 

and/or different contractual arrangements for procuring renewable power. Lower cost 

financing and more favorable contract assumptions include lower finance rates and longterm, fixed-price contracts for bundled power. 

• Availability of imports: Reflects variations in the treatment of renewable power or RECs 

that are imported from nearby states or regions. In addition to policy considerations, other 

factors, such as technical constraints (e.g. transmission capacity constraints) and economic 

constraints (e.g. wheeling charges) can also influence import availability. Increased import 

availability increases renewable supply and results in lower expected state RPS rate impacts. 

• Carbon credit value: Reflects the value of renewable energy in reducing carbon dioxide 

emissions, especially if future regulations limit such emissions. Applying this credit to 

renewable energy (or, conversely, applying an additional cost to fossil fuel-based generation) 

reduces the expected/effective cost of state RPS compliance. 

• Resource eligibility: Reflects different definitions of RPS-eligible renewable generating 

technologies. Looser, or less restrictive, eligibility provisions increase the supply of RPSeligible resources and result in lower expected rate impacts.

• Demand for renewable energy from other sources: Reflects changes in demand for -

eligible renewable energy supply from other sources, such as voluntary green power 

programs or RPS policies in neighboring states. Increased demand for renewable energy – 

regardless of the source – results in higher expected state RPS rate impacts.

• Maximum compliance penalty cost: Reflects the assumption that electricity suppliers pay 

the non-compliance penalty or alternative compliance payment that is assumed to apply to 

the state RPS. Penalties and alternative compliance payments can sometimes bound the 

maximum possible cost of a state RPS, because suppliers may choose to pay the penalty or 

alternative compliance payment when it presents a less costly alternative to purchasing 

renewable energy or RECs. 

• Load growth: Reflects changes to load growth assumptions. Higher load growth increases 

renewable power obligations in MWh terms, which may result in higher state RPS rate 

impacts.

• Portfolio risk: Reflects the cost risk associated with a given electricity generation portfolio. 

In theory, a state RPS will reduce portfolio risk by reducing exposure to variable fuel costs, 

but this reduction in risk may result in higher average rate impacts. Depending on their 
20

resource constitution, state RPS generation portfolios may have different levels of risk (with 

corresponding differences in rate impacts). 

Due to the wide range of scenarios modeled and the different assumptions used within each type 

of scenario, it is difficult to draw definitive conclusions about the relative impact of different cost 

drivers. Figure 11 and Figure 12, however, show the expected cost impacts of all of the scenario 

types modeled in the state RPS cost studies that we reviewed.34 Within a data column, each 

marker represents the change in base-case monthly residential electricity bill impacts caused by 

an individual scenario from a single state RPS cost study. Figure 11 presents data on scenario 

types that result in lower state-RPS-induced electricity rate/bill impacts, while Figure 12 presents 

data on scenario types that generally result in higher electricity rate/bill impacts. 

Most individual scenarios do not appear to have major impacts on base-case state RPS costs. 

With few exceptions, the residential electricity bill impacts of these scenarios – as measured by 

changes from the base case – are less than $1 per month. Though such changes are not 

overwhelming, it is important to recognize that the median base-case residential electricity bill 

impact among the studies in our sample is just $0.38/month, with a range of ($5.19)/month to 

$7.14/month. Therefore, even a $1/month change from this base-case is sizable in percentage 

terms, and demonstrates significant cost sensitivity to input parameters. 

In some cases, scenarios result in incremental costs well above $1/month for an average 

household. The most conspicuous example is the New Jersey “high technology cost” scenario, 

which exceeds the base-case bill impact by about $14/month. This is largely explained by the 

relatively high amount of solar energy required by the New Jersey RPS, which would result in 

substantially higher costs if the technology does not become more economic over time.35 

A confluence of multiple scenarios can also impact costs more dramatically. In the 

Massachusetts (SEA) study, for example, none of the individual cost-saving scenarios results in 

monthly electricity bill savings of more than $0.40/month relative to the base case.36 However, 

the study also models the combined impacts of all of these cost-reducing scenarios and finds that 

they could save average residential customers $1.19 per month compared to the base case (the 

numbers are roughly reversed for the cost-increasing scenarios). This convergence of costreducing factors represents a “best case” scenario, though bill savings could be higher still if 

more aggressive assumptions are used. 

It is also apparent from the data that rate impacts are far from symmetrical within each scenario, 

at least among our sample of studies. In three instances, for example, higher expected fossil fuel 

prices result in rate savings for an average household of over $1 per month relative to the base 

case, but the highest rate increase due to lower fossil fuel prices is just $0.79 per month. This 

 

34 Some studies model more than two scenarios for each scenario type, e.g. three different natural gas price forecasts 

instead of just a high and low forecast. In these instances, we include only the two scenarios (one cost-decreasing 

and one cost-increasing) that have the greatest impact on rates. 35 In reality, higher-than-expected solar technology costs would probably cause legislators to change the RPS policy 

to require less solar energy rather than allow state RPS rate impacts to reach such an extreme level. 36 These individual cost-saving scenarios include: lower wholesale market price, less demand for renewable energy 

from other sources, PTC extension, more favorable financing, lower import costs, and lower renewable technology 

costs and fuel costs. 
21

asymmetry may result from uneven assumptions in the high and low scenarios (i.e. a high natural 

gas price forecast that departs from the base case forecast by more than the low forecast), or it 

may result from significant non-linearity in the study’s model. Natural gas price scenarios are 

further discussed in Section 5.2. 

($2.25)

($1.75)

($1.25)

($0.75)

($0.25)

$0.25

High Fossil Price

High Wholesale Price

PTC Extension

Favorable Financing

Lower Tech Cost

Softer RPS Target

More Imports

Looser Resource Eligibility

Lower Demand from Other Source

Increased Carbon Credit Value

Higher Portolio Risk

Change in Bill Impact in 1st Peak Target Yr. (2003$/month)

Figure 11. Changes to Base-Case Residential Monthly Electricity Bill Impacts by Individual Driver 

(Cost Decreasing Scenarios) 

($0.50)

$0.00

$0.50

$1.00

$1.50

$2.00

$2.50

$3.00

Low Fossil Price

Low Wholesale Price

No/Limited PTC

Unfavorable Financing

Higher Tech Costs

Fewer Imports

Harder RPS Target

Stricter Resource Eligibility

Hits Compliance Penalty

Change in Bill Impact in 1st Peak Target Yr. (2003$/month)

NJ (Rutgers)

$14.02

TX ($1.04) 

not show n 

on chart

 

Figure 12. Changes to Base-Case Residential Monthly Electricity Bill Impacts by Individual Driver 

(Cost Increasing Scenarios) 
22

5. Projected Benefits 

Many of the studies also evaluate the potential public benefits of state RPS adoption. These 

benefits can be divided into three main categories: macroeconomic, risk mitigation, and 

environmental. Figure 13 identifies the number of studies that model each of these potential 

benefits. Though the figure includes only the primary metrics of employment, gross state 

product, and income under the category of macroeconomic benefits, a smaller number of studies 

also quantify revenues from state income, sales, and property taxes, and land lease payments.37 

Of the benefits covered in this section, only the risk mitigation benefits affect the direct costs

shown in Section 3.3, and they do so only to the extent that they affect electricity prices. For 

example, the natural gas price suppression effect described later presumably reduces wholesale 

electric prices by decreasing the price of natural gas used in the electricity sector; these effects 

(where modeled) were included in the direct cost results presented earlier. In contrast, the 

benefits of lower natural gas prices for consumer natural gas bills (which can be much larger on 

a dollar-per-customer basis) are not included in the direct cost impacts reported earlier. 

2

7

14

5

7

8

8

9

0 5 10 15

Water Use Reduction

Criteria Pollutant Emissions Reduction

CO2 Emissions Reduction

Natural Gas Price Reduction

Wholesale Electric Price Reduction

Income

Gross State Product

Employment

Environmental

Risk 

Mitigation Macroeconomic

Number of Studies Considering Each Scenario

Figure 13. Potential Benefits Modeled by State RPS Cost Studies 

 

37 Arizona (PIRG) and New Jersey (Rutgers) quantify at least one type of tax effect. Arizona (PEG) quantifies the 

combined impact of tax revenue and income, but does not provide a separate estimate of each individual effect. In 

addition to tax impacts, Colorado (UCS), Nebraska (UCS), Texas (UCS), Washington (UCS), and Wisconsin (UCS) 

also quantify land lease payments to rural landowners. 
23

5.1 Macroeconomic Impacts 

State RPS cost studies are increasingly considering macroeconomic impacts; seven of the nine 

studies in our review that estimate state RPS employment benefits were published in 2004 or 

later. In addition, over the last several years, a number of state RPS studies have exclusively 

modeled macroeconomic impacts; these studies are not included in our review, as we are 

primarily interested in expected rate impacts.38 The recent emphasis on the potential 

macroeconomic benefits of state RPS policies may signify that RPS proponents are increasingly 

accentuating these impacts to justify policy action. 

Figure 14 and Table 3 show the projected employment impacts from the nine cost studies in our 

sample that model these effects (Table 3 also shows gross state product effects).39 All of the 

studies predict some level of net employment gain, ranging from a few hundred to several 

thousand jobs created. That growth in renewable energy generation may increase net 

employment is consistent with past analyses, which have often shown renewable energy to be 

more labor-intensive than conventional forms of electricity production (see, e.g., REPP 2001; 

Kammen et al. 2004). 

Since the studies use different methods40 and units41, and estimate employment impacts of state 

RPS policies of vastly different size, it is difficult to directly compare the results of one study to 

another. With this in mind, the employment figures in Table 3 do not appear to be strongly 

correlated with the incremental renewable energy generation required to meet modeled state RPS 

policies. This may be due to different mixes of renewable technologies developed, different 

assumptions concerning in-state versus out-of-state renewable energy project development and 

manufacturing, and different approaches to the incorporation (or lack thereof) of energy bill 

impacts into the macroeconomic analysis.42 

 

38 These include: Bournakis et al. (2005), Madsen et al. (2007), Perryman et al. (2005), and Altman et al. (2002). 

39 Though we label the employment impacts in Figure 14 and Table 3 as “Incremental Net Jobs in Peak Target 

Year,” they may actually represent cumulative impacts in some cases, e.g. they may count increases in short-term 

construction jobs from earlier years that no longer exist in the peak target year. These and other timeframe and 

employment-type distinctions are not always clear in the studies, so we present the data with significant caveats. 40 With the exception of Arizona (PEG), all of the studies in Figure 14 conduct input-output analysis that considers 

not only direct effects, but also indirect and induced employment gains and gross state product impacts. The 

specific tools used to conduct this analysis, however, vary by study. Arizona (PEG) is the only study that uses a 

“back-of-the-envelope” calculation to estimate direct economic effects 41 Employment impacts, for instance, are typically reported using one of two metrics: “jobs,” which are often of 

indeterminate length, and “job-years”, which quantify both the number of jobs created and the duration of those 

jobs. 

42 Specifically, all of the studies summarized here except for Arizona (PEG) review the net employment gains of 

renewables deployment inclusive of job losses associated with the reduction in conventional forms of electricity 

production (Arizona (PEG) apparently does not account for these job losses). A majority of the studies – Arizona 

(PIRG), Colorado (UCS), New Jersey (Rutgers), Texas (UCS), Washington (UCS), and Wisconsin (UCS) – evaluate 

the influence of RPS-induced retail rate impacts (either positive or negative) on employment (e.g., if an RPS is 

expected to raise retail electricity rates, those increases would be expected to result in some loss in statewide 

employment). The Washington (UCS) results presented in this report, however, do not include these retail rate 

impacts because UCS was unable to provide impacts that are uniquely associated with renewable energy (their 

analysis also included energy efficiency impacts). 
24

0

30

60

90

120

WA (UCS) AZ (PIRG) WI (UCS) PA (B&V) NE (UCS) CO (UCS) TX (UCS) NJ

(Rutgers)

AZ (PEG)

Net Jobs Created/Incremental TWh of

Renewable Gen.

0

4,000

8,000

12,000

16,000

Net Jobs Created Over Analysis Timeframe

Net Jobs Created Per Renewable TWh - left axis

Cumulative Net Jobs Created - right axis

 

Figure 14. Employment Impacts Predicted by State RPS Cost Studies 

Table 3. Employment and Gross State Product Impacts Predicted by State RPS Cost Studies 

Cost Study Incremental 

Net Jobs In 

Peak Target 

Year 

Timeframe of 

Analysis 

Cumulative 

Incremental 

RPS Target 

(GWh) 

Change in Gross 

State Product 

($2003 Millions) 

Model Used 

Arizona (PIRG) 308 2005-2020 96,500 $374 (in 2020) IMPLAN 

Wisconsin (UCS) 380 2006-2020 61,300 $95 (in 2020) IMPLAN 

Nebraska (UCS) 357 2003-2012 14,500 $37 (in 2012) IMPLAN 

Arizona (PEG) 600 1998-2010 5,700 n/a Spreadsheet 

Colorado (UCS) 1,290 2005-2020 46,500 $51 (in 2015) IMPLAN 

Pennsylvania 

(B&V) 

3,747 2006-2025 186,600 $9,038 RIMS II 

New Jersey 

(Rutgers) 

2,600-11,700 2005-2020 90,300 $203-1014 

(in 2020) 

R/ECON I-O 

Texas (UCS) 14,600 2005-2025 225,800 $61 (in 2025) IMPLAN 

Washington (UCS) 30 2010-2025 76,400 $10 (in 2020) IMPLAN 

Note: All employment figures represent employment gains that occur in the state of the modeled state RPS. The 

employment figures from Pennsylvania (B&V) are based on a model of the state’s Alternative Energy Portfolio 

Standard, which includes requirements for energy efficiency and other “Tier II” alternate (mostly non-renewable) 

energy sources. Employment and gross state product figures from Washington (UCS) represent only the impacts of 

the renewable energy additions of the state RPS (the study also models an efficiency standard), and do not include 

induced impacts from energy price changes. The Pennsylvania and Arizona employment figures are calculated by 

dividing the job-years reported in the studies by the length of the study’s timeframe. Lower and upper bounds of 

range of New Jersey (Rutgers) impacts represent results from two renewable technology manufacturing scenarios: 

one in which all renewable technology is manufactured out-of-state, and one in which 100% of renewable 

technology is manufactured in-state (the data in Figure 14 represents the average of these two scenarios). 

Wisconsin (UCS) provides Scenario 2 results in the report text, but data shown here is from Scenario 1 (to be 

consistent with our base-case designation). 
25

5.2 Risk Mitigation Benefits 

Two distinct types of risk mitigation benefits have been evaluated in the state RPS cost studies in 

our sample: energy price suppression effects and hedging energy price uncertainty. 

5.2.1 Suppression of Electricity and Natural Gas Prices 

The effect of incremental renewable generation on reducing wholesale electricity and natural gas 

prices is a significant potential benefit of state RPS policies. However, few studies have 

attempted to quantify these price suppression effects, and the magnitude of these effects is 

somewhat uncertain. 

5.2.1.1 Electricity price effects 

In some instances, the increased cost of renewable generation relative to conventional fossil 

generation may be mitigated by cost savings that derive from reductions in wholesale electricity 

prices. Adding a substantial amount of low-marginal-cost renewable generation to the electricity 

system reduces the demand for generation from conventional sources and may thereby suppress 

competitive wholesale energy prices. This effect may only be significant and important in 

markets with liquid wholesale spot markets, such as New England, New York, and PJM. 

Moreover, the magnitude of this effect is debatable: some studies believe that significant longterm reductions in wholesale energy prices are possible, while others have concluded that such 

reductions are likely to be minimal and fleeting as suppliers adjust to the new market conditions. 

According to the latter argument, to fairly evaluate this effect, it is critical to consider supplier 

response to lower prices (e.g., lower prices may slow capacity expansion, thereby increasing 

prices up to their pre-RPS levels, either through wholesale energy prices and/or through separate 

capacity markets). 

The four state RPS cost studies in our review that quantify these potential savings are all 

analyses of RPS proposals in New York (Figure 15).43 Each of the studies projects overall longterm firm price reductions, ranging from 0.4% (NY CCAP and NY Potomac) to 2.6% (NY 

ICF).44 These overall effects are influenced by impacts on both wholesale energy and capacity 

prices. Three of the four studies, for example, predict increases in capacity prices (capacity 

prices have been converted to $/MWh in the figure, for ease of comparison), suggesting that any 

reduction in wholesale energy prices due to a state RPS may be partially or almost entirely offset 

by increases in capacity prices.45 For instance, the Potomac study predicts that increases in long-

 

43 Three other studies, Colorado (UCS), Rhode Island (Tellus), and Texas (UCS), also include some electricity price 

suppression effect (at a minimum through lower natural gas prices), but those effects are not specifically and 

separately quantified, and are therefore not reported here. In addition, New Jersey (Rutgers) includes a very small 

wholesale electricity price suppression effect that is induced by reductions in electricity demand that themselves are 

caused by higher overall electricity prices. Because this effect is fundamentally different from the price suppression 

effect that results from increased renewable generation, we do not include New Jersey (Rutgers) among the seven 

studies shown in Figure 13. 

44 The other study, New York (DPS), projects that firm wholesale prices will be reduced by 1.7%. For simplicity, 

we use price reduction data from Scenario 1 of New York (ICF). The price reduction effect is negligibly higher in 

Scenario 2. 

45 This effect is likely to occur in markets where the capacity factor of renewable generators is significantly higher 

than the capacity credit that they receive, thus shifting revenues from the energy to the capacity market. 
26

run capacity prices will offset the majority of the wholesale energy price reductions. However, 

even small reductions in overall wholesale energy/capacity prices hold the prospect of offering 

significant aggregate savings to electricity consumers (which come at the expense of producers, 

so may not reflect a net societal gain).46 Table 4 shows that projected residential electricity bills 

would range from $0.11/month to $0.57/month higher if the four New York cost studies had not 

modeled the effects of state RPS generation on wholesale market prices. 

$(0.42)

$(1.59)

$(0.60)

$(0.95)

$0.24

$1.40

$0.02

$(0.18) $(0.19)

$(0.58)

$(1.06)

$(0.11)

$(2.00)

$(1.00)

$-

$1.00

$2.00

NY (CCAP) NY (Potomac) NY (DPS) NY (ICF)

Change in 1st Peak Target Yr. (2003$/MWh)

Energy

Capacity

Firm power

Figure 15. Wholesale Energy and Capacity Price Changes Projected by State RPS Cost Studies 

Table 4. Effect of Wholesale Market Price Reductions on State RPS Rate Impacts in 1st Year of 

Peak State RPS Target 

Cost Study Retail Rate Impact 

Accounting for 

Wholesale Price 

Effects (i.e. as 

Modeled) 

Retail Rate Impact 

Excluding 

Wholesale Price 

Effects 

Residential 

Electricity Bill 

Impact Accounting 

for Wholesale Price 

Effects ($/mo.) 

Residential 

Electricity Bill 

Impact Excluding 

Wholesale Price 

Effects ($/mo.) 

NY (CCAP) 2.7% 3.0% $1.63 $1.76 

NY (Potomac) 0.8% 1.0% $0.42 $0.53 

NY (DPS) 0.3% 1.0% $0.14 $0.53 

NY (ICF) 5.9% 7.0% $3.25 $3.82 

5.2.1.2 Natural gas price effects 

State RPS policies will also reduce fossil fuel consumption by avoiding generation from 

conventional sources, primarily natural gas and coal. Many recent reports have shown that 

increased renewable energy and energy efficiency deployment may put downward pressure on 

 

46 As noted earlier, these electricity-sector consumer savings (where modeled) are embedded in the direct cost 

results presented in Section 3.3. 
27

natural gas prices by reducing national and regional gas demand and thereby easing supply 

constraints (see, e.g., Elliot and Shipley 2005; Wiser et al. 2005). Reduced gas prices will result 

not only in lower wholesale electricity prices (this effect, where modeled, is included in the 

direct cost results presented in Section 3), but also in lower end-use natural gas bills. 

Increasingly, renewable energy proponents cite this benefit in support of state RPS policies, 

though it deserves note that these consumer savings come at least in part at the expense of 

natural gas producers. 

Five of the studies in our sample quantify natural gas price effects in their base case analysis. 

Three of these five studies quantify the expected natural gas price savings in terms of in-state or 

intra-regional delivered natural gas prices, which (relative to the reference case scenario) are 

estimated to decline by an average of 0.1% in Rhode Island (Tellus), 0.6% in Texas (UCS), and 

0.8% in Colorado (UCS) (Figure 16).47 Of the other two studies, the New York ICF analysis 

concludes that the natural gas savings are negligible, despite electricity sector natural gas 

demand reductions of 4-5% in the last year of the study (2013). The remaining study, New York 

(CCAP), also does not specifically enumerate its natural gas price reductions, but concludes that 

natural gas prices will decline slightly in 2010 and increase slightly in 2020, in response to 

decreases in electricity sector natural gas consumption of 8% and 7%, respectively. 

$(0.06)

$(0.05)

$(0.04)

$(0.03)

$(0.02)

$(0.01)

$-

RI (Tellus) TX (UCS) CO (UCS)

Average Savings Relative to Reference Case 

(2003$/MMBtu)

Figure 16. Delivered Natural Gas Price Savings Projected by State RPS Cost Studies, Averaged 

Over Each Study’s Timeframe 

Three other studies – California (CRS), Maryland (Synapse), and Virginia (CEC) – externally 

estimate potential natural gas price savings for illustrative purposes only (i.e., these savings did 

not factor into the analysis of direct state RPS costs). California (CRS) applies the empirical 

method developed by Wiser et al. (2005) to estimate potential price reductions. California (CRS) 

finds that gas prices would be reduced by an average of $0.02-0.06/MMBtu during the 2011-

 

47 These percentage reductions, and the data in Figure 16, represent the average change in delivered natural gas 

prices over the time period during which the RPS ramps up to its ultimate target level. This timeframe is 2005-2020 

for Rhode Island (Tellus), 2005-2025 for Texas (UCS), and 2005-2015 for Colorado (UCS). Rhode Island and 

Colorado data are for non-electric customers only; Texas data includes savings for electric generators. 
28

2020 timeframe. In lieu of estimating natural gas price reductions, Virginia (CEC) simply 

assumes that each MWh of renewable generation will result in three dollars of consumer savings, 

using the Wiser et al. (2005) results as a benchmark. Maryland (Synapse) models two scenarios 

in which natural gas prices are assumed to fall by 2% and 4% relative to the reference case 

forecast. 

Natural gas price reductions caused by increased renewables deployment will benefit consumers 

nationwide, with a relatively small proportion of this benefit being gained by consumers in the 

state in which the RPS is adopted. Nonetheless, though the expected price savings in Figure 16 

may appear insubstantial, consumer natural gas bill savings are sometimes projected to be large 

enough to eclipse the electricity bill impacts of some state RPS policies.48 This is true of the 

Colorado (UCS) study, which estimates that average residential natural gas bill savings in 

Colorado will reach $1.25 per month by 2015, compared to an estimated increase in electricity 

bills of just $0.13 per month in that year. By contrast, the Rhode Island (Tellus) and Texas 

(UCS) studies project much smaller natural gas savings. Rhode Island (Tellus) estimates average 

residential natural gas bill savings of $0.22 per month in 2020, compared to an expected increase 

in electricity bills of $2.64 per month in that year. Texas (UCS) estimates natural gas bill 

savings of $0.31 per month over the 2020-2025 timeframe, which is far smaller than the 

projected electricity bill savings of $4.26 per month over the same time period.49 The authors of 

one of the New York (ICF) studies predict no change in natural gas prices despite a 4-5% 

reduction in New York’s natural gas demand. 

These contrasting results arise in part from very different assumptions concerning the price 

elasticity of natural gas supply nationally, and the impact of regional transportation constraints. 

The model used in the New York (ICF) study assumes that natural gas supply is inelastic at the 

demand reduction level induced by the New York RPS, while the model used in the Colorado 

(UCS) study assumes that reductions in natural gas demand in Colorado will have a relatively 

sizable impact on regional gas prices. More generally, one would expect that at least two 

conditions would be necessary to achieve significant in-state natural gas bill savings from a state 

RPS: (1) the state RPS would need to result in sizable reductions in natural gas demand, such 

that those reductions can influence national natural gas prices; and (2) the state would need to 

have significant aggregate natural gas demand, such that even modest price reductions could 

have significant overall bill impacts. Regional natural gas pipeline constraints may further 

increase projected regional savings in that reduced gas demand would alleviate both national 

supply and local transportation constraints. 

5.2.2 Hedging Energy Price Uncertainty 

Though natural gas and wholesale electricity prices are uncertain and prone to significant 

fluctuation, the price of renewable energy is largely fixed. In the broader literature, a variety of 

 

48 Because the data in Figure 16 are averaged over the “ramp-up” period of each state RPS, the data likely underrepresent the long-run natural gas savings that would result from state RPS policies once they reach their ultimate 

renewable target level. We have presented average, rather than “initial peak year,” savings because data from these 

studies occasionally show unpredictable effects in individual years where gas prices may fluctuate up or down from 

year to year without any discernible pattern. This is likely an artifact of the NEMS model, which these studies have 

used to quantify RPS impacts. 

49 We present average, rather than peak year, data for Texas because the natural gas savings data from the study 

exhibit substantial year-to-year variability as discussed generally in the previous footnote. 
29

methods have been developed to try to quantify the benefit of the price certainty that renewable 

energy can provide (see, e.g., Bolinger et al. 2006; Awerbuch 1993, 2003). With few exceptions, 

however, these methods have not been directly used in the state RPS cost studies in our sample.50 

Despite this, the value of renewable energy as a hedge against price uncertainty is implicitly 

considered by those studies that model natural gas and wholesale electricity price scenarios that 

differ from the base-case price scenario. The results of these analyses demonstrate that the value 

of renewable energy is especially great under scenarios of unexpectedly high natural gas and 

wholesale electricity prices. 

Figure 17 illustrates the sensitivity of state RPS costs to expected natural gas prices, for those 

studies that analyze multiple natural gas price scenarios.51 With few exceptions, the expected 

cost of state RPS policies appears to be moderately sensitive to changes in expected natural gas 

prices. A linear regression of this relationship has an R-squared value of 0.68 and a slope of -

0.40. Taken at face value, this implies that a $1.00 per MMBtu increase in expected natural gas 

prices is projected to reduce the monthly incremental cost (or increase the monthly savings) of a 

typical state RPS policy for an average household by $0.40 (relative to the base-case RPS 

scenario).52 

Figure 18 shows the sensitivity of state RPS costs to expectations of wholesale electric prices in 

the reference case. As with the natural gas results, changes in wholesale market prices have 

moderate effects on the expected bill impacts of state RPS policies. A linear regression of this 

relationship yields an R-squared of 0.95 (though this is inflated by the high incidence of 

symmetrical data points across the y-axis) and a slope of -0.69. This suggests that a 1 ¢/kWh 

increase in (reference-case) expected wholesale electric prices will reduce the monthly 

incremental cost (or increase the monthly savings) of a typical state RPS policy for an average 

household by $0.69 (relative to the base-case RPS scenario). 

 

50 Based on the work conducted at Berkeley Lab (see Bolinger et al. 2006), the original Pennsylvania (B&V) study 

included a hedge adder of $0.50/MMBtu in its natural gas price forecast for one of its sensitivity scenarios; however, 

the updated version of the B&V analysis does not model this scenario. Virginia (CEC) uses a “mean-variance 

portfolio” analysis to highlight the risk reduction benefits of an RPS, but does not include hedge adders for fuel or 

wholesale electricity prices. The methodology of the Virginia study is described in greater detail in Section 6.1. 51 In addition to those studies included in the figure, the Colorado (PPC) study estimates that residential customers 

would save $0.46 to $0.67 on their monthly electricity bills with a state RPS in place during the two years in which 

hypothetical natural gas price spikes (the magnitude of which are not fully disclosed) occur. 

52 In reality, the relationship between state RPS cost impacts and natural gas prices is probably not a linear one, due 

in part to fuel substitution effects, but we assume a linear trend for simplification. 
30

($1.50)

($1.00)

($0.50)

$0.00

$0.50

$1.00

($2.00) ($1.00) $0.00 $1.00 $2.00 $3.00

Change in Natural Gas Price in 1st Yr. of Peak RPS Target (2003$/MMBtu)

Change in Residential Bill Impact in 1st Yr. of Peak

RPS Target (2003$/mo.)

Figure 17. Sensitivity of State RPS Bill Impacts to Alternative Natural Gas Price Scenarios 

($1.00)

($0.50)

$0.00

$0.50

$1.00

(1.5) (1.0) (0.5) - 0.5 1.0 1.5

Change in Wholesale Electric Price in 1st Yr. of Peak RPS Target (2003 cents/kWh)

Change in Residential Bill Impact in 1st Yr. of Peak RPS

Target (2003$/mo.)

Figure 18. Sensitivity of State RPS Bill Impacts to Alternative Wholesale Electric Price Scenarios 

Data from these sensitivity scenarios show that state RPS policies do offer some insurance in the 

event of higher-than-expected natural gas and wholesale electricity prices. The results suggest 

that a 20-25% change in wholesale electric price assumptions alters the expected cost of state 
31

RPS policies by roughly 1% of overall retail rates, relative to the base-case scenario. This effect 

is roughly half as large in the case of natural gas prices: a 20-25% change in natural gas prices 

adds or subtracts roughly 0.5% of overall rates to the base-case state RPS rate impact. These 

effects are far from negligible. More than two-thirds of the state RPS cost studies report basecase retail cost increases of 1% or less, as reported earlier. Our findings show that increases in 

wholesale electric price expectations of 20-25%, or natural gas price expectations of 40-50%, 

could entirely offset a base-case state-RPS-induced rate impact of 1%.53 

These results are especially notable in light of the relatively low natural gas price forecasts that 

were used by many of the studies in our sample. The average base-case delivered natural gas 

price forecast in the initial peak target year of each study (2010 to 2023, depending on the study) 

shown in Figure 17 is just $4.52/MMBtu. This compares to 2010 NYMEX Henry Hub futures 

prices that in 2005-2006 have regularly exceeded $6.00/MMBtu when converted to delivered 

prices in 2003 dollars.54 If one used today’s expectations for future natural gas prices (whether 

EIA forecasts or extrapolated NYMEX forward curves), the projected cost of state RPS policies 

would be significantly below (or savings significant above) the base-case cost study projections 

summarized in this paper.55 We devote further discussion to the importance of natural gas price 

assumptions in Section 8.3. 

5.3 Environmental Benefits 

Of the potential environmental benefits quantified in the cost studies, carbon dioxide emissions 

reductions are the most common, appearing in half of the reviewed studies. Less than a third of 

the studies quantify reductions in criteria pollutants such as nitrogen oxides, sulfur dioxide, and 

mercury,56 and only two studies quantify reductions in water use resulting from less cooling 

water consumption at fossil fuel plants. 

 

53 Of course, these calculations assume that renewable energy offsets natural gas-fired electricity production. As gas 

prices have risen, the prospect for coal displacement has increased. In this instance, the “hedge” benefits of 

renewable energy – at least relative to natural gas prices – would be diminished. 

54 The EIA projects lower long-term delivered natural gas prices ranging from $4.97 to $5.27/MMBtu from 2015 to 

2020 (EIA 2006a). Bolinger and Wiser (2005), however, observe that EIA natural gas price forecasts have been 

consistently below contemporaneous long-term forward prices in recent years, and argue that the cost of fixed-price 

renewable generation should be compared against long-term forward natural gas prices (that can be locked in with 

certainty) rather than uncertain EIA gas-price forecasts. 55 Again, assuming that renewable energy continues to offset natural gas, rather than coal, generation. 

56 One reason that some studies may fail to estimate criteria air pollution reductions is that in many instances 

increased use of renewable energy will have little aggregate impact on those emissions. In particular, for pollutants 

covered by national or regional cap-and-trade programs, increased use of renewable energy may put downward 

pressure on the cost of compliance with the environmental regulations, but is unlikely to reduce aggregate emissions 

per se (except in the unlikely event that emissions allowances are explicitly retired). It appears that Indiana (EEA), 

New York (ICF) and New York (CCAP) are the only studies in our review that explicitly model the impact of state 

RPS policies on emission allowance prices (the NEMS model, which is used by a number of additional studies to 

estimate RPS impacts, also typically addresses impacts on emission allowance prices, in which case CA/OR/WA 

(Tellus), Colorado (UCS), Rhode Island (Tellus), Texas (UCS) also incorporate these effects).. Future state RPS 

cost studies may wish to evaluate these compliance cost effects, but without additional documentation and 

reasoning, should generally not claim RPS-induced emissions reductions from pollutants covered under cap-andtrade programs. 
32

None of the studies directly quantifies the value of the health and economic impacts of 

reductions in air pollutant emissions and water use. Lack of agreement on a credible 

methodology for estimating these impacts, in dollar terms, makes quantification challenging. 

The New Jersey (Rutgers) study provides illustrative calculations of potential environmental and 

public health benefits using generic externality adders, but the calculations are not included in 

the study’s cost results. A few other studies include qualitative discussions of these impacts. 

Focusing on those studies that evaluate possible carbon reductions, Figure 19 presents the CO2

reductions and implied CO2 emissions rates of generation displaced by RPS resources in each 

study’s peak RPS target year.57 The magnitude of CO2 emissions reductions, which is a function 

of the amount of incremental renewable generation and the emissions profile of displaced 

generation, varies tremendously across the studies, from a low of 0.88 million metric tons 

(MMT) in Rhode Island (Tellus) to a high of 26.0 MMT in California (Tellus). The displaced 

emissions rate also varies considerably, from a low of 0.22 metric tons of CO2 (MTCO2) per 

MWh in the Washington (Tellus) study to a high of 0.73 MTCO2/MWh in the Colorado (UCS) 

analysis. The median displaced CO2 emissions rate of 0.46 MTCO2/MWh is low compared to 

the national electricity-sector average emissions rate of 0.60 MTCO2/MWh (EIA 2006b), 

reflecting an expectation that RPS resources will largely displace generation from natural gas 

plants. The median displaced CO2 emissions rate is 25% higher than the emissions rate of a new 

combined-cycle natural gas generator. 

0.00

0.25

0.50

0.75

1.00

WA (Tellus)

WA (UCS)

NY (CCAP)

CO (PPC)

TX (UCS)

NY (ICF)

AZ (PIRG)

RI (Tellus)

OR (Tellus)

NY (DPS)

MA (SEA)

VA (CEC)

CA (UCS)

CA (Tellus)

WI (UCS)

CO (UCS)

Displaced Emissions Rate in 1st Peak Target Yr. 

(MTCO2/MWh)

0

5

10

15

20

25

30

CO2 Reductions in 1st Peak Target Yr. 

(Million metric tons)

Displaced Emissions Rate

CO2 Reductions

Conventional Coal Plant 

Emissions Rate

NGCC Plant 

Emissions Rate

US Electric 

Sector Average

Figure 19. Projected CO2 Emissions Displaced by State RPS Policies in Initial Peak Year of RPS 

The low displaced CO2 emissions rates predicted by some studies appear puzzling at first. One 

study projects avoided CO2 emissions rates that are lower than the emissions rate of a new 

 

57 Not all of the studies that quantify CO2 reductions provide sufficient data to be included in the figure. Colorado 

(PPC) provides emissions reduction estimates for two scenarios: RPS generation displaces natural gas combinedcycle generation, and RPS generation displaces coal-fired generation. Since the study uses the estimated cost of a 

natural gas combined-cycle plant to calculate avoided costs, we use the CO2 reductions from the former scenario. 
33

combined-cycle natural gas plant. A few other studies seem to predict avoided CO2 emissions 

rates that are roughly equal to or barely higher than that of a new combined-cycle natural gas 

plant, perhaps suggesting that RPS generation in these states will not displace any other 

conventional generators. This unexpected result can be partly explained by the fact that some 

studies predict that RPS generation will displace, to some degree, in-state hydropower 

generation. But the cost studies’ treatment of renewable energy imports is perhaps the more 

significant factor. Specifically, a few of the studies with low rates of CO2 displacement may not 

account for the CO2 emissions of displaced fossil-fuel generation imports (focusing instead on 

just the displacement of in-state sources of CO2).58 If the state RPS policy is expected to 

displace large amounts of imported fossil power, in-state CO2 reductions can be significantly 

lower than the total reductions induced by the state RPS. As a result, in-state CO2 reductions 

may not appear commensurate with the total amount of incremental renewable generation. 

Though reductions in carbon emissions is not the sole – or even primary – justification used to 

support many state RPS policies, Figure 20 shows the implied CO2 abatement costs projected by 

those studies that estimate CO2 reductions, focusing again on the peak RPS target year of each 

study.59 CO2 abatement costs vary widely, from a low of -$427/MTCO2 in Texas (UCS) to a 

high of $181/MTCO2 ton in New York (ICF), with a median value of $3/MTCO2. The wide 

variation in CO2 abatement costs is a reflection of the variation in retail rate impact projections 

among the studies. Not surprisingly, the four studies with the highest per-ton abatement cost 

projections in Figure 20 represent four of the six studies in our review with the highest expected 

base-case retail rate impacts. Another factor may be the analytic assumptions used to draw 

system boundaries. In some studies, a significant amount of the conventional generation 

expected to be displaced is projected to come from out-of-state generators, and if the 

corresponding emissions reductions are not counted, the per-ton abatement costs will be unduly 

inflated. 

These implied CO2 abatement costs can be benchmarked against the assumed CO2 regulatory 

compliance costs that are incorporated in the long-term resource plans of electric utilities. 

According to Bolinger and Wiser (2005), for example, the recent resource plans of seven 

Western utilities assume CO2 compliance costs (often as scenarios, not necessarily in the basecase analysis) ranging from $0-$64/MTCO2 (levelized over each utility’s planning horizon, in 

2003 dollars). As reported in the same paper, this range is not inconsistent with the expected 

compliance costs shown in the broader modeling literature under a range of carbon reduction 

scenarios, including those estimated by the EIA under various regulatory regimes. The spread of 

abatement costs in Figure 20 is obviously far broader, but 13 of the 16 state RPS cost studies 

included in the figure (if we count the California/Oregon/Washington Tellus study as three 

separate studies) project CO2 reduction costs in the peak target year that fall within the 

 

58 In cases where a state RPS cost study reports CO2 emissions reductions both in-state and regionally, we use 

regional data to present CO2 reductions and to calculate CO2 emissions displacement rates and implied abatement 

costs. 

59 These costs were calculated by dividing by the base-case direct state RPS electricity cost impacts (which do not 

include natural gas bill reductions) in the initial peak target year of each study by the corresponding CO2 reductions 

in Figure 19. Since these are single-year costs, they do not represent the average costs of CO2 abatement over the 

lifetime of each modeled RPS policy. Furthermore, these costs are consumer costs, which often include wealth 

transfers to generators and do not necessarily reflect the true social cost of each state RPS policy. 
34

$64/MTCO2 upper bound of the Western utility compliance cost assumptions. Seven of these 

studies project reductions in CO2 emissions that come with net savings to electricity consumers. 

($50)

($25)

$0

$25

$50

$75

$100

TX (UCS)

CO (UCS)

WA (UCS)

CA (Tellus)

CO (PPC)

CA (UCS)

OR (Tellus)

WI (UCS)

VA (CEC)

NY (DPS)

WA (Tellus)

MA (SEA)

RI (Tellus)

AZ (PIRG)

NY (CCAP)

NY (ICF)

CO2 Reduction Cost in 1st Peak Target Yr. 

(2003$/MTCO2)

$181

$(427)

Median Abatement Cost = $3/MTCO2 

Standard Deviation = $125/MTCO2

Figure 20. Projected CO2 Abatement Costs in Initial Peak Year of State RPS 

More generally, the laws and regulations governing the environmental impacts of electricity 

generators are likely to change over the lifetime of electricity supply investments, as will the cost 

of compliance with existing environmental regulations. These changes could impose substantial 

costs on electricity-sector shareholders and customers (Repetto and Henderson 2003). 

Traditional air pollutants (SO2, NOx, mercury, fine particulates, etc.) may be regulated more 

tightly in the future. Perhaps more significantly, new state or federal carbon regulations are 

possible over the 10+ year time horizons of state RPS policies. Because renewable energy 

sources are unlikely to be affected by these future requirements, purchasing or owning renewable 

energy assets may reduce exposure to these environmental compliance risks. This potential 

financial value has only been specifically quantified by four of the reviewed studies: Indiana 

(EEA) applies a carbon tax of $19/MTCO2 to fossil fuel-based generation in a scenario analysis; 

Iowa (WUC) considers a carbon credit value of $1-$46/MTCO2 in various scenario analyses; 

Washington (UCS) assumes an emission allowance cost of $5/MTCO2 in 2013, gradually 

increasing to $28/MTCO2 by 2025; and Wisconsin (UCS) assumes an emission allowance cost 

that starts at $9/MTCO2 and increases 5% per year thereafter. Given recent state activity on 

carbon emissions regulations, the carbon credit value of state RPS policies may be worth further 

exploration in future analyses. 
35

6. Comparison of Study Methodologies and Assumptions 

Previous sections of this report have summarized some of the key findings of the state RPS cost 

studies. We now turn to a description of the general modeling approaches used by these studies, 

and then to the major assumptions and sources that have been employed. In this section, we 

describe the four basic modeling approaches used by the state RPS cost studies in our review and 

how assumptions about state RPS market structure can significantly affect cost results. The 

remainder of the report provides more detailed descriptions of the methodologies and 

assumptions used to estimate renewable (Section 7) and avoided generation costs (Section 8). 

6.1 General Modeling Approaches 

The studies use a range of different cost estimation methods that do not always lend themselves 

to clear categorization. For descriptive purposes, we identify four broad categories of state RPS 

cost estimation models, listed below in approximate order of increasing complexity (as we note 

later, more complexity does not necessarily equate with model superiority).60 These approaches 

differ in the methods used to characterize the cost of renewable energy and the avoided cost of 

conventional fuels that are displaced by renewables deployment. Table 5 summarizes some the 

key aspects of the four modeling approaches described below, while Figure 21 identifies the 

studies that use each approach. 

• Category A: Spreadsheet model of renewable generation and avoided utility cost 

Under this approach, both renewable generation and avoided utility costs are estimated with a 

spreadsheet model; the projected cost impact of a state RPS is simply the difference in 

renewable generation and avoided utility costs. Sixteen of the 26 studies utilize cost 

estimation models that can be described by this broad category. The level of model 

complexity and sophistication varies widely – models range from simple estimates with few 

inputs to detailed supply curve models built from original research on the cost and 

availability of different generation options. The state-RPS-driven renewable resource mix 

can either be an input (in simpler approaches) or an output (in more detailed supply curve 

based approaches) of the model. 

The general advantage of a spreadsheet model comes in its transparency, simplicity, and 

relatively low cost. The input parameters in a spreadsheet model can typically be easily 

changed to accommodate scenario analysis and alternate assumptions. However, a 

spreadsheet model is typically unable to capture wholesale electricity and fossil fuel price 

feedbacks, and may not be well suited for modeling state RPS policies in situations where 

these effects are expected to be sizable. A spreadsheet model also does not provide the same 

level of detail about avoided costs as a generation dispatch model. The additional detail 

offered by dispatch models can enable more accurate comparisons of the wholesale energy 

and capacity value of renewable generation relative to the value of conventional generation. 

 

60 Some of these characterizations are based on previous work by Grace et al. (2003). 
36

• Category B: Spreadsheet model of renewable generation and generation dispatch model 

of avoided utility cost using reference-case (non-RPS) resource mix 

This approach uses the same general method for estimating renewable generation cost as the 

approach described in Category A, but estimates avoided utility cost through the use of a 

generation dispatch model (e.g. GE MAPS, PROSYM). These models are complex software 

programs that simulate the interaction of supply and demand in an electric system and 

provide detailed wholesale electricity price projections as a model output. Four of the studies 

in our sample employ this approach. 

The advantage of using a dispatch simulation model for this purpose is that it yields a 

potentially more accurate forecast of avoided utility costs than would a spreadsheet-based 

approach, and is capable of capturing the time-varying price of wholesale electricity. The 

latter is useful in estimating the true wholesale market value of renewable generation sources 

whose output profiles are temporally dependent.61 Like Category A, however, this approach 

does not capture electricity and fossil fuel price feedbacks, because the dispatch model is 

only run based on the reference-case (pre-RPS) resource mix. This approach may also add 

additional costs because generation dispatch models often require specific training and can 

entail significant software or licensing costs. 

• Category C: Spreadsheet model of renewable generation and generation dispatch model 

of avoided utility cost using implied state RPS resource mix 

This approach again relies on a spreadsheet model to estimate the cost and availability of 

renewable generation. The dispatch model, however, is now run under two different resource 

supply scenarios: (1) the reference case, non-RPS resource mix (as in Category B); and (2) 

the implied state RPS resource mix. The implied state RPS resource mix is an output of the 

spreadsheet model, and the capacity of each renewable generator type is input into the 

generation dispatch model along with conventional generators. Though the renewable 

generators are included in the dispatch model run of the state RPS case, the cost of these 

generators is modeled separately in the spreadsheet model.62 The dispatch model thereby 

provides electricity production costs for the reference and state RPS scenarios, and the model 

of the implied state RPS resource mix will have a lower cost result because the renewable 

generators are modeled at zero-cost. The avoided cost of the state RPS policy is then 

calculated as the difference between the total costs of these two scenarios, and can then be 

compared to renewable generation costs (which are an output of the spreadsheet model) to 

determine overall projected cost impacts. The two studies in our sample that employ this 

approach both evaluate the New York RPS using GE-MAPS software. 

Unlike the approaches used by Category A and Category B models, this approach has the 

advantage of quantifying the effect of state-RPS-eligible generation on reducing wholesale 

electricity prices. It can also provide specific information about which conventional 

 

61 Though, as we note in Section 8.1, spreadsheet models can also approximate the time-varying value of renewable 

generation. 

62 In these dispatch models, renewable generators are often modeled as “must-run” resources and are not subject to 

least-cost dispatch. 
37

generators are likely to be displaced by increasing levels of renewable generation, which may 

provide a more accurate forecast of avoided pollution emissions than would other techniques. 

This approach does not, however, model the natural gas demand and price reductions that 

might result from state RPS policies. Further, some generation dispatch models are not well 

suited to model renewable generation, and modeling the implied RPS resource mix within a 

dispatch model may require an immense number of input assumptions (e.g., location and 

temporal generation profile of each RPS resource). 

• Category D: Integrated energy model 

An integrated energy model is an energy-sector model that endogenously determines fuel 

prices, capacity expansion, and electricity prices. The two most commonly used integrated 

energy models used for state RPS cost studies thus gar are NEMS (National Energy 

Modeling System), developed by the U.S. Energy Information Administration (EIA), and 

IPM (Integrated Planning Model), developed by ICF Consulting. With this approach, the 

mix of state RPS resources and the production costs of those resources (as well as the cost of 

the resources that they offset) are estimated using the integrated energy model. Six of the 

studies in our sample employ this method. 

Perhaps the most advantageous feature of these models in the context of state RPS cost 

analysis (relative to Category C models) is their ability to capture fossil fuel price feedbacks. 

Integrated energy models are also capable of endogenously estimating renewable technology 

costs (based, of course, on model input assumptions), though, as we explain in Section 7.3.2, 

their methods of doing so are sometimes controversial. Because these models often come 

with built-in assumptions already in place, it may not be critical to conduct a bottoms-up 

analysis to develop refined assumptions. Unfortunately, these models tend to be less 

transparent than others, and without detailed knowledge of the model’s functionality, it can 

be difficult to understand how input assumptions lead to model results, particularly when the 

model and its source code are proprietary. Furthermore, an integrated energy model such as 

NEMS is designed to analyze the national energy sector and may require substantial 

modification to obtain the specificity and detail that is necessary to accurately model statelevel policies. 

Though we use these four categories to loosely summarize the modeling approaches employed 

by the state RPS cost studies in our sample, not every study in our review fits neatly into one of 

the four categories. One study in particular deserves mention for the uniqueness of its modeling 

approach. Virginia (CEC) uses “mean-variance portfolio analysis” to show the cost impacts of 

electricity generation profiles at varying levels of financial risk.63 The study’s model is an Excel 

workbook that finds the minimum-cost electricity portfolio at a given level of risk, subject to 

other constraints. The model’s use of a risk constraint distinguishes it from other studies that use 

Category A modeling approaches. 

. 

 

63 The model defines portfolio risk as a weighted average of the individual technology cost variances, adjusted for 

their co-variances. 
38

Table 5. Summary of Basic Modeling Approaches 

Model type Category A Category B Category C Category D 

Description Spreadsheet 

model of 

renewable 

generation and 

avoided utility 

cost 

Spreadsheet model of 

renewable generation 

and generation 

dispatch model of 

utility avoided cost 

using reference-case 

resource mix 

Spreadsheet model of 

renewable generation 

and generation dispatch 

model of utility avoided 

cost using implied RPS 

resource mix 

Integrated energy 

model 

Model platform Spreadsheet Spreadsheet + dispatch 

model (e.g. PROSYM, 

GE-MAPS) 

Spreadsheet + dispatch 

model (e.g. PROSYM, 

GE-MAPS) 

Macro energy-sector 

model (e.g. NEMS, 

IPM) 

Ability to capture 

possible power 

market price 

suppression effect? 

No No Yes Yes 

Ability to directly 

capture possible 

natural gas price 

suppression effect? 

No No No Yes 

Ability to capture 

time value of 

renewable energy 

Limited Yes Yes Yes, but details 

depend on model 

General 

transparency 

High Medium Medium Low 

Number of required 

input assumptions 

Few to many Few to many Typically many Typically many, but 

may already be 

build into model 

Advantages Simplicity; 

transparency; 

flexibility; 

relatively low 

cost 

May provide more 

accurate forecast of 

utility avoided cost 

and more detailed 

information about 

time-value of 

renewable generation 

In addition to benefits 

of Category B, can 

capture market price 

suppression effect and 

provides detail on 

individual generators 

and avoided emissions 

In addition to 

benefits of Category 

B/C, can capture 

effects on fossil fuel 

prices; can provide 

regional-level 

impacts; comes with 

built-in assumptions 

Disadvantages Inability to 

capture price 

feedbacks; 

avoided cost 

and renewable 

cost estimates 

can be crude 

Additional expense 

and training required; 

inability to capture 

price feedbacks 

May require large 

number of input 

assumptions; dispatch 

models may not be well 

suited to modeling 

renewable generators; 

does not model 

reductions in fuel price 

Lack of model 

transparency; model 

may require 

modification for 

state-level analysis; 

can be difficult and 

costly to use 
39

Category A

(16 studies) Category D

(6 studies)

AZ (PIRG), AZ

(PEG), CA (CRS),

CA LADWP (EC),

CO (PPC), HI (GDS), IA 

(WUC), IN (EEA),

MD (Sy napse), MN (WUC),

NE (UCS), PA (B&V), VA 

(CEC), VT (Sy napse), WA 

(Lazarus), WA (UCS)

CA/OR/WA, CO 

(UCS), NY (CCAP), 

NY (ICF), RI (Tellus), 

TX (UCS)

Category C

(2 studies)

Category B

(4 studies)

CA (UCS), MA (SEA),

NJ (Rutgers),

WI (UCS)

NY (DPS), 

NY (Potomac)

 

Figure 21. General Modeling Approaches Used for Estimating State RPS Costs 

Overall, this diversity of modeling approaches indicates that a standard template for state RPS 

cost estimation has yet to emerge. One might assume that accuracy increases with each 

modeling approach from Category A to Category D, as each successive model tends to provide 

more detail and captures more complexity. However, not enough is yet empirically known about 

the actual cost impacts of state RPS policies to validate the accuracy of one model over another. 

Energy markets are subject to significant uncertainty, and future renewable energy costs, while 

less volatile than conventional electricity prices, are also uncertain. As a result, the assumptions 

governing these costs may ultimately prove more important than the choice or complexity of the 

model itself. In fact, as described above, each modeling approach possesses advantages and 

disadvantages, and no single approach is clearly superior to the rest. Instead, the choice of the 

modeling approach should be linked to the time and resources available to the study team, the 

goals of the study, the need for transparency and multi-stakeholder involvement, and the 

availability and quality of input data, among other factors 

6.2 State RPS Market Structure 

The presumed structure of the state RPS market in a given state or region is an important 

consideration for modeling the cost impacts of a state RPS policy. In the paragraphs that follow, 

we describe possible state RPS market structures and the modeling approaches that cost studies 

have used to represent these structures. 

Much as electricity suppliers can purchase power to meet their load through a variety of different 

contract types, suppliers in some markets may be able to meet their state RPS obligations 

through multiple contract and compliance strategies. Renewable energy certificates, or RECs, 

are used in many RPS markets to demonstrate compliance with renewable mandates. RECs can 

often be sold or traded separately from the electricity commodity itself, and thereby create a 
40

supplemental revenue stream for renewable generators. RECs can be bought under long-term 

contract, or in short-term markets. The use of short-term REC transactions is most common in 

restructured electricity markets, where retail suppliers are less likely to pursue long-term 

contracts with renewable generators. Brokered REC markets have also emerged in these regions. 

In situations where RECs are primarily obtained in spot or short-term forward markets rather 

than under long-term contracts, there is little incentive for renewable generators to sell below the 

current spot price of RECs. In this market-clearing model, the incremental cost of the highestcost marginal renewable generator at any given time effectively determines the REC price, which 

is then paid to all renewable generators regardless of their actual costs (see Figure 22). This 

allows lower-cost generators to earn revenues that exceed their costs – not unlike any other 

commodity market – and can lead to higher state RPS compliance costs for ratepayers.64 At a 

minimum, this market-clearing model is appropriate for estimating the retail rate impacts of state 

RPS policies in competitive electricity markets that are expected to primarily feature short-term 

REC contracts. The market-clearing model is exemplified by the state RPS policies in much of 

the Northeastern U.S., where retail electricity suppliers commonly purchase RECs in short-term 

markets to meet their RPS obligations.65 However, medium- and long-term contracts for RECs 

have been executed in all of these markets, though in many cases annual or spot purchases for 

RECs are still the most common form of RPS compliance. Levelized Cost of Generation ($/MWh) P0

RPS Resource Supply (GWh)

RPS Demand Level 

Total cost of RPS = 

Total area under line 

Figure 22. Graphical Representation of Market-Clearing State RPS Compliance Model 

 

64 Note that the social cost of the policy still represents the area under the stair-stepped renewables supply curve. 

The area below the dashed horizontal line and above the step function represents producer surplus: profit received 

by generators that comes at the expense of consumers, and that is properly considered a wealth transfer, not a true 

social cost. 

65 A shortage of qualifying renewable supply in the early years of the Massachusetts RPS has driven REC prices to 

roughly $50/MWh. RPS officials in New England and elsewhere are understandably interested in avoiding future 

REC supply shortages that lead to such high costs, and some states now provide technical and financial support to 

facilitate long-term renewables contracts. 
41

Even in markets dominated by longer-term contracts (with or without RECs), the pricing of those 

contracts may tend to rise to the cost of the marginal renewable generator, and may therefore 

approximate a market-clearing model. Because prices are fixed at contract signature, however, 

the market-clearing contract price in this case would effectively be set by each solicitation and 

would then be fixed for generators selected under that solicitation for the duration of their 

contracts. Each solicitation or time period thereby would yield a new market-clearing-based 

long-term contract price.66 

An alternative approach altogether is to assume an average-cost pricing model, which is likely 

to be most appropriate where longer-term (RECs, or RECs plus electricity) contracts are the 

predominant form of compliance (especially in still-regulated markets). Under this model, it is 

assumed that these long-term contracts are priced based on the actual cost of each renewable 

energy project (which will differ by technology), and are not influenced by the bid prices of 

other developers (see Figure 23). The total cost of RPS compliance is determined by the 

weighted average cost of all RPS resources, rather than by the marginal cost resource, leading to 

lower compliance and ratepayer costs. Levelized Cost of Generation ($/MWh) 

RPS Resource Supply (GWh)

RPS Demand Level 

Total cost of RPS = 

Area under curve 

Figure 23. Graphical Representation of Average-Cost State RPS Compliance Model 

Figure 24 presents the number of studies that utilize the different approaches to characterizing 

the state RPS market structure. Ten of the studies we reviewed adopt an average-cost approach 

for estimating state RPS cost impacts. Another 14 studies adopt the market-clearing approach 

(including two studies that also use an average-cost approach in a different scenario). Those 

studies that take the market-clearing approach are typically those that analyze RPS policies in 

states with competitive electricity markets (e.g., Massachusetts, New York, and New Jersey). Of 

 

66 With this method, the contract price received by an RPS generator is not affected by the marginal unit price in 

subsequent years. 
42

these studies, seven assume that the prices received by all renewable generators in any given year 

are determined by the price of the marginal renewable energy unit in that year, while the other 

seven use the longer-term contract-based market-clearing approach described earlier. A final six 

studies do not clearly take either the average-cost or the market-clearing approach. This is 

because in these cases a supply-curve method for characterizing renewable costs is not used, and 

these studies instead simply assume that all RPS-eligible resources are of the same cost in a 

given year. 

AverageCost

(9)

Market 

Clearing:

Annual Spot

(7)

Market 

Clearing: 

ContractBased

(7)

Figure 24. Approaches for Characterizing State RPS Market Structure 

Two studies, New York (DPS) and Vermont (Synapse), model state RPS costs using both the 

market-clearing and average-cost methods.67 These studies find that the two approaches can 

yield substantially different results: a retail cost increase of 0.39% (market clearing) vs. 0.13% 

(average cost) in the case of New York (DPS), and an increase of 0.84% (market clearing) vs. 

0.33% (average cost) in Vermont (Synapse). The difference between market-clearing and 

average-cost results should be most pronounced when the renewable energy supply curve is 

sharply upward sloping.

Given the limited early experience with state RPS markets, it is perhaps premature to judge the 

relative accuracy of these different cost-estimation assumptions. Considering the significant 

structural and regulatory variations among RPS compliance markets, it is also reasonable to 

expect that the average-cost approach will be more accurate in some states (especially stillregulated states where long-term contracting is prevalent), while the market-clearing approach 

will be better suited for others (competitive markets where short-term trade in RECs is common). 

In many instances, actual RPS contracting may resemble elements of each approach. 

 

67 New York DPS adopts the long-term contract approach to model the market-clearing scenario. The Vermont 

study adopts the spot market approach. 
43

7. Modeling Renewable Technologies, Resources, and Costs 

The renewable technology costs of a state RPS policy are a function of the resource eligibility 

requirements of the policy, the presumed resource availability within the state or region (if outof-state imports are allowed), the present and projected costs of the technologies, and the 

expected demand for renewable generation (which may include non-RPS-driven demand and/or 

out-of-state demand). In this section, we examine how the state RPS cost studies in our sample 

have accounted for each of these factors. Section 7.1 identifies the technologies that are modeled 

by the studies, and whether renewable technology deployment is treated as an input or output of 

the studies. Section 7.2 and 7.3 describe the studies’ methods for estimating resource availability 

and production cost, respectively. Section 7.3 also presents a few of the key renewable cost 

assumptions that are used by the studies. 

7.1 Technologies Modeled 

The number and type of renewable technologies considered in each state RPS cost study is 

dependent on the resource eligibility provisions of the proposed RPS, the predicted degree of 

competition among different renewable resource options, the availability of existing resource 

assessment data, and the time and funding available to model different technologies. Table 6 

shows the technologies that are modeled by each state RPS cost study in our sample.68

Even if a technology is analyzed by a state RPS cost study, it may not be expected to contribute 

to RPS requirements if its cost is expected to be too high or its resource potential is deemed too 

low. This is often the case with solar technology, which, despite being included in the 

technology assessments of many cost studies, is predicted by some of these studies to be noneconomic compared to other technologies, and is thus assumed to contribute negligible 

generation towards meeting state RPS targets.69 

The simplest approach to estimating renewable resource availability is to assume the existence of 

sufficient resource potential of a single type of renewable technology to fully meet the state RPS 

requirements with that single technology type. This is most commonly done for wind power, 

due to its abundant resource potential in many parts of the country as well as an expectation that 

it will be a least-cost renewable resource. Studies that rely on more detailed resource 

assessments are likely to model wind resources with much greater specificity, and may model 

both offshore and onshore sites of different sizes, each with varying cost and performance 

characteristics. As shown in Table 6, three of the studies in our sample only consider wind 

power. The majority of the studies, however, consider a wider variety of resource types. In 

addition to wind, studies regularly include landfill gas, photovoltaic and/or central station solar, 

and one or more biomass technologies. A smaller number of studies include geothermal, hydro 

fuel cells, anaerobic digestion, and MSW incineration. 70 

 

68 Some studies model more detailed categories of renewable technologies than those shown in Table 6. For 

instance, NY (DPS) models hydro upgrades at existing facilities as well as new hydro facilities. 69 Of course, studies that analyze state RPS policies with solar set-asides, such as New Jersey (Rutgers) will predict 

a non-trivial amount of solar energy production, even if solar is not cost-competitive with other renewable 

technologies. 

70 A few studies also consider less commercial technologies, such as tidal or wave power. These technologies are 

not individually identified in Table 6, but they are included in the “Other” column. 
44

Table 6. Technologies Modeled by Each State RPS Cost Study 

State RPS Cost 

Study 

Wind 

Offshore Wind 

Geothermal 

Hydro 

Landfill Gas 

Biomass Direct 

Biomass CoFire 

Biomass 

Gasification 

Digestion 

Anaerobic 

MSW 

Photovoltaic 

Concentrating 

Solar Power 

Fuel Cell 

Other 

Notes 

AZ (PIRG) 3 3 3 3 3 3 3 a,b 

AZ (PEG) 3 

CA (CRS) 3 3 3 3 3 3 3 3 

CA (Tellus) 3 3 3 3 3 c,g 

CA (UCS) 3 3 3 3 3 3 3 d,e 

CA LADWP (EC) 3 3 3 

CO (PPC) 3 3 3 

CO (UCS) 3 3 3 3 3 c 

HI (GDS) 3 3 3 3 

IA (WUC) 3 

IN (EEA) 3 

MA (SEA) 3 3 3 3 3 3 3 f 

MD (Synapse) 3 3 3 3 

MN (WUC) 3 

NE (UCS) 3

NJ (Rutgers) 3 3 

NY (CCAP) 3 3 3 3 g 

NY (DPS) 3 3 3 3 3 3 3 3 3 3 f 

NY (ICF) 3 3 3 3 3 3 f 

NY (Potomac) 3 3 3 3 3 3 3 3 

OR (Tellus) 3 3 3 3 3 c,g 

PA (B&V) 3 3 3 3 3 3 3 3 3 3 e,h 

RI (Tellus) 3 3 3 3 c 

TX (UCS) 3 3 3 3 c 

VT (Synapse) 3 3 3 3 3 3 3 

VA (CEC) 3 3 3 3 3 3 3 a 

WA (Lazarus) 3 3 3 3 

WA (Tellus) 3 3 3 3 3 c,g 

WA (UCS) 3 3 3 3 3 

WI (UCS) 3 3 3 3 3 3 

Notes: 

a - Study does not disaggregate biomass resource into different technologies, and may model other technologies in 

addition to direct biomass. 

b - Based on text of report, we assume that the biomass technologies modeled in the study are biomass direct, 

landfill gas, and anaerobic digestion. 

c - Study uses NEMS. The technologies listed here are only those specifically identified in the study, and do not 

represent all technologies implicitly considered within NEMS, which contains resource potential and cost 

assumptions for most of the technologies in the table above. 

d - Based on text of report, we assume that the biomass technology modeled in the study is biomass co-firing. 

e - Study models fuel cells running on renewable fuels. 

f - Study models fuel cells running on non-renewable fuels (i.e. natural gas). 

g - Study does not specify which technologies are included in “Other.” 

h -“Other” technologies considered are ocean energy (i.e. ocean thermal, wave, and tidal). 
45

It is important to note that renewable technology deployment is an input assumption to some 

studies and a model output of others. The former approach is used by studies that assume that 

the entirety of the RPS generation requirement is met with a single type of technology: Arizona 

(PEG), Indiana (EEA), Iowa (WUC), Minnesota (WUC), Nebraska (UCS). In addition, a few 

other studies – Arizona (PIRG), Colorado (PPC), Maryland (Synapse), New Jersey (Rutgers), 

and Washington (Lazarus) – assume a specified RPS generation mix from the onset that consists 

of more than one technology type.71 This approach lacks the analytical rigor of more 

sophisticated modeling approaches, but may be sufficient for a study whose primary focus is 

providing cost estimates, especially if only one or two technologies are expected to effectively 

determine the cost impacts of the state RPS. 

The other 17 studies in our review treat renewable technology deployment as a model output, 

i.e., the model “selects” renewable technologies in order of ascending cost until sufficient 

resources are developed to meet the state RPS target. In addition to providing more credible 

estimates of the contribution of each renewable technology to meeting state RPS goals, this 

approach also provides more precise (though not necessarily more accurate) estimates of the 

aggregate renewable production cost of the RPS policy. Such precision may be desirable when 

modeling a state RPS policy whose cost impacts likely depend on the cost and availability of a 

number of different technologies. 

7.2 Renewable Resource Characterization 

State RPS cost impacts are, to some degree, a function of the available renewable resource 

potential within a state or region. Though most of the state RPS cost studies in our sample 

assume that only the most cost-effective renewable technologies will be built, there are limits to 

the resources that can be developed within a specific geographic area and time frame. 

Estimating the available renewable resource supply is thus an important component of most state 

RPS cost studies. 

The availability of a particular renewable technology is highly dependent on the geographic 

limits within which the resource potential for that technology is analyzed. These limits, which 

are prescribed by the state RPS policy that is modeled, may be as small as a single state or as 

large as multiple NERC regions spanning several states and Canadian provinces.72 

The methods and sources that the studies use to characterize renewable resource supply are as 

varied in complexity and detail as the general modeling approaches described in Section 6. As 

mentioned previously, some studies do not estimate renewable resource availability, and instead 

simply assume the existence of adequate supply to meet state RPS targets at a given price 

point.73 Occupying the opposite end of the spectrum are studies that perform original detailed 

 

71 In addition, one study – California LADWP (EC) – assumes a non-specified RPS generation mix of multiple 

technologies. 

72 Since non-hydro renewable power transactions have not regularly crossed international borders in North America, 

it is not clear whether such transactions, even if allowed, will actually result from RPS policies. A few cost studies 

predict that such imports will be used to meet state RPS requirements (either in the base case or in a scenario 

analysis), but the likelihood of these transactions remains a source of uncertainty. 73 This may be a defensible approach if the required amount of renewable generation is relatively low and the 

resource supply is known to be more than sufficient. 
46

resource assessments, sometimes analyzing wind speed data or quantifying the potential 

renewable capacity at specific individual sites. The substantial regulatory, technical, and 

economic uncertainties affecting renewable resource availability and cost are such that 

renewable resource characterization is, by nature, a somewhat speculative exercise. Due to the 

considerable degree of guesswork involved and the potential for diminishing returns (in terms of 

predictive accuracy) from increasing specificity, it is unclear whether highly detailed resource 

assessments necessarily lead to significantly improved state RPS cost estimates. 

For the purpose of a state RPS cost study, renewable resource assessments typically begin with a 

survey of existing estimates of resource potential in the state or region of interest. These 

estimates may come from government sources, such as the EIA, the National Renewable Energy 

Laboratory, or state energy agencies. Estimates are also sometimes done by consultants with 

expertise in analyzing resource potential, typically funded by public agencies. 

Figure 25 identifies the principal sources of renewable resource availability data among the 

studies in our sample. Though many of the reviewed studies use data from existing resource 

estimates (some may also make adjustments to these estimates based on knowledge of the 

renewable energy market), some rely on primary research to develop original resource 

assessments. This research generally entails collecting market intelligence through interviewing 

renewable energy project developers, energy analysts, and other industry experts. At a more 

technical level, it may also include deriving resource potential estimates from existing data that 

is in another form; for instance, calculating potential wind capacity and capacity factors from 

wind speed data. Detailed original resource assessments can be costly, and the relatively small 

number of studies that perform such assessments is more likely due to the limited financial 

resources available for these studies than to an abundance of existing reliable data on renewable 

resource availability. 

NonGovt. 

Source

(5)

Govt. 

Source

(9)

None

(7)

Primary 

Research 

(7)

Figure 25. Principal Sources of Renewable Resource Data 
47

7.3 Renewable Energy Cost Characterization 

Renewable generation costs can be loosely divided into two categories: busbar and secondary. 

Busbar costs are the direct costs of a generation facility at the individual plant level, without 

accounting for any secondary effects that the plant may have on utility operations or transmission 

expansion. We start our discussion below on busbar costs, and cover secondary costs later in this 

section. 

7.3.1 Sources and Methods for Estimating Busbar Costs 

Figure 26 identifies the principal sources for busbar cost data among the studies in our sample. 

The most common are government estimates, such as from the EIA’s Annual Energy Outlook or 

the EPRI/DOE Renewable Technology Characterization. As with resource potential, however, 

the studies employ a wide range of methods to estimate busbar costs, from simple top-down 

estimates derived from a single data source, to original bottom-up estimates requiring several 

input assumptions from multiple data sources. 

As mentioned previously, many of the studies develop a renewable resource supply curve to 

estimate state RPS cost impacts. These supply curves identify the quantity (either aggregate or 

incremental to the previous year) of RPS-eligible resources that are available at a given levelized 

cost in a given year. To avoid the potentially cumbersome task of creating supply curves for 

each year of the study timeframe, some studies construct supply curves for a few “snapshot” 

years, typically spaced at three- to five-year intervals.74 The point at which the supply curve 

intersects RPS demand level in a given year determines the marginal cost resource. Depending 

on the assumed structure of the RPS, this marginal cost resource may in effect set the levelized 

price that all renewable resources are assumed to receive (a further discussion of this issue is 

provided in Section 6.2). When multiple resource tiers are included in a state RPS proposal, a 

separate supply curve for each resource tier is required. 

NonGovt. 

source

(5)

Govt. 

Source

(15)

Primary 

Research 

(8)

Figure 26. Principal Sources of Renewable Energy Busbar Cost Data 

 

74 Costs in interim years are then interpolated, or simply not estimated. 
48

The majority (93%) of the studies we reviewed account for technology learning in some form 

(i.e., expected future reductions in renewable cost due to technological improvements). In some 

studies, however, these cost reductions are counterbalanced by expected cost increases as the 

best renewable resource sites are developed. 

The busbar cost of renewable energy is a function of multiple input assumptions, including: 

• capital cost 

• operating and maintenance (O&M) cost 

• fuel cost (if applicable) 

• capacity factor 

• finance terms and rates 

• financial incentives 

These assumptions can vary not only with time and region but also within a technology type. 

Not surprisingly, the assumptions differ considerably across the state RPS cost studies we 

reviewed. Often, these variations can be explained by regional factors (e.g., higher wind 

capacity factors in windier states), by differences in financing structures (e.g., municipally 

owned vs. privately developed resources), or by expectations of technology cost and/or incentive 

availability that may vary according to when the study was completed. 

The lack of agreement in renewable generation cost estimates points to a larger problem 

involving renewable resource data. As the renewable energy market continues to rapidly expand 

and evolve, the need for accurate, rigorous, and up-to-date estimates of renewable resource cost, 

performance, and potential is as important as ever. Unfortunately, the most commonly used data 

sources for these variables sometimes do not reflect the most recent knowledge about renewable 

technology performance and cost. As a result, the assumptions underpinning renewable cost and 

performance estimates in some studies may be dated, inaccurate, or inconsistent with current 

market conditions. State RPS cost studies are not necessarily culpable for this, since developing 

better estimates of renewable cost and performance would require time and resources that are 

beyond the scope of many state RPS cost studies. Such an ambitious undertaking would 

probably be best managed by a government research agency. The availability of better estimates 

of renewable cost and performance would improve the credibility of state RPS cost analysis and 

lend more weight to economic analysis of renewable technologies in general. 

Even if more accurate data sources were available, however, substantial uncertainties would still 

exist. Below, we provide a comparison of the assumptions for two key state RPS cost drivers 

that underscores both the need for more current data and the uncertainties surrounding future 

renewable generation costs. These two key assumptions – wind capital cost and the duration of 

PTC availability – are specified in many of the state RPS cost studies and, as evidenced in Figure 

11 and Figure 12, both are potentially significant cost drivers. Though these two factors are 

among the most important determinants of RPS cost impacts, a more thorough review of the cost 

studies would need to evaluate other factors as well. However, because the many assumptions 

affecting renewable energy costs are often not explicitly provided in the cost studies, it can be 

difficult to unpack individual cost drivers. 
49

7.3.2 Wind Capital Cost Assumptions 

The assumed cost of constructing wind projects varies considerably among the reviewed studies 

(Figure 27).75 Among the 17 studies that present these data, the highest capital cost estimate in 

the 2010-2015 timeframe (from Scenario 1 of the New York ICF study) is four times higher than 

the lowest estimate (from the Vermont study).76 A majority of the studies use wind capital cost 

assumptions that are lower than EIA estimates from the 2006 Annual Energy Outlook; the 

median capital cost among the studies is $41/kW lower than the EIA estimate in 2010 and 

$71/kW lower in 2020. 

Many of the studies assume that capital costs will gradually decline over time.77 On the other 

hand, wind costs are endogenously modeled and actually increase over time in at least six of the 

reviewed studies: CA/OR/WA (Tellus), Colorado (UCS), New York (ICF), New York (CCAP), 

Rhode Island (Tellus), and Texas (UCS).78 This is consistent with the idea that capital costs will 

increase in a given region after the best sites are developed, and therefore that capital costs are a 

function of installed capacity as well as time.79 In some studies, the increase in capital cost with 

increased wind development significantly outweighs any learning effects that would otherwise 

reduce costs over time. 

The assumed capital cost of wind projects can significantly affect the predicted cost of state RPS 

policies. For example, a change in capital costs of $100/kW roughly corresponds to a $5/MWh 

change in levelized generation costs.80 Of the studies reviewed here, most predict wind capital 

costs of under $1200/kW, and some predict long-term costs well below this figure. Notable is 

that current wind costs are reportedly in the $1400-2000/kW range, driven higher in recent 

months by adverse exchange rate movements, rising energy and steel prices, tight wind turbine 

manufacturing capacity, and a general rush to install wind projects while the PTC remains in 

place. As a result, the wind cost assumptions employed in most of the state RPS analyses 

presented here do not accurately reflect the current cost to build a wind project. This disparity 

 

75 Most of the capital costs shown here are “overnight” capital costs, which refer to the total construction cost if the 

wind farm could be built instantaneously, i.e., without including interest on the construction funds. In some cases, 

however, the capital costs presented may represent rolled-in capital cost, which include construction financing costs. 

None of these capital costs include transmission costs. Where studies provide estimates for capital costs for wind 

projects in different regions, we show cost estimates for projects within the region of the modeled RPS policy. 

76 The high capital cost assumption from Scenario 1 of the New York (ICF) study results from that study’s reliance 

on EIA “cost adjustment steps,” which multiply initial capital cost estimates by up to a factor of three to reflect 

expected resource degradation. We include the capital cost assumptions of both Scenario 1 and Scenario 2 of the 

study in Figure 27 because they are substantially different, and neither scenario is clearly identified as the base case. 77 Several studies use current and projected wind capital cost estimates from EPRI/DOE Renewable Energy 

Characterization publications or from estimates published by the Government Performance Review Act (GPRA). 

Both of these sources project more aggressive cost reductions than those estimated by EIA. 78 However, Colorado (UCS), New York (CCAP), and Texas (UCS) do not provide enough data to be included in 

the chart. Though Pennsylvania (B&V) does not endogenously model wind costs, the study applies a capital cost 

adder of $500/kW to 50% of the available wind resource (this cost adder is not reflected in Figure 27). It is unclear 

how much this more expensive wind resource is assumed to contribute to RPS requirements. 

79 These cost increases may also be reflected in some studies that do not endogenously model wind costs. These 

studies may assume that a certain fraction of wind resources are of higher cost than others. As the lower cost wind 

resources are used up, the higher cost resources are then developed, which may increase average wind costs over 

time. 

80 Assuming a simple capital recovery factor of 15% and a capacity factor of 34%. 
50

between study expectations and current market reality suggests that (all else being equal) the 

actual cost impacts of state RPS policies may exceed those estimated in our sample of studies, 

especially if higher wind costs persist.81 

Figure 27. Wind Power Capital Cost Assumptions (Base-Case Analysis) 

7.3.3 Federal Production Tax Credit Availability 

The federal PTC can “buy-down” the cost of renewable energy by roughly $20/MWh on a longterm, levelized cost basis. As such, assumptions about the availability and level of the PTC can 

greatly impact the predicted cost of state RPS policies. 

Figure 28 illustrates the duration of PTC availability assumed by the studies in our sample.82 

The lack of consistency in these assumptions reflects the political uncertainty surrounding PTC 

extension. Given the changes in the status of the PTC over time, it is not surprising that a state 

RPS cost study from 2002 (when existing legislation did not provide for PTC extension beyond 

2003) might assume that the PTC would extend to a different year than an RPS cost study 

conducted in 2005 (when Congress extended the PTC through 2007). 

 

81 It is somewhat unclear whether this substantial increase in cost is a short-term phenomenon or if it marks a more 

permanent shift in the wind energy market. Some experts believe that a supply imbalance caused by the boom in 

turbine demand following the recent extension of the PTC is largely responsible for the recent cost run-up, but other 

factors (e.g., high steel prices, a weak U.S. dollar, or a move by manufacturers to increase profits to sustainable 

levels) may also be significant. 

82 In 2006, the inflation-adjusted PTC was worth 1.9 cents per kWh. The credit is available for the first 10 years of a 

plant’s lifetime. 

$600

$800

$1,000

$1,200

$1,400

$1,600

$1,800

$2,000

2003 2005 2007 2009 2011 2013 2015 2017 2019

Capital Cost ($2003/kW)

CA (CRS)

CA/OR/WA

CA LADWP (EC)

IA (WUC)

IN (EEA)

MD (Synapse)

MN (WUC)

NE (UCS)

NY (Potomac)

NY (DPS)

NY (ICF 1)

NY (ICF 2)

PA (B&V)

RI (Tellus)

VT (Synapse)

WA (Lazarus)

WA (UCS)

WI (UCS)

US EIA (AEO2006)

$3,155 

Current costs

range from 

$1400/kW

to $2000/kW
51

The final year of PTC availability is most commonly assumed to be 2006. The last six studies 

shown in the figure assume PTC availability throughout the entire timeframe of their analysis, 

while seven studies do not appear to include the PTC in their analysis at all.83 

2012

2010

2009

2007

2007

2006

2006

2006

2006

2006

2006

2006

2005

2005

2003 2006 2009 2012

IA (WUC)

NY (CCAP)

NY (Potomac)

NY (DPS)

NY (ICF)

MD

WA (UCS)

MN (WUC)

PA (B&V)

IN (EEA)

WI (UCS)

WA (Lazarus)

VT (Synapse)

NE (UCS)

MA (SEA)

CO (PPC)

CA (UCS)

NJ (Rutgers)

TX (UCS)

CO (UCS)

Final year of PTC extension*

* These studies assume no PTC availability: AZ (PEG), AZ (PIRG), CA LADWP (EC), 

 CA (CRS), CA/OR/WA (Tellus), HI (GDS), RI (Tellus), VA (CEC)

 

Figure 28. Duration of PTC Availability Assumed by State RPS Cost Studies (Base-Case Analysis) 

The studies are also not consistent in their assumptions of which specific technologies will 

qualify for the PTC. In addition to wind, some studies assume that the PTC is available for some 

or all types of biomass, including landfill gas. Though the PTC was recently extended through 

the end of 2008 and expanded to include geothermal, open-loop biomass, and other previously 

excluded resources, its long-term fate remains highly uncertain. Several studies have reflected 

this uncertainty in their analysis by modeling various PTC availability scenarios. 

7.3.4 Treatment of Secondary Costs 

To accurately reflect the true cost of renewable energy, it is not sufficient to only estimate busbar 

economics. Instead, a variety of secondary costs must also be considered: transmission costs, 

integration costs, resource adequacy or capacity costs (or capacity value), and administration and 

transaction costs. Table 7 identifies the studies that incorporate these costs into their calculation 

of overall cost impacts. 

 

83 Some of these studies do not consider the PTC because they exclusively model other renewable technologies (i.e., 

Arizona PEG solely considers solar technologies) or they assume that all wind energy is obtained through public 

power ownership (i.e., CA LADWP EC). 
52

Table 7. Secondary Costs of Renewable Generation Considered by State RPS Cost Studies 

Cost Variable Number of 

Studies

Studies

Capacity value 20 

AZ (PEG), CA (CRS), CA/OR/WA (Tellus), CO (PPC), CO (UCS), IA 

(WUC), IN (EEA), MD (Synapse), MA (SEA), MN (WUC), NE (UCS), 

NY (CCAP), NY (DPS), NY (ICF), NY (Potomac), PA (B&V), RI 

(Tellus), TX (UCS), WA (UCS), WI (UCS) 

Transmission costs 15 

CA (CRS), CA (UCS), CA/OR/WA (Tellus), CA LADWP (EC), CO 

(PPC), CO (UCS), IA (WUC), MA (SEA), MN (WUC), NE (UCS), PA 

(B&V), TX (UCS), VT (Synapse), WA (UCS), WI (UCS) 

Integration costs 12 

CA (CRS), CA/OR/WA (Tellus), CO (PPC), CO (UCS), IA (WUC), IN 

(EEA), MN (WUC), NJ (Rutgers), TX (UCS), WA (Lazarus), WA (UCS), 

WI (UCS) 

Administration & 

transaction costs 5 CA (UCS), MA (SEA), WA (Lazarus), WA (UCS), WI (UCS) 

These costs can be significant, especially in regions with transmission constraints and aggressive 

state RPS targets. They are especially relevant for wind power, which offers a variable 

production pattern from projects often located at some distance from load. The fact that many of 

the studies in our sample ignore many of these costs suggests that state RPS cost-impacts may be 

underestimated by these studies, all else being equal. 

 

Perhaps the most significant secondary cost, especially for wind power, is transmission. 

Transmission costs have become a significant constraint for many wind power project developers 

(see, for instance, CDEAC 2006). These transmission costs are extremely site-specific, however, 

and do not lend themselves to the simplifying assumptions that are often made to model other 

parameters. It is also not always clear what specific costs should be allocated to the cost of the 

state RPS, especially in the event that transmission expansion would have been necessary to 

meet the needs of growing loads and conventional generators.84 Roughly half of the cost studies 

in our sample include transmission in their analysis, but few if any of the studies analyze these 

costs in a detailed fashion. 

As wind power penetration has increased, the question of how much dependable capacity wind 

power can provide to a system has taken on increased relevance. Wind projects do not offer the 

same value to an electricity system as a base-load coal plant or a dispatchable gas plant, but still 

provide some contribution to resource adequacy and therefore have some capacity value. Of the 

20 studies that specifically analyze the capacity value of renewable energy, most assume that the 

capacity value of wind generation is likely to be commensurate with the expected capacity factor 

of these plants. Absent more detailed study, this approximation may not be a bad one, at least at 

relatively low levels of wind penetration (Giebel 2005). 

Wind integration costs represent the combined impact of incorporating variable or “as-available” 

wind power into the grid. The science of understanding and quantifying the integration impacts 

and costs of wind power has solidified over the last several years, with most studies concluding 

 

84 See CDEAC (2006) for a detailed discussion of transmission cost allocation and recovery issues for wind 

generators. 
53

that these costs represent only a small fraction of overall renewable production costs, typically 

under 0.5 ¢/kWh at levels of wind penetration as high as 10-20% (Bolinger and Wiser 2005; 

EWEA 2005; Smith et al. 2004). Only 12 of the studies in our sample include these potential 

costs in their analysis of state RPS impacts. 

Finally, a few of the state RPS cost studies include administration and transaction costs that 

accompany state RPS implementation, typically finding that these costs are expected to be 

small.85 

7.4 Renewable Demand Characterization 

Cost studies that use a supply curve to represent renewable resource costs and availability must 

also develop a renewable demand curve to estimate state RPS rate impacts. The most basic 

version of such a curve would simply assume that all demand for renewable resources is driven 

by the RPS in question; in this case, yearly renewable energy demand is simply calculated by 

multiplying the state RPS target percentage by the expected retail electric load of the year of 

interest. 

This simplified approach, however, may well be inaccurate. In reality, multiple sources of 

demand for RPS-eligible renewable energy may exist. These sources include competing RPS 

policies from neighboring states, government agency green power commitments, and customerdriven green power programs. Of these demand drivers, existing or future RPS policies from 

neighboring states are likely to be the most significant in many regions. In a small Northeastern 

state such as Rhode Island or Vermont, this external RPS demand may dwarf the demand from 

the state’s own policy. Especially in these regions, it is essential to develop estimates of regional

renewable energy supply and demand, a task that can be greatly complicated by differences in 

the types of technologies that are considered eligible by each competing state RPS policy. 

Ignoring these other demands could yield a sizable underestimate of state RPS costs if the 

renewable energy supply curve is sharply upward sloping. Seven of the cost studies we reviewed 

account for competing renewable energy demands from existing state RPS (or other renewable 

incentive) policies in nearby states, while four consider customer-driven green power demand.86 

None of the studies considers the potential incremental demand that could come if other states in 

the region were to adopt state RPS policies in the future. 

 

85 For example, California (UCS) expects these costs to vary from roughly 0.01% to 0.03% of total retail costs 

through the course of the study. 

86 One of these studies, New York (Potomac), implicitly accounts for out-of-state RPS and green power demand by 

using the renewable generation cost estimate developed in New York (DPS) (The latter study accounts for both 

demand sources). 
54

8. Modeling Avoided Cost 

The difference between renewable energy costs (busbar and secondary) and the cost of 

conventional power that would otherwise be used to meet load (avoided costs) determines the 

projected rate impacts of state RPS policies. Depending on the approach, these avoided costs 

may be either an input or an output. If an input, then avoided costs are static, and are assumed to 

not be affected by increased renewables generation. In other words, wholesale market prices are 

the same in both the reference-case and state RPS scenario. When avoided costs are a model 

output, on the other hand, incremental renewable generation may cause the wholesale market 

price to differ in the RPS scenario relative to the reference-case scenario. 

Here we discuss three important elements of avoided cost calculations: (1) the general 

methodology that is used to estimate these costs, (2) temporal and geographic variations in 

avoided costs, and (3) the natural gas price forecast that is used. 

8.1 General Methodologies 

The most common method for estimating avoided costs is to use a conventional fossil-fuel plant 

proxy (Figure 29). The fuel type of this plant has typically been natural gas (7 of 13 studies), but 

some studies have estimated the cost using a mix of coal, gas, and other conventional generators 

(6 of 13 studies).87 Estimating the levelized cost of the proxy plant typically requires input 

assumptions on capital and O&M costs, fuel prices, and financing terms. In most studies, the 

levelized cost of the proxy plant is assumed to set the long-term marginal wholesale price, which 

is then used as the long-run avoided cost forecast. In the short run, the avoided cost forecast is 

sometimes approximated using utility filings or forward market prices.88 The fossil-fuel plant 

proxy approach is relatively simple and straightforward, but does not account for wholesale 

electric or natural gas price feedbacks, and may not be the preferred method for modeling RPS 

impacts in situations where these feedbacks may be important. The proxy plant method is also 

not well-tuned to analyze the wholesale market value of temporally-dependent renewable energy 

production profiles, though it can be substantially modified to approximate these values.89 

 

87 As natural gas prices have risen in recent years, it has become increasingly likely that renewable generation will 

offset coal production over time. 88 There may also be an interim forecast period between the short and long run where market prices are interpolated. 

89 For example, the avoided cost estimate in one of the California studies (CRS) is based on the California Public 

Utilities Commission’s avoided cost forecast methodology, the production cost component of which is based on the 

estimated cost of a new combined-cycle natural gas plant. The methodology then maps the hourly profile of a 

historical dataset of wholesale market prices to the long-run average cost of the natural gas plant to develop an 

hourly market price forecast 
55

Integrated 

energy model

6 studies

CA/OR/WA (Tellus), 

CO (UCS), NY 

(CCAP), NY (ICF), RI 

(Tellus), TX (UCS)

Other

3 studies

AZ (PEG),

WA (Laz.), 

WA (UCS)

 Conventional 

 plant proxy

 13 studies

AZ (PIRG), CA (CRS),

CA LADWP (EC), CO (PPC),

HI (GDS), IA (WUC), IN (EEA),

MD (Synapse), MN (WUC),

NE (UCS), PA (B&V), VA 

(CEC), VT (Synapse)

 Dispatch

simulation w/o

RPS resources

4 studies

CA (UCS), MA

(SEA), NJ

 (Rutgers),

 WI (UCS)

Dispatch 

simulation 

w/RPS 

resources

2 studies

NY (DPS), NY 

(Potomac)

Figure 29. State RPS Cost Study Methodologies for Estimating Avoided Cost 

Six of the studies rely on dispatch simulation models to generate avoided cost forecasts. These 

models differ on one main point: whether or not RPS resources are included in the dispatch 

simulation. The California (UCS), Massachusetts (SEA), New Jersey (Rutgers), and Wisconsin 

(UCS) dispatch simulations model only the reference-case resource mix, and thereby provide a 

static avoided cost forecast.90 On the other hand, the New York (DPS) and New York (Potomac) 

simulations model both the reference-case resource mix and the implied RPS resource mix, 

allowing wholesale market prices to vary between these two cases and therefore providing a 

dynamic avoided cost estimate.91 Dispatch simulation models should be able to provide more 

specific and precise estimates of electricity system operating characteristics (such as hourly and 

seasonal market prices, hourly emissions, and displaced generation types) than would proxyplant methods, but many of these characteristics can also be roughly estimated with relatively 

simple spreadsheet models. 

 

Six of the studies employ an integrated energy model, either NEMS or IPM, to analyze potential 

cost impacts. These models are capable of endogenously determining fuel prices and capacity 

expansion, in addition to electricity prices. The avoided cost in this instance is not a direct 

output of the model – instead, the model calculates the entire electricity (or energy) system cost 

of the reference-case and RPS scenarios, and the incremental state RPS cost is simply the 

difference between the costs of the two scenarios. 

 

90 The wholesale market price forecast used by the New Jersey study was determined in the New Jersey Renewable 

Market Assessment, prepared by Navigant Consulting and published in August 2004. 

91 In this case, state-RPS-eligible renewable resource types and generation costs are estimated externally to the 

dispatch simulation using a linear spreadsheet model, and renewable generators are modeled as zero-cost in the 

dispatch simulations. 
56

Finally, three studies rely on alternative approaches altogether. Arizona (PEG) simply assumes 

that average retail rates will decline by 2% a year for the first 12 years of the study, and remain 

constant thereafter. Washington (Lazarus) assumes a constant avoided generation cost 

throughout the course of the study. Washington (UCS) bases its avoided cost assumptions on the 

“high fuel price” case of the Northwest Power and Conservation Council’s Fifth Power Plan. 

8.2 Temporal and Geographic Variations in Avoided Cost 

The avoided costs of increased renewable generation are dependent on the timing of the 

displaced generation (i.e. peak, off-peak, summer, etc.) and, in some instances, the location of 

that generation. Twelve of the studies account for the time-differentiated value of renewable 

energy. This practice may be particularly important in regions of the country that experience 

high variability in diurnal and seasonal wholesale market prices, and where renewable energy 

output profiles are either especially well or poorly matched to those changes. 

Only a few studies account for the geography of displaced generation within a specific state. 

The advent of locational marginal pricing in some regions of the country has improved our 

intuitive understanding of, and capability to model, geographically varying avoided costs. Only 

the Hawaii (GDS) and New York (DPS) studies actually model generator location, however.92 

Predicting the likely location of renewable resource additions requires a detailed resource 

assessment – a feature that is available to few of the state RPS cost studies we reviewed. 

8.3 Natural Gas Price Forecasts 

In many studies, the most important input to the avoided cost calculation is the natural gas price 

forecast. This is due to two factors: (1) natural gas prices are highly uncertain, especially when 

compared to coal prices, making gas prices particularly difficult to predict; and (2) the majority 

of studies expect that increased renewable generation will largely displace natural gas-fired 

generation. The importance of the natural gas price forecast is also reflected by the relatively 

large number of studies that examine state RPS cost sensitivity to natural gas prices, as discussed 

earlier in this report. 

Figure 30 presents the delivered natural gas price forecasts used by the state RPS cost studies in 

their base-case analyses (alternate price forecasts used in sensitivity scenarios are not included in 

the figure).93 These forecasts reflect the upward shift in natural gas prices projections over the 

past several years, with most of the higher forecasts in the figure coming from more-recent 

studies. Though significant price discrepancies are apparent in the short term, projected prices 

converge to some degree in the longer term. The thick line marked by squares represents the 

EIA’s 2006 forecast for average natural gas prices delivered to lower-48 electric generators 

 

92 The other three studies (Colorado PPC, Colorado UCS, and Washington Lazarus) allocate aggregate RPS cost 

impacts to utilities according to utility-specific resource requirement provisions in the state’s RPS legislation or by 

comparing a proportional amount of total incremental RPS generation costs to avoided cost forecasts that vary by 

utility. 

93 Not all of the reviewed state RPS cost studies are represented in the figure. Some did not use a natural gas price 

forecast as a model input, and others did not provide the natural gas price data that they used. 
57

(EIA’s more recent 2007 forecast is similar). The EIA forecast is close to the median value of 

the state RPS cost study forecasts, at least in the later years of the forecasts. 

 

Figure 30. Base-Case Natural Gas Price Assumptions of State RPS Cost Studies 

Prices for 2007-2011 NYMEX natural gas futures have recently been trading at much higher 

levels than the vast majority of the forecasts shown in Figure 30.94 As described in Bolinger et 

al. (2006), NYMEX futures prices – which can be locked in with certainty – may offer a moreappropriate proxy for the avoided cost of fuel-free renewable generation than do uncertain 

fundamentals-based price forecasts. This suggests that the majority of the state RPS cost studies 

we reviewed rely on natural gas price forecasts that are lower than what arguably “should” be 

used, if the analysis was prepared today, at least for gas prices through 2011 and probably for 

later years as well. This has important implications for evaluating the results of the cost studies. 

As we note in Section 5.2.2, a change in natural gas prices of 20-25% can lead to a 0.5% impact 

on retail rates. Because many of the state RPS cost studies predict retail rate impacts of less than 

1%, this implies that an increase in natural gas prices of approximately $2/MMBtu relative to the 

base case – not an unreasonable expectation given current natural gas prices – could entirely 

offset the expected incremental cost of the RPS in a number of states.95 

 

94 The NYMEX data series was downloaded on March 5, 2007. Annual prices were estimated by averaging monthly 

prices (without weighting for consumption), and these estimated annual Henry Hub prices were converted to 

average wellhead prices for the lower 48 states using a method published by EIA (Budzik 2002). Wellhead prices 

were then converted to average delivered prices using the differential between average 2006 wellhead prices and 

delivered prices to electric generators in EIA 2006a. 

95 Of course, with higher natural gas prices, renewable energy is more likely to be competing with coal-fired 

generation in the future. Moreover, the effect of higher natural gas prices in mitigating potential RPS costs may be 

counterbalanced by other uncertainties that lead to higher-than-expected renewable generation costs. For instance, 

few of the reviewed studies predicted that wind capital costs would rise, rather than fall. If wind turbine costs 

remain at their current levels, it is possible that the unexpectedly high cost of wind generation will mitigate any cost 

$0.00

$2.00

$4.00

$6.00

$8.00

$10.00

2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024

Delivered Natural Gas Prices ($2003/MMBtu

AZ (PIRG) CA (CRS) CA (UCS) CA/OR/WA CA (LADWP) CO (PPC)

CO (UCS) IA (WUC) MD (Synapse) MN (WUC) NE (UCS) NY (DPS)

PA (B&V) RI (Tellus) TX (UCS) VT (Synapse) WA (Lazarus) WI (UCS)

US EIA VA (CEC) WA (UCS)

NYMEX Futures Strip 

03/05/07
58

9. Conclusions 

With a few exceptions, the long-term rate impacts of state RPS policies are projected to be 

relatively modest. Only two of the 28 state RPS cost studies in our sample predict rate increases 

of greater than 5%, and 19 of the studies project rate increases of no greater than 1% (and six of 

these studies predict rate decreases). The median residential electric bill impact is +$0.38 per 

month. When combined with possible natural gas price reductions and corresponding gas bill 

savings, the overall cost impacts are even more modest, resulting in net consumer savings in at 

least one additional case. 

Not surprisingly, wind power is expected to be the dominant state RPS resource, comprising 

62% of incremental state RPS generation across the reviewed studies. The prevalence of wind 

suggests that wind generation costs (including transmission, capacity, and integration costs, as 

well as capital costs) are particularly important input assumptions to most state RPS cost models. 

The studies in our sample utilize a variety of modeling approaches, methods, and data sources to 

estimate state RPS costs and benefits. A standard cost template has not yet emerged. This is in 

part due to regional differences in state RPS policies and electricity markets, as different 

situations call for different modeling approaches. However, a more important factor may be the 

time and funding constraints imposed on individual studies. State RPS cost studies are typically 

done with limited budgets on short timeframes, and the sophistication and detail of the analysis 

may largely be a function of these factors. 

More-sophisticated models can account for interesting and potentially significant natural gas and 

wholesale electricity price feedbacks and may therefore be better-received by policymakers and 

state RPS stakeholders. These models may also be better able to capture the benefits of 

increased renewable energy deployment. It is not entirely clear, however, that such models 

necessarily improve predictive accuracy. The assumptions for the primary and secondary costs 

of renewable energy, as well as the cost of conventional generation offset by increased 

renewable energy deployment, are likely of far more importance than the type of model used. 

Though this report has focused most heavily on state-RPS-induced rate impacts, an increasing 

number of studies are modeling the macroeconomic or other public benefits of state RPS 

policies, either in addition to, or exclusive of, rate impacts. Similarly, studies are increasingly 

evaluating the sensitivity of state RPS costs to uncertain input parameters, and are considering 

the potential value of renewable energy in reducing certain electricity sector risks. 

State RPS cost studies are becoming more sophisticated, but improvements are still possible. 

Based on our review, we identify a number of areas of possible improvement for future state 

RPS cost studies: 

• Improved treatment of transmission costs, integration costs, and capacity values. 

Transmission availability and transmission expansion costs have become among the most 

 advantages that renewable generation might have achieved under a scenario of high natural gas prices. The 

uncertainties involved with predicting natural gas prices and wind capital costs (to name but two of a plethora of 

uncertain cost-driving factors) underscore the importance of performing scenario analysis. 
59

important barriers to renewable energy in many states, but these costs are often poorly 

understood and imprecisely modeled in state RPS cost studies. The capacity value of 

renewable energy (wind, in particular), as well as the cost of integrating renewable energy 

into larger electricity systems, are likewise emerging as potentially important variables, and 

studies analyzing state RPS policies with relatively high incremental targets must be careful 

to properly account for these potential costs and impacts. 

• More rigorous estimates of the future cost and performance of renewable technologies. As 

the renewable energy market continues to rapidly evolve and expand, the need for accurate, 

rigorous, and up-to-date estimates of renewable resource cost, performance, and potential is 

as acute as ever. Unfortunately, some of the most commonly used data sources for the cost 

and potential of renewable generation technologies are somewhat dated and arguably not up 

to the task. Developing better estimates of future renewable technology cost and 

performance would require time and resources that are beyond the scope of many state RPS 

cost studies, and would probably be best managed by a government agency. The availability 

of such information would improve the credibility of state RPS cost analysis and lend more 

weight to economic analysis of renewable technologies in general. 

• Consideration of competing state RPS requirements. As the number of states that have 

adopted RPS policies continues to grow, the available supply of renewable energy in regions 

with limited renewable potential (e.g., New England) may become more costly due to 

increased demand. Future cost studies would be well served to consider renewable demand 

from existing and potentially new RPS policies in neighboring states and regions and 

evaluate the potential effect of this demand on state RPS rate impacts. 

• Estimating the future price of natural gas. Where possible, base-case natural gas price 

forecasts should arguably be benchmarked to then-current NYMEX futures prices (Bolinger 

et al. 2006). Furthermore, given fundamental uncertainty in future gas prices, a healthy range 

of alternative price forecasts should be considered through sensitivity analysis. To calculate 

the potential secondary impacts of increased renewable energy deployment on natural gas 

prices, either an integrated energy model or the simplified tool developed by Wiser et al. 

(2005) might be used. 

• Evaluation of coal as the marginal price setter. With high natural gas prices, some states 

are shifting away from natural gas towards other resources, especially coal. A few of the 

state RPS cost studies already assume that coal is the marginal fuel type that is offset by 

increased renewable generation, but most of the studies assume that natural gas will be the 

primary source of displaced electricity generation. New studies should more closely 

investigate the possibility that RPS generation may increasingly displace coal-fired and other 

non-gas-fired generation. Such a shift would likely reduce the importance of natural gas bill 

savings, but could also increase the importance of carbon emissions reductions. 

• Greater use of scenario analysis. The inaccuracy of long-term fundamental gas price 

forecasts from the EIA and other private sector firms in recent years underscores the 

importance of using scenario analysis to bound possible outcomes. Not only is the future 

cost of conventional generation unknowable, renewable technologies themselves are 

experiencing rapid changes, both of which render the long-term impacts of state RPS policies 

highly uncertain. Such uncertainty can be evaluated, to a degree, through greater use of 

scenario analysis. Some of the variables that may be most appropriate for scenario analysis 

include renewable technology potential and costs, future natural gas and wholesale electric 

prices, the period of PTC extension, and the potential impact of future carbon regulations. 
60

• Consideration of future carbon regulations. As some states and regions begin to implement 

carbon regulations, renewable generators may stand to benefit. It is also possible that federal 

carbon regulations will be developed within the time horizon of state RPS policies. Although 

these trends may significantly reduce the incremental cost of the renewable generation that is 

required by state RPS policies, the risk of future carbon regulation has only been modeled by 

four of the studies in our sample. In future studies, we recommend that the risk of future 

carbon regulations be explicitly considered, at a minimum though scenario analysis. 

• Accurate representation of state RPS market structure. In some regions of the country, 

state RPS compliance strategies based on short-term markets for RECs have led to 

unexpected cost impacts. All other factors being equal, markets in which electricity suppliers 

primarily rely on spot market REC transactions for RPS compliance may result in 

substantially different consumer cost impacts than markets that are dominated by traditional 

long-term contracting for renewables procurement. Where the former conditions apply, the 

market-clearing model should be used to estimate consumer costs. Where long-term 

contracts are more common, the average-cost model may be more appropriate for estimating 

these cost impacts. Future state RPS cost studies should seek to adopt modeling approaches 

that are consistent with probable RPS market structures. 

• More robust treatment of public benefits. Though an increasing number of studies have 

modeled macroeconomic benefits, the assumptions driving these analyses are often 

inconsistent, and the wide range of results may detract from the credibility of such studies. 

More work is needed to identify the most feasible and defensible assumptions governing the 

public benefits of renewable energy, including the fossil fuel hedge value of renewable 

energy and the benefits of reduced carbon emission, in addition to employment and economic 

development impacts. 

Actual state RPS costs may differ from those estimated in the state RPS cost studies. The 

improvements listed above, if adopted, should lead to more accurate and realistic projections of 

the costs and benefits of state RPS policies in the future. In the meantime, it is difficult to assess 

whether the state RPS impact studies reviewed in this report present overly optimistic or overly 

conservative estimates of future costs. Some of the assumptions in the state RPS cost studies 

that may result in an underestimation of actual state RPS costs include: 

• Wind capital cost assumptions that appear too low in many cases, given recent increases 

in wind costs; 

• Transmission and integration costs that are not fully considered in some instances; 

• Use of an “average cost” model in some situations when a “market-clearing” model may 

be more appropriate; 

• Lack of full consideration for the potential demand for renewable energy from other 

sources, such as demand from other state RPS policies; 

• Increased likelihood that coal-fired generation will set wholesale market prices in some 

regions which, in the absence of carbon regulations, may make renewable generation less 

economic than when renewable energy is presumed to compete with natural gas; and, 

• Expectations in some cases that the federal production tax credit (PTC) will be available 

indefinitely, which may be overly optimistic given the political uncertainty affecting PTC 

extension. 
61

Conversely, a number of other cost study assumptions may result in an overestimation of actual 

state RPS costs, including: 

• Reliance on natural gas price forecasts that are almost universally substantially below 

current price expectations; 

• Secondary natural gas and/or wholesale electric price reductions that have not been 

modeled in many of the studies; 

• The potential for future carbon regulations, which are ignored in most of the studies in 

our sample; and 

• Expectations in many cases that the PTC will only be available for either a very limited 

period or not at all, which may be overly conservative given the recent two-year 

extension of the PTC and the possibility for longer-term extension. 

As states accumulate more empirical experience with actual state RPS policies, future analyses 

should benchmark the cost projections from RPS cost studies against actual realized cost impacts 

as a way to both inform future state RPS modeling efforts and better weigh the potential costs 

and benefits of state RPS policies. 
62

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Berkeley National Laboratory. 

Bolinger, M., Wiser, R. and W. Golove. 2006. “Accounting for Fuel Price Risk When 

Comparing Renewable to Gas-Fired Generation: The Role of Forward Natural Gas Prices.” 

Energy Policy, 34 (6): 706-720. 

Bournakis, A., Cuttica, J., Mueller, S., and G. Hewings. 2005. The Economic and 

Environmental Impacts of Clean Energy Development in Illinois. Chicago, Illinois: Energy 

Resources Center, University of Illinois at Chicago. 

Budzik, P. 2002. U.S. Natural Gas Markets: Relationship Between Henry Hub Spot Prices and 

U.S. Wellhead Prices. Washington D.C.: U.S. Energy Information Administration. 

Western Governor’s Association Clean and Diversified Energy Advisory Committee (CDEAC). 

2006. Wind Task Force Report. 

 

Elliot, R. and A. Shipley. 2005. Impacts of Energy Efficiency on Natural Gas Markets: Updated 

and Expanded Analysis. Report Number E052. Washington, D.C.: American Council for an 

Energy-Efficient Economy. 

Energy Information Administration (EIA). 2004. Electric Sales, Revenue, and Average Price 

2003. Washington D.C.: U.S. Energy Information Administration. 

Energy Information Administration (EIA). 2005. Annual Energy Outlook 2005 with Projections 

to 2025. DOE/EIA-0383(2005). Washington D.C.: U.S. Energy Information Administration. 

Energy Information Administration (EIA). 2006a. Annual Energy Outlook 2006 with 

Projections to 2030. Report No. DOE/EIA-0383(2006). Washington D.C.: U.S. Energy 

Information Administration. 

Energy Information Administration (EIA). 2006b. Annual Energy Review 2005. Report No. 

DOE/EIA-0384(2005). Washington D.C.: U.S. Energy Information Administration. 
63

European Wind Energy Association (EWEA). 2005. Large Scale Integration of Wind Energy in 

the European Power Supply: Analysis, Issues and Recommendations. Brussels, Belgium: 

European Wind Energy Association. 

Giebel, G. 2005. “Wind Power has a Capacity Credit: A Catalogue of 50+ Supporting Studies.” 

e-WindEng, E-publishing (002). 

Gittell, R. and M. Magnusson. 2007. Economic Impact of a New Hampshire Renewable 

Portfolio Standard. University of New Hampshire. 

Grace, B., Wiser, R. and D. Smith. 2003. “Modeling Alternatives in Support of RPS.” 

Memorandum to New York State Energy Research and Development Authority and New York 

Department of Public Service. 

Kammen, D., Kapadia, K. and M. Fripp. 2004. Putting Renewables to Work: How Many Jobs 

can the Clean Energy Industry Generate? Berkeley, California: Renewable and Appropriate 

Energy Laboratory. 

LaCapra, GDS and Sustainable Energy Advantage. 2006. Analysis of a Renewable Portfolio 

Standard for the State of North Carolina. Prepared for the North Carolina Utilities Commission. 

Madsen, T., Telleen-Lawton., Coyne, W. and M. Baker. 2007. Energy for Colorado’s Economy 

Creating Jobs and Economic Growth with Renewable Energy. Environment Colorado Research 

& Policy Center. 

Perryman Group (Perryman). 2005. Texas Power: The Impact of a Substantial Expansion in 

Renewable Power Capacity and Electric Transmission Infrastructure on Business Activity in 

Texas. Waco, Texas. 

Rader, N. and S. Hempling. 2001. The Renewables Portfolio Standard: A Practical Guide. 

Washington, D.C.: National Association of Regulatory Utility Commissioners. 

Renewable Energy Policy Project (REPP). 2001. The Work that Goes into Renewable Energy. 

Research Report No. 13. Washington, D.C.: Renewable Energy Policy Project. (Authors: V. 

Singh and J. Fehrs). 

Repetto, R. and J. Henderson. 2003. “Environmental Exposures in the US Electric Utility 

Industry.” Utilities Policy, 11: 102-111.

Smith, J.C., DeMeo, E., Parsons, B. and M. Milligan. 2004. Wind Power Impacts on Electric 

Power System Operating Costs: Summary and Perspective on Work to Date. Golden, Colorado: 

National Renewable Energy Laboratory. 

van der Linden, N.H., Uyterlinde, M.A., Vrolijik, C., Nilsson, L.J., Khan, J., Astrand, K., 

Ericsson, K. and R. Wiser. 2005. Review of International Experience with Renewable Energy 
64

Obligation Support Mechanisms. ECN-C—05-025. Petten, Netherlands: Energy research 

Centre of the Netherlands. 

Virtus Energy Research Associates (Virtus). 2002. Renewable Resources: The New Texas 

Energy Powerhouse. Austin, Texas: Sustainable Energy and Economic Development Coalition 

and Public Citizen. (Authors: P. Altman, T. Smith, L. Hall, and A. Lonial). 

Wiser, R. and M. Bolinger. 2004. An Overview of Alternative Fossil Fuel Price and Carbon 

Regulation Scenarios. LBNL-56403. Berkeley, California: Lawrence Berkeley National 

Laboratory. 

Wiser, R., Porter, K. and R. Grace. 2004. Evaluating Experience with Renewables Portfolio 

Standards in the United States. LBNL-54439. Berkeley, California: Lawrence Berkeley 

National Laboratory. 

Wiser, R., Bolinger, M. and M. St. Clair. 2005. Easing the Natural Gas Crisis: Reducing 

Natural Gas Prices Through Increased Deployment of Renewable Energy and Energy Efficiency. 

LBNL-56756. Berkeley, California: Lawrence Berkeley National Laboratory. 
65

Appendix A. Bibliography of RPS Cost Studies Included in Sample 

Studies listed in alphabetical order by state. 

Wenger, H. and R. Williamson (PEG). 1998. [Arizona] Solar Portfolio Standard Analysis. 

Presented at the American Solar Energy Society’s Solar ’98 Conference. Albuquerque, New 

Mexico: American Solar Energy Society. Available at: 

http://www.millionsolarroofs.org/articles/static/1/binaries/Solar_Portfolio_Standard_Analysis_A

rizona.pdf 

Madsen, T. and D. Brown (PIRG). 2005. Renewing Arizona’s Economy: The Clean Energy 

Path to Jobs and Economic Growth. Phoenix, Arizona: Arizona PIRG Education Fund. 

Available at: http://www.arizonapirg.org/reports/economyreport.pdf 

Donovan, D., Clemmer, S., Nogee, A. and P. Asmus (UCS). 2001. Powering Ahead: A New 

Standard for Clean Energy and Stable Prices in California. Cambridge, Massachusetts: Union 

of Concerned Scientists. Available at: 

http://www.ucsusa.org/clean_energy/clean_energy_policies/powering-ahead-a-new-standard-forclean-energy-and-stable-prices-in-california.html 

Marcus, B (EC). 2003. Clean and Affordable Power: How Los Angeles Can Reach 20% 

Renewables Without Raising Rates. Los Angeles and Sacramento, California: Environment 

California Research and Policy Center and the Center for Energy Efficient and Renewable 

Technologies. Available at: 

http://www.environmentcalifornia.org/uploads/W2/81/W281yw_w4cQcZb5W8NOBNA/Clean_

And_Affordable_Power_2003.pdf 

Del Chiaro, B. and B. Marcus (EC). 2004. Clean and Affordable Power: Updated Cost Analysis 

for Meeting a 20% Renewables Portfolio Standard by 2017 at the Los Angeles Department of 

Water and Power. Los Angeles, California: Environment California Research and Policy 

Center. Available at: 

http://www.environmentcalifornia.org/uploads/W2/81/W281yw_w4cQcZb5W8NOBNA/Clean_

and_Affordable_Power.pdf 

Bailie, A., Dougherty, B., Heaps, C. and M. Lazarus (Tellus). 2004. Turning the Corner on 

Global Warming Emissions: An Analysis of Ten Strategies for California, Oregon, and 

Washington. Boston, Massachusetts: Tellus Institute. Available at: 

http://www.ef.org/westcoastclimate/B_Tellus_Turning_%20Corner.pdf 

Hamrin, J., Dracker, R., Martin, J.. R., Wiser, Porter, K., Clement, D. and M. Bolinger (CRS). 

2005. Achieving a 33% Renewable Energy Target [California]. Prepared for the California 

Public Utilities Commission. San Francisco, California: Center for Resource Solutions. 

Available at: http://www.resourcesolutions.org/lib/librarypdfs/Achieving_33_Percent_RPS_Report.pdf 
66

Binz, R. (PPC). 2004. The Impact of a Renewable Energy Portfolio Standard on Retail Electric 

Rates in Colorado. Denver, Colorado: Public Policy Consulting. Available at: 

http://www.environmentcolorado.org/reports/ImpactOfRPS.pdf 

Binz, R. (PPC). 2004. The Impact of the Renewable Energy Standard in Amendment 37 on 

Retail Electric Rates in Colorado. Denver, Colorado: Public Policy Consulting. Available at: 

http://www.rbinz.com/Files/Binz%202004%20Colo%20RES%20Report.pdf 

Deyette, J. and S. Clemmer (UCS). 2004. The Colorado Renewable Energy Standard Ballot 

Initiative: Impact on Jobs and the Economy. Boston, Massachusetts: Union of Concerned 

Scientists. Available at: 

http://www.ucsusa.org/clean_energy/clean_energy_policies/the-colorado-renewable-energystandard-ballot-initiative.html 

GDS Associates, Inc. (GDS). 2001. Analysis of Renewable Portfolio Standard Options for 

Hawaii. Submitted to the State of Hawaii Department of Business, Economic Development, & 

Tourism. Marietta, Georgia: GDS Associates, Inc. Available at: 

http://www.hawaii.gov/dbedt/info/energy/publications/rps01.pdf 

Boerger, P. (EEA). 2006. Rate Impact of a Renewable Electricity Standard in Indiana. 

Indianapolis, Indiana: Engineering Economic Associates, LLC. 

Wind, T. (WUC). 2000. Projected Impact of a Renewable Portfolio Standard on Iowa’s 

Electricity Prices. Jefferson, Iowa: Wind Utility Consulting. 

Chen, C., White, D., Woolf, T. and L. Johnston (Synapse). 2003. The Maryland Renewable 

Portfolio Standard: An Assessment of Potential Cost Impacts. Prepared for the Maryland Public 

Interest Research Group. Cambridge, Massachusetts: Synapse Energy Economics, Inc. 

Available at: http://www.synapse-energy.com/Downloads/SynapseReport.2003-03.MarylandPIRG.MD-RPS.03-12.pdf 

Smith, D., Cory, K., Grace, R. and R. Wiser (SEA). 2000. Massachusetts Renewable Portfolio 

Standard Cost Analysis Report. Boston and Natick, Massachusetts: La Capra Associates and 

Sustainable Energy Advantage. Available at: http://www.mass.gov/doer/programs/renew/rpsdocs/fca.pdf 

Grace, R. and K. Cory (SEA). Massachusetts RPS: 2002 Cost Analysis Update – Sensitivity 

Analysis. Natick and Boston, Massachusetts: Sustainable Energy Advantage and La Capra 

Associates. Available at: 

http://www.mass.gov/doer/programs/renew/rps-docs/cau-sap.pdf 

Wind, T. (WUC). 2001. Projected Impact of a Renewable Portfolio Standard on Minnesota’s 

Electricity Prices. Jefferson, Iowa: Wind Utility Consulting. 

Clemmer, S. (UCS). 2001. Strong Winds: Opportunities for Rural Economic Development Blow 

Across Nebraska. Cambridge, Massachusetts: Union of Concerned Scientists. Available at: 
67

http://www.ucsusa.org/clean_energy/clean_energy_policies/strong-winds-opportunities-forrural-economic-development-blow-across-nebraska.html 

Rutgers, the State University of New Jersey (Rutgers). 2004. Economic Impact Analysis of New 

Jersey’s Proposed 20% Renewable Portfolio Standard. Prepared for New Jersey Board of 

Public Utilities, Office of Clean Energy. New Brunswick, New Jersey: Edward J. Blounstein 

School of Planning and Public Policy. Available at: 

http://www.state.nj.us/bpu/reports/EIAreport.pdf 

Center for Clean Air Policy (CCAP). 2003. Recommendations to Governor Pataki for Reducing 

New York State Greenhouse Gas Emissions. Washington, D.C.: Center for Clean Air Policy. 

Available at: http://www.ccap.org/pdf/04-2003_NYGHG_Recommendations.pdf 

ICF Consulting (ICF). 2003. Report of Initial Analysis of Proposed New York RPS. Fairfax, 

Virginia: ICF Consulting. 

New York State Department of Public Service, New York State Energy Research and 

Development Authority, Sustainable Energy Advantage, and La Capra Associates (DPS). 2004. 

New York Renewable Portfolio Standard Cost Study Report II. Albany, New York: New York 

Department of Public Service. Available at: http://www.dps.state.ny.us/rps/RPS-COSTSTUDY-II-Volume-A-2-27-04rev1.pdf 

New York State Department of Public Service, New York State Energy Research and 

Development Authority, Sustainable Energy Advantage, and La Capra Associates (DPS). 2004. 

Renewables Portfolio Standard Order Cost Analysis. Albany, New York: New York Public 

Service Commission. Available at: 

http://www3.dps.state.ny.us/pscweb/WebFileRoom.nsf/Web/85D8CCC6A42DB86F85256F1900

533518/$File/301.03e0188.RPS.pdf?OpenElement 

Potomac Economics (Potomac). 2005. Estimated Market Effects of the New York Renewable 

Portfolio Standard. Fairfax, Virginia: Potomac Economics. Available at: 

http://www.ksg.harvard.edu/hepg/Papers/Potomac.RPS.Market.Impact.Study.6.6.2005.pdf 

Pletka, R., Wynne, J., Abiecunas, J., Scupham, S., Lindstrom, N., Jacobson, R. and B. Stevens 

(B&V). 2004. Economic Impact of Renewable Energy in Pennsylvania. Overland Park, 

Kansas: Black & Veatch. Available at: 

http://www.bv.com/energy/eec/studies/PA_RPS_Final_Report.pdf 

Pletka, R., Wynne, J., Abiecunas, J., Scupham, S., Lindstrom, N., Jacobson, R. and B. Stevens 

(B&V). 2004. Economic Impact of Renewable Energy in Pennsylvania: Analysis of the 

Advanced Energy Portfolio Standard. Overland Park, Kansas: Black & Veatch. Available at: 

http://www.bv.com/energy/eec/studies/PA_RPS_F_AEPS_Analysis.pdf 

Bernow, S. and A. Bailie (Tellus). 2002. Modeling Analysis: Renewable Portfolio Standards for 

the Rhode Island GHG Action Plan. Boston, Massachusetts: Tellus Institute. Available at: 

http://righg.raabassociates.org/Articles/RPS-Modeling.finalb.doc 
68

Deyette, J. and S. Clemmer (UCS). 2005. Increasing the Texas Renewable Energy Standard: 

Economic and Employment Benefits. Cambridge, Massachusetts: Union of Concerned Scientists. 

Available at: http://www.ucsusa.org/clean_energy/clean_energy_policies/increase-the-texasrenewable-energy-standard.html 

Woolf, T., White, D., Chen, C. and A. Sommer (Synapse). 2003. Potential Cost Impacts of a 

Vermont Renewable Energy Standard. Prepared for the Vermont Public Service Board. 

Cambridge, Massachusetts: Synapse Energy Economics. Available at: http://www.synapseenergy.com/Downloads/SynapseReport.2003-10.VT-PSB.Cost-Impacts-VT-RPS.03-32.pdf 

DeLaquil, P., Awerbuch, S. and K. Stroup (CEC). 2005. A Portfolio Risk Analysis of Electricity 

Supply Options in the Commonwealth of Virginia. Prepared for the Chesapeake Climate Action 

Network. Annapolis, Maryland: Clean Energy Commercialization. Available at: 

http://www.chesapeakeclimate.org/doc/VA_RPS_Portfolio_Study_Report-Final_Updated.doc 

Lazarus, M., Lazar, J. and R. Hammerschlag (Lazarus). 2003. Economics of a Washington 

Energy Portfolio Standard: Effects on Ratepayers. Prepared for the Northwest Energy Coalition. 

Deyette, J. and S. Clemmer (UCS). 2006. The Washington Clean Energy Initiative: Effects of I937 on Consumers, Jobs and the Economy. Cambridge, Massachusetts: Union of Concerned 

Scientists. Available at: 

http://www.ucsusa.org/clean_energy/clean_energy_policies/washington-clean-energy-i-937.html 

Clemmer, S., Grace, B. and K. Cory (UCS). 2003. A Study to Evaluate the Impacts of 

Increasing Wisconsin’s Renewable Portfolio Standard. Madison, Wisconsin: Wisconsin 

Division of Energy. Available at: http://energytaskforce.wi.gov/docview.asp?docid=2 

Union of Concerned Scientists (UCS). 2006. Fact Sheet: Increasing Wisconsin’s Renewable 

Portfolio Standard Will Create Jobs and Help Stabilize Energy Bills. Cambridge, 

Massachusetts: Union of Concerned Scientists. Available at: 

http://www.ucsusa.org/clean_energy/clean_energy_policies/increase-wisconsin-rps.html 
69

Appendix B. Base-Case Scenarios Chosen for State RPS Cost Studies 

Study Scenario Chosen for 

Base-Case Rationale

AZ (PEG) Scenario 1, Option 1 Most similar to existing Arizona RPS 

CA (LADWP) “Renewables Above 

Gas/Coal Mix” 

Cost results of this scenario are between the results of the other 

two scenarios 

HI (GDS) “9.5% RPS/Reference Oil 

Price Case” Text of report appears to indicate that this is more likely scenario

IN (EEA) Scenario 1 All other scenarios in the report are compared against this 

scenario 

NY (DPS) 

Average of “Market 

Clearing” and “Average 

Cost” approaches 

Study does not indicate which scenario is more likely 

NY (ICF) Average of Scenario 1 and 

Scenario 2 Study does not clearly identify a base case scenario 

RI (Tellus) 20% RPS target 

Study provides complete set of sensitivity results for this target 

level, but does not provide complete sensitivity results for other 

levels 

TX (UCS) “TEPC Proposal, More 

Likely Scenario” 

TEPC proposal of 10,000 MW by 2025 more closely resembles 

actual legislation 

VA (CEC) 20% RPS, Low Imports 

20% is the RPS target level currently being considered in the 

state legislature. To be conservative, “Low Imports” scenario 

chosen because it has higher cost impacts than “Low Natural 

Gas” scenario. 

VT (Synapse) 

“Vermont-Only 

renewables, Excluding 

Hydro-Québec” 

Although text of report appears to indicate that Hydro-Quebéc 

resources are included in base case, the study provides more 

comprehensive results for the scenario in which these resources 

are excluded 

WI (UCS) “PTC to 2007 no CO2 

savings” 

Supplemental information provided by study authors identifies 

this scenario as the base case 

Note: For studies not included in the table, the base-case is specifically identified in the cost-impact report. 
70

Appendix C. Methodology for Converting Cost Results 

In general, the cost studies provided cost results in at least one of the following formats: 

1) Percentage changes in retail electricity rates 

2) Monthly electricity bill impacts for typical customers 

3) Cents/kWh retail rate impacts 

4) $/MWh cost premium of state RPS generation (RECs) 

5) Annual cost premium/savings of state RPS 

As noted in Section 3.1, we have converted all cost results to the first three of these metrics. We 

consistently relied upon two data sources to perform these conversions: 

• 2003 average monthly residential electricity consumption data and retail rates for each 

state, from EIA (2004) 

• Projected average retail and residential electricity rates by Electricity Market Module 

(EMM) region from the EIA (2005) Annual Energy Outlook (AEO). Where necessary, 

we normalized these rates for each state according to the following formula: 

Expected retail rate in state in 20xx = 

Projected retail rate in EMM region in 20xx * Average retail rate in EMM region in 2003

Average retail rate in state in 2003

Specific calculation steps to arrive at percentage changes in electricity rates, monthly bill 

impacts, and cents/kWh retail rate impacts are detailed below (italicized terms represent 

variables mentioned above): 

1) Percentage changes in retail electricity rates 

To calculate percentage changes in retail rates, it was necessary to first calculate ¢/kWh rate 

impacts (if they were not already provided by the cost study).96 We converted monthly bill

impacts to ¢/kWh by dividing them by average residential monthly electricity consumption

figures from EIA (2004).97 We converted annual cost impacts to ¢/kWh by dividing them by the 

projected electricity load (that is subject to the state RPS) in the relevant year. We then 

calculated the percent change of this ¢/kWh rate impact from the expected retail rate in the 

relevant year. 

2) Monthly electricity bill impacts for typical customers 

To calculate monthly electricity bill impacts, it is also necessary to first calculate ¢/kWh rate 

impacts. We converted percentage changes in retail rates to ¢/kWh by applying these changes to 

the expected retail rate in the relevant year. To convert other cost metrics to ¢/kWh, we 

 

96 If the study provided projected REC prices instead of ¢/kWh retail rate impacts, we used RECs prices as a proxy 

for ¢/kWh. 

97 Note that this calculation yields the ¢/kWh impact for residential customers, which may, depending on the study’s 

cost allocation assumptions, differ from the ¢/kWh impact for all retail customers. This difference, if it exists, is 

likely to be negligible. 
71

followed the calculation steps outlined above. Once we estimated the ¢/kWh rate impact, we 

multiplied it by average residential electricity consumption to arrive at the monthly bill impact. 

 

3) Cents/kWh retail rate impacts 

We converted other cost metrics to ¢/kWh retail rate impacts using one of the methods described 

in the two numbered items above.