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. Depa rtment of Energy under Contract No. DE-AC02-05CH11231. The authors are solely responsible for any omissions or

errors contained herein. 

# E RNEST 

# O RLANDO 

# L AWRENCE 

# B ERKELEY 

# N ATIONAL 

# L ABORATORYDisclaimer

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

Government. While this document is believed to contain correct information, neither the United States Government nor any ag ency thereof, nor The Regents of the University of California, nor any of thei r employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, prod uct, or process disclo sed, or represents that its use would not infringe privately ow ned rights. Reference herein to any specific commercial product, process, or service by it s trade name, trademark, manufacturer, or 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 a nd opinions of authors expressed herein do not necessarily state or reflect those of th e United States Government or any agency thereof, or The Regents of the University of California.

Ernest Orlando Lawrence Berkeley Nati onal 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 Be rkeley 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 Electric ity Delivery and Energy Reliability (Permitting, Siting and Analysis Divi sion) 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. Th e authors are solely responsible for any

omissions or errors contained herein.

### Acknowledgements

The work described in this report was funded by the Office of Electric ity Delivery and Energy Reliability (Permitting, Siting and Analysis Divi sion) 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. Th e authors are solely responsible for any

omissions or errors contained herein. We a ppreciate the funding and c ontinued support of our U.S. Department of Energy colleagues, espe cially 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 inva luable feedback. In addition to those mentioned above, we also thank Bernadette De l 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 Resour ce Mix and Direct Costs..............................................................10 3.1 Methodology for Comparing Re sults from Multiple Studies ............................................10 3.2 Projected Renewable Resour ce 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 Electric ity 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 Technologi es, 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 Va riations in Avoided Cost.....................................................56

8.3 Natural Gas Price Forecasts...............................................................................................5 6

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 Polic ies Currently in Place........................................................................... 1 Figure 2. State RPS Cost-Impact St udies Included in Report Scope ........................................... 4 Figure 3. Primary Authorship and F unding Source of State RPS Cost Studies........................... 8 Figure 4. Incremental Renewable Ener gy Deployment by Study and Technology ................... 12 Figure 5. Mix of Incremental Renewable Gene ration from All Studies Combined (GWh, %). 13 Figure 6. Distribution of Base-Case Imp acts on Average Retail Electricity Rates.................... 14 Figure 7. Projected Electricity Ra te 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 Project ed by State RPS Cost Studies.... 17 Figure 10. Sensitivity Scenarios Modeled by State RPS Cost Studies........................................ 18 Figure 11. Changes to Base-Case Residential M onthly Electricity Bill Impacts by Individual Driver (Cost Decreasing Scenarios)............................................................................ 21 Figure 12. Changes to Base-Case Residential M onthly Electricity Bill Impacts by Individual Driver (Cost Increasing Scenarios)............................................................................. 21 Figure 13. Potential Benefits M odeled by State RPS Cost Studies ............................................. 22 Figure 14. Employment Impacts Pred icted by State RPS Cost Studies ...................................... 24 Figure 15. Wholesale Energy and Capacity Pric e 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 Imp acts to Alternative Wholesale Electric Price

Scenarios..................................................................................................................... 3 0 Figure 19. Projected CO 2 Emissions Displaced by State RPS Po licies in Initial Peak Year of

RPS ............................................................................................................................ . 32 Figure 20. Projected CO 2 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 Ma rket-Clearing State RPS Compliance Model .......... 40 Figure 23. Graphical Represen tation of Average-Cost St ate RPS Compliance Model ............... 41 Figure 24. Approaches for Characteriz ing State RPS Market Structure ..................................... 42 Figure 25. Principal Sources of Renewable Resource Data ........................................................ 46 Figure 26. Principal Sources of Re newable Energy Busbar Cost Data ....................................... 47 Figure 27. Wind Power Capital Cost A ssumptions (Base-Case Analysis).................................. 50 Figure 28. Duration of PTC Availability A ssumed by State RPS Cost Studies (Base-Case Analysis) ..................................................................................................................... 5 1 Figure 29. State RPS Cost Study Methodol ogies for Estimating Avoided Cost ......................... 55 Figure 30. Base-Case Natural Gas Price A ssumptions 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 24Table 4. Effect of Wholes ale Market Price Reductions on State RPS Rate Impacts in 1 st Year of Peak State RPS Target ................................................................................... 26 Table 5. Summary of Ba sic Modeling Approaches .................................................................. 38 Table 6. Technologies Modeled by Each State RPS Cost Study.............................................. 44 Table 7. Secondary Costs of Renewable Gene ration Considered by State RPS Cost Studies . 52i 

### Executive Summary

Introduction

State renewables portfolio standards (RPS) have emerged as one of the most important policy drivers of renewable energy capac ity expansion in the U.S. Collectively, these policies now apply to roughly 40% of U.S. el ectricity load, and may have s ubstantial impacts on electricity markets, ratepayers, and local economies. As R PS 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 prim arily on projecting cost impacts, but sometimes also estimating macroeconomic and environmental effects.

This report synthesizes and analyz es the results and methodologies of28 distinct state or utilitylevel RPS cost impact analyses completed since 1998. 1 Together, these studies model proposed or adopted RPS policies in 18 diffe rent 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 mo deling 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 re aches 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 studi es predict rate increases above 1%, and two of these studies predict rate incr eases of more than 5%. Though most of the studies project relatively limited impacts on retail electricity rate s, the wide range of impacts shown in Figure ES - 1 underscores the large variability among the st udies’ 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 in crease of over seven dollars per month. 3 However, the median bill impact across all of the studies in our samp le 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 Ma gnusson (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 st ate RPS scenario, while we use the term “reference case” to refer to the business-as-usual, non-RPS sc enario. 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 comparin g the long-term state RPS impa cts of studies that provide projected impacts in widely varying fo rmats 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 tec hnology 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 st udies that forecast the renewable technology mix). The renewable generation mix is an input assumption to some st udies and a model output to others. Perhaps not surprisingly, wind is expected to be the dominan t technology, representing in aggregate 62% of incremental state RPS generation across all of these studies combined. Projected wind development is particularly prevalent in the Mi dwest 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 di stant second, and almost all of the expected geothermal additions are from three California st udies. Biomass co-firing and direct combustion account for approximately 8% of expected increm ental generation, while hydro, landfill gas, and solar each comprises less than 4%. 4

4 These percentages are purely intended for illustrative purp oses. 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 po licies on these percentage figures.iii - 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000VT (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 Midwest East West

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

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

studies we reviewed include some form of scenar io 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 pr oduction tax credit, varying projec tions of renewable technology cost, fossil fuel price uncertainty, and wholesal e 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 fact ors 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 dol lar 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 th e macroeconomic effects of state RPS policies. 5 All of these studies predict some level of net empl oyment 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 strong ly on the assumptions of the studies than on the amount of incremental renewable generation requir ed to meet the modeled state RPS policies. These assumptions include the different mixe s of renewable technologies developed, the proportion of in-state versus out-of-state renewa ble project development and manufacturing, and the incorporation (or lack thereof) of energy bill impacts into the m acroeconomic analysis. About a quarter of the studies in our sample also model the risk mitigation benefits of state RPS generation, estimating a broad rang e of reductions in wholesale elect ricity and natural gas prices; still other studies evaluate the sensitivity of the projected cost of state R PS policies to variations in the projected price of natural gas. Half of the studies we reviewed quantify potential environmental benefits, most commonly carbon dioxide (CO 2 ) emissions reductions. Most of these studies indicate that RPS generation is expected to displace CO 2 emissions at a rate that is, on average, slightly higher than that of a natural gas plant. Although the spread of projected CO 2 abatement costs across the studies is extremely broad, a majority of these studies project CO 2 reduction costs that fall within the range of th e 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 methodol ogy for analyzing state RPS cost impacts, the studies in our sample employ a diverse array of modeling appro aches, 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 R PS policies and electricity markets, as different situations call for different modeling approach es. 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 deta il of the analysis is likely to be constrained by these limiting fact ors. Though more sophisticated models can account for interesting and potenti ally significant price feedbacks and may be better received by policymakers and state RPS stakeholders, it is no t entirely clear that such models necessarily improve predictive accuracy. Given the signif icant uncertainty surrounding numerous state RPS cost factors, it is likely that the assumptions govern ing these factors, such as the natural gas price forecast and the presumed availability of the produc tion tax credit, are as or more important than the type of model used.

Studies appear to have underestimated both rene wable technology costs and avoided fuel

costs. The vast majority of studies we reviewed a ppear to have underestimated two major state RPS cost factors: wind power capital costs and na tural gas prices. Since wind is expected to be the dominant contributor to state RPS generati on requirements, wind cost assumptions are critically important for estimating the cost impact s of state RPS policies. Since the studies did not anticipate the sudden leap in wind costs ove r the past several year s, the wind capital cost assumptions in most of the studies, which t ypically fall between $800-1300/kW in the 2005-2010 timeframe, are significantly be low current costs (which are reportedly in the $1400-2000/kW range). This disparity between study expectations and current ma rket reality suggests that (all else being equal) the actual co st impacts of state RPS policie s 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 substa ntially underestimated natural gas prices, which arev perhaps the most important input to the a voided cost estimates of several studies. 6 Current natural gas prices (and near-ter m 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 na tural gas prices will ne gate the effects of underestimating current wind costs; the uncertain ties involved with pred icting 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 co st impacts are combined with possible state RPSinduced natural gas price reductions and corres ponding 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 st ill an evolving process, and that a standard template has not yet emerged. Moreover, like mo st prospective analyses of electricity markets, state RPS cost analysis is an inherently un certain practice, highlighting the importance of evaluating the sensitivity of proj ected state RPS costs to uncertai n 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 sophistic ated, but improvements are still possible. We identify a number of areas of possible improve ment for future state RPS cost studies:

• Improved treatment of transmission costs, integration costs, and capacity values: Transmission availability and transmission e xpansion 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 we ll as the cost of inte grating renewable energy into larger electricity systems, are likewise em erging as potentially important variables, and studies analyzing state RPS policies with relativ ely 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 rapidl y evolve and expand, the need for accurate, rigorous, and up-to-date estimates of renewable resource cost, performa nce, and potential is as acute as ever. Unfortunately, some of the most commonly used data sources for the cost and potential of renewable generation technol ogies are somewhat dated and arguably not up

6 This is not true of studies that assume that avoided cost s 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 be tter estimates of future re newable 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 mana ged by a government agency. The availability of such information would improve the credibil ity of state RPS cost analysis and lend more weight to economic analysis of renewa ble technologies in general.

• Consideration of competing state RPS requirements: As the number of states that have adopted RPS policies continues to grow, the av ailable 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 polic ies in neighboring states and regions and evaluate the potential effect of th is 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 funda mental uncertainty in future gas prices, a healthy range of alternative price forecasts shou ld 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 th e marginal price setter: With high natural gas prices, some states are shifting away from natural gas towards other resources, es pecially 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 gene ration. 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 lik ely reduce the importance of natural gas bill savings, but could also increase the im portance 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 ev aluated, to a degree, through greater use of scenario analysis. Some of the variables that may be most appropriate for scenario analysis include renewable technology poten tial and costs, future natura l gas prices, the period of PTC extension, and the potential im pact 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 increm ental cost of renewable generation required by state RPS policies, the risk of future carbon regulation has only been modeled by four ofvii 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 shor t-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 deve lopment (coupled with high demand for RECs and difficulties in siting and permitting) has result ed in ratepayer costs that are substantially higher than anticipated. State RPS cost studies should seek to adopt modeling approaches that are consistent with probable st ate RPS market structures.

• More robust treatment of public benefits: Though an increasing number of studies have modeled macroeconomic benefits, the assump tions 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 f easible and defensible assumptions governing the public benefits of renewable energy, includi ng 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 es timated 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 t oo 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 potentia l 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 regul ations, may make renewable generation less economic than when renewable energy is presumed to compete with natural gas; and,

• Expectations in some cases that the federa l 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 assumpti ons 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 elect ric price reductions that have not been modeled in many of the studies;

• The potential for future carbon regulations, wh ich 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 R PS modeling efforts and bett er 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 Dist rict of Columbia, have adopted such standards (Figure 1). Additional states, su ch as Illinois and Vermont, have established voluntary standards, while still others are considering enacting obligat ory 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 m eet their renewable purchase obligations in a leastcost fashion. In part to accommodate diverse goals and regional differe nces, however, state RPS policies differ in their design. The definition of eligible renewable proj ects and the amount of renewable energy that is required varies. In many – but not all – juri sdictions, 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 co mpliance 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 lo wer priority technologies, which comprise a second tier. Credit multipliers provide additional state RPS compliance “cr edit” 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 20202 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 re newable energy may lead to subs tantial increases in electricity prices. State RPS proponents often counter thes e 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 stakeholde rs – often proponents or neutral parties, but possibly opponents as well – have authored or commissioned studies to analyze the potential

costs and benefits of su ch policies.

This report summarizes the results and methodol ogies 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 r eality in which no nationa l 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., e ffects 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, unl ess RPS-specific costs are provided. 12 Finally, we exclude

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

The primary purpose of this report is to summari ze, 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 cas es, critique the various methods used by these studies, with a goal of identifyi ng 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 st ate-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 va n 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 Chan ge Action Plan, which models a state RPS as one of several climate change mitigation policies but does not id entify RPS-specific costs, an d we exclude a recent study for Michigan that looks at th e impacts of renewable energy and energy ef ficiency 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 importa nt comparison for future work.3 • Section 4 identifies any alternative scenarios that ar e 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 CO 2 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 stat e RPS market structure.

• Section 7 describes the methodologies and assumptions that the state RPS cost studies have used in modeling renewable re source 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 highlight s 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 re gions of the country, are repres ented 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 Appe ndix 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, Or egon, 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 Scope 15

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

A Z 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

20065 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 th e 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 RPS6 2.2 Design of State RPS Policies in Study Sample

The publication of most of these st udies was timed to coincide with state RPS legislation that had been proposed or implemented, and many studi es 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 ad option. Because some of the studies analyze state RPS proposals that were late r substantially modified or neve r adopted, the RPS design that each study analyzes does not neces sarily reflect the policy that was eventually adopted (if a policy was adopted at all). A few of the studie s have updated their original analysis to more accurately reflect the state RPS design that wa s ultimately adopted; in these instances, we include only the quantita tive results from the updated analysis, though we sometimes describe qualitative aspects of the original studies.

As one might expect, the state RPS policies mode led 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-cas e” 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 exp licitly identify a “base-case” scenario; Appendix B

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

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

incremental amount of new renewable generation needed to ac hieve 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 bene fits of state RPS policies, these few studies compare the modeling output of the state RPS scenario relativ e 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 Tabl e 1. Appendix A contains URLs for th ose 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 increa sed 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 R PS, 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 Include s 0.15% customer-site resource tier

OR (Tellus) 20% 10.6% 2020

PA (B&V) 10% 7.2% 2020 Update to earli er 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 ef ficiency, 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 study8 A few of the studies evaluate state RPS policie s 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 f acilities, integrated gasificatio n 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 gene ration 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 type s of organizations that have served as the pr imary 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%) a nd non-governmental orga nizations (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 grou ps, and in some cases were part of a larger, state-sponsored regulator y proceeding that allowed for public comments on

21 When studies have been funded by multiple entitie s, we only identify the primary funding source.9 draft versions of the study. Mo st of the studies in our sample, however, were not distributed for broad public review prior to publicat ion. It is also noteworthy th at many of the reviewed studies have been produced by organizations and author s that are strongly s upportive of state RPS policies, whereas few of the st udies have been funded or co nducted by state RPS opponents. This report does not attempt to account for any potenti al bias that might result from the type of study author or funding source, though it does sc rutinize the studies’ methods and assumptions more generally.10 

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

This section summarizes two of the most importa nt 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 polic ies. In the latter case, we define direct costs to include the imp act of renewables deploym ent on retail electricity

rates and bills. These direct costs do not incl ude the effect of increa sed renewable generation on the price of input fuels such as natural gas, whic h may then generate consumer savings outside of the electricity sector (this potenti al 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 incl uded 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 th e 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 additi on, the studies use different units to convey cost results, including percentage change in costs (e ither on a retail- or gene ration-cost basis), total incremental system costs in dollars, changes to retail rates in cents per kilowatt-hour (kWh), changes to monthly electricity bills, and re newable energy certifi cate (REC) prices.

Developing a consistent set of metrics for compar ing cost projections across studies is therefore necessary. To do so, we compare cost projections using two metr ics that are easily understood and, where necessary, are readily converted from other data: (1) percenta ge changes in retail electricity rates, and (2) monthly electricity bill impacts for a t ypical residential household. The specific approaches that we used to convert cost data to these metrics are described in Appendix B. To further facilitate compar isons, all cost data have been converted to real 2003 dollars.

It is also difficult to create a method for comp aring 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 repr esent 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 impor tant from a strictly economic perspectiv e, electricity market analyses often emphasize consumer cost impacts. Thoug h these cost impacts may sometimes repr esent wealth transfers rather than true social costs, consumer cost proj ections 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 an d renewable generation results for the 2006-2013 period, which coincides with the time interval during which the Ne w 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 re port annual costs, costs averaged over a given timeframe, and/or the present or net present valu e of RPS-induced costs. The use of averaged data or individual “snapshot” years also compli cates comparisons. More generally, comparing

results from studies that themselves have b een conducted over a span of several years is potentially problematic because underlying condi tions may have changed over this period. Perhaps most obviously, natural gas prices (and pr ice expectations) are much higher today than they were in years past, so a state RPS study co nducted several years ago would naturally yield different results than one conducted in the same manner today.

Given these challenges, complete comparability acro ss all of the studies in our sample is simply not possible. Nonetheless, we temporally nor malize the results from the different studies by presenting results from the first year that each st ate 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 percenta ge target of 10%. 25 (Note that the absolute amount of renewable energy may increase somewhat after the state RPS target ini tially 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 re sults from the initial p eak year is a tractable and consistent method for comparing the projected impacts across studies with very different timeframes, especially considering the data limita tions 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 pot ential long-term costs of state RPS policies). 26 Presenting data from the initial peak ta rget 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 fo cused on cost impacts, the majority (23 of 28 studies) also forecast the mix of renewable technol ogies 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 requi red 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 c ontinued 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 su fficient data to enab le such a comparison.

28 Again, for consistency the data ar e taken from the first year in whic h each modeled RPS reaches its ultimate target. Here and elsewhere in this report, results from Rh ode Island (Tellus) reflect th e impacts of state RPS policies12 Perhaps not surprisingly, wind is expected to be the dominant technology, representing 62% of incremental state RPS generation across all of th e studies combined. Projected wind deployment is particularly prevalent in the Midwest and Te xas, 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 45,000VT (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 Midwest East West

Figure 4. Incremental Renewable Ener gy Deployment by Study and Technology

in Connecticut and Massachusetts as well as in Rhode Isla nd. 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 purp oses. 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 a ll states, and multiple cost studies exist in some states, thereby “double counting” the impacts of those st ates’ 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 me ntioning 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 mo del output. When treated as an input, the renewable energy mix is typically crudely es timated according to the cost study author’s knowledge of the situation in the state(s) being m odeled. When treated as an output, renewable energy deployment is usually estimated by cons tructing an aggregate renewable resource supply curve, with the state RPS target level determ ining 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 ra te 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 typical ly 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 pr edict 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 in1st Peak Target Yr.

Number of Studies

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

Figure 7 summarizes projected electricity rate im pacts 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 ra te increase is predicted by th e 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 addi tions 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 ca se 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 hi gh-cost solar technologies (for reference, the average contribution of solar tec hnologies 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 reta il 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 anal yze essentially the same RPS design in New York estimate retail rate increases of less than one pe rcent (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 imp acts on electric rates agai nst the incremental state RPS target as modeled by each cost study. There is a faint correlation be tween 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 repr esent the high- and low-cost estimates for each study that conducted scenario anal ysis around the base-case results . In some instances, these ranges can be extremely large. For example, th e 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 th e other studies that conduct s cenario 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 TargetChange in Avg. Retail Elec. Rates in 1stYear of Peak RPS Target

n = 30

R

2 = 0.19Diamonds indicate basecase rate impacts.

Vertical error bars represent rate impacts of

alternate scenarios.

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

Direct costs can also be presente d as the expected increase in an average residential consumer’s monthly electricity bill. Figure 9 presents projected cost impact s in this form, along with error bars for those studies that include scenario analys es. As shown in these figures, cost studies of state RPS policies in Eastern stat es (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 im pacts are from studies of Eastern states. The higher expected costs in the East are most-likel y 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 Northeas t. 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 st udies is still modest, at $0.82/month. Among the other (non-Eastern) states, the median m onthly 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 $10PA (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 West Midwest

Base case

Alternate scenarios

Figure 9. Typical Residential Electricity B ill Impacts Projected by State RPS Cost Studies18 

### 4. Scenario Analysis

Twenty-one of the 28 studies we reviewed includ e 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 scenario s that are most commonly modeled are the availability of the federal pr oduction tax credit, varying projec tions of renewable technology cost, fossil fuel price uncertainty, and whol esale 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 pa rticular factors than to others. State RPS cost sensitivity may be caused by a scenario’s potential ly large effect on elec tricity 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 Availab ility

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 briefl y described below, in qualitative terms (as before, we use the term “b ase 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 th eir 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 expect ed 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 ma rket 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 stat e 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-cas e renewable financing terms and/or different contractual arrangements fo r procuring renewable power. Lower cost financing and more favorable contract assump tions 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 in fluence import availability. Increased import availability increases renewable supply and result s 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 a dditional 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, e ligibility provisions incr ease 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 ot her 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 al ternative compliance payment th at 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 presen ts a less costly alte rnative to purchasing renewable energy or RECs.

• Load growth: Reflects changes to load growth assump tions. 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 expos ure to variable fuel costs,

but this reduction in risk ma y result in higher average rate impacts. Depending on their20 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 conclu sions about the relative impact of different cost drivers. Figure 11 and Figure 12, however, show th e expected cost impacts of all of the scenario types modeled in the state RPS co st studies that we reviewed. 34 Within a data column, each marker represents the change in base-case mont hly residential electricity bill impacts caused by an individual scenario from a single state RPS co st study. Figure 11 pres ents data on scenario types that result in lower state-RPS-induced electr icity rate/bill impacts, while Figure 12 presents data on scenario types that ge nerally result in higher elect ricity 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 bi ll 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 rang e 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 th e 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 technol ogy 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 ex ample, none of the individual co st-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 s cenarios 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-increas ing scenarios). This convergence of costreducing factors represents a “best case” scenar io, though bill savings co uld be higher still if more aggressive assumptions are used.

It is also apparent from the data that rate imp acts are far from symmetrical within each scenario, at least among our sample of studies. In three in stances, for example, higher expected fossil fuel prices result in rate savings for an average hous ehold of over $1 per mont h 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 scenar io type, e.g. three different natural gas price forecasts instead of just a high and lo w 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 R PS rate impacts to reach such an extreme level.

36 These individual cost-saving scenarios include: lower wh olesale market price, less de mand 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 scenario s (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 H i g h

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Figure 11. Changes to Base-Case Residential Mont hly 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

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Figure 12. Changes to Base-Case Residential Mont hly Electricity Bill Impacts by Individual Driver (Cost Increasing Scenarios)22 

### 5. Projected Benefits

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

benefits can be divided into three main cate gories: macroeconomic, risk mitigation, and environmental. Figure 13 identifies the numbe r of studies that mode l each of these potential benefits. Though the figure includes only the primary metrics of employment, gross state product, and income under the category of macro economic 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 ex tent that they affect electricity prices. For example, the natural gas price suppression effect described later presumably reduces wholesale electric prices by decreasing the pr ice 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 th e 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 EmploymentEnvironmentalRiskMitigation 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 l east one type of tax effect. Arizona (PEG) quantifies the combined impact of tax revenue and income, but does not prov ide 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 consider ing macroeconomic impacts; seven of the nine

studies in our review that es timate 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 employmen t 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 methods 40 and units 41 , 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 cumu lative impacts in some cases, e.g. th ey 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 alwa ys clear in the studies, so we presen t 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 in direct and induced employment gains an d gross state product impacts. The specific tools used to conduct this an alysis, however, vary by study. Arizona (PEG) is the only study that uses a “back-of-the-envelope” calculation to es timate 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 summ arized 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 (U CS), 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 prov ide impacts that are uniquely associ ated with renewable energy (their analysis also included ener gy 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 ofRenewable Gen.

0 4,000 8,000 12,000 16,000Net 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 efficie ncy 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 fr om 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 effect s and hedging energy price uncertainty.

5.2.1 Suppression of Electricity and Natural Gas Prices

The effect of incremental renewable generation on reducing wholesale elect ricity and natural gas prices is a significant potentia l benefit of state RPS policies. However, few studies have attempted to quantify these price suppression eff ects, 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-margin al-cost renewable genera tion to the electricity system reduces the demand for generation from conventional sources an d may thereby suppress competitive wholesale energy prices. This eff ect 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 debata ble: some studies believe that significant longterm reductions in wholesale energy prices are po ssible, 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 criti cal to consider supplier response to lower prices (e.g., lower prices ma y 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 im pacts 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 fi gure, for ease of comparison), suggesting that any reduction in wholesale energy prices due to a state RPS may be part ially 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 (Tellu s), and Texas (UCS), also include some electricity price suppression effect (at a minimum through lower natural gas prices), but those eff ects are not specifically and separately quantified, and are therefore not reported here. In addition, New Jersey (Rutgers) includes a very small wholesale electricity price supp ression effect that is induced by reductions in electricity demand that themselves are caused by higher overall electric ity prices. Because this effect is fundame ntally different from the price suppression

effect that results from increased re newable 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 ma rkets where the capacity factor of renewa ble 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 pric e reductions. However, even small reductions in overall wholesale energy /capacity prices hold the prospect of offering significant aggregate savings to electricity consum ers (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 generati on on wholesale market prices.

$(0.42)

$(1.59) $(0.60)

$(0.95) $0.24 $1.40

$0.02

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$(0.58)

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$- $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 Pri ce Changes Projected by State RPS Cost Studies

Table 4. Effect of Wholesale Market Price Reductions on State RPS Rate Impacts in 1 st 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 a nd coal. Many recent reports have shown that increased renewable energy and energy efficien cy deployment may put downward pressure on

46 As noted earlier, these electricity-sector consumer savi ngs (where modeled) are embe dded 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; Wise r et al. 2005). Reduced gas prices will result not only in lower wholesale electricity prices (t his effect, where modeled, is included in the direct cost results presented in Section 3), but also in lower end-us e natural gas bills. Increasingly, renewable energy pr oponents cite this benefit in support of state RPS policies, though it deserves note that these consumer savings come at least in pa rt at the expense of natural gas producers.

Five of the studies in our sample quantify natura l gas price effects in their base case analysis. Three of these five studies quantif y the expected natural gas price sa vings 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 reducti ons, but concludes that natural gas prices will decline slightly in 2010 an d increase slightly in 2020, in response to decreases in electricity sect or 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 Pr ojected 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 pri ce savings for illustrative purposes only (i.e., these savings did not factor into the analysis of direct state R PS 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 ga s 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 benchm ark. Maryland (Synapse) models two scenarios in which natural gas prices are assumed to fa ll by 2% and 4% relative to the reference case

forecast.

Natural gas price reductions caused by increase d 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. Nonethele ss, though the expected pr ice 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 aver age residential natural gas bill savings in Colorado will reach $1.25 per month by 2015, compar ed 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 sa vings. Rhode Island (Tellus) estimates average residential natural gas bill savings of $0.22 per m onth 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 time frame, 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 ch ange in natural gas pr ices 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 assume s 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 natura l gas demand in Colorado will have a relatively sizable impact on regional gas prices. More ge nerally, one would expe ct 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. Regiona l 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 uncerta in and prone to significant fluctuation, the price of renewabl e energy is largely fixed. In th e broader literature, a variety of

48 Because the data in Figure 16 are averaged over the “r amp-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 presen ted average, rather than “initial peak year,” savings because data from these studies occasionally show unpredictable e ffects in individual years where gas prices may fluctuate up or down from year to year without any discernible pattern. This is likel y an artifact of the NEMS model, which these studies have used to quantify RPS impacts.

49 We present average, rather than p eak year, data for Texas because the na tural gas savings data from the study exhibit substantial year-to-year variability as di scussed generally in the previous footnote.29 methods have been developed to try to quantify th e benefit of the price cer tainty 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 ga s 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 sc enarios of unexpectedly high natural gas and wholesale electricity prices.

Figure 17 illustrates the sensitivit y 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 incremen tal cost (or increase th e monthly savings) of a typical state RPS policy for an average house hold 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 th e natural gas results, changes in wholesale market prices have moderate effects on the expected bill impacts of st ate 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 sl ope of -0.69. This suggests that a 1 ¢/kWh increase in (reference-case) expected whol esale 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 Berk eley Lab (see Bolinger et al. 2006), th e original Pennsylvania (B&V) study included a hedge adde r of $0.50/MMBtu in its natural gas price forecast for one of its sensitivity scenar ios; however, the updated version of the B&V analys is 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 $0.6 7 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$/MMBtu)Change in Residential Bill Impact in 1st Yr. of PeakRPS Target (2003$/mo.)

Figure 17. Sensitivity of State RPS Bill Impact s to Alternative Natura l 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 RPSTarget (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 wh olesale electricity prices. The results suggest that a 20-25% change in wholesale electric price assumptions alters the expected cost of state31 RPS policies by roughly 1% of overall retail rates, rela tive to the base-case s cenario. This effect is roughly half as large in the case of natural ga s 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 report ed earlier. Our findings show that increases in wholesale electric price expecta tions of 20-25%, or natural gas price expectations of 40-50%, could entirely offset a base-case st ate-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 in itial peak target year of each study (2010 to 2023, depending on the study) shown in Figure 17 is just $4.52/MMBtu. Th is compares to 2010 NYMEX Henry Hub futures prices that in 2005-2006 have regularly exceed ed $6.00/MMBtu when converted to delivered prices in 2003 dollars. 54 If one used today’s expectations fo r future natural gas prices (whether EIA forecasts or extrapolated NYMEX forward curv es), the projected cost of state RPS policies would be significantly below (or savings signifi cant 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 quantifie d 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 th at renewable energy offsets natural gas-fired electricity production. As gas prices have risen, the prospect for coal displacement ha s increased. In this instan ce, 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 na tural gas price forecasts have been consistently below contemporaneo us 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 uncerta in 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 ha ve 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 allowa nce prices, in which case CA/OR/WA (Tellus), Colorado (UCS), Rhode Island (Tellus), Texas (U CS) also incorporate these ef fects).. Future state RPS cost studies may wish to evaluate these compliance cost effects, but without additional documentation andreasoning, 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 h ealth and economic impacts of reductions in air pollutant emissions and water use. Lack of agreement on a credible methodology for estimating these impacts, in dolla r terms, makes quantification challenging. The New Jersey (Rutgers) study provides illustra tive calculations of poten tial environmental and public health benefits using gene ric externality adders, but the calculations are not included in the study’s cost results. A few ot her studies include qualitative di scussions of these impacts.

Focusing on those studies that evaluate possible carbon reduct ions, Figure 19 presents the CO 2 reductions and implied CO 2 emissions rates of generation di splaced by RPS resources in each study’s peak RPS target year. 57 The magnitude of CO 2 emissions reductions, which is a function of the amount of incremental renewable gene ration and the emissions profile of displaced generation, varies tremendously across the studi es, 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 CO 2 (MTCO 2 ) per MWh in the Washington (Tellus) study to a high of 0.73 MTCO 2 /MWh in the Colorado (UCS) analysis. The median displaced CO 2 emissions rate of 0.46 MTCO 2 /MWh is low compared to the national electricity-sector av erage emissions rate of 0.60 MTCO 2 /MWh (EIA 2006b), reflecting an expectation that RPS resources wi ll largely displace generation from natural gas plants. The median displaced CO 2 emissions rate is 25% higher th an the emissions rate of a new combined-cycle natural gas generator.

0.00 0.25 0.50 0.75 1.00

W

A

( Tellus ) WA (U C S)

N Y

( C CAP) CO

( P PC

) TX (UCS) NY (ICF)

AZ

(P

IRG)

RI

(Tell u s ) OR (Tell u s)

N Y (D

P S )
MA

( S EA

) VA (CE C ) CA

( UC

S )

C A

(Tellus) WI (

U CS)

CO (U C S)Displaced Emissions Rate in 1st Peak Target Yr.(MTCO2/MWh)

0 5 10 15 20 25 30CO2 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 CO 2 Emissions Displaced by State RPS Policies in Initial Peak Year of RPS

The low displaced CO 2 emissions rates predicted by some stud ies appear puzzling at first. One study projects avoided CO 2 emissions rates that are lower than the emissions rate of a new

57 Not all of the studies that quantify CO 2 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 CO 2 reductions from the former scenario.33 combined-cycle natural gas plant. A few other studies seem to predict avoided CO 2 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 disp lace, to some degree, in-state hydropower generation. But the cost studi es’ treatment of renewable ener gy imports is perhaps the more significant factor. Specifically, a few of the studies with low rates of CO 2 displacement may not

account for the CO 2 emissions of displaced fossil-fuel generation imports (focusing instead on just the displacement of in-state sources of CO 2 ). 58 If the state RPS policy is expected to displace large amounts of impor ted fossil power, in-state CO 2 reductions can be significantly

lower than the total reducti ons induced by the state RPS. As a result, in-state CO 2 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 CO 2 abatement costs projected by

those studies that estimate CO 2 reductions, focusing again on the peak RPS target year of each study. 59 CO 2 abatement costs vary widely, from a low of -$427/MTCO 2 in Texas (UCS) to a high of $181/MTCO2 ton in New York (ICF), with a median value of $3/MTCO 2 . The wide

variation in CO 2 abatement costs is a reflection of the vari ation in retail rate impact projections among the studies. Not surprisingly, the four st udies 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 signifi cant amount of the conventional generation expected to be displaced is projected to co me from out-of-state generators, and if the corresponding emissions reductions are not counted, the per-t on abatement costs will be unduly

inflated.

These implied CO 2 abatement costs can be benchmarked against the assumed CO 2 regulatory compliance costs that are incorporated in the lo ng-term resource plans of electric utilities. According to Bolinger and Wiser (2005), for example, the recent resource plans of seven

Western utilities assume CO 2 compliance costs (often as scenarios, not necessarily in the basecase analysis) ranging from $0-$64/MTCO 2 (levelized over each util ity’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 modeli ng literature under a ra nge of carbon reduction scenarios, including those estimated by the EIA unde r various regulatory regimes. The spread of abatement costs in Figure 20 is obviously far br oader, but 13 of the 16 state RPS cost studies included in the figure (if we count the Calif ornia/Oregon/Washington Tellus study as three separate studies) project CO 2 reduction costs in the peak target year that fall within the

58 In cases where a state RPS cost study reports CO 2 emissions reductions both in-state and regionally, we use
regional data to present CO 2 reductions and to calculate CO 2 emissions displacement ra tes and implied abatement

costs.

59 These costs were calculated by dividing by the base-case direct state RPS el ectricity cost imp acts (which do not
include natural gas bill reductions) in the initial peak target year of each study by the corresponding CO 2 reductions
in Figure 19. Since these are single-year cost s, they do not represent the average costs of CO 2 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/MTCO 2 upper bound of the Western u tility compliance cost assumptions. Seven of these studies project reductions in CO 2 emissions that come with net savings to electricity consumers.

($50) ($25)

$0 $25 $50 $75 $100

T X (U

C S)

CO

( UCS) WA ( U CS)

CA

(T

e llus

)

CO

( PP

C )
CA

(UC

S )
OR

( Tellu s )

WI (

U CS

)

V A

(C

E C)

NY

(DPS) WA ( T ell

u s)

MA

( SE

A )

RI

(T

e l l u s)

A Z (PIRG)

NY

(CC

A P) NY (I CF

)CO2 Reduction Cost in 1st Peak Target Yr.(2003$/MTCO2) $181

$(427) Median Abatement Cost = $3/MTCO2 Standard Deviation = $125/MTCO2

Figure 20. Projected CO 2 Abatement Costs in Initial Peak Year of State RPS

More generally, the laws and re gulations governing the environm ental impacts of electricity generators are likely to change over the lifetime of elect ricity supply investments, as will the cost of compliance with existing environmental regula tions. These changes could impose substantial costs on electricity-sector shareholders and customers (Repetto and Henderson 2003). Traditional air pollutants (SO 2 , NOx, mercury, fine particulates , etc.) may be regulated more tightly in the future. Perhaps more significantl y, new state or federa l 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 futu re requirements, purchasing or owning renewable energy assets may reduce exposure to these envi ronmental compliance risks. This potential financial value has only been specifically quan tified by four of the reviewed studies: Indiana (EEA) applies a carbon tax of $19/MTCO2 to fossil fu el-based generation in a scenario analysis; Iowa (WUC) considers a carbon credit value of $1-$46/MTCO 2 in various scenario analyses; Washington (UCS) assumes an emission allowa nce 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/MTCO 2 and increases 5% per year thereaf ter. Given recent state activity on carbon emissions regulations, the carbon credit valu e of state RPS policies may be worth further exploration in future analyses.35 

### 6. Comparison of Study Methodologies and Assumptions

Previous sections of this repor t have summarized some of the ke y findings of the state RPS cost studies. We now turn to a description of the ge neral 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 detail ed 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 differe nt 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 approxima te 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 co st of renewable energy an d the avoided cost of conventional fuels that are displaced by renewabl es deployment. Table 5 summarizes some the key aspects of the four modeling approaches de scribed 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 a nd 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 si mpler approaches) or an output (in more detailed supply curve based approaches) of the model.

The general advantage of a spreadsheet mode l 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 we ll 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 renewabl e 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 ref erence-case (non-RPS) resource mix

This approach uses the same general method fo r estimating renewable generation cost as the approach described in Category A, but estimate s avoided utility cost through the use of a generation dispatch model (e.g. GE MAPS, PROS YM). These models are complex software programs that simulate the interaction of supply and demand in an electric system and provide detailed wholesale electric ity 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 wholes ale electricity. The latter is useful in estimating the true wholes ale 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 pri ce feedbacks, because the dispatch model is only run based on the reference-case (pre-RPS) re source mix. This approach may also add additional costs because generation dispatch mo dels 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 mode l 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-R PS resource mix (as in Category B); and (2) the implied state RPS resource mix. The implie d state RPS resource mix is an output of the spreadsheet model, and the cap acity of each renewable genera tor type is input into the generation dispatch model along with conve ntional generators. Though the renewable generators are included in the dispatch model run of the state RPS cas e, the cost of these generators is modeled separate ly in the spreadsheet model. 62 The dispatch model thereby provides electricity production co sts 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 a voided cost of the stat e RPS policy is then

calculated as the difference between the total co sts 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. Th e 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 increa sing levels of renewable generation, which may provide a more accurate forecast of avoided pollu tion emissions than would other techniques. This approach does not, however, model the na tural 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 m odeling the implied RPS resource mix within a dispatch model may require an immense numbe r 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-sect or model that endogenously determines fuel prices, capacity expansion, and electricity pri ces. The two most commonly used integrated energy models used for state RPS cost st udies thus gar are NE MS (National Energy Modeling System), developed by the U.S. En ergy Information Administration (EIA), and IPM (Integrated Planning Model), developed by ICF Consulting. With this approach, the mix of state RPS resources and the production cost s of those resources (as well as the cost of the resources that they offset) are estimated us ing the integrated energy model. Six of the studies in our sample employ this method.

Perhaps the most advantageous feature of thes e 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 assumpti ons), though, as we explain in Section 7.3.2, their methods of doing so are sometimes contr oversial. 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. Un fortunately, 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 l ead to model results, particularly when the model and its source code are proprietary. Furt hermore, an integrated energy model such as NEMS is designed to analyze the national en ergy 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 loosel y summarize the modeling approaches employed by the state RPS cost studies in our sample, not ev ery study in our review fits neatly into one of the four categories. One study in particular deserves mention fo r the uniqueness of its modeling approach. Virginia (CEC) uses “mean-variance por tfolio analysis” to show the cost impacts of electricity generation profiles at va rying levels of financial risk. 63 The study’s model is an Excel workbook that finds the minimum-cost electricity por tfolio at a given leve l of risk, subject to

other constraints. The model’s us e of a risk constraint distinguish es 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 use39 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 mode ling 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. Ho wever, not enough is yet empirically known about the actual cost impacts of state RPS policies to va lidate the accuracy of one model over another. Energy markets are subject to si gnificant uncertainty, and future renewable energy costs, while

less volatile than conventional elec tricity prices, are also uncertain. As a result, the assumptions governing these costs may ultimately prove more im portant 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 s uperior to the rest. Instead, the choice of the modeling approach should be li nked to the time and resources av ailable to the study team, the goals of the study, the need for transparen cy 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 st ate RPS market in a given stat e 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 ma rket 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 throu gh a variety of different contract types, suppliers in some markets may be able to meet their state RPS obligations through multiple contract and compliance strategi es. Renewable energy certificates, or RECs, are used in many RPS markets to demonstrate co mpliance with renewable mandates. RECs can often be sold or traded separately from the electricity commodity itse lf, and thereby create a40 supplemental revenue stream fo r renewable generators. RECs can be bought under long-term contract, or in short-term mark ets. 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 ma rkets 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, ther e is little incentive for renewabl e 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 regard less 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 ex emplified 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, t hough in many cases annual or spot purchases for

RECs are still the most common fo rm of RPS compliance.Levelized Cost of Generation ($/MWh)

P 0

RPS Resource Su pp l y ( GWh ) RPS Demand Level

Total cost of RPS =

Total area under line

Figure 22. Graphical Representation of Mark et-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 repres ents producer surplu s: 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 th e cost of the marginal renewabl e generator, and may therefore approximate a market-clearing model. Because pr ices are fixed at contract signature, however, the market-clearing contract pri ce 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 th ereby 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 st ill-regulated markets). Under this model, it is assumed that these long-term contracts are pri ced based on the actual cost of each renewable energy project (which will differ by technology), a nd 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 Su pp l y ( 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 ut ilize the different approaches to characterizing

the state RPS market structure. Ten of the studi es 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-co st 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 rene wable 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-co st 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 co st 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-estima tion assumptions. Considering the significant structural and regulatory varia tions 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 ma y resemble elements of each approach.

67 New York DPS adopts the long-term contract approach to model the mark et-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 po licy 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 a nd projected costs of the technologies, and the expected demand for renewable generation (whi ch may include non-RPS-driven demand and/or out-of-state demand). In this section, we examin e how the state RPS cost studies in our sample

have accounted for each of these f actors. 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 op tions, the availability of existing resource assessment data, and the time and funding availabl e 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 co st 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 t echnology, which, despite being included in the technology assessments of many cost studies, is pr edicted 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 res ource availability is to a ssume the existence of sufficient resource potential of a si ngle type of renewable technol ogy 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 si zes, 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, co nsider 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 sma ller 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 exis ting facilities as well as new hydro facilities.

69 Of course, studies that analyze state RPS policies with so lar 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 commer cial 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 StudyWindOffshore WindGeothermalHydroLandfill GasBiomass DirectBiomass CoFireBiomassGasificationAnaerobicDigestionMSWPhotovoltaicConcentratingSolar PowerFuel CellOtherNotes

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 re newable 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 generati on requirement is met with a si ngle 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 R PS 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, especia lly 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 re newable 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 ta rget. In addition to providing more credible

estimates of the contribution of each renewabl e technology to meeting state RPS goals, this approach also provides more precise (though not necessarily more accurate) estimates of the aggregate renewable production co st 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 availa ble 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 t echnology is highly dependent on the geographic

limits within which the resource potential for that t echnology is analyzed. These limits, which are prescribed by the state RPS policy that is mode led, 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 tran sactions 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 wi nd speed data or quantifying the potential renewable capacity at specific individual sites. The substantial regu latory, technical, and economic uncertainties affecting renewable reso urce availability and cost are such that

renewable resource characterizati on 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 increas ing specificity, it is unclear wh ether highly detailed resource assessments necessarily lead to significantly improved state RPS cost estimates.

For the purpose of a state RPS cost study, renewa ble 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 res ource potential, typically f unded by public agencies.

Figure 25 identifies the principa l sources of renewable resour ce availability data among the studies in our sample. Though many of the revi ewed 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 prim ary research to develop original resource assessments. This research generally entails collecting market intellig ence through interviewing renewable energy project developers, energy anal ysts, and other industry experts. At a more technical level, it may also in clude deriving resource potential es timates from existing data that is in another form; for instance, calculating poten tial wind capacity and capacity factors from wind speed data. Detailed original resource asse ssments can be costly, and the relatively small number of studies that perform such assessmen ts is more likely due to the limited financial

resources available for these stud ies than to an abundance of ex isting reliable data on renewable resource availability.

NonGovt.

Source (5) Govt.

Source (9)

None

(7) Primary

Research (7)

Figure 25. Principal Sources of Renewable Resource Data47 7.3 Renewable Energy Cost Characterization

Renewable generation costs can be loosely divided into two categ ories: busbar and secondary. Busbar costs are the direct costs of a generation facility at the individu al plant level, without accounting for any secondary effects that the plant may have on util ity operations or transmission expansion. We start our discus sion below on busbar costs, and cove r secondary costs later in this

section.

7.3.1 Sources and Methods for Estimating Busbar Costs

Figure 26 identifies the principal so urces 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 Characteriza tion. 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 cu rves identify the quantity (either aggregate or incremental to the previous year) of RPS-eligible resources that are availa ble at a given levelized cost in a given year. To avoid the potentiall y 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 dete rmines 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 furt her 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 le arning in some form (i.e., expected future reductions in renewable co st due to technological improvements). In some studies, however, these cost reductions are counte rbalanced 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 consid erably across the state RPS cost studies we reviewed. Often, these varia tions can be explained by regi onal factors (e.g., higher wind capacity factors in windier states), by differen ces in financing structures (e.g., municipally owned vs. privately developed reso urces), 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 generati on cost estimates points to a larger problem involving renewable resource data. As the renewable energy ma rket 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 ev er. 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 co st 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 availabl e, however, substantial un certainties 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 curren t data and the uncertainties surrounding future renewable generation costs. These two key assu mptions – wind capital cost and the duration of PTC availability – are specified in many of the st ate RPS cost studies and, as evidenced in Figure 11 and Figure 12, both are potential ly 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 explic itly 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 va ries considerably among the reviewed studies (Figure 27). 75 Among the 17 studies that pr esent these data, the highest capital cost estimate in the 2010-2015 timeframe (from Scenario 1 of the Ne w 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 graduall y decline over time. 77 On the other hand, wind costs are endogenously m odeled and actually incr ease 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 th e idea that capital costs will increase in a given region after the best sites ar e 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 signi ficantly affect the predicted cost of state RPS policies. For example, a change in capita l costs of $100/kW roughly corresponds to a $5/MWh change in levelized generation costs. 80 Of the studies reviewed he re, most predict wind capital costs of under $1200/kW, and some predict long-ter m 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, ri sing energy and steel pri ces, 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 ar e “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 th e 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 in clude the capital cost assumptions of both Scenario 1 and Scenario 2 of the study in Figure 27 because they are substa ntially 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 proj ect 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 co st 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 studi es 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, whic h 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 r eality suggests that (all else being equal) the actual cost impacts of state R PS policies may exceed those estimated in our sample of studies, especially if highe r 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 availa bility assumed by the studies in our sample. 82
The lack of consistency in these assumptions reflects the political un certainty 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 legi slation 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 ex tended 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 ma rket. Some experts believe that a supp ly imbalance caused by the boom in turbine demand following the recent exte nsion of the PTC is largely responsibl e for the recent cost run-up, but other factors (e.g., high steel prices, a weak U.S. dollar, or a move by manufactur ers 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 2019Capital 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/kW51 The final year of PTC availabil ity is most commonly assumed to be 2006. The last six studies shown in the figure assume PTC availability th roughout the entire timeframe of their analysis, while seven studies do not appear to incl ude 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 sp ecific technologies will qualify for the PTC. In addition to wind, some st udies assume that the PTC is available for some or all types of biomass, incl uding landfill gas. Though the PTC was recently extended through the end of 2008 and expanded to include geotherm al, open-loop biomass, and other previously excluded resources, its long-term fate remains hi ghly 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 en ergy, it is not sufficient to only estimate busbar economics. Instead, a variety of secondary costs must also be c onsidered: 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 cost s into their calculation of overall cost impacts.

83 Some of these studies do not consid er the PTC because they exclusively m odel 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 Gen eration Considered by State RPS Cost Studies

Cost Variable Number of
Studies Studies

Capacity value 20 AZ (PEG), CA (CRS), CA/OR/WA (T ellus), 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 (L azarus), WA (UCS), WI (UCS)

These costs can be significant, es pecially in regions with transm ission constraints and aggressive state RPS targets. They are especially rele vant for wind power, which offers a variable production pattern from projects of ten located at some distance from load. The fact that many of the studies in our sample ignore many of these co sts suggests that state RPS cost-impacts may be underestimated by these studies, all else being equal.

Perhaps the most significant secondary cost, es pecially for wind power, is transmission. Transmission costs have become a significant cons traint for many wind power project developers (see, for instance, CDEAC 2006). These transmissi on costs are extremely site-specific, however, and do not lend themselves to the simplifying assu mptions 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 ev ent 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 analys is, 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 de pendable capacity wind power can provide to a system has taken on incr eased 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 a nd therefore have some capacity value. Of the 20 studies that specifically anal yze the capacity value of renewabl e 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, th is approximation may not be a bad one, at least at relatively low levels of wi nd penetration (Giebel 2005).

Wind integration costs represent the combined imp act of incorporating vari able or “as-available” wind power into the grid. The science of unde rstanding and quantifying the integration impacts and costs of wind power has solidif ied 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 produ ction costs, typically under 0.5 ¢/kWh at levels of wind penetrati on 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 in clude administration and transaction costs that accompany state RPS implementation, typically fi nding that these costs are expected to be

small. 85

7.4 Renewable Demand Characterization

Cost studies that use a supply curve to represen t 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 th at 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 ex ist. These sources include competing RPS policies from neighboring states, government ag ency 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 regi ons. 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 c ould yield a sizable underestimat e of state RPS costs if the renewable energy supply curve is sh arply 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 th e potential incremental demand that could come if other states in the region were to adopt state RPS po licies 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 (Pot omac), implicitly accounts fo r 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 cost s (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 R PS scenario. When avoided costs are a model output, on the other hand, incremental renewabl e generation may cause the wholesale market price to differ in the RPS scenario rela tive 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 cost s, (2) temporal and ge ographic variations in avoided costs, and (3) the natural ga s price forecast that is used.

8.1 General Methodologies

The most common method for estimating avoided cost s 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 avoi ded 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 simp le and straightforward, but do es not account for wholesale electric or natural gas price feedbacks, and may not be the preferred method for modeling RPS impacts in situations where th ese 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 substantia lly 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 produc tion 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 ba sed 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 wholesal e market prices to the long- run average cost of the natural gas plant to develop an hourly market price forecast55 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

A Z (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 mode ls to generate avoided cost forecasts. These models differ on one main point: whether or no t RPS resources are included in the dispatch simulation. The California (UCS ), Massachusetts (SEA), New Jersey (Rutgers), and Wisconsin (UCS) dispatch simulations model only the refe rence-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 res ource mix and the implied RPS resource mix, allowing wholesale market prices to vary betw een 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 syst em 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 mode l, either NEMS or IPM, to analyze potential cost impacts. These models are capable of e ndogenously determining fuel prices and capacity expansion, in addition to electric ity prices. The avoided cost in this instance is not a direct output of the model – instead, the model calculates the entire electricity (o r 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 Jers ey 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 ty pes 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 alte rnative approaches altogether. Arizona (PEG) simply assumes that average retail rates will dec line 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 (U CS) bases its avoided cost assumptions on the “high fuel price” case of the Northwest Powe r and Conservation Council’s Fifth Power Plan.

8.2 Temporal and Geographic Variations in Avoided Cost

The avoided costs of increased renewable ge neration 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 impor tant in regions of the country that experience high variability in diurnal and seasonal wholes ale 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 di splaced 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) natura l gas prices are highly unc ertain, especially when compared to coal prices, making gas prices partic ularly difficult to predict; and (2) the majority of studies expect that increased renewable ge neration will largely displace natural gas-fired generation. The importance of the natural gas pr ice 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 fo recasts 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. Th e 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 provid e the natural gas price data that they used.57 (EIA’s more recent 2007 forecast is similar). Th e EIA forecast is close to the median value of the state RPS cost study forecasts, at l east 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 ha ve 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 certain ty – may offer a moreappropriate proxy for the avoided cost of fuel -free renewable genera tion than do uncertain fundamentals-based price forecasts. This suggests th at 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 th rough 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 ma ny of the state RPS cost studies predic t 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 es timated annual Henry Hub prices were converted to average wellhead prices for the lower 48 states using a me thod published by EIA (Budzik 2002). Wellhead prices were then converted to average delivered prices using the differential between averag e 2006 wellhead prices and delivered prices to electric ge nerators 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 na tural gas prices in mitigating potential RPS costs may be counterbalanced by other uncertainties that lead to higher-than-expected rene wable generation costs. For instance, few of the reviewed studies predicted that wind capital co sts would rise, rather than fall. If wind turbine costs remain at their current levels, it is possible that the unexp ectedly 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 2024Delivered 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 /05/0758 

### 9. Conclusions

With a few exceptions, the long-term rate impact s of state RPS policies are projected to be relatively modest. Only two of th e 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 reduc tions 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 th e reviewed studies. The prevalence of wind suggests that wind generation cost s (including transmission, capacity , and integration costs, as well as capital costs) are particularly important in put assumptions to most state RPS cost models.

The studies in our sample utilize a variety of mo deling approaches, methods, and data sources to

estimate state RPS costs and benefits. A standard co st 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 th e sophistication and detail of the analysis may largely be a function of these factors.

More-sophisticated models can account for interes ting and potentially significant natural gas and wholesale electricity pr ice feedbacks and may therefore be better-received by policymakers and state RPS stakeholders. These models may also be better able to cap ture the benefits of increased renewable energy deployment. It is not entirely clear, however, that such models necessarily improve predictive accuracy. The as sumptions for the primary and secondary costs of renewable energy, as well as the cost of conventional generation offset by increased renewable energy deployment, are lik ely of far more importance than the type of model used.

Though this report has focused most heavily on st ate-RPS-induced rate impacts, an increasing number of studies are modeling the macroeconom ic 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 re ducing certain electricity sector risks.

State RPS cost studies are becoming more sophi sticated, 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 e xpansion 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 R PS cost studies. The capacity value of renewable energy (wind, in particular), as we ll as the cost of inte grating renewable energy into larger electricity systems, are likewise em erging as potentially important variables, and studies analyzing state RPS policies with relativ ely 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 rapidl y evolve and expand, the need for accurate, rigorous, and up-to-date estimates of renewable resource cost, performa nce, and potential is as acute as ever. Unfortunately, some of the most commonly used data sources for the cost and potential of renewable generation technol ogies are somewhat dated and arguably not up to the task. Developing be tter estimates of future re newable 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 mana ged by a government agency. The availability of such information would improve the credibil ity of state RPS cost analysis and lend more weight to economic analysis of renewa ble technologies in general.

• Consideration of competing state RPS requirements. As the number of states that have adopted RPS policies continues to grow, the av ailable 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 polic ies in neighboring states and regions and evaluate the potential effect of th is demand on state RPS rate impacts.

• Estimating the future price of natural gas

. Where possible, base-c ase natural gas price forecasts should arguably be benchmarked to th en-current NYMEX futures prices (Bolinger et al. 2006). Furthermore, given fundamental un certainty in future gas prices, a healthy range of alternative price forecasts should be consid ered 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 th e marginal price setter. With high natural gas prices, some states are shifting away from natural gas towards other resources, es pecially 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 gene ration. 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 lik ely reduce the importance of natural gas bill savings, but could also increase the im portance 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 rende r the long-term impacts of state RPS policies highly uncertain. Such uncertainty can be ev aluated, 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 cost s, future natural gas and wholesale electric prices, the period of PTC extension, and the pot ential 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 th e time horizon of state RPS policies. Although these trends may significantly reduce the incremen tal cost of the renewable generation that is required by state RPS policies, th e 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 s hort-term markets for RECs have led to unexpected cost impacts. All other factors bei ng equal, markets in whic h electricity suppliers primarily rely on spot market REC trans actions for RPS compliance may result in substantially different consumer cost impacts than markets that are dominated by traditional long-term contracting for renewables procuremen t. 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 st udies 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 assump tions 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 f easible and defensible assumptions governing the public benefits of renewable energy, includi ng the fossil fuel hedge value of renewable energy and the benefits of reduced carbon emi ssion, in addition to employment and economic development impacts.

Actual state RPS costs may differ from those es timated 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 t oo 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 situ ations when a “market-clearing” model may be more appropriate;

• Lack of full consideration for the potentia l 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 regul ations, may make renewable generation less economic than when renewable energy is presumed to compete with natural gas; and,

• Expectations in some cases that the federa l 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 assumpti ons 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 elect ric price reductions that have not been modeled in many of the studies;

• The potential for future carbon regulations, wh ich 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 co st studies against actual realized cost impacts as a way to both inform future state RPS mode ling efforts and better we igh the potential costs and benefits of state RPS policies.62 

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### Appendix A. Bibliography of RPS Cost Studies Included in Sample

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### Appendix B. Base-Case Scenarios Ch osen 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 iden tify 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 co st 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 scen ario 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-c ase is specifically identified in the cost-impact report.70 

### Appendix C. Methodology for Converting Cost Results

In general, the cost studies provided cost resu lts in at least one of the following formats:

1) Percentage changes in retail electricity rates 2) Monthly electricity bill im pacts 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 s ources to perform these conversions:

• 2003 average monthly residential electricity c onsumption 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 * 2003 in region EMM in rate retail Average 2003 in state in rate retail Average

Specific calculation steps to arrive at percenta ge 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 re tail electricity rates To calculate percentage changes in retail ra tes, 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 el ectricity 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 th e state RPS) in the relevant year. We then calculated the percent change of th is ¢/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 pri ces instead of ¢/kWh retail rate impact s, 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. On ce we estimated the ¢/kWh rate impact, we multiplied it by average residential el ectricity 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.