A Prospective Analysis of the Costs, Benefits, and
Impacts of U.S. Renewable Portfolio Standards
NREL and LBNL are national laboratories of the U.S. Department of Energy.
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Laboratory (NREL) at www.nrel.gov/publications.
Contract Nos. DE-AC36-08GO28308 (NREL) and
DE-AC02-05CH11231 (LBNL)
A Prospective Analysis of the
Costs, Benefits, and
Impacts of U.S. Renewable
Portfolio Standards
Trieu Mai
1
, Ryan Wiser
2
, Galen Barbose
2
,
Lori Bird
1
, Jenny Heeter
1
, David Keyser
1
,
Venkat Krishnan
1
, Jordan Macknick
1
, and
Dev Millstein
2
1
National Renewable Energy Laboratory
2
Lawrence Berkeley National Laboratory
Technical Report
NREL/TP-6A20-67455
LBNL-1006962
December 2016
NOTICE
This report was prepared as an account of work sponsored by an agency of the United States government.
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express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of
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or favoring by the United States government or any agency thereof. The views and opinions of authors
expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.
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iii
CITATION
Mai, Trieu, Ryan Wiser, Galen Barbose, Lori Bird, Jenny Heeter, David Keyser, Venkat
Krishnan, Jordan Macknick, and Dev Millstein. 2016. A Prospective Analysis of the Costs,
Benefits, and Impacts of U.S. Renewable Portfolio Standards. NREL/TP-6A20-67455/LBNL-
1006962. Golden, CO and Berkeley, CA: National Renewable Energy Laboratory and Lawrence
Berkeley National Laboratory. http://www.nrel.gov/docs/fy17osti/67455.pdf.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
iv
PREFACE
This is one report in a series that explores the costs, benefits, and other impacts of state renewable
portfolio standards (RPS), both retrospectively and prospectively. The first report, A Survey of State-Level
Cost and Benefit Estimates of Renewable Portfolio Standards, published in 2014, comprehensively
summarized historical RPS compliance costs, drawing in part on estimates developed by utilities and state
regulatory agencies. The study also reviewed analyses of the broader societal benefits and impacts of
several states’ RPS policies, typically conducted for or by the regulatory agencies or RPS administrators
in those states. However, the small number of such studies, and their widely varying methods and scopes,
ultimately limited the ability to compare benefits across states or to generalize beyond the specific studies
performed. That limitation set the stage for future reports.
The second report in the series, A Retrospective Analysis of the Benefits and Impacts of U.S. Renewable
Portfolio Standards, published in January 2016, analyzed historical benefits and impacts of renewable
energy (RE) used to meet all state RPS policies, in aggregate, employing a consistent and well-vetted set
of methods and data sets. The analysis focuses on three specific benefits: air pollution, greenhouse gas
emissions, and water use. It also analyzes three other impacts: gross job additions, wholesale electricity
market price suppression, and natural gas price suppression. These are an important subset, but by no
means a comprehensive set, of all possible effects associated with RPS policies. That report did not
include a comparison of costs and benefits of renewables used to meet RPS policies, and nor did it assess
the potential costs, benefits, and impacts prospectively based on future increases in RPS targets.
The present report fills that gap by evaluating the future costs, benefits, and other impacts of renewable
energy used to meet current state RPS polices. It also examines a future scenario where RPSs are
expanded. The analysis examines changes in electric system costs and retail electricity prices, which
include all fixed and operating costs, including capital costs for all renewable, non-renewable, and
supporting (e.g., transmission and storage) electric sector infrastructure; fossil fuel, uranium, and biomass
fuel costs; and plant operations and maintenance expenditures. The analysis uses the same framework as
the second report to analyze three specific benefits: air pollution, greenhouse gas emissions, and water
use. It also analyzes two other impacts, RE workforce and economic development, and natural gas price
suppression.
The terminology applied in this series does not align precisely with the traditional concepts of costs and
benefits, but rather is a function of how RPS programs have often been evaluated in practice. This
analysis series, particularly the present report, evaluates renewable energy used to meet RPS policies in
terms of costs, benefits, and other impacts:
• Cost metrics presented in this report include national electric system expenditures and national and
regional retail electricity prices. Previous reports examined the cost of compliance from the
perspective of the utility or other load-serving entity, compared to the costs that would have been
borne in the absence of the RPS.
• Benefits, as analyzed in this report series, consist specifically of environmental and health benefits
that accrue to society at large, rather than to individual utilities. In theory, such benefits may be
negative, representing net environmental costs, if the renewable electricity used for RPS compliance
leads to more harmful environmental impacts than it avoids. These benefits have been of interest to
state policymakers as they adopt RPS polices.
• Other impacts, in the form of resource transfers from one market participant or segment to another,
are also evaluated. These impacts may also entail net costs or benefits to society at large, but our
analyses focus only on the gross impacts, not the net cost or benefit.
The present report is intended to help policymakers, RPS administrators, and other decision makers gauge the
potential significance of the costs as well as a number of key benefits and impacts from state RPS programs.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
v
ACKNOWLEDGMENTS
The authors would like to thank the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency
and Renewable Energy’s (EERE) Office of Strategic Programs for primary funding support for this
analysis. In particular, the authors are grateful to Stephen Capanna and Kara Podkaminer of the Office of
Strategic Programs for their support of this project.
The authors would also like to thank the following individuals for their thoughtful review: Stephen
Capanna and Kara Podkaminer, DOE EERE Office of Strategic Programs; Caitlin Murphy, DOE Office
of Energy Policy and Systems Analysis; J. David Coop, Christopher Hall, and Doreen Harris, New York
State Energy Research and Development Authority; Ed Holt, Ed Holt and Associates; Warren Leon,
Clean Energy States Alliance; and Jeffrey Logan, David Mooney, Robin Newmark, Gian Porro, Gary
Schmitz, and Daniel Steinberg, National Renewable Energy Laboratory (NREL). We also thank Karin
Haas of NREL for editorial support, Nicholas Gilroy of NREL for GIS work, and Stacy Buchanan of
NREL for design support.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
vi
ACRONYMS
AP2 Air Pollution Emission Experiments and Policy (analysis model)
Btu British thermal unit
CO
2
carbon dioxide
CPP Clean Power Plan
DOE U.S. Department of Energy
EASIUR Estimating Air pollution Social Impacts Using Regression model
EIA U.S. Energy Information Administration
EPA U.S. Environmental Protection Agency
GHG greenhouse gas
GW gigawatt
IWG Interagency Working Group
JEDI Jobs and Economic Development Impacts model
kW kilowatt
kWh kilowatt-hour
LSE load-serving entity
MW megawatt
MWh megawatt-hour
NO
x
nitrogen oxides
NREL National Renewable Energy Laboratory
O&M operations and maintenance
PM particulate matter
RE renewable energy
REC renewable energy certificate
ReEDS Regional Energy Deployment System
RGGI Regional Greenhouse Gas Initiative
RPS renewable portfolio standard
SCC social cost of carbon
SO
2
sulfur dioxide
TWh terawatt-hour
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
vii
EXECUTIVE SUMMARY
As states have gained experience with renewable portfolio standards (RPS) policies, many have made
significant revisions to existing programs. In 2015 and 2016, seven states raised and extended their final
RPS targets, while another state enacted a new RPS policy (Barbose 2016b). Interest in expanding and
strengthening state RPS programs may continue, while efforts like recent proposals in many states to
repeal or freeze existing RPS policies may also persist. In either context, questions about the potential
costs, benefits, and other impacts of RPS programs are usually central to the decision-making process.
This report follows on previous analyses that have focused on the historical costs, benefits, and other
impacts of existing state RPS programs (Heeter et al. 2014; Wiser et al. 2016a). This report examines
RPS outcomes prospectively, considering both current RPS policies as well as a potential expansion of
those policies. The goal of this work is to provide a consistent and independent analytical methodology
for that examination. This analysis relies on National Renewable Energy Laboratory’s (NREL’s)
Regional Energy Deployment System (ReEDS) model to estimate changes to the U.S. electric power
sector across a number of scenarios and sensitivity cases, focusing on the 2015–2050 timeframe. Based
on those modeled results, we evaluate the costs, benefits, and other impacts of renewable energy
contributing to RPS compliance using the suite of methods employed in a number of recent studies
sponsored by the U.S. Department of Energy (DOE): a report examining retrospective benefits and
impacts of RPS programs (Wiser et al. 2016a), the Wind Vision report (DOE 2015), the On the Path to
SunShot report focusing on environmental benefits (Wiser et al. 2016b), and the Hydropower Vision
report (DOE 2016).
The analysis is structured around three scenarios: a No RPS scenario, which assumes RPSs do not exist
beyond 2014 and limited economic growth in renewable energy (RE); an Existing RPS scenario, which
assumes RPS requirements continue to grow based on existing state RPS policies as of July 2016; and a
High RE scenario, which assumes that nearly all states adopt an RPS with relatively aggressive targets. In
this analysis, we estimate the costs, benefits, and other impacts of higher levels of renewable energy by
analyzing the Existing RPS and High RE scenario results relative to those from the No RPS scenario.
This approach is used to measure the costs, benefits, and impacts of the RE generation used to meet RPS
policies. There are multiple drivers of new renewable energy—including other policies such as tax
incentives and non-policy drivers—and we do not seek to attribute all of the estimated costs, benefits, and
impacts solely to the RPS policies themselves. With that in mind and based on this approach, the key
findings of the analysis are as follows and summarized in Figures ES-1 and ES-2.
Renewable Generation: In the Existing RPS scenario, renewables (including hydro) reach 26% of total
U.S. electricity generation by 2030 and 40% by 2050, compared to 21% and 34% under the No RPS
scenario. Under the High RE scenario, renewables reach 35% by 2030 and 49% by 2050. Because RE
generation is greater in the Existing RPS and High RE scenarios than in the No RPS scenario, fossil fuel-
based generation is correspondingly lower.
Costs: We estimate incremental costs, relative to the No RPS scenario, in terms of both the net present
value of electric system costs over 2015–2050 and the difference in retail electricity prices. Both
measures of costs are evaluated over a set of sensitivity cases related to future natural gas prices and
renewable technology costs. Both measures consider fuel costs, operations and maintenance (O&M)
costs, and capital costs for new generation, storage, and transmission infrastructure.
• Electric System Costs: For the Existing RPS scenario, incremental system costs (2015–2050) range
from ±$31 billion (-0.7% to 0.8% of the No RPS total system costs) across the sensitivity cases. On a
levelized basis, these costs equate to about ±0.75¢ per kilowatt-hour of renewable energy (kWh-RE).
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
viii
In the High RE scenario, incremental system costs range from $23 billion (0.6%) to $194 billion
(4.5%), which equates to an incremental levelized cost ranging from 0.26¢/kWh-RE to 1.5¢/kWh-RE.
• Electricity Prices: Across the various sensitivity cases and census regions, retail electricity prices in
the Existing RE scenario are up to a maximum of roughly 1¢/kWh higher than electricity prices in the
No RPS scenario. However, estimated incremental prices vary significantly between regions and
years and depend on future RE technology costs and fossil fuel prices. For some sensitivity cases and
regions, we find that electricity prices are lower in the Existing RPS scenario than in the No RPS
scenario. The High RE scenario has a considerably higher upper bound to the range of possible
electricity price increases, with up to a 4.2¢/kWh increase in the most expensive case. Under certain
conditions, however, even the High RE case may result in electricity price reductions in some regions
and years.
Benefits: The study evaluates benefits associated with reduced air pollutant emissions and avoided human
health damages, reduced greenhouse gas (GHG) emissions and avoided climate change damages, and
reduced water use for electric power generation. These benefits are associated with all RE used to meet
the RPS requirements and not with the RPS policies specifically.
• Air Quality Benefits: Cumulative (2015–2050) national emissions of sulfur dioxide (SO
2
), nitrogen
oxides (NO
x
), and fine inhalable particles with diameters that are generally 2.5 micrometers and
smaller (PM
2.5
) decrease by 5.5%, 5.7%, and 4.5%, respectively, in the Existing RPS scenario. As a
result of these reductions, we estimate the health and environmental benefits of the Existing RPS
scenario to be equal to $97 billion using a “central” estimate, equivalent to 2.4¢/kWh-RE. For the
High RE scenario, we estimate cumulative emission reductions of 29% for each of the three air
pollutants assessed (SO
2
, NO
x
, and PM
2.5
), resulting in health and environmental benefits of $558
billion using the central estimate, equivalent to 5.0¢/kWh-RE. In both scenarios, air quality benefits
come primarily from avoided premature mortality, particularly in eastern United States.
• Reductions in GHG Emissions: Cumulative (2015–2050) life-cycle GHG emissions decrease by 6%
in the Existing RPS scenario, which translates into $161 billion of global benefits when applying a
“central value” for the social cost of carbon. These global benefits equal 3.9¢/kWh-RE. In the High
RE scenario, cumulative life-cycle GHG emissions decrease by 23%, resulting in $599 billion of
global benefits when using the central value for the social cost of carbon, equivalent to 5.4¢/kWh-RE.
• Water Use Reduction: Cumulative water consumption in the Existing RPS scenario is 4% lower and
water withdrawals are 3% lower compared to the No RPS scenario. On average, we find that each
megawatt-hour of renewable energy used to meet Existing RPS targets saves withdrawal of 3,400
gallons of water and consumption of 290 gallons of water.
1
In the High RE scenario, water
consumption and withdrawals are both 18% lower. To put these figures in context, the 2030 annual
consumption savings are equal to the water demands of 420,000 U.S. households in the Existing RPS
scenario, and 1.9 million households in the High RE scenario. Many U.S. regions, including water-
stressed regions, see water savings. We do not estimate the monetized water savings benefits because
no standard valuation methods exist.
1
Withdrawals are defined as the amount of water removed or diverted from a water source for use, while
consumption refers to the amount of water that is evaporated, transpired, incorporated into products or crops, or
otherwise removed from the immediate water environment.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
ix
Impacts: The study evaluates two other “impacts,” which are best considered resource transfers rather
than societal benefits.
• RE Workforce Requirement and Economic Development: The Existing RPS scenario requires 4.7
million full-time job-years in RE-related employment, about a 19% increase over the 2015 to 2050
study period compared to the No RPS scenario. The High RE scenario requires in 11.5 million job-
years of RE-related employment, or a 47% increase from the No RPS scenario. Gross RE jobs include
onsite, supply chain, and induced jobs. Gross onsite jobs, which include construction and O&M jobs,
represent 28% and 29% of all gross jobs, respectively, in the Existing RPS and High RE scenarios.
We do not estimate economy-wide net impacts, and the increased RE jobs noted here will be offset by
job contraction in other parts of the economy.
• Natural Gas Price Reductions: Achieving the Existing RPS and High RE scenarios reduces
cumulative (2015–2050) electricity-sector natural gas demand by a total of 35 quads and 46 quads,
respectively, relative to the No RPS baseline. These reductions represent 3.3% and 4.3% of total
projected economy-wide natural gas consumption in the United States over the same period, and tend
to suppress natural gas prices. As a result, natural gas consumer bill savings outside the electric sector
from the Existing RPS scenario total $78 billion on a discounted, present-value basis, which is equal
to a levelized impact of 1.9¢/kWh-RE. Under the High RE scenario, total consumer savings equal $99
billion, or 0.9¢/kWh-RE. These savings come at the expense of producers, and therefore do not
represent societal net benefits but rather resource transfers.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
x
Figure ES-1. Cost, benefits, and impacts of the Existing RPS scenario relative to the No RPS
Scenario, 2015–2050
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
xi
When comparing the costs and monetized benefits, we find that the benefits exceed the costs, even when
considering the highest cost and lowest benefit outcomes (Figure ES-2). Under the Existing RPS scenario,
the high-end costs are 0.75¢/kWh-RE, while air pollution and health benefits total at least 1.2¢/kWh-RE
and GHG benefits total at least 0.9¢/kWh-RE. Under the High RE scenario, the high end costs are
1.5¢/kWh-RE while air pollution and health benefits total at least 2.7¢/kWh-RE and GHG benefits total at
least 1.2¢/kWh-RE. The figures here are presented on a national basis and reflect levelized 2015–2050
values.
Figure ES-2. Comparison of national systems costs and monetized benefits under the Existing
RPS and High RE scenarios
Note: Positive values reflect benefits in the Existing RPS and High RE scenarios, whereas negative values reflect
higher costs relative to the No RPS scenario. Water benefits, gross RE job needs and economic impacts, and natural
gas impacts are not shown here.
Our analysis has several limitations. First, we recognize that there may be more cost-effective ways than
RPS policies to achieve the benefits and impacts discussed. Second, while our analysis examines the RE
needed to meet RPS demand growth going forward, it does not seek to attribute those effects solely to
RPS policies. In other words, the estimates provided reflect an upper bound to the impacts of the policies
themselves because other drivers might lower the influence of RPS policies in adding new RE. Third, our
work distinguishes between the potential benefits and impacts of RPS programs. Impacts are best
considered resource transfers, benefiting some stakeholders at the expense of others, though such impacts
might still be relevant considerations when evaluating state RPS programs. We do not evaluate the net
effects of these impacts and, as such, cannot assess whether or not these impacts reflect net costs or
benefits at a national scale. Fourth, we consider the impacts of RE needed to meet all existing (and
expanded) RPSs in aggregate and do not estimate the impacts of any individual RPS policy. Finally, our
analysis considers an important subset of—but not all—potential benefits and impacts; for example, we
do not quantify land use and wildlife impacts. Despite these limitations, the analysis can inform decision
makers about the prospective costs, merits, and value of state RPS programs as they consider revisions to
existing policies and development of new policies.
-4
-2
0
2
4
6
8
10
12
14
16
18
Electric System
Cost
Air Pollution
and Health
Benefit
GHG Benefit
Benefits (¢/kWh-RE)
Central
Estimate
Existing RPS
Electric System
Cost
Air Pollution
and Health
Benefit
GHG Benefit
High RE
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
xii
TABLE OF CONTENTS
1. Introduction ............................................................................................................................................. 1
Background ............................................................................................................................................. 1
Scope, Methods, and Contribution ......................................................................................................... 2
General Limitations ................................................................................................................................. 3
Roadmap ................................................................................................................................................ 4
2. Methods: Electric Sector Modeling and Impact Assessment Methods ............................................. 5
Overview ................................................................................................................................................. 5
Electricity Sector Modeling and Scenario Definitions ............................................................................. 6
Methods for Assessing Benefits and Impacts ...................................................................................... 11
3. Renewable Deployment and Avoided Fossil Generation under Existing RPSs and
Expanded RE Scenarios .................................................................................................................... 18
4. Costs of Existing RPSs and Expanded RE......................................................................................... 23
System Costs ........................................................................................................................................ 23
Electricity Prices ................................................................................................................................... 24
5. Benefits of Existing RPSs and Expanded RE .................................................................................... 26
Air Pollutant Emissions, Human Health, and Environmental Benefits ................................................. 26
GHG Emissions Reduction Benefits ..................................................................................................... 32
Water Use Reduction Benefits ............................................................................................................. 35
6. Impacts of Existing RPS and Expanded RE ....................................................................................... 39
Gross Renewable Energy Workforce Requirement and Associated Economic Development
Impacts .................................................................................................................................... 39
Natural Gas Price Reduction Impacts .................................................................................................. 43
7. Summary and Conclusions .................................................................................................................. 45
References ................................................................................................................................................. 48
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at
www.nrel.gov/publications.
xiii
LIST OF FIGURES
Figure ES-1. Cost, benefits, and impacts of the Existing RPS scenario relative to the No RPS Scenario,
2015–2050 ....................................................................................................................................... x
Figure ES-2. Comparison of national systems costs and monetized benefits under the Existing RPS and
High RE scenarios ........................................................................................................................... xi
Figure 1.1. States with RPS policies as of November 2016 ......................................................................... 1
Figure 2.1. Modeling and data analysis to support cost, benefit, and impact estimates .............................. 5
Figure 3.1. U.S. renewable energy penetration under three modeled scenarios ....................................... 18
Figure 3.2. Difference in capacity (top) and generation (bottom) between the Existing RPS and No RPS
scenarios through 2050.................................................................................................................. 19
Figure 3.3. Difference in capacity (top) and generation (bottom) between the High RE and No RPS
scenarios through 2050.................................................................................................................. 20
Figure 3.4. Difference in cumulative generation between the Existing RPS (left) and High RE (right)
scenarios relative to No RPS ......................................................................................................... 21
Figure 3.5. Regional incremental RE generation relative to No RPS ......................................................... 22
Figure 4.1. Present value incremental system cost compared to No RPS, 2015–2050 ............................. 24
Figure 4.2. Range of electricity prices relative to No RPS scenario ........................................................... 25
Figure 4.3. Regional ranges of 2030 retail electricity prices relative to No RPS scenario ......................... 25
Figure 5.1. Emissions of (a) SO
2
, (b) NO
x
, and (c) PM
2.5
in three modeled scenarios ............................... 27
Figure 5.2. Regional (a) SO
2
, (b) NO
x
, and (c) PM
2.5
emissions relative to No RPS .................................. 29
Figure 5.3. Present value (2015–2050) air pollution benefits, Existing RPS relative to No RPS ............... 29
Figure 5.4. Present value (2015–2050) air pollution benefits, High RE relative to No RPS ....................... 30
Figure 5.5. Electricity system life-cycle GHG emissions in three modeled scenarios ................................ 32
Figure 5.6. Regional avoided direct-combustion CO
2
emissions relative to No RPS ................................. 33
Figure 5.7. Present value (2015–2050) GHG reduction benefits, Existing RPS relative to No RPS.......... 34
Figure 5.8. Present value (2015–2050) GHG reduction benefits, High RE relative to No RPS ................. 34
Figure 5.9. Electricity sector water (a) consumption and (b) withdrawal in three modeled scenarios ........ 36
Figure 5.10. Regional water consumption (a) and withdrawal (b) savings relative to No RPS .................. 38
Figure 6.1. Gross domestic RE-related jobs in three modeled scenarios .................................................. 39
Figure 6.2. Total domestic RE-related job-years (2015–2050) by (a) renewable technology; (b) onsite,
supply chain, and induced; and (c) construction and O&M ........................................................... 41
Figure 6.3. Regional gross onsite jobs relative to No RPS ......................................................................... 42
Figure 6.4. Electricity sector natural gas (a) consumption and (b) delivered prices in three modeled
scenarios ........................................................................................................................................ 43
Figure 6.5. Present value (2015–2050) non-electric natural gas consumer savings relative to No RPS ... 44
Figure 6.6. Regional (non-electric) natural gas consumer savings relative to No RPS .............................. 44
Figure 7.1. Comparison of national systems costs and monetized benefits under the Existing RPS and
High RE scenarios ......................................................................................................................... 47
LIST OF TABLES
Table 5.1. Emissions reductions, monetized benefits, and mortality and morbidity benefits (2015–2050),
Existing RPS and High RE relative to No RPS .............................................................................. 31
Table 6.1. Gross Total (2015–2050) RE-Related Earnings, Output, and Gross Domestic Product in Three
Modeled Scenarios ........................................................................................................................ 42
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
1
1. INTRODUCTION
BACKGROUND
State renewable portfolio standards (RPS), which require that electric load-serving entities (LSEs) meet a
minimum portion of their load with eligible forms of renewable electricity (RE), currently exist in 29 U.S.
states and Washington, D.C. (Figure 1.1). Along with federal tax credits and other state policies, RPS
programs have been one of the key policy drivers for RE growth in the United States, with more than half
of all renewable capacity additions since 2000 serving RPS demand (Barbose 2016a). State RPS
requirements are scheduled to continue ramping up over time, with most states reaching their maximum
targets between 2020 and 2025, though eight states have targets that increase until 2030 or beyond. Under
existing policies, aggregate RPS requirements will roughly double from 2015 to 2030, reaching 10% of
total U.S. retail electricity sales by 2030 (Barbose 2016b).
Figure 1.1. States with RPS policies as of November 2016
As states have gained experience with RPS policies, many have made significant revisions to existing
programs, including increases to their final targets. In 2015 and 2016, seven states raised and extended
their final RPS targets, while another enacted a new RPS policy (Barbose 2016b).
2
Interest in expanding
and strengthening state RPS programs may continue, while efforts like recent proposals in many states to
repeal or freeze existing RPS policies may also persist. In either context, questions about the potential
costs, benefits, and other impacts of RPS programs are usually central to the decision-making process.
A variety of sources—including several prior studies in the same series as this report—provide insight
into the historical costs, benefits, and other impacts of RPS programs.
3
LSEs in many states are required
to submit compliance cost data to state regulatory agencies, which often issue annual RPS compliance
cost reports. Although the definitions and methods used to calculate RPS compliance costs can vary
2
California, Connecticut, Hawaii, Michigan, Oregon, Rhode Island, and Washington, D.C. raised and extended their
targets; Vermont enacted a new RPS.
3
Previous studies include Wiser et al. (2016a), Barbose et al. (2015), and Heeter et al. (2014).
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
2
across states, total reported costs in 2014 were $2.6 billion, equivalent to 1.3% of retail electricity bills in
RPS states (Barbose 2016a). Compliance costs vary considerably across states, and are generally
increasing over time as states’ targets ramp up (Heeter et al. 2014). A number of econometric analyses
have also estimated the net effect of state RPS policies on average retail electricity prices at the state or
utility level, with results across the studies ranging from a 3% to 7% increase (Morey and Kirsch 2013;
Tra 2016; Wang 2015). Compliance cost data and econometric analyses of the effects on retail electricity
prices both reflect net costs to LSEs or electric customers, but do not reflect broader costs and benefits.
A small number of states have commissioned analyses of broader environmental or other societal benefits
of their RPS programs, though these studies have widely varying methods and scopes and thus offer
limited opportunities to synthesize their findings (Heeter et al. 2014). Responding to that need, Wiser et
al. (2016a) developed the first-ever national assessment of the benefits and impacts of state RPS
programs, focusing on the year 2013. The study estimated $7.4 billion in benefits from reduced climate
change and air pollution damages, along with additional benefits from reduced water consumption. The
study also estimated that RPS policies in 2013 helped support roughly 200,000 gross jobs related to
renewable energy (RE), and led to $1.3 billion to $4.9 billion in consumer savings from reduced
electricity and natural gas prices.
Notwithstanding the insights gained from these various prior studies, two important gaps remain in terms
of informing future policy development. The first is to provide a forward-looking perspective, considering
currently scheduled increases in RPS targets as well as the incremental effects of possible revisions to
those targets. The second is to estimate both costs and benefits in an integrated manner to allow direct
comparison. The present study is intended to address both of these gaps.
SCOPE, METHODS, AND CONTRIBUTION
This study evaluates the costs, benefits, and other impacts of all RE needed to meet RPS demand growth
through 2050, under both existing RPS policies as well as possible expansions. Estimated costs include all
electric infrastructure and operating costs and are measured in terms of both the net present value of
electric system expenditures as well as changes to average electricity prices. Net costs to the electric
system are evaluated under a range of sensitivity cases related to natural gas prices and RE technology
costs. Broader societal benefits and impacts evaluated within the study include: reduction in greenhouse
gas (GHG) emissions, reduction in air pollution emissions, reduction in water use, development of jobs,
economic development, and natural gas price suppression. As discussed below, the latter three items are
best considered “impacts” rather than strict societal benefits in the context of this analysis. For each
benefit and impact, we quantify effects in physical units (e.g., tons of pollutants, gallons of water) and,
where possible, monetize them into dollar-value terms and quantify uncertainty. In presenting results, we
focus on the aggregate effects of RPS programs nationally and regionally; we do not analyze the costs and
benefits of individual state RPS programs.
The analysis of costs, benefits, and impacts is structured around three scenarios: Existing RPS, High RE,
and No RPS. The Existing RPS scenario assumes RPS requirements continue to grow based on state RPS
policies as of July 2016.
4
Additional economic growth in RE above and beyond what is needed to meet
RPS requirements also takes place in the Existing RPS scenario. The High RE scenario represents a
hypothetical scenario in which all states adopt an RPS with expanded targets. The No RPS scenario
assumes RPSs do not exist beyond 2014. Continued economic additions of RE take place under the No
4
This cut-off date was a function of the timing of the analysis. As a result, the analysis does not account for the
increase and extension to New York’s RPS of 50% by 2030, up from 29% by 2015 (adopted in August 2016) or
any other changes since July 2016.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
3
RPS scenario, but RE additions are capped at the levels seen in the Existing RPS scenario. Further details
are described in Section 2.
The study relies on a suite of models and analytical techniques, described in detail in Section 2. We use
the National Renewable Energy Laboratory’s (NREL’s) Regional Energy Deployment System (ReEDS)
and dSolar models to estimate differences in generation, capacity, fuel use, water use, emissions, and
costs under each scenario and sensitivity case. Based on those ReEDS outputs, individual benefits and
impacts are then estimated using methods developed and vetted through a number of earlier U.S.
Department of Energy (DOE) reports. These include the prior report in this series focusing on
retrospective benefits and impacts (Wiser et al. 2016a), as well as the Wind Vision Report (DOE 2015),
the On the Path to SunShot report focusing on environmental benefits (Wiser et al. 2016b), and the
Hydropower Vision report (DOE 2016). Moreover, the scenario construct in our analysis is consistent
with these earlier studies wherein we evaluate differences in a scenario with higher levels of renewable
generation and a baseline scenario where renewable generation is capped.
This report represents the first national-level, integrated assessment of the prospective costs, benefits, and
impacts of state RPS policies. However, we are not alone in analyzing related topics. Individual states
often collect and publish historical data on RPS compliance costs, and several states have evaluated
broader benefits and impacts (Heeter et al. 2014). Various researchers have applied statistical methods to
try to isolate the historical effects of RPS programs on specific policy-relevant criteria, such as GHG
emissions (Eastin 2014; Yi 2015), air pollution (Eastin 2014; Werner 2014), jobs (Bowen et al. 2013; Yi
2013), and retail electricity prices (Caperton 2012; Johnson 2014; Morey and Kirsch 2013; Tra 2016;
Wang 2015). In addition, a previous study in this series provided a national-level assessment of historical
benefits and impacts (Wiser et al. 2016a).
Among studies that have taken a prospective look, many states estimated future RPS costs prior to initial
enactment of their RPS, as summarized by Chen et al. (2007). Prospective cost-effectiveness evaluations
for several states have also been conducted in concert with major revisions to state RPS programs (e.g.,
Rouhani et al. 2016). Prospective studies with a national scope have focused primarily on the potential
effects of a national RPS or clean energy standard (EIA 2012; Goulder et al. 2016; Logan et al. 2009;
Mignone et al. 2012; Paul et al. 2014). To date, however, we are aware of no prior work that has sought to
prospectively evaluate the cost and benefits of all state RPS programs in aggregate.
GENERAL LIMITATIONS
Caveats and limitations associated with individual analytical elements in this study are discussed in
Section 2. In addition to those, a number of general, cross-cutting considerations deserve explicit note.
• Benefits versus Impacts: In presenting the approach and results of our analysis, we distinguish
between potential societal benefits of RPS programs (GHG, air pollution, and water use reductions)
and other impacts of those programs (gross jobs and economic development and natural gas price
reductions). In evaluating potential benefits, we consider both beneficial and detrimental effects from
RPS resources—the latter including, for example, air pollutants emitted by biomass and water use by
concentrating solar power facilities. The impacts that we evaluate are sometimes described as
benefits, and in some cases may be benefits from the perspective of an individual state or entity.
However, at a national or global scale, the impacts of RPS programs related to gross jobs and
economic development and to natural gas price suppression are best considered resource transfers:
benefits for some stakeholders at the expense of others. For these impact categories, we do not
evaluate the net effects over the entire economy and, as such, cannot assess whether or not these
impacts reflect net costs or benefits. Net impacts between stakeholders are possible, but our
assessment does not determine the magnitude or direction of such net effects. Such impacts may still
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4
be relevant to evaluating state RPS programs, especially by state policymakers. However, it is
important to acknowledge any offsetting effects that may take place at the sectoral, regional, national,
or even international scales.
• Scope of Costs, Benefits, and Impacts: Our analysis considers an important subset of—but not all—
potential costs, benefits, and other impacts. For example, we do address integration of utility-scale RE
into the transmission grid within the capabilities of our electric sector models but do not assess the
challenges of integrating RE into distribution grids or the full set of impacts on economic
development or energy supply risks. We also do not quantify the land use impacts from the RE
deployment serving state RPS policies nor the offsetting reduction in impacts from fossil fuel energy
supplies. Other non-quantified environmental impacts include heavy metal releases, radiological
releases, waste products, and water quality impacts associated with power and upstream fuel
production, as well as wildlife, noise, aesthetics, and others.
• Cost-Effectiveness: RPS programs are not the only possible way to achieve the outcomes discussed
in this paper. Rather, as widely recognized in the economics literature, “internalizing externalities” is
generally most cost-effectively achieved by directly pricing those externalities rather than through
technology- or sector-specific policies. This is partly due to possible economy-wide rebound,
spillover, or leakage effects, and also because such policies more directly target the achievement of
public benefits (Borenstein 2012; Edenhofer et al. 2013; IPCC 2011; IPCC 2014; Kalkuhl et al. 2013;
McKibbin et al. 2014; Tuladhar et al. 2014). Research focused on RPS policies has highlighted these
possible effects, finding that the desired benefits of RPS programs may not be fully achieved or
achieved as cost effectively as might be desired (Bushnell et al. 2007; Carley 2011; Fischer and
Newell 2008; Fell and Linn 2013; Rausch and Karplus 2014), though other research suggests that the
pitfalls of such “second-best” policies may be modest (Kalkuhl et al. 2012).
• Additionality: Our analysis estimates the costs, benefits, and other impacts associated with RE used
to meet RPS demand growth going forward, but it does not seek to attribute those effects solely to
RPS policies. Because of the potential leakage and spillover effects noted above, and also because of
the multiple drivers for RE additions—of which state RPS programs are but one—the estimates
presented in this paper may overstate the effects (including both costs and benefits) solely attributable
to state RPS programs. Previous research, for example, has come to mixed conclusions about the
incremental effect of RPS programs on RE deployment (Carley 2009; Carley and Miller 2012; Menz
and Vachon 2006; Sarzynski et al. 2012; Shrimali et al. 2015; Shrimali and Jenner 2013; Shrimali and
Kniefel 2011; Staid and Guikema 2013; Yin and Powers 2010).
• Uncertainty: Considerable uncertainty underlies many elements of our analysis. Where possible, we
quantify the range of possible outcomes. In other cases, we qualify the study results and highlight
areas of uncertainty not explicitly addressed in the analysis.
ROADMAP
The remainder of this paper is structured as follows. Section 2 describes the methods used to model
dispatch and capacity expansion of the U.S. electric generation fleet under each scenario and to estimate
each of the benefits and impacts covered in this report. Section 3 presents modeled shifts in renewable
generation and displacement of fossil fuel generation under each scenario, while Section 4 describes the
modeled costs of the RPS scenarios, both in terms of total system costs and average electricity prices
relative to the baseline scenario. Sections 5 and 6 then present estimates for each of the potential benefits
and impacts in turn: GHG emissions, air pollution emissions, water use, development of jobs, economic
development, and natural gas price suppression. We conclude in Section 7 with a summary of our
findings and their implications.
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5
2. METHODS: ELECTRIC SECTOR MODELING AND IMPACT
ASSESSMENT METHODS
OVERVIEW
This analysis is built on a suite of analytical tools and methods, depicted in Figure 2.1. These tools closely
follow a number of reports conducted for and by the DOE (DOE 2015; DOE 2016; Wiser et al. 2016b),
including our 2016 study evaluating the historical benefits and impacts of existing RPS programs (Wiser
et al. 2016a).
The ReEDS electricity capacity expansion model forms the core of the analysis and is used to estimate
renewable and non-renewable capacity and generation through 2050 under each scenario and sensitivity
case. The dSolar customer adoption model provides inputs to ReEDS for rooftop photovoltaic capacity
additions (Sigrin et al. 2015). In addition to estimating generation and capacity by fuel type, ReEDS also
directly computes total electric system costs, air pollutant (nitrogen oxides [NO
x
] and sulfur dioxide
[SO
2
]) emissions, life-cycle GHG emissions, water consumption and withdrawals, and changes in
regional natural gas prices associated with natural gas consumption by electric power generation. Each of
those outputs is then used to estimate individual benefits and impacts, in many cases requiring additional
analytical tools and computations outside of ReEDS. Each of these steps is described in detail throughout
the remainder of this section.
Figure 2.1. Modeling and data analysis to support cost, benefit, and impact estimates
Note: Red letters in parentheses indicate in which benefit/impact analyses each data set was used.
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6
ELECTRICITY SECTOR MODELING AND SCENARIO DEFINITIONS
The ReEDS model is the core analytic backbone of our analysis (Short et al. 2011; Eurek et al. 2016).
5
It
is a capacity expansion model used to develop future scenarios of the electricity system in the contiguous
United States. Specifically, ReEDS relies on an optimization approach to determine new capacity of
multiple renewable and non-renewable technologies, and the generation from new and existing power
plants, from the present to 2050 while meeting electricity consumption, other grid services, and policy
requirements. ReEDS is designed to address the unique characteristics of many renewable electricity
technologies, particularly the variability and uncertainty of solar and wind resources and the location-
dependence of RE resources. The model accomplishes this through high spatial resolution and statistical
parameterizations (Short et al. 2011) to estimate renewable capacity value, curtailment, and operating
forecast error reserve requirements that inform model investment and dispatch decisions. ReEDS
considers transmission expansion, which is needed to compare, for example, the cost competitiveness of
remote high-quality renewable resources versus lower-quality local resources. The high spatial resolution
in the model is also used to reflect regional differences in policies and to self-consistently project national
and state policy compliance mechanisms under different future conditions.
In this study, we use ReEDS to simulate three scenarios of the U.S. electricity system through 2050, each
of which is described further in the remainder of this section:
• No RPS: A counterfactual scenario that assumes no further growth in RPS requirements beyond 2014
and that economic growth in RE is capped at the same level as in the Existing RPS scenario
• Existing RPS: Based on existing state RPS policies as of July 2016
• High RE: A hypothetical scenario in which all states adopt expanded RPS targets.
For each scenario, the primary ReEDS outputs are annual generation and installed capacity by technology
type from 2014 to 2050. From those outputs, the various costs, benefits, and other impacts reported within
this study are estimated. Costs are estimated directly within ReEDS, while benefits and other impacts are
often estimated using additional analytical techniques that rely directly on ReEDS outputs, as described
throughout the remainder of this section. Incremental impacts are calculated by comparing the Existing
and High RE Scenarios to the No RPS scenario.
Key Input Assumptions
The core assumptions used in the electric sector modeling in this study are consistent with those from
NREL’s 2016 Standard Scenarios report (Cole et al. 2016). In particular, we rely on demand growth and
fossil fuel prices from the EIA Annual Energy Outlook 2016 Reference Case (EIA 2016) and renewable
and non-renewable technology cost and performance assumptions based on the NREL Annual
Technology Baseline 2016 “mid” projections.
6
In these projections, levelized costs for land-based wind
energy are estimated to decline from 2014 levels by 16% by 2030 and 21% by 2050. The unsubsidized
levelized costs of energy for utility photovoltaics are estimated to decline from 2014 levels by as much as
68% by 2050. Other generation technologies are also estimated to experience cost declines of varying
levels as reported in the Annual Technology Baseline 2016 data. Storage and transmission costs are based
5
ReEDS has been widely used in previous analysis of long-term renewable futures (DOE 2015, 2016; Wiser et al.
2016a; NREL 2012) and to simulate scenarios for analyses of a broad range of state and federal energy policies
and regulations (Mai et al. 2016; Cole et al. 2015; Lantz et al. 2014; Mignone et al. 2012; Bird et al. 2011; Logan
et al. 2009).We use the 2016 Final Release version of ReEDS in our analysis. Additional information about
ReEDS can be found on the model website: http://www.nrel.gov/analysis/reeds/
.
6
http://www.nrel.gov/analysis/data_tech_baseline.html
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7
on the same assumptions from the NREL 2016 Standard Scenarios report (Cole et al. 2016; Eurek et al.
2016). All three model scenarios—No RPS, Existing RPS, and High RE—rely on the same set of
assumptions.
7
Recognizing the uncertainties in future technology costs and fuel prices, we also model four additional
sets of sensitivities that include high and low assumptions for natural gas prices and renewable
technology costs. The natural gas price scenarios are based on the Annual Energy Outlook 2016 High and
Low Oil & Gas Resource cases (EIA 2016), and the renewable technology sensitivities are based on the
Annual Technology Baseline 2016 High and Low RE cost projections. For all sensitivities, only one set
of parameters was varied at a time, and all other assumptions were kept consistent with the primary
scenario. The full set of three scenarios (Existing RPS, High RE, and No RPS) was modeled across the
natural gas price and renewable cost scenarios, and the results were used to estimate a range in costs. The
various benefit and impact categories were evaluated using only the central-case assumptions for fuel
prices and RE technology costs, although the cost categories are presented as a range based on the results
from the full set of natural gas and RE price sensitivities. Uncertainties in many of those benefit and
impact categories were also estimated but were based on other underlying sources of uncertainties.
Existing RPS Scenario
The Existing RPS scenario is based on all existing state RPS policies, as of July 2016.
8
We model primary
RPS requirements as well as technology-specific carve-outs and account for state-specific technology
eligibility requirements. RPS demand projections developed by Lawrence Berkeley National Laboratory
are used to inform the modeled RPS requirements, resource eligibility, and technology carve-outs.
9
The
Lawrence Berkeley National Laboratory RPS demand projections are converted to a percent of electricity
sales in ReEDS, to ensure consistency with the ReEDS electricity consumption growth and behind-the-
meter distributed generation projections.
7
We do not model technology learning in ReEDS. In addition, although base assumptions around fuel resource
supply are consistent across all scenarios, ReEDS includes supply curves to reflect the price elasticity of power
sector natural gas consumption. As a result, the output gas prices differ between scenarios, and these differences
are used in our assessment of impacts to natural gas consumers and producers.
8
The scenario includes recent revisions to state RPS policies in Oregon, Rhode Island, Vermont, and the District of
Columbia. ReEDS does not have a region specifically for the District of Columbia, but includes any electricity
demand, generation, or policies in the District of Columbia in the Maryland region. Importantly, the scenario does
not include the New York clean energy standard passed in August 2016 or any other policies enacted since then
because the model scenarios were complete by the time the policies were announced. Unless otherwise noted by
the policy, we maintain the same RPS requirement (in percent terms) for all years after the terminal year of the
policy. Model representation of existing RPS policies can be found in the appendix of Frew et al. (2016) and Eurek
et al. (2016).
9
Some analyst judgment was needed to represent resource eligibility, particularly for existing and new hydropower
resources and for qualifying biomass facilities. Judgments were also applied when modeling in-state requirements
and incentives. In addition to primary-tier RPS requirements, we also model three technology-specific carve-outs
for wind, solar, and distributed generation. For the latter two carve-outs, we assume for each state whether or not
the carve-out is met by utility-scale or distributed solar based on policy requirements or, when no clear
specification is available, historical practices. Finally, we reviewed existing (2015) RE capacity to assess whether
it was contractually available to meet future RPS compliance. This assessment resulted in an estimate of about 40
terawatt-hours (TWh) (out of about 160 TWh excess RE generation beyond RPS requirements) of 2015 RE
generation in the existing fleet available for future RPS obligations. These 40 TWh are not deducted in the No
RPS scenario and therefore we do not fully capture the impacts of these potentially “banked” renewable energy
certificates. They do, however, help to reduce compliance costs as they lower the need for investments in new
(post-2014) RE capacity to meet growing RPS requirements. See Barbose (2016a) and http://rps.lbl.gov
for further
details on the RPS data used in the ReEDS modeling.
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8
RPS policies are represented in ReEDS in terms of constraints requiring a minimum number of renewable
energy credits (RECs) from RPS-eligible technologies to be retired in each year for load served in RPS
states. We allow interstate REC trading based on legislated eligibility rules and historical practices (Holt
2016; Heeter et al. 2015).
10
We also allow states to satisfy compliance obligations via alternative
compliance payment mechanisms rather than by retiring RECs, where applicable.
11
Further details on
ReEDS’ representation of RPS policies are described in Frew et al. (2016) and in the latest ReEDS
documentation (Eurek et al. 2016).
Also important to note is that RE generation is allowed to exceed RPS requirements, thus some additional
“economic RE” is generated. Economic RE is assessed within the least-cost framework of the ReEDS
model based on the assumed costs of all generation technologies, including interconnection, transmission,
and other factors, as well as the relative value of the different modeled options to the system. That
incremental economic RE generation, above and beyond what is needed to meet RPS demand growth, is
used as a constraint on RE growth in the No RPS scenario, as described below. Finally, other major
existing policies are modeled in this scenario, including federal RE tax credits and state and regional
carbon policies. The one major exception is the Clean Power Plan (CPP), which is used as the point of
reference for constructing the High RE scenario and is excluded from the Existing RPS scenario.
High RE Scenario
This scenario represents a hypothetical future in which all states choose to adopt relatively aggressive
RPS policies and is intended to help inform efforts by states who may be considering new or expanded
RPS targets. As a proxy for what this might entail, RE targets under this scenario are based on the level of
RE growth that would occur if states were to meet the entirety of their CPP compliance obligations solely
with RE generation.
12,13
This is implemented in ReEDS by limiting annual natural gas-fired generation to
the amounts in the Existing RPS scenario.
14
To be clear, this scenario specification is not intended to
suggest that such a strategy will be least-cost nor does it presuppose that the CPP will be implemented.
However, given that the CPP has been a common consideration among stakeholders in recent discussions
about new or revised RPS policies, it provides a useful point of reference. Also important to note is that
this scenario does not represent any particular economic or technical upper limit on RE.
No RPS Scenario
This scenario serves as the counterfactual baseline from which the costs, benefits, and other impacts in the
Existing RE and High RE scenarios are measured. This scenario holds RPS requirements constant after
10
Restricting REC trading based on historical practices may be overly stringent on future REC trading. The model
REC trading representation and trading limits are applied to ensure that RECs are only retired once and to avoid
REC trading between states that are not allowed by legislation.
11
The model treats all RPS states as though alternative compliance payment mechanisms are available. However,
for states that, in fact, do not allow alternative compliance payments, we assume arbitrarily high alternative
compliance payment levels ($200/megawatt-hour (MWh) for primary-tier RPS requirements and $400/MWh
values for carve-out requirements) in order to ensure that the modeled RPS requirements are met with RECs.
12
The CPP regulations allow states various options for compliance, including state measures plans, which could
include RPS policies that are “federally enforceable” and can be used to comply with the CPP.
13
We model this scenario as though the CPP were implemented with “mass-based” emission targets for each state,
with no allowance trading between states.
14
By restricting coal-to-gas fuel switching beyond that found in the Existing RPS scenario, this implementation
forces the model to reduce generation from CO
2
-emitting plants. Because ReEDS does not have other endogenous
mechanisms to reduce emissions (e.g., heat rate upgrades or energy efficiency), it deploys greater amounts of
renewable energy to replace the reduced generation.
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9
2014 (also reducing the amount of exogenously specified rooftop photovoltaics associated with solar and
distributed generation RPS carve-outs).
Aside from removing RPS requirements, the other key feature of the No RPS scenario is that economic
RE growth is allowed to take place, but is capped at the level of economic RE in the Existing RPS
scenario. The rationale for this approach is to be able to assess the effects of all RE used to meet RPS
demand growth and to provide a baseline for assessing system costs. To implement the cap on economic
RE growth, we apply an upper limit on annual RE generation for each individual census region.
15
Specifically, the limit is equal to the difference between annual RE generation and annual RPS
requirements in the Existing RPS scenario.
16
The cap is applied at the census division level rather than the
state level to account for interstate REC transactions and electricity transmission.
17
Similar to the Existing RPS scenario, the No RPS scenario also excludes the CPP but includes all other
existing state and federal policies. However, the stringency of state and regional carbon policies (in
particular, the Regional Greenhouse Gas Initiative (RGGI) and California’s cap-and-trade program) was
reduced in the No RPS scenario. New RE is one mechanism used to comply with those state and regional
carbon policies; however, because we are restricting RE generation in the No RPS scenario, the
stringency of the carbon emissions requirements was reduced as to not exaggerate costs in this scenario.
18
Metrics to Estimate Electric System and Consumer Costs
Two cost metrics are calculated in ReEDS and reported in this study. The first is the net present value of
system costs, which includes all electric system expenditures through the study horizon (2015–2050). The
expenditures include all fixed and operating costs, including capital costs for all renewable, non-
renewable, and supporting (e.g., transmission and storage) electric sector infrastructure; fossil fuel,
uranium, and biomass fuel; and plant operations and maintenance (O&M). Future costs are discounted
15
The scenarios are constructed as follows: First, the Existing RPS scenario, which allows for RE generation in
exceedance of RPS requirements, is modeled. Then, for each census region and year we evaluate the difference
between RE generation in this scenario and the aggregate RPS requirement in the region. This difference is then
used to construct a constraint that caps RE generation for the No RPS scenario. (We also reduce rooftop PV
generation according to any regional solar or distributed generation carve-outs.) Finally, using the new caps, we
simulate the No RPS scenario. As a result, differences in RE generation between the Existing RPS and No RPS
scenarios reflect the RPS requirements.
16
This scenario construct implicitly associates the more-expensive RE generation to the RPS required share because
the cap on RE generation in the No RPS scenario would result in lower-cost RE to be deployed first in that
scenario.
17
RPS compliance can often be met by out-of-state resources and can impact the dispatch of out-of-state generators.
We acknowledge that census division boundaries do not align perfectly with REC and electricity markets, but
implementing the caps at the census region level captures these regional effects to some degree. This regional
representation also helps to mitigate the inability of ReEDS to track REC banking.
18
We apply an iterative process to estimate the revised carbon cap levels. This process required running the No RPS
scenario without any deviations from the carbon caps in California and the RGGI states. We estimate the carbon
intensity of incremental RE—defined as the different in RE generation in the Existing RPS and No RPS
scenarios—by using the ratio of the incremental RE generation to avoided CO
2
emissions. These carbon intensities
are estimated for in-state/in-region generation and imported generation. The carbon caps are then adjusted by these
estimated carbon intensities and the amount of incremental generation. The ReEDS documentation (Eurek et al.
2016) describes the standard representations of RGGI and the California cap-and-trade programs.
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10
using a 3% (real) social discount rate.
19
In addition, due to the long lifetimes of electric system
infrastructure, we only include fractions of the capital expenditures that are incurred by 2050. The ReEDS
documentation (Eurek et al. 2016; Short et al. 2011) explains this apportionment in detail.
20
Although the
production and investment tax credits are included in the model decision-making, they are not included
when estimating the net present value of system costs because these RE tax credits reflect a transfer of
costs between taxpayers and the electricity system. Including the tax credits in this metric would lower
the estimated values from what are otherwise presented here.
The second cost metric reported is the retail electricity price. For this study, electricity prices are based on
the marginal values of key restrictions used in the model, including load balancing, operating reserves,
planning reserves, and policy constraints.
21
These prices are akin to hourly locational marginal prices used
by system operators for electricity market clearing, but the prices output by ReEDS are averages over all
hours of the year and include capacity prices and ancillary service prices. Thus, they implicitly cover both
fixed and variable costs for all generators. Although competitive pricing is not ubiquitous in all U.S.
regions, a majority of electricity sales occur in restructured markets, thus these modeled prices are a
useful metric for estimating relative costs incurred by the bulk power system.
22
ReEDS does not model
the distribution system—including any needed upgrades or maintenance—and we do not make any
assumptions or estimates for how distribution system costs might evolve across scenarios. ReEDS also
does not estimate retail mark-ups. We therefore assume that differences in ReEDS-estimated electricity
prices are passed through directly to retail electricity prices.
As with any model, ReEDS requires many simplifications. These simplifications can impact all model
results, but can have particularly significant impacts on estimated costs. We note some of the key
limitations and caveats of ReEDS with respect to estimated scenario costs below:
• System-wide optimization. The model decision-making seeks to optimize system-wide costs for the
contiguous United States as a whole. Actual decision making in utility and transmission planning,
procurement, and dispatch processes may not be optimized. As such, costs in all scenarios will likely
be higher than those estimated by ReEDS.
23
Although this likely impacts all scenarios, it is probable
that this underestimation of costs in ReEDS is more prominent for scenarios with higher RE shares
due to the location-restricted and variable nature of many renewable resources. Furthermore, ReEDS
does not model non-economic decisions, which might lead to higher actual costs than those reported.
• Siting and supply chain. ReEDS does not explicitly model siting and supply chain constraints.
Again, because these factors might impact RE to a greater degree than other generation technologies,
19
The use of a social discount rate in our system cost metric is consistent with discount rates used by the EIA, the
International Energy Agency, and the Intergovernmental Panel on Climate Change to estimate long-term costs and
benefits. A 3% discount rate is also in line with guidance from the White House Office of Management and
Budget for “cost-effectiveness” analysis that spans multiple decades. Note that this discount rate differs from the
higher discount rate (5.3% real, weighted average cost of capital) used in most cases for the ReEDS investment
and dispatch decision-making.
20
ReEDS uses a 20-year economic lifetime for all generation technologies. As such, only a fraction of the 20-year
net present value costs of new capital equipment installed after 2030 are included in the system cost metric.
21
Endogenously- estimated REC prices are rolled into the competitive electricity price.
22
ReEDS also includes a cost-of-service price (Eurek et al. 2016) that is more reflective of pricing in regions
without restructured markets. For this study, we report competitive prices only but note that the estimated
incremental cost-of-service prices are similar in magnitude.
23
However, reported costs from ReEDS for a particular region might be either higher or lower than real costs under
the same set of scenarios. For example, a region might find lower cost solutions for it than the system-wide
minimum cost solution.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
11
their lack of representation might skew the reported incremental costs of scenarios with higher
amount of RE to be lower than they might otherwise be.
• Foresight. Only limited foresight is modeled in ReEDS.
24
It is not clear how real investor foresight
compares with that modeled in ReEDS. And while accurate foresight may yield lower costs in all
scenarios, it is not clear how better foresight might impact cost differences across scenarios.
METHODS FOR ASSESSING BENEFITS AND IMPACTS
Evaluating the Benefits and Impacts of State RPS and High RE Scenarios
In addition to analyzing the possible costs associated with the Existing RPS and High RE scenario, we
evaluate a diverse set of possible benefits and impacts. We report results in physical units and—where
credible methods exist—in monetary terms. We evaluate the Existing RPS and High RE scenarios relative
to the No RPS scenario to identify the full set of benefits and impacts of all additional RE demand
associated with those scenarios. We report results for the full analysis timeframe of 2015–2050, in some
cases also highlighting nearer-term results to 2030.
Various aspects of our methods to estimate GHG and air pollution benefits build on or complement
approaches used by U.S. regulatory agencies (GAO 2014; EPA 2015c) and academic researchers (NRC
2010; Arent et al. 2014; Buonocore et al. 2016a; Buonocore et al. 2016b; Callaway et al. 2015; Chiang et
al. 2016; Cullen 2013; Graff Zivin et al. 2014; Driscoll et al. 2015; Fann et al. 2012; Johnson et al. 2013;
Kaffine et al. 2013; McCubbin and Sovacool 2013; Novan 2014; Rouhani et al. 2016; Siler-Evans et al.
2013; Shindell 2015). The basic approach used for estimating water use impacts has also been applied in
multiple studies (Clemmer et al. 2013; Macknick et al. 2012; Macknick et al. 2015; Rogers et al. 2013).
The same is true for our assessments of gross RE-workforce and economic development impacts
(Bamufleh et al. 2013; Croucher 2012; Flores et al. 2014; Keyser et al. 2014; Lantz and Tegen 2008;
Loomis and Carter 2011; Navigant 2013; Slattery et al. 2011; Steinberg et al. 2012; You et al. 2012), and
natural gas price impacts (Fischer 2009; Wiser and Bolinger 2007).
Air Pollutant Emissions and Human Health and Environmental Benefits
Our methods to value the potential air quality benefits of the Existing RPS and High RE scenarios involve
first estimating the net reductions in direct combustion-related emissions of SO
2
, NO
x
, and fine inhalable
particles with diameters that are generally 2.5 micrometers and smaller (PM
2.5
)
relative to the No RPS
scenario. We then quantify the public health and environmental benefits of those changes in emissions in
the form of reduced mortality and morbidity associated with exposure to particulate matter and ground-
level ozone, and translate those effects into monetary terms.
25
Combustion-related electric-sector emissions of SO
2
and NO
x
are estimated within ReEDS using emission
rates developed based on recent measurements of power plant emissions (Ventyx 2013) and adjusted over
time to reflect EPA’s Mercury Air Toxics Standard. Combustion-related PM
2.5
emission estimates are less
certain and estimated outside of ReEDS as the product of ReEDS generation outputs (megawatt-hours, by
generation type and vintage) and average emission rates (grams per megawatt-hour, by generation type
24
ReEDS includes imperfect foresight related to changing natural gas fuel prices. The foresight term includes a
expectation of changing natural gas fuel prices based on data from the 2016 Annual Energy Outlook scenarios
(EIA 2016), but since ReEDS includes an endogenous representation of power sector natural gas price and demand
dynamics, future prices observed in the model ultimately differ from these projections.
25
These represent a subset—but arguably the most important—of emissions impacts. Due to methodological and
data limitations, we do not evaluate impacts from heavy metal releases, radiological releases, waste products, and
land use impacts associated with power and upstream fuel production as well as noise, aesthetics, and others. Note
also that we only consider SO
2
, NO
x
, and PM
2.5
emissions from power plant operation, and so do not assess
upstream and downstream life-cycle impacts.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
12
and state).
26
Biomass emissions are also estimated outside of ReEDS and are more uncertain than
emissions from fossil fuel units. Specifically, biomass emissions rates of SO
2
and PM
2.5
are based on the
national average values developed in Wiser et al. (2016b). The NO
x
emission rates are newly updated
with EIA data (EIA 2015).
27
Overall, our methodology presumes that the Mercury Air Toxics Standard is
maintained or replaced with a similar regulation so that SO
2
and NO
x
cap-and-trade programs, such as the
Cross-State Air Pollution Rule (which is also modeled in ReEDS), are essentially non-binding over
time.
28
If emissions regulations become more stringent than assumed in our analysis or if binding cap-
and-trade programs are created, then the resulting impacts of the Existing RPS and High RE scenarios
would differ from those shown here.
The marginal impacts of air pollutant emissions on health outcomes (including morbidity and mortality
outcomes and total monetary value) are an area of active research, and we reflect some of this uncertainty
by calculating benefits using three distinct methods. All three approaches include representation of
pollutant transport and chemical transformation to assess population exposure and response. Each does so
differently, however, and considers different health and environmental outcomes. We use: (a) the Air
Pollution Emission Experiments and Policy analysis model (AP2, formerly APEEP; described in Muller
et al. 2011)
29
; (b) EPA’s marginal benefit methodology (EPA 2015a; EPA 2015c)
30
; and (c) The
Estimating Air pollution Social Impacts Using Regression (EASIUR) model (Heo et al. 2016).
31
To incorporate differences across epidemiological studies, EPA and EASIUR both include two estimates
of health impacts, a low estimate and a high estimate. Moreover, the EPA methods allow us to estimate
specific health outcomes (mortality and specific forms of morbidity); all methods allow for monetary
quantification. We report a “central” value estimate as the simple average of all other estimates.
32
26
Average PM
2.5
emissions rates (Cai et al. 2012; Cai et al. 2013) are differentiated by generation type (coal, gas, or
oil) and U.S. state, and are adjusted over time to comply with scheduled PM
2.5
limits in the Mercury Air Toxics
Standard for existing plants.
27
Although EIA data for biomass were incomplete for PM
2.5
and SO
2
, we were able to derive and use state-level
estimates of NO
x
emission rates. These state-level estimates produced an average national NO
x
emission rate for
biomass that was within 20% of the NO
x
emission rate in Wiser et al (2016b).
28
Otherwise, emissions impacts should arguably be valued at allowance prices to reflect savings in the cost of
complying with the cap (Siler-Evans et al. 2013). Note that we do not include some more-localized existing
binding cap-and-trade programs. The geographic extent of these programs is limited, so they will not substantially
bias our results.
29
AP2 contains monetized benefit-per-ton estimates based on emissions in the year 2008, so damages from AP2 are
scaled over time based on Census population projections (U.S. Census Bureau 2012) and per capita income growth
projections used by EIA (2014), using an elasticity of the value of statistical life to income growth consistent with
National Research Council (2010).
30
EPA’s benefit-per-ton values are developed for each year within each of three large regions by linearly
extrapolating EPA’s provided benefit-per-ton values. The 2015–2025 benefit-per-ton values are based on the
linear trend established by EPA's 2020 and 2025 values. The 2026–2050 benefit-per-ton values are based on the
linear trend established by EPA's 2025 and 2030 values. The same process is used for EPA's health incidence-per-
ton (mortality and morbidity outcomes) estimates.
31
EASIUR contains monetized benefit-per-ton estimates based on population and income in the year 2005, so
damages from EASIUR are scaled over time based on Census population projections (U.S. Census Bureau 2012)
and per capita income growth projections used by EIA (2014), using an elasticity of the Value of Statistical Life
(VSL) to income growth consistent with National Research Council (2010).
32
A critical value within each approach is the monetary value of preventing a premature mortality (or the VSL).
Each approach is based on a VSL of approximately $6 million (in 2000$), which is consistent with the broader
literature.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
13
Greenhouse Gas Emissions Reduction Benefits
We estimate the potential life-cycle GHG benefits of the Existing RPS and High RE scenarios, relative to
the No RPS scenario, by quantifying the economic value of those GHG reductions in mitigating the
severity of climate-related damages and in meeting potential future carbon-reduction compliance
obligations.
ReEDS calculates operational combustion-related CO
2
emissions that result from electric sector
dispatch.
33
Additionally, based on the comprehensive literature assessment conducted under the auspices
of NREL’s Life Cycle Assessment Harmonization project,
34
embedded within and calculated by ReEDS
are assumptions that enable an evaluation of the full life-cycle GHG impacts associated with: (1) ongoing
fuel-cycle emissions from the production and transport of fuels and from other aspects of power plant
operations, (2) construction-related emissions, and (3) emissions from end-of-life decommissioning.
35
By
applying these life-cycle adjustments, we capture avoided fuel cycle, construction, and decommissioning
emissions from displaced fossil generation and capacity while also accounting for increased fuel cycle,
construction, and decommissioning emissions from renewable generation and capacity.
We estimate the monetary benefits of reduced climate-change damages from GHG reductions using social
cost of carbon (SCC) estimates. The SCC provides an estimate of climate change-induced monetary
damages to agricultural productivity, human health, property, ecosystem services, and other systems,
presented below in units of $/(metric ton CO
2
). There is a wide range of SCC estimates in the scientific
literature, illustrating the deep uncertainties involved. SCC estimates are particularly sensitive to the
choice of discount rates, estimates of future climate change damage and the potential for catastrophic
climate tipping points, as well as the representation of abatement policies (Nordhaus 2014). Meta-
analyses (Tol 2008; Tol 2011; Tol 2013) of independent SCC estimates have been conducted, with the
most recent work (Tol 2013) finding mean and median SCC values of $53 and $37, respectively, and an
associated standard deviation of $88. Tol (2013) developed these values based on 75 studies containing
588 estimates of the SCC. Havranek et al. (2015) build on the work by Tol (2013) and attempt to correct
for selective reporting bias, finding a mean SCC estimate between $0 and $39. Still others, such as van
den Bergh and Botzen (2014), argue for a lower bound SCC value of $125.
33
We do not consider the possible erosion of the GHG or air emissions benefits due to the increased cycling,
ramping, and part loading required of fossil fueled generators in electric systems with higher penetrations of
variable renewable generation, as these impacts are not fully considered in ReEDS. This omission will not
meaningfully bias our results, however, because the available literature demonstrates that these impacts are
generally relatively small (Fripp 2011; Göransson and Johnsson 2009; Gross et al. 2006; Pehnt et al. 2008; Perez-
Arriaga and Batlle 2012; Oates and Jaramillo 2013; Valentino et al. 2012; GE Energy Consulting 2014; Lew et al.
2013).
34
See http://www.nrel.gov/harmonization.
35
Specifically, median life-cycle, non-combustion GHG emission values were identified for each generation
technology and for the fuel cycle, construction, and decommissioning phases based on the NREL’s Life Cycle
Assessment Harmonization project literature assessment. We use the same emission factors as those employed in
the Hydropower Vision study (DOE 2016, Appendix G). To estimate non-combustion GHG emissions from the
fuel cycle, we use the electricity-production estimates (in ) provided by ReEDS for all generation technologies and
apply the median, literature-derived estimates of technology-specific, non-combustion fuel-cycle GHG emissions.
There is uncertainty in these estimates (Brandt et al. 2014, Arent et al. 2015). We assume that biomass GHG
combustion emissions are entirely offset by carbon absorption to produce the biomass feedstocks (i.e., we do not
estimate land-use related emissions), and that any landfill gas used for electric production would otherwise have
been flared. To estimate GHG emissions from construction, we use the capacity estimates (in megawatts) provided
by ReEDS over the 2015 to 2050 timeframe and apply the median, literature-derived estimates of technology-
specific, construction-related GHG emissions. Finally, to estimate GHG emissions from decommissioning, we use
decommissioning capacity estimates (in megawatts) provided by ReEDS over the 2015 to 2050 timeframe and
apply the median, literature-derived estimates of technology-specific, decommissioning-related GHG emissions.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
14
U.S. government regulatory bodies use estimates for the SCC that were determined by the U.S.
Interagency Working Group (IWG) on the Social Cost of Greenhouse Gas Emissions (IWG 2010; IWG
2015) when formulating policy (GAO 2014; Kopp and Mignone 2012).
36
The SCC estimates from the
IWG represent global future damages from GHG emitted in a particular year, with the future damages
calculated in present value terms using a selected range of discount rates. To address the range of
assumptions that can be used to formulate the SCC, the IWG provides four different SCC estimates (e.g.
for 2010: $11, $33, $53, and $90, with the value of $33 described as the central estimate). Thus, the IWG
SCC estimates cover a similar range to that found in the meta-analyses described above.
As an alternative to valuing GHG reductions based on the SCC, we also value those reductions based on
the possible cost of complying with legal requirements to reduce GHG emissions. The EPA may limit
GHG emissions from existing and new power plants through the CPP (EPA 2015a; EPA 2015b; Luckow
et al. 2016), though the legal fate of the CPP is unclear.
When binding cap-and-trade programs are used to
limit GHG emissions, the climate-change benefits of RE might best be valued based on the cost of
complying with those legal requirements (Barbose et al. 2008; Cullen 2013; Siler-Evans et al. 2013).
In
this case, the GHG benefits of RE come in the form of helping to meet the carbon-reduction target and
thereby offsetting some of the “marginal” costs of complying with the policy.
Specifically, we value the potential compliance-cost savings of GHG reductions based on two sets of
estimates. First, we use EPA estimates of the average national cost of complying with the CPP under both
mass-based and rate-base application (EPA 2015a).
37
Those estimates are provided by EPA for 2020,
2025, and 2030; we interpolate between these years to estimate costs in intervening periods and further
assume the 2030 compliance cost estimates remain constant through 2050. Second, we use Synapse
Energy Economics projections of carbon costs under “low,” “medium,” and “high” trajectories (Luckow
et al. 2016). Synapse considers the possibility of more stringent long-term carbon-reduction goals than
envisioned by the CPP and so estimates higher costs than those from EPA (2015a).
38
Water Use Reduction Benefits
Electric sector water usage comes in two forms. Withdrawal is defined as water removed from and then
returned to a source. Consumption is defined as water that is removed from but not returned to its source.
Withdrawals include cooling water returned to its source at a higher temperature; consumption includes
water evaporated for cooling. We estimate the impacts of the Existing RPS and High RE scenarios on
both withdrawals and consumption relative to the No RPS scenario and present results based on the 18
U.S. Geological Survey Hydrologic Unit Code watershed regions (Seaber et al. 1987).
Electric sector water withdrawal and consumption were estimated within ReEDS for all generation types.
ReEDS includes representations of cost, performance, and water-use characteristics by generation type
and cooling system technology, and new power plant construction is limited by water availability
36
The Technical Support Document “Technical Update of the Social Cost of Carbon for Regulatory Impact
Analysis Under Executive Order 12866,” describes recent updates to the IWG work, including additional
discussion of uncertainty.
37
While EPA has also estimated state-level marginal carbon-abatement costs, we prefer to use national average
estimates because of the uncertainty introduced at the state- level due to the flexibility allowed in a state achieving
the CPP. Also, average cost estimates may be more appropriate than marginal estimates as the carbon reductions
associated with the two core scenarios analyzed for this report represent either a sizable contribution to CPP
compliance (Existing RPS scenario) or reflect full compliance with the CPP (High RE scenario).
38
Note that we apply the IWG SCC estimates to the estimated life-cycle GHG emissions savings (in CO
2
-
equivalent) whereas we apply compliance cost estimates only to combustion-related electric-sector emissions
(only CO
2
) because EPA CPP regulations, if enacted, would apply only to the electric sector.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
15
(Macknick et al. 2015). ReEDS incorporates a representation of cooling systems for the existing
generation fleet based on an analysis of individual power-generating units as described elsewhere (Averyt
et al. 2013; UCS 2012). Various changes in the electricity sector, such as coal plant retirements, new
combined-cycle natural gas plant construction, and increased dry cooling use, can impact water use.
These changes, in turn, may be driven in part by future water policies, which could affect estimated water
impacts. Following Macknick et al. (2012) and Tidwell et al. (2013), new power plants modeled in
ReEDS do not have the option of installing once-through cooling technologies.
We focus exclusively on operational water-use requirements because thermoelectric water withdrawals
and consumption during plant operations are orders of magnitude greater than the demands from other
life-cycle stages (Meldrum et al. 2013). Thermal power plants using once-through cooling withdraw far
more water for every megawatt-hour of electricity generated than do plants using recirculating cooling
systems. For water consumption, however, once-through cooling has lower demands than recirculating
systems. Dry cooling can be used to reduce both withdrawal and consumption for thermal plants but at a
cost and efficiency penalty (EPA 2009). Non-thermal RE technologies, such as photovoltaics and wind,
do not require water for cooling and thus have low operational water-use intensities. All new
concentrating solar power capacity is modeled with cost and performance characteristics consistent with
dry cooling, landfill gas facilities are assumed to require no water for operations, and hydropower
evaporation is not considered.
Electric-sector water savings provide economic and environmental benefits, especially in regions where
water is limited and could be used for other ecosystem or societal services. Reducing electric sector
dependence on water can also reduce the vulnerability of electricity supply to the availability or
temperature of water, and help ease concerns over energy sector vulnerabilities to climate change (DOE
2013). And the lower life-cycle water requirements of some renewable technologies can help alleviate
other energy-sector impacts on water resource quality and quantity that take place during upstream fuel
production for other technologies (Averyt et al. 2011). Because there is no standard, literature-based
methodology to quantify the monetary benefits of these water-use reductions (DOE 2015, we do not
quantify the benefits of water-use reductions in monetary terms.
Gross Renewable Energy Workforce Requirement and Economic Development Impacts
We estimate the potential gross domestic RE-related jobs and other economic impacts associated with the
Existing RPS and High RE scenarios relative to the No RPS scenario. Such estimates may provide
governments, stakeholders, and other interested parties information about how RE expenditures could
translate to gross RE workforce needs and associated gross domestic product, earnings, and economic
activity in the United States.
It must be emphasized, however, that our analysis does not include an assessment of economy-wide net
impacts, and we therefore make no claim of net benefits or costs.
39
Accordingly, we refer to the estimated
RE workforce and economic effects as impacts rather than benefits. Impacts such as displacement of jobs
in fossil fuels or displaced investment in general are not included.
39
Increased renewable generation displaces demand for other sources of electric generation, impacting job totals and
economic development associated with those sectors. Additionally, to the extent that increased renewable energy
impacts the cost of energy, or has other macroeconomic effects, this too may affect employment in the broader
economy. In general, there is little reason to believe that net impacts are likely to be sizable in either the positive or
negative direction, at least at an international or national scale (e.g., Rivers 2013). Moreover, even were net
positive effects likely, questions remain as to whether such effects serve as economic justification for government
policy (e.g., Borenstein 2012; Edenhofer et al. 2013; Gillingham and Sweney 2010; Morris et al. 2012).
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
16
To assess the potential gross RE-related workforce requirements and economic-development impacts of
the Existing RPS and High RE scenarios, we use NREL’s Jobs and Economic Development Impacts
(JEDI) suite of models as well as IMPLAN, applying those models to estimate the gross impacts
associated with ReEDS-estimated capacity additions and generation of wind, solar, geothermal, landfill
gas, hydropower, and biomass. JEDI is an input-output model based on IMPLAN and has been used
extensively in national and regional assessments. Because there is no landfill gas module in the JEDI
suite, we use IMPLAN parameterized with cost assumptions from Jensen et al. (2010). Assumptions for
“domestic content”—the portion of expenditures made in the United States—for RE technologies are
based on JEDI default data and Jensen et al. (2010).
40,41
We estimate gross domestic job needs and economic development impacts associated with both the
operation and construction (including domestic manufacturing of equipment subsequently installed) of
RE facilities. Four metrics are presented: gross jobs, earnings, output, and gross domestic product. Jobs
are expressed as full time equivalent, which is the same as one job-year, the equivalent of one person
working 40 hours per week for one year. Earnings include wages, salaries, and employer-provided
supplements such as health insurance and retirement contributions. Output is a measure of overall
economic activity. At an individual business, it could be thought of as revenue. This revenue includes
payments for inputs as well as payroll and taxes, and property-type payments (including profits or
payments to investors). Gross domestic product is solely a measure of the value of production. It includes
payments to workers, property-type income, and taxes.
These four metrics can be segmented into three categories: onsite, supply chain, and induced. Onsite
impacts are most directly related to the project; these include installers, construction workers, plant
operators, and other maintenance personnel. Supply chain impacts arise as a result of domestic
expenditures by the developer, installer, or operator; these include construction material providers,
equipment manufacturers, and consultants. Induced impacts arise as a result of onsite and supply chain
workers making expenditures within the United States; these impacts are often in retail sales, leisure and
hospitality, education, and health services. In all cases, we only report domestic jobs and impacts. All
results produced by JEDI and IMPLAN are for the equivalent of a single year, although results are
presented in both single years and cumulatively over multiple years (e.g., 2015–2050). Results are
reported on a national basis and, for onsite jobs only, on a regional basis.
42
Aside from only estimating gross impacts, there are several limitations to using input-output models such
as JEDI and IMPLAN. Perhaps most importantly, these models do not make assumptions about future
changes in the economy such as technology improvements, changes in relative prices, or changes in tax
rates that could cause producers to substitute inputs or change how much they purchase in the United
States. Model results should therefore be interpreted as gross impacts that could take place given the
structure of the economy as it exists at the time of publication of this report.
Natural Gas Price Reduction Impacts
Though RE is not free of risk, it relies on a domestic “fuel” stream not subject to significant resource
exhaustion or price uncertainty. Various methods have been used to assess the benefits of these
characteristics as well as the benefits of electricity supply diversity more generally (Awerbuch 1993;
40
Default data are used to ensure that each technology is treated as similarly as possible to maintain consistency.
Model estimates using default data have historically come close to job counts at actual projects and numbers
published by other researchers (Billman and Keyser 2013).
41
Domestic content is specified for project expenditures.
42
We do not estimate supply chain or induced economic impacts by region because of uncertainty relating to where
in the United States those impacts take place.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
17
Bazilian and Roques 2008; Bolinger 2013; Bolinger et al. 2006; Jenkin et al. 2013; Stirling 2010; Wiser
and Bolinger 2006). Many of these methods have proven to be incomplete or even controversial,
however, and standardized approaches to benefit quantification have not emerged.
Increased use of RE mitigates long-term fossil fuel price risks in one way that can be readily quantified.
Specifically, by reducing demand for fossil fuels, RE can place downward pressure on fossil fuel prices
with benefits to energy consumers both within and outside of the electricity sector. We estimate these
effects for the Existing RPS and High RE scenarios relative to the No RPS scenario, focusing on impacts
to natural gas prices.
43
Specifically, ReEDS is used to estimate the impact of the various scenarios on natural gas demand and
regional gas prices; these price impacts are estimated within ReEDS based on results from the EIA’s
Annual Energy Outlook model scenarios.
44
Natural gas price impacts within the electric sector are
addressed within ReEDS and are embedded within the cost and electricity price metrics discussed earlier.
The effects of natural gas price changes outside of the electric sector, however, are not included within
ReEDS. To estimate these consumer natural gas bill savings we apply the regional natural gas price
changes estimated by ReEDS to EIA estimates of future non-electric sector regional gas demand.
45
These consumer natural gas bill savings come at the expense of natural gas producers and so represent a
transfer payment. While individual states may experience net benefits from these transfers—e.g., states
that consume more natural gas than they produce—others may experience the opposite. We therefore
refer to this effect as an impact rather than a benefit and make no claim of a net societal gain.
43
Within a simple economics supply/demand framework, this gas price effect can be represented by the demand
curve for natural gas shifting inward along the supply curve. Presuming the supply curve has an upward slope and
does not change in response to the demand shift, the demand shift will result in a lower price. Although demand
for coal within the electricity sector also declines as a result of increased renewable generation, we do not analyze
potential impacts on coal prices because the long-term inverse price elasticity of supply is generally thought to be
lower for coal than for natural gas (Wiser and Bolinger 2007).
44
Cole, Medlock, and Jani (2016) and Eurek et al. (2016) describe the methodology behind the regional and national
natural gas supply curves (price vs. electric sector consumption) used in ReEDS. The parameterization of these
supply curves are based on data from scenarios in the EIA Annual Energy Outlook (EIA 2014; EIA 2016).
45
There is inherent uncertainty in the response of natural gas prices to natural gas demand. The results estimated
here are consistent with assumptions and approaches used by EIA, but are inherently uncertain. Consistent with
EIA, we further assume that national wellhead gas price changes flow through fully to delivered natural gas prices
in all regions and across all sectors, and that consumers are fully exposed to those price changes. We do not fully
account for the possibility that any price changes could yield a rebound in natural gas demand, including
possibilities for natural gas exports.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
18
3. RENEWABLE DEPLOYMENT AND AVOIDED FOSSIL GENERATION
UNDER EXISTING RPSS AND EXPANDED RE SCENARIOS
This section describes projected annual energy generation and installed capacity by technology type under
each of the scenarios examined in the analysis. Differences in generation and capacity across the
scenarios are the basis for the estimated costs, benefits, and other impacts presented in Sections 4 and 6.
Figure 3.1 shows the RE penetration as a fraction of total annual generation under each of the three
scenarios, as calculated by the ReEDs model. By 2030, renewables (including hydropower) are estimated
to grow to 26% of energy generation under the Existing RPS scenario, compared to 21% under the No
RPS scenario and 35% under the High RE scenario. These estimates are up from a 14% renewable
penetration level in 2015, of which about half consists of hydropower. By 2050, renewables reach 34%
under the No RPS scenario, 40% under the Existing RPS scenario, and 49% under the High RE scenario.
The general trends in RE penetration shown in Figure 3.1—rapid growth through 2020, followed by
steady or flat growth between 2020 and 2040, and increasing growth rates after 2040—are primarily
driven by the underlying policy, technology cost, and fuel price assumptions. More specifically, the
modeling analysis finds that the federal RE tax credits to be effective at driving significant RE growth in
the near term, but after their expirations, growth in RE generation does not significantly exceed demand
growth. In the longer term, the scenarios show that the assumed reductions in RE technology costs
combined with increasing natural gas prices result in rapid RE growth during the last decade of the
analysis.
46
Figure 3.1. U.S. renewable energy penetration under three modeled scenarios
The Existing RPS scenario leads to 66 gigawatts (GW) of renewables above the No RPS scenario by
2030, or an additional 218 terawatt-hours (TWh) of renewable generation in 2030 (see Figure 3.2).
47
Most
of the additional capacity and generation is composed of wind and solar, but there is also significant
incremental generation from biomass and geothermal. Specifically, by 2030, the Existing RPS scenario
shows an additional 42 GW of solar (both utility scale and distributed), 19 GW of wind, and the
remaining 5 GW from biomass, geothermal, and hydropower. On a generation basis, contributions are
46
The NREL 2016 Standard Scenarios report (Cole et al. 2016) provides results and discussion of a broader set of
scenarios that use very similar, though not precisely the same, assumptions as the scenarios here.
47
The Existing RPS scenario allows for economic RE deployment, i.e. deployment beyond the RPS requirements.
For example, we estimate annual RE generation to grow by nearly 600 TWh between 2014 and 2030, and by about
1570 TWh by 2050; these levels well exceed the increase in estimated growth in RPS requirements over the
corresponding periods.
0%
10%
20%
30%
40%
50%
2015 2020 2025 2030 2035 2040 2045 2050
Renewable Penetration
High RE
Existing RPS
No RPS
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
19
more evenly spread across technologies, with solar contributing 83 TWh, 74 TWh from wind, and 61
TWh from the combination of biomass, geothermal, and hydropower in 2030.
48
Figure 3.2. Difference in capacity (top) and generation (bottom) between the Existing RPS and No
RPS scenarios through 2050
By 2050, the Existing RPS scenario requires an additional 122 GW of renewable capacity beyond that in
the No RPS scenario, or 296 TWh of incremental RE generation. Solar represents a substantially larger
fraction of the incremental capacity and generation in the 2050 timeframe.
The new RE generation in the Existing RPS scenario largely offsets fossil generation, with a greater
amount of coal offset before 2030 but more natural gas offset in the longer term. In 2030, the renewable
generation fairly evenly offsets coal generation and natural gas generation from combined cycle units (see
Figure 3.2). By 2050, the vast majority of displaced generation is from natural gas combined cycle units.
Avoided fossil capacity is also found in the Existing RPS scenario with nearly 40 GW of avoided natural
gas combined cycle capacity by the 2040s and additional coal capacity retirements of less than about 5
48
The scenario outcomes between renewable technologies can be sensitive to the assumptions used. Given
uncertainties associated with future technology costs, different technology distributions for RE used to meet
growing RPS requirements from those in Figures 3.2 and 3.3 are ultimately likely. However, we note that prior
studies (DOE 2015; DOE 2016; Wiser et al. 2016b) have found similar benefits between different RE options.
-50
0
50
100
150
2014
2016
2018
2020
2022
2024
2026
2028
2030
2032
2034
2036
2038
2040
2042
2044
2046
2048
2050
GW
Storage
Solar
Wind
Bio + Geo
Hydropower
NG-CT
NG-CC
Oil-Gas-Steam
Coal
-400
-300
-200
-100
0
100
200
300
400
2014
2016
2018
2020
2022
2024
2026
2028
2030
2032
2034
2036
2038
2040
2042
2044
2046
2048
2050
TWh
Imports
Solar
Wind
Bio + Geo
Hydropower
NG-CT
NG-CC
Oil-Gas-Steam
Coal
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
20
GW.
49
However, we find greater deployment of natural gas combustion turbine peaking capacity in the
Existing RPS scenario compared with the No RPS scenario. This result, which is most prominent in the
long term after 2030, reflects the need for firm capacity with growing electricity consumption. On net,
fossil capacity is lower in the Existing RPS compared to the No RPS scenario, suggesting that the
incremental RE from the former scenario possesses some capacity credit.
The High RE scenario results in 215 GW of renewables above the No RPS scenario by 2030, or 627 TWh
of incremental renewable generation (see Figure 3.3). Wind and solar are the dominant RE technologies
deployed in the High RE scenario, although we estimate some incremental biomass, geothermal, and
hydropower generation in the 2030 timeframe. Specifically, the High RE scenario includes an incremental
137 GW of solar and 71 GW of wind. On a generation basis, the incremental wind and solar generation
are of very similar magnitude (280 TWh wind, 273 TWh solar), while biomass, geothermal, and
hydropower contribute an additional 73 TWh above the No RPS scenario by 2030.
Figure 3.3. Difference in capacity (top) and generation (bottom) between the High RE and No RPS
scenarios through 2050
49
ReEDS does not install new coal capacity in any of the scenarios. Differences in coal capacity reflect greater
retirements of coal plants with high RE shares. Coal retirements in ReEDS are modeled based on lifetimes,
announced retirements, and estimated utilization (Eurek et al. 2016). The latter method is responsible for any
differences in coal capacity between the scenarios.
-100
-50
0
50
100
150
200
250
300
350
400
450
2014
2016
2018
2020
2022
2024
2026
2028
2030
2032
2034
2036
2038
2040
2042
2044
2046
2048
2050
GW
Storage
Solar
Wind
Bio + Geo
Hydropower
NG-CT
NG-CC
Oil-Gas-Steam
Coal
-1000
-800
-600
-400
-200
0
200
400
600
800
1000
2014
2016
2018
2020
2022
2024
2026
2028
2030
2032
2034
2036
2038
2040
2042
2044
2046
2048
2050
TWh
Imports
Solar
Wind
Bio + Geo
Hydropower
NG-CT
NG-CC
Oil-Gas-Steam
Coal
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
21
In the longer term, by 2050 the High RE scenario requires 331 GW of RE capacity above the No RPS
scenario, or 765 TWh of incremental renewable generation. By 2050, capacity additions are dominated by
solar, but there is also significant incremental wind and natural gas combustion turbine capacity compared
to the No RPS scenario. Incremental generation in 2050 is still split relatively evenly between solar and
wind.
Coal is the dominant form of avoided generation in the High RE scenario, even more so than in the
Existing RPS scenario. In 2030, more than 80% of the displaced fossil fuel generation is coal, with the
remainder primarily from natural gas combined cycle units. In the longer term, more natural gas
generation is displaced, but coal displacement is still substantial.
Figure 3.4 further highlights the differences in cumulative displacement of fossil generation under the
Existing RPS and High RE scenarios relative to the No RPS scenario, as well as how displacement
changes in the near term (before 2030) and the longer term. While the Existing RPS scenario offsets more
natural gas in the longer term, the High RE scenario displaces substantial coal generation in both the short
and longer term. Under the Existing RPS scenario, more than half of cumulative displacements are coal
generation in the near term, but coal falls to about one-third of displacements in the longer term as more
natural gas generation from combined cycle units is displaced. For the High RE scenario, coal represents
more than 80% of cumulative displacements in the near term, and a little more than two-thirds of
displacements in the longer term.
Figure 3.4. Difference in cumulative generation between the Existing RPS (left) and High RE (right)
scenarios relative to No RPS
The amount and type of displaced fossil generation is explained in part by regional deployment of
renewables as well as the assumed drivers behind incremental RE deployment in the scenarios. For the
Existing RPS scenario, regional variations in renewable generation depend on state RPS policies,
including the magnitude and timing of RPS targets, while fossil displacement is also affected by other
regional factors such as the existing generator fleet, anticipated coal plant or other plant retirements, and
future fuel prices. In general, states with RPS policies are typically not as coal-reliant as non-RPS states,
thereby resulting in relatively low levels of avoided coal generation in the Existing RPS scenario. In
contrast, the High RE scenario effectively applies to all states. Moreover, emission constraints are the
primary drivers in the High RE scenario compared to the direct RE requirements under the Existing RPS
scenario. As a result, the High RE scenario results in much greater levels of avoided coal generation than
in the Existing RPS scenario.
-10000
-8000
-6000
-4000
-2000
0
2000
4000
6000
8000
10000
TWh
RE
Natural Gas
Coal
-25000
-20000
-15000
-10000
-5000
0
5000
10000
15000
20000
25000
TWh
RE
Natural Gas
Coal
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
22
Figure 3.5 shows cumulative RE generation for the Existing RPS scenario and High RE scenario relative
to the No RPS scenario in the nine census divisions. Under the Existing RPS scenario, the Pacific region
in particular results in substantial incremental RE generation in the near term as well as the longer term.
Other regions with lesser but still substantial incremental renewable generation under the Existing RPS
scenario, particularly by 2050, include the East North Central region, the Middle Atlantic, New England,
and the Mountain region. Notably and as described previously, the significant RPS-required renewable
generation in New England and the Pacific region largely offsets gas while smaller amounts of
incremental renewables elsewhere help reduce more coal.
In general, the distribution of incremental renewable generation is more uniform in the High RE scenario.
The greatest absolute amounts of incremental renewables under the High RE scenario are found in the
Midwest (East and West North Central regions), Pacific, and South Atlantic regions. On a percent basis,
incremental renewable generation is even more uniformly distributed between census regions.
Figure 3.5. Regional incremental RE generation relative to No RPS
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
23
4. COSTS OF EXISTING RPSS AND EXPANDED RE
The ReEDS model directly estimates costs in each scenario. Here we present those results by comparing
costs for the Existing RPS and High RE scenarios relative to the No RPS scenario in terms of the two cost
metrics introduced earlier: (1) the net present value of system costs, and (2) average retail electricity
prices. For both metrics, we present the results in terms of the incremental cost relative to the No RPS
scenario. Rather than focusing on central-case cost estimates, we instead present the range in incremental
costs across sensitivity cases related to natural gas prices and renewable technology costs.
50
We also compare the estimated prospective incremental electric system costs with historical compliance
costs reported by utilities, state regulatory agencies, and others (Heeter et al. 2014; Barbose et al. 2015).
SYSTEM COSTS
The national electric system costs presented in Figure 4.1 are based on the net present value of all electric
system expenditures from 2015 to 2050, including fuel, O&M, and capital costs for new generation,
interconnection, transmission, and storage infrastructure. For the Existing RPS scenario, incremental costs
were estimated to range from ±$31 billion (-0.7% to 0.8% on a national basis inclusive of the states with
and without RPS polices) relative to the No RPS scenario. On a levelized basis, this equates to about
±0.75 cents per kWh of renewable electricity (¢/kWh-RE).
51
The fact that the low end of the cost range is
negative implies that, under certain conditions (namely, high natural gas prices or low renewable energy
technology costs), RE used to meet existing RPS policies results in a net savings to the electric system.
Conversely, when gas prices are relatively low or when renewable energy technology costs are high, RE
used for existing RPS requirements results in a net cost. In either case, however, the net effect (whether
positive or negative) is quite small as a share of overall system costs.
For the High RE scenario, the range in incremental system costs is much wider. Under this scenario,
incremental system costs ranged from $23 billion (0.6% of total system costs) to $194 billion (4.5% of
total system costs) across the sensitivity cases. This equates to 0.26¢/kWh-RE to 1.5¢/kWh-RE on a
levelized basis. The upper bound of the range corresponds to the sensitivity with high RE technology
costs.
50
Whereas the benefits and impacts presented in Sections 5 and 6 are based only on scenarios using central
assumptions for renewable energy technology costs and natural gas prices.
51
Reported levelized system costs are defined as the incremental present value of system costs (in dollars) divided
by the present value of incremental RE generation (in kilowatt-hours or megawatt-hours) relative to the No RPS
scenario. We use a 3% real discount rate for both present value calculations.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
24
Figure 4.1. Present value incremental system cost compared to No RPS, 2015–2050
As a point of comparison, previous work by Lawrence Berkeley National Laboratory and NREL
estimated historical RPS compliance costs based on data reported by utilities, state regulatory agencies,
and others (Barbose et al. 2015; Heeter et al. 2014).
52
Those analyses and subsequent data updates found
that for 2012–2014, the incremental cost of RPS compliance averaged around 1¢/kWh-RE, though costs
varied by state. This is roughly in line with the upper end of the range estimated here for the Existing RPS
scenario, which is about 0.75¢/kWh-RE.
ELECTRICITY PRICES
Figure 4.2 shows average electricity prices in the Existing RPS and High RE scenarios relative to the No
RPS scenario, with ranges reflecting variations across sensitivity cases as well as across regions. A
positive number implies higher electricity prices and a negative number implies lower electricity prices
than in the No RPS scenario. One important difference relative to the incremental system costs described
earlier is that the modeled impacts on electricity prices reflect RE tax credits (which are not included
when calculating system costs for reasons noted previously). As a result, the incremental effects on
electricity prices tend to be lower than the effects on system costs.
Based on our modeled results, the Existing RPS scenario may result in up to roughly a 1¢/kWh increase
in electricity prices on the high end. The upper-bound estimate generally grows until 2030 and stays
relatively flat thereafter, with an average effect of about 0.7¢/kWh per year. On the low end of the range,
the Existing RPS scenario results in a reduction in electricity prices relative to the No RPS scenario,
although the magnitude of this effect varies significantly from year to year (and across regions).
For the High RE scenario, the upper bound to the range of electricity price effects is considerably higher
than in the Existing RPS scenario. Specifically, relative prices are estimated to peak at about 4.2¢/kWh
higher than the No RPS case with an average over all years of about 2.9¢/kWh. The low end of the range
of incremental prices for the High RE scenario is similar to those for the Existing RPS scenario.
52
As discussed in Section 2, our scenario constructs evaluate the impacts of all RE used to meet RPS demand
growth. Incremental costs are estimated relative to the No RPS scenario, which has an upper limit on RE
generation. Because of issues around additionality, these incremental values do not reflect directly the incremental
cost of the RPS policies, and therefore care is warranted in comparing our reported incremental costs with other
estimates.
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
-100
-50
0
50
100
150
200
250
Low High Low High
Existing RPS High RE
¢/kWh-RE
Billion 2015$
billion $2015
¢/kWh-RE
-0.7%
0.8%
0.6%
4.5%
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
25
Figure 4.2. Range of electricity prices relative to No RPS scenario
While Figure 4.2 captures the full range of incremental prices across all regions and scenarios, electricity
price impacts also vary across regions. Figure 4.3 shows the estimated range of 2030 incremental prices
by census division across the full set of natural gas and renewable technology cost sensitivities. For the
Existing RPS scenario, we find a narrow range of 2030 incremental prices. In most regions, 2030
incremental prices fall within a band of ±0.35¢/kWh. The New England and Pacific regions, which
contain states with some of the more stringent RPS policies, are exceptions with 2030 incremental prices
ranging from about -0.4¢/kWh up to about 1¢/kWh. For the High RE scenario, high-end 2030 incremental
prices are estimated to be highest in the New England (4¢/kWh) and Middle Atlantic (3.6¢/kWh) regions.
All other regions have high-end 2030 incremental price estimates in the range of 2–3.2¢/kWh. On the low
end of the ranges, regional 2030 incremental prices for the High RE scenario vary from -0.1¢/kWh
(Pacific region) to about 0.4¢/kWh (New England and Middle Atlantic regions).
Figure 4.3. Regional ranges of 2030 retail electricity prices relative to No RPS scenario
-5
-4
-3
-2
-1
0
1
2
3
4
5
2015 2020 2025 2030 2035 2040 2045 2050
Electricity price relative to No
RPS scenario (2015
¢/kWh)
High RE
Existing RPS
Note: ranges reflect incremental prices across
all census divisions and sensitivities
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26
5. BENEFITS OF EXISTING RPSS AND EXPANDED RE
Using the methods discussed earlier, we evaluate three potential benefits associated with the Existing RPS
and High RE scenario, both relative to the No RPS scenario:
53
• Air pollutant emissions and human health and environmental benefits
• GHG emissions reduction benefits
• Water use reduction benefits.
We report results in physical units and—where credible methods exist—in monetary terms.
AIR POLLUTANT EMISSIONS, HUMAN HEALTH, AND
ENVIRONMENTAL BENEFITS
The Existing RPS scenario reduces national electricity sector emissions of SO
2
, NO
x
, and PM
2.5
by 7.8%,
7.2%, and 6.0% in 2030 and by 4.6%, 5.0%, and 4.1% in 2050 relative to the No RPS baseline scenario
(Figure 5.1). The High RE scenario drives greater reductions: SO
2
(35% in 2030, 34% in 2050), NO
x
(34% in 2030, 32% in 2050), and PM
2.5
(33% in 2030, 32% in 2050). Percentage savings do not grow
with time because, as shown earlier, greater proportions of natural gas are found to be offset (and lower
proportions of coal) during the later years of the forecast period.
Cumulative emission savings under the Existing RPS scenario from 2015 to 2050 (and as a percentage of
total electricity sector emissions) equal 2.1 million metric tons of SO
2
(5.5%), 2.5 million metric tons of
NO
x
(5.7%), and 0.3 million metric tons of PM
2.5
(4.5%). The High RE scenario leads to emission savings
of 11.1 (29%), 12.8 (29%), and 1.8 (29%) million metric tons of SO
2
, NO
x
, and PM
2.5
, respectively.
53
The benefits and impacts presented in this section and the next are based only on scenarios using central
assumptions for renewable energy technology costs and natural gas prices. The ranges presented reflect underlying
uncertainties within each benefit or impact category but, unlike the cost results from Section 4, do not reflect the
full range of scenario results.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
27
(a) SO
2
emissions
(b) NO
x
emissions
(c) PM
2.5
emissions
Figure 5.1. Emissions of (a) SO
2
, (b) NO
x
, and (c) PM
2.5
in three modeled scenarios
As shown in Figure 5.2, regional reductions are concentrated in areas with significant RE deployment that
offsets coal-fired generation. Under the Existing RPS scenario, reductions are most sizable in the West
South Central region. Under the High RE scenario, significant emission reductions accrue throughout
much of the eastern half of the country, especially in the East North Central and South Atlantic areas.
Some regions see small emissions increases under the Existing RPS and High RE scenarios; where this
takes place, it is due to the estimated growth in biomass generation in these regions.
0.0
0.5
1.0
1.5
2.0
2.5
2015 2020 2025 2030 2035 2040 2045 2050
Million metric tonnes SO
2
No RPS
Existing RPS
High RE
0.0
0.5
1.0
1.5
2015 2020 2025 2030 2035 2040 2045 2050
Million metric tons NO
x
No RPS
Existing RPS
High RE
0.00
0.05
0.10
0.15
0.20
0.25
2015 2020 2025 2030 2035 2040 2045 2050
Million metric tons PM
2.5
No RPS
Existing RPS
High RE
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
28
(a) SO
2
emissions
(b) NO
x
emissions
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
29
(c) PM
2.5
emissions
Figure 5.2. Regional (a) SO
2
, (b) NO
x
, and (c) PM
2.5
emissions relative to No RPS
All units in thousands of metric tons; note smaller scale for PM
2.5
.
These emissions reductions lead to improved air quality and health outcomes across the continental
United States. Estimates of the monetary value of these benefits are shown in Figure 5.3 (Existing RPS)
and Figure 5.4 (High RE) across the full range of methods applied.
Existing RPS: Total health and environmental benefits from the Existing RPS scenario fall in the range
of $48–$175 billion on a discounted, present-value basis. The average “central” estimate is $97 billion,
which is equivalent to a levelized benefit of RE of 2.4¢/kWh-RE; the total range is 1.2–4.2¢/kWh-RE
(Figure 5.3). The range of benefits estimates reflects uncertainties in how to value emissions reductions.
Figure 5.3. Present value (2015–2050) air pollution benefits, Existing RPS relative to No RPS
Note: The “Central Estimate” represents the simple average of all other estimates.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
0
20
40
60
80
100
120
140
160
180
200
AP2 EASIUR
Low
EPA Low Central
Est.
EASIUR
High
EPA High
¢/kWh-RE
Billion $2015
Billion $
¢/kWh-RE
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
30
High RE: Under the High RE scenario, total benefits range from $303 billion to $917 billion on a
present-value basis. The average “central” estimate is $558 billion, which is equivalent to a levelized
benefit of 5.0¢/kWh-RE; the total range is 2.7–8.2¢/kWh-RE (Figure 5.4). The divergence between the
per-kilowatt-hour benefits of the High RE scenario relative to the Existing RPS scenario is again a
consequence of the higher proportion of coal avoided under the former scenario.
Figure 5.4. Present value (2015–2050) air pollution benefits, High RE relative to No RPS
Note: The “Central Estimate” represents the simple average of all other estimates.
Across both scenarios, reduction of SO
2
and the subsequent reduction of particulate sulfate concentrations
account for the majority of the monetized benefits (Table 5.1). These benefits accrue primarily due to
displacement of coal generation in the central and eastern United States.
Most of the health benefits come from avoided premature mortality, primarily associated with reduced
chronic exposure to ambient PM
2.5
(largely derived from the transformation of SO
2
to sulfate particles,
but also from transformation of NO
x
to nitrate particles and direct PM
2.5
exposure). Based on the EPA
approach, achieving the Existing RPS scenario prevents 12,000–28,000 premature mortalities in total
from 2015 to 2050; the High RE scenario avoids 70,000 to 166,000 premature mortalities. These futures
also result in numerous forms of avoided morbidity outcomes, as summarized in Table 5.1.
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
0
100
200
300
400
500
600
700
800
900
1000
AP2 EASIUR
Low
EPA Low Central
Est.
EASIUR
High
EPA High
¢/kWh-RE
Billion $2015
Billion $
¢/kWh-RE
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
31
Table 5.1. Emissions reductions, monetized benefits, and mortality and morbidity benefits (2015–2050), Existing RPS and High RE
relative to No RPS
Note: Monetized benefits are discounted at 3% (real), but emissions reductions and mortality and morbidity values are simply summed over the 2015–2050 period. EPA benefits derive
from mortality and morbidity estimates based on population exposure to direct emissions of PM
2.5
and secondary PM
2.5
(from SO
2
and NO
X
emissions) as well as ozone exposure from
NO
X
emissions during the ozone season (May–September). AP2 benefits are derived from mortality and morbidity estimates based on population exposure to direct emissions of PM
2.5
and secondary PM
2.5
(from SO
2
and NO
X
emissions) as well as ozone exposure from NO
X
emissions during the ozone season (May–September). AP2 benefits also include
consequences from decreased timber and agriculture yields, reduced visibility, accelerated degradation of materials, and reductions in recreation services. EASIUR benefits derive
from mortality estimates based on population exposure to direct emissions of PM
2.5
and secondary PM
2.5
(from SO
2
and NO
X
emissions), but do not include morbidity benefits or ozone
benefits. Morbidity incidences estimates are derived from EPA; unlike mortality estimates, EPA does not assign separate high and low estimates.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
32
GHG EMISSIONS REDUCTION BENEFITS
Achieving the Existing RPS scenario reduces life-cycle GHG emissions from the electricity sector by 7%
in 2030 and 6% in 2050 relative to the No RPS baseline scenario (Figure 5.5). The High RE scenario
results in far greater GHG reductions: 27% in 2030 and 25% in 2050. Cumulative GHG savings from
2015 to 2050 in the Existing RPS scenario equal 4.7 billion metric tons of CO
2
-equivalent, representing
6% of total life-cycle emissions from the electricity sector over that same time period. Under the High RE
scenario, GHG savings equal 18.1 billion metric tons, equivalent to 23% of total life-cycle emission in the
electricity sector from 2015 to 2050.
Figure 5.5. Electricity system life-cycle GHG emissions in three modeled scenarios
Emissions reductions from avoided fossil-fuel combustion (2015 to 2050) are estimated with ReEDS at
4.5 billion and 17.4 billion metric tons of CO
2
under the Existing RPS and High RE scenarios,
respectively. These savings are somewhat smaller than the full life-cycle reductions, demonstrating that
the emissions from the non-combustion phases of RE deployment are lower than for fossil fuels.
As shown in Figure 5.6, combustion-related CO
2
savings vary by region, timeframe, and scenario. Under
the Existing RPS scenario, significant reductions accrue in most regions outside of the Southeast. Overall
reductions are substantially greater in the High RE scenario and are particularly sizable in the East North
Central and South Atlantic regions. These results are driven both by the relative amount and location of
RE deployment in these scenarios and by the degree to which higher-carbon-emitting plants are displaced.
The non-combustion, life-cycle impacts are not assigned to regions (and so are not included in Figure 5.6)
because of the challenges of estimating the location of upstream and downstream emissions.
0
500
1000
1500
2000
2500
3000
2015 2020 2025 2030 2035 2040 2045 2050
Million metric tons CO2e
No RPS
RPS
High RE
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
33
Figure 5.6. Regional avoided direct-combustion CO
2
emissions relative to No RPS
Estimates of the monetary value of these GHG emissions reductions are sizable but span a large range
(see Figure 5.7 for Existing RPS and Figure 5.8 for High RE)
54
:
Existing RPS: Using the “central” trajectory for the SCC, discounted present-value global climate
damage reductions from the Existing RPS scenario equal $161 billion. This is equivalent to a levelized
benefit of RE of 3.9¢/kWh-RE. Across the full range of SCC estimates, total benefits span $37 billion
(0.9¢/kWh-RE) to $487 billion (11.8¢/kWh-RE), a sizable range but significant even in the lower case. If,
alternatively, RE is viewed as a way to meet future carbon-reduction requirements, then the present-value
benefits of achieving the Existing RPS scenario range from $34 billion to $140 billion (0.8–3.4¢/kWh-
RE) depending on the valuation approach used. Under Synapse’s “medium” trajectory for the cost of
carbon, the Existing RPS scenario yields $96 billion in savings, which is equivalent to a levelized benefit
of 2.3¢/kWh-RE.
54
In all cases, valuation estimates are based on emissions reductions that take place from 2015 through 2050: any
emissions reductions after 2050 are not considered in the analysis.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
34
Figure 5.7. Present value (2015–2050) GHG reduction benefits, Existing RPS relative to No RPS
High RE: Using the “central” trajectory for the SCC, present-value global climate damage reductions
from the High RE scenario equal $599 billion, equivalent to a levelized benefit of 5.4¢/kWh-RE. Across
the full range of SCC estimates, benefits span $132 billion (1.2¢/kWh-RE) to $1,821 billion (16.3¢/kWh-
RE). If, alternatively, RE is viewed as a way to meet future carbon-reduction requirements, then the
present-value benefits of achieving the High RE scenario range from $131 billion to $614 billion (1.2–
5.5¢/kWh-RE). Under Synapse’s “medium” trajectory for the cost of carbon, the High RE scenario yields
$418 billion in savings, which is equivalent to a levelized benefit of 3.8¢/kWh-RE. The per-kilowatt-hour
benefits of the High RE scenario are greater than for the Existing RPS scenario due to the larger
proportion of coal (and lower proportion of natural gas) that is offset by growth in RE.
Figure 5.8. Present value (2015–2050) GHG reduction benefits, High RE relative to No RPS
0
2
4
6
8
10
12
14
16
18
20
0
50
100
150
200
250
300
350
400
450
500
mass-based rate-based low medium high low central high higher-
than-
expected
EPA CPP Final Rule Synapse Estimates Interagency Working Group
¢/kWh-RE
Billion 2015$
Billion $
cent/kWh-RE
Global Climate Damages
Carbon Reduction Compliance
0
2
4
6
8
10
12
14
16
18
20
0
200
400
600
800
1000
1200
1400
1600
1800
2000
mass-based rate-based low medium high low central high higher-
than-
expected
EPA CPP Final Rule Synapse Estimates Interagency Working Group
kWh-RE
Billion 2015$
Billion $
cent/kWh-RE
Global Climate Damages
Carbon Reduction Compliance
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
35
WATER USE REDUCTION BENEFITS
On a national level, electricity sector water withdrawals are estimated to decline substantially over time
under all scenarios, largely owing to the retirement and reduced operations of once-through-cooled
thermal facilities and the assumed replacement of those plants with newer, less water-intensive generation
and cooling technologies (Figure 5.9). National electricity sector water consumption also eventually
declines in all scenarios, but to a lesser extent than water withdrawals because recirculating cooling has
higher water consumption than once-through cooling.
Achieving the Existing RPS and High RE scenarios further reduces electricity sector water use, both
compared with recent use and compared with the No RPS baseline scenario (Figure 5.9). Specifically,
under the Existing RPS scenario, water consumption is 4% lower in 2030 and 7% lower in 2050 relative
to the No RPS baseline, and water withdrawal is 4% lower in 2030 and 3% lower in 2050. On a
cumulative basis (2015–2050), water consumption and withdrawal savings are 4% (2,200 billion gallons)
and 3% (26,000 billion gallons) lower, respectively, in the Existing RPS scenario. Greater impacts are
seen in the High RE scenario: relative to the No RPS baseline, water consumption is 20% lower in 2030
and 25% lower in 2050, whereas water withdrawal is 20% lower in 2030 and 26% lower in 2050.
Cumulative water use is estimated to be 18% lower in the High RE scenario, with about 9,000 billion
gallons in in reduced water consumption and 169,000 billion in reduced water withdrawal.
To put these figures into context, the 2030 annual consumption savings are equivalent to the water
demands of 420,000 U.S. households in the Existing RPS scenario, and 1.9 million households in the
High RE scenario. On average and over the entire 2015–2050 period, each megawatt-hour of RE meeting
the Existing RPS targets is found to save 3,400 gallons of water withdrawal and 290 gallons of water
consumption.
Water consumption and withdrawal impacts are not uniform throughout the continental United States.
Figure 5.10 presents regional cumulative water savings (2015–2050) in absolute terms and as a percent of
power sector water use compared with the No RPS baseline for 18 distinct watershed regions. The
amount of water saved—whether consumption or withdrawal—is affected by the amount and type of
incremental RE supply and the water use associated with the displaced fossil generation units. Given
these dynamics, the largest water savings—especially under the High RE scenario—accrue in regions of
the United States that are not generally considered water stressed and that currently withdraw and
consume larger quantities of water for power generation. Figure 5.10 highlights that although absolute
water withdrawal and consumption savings are lower in the water-stressed southwestern United States,
the percent savings under the Existing RPS scenario are more-consistent with savings in other regions.
Under the High RE scenario, absolute and percent water savings are higher in regions other than the
southwestern United States and water savings are typically greater than those found in the Existing RPS
scenario. The watershed region comprising most of California shows a slight increase in cumulative water
withdrawals by 2050, which are the result of higher generation levels of natural gas combined cycle
technologies in California under the Existing RPS and High RE scenarios.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
36
(a) Water consumption
(b) Water withdrawal
Figure 5.9. Electricity sector water (a) consumption and (b) withdrawal in three modeled scenarios
0
500
1,000
1,500
2,000
2015 2020 2025 2030 2035 2040 2045 2050
Billion gallons
No RPS
Existing RPS
High RE
0
10,000
20,000
30,000
40,000
2015 2020 2025 2030 2035 2040 2045 2050
Billion gallons
No RPS
Existing RPS
High RE
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
37
(a) Water Consumption (percent savings and gallon savings from No RPS scenario)
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
38
(b) Water Withdrawal (percent savings and gallon savings from No RPS scenario)
Figure 5.10. Regional water consumption (a) and withdrawal (b) savings relative to No RPS
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
39
6. IMPACTS OF EXISTING RPS AND EXPANDED RE
Using the methods discussed earlier, we evaluate two potential impacts associated with the Existing RPS
and High RE scenario, both relative to the No RPS scenario:
• Gross RE workforce and economic development impacts
• Natural gas price reduction impacts.
We make no claim of net societal benefits for these two impacts. While individual states may benefit from
an increase in gross RE jobs or reduced natural gas prices, on an international or national basis these
impacts are most likely to represent resource transfers. Growth in RE workforce, for example, may come at
the expense of job losses elsewhere in the economy, and though net job changes are possible we do not
evaluate them here. And while natural gas price reductions will unambiguously benefit energy consumers,
those benefits come at the expense of natural gas producers and those who benefit from natural gas
production.
GROSS RENEWABLE ENERGY WORKFORCE REQUIREMENT AND
ASSOCIATED ECONOMIC DEVELOPMENT IMPACTS
As a consequence of the greater amount of RE deployment, the Existing RPS and High RE scenarios drive
increased gross domestic RE-related jobs relative to the No RPS baseline (Figure 6.1
). In terms of total
cumulative job-years over the entire 2015–2050 period, the Existing RPS scenario yields 4.7 million
additional job-years compared to the No RPS scenario, a 19% increase in RE-related employment required.
This is equivalent to the renewable energy sector needing approximately 134,000 more workers annually,
on average, in comparison to the No RPS scenario. The High RE scenario, meanwhile, is estimated to
require 11.5 million additional job-years, again relative to the No RPS baseline, a 47% boost. We do not
estimate economy-wide net impacts, and the increased RE jobs noted here will be expected—at a national
or international scale—to be partially or fully offset by job contraction in other parts of the economy.
Figure 6.1. Gross domestic RE-related jobs in three modeled scenarios
0
500,000
1,000,000
1,500,000
2,000,000
2015 2020 2025 2030 2035 2040 2045 2050
Total Gross Jobs (FTE)
No RPS
Existing RPS
High RE
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
40
Figure 6.2 illustrates the breakdown among these job totals by RE technology, by onsite vs. supply chain
vs. induced, and by construction versus O&M.
55
The distribution of jobs among renewable technologies
reflects both the contribution of each technology to generation and capacity, as well as the labor-intensity of
their construction and operation phases. As shown, jobs related to solar PV—including both distributed and
utility scale—dominate the totals in all three scenarios, followed by wind. Supply-chain job totals are
somewhat larger than onsite and induced jobs in all three scenarios. In part, this reflects the fact that one-
time construction jobs, which directly impact supply-chain totals, are somewhat greater in magnitude than
ongoing RE O&M jobs.
55
For each scenario, the total job-years are equal across all three breakdown categories shown in Figure 6.2 because
all jobs are allocated to each subcategory. For example, “Solar” jobs include onsite, supply chain, and induced jobs
that resulted from the construction and operations of new solar generation. Similarly, “Construction” jobs in this
figure include onsite construction as well as supply chain (e.g., materials) needed for and induced jobs resulting from
the construction activity.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
41
(a) Renewable technology
(b) Onsite vs. supply chain vs. induced
(c) Construction vs. O&M
Figure 6.2. Total domestic RE-related job-years (2015–2050) by (a) renewable technology; (b) onsite,
supply chain, and induced; and (c) construction and O&M
-
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
40,000,000
No RPS Existing RPS High RE
Job-Years
Solar
Wind
Landfill Gas
Biopower
Geothermal
Hydropower
-
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
40,000,000
No RPS Existing RPS High RE
Job-Years
Induced
Supply Chain
Onsite
-
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
40,000,000
No RPS Existing RPS High RE
Job-Years
O&M
Construction
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
42
Of the three categories of jobs—onsite, supply chain, and induced—only onsite jobs can be readily linked
to specific regions. The distribution of onsite jobs (including those associated with both construction and
O&M) across regions under the Existing RPS and High RE scenarios relative to the No RPS baseline,
largely corresponds to the distribution of incremental RE deployment (see Figure 6.3). In particular, the
Existing RPS case yields the greatest number of incremental onsite jobs in the Pacific, Middle Atlantic, and
New England Regions. The High RE case, on the other hand, yields the largest incremental onsite job
increases in the South Atlantic region.
56
Figure 6.3. Regional gross onsite jobs relative to No RPS
Finally, Table 6.1 summarizes the results for earnings, output, and gross domestic product across the entire
analysis time period for each of the three scenarios. The results largely mirror those presented above for
employment effects. We again emphasize that these figures represent gross impacts associated with RE
deployment and not net economy-wide effects.
Table 6.1. Gross Total (2015–2050) RE-Related Earnings, Output, and Gross Domestic Product in
Three Modeled Scenarios
Gross Impact (2015$
million)
No RPS Existing RPS High RE
Earnings 1,281,000 1,562,000 1,962,000
Output 3,956,000 4,727,000 5,841,000
GDP 2,827,000 3,562,000 3,573,000
56
RE employment is tied to both capacity expansion and the type of RE deployed. As shown in figure 6.2, some
technologies require more workers for either installation or O&M. This is particularly true for solar PV installation,
which is relatively more labor-intensive than other RE technologies.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
43
NATURAL GAS PRICE REDUCTION IMPACTS
Achieving the Existing RPS and High RE scenarios reduces cumulative 2015–2050 electricity sector
natural gas consumption by a total of 35 quads and 46 quads, respectively, relative to the No RPS baseline
(Figure 6.4). These reductions represent 3.3% and 4.3% of total economy-wide natural gas consumption in
the United States over the same time period. Despite the much-more aggressive RE deployment in the High
RE scenario, natural gas demand reduction is only modestly higher than under the Existing RPS scenario
due to the higher-degree of coal displacement under the High RE case.
These estimated economy-wide natural gas demand reductions place downward pressure on natural gas
prices, illustrated on a national basis in Figure 6.4. Though the figure presents national average results, the
price changes vary somewhat by region; regional reductions in 2050 of $0.36 to $0.59 per million Btu
(MMBtu) are estimated in the Existing RPS scenario, whereas reductions of $0.69 to $0.89/MMBtu are
estimated in the High RE scenario.
(a) Natural gas consumption
(b) Natural gas delivered prices
Figure 6.4. Electricity sector natural gas (a) consumption and (b) delivered prices in three modeled
scenarios
These price changes, as they affect the electricity sector, are reflected in the results presented earlier on the
cost and retail electricity price impacts of the various scenarios. In addition gas price reductions provide
consumer benefits in the form of lower natural gas costs outside of the electricity sector. In particular, as
shown in Figure 6.5, natural gas bill savings from the Existing RPS scenario total $78 billion on a
discounted, present-value basis, which is equivalent to a levelized impact of RE of 1.9¢/kWh-RE. Under the
0
5
10
15
20
2015 2020 2025 2030 2035 2040 2045 2050
quads
No RPS
Existing RPS
High RE
$0
$1
$2
$3
$4
$5
$6
$7
$8
2015 2020 2025 2030 2035 2040 2045 2050
$2015/MMBtu
No RPS
Existing RPS
High RE
Note: prices vary by region
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
44
High RE scenario, total consumer savings equal $99 billion, or 0.9¢/kWh-RE. While absolute consumer
savings are greater under the High RE scenario compared with the Existing RPS, levelized impacts are
lower due to the greater amount of incremental RE in the former.
Figure 6.5. Present value (2015–2050) non-electric natural gas consumer savings relative to No RPS
Consumer benefits vary by region depending on the estimated regional gas price reduction and total
regional non-electric natural gas consumption (Figure 6.6). Importantly, while individual regions or states
may experience net benefits associated with these natural gas price changes, the potential price reductions
and consumer savings are likely to be primarily, or even exclusively, transfer payments from gas producers
(and those that benefit from gas production, such as owners of mineral rights) to gas consumers on a
national basis.
Figure 6.6. Regional (non-electric) natural gas consumer savings relative to No RPS
0.0
0.4
0.8
1.2
1.6
2.0
2.4
0
20
40
60
80
100
120
Existing RPS High RE
¢/kWh-RE
Billion $2015
Billion $
¢/kWh-RE
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
45
7. SUMMARY AND CONCLUSIONS
RPS policies have been a driver of growth in renewable capacity and generation in the past and, based on
already established future-year policy targets, are expected to continue to require substantial new renewable
capacity in coming years (Barbose 2016a). This analysis estimates the prospective benefits and costs
associated with new renewables needed to meet future-year targets of existing RPS policies as well as those
associated with higher renewable penetrations that could be achieved under an expanded RPS adoption
scenario. Benefits are estimated based on changes in the generation mix and the resulting physical impacts
calculated through 2050 using the ReEDS model, with monetary estimates applied as feasible. Costs
include all electric infrastructure and operating costs and are estimated in terms of both the net present
value of electric system expenditures as well as changes to average retail electricity prices.
Our analysis finds that the Existing RPS scenario will require 66 GW of renewables (218 TWh of
incremental renewable generation) above the No RPS scenario by 2030 and 122 GW (296 TWh of
renewable generation) by 2050. These values reflect the amount of incremental RE needed to satisfy RPS
requirements beyond 2014 and serve as the basis for which we evaluate the costs, benefits, and impacts.
The Existing RPS scenario results in 26% renewables (including hydropower) penetration by 2030 and 40%
by 2050, compared to 21% and 34%, respectively, under the No RPS scenario. Wind and solar dominate
capacity additions, but significant incremental generation is also derived from biomass and geothermal
resources. The new RE generation largely offsets fossil generation, with a greater amount of coal offset
before 2030, but more natural gas offset in the longer term. RE used to meet existing RPS requirements
from 2015 to 2050 results in cumulative emissions reductions equal to 2.1 million metric tons of SO
2
(5.5%
as a percentage of total electricity sector emissions), 2.5 million metric tons of NO
x
(5.7%), and 0.3 million
metric tons of PM
2.5
(4.5%). Based on these reductions, total health and environmental benefits are
estimated to be $97 billion (central estimate), or 2.4¢/kWh-RE. In addition, the generation mix in the
Existing RPS scenario results in cumulative GHG savings from 2015 to 2050 equal to 4.7 billion metric
tons of CO
2
-equivalent, or 6% of total life-cycle emissions from the electricity sector. Under the central
estimate, global climate damage reductions equal $161 billion on a discounted, present value basis, or
3.9¢/kWh-RE.
The High RE scenario results in substantial additional capacity of 215 GW of renewables above the
baseline No RPS scenario by 2030 (or 627 TWh of incremental renewable generation) and 331 GW of
incremental renewables (or 765 TWh of generation) by 2050. This translates to an RE penetration of 35%
by 2030 and 49% by 2050. Again, wind and solar are the dominant RE technologies deployed. Coal is the
dominant form of avoided generation in the High RE scenario, even more so than under the Existing RPS
scenario. The High RE scenario leads to cumulative (2015–2050) air emission savings of 11.1 (29%), 12.8
(29%), and 1.8 (29%) million metric tons of SO
2
, NO
x
, and PM
2.5
, respectively. The health benefits of these
reduced emissions are estimated to be $558 billion on a present-value basis (central estimate), or 5.0¢/kWh-
RE. In addition, under the High RE scenario, GHG savings equal 18.1 billion metric tons, equivalent to
23% of total life-cycle emission in the electricity sector from 2015 to 2050. Using central estimates,
present-value global climate damage reductions from the High RE scenario equal $599 billion, equivalent
to a levelized benefit of 5.4¢/kWh-RE.
In addition to the air and health benefits and GHG savings, the Existing RPS and High RE scenarios also
yield benefits or impacts in the form of reduced water consumption and withdrawals, increased renewable
energy-related employment, and reductions in natural gas demand that lower consumer gas bills. Relative to
the No RPS baseline, total water consumption in the electricity sector from 2015–2050 is 4% and 18%
lower under the Existing RPS and High RE scenarios, respectively. The scenarios also require an increase
in gross domestic RE-related jobs, although these could lead to offsets by job contraction in other parts of
the economy. Over the entire 2015–2050 period, the Existing RPS scenario requires 4.7 million additional
cumulative job-years, a 19% increase in RE-related employment, while the High RE scenario requires 11.5
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
46
million additional job-years, a 47% boost. Gross onsite jobs, which include construction and O&M jobs,
represent 28% and 29% of all gross jobs, respectively, in the Existing RPS and High RE scenarios. Finally,
achieving the Existing RPS and High RE scenarios reduces cumulative 2015–2050 electricity sector natural
gas demand by 35 quads and 46 quads, respectively, relative to the baseline. Gas price reductions that result
from reduced consumption provide consumer benefits in the form of lower natural gas bills, with bill
savings from the Existing RPS scenario totaling $78 billion on a discounted, present-value basis, or
1.9¢/kWh-RE. Under the High RE scenario, total consumer savings equal $99 billion, or 0.9¢/kWh-RE.
In comparison to the benefits, incremental system cost impacts are 1.5¢/kWh-RE or less in both scenarios.
Figure 7.1 shows a comparison of the incremental system cost impacts relative to RPS benefits that can be
monetized. Estimated costs include all electric infrastructure and operating costs and are evaluated under a
range of sensitivity cases related to natural gas prices and RE costs. For the Existing RPS scenario,
incremental system costs are estimated to range from ±$31 billion (-0.7% to 0.8%). On a levelized basis,
these costs are estimated to be about ±0.75¢/kWh-RE. On the low end of the ranges, incremental costs are
found to be negative, suggesting that the RE used to meet aggregate existing RPS policies is economically
competitive with other generation sources, when high natural gas prices are assumed. On the other hand,
higher RE costs or lower natural gas prices can result in positive compliance costs. In either case, our
scenarios suggest likely small national-level electric system cost impacts in either direction for the Existing
RPS scenarios. For the Existing RPS scenario, we find incremental retail electricity prices of up to about
1¢/kWh on the high end and reduced prices on the low end.
For the High RE scenario, we find a larger range of incremental costs, ranging from $23 billion (0.6% of
total system costs) to $194 billion (4.5% of total system costs), which is equivalent to 0.26¢/kWh-RE to
1.5¢/kWh-RE on a levelized basis. All of the sensitivities conducted for the High RE scenario were found
to have greater system costs than the No RPS scenario, with the highest incremental costs found when high
RE technology costs are assumed. The range of incremental electricity prices is greater for the High RE
scenario compared to the Existing RPS scenario, particularly after 2020. On the high end of the range,
regional incremental prices are estimated to peak at as much as 4.2¢/kWh and average (for all years) about
2.9¢/kWh. The low end of the range of incremental prices for the High RE scenario is similar to that for the
Existing RPS scenario.
Summarizing the comparison, we find that the benefits exceed the costs, even when considering the highest
cost and lowest benefit outcomes (Figure 7.1). Under the Existing RPS scenario, the high end costs are
0.75¢/kWh-RE, while air pollution and health benefits total at least 1.2¢/kWh-RE and GHG benefits total at
least 0.9¢/kWh-RE. Under the High RE scenario, the high end costs are 1.5¢/kWh-RE while air pollution
and health benefits total at least 2.7¢/kWh-RE and GHG benefits total at least 1.2¢/kWh-RE. The figures
here are presented on a national basis.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
47
Figure 7.1. Comparison of national systems costs and monetized benefits under the Existing RPS
and High RE scenarios
Note: Positive values reflect benefits in the Existing RPS and High RE scenarios, whereas negative values reflect
higher costs relative to the No RPS scenario. Water benefits, gross RE workforce and economic impacts, and natural
gas impacts are not shown here.
While we did not evaluate state RPS policies individually, national and regional results can help
policymakers understand regional trends and inform state-level decisions about policy changes. Under the
Existing RPS scenario, the Pacific region in particular shows considerable (relative to other regions)
incremental renewable generation in both the near and longer term. Other regions with lesser, but still
substantial, incremental renewable generation under the Existing RPS scenario relative to the No RPS
scenario, particularly by 2050, include the East North Central, Middle Atlantic, New England, and
Mountain regions. Notably, the significant RPS required renewable generation in New England and the
Pacific region largely offset gas while smaller amounts of incremental renewables elsewhere help reduce
more coal. In general, there is a more uniform regional distribution of incremental renewable generation in
the High RE scenario and greater reduction in coal usage as a result of the construction of the scenario.
Our analysis has several limitations. First, we recognize that there may be more cost-effective ways to
achieve the benefits and impacts discussed in this paper. Second, while our analysis examines the costs,
benefits, and impacts of RE needed to meet RPS requirements going forward, it does not seek to attribute
those effects solely to RPS policies. Third, our work distinguishes between the potential benefits and
impacts of RPS programs. Impacts are best considered as resource transfers, benefiting some stakeholders
at the expense of others, although such impacts might still be relevant considerations when evaluating state
RPS programs. We do not evaluate the net effects of these impacts over the entire country and thus cannot
assess whether or not these impacts reflect net costs or benefits at a national scale. Fourth, we consider the
impacts of RE needed to meet all existing (and expanded) RPS in aggregate and do not estimate the impacts
of any individual RPS policy. Finally, our analysis considers an important subset of, although not all,
potential benefits and impacts; for example, we do not quantify land use and wildlife impacts. Despite these
limitations, the analysis can inform decision makers about the prospective costs, merits, and value of state
RPS programs as they consider revisions to existing policies and development of new policies.
-4
-2
0
2
4
6
8
10
12
14
16
18
Electric System
Cost
Air Pollution
and Health
Benefit
GHG Benefit
Benefits (¢/kWh-RE)
Central
Estimate
Existing RPS
Electric System
Cost
Air Pollution
and Health
Benefit
GHG Benefit
High RE
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
48
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