Modeling Decarbonization: Report Summary and Policy Brief — Virginia Senate Document No. 17 (2021)
Summary
Senate Document No. 17 of 2021, a report of the Virginia Secretary of Natural and Historic Resources and the Virginia Secretary of Commerce and Trade to the General Assembly of Virginia, titled Modeling Decarbonization: Report Summary and Policy Brief (Chapter 1194, 2020). The cover letter, dated January 1, 2022, states that clean energy employment in the Commonwealth is more than 88,000 and describes the Virginia Clean Economy Act of 2020 (VCEA), which requires the report. The report presents modeling of electricity generation and power plant capacity in the twelve-state RGGI region through 2040.
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REPORT OF THE VIRGINIA SECRETARY OF
NATURAL AND HISTORIC RESOURCES AND
VIRGINIA SECRETARY OF COMMERCE AND
TRADE
Modeling Decarbonization:
Report Summary and Policy
Brief for Virginia Governor’s
Office Administration and
Policymakers (Chapter 1194,
2020)
TO THE GENERAL ASSEMBLY OF VIRGINIA
SENATE DOCUMENT NO. 17
COMMONWEALTH OF VIRGINIA
RICHMOND
2021
January 1, 2022
To Members of the Virginia General Assembly:
The clean energy sector is a rapidly growing sector of Virginia’s economy. Total clean
energy employment is currently more than 88,000 across the Commonwealth, according to the
recent “Clean Jobs America” report by E2 from 2021, placing Virginia in the top 10 states
nationwide. Tens of thousands of new jobs are projected to emerge to support growth in a variety
of clean energy sectors. Additional jobs, economic development, and increased revenues to
localities are just a few of the tangible economic benefits of the Virginia Clean Economy Act of
2020 (“VCEA”), in addition to cleaner air and water.
The VCEA requires a report to the General Assembly on (1) how Virginia can achieve
100 percent carbon-free electricity by 2045 at least-cost to ratepayers and (2) a recommendation
on “whether the General Assembly should permanently repeal the ability to obtain a certificate of
public convenience and necessity (CPCN) for any electric generating unit that emits carbon as a
by-product of combusting fuel to generate electricity.” Over the past year, staff from the Virginia
Department of Energy (Virginia Energy) and the Virginia Department of Environmental Quality
(DEQ) led modeling research, a mandated stakeholder engagement process, and report summary
for determining the least-cost path of implementation.
The 2045 carbon-free electricity generation goal can be met through existing natural gas
infrastructure, existing nuclear energy facilities (with renewed permits) and new renewable
energy investments. A moratorium on new carbon-emitting generating units is not required to
meet clean energy goals set forth by the VCEA. The VCEA is working to put Virginia on a path
to reach mid-century decarbonization goals at least cost to ratepayers while boosting economic
competitiveness for the coming decades.
Extending a moratorium on the issuance of CPCNs for carbon-emitting generation
facilities in the Commonwealth appears unnecessary given the advantages carbon-free generation
receives through the VCEA and the declining emissions limits from the Virginia Carbon Rule
(implemented through the Regional Greenhouse Gas Initiative). Further, a moratorium may
prove counterproductive if it prohibits vital reliability projects and stifles other forms of clean
energy innovation. While these risks may appear small, it may be imprudent to remove elements
Patrick Henry Building • 1111 East Broad Street • Richmond, Virginia 23219
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January 1, 2022
Page 2
of flexibility from a 30-year project when the elimination of this flexibility will not adversely
affect achieving VCEA’s ultimate goals. A moratorium is unlikely to accelerate the expansion
of the clean energy industry in Virginia as carbon free sources will continue to be highly
advantaged by the provisions of the VCEA and by market dynamics, including customer
preference and falling costs of renewable energy production.
Thus, our formal recommendation to the General Assembly is to not permanently
repeal the ability to obtain a CPCN for carbon emitting electricity units at this time. Other
measures from the VCEA remain in place to ensure a robust process to screen applications for
economic, social and environmental factors and the Virginia Carbon Rule will guarantee
certainty in emissions reductions from in-state electricity generation. Key subject matter experts
should consider studying reliability factors in Virginia’s electricity sector as the energy economy
transitions to cleaner and distributed sources of power. Further, continuing to advance the type of
research reflected in this report will support the Commonwealth’s economy, environmental
resources and social communities to reach mutual goals.
A diverse energy landscape including energy efficiency, solar energy, energy storage,
wind energy, smart grid investments and existing natural gas and nuclear energy facilities will
contribute to a reliable Virginia energy grid. Further reliability analysis and reporting on a
regular (bi-annual) basis should be conducted, building on the success of this research project.
Virginia Energy, DEQ, the State Corporation Commission, investor-owned utilities, PJM, and
other key stakeholders should continue to work on this topic and report their findings through a
public process.
We wish to extend appreciation to the full research team, as led by the state agencies and
supported by the University of Virginia and Georgetown University, the modeling organization
Resources for the Future, as well as other key partners such as the State Corporation Commission
staff, members of the Council on Environmental Justice, and all parties who participated in the
stakeholder engagement process.
Respectfully,
Brian Ball Ann Jennings
Secretary of Commerce and Trade Secretary of Natural and Historic Resources
Modeling Decarbonization: Report Summary and Policy Brief for
Virginia Governor’s Office Administration and Policymakers
Virginia Department of Energy
December 14, 2021
1
Purpose and Background:
Over the past year, Virginia Department of Energy and Virginia Department of Environmental
Quality staff have led a legislation-mandated stakeholder engagement process, modeling
research, and report summary for determining the least-cost path of implementation for the
Virginia Clean Economy Act of 2020 (HB1526/SB851). The enclosed report describes the
modeling project, results from the modeling research, and stakeholder comments. As with any
technical analysis or academic research, the output requires further contextualization and
emphasis of key points for the administration, policymakers, and related stakeholders in
Virginia. This brief will present high level considerations as well as provide a standalone
summary of the report.
The Virginia Clean Economy Act (VCEA) mandates a report to the General Assembly on (1)
how Virginia can achieve 100 percent carbon-free electricity by 2045 at least-cost to ratepayers
and (2) a recommendation on “whether the General Assembly should permanently repeal the
ability to obtain a certificate of public convenience and necessity for any electric generating unit
that emits carbon as a by-product of combusting fuel to generate electricity.”
Shifting to 100 percent clean electricity will result in significant investment in new infrastructure,
with its associated jobs, business opportunities and local revenues. Furthermore, the shift from
fossil-fuel generation to clean, renewable energy results in direct benefits to the residents of
Virginia, creating cleaner air and water and mitigating the costly impacts from climate change
including sea level rise and increased damages in other flood prone areas.
The state agencies in this memo are addressing the first charge on the least-cost pathway to
reach carbon free electric generation in Virginia. The Governor’s office, in collaboration with the
Secretary of Commerce and Trade and Secretary of Natural and Historic Resources, will be
making the final recommendation to the General Assembly on the latter CPCN consideration.
Key Findings in the Report:
The VCEA primarily relies on four key pillars for accomplishing electricity sector
decarbonization: (1) a renewable portfolio standard (RPS) and energy efficiency resource
standard (EERS), (2) joining the Regional Greenhouse Gas Initiative (RGGI) carbon pricing
program, (3) a set of capacity targets for specific renewables technologies and (4) shutting
down carbon emitting power plants by 2045.
The state agencies worked closely with a reputable modeling team at Resources for the Future
(RFF), including Virginia-based research institutions, and used RFF’s Haiku electricity model to
test various policy, technology, and fuel cost scenarios. Based on this modeling, three key
conclusions emerged:
1. The near and long term RPS, EERS, and RGGI targets in the VCEA can be met with
existing, known technologies — supported by the 2035 capacity targets;
2
2. Cost reduction pathways are available, mostly centering on effective implementation of
the VCEA provisions;
3. There are several elements not directly examined in the model that could limit costs
further, including: innovative ownership and financing models for clean energy and
energy efficiency, grid modernization and expansion, and technological innovation.
By implementing these policies, the Commonwealth ratepayers including residents, businesses
and other organizations will all benefit from cost effective clean energy including economic
development, job creation, investment in clean energy infrastructure and services, as well as the
known health and environmental cost savings that occur from a shift to cleaner air and water. By
joining the regional carbon market (RGGI), Virginia ratepayers will benefit greatly through
reduced carbon emissions in the power sector. Virginia’s participation in the RGGI market
captures auction proceeds to reinvest in funding for low-income energy efficiency investments
as well as climate related flood prone community infrastructure funding.
Policy and Qualitative Analysis of the Report:
These are important findings for understanding the impact and implementation challenges of the
VCEA. The Haiku electricity model was chosen for its ability to model and analyze the electricity
sector specifically. This is foundational for any modeling exercise and developing useful
conclusions. That said, all models have limits. As such, interpreting the findings in a broader
context is also essential when analyzing the results. The nature of the modeling report requires
further policy context to elaborate on more qualitative measures. Among these include the
importance of the clean energy sector to Virginia’s economy, why a diverse set of clean energy
resources is prudent to invest in, even if not cost-minimizing, and how the VCEA supports new
industries as it works to reduce damages from emissions.
- Virginia’s Clean Energy Economy is Strong and Poised for Growth
The most important qualitative result worth emphasizing is the economic strength of the clean
energy sector. The emerging/advanced/clean energy sector is already a significant share of the
energy economy in the Commonwealth, and is only poised to grow — largely because of the
VCEA and the policy certainty it provides.
Existing VCEA/Future Estimates*
Economic
Activity Taxes Economic Taxes
Jobs (annual) (annual) Jobs Activity (annual) (annual)
Energy
Efficiency 71,505 $1.5B Unknown 110,000 $2.3B Unknown
3
Existing VCEA/Future Estimates*
Economic
Activity Taxes Economic Taxes
Jobs (annual) (annual) Jobs Activity (annual) (annual)
Offshore $117M
Wind Unknown Unknown Unknown 15,520 $2.2B
Land-based
wind** - - - 4-8 per MW $0.95M per MW $10k per MW
Nuclear*** 2,374 $1.7B $33.4M 2,374 $1.7B $33.4M
280,000
(cumulative
Solar**** 4,489 $1.3B $81M (est) job-years) $8.1B $502M
Energy
Storage 100-400 $3M-$9M Unknown 12,000 $1.5B Unknown
*All future values are inherently speculative and represent annual figures unless noted
**No land-based wind has been developed in Virginia as of Q4 2022
***It is unclear if/how the VCEA impacts nuclear energy growth, assumed to be unchanged
****Utility scale and distributed
Markets working to drive clean energy growth will be limited without policy to help drive that
growth. Low-carbon energy technology is widely acknowledged as the cheapest source of new
electricity capacity, but strong policy signals ensure that the market responds at a rate reflecting
the pressing need to eliminate greenhouse gas emissions. Though the modeling indicates that
the VCEA capacity targets are not economically optimal based on a narrow focus on technology
costs, removing them could send a negative signal to industries already investing in Virginia.
Offshore wind is the clearest example of this dynamic — the capacity target and Virginia’s
commitment has already stimulated long-term investment in the Commonwealth. Without the
capacity target, that investment would be delayed, which could, in turn, delay the cost
reductions from early investment in the technology.
Because of the clear policy signal of the VCEA and favorable economics, the market is
responding and investment in Virginia is being planned and executed right now.
- The VCEA supports Multiple Technological and Policy Pathways to
Decarbonization
This points to the importance of having multiple policy and technology paths to decarbonization.
The VCEA builds in multiple mechanisms for achieving the Commonwealth’s policy goals. It
limits emissions via RGGI and the RPS while also encouraging targeted clean energy and
energy efficiency deployment via the capacity targets and EERS.
The report is quite clear in verifying that existing technology at existing costs (largely solar and
the first 2.6GW of offshore wind) will ensure Virginia meets the VCEA targets in the near term.
Looking further into the future, there are other reasons to encourage multiple clean energy
4
pathways that are not solely relying on technology costs. The most important is that the path to
decarbonization is less clear as clean energy deployment ramps up in the following decades
and new, hard-to-model risks emerge such as land use or supply chain challenges.
Developing multiple resources reduces the likelihood of challenges with any single technology
delaying decarbonization in addition to unlocking additional benefits such as complimentary
seasonal and diurnal generation profiles of solar and offshore wind reducing total deployment
needs and necessary grid expansion. This will also allow further innovation in emerging energy
technology necessary to reach full decarbonization (such as long-duration energy storage or
advanced nuclear, geothermal, bio-energy with carbon capture or zero-carbon synthetic fuels,
or green hydrogen) to progress and be assessed.
- Smart Implementation is Critical to Success
The last major theme that emerges from the report is the need for effective implementation of
the VCEA. The report outlines several “no regrets” solutions: including energy efficiency, grid
modernization and expansion, demand response, and innovative financing and ownership
models. All depend on smart planning in addition to continued implementation, measurement,
and verification to achieve their maximum benefit and thereby reduce costs to ratepayers.
Conclusions:
Modeling is, in a sense, an idealized representation of the real world — especially when
forecasting decades into the future. This brief compliments the quantitative model results and
analyzes the effectiveness of the VCEA, the potential of Virginia’s clean energy economy, and
the remaining implementation challenges. Clean energy is and will continue to develop
Virginia’s economy in an enormous new industry — lifting all corners of the Commonwealth with
it. The technical findings in this report confirm that VCEA is feasible and affordable, and makes
crystal clear that smart and rigorous implementation will be key to its success at least-cost for
ratepayers. All this is to say, the VCEA is on track and working as intended so far.
References:
1. DOE US Energy & Employment Report (2021): link
2. Princeton Net-Zero America project: link
3. Magnum report on Virginia offshore wind economic impacts: link
4. VCU CURA solar report: link
5. Nuclear Energy Institute Report: link
6. NREL JEDI (economic impact estimator) tool: link
7. Strategen report: link
8. Virginia Advanced Energy Economy report: link
9. SEIA Virginia fact sheet: link
5
Achieving Clean Electricity
Generation at Least Cost to
Ratepayers by 2045
DECEMBER 11, 2021
Prepared for the Virginia Department of Energy
by the Weldon Cooper Center for Public Service, University of Virginia
William Shobe and Anthony Artuso, University of Virginia
Maya Domeshek, Resources for the Future
LIST OF CONTRIBUTORS
Virginia Department of Energy
(Virginia Energy)
Principle Investigator
Virginia Department of Environmental
Quality (DEQ)
Resources for the Future (RFF)
Modeling Team
University of Virginia, Weldon Cooper
Center for Public Service
Lead Report Author
Georgetown Climate Center
ACKNOWLEDGEMENTS
• DEQ: Christopher Bast, Thomas Ballou, • RFF: Dallas Burtraw, Karen Palmer, Nicholas
Michael Dowd Roy
• Dominion Energy • Southern Environmental Law Center
• Georgetown Climate Center:
• State Corporation Commission
Franz Litz, James Bradbury
• Office of the Governor: Trieste Lockwood • UVA: Will Olsen, Art Small
• Office of the Secretary of Commerce and • Virginia Energy: Carrie Hearne, Al
Trade: John Begala Christopher, Michael Skiffington, Erik Olson
• Numerous contributions from individuals and
• PJM
organizations
• Council on Environmental Justice and its Subcommittee on Just Transition and Infrastructure
EXECUTIVE SUMMARY
The Virginia Clean Economy Act (VCEA) 1 passed in 2020 requires the elimination of most CO2
emissions from Virginia’s electricity sector by 2050. The VCEA relies on four key pillars for
accomplishing electricity sector decarbonization: (1) a clean energy standard, (2) joining the
Regional Greenhouse Gas Initiative (RGGI) carbon pricing program, (3) a set of capacity targets
for specific renewables technologies and (4) shutting down most CO2 emitting power plants by
2045. The VCEA requests that the Secretary of Natural Resources and the Secretary of
Commerce and Trade report to the General Assembly by January 1, 2022 recommendations “to
achieve 100 percent carbon-free electric energy generation by 2045 at least cost for ratepayers.
Such report shall include a recommendation on whether the General Assembly should
permanently repeal the ability to obtain a certificate of public convenience and necessity for any
electric generating unit that emits carbon as a by-product of combusting fuel to generate
electricity.”
To identify potential strategies for achieving least-cost decarbonization by 2045, the Virginia
Department of Energy (Virginia Energy) and the Virginia Department of Environmental Quality
(DEQ), with the support of the Georgetown Climate Center, led a research project to model
possible strategies to achieve least-cost elimination of CO2 emissions by 2045. A research team
at Resources for the Future (RFF) in consultation with the Weldon Cooper Center for Public
Service at the University of Virginia (Cooper Center) used RFF’s electricity planning model, Haiku,
to explore potential strategies for lowering the costs of meeting VCEA emissions targets. The
Cooper Center had the primary responsibility for reporting the modeling results and evaluating
other potentially important elements of a least-cost approach to decarbonizing Virginia’s
electricity sector.
The results of this investigation suggest a number of key strategies for lowering the costs of
decarbonization. The modeling results are broadly consistent with the results of other studies of
least-cost emissions reduction. Underlying many of the specific suggestions in this report are
the principle that flexibility in technology, timing and geography all contribute to lower costs.
Strategies that focus directly on reducing aggregate greenhouse gas emissions tend to have
lower costs than strategies that specify particular techniques or locations for achieving those
emissions. Elements of a flexible approach might include:
• Technology-neutral, competitive procurement of non-emitting generation
• Procurement of non-emitting generation from the PJM interconnection region
• Emphasizing market-based approaches such as the Regional Greenhouse Gas Initiative
1
HB1526/SB851
EXECUTIVE SUMMARY
MODELING STRATEGY AND KEY RESULTS
RFF’s Haiku electricity model calculates the least-cost mix of electricity generation resources,
given a set of input assumptions about electricity demand, generation costs and availability of
renewable energy resources such as sun and wind. For this inquiry, a set of widely-used,
standard assumptions from the U.S. Energy Information Administration and the National
Renewable Energy Laboratory were used. The technology cost estimates are only for
technologies that can be reasonably subjected to cost analysis in their current state of
development. These estimates do not include technologies currently in the early stages of
development. In particular, new nuclear, floating wind turbines and advanced geothermal
technologies are not represented here. Should the cost of these technologies fall faster than
expected, then a cost-effective strategy would include these in the mix of resources available for
competitive procurement.
The Baseline Scenario reflects the current electricity sector stock of generation assets and the
current policy environment, including the provisions of the VCEA such as the renewable
portfolio standard, RGGI membership and the specific technology capacity goals for renewables
and storage. Several results from the Baseline Scenario are true in the alternative scenarios as
well:
• The four existing nuclear reactors are retained through the planning horizon
• Existing natural gas capacity is retained but with falling rates of utilization
• Utility-scale solar is the largest addition to generation capacity
• Between 2025 and 2035, the RPS is not binding (i.e. renewables investments exceed RPS
requirements); offshore wind deployment substitutes for solar
• Electricity demand can be met with non-emitting resources by 2045
A series of policy scenarios were used to calculate the change in ratepayer costs likely to occur
given a change in the VCEA pathway. Several possible cost-lowering adjustments were
identified.
• Coal generation would retire for economic reasons by 2025
• Savings are available from the increased use of renewable energy credits from outside
Virginia
• Investing in energy efficiency is cost effective, although improvements in evaluation and
monitoring are necessary
• Competitive capacity procurement can result in significant savings over specific
technology capacity targets
There are several possible cost-saving initiatives that were not easily incorporated into the
electricity sector planning framework used in this study. In particular, enhanced grid planning
and finance, more aggressive energy conservation efforts, distributed energy resources and
ii
EXECUTIVE SUMMARY
demand management are all very fruitful areas for activities that have the potential to
substantially lower the cost of decarbonization to ratepayers:
• There are potentially billions of dollars in savings from enhanced regional planning for
grid modernization and finance
• The size of the energy efficiency resource may be very large but is unlikely to be be
realized cost effectively without a significant investment in state agency capacity for
advanced monitoring and evaluation
• Some recent studies suggest that distributed energy resources can be cost-effective
notwithstanding their relatively high levelized cost of energy but further research is
necessary
• Demand response, especially as EV penetrations rise, has a very large potential for cost-
saving. Harvesting this resource requires a substantial investment in institutional
innovation and creative tariff design
This modeling effort, along with many similar ones, indicates that eliminating CO2 emissions
from Virginia’s electricity sector are technically achievable, but the approach taken to
decarbonization can have a substantial effect on the cost of doing so. In the short run, much of
the decarbonization effort centers around the buildout of utility-scale solar, our least expensive
new energy resource, is central to a cost-effective decarbonization effort. In the medium to long
term, the cost effectiveness of the energy transition will depend on maintaining a policy
environment that promotes investment in least cost technologies as new clean energy and
energy storage technologies emerge and the relative cost of currently available technologies
continues to shift.
iii
TABLE OF CONTENTS
Introduction .................................................................................................................... 1
The purpose of this study .............................................................................................................................. 1
Modeling team ................................................................................................................................................. 2
Public comment............................................................................................................................................... 2
Plan for this report .......................................................................................................................................... 3
A quick look at Virginia’s electricity sector .............................................................. 4
Modeling methodology and assumptions ................................................................ 6
Discussion of baseline assumptions ......................................................................................................... 8
Model results ................................................................................................................14
The Baseline Scenario .................................................................................................................................. 14
Sensitivity scenarios .................................................................................................................................... 18
Policy scenarios ............................................................................................................................................. 24
Other possible technologies and approaches ...................................................................................... 31
Compliance flexibility and lowering decarbonization costs ................................ 34
Additional factors for least-cost decarbonization pathways .............................. 37
Resource variability, reliability and resilience....................................................................................... 37
Transmission and distribution system costs ........................................................................................ 37
Harvesting energy efficiency ..................................................................................................................... 40
Demand Response ....................................................................................................................................... 43
Distributed Energy Resources .................................................................................................................. 46
Conclusions .................................................................................................................49
References ....................................................................................................................51
Appendix 1: Public comment on the CPCN permanent moratorium ................. 54
Appendix 2: Public presentation on modeling, September 9, 2021 ................... 57
INTRODUCTION
THE PURPOSE OF THIS STUDY
The Virginia Clean Economy Act (VCEA) passed in 2020 requires the elimination of most CO2
emissions from Virginia’s electricity sector by 2050. 2 In addition to its many other provisions, the
VCEA provides:
That in developing a plan to reduce carbon dioxide emissions from covered units described in
§ 10.1-1308 of the Code of Virginia, as amended by this act, the Secretary of Natural
Resources and the Secretary of Commerce and Trade, in consultation with the State
Corporation Commission and the Council on Environmental Justice and appropriate
stakeholders, shall report to the General Assembly by January 1, 2022, any recommendations
on how to achieve 100 percent carbon-free electric energy generation by 2045 at least cost for
ratepayers. Such report shall include a recommendation on whether the General Assembly
should permanently repeal the ability to obtain a certificate of public convenience and
necessity for any electric generating unit that emits carbon as a by-product of combusting fuel
to generate electricity…
This report is in response to this request by (1) reporting to the General Assembly on how Virginia
can achieve 100 percent carbon-free electricity by 2045 and (2) making a recommendation “on
whether the General Assembly should permanently repeal the ability to obtain a certificate of
public convenience and necessity for any electric generating unit that emits carbon as a by-product
of combusting fuel to generate electricity.”
Although it contains provisions addressing a broad array of policies, the VCEA primarily relies on
four key pillars for accomplishing electricity sector decarbonization: (1) a renewable portfolio
standard, (2) joining the Regional Greenhouse Gas Initiative (RGGI) carbon pricing program, (3) a
set of capacity targets for specific renewables technologies and (4) shutting down CO2 emitting
power plants by 2045.
This study relies on modeling of Virginia’s electricity sector and on other recent studies to examine
how the Commonwealth might cost-effectively achieve by 2045 the goal of zero CO2 emissions
from the electricity sector. The available evidence suggests that increasing the flexibility of the
approach to reducing CO2 emissions can save Virginia ratepayers hundreds of millions of dollars
per year by 2035 and beyond without relaxing the level of climate ambition. Replacing certain
specific technology and geographic mandates with more a more flexible approach specifically
targeting CO2 emissions can substantially reduce the cost of achieving emission reduction goals.
The results of the Virginia-specific modeling carried out for this study are in broad agreement with
2
HB1526/SB851
1
INTRODUCTION
other detailed studies of decarbonization strategies for the region and nation (Energy and
Environmental Economics, 2020; Larson et al., 2020).
MODELING TEAM
Working on behalf of the Secretary of Commerce and Trade (SOCT) and Secretary of Natural and
Historic Resources (SNHR), Virginia Department of Energy (“Virginia Energy”) and Virginia
Department of Environmental Quality (“DEQ”) together (“the agencies”) have led this research
project. The agencies identified existing partnerships within the Commonwealth’s network of public
research institutions to support the agency’s research and analysis on this technical mandate. The
Georgetown Climate Center and the University of Virginia’s Weldon Cooper Center for Public
Service were selected as institutions where researchers have been actively engaged on the topics of
decarbonization, electricity market transformation, regional carbon markets, economic impact
analysis and greenhouse gas emission reductions, energy and environmental policy, among other
topics.
In particular, the Georgetown Climate Center supported the modeling research through its
partnership with Resources for the Future (“RFF”), a leading independent, global nonprofit research
institution with deep expertise in environmental economics and energy policy. RFF uses the “Haiku”
model to simulate regional electricity markets and interregional electricity trade in the continental
United States. The model accounts for capacity planning, investment, and retirement over a multi-
year horizon and for system operation over seasons of the year and times of day. The model is
discussed in more detail below and throughout the report.
Throughout 2021, the modeling team worked to analyze the VCEA, build out the assumptions in
the model to match the VCEA policy framework, and run a variety of modeling scenarios to
determine how Virginia can achieve its 100 percent carbon free (“clean”) electricity goals while
keeping costs to ratepayers at a minimum.
PUBLIC COMMENT
In addition to direct analysis from the core modeling team, input was sought from key
stakeholders, including but not limited to the following organizations: the State Corporation
Commission (“SCC”), members of the Council on Environmental Justice and its subcommittee on
Just Transition and Infrastructure, the impacted investor-owned utilities, Dominion Energy and
Appalachian Power Company, the regional transmission operator, PJM, and the Southern
Environmental Law Center (“SELC”). Further input was solicited from members of the public and key
Virginia organizations. A list of all public commenters can be found in Appendix 1 of this report. 3
3
A full listing of the public comments is available from Virginia Energy
2
INTRODUCTION
The agencies and the modeling team greatly appreciate the individuals and organizations who
made contributions to this process, as it helped the team think through a variety of complex issues
which are reflected in this report. While not all of the topics referenced in public comments have
been addressed in this project and report, the agencies are committed to tracking these issues as
the Commonwealth’s energy sector shifts to a clean electricity system. Future reports may address
certain elements that were raised in public comments, such as how certain income groups may
experience benefits and/or burdens from the VCEA.
PLAN FOR THIS REPORT
Section 2 briefly reviews the recent emission history of Virginia’s electricity sector. Section 3
discusses some general principles for reducing decarbonization costs, principles gleaned from
recent studies at the regional and national level. Section 4 describes in detail the specific modeling
for this study of cost-reducing strategies for Virginia. Section 5 and Section 6 discuss some other
components of a least-cost approach. Section 7 concludes.
3
A QUICK LOOK AT VIRGINIA’S ELECTRICITY
SECTOR
The electricity sector in Virginia is changing rapidly. Recent emission trends are dominated by three
factors, declining imports, the rapid substitution of natural gas for coal as a primary energy source
and, more recently, the increasing penetration of solar generation. Electricity sector emissions in
Virginia have fallen by more than 25% since their high in the year 2000, even as generation rose by
33% during the same period. (Through 2019, this decrease in emissions was primarily due to
increasing substitution of natural gas for coal.) Because of the 50% decrease in imports, Virginia’s
own emissions of CO2 have been relatively flat since 2015, as in-state generation has substituted for
imports. The CO2 emissions per unit of electricity produced has fallen by more than half since its
high in 2001 and is expected to continue to improve in the future.
Figure 1: Virginia Electricity Generation By Fuel Type: 2001-2020
Recently, as the transition away from coal-fired generation accelerated, an increasing share of
renewable energy, solar generation in particular, has begun to displace fossil-fired generation. In
the first eight months of 2021, utility-scale and distributed solar generated the same amount of
electricity in Virginia as coal (3.8%). Utility-scale solar will account for more than 3% of electricity
generation in 2021, double that of 2020 and up from zero in 2015. Virginia currently has more than
4
MODELING METHODOLOGY AND ASSUMPTIONS
2 gigawatts (GW) of utility-scale solar capacity in operation, with 3 GW in advanced stages of
development. In 2022, solar will be the third largest source of electricity in Virginia after natural gas
and nuclear.
Demand for electricity has grown relatively slowly since 2007 due to moderating population growth
and improved energy efficiency in the residential and commercial sectors. Sales of electricity to
data centers has been the only growing sector of electricity sales in recent years, and these sales
can be expected to continue to grow for the next several years. Increasing sales for electric vehicles
(EVs) should begin have an effect on growth rates even as early as 2022. 4 Even with the recent
growth of data center sales, the electricity intensity of economic activity in Virginia has continued
to fall. A dollar of gross state product now requires half as much electricity as it did in the year
2000.
Figure 2: Annual Virginia Electricity Sales by End Use
4 For a detailed forecast of future electricity demand in Virginia, see (Shobe 2021).
5
MODELING METHODOLOGY AND
ASSUMPTIONS
Virginia’s electricity sector is integrated into a much larger regional grid. Dispatch of generation
resources and balancing supply and demand are managed centrally by the PJM independent
system operator (ISO). Decisions about Virginia’s energy future must account for the
interconnectedness with the rest of PJM and with neighboring states and accommodate the variety
of policy perspectives across the region.
The Haiku electricity model, used in this study, is a long-term planning model of the US electricity
sector. The model calculates the least-cost way to meet Virginia and US electricity demand over the
period from 2019-2045 given the existing power plants in each state, planned additions and
subtractions from the existing fleet of power plants, the costs to build new facilities, the cost of
running such facilities, and the availability of sun and wind renewable resources. The model also
includes existing environmental regulations at the federal and state level. Each state is represented
as a separate node with transmission between states and individual power plants grouped into
categories by fuel and technology. The model calculates the generation mix, emissions, and power
plant capacity for each state and year that will minimize total cost. It also provides information
about resource cost—the cost to run the electricity grid—including fuel costs, capital costs,
operations and maintenance costs, and costs to import and export electricity.
The model includes information about existing and planned powerplant capacity from the S&P
Global database. It chooses to build new model plants or add or remove capacity from existing
model fossil plants plants based on costs from the Energy Information Administration’s Annual
Energy Outlook 2021 (AEO2021) reference case, and for renewable plants based on costs from the
National Renewable Energy Lab’s Annual Technology Baseline 2020 (NREL ATB 2020). Demand is
fixed to the level projected in the Annual Energy Outlook 2021 reference case, subject to the
investments in energy efficiency that are explicitly part of this study. The load profile is based on
the US Energy Information Agency’s form 930 for historical hourly demand. This hourly demand is
grouped into 24 representative hours (for three seasons and eight times of day) and the model
represents system operations within each year for each of those hours. Solar and wind availability
are represented at the state-level for the same time blocks used to represent system operations.
The model includes PJM reserve capacity requirements and constrains inter-state transmission
capability to the maximum observed.
Haiku does not represent emerging technologies that have yet to reach commercial scale such as
carbon capture and storage or synthetic fuels, hydrogen and ammonia, nor does it permit
economic investments in new nuclear units, new hydro units, or new pumped storage units,
although existing and planned new facilities of those types are included in the model. It does allow
economic investment in commercially demonstrated technologies including 4- hour batteries, solar,
6
MODELING METHODOLOGY AND ASSUMPTIONS
and on and offshore wind. The technology mix chosen by the model thus reflects those most
readily available in the next fifteen years without further regulatory developments or dramatic shifts
in technology.
The Haiku model does not include the cost of expanding transmission capacity. Transmission
modernization planning will be discussed in Section 6.
Haiku, like other capacity planning models, chooses the cost-minimizing mix of technologies for
the U.S., given set of input assumptions. Thus, given any starting assumptions about inputs, the
model is designed to choose the capital investments and operating plan that minimize the cost of
building and operating the electricity sector. Since cost-minimization is built into the modeling
approach, differences in costs among the various scenarios arise due to the different assumptions
made about model inputs and policy assumptions.
Because of its high current costs relative to utility-scale solar, the model will not choose to rely on
distributed solar for more than a marginal contribution to the electricity supply. The potential
benefits of greater reliance on distributed solar that are not fully captured in the model are
discussed in more detail below.
The Haiku model focuses on generation planning and does not address energy efficiency
investments and demand response load shifting strategies. These topics are addressed separately
in Section 6.
This report did not include explicit modeling of grid modernization and regional transmission
planning. The changes that are underway in electricity generation and use are, in turn, forcing a
rethinking of strategies for planning, building, and managing the transmission and distribution
infrastructure (Fox-Penner, 2020). Even in the absence of the need to decarbonize the economy, the
basic design and operation of the grid will require substantial changes. Coordinating these
essential grid modernization efforts with the transition of the electricity supply to one that is more
diverse and distributed will shift grid design away from one-way electricity transport to one of
resource and consumer connectedness. In the future, electricity generation, demand response and
storage may take place anywhere. This connectedness will ultimately allow us to treat even
household consumer devices as providers of grid services.
Long-run uncertainty about the economics of emerging technologies does not need to delay
action today. Virtually every long-range energy plan, whether it requires full decarbonization or not,
starts with much the same short-run plan for generation capacity expansion. The least-cost path
forward, regardless of the policy environment, involves emphasizing the construction of utility-scale
solar and on-shore wind generation. In the longer-run there will be new options to consider, but
Virginia’s short-run generation choices do not appear likely to lock us in to a given long-run path.
7
MODELING METHODOLOGY AND ASSUMPTIONS
State policy makers have time to re-evaluate changing options and make mid-course corrections as
needed, once some of the uncertainty over future technologies and projected costs of currently
available technologies is resolved.
Given the great uncertainty over the final five years of the policy horizon, that is 2041 – 2045, the
cost estimates become more speculative, relying on predictions of how technologies will develop
more than two decades in the future. Actual decarbonization pathways will involve deployment of
technologies that are not yet commercially viable. This report only considers currently available
technologies for both generation and storage. It does not include carbon capture and storage, long-
term storage, synthetic fuels, geothermal power or small modular nuclear reactors. Th exclusion of
these technologies makes the out-year cost estimates higher than what is likely to occur, but has
the advantage of demonstrating the technical feasibility of a zero carbon electricity system even
without significant technological breakthroughs. This methodology is an appropriately conservative
approach, demonstrating feasibility under current, gradually improving technology rather than
starting with more optimistic technology assumptions.
DISCUSSION OF BASELINE ASSUMPTIONS
Table 1 shows the key assumptions used for the Baseline Scenario. The national AEO2021 demand
forecast is down-scaled to Virginia based on historical share of regional demand minus the energy
savings from the energy efficiency resource standard (EERS). Electricity consumption grows at
about one percent per year. (It should be noted that EV penetration is very slow in the AEO2021
Reference Case, amounting to only about one percent of electricity consumption by 2040.) The
AEO2021 Reference Case is comparable to the PJM electricity demand forecast for the DOM Zone.
The choice of AEO2021 over the PJM forecast does not make an appreciable difference in the
analysis. Using the AEO2021 provides a consistent national forecast that can be used for all of the
regions in the model.
The ATB2020 cost estimates for offshore wind includes costs for laying underwater cable. The cost
forecasts in the ATB2020 are illustrated in Figure 1, showing expected levelized cost of energy
(LCOE). 5 A cost-minimizing planning model will deploy technologies approximately according to
the cost estimates illustrated in Figure 1, but several observations are in order:
• An important implicit assumption in the ATB2020 estimates is that the costs of deployment
of a given technology for Virginia (and other places) do not change based on the amount of
deployments that have already occurred. Under the assumptions of the ATB2020, the cost
of deployment of solar and wind does not vary as the scale of deployment increases. This
assumption may not hold in practice as solar and wind deployment expand, since the land
most appropriate for solar or onshore wind deployment may be subject to increasing costs
5
The levelized cost of energy (LCOE) is the average net present value of the cost of generation over the
expected lifetime of the plant.
8
MODELING METHODOLOGY AND ASSUMPTIONS
as the scale of deployment increases. Changes in costs due to changes in the relative
scarcity of appropriate land resources could be an important factor in the later years of the
policy horizon, past 2035, and could change the relative cost-effectiveness of different, non-
emitting generation technologies. The magnitude of this effect depends on other policy
choices such as grid planning and deployment.
• The technology cost measure does not take into account any other policy priorities that
might lead policy makers to choose a more expensive technology over a cheaper one. This
approach is consistent with the mandate for this study, which focuses on minimizing costs
to ratepayers.
• The ATB2020 estimates do not include technologies currently in the early stages of
development. In particular, new nuclear and advanced geothermal technologies are not
represented here. Even if they were to achieve some level of deployment, they would only
begin to become competitive with other sources in the last few years of the policy horizon.
Figure 3: Estimated Levelized Cost of Energy for Various Technologies
9
MODELING METHODOLOGY AND ASSUMPTIONS
Table 1: Levelized Cost of Energy ($/MWh) 2020 – 2050 (NREL ATB 2020)
2020 2050
Utility-scale Solar 37.9 16.3
Solar + Storage 54 20.4
Commercial Solar 72.3 25.8
Rooftop Solar 114 33.8
Offshore Wind 120.8 62.5
Onshore Wind 30.4 17.2
Geothermal (Flash) 151.5 102
Nuclear 79.6 69.6
10
MODELING METHODOLOGY AND ASSUMPTIONS
Table 2: Important Reference Case Assumptions
Category Source/Approach for Reference Case Assumptions
Haiku Reporting Years Reporting years for the model: 2021, 2022, 2023, 2024, 2025, 2027, 2030, 2033, 2035, 2036, 2040, 2045
Demand - Load and Peak Growth Demand from Energy Information Administration 2021 Annual Energy Outlook (AEO2021)
Gas Prices Annual Energy Outlook 2021 Reference Case gas prices
Build Costs - Renewables National Renewable Energy Laboratory 2020 ATB mid-cost scenario for renewable overnight capital costs
Minimum Generation Minimum Generation for some technologies such as Combined Heat and Power and Conventional Hydro
Maximum Generation VA pumped storage has seasonal maximum generation based on historical generation
Latest information from S&P Global Market Intelligence
Firm Capacity Changes
VA - Any additional capacity changes not accounted for in S&P Global
Nuclear Lifetime 80 years, or as planned by owners
State-level RPS for all states with statutory RPSs
VA: 100% by 2050. Remove nuclear and data centers from denominator.
Renewable Portfolio Standards (RPS)
Allow 25% of RECs to come from outside of VA after 2025.
Alternative Compliance Payment of $45
Solar & Onshore Wind Procurement From the VCEA: VA: 16,100 MW of combined solar and onshore wind 2036
Minimum based on input from the states and expectations based on state policies/announcements
NY: 9,000 MW Offshore Wind by 2035 / 3,000 MW Storage by 2030
MA: 3,200 MW Offshore Wind by 2030/ 1,000 MW Storage by 2025
Offshore Wind/Storage CT: 2,000 MW Offshore Wind by 2030
Requirements RI: 400 MW Offshore Wind by 2030
MD: 1,568 MW Offshore Wind by 2030
NJ: 4,000 MW Offshore Wind by 2030 / 2,000 MW Storage by 2030
VA: 2,600 MW Offshore Wind by 2026 / 3,100 MW Storage by 2035
RGGI Program Assumptions 11 Current RGGI States + PA. Emissions cap declines 3% per year of 2020 levels (NJ 2021, VA 2021, PA 2022)
EE Assumptions VCEA EERS extended at 1% per year from 2026 removed from AEO demand projections
11
MODELING METHODOLOGY AND ASSUMPTIONS
Except as noted, most existing generation assets remain in place through the policy horizon. In all
scenarios modeled, the four in-service nuclear reactors are relicensed by the Nuclear Regulatory
Commission for operation beyond 2050. All coal plants close by the end of 2024, except for the
Virginia City Hybrid Energy Center (VCHEC) in Wise. 6 This plant remains in service through 2045 as
specified in Dominion Energy’s 2020 Integrated Resource Plan, operating at a very low capacity
factor due to its very low cost-effectiveness. All existing natural gas boilers not currently scheduled
for retirement are retained through the policy period. Planned builds, such as the Chickahominy
Gas plant, are assumed to be in operation by 2025. 7
Four fossil plants, Doswell Energy Center, Panda Stonewall Power Project, Marsh Run Generation
Facility and Louisa Generation Facility are not directly required to close by the provisions of the
VCEA. Over the period covered by the modeling effort, these plants will become increasingly
uncompetitive with the expansion of zero marginal cost generation from wind and solar facilities
and the declining costs of energy storage technologies. By 2045 their utilization will fall to low
levels partly due to competition with expanding renewables but also due to the rising price of
emitting CO2 under the RGGI cap. Since the VCEA requires that the Virginia’s budget under the
RGGI cap reach zero in 2049, CO2 emitting facilities will become increasingly uneconomic. As a
result, these plants are assumed to close by the end of 2045.
The mandate for this study requires that the electricity sector achieve zero emissions by 2045. This
requirement achieves zero emissions five years earlier than the VCEA RPS and RGGI provisions. The
VCEA requires the retirement of all emitting facilities by the end of 2045, so the model forces all
remaining generation facilities with CO2 emissions to retire on January 1st 2046.
It is assumed that the transmission system has adequate capacity to accommodate the generation
resources used to meet consumer demand. Although a detailed analysis of transmission and
distribution adequacy was not a focus of this study, it is a topic that warrants substantial attention
in the near term and is discussed in Section 6. Grid modernization is becoming a pressing issue as
the mix of low-cost generation resources changes and as the cost advantages of grid
modernization become more apparent.
While the current RGGI memorandum of understanding only specifies cap reductions through
2030, it is generally expected that cap reductions will continue after 2030, so the base-case
6
The Clover Power Station, jointly owned by Dominion Energy and the Old Dominion Electric Cooperative, is
slated to close in Dominion Energy’s latest integrated resource plan and so is assumed to close. In 2021,
through the month of September, the Clover Power Station operated at only 17% of its rated capacity.
7
A natural gas power plant slated for Charles City was cancelled during this modeling effort. The inclusion of
the Charles City plant does not make an appreciable difference in the model outcome. The main effect of the
additional plant is to reduce the operating capacity of the other natural gas plants in Virginia.
12
MODELING METHODOLOGY AND ASSUMPTIONS
assumption regarding RGGI implementation is a continued reduction of the regional cap by three
percent of the 2020 cap level per year through the policy horizon. 8
Recent and pending federal legislation: The recently passed federal infrastructure funding bill
contains several billion dollars for grid modernization, CO2 capture technologies, CO2 transport,
clean hydrogen and subsidies for continued operation of existing nuclear plants. The likely effects
of these changes in federal policies are not reflected in the model assumptions.
8
The VCEA specifies that any emission cap reach zero by 2049. This provision is not reflected in the model
scenarios. Doing so would not appreciably change the results, since other emission constraints are more
binding than the 2049 cap provision.
13
MODEL RESULTS
THE BASELINE SCENARIO
The Baseline Scenario results are illustrated in Figures 4, 5 and 6. Figure 4 depicts the capacity mix
expected under the VCEA. All coal except the VCHEC retires by 2025. Legacy natural gas and
nuclear capacity are retained through the policy period. The additions to capacity are all renewable
resources and storage. Utility-scale solar is the preferred new resource due to its low capital and
operating costs, reaching 30GW installed by 2045. Figure 5 shows annual generation by resource
type. The expanding supply of renewables gradually displaces natural gas generation. Interestingly,
there is no systematic increase in the level of imports due to the relatively aggressive addition of
offshore wind and solar. The existing nuclear capacity has an outsized share of generation due to
its high capacity factor.
Emissions are shown in Figure 6. They decline steadily until dropping to zero on the first day of
2046, since the modeling constrains all fossil generation to shut down on the last day of 2045.
14
MODELING RESULTS
Figure 4: Baseline Scenario Capacity (GW) and Generation (TWh)
15
MODELING RESULTS
Figure 5: Virginia CO2 Emissions - Actuals and Predicted
The Baseline Scenario results illustrate several important points:
1. No additional fossil capacity is built to meet expected consumption, including seasonal
variation in supply and demand. (Since the model used here does not evaluate the potential
for local transmission constraints, it does not address the possible need for local additions
to generation needed to address geographically localized supply constraints.)
2. Nuclear generation remains constant through the policy horizon. New nuclear technologies
were not considered in the model scenarios, since the technology is currently considered
too speculative for inclusion. Given the recent increase in federal funding for advanced
nuclear research and deployment, the question of advanced nuclear generation availability
should be revisited if the technology advances faster than current expectations suggest.
3. The RPS is not a binding upper-bound constraint on emissions (fossil generation) during
the period from 2025 to 2035. The aggressive capacity targets explicitly identified in the
VCEA for solar and offshore wind (combined with assumed slow demand growth) keep the
non-emitting generation portfolio above the minimum RPS levels during those years. In the
years up to 2025, whether the RPS is binding (i.e. sets the pace for use of renewables)
depends on the availability of renewable energy credits (RECS) from the other states in PJM.
16
MODELING RESULTS
A high availability of RECS from PJM would result a in greater use of renewables than
required by the RPS, and lower costs, during some early years.
4. All new capacity, aside from two natural gas plants currently in the permitting process, is
renewables and storage.9 Solar and onshore wind are the cheapest incremental generation
technologies. The offshore wind is built by the model because it is a capacity target
specified in the VCEA. The additional storage deployed before 2040 is also due to VCEA
targets, rather than least-cost supply and demand balancing.
5. Wind and solar are, in the early years, largely substitutes for each other. The onshore wind
capacity chosen by the cost minimization algorithm may appear large given the current low
levels of planned onshore wind capacity for Virginia. At the time of this writing, the PJM
interconnection queue only shows 400 MW of wind for Virginia. Were this wind not
available, the model would substitute additional solar generation at approximately the same
levelized cost. The first 2.7 GW of offshore wind substitutes for deployment of additional
solar, since the RPS is being satisfied by the target offshore wind generation.
6. Existing natural gas generation capacity is retained through the policy horizon but operates
at declining capacity factors. The role of legacy gas plants shifts from primarily baseload,
bulk power to a role of seasonal supply and load following to compensate for the
increasing penetration of variable renewable resources.
7. By 2040, less than 20% of generation is from legacy fossil fuel plants. Because solar, wind
and nuclear have very low marginal costs, these resources, once built will tend to reduce the
level of imports of electricity.
8. Emissions fall rapidly at first due to the RPS and the renewables capacity targets in the
VCEA. Later on, after 2035, the RPS (and the RGGI cap) push emissions down, but on a path
that reaches zero by 2050 under the terms of the VCEA. In the base case, emissions stay on
this path until the end of 2045, when they drop to zero.
9. Given the standard forecast for natural gas prices, the addition of natural gas capacity
between now and 2025 does not appear to increase net natural gas generation in Virginia.
In the base case, natural gas generation falls between 2019 and 2025 even with the addition
of the Charles City and Chickahominy plants. The addition of these plants substitutes for
other natural gas generation in the state.
10. In broad outline, these results are quite similar to those of other modeling exercises on
decarbonizing Virginia’s economy(Cole et al., 2020; Energy and Environmental Economics,
2020).
9
A natural gas power plant slated for Charles City was cancelled during this modeling effort. The inclusion of the
Charles City plant does not make an appreciable difference in the model outcome. The main effect of the additional
plant is to reduce the operating capacity of the other natural gas plants in Virginia.
17
MODELING RESULTS
SENSITIVITY SCENARIOS
The modeling strategy for this report starts with the definition of the base case, the Baseline
Scenario. The main assumptions used to define this case were discussed in the previous section.
This section reports on the Baseline Scenario results along with three sensitivity cases that
investigate how the results change in response to different assumptions regarding renewable
technology costs, natural gas prices and the rate of growth in electricity demand. Following the
sensitivity cases are a set of policy scenarios that investigate how different policy choices will affect
ratepayer costs.
Scenario label Description
LowNG Sensitivity with low NG prices
HighRECost Sensitivity with high renewable cost. (how high)
HighCons Sensitivity with high data center and EV growth
1) Low natural gas prices [LowNG]
The Low Natural Gas Price scenario assumes that natural gas prices follow the AEO2021 High Oil
and Gas Resource Case. In this case, NG price is about 10 percent below that in the base case.
Virginia emissions increase in this case as increased gas generation in Virginia substitutes for coal
generation in the rest of PJM. Total emissions in PJM fall due to increased substitution of gas for
coal. Interestingly, the Virginia RPS is not binding during some years due to the aggressive
renewables capacity targets. In the absence of the capacity targets, the RPS would be binding and
would still prevent the expansion of natural gas capacity. The combination of the RPS, the declining
RGGI cap and the capacity targets for solar and wind in the VCEA are sufficient to prevent the
building of any new NG generation even in a scenario favorable to new NG capacity. The low NG
prices specified in the AEO2021 high resource availability case save Virginia ratepayers about $500
million per year in fuel costs and avoided imports.
2) High renewables costs [HighRECost]
This sensitivity test uses the NREL ATB2020 conservative (high capital costs) case. The results may
appear somewhat unexpected at first. The main difference between this case and the Baseline is an
increase in solar capacity in Virginia during the years prior to 2035. This counter-intuitive result
arises due to the federal investment tax credit for solar. The tax credit is determined as a percent of
capital costs, consequently a given increase capital costs of solar has a smaller impact on ratepayers than
the same percentage increase in capital costs of other electric generation technologies. Nevertheless,
higher renewables costs in this scenario do increase ratepayer costs by around $100 million per
18
MODELING RESULTS
year by 2040. Increased federal support for research and development can have substantial
benefits for Virginia’s economy by reducing the cost of energy services.
3) The high electricity demand case [HighCons]
The AEO2021 forecast of electricity demand growth has electricity sales growing slowly from the
current 118 terawatt-hours (TWh) to 140 TWh by 2045. Due to the slow forecast penetration of EVs
assumed in the AEO2021 forecast, only 2.5 TWh are consumed by EVs in 2045. There are at least
two reasons to believe that electricity demand growth in Virginia will outpace the growth assumed
in AEO2021. First, current forecasts of EV sales by private sector analysts and by the automakers
themselves anticipate a considerably faster transition to electric vehicles. Second, growth in
electricity consumption by data centers has continued to grow at an increasing rate, at least
through the end of 2020, the most recent data available.
To better understand the likely effect of these two factors on future electricity consumption, UVA’s
Weldon Cooper Center for Public Service developed an independent electricity sales forecast for
Virginia for 2020 through 2050 (Shobe, 2021). The Cooper Center analysis found that electricity
demand has not been growing appreciably in any sector: residential, commercial or industrial, with
one exception: data centers. The Cooper Center’s forecast separates Dominion Energy data center
sales from other commercial sales and uses monthly data center electricity consumption through
2020 to estimate future sales. Accounting for likely future growth of data center demand results in
a considerably higher forecast of electricity demand compared to the AEO2021 forecast, as much
as 40% higher.
The Cooper Center forecast report also developed two scenarios for the stock of EVs between now
and 2050. Both of these scenarios are intended to reflect the rapidly increasing private estimates of
the likely turnover of the vehicle fleet. The scenarios describe paths to 75% and 98% of the total
light duty vehicle stock by 2050. These scenarios were designed to estimate the upper range of
light duty EV electricity use by 2050. EV electricity demand could increase overall demand by nearly
20% in 2050, compared to the standard demand forecast. No estimates were made concerning
heavy duty transport, since electrification of this sector is still highly speculative.
The combined effect of increased data center and EV consumption results in total electricity sales
of almost 210 TWh by 2045. Data center sales amount to over 80 TWh in this high sales forecast. EV
sales amount to 23 TWh. Statewide CO2 emissions fall considerably in this case because the vehicle
fleet converts from internal combustion engines, which generate, 602 grams of CO2 per mile, to
electricity generated primarily by non-emitting technologies. Based on the Cooper Center high-
vehicle penetration scenario, economy-wide CO2 emissions actually fall by 45 million metric tons
per year by 2050, or by much more than the current emissions of the entire electricity sector, due
to the substitution of EVs for internal combustion engines in light-duty transport.
19
MODELING RESULTS
There are also considerable cost savings from converting the vehicle fleet to EVs. As reported
below, consumers/ratepayers save money on fuel and repair costs. And each electric car is a rolling
battery. Consequently, the cumulative electricity storage capacity will be much higher than that
measured by storage-only battery installations. New vehicle-to-grid technologies are expected to
enable grid utilization of EV battery storage, facilitate renewables integration and produce savings
for ratepayers, but this technology was not included in the modeling, since its effects are still quite
speculative.
20
MODELING RESULTS
Figure 6: Electricity Sales - Baseline and High Demand Scenarios
21
MODELING RESULTS
The High Demand scenario uses the combined high data center/high EV forecast to assess how
high demand growth might affect ratepayer costs and the ability to meet the VCEA requirements.
The high EV penetration scenario is not a forecast but rather an illustrative calculation of the net
benefits that might be achieved by a rapid transformation of the light duty vehicle fleet.
1. Virtually all of the increased expenditures on electricity are data center sales and EV
charging. Residential and commercial (ex. data centers) payments do not increase.
2. Expenditures on EV charging represent a net savings for consumers and a large net
reduction in CO2 emissions.
3. Consumers in 2040 will be spending on the order of $2 billion for electricity and saving
approximately $5 billion per year in fuel and maintenance costs. These amounts double by
2050 when, according to this scenario, EVs nearly fully replace internal combustion engines.
4. CO2 emissions from the transportation sector fall by 22 million short tons per year by 2040
and double that in 2050, when CO2 emissions from light duty vehicles are almost
completely eliminated.
5. Emissions from the electricity sector do not change appreciably due to the combined effect
of the RPS, RGGI and the renewables capacity targets.
6. All of the increased generation needed to meet the increased demand is met with increased
solar and battery storage. The solar capacity built is greater than the targets specified in the
VCEA. Assuming that the costs of solar installations do not rise with the scale of
implementation, the model envisions the construction of nearly 65 GW of utility-scale solar
by the end of the policy horizon. If the costs of utility-scale solar installations do rise at
these high saturation levels, then, according to the ATB2020, the cost-effectiveness
advantage would shift in favor of onshore wind along with commercial and residential solar.
7. With the model’s choice of 3.3 GW of onshore wind, solar capacity is 17 GW in 2040.
Without the onshore wind, the solar capacity would need to expand to 25 GW.
8. Imports would increase from around 20 TWh/year to 40 TWh/year. While, at first, it might
seem that this would increase emissions for PJM or the nation, this is probably not true. The
services produced by data centers locating in Virginia are a near perfect substitute for the
services produced by data centers built elsewhere. This demand substitution is not
accounted for in the model. Thus, increased imports due to increased data center demand
in Virginia compensate for increased data center demand that would otherwise occur
outside of Virginia. And since the VCEA defines the RPS targets in terms of energy sales in
Virginia, increased imports should not significantly affect global emissions.
22
MODELING RESULTS
Figure 7: Capacity and Generation – High Electricity Demand Scenario
23
MODELING RESULTS
POLICY SCENARIOS
Scenario Name Description
noCoalMin Policy without coal minimum generation; economic shutdown
highEE Policy with EE extended past 2026
noVCEACapTarget Policy without capacity targets, RPS is in place
RPS2045 Policy with 100% RPS in VA by 2045 (75% of compliance in state)
RPS2045in Policy with 100% RPS in VA by 2045 (all compliance in state)
VACap Policy with 0 emissions in 2045 in VA cap
Policy with high demand but data centers can meet their renewable
highConsDCout
requirements in PJM
Policy with high demand but data centers must meet their renewable
highConsDCin
requirements in VA
The next set of scenarios explore what features of clean energy policies can help lower costs to
ratepayers. The focus of these modeling exercises is on the cost of building, operating and
maintaining the generating equipment necessary for meeting the realized demand. Accordingly,
costs include the resource costs for building, fueling and maintaining generation equipment. The
cost of importing electricity is included since in most years, in all scenarios, Virginia is a net
importer of electricity, although generally less so than in the recent past.
The scope of this analysis is limited in a number of important ways:
• The analysis does not include an evaluation of the regulatory structure under which the
electricity sector operates. The model applies a standard, economy-wide rate of return for
all investments and does not distinguish between resources built by regulated and non-
regulated entities. The question of the organizational structure under which resources are
managed applies equally to all energy resources and is not limited to the case of planning
for the elimination of CO2 emissions from the electricity sector, which is the subject of this
study.
• Since the focus of the analysis is on total resource costs, reported costs do not include pure
transfers of value from one set of ratepayers to another. For example, the earnings from the
RGGI allowance auction reflect a transfer of value rather than a use of resources. By law,
utilities can recover the costs of the allowances through a surcharge on ratepayers, while
the revenues are applied by the state to energy efficiency programs benefitting low-income
households and to assisting localities in managing increased flood risk, a risk directly tied to
increased concentrations of CO2 in the atmosphere.
24
MODELING RESULTS
• This study does not analyze differential effects on different classes of ratepayers. The focus
is on least-cost to ratepayers in aggregate, as specified in the study mandate. The VCEA
explicitly provides for periodic studies of differential rate impacts. Analyses of these
differential rate impacts, for example, studies of the benefits and burdens that may be felt
by historically economically disadvantaged communities, will be undertaken separately from
this effort.
1) Least-cost pathways without specific capacity targets
Offshore wind is currently a relatively expensive renewable energy resource. (See Figure 3:
Estimated Levelized Cost of Energy for Various Technologies.) Given the current trends in
renewable energy costs estimated by NREL, expanded offshore wind is expected to continue to be
more expensive than other available generation technologies, especially utility-scale solar. The
same consideration applies to batteries for storing electricity. While there is understandable
excitement about potential new energy storage technologies, model results using the NREL mid-
range cost estimates for battery technology do not support an economic case for installing
substantial amounts of local electricity storage in the near term. The modeling does indicate that
investments in battery storage capacity are likely to be cost-effective in later years of the planning
period as battery costs decline and as the percentage of generation from intermittent renewables
increases.
Several factors could make increased battery installation more attractive than is justified purely on
the basis of overnight capital costs for batteries that smooth the availability of renewables.
Batteries can provide a number of other valuable services to the grid and can contribute to grid
resilience. If these battery services are compensated according to the value they provide, then
investment in battery storage should occur in the course of a least-cost planning and
implementation for a decarbonized grid, rather than as a mandated capacity target. This suggests
that there should be an increased focus on correctly compensating storage capacity for its full
value to the grid.
The noVCEACapTarget scenario includes all of the current provisions of the VCEA except for the
specific technology capacity targets for wind, solar and storage. Because of its relatively advanced
stage of development, the capacity target for the first 2.7 GW of offshore wind was retained for this
scenario. All other specific capacity targets were removed in order to explore which technology mix
will result in the least-cost path for ratepayers. Because utility-scale solar and onshore wind have
the lowest levelized cost of energy in the ATB2020, the RPS and RGGI components of the VCEA are
sufficient to encourage the deployment of utility-scale solar, at levels in excess of the explicit
capacity targets included in the VCEA. Based on information from PJM, it is very likely that Virginia
will have 3 GW of solar generation in service by the end of 2022 with much more in the early
planning stages.
25
MODELING RESULTS
The offshore wind capacity and some of the targeted storage are more expensive
than other available options, including the expansion of commercial and
residential solar. Eliminating the capacity targets for expanded offshore wind and
for electricity storage, and allowing deployment of these resources to be guided
by investor decisions about how to meet the RPS and RGGI requirements cost-
effectively will likely save money for ratepayers.
The explicit capacity expansion goals for renewables and storage specified in the VCEA may be cost
effective if costs or performance characteristics of these technologies continue to rapidly improve.
Nevertheless, the RPS and RGGI policies are sufficient for meeting the state’s goals for eliminating
carbon emissions from electric generation. Specific technology targets in addition to those policies
will tend to increase costs to ratepayers. Removing the additional offshore wind capacity target
causes the substitution of lower cost solar for the added offshore wind. The modeling suggests that
the explicit off-shore wind and energy storage targets could cost ratepayers more than $250
million per year in 2035 compared to the least-cost strategy. This cost penalty for ratepayers rises
to $450 million per year by 2040, given the baseline cost assumptions for wind, solar and storage
technologies.
There is an argument to be made that the cost of offshore wind deployment is overestimated, in
particular for the years after 2025. It may be that there will be significant cost reductions from
“learning by doing”. The building of the first few wind farms could cause a sharp downward shift in
costs. If this is true, then Virginia ratepayers may gain by a delay in the building of offshore wind
until further cost reductions in off-shore wind technology have been realized as a result of
investments by other states and countries. Once firms have experience with offshore wind
deployment in the U.S., then Virginia could hold a procurement auction for future wind
deployments off its own coast.
The analysis did not include possible regional economic benefits of new industrial development in
Virginia that could arise because of deployment of offshore wind generation resources. Insofar as
such economic development benefits may arise, they are likely to be largely confined to coastal
regions of the state, while the effects on ratepayers will be statewide.
26
MODELING RESULTS
If a decision is made to develop offshore wind for potential economic
development benefits, it may be worth considering whether there are mechanisms
for matching more closely the payments made and the economic development
benefits received.
2) Regional flexibility and procurement of renewables from within
PJM and beyond
The cost-effective use of renewable energy has an important geographic component. First, since
the potential for low-cost renewables generation is not evenly distributed across the country,
different regions may have distinct advantages in renewables generation; for example, wind in the
Midwest and solar in the South and Southwest. Second, and less obvious, is the temporal
complementarity between wind and solar within and across regions of wind and solar generation.
Connecting the renewables supply across resources and regions smooths local renewables
variability. For these reasons, there may be considerable savings to ratepayers from allowing
renewables procurement from other places. An obvious geographic extension for renewables
procurement in Virginia is the PJM region, since the supply of electricity across this region is
simultaneously determined by PJM operators given all of the encompassed generation resources.
The model scenarios demonstrate the dramatic savings possible by extending the geographic
scope of sourcing renewable energy. The modeled scenarios include two different Virginia RPS
scenarios, both of which achieve 100 percent non-emitting power by 2045. In one scenario,
RPS2045in, all RECS must be acquired within Virginia. In the alternative scenario, RPS2045, RECS
could be acquired anywhere in the PJM service region. Implementing a renewable portfolio
standard requiring 100% non-emitting energy by 2045 is technically feasible with all in-state
sources but would likely be much more expensive than an equivalent RPS allowing imported
renewable energy credits to cover a significant share of generation. The modeling results suggest
that the go-it-alone approach to the RPS would cost Virginia ratepayers an extra $400 million per
year by 2040 compared to allowing 25 percent imported RECS, and the costs would rise
substantially as the state approached the 100 percent goal in 2045. Increasing the allowable REC
imports beyond 25 percent would further reduce ratepayer costs.
The strong conclusion one can draw from these modeling exercises is that costs to
ratepayers can be reduced by increasing geographic flexibility in the source of
renewable energy. This can be done without increasing CO2 emissions. The CO2
emission profiles of these competing approaches are virtually identical.
27
MODELING RESULTS
The broader lesson supported by the modeling results is that restricting the focus to Virginia in
designing an emission reduction strategy will generally increase the cost of achieving a given
reduction in emissions. This is certainly true in the case of the procurement of renewables but
applies equally to the overall approach to emission reductions. A regional approach will generally
cost less than a go-it-alone strategy.
Another model comparison reinforces this point. One of the modeling scenarios imposes a cap on
Virginia emissions separately, and this cap declines to zero by 2045 but does not allow any trading
in RECS or RGGI allowances for compliance. It is instructive to compare this go-it-alone cap
scenario, VACap, with the baseline assumptions but without the specific technology capacity
targets, noVCEACapTarget. The only substantive difference between these scenarios is the
reduced geographic flexibility in compliance. Again, the cost of restricting trade is large, amounting
to over $600 million per year in higher costs by 2040, without any gain in emission reductions.
A regional approach to capping emissions is effective in controlling emissions and
produces considerable savings for ratepayers.
3) Pathway without a minimum on coal generation in years prior to
2045
The Baseline Scenario retains the Virginia City Hybrid Energy Center coal generation as proposed in
Dominion Energy’s Integrated Resource Plan. As a result, the VCHEC operates at a very low capacity
factor in our Baseline Scenario, essentially operating as a seasonal peaking facility during winter
and summer days, when demand spikes due to high heating and cooling loads. In the 12 months
prior to August of 2021, the VCHEC had four months where it did not generate any electricity. The
noCoalMin scenario relaxed the Baseline Scenario assumption that the VCHEC operate through to
the end of the policy horizon. This scenario allowed the plant to be retired on purely economic
grounds, in which case, the plant retires by 2025. Operating cost savings alone are on the order of
millions of dollars per year. The Virginia State Corporation Commission staff estimates that the
plant has a negative net present value of over $472 million over the next 10 years. 10 The Haiku
model estimated savings are not directly comparable to the SCC calculations but are entirely
consistent with the conclusion that the VCHEC should retire by 2025 on economic grounds and
that the losses associated with keeping it open rise over time.
In weighing the costs and benefits of closure, policy makers may want to consider both the local
economic effects of shifting generation and the gains in health outcomes resulting from lower
fossil fuel combustion.
10
Virginia State Corporation Commission. Prefiled Staff Testimony. September 29, 2020.
28
MODELING RESULTS
Local communities face concentrated local costs due to losing an important local industrial plant.
This must, in turn, be weighed against the more diffuse but equally real cost to the rest of the state
economy. As a result of the payments made to keep the VCHEC open, ratepayers in the rest of the
state have less to spend in their own local communities.
The health costs from emissions by fossil fuel power plants are also unequally distributed. There is
strong evidence of persistent inequalities in exposure to damaging pollutants, with lower income
communities suffering disproportionate health effects from current levels of emissions (Colmer et
al., 2020).
The economically optimal retirement date for all of the remaining coal generation
fleet is prior to 2025. With the VCHEC having a large negative net present value,
there are clear opportunities for reducing ratepayer costs, improving health
outcomes and providing economic relief and development assistance to the
community around the VCHEC.
4) Land-use and utility-scale solar
The VCEA’s solar and onshore wind capacity target of 16.1 GW of capacity by 2035 is broadly
consistent with the least-cost generation capacity choice for that year. Given the current available
technology, it takes approximately 6 acres of land for each MW of solar capacity. 11 This implies that
in 2035, the modeled utility-scale solar deployment would require about 96 thousand acres. This
amounts to approximately 1.1 percent of Virginia’s land area. This level of land use change is
comparable in size to other land-use changes that have taken place in the past, for example, for
roads and urban development, but the solar deployment would occur at a much faster pace.
In the later years of the planning horizon, and especially in the high consumption scenarios, the
amount of land required for solar deployment would be considerably larger. As an extreme
example, in the high consumption case where only in-state renewable resources were allowed, the
land required would account for more than 5 percent of the total land area of the state. This case
would certainly face increasing prices for land for solar deployment and, potentially, conflicts with
other land uses. In this case, there would be the substitution of other resources for utility-scale
solar. In particular, commercial and rooftop solar would become increasingly competitive, as would
offshore wind.
11
https://www.solarlandlease.com/how-much-land-does-a-solar-farm-need
29
MODELING RESULTS
These potential constraints on land-use changes are not accounted for in the modeling exercises.
But the model does demonstrate that, allowing for compliance using renewable resources from
outside of Virginia keeps solar land area requirements to a more manageable two percent of the
state’s land area. This illustrates, again, the value of regional flexibility in meeting the renewable
energy requirements.
30
MODELING RESULTS
OTHER POSSIBLE TECHNOLOGIES AND APPROACHES
The modeled scenarios have considered only a subset of the many technologies for generation and
storage that may become cost-effective in the later years of the policy horizon. To provide
guidance relevant to short-run decision making, the modeling exercise is restricted to consider
technologies for which costs and operating characteristics can be readily determined. This
restriction excludes from the modeling a number of promising technologies, currently in
development, that may be available to make cost-effective contributions before the 2045 policy
horizon. It is almost certain that some technology breakthroughs will bring the cost of one or more
of these alternatives into competitive range. Virginia needs to track the availability of alternative
technologies and ensure that the most cost-effective resources are being encouraged in the energy
procurement process, subject to social and environmental constraints. One successful strategy that
has been used is to hold open procurement auctions that are technology neutral.
Among the potential technologies that may be available before the end of the policy horizon are:
1. Technologies for generation and storage
• New nuclear technologies: small modular fission reactors or even nuclear fusion
• Geothermal energy for electricity generation
• Floating wind turbines for deep offshore wind (2030 cost target, ~$50-60/MWh)
• Natural gas and bio-energy with carbon capture and storage
• Landfill gas (LFG) 12
2. Storage
• Hydrogen, ammonia and other synthetic fuels
• Advanced physical and chemical storage
• Compressed air storage
• Advanced batteries including bulk storage technologies
• Vehicle-to-grid technology
12 Large landfills in Virginia already capture and burn landfill gas. This study did not address where there is a
significant opportunity to expand the electricity generated from LFG.
31
MODELING RESULTS
3. Competitive procurement of new generation
Evidence from a variety of sources suggests that competitive procurement of new renewable
generation capacity has been a key driver of declines in wind and solar costs (Fitch-Roy et al.,
2019). Given that wind and solar resources will almost certainly be central to Virginia’s energy
future, some consideration to enhancing the state’s capacity for using procurement auctions to
acquire new generation could help lower future deployment costs, facilitate integration of cost-
competitive emerging technologies, and reduce the cost to ratepayers of achieving a decarbonized
energy supply. 13
Virginia should enhance the state’s capacity for using procurement auctions to
acquire new renewables generation.
4. Enhanced energy efficiency
Energy efficiency as a resource will be discussed in more detail in a subsequent section of this
report. Energy efficiency (EE) is an unusual resource in that the focus is on reducing demand rather
than increasing supply. There is good evidence that energy efficiency represents a large and
potentially inexpensive resource (Lovins, 2018). If successful at reducing energy demand, EE
substitutes for the most expensive energy source, lowering consumer costs. If peak demand is
reduced, the benefits are greater still. It has the added benefit of potentially eliminating the need
for costly upgrades to the transmission and distribution system.
The high energy efficiency scenario demonstrates the potential value of energy efficiency
investments by extending the current energy efficiency resource standard (EERS) beyond 2025. The
modeling suggests that the EERS does not have its effect primarily on generation in Virginia; in-
state generation changes very little. The extended EERS acts by reducing imports. The reason for
this is that the existing VCEA policies, including the renewable and storage capacity targets result in
the deployment of a minimum capacity of non-emitting resources regardless of the level of
demand. Wind, solar and storage capacity have low marginal costs of operation, and substitute
directly for higher marginal cost resources, especially coal, in the rest of PJM.
Assuming that adequate monitoring and evaluation mechanisms can be implemented, even the
modest existing EERS could save around 10 TWh of electricity per year by 2035, resulting in over $1
billion in consumer savings per year by 2035. Based on an estimated average cost of $58/MWh for
energy efficiency savings, consumers would have net savings of over $400 million per year. This
estimate does not include cost savings from avoided capacity upgrades to the transmission and
13
For additional discussion see: https://www.irena.org/policy/Renewable-Energy-Auctions and
http://www.open-electricity-economics.org/book/text/09.html and https://www.usaid.gov/energy/auctions.
32
MODELING RESULTS
distribution system. What is not as well understood is the true cost of implementing these
measures, and the likelihood of achieving the targeted demand reductions. Empirical studies have
shown that many seemingly sensible policies for reducing energy intensity do not achieve the
predicted savings (Fowlie et al., 2018). Ensuring cost-effective implementation of efficiency
programs depends critically on implementing effective incentives along with effective institutional
arrangements in place for truly independent monitoring and evaluation. This is discussed in more
detail in Section 6.
It is important to note that energy efficiency efforts for the whole economy may increase demand
for electricity. As already discussed, the shift to EVs increases both electricity demand and efficiency
of energy end-use. EVs are much more efficient than internal combustion engines in the
transformation of primary energy into transportation services. The electrification of building
heating and cooling has the same dual effect of increasing electricity use while decreasing overall
energy use. Models of economy-wide decarbonization uniformly show the importance of
electrification of energy end-uses for both improved efficiency and decarbonization. Broader
electrification of energy services adds to the need for an expanded, non-emitting energy supply.
33
COMPLIANCE FLEXIBILITY AND LOWERING
DECARBONIZATION COSTS
The modeling confirms a general result that is supported by both economic theory and evidence:
for a given emission constraint pathway, increasing compliance flexibility reduces compliance costs.
Flexibility can come in many dimensions, but three merit special mention: flexibility in timing,
technology and geography. Allowing compliance flexibility in each of these dimensions can
produce substantial savings in ratepayer costs. Because greenhouse gas emissions have their
effects at the global level and depend on the total stock of emissions rather than on emissions in a
given year, flexibility offers gains with no significant changes to the level of ambition of the
emission reduction goals. Flexible compliance implies that the mechanism for controlling emissions
focus on total emissions rather than on the specific mechanisms used to reduce them.
Compliance flexibility and the associated cost savings are the key advantages of “cap and trade”
emission control programs such as RGGI. The total cap over the compliance horizon defines the
level of ambition. Given this level of ambition, sources have the greatest possible flexibility in
choosing when, where and how to meet the target. The observed market price of allowances
guides compliance choices and induces innovation in the same way that the price provides these
same incentives in normal markets for goods. Sources can shift compliance obligations across time.
In the case of an emission cap that declines over time, sources can be expected to comply early and
bank emission allowances for later periods. The market price also provides policy makers with
important information about the market participants’ perceptions regarding future levels of
scarcity. This information can be used by policy makers to adjust the level of ambition accordingly.
In practice, the Regional Greenhouse Gas Initiative has lived up to expectations, driving lower
compliance costs and ultimately greater ambition in future emission reductions.
The revenues from the sale of RGGI allowances can be recycled to the benefit of ratepayers in a
number of ways. The VCEA mandates that the allowance revenues be split between energy
efficiency measures and flood resilience planning. Other possible strategies for recycling revenues
include rebates of some share of revenues directly back to ratepayers or reductions in other state
tax rates. These strategies retain the cost-savings inherent in the market-based allowance trading
framework but passes some revenue directly back to ratepayers rather than allocating all of the
allowance revenues to fund specific programs such as energy efficiency and flood resilience
programs. An alternative mechanism for managing allowance value is the strategy chosen by DEQ
in its initial regulation for joining RGGI. DEQ’s innovative approach used a consignment sale to
return all net allowance value directly back to ratepayers. Each of these revenue assignment
strategies retains the cost effectiveness of the market-based approach but makes a different choice
about how allowance value is returned to the economy.
34
COMPLIANCE FLEXIBILITY AND LOWERING DECARBONIZATION COSTS
Economic theory and a considerable body of empirical evidence shows that market-based emission
controls such as RGGI are most likely to achieve the least cost emission reduction pathway.
RGGI should be considered a key mechanism for reducing costs to ratepayers of
achieving zero emissions by 2045.
A recent study for PJM by the firm E3 on how to achieve 80% reductions in greenhouse gas
emissions by 2050 reached very similar conclusions to this study about least cost pathways (Energy
and Environmental Economics, 2020). 14
• All PJM coal plants retire by 2030
• Legacy nuclear generation is kept in service
• Natural gas capacity is kept in service, and possibly converted to low carbon fuels, but is run
at lower capacity factors in later years
• Solar and onshore wind are the primary additions to capacity, becoming the majority of
generation later in the policy horizon
• Offshore wind supplements onshore wind in later years.
The PJM study also emphasized the value of flexibility in achieving any given decarbonization goal.
It shows that a policy that focuses directly on greenhouse gas reductions is more cost-effective
than mandating the use of specific technologies for achieving those reductions. The least cost
scenario relies on capping GHG emissions and allowing trading of allowances among sources, as
with the RGGI program. A cap that declines over time to the emission objective can reliably achieve
the desired emissions reduction goal but with the maximum flexibility in how that reduction is
accomplished. Such a policy is technology and geographically neutral, and encompasses all
possible modes of reduction including demand reduction, carbon free generation and GHG capture
by sources. A declining cap can also allow temporal flexibility by defining a total emission budget
during for a given period of time and allowing some flexibility in the rate of reduction.
A close-second policy choice, in terms of cost-effectiveness, according to the E3 study, is a
technology neutral clean energy standard (CES) with the opportunity to trade clean energy credits.
A technology neutral CES provides flexibility in mode, geography and timing of reductions. The
focus is on emission-free energy sources rather than on renewables in particular. This approach
makes available a number of additional opportunities for GHG reduction such as bio-energy with
carbon capture or zero-carbon synthetic fuels.
14
See Energy and Environmental Economics. (October 2020) Least Cost Carbon Reduction Strategies in PJM.
https://www.pjm.com/-/media/committees-groups/task-forces/cpstf/2020/20201208/20201208-item-03b-e3-
least-cost-carbon-reduction-policies-in-pjm.ashx
35
COMPLIANCE FLEXIBILITY AND LOWERING DECARBONIZATION COSTS
One concern raised by the E3 study is the potential for “leakage” where some member utilities in
PJM are not subject to emission charges or constraints. As emission reduction costs rise, the
amount of generation shifting is also likely to rise. Without “border adjustments” that require
imported electricity to face the same abatement costs as local generation, leakage can undo much
of the local emission reductions. Appropriate border adjustments can retain the advantage of
compliance flexibility without resulting in emission leakage.
36
ADDITIONAL FACTORS FOR LEAST-COST
DECARBONIZATION PATHWAYS
RESOURCE VARIABILITY, RELIABILITY AND RESILIENCE
In approaching the task of eliminating CO2 emissions from the state’s electricity supply, Virginia
starts from a position of relative strength. With 3.7 GW of nuclear baseload generation, 14 GW (or
more) of natural gas capacity and substantial interstate transmission capacity, the electricity sector
is in a position to absorb substantial quantities of solar and wind without generating concerns over
resource reliability. Even with the VCEA capacity targets, the electricity supply will be less than 30
percent variable renewable resources up until about 2035. Considerable time exists to prepare for
an electric grid in which renewable generation exceeds 50 percent, which will likely occur around
2040. Experience from other states and countries suggests that, until this point is reached, the
increasing contribution of renewables need not force major changes in supply and demand
management to maintain system reliability. Current uncertainty over what will be the best options
and approaches in the later years of the policy horizon need not slow transition efforts, which in
the near term should focus on development of onshore wind and solar generating capacity. There
is room to plan and adjust as new technologies and techniques become available.
TRANSMISSION AND DISTRIBUTION SYSTEM COSTS
A recent PJM study evaluated the cost of a coordinated set of grid upgrades necessary and
appropriate for harvesting approximately 14 GW of wind off the coast between New Jersey and
North Carolina. 15 The study estimated that the coordinated set of grid upgrades would cost
between $2.2 and $3.2 billion over the next several years. The implied costs (undiscounted) are
between $188/kW and $210/kW. But not all of these costs should be counted as costs of offshore
wind development. As acknowledged in the PJM analysis, many of these upgrades have value
whether the offshore wind projects are built or not. Several important market efficiency
improvements are generated by the transmission upgrades: congestion relief, reduced CO2
emissions across PJM, a decrease in other renewable curtailments (especially solar) and a decrease
in congestion load payments and locational marginal price differentials. In other words, this grid
upgrade proposal creates very substantial benefits beyond the mere transfer of offshore wind
electricity. Because these benefits of grid upgrades are overlapping and complementary, there is no
obvious method to allocate the costs for the upgrade among the wider set of public goods.
15
Offshore Wind Transmission Study: Phase 1 Results. (October 2021). PJM.
37
ADDITIONAL FACTORS FOR LEAST-COST DECARBONIZATION PATHWAYS
A study by The Brattle Group and Grid Strategies pointed out a striking feature of this grid planning
exercise. 16 The cost of upgrades resulting from a careful, integrated regional analysis is half the cost
of the transmission upgrades that would result from the piecemeal grid modifications that would
occur through the normal interconnection queue approval process. As the Brattle Group study
(along with many others) shows, the current grid planning and resource interconnection processes
are not working well and are key friction points preventing more rapid and cost-effective
incorporation of renewables to the grid.
The current interconnection process leads to a higher-cost transmission system, raising electricity
costs and reducing the benefits of renewable integration. The current system also unfairly allocates
costs to new projects because it fails to ensure that the beneficiaries of grid services “pay costs that
are roughly commensurate with the benefits they receive.” (Brattle 2021). Finally, the current
process imposes unnecessary costs on renewables developers. This has several damaging effects. It
reduces the profitability of renewables projects. It raises consumer costs. And because the
interconnection process is a fixed cost of project development, it tends to push developers to
propose larger projects than otherwise, generating frictions with local land-use planning
authorities.
The modern grid, like the interstate highway system, is a public infrastructure that generates
enormous social value. Its role is already shifting dramatically as new, more distributed, two-way
energy services develop. The current grid structure is no longer a perfect match to the new energy
technology landscape. Consequently, investment in grid modernization will be essential whatever
mix of renewable and non-renewable energy technologies are chosen. Consumers will benefit from
the investments, but allocating the costs to specific projects or users will often not be possible or
beneficial.
A significant portion of the land in Virginia most suited for solar and wind deployment are
sufficiently far from current transmission infrastructure that solar deployment in these areas is not
currently cost-effective. Planning some grid expansion for the sole purpose of harvesting Virginia’s
solar resource cost-effectively may have significant public benefit, but Virginia has no ready
mechanism for engaging in this anticipatory resource planning and expansion.
The approach to grid modernization and management should no longer follow the old model built
around adding a few large generation sources and connecting them by high-voltage transmission
to load centers. The electric grid is an energy services harvesting tool as well as a system for bulk,
unidirectional transfer of electricity. Continuing to use antiquated processes for costly, incremental
grid upgrades would raise the cost of grid services and reduce the value of the grid in facilitating
the shift to non-emitting resources.
16
Transmission Planning for the 21st Century: Proven Practices that Increase Value and Reduce Costs.
(October 2021).
38
ADDITIONAL FACTORS FOR LEAST-COST DECARBONIZATION PATHWAYS
The Brattle/Grid Strategies study recommends that the grid planning process be organized around
five planning practices:
1. Proactively plan for future generation and load
2. Apply a multi-value planning framework to all transmission projects
3. Use scenario-based planning to address uncertainties
4. Capture portfolio-level cost advantages and use portfolio-based cost recovery
5. Perform joint, interregional planning
The purpose of approaching grid planning in this way is that it reduces the cost of grid services and
provides a better match between grid architecture and grid services value.
At the end of October 2021, the PJM interconnection queue for Virginia had 31 GW of solar
capacity listed as “Active” and 3 GW at the more advanced “Engineering and Procurement” stage. 17
Unfortunately, based on recent experience less than a fifth of the “active” projects are likely to be
completed. Working through the queue process takes years and involves a series of expensive
interconnection studies that subject developers to wildly varying estimates of the grid upgrade
costs that are to be assigned to the particular project.
Smaller renewable projects must use the interconnection queue managed by Dominion Energy. For
solar projects in this queue, of those with completed interconnection agreements, the mean time
between queue assignment and an executed interconnection agreement is over 682 days. This
does not count the period before queue assignment. The delay and uncertainty impose large costs
on developers, causing many to drop out and ultimately raise the cost of electricity to ratepayers.
Virginia would benefit by developing an active grid planning framework within the Virginia
Department of Energy. The grid planning group could initiate planning and modeling activities
outside of the traditional adversarial process of utility regulation. The group should be charged
with helping coordinate a regional grid planning process to complement and expand upon the grid
planning process at PJM. The grid for a decarbonized Virginia will not plan and organize itself. The
savings to ratepayers is potentially very large. The Brattle Group/Grid Strategies study is the only
exercise to date to compare the current ad hoc approach and the alternative coordinated regional
planning approach. That study suggests that the coordinated approach could reduce transmission
upgrade costs by half over the policy horizon, amounting to billions of dollars saved.
Twenty-five years is not a long time in the context of grid planning and deployment. The key issues
extend beyond engineering and technical design questions. The new grid will require new means
17
See: PJM New Services Queue.
39
ADDITIONAL FACTORS FOR LEAST-COST DECARBONIZATION PATHWAYS
for the financing of grid upgrades and the pricing of grid services. In many respects, these soft
design questions are as challenging as those faced by the engineers.
HARVESTING ENERGY EFFICIENCY
Energy efficiency appears, on the surface, to be a large and low-cost resource for reducing
emissions by increasing the end-use energy services obtained for each unit of energy input.
Opportunities exist for increasing energy efficiency because the energy intensity of developed
economies has been declining for several decades. This pattern, along with engineering studies, has
led many observers to the conclusion that investing in energy efficiency is one of the least-cost
resources available to us. Measuring the actual results of energy efficiency investments is very
difficult (Tietenberg, 2009). Given the difficulty of measuring actual energy savings, much of the
evaluation of energy efficiency efforts relies on estimated outcomes based on inputs or effort
rather than actual energy savings. Fortunately, recent research suggests strategies for improved
program design along with improved monitoring and evaluation of actual outcomes.
The key difficulty with estimates of the energy efficiency potential is understanding why we are not
already getting the “right” amount of savings. In 2009, McKinsey and Company published a study
claiming that there existed a huge energy efficiency resource that had negative costs, meaning it
would be profitable for a firm to borrow at the market interest rate to invest in savings. The
difficulty with this result was immediately apparent. If these investments were so profitable, why
were investors not taking advantage of profitable investments? If they weren’t, then either there
was something getting in the way of profitable activities or the investments might not be profitable
after all. This “energy efficiency gap” has been the subject of intense research since the publication
of the McKinsey study.
A number of conclusions are now possible. First, many programs that appear cost effective don’t
account for the true opportunity cost of investing in efficiency, once you add up all of the costs.
Second, some investments in energy efficiency do produce economic gains but don’t save energy.
Investments in efficiency lower the costs of using the services that the energy creates. As the
services become cheaper, people will choose to consume more of the now cheaper services. This is
often called “the rebound effect”. There is a benefit to the consumer, just less energy savings than
you expected.
The third issue is identifying the “market failures” that prevent people or investors from investing in
the right amount of efficiency. Implementing a policy that tries to reap efficiency but doesn’t
address the reason for under-investment is likely to have unintended (or even perverse)
consequences. There is a vast literature on this issue but still no consensus on what policies are
best for addressing what market failures we think are present and what we can expect to reap from
a given intervention (Gillingham & Palmer, 2014).
40
ADDITIONAL FACTORS FOR LEAST-COST DECARBONIZATION PATHWAYS
The fourth reason is possibly the most important. The true reductions in energy use due to a given
public expenditure are devilishly hard to measure (Fowlie et al., 2018). Carrying out carefully
designed, randomized control trials, can provide strong evidence about effectiveness. In cases
where controlled experiments have been done, the results indicate that savings are generally much
less than the engineering estimates. Some programs produce savings, others can actually increase
energy use. Unfortunately, randomized trials are very expensive and administratively burdensome.
Recent advances in machine learning have provided an alternative, more feasible evaluation tool
that can provide reasonably trustworthy estimates of the value of different efficiency
interventions(Burlig et al., 2020; Prest et al., 2021). These evaluation methods use high time-
resolution electricity meter data on individual customers to discover patterns of differences
between those receiving the policy treatment and those not receiving the treatment. Researchers
have recently used these techniques to effectively reproduce the results of randomized trials. This is
an extremely promising area for advancing the ability to monitor, evaluate and re-target efficiency
expenditures to maximize the gains in energy saved.
If Virginia is to depend on energy efficiency as a cost-effective contribution to decarbonization,
then it is critical that the measure of success is not measured based on level of investment or
numbers of specific efficiency measures installed but instead on empirically measured outcomes.
To ensure this occurs, performance incentives mechanism need to be designed accordingly and
there needs to be the appropriate administrative capacity outside of the organizations actually
implementing the programs for oversight, monitoring and evaluation.
In 35 states, including Virginia, the administration and procurement of energy efficiency resources
is the responsibility of the utility, pursuant to state legislative mandates. There are other
alternatives to this model, such as a state agency serving as administrator, third-party
administration, and hybrid models that several states have chosen to adopt (Sergici & Irwin, 2019).
Oregon adopted the third-party administrator model as a result of state redistricting proceedings
in 2002. The Oregon Public Utility Commission (PUC) created the Energy Trust of Oregon (ETO) to
set energy efficiency resource standards and administer statewide energy efficiency programs. ETO
is led by an elected non-stakeholder board of director and overseen by three advisory councils. The
PUC defines metrics to evaluate ETO’s performance and sets stringent evaluation, measurement,
and verification (EM&V) standards (Sedano, 2011).
Accurate and verifiable EM&V is critical for a successful energy efficiency program. If programs are
not thoroughly assessed, performance incentives lose their effectiveness and there is no basis on
which to continually improve program design. To ensure transparent evaluation, some states
require that eligible third parties have access to anonymized customer usage data. In Illinois, the
Open Data Access Framework sets guidelines for securely authorizing and sharing customer data.
The Illinois Commerce Commission has also approved data-sharing programs for ComEd, the states
41
ADDITIONAL FACTORS FOR LEAST-COST DECARBONIZATION PATHWAYS
largest electric utility. These policies allow companies and independent researches to access smart
meter data which increases transparency (Munson, 2017).
42
ADDITIONAL FACTORS FOR LEAST-COST DECARBONIZATION PATHWAYS
DEMAND RESPONSE
Demand response involves activities that shed, shift or shape behind-the-meter (BTM) electricity
demand and supply to enable more efficient use of grid resources. The most expensive electricity
to supply has generally been the kilowatt hours needed to meet peak demand. Capital costs of
generation, transmission, and distribution system capacity utilized only during periods of high
demand must be recovered from a relatively few kilowatt hours of output. Demand response
technologies and programs that reduce peak loads can therefore provide substantial savings. One
recent study estimated that investing in demand response capabilities and technologies could
reduce peak loads by 20% and save U.S. ratepayers $15 billion annually by 2030 through avoided
capacity and energy costs (Hledik et al., 2019). If enhanced demand response in Virginia yielded
proportionate savings, then based on Virginia’s share of national electric demand, Virginia
ratepayers could realize more than $400 million per year in savings by 2030. This is likely a
conservative estimate since demand response technology and business model innovation have
continued to accelerate since the study was completed. A recent energy system modeling study
estimated that demand response measures for building heating, cooling and hot water applications
could reduce battery energy storage requirements for a decarbonized electric grid by nearly 60%
in a climate zone of the U.S. that includes most of Virginia (Houssainy & Livingood, 2021).
Use of intermittent renewables as a significant source of electric generation increases the value and
range of applications of demand response, extending it beyond conventional peak demand
shaving to include a wide variety of measures designed to cost-effectively manage periods of
excess demand relative to real-time renewable supply. Virginia is still in the early stages of the
transition to an electric grid with high levels of intermittent renewables that also supports increased
electrification of transportation and other end uses. Other states and countries have already
advanced further down that path. In the process, demand response is evolving to encompass a
much more diverse set of technologies, use cases and value streams. For Virginia, this provides the
opportunity to learn from and build upon technology applications and business model innovations
that have already been deployed and tested elsewhere. As Virginia moves to the forefront of the
clean energy transition, demand response offers the potential to deliver substantial cost savings to
ratepayers. Given the concentration of major technology companies, data centers, and large public
sector energy users within the state, there is also an opportunity for the Commonwealth to become
a hub for smart grid technology and business development.
1. Enabling behind-the meter supply and demand response
Conventional peak load shedding demand response has generally involved a direct interaction
between utilities and their customers. The interaction has occurred through rate structures
intended to reduce customer demand during peak periods and/or by utilities providing customers
a bill credit in exchange for allowing the utility to install a device that can cycle energy using
appliances, typically hot water heaters and air conditioners, on and off during periods of peak
43
ADDITIONAL FACTORS FOR LEAST-COST DECARBONIZATION PATHWAYS
demand. Some large commercial and industrial ratepayers have employed energy service
companies to assist in managing their load curves to minimize utility bills but savings available to
residential and smaller commercial customers were rarely substantial enough to support use of
third-party services and expertise.
With the expansion of smart metering and smart BTM technologies, this landscape is changing
rapidly. An expanding set of energy management companies are using smart communication
enabled technologies, to manage BTM resources for commercial and industrial as well as
residential accounts. Based on customer defined preferences and price points, these companies
remotely and automatically access demand response resources across their customer base, and on
behalf of their customers, sell the aggregated demand response capacity and output on retail or
wholesale electricity markets. Management of the resource and execution of energy market
transactions occur without any ongoing involvement from the ratepayer with the savings credited
on the ratepayer’s utility bill.
2. EV integration
Continued market penetration and cost reductions of EVs has the potential to significantly expand
demand response and improve grid capacity utilization, while also reducing transportation related
emissions of CO2 and other air pollutants (Blonsky et al., 2019; Malmgren, 2016). Miles traveled per
charge for electric vehicles is steadily increasing which provides most EV owners with substantial
flexibility regarding when to recharge their vehicles. Studies have shown that EV owners are willing
to modify charging times given appropriate incentives to do so (EA Technology, 2019). This creates
a very substantial demand response opportunity. Realizing the load leveling potential of EV’s will
require coordinated deployment of distribution system and charging infrastructure investments,
dynamic rate structures, and smart grid capabilities.
Demand response and grid management benefits of EV’s will be further magnified as two-way
charging technology is commercialized. Bidirectional vehicle-to-grid (V2G) and vehicle to home,
business, etc. (V2X) charging technology enables EV’s to draw electricity from the grid to recharge
batteries and to discharge power from the batteries back to the grid or to meet on-site customer
demand. With V2G or V2X charging, EV’s provide the grid with time flexible load as well as
distributed storage capacity.
Since EV owners will be purchasing their vehicles primarily for transportation, a substantially lower
level of ratepayer funded capital investment is required to benefit from the energy storage capacity
of EV’s as compared with development of dedicated utility scale storage capacity. Conversely,
providing financial incentives to EV owners for optimized EV charging and V2G supported energy
storage and discharge back to the grid would be expected to accelerate EV adoption and charging
technology development.
44
ADDITIONAL FACTORS FOR LEAST-COST DECARBONIZATION PATHWAYS
3. Tariff design
Dynamic pricing of electricity, including time-of-use (TOU) rates, peak demand surcharges (or
rebates for reductions), and real-time pricing (RTP), provides customers with financial incentives to
reduce or shift loads and manage distributed energy resources (DERs), EVs, and energy storage to
minimize energy costs. Rate based financial incentives are a critical driver of adoption and
optimized use of systems and technologies that augment demand response.
Combining dynamic rates with supporting technologies, including programmable thermostats as
well as smart, communication-enabled thermostats and appliances, increases demand response by
a factor of two or more versus deployment of dynamic rates without accompanying BTM
technologies (Faruqui et al., 2017). Dynamic pricing provides the incentive for ratepayers to provide
demand response, while smart systems and equipment make it easier for ratepayers to respond to
those incentives. Analysis of dynamic pricing programs also indicates that rate structure design is
critically important. Larger differences in rates between non-preferred/high-cost and
preferred/low-cost demand periods and shorter duration non-preferred/high cost demand periods
generate larger demand response per customer and higher rates of customer participation (Faruqui
et al., 2019; Sherwood et al., 2016). Smart systems that include smart meters, thermostats,
appliances, and energy storage systems allow for more precise and automated opportunities for
households and businesses to react to time-varying rates. The converse is also true. Dynamic
pricing unlocks the potential of smart meters and smart BTM systems and devices, augmenting
customer demand response.
4. Smart grid technologies
In addition to smart BTM technologies and dynamic rate structures, smart grid technologies, such
as smart meters and associated information management systems, are needed to maximize grid-
level demand response capabilities. Smart meters enable design and implementation of more
sophisticated and time sensitive rate structures. A series of studies conducted using detailed
customer use data in Illinois found that smart meters combined with rates that reflect the time
varying costs of service delivery can also have significant benefits for many lower income rate
payers (Zethmayr & Kolata, 2018; Zethmayr & Makhija, 2019).The analysis found that many lower
income households have flatter electricity use patterns throughout the day and across seasons
compared with commercial and more affluent residential ratepayers. This allows for the design of
rate structures that provide effective incentives to reduce peak or excess demand, while reducing
total utility costs for low- and moderate-income households.
Smart metering technologies and associated customer data management systems also provide
ratepayers and third-party energy service companies with more detailed data with which to
optimize use of BTM technologies. Residential and business customers can obtain much more
detailed on-line data and reports on their electricity use over time and in comparison to other
45
ADDITIONAL FACTORS FOR LEAST-COST DECARBONIZATION PATHWAYS
customers in their rate class. Third-party demand response aggregators utilize these data and the
enhanced communication capabilities of smart metering systems to assist customers in optimally
responding to time varying rate structures and other demand response incentives.
5. State policies
Numerous states have implemented utility performance incentive mechanisms to promote
investment in demand response and ensure those investments yield benefits for ratepayers (Gold et
al., 2020). A review of California’s experience with demand response indicates that the design of
state policies and regulatory mechanisms, rather than technical or economic factors, have limited
the use of demand response as a resource for cost-effectively balancing supply and demand (St.
John, 2020). Massachusetts has implemented a Clean Peak Energy Standard (CPS) to accelerate
investment in technologies and operational management systems that can supply clean electricity
or reduce electric demand during specified peak periods defined by the state. Any clean energy or
energy storage resource that generates, dispatches, or discharges electricity to the grid, or reduces
demand, during designated peak periods generates Clean Peak Certificates. Retail electricity
suppliers can develop or purchase CPCs to achieve annual legislative mandated targets.
DISTRIBUTED ENERGY RESOURCES
Due in part to prior restrictions on development of distributed energy resources (DERs) in Virginia,
distributed solar currently supplies well under 1% of Virginia’s electricity demand. Other states have
demonstrated that much higher levels of distributed solar output can be achieved and successfully
integrated into local and regional power grids. U.S. Energy Information Agency data indicate that in
August 2021 distributed solar contributed more than 14% of Hawaii’s electric generation and nearly
14% of electricity generated in California.
For purposes of this report, a key question is whether relying more significantly on DERs will
increase costs to ratepayers of achieving a zero-carbon electricity supply for Virginia. Estimates of
the levelized cost of electricity from different technologies indicate that distributed solar is several
times more expensive than utility scale solar but is currently less expensive than offshore wind.
Although the absolute magnitude of the cost differential between distributed and utility-scale solar
is projected to decline over time as costs of both technologies continue to decrease, distributed
solar is expected to remain more expensive.
Given the VCEA’s steadily increasing RPS mandates, the financial value to ratepayers resulting from
DER investments is not a function of overall generation capacity mix and average fuel costs for
Virginia’s investor owned utilities. Instead, it is the avoided marginal cost of obtaining renewable
energy credits from utility scale renewables plus avoided transmission and distribution costs. One
least-cost grid decarbonization study found that including analysis of transmission and distribution
capacity requirements, as well as the demand response potential of distributed solar and storage,
46
ADDITIONAL FACTORS FOR LEAST-COST DECARBONIZATION PATHWAYS
boosted investments in DERs, reduced utility scale storage investments and yielded substantial cost
savings compared with scenarios that did not take these costs and capabilities into account (Clack
et al., 2020).
In addition to these direct ratepayer impacts, increased development of DERs can help to avoid or
reduce land-use conflicts associated with utility scale renewables. Energy system modeling
conducted for this report indicates that achieving the RPS mandates of the VCEA could require
utility scale solar development on over 1% of the land area of Virginia. Since prime locations for
utility scale solar (relatively flat land close to existing infrastructure) often overlap with productive
farmland and areas with high potential for residential or commercial development, there will be
competing interests and proposals for use of land that is well-suited for utility-scale solar
development. These land use conflicts can delay development of utility scale solar projects and
increase costs if utility scale solar must utilize sites with steeper slopes and/or greater distance to
transmission lines.
The advantage of distributed solar is that it does not require a greenfield site. Almost all distributed
solar facilities, whether rooftop, canopy, or ground mount, are located on parcels that are also
being used for other purposes. Supporting expanded DER development will reduce the need for
utility scale solar which would be expected to alleviate potential land use conflicts involved in
meeting the RPS mandates of the VCEA. A recent study of the rooftop solar production potential of
all 50 states found that installing rooftop solar on existing buildings with high solar potential could
provide more than 30% of Virginia’s current annual electricity consumption (Gagnon et al., 2018).
Additional distributed solar capacity could be provided from canopy systems on parking areas, as
well as ground mount systems located on landfills, publicly owned roadway buffers, and larger
suburban or ex-urban parcels.
Continued investment in distributed solar is occurring alongside an accelerating transition to
electric vehicles and rapid innovation in BTM storage and energy management systems. New
technologies reaching commercial stage include bi-directional V2G and V2X charging systems for
EVs and smart inverters that can automatically isolate DERs from the grid to provide power during
system outages, help manage voltage fluctuations on the grid, and enable more efficient
integration with distributed storage (Silvia, 2021; St. John, 2021).
Studies have also shown that households and businesses choosing to invest in distributed solar are
more likely to be early adopters of EVs, distributed storage and smart BTM technologies (Delman,
2018). They are also more likely to opt-in to time of use or other dynamic rate structures and be
prepared to optimize energy use in response. Analysis of the costs and benefits of policies
supporting expanded development of DERs must account for these linkages between adoption of
distributed solar, EVs and BTM energy storage systems and other smart technologies that expand
demand response capacity.
47
ADDITIONAL FACTORS FOR LEAST-COST DECARBONIZATION PATHWAYS
Continued market penetration of distributed solar also increases public participation and
community engagement in Virginia’s energy transition. The most significant factor affecting
whether a household has installed a rooftop solar energy system is whether one or more of their
neighbors has recently installed one (Barton‑Henry et al., 2021; Graziano & Gillingham, 2015). This
suggest that an effective means of expanding the installed capacity and income distribution of
households with rooftop solar is to target incentives to neighborhoods with lower concentrations
of rooftop solar.
48
CONCLUSIONS
The modeling carried out for this study confirms a general principle, borne out by other studies,
that flexibility in meeting GHG reduction goals lowers costs of achieving any given reduction target.
Flexibility has many dimensions including technology, geography and time. The first step in
achieving compliance flexibility is in choosing the policy target, which in the case of the VCEA is
reductions in GHG emissions from Virginia’s electricity sector. The targeted reductions can be
achieved through various combinations of energy efficiency improvements, renewable energy,
energy storage and other still-emerging technologies. Defining the goals to be met and providing
appropriate incentives to achieve them while allowing utilities, independent power producers, and
energy users flexibility in determining how best to balance supply and demand offers the greatest
potential for achieving cost-effective results.
The method used in this study for exploring cost-effective strategies started with a Baseline
Scenario intended to capture the key features of the VCEA. After exploring the sensitivity of the
initial results to some of the key assumptions, alternative pathways were modeled. In every case,
the modeling required that the electricity sector achieve full decarbonization by the end of 2045.
The model results confirm the general observation that flexibility in compliance reduces ratepayer
costs. Policies that specify technologies, local versus regional compliance or overly rigid time paths
for reduction raise costs. Policies that focus directly on GHG emissions rather than on techniques
for reducing them generate the greatest gains. Virginia, is a member of the Regional Greenhouse
Gas Initiative, an example of a flexible GHG-focused approach.
The most significant, but limited exception to this general rule on flexibility is cases where a known
“market failure” prevents consumers and producers from making effective responses to a price on GHG
emissions. There is some reason to believe that energy efficiency is a large available resource for
decarbonization, but that some efficiency opportunities are hampered by ineffective price incentives. In
this case, and in the case of equity concerns, supplemental policies may be cost-reducing or result in
increased public benefits. Available evidence suggests that cost-effectively harvesting this efficiency
resource requires a demanding regime of monitoring and evaluation. Without effective monitoring,
demand reduction due to efficiency efforts will be much less than expected and will be more costly
than necessary.
Modeling results, and additional analysis conducted for this report, point to several possible ways of
lowing the cost of achieving the goals of the VCEA:
• Allow compliance flexibility over technology, geography and time
• Provide flexibility in the timing of new renewables and storage
• Allow legacy coal plants to retire on an economic basis
• Explore options for reducing the “soft” costs of distributed energy resources
• Encourage the rapid transition to electrified light duty vehicles
49
ADDITIONAL FACTORS FOR LEAST-COST DECARBONIZATION PATHWAYS
• Recognize load shifting and demand management as an increasingly important resource for
cost-effectively balancing supply and demand, especially as the share of intermittent
renewable generation increases
• Develop a robust monitoring and evaluation framework for energy efficiency efforts
• Build the institutional capacity for regional transmission planning and finance
Making these marginal adjustments can be expected to substantially lower the cost of eliminating
GHG emissions from the electricity sector by 2045.
50
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Delman, B. (2018). Survey examines solar’s strong connection to electric vehicles—Solar United
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examines-solars-strong-connection-electric-vehicles/
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Faruqui, A., Sergici, S., & Warner, C. (2017). Arcturus 2.0: A meta-analysis of time-varying rates for
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Fitch-Roy, O. W., Benson, D., & Woodman, B. (2019). Policy Instrument Supply and Demand: How
the Renewable Electricity Auction Took over the World. Politics and Governance, 7(1), 81–91.
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53
APPENDIX 1: PUBLIC COMMENT ON THE CPCN
PERMANENT MORATORIUM
A listing of all public comments received is available from Virginia Energy at this link.
For a Against a
Encourage No
Permanent Permanent
Organization Reliability Comment
Moratorium/ Moratorium/
Analysis on CPCN
Repeal Repeal
VCEJ (Virginia Council on
X
Environmental Justice)
SCC (State Corporation
X
Commission Staff)
PJM X
Dominion Energy Virginia X
Appalachian Voices X
Framatome X
New Virginia Majority X
NFIB (National Federation of
X
Independent Businesses)
NRDC X
Nuclear Energy Institute X
Power For Tomorrow (PFT) X
Sierra Club X
The Nature Conservancy X
Virginia Asian Chamber of
X
Commerce
Virginia Chamber X
Virginia Clean Energy X
Virginia Manufacturers
X
Association
Virginia Nuclear Energy
X
Consortium
54
For a Against a
Encourage No
Permanent Permanent
Individuals Reliability Comment
Moratorium/ Moratorium/
Analysis on CPCN
Repeal Repeal
Alleyn Harned X
Charles Meyers X
William Welkowitz X
Loralee Clark X
Vernon Savage X
Bill Staley X
Gene Whitaker X
Martha Owen X
Bryan Ramirez X
Stanley Naimon X
Karen Gavidia X
Michael Carter X
Karen Fostel X
Susan Brashaw X
AJ X
Raymon Nuesch X
Jennifer Yarrington X
Erika B X
Travis Allen X
Ann Creasy X
Marjories Leach-Parker X
Paula Chow X
Anne Baker X
Marvin Winfield X
Cynthia Howell X
Peter Yadlowsky X
Caroline Fleet X
Bridget Baiss X
Becky Daiss X
55
For a Against a
Encourage No
Permanent Permanent
Individuals Reliability Comment
Moratorium/ Moratorium/
Analysis on CPCN
Repeal Repeal
Mary Finlay X
Judy Gayer X
Robert Reed X
Marilyn Clark X
Bonnie Farmer X
Dragutin Cvijanovic X
Marjorie Baker X
Deedee Tostanoski X
Morgan Blade X
John Kasper X
Joseph J. James X
Joy Loving X
Kathleen Owens X
Mary Thompson X
Paula Kallay X
Rhonda Johnson X
Laura Lavertu X
William Gathright X
56
APPENDIX 2: PUBLIC PRESENTATION ON
MODELING, SEPTEMBER 9, 2021
57
Getting to Carbon-Free Electric
Generation at Least Cost to Virginia
Ratepayers
Public Virtual Presentation
September 9, 2021
From Virginia Energy, DEQ and Research Partners
CLEAN ENERGY VIRGINIA
Decarbonization Modeling Report
Virginia Clean Economy Act (2020)
► Directs the Secretaries of Natural Resources and Commerce &
Trade, in consultation with the State Corporation Commission
(SCC) and the EJ Council, to report to the General Assembly (GA)
by January 1, 2022, any recommendations on how to achieve 100
percent carbon-free electric energy generation by 2045 at least
cost for ratepayers.
► Such report shall include a recommendation on whether the GA
should permanently ban the construction of new fossil fuel-based
electric generation facilities.
CLEAN ENERGY VIRGINIA
VCEA Language (2020)
6. That in developing a plan to reduce carbon dioxide emissions from covered units
described in § 10.1-1308 of the Code of Virginia, as amended by this act, the
Secretary of Natural Resources and the Secretary of Commerce and Trade, in
consultation with the State Corporation Commission and the Council on
Environmental Justice and appropriate stakeholders, shall report to the General
Assembly by January 1, 2022, any recommendations on how to achieve 100 percent
carbon-free electric energy generation by 2045 at least cost for ratepayers.
Such report shall include a recommendation on whether the General Assembly
should permanently repeal the ability to obtain a certificate of public convenience
and necessity for any electric generating unit that emits carbon as a by-product of
combusting fuel to generate electricity.
Until the General Assembly receives such report, the State Corporation Commission
shall not issue a certificate of public convenience and necessity for any investor-
owned utility to own, operate, or construct any electric generating unit that emits
carbon as a by-product of combusting fuel to generate electricity.
CLEAN ENERGY VIRGINIA
Process and Project Partners
► DMME is taking lead on the project by coordinating research, conducting
key consultations, engaging the public, drafting recommendations and
working with DEQ and the Secretary offices to finalize the submission to
the General Assembly.
► Project partners include DEQ, Resources for the Future, UVA Weldon
Cooper Center for Public Service and Georgetown Climate Center.
► Additional public comment will be solicited in the coming weeks (e.g.
webinar and supporting communication channels).
► Final report due to GA by January 1, 2022.
CLEAN ENERGY VIRGINIA
2022: Community Burden Analysis
► Virginia Clean Economy Act (2020)
► Beginning next year (September 1, 2022) and every three years
thereafter, DMME, in consultation with the EJ Council and appropriate
stakeholders, shall determine whether implementation of the VCEA
imposes a disproportionate burden on historically economically
disadvantaged communities.
► Final report to the GA due by January 1, 2023, and every three years
thereafter.
CLEAN ENERGY VIRGINIA
Today: Input on Modeling, Report
► Modeling team will present results
► Questions, comments or recommendations on the modeling or the
final report may be submitting in writing, using “Q&A” feature
► Moderated discussion will follow presentation
► Public comment may be submitted by email to Virginia Energy no
later than Saturday, October 9th
► modeling@dmme.Virginia.gov
► https://dmme.virginia.gov/decarbonization.shtml
CLEAN ENERGY VIRGINIA
Questions? Contact
Carrie Hearne
Associate Director, Energy Equity
Virginia Energy (DMME)
Carrie.Hearne@DMME.Virginia.gov
CLEAN ENERGY VIRGINIA
Modeling for Legislatively Mandated Report on
Virginia’s Clean Energy Goals
Dallas Burtraw, Maya Domeshek, Karen Palmer, Nick Roy / RFF
Franz Litz, James Bradbury / Georgetown Climate Center
Bill Shobe, Anthony Artuso, Art Small / University of Virginia
Public Webinar Presentation
S e pte m b e r 9 , 2 0 2 1
Presentation Roadmap
• Introduction
• Context for this work under Virginia’s Clean Economy Act (VCEA)
• The reference case outcome under the VCEA
• Initial sensitivity case with higher electricity demand
• Process for completing the report
• Discussion
9
Mandate for the Report
The VCEA commissions a report to the General Assembly due by
January 1, 2022, that in consultation with the Council on Environmental
Justice, State Corporation Commission and other appropriate
stakeholders includes “recommendations on how to achieve 100
percent carbon free electric energy generation by 2045 at least cost for
ratepayers.”
10
Details of the Virginia Clean Economy Act :
• Greatly expands construction of solar, onshore wind, offshore wind, and energy
storage with 35% of these facilities owned by entities other than Dominion
• Establishes a schedule for retiring all carbon-emitting generating units by 2045
• Establishes a standard for 100% renewables by 2045 (Dominion) or 2050 (APCo)
• Establishes an energy efficiency resource standard (affecting all customers)
growing to 5%/year (Dominion) or 2% (APCo) by 2025, to be subsequently revised
11
Modeling to Support the Report
We have two principal questions:
• What electricity sector emissions pathway will the existing
requirements of the VCEA achieve?
• What additional measures, if any, are necessary to achieve the
VCEA emissions goal (zero carbon emissions by 2045) at least cost
(including environmental cost)?
12
How the Model Works
Inputs:
• Existing Power Plants
• Planned New Investments
• Fuel Costs Outputs:
• Operations and Maintenance • Additional Power Plant
Costs Haiku Investments
• Capital Costs of New Plants Model • Generation by Power Plants
• Transmission Capability • Emissions
• Solar and Wind Availability • Resource Costs
• Annual Demand in 24 Time
Blocks
Modeling will cover the 2020-2040 time period
13
Modeling Approach
Reference case Sensitivity cases will Policy cases will analyze:
includes: analyze alternative:
• Potential additional
• VCEA requirements: • Natural gas prices measures, if any,
‒ Renewable • Levels of demand necessary to achieve
procurement • Federal policies the VCEA emissions goal
‒ Renewable
portfolio standard
‒ Energy efficiency
provisions
• 12-State RGGI (with
Pennsylvania)
14
Expected Electricity Generation in Virginia Through 2040
• New wind and solar constitute roughly half of all generation in Virginia by 2040
• Coal generation falls to very low levels in Virginia by 2030
15
Expected Power Plant Capacity in Virginia through 2040
• Renewable capacity grows with capacity targets
• Natural gas capacity survives through 2040, although with diminishing utilization
16
Expected CO2 Emissions in Virginia through 2040 with VCEA Goals
• VCEA policies extend and continue emissions trends since 2005
17
Expected CO2 Emissions in the Twelve State RGGI Region through 2040
• The VCEA helps the twelve-state RGGI region achieve its cap, if extended at the
current pace out from 2030.
18
High Demand Sensitivity: Expected Electricity Demand
in Virginia Through 2040
19
High Demand Sensitivity: Expected Electricity Generation in
Virginia Through 2040
20
High Demand Sensitivity: Expected Power Plant Capacity in
Virginia Through 2040
21
High Demand Sensitivity: Expected CO2 Emissions in Virginia through
2040 with VCEA Goals
22
Scenarios under Consideration
Sensitivity Scenario Specification Motivation
1. Higher demand The following, in combination: • Data center growth
• UVA high demand scenario • Electrification including EVs
• High national demand
2. Federal policies The following, in combination: • Report could be completed in time to
• PTC extension (CEAA) influence delegation from VA
• CEPP modeled as CES with banking • This CEPP scenario will achieve close to
to achieve 90% clean in 2040 80% clean in 2030
3. Demand with federal • Combine #1 & #2 • The EV portion of demand
policies growth would be accelerated by the
federal policies
4. Lower gas/ higher The following, in combination: • Higher renewable costs partially
renewable costs • Higher renewable capital costs represents difficulties with siting
• Lower natural gas prices
Policy Scenario Specification Motivation
A. Lower demand • Higher energy efficiency • EE goals greater than VCEA
• Each scenario analyzes, among other things, the relationship between importing clean energy compared to
23 the benefits of in-state investment and job creation.
Process and Next Steps
• By the end of September, the modeling team will complete sensitivity analysis and evaluation of
other scenarios to develop policy options for possible inclusion in the report.
• The DMME will provide opportunities for stakeholder input on the modeling results.
• University of Virginia’s Weldon Cooper Center for Public Service will lead in completing a draft
report to DMME.
• DMME will report results and recommendations to the General Assembly.
Additional Resources
• Decarbonizing Virginia's Economy: Pathways to 2050. (Jan. 2021) William Shobe, Arthur Small,
Anthony Artuso, Ben Haley and Gabe Kwok. Weldon Cooper Center for Public Service, Univ. of
Virginia. https://energytransition.coopercenter.org/reports/decarbonizing-virginia-pathways-
2050
24
DISCUSSION • The VCEA puts us almost on the
trajectory to zero by 2045
• Do we have the key assumptions
right that get us to these results?
• What potential additional policy
measures do we consider to close
the gap? When should additional
policies be considered?
• What additional matters should
the report drafters take into
account?
• Other thoughts or questions?
25
Important Reference Case Assumptions
Category Source/Approach for Reference Case Assumptions
Haiku Reporting Years Reporting years for the model: 2021, 2022, 2023, 2024, 2025, 2027, 2030, 2033, 2035, 2036, 2040
Demand - Load and Peak Growth Demand from Energy Information Administration 2021 Annual Energy Outlook
Gas Prices Annual Energy Outlook 2021 Reference Case gas prices
Build Costs - Renewables National Renewable Energy Laboratory 2020 mid-cost scenario for renewable overnight capital costs
Minimum Generation for some technologies such as Combined Heat and Power and Conventional Hydro
Minimum Generation
Minimum / maximum generation at VA coal plants from Dominion IRP
Maximum Generation VA pumped storage has seasonal maximum generation based on historical generation
Latest information from S&P Global Market Intelligence
Firm Capacity Changes
VA - Any additional capacity changes not accounted for in S&P Global
Nuclear Lifetime 80 years, or as planned by owners
State-level RPS for all states with statutory RPSs
VA: 100% by 2050. Remove nuclear and data centers from denominator.
Renewable Portfolio Standards (RPS)
Allow 25% of RECs to come from outside of VA after 2025.
Alternative Compliance Payment of $45
Solar & Onshore Wind Procurement From the VCEA: VA: 16,100 MW of combined solar and onshore wind 2036
Minimum based on input from the states and expectations based on state policies/announcements
NY: 9,000 MW Offshore Wind by 2035 / 3,000 MW Storage by 2030
MA: 3,200 MW Offshore Wind by 2030/ 1,000 MW Storage by 2025
CT: 2,000 MW Offshore Wind by 2030
Offshore Wind/Storage Requirements
RI: 400 MW Offshore Wind by 2030
MD: 1,568 MW Offshore Wind by 2030
NJ: 4,000 MW Offshore Wind by 2030 / 2,000 MW Storage by 2030
VA: 2,600 MW Offshore Wind by 2026, 5,200 by 2035 / 3,100 MW Storage by 2035
26 Program Assumptions
RGGI 11 Current RGGI States + PA. Emissions cap declines 3% per year of 2020 levels (NJ 2021, VA 2021, PA 2022)
EE Assumptions VCEA EERS flat after 2025 removed from AEO demand projections
Expected Electricity Generation in the Twelve State RGGI Region
Through 2040
• Coal generation falls to low levels throughout RGGI by 2030
27
Expected Power Plant Capacity in the Twelve State RGGI
Region Through 2040
28
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