Interstate Leakage of Carbon Pollution Abatement
An Analysis of State Considerations for Using Energy Efficiency to Comply
With the Proposed Clean Power Plan
Sam Hile
April 24, 2015
Contents
Acknowledgements....................................................................................................................................... 2
Executive Summary....................................................................................................................................... 3
Background ................................................................................................................................................... 4
Clean Power Plan ...................................................................................................................................... 4
Energy Efficiency and Interstate Effects ................................................................................................... 5
Quantifying Interstate Effects ................................................................................................................... 9
Analytical Methods ..................................................................................................................................... 13
Model ...................................................................................................................................................... 13
Methodology........................................................................................................................................... 13
Modeled Scenarios ................................................................................................................................. 18
Measuring Interstate Effects................................................................................................................... 19
Results ......................................................................................................................................................... 20
Interstate Effects Are Significant ............................................................................................................ 20
Program Costs Incentivize Mitigation ..................................................................................................... 21
Discussion.................................................................................................................................................... 24
Mitigation via Market Mechanisms ........................................................................................................ 24
Valuing EE................................................................................................................................................ 25
States vs. Power Pools ............................................................................................................................ 26
Costs ........................................................................................................................................................ 27
Rate vs Mass-Based State Plans .............................................................................................................. 27
Remaining Challenges ................................................................................................................................. 28
Uncertainty in MRV ................................................................................................................................. 28
Carbon Constraints ................................................................................................................................. 29
Modeling Actions of Neighboring States ................................................................................................ 29
Political barriers ...................................................................................................................................... 30
Appendix ..................................................................................................................................................... 31
Result tables ............................................................................................................................................ 31
Scope of Crediting for Interstate Effects................................................................................................. 32
Works Cited ................................................................................................................................................. 35
1.
Acknowledgements
The author would like to thank Jonas Monast and Jeremy Tarr at the Nicholas Institute for
Environmental Policy Solutions for providing guidance and sharing their expertise on the
Clean Power Plan. In addition, Etan Gumerman (also of NIEPS) was instrumental in
facilitating the energy modeling component of the project.
Many elements of this project were inspired by previous work done by the author at the
Center for Climate and Energy Solutions. Jeff Hopkins and Kyle Aarons provided valuable
feedback that led to the publication of a policy brief upon which this report builds.
2.
Executive Summary
The U.S. Environmental Protection Agency (EPA) proposed state-specific limits on carbon
pollution from existing sources in its recent “Clean Power Plan” proposal. These goals
reflect the potential of states to reduce carbon emissions by, among other things, reducing
demand for electricity generated from fossil fuels via end-use energy efficiency. The
interconnected nature of the electricity grid, however, frequently causes the fossil
generation reductions associated with these policies to transpire outside the state
responsible for their implementation. This report shall refer to these phenomena as
“interstate effects.”
States implementing the Clean Power Plan (CPP) may only be interested in pursuing energy
efficiency programs for compliance purposes if the associated carbon savings can be
counted regardless of where they physically occur. However, the proposed rule would
require states implementing such policies to ensure that the associated reductions are not
double-counted by the state whose generation and emissions levels are affected. Interstate
effects could thus influence the development of state compliance strategy.
This Master’s Project seeks to develop a deeper understanding of the mechanisms and
range of magnitude of interstate effects between power pools, under various levels of
energy efficiency. It will demonstrate that up to nearly 40% of total carbon pollution
abatement may occur outside the pool implementing the energy efficiency program, and
that in such instances, regional cooperation among states may be needed to ensure
sufficient signals for them to invest in energy efficiency as a compliance measure.
3.
Background
Clean Power Plan
Emissions from the power sector collectively account for almost 40 percent of the United
States’ domestic carbon dioxide (CO2) emissions.1 Section 111(b) of the Clean Air Act
requires EPA to regulate new sources of pollution, while Section 111(d) provides for the
regulation of existing sources. EPA proposed guidelines for the regulation of CO2 from new
power plants under Section 111(b) on September 20, 2013.2 This rulemaking action in turn
triggered the agency’s obligation to promulgate regulations for emissions from existing
sources under Section 111(d). EPA proposed these regulations in its Clean Power Plan
(CPP) on June 2, 2014.3 The proposed rule4 is projected to cut power sector emissions by
30 percent from 2005 levels.5 It covers most fossil fuel units over 25 megawatts (MW) in
capacity, and excludes most existing nuclear as well as hydroelectric capacity.6
A key feature of Section 111 is its mandate that EPA establish the form and level of its
standards based on its determination of the Best System of Emission Reduction (BSER).
Section 111(a) requires that the BSER be “adequately demonstrated” and that its cost,
energy requirements, and environmental impacts to be taken into account.7 EPA identified
four BSER components with respect to existing power plants. These were adapted into four
“building blocks” which were used to derive each state’s emission rate goal for the year
20308:
1. Improve heat rates of coal power plants
2. Increase utilization of natural gas combined cycle plants where possible
1
Center for Climate and Energy Solutions, “Q&A:EPA Regulation of Greenhouse Gas Emissions from Existing Power
Plants,” http://www.c2es.org/federal/executive/epa/q-a-regulation-greenhouse-gases-existing-power
2
US EPA, “Carbon Pollution Standards: What EPA is Doing,” http://www2.epa.gov/carbon-pollutionstandards/what-epa-doing
3
US EPA, “Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units,”
pg. 14
4
The rule is expected to be finalized by summer 2015
5
Ibid
6
US EPA, “Goal Computation Technical Support Document,” pg. 28
7
“42 U.S Code Section 7411 – Standards of performance for new stationary sources,”
https://www.law.cornell.edu/uscode/text/42/7411
8
US EPA, “Goal Computation Technical Support Document,” pg. 3
4.
3. Use more renewable energy (RE) and nuclear resources
4. Reduce demand for electricity through energy efficiency (EE).
Section 111 employs a cooperative federalist approach wherein the federal government
gives states considerable flexibility to meet their mandates. In fact, states are not actually
required to follow the BSER practices upon which their targets are set, so long as the total
emissions from their covered sources collectively meet the targets.9 Nor are states
constrained to reducing emissions solely from their own fleets; the proposed rule would in
certain instances grant states the flexibility to administratively apply CO2 reductions gained
through participation in credit markets and other schemes to individual power plants in
their fleets. This flexibility, in addition to its numerous economic advantages, would enable
states to collaborate on resolving unfavorable interstate dynamics that often result from
systematic changes to the regional dispatch of electricity.10
Energy Efficiency and Interstate Effects
The CPP contains ample guidance for states seeking to evaluate potential compliance
pathways. Throughout the proposal and accompanying documents, EPA also repeatedly
seeks comment from states and other stakeholders regarding their current or anticipated
concerns about crafting and implementing approvable state plans. One such concern
relates to the undesirable interstate trade effects that may result from states using energy
efficiency as a compliance tool. This section will explain in detail this issue, which is the
focus of this report.
In its simplest terms, energy efficiency (EE) is “using less energy to provide the same
service.”11 Although they are often lumped together in discussions, EE is distinct from
energy conservation, which means directly modifying consumption behavior in order to
use less energy.12 Examples of demand-side EE include purchasing low-power electronics
9
US EPA, “Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units,”
pg. 39
10
US EPA, “Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units: State Plan
Considerations,” pg. 87
11
Lawrence Berkeley National Laboratory, “What’s Energy Efficiency?” http://eetd.lbl.gov/ee/ee-1.html
12
Ibid
5.
and appliances, as well as upgrading home insulation and undertaking home energy audits.
In the commercial sector, building energy codes and technologies such as optimized
heating, ventilation and air conditioning (HVAC) systems tend to excel at reducing energy
use and lowering operating costs. Finally, opportunities for EE in the industrial sector
typically include waste heat recovery, motor upgrades and enhanced controls systems.13
In order to calculate the contribution of EE to each state’s final emission rate goal (Building
Block 4), EPA developed a “best practices” scenario for demand-side EE. This scenario
established current and optimal levels of EE performance, based on historical data for
incremental energy savings as a percentage of retail sales. Table 1 reports estimates for
the energy-saving potential of such programs at the state level. EPA’s analysis ultimately
led it to identify 1.5% incremental savings as a percentage of retail sales as the best
practices level of performance. 14
Table 1: Summary of state EE policy targets as a fraction of retail sales.
Demand-side EE is widely considered one of the most cost-effective ways of reducing
power sector emissions, and is not without its success stories. For example, EE has been
responsible for saving 35% to 70% of sector emissions in the ten states with statutory
requirements to reduce greenhouse gases including CO2.15 However, investment in EE has
been weaker in other states due to a combination of factors including imperfect
13
US EPA, “Technical Support for Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric
Generating Units: GHG Abatement Measures,” pg. 5-3
14
Ibid, pg. 5-32
15
Ibid, pg. 5-8
6.
information, imperfect supply markets, behavioral issues, transaction costs, and risk and
uncertainty.16
In addition to these market challenges, EPA has identified a technical challenge to using EE
for compliance with the CPP that is related to the underlying structure of the electricity
grid and may be summarized as follows17:
“The interconnected nature of the grid means that one state’s energy efficiency or
renewables (EE/RE) program may reduce output and emissions from electrical
generating units (EGUs) in neighboring states. When a state decreases its demand
for fossil electricity via an EE/RE policy, one or more fossil EGUs respond by
producing less so that system supply and demand remain precisely balanced. As a
simple example, consider two hypothetical neighboring states, State A and State B.
Suppose State A (the program state) decreases its annual demand, also called load,
by 1,000 megawatt-hours (MWh) due to an EE program. If it imports 10 percent of
its electricity consumption from State B (the generator state), State B may produce
100 MWh fewer than it would have otherwise over the course of the year. This
avoided generation in State B represents emission savings.
Equity principles might suggest State A should get full credit for these savings
because the savings derive from their efforts alone, and State B should get none of
the credit. That is, State B would get no credit for the reductions that took place
within its borders that were the result of EE policies put in place by State A. In the
proposed CPP, EPA affirmed this with regard to RE programs, proposing that
‘Consistent with existing state RPS policies, a state could take into account all of the
CO2 emission reductions from renewable energy programs and measures
implemented by the state, whether they occur in the state and/or in other
states.’1819 In contrast, EPA proposed for EE that ‘A state may take into account in its
plan only those CO2 emission reductions occurring in the state that result from
demand-side energy efficiency programs and measures implemented in the state
(emphasis added).’ 20
16
Ibid, pg. 5-5
Sam Hile, “Cross-State Load Reductions Under EPA’s Proposed Clean Power Plan,” November 2014,
http://www.c2es.org/publications/cross-state-electricity-load-reductions-under-epas-proposed-clean-power-plan
18
US EPA, “Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units: State Plan
Considerations,” pg. 87
19
According to EPA, CO2 reductions from RE programs are not subject to the same double-counting issue as those
from EE because of the rigorous tracking systems used with Renewable Energy Certificates. For the remainder of
this report, “interstate effects” refer to those from EE only.
20
Ibid
17
7.
The problem of interstate effects is two-fold: quantifying savings in a state that
transpire due to efforts taken in a different state, and ensuring that they are not
double-counted. States that import or export a significant portion of their electricity
are most affected and could create tension in their region by attempting to secure as
much credit for themselves as possible. Yet virtually all states will be affected to
some degree, and many will depend on EE/RE programs to achieve compliance.
Therefore, states have a vested interest in ensuring their abatement efforts are
recognized to the fullest extent possible.”
This issue has gained modest attention since the release of the proposed rule, and some
groups have offered ideas on how to help State A secure at least partial credit and prevent
State B from double-counting the savings. There are several proposals to establish an EE
credit market based on the same tracking systems used by Renewable Energy Certificate
markets,21 but while a few states currently operate markets for Energy Savings Certificates,
none of these markets currently allow for interstate trade.22 Other groups have proposed
joint accounting systems that might be attractive to states seeking to collaborate on
interstate effects.23 Another idea is for states to authorize EGU operators with “common
elements” to work together to mitigate interstate effects without formal state
involvement.24 The details of these methods, however, are beyond the scope of this project.
Instead, the project will broadly confirm that interstate effects are nontrivial and should be
planned for by states, particularly those that import significant quantities of carbonintensive electricity.
21
See for example APX Research, “Using Tracking Systems with the Implementation of Section 111(d) State Plans,”
http://www.narecs.com/wp-content/uploads/sites/2/2014/10/APXAnalytics_1_Section111d.pdf
22
World Resources Institute, “Energy Savings Certificates,”
http://www.wri.org/sites/default/files/pdf/bottom_line_energy_savings_certificates.pdf
23
See, for example, Daniel A. Lashof et al., “Potential Emission Leakage
Under the Clean Power Plan and a Proposed Solution,” NextGen Climate America,
http://www.scribd.com/doc/248965004/NextGen-Climate-America-Comment-Leakage-Potential-in-Clean-PowerPlan#scribd
24
Jonas Monast et al., “Enhancing Compliance Flexibility under the Clean Power Plan: A Common Elements
Approach to Capturing Low-Cost Emissions Reductions,” March 2015,
http://nicholasinstitute.duke.edu/sites/default/files/publications/ni_pb_15-01.pdf
8.
Quantifying Interstate Effects
Interstate effects are a routine phenomenon in power markets with large EE/RE programs.
However, it appears that states have not previously sought to quantify them. One possible
explanation is that precise estimates were impossible until the relatively recent advent of
sophisticated computer models. In addition, state policies like Renewable Portfolio
Standards (RPS) and Energy Efficiency Resource Standards (EERS) typically only require
utilities to generate a certain portion of their power from these resources.25 Although these
policies often help states meet other environmental regulations such as National Ambient
Air Quality Standards (NAAQS)26, they are themselves not contingent upon demonstration
of improved environmental outcomes. Similarly, while out-of-state resources (mainly
renewables) can often also be used to satisfy these state policies, there is still little
incentive for compliance entities to address out-of-state dynamics. According to EPA, “in
many cases the intent of the state policy is often to affect the characteristics of the regional
electric generation mix, rather than the state generation mix.”27
Quantifying interstate effects begins with quantifying savings from the underlying EE
program. Whether for the purpose of CPP compliance or routine program evaluation, there
are three fundamental steps to estimating the emissions savings from an EE program:
1. Estimate the quantity of energy saved (in MWh)
2. Identify the generator(s) at which these energy savings physically occured
3. Translate the energy savings at the affected generator(s) into emissions savings (in
tons/pounds) using generator-specific emission factors
The first step is challenging because it is requires a conception of how much energy would
have been consumed in a “business as usual” (BAU) scenario, relative to consumption in
the EE policy case. Since it is impossible to observe both outcomes in reality, the savings
can only be estimated via comparison to a carefully constructed counterfactual scenario.
25
Center for Climate and Energy Solutions, “Energy Efficiency Standards and Targets,” http://www.c2es.org/usstates-regions/policy-maps/energy-efficiency-standards
26
See for example US Environmental Protection Agency, “Roadmap for Incorporating Energy Efficiency/Renewable
Energy Policies and Programs into State and Tribal Implementation Plans,” July 2012,
http://epa.gov/airquality/eere/pdfs/EEREmanual.pdf
27
US EPA, “Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units: State Plan
Considerations,” pg. 86
9.
However, the advantage of using a deterministic energy model is that it explicitly allows
the user to observe both outcomes (see later sections).
The second step is equally challenging, due to the complex economics governing power
dispatch. Power generation resources are typically dispatched in order of increasing
marginal cost until total system capacity is enough to meet anticipated demand plus a small
margin for reliability purposes. This ordering minimizes system cost while ensuring
enough supply will be available to meet anticipated demand.28 When EE/RE programs
come online, they alter regional dispatch by displacing generation from the highestmarginal cost generation resource(s) that would have otherwise been dispatched.
The identity of the specific generator(s) on the margin at a given level of load and time can
only be pinpointed using power dispatch models. Yet the dispatch order of power
generation resources is consistent enough to allow for predicting the type of plant that will
be on the margin. Oftentimes, this is sufficient information for approximating the emissions
saved by displacing generation from this marginal unit.
Power generation resources may be broadly classified as baseload, intermediate, or peak,
depending on how frequently they are called on to meet system demand. Figure 1 shows
that baseload sources (e.g. coal or nuclear) operate nearly year-round, in contrast to nonbaseload EGUs that run less often because they tend to be more expensive. These EGUs are
therefore more likely to be displaced by EE programs in order to minimize system cost.
Intermediate load plants include natural gas turbines and renewables, while peak load
combustion turbines tend to be less efficient and more carbon-intensive.29
28
Seth Blumsack, “Economic Dispatch and Operations of Electric Utilities,” https://www.eeducation.psu.edu/eme801/node/532
29
US Energy Information Administration, “Electric generator dispatch depends on system demand and the relative
cost of operation,” August 17, 2012, http://www.eia.gov/todayinenergy/detail.cfm?id=7590
10.
Figure 1: idealized load duration curve composed of baseload, intermediate and peak load-serving generation capacity.
Source: Sam Hile, “Cross-State Electricity Load Reductions.”
This economic reasoning may be used to obtain a first-order approximation of the diffuse
emissions savings from EE, an approach best exemplified by AVERT (Avoided Emissions
Regeneration Tool). AVERT was originally created by EPA to assist states with developing
compliance plans for NAAQS. The region-based planning tool takes user-input EE/RE
profiles and estimates the magnitude and location of both energy and air pollution savings
across the area of interest.
11.
Figure 2: AVERT estimates the energy and emissions benefits of EE/RE programs across the grid regions shown. Source:
Climate Protection Partnerships Division, “aVoided Emissions and generation Tool”,
http://epa.gov/statelocalclimate/documents/pdf/AVERT%20User%20Manual_02-13-2014%20Final_508.pdf
Although the AVERT model was developed primarily to assess the air quality co-benefits of
reducing electricity consumption, EPA has indicated that it may also be useful for
compliance with the CPP. Indeed, the scope of AVERT is sufficiently broad and its statistical
algorithms powerful enough to capture most of the aggregate emissions benefits of EE/RE
programs across any of its pre-defined regions. The model is less useful for assessing
interstate effects, however, because it does not allow for directed load reductions within a
region. This means that the user cannot assign the location of a simulated EE program to a
specific state, and will thus be unable to assess the balance of interstate effects.
Furthermore, although AVERT allows for the manual addition/retirement of EGUs, it is not
designed to make projections for how this affects regional dispatch decisions more than
five years into the future due to uncertainty in the long-term behavior of fuel prices, control
technologies and other parameters. For these reasons, this analysis will quantify interstate
effects using more advanced energy modeling methods.
12.
Analytical Methods
Model
The primary tool of analysis in this report is AURORAxmp© (Aurora), a production-cost
model for the U.S. electricity sector developed by EPIS, Inc. The model solves for the
security-constrained economic dispatch at each hour. It also performs long-term
optimizations by introducing and/or retiring resources. Its input database includes all
parameters needed to estimate an EGU’s hourly power output, including capacities, heat
rates, and fuel costs. This database also contains BAU projections for key economic
parameters such as demand growth, as well as for most on-the-books energy and
environmental policies.30 This information is gathered from a combination of public and
private commercial sources and regularly updated by EPIS.
Methodology
The United States electricity system is divided into numerous regional markets or “power
pools.” This project seeks to identify the likely range of magnitudes of interstate effects
associated with EE at the power pool level. Because the CPP regulates emissions from
power plants on a state-by-state basis, one might wonder why this analysis chose to
examine leakage between power pools and not states. The primary reason is that the model
does not have the native capability to isolate a state from its power pool and specify a
demand growth rate independent of that of the larger pool.31 Despite this limitation, there
is still great value in conducting an analysis at the zonal level. After all, if leakage of carbon
abatement is problematic at the power pool level, it is likely that it will be at least as
important at the state level.
30
Any present-day EE programs included in the model’s BAU case are distinct from the EE introduced exogenously
into the model.
31
Instead, the state would have to be approximated by introducing a custom demand zone into the model whose
properties are sufficiently similar. This in turn would require a myriad of precise assumptions regarding the new
zone’s baseline demand growth, prices, transmission links, capacity, and other such parameters. Such an exercise
was beyond the scope of this project.
13.
Figure 3: Map of electric grid sub-regions in the Southeast. Source: FERC: Electric Power Markets, Southeast,
http://www.ferc.gov/market-oversight/mkt-electric/southeast.asp
This analysis is presented as a case study of the VACAR South power pool, which is
comprised of North and South Carolina (blue region in Figure 3). This selection was
motivated by several criteria. For one, the analysis began with the hypothesis that
interstate effects from EE are most pronounced in markets with a high volume of interstate
trade and a high degree of linkage with other markets. According to EPA, centralized
dispatch in contiguous electric grid regions, including the territories of Independent
System Operators (ISOs), renders attribution of interstate effects unnecessary because
power flows are effectively contained within the region.32 VACAR South was accordingly
selected because it does not participate in an ISO but regularly trades power with
neighboring pools in volumes large enough to produce measurable interstate effects. For
instance, North Carolina ranked 7th in net electricity imports in 2012 (
Table 2).
32
US EPA, “Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units: State Plan
Considerations,” pg. 87-88
14.
15.
State
California
Virginia
Ohio
Maryland
Tennessee
Massachusetts
North Carolina
Minnesota
Georgia
New York
2012 adjusted
generation
(thousand MWh)
184,535
65,427
120,002
34,970
71,887
33,480
107,919
48,274
113,121
125,572
2012
consumption
(thousand MWh)
259,538
107,795
152,457
61,814
96,381
55,313
128,085
67,989
130,979
143,163
Imports (MWh)
75,003
42,368
32,455
26,844
24,494
21,833
20,166
19,715
17,858
17,591
Table 2: top-ten state importers of electricity with generation and consumption data. Generation data adjusted
downward 7.51% to reflect average line losses. Source: EIA Electric Power Annual, http://www.eia.gov/electricity/annual;
EIA Electricity Consumption Estimates,
http://www.eia.gov/state/seds/data.cfm?incfile=/state/seds/sep_fuel/html/fuel_use_es.html&sid=ALdata
This pool was also selected because its simple composition may make it easier to draw
targeted policy conclusions from the modeling. For example, if modeling indicates that
VACAR South is particularly vulnerable to interstate effects from EE, this could give North
and South Carolina incentive to collaborate on their respective plans and attempt to
mitigate these effects. Yet if this same result were found for a pool that spans various
portions of different states (e.g. SERC Entergy), it would be much more difficult to
determine which states should collaborate to mitigate the effects.
Finally, the southeastern United States is currently ranked fairly low in terms of its
adoption of EE. In particular, North and South Carolina are ranked 24th and 42nd,
respectively, in terms of their financial incentives, disclosure practices, investment in
research and development and other areas.33 There is speculation that the primary barrier
to EE in this region is the perception that its benefits are outweighed by its costs.34 It is
hoped that this analysis will help planners in the Carolinas to conceptualize and ultimately
33
See for example American Council for an Energy-Efficient Economy, “The State Energy Efficiency Scorecard,”
http://aceee.org/state-policy/scorecard
34
American Council for an Energy-Efficient Economy, “Energy Efficiency Gains Traction in Lagging States,” May 16,
2012, http://aceee.org/press/2012/05/energy-efficiency-gains-traction-lag
16.
begin to address the issue of interstate effects that might otherwise have discouraged them
from incorporating EE into their energy planning portfolios.
In Aurora there are several potential mechanisms for incorporating energy efficiency. One
option is to exogenously introduce a demand side management (DSM) or conservation
resource into the model. Aurora treats these resources like any other in its database, but
grants them the unique ability to alter and/or shift demand on an hourly basis. However,
this project chose not to simulate EE in this fashion. This was primarily done because the
location of the resource and the extent of its ability to lower hourly demand are sensitive
parameters whose specification would require a granularity deeper than that of the
project.
Instead, this analysis simulated the introduction of EE programs at the power pool level by
exogenously reducing average annual demand growth in the pool according to the
following equation:
𝐷𝐸𝐸 = 𝐷𝐵𝐴𝑈 − 𝑌
where 𝐷𝐸𝐸 is the adjusted growth rate, 𝐷𝐵𝐴𝑈 is the default growth rate, and 𝑌 describes the
size of the EE program (all percentages). As an example, Figure 4 compares demand
growth in VACAR South between the BAU and 2% EE cases. In this example demand
growth is negative in the EE case because the savings from EE outweigh BAU demand
growth. Note that this growth rate percentage is technically distinct from the retail sales
percentage metric used by EPA to support its rulemaking. However, the two metrics will be
closely related so long as the average retail price of electricity stays relatively constant over
the period of analysis.35
35
If price remains constant, a 2% growth in regional demand is the same thing as a 2% growth in regional sales.
17.
Millions
VACAR South Demand, 2020-25
300
250
MWh
200
150
Base
100
2%
50
0
Figure 4: example comparison of demand in Base/BAU and EE cases.
It is important to note that this approach assumes nothing about the EE program in
question, other than that it displaces generation evenly across all hours of the year (in
contrast to conservation programs). The demand growth reductions used to represent EE
may be thought of as the result of a single program (e.g. utility-wide rebates for compact
fluorescent lightbulbs) or as the combined impact of several concurrent measures. While in
practice the scale of these impacts may differ depending on the specific location of program
implementation (e.g. western vs. eastern North Carolina), these differences are not readily
retrieved from the model and are thus ignored here.
It should also be noted that the model only simulated the regional effects of an EE program
based in VACAR South, and used default assumptions for neighboring pools. That is, it did
not investigate any combinations of pools simultaneously implementing beyond-BAU EE
programs, nor did it seek to represent any other energy or environmental policies not
contained in the reference model outlook. This is primarily due to the prohibitive difficulty
of separating out the impacts of concurrent EE programs. Moreover, this project is not
intended to identify or simulate a regional compliance approach to 111(d), since any
attempts to model such interactions would be entirely context-specific and lack external
18.
validity. The results contained in this report are instead intended to give planners in the
Southeast a rough estimate of the potential range of interstate effect magnitudes. As will be
discussed, the results indicate that state planners may wish to examine the implications of
interstate effects with more detailed, state-specific models in the future.
Modeled Scenarios
This project simulated the regional impacts of 1, 1.5, 2, 2.5 and 3% EE programs in VACAR
South. These choices were initially inspired by EPA’s average 1.5% market potential figure
and also driven by a research interest in probing the consequences of more ambitious state
EE portfolios. They were more formally validated ex-post using EPA’s data files that
accompany its GHG Abatement Measures Technical Support Document. Between 2020-25,
EPA expects North and South Carolina combined to save nearly 69 million MWh with EE
due to the CPP36, which is roughly consistent with this report’s estimated savings of 55 and
78 million MWh in the 1.5% and 2% EE cases, respectively (see
36
US EPA, “Data File: GHG Abatement – Scenario 1 (XLS),” http://www2.epa.gov/carbon-pollutionstandards/clean-power-plan-proposed-rule-technical-documents
19.
Appendix). Programs smaller than 1% were not simulated due to potential issues with
statistical significance, and cases larger than 3% were explored but ultimately not reported
because they seemed too ambitious to be policy relevant.
All EE programs were simulated to take effect on January 1, 2020 and remain in operation
throughout the duration of the study ending December 31, 2025. While the study period is,
strictly speaking, limited to 2020-2025, it was important to also model the years leading up
to the program in order to capture the full market response to the EE program. For
example, some types of units might be retired or constructed as an indirect result of lower
regional demand for electricity. An analysis that only examines the post-program effects
will not capture the full effect of the program.
While the default Aurora database contains all major regional power markets in the U.S.,
not all markets need be included in the simulation in order to capture most of the effects of
an EE program in a particular pool. Since the projected simulated an EE program based in
VACAR South, only key southeastern power pools were included in the sampling. These
are: FRCC, MISO Central, MISO North, PJM, SERC South, SERC TVA, SERC Virginia Power,
and SPP. The chief advantages of limiting the scope of analysis to these pools were ease of
interpreting results and greatly reduced computational expense.
Measuring Interstate Effects
This analysis primarily seeks to compare how in-pool and out-of-pool generation and
emissions change over time following the implementation of a systematic energy efficiency
program in a given power pool. There are two ways to calculate these changes, as shown
below with respect to emissions:
∆𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑛𝑒𝑡 = ∑(𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑖𝐵𝑎𝑠𝑒 − 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑖𝐸𝐸 )
𝑖
∆𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑡𝑜𝑡𝑎𝑙 = ∑𝑖(𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑖𝐵𝑎𝑠𝑒 − 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑖𝐸𝐸 ) 𝑠. 𝑡. 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑖𝐵𝑎𝑠𝑒 ≥ 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝑖𝐸𝐸
The key difference between these methodologies is that the first sum allows for post-EE
emissions to increase, whereas the second sum ignores such instances and only tallies
cases in which emissions were reduced relative to the base case. Preliminary analysis
20.
showed that in certain instances, generation and/or emissions in neighboring power pools
can slightly increase in response to another pool’s EE program. The reasons for this are not
readily apparent, but likely stem from localized price responses. This project will report
both result types for completeness.
It will also be shown that interstate effect estimates are even more sensitive to the choice
of pools included in the analysis. That is, the results vary depending on whether the
analysis looks at reductions across all pools versus the subset of pools that directly export
electricity to the program pool. These direct supplier pools were identified by constructing
a table from model outputs of average yearly net power flow by origin and destination
zones. Only pools with nonzero average net flow into VACAR South are included in this
subset. These pools are: PJM, SERC Southeast, SERC TVA, and SERC Virginia Power. The set
of results limited to these pools can be found in the Appendix.
Results
Interstate Effects Are Significant
The results indicate that anywhere from 26% to 37% of regional CO2 reductions resulting
from EE programs deployed in VACAR South may physically occur in other pools (Figure
5). Since the only parameter that varies between the BAU and EE cases is demand growth
in VACAR South, it follows that all emissions reductions from baseline must be somehow
attributable to the EE programs in VACAR South.
21.
VACAR % of total CO2 reductions, 2020-25
74%
72%
70%
68%
66%
All
64%
Direct
62%
60%
58%
1
1.5
2
2.5
3
% EE
Figure 5: program pool share of total emissions reductions by EE case.
Figure 5 also reveals that the program pool’s share of total emissions savings generally
decreased as the scale of the EE program increased. This outcome was expected since
larger EE programs reduce more demand and are thus more disruptive to the regional
dispatch. However, the incremental difference in share of reductions is relatively minor
compared to the incremental CO2 savings between policy cases (Figure 6). For example,
CO2 savings doubled from the 1% to 2% EE cases while interstate effects increased by less
than four percentage points.
22.
lbs CO2
Millions
Total CO2 reductions, 2020-25
70
60
50
40
30
All
20
Direct
10
0
1
1.5
2
2.5
3
% EE
Figure 6: Total CO2 reductions by EE case.
Finally, the two figures reveal that both total emissions reductions and VACAR’s share of
these reductions differ depending on whether the scope of analysis is limited to the subset
of pools that directly import to VACAR. More precisely, interstate effects appear smaller
when only considering direct imports (Figure 5), but the total emissions savings
potentially eligible for compliance are also diminished (Figure 6). States might prefer one
approach over the other depending on the perceived salience of interstate effects to
obtaining overall compliance. However, EPA should define one of these two approaches as
an approvable accounting practice in order to ensure consistency across state compliance
plan submissions.
Program Costs Incentivize Mitigation
Although states presumably seek to minimize interstate effects in order to maximize the
compliance value of their EE programs, the decision to collaborate with neighbors to
capture these effects is also sensitive to anticipated transactional and computational costs.
It is conceivable that the cost of mitigating these effects might outweigh the additional
compliance benefit of capturing these additional out-of-state tons.
23.
According to Figure 7, average wholesale electricity prices in VACAR37 consistently
declined relative to the base case due to VACAR South’s EE program. Prices at other hubs,
in contrast, stayed nearly constant. This suggests that one effect of EE is to make the
program pool’s electricity cheaper relative to neighboring pools. Indeed, there is some
evidence to suggest that EE programs can lower wholesale power prices: one study found
that Ohio’s EERS could save its customers almost $3.37 billion in reduced energy
expenditures by 2020, in part due to downward price pressure in the wholesale market.38
Change in average VACAR hub price, 2020-25
0.0%
-0.2%
1
1.5
2
2.5
3
-0.4%
-0.6%
-0.8%
-1.0%
-1.2%
-1.4%
-1.6%
-1.8%
EE Run Case (%)
Figure 7: Change in average price of electricity at the VACAR hub over the period 2020-25, relative to the base case.
The results indicate, however, that EE’s benefit of lower electricity rates for VACAR
customers would likely be outweighed by its implementation cost. From 2020-25, average
annual wholesale prices in VACAR declined from $64.19/MWh in the BAU case to
$63.57/MWh in the 2% EE case. This translates to a net total savings of over $67 million
37
Reported prices are for the entire VACAR region, which includes both VACAR South and VACAR Virginia Power.
Aurora does not break out prices into sub-regions, but it is safe to assign most of this decrease to VACAR South by
assuming that VACAR Virginia Power prices stayed nearly constant.
38
Max Neubauer et. al, “Ohio’s Energy Efficiency Resource Standard: IMpacxts on the Ohio Wholesale Electricity
Market and Benefits to the State,” http://www.ohiomfg.com/legacy/communities/energy/OMAACEEE_Study_Ohio_Energy_Efficiency_Standard.pdf, pg. 20
24.
Table 3). However, applying EPA’s assumed levelized cost of $0.0911/kWh for EE39 to the
approximately 108 million MWh saved everywhere from 2020-25 in the 2% EE case, the
estimated cost of this program would be nearly $10 billion. Of this total cost, approximately
$1.4 billion represents the cost of installing EE capacity that ultimately produced
generation and emission reductions elsewhere.
Total energy savings (GWh)
VACAR
Interstate
Total levelized cost of EE ($ M)
VACAR
Interstate
Interstate: program only
BAU VACAR price ($/MWh)
EE VACAR price ($/MWh)
Avoided costs from EE ($ M)
Total benefit of EE ($)
1
58,745
41,401
17,345
5,3512
3,772
1,580
791
64.2
63.9
14.8
(5,337)
EE Case (%)
1.5
2
2.5
73,858
108,397
123,096
55,018
78,377
91,436
18,840
30,020
31,659
6,728
9,875
11,214
5,012
7,140
8,330
1,716
2,735
2,884
859
1,369
1,444
64.2
64.2
64.2
63.7
63.6
63.3
33.1
67.1
108.7
(6,695)
(9,808)
(11,105)
3
142,027
106,238
35,788
12,939
9,678
3,260
1,632
64.2
63.1
147.9
(12,791)
Table 3: estimated costs and benefits of simulated EE.
This figure represents over 10% of VACAR’s total EE expenditures and is certainly not
insignificant. These results indicate that the utility to VACAR of mitigating interstate effects
likely exceeds anticipated associated transactional or administrative costs. A more formal
cost-benefit analysis, however, is needed to completely answer this question. Additional
research on EE markets in VACAR would also be beneficial. For one, the actual levelized
cost of EE in the VACAR region may vary from that assumed by EPA in its analysis (which is
invariant across states). In either case, the data suggest that interstate effects will likely be
nontrivial to state planning authorities based on cost alone, and that potential mechanisms
for their resolution merit further research.
39
US EPA, “Data File: GHG Abatement – Scenario 1 (XLS),” http://www2.epa.gov/carbon-pollutionstandards/clean-power-plan-proposed-rule-technical-documents
25.
Discussion
Mitigation via Market Mechanisms
This analysis suggests that states have reason to attempt to mitigate interstate effects.
According to the proposed rule, states have the option to file multistate compliance plans,
which would make accounting for interstate effects within their territory unnecessary.40 In
instances where states are either unwilling or unable to participate in multistate plans,
however, cooperative accounting mechanisms or credit trading markets may be attractive
as a means of effect mitigation. EPA has suggested that states could distribute “credits” and
“debits” for avoided emissions from EE among themselves according to an agreed-upon
formula. Similarly, they could establish a tradable regional EE credit market whose
currency would be based on either avoided megawatt-hours or avoided tons of CO2.41
Both mitigation options—joint accounting and credit market—can accurately redistribute
interstate reductions and prevent double-counting when coupled with appropriate
safeguards. Given the choice, it is unlikely that a program state would opt to participate in a
credit market since it would essentially have to buy-back the out-of-state avoided
emissions from its EE program. This would amount to overpaying for those EE reductions.
Similarly, generator states affected by EE programs in neighbor states would likely prefer
to earn revenue from these reductions rather than transfer them back to the program state
free of charge. The only reason a program state would exercise this option is if the
generator state refused to participate in a joint accounting system and other domestic
compliance options (e.g. coal plant retrofits or RE) for the program state were too costly.
Regardless of these concerns, it is unclear if a credit market would necessarily accurately
reflect the marginal emissions abatement value of EE. Simple economic theory suggests
that the market price of credits should vary to reflect the heterogeneity of carbon
intensities across the power fleet. That is, a credit from a coal plant would presumably cost
40
US EPA, “Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Generating Units,” pg.
495
41
US EPA, “Technical Support Document for Carbon Pollution Emission Guidelines for Existing Stationary Sources:
Electric Utility Generating Units: State Plan Considerations,” pg. 89
26.
more than a credit from a natural gas plant, since the marginal cost of abatement for the
coal plant is higher due to its relatively more limited and expensive abatement
opportunities. Yet whereas a credit for avoided emissions directly takes this into account, a
credit based on avoided generation only indirectly prices the underlying carbon
commodity and could easily lead to a mismatch between generation and emission savings.
For example, suppose that a state applies a purchased EE credit (in MWh) to the emission
rate of one of its EGUs and reports a reduction of CO2 reduction of 1,000 lbs. For the
environmental integrity of the market to be preserved, the purchased credit must also have
resulted in a 1,000 lb reduction at its original EGU. Yet the purchasing state could easily
apply the same credit to a dirtier EGU and calculate a savings of 1,500 lbs. If the actual
carbon savings was only 1,000 lbs, this would overstate the regional environmental benefit
of the EE credit by 50%. These considerations suggest that avoided tons are the most direct
and reliable currency that states could use to mitigate interstate effects via market
mechanisms.42
Valuing EE
This report argues that both the estimated magnitude and costs of interstate effects
warrant further analysis by states considering using EE to comply with the CPP. However,
the importance of interstate effects to state planners depends on how integral EE is to their
state’s compliance strategy. A state that expects the vast majority of its emission reductions
to come from implementing Building Blocks 1 and 2, for example, might not be concerned
about recovering interstate reductions from its comparatively modest EE program.
Alternatively, a state may be less concerned about interstate effects if the levelized cost of
EE becomes lower than expected, or if its neighbors make significant investments in clean
energy and thus lower the potential for interstate effects.
In regional credit markets, such considerations would likely overtly manifest themselves as
observable economic parameters. The value of an EE credit would be driven by regional
market conditions in addition to its underlying abatement cost. In a market composed of
42
The choice also depends on whether the state is rate- or mass-based (see Rate vs Mass-Based State Plans).
27.
predominantly coal- and gas-heavy states with limited renewable energy resource
potential, for example, EE credits would presumably be scarce and valuable. In contrast, a
market whose participants heavily invest in EE would likely see a surplus of available
credits and thus comparatively lower trading prices.
It should also be remembered that interstate effect credits are potentially valuable to
generator as well as program states. This could potentially represent a barrier to
collaboration. When interstate effects are large, there is arguably a disincentive for affected
generator states to cooperate, since cooperation in this case amounts to sacrificing
allowances that they might otherwise have secured. By choosing not to participate in a
collaborative effort, the generator state could theoretically take credit for emissions
reductions that were really the fruit of its neighbor’s efforts. The strength of this
disincentive will be entirely dependent on whether these extra tons of CO2 are integral to
the affected state’s overall compliance status.
States vs. Power Pools
It is challenging to extrapolate the results of this analysis from the power pool to state
level. On the one hand, VACAR South may simply be thought of as an electrical
amalgamation of North and South Carolina. Therefore, one conclusion might be that the
two states stand to be individually impacted by interstate effects and thus have a vested
interest in mitigation. However, the caveat to a pool- rather than state-level analysis is that
it brushes over the potentially substantial heterogeneity between states that makes
drawing conclusions at the state level problematic.
For example, a state-level analysis might find that North Carolina is twice as vulnerable to
interstate effects as South Carolina because it imports more carbon-intensive electricity
from other neighboring states. In this scenario, from the perspective of South Carolina it
may not make sense for the two states to group themselves together and in turn develop
credit markets with other pools. Similarly, this analysis presents estimates of interstate
effects spread across neighboring power pools, not states. Effect estimates in PJM, for
example, gloss over potential variation in estimates across each of its individual member
28.
states.43 In summary, while the results of this project suggest interstate effects can be
problematic at the power pool level, they should not necessarily be applied to the state
level without more research.
Costs
The initial hypothesis of this analysis was that interstate effects represent a potential
threat to investment in EE as a compliance strategy. A simple exploration of EE program
costs broadly confirms this notion. EE reductions are frequently hailed as “low-hanging
fruit,” and EE is indeed often the cheapest resource in competitive wholesale energy
and/or capacity markets. Its reductions are certainly not free, however. Despite its
generally cost-effective nature, the costs of implementing EE at a scale relevant to CPP
compliance strategy are nontrivial. Furthermore, even the small fraction of EE capacity that
ultimately results in out-of-state reductions carries substantial cost. Unless the average
levelized cost of EE steeply declines in the future, the economics of EE may very well
influence states to work to mitigate interstate effects.
As described earlier, EPA assumed a single levelized cost of EE for all states when it
formulated the cost projections that accompany the proposal. However, there could be
heterogeneity in actual EE program costs across states. One way states could address this
would be to establish a credit trading market for EE and equalize marginal abatement costs
across firms. This would both address interstate effects within the region and increase
economic efficiencies. Alternatively, a state could invest in EE programs physically sited in
one of its neighbors if these investments prove cheaper than available opportunities at
home.
Rate vs Mass-Based State Plans
The CPP proposes to give states the flexibility to translate their emission rate-based goals
to mass-based ones.44 This option may be preferable to states interested in building
43
Not to be confused with estimates of interstate effects from EE programs implemented within PJM, which are
presumably negligible.
44
US EPA, “Translation of the Clean Power Plan Emission Rate‐
Based CO2 Goals to Mass‐Based Equivalents,” pg. 2
29.
regional mass-based carbon trading regimes or using existing ones (e.g. RGGI) for
compliance purposes.45 The choice may also be driven by interest in mitigating interstate
effects.
For the sake of simplicity, states may opt to only collaborate with neighbors whose plans
are based on the same standard. In this scenario, a program state might be influenced to
conform to the same standard as its neighbors in order to participate in a joint accounting
system and mitigate interstate effects. EPA notes that in the case of a regional EE credit
market, a mix of mass and rate-based states would lead to double-counting absent a
cooperative joint accounting system.46 Such a system is certainly possible yet would likely
be exceedingly complex. In short, a state’s need to address anticipated interstate effects
could at least partially influence its ultimate selection of rate versus mass. If these effects
are as significant to VACAR as indicated in this analysis, this dynamic could play an
interesting role in planning decisions.
Remaining Challenges
Uncertainty in MRV
Perhaps the most formidable obstacle to EE adoption is estimating the anticipated energy
savings, also sometimes called Measurement, Reporting and Verification (MRV). Whereas
this project easily simulated EE by exogenously reducing projected demand growth in a
power pool, real-world EE programs are typically much more complex. For example, the
efficacy of demand-side EE in the residential sector is often subject to perverse and
sometimes unpredictable consumer behaviors. Households that install new air
conditioning units have incentive to operate them for more hours per day than before (the
so-called “rebound effect”). Utility customers might refuse to switch to more efficient light
bulbs simply due to personal preference. Such factors can make forming a reasonable
45
Jonathan Ramseur, “EPA’s Proposed Clean Power Pan: Conversion to Mass-Based Emission Targets,” March 17,
2015, http://www.fas.org/sgp/crs/misc/R43942.pdf pg. 2
46
US EPA, “Technical Support Document for Carbon Pollution Emission Guidelines for Existing Stationary Sources:
Electric Utility Generating Units: State Plan Considerations,” pg. 94
30.
projection of the anticipated energy savings from an EE program as difficult as
subsequently translating these energy savings into emissions savings.
Carbon Constraints
One caveat to this analysis is that the scenarios were run without any carbon constraints. A
more sophisticated exercise would impose constraints on both existing and new power
plants as per the proposed guidelines under sections 111(d) and 111(b), respectively. In
fact, this approach was explored using the latest model version’s (11.5) new ability to
impose emission rate constraints on all or selected resources, in response to the demand
for modeling services related to implementation of the CPP. However, it was ultimately
abandoned because preliminary results indicated that demand in other, non-program pools
was not perfectly exogenous. Nevertheless, further analysis is needed to examine how
interstate effects can vary in scope and/or magnitude in a carbon-constrained future.
Modeling Actions of Neighboring States
The purpose of this project is not to articulate a realistic compliance scenario for the
VACAR region, but to demonstrate that interstate effects could be worthy of more detailed
analysis by states. Such an analysis would have to incorporate assumptions regarding other
CPP compliance actions anticipated to be taken by both the program state and its
neighbors. These could include future EE programs developed in neighboring states.
While this analysis has only considered the interstate effects of one EE program at a time,
in the real world multiple states will be simultaneously planning and implementing their
own EE programs in a given grid region. Those states interested in addressing interstate
effects will have to estimate the tons of CO2 that could potentially be double-counted
among themselves. More EE from multiple states in a region should lower its emissions,
which in turn could lower the magnitude of interstate effects experienced by program
states in the region. How this would impact states’ incentives to recover these interstate
effects is unclear. If the states have similar generation mixes and demand curves, the
interstate portions of reductions from their EE programs could end up offsetting one
another and reducing the need for mitigation. In any case, such analysis would be much
more complicated than the one presented here.
31.
Energy models for state planning will also have to consider how the regional energy
landscape is likely to evolve due to other compliance actions. For example, if several
generator states re-dispatch increasing amounts of natural gas capacity, or deploy more RE
capacity, the regional impact of a program state’s planned EE program could be weaker
relative to BAU. EPA has indicated it that it may jointly assess the net balance of regional
interstate effects in instances where states lack the information and/or resources to
represent such complexities in energy models, but only “if regional performance falls short
of the aggregated identified performance levels for affected EGUs in individual state
plans.”47
Political barriers
It is worth noting that not all obstacles related to state plan implementation are purely
technical. While “collaboration” is a frequent refrain in EPA guidance documents, in certain
instances fraught political relations between states may reduce the likelihood of successful
cooperation (even if both parties stand to benefit). If such diplomatic obstacles prove
intractable, it is possible that authorized EGU operators in the two states could work
together to mitigate interstate effects without formal state involvement.48 Alternatively,
states with regulated power markets could instruct their respective utilities to work
together.
47
US EPA, “Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units: State Plan
Considerations,” pg. 89
48
Jonas Monast et al., “Enhancing Compliance Flexibility under the Clean Power Plan: A Common Elements
Approach to Capturing Low-Cost Emissions Reductions,” March 2015,
http://nicholasinstitute.duke.edu/sites/default/files/publications/ni_pb_15-01.pdf
32.
Appendix
Result tables
Interstate effects are driven by the link between changes in generation and corresponding
changes in emissions. Accordingly, Table 4 through Table 7 compare emissions and
generation patterns for each EE simulation relative to the base case.
CO2 Reductions (2020-25)
Reductions
Net reductions
Reductions in
VACAR %
VACAR
Run case
(thousand tons)
(thousand tons)
VACAR South
of total
% of net
1
10.2
7.9
7.2
71.0%
91.1%
1.5
16.1
15.5
10.8
66.8%
69.5%
2
22.1
21.2
14.0
63.2%
66.1%
2.5
26.0
25.8
16.9
65.1%
65.4%
3
31.5
31.5
20.2
63.9%
64.1%
Table 4
CO2 Reductions (2020-25) – Direct Importers ONLY
Reductions
Net reductions
Reductions in
VACAR %
VACAR
Run case
(thousand tons)
(thousand tons)
VACAR South
of total
% of net
1
10.0
9.5
7.2
71.0%
91.1%
1.5
15.2
15.2
10.8
66.8%
69.5%
2
20.4
20.4
14.0
63.2%
66.1%
2.5
23.6
23.6
16.9
65.1%
65.4%
3
28.3
28.3
20.2
63.9%
64.1%
Table 5
33.
Generation Reductions (2020-25)
Run case
Reductions
Net reductions
Reductions in
VACAR %
VACAR
(%)
(million MWh)
(million MWh)
VACAR South
of total
% of net
1
41.4
36.2
24.1
58.1%
66.4%
1.5
55.0
54.1
36.2
65.8%
66.9%
2
78.4
71.6
48.4
61.7%
67.5%
2.5
91.4
90.7
59.8
65.4%
65.9%
3
106.2
105.9
70.5
66.3%
66.5%
Table 6
Generation Reductions (2020-25) -- Direct Importers ONLY
Run case
Reductions
Net reductions
Reductions in
VACAR %
VACAR
(%)
(million MWh)
(million MWh)
VACAR South
of total
% of net
1
41.2
39.6
24.1
58.4%
60.8%
1.5
51.0
51.0
36.2
70.9%
70.9%
2
71.8
71.7
48.4
67.3%
67.5%
2.5
83.0
82.8
59.8
72.0%
72.2%
3
97.0
97.0
70.5
72.7%
72.7%
Table 7
Scope of Crediting for Interstate Effects
EPA has yet to precisely define the scope of interstate effects states may consider when
calculating out-of-state compliance credits. The primary questions that determine this
scope are:
1. Should states account for emission increases that indirectly result from their EE
programs?
2. Should states account for displaced emissions in areas from which they do not
directly import electricity?
The only model parameter that differs between the base and policy cases is annual demand
growth in VACAR South over the period 2020-25. Therefore, one might surmise that any
difference in emissions between the two cases, positive or negative, must be attributable to
34.
the EE program. While this assumption is correct,49 it may nonetheless be more
appropriate from a policy perspective to ignore emission increases since these are
secondary effects of displaced imports.
In its guidance on interstate effects, EPA has focused on preventing the double-counting of
emissions savings by the program pool and affected generator pool(s).50 It also has
highlighted possible methods for crudely calculating interstate effects that examine annual
imports and exports with respect to the program pool. These indicate that the agency is
primarily concerned with interstate effects on direct importers. This analysis agrees that,
due to simplicity and practicality, the emission reductions from this subset of direct
importers are a better approximation than the full sample of the potential pool of
allowances eligible for compliance credit.
Furthermore, across all model runs negative emissions impacts represented increases of
less than 0.5% relative to their respective baselines. This indicates that a modest EE
program such as the ones simulated is unlikely to cause emissions to significantly increase
elsewhere in the grid region. However this is also a matter of perspective: in the 1% EE
case, emissions in MISO Central increased by nearly 2,150,000 tons over the period 202025. While this represents only a 0.11% increase for MISO Central, it decreases the impact of
VACAR’s EE program by over 10%, relative to the tons saved when these instances of
emission increases are excluded. Thus the incentive for the program pool to ignore
negative emissions impacts of its program may be stronger than that of affected pools to
seek redress for these impacts. Figure 8 shows how, with the exception of the 1% run case,
the difference between total and net emissions savings is fairly small and in some cases
negligible.
49
Although carbon-constrained runs suggest this assumption does not hold across all power pools, perhaps
because demand growth is linked between certain areas and thus not perfectly exogenous.
50
See for example US EPA, “Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units:
State Plan Considerations,” pg. 33
35.
Difference Between Total and Net CO2
Reductions
30%
25%
20%
15%
10%
5%
0%
1
1.5
2
2.5
3
EE Run Case (%)
Figure 8: larger EE programs generally lead to a smaller difference between total and net emission savings across all
regions.
The second scoping question has a stronger bearing on the magnitude of interstate effects.
As described in earlier sections, an EE program in one power pool may shift the patterns of
imports and exports between other pools in ways that are difficult to trace. Indeed, this
may be near-impossible when concurrent EE efforts of neighboring pools are taken into
consideration. Yet according to Figure 5, the impacts of VACAR’s EE program are on average
roughly five percent greater when the analysis is limited to direct sources of electricity
imports. Since this differential is unlikely to make-or-break a state’s overall compliance
status – and because analyzing direct imports only is simple and consistent with previous
EPA guidance – this project recommends this approach.
36.
Works Cited
American Council for an Energy-Efficient Economy. (2012, May 16). Energy Efficiency Gains Traction in
Lagging States. Retrieved April 19, 2015, from http://aceee.org/press/2012/05/energy-e
American Council for an Energy-Efficient Economy. (2014). The State Energy Efficiency Scorecard.
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