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An Assessment of Organic Conservation
in Xcel Energy’s Northern States Power
Service Territory
PREPARED FOR
Xcel Energy
PREPARED BY
Ahmad Faruqui
Ryan Hledik
Wade Davis
April 1, 2014
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Table of Contents
Introduction .......................................................................................................................................... 1 The Survey of Expert Opinion ............................................................................................................. 2 The Residential Lighting Case Study ................................................................................................... 5 The Commercial Lighting Case Study ............................................................................................... 11 The Residential Displays Case Study ................................................................................................. 13 Conclusions and Policy Implications ................................................................................................. 15 Recommendations for Further Research ........................................................................................... 16 References ........................................................................................................................................... 18 Appendix A: The Email Survey of Expert Opinion .......................................................................... 21 Appendix B: Additional Study Detail ................................................................................................ 23 13
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Introduction
U.S. electricity sales growth has slowed down, even several years past the ending of the Great
Recession of 2008-09. A survey of two dozen utility load forecasters carried out by The Brattle
Group suggests that future utility sales growth will be less than one percent annually on average.1
Some utilities have observed a complete flattening of their sales growth. On a per-capita basis,
sales growth has been negative recently and could remain this way into the foreseeable future.2
Part of this reduction in sales growth can be attributed to utility demand-side management
(DSM) programs and state and federal codes and standards for electric efficiency.3
There is a prevalent belief among many electricity industry experts, however, that some
improvements in energy efficiency happen naturally and are not directly attributable to codes
and standards or DSM programs. These improvements are driven by factors such as the
“greening” of consumer attitudes toward energy, scientific discoveries in universities and labs,
competition among manufacturers to differentiate product offerings and add value by
incorporating new features in their products (i.e., technical innovation), and consumer response
to rising energy prices. In this report, we refer to these naturally occurring improvements in
energy efficiency as “organic conservation.”
If the impact of organic conservation on sales growth is significant and persists into the future,
there are important implications for state and federal energy policy. For example, it will be
necessary to account for the combined impact of organic conservation and increasingly stringent
codes and standards when establishing utility energy savings targets. But while there are
detailed studies on the impacts of codes and standards and utility DSM programs, organic
conservation remains a relatively under-researched area.
Xcel Energy retained The Brattle Group to conduct a first-of-its-kind assessment of the impacts
of organic conservation. We developed a series of case studies establishing an order-ofmagnitude estimate of the likely impact that organic conservation has had on energy
consumption for three specific end-uses.4 This report summarizes the key findings of that
research and briefly describes our methodological approach. It is written for an executive
audience. Additional details are provided in the appendix.
We begin by discussing the findings of a survey of expert opinion on organic conservation. We
then describe our estimates of organic conservation for three case studies in Xcel Energy’s
1
2
3
4
Ahmad Faruqui, “Surviving Sub One Percent Sales Growth,” Electricity Policy, June 2013.
Derived from U.S. EIA data in the 2013 Annual Energy Outlook and 2012 Annual Energy Review.
Utility DSM programs provide a financial incentive for customers to consume electricity more
efficiently. Codes and standards establish minimum efficiency levels for certain end uses.
We use the terms “energy consumption,” “sales,” and “usage” interchangeably throughout the report.
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Northern States Power (NSP) service territory: residential lighting, commercial lighting, and
residential displays. We conclude with a summary of key findings and recommendations for
further research.
The Survey of Expert Opinion
Given organic conservation’s evolving nature, we began the project by reaching out to over 100
energy efficiency experts and sought their opinion on the likely impact of organic conservation
on future electricity sales.5 We received over 60 responses from utilities, state regulators,
environmental advocacy groups, energy policy think tanks, appliance/equipment manufacturers,
government energy research labs, consultants, academics, and large national customers. The
responses provided us with a variety of perspectives and opinions.
We found that most respondents were familiar with the concept of organic conservation, but
knew it by a different name. The concept is alternatively known to others in the industry as:







Naturally occurring conservation
Natural energy efficiency
Naturally occurring market adoption of efficiency
Autonomous technological change
Non-programmatic energy efficiency
Normally occurring market adoption (NOMAD)
Autonomous Rate of Energy Efficiency Improvement (AEEI)
Most experts acknowledged that organic conservation exists but there was a divergence of views
on its magnitude and persistence. Some opined that it has already been quantified when utilities
reported their estimates of free-ridership in their DSM programs. Free-ridership measures that
fraction of customers who would have taken the actions that are incentivized through a DSM
program even if the incentives had not been offered.6
Others felt that the impact of organic conservation extends beyond DSM free-ridership, and to
confine its impact only to that of free-ridership would define it too narrowly. These respondents
stated that evolving customer attitudes toward energy consumption – and toward efficiency and
sustainability in particular – are driving an additional natural increase in the adoption of energy
efficient appliances. Some respondents believed that market competitiveness among equipment
and technology manufacturers is leading to the introduction of energy efficient features as a way
to differentiate product lines. Others believed that such efficiency improvements are occurring
generally as a byproduct of overall technological improvements. For example, a large
5
6
The email survey and a list of respondents are included in Appendix A.
In some states, utilities are required to estimate the impact of free-ridership and net it out of the
impacts attributed to DSM programs.
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semiconductor manufacturer pointed to Moore’s Law as evidence of improvements in computer
processing that are not driven by any programs or standards.7
Respondents from utilities tended to share the view that organic conservation is large in
magnitude. One respondent from a Midwestern utility felt that organic conservation has had a
larger impact in their service territory than either codes and standards or the utility’s DSM
programs. Some of those who felt that the impacts of organic conservation were very large
suggested that targets and mandates for utility DSM are no longer needed, because conservation
had now become a natural occurrence. In other words, they felt that the market would adopt
energy efficient appliances in the absence of intervention through new programs or standards,
suggesting that rebates for more efficient technologies were unnecessary subsidies.
A minority of respondents did not believe that organic conservation is significant in magnitude.
In these instances, the respondents felt that “naturally occurring” efficiency improvements can
ultimately be traced to either utility or governmental initiatives. For example, some indicated
that the cumulative impacts of utility DSM programs persist long after the programs have ended
since DSM programs transform the energy marketplace. While a utility may only be given credit
for the efficient appliance purchases that are formally made through its DSM program, the
customers purchasing the appliances may permanently change their preferences as a result and
continue to purchase the more efficient appliances long after the program has ended.
Respondents indicated that these impacts are often attributed to organic conservation, but should
instead be attributed to the utility DSM programs. This is commonly referred to as the “spillover
effect.”
Others felt that efficiency improvements are occurring outside of DSM programs and codes and
standards, but that these improvements are attributable to other types of “market intervention”
and that they would not have occurred on their own. For example, some felt that the
development of many efficient technologies should be attributed to federal funding for research
and development. Others felt that lobbying efforts by trade associations and “soft” programs like
Energy Star labels are driving efficiency improvements. In all of these cases, regardless of
whether or not the impacts are attributed to organic conservation or some form of market
intervention, they still generally fall under the rubric of initiatives whose impacts should be
accounted for when developing new utility energy efficiency policies.
Finally, a few skeptics of organic conservation believed that any naturally occurring efficiency
improvement that happens “coincidentally” in one technology is likely offset by a coincidental
reduction in efficiency in another technology (due to the addition of energy-intensive new
features). They felt that these naturally occurring impacts would occur in roughly equal
proportions in both directions, yielding a negligible impact in the end.
7
Moore’s Law, named after Gordon Moore, the co-founder of Intel, is the observation that computing
efficiency doubles approximately every two years. See www.mooreslaw.org for more information.
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Respondents all agreed that it will be very challenging to isolate and quantify the impact of
organic conservation. Very little literature exists on the topic, and we did not identify any
studies that comprehensively establish quantitative estimates of the impact of organic
conservation (akin to those that exist for utility DSM programs and governmental codes and
standards). This suggests the need for a new and original approach to the topic. Therefore, we
designed our approach to address the four key challenges identified through our survey:

Challenge #1: Utility sales forecasting models do not typically include end-use
granularity. While some utilities claim to implicitly account for organic conservation in
their sales forecasting processes, its impact is difficult to isolate. To address this
challenge, we have used a bottom-up case study approach to quantifying organic
conservation for specific end-uses, rather than relying on a top-down econometric
modeling approach.8

Challenge #2: It is difficult to account for the indirect impact of codes and standards and
DSM programs (e.g., the “spillover effect”) on efficiency improvements. For the purpose
of our analysis, we have defined organic conservation to include any efficiency
improvements that are not directly attributable to codes and standards or DSM. Any
indirect impacts, such as the “spillover effect” described earlier, are accounted for in our
estimate of organic conservation.

Challenge #3: It is difficult to account for substitution across technologies. Naturally
occurring energy savings can occur in the form of switching from one technology (e.g. a
desktop computer) to a different technology (e.g. an iPad). In our analysis, we consider
this a secondary effect and focus specifically on the primary effect, i.e., efficiency
improvements in individual technologies. Inclusion of this secondary effect would
possibly lead to larger estimates of organic conservation (although a scenario can also be
envisioned in which the opposite occurs and consumption increases).

Challenge #4: There is uncertainty in the future impact of any standard or DSM program.
There is undoubtedly uncertainty in any forecast of future technology adoption and
conservation-related behavior. In recognition of this uncertainty, and to better
understand the key drivers of our estimates of organic conservation, we have included
sensitivity cases in our analysis.
In summary, all experts were familiar with the concept of organic conservation, although
virtually everyone knew it by a different name. Most experts felt that it exists and many believe
its impacts are significant. A few argued that efficiency improvements are driven largely by
8
Such an approach, however, would be a valuable research activity and is included in our
recommendations for further analysis.
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market intervention (i.e., policy initiatives, DSM programs, lobbying, etc.). In all cases, it was
difficult to disentangle sentiments about organic conservation from the respondents’ own
professional agendas. However, all of the experts agreed that the impact of organic conservation
is difficult to isolate and quantify, that little research exists on the topic, and that it is necessary
to better understand its potential future impact – whether large or small - on electricity
consumption.
The Residential Lighting Case Study
We began our quantitative assessment of organic conservation by estimating its impact on
residential lighting. Our first step was to establish the efficiency level of the average household
light bulb in NSP’s service territory. This average bulb is a composite of incandescents, halogens,
compact fluorescents (CFLs), and light-emitting diodes (LEDs).9 Based on data provided by NSP
and other publicly available sources (e.g. data from the U.S. Energy Information Administration,
or EIA), we estimated that the average household light bulb consumes 34 kWh of electricity per
year. We then propound a frozen efficiency case in which this value continues into the
indefinite future. The frozen efficiency case assumes no change in light bulb efficiency or in
consumer behavior and forms an important analytical baseline against which the future impact
of DSM programs, codes and standards, or organic conservation can be envisioned. The frozen
efficiency case is illustrated by the horizontal line in Figure 1.
9
Slightly over half of the bulbs in the average home are incandescents, roughly a quarter are CFLs,
around 1 percent are LEDs, and the rest are other types of bulbs.
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Figure 1: Annual Energy Consumption per Average Bulb (Frozen Efficiency) 35
Frozen Efficiency
Annual kWh/Bulb
33
31
29
27
2015
2014
2013
2012
25
Future deviations from this frozen efficiency case will be the result of two factors. The first
factor is change in consumer behavior. Evolving customer attitudes and increasing energy
awareness could lead to reductions in lighting use. For example, customers may become more
likely to turn off lights in empty rooms as their energy awareness increases. The second factor is
technological change. Over time, customers will purchase more efficient light bulbs and the
overall existing bulb stock will shift toward these more efficient options. Commercially available
options which consumers can purchase today include halogens, which use 28 percent less energy
than incandescents (a 40 percent improvement in efficiency, as measured in lumens per watt),
and CFLs and LEDs, which use 75 percent to 80 percent less energy (a 300 to 400 percent
improvement in efficiency).
Codes and standards will be a key driver of the adoption of these more efficient bulbs.
Specifically, the Energy Independence and Security Act (EISA) of 2007 mandates that minimum
bulb energy consumption be reduced by 28% relative to that of an incandescent (beginning in
2012). This effectively establishes halogens as the least efficient residential lighting option in the
market. And beginning in 2020, the standard requires roughly 65% energy savings per bulb
relative to an incandescent. This will establish CFLs as the least efficient residential lighting
option among existing technologies.
EISA will gradually lead to the phasing out of incandescents in NSP’s service territory, except in
specialty applications. The switch to more efficient bulbs is expected to occur over a relatively
long time horizon, as incandescents that are currently in use will eventually burn out and be
replaced. As a starting point for quantifying the impact of EISA, we have adopted a relatively
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conservative methodology that was developed by NSP. This projected impact of EISA is
illustrated in Figure 2.10 It produces a two percent reduction in per-bulb energy consumption by
2015.
Figure 2: Annual Energy Consumption per Average Bulb (After Codes & Standards) 35
Codes and Standards
Annual kWh/Bulb
33
31
29
27
2015
2014
2013
2012
25
NSP’s approved DSM programs will lead to incremental lighting improvements above and
beyond those resulting from EISA. NSP’s residential lighting program has been approved
through 2015 and provides rebates that are between 30 percent and 40 percent of the
incremental cost for CFL and LED purchases. This will accelerate the purchase of light bulbs
that not only meet but also exceed the minimum efficiency requirements established in EISA.
Based on NSP’s projections, roughly 1.4 million CFLs are expected to be sold per year through
the program, to roughly 225,000 participants per year. Annual LED sales through the program
will average around 78,000 units per year, to roughly 75,000 participants per year. The result,
when combined with the impact of EISA, is an average reduction in per-bulb energy
consumption of about 11 percent by 2015. This is illustrated in Figure 3.
10
Under this methodology, since EISA only mandates a roughly 30% improvement in lighting
efficiency, only 30% of the 26% of residential lighting energy consumption that is currently from
CFLs is attributed to EISA. This impact is fully reached in 2020, with a linear ramp-up in prior years.
An alternative and more aggressive assumption about EISA-driven efficient lighting adoption is
included in our sensitivity analysis.
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Figure 3: Annual Energy Consumption per Average Bulb (After Utility DSM) 35
Codes and Standards
Annual kWh/Bulb
33
Utility DSM
31
29
27
2015
2014
2013
2012
25
A portion of NSP’s projected DSM program impacts includes free-riders. As discussed above,
free-ridership is considered a form of organic conservation, because it represents the adoption of
energy efficient light bulbs that would have happened even if the incentive payments had not
been offered. A 2012 consultant study for NSP found that 46% of its residential lighting DSM
impacts were attributable to free-ridership.11 This estimate was based on customer surveys,
corporate interviews, and an econometric model with sales tracking data. In the corporate
interviews, retailers were asked to estimate sales in the absence of the utility program. The
customer surveys focused on consumer purchasing habits. Figure 4 reflects the impact of freeridership on lighting efficiency.
11
The Cadmus Group. “Minnesota Home Lighting Program Evaluation.” Prepared for Xcel Energy.
November 12, 2012. P. 48.
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Figure 4: Annual Energy Consumption per Average Bulb (After Free‐Ridership) 35
Codes and Standards
Annual kWh/Bulb
33
Utility DSM
31
Freeridership (Organic
Conservation)
29
27
2015
2014
2013
2012
25
As we have defined it for this study, organic conservation includes all expected efficiency
improvements not directly driven by DSM programs or codes and standards. Therefore, it is
possible that there is additional organic conservation that is not accounted for in the freeridership measure. To capture this additional organic conservation, we established an allinclusive forecast of residential lighting efficiency improvements, with the incremental
difference between this forecast and the one in Figure 4 being implicitly attributable to organic
conservation. We relied on projections in the EIA’s 2013 Annual Energy Outlook (AEO) to
establish our all-inclusive lighting efficiency case.12
The AEO provides a reasonable all-inclusive forecast of lighting efficiency improvements,
because it explicitly accounts for the impact of codes and standards and – based on our review of
the EIA’s methodology – implicitly accounts for the impact of utility DSM programs.13 It also
accounts for organic conservation in several different ways:
12
U.S. EIA. “Annual Energy Outlook 2013.” April 2013.
13
The AEO forecast does not explicitly account for the impact of new utility DSM programs. However,
it is calibrated to historical trends in lighting technology adoption. To the extent that utility DSM
programs have helped to drive these trends, their impacts should be embedded in the forecast.
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
Technology efficiency improvements:

Technology cost reductions: Consultant forecasts are used to develop projections of
The efficiency of new technology options is
projected based primarily on interviews with manufacturers. This accounts for marketdriven changes to product features.
technology cost reductions over time. As the relative cost of efficient technologies drops,
projected customer purchases increase.

Changing electricity prices: The EIA’s electricity price projections affect the payback
period for new technologies; as electricity prices rise, so does the financial attractiveness
of more efficient equipment.

Customer choice: The EIA’s technology choice module accounts for observed customer
preferences for efficient equipment based on historical data.

Consumer behavior: The EIA’s demand module can account for changes in customer
behavior such as reducing the number of hours per year that a given piece of equipment
(e.g., a light bulb) is used.
Organic conservation is calculated as the difference between the AEO forecast (scaled to the
characteristics of NSP’s service territory) and NSP’s projected impact of codes and standards and
DSM programs. This is illustrated in Figure 5. Including the impact of free-ridership, organic
conservation will account for roughly 65% of total household lighting efficiency improvement
between 2012 and 2015.14
14
Sensitivity cases are described in the appendix. Under different assumptions and methodologies, we
find that the organic conservation could represent 42% to 65% of total efficiency improvement.
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Figure 5: Annual Energy Consumption per Average Bulb (With Organic Conservation) y
35
Codes and Standards
Annual kWh/Bulb
33
Utility DSM
31
Freeridership (Organic
Conservation)
29
Additional Organic
Conservation
27
2015
2014
2013
2012
25
The Commercial Lighting Case Study
We used a very similar approach to estimate the impact of organic conservation in commercial
lighting as we had used for residential lighting. The impact of codes and standards was derived
from a projection by NSP and accounts for the impact of both EISA and the Energy Policy Act
(EPACT) of 2005.15 Utility DSM impacts were also provided by NSP based on its basic
commercial lighting program, and assume a very small number of participants (roughly 37 per
year) and rebates of roughly 10 percent to 30 percent of the incremental cost of various efficient
lighting packages. Free-ridership was assumed to account for 17 percent of the utility DSM
impacts, based on a meta-analysis conducted by Lawrence Berkeley National Laboratory.16 The
15
16
EISA includes a maximum allowable wattage for incandescent and halogen lamps (2012), and certain
metal halide lamp fixtures must meet minimum ballast efficiency requirement (2009). EPACT
includes standards for medium base CFLs (2006), for ballasts for Energy Saver fluorescent lamps (2009
and 2010), and bans mercury vapor lamp ballasts (2008).
Vine, Edward, Joseph Eto, Leslie Shown, Richard Sonnenblick, and Christopher Payne. Lawrence
Berkeley National Laboratory. “Evaluation of Commercial Lighting Programs: A DEEP Assessment.
Lawrence Berkeley National Laboratory.” 1994. P. 8.243. http://emp.lbl.gov/sites/all/files/lbnl36522.pdf
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incremental impact of additional organic conservation was derived using the commercial lighting
forecast in the 2013 AEO. The results are illustrated in Figure 6.17
Figure 6: Annual Commercial Lighting Energy Consumption per Square Foot Codes and Standards
Annual Lighting kWh/Square Foot
3.38
Utility DSM
Freeridership (Organic Conservation)
Additional Organic Conservation
3.28
2015
2014
2013
2012
3.18
Unlike the large gains seen in residential lighting, commercial lighting efficiency is only
expected to improve by 6.7% between 2012 and 2015. This is likely because the most stringent
codes and standards for commercial lighting were introduced back in the 2008-2009 timeframe
and have already had a significant impact. Presumably, large commercial customers have a more
sophisticated approach to energy management than individual households and therefore require
less market intervention to encourage adoption of efficient technologies. Organic conservation
represents 77 percent of the total efficiency improvement in this case.18 Its share of the total
efficiency gain is larger than that of residential lighting, but it is smaller in overall magnitude of
efficiency improvement.
17
The chart is only representative of customers participating in the basic commercial lighting program,
which is limited to medium and large businesses that apply for a single technology change. Utility
DSM impacts in this graph do not include any bundle approaches to energy efficiency or other
lighting-focused programs.
18
Sensitivity analysis is described in the appendix. Based on an alternative case, we found that organic
conservation could account for as much as 83 percent of the total efficiency improvement.
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The Residential Displays Case Study
Residential displays (i.e., personal computers, TVs) are an interesting case study, because there
are no codes and standards and few successful utility DSM programs to drive the market toward
more efficient products. Therefore, all observed efficiency gains can be attributed to organic
conservation. The residential displays case study is based entirely on historical and projected
stock efficiency as derived from region-specific data reported in the 2013 AEO.
Prior to 2008, the amount of electricity consumed by personal computers (PCs) was increasing on
a per-unit basis. This could possibly be attributed to monitors that were increasing in size and in
output, or to an increase in the amount of time that owners were spending using their
computers. However, as monitors and computer processors became more efficient over time,
overall energy consumption per computer decreased significantly. Between 2008 and 2012,
energy use per PC dropped by 8%. By 2020, the AEO projects that it will decrease by 24%
relative to the 2008 peak. This is all due to organic conservation. The trend in energy
consumption per PC is illustrated in Figure 7.
Figure 7: Annual Energy Consumption per Personal Computer Energy consumption per TV has exhibited a similar trend. Prior to 2009, TV size increased as
plasma TVs and LCDs replaced cathode ray tube TVs. The associated increase in average TV
screen size more than offset improvements in TV efficiency, and the result was an overall
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increase in TV energy consumption.19 However, a transition toward even more efficient TVs like
LED-backlit LCDs has helped reverse this trend beginning in 2009.20 By 2020, TVs are projected
to consume 16% less energy than at the peak in 2008. This is illustrated in Figure 8.
Figure 8: Annual Energy Consumption per TV New standards for residential displays may be on the horizon. A 2012 study by the American
Council for an Energy-Efficient Economy (ACEEE) posited that an efficiency standard for
personal computers could come into being as early as 2019.21 ACEEE’s analysis assessed the
impact of a standard that is consistent with the Energy Star version 5.0 requirements (computers
meeting this standard use 65 percent less energy than the least efficient new products). Such a
standard would produce national annual energy savings of 11.8 TWh by 2035 at a net present
value of $8.6 billion, according to ACEEE. Similarly, ACEEE envisioned a potential efficiency
standard for TVs. By 2016, ACEEE estimates that TVs could meet the Energy Star 5.3 efficiency
19
20
21
Herter, Karen. Smart Electronics Initiative. “Get Smart Guide: Energy Innovation for the Consumer
Electronic Industry.” 2012. P. 6. http://greentechleadership.org/documents/2013/07/get-smartguide.pdf
Park, Won Young, Amol Phadke, Nihar Shah, and Virginie Letschert. Lawrence Berkeley National
Laboratory. “TV Energy Consumption Trends Energy-Efficiency Improvement Options.” P. xv.
https://isswprod.lbl.gov/library/view-docs/public/output/rpt81012.PDF
Amanda Lowenberger, Joanna Mauer, et al. ASAP/ACEEE. “The Efficiency Boom: Cashing in on
Savings from Appliance Standards.” March 2012. P. 27.
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requirements, which would lead to 10 TWh of annual energy savings at a present value of $8.3
billion nationally.22
Conclusions and Policy Implications
The findings of our study support the existence of organic conservation. We have identified
three case studies in which energy efficiency improvements are expected to occur above and
beyond any impacts of DSM programs or codes and standards. This conclusion is further
supported by our survey of expert opinion. Most industry experts, based on firsthand experience
and general intuition, agree that some improvements in energy efficiency occur naturally.
The magnitude of the impact of organic conservation varies widely across our three case studies.
It depends not only on the characteristics of the technology or appliance that is being evaluated,
but also on timing in that technology’s development cycle. As observed historically in the case
of residential displays, there are points where technology can naturally become less energy
efficient due to customer preferences for other energy intensive features (e.g., larger TV screen
sizes). Organic conservation may be cyclical in this sense for some technologies. But as
technologies mature, there appears to be a trend toward improving efficiency.
There is debate about what causes organic conservation. Some attribute it to evolving customer
attitudes. Others feel it is driven naturally by the demands of the market. Others argue that it is
the byproduct of policy initiatives that are not strictly considered DSM programs or codes and
standards, but are still forms of “market intervention” nonetheless. However, from an energy
efficiency policy perspective, the exact cause of organic conservation may not matter. The
simple conclusion that efficiency gains are happening outside of both utility DSM programs and
codes and standards have significant implications for energy efficiency policies.
Consider energy savings targets - also known as energy efficiency resource standards - which
exist for utilities in many states, including Minnesota. These targets are based on an assumption
that, through DSM programs, utilities can achieve incremental sales reductions relative to a
baseline forecast of electricity sales. If that baseline does not fully account for the impact of
organic conservation (or, for that matter, codes and standards), the utilities may have to pursue
unexpectedly expensive DSM programs in order to achieve the stated targets. Whether these
more expensive DSM programs are cost-effective will depend on the specific system conditions
of the utility.
Decoupling is another energy efficiency related policy mechanism for which organic
conservation has implications. Decoupling mechanisms can be structured many different ways.
In most cases, utilities are made “whole” for sales reductions due to efficiency improvements. If
22
Ibid, p. 31
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the estimate of these sales reductions does not include the impact of organic conservation, the
utilities could under-recover their costs.
Utility DSM programs should also be designed with organic conservation impacts in mind.
Certain end-uses are naturally experiencing significant improvements in efficiency. It will be
important to account for this effect when assessing the cost-effectiveness of the programs. Some
utilities already do this by accounting for free-ridership when establishing the impacts that are
attributable to the DSM program. Similar considerations exist for codes and standards. The costs
associated with establishing a new standard should be weighed against the rate at which the
intended efficiency improvement is likely to happen naturally in the absence of the standard.
Recommendations for Further Research
The organic conservation impact projections presented in this study are order-of-magnitude
estimates. They illustrate the general degree of efficiency improvement that is happening
outside of DSM programs and codes and standards. As the first study of its kind, our findings
could be strengthened significantly through further research in a number of key areas. We have
identified six research activities that would be particularly valuable in further extending the
industry’s understanding of organic conservation:
1. Estimate organic conservation using a Delphi approach. As a follow-up to the survey of
expert opinion, manufacturers could be interviewed to assess the degree to which
appliances are being manufactured and sold above and beyond required efficiency levels.
The manufacturers and other experts would be asked to quantify the magnitude of
organic conservation’s likely impacts, and the collection of estimates would be used to
derive a meaningful conclusion about the likely magnitude of impacts.
2. Back out the impact of organic conservation from utility sales forecasts using a regressionbased approach. It might be possible to establish a sales forecasting model, which, based
on historical data, controls for the effects of the electricity price, weather, the economy,
DSM programs, codes and standards, and other important factors. If the model is
designed well, the remaining energy savings trend observed in the model’s forecast can be
attributed to organic conservation. Alternatively, rather than building a model from
scratch with publicly available data, this activity could also be implemented using an
existing utility sales forecasting model, controlling for any of the above described factors
that are not already accounted for, and adding a time trend to the model. In either case,
this would be a nice complement to the bottom-up case study approach, because it would
provide an estimate of organic conservation at the class or system level.
3. Expand the sensitivity analysis. More robust sensitivity analysis could be conducted as an
enhancement of the case studies. It would be possible to establish a plausible distribution
values for each uncertain variable in the analysis, and then run Monte Carlo simulations
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to create a measure of the overall uncertainty in the results. This would also help to
identify the key drivers of the findings.
4. Develop additional case studies. It would be valuable to include additional appliance and
end-use case studies, and develop an estimate of their impacts using a methodology
similar to that described above. Industrial motors are one example of a potentially
interesting new case study.
5. Incorporate historical assessments into the case studies. It may be possible to expand the
case studies in our assessment to include a historical timeframe. This would require
additional data gathering and may or may not be feasible given available data.
6. Conduct a pre-DSM era assessment of efficiency improvement. Prior to the origin of
DSM programs and efficiency codes and standards in the 1970’s, all improvements in percapita energy efficiency could be considered organic conservation (or vice versa). It
should be possible to quantify this trend using historical energy data.
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References
Primary References
The Cadmus Group. “Minnesota Home Lighting Program Evaluation.” November 12, 2012.
Fraunhofer Center for Sustainable Energy Systems. “Energy Consumption Of Consumer
Electronics In U.S. Homes In 2010.” December 2011.
The Home Depot. “Fluorescent Bulbs.” http://www.homedepot.com/b/Electrical-Light-BulbsFluorescent-Bulbs/%20/b/Electrical-Light-Bulbs-Fluorescent-Bulbs/N-5yc1vZbm3z
The Home Depot. “Halide: Top Sellers.” http://www.homedepot.com/b/N-5yc1v/Ntk-All/Ntthalide?Ns=P_Topseller_Sort%7C1
The Home Depot. “Halogen Light Bulbs.” http://www.homedepot.com/b/Electrical-Light-BulbsHalogen-Light-Bulbs/N-5yc1vZbmg5
KEMA. “Xcel Energy Minnesota DSM Market Potential Assessment.” April 20, 2012.
Lowenberger, Amanda, Joanna Mauer, et al. ASAP/ACEEE. “The Efficiency Boom: Cashing in on
Savings from Appliance Standards.” March 2012.
Mauer, Joanna et al. ACEEE. “Better Appliances: An Analysis of Performance, Features, and
Price as Efficiency has Improved.” May 2013.
Park, Won Young, Amol Phadke, Nihar Shah, and Virginie Letschert. Lawrence Berkeley
National Laboratory. “TV Energy Consumption Trends Energy-Efficiency Improvement
Options.” P. xv. https://isswprod.lbl.gov/library/view-docs/public/output/rpt81012.PDF
U.S. DOE. “2010 U.S. Lighting Market Characterization.” January 2012.
http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/2010-lmc-final-jan-2012.pdf
U.S. EIA. “Annual Energy Outlook 2013.” April 2013.
http://www.eia.gov/forecasts/aeo/pdf/0383%282013%29.pdf
U.S. EIA. “NEMS Commercial Database: AEO 2013 Reference Case.” Filename:
DB_Commercial_ref2013d102312a.xlsm.
U.S. EIA. “NEMS Commercial Database: AEO 2013 High Technology Case.” Filename:
DB_Commercial_hightechd120712a.xlsm.
U.S. EIA. “NEMS Residential Database: AEO 2013 Reference Case.” Filename: resDB aeo2013.xls.
Vine, Edward, Joseph Eto, Leslie Shown, Richard Sonnenblick, and Christopher Payne.
Lawrence Berkeley National Laboratory. “Evaluation of Commercial Lighting Programs: A DEEP
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Assessment.
Lawrence
Berkeley
National
http://emp.lbl.gov/sites/all/files/lbnl-36522.pdf
Laboratory.”
1994.
P.
8.243.
Xcel Energy/NSP-MN. “Anticipated Monthly Impacts: Residential Lighting Codes and Standards
Impacts on Electricity Sales.” Filename: res+lighting+adjustment_v2.xls.
Xcel Energy/NSP-MN. “Anticipated Monthly Impacts: Commercial Lighting Codes and Standards
Impacts on Electricity Sales.” Filename: biz+lighting+adjustment.xls.
Xcel Energy/NSP-MN. “Technical Assumptions for the 2010/2012 Demand-Side Management
Triennial Plan: Residential.” Filename: MN Home Lighting.xls.
Xcel Energy/NSP-MN. “Technical Assumptions for the 2010/2012 Demand-Side Management
Triennial Plan: Commercial.” Filename: MN Lighting Efficiency.xls.
Xcel Energy/NSP-MN and Wise Research Associates. “2012 Residential Energy Use Survey:
Minnesota Service Area.” June 2012.
Xcel Energy/NSP-MN and Wise Research Associates. “2010 Residential Energy Use Survey:
Minnesota Service Area.” June 2010.
Xcel Energy/NSP-MN and Wise Research Associates. “2008 Residential Energy Use Survey:
Minnesota Service Area.” December 2008.
Other Supporting Material
EPRI. “Assessment of Achievable Potential from Energy Efficiency and Demand Response
Programs in the US.” January 2009. http://www.isa.org/FileStore/Intech/WhitePaper/EPRI.pdf
Fox, Eric. Itron. “Using Load Research Data to Develop Long-Term Peak Demand Forecasts.”
2010 AEIC Load Research Conference. August 15, 2010.
http://www.aeic.org/load_research/docs/LRToDevelopLongTermPeakDemandForecasts.pdf
Goldman Sachs. “Clean Currents: Seeing the (LED) light.” November 24, 2013.
Herter, Karen. Smart Electronics Initiative. “Get Smart Guide: Energy Innovation for the
Consumer Electronic Industry.” 2012.
Laitner, John. “Linking Energy Efficiency to Economic Productivity: Recommendations for
Improving the Robustness of the U.S. Economy.” ACEEE, July 2013.
McKinsey & Company. “Sizing the Potential of Behavioral Energy-Efficiency Initiatives in the
US Residential Market." May 2013.
Meyers, Stephen, Alison Williams, and Peter Chan. “Energy and Economic Impacts of U.S.
Federal Energy and Water Conservation Standards Adopted From 1987 Through 2010.”
Lawrence Berkeley National Laboratory, December 2011.
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Newell, Richard, Adam Jaffe, and Robert Stavins. “The Induced Innovation Hypothesis and
Energy-Saving Technological Change." The Quarterly Journal of Economics, 114:3 (August
1999), pp. 941–975.
Nordhaus, William. “Do Real Output and Real -Wage Measures Capture Reality? This History of
Lighting Suggests Not.” Cowles Foundation Research in Economics at Yale University, 1998.
Power. “Unlocking the Potential of Behavioral Energy Efficiency." Arlington, Virginia. 2013.
Rohmund, Ingrid et al. “Factors Effecting Electricity Consumption in the United States (20102035).” Institute of Electric Efficiency, March 2013.
Smith, Sarah. SNL. “Gas furnace efficiency rule struggles to balance technological extremes.”
December 9, 2013. http://www.snl.com/InteractiveX/article.aspx?ID=26203895&KPLT=4
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Appendix A: The Email Survey of Expert Opinion
Each respondent to our survey of expert opinion was sent an email similar to the following:
I am reaching out to see if you are familiar with the concept of organic conservation and if you
have had success in quantifying it, either historically or in a forecast.
By organic conservation, I mean the amount of naturally occurring improvement in energy
efficiency that occurs independently of governmental codes and standards and utility DSM
programs. It may arise due to changes in energy prices. But by and large it is an autonomous
process associated with scientific discovery, technological innovation and commercialization. It
may be driven by the desire of manufacturers to compete with each other or it may be driven by
the emergence of green attitudes and preferences among consumers. Some might even argue
that it is driven by prior codes and standards and utility DSM programs which have transformed
the energy market by changing not only the buying habits of consumers but also the business
practices architects and engineers, equipment manufacturers, dealers and installers that lie
upstream of the consumer.
Commonly cited examples of organic conservation include the energy efficiency improvements
we are seeing in laptop computers and LED TV’s. Some might even consider the emergence of
LED light bulbs as organic conservation.
I would be interested in any studies or presentations you have on the subject. This will help
guide my research on a new project.
68 respondents spanned 45 different organizations:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
American Council for an Energy-Efficient Economy (ACEEE)
American Electric Power (AEP)
Association of Home Appliance Manufacturers
Ameren Corporation
Appliance Standards Awareness Project (ASAP)
American Society of Heating, Refrigerating and Air Conditioning Engineers (ASHRAE)
BC Hydro
Baltimore Gas and Electric (BGE)
California Energy Commission (CEC)
Cave Creek Institute
Commonwealth Edison (ComEd)
Consolidated Edison Company of New York (ConEd)
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13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
Economic and Human Dimensions Research Associates
Environmental Defense Fund (EDF)
Institute for Electric Efficiency (IEE)
Eastern Interconnection States’ Planning Council (EISPC)
Emerson Network Power
Electric Reliability Council of Texas (ERCOT)
Florida Power and Light (FPL)
Georgia Tech
Hydro One
Hydro Quebec
Intel
Lawrence Berkeley National Laboratory
National Electric Manufacturer’s Association (NEMA)
Northeast Utilities
National Resources Defense Council (NRDC)
Northwest Power & Conservation Council
Ontario Power Authority
PacifiCorp
Pacific Gas & Electric (PGE)
PNM Resources
Regulatory Assistance Project (RAP)
Southern California Edison (SCE)
San Diego Gas & Electric
Sacramento Municipal Utility District
Texas PUC
Tennessee Valley Authority
U.S. Department of Energy (DOE) / U.S. Energy Information Administration (EIA)
U.S. Environmental Protection Agency (EPA)
University of Vermont
Vectren Corporation
Vermont Electric Power Company (VELCO)
Walmart
Westar Energy
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Appendix B: Additional Study Detail
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An Assessment of Organic
Conservation in Northern States
Power’s Service Territory
Final
PRESENTED TO
Xcel Energy
PRESENTED BY
Ahmad Faruqui
Ryan Hledik
Wade Davis
February 3, 2014
Copyright © 2013 The Brattle Group, Inc.
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Today’s discussion is organized into six topics
1. Introduction
2. The survey of expert opinion
3. Residential lighting case study
4. Commercial lighting case study
5. Residential displays case study
6. Conclusions and next steps
Appendices
Bibliography
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Introduction
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Our purpose
  NSP believes that the potential impact of its future demand‐
side management (DSM) programs is being stymied by increasingly stringent codes and standards and the emergence of “organic conservation”
  The purpose of our study is to quantify the impact of OC on energy sales/usage   We use a case study approach that focuses on residential lighting, commercial lighting, and residential displays
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What is organic conservation?
  Organic conservation encompasses all improvements in end‐use energy efficiency that are not directly attributable to codes and standards or utility DSM programs
  Organic conservation is driven by
▀
▀
▀
▀
The “greening” of consumer attitudes toward energy efficiency which are motivating both behavioral changes and equipment changes
Scientific discoveries in the universities and labs
Competition among manufacturers to differentiate product offerings and add value through new features, i.e., technical innovation
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Given the complexity of the inquiry, we use a
three-pronged approach: an expert survey, a
literature review, and quantitative modeling
  Survey of expert opinion
▀ Received responses from over 50 industry experts
▀ Responders included utilities, policymakers, researchers, and trade associations
▀ Provided a rich perspective on the role that OC plays in the overall conservation and energy efficiency landscape
  Literature review
▀ The survey identified a number of key sources
▀ This was supplemented with our own research
▀ Very little quantitative research exists on OC   Quantitative modeling
▀ We combined NSP data and projections with data and projections from other publicly available sources to develop estimates of the relative impacts of OC, codes and standards and DSM
▀ We also conducted sensitivity analysis around key assumptions
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The Survey of
Expert Opinion
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Our analysis was informed by an email survey of
experts and a comprehensive literature review
  We reached out to over 100 energy efficiency experts by email to get their perspective on the impact of organic conservation
  Over 50 responses were received:
▀ Utilities
▀ State regulators
▀ Environmental advocacy groups
▀ Energy policy think tanks
▀ Appliance/equipment manufacturers
▀ Government energy research labs
▀ Consultants
▀ Academics
▀ Large national customers
  Surveys were supplemented with follow‐up conversations when further clarification was needed, or where a data source was particularly rich
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The term “organic conservation” was unfamiliar
to the survey respondents…
  … but the underlying concept resonated with several experts   Organic conservation is alternatively known to others as
▀
▀
▀
▀
▀
▀
▀
Naturally occurring conservation
Natural energy efficiency
Naturally occurring market adoption of efficiency
Autonomous technological change
Non‐programmatic energy efficiency
Normally occurring market adoption (NOMAD)
Autonomous Rate of Energy Efficiency Improvement (AEEI)
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Some experts state that organic conservation
exists and is large in magnitude
  Most respondents believe OC exists and point to free‐ridership in utility DSM programs as an example
  Several respondents also more broadly recognize the impact of evolving customer attitudes and market competitiveness among manufacturers as additional drivers of OC
  One Midwestern utility argues that OC has a larger impact than codes and standards or utility DSM
  A large semiconductor manufacturer points to Moore’s Law* as evidence of improvements in computer processing that are not driven by any programs or standards
  To some, significant OC suggests that targets and mandates for DSM are no longer needed, because it will happen naturally anyway
* Moore’s Law is the observation that computing efficiency doubles approximately every two years.
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Others deny the presence of significant organic
conservation
  Arguments include…
▀ Cumulative impacts from utility DSM programs persist long after the programs have ended; these impacts often are attributed to organic conservation, but should instead be attributed to the DSM programs
▀ Most customers (roughly 90%) are not interested in paying a premium for improved efficiency due to long payback periods; therefore, efficiency improvements are not market‐driven but are caused by codes and standards
▀ Efficiency improvements may be occurring outside of DSM programs and codes and standards, but these improvements are attributable to other types of “market intervention” and would not occur naturally otherwise; for example, federal R&D funding and "soft" initiatives like lobbying efforts and Energy Star labels (note: these fall under our definition of OC for the purposes of this study)
▀ For any naturally occurring efficiency improvement that happens “coincidentally” in one technology, there is likely to be an offsetting coincidental reduction in efficiency in another technology; it goes both ways
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There are many challenges to isolating and quantifying
the impact of organic conservation
Challenge #1: Utility sales forecasting models do not typically include end‐
use granularity. Therefore, while some utilities claim to implicitly account for OC in their sales forecasting, its impact is difficult to isolate. To address this, we have used a bottom‐up case study approach to quantifying OC for specific end‐uses
Challenge #2: It is difficult to account for the indirect impact of codes and standards and DSM. For the purpose of our analysis, we have defined OC to include any efficiency improvements that are not directly attributable to codes and standards or DSM
Challenge #3: It is difficult to account for substitution across technologies. Naturally occurring energy savings can occur in the form of switching from one technology (e.g. a desktop computer) to a different technology (e.g. an iPad). In our analysis, we consider this a secondary effect and focus specifically on efficiency improvements in individual technologies
Challenge #4: There is uncertainty in the future impact of any standard or DSM program. To address this, we have included sensitivity cases in our analysis
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The bottom line…
 All respondents were familiar with the concept of organic conservation, but virtually no one knew it by that term
 Most experts feel that it exists and most believe its impacts are significant
 A few argue that all efficiency improvements are driven by market intervention
 In all cases, it is difficult to disentangle sentiments about organic conservation from the respondent’s own agenda
 All agree that the impact of organic conservation is difficult to isolate and quantify, and little research exists on the topic – we are breaking new ground with this study
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The Residential
Lighting Case Study
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What is the annual energy consumption of the
average household light bulb in Minnesota?
Share of Bulb Types per Home
Comments
▀
LED, 1%
Other, 10%
▀
Halogen, 9%
▀
Incandescent, 54%
CFL, 26%
▀
▀
We begin by establishing the efficiency level of the average light bulb owned by residential customers in NSP’s service territory
It is largely a composite of incandescents, halogens, CFLs, and LEDs
Total NSP Minnesota residential sales for 2012 (as reported by EIA) are multiplied by 17.4% (residential lighting share of total) and divided by total number of customers and the average number of bulbs per household (published estimates range from 40 to 55)
The result is 34 kWh per bulb per year
This estimate aligns well with alternative data sources (EIA) and methodologies (e.g., a bottom‐up estimate)
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We use a "frozen efficiency” case as the
baseline from which to measure OC impacts
Comments
Annual Energy Consumption per Avg. Bulb
▀
35
Frozen Efficiency
31
29
▀
27
2015
2014
2013
25
2012
Annual kWh/Bulb
33
The frozen efficiency case is the business‐
as‐usual baseline assuming no change in light bulb efficiency or in customer behavior
It does not account for the future impact of DSM programs, codes and standards, or organic conservation
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Future reductions in average bulb energy use
will be influenced by two basic factors
  Changes in consumer behavior: Evolving customer attitudes could lead to changes in lighting use (i.e. turning off lights in empty rooms)
  Changes in the bulb mix: Over time, the stock of bulbs will shift toward more efficient options. The primary options available to consumers are summarized below (we focus on “general service” A19 bulbs)
Wattage (for equivalent lumens)
Incandescent
Halogen
CFL
LED
60
43
15
12
Implied efficiency Energy savings relative improvement relative to incandescent
to incandescent
(kWh)
(lumens/watt)
28%
75%
80%
40%
300%
400%
Notes:
60 watts used as average incandescent wattage; other bulbs are equivalent
Efficiency (lumens/watt) is very similar between CFLs and LEDs
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Codes and standards will play an important role in reducing
future residential lighting energy consumption
  The Energy Independence and Security Act (EISA) of 2007 mandates that minimum bulb energy consumption be reduced by 28% relative to an incandescent (beginning in 2012)
  This effectively establishes halogens as the least efficient residential lighting option on the market
  Beginning in 2020, the standard requires 45 lumens/watt as the minimum lighting efficiency (roughly 65% energy savings per bulb relative to incandescent)
  This establishes CFLs as the least efficient residential lighting option among existing technologies that will be sold in 2020
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Our base case codes and standards impact is
based on NSP’s estimate
Annual Energy Consumption per Avg. Bulb
Assumptions in NSP Estimate
▀
35
Codes and Standards
▀
31
▀
29
27
▀
2015
2014
2013
25
2012
Annual kWh/Bulb
33
Note: NSP’s codes and standards impact projection is confidential and for internal use only
▀
26% of residential lighting energy consumption will be from CFLs in 2020
The adoption of CFLs is driven by codes and standards and NSP’s DSM programs
Since EISA only mandates a ~30% improvement in lighting efficiency, only 30% of the 26% CFL residential lighting energy consumption is attributable to the codes and standards
This impact is fully attributed to EISA in 2020, with linear ramp‐up in prior years
This is very conservative; sensitivity cases are discussed later in this presentation
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NSP’s approved DSM programs will also lead to
lighting efficiency improvements
  Our understanding of NSP’s residential lighting program is that it will provide rebates for both CFL and LED purchases
▀
▀
CFL rebate per unit = Roughly 40% of incremental cost
LED rebate per unit = 30% to 40% of incremental cost
  The program has been approved through 2015
  Impacts of the program were provided by NSP and we understand them to be incremental to the impact of codes and standards
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NSP’s residential lighting program is expected to produce
larger impacts than EISA
Annual Energy Consumption per Avg. Bulb
Assumptions in NSP Estimate
▀
35
Codes and Standards
Utility DSM
31
▀
29
27
2015
2014
2013
25
2012
Annual kWh/Bulb
33
Roughly 1.4 million CFLs are expected to be sold per year through the program, to roughly 225,000 participants per year
Annual LED sales will average around 78,000 units per year, to roughly 75,000 participants per year
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Free-ridership represents a portion of NSP’s projected DSM
impacts and is a form of organic conservation
Annual Energy Consumption per Avg. Bulb
Comments
▀
35
Codes and Standards
Utility DSM
31
Freeridership (Organic
Conservation)
▀
29
▀
27
2015
2014
2013
25
2012
Annual kWh/Bulb
33
A 2012 consultant study for NSP found that 46% of residential lighting DSM impacts are attributable to free‐ridership
These are customers who would have bought more efficient light bulbs even in the absence of the rebate
The 46% estimate is subject to uncertainty and is the average of several different estimates of free‐ridership that were developed by the consultant
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The final piece of the “efficiency wedge” is
organic conservation
  Organic conservation includes all additional expected efficiency improvements that are not attributable to codes and standards or DSM programs
  Therefore, to quantify organic conservation, we must establish an all‐
inclusive forecast of residential lighting efficiency improvements
  We rely on projections in EIA’s 2013 Annual Energy Outlook (AEO) to establish our lighting efficiency case
▀ The AEO explicitly accounts for the impact of codes and standards and implicitly accounts for the impact of utility DSM programs
▀ It also accounts for organic conservation through projections of technology cost reductions and changes in customer preferences and behavior
▀ Energy consumption is reported by sector and end‐use for each census division; NSP is represented by the West North Central division
▀ See appendix for further details about the AEO forecast methodology
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With these assumptions, organic conservation plays a
large role in residential lighting efficiency improvement
Annual Energy Consumption per Avg. Bulb
y
Comments
▀
35
Codes and Standards
Utility DSM
31
Freeridership (Organic
Conservation)
29
▀
Additional Organic
Conservation
27
2015
2014
2013
25
2012
Annual kWh/Bulb
33
OC is the difference between the AEO forecast and NSP’s projected impact of codes and standards and DSM programs
Including the impact of free‐ridership, organic conservation will account for roughly 65% of the total efficiency improvement between 2012 and 2015
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We tested the sensitivity of the results to different
assumptions and methodologies
  Sensitivity #1: Alternative EISA impact projection
▀ Based on information about NSP’s existing light bulb stock, roughly 70% of all residential lighting energy consumption is from incandescents
▀ EISA will eventually phase out all incandescents and they will be replaced – at a minimum – by bulbs that use 28% less energy
▀ In our sensitivity case, we assume that by 2020 all incandescents are replaced by bulbs that use 28% less energy (the transition is assumed to happen in a linear fashion)
▀ We recommend considering this approach as an alternative to the one currently being used by NSP
  Sensitivity #2: Alternative methodology
▀ Using EIA data, we established pre‐EISA efficiency improvements as the OC trend and considered incremental improvements to be the impact of codes and standards and new utility DSM programs
▀ See appendix for further detail
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The sensitivity cases demonstrate that there is
considerable uncertainty in the projections…
  … however, in all cases, organic conservation plays a significant role, representing between 42% and 65% of total efficiency improvement
Share of Efficiency Improvement by Scenario (2012‐2015)
100%
10%
Share of Total Efficiency Improvement
90%
80%
28%
33%
25%
70%
60%
50%
25%
25%
21%
Utility DSM
Free‐ridership (OC)
40%
21%
21%
30%
20%
Codes and Standards
Additional OC
44%
25%
10%
21%
0%
Base Case
Alternative C&S
Assumption
Alternative
Methodology
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The Commercial
Lighting Case Study
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The commercial lighting case study was
developed using a similar approach
  Codes and standards
▀
▀
▀
EISA 2007: Maximum allowable wattage for incandescent and halogen lamps (2012), and certain metal halide lamp fixtures must meet minimum ballast efficiency requirement (2009)
Energy Policy Act (EPACT) of 2005: Standards for medium base CFLs (2006), for ballasts for Energy Saver fluorescent lamps (2009 and 2010), and bans mercury vapor lamp ballasts (2008)
Impacts provided by Xcel and approximately based on DOE projection
  Utility DSM
▀
▀
Impact projections were provided by NSP
Assumes a small number of participants (~54/year) and rebates of roughly 10% to 30% of the incremental cost of various efficient lighting packages
  Free‐ridership
▀
▀
Represents 17% of utility DSM impacts
Based on meta‐analysis by Lawrence Berkeley National Lab
  Additional organic conservation
▀
Projected total improvement in commercial lighting efficiency based on forecast in the AEO
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Codes and standards and OC dominate projected
improvements in commercial lighting efficiency
Annual Lighting Energy Consumption per Square Ft.
Comments
▀
Codes and Standards
Utility DSM
Freeridership (Organic Conservation)
▀
Additional Organic Conservation
3.28
▀
▀
2015
2014
2013
3.18
2012
Annual Lighting kWh/Square Foot
3.38
Unlike the large gains seen in residential lighting, commercial lighting efficiency is only expected to improve by 6.7% between 2012 and 2015
This is likely because the most stringent codes and standards for commercial lighting were introduced back in the 2008‐09 timeframe
Organic conservation represents 77% of the total efficiency improvement
Presumably, large commercial customers have a more sophisticated approach to energy management than individual households and therefore require less market intervention to encourage adoption of efficient technologies
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We analyzed a sensitivity case in the commercial
lighting case study
Annual Lighting Energy Consumption per Square Ft.
▀
100%
Share of Total Efficiency Improvement
90%
Comments
15.6%
21.2%
80%
1.2%
0.2%
1.6%
0.3%
▀
70%
60%
Codes and Standards
Utility DSM
50%
40%
▀
76.9%
83.0%
Free‐ridership (OC)
Additional OC
30%
20%
▀
10%
0%
Base Case
Alternative Methodology
▀
Data is not available to conduct the same sensitivities that were analyzed in the residential lighting case
Instead, we test a scenario in which the total efficiency improvement is greater than the AEO Reference Case projection
We base our estimate on the “High Demand Technology” case of the AEO, in which customers are assumed to be more accepting of longer payback periods when making purchase decisions about efficient technologies
The case also assumes accelerated market availability and cost reductions for some efficient technologies
See appendix for details
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The Residential
Displays Case Study
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There are no codes and standards or utility DSM
programs for residential displays
  Residential displays include personal computers and TVs
  Residential displays are an interesting case study because there are no codes and standards or utility DSM programs to drive the market toward more efficient products
  In this case, all efficiency gains can be attributed to organic conservation
  Our analysis is based entirely on both historical and projected stock efficiency as reported in the AEO
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Personal computers have seen both an organic increase
and decrease in efficiency over the past decade
Annual Energy Consumption per Personal Computer
Comments
▀
▀
▀
▀
▀
▀
Prior to 2008, the energy efficiency of personal computers was decreasing
This could be attributable to an increase in the amount of time people use the computers, or to monitors that were increasing in size and in output
Over time, as monitor energy usage decreased and computer processing became more efficient, overall efficiency improved significantly
Between 2008 and 2012, energy use per PC dropped by 8%
By 2020, the AEO projects that it will decrease by 24% relative to the 2008 peak
This is all organic conservation
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A similar pattern is observed in TVs
Annual Energy Consumption per TV
Comments
▀
▀
As cathode ray tube TVs were replaced with plasma TVs and LCDs, and as TV size increased, there was an organic decrease in efficiency
A transition toward LEDs has reversed this trend, and by 2020 TVs are projected to consume 16% less energy than at the peak in 2008
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New standards for residential displays may be
on the horizon
  Personal computers
▀
▀
ACEEE expects a DOE standard to become effective in 2019
This rule is expected to be based on the Energy Star 5.0 standard; equipment meeting this standard uses 65% less energy than the least efficient new products
  Televisions
▀
▀
Industry groups expect a new DOE standard for TVs effective 2016
This could be based on the Energy Star version 5.3 standard, which would result in 30% energy savings
  The impact of these potential standards is not reflected in the AEO projections
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Conclusions and
Recommendations
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Conclusions
  There is a consensus among experts that organic conservation exists but disagreement on whether it is truly independent of past utility and governmental programs
  Irrespective of its specific cause, the simple conclusion that not all efficiency improvements are currently being accounted for in energy efficiency policy development has significant implications
  Future energy savings targets, decoupling mechanisms, and utility DSM program planning initiatives should all take into account the likely impact of organic conservation
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Suggestions for further research
  Estimate OC using a Delphi approach. Interview manufacturers to assess the degree to which appliances are being manufactured and sold above and beyond required efficiency levels. Ask experts to quantify the magnitude of OC impacts.
  Back out OC from sales forecasts using regression‐based approach.
Establish a sales forecasting model that controls for price, weather, economy, utility DSM, codes & standards, etc.; the remaining energy savings can be attributed to OC at the class or system level. Could be done using existing sales forecasting model by adding a time trend.
  Expand the sensitivity analysis. Establish distribution of range of possible values for each uncertain variable and run Monte Carlo Simulation for more robust sensitivity analysis. Benchmark against 2012 DSM potential estimates.
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Suggestions for further research (concluded)
  Develop additional case studies. Look at other appliances and end‐uses where organic conservation might be observed and quantified; industrial motors is one such example
  Incorporate historical assessment into the case studies. This would require additional data gathering and may or may not be feasible given the available data
  Conduct pre‐DSM era assessment. Look at trends in per‐capita energy consumption prior to the “DSM era” (1970s) and consider all efficiency improvements to be organic conservation
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Appendix A:
Additional Documentation on
the EIA’s Annual Energy
Outlook Forecasting
Methodology
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Energy consumption is projected in the AEO by
sector and end-use for each Census Division
NSP is in the West North Central Division
Source: EIA, 2013 Annual Energy Outlook
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The AEO uses a stock/flow model to forecast
technology adoption
  The regional existing equipment stock is based on recent RECS data
  Change in the existing stock is driven by retirements (based on maximum equipment life) and a “technology choice module”
  The parameters of the technology choice module are calibrated using historical data, which is used to predict customer purchases from a menu of new technology options
  The purchase decision is a function of the financial payback of the investment – it is a comparison of the relative installed capital costs and ongoing operations costs of each competing option
  Codes and standards affect the menu of technology options that are available to customers by establishing the minimum efficiency level of the options in any given year
  Utility programs are implicitly – but not explicitly – accounted for in the calibration of the technology choice parameters to historical data
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The AEO forecast accounts for organic
conservation in many different ways
  Technology efficiency improvements: Based primarily on interviews with manufacturers, the efficiency of new technology options is projected. This accounts for market‐driven changes to product features
  Technology cost reductions: Consultant forecasts are used to develop projections of technology cost reductions over time. As the relative cost of efficient technologies drops, customer purchases increase
  Changing electricity prices: The EIA’s electricity price projections affect the payback period for new technologies; as electricity prices rise, so does the financial attractiveness of more efficient equipment
  Customer choice: The technology choice module accounts for observed customer preferences for efficient equipment based on historical data
  Customer behavior: The EIA’s demand module can account for changes in customer behavior such as reducing the number of hours per year that a given piece of equipment is used
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Three sensitivity cases in the AEO may be of
interest for further sensitivity analysis
Residential Energy Intensity as Projected in AEO
(Indexed to 2005 value)
Comments
▀
The High Demand Technology Case
assumes higher efficiency, earlier availability, lower cost, and more frequent energy‐efficient purchases for some equipment
▀
The Best Available Demand Technology Case limits customer purchases of new and replacement equipment to the most efficient models available at the time of purchase—regardless of cost. This case also assumes that new homes are constructed to the most energy‐
efficient specifications
▀
The Extended Policies Case (not shown at left) assumes the enactment of new rounds of standards, generally based on improvements seen in current ENERGY STAR equipment
Source: EIA, 2013 Annual Energy Outlook
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In both the residential and commercial sectors, the AEO identifies
lighting as the biggest opportunity for efficiency improvement
Change in Residential End‐use Energy Consumption, by Case
Source: EIA, 2013 Annual Energy Outlook
Change in Commercial End‐use Energy Intensity, by Case
Source: EIA, 2013 Annual Energy Outlook
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The residential demand module structure
Source: EIA, The National Energy Modeling System: An Overview 2009, October 2009
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Appendix B:
Residential Lighting
Sensitivity Cases
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Our first sensitivity case considers an increase
impact from codes and standards
Annual Energy Consumption per Avg. Bulb
Assumptions
▀
35
▀
Codes and Standards
Utility DSM
31
29
▀
Freeridership (Organic
Conservation)
27
Additional Organic
Conservation
2015
2014
2013
25
2012
Annual kWh/Bulb
33
▀
By 2020, all incandescents will be replaced by bulbs that use 28% less energy, thus satisfying the minimum EISA requirement
Based on data provided by NSP, we estimate that roughly 70% of current residential lighting energy consumption is attributable to incandescents
The result of the codes and standards is a roughly 16% reduction in total residential lighting energy consumption by 2020 – we assume a linear improvement starting in 2012 until this level of efficiency is reached in 2020
Note that an even more aggressive scenario could be envisioned, if incandescents were replaced primarily by CFLs or LEDs during this timeframe
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Our second sensitivity case relies on historical
AEO data to establish an OC trend
Annual Energy Consumption per Avg. Bulb
45
▀
Historical
Projected
40
▀
35
30
▀
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
25
2005
Annual kWh/Bulb
Assumptions
The AEO includes historically‐calibrated end‐
use data going back to 2005
Between 2005 and 2012, before EISA took effect, we observe a roughly 3.6% per year improvement in lighting efficiency
This trend can be attributed to organic conservation and the impact of historical utility DSM programs
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The historical impact of OC and DSM is assumed
to continue into the future
Annual Energy Consumption per Avg. Bulb
45
▀
Historical
Projected
40
3.6% annual efficiency improvement trend
35
We assume that the historical rate of efficiency improvement that is attributable to OC and DSM will persist throughout our forecast horizon
30
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
25
2005
Annual kWh/Bulb
Assumptions
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The AEO projects an increase in efficiency relative to
historical trends; this is the incremental impact of EISA
Annual Energy Consumption per Avg. Bulb
▀
45
Historical
Projected
40
35
▀
Incremental increase largely attributable to EISA
30
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
25
2005
Annual kWh/Bulb
Assumptions
The increase in efficiency improvement relative to the historical trend is assigned to codes and standards, as this approximately represents the impact of EISA in EIA’s modeling
It is the difference between the projected efficiency improvement, and an average efficiency improvement of 3.6% per year
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The AEO projects an increase in efficiency relative to
historical trends; this is the incremental impact of EISA
Annual Energy Consumption per Avg. Bulb
45
▀
Historical
Projected
40
▀
35
Codes and Standards
Utility DSM
30
▀
Freeridership (Organic Conservation)
Additional Organic Conservation
▀
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
25
2005
Annual kWh/Bulb
Assumptions
We establish a frozen efficiency case based on energy consumption per bulb in 2012
The incremental impacts of NSP’s new DSM programs are assumed to account for some of the incremental growth in efficiency
Free‐ridership is accounted for using the same 46% assumption as in the base case
The forecast could be extended beyond 2015 given data availability in the AEO
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Appendix C:
Commercial Lighting
Sensitivity Case
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We considered one sensitivity case for
commercial lighting
The staggered timing of the effective period for recent commercial lighting standards does not allow for the impacts to be easily isolated, making it difficult to implement sensitivity cases using the same approaches that were used for residential lighting
Further, there is likely less uncertainty in the projections of codes and standards for commercial lighting, given that some of the standards have already been in place for several years
Instead, we considered a sensitivity around the AEO projection of total commercial lighting efficiency improvement; our sensitivity is based on the AEO’s “High Technology Demand” case
The “High Technology Demand” case assumes that customers are more likely to pay a premium for more efficient technologies, that new technologies make it to market sooner, and that the cost of the technologies is reduced
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Despite the more aggressive assumptions about
efficiency improvement, impacts are still relatively small
Annual Lighting Energy Consumption per Square Ft.
Assumptions
▀
Codes and Standards
Utility DSM
Freeridership (Organic Conservation)
3.30
Additional Organic Conservation
3.20
▀
2015
2014
2013
3.10
2012
Annual Lighting kWh/Square Foot
3.40
The 6.7% reduction in energy consumption per square foot between 2012 and 2015 from the base case increases slightly to 9.0% in the sensitivity case
The impact of organic conservation accounts for this increase in efficiency
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Bibliography
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68 respondents to the survey of expert opinion
spanned 45 different organizations
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
American Council for an Energy‐Efficient Economy (ACEEE)
American Electric Power (AEP)
Association of Home Appliance Manufacturers
Ameren Corporation
Appliance Standards Awareness Project (ASAP)
ASHRAE
BC Hydro
Baltimore Gas and Electric (BGE)
California Energy Commission (CEC)
Cave Creek Institute
ComEd
ConEd
Economic and Human Dimensions Research Associates
Environmental Defense Fund
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Respondents to the survey of expert opinion (cont.)
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
Institute for Electric Efficiency (IEE)
Eastern Interconnection States’ Planning Council (EISPC)
Emerson Network Power
Electric Reliability Council of Texas (ERCOT)
Florida Power and Light (FPL)
Georgia Tech
Hydro One
Hydro Quebec
Intel
Lawrence Berkeley National Laboratory
National Electric Manufacturer’s Association (NEMA)
Northeast Utilities
National Resources Defense Council (NRDC)
Northwest Power & Conservation Council
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Respondents to the survey of expert opinion
(concluded)
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
Ontario Power Authority
PacifiCorp
Pacific Gas & Electric (PGE)
PNM Resources
Regulatory Assistance Project (RAP)
Southern California Edison (SCE)
San Diego Gas & Electric
Sacramento Municipal Utility District
Texas PUC
Tennessee Valley Authority
U.S. DOE and U.S. EIA
U.S. EPA
University of Vermont
Vectren Corporation
Vermont Electric Power Company (VELCO)
Walmart
Westar Energy
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Primary sources
  The Cadmus Group. “Minnesota Home Lighting Program Evaluation.” November 12, 2012.   Fraunhofer Center for Sustainable Energy Systems. “Energy Consumption Of Consumer Electronics In U.S. Homes In 2010.” December 2011.
  The Home Depot. “Fluorescent Bulbs.” http://www.homedepot.com/b/Electrical‐Light‐
Bulbs‐Fluorescent‐Bulbs/%20/b/Electrical‐Light‐Bulbs‐Fluorescent‐Bulbs/N‐5yc1vZbm3z
  The Home Depot. “Halide: Top Sellers.” http://www.homedepot.com/b/N‐5yc1v/Ntk‐
All/Ntt‐halide?Ns=P_Topseller_Sort%7C1
  The Home Depot. “Halogen Light Bulbs.” http://www.homedepot.com/b/Electrical‐Light‐
Bulbs‐Halogen‐Light‐Bulbs/N‐5yc1vZbmg5
  KEMA. “Xcel Energy Minnesota DSM Market Potential Assessment.” April 20, 2012. 59 | brattle.com
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Primary sources (cont.)
  Lowenberger, Amanda, Joanna Mauer, et al. ASAP/ACEEE. “The Efficiency Boom: Cashing in on Savings from Appliance Standards.” March 2012.   Mauer, Joanna et al. ACEEE. “Better Appliances: An Analysis of Performance, Features, and Price as Efficiency has Improved.” May 2013.
  U.S. DOE. “2010 U.S. Lighting Market Characterization.” January 2012. http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/2010‐lmc‐final‐jan‐2012.pdf
  U.S. EIA. “Annual Energy Outlook 2013.” April 2013. http://www.eia.gov/forecasts/aeo/pdf/0383%282013%29.pdf
  U.S. EIA. “NEMS Commercial Database: AEO 2013 Reference Case.” Filename: DB_Commercial_ref2013d102312a.xlsm.
  U.S. EIA. “NEMS Commercial Database: AEO 2013 High Technology Case.” Filename: DB_Commercial_hightechd120712a.xlsm.
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Primary sources (cont.)
  U.S. EIA. “NEMS Residential Database: AEO 2013 Reference Case.” Filename: resDB aeo2013.xls.
  Vine, Edward, Joseph Eto, et al. Lawrence Berkeley National Laboratory. “Evaluation of Commercial Lighting Programs: A DEEP Assessment.”
  http://emp.lbl.gov/sites/all/files/lbnl‐36522.pdf
  Xcel Energy/NSP‐MN. “Anticipated Monthly Impacts: Residential Lighting Codes and Standards Impacts on Electricity Sales.” Filename: res+lighting+adjustment_v2.xls.
  Xcel Energy/NSP‐MN. “Anticipated Monthly Impacts: Commercial Lighting Codes and Standards Impacts on Electricity Sales.” Filename: biz+lighting+adjustment.xls.
  Xcel Energy/NSP‐MN. “Technical Assumptions for the 2010/2012 Demand‐Side Management Triennial Plan: Residential.” Filename: MN Home Lighting.xls.
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Primary sources (concluded)
  Xcel Energy/NSP‐MN. “Technical Assumptions for the 2010/2012 Demand‐Side Management Triennial Plan: Commercial.” Filename: MN Lighting Efficiency.xls.
  Xcel Energy/NSP‐MN and Wise Research Associates. “2012 Residential Energy Use Survey: Minnesota Service Area.” June 2012.
  Xcel Energy/NSP‐MN and Wise Research Associates. “2010 Residential Energy Use Survey: Minnesota Service Area.” June 2010.
  Xcel Energy/NSP‐MN and Wise Research Associates. “2008 Residential Energy Use Survey: Minnesota Service Area.” December 2008. 62 | brattle.com
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Other supporting material
  EPRI. “Assessment of Achievable Potential from Energy Efficiency and Demand Response Programs in the US.” January 2009. http://www.isa.org/FileStore/Intech/WhitePaper/EPRI.pdf
  Fox, Eric. Itron. “Using Load Research Data to Develop Long‐Term Peak Demand Forecasts.” 2010 AEIC Load Research Conference. August 15, 2010. http://www.aeic.org/load_research/docs/LRToDevelopLongTermPeakDemandForecasts.pdf
  Goldman Sachs. “Clean Currents: Seeing the (LED) light.” November 24, 2013.   Herter, Karen. Smart Electronics Initiative. “Get Smart Guide: Energy Innovation for the Consumer Electronic Industry.” 2012.
  Laitner, John. “Linking Energy Efficiency to Economic Productivity: Recommendations for Improving the Robustness of the U.S. Economy.” ACEEE, July 2013.   McKinsey & Company. “Sizing the Potential of Behavioral Energy‐Efficiency Initiatives in the US Residential Market.“ May 2013. 63 | brattle.com
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Other supporting material (concluded)
  Meyers, Stephen, Alison Williams, and Peter Chan. “Energy and Economic Impacts of U.S. Federal Energy and Water Conservation Standards Adopted From 1987 Through 2010.” Lawrence Berkeley National Laboratory, December 2011.   Newell, Richard, Adam Jaffe, and Robert Stavins. “The Induced Innovation Hypothesis and Energy‐
Saving Technological Change.“ The Quarterly Journal of Economics, 114:3 (August 1999), pp. 941–
975.
  Nordhaus, William. “Do Real Output and Real ‐Wage Measures Capture Reality? This History of Lighting Suggests Not.” Cowles Foundation Research in Economics at Yale University, 1998.
  OPower. “Unlocking the Potential of Behavioral Energy Efficiency.“ Arlington, Virginia. 2013.   Rohmund, Ingrid et al. “Factors Effecting Electricity Consumption in the United States (2010‐
2035).” Institute of Electric Efficiency, March 2013.   Smith, Sarah. SNL. “Gas furnace efficiency rule struggles to balance technological extremes.” December 9, 2013. http://www.snl.com/InteractiveX/article.aspx?ID=26203895&KPLT=4
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