IEEE Power & Energy Magazine - September/October 2016 - 71
Since 1999, energy use per square foot
has dropped by about 18% in the U.S. commercial
business sector.
energy-efficiency programs used a comprehensive set of regu-
latory tools that helped to align utility business models with
energy efficiency. The three most important characteristics
include mechanisms 1) that allow utilities to recover the direct
costs of energy-efficiency programs, 2) remove the through-
put incentive by allowing the recovery of lost contributions to
fixed costs, and 3) create earnings opportunities for efficiency
investments and performance. in the eight states that combine
all three of these tools, the average energy efficiency savings
in 2013 was approximately 1.5% of sales as compared to
about 0.6% in those states with only a partial set of policies.
among the backdrop of changes in regulatory policy, there
are a number of other drivers at work in the economy that are
promoting greater customer interest in energy efficiency. one
major driver has been the adoption of corporate sustainability
policies at major corporations, policies that recognize the risk
inherent in not addressing the impacts of climate change. as a
result, many corporations now have specific targets for green-
house gas emissions reductions. efforts have even emerged
such as the carbon disclosure project, a voluntary program
that promotes the disclosure of carbon footprint information.
since 1999, energy use per square foot has dropped by about
18% in the u.s. commercial business sector, and much of this
can be attributed to greater awareness of the importance of
energy efficiency and a reduced carbon footprint.
There is clear evidence that private entities are taking
action to reduce energy consumption not only as a result of
utility incentives. between 2010 and 2015, for instance, the
61 largest commercial office buildings in downtown char-
lotte, north carolina, reduced overall consumption by some
16%. some of this reduction was due in part to utility initia-
tives led by duke energy, but the majority of the reduction
was driven by progressive corporate policy. specifically,
major leaders within in the city, including bank of america
and Wells Fargo, undertook various energy-efficiency proj-
ects in the buildings they occupy. These projects were pro-
moted primarily to help meet corporate sustainability goals.
as a side effect, however, the most valuable spaces within
the city also tend to have some of the lowest energy use per
square foot. This trend, a connection between real-estate
value and energy intensity, is one that is seen throughout the
country. efforts such as leed (leadership in energy and
environmental design) are helping to drive this change.
on the demand side, there is also growing interest in the
applicability of the internet of Things and associated ana-
lytics software to help identify operational inefficiencies
in buildings and help to better integrate demand response.
september/october 2016
although these activities tend to have benefits, they are still
in their infancy, and much uncertainty still surrounds the
expected payback to customers. Furthermore, most analytic
packages are not yet equipped to properly integrate into the
business practices of facilities management teams. as these
offerings evolve in the coming years, it is expected that they
will have a greater impact on achieving energy efficiency
and better integrating utility demand-response programs.
Utility Business Models
Challenges and Implications
The technical advancements and resulting challenges discussed
here have two systemic impacts, irreducible heterogeneity and
ubiquitous agency, that threaten to upend a century of assump-
tions about how regulated utility businesses function.
Irreducible Heterogeneity
For purposes of planning and pricing, utilities for decades
have made the assumption that customers can be divided into
classes, groups with similar electricity usage needs and load
profiles, even though absolute demand levels may vary. For
example, a residential class customer likely has a demand
peak in the evening, i.e., when many people return home from
work. a commercial class customer likely has relatively steady
demand during the daytime hours, declining overnight when
many businesses are closed. Many industrial class custom-
ers have steady demand 24-7, if their operations run on three
shifts (see Figure 3). While absolute demand will vary (e.g., by
square footage), the relative similarity of these load profiles for
all customers within a class underpins utility logic in cost allo-
cation and, ultimately, in setting pricing tariffs for each class.
To the degree that load profiles within a class are not homo-
geneous, it is an indication of a cross-subsidy. For example, if
industrial and residential customers were grouped together in a
single class, the industrial customers would effectively be sub-
sidizing the residential customers for the extra (less economic,
largely idle) generation capacity needed to meet the evening
residential demand peak.
The idea of homogeneity within a customer class has
always been an approximation, but its validity is eroded by
the proliferation of distributed energy. in terms of load pro-
files, a traditional residential load is as dissimilar to a residen-
tial-with-rooftop-solar load as it is to an industrial load. some
utilities and advocates support the creation of new customer
classes and their related tariffs to resolve this problem, such
as the "value of solar" tariffs adopted in austin, Texas, and in
Minnesota. While these tariffs are a step in the right direction,
ieee power & energy magazine
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