Alexis Abramson Building Technologies Office Prioritization Tool

16 Pages·2013·1.89 MB·English

Building Technologies Office
Program Peer Review
Alexis Abramson
Building Technologies Office
Chief Scientist
[email protected]
Prioritization Tool
April 2, 2013
1 | Program Name or Ancillary Text eere.energy.gov
The prioritization tool provides
quantitative insight to decision-making
Vision: develop an analytical tool that considers building
efficiency measures and technologies, and assesses and
compares their potential value into the future

Uses:
• Inform programmatic decision-making
• Examine “what if” scenarios
• Create targets for FOAs
• Set programmatic goals
• Provide public with a comprehensive analytical tool

FY13 peer review completed March 2013
BTO review of outputs ongoing in April 2013 – no output review today

2 | Building Technologies Office eere.energy.gov
The analytical framework involves defining inputs,
calculating, and generating outputs
• Performance improvement
• Cost U = S X E -1 X SD = S X C
• Market
Inputs
• Lifetime
Consumption
• Stock and flow dynamics
Service Demand per
• Technology diffusion
unit stock
• Cost of Conserved Energy (CCE)
Analysis
• Staging framework
Efficiency
• CCE and annual energy savings
• Technical potential
• Full-Adoption potential
Equipment Stock
Outputs • BTO-Adoption adjusted potential
• Staged potential
Total energy use
3 | Building Technologies Office eere.energy.gov
Outputs are viewed through different
‘lenses’
LENS 1: Imagine replacing all existing stock with
the new measure overnight = Technical
Baseline

Potential s
U energy
T consumed Technical
B
LENS 2: A ‘stock and flow’ model accounts for potential
today
in 2030
unit replacement, elimination or addition =
Maximum Adoption Potential (unstaged) Energy technology/measure

LENS 3: To avoid ‘double counting,’ measures
with lowest CCE “stage” first to capture their B
t ) D
share of the market = Maximum Adoption s s
o U
c T
Potential (staged) e B
c / C
$
u ( E
d E
LENS 4: Market penetration and BTO influence e C A
R
C
on acceleration can be examined using the Bass
BTUs saved
Diffusion Model to represent more “realistic”
Improve
market diffusion = Adjusted Adoption Potential
performance

4 | Building Technologies Office eere.energy.gov
Reviewing…
#1: How much energy is currently used for lighting Business
As Usual
and is expected to be used in the future?
(BAU)

#2: Imagine that every lamp was replaced with an
Technical
LED lamp overnight. What would be the reduction
Potential
in energy use?

#3: What if we replaced every lamp at the end of its
Maximum
life with an LED lamp (or at an accelerated rate
Adoption
when it was cost effective to do so)? How much
Potential
energy would be saved then?

#4: Now we want as accurate a depiction as
Adjusted
possible of the rate our LED lamps are adopted by
Adoption
the market, taking into consideration the impact of
Potential
DOE’s spending on R&D, deployment, or standards
activity.

5 | Building Technologies Office eere.energy.gov
Imagine replacing existing stock with the new
measure overnight = Technical Potential
Accounts for market size and relative performance between measure and baseline,
but does not account for cost effectiveness
Residential R-10 Windows
BAU
2000
s 1800
U
T
B 1600
T

y 1400
g
r
e 1200
n
e

y 1000
Energy saved!
r
a
m
800
i
r
P
600
400
200
Technical potential
0
2010 2030 2050
Year
Fit BAU: Extrapolation of BAU based on NEMS
Preliminary
6 | Building Technologies Office eere.energy.gov
‘Stock and flow’ accounts for replacement,
elimination or addition = Maximum Adoption
Potential (unstaged)
Stock turnover slowly brings energy use closer to technical potential

Residential R-10 Windows
2000 BAU

1800

s
U
T 1600

B Maximum
Energy saved!
T

y adoption
g
1400
r
e potential
n
e
y
r 1200
a
m

i
r
P
1000
Technical potential

0

2010 2020 2030 2040 2050
Year
Preliminary
Staging is then used on ‘Maximum Adoption Potential’ to avoid double counting
7 | Building Technologies Office eere.energy.gov
FY13 output example: water heating maximum
adoption potential, 2030 (unstaged)
$50 R: Gas water heater
electronic ignition > 500 TBTUs TP
$45
100–500 TBTUs TP
C: Solar WH gas back
< 100 TBTUs TP
up
$40

)
y
r
a
m $35
R: More insulation for
i
r
p
water heaters
U, $30
T
B
M
$25 R: Solar WH gas back
M
R: condensing gas WH
/ up
$
( $20

y C: HPWH (COP 2.0)
g
er $15 R+C: gas absorption
n
e HPWH

d C: condensing gas WH R: solar WH electric
e $10
v
r R: HPWH COP 2.2 back up
e
s R: WH insulation
n
$5
o blankets
c
R+C: Integrated heat
f
o C: HPWH
t $- pump
s
o
C 0 200 400 600 800 1000 1200 1400 1600

Primary energy savings: 2030 (TBTUs)
Preliminary
8 | Building Technologies Office eere.energy.gov
FY13 output example: water heating
maximum adoption potential, 2030 (staged)
R+C: gas HPWH, tech
$50 limit
R+C: ASHPWH, tech
) limit > 500 TBTUs TP
y $45
r 100–500 TBTUs TP
a R: Solar WH gas
m
< 100 TBTUs TP
i backup
r $40
p

,
U
R: solar WH electric
T
B $35
M backup
M
R: More insulation for
/ $30
$
water heaters
(

y
g R: condensing gas
r $25
e
WH
n
e

d $20
e
v C: Solar WH gas back
r
e
s up
n $15 R+C: Gas absorption
o
c HPWH

f
t o $10 R: WH insulation R+C: Integrated heat
s
o blankets
C pump
$5
C: HPWH R: HPWH COP 2.2
$-
0 200 400 600 800 1000 1200 1400 1600
Primary energy savings: 2030 (TBTUs)
Preliminary
9 | Building Technologies Office eere.energy.gov
Market penetration scenarios can also be
explored = Adjusted Adoption Potential
• Bass Diffusion Model (p’s and q’s) accounts for market penetration
• Market acceleration can be further enhanced through BTO investment
• Standards/codes action initiates path towards full adoption
Residential R-10 Windows BAU
2000
1800

s
Energy
U Adjusted
T
B 1600 saved!
adoption
T

y potential
g
r 1400
e
n
e Maximum

y
r adoption
a 1200
m
potential
i
r
P
1000
Technical potential
0
2010 2020 2030 2040 2050
Preliminary Year
10 | Building Technologies Office eere.energy.gov