technical assistance for states: grid-interactive efficient buildings · 2020-07-17 · *...
TRANSCRIPT
Technical Assistance for States: Grid-Interactive Efficient Buildings
GEB Potential Working Group Call
July 17, 2020
Chioke Harris1, Sam Koebrich1, Joyce McLaren1
1National Renewable Energy Laboratory
Agenda1. Present regional level GEB potential
data
2. Collect feedback
3. Present ideas on exploring customer participation
4. Collect feedback & data source ideas
Discussion1. Would you benefit from seeing similar results, specific
to your region?
2. Is the granularity of the results sufficient for your use?
3. What other data or information would be particularly
useful? How can we provide more helpful information
that can be applied to your needs?
4. How would/will you use data on GEB potential? (Inform
goal-setting? Inform program design?)
Assessing the potential for energy flexibility from buildings at the regional level
Chioke Harris2
with Jared Langevin1, Handi Putra1, Andrew Speake2, Elaina Present2, Rajendra
Adhikari2, and Eric Wilson2
1 Lawrence Berkeley National Laboratory
2 National Renewable Energy Laboratory
Motivating question
How much can grid-interactive
efficient building technologies impact
system load shape in the SRVC
region?
6
What is the electric load “resource” available from buildings if
they can operate flexibly?
• Buildings comprise 75% of U.S. electricity demand.
• Demand-side flexibility can support variable renewable electricity penetration cost-effectively.
• The magnitude of the potential grid resource from flexible building technologies has not yet been quantified.
Comparison of the costs per MWh of shifting renewable energy from generation sources, and battery storage/distributed energy resources.
Aggregated demand-side flexibility resources are found to be cost-effective and frequently cheaper than the generation alternative. Source: McKinsey.
7
A bottoms-up stock-and-flow model of buildings is used to evaluate the time- and location-specific effect of efficiency, flexibility measures
1. Define individual measures, rolled up into three (3) measure portfolios:
2. Develop 8760 (hourly) fractions of annual baseline electricity demand by location, building type, and end use
3. Develop bottom-up EnergyPlus measure simulations and hourly savings fractions based on regional system needs
4. Translate measures to Scout and assess regional/national portfolio potential, annually and sub-annually (2015-2050)
* “Flexibility” measures can reduce load during peak hours (“shed”) or move electricity use out of the peak period (“shift”).
Further details on demand flexibility can be found in the Building Technologies Office Grid-interactive Efficient Buildings Overview.
• energy efficiency (EE)
• demand flexibility* (DF)
• EE + DF
Residential measures were modeled using ResStock
8
Scenario Measure Name End Use(s) Description
Energy Efficiency (EE)
Scout “Best Available” ECM portfolio
All major end usesCurrent best available residential efficiency ECMs, definitions posted on Scout GitHub repository
Programmable thermostat (PCT) setups and setbacks
HVACApply thermostat setups and setbacks while maintaining temperature setpoint diversity
Demand Flexibility (DF)
PCT + pre-cooling and heating HVAC Decrease/increase temperature set points during peak period
Grid-responsive water heater Water HeatingIncrease temperature setpoint at beginning of take period, decrease setpoint at beginning of peak period
Grid-responsive washer/dryer, variable-speed pool pump
AppliancesShift washer/dryer cycles and pool pump power to off-peak hours
Low priority plug load adjustments ElectronicsShift or switch off/unplug some low-priority electronics during peak hours (e.g., TVs, set top boxes, laptops/PCs)
EE + DF
PCT + pre-cool/heat + efficient envelope and HVAC equipment
HVAC, LightingCombine EE HVAC and envelope upgrades with DF HVAC controls
Grid-responsive cycling/control + efficient equipment
Appliances, WH, Electronics
Combine DF WH, appliance, and electronics strategies with most efficient equipment
All remaining EE ECMs Refrigeration Account for efficiency outside of other EE+DF measures
ResStock, a framework for simulating a statistically representative sample of residential buildings in OpenStudio
and EnergyPlus, was used to explore the effect of various measures on hourly residential building energy use.
Commercial measures were modeled with prototype buildings
9
Scenario Measure Name End Use(s) Description
Energy Efficiency (EE)
Scout “Best Available” ECM portfolio
All major end uses
Current best available commercial ECMs, definitions posted on Scout GitHub repository
Demand Flexibility (DF)
Global temperature adjustment (GTA)
HVAC
Increase zone temperature set points for one or more peak hours
GTA + pre-cooling Decrease zone set points prior to peak period
GTA + pre-cooling + ice storage Charge ice storage overnight and discharge during peak period
Continuous dimming LightingDim lighting, and shut off lighting in unoccupied spaces, for one or more peak hours
Low priority device switching ElectronicsSwitch off low-priority devices (e.g., unused PCs, equipment) for one or more peak hours
EE + DF
GTA + pre-cool/heat + efficient envelope and HVAC equip.; daylighting controls + dimming
HVAC, LightingCombine DF HVAC/lighting strategies with more efficient envelope/equipment, daylighting, and controls to maximize EE and DF
Device switching + efficient electronics
ElectronicsCombine DF electronics strategy with the most efficient electronic equipment
All remaining EE ECMsRefrigeration,
WHAccount for efficiency outside of combined EE+DF measures above
The Commercial Prototype Reference Models were used with OpenStudio and EnergyPlus to explore the effect
of various measures on hourly commercial building energy use.
County-level
mapping
10
Results are generated across multiple scales, each with specific
geographic resolution, and aligned by county
Building-scale simulation
Tools: ResStock, DOE Commercial Prototype Models
Boundaries: Representative cities for 14 mainland ASHRAE 90.1-2016 climate zones
December 2018
U.S. Energy Information Administration | Electricity Market Module of the National Energy Modeling System: Model Documentation 2018 6
Figure 3. Market model supply regions
1 ‐ Texas Reliability Entity (ERCT) 2 ‐ Florida Reliability Coordinating Council (FRCC) 3 ‐ Midwest Reliability Organization / East (MROE) 4 ‐ Midwest Reliability Organization / West (MROW) 5 ‐ Northeast Power Coordinating Council / New England (NEWE) 6 ‐ Northeast Power Coordinating Council / NYC‐Westchester (NYCW) 7 ‐ Northeast Power Coordinating Council / Long Island (NYLI) 8 ‐ Northeast Power Coordinating Council / Upstate New York (NYUP) 9 ‐ ReliabilityFirst Corporation / East (RFCE) 10 ‐ ReliabilityFirst Corporation / Michigan (RFCM) 11 ‐ ReliabilityFirst Corporation / West (RFCW) 12 ‐ SERC Reliability Corporation / Delta (SRDA) 13 ‐ SERC Reliability Corporation / Gateway (SRGW) 14 ‐ SERC Reliability Corporation / Southeastern (SRSE) 15 ‐ SERC Reliability Corporation / Central (SRCE) 16 ‐ SERC Reliability Corporation / Virginia‐Carolina (SRVC) 17 ‐ Southwest Power Pool Regional Entity / North (SPNO) 18 ‐ Southwest Power Pool Regional Entity / South (SPSO) 19 ‐ Western Electricity Coordinating Council / Southwest (AZNM) 20 ‐ Western Electricity Coordinating Council / California (CAMX) 21 ‐ Western Electricity Coordinating Council / Northwest Power Pool Area (NWPP) 22 ‐ Western Electricity Coordinating Council / Rockies (RMPA)
Region-scale simulation
Tools: Scout
Boundaries: 22 U.S. EIA Electricity Market Module (EMM) regions
2A (FRCC)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Winter Month
Peak DayTypical WeekdayTypical Weekend
Avg. Peak Load Hr./RangeAvg. Low Load Hr./Range
2B, 4B (AZNM)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Winter Month
Avg. Peak Load Hr./RangeAvg. Low Load Hr./RangePeak DayTypical WeekdayTypical Weekend
3A−1 (SRVC)
Hour Ending (Local Standard Time)Fr
actio
n Pe
ak N
et L
oad
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Winter Month
Avg. Peak Load Hr./RangeAvg. Low Load Hr./RangePeak DayTypical WeekdayTypical Weekend
3A−2 (SPSO)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
−0.2
0
0.2
0.4
0.6
0.8
1
Winter Month
Peak DayTypical WeekdayTypical Weekend
Avg. Peak Load Hr./RangeAvg. Low Load Hr./Range
3B (ERCOT)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Winter Month
Peak DayTypical WeekdayTypical Weekend
Avg. Peak Load Hr./RangeAvg. Low Load Hr./Range
3C (CAMX)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Winter Month
Peak DayTypical WeekdayTypical Weekend
Avg. Peak Load Hr./RangeAvg. Low Load Hr./Range
4A−1 (NYCW)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Winter Month
Avg. Peak Load Hr./RangeAvg. Low Load Hr./RangePeak DayTypical WeekdayTypical Weekend
4A−2 (SPNO)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
−0.4
−0.2
0
0.2
0.4
0.6
0.8
1
Winter Month
Peak DayTypical WeekdayTypical Weekend
Avg. Peak Load Hr./RangeAvg. Low Load Hr./Range
4C, 5C, 6B (NWPP)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Winter Month
Avg. Peak Load Hr./RangeAvg. Low Load Hr./RangePeak DayTypical WeekdayTypical Weekend
5A−1 (NYUP)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Winter Month
Avg. Peak Load Hr./RangeAvg. Low Load Hr./RangePeak DayTypical WeekdayTypical Weekend
5A−2 (RFCW)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Winter Month
Avg. Peak Load Hr./RangeAvg. Low Load Hr./RangePeak DayTypical WeekdayTypical Weekend
5B (RMPA)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Winter Month
Peak DayTypical WeekdayTypical Weekend
Avg. Peak Load Hr./RangeAvg. Low Load Hr./Range
6A−1, 7 (MROW)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Winter Month
Peak DayTypical WeekdayTypical Weekend
Avg. Peak Load Hr./RangeAvg. Low Load Hr./Range
6A−2 (NEWE)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Winter Month
Avg. Peak Load Hr./RangeAvg. Low Load Hr./RangePeak DayTypical WeekdayTypical Weekend
11
Net load shapes projected for 2050 for summer and winter
inform flexible measure operation
Data: EIA EMM, projection year 2050
• Flexibility measures are designed to remove load during net peak periods and build load during low net demand periods (if possible), flattening the net load shape.
• The year 2050 is used because it includes the highest renewable penetration levels.
2A (FRCC)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Summer Month
Peak DayTypical WeekdayTypical Weekend
Avg. Peak Load Hr./RangeAvg. Low Load Hr./Range
2B, 4B (AZNM)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Summer Month
Avg. Peak Load Hr./RangeAvg. Low Load Hr./RangePeak DayTypical WeekdayTypical Weekend
3A−1 (SRVC)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Summer Month
Avg. Peak Load Hr./RangeAvg. Low Load Hr./RangePeak DayTypical WeekdayTypical Weekend
3A−2 (SPSO)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
−0.2
0
0.2
0.4
0.6
0.8
1
Summer Month
Peak DayTypical WeekdayTypical Weekend
Avg. Peak Load Hr./RangeAvg. Low Load Hr./Range
3B (ERCOT)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Summer Month
Peak DayTypical WeekdayTypical Weekend
Avg. Peak Load Hr./RangeAvg. Low Load Hr./Range
3C (CAMX)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Summer Month
Peak DayTypical WeekdayTypical Weekend
Avg. Peak Load Hr./RangeAvg. Low Load Hr./Range
4A−1 (NYCW)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Summer Month
Avg. Peak Load Hr./RangeAvg. Low Load Hr./RangePeak DayTypical WeekdayTypical Weekend
4A−2 (SPNO)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
−0.4
−0.2
0
0.2
0.4
0.6
0.8
1
Summer Month
Peak DayTypical WeekdayTypical Weekend
Avg. Peak Load Hr./RangeAvg. Low Load Hr./Range
4C, 5C, 6B (NWPP)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Summer Month
Avg. Peak Load Hr./RangeAvg. Low Load Hr./RangePeak DayTypical WeekdayTypical Weekend
5A−1 (NYUP)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Summer Month
Avg. Peak Load Hr./RangeAvg. Low Load Hr./RangePeak DayTypical WeekdayTypical Weekend
5A−2 (RFCW)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Summer Month
Avg. Peak Load Hr./RangeAvg. Low Load Hr./RangePeak DayTypical WeekdayTypical Weekend
5B (RMPA)
Hour Ending (Local Standard Time)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Summer Month
Peak DayTypical WeekdayTypical Weekend
Avg. Peak Load Hr./RangeAvg. Low Load Hr./Range
6A−1, 7 (MROW)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Summer Month
Peak DayTypical WeekdayTypical Weekend
Avg. Peak Load Hr./RangeAvg. Low Load Hr./Range
6A−2 (NEWE)
Frac
tion
Peak
Net
Loa
d
1 3 5 7 9 11 13 15 17 19 21 23
0
0.2
0.4
0.6
0.8
1
Summer Month
Avg. Peak Load Hr./RangeAvg. Low Load Hr./RangePeak DayTypical WeekdayTypical Weekend
SRVC, Climate Zone 3A — Summer
Frac
tion
Net
Pea
k Lo
ad
Hour Ending (Local Standard Time)Hour Ending (Local Standard Time)Fr
actio
n N
et P
eak
Load
SRVC, Climate Zone 3A — Winter
12
Current limitations
• Primary focus is on technical potential results
• Results do not generally consider market conditions, consumer preferences,
payback period, or price elasticity
• Measures are based on the highest performance technologies currently
available
• Does not include prospective technologies currently in development
• Measure operation is not based on real-time signals
• Flexible operation is defined based on preset net peak (high demand) and
low demand periods set by EMM region
13
EE measures reduce load throughout the day, DF measures
decrease load at peak times
5
10
15
20
25
30
35
Tota
l Ele
ctric
Loa
d (M
Wh)
0 4 8 12 16 20
Time of Day0 4 8 12 16 20
14
Combining EE and DF measures together has the effect of
combining the savings from EE with the shift/shed of DF
5
10
15
20
25
30
35
Tota
l Ele
ctric
Loa
d (M
Wh)
0 4 8 12 16 20
Time of Day0 4 8 12 16 20
15
Efficiency and flexibility are complementary for peak demand
reduction
Data: Scout Acronyms: Energy Efficiency (EE), Demand Flexibility (DF)
Increased demand
Decreased demandDecreased electricity use
Increased demand
Decreased demand
*DRAFT*
EE+DF DF EE−100
−80
−60
−40
−20
0
20
Scenario
Cha
nge
in A
nnua
l Ele
ctric
ity U
se (T
Wh)
Annual Impacts (2020)
Technical Potential (TP)
−66
1
−69
EE+DF DF EE−30
−20
−10
0
10
20
Scenario
Avg.
Cha
nge
in H
ourly
Dem
and
(GW
)
Net Peak Period Impacts (2020)
TP−SummerTP−Winter
−15−11
−9
−4
−9 −8
EE+DF DF EE−30
−20
−10
0
10
20
Scenario
Avg.
Cha
nge
in H
ourly
Dem
and
(GW
)
Low Net Demand Period Impacts (2020)
TP−SummerTP−Winter
−9 −8
1 1
−11 −10
EE+DF DF EE−100
−80
−60
−40
−20
0
20
Scenario
Cha
nge
in A
nnua
l Ele
ctric
ity U
se (T
Wh)
Annual Impacts (2020)
−66
1
−69
Technical Potential (TP)
EE+DF DF EE−30
−20
−10
0
10
20
Scenario
Avg.
Cha
nge
in H
ourly
Dem
and
(GW
)
Net Peak Period Impacts (2020)
TP−SummerTP−Winter
−15−11
−9
−4
−9 −8
Residential (New)Residential (Existing)Commercial (New)Commercial (Existing)
EE+DF DF EE−30
−20
−10
0
10
20
Scenario
Avg.
Cha
nge
in H
ourly
Dem
and
(GW
)
Low Net Demand Period Impacts (2020)
TP−SummerTP−Winter
−9 −8
1 1
−11 −10
16
Residential buildings drive changes in load across metrics
Data: Scout Acronyms: Energy Efficiency (EE), Demand Flexibility (DF)
*DRAFT*
EE+DF DF EE−100
−80
−60
−40
−20
0
20
Scenario
Chan
ge in
Ann
ual E
lect
ricity
Use
(TW
h)
Annual Impacts (2020)
−66
1
−69
Technical Potential (TP)
EE+DF DF EE−30
−20
−10
0
10
20
Scenario
Avg.
Cha
nge
in H
ourly
Dem
and
(GW
)
Net Peak Period Impacts (2020)
TP−SummerTP−Winter
−15−11
−9−4
−9 −8
HeatingCoolingVentilationLighting
Water HeatingRefrigerationPlug LoadsOther
EE+DF DF EE−30
−20
−10
0
10
20
Scenario
Avg.
Cha
nge
in H
ourly
Dem
and
(GW
)
Low Net Demand Period Impacts (2020)
TP−SummerTP−Winter
−9 −8
1 1
−11 −10
Cooling and heating drive peak reductions, water heating adds
load
17
*DRAFT*
Data: Scout Acronyms: Energy Efficiency (EE), Demand Flexibility (DF)
18
Residential measures show a diversity of summer peak and
total impacts among scenarios
●● ●● ● ●●0
2
4
6
−0.5 0.0 0.5 1.0 1.5 2.0Avg. Decrease in Summer Net Peak Demand (GW)
Dec
reas
e in
Ann
ual E
lect
ricity
Use
(TW
h) ● DF
EE
EE+DF
End Use●
●
●
●
●
●
●
●
Appliances
Envelope
HVAC
Lighting
Plug Loads
Pool Pumps
Refrigeration
Water Heating
●● ●● ● ●●0
2
4
6
−0.5 0.0 0.5 1.0 1.5 2.0Avg. Decrease in Summer Net Peak Demand (GW)
Dec
reas
e in
Ann
ual E
lect
ricity
Use
(TW
h) ● DF
EE
EE+DF
End Use●
●
●
●
●
●
●
●
Appliances
Envelope
HVAC
Lighting
Plug Loads
Pool Pumps
Refrigeration
Water Heating
Scenario
19
Efficiency measures that yield larger decreases in overall
demand tend to also show larger decreases in peak demand
0
2
4
6
−0.5 0.0 0.5 1.0 1.5 2.0Avg. Decrease in Summer Net Peak Demand (GW)
Dec
reas
e in
Ann
ual E
lect
ricity
Use
(TW
h)
End UseAppliances
Envelope
HVAC
Lighting
Plug Loads
Pool Pumps
Refrigeration
Water Heating
●● ●● ● ●●0
2
4
6
−0.5 0.0 0.5 1.0 1.5 2.0Avg. Decrease in Summer Net Peak Demand (GW)
Dec
reas
e in
Ann
ual E
lect
ricity
Use
(TW
h) ● DF
EE
EE+DF
End Use●
●
●
●
●
●
●
●
Appliances
Envelope
HVAC
Lighting
Plug Loads
Pool Pumps
Refrigeration
Water Heating
Scenario
20
Demand flexibility measures do not substantially influence
total annual electricity use
●● ●● ● ●●0
2
4
6
−0.5 0.0 0.5 1.0 1.5 2.0Avg. Decrease in Summer Net Peak Demand (GW)
Dec
reas
e in
Ann
ual E
lect
ricity
Use
(TW
h)
End Use●
●
●
●
●
Appliances
HVAC
Plug Loads
Pool Pumps
Water Heating
●● ●● ● ●●0
2
4
6
−0.5 0.0 0.5 1.0 1.5 2.0Avg. Decrease in Summer Net Peak Demand (GW)
Dec
reas
e in
Ann
ual E
lect
ricity
Use
(TW
h) ● DF
EE
EE+DF
End Use●
●
●
●
●
●
●
●
Appliances
Envelope
HVAC
Lighting
Plug Loads
Pool Pumps
Refrigeration
Water Heating
Scenario
21
Combining efficiency and flexibility splits the difference in
peak demand reductions and annual electricity use reductions
0
2
4
6
−0.5 0.0 0.5 1.0 1.5 2.0Avg. Decrease in Summer Net Peak Demand (GW)
Dec
reas
e in
Ann
ual E
lect
ricity
Use
(TW
h)
End UseAppliances
Plug Loads
Pool Pumps
Water Heating
●● ●● ● ●●0
2
4
6
−0.5 0.0 0.5 1.0 1.5 2.0Avg. Decrease in Summer Net Peak Demand (GW)
Dec
reas
e in
Ann
ual E
lect
ricity
Use
(TW
h) ● DF
EE
EE+DF
End Use●
●
●
●
●
●
●
●
Appliances
Envelope
HVAC
Lighting
Plug Loads
Pool Pumps
Refrigeration
Water Heating
Scenario
22
Residential measures show a diversity of summer peak and
total impacts among scenario
●● ●● ● ●●0
2
4
6
−0.5 0.0 0.5 1.0 1.5 2.0Avg. Decrease in Summer Net Peak Demand (GW)
Dec
reas
e in
Ann
ual E
lect
ricity
Use
(TW
h) ● DF
EE
EE+DF
End Use●
●
●
●
●
●
●
●
Appliances
Envelope
HVAC
Lighting
Plug Loads
Pool Pumps
Refrigeration
Water Heating
●● ●● ● ●●0
2
4
6
−0.5 0.0 0.5 1.0 1.5 2.0Avg. Decrease in Summer Net Peak Demand (GW)
Dec
reas
e in
Ann
ual E
lect
ricity
Use
(TW
h) ● DF
EE
EE+DF
End Use●
●
●
●
●
●
●
●
Appliances
Envelope
HVAC
Lighting
Plug Loads
Pool Pumps
Refrigeration
Water Heating
Scenario
InsulationWindowsAir Sealing
Heat Pump Water Heater
Air Source Heat Pump
Preconditioning
Central AC
Clothes Dryer
23
Commercial measures also show significant diversity,
particularly among various HVAC strategies
●●
●
●●
0
2
4
6
−0.5 0.0 0.5 1.0 1.5 2.0Avg. Decrease in Summer Net Peak Demand (GW)
Dec
reas
e in
Ann
ual E
lect
ricity
Use
(TW
h) Portfolio● DF
EE
EE+DF
End Use●
●
●
●
●
●
Envelope
HVAC
Lighting
Plug Loads
Refrigeration
Water Heating
DF, EE+DF Plug Loads
Ice StorageHVAC + Ice Storage
HVAC
GTA
HVAC + GTAHVAC + GTA + Precooling
HVAC + Precooling
All Envelope
WindowsOpaque Envelope
Scenario
Conclusion
24
Results from Scout can help quantify the potential for individual technologies, portfolios to impact peak, total electricity use
A quantitative framework was established for time-sensitive evaluation of
building efficiency and flexibility measures, with results shown for a single
region (and available for other regions of the U.S.)
• Adapts the Scout impact analysis software to enable assessment of hourly building
electricity use under baseline conditions and with efficiency/flexibility measure adoption
• Leverages ResStock (residential) and DOE Prototype Models (commercial) to develop
hourly baseline and measure electric load shapes across 14 climate zones
Initial results show a large potential peak reduction resource from buildings,
interactions between efficiency and flexibility
Residential and commercial cooling, residential heating, and commercial plug
loads show large potential for impacts on peak electricity demand and overall
electricity use
25
Discussion1. Would you benefit from seeing similar results, specific
to your region?
2. Is the granularity of the results sufficient for your use?
3. What other data or information would be particularly
useful? How can we provide more helpful information
that can be applied to your needs?
4. How would/will you use data on GEB potential? (Inform
goal-setting? Inform program design?)
Additional Tool Development
Additional Tool Development and Next Steps
29
NREL is preparing to conduct additional analysis in 2020/2021 expanding the Scout modeling to provide:
• Estimate upper and lower bound of customer participation for various DR technologies.
• Speed of adoption of DR technologies and diffusion into customer base.
• Analysis of elasticity of customer participation when influenced through changes in compensation (i.e., rates, tariffs)
• Using this analysis, we are hoping to develop a tool or simple web-app allowing states and utilities to simulate novel compensation mechanisms and estimate their impact on participation.
Additional Tool Development and Next Steps
30
In order to conduct this analysis and begin developing a tool, NREL is looking for data and input including:
• Any anonymized data on residential or commercial utility demand response programs (i.e., participation rates, compensation structures, availability)
• Data detailing customer participation in any ‘EV’ or ‘smart’ rates.
Additionally, and fitting with the larger technical assistance efforts of GEB, NREL is looking for an initial cohort of users that may be interested in piloting and contributing to the specification of the tool.
This work was authored by Alliance for Sustainable Energy, LLC, the manager and operator of the National Renewable Energy Laboratory for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308 and by The Regents of the University of California, the manager and operator of the Lawrence Berkeley National Laboratory for the DOE under Contract No. DE-AC02-05CH11231. Funding was provided by the DOE Office of Energy Efficiency and Renewable Energy Building Technologies Office. The views expressed in this presentation and by the presenter do not necessarily represent the views of the DOE or the U.S. Government.
Thank youChioke Harris [email protected]
Joyce McLaren [email protected]
Sam Koebrich [email protected]
Scout: scout.energy.gov
ResStock: www.nrel.gov/buildings/resstock.html
Commercial Prototypes:https://www.energycodes.gov/development/commercial/prototype_models