HPWH Demand Flexibility Study - ACEEE · Study objective: Assess heat pump water heater demand...
Transcript of HPWH Demand Flexibility Study - ACEEE · Study objective: Assess heat pump water heater demand...
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HPWH Demand Flexibility Study
Preliminary Results
Pierre Delforge, NRDC, [email protected]
Ben Larson, Ecotope, [email protected]
Sponsored by:
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Agenda
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1. Objective and Scope
2. Methodology
3. Preliminary Findings
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Study objective: Assess heat pump water heater
demand flexibility potential in California
Shed on peak
Load up off peak
➢ How much can HPWH thermal storage reduce customer and grid costs?
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Why does NRDC care? Fully valuing benefits of advanced
electric water heating is key accelerate its adoption
Advanced Water Heating
Efficiency
Emissions
Grid Services
(thermal storage+)
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Key questions
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1. Myth or reality?
• Evaluate common perception that “HPWH are not well suited to DR…”
2. Thermal storage
• Evaluate HPWH thermal storage capacity and cost
3. Costs benefits
• Customer bills
• Utility/societal marginal costs
4. Energy efficiency
• Storage efficiency penalty
• Resistive element avoidance
5. GHG emissions benefits
• GHG reductions from HPWH load shifting
6. Load coincidence
• Grid peak
• Solar / duck curve
7. Assurance of service
• Ensure load shifting does not compromise customer hot water delivery
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Study approach
2 parts:
1. Simulation
Ecotope HPWHsim simulation model
2. Lab testing
4 HPWH models:
– Rheem, 50 gallons
– AO Smith, 66 gallons
– Bradford White, 80 gallons
– Sanden, 83 gallons
Calibrate Ecotope’s model
Validate simulation results
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Agenda
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1. Objective and Scope
2. Methodology
3. Preliminary Findings
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Price Signals: What to Optimize HPWH Operation for?
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Price Signal Objective
Utility marginal costs (PG&E 2024), including
energy, emissions, capacity, T&D, no retail rate
adder.
Grid energy/societal
cost perspective
Residential TOU rate: hypothetical “Flexible water
heating” rate, developed by NRDC based on PG&E
2024 marginal costs
Consumer bills
TDV + NEM2: CEC’s 30-yr present value projection
of grid energy costs + Net Energy Metering (NEM2)
CA 2019 building
code
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Hourly Marginal Costs (PG&E 2024) –Annual Average
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Chart shows annual average of hourly values for simplicity. Price schedule has 8760 hourly values for entire year.
-50
0
50
100
150
200
250
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
$/M
Wh
Hour of day
Distribution
Transmission
Capacity
Energy
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Average of CZ3($/kWh)
Hour of Day
Month 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
1 0.05 0.04 0.04 0.04 0.05 0.06 0.06 0.06 0.05 0.01 0.01 0.01 0.00 0.01 0.02 0.05 0.09 0.12 0.15 0.15 0.15 0.11 0.07 0.05
2 0.05 0.05 0.05 0.05 0.05 0.06 0.07 0.06 0.04 0.02 0.01 0.01 0.01 0.01 0.03 0.05 0.07 0.10 0.13 0.13 0.12 0.09 0.07 0.06
3 0.05 0.05 0.05 0.05 0.05 0.05 0.06 0.06 0.03 0.00 -0.01 -0.01 -0.01 -0.01 0.00 0.02 0.04 0.07 0.11 0.12 0.12 0.10 0.08 0.06
4 0.05 0.04 0.04 0.03 0.03 0.04 0.04 0.04 0.00 -0.02 -0.03 -0.03 -0.03 -0.03 -0.02 -0.02 0.00 0.04 0.07 0.10 0.11 0.11 0.08 0.06
5 0.05 0.04 0.03 0.03 0.03 0.03 0.04 0.02 0.00 -0.02 -0.02 -0.02 -0.02 -0.02 -0.01 0.00 0.03 0.06 0.08 0.10 0.10 0.10 0.08 0.06
6 0.06 0.05 0.05 0.04 0.04 0.04 0.04 0.02 0.00 -0.01 -0.01 -0.01 -0.01 -0.01 0.00 0.02 0.04 0.06 0.09 0.13 0.14 0.13 0.09 0.07
7 0.07 0.06 0.06 0.06 0.05 0.05 0.06 0.04 0.03 0.01 0.01 0.02 0.02 0.03 0.04 0.05 0.07 0.09 0.16 0.18 0.20 0.17 0.11 0.08
8 0.07 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.04 0.02 0.01 0.02 0.02 0.03 0.05 0.06 0.08 0.14 0.30 0.32 0.28 0.21 0.11 0.08
9 0.06 0.06 0.06 0.05 0.05 0.06 0.06 0.06 0.03 0.00 -0.01 -0.01 0.00 0.00 0.02 0.05 0.12 0.51 1.04 0.85 0.56 0.40 0.17 0.08
10 0.06 0.06 0.06 0.05 0.05 0.06 0.06 0.06 0.05 0.01 -0.01 0.00 0.00 0.00 0.02 0.04 0.07 0.13 0.34 0.27 0.19 0.15 0.10 0.07
11 0.06 0.06 0.05 0.05 0.06 0.06 0.07 0.06 0.03 0.01 0.00 0.00 0.00 0.01 0.03 0.06 0.08 0.11 0.13 0.12 0.12 0.10 0.08 0.07
12 0.06 0.05 0.05 0.05 0.06 0.06 0.07 0.07 0.05 0.02 0.01 0.01 0.01 0.02 0.04 0.07 0.09 0.13 0.15 0.15 0.14 0.11 0.07 0.06
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1. Chart shows monthly average of hourly values, $/kWh. Simulation uses hourly price schedule (8760 hours/year).
2. Calculation of total hourly marginal cost based on PG&E’s 2024 variable marginal costs. Includes energy, capacity, transmission, and distribution.
Hourly Marginal Costs (PG&E 2024) –Monthly Average
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Residential Time of Use: Hypothetical NRDC “Flexible Water Heating” RateReflective of PG&E 2024 marginal costs. 3x peak/off-peak price differential. Morning partial-peak to reflect morning energy marginal cost mini-peak.
$0.00
$0.05
$0.10
$0.15
$0.20
$0.25
$0.30
$0.35
$0.40
$0.45
$0.50
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Summer weekday Summer Weekend Winter Weekday Winter WeekendSummer weekday Summer weekend
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Control Strategies: How to optimize HPWH
operation for price schedules
3 levels of “smartness”, to evaluate their relative effectiveness:
1) Simplest: On/off timer
• Can be installed by user/electrician/plumber, available with current technology.
• Response only to a known, fixed price TOU price schedule
2) Smarter: Load-up / shed
• Load up to 135F/145F/155F during price trough, shed on peak, 125F rest of the
time
• Site or cloud controls
• Fixed price TOU price schedule
3) Smartest: Advanced price optimization, grid-connected
Hourly optimization based on look-ahead price signal received via outside
communication.
• Grid connectivity and new control functions needed
• Responds to any price schedule: dynamic or TOU
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Simulation Runs
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Optimization parameters: California climate zones:
Input values # of
values
Price signals: Utility marginal costs, TOU,
TDV-NEM2
3
Units: Hybrid HPWH (50, 65, 80-gal),
HP-only (50, 80-gal, Sanden Gen3-80),
ERWH (50, 65, 80-gal)
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Max water temp: 125, 135, 145, 155 4
Climate zones: all 16 CA climate zones 16
Draw patterns: 1-5 bedrooms
(from CEC compliance tool CBECC-Res)
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Control strategies: On/Off Timer,
Load-up/Shed, Advanced
3
Total Scenarios 31,680
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How to assess if a simulation scenario is
successful?
Simulation scenario successful if:
1. Controls do not compromise customer hot water
delivery
(# gallons delivered < 105F) < 0.3% *
AND
2. Costs no higher than uncontrolled case
Price arbitrage gains > cost of increased energy use
* Hiller C., ASHRAE 1998, DHW sizing guideline: 12 runouts / year. Corresponds to 0.3% missed
gallons
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Agenda
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1. Objective and Scope
2. Methodology
3. Preliminary Findings
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Compressor efficiency decreases and thermal
losses increase at higher set points
• Lab testing measured compressor efficiency at higher water
temperatures
➢ Trade-off between thermal storage and energy efficiency
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125F 135F 145F 155F
Coe
ffic
ient o
f p
erf
orm
ance
(eff
icie
ncy)
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Sample results
CZ12, 3 bedrooms, 50G ERWH, 66G HPWH
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Average of Res50
Average of Res50Electric resistance WH
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Sample results
CZ12, 3 bedrooms, 50G ERWH, 66G HPWH
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Average of Res50
Average of AOS50BC
Electric resistance WH
Heat pump WH, unmanaged
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Sample results
CZ12, 3 bedrooms, 50G ERWH, 66G HPWH
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Average of Res50
Average of AOS50BC
Average of AOS50OP
Electric resistance WH
Heat pump WH, unmanagedHeat pump WH, managed
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Sample results
CZ12, 3 bedrooms, 50G ERWH, 66G HPWH
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Average of Res50
Average of AOS50BC
Average of AOS50OP
Electric resistance WH
Heat pump WH, unmanagedHeat pump WH, managed
Efficiency
Thermal
storage
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Findings: Cost Savings by Control Strategies
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R134a Hybrid CO2 Heat Pump
➢ On/off strategy yields limited savings with R134a hybrid technology, and
causes significant runouts
➢ Advanced strategy is work-in-progress
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+0%
-15%-12%
-7%
-2%
-34% -36% -38%
+0% +2%
+8%
+14%
-50%
-40%
-30%
-20%
-10%
+0%
+10%
+20%
125 F 135 F 145 F 155 F
MAXIMUM SET POINT
Cost and Energy Savings By Set Point(Load-up/Shed Control Strategy)
Customer Costs Utility Costs Energy Use
Findings: Optimal Control Temperature
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Optimal temperature
for cost / efficiency
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Findings: HPWH thermal storage
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Modest storage capacity per HPWH, but significant in aggregate:
– Effective storage capacity: Roughly 0.3 to 0.6 kWh per evening per HPHW
– “If all water heaters in CA were managed…” it would provide 1 to 2 GW storage capacity
– Limited by peak-coincidence, efficiency penalty, and run-outs
– Varies by household size, climate zone, season.
Roughly half the cost per kWh of stored energy vs. battery storage:
– $80-$600 / kWh for HPWH thermal storage
– Compared to $400-$800 / kWh for battery storage
Sources and assumptions:
– HPWH storage: $50-$200 for mixing valve and control module
– Home batteries: $400-$800/kWh (Business Insider, “10 home batteries that rival Tesla's Powerwall 2”, May 18, 2017)
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Findings: operational costs savings
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Operational savings depend on what controls optimize for:
➢ Optimizing for customer bills yields significant cost savings for both customers and grid/society
➢ Optimizing for grid marginal costs can potentially increase customer bills.
➢ Would requires different mechanism to compensate customers, e.g. free or discounted water heater, annual cash payment, etc…
Customer bill
savings
Utility marginal
cost savings
Optimizing for
customer costs
(TOU)
-15% to -20% -35%
Optimizing for grid
marginal costs0% to +5% -60%
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Outcomes scorecard
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ERWH
Unmanaged
ERWH
Managed
HPWH
Unmanaged
HPWH
Managed
Peak coincidence
(5pm-9pm)20% 0% 15% 1%
Solar coincidence
(8am-5pm)50% 80% 55% 65%
Effective storage
capacity / evening- 1-2 kWh - 0.3-0.6 kWh
Energy use (kWh/y)2,570 2,640
(+3%)
1,030
(-60%)
1,040
(-60%/+1%)
Resistive kWh 100% 100% 16% 14%
Consumer bills$500 $380
(-25%)
$180
(-65%)
$150
(-70%/-16%)
Utility marginal costs$180 $80
(-55%)
$57
(-70%)
$37
(-80%/-35%)
3-bedroom house, CZ12, ERWH 50-gallon + 30F thermal storage /
HPWH 65-gallon +10F
* Pending further
control optimizations
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How about GHG reductions?
Wait, why such low GHG reductions from load management?
➢ GHG bean counting issue:
o CPUC ACM emissions factors have low peak/off-peak differentiation
o Uses RPS as both floor and ceiling
o Not appropriate to value load shifting
• Highly differentiated emissions factors could yield > 50% GHG reductions!
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ERWH
Unmanaged
ERWH
Managed
HPWH
Unmanaged
HPWH
Managed
CO2e (kg)700 650
(-7%)
270
(-60%)
265
(-61%/-2%)
* Avoided Cost Model: http://www.cpuc.ca.gov/General.aspx?id=5267
* RPS: Renewable Portfolio Standard
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 2 4 6 8 10 12 14 16 18 20 22
CO
2e
(sh
ort
to
n /
MW
h)
Hour of Day
Hourly Emissions Factors
ACM 2030 Alternative Emissions Factor (illustrative)
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Key Takeaways
1. Significant potential for cost-effective HPWH load shifting
– 130-140F sweet spot (“sweet range”)
– 15-20% customer savings potential
– 30-60% utility savings potential
2. Requires:
1. Smart control technology
2. Load flexibility programs
3. TOU rates: cost-reflective and sufficiently differentiated OR alternative customer compensation mechanism
4. Incentive programs and supportive regulatory environment (e.g. building code)
5. Appropriate GHG accounting methodology for load shifting
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Thanks! Questions?
Pierre Delforge, [email protected]
Ben Larson, [email protected]
Project team: Nick Carew and Logan Piepmeier (Ecotope),
Eddie Huestis, Peter Grant (Frontier Energy), Mary Reagan
Steering Committee: David Rivers (SCE), Owen Howlett
(SMUD), Beckie Menten (MCE), Rachel Kuykendall (SCP),
Geoff Wickes (NEEA), Christine Tam (Palo Alto), Bruce
Wilcox, Jim Lutz, Ram Narayanamurthy (EPRI)