PREDICTIVE PRE-COOLING CONTROL FOR LOW LIFT RADIANT COOLING USING BUILDING THERMAL MASS.
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Transcript of PREDICTIVE PRE-COOLING CONTROL FOR LOW LIFT RADIANT COOLING USING BUILDING THERMAL MASS.
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PREDICTIVE PRE-COOLING CONTROL FOR LOW LIFT RADIANT COOLING USING BUILDING THERMAL MASS
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Motivation: energy and climate
Addressing energy, climate and development challenges
Buildings use 40% of energy and 70% of electricity1
Buildings represent low cost carbon emission reduction potential2
Most rapidly developing cities in cooling-dominated climates3
Increasing demand for A/C where grids are already stressed4
1. USDOE 2006. Building Energy Databook2. IPCC 2007. Fourth Assessment Report3. Sivak 2009. Energy Policy 374. McNeil and Letschert 2007. ECEEE 2007 Summer Study
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Predictive pre-cooling control for low lift radiant cooling using building thermal mass can lead to significant sensible cooling energy savings.
What is a low-lift cooling system (LLCS)?
How is it implemented using building thermal
mass?
How is predictive pre-cooling control achieved?
How significant are the energy savings?
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Low lift cooling systems (LLCS)
Cooling strategy that lever-ages existing technologies:
Variable speed compressor
Variable flow hydronic distribution
Radiant cooling Thermal energy storage
(TES) Pre-cooling control Dedicated outdoor air
system (DOAS)
…to save cooling energy:
Operate chillers at part load and low lift more of the time by predictive pre-cooling control
Night time operation Load leveling Radiant cooling
Reduce transport energy req’d to deliver cooling to a space
Efficient dehumidification
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ML\wk\BT\finalFY07\TOSenergy\TOSmoEnergy5.xls kWh|case5C
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
Houston Memphis Los Angeles Baltimore Chicago
Climate (Represented by City)
Sy
stem
In
pu
t E
ner
gy
(kW
h/y
r)
2-Speed Chiller, VAV
Var-Speed Chiller, VAV
2-Speed Chiller, VAV, TES
Var-Speed Chiller, VAV, TES
2-Speed Chiller, RCP/DOAS
Var-Speed Chiller, RCP/DOAS
2-Speed Chiller, RCP/DOAS, TES
Var-Speed Chiller, RCP/DOAS, TES
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Simulation of LLCS shows significant cooling energy savings potentialSimulated energy savings: 12 building types in 16 cities relative to a DOE benchmark HVAC system
Total annual cooling energy savings 37 to 84% in standard buildings, average 60-70% -9 to 70% in high performance buildings, average
40-60%
(Katipamula et al 2010, PNNL-19114)
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LLCS cooling energy savings in Atlanta
Simulated total annual cooling energy savings: in a medium size office building in Atlanta over a full year with respect to a variable air volume (VAV) system
served by a variable-speed chiller with an economizer and ideal storage
similar to a split-system air conditioner (SSAC) used as an experimental base line, with some differences
28 % annual cooling energy savings
(Katipamula et al 2010, PNNL-19114)
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0
20
40
T
-
Tem
pera
ture
(°C
)
60
1 1.2 1.4S - Entropy (kJ/kg-K)
1.6 1.8
100
200
300
400500600700 psia
Radiant cooling and variable speed pump
Predictive pre-cooling of thermal storage and variable speed fans
Low-lift refers to a lower temperature difference between evaporation and condensation
Low lift vapor compression cycle requires less work
Vapor compression cycles shown under typical and low-lift conditions
Variable-speed compressor adapts exactly and efficiently to conditions
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0
0.5
1
0
50
100
0
10
20
30
40
50
part load ratio
Chicago---baseline
outdoor dry-bulb temperature (deg F)
FLE
OH
5
10
15
20
25
30
35
40
45
0
0.5
1
0
50
100
0
10
20
30
40
50
part load ratio
Chicago---variable-speed chiller with RCP/DOAS
outdoor dry-bulb temperature (deg F)
FLE
OH
5
10
15
20
25
30
35
40
24
1 )(
)(
t tCOP
tQJ
24
1
24
1
)()(tt
Load tQtQ
where COP = f(Tx,Tz,Q) [kWth/kWe],
subject to just satisfying the daily load requirement:
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Chi
ller
Sys
tem
Spe
cific
Pow
er,
1/C
OP
(kW
/kW
)
Part-Load Fraction
43.3337.7832.2226.6721.1115.56 10
Tx (C)
LLCS operates a chiller at low lift more of the time
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Are the simulation results REAL?
Develop the pre-cooling control and experimentally test an LLCS
Optimize control of a chiller over a 24-hour look-ahead schedule to minimize daily chiller energy consumption by operating at low lift conditions while maintaining thermal comfort
Informed by data-driven zone temperature response models and forecasts of climate conditions and loads
Informed by a chiller performance model that predicts chiller power and cooling rate at future conditions for a chosen control
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Low lift chiller performance
Predictive pre-cooling control requires a chiller model to predict chiller power consumption, cooling capacity and COP at low-lift
To identify a chiller model under low lift conditions:
Build a heat pump test stand
Experimentally test the performance of a heat pump at low pressure ratios, which was later converted to a chiller for LLCS
Identify empirical model of chiller performance useful for predictive control
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Outdoor temp
(C)
Indoor temp (C)
Compressor
speed (Hz)
Fan speed (RPM)
1522.530
37.545
142434
19306095
30045060075090010501200
Measured heat pump performance at many steady state conditions
Tested 131 combinations of the following conditions
To model chiller power, cooling rate, and COP as a function of all 4 variables
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1 2 3 4 50
0.2
0.4
0.6
0.8
Pressure ratio (kPa/kPa)
Com
pres
sor
EIR
(kW
e/kW
th)
1 2 3 4 50
5
10
15
20
25
Pressure ratio (kPa/kPa)C
ompr
esso
r C
OP
(kW
th/k
We)
1 2 3 4 50
0.2
0.4
0.6
0.8
Pressure ratio (kPa/kPa)
Out
door
uni
t E
IR (
kWe/
kWth
)
1 2 3 4 50
5
10
15
Pressure ratio (kPa/kPa)
Out
door
uni
t C
OP
(kW
th/k
We)
EER
34
17
51
Typical operationCOP ~ 3.5
Low lift operation
COP ~ 5-10
Test results show expected higher COPs at low lift conditions
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4-variable cubic polynomial models
0 500 1000 15000
500
1000
1500
2000Measured vs Predicted Power Consumption
Measure power consumption (W)
Mod
el p
redi
cted
pow
er c
onsu
mpt
ion
(W)
Relative RMSE = 5.5 %Absolute RMSE = 27 W
0 1000 2000 3000 4000 50000
1000
2000
3000
4000
5000Measured vs Predicted Cooling Capacity
Measured cooling capacity (W)
Model pre
dic
ted c
oolin
g c
apacity (
W)
Relative RMSE = 1.7 %Absolute RMSE = 40 W
),,T,T(fP fancompressornevaporatiooutdoorair ),,T,T(gQC fancompressornevaporatiooutdoorair
Empirical models accurately represent chiller cooling capacity, power and COP
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Zone temperature model identification
LLCS control requires zone temperature response models to predict temperatures and chiller performance
Develop data-driven models from which to predict Zone operative temperature (OPT) The temperature underneath the concrete slab (UST) Return water temperature (RWT) and ultimately chiller
evaporating temperature (EVT) from which chiller power and cooling rate can be calculated
Assume ideal forecasts of outdoor climate and internal loads
Implement data-driven modeling on a real test chamber
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OPT = operative temperatureOAT = outdoor air temperatureAAT = adjacent zone air temperatureQI = heat rate from internal loadsQC = cooling rate from mechanical
systema,b,c,d,e = weights for time series of
each variable
(Inverse) comprehensive room transfer function (CRTF) [Seem 1987]
Steady state heat transfer physics constrain CRTF coefficients
Nt
tkk
Nt
tkk
Nt
tkk
Nt
1tk
Nt
tkkk )k(QCe)k(QId)k(AATc)k(OATb)k(OPTa)t(OPT
Existing transfer function modeling methods can be applied to predict zone temperature
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Chiller power and cooling rate depend on evaporating temperature, which is coupled to return water temperature, and thus to the state of thermal energy storage, in this case a radiant
concrete floor
Predict concrete floor under-slab temperature (UST) using a transfer function model
Predict return water temperature (RWT) using a low-order transfer function model in UST and cooling rate QC
Superheat relates RWT to evaporating temperature (EVT)
2t
tkk
2t
tkk
2t
1tkk )k(QCh)k(USTg)k(RWTf)t(RWT
Evaporating temperature is predicted from intermediate temperature response models
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Temperature sensors: OPT, OAT, AAT, UST, RWT
Power to internal loads: QI
Radiant concrete floor cooling rate: QC
Data-driven models identified for a test chamber with a radiant concrete floor
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Models validated based on accuracy of predicting different data 24-hours-ahead
0 20 40 60 80 100 120275
280
285
290
295
300
305
hour
tem
p (K
)
Cooling validation data temperatures
OpTZAT
MRT
AAT
OAT
USTRWT
0 20 40 60 80 100 120-1500
-1000
-500
0
500
1000
hour
heat
rat
e (W
)
Cooling validation data heat inputs
Internal load
Floor cooling
Sample validation temperature data
Sample validation thermal load data
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0 5 10 15 20 25292
294
296
298
300
hour
tem
p (K
)
ZAT
p-ZAT
0 5 10 15 20 25292
294
296
298
hour
tem
p (K
)
MRT
p-MRT
0 5 10 15 20 25292
294
296
298
hour
tem
p (K
)
OpT
p-OpT
0 5 10 15 20 25286
288
290
292
294
hour
tem
p (K
)
UST
p-UST
0 2 4 6 8 10275
280
285
290
295
hour
tem
p (K
)
Validation data RMSEs for 24 hour look ahead: RMSE ZAT = 0.06 KRMSE MRT = 0.09 KRMSE OPT = 0.08 KRMSE UST = 0.15 KRMSE RWT = 0.84 K p = N step ahead predicted variable
RWT
p-RWT
Operative temperature (OPT) Under-slab temperature (UST)
Return water temperature (RWT)Root mean square error (RMSE) for 24 hour ahead prediction of validation data
OPT RMSE = 0.08 KUST RMSE = 0.15 KRWT RMSE = 0.84 K
Transfer function models accurately predict zone temperatures 24-hours-ahead
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rt = electric rate at time t, or one for energy optimizationPt = system power consumption as a function of past compressor speeds and exogenous variables = weight for operative temperature penaltyPENOPTt = operative temperature penalty when OPT exceeds ASHRAE 55 comfort conditionsPENEVTt = evaporative temperature penalty for
temperatures below freezing = Vector of 24 compressor speeds, one for each hour of the 24 hours ahead
24
1ttttt )(PENEVT)(PENOPT)(PrJminarg
Optimization minimizes energy, maintains comfort, and avoids freezing the chiller
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Pattern search initial guess at current hour
Pattern search algorithm determines optimal
compressor speed schedule for the next 24 hours
Operate chiller for one hour at optimal state
24-hour-ahead forecasts of outdoor air temperature, adjacent zone temperature, and internal loads (OAT, AAT, QI)
0241i0241iinitial 0
optimal241ioptimal
optimal,1
)RWT,OAT,(ff optimal,1
0,optimal242iinitial
Perform optimization at every hour with current building data and new forecasts
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Pre-cooling the concrete floor maintains comfort and reduces energy consumption
6 pm 12 am 6 am 12 pm 6 pm0
10
20
30
40
hour
Tem
pera
ture
(C
)
Zone temperature response
6 pm 12 am 6 am 12 pm 6 pm0
500
1000
1500
2000
Hour
Cum
ulua
tive
ener
gy
cons
umpt
ion
(Wh)
Chiller energy consumption
6 pm 12 am 6 am 12 pm 6 pm0
50
100
150
200
250
Hour
Chi
ller
Pow
er (
W)
Chiller power
6 pm 12 am 6 am 12 pm 6 pm0
5
10
15
20
25
30
hour
Com
pres
sor
spee
d (H
z)
Chiller control schedule
OPTOAT
RWT
UST
EVTOPT
max
OPTmin
Total energy consumption over 24 hours = 1921 Wh
Occupied
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Pre-cooling the concrete floor maintains comfort and reduces energy consumption
6 pm 12 am 6 am 12 pm 6 pm0
10
20
30
40
hour
Tem
pera
ture
(C
)
Zone temperature response
6 pm 12 am 6 am 12 pm 6 pm0
500
1000
1500
2000
Hour
Cum
ulua
tive
ener
gy
cons
umpt
ion
(Wh)
Chiller energy consumption
6 pm 12 am 6 am 12 pm 6 pm0
50
100
150
200
250
Hour
Chi
ller
Pow
er (
W)
Chiller power
6 pm 12 am 6 am 12 pm 6 pm0
5
10
15
20
25
30
hour
Com
pres
sor
spee
d (H
z)
Chiller control schedule
OPTOAT
RWT
UST
EVTOPT
max
OPTmin
Total energy consumption over 24 hours = 1921 Wh
Occupied
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Experimental assessment of LLCS
Prior research shows dramatic savings from LLCS, but
Based entirely on simulation Assumes idealized thermal storage, not a real
concrete floor Chiller power and cooling rate are not coupled to
thermal storage, as it is for a concrete radiant floor
How real are these savings?
What practical technical obstacles exist?
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Build chiller by modifying an inverter heat pump outdoor unit
Install a radiant concrete floor in MIT and Masdar test rooms
Implement the pre-cooling optimization control
Test LLCS under a typical summer week in Atlanta (and next Phoenix) subject to internal loads—then in real AD weather
Compar the LLCS performance to a baseline system - a high efficiency (SEER~16, SCOP~4.7) variable capacity split-system air conditioner (SSAC)
Building and testing a Low-Lift System
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CONDENSER
ELECTRONIC EXPANSION VALVE
COMPRESSOR
BPHX
TO RADIANT FLOOR
FROM RADIANT FLOOR
FROM INDOOR UNIT (CLOSED)
TO INDOOR UNIT (CLOSED)
TEST CHAMBER CLIMATE CHAMBER
IDENTICAL FOR LLCS AND BASE CASE SSAC
LLCS and SSAC use the same outdoor unit
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TEST CHAMBER CLIMATE CHAMBER
BPHX
WATER PUMP
TO CHILLER
FROM CHILLER
FILTER
EXPANSION TANK
12’
17’
RADIANT MANIFOLD
RADIANT FLOOR
LLCS provides chilled water to a radiant concrete floor (thermal energy storage)
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Chiller/heat pump
Radiant concrete floor
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Atlanta typical summer week and standard efficiency loads Based on typical meteorological year weather data Assuming two occupants and ASHRAE 90.1 2004
loads
Run LLCS for one week *(after a stabilization period)Run split-system air conditioner (SSAC) for one week* Compare sensible cooling only Mixing fan treated as an internal load
Repeat for Phoenix typical summer week, high efficiency loads – to be completed after climate chamber HVAC repairs
Tested LLCS for a typical summer week in Atlanta subject to standard internal loads
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0 20 40 60 80 100 120 140 160
20
25
30
35
40
Tem
pera
ture
(C)
Hours
Atlanta typical summer week OAT
0 20 40 60 80 100 120 140 160
20
25
30
35
40
Tem
pera
ture
(C)
Hours
Phoenix typical summer week OAT
5 10 15 200
100
200
300
400
500
600
700
800
Load
(W
)
Hour
Standard efficiency load schedule
Peak load density = 3.4 W/sqft
5 10 15 200
100
200
300
400
500
600
700
800
Load
(W
)
Hour
High efficiency load schedule
Peak load density = 2 W/sqft
5 10 15 200
100
200
300
400
500
600
700
800
Load
(W
)
Hour
Standard efficiency load schedule
Peak load density = 3.4 W/sqft
5 10 15 200
100
200
300
400
500
600
700
800
Load
(W
)
Hour
High efficiency load schedule
Peak load density = 2 W/sqft
0 20 40 60 80 100 120 140 160
20
25
30
35
40
Tem
pera
ture
(C)
Hours
Atlanta typical summer week OAT
0 20 40 60 80 100 120 140 160
20
25
30
35
40
Tem
pera
ture
(C)
Hours
Phoenix typical summer week OAT
Atl
anta
te
stPhoen
ix
test
Outdoor climate conditions
Internal loads
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LLCS energy consumption
(Wh)
SSAC (SEER~16) energy consumption (Wh)
Measured
10,982
Measured 14,645 25%
Deducting latent cooling1
14,053 22%2
1 Latent cooling is deducted by measuring condensate water from the SSAC, calculating the total enthalpy associated with its condensation, and dividing it by the average SSAC COP over the week.
2 Assuming no latent cooling by the LLCS
LLCS ENERGY SAVINGS relative to SSAC in Atlanta subject to standard loads
Similar to simulated total annual cooling energy savings, 28 percent, by (Katipamula et al 2010)
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SSAC Thermostatic
control
SSAC Predictive
control
Radiant panel predictive
control
Weekly average COP 4.32 4.97 7.46
Cooling delivered (Wh) -47,940 -39,920 -39,420
Simulated energy (Wh) 11,110 8,038 5,285
Measured energy (Wh) 14,053 n/a n/a
Error in simulation 20.9% n/a n/a
Savings relative to simulated base case
base 27.6% 52.3%
Simulated the performance of predictive pre-cooling control on the SSAC and with radiant ceiling panels
Significant savings potential for predictive control on other systems
Predictive pre-cooling control can be applied to other systems to achieve low lift
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Masdar Test Building
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Slab Temperatures
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Freshly Poured
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Test Building Instrumentation
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MASDAR CITY PHASE 1B DEMO
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Future LLCS research
Refine LLCS methods Determine evaporating temperature without measuring
under-slab concrete temperature Refine temperature response model identification
methods, e.g. real-time model identification with updated training data
Simplify and improve the pre-cooling optimization and control
Combine concrete-core with direct cooling (e.g. chilled beams) and adapt the predictive control algorithm
Perform testing subject to actual outdoor conditions at MASDAR
Install and test LLCS in a real building (medium size office)
Pre-cooling control for other LLCS configurations
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Future of LLCS in real buildings Concrete-core and radiant systems gaining market
share, and familiarity among architects and engineers (primarily in Europe)
Automation systems are becoming more prevalent/sophisticated
Capital cost savings for LLCS in medium office buildings, -0.58 $/sqft incremental cost relative to $7.91/sqft base cost1
Adapt components of LLCS to existing buildings and different new and existing building types, e.g. Direct cooling combined with active or passive
thermal storage Radiant concrete-core using a “topping slab” for
existing buildings Adapt low-lift predictive control to existing concrete-
core buildings
1. Katipamula et al 2010, PNNL-19114
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Summary
Detailed data on the performance of an inverter-driven rolling-piston compressor heat pump over a wide range of conditions including low lift, over a capacity range of 5:1
Methodology for integrating chiller models and zone temperature response models into a pre-cooling optimization algorithm for controlling LLCS with real building thermal mass
Experimental validation of significant LLCS sensible cooling energy savings relative to a state-of-the-art split system air conditioner (SEER 16), 25 percent in Atlanta with standard efficiency internal loads. >50% clearly achievable.