Model predictive control for energy efficient

cooling and dehumidification

Tea Zakula

Leslie Norford

Peter Armstrong

Introduction Software environment LLCS assessment Conclusion

Motivation

IBO Workshop, Boulder, Colorado, June 2013

Total U.S. energy consumption

U.S. EU World China India0

10002000300040005000600070008000

kg o

il eq

uiva

lent

/cap

ita

average

19%

22%

31%

28%

Commercialbuildings

Residentialbuildings

Transportation

Industrial

Primary energy use

Source: The World Bank (2010) Source: U.S. Energy Information Administration (2012)

1

Introduction Software environment LLCS assessment Conclusion

Low-Lift Cooling System (LLCS) delivers cold water to Thermally Activated Building Surfaces

(TABS). Cooling is optimized by the Model Predictive Control (MPC) algorithm.

Model predictive

control

Heat pump

Cold water

Building with TABS and

thermal storage

Dedicated outdoor air

system (DOAS)

Ventilation and dehumidification air

LLCS description

IBO Workshop, Boulder, Colorado, June 20132

Introduction Software environment LLCS assessment Conclusion

Model Predictive Control (MPC) – Cooling is optimized over 24-hours for the lowest energy

consumption. Building is precooled during night when the cooling process is more efficient.

LLCS description

OccupiedNon-occupied Non-occupied

Zon

e te

mpe

ratu

re Temperature limits

Optimized cooling

Model predictive

control

Heat load predictions

Cool

IBO Workshop, Boulder, Colorado, June 20133

Introduction Software environment LLCS assessment Conclusion

T

s

Toutisde

Tfluid

With conventional systemWith low-lift cooling technology

Cooling cycle in T-s diagramThermally Activated Building Surface (TABS) - increases evaporating temperature and reduces transport power.

Thermal storage – reduces condensing temperature, peak loads and daytime loads.Use building as thermal storage saves useful building space.

Dedicated Outdoor Air System (DOAS) – provides better ventilation and humidity control.

Model Predictive Control (MPC) – enables strategic cooling, shifting cooling toward night time.

LLCS savings strategies

IBO Workshop, Boulder, Colorado, June 20134

Introduction Software environment LLCS assessment Conclusion

Gayeski’s experimental measurements (2010) – Tested LLCS in experimental room at MIT. For a typical summer week showed 25% electricity savings for Atlanta and 19% for Phoenix climate.

Previous work on LLCS

Pacific Northwest National Laboratory (2009, 2010) – Proposed LLCS and assessed its performance for 16 different climates and several building types. Showed annual electricity savings up to 70%.

IBO Workshop, Boulder, Colorado, June 20135

Introduction Software environment LLCS assessment Conclusion

Model predictive

control

Heat load predictions

Heat pump

Cold water

Building with TABS and

thermal storage

Building data

Dedicated outdoor air

system (DOAS)

Ventilation and dehumidification air

Building model

Software environment components

IBO Workshop, Boulder, Colorado, June 20136

Introduction Software environment LLCS assessment Conclusion

Building model

Data-driven (inverse) model

- Used for optimization

- Validated using TRNSYS model

TRNSYS model

- Used after the optimization to give more accurate building response

- Validated using experimental measurements

Building model

IBO Workshop, Boulder, Colorado, June 20137

Introduction Software environment LLCS assessment Conclusion

Inverse building model

Inverse model of the experimental room – proposed by Armstrong (2009)

Coefficients a … g are found using linear regression to TRNSYS data.

For zone, operative and floor temperature:

For water return temperature:

𝑇𝑤 ,𝑟𝑒𝑡𝑢𝑟𝑛=∑𝑘=1

𝑛

𝒆𝑘𝑇𝑤 ,𝑟𝑒𝑡𝑢𝑟𝑛❑𝑘 +∑

𝑘=0

𝑛

𝒇 𝑘𝑇 𝑓𝑙𝑜𝑜𝑟❑𝑘 +∑

𝑘=0

𝑛

𝒈𝑘𝑄𝑐𝑜𝑜𝑙𝑖𝑛𝑔❑𝑘

𝑇=∑𝑘=1

𝑛

𝒂𝑘𝑇❑𝑘+∑

𝑘=0

𝑛

𝒃𝑘𝑇 𝑜𝑢𝑡𝑖𝑠𝑑𝑒❑𝑘 +∑

𝑘=0

𝑛

𝒄𝑘𝑄𝑖𝑛𝑡𝑒𝑟𝑛𝑎𝑙❑𝑘 +∑

𝑘=0

𝑛

𝒅𝑘𝑄𝑐𝑜𝑜𝑙𝑖𝑛𝑔❑𝑘

k=3 k=2 k=1 k=0Time

Tpast

Toutside,past+present

Tpresent

Qinternal,past+present

Qcooling,past+present

IBO Workshop, Boulder, Colorado, June 20138

Introduction Software environment LLCS assessment Conclusion

Software environment components

Model predictive

control

Heat load predictions

Cold water

Building with TABS and

thermal storage

Building data

Dedicated outdoor air

system (DOAS)

Ventilation and dehumidification air

Heat pump

Heat pump optimization

IBO Workshop, Boulder, Colorado, June 20139

Introduction Software environment LLCS assessment Conclusion

Parameters required to achieve the optimal point

Toutside = 30 oC

Tw,return = 20 oCTw,return = 17 oCTw,return = 14 oCTw,return = 11 oC

Specific power consumption in the optimal point

Qcooling/Qcooling,max

• Evaporator airflow • Condenser airflow • Compressor frequency• Subcooling on condenser

Physics based heat pump model (Zakula, 2010) used to find power consumption in optimal point and optimal set of parameters required to achieve that point.

1/C

OP

Results of static optimization are used in software environment to calculate

electricity required for cooling.

Heat pump optimization results

IBO Workshop, Boulder, Colorado, June 201310

Introduction Software environment LLCS assessment Conclusion

Model predictive

control

Heat load predictions

Heat pump

Cold water

Building with TABS and

thermal storage

Building data

Dedicated outdoor air

system (DOAS)

Ventilation and dehumidification air

Model predictive control

Software environment components

IBO Workshop, Boulder, Colorado, June 201311

Introduction Software environment LLCS assessment Conclusion

Model predictive control

0 1 2 23

Qc0 Qc1 Qc2 Qc23

Planning horizon

Find the optimal cooling rates for the lowest electricity consumption over the planning horizon.

Objective funtion=∑i=0

23

Cooling power+∑i=0

23

Transport power+∑i=0

23

Temperature penalty

Using heat pump optimization results Using inverse building model

Time (h)

Cooling rate optimization

IBO Workshop, Boulder, Colorado, June 201312

Introduction Software environment LLCS assessment Conclusion

MATLABOptimization of cooling rates

• Building response from inverse model• Cooling electricity consumption from

heat pump static optimization results

TRNSYS

Buildingthermal response

Optimal values

[Qc0, Qc2, …, Qc23]

Building response

Optimization

Tz,history, To,history, Tfloor,history,Tw,history

Qc … cooling rateTw … water temperatureTo … operative temperatureTz … room temperatureTfloor … floor temperature

Model predictive control

Execution

IBO Workshop, Boulder, Colorado, June 201313

Introduction Software environment LLCS assessment Conclusion

• Model is fast enough for implementation in a real building (computational time to optimize one week is 5 – 10 min).

Model predictive control main findings

• Software environment can be used for the LLCS analysis, but also for the analysis of other heating and cooling systems that employ MPC.

Building withLLCS

MPC

Building withVAV

MPC

Building withsplit-system

MPC

IBO Workshop, Boulder, Colorado, June 201314

Zo

ne

te

mp

era

ture

(o C

)

Time (h)

• Inverse model can adequately replicate results from TRNSYS.

Inverse modelTRNSYS

Introduction Software environment LLCS assessment Conclusion

Model predictive

control

Heat load predictions

Heat pump

Cold water

Building with TABS and

thermal storage

Building data

Dedicated outdoor air

system (DOAS)

Ventilation and dehumidification air

DOAS configurations

Software environment components

IBO Workshop, Boulder, Colorado, June 201315

Introduction Software environment LLCS assessment Conclusion

Proposed DOAS configurations

Enthalpy wheelSystem C

Eva

po

rato

r

Co

nd

ense

r

Co

nd

ense

r

Enthalpy wheel

System ECondenser

Run-around heat pipe

Eva

po

rato

r

Enthalpy wheelSystem B

Eva

po

rato

r

Co

nd

ense

r

Enthalpy wheel

System A

Eva

po

rato

r

Condenser

Enthalpy wheelSystem D

Eva

po

rato

r

Co

nd

ense

r

Condenser

IBO Workshop, Boulder, Colorado, June 201316

Introduction Software environment LLCS assessment Conclusion

LLCS vs conventional VAV

LLCS vs VAV with MPC

LLCS vs conventional split-system

LLCS vs split-system with MPC

IBO Workshop, Boulder, Colorado, June 201317

Introduction Software environment LLCS assessment Conclusion

LLCS vs conventional VAV

Fresh air for ventilation and dehumidification

DOAS

Water for cooling

Air for cooling, ventilation and dehumidification

LLCS VAV

Operated under MPCwith temperatures allowed to float

between 20 and 25oC during occupied hours

Operated under conventional control (only during the operating hours to

maintain constant temperature of 22.5oC)

Simulating a typical summer week and 22-week period across 16 climates assuming standard internal loads (from people and equipment) for an office.

Condenser Condenser

Eva

pora

tor

Eva

pora

tor

IBO Workshop, Boulder, Colorado, June 201318

Introduction Software environment LLCS assessment Conclusion

DOAS configurations analyzed with LLCS

Enthalpy wheelSystem C

Eva

po

rato

r

Co

nd

ense

r

Co

nd

ense

r

Enthalpy wheel

System ECondenser

Run-around heat pipe

Eva

po

rato

r

Enthalpy wheelSystem B

Eva

po

rato

r

Co

nd

ense

r

Enthalpy wheel

System A

Eva

po

rato

r

Condenser

Enthalpy wheelSystem D

Eva

po

rato

r

Co

nd

ense

r

Condenser

IBO Workshop, Boulder, Colorado, June 201319

Introduction Software environment LLCS assessment Conclusion

Results: zone temperatures and cooling rates for Phoenix climate

Tem

pera

ture

(oC

)

Time (h)

LLCS under MPC Conventional VAV

Internal sensible gainTABS cooling rateDOAS cooling rate

Tem

pera

ture

(oC

)

Time (h)

Time (h)

The

rmal

load

(W

)

The

rmal

load

(W

)

Time (h)

Internal sensible gain

VAV cooling rate

LLCS vs conventional VAV

Temperature limitsOperative temperature

IBO Workshop, Boulder, Colorado, June 201320

Introduction Software environment LLCS assessment Conclusion

LLCS vs conventional VAV

Results: LLCS electricity savings for a typical summer week

Ele

ctric

ity s

avin

gs (

%)

A LLCS with condenser placed outside C LLCS with parallel condensers, one in supply, the other in return streamD LLCS with parallel condensers, one in supply stream, the other outsideE LLCS with condenser placed outside and run-round heat pipe

→ typical and best performing

→ second best performing

IBO Workshop, Boulder, Colorado, June 201321

Introduction Software environment LLCS assessment Conclusion

VAV under MPC

Model predictive

control

Heat load predictions

Heat pump

Building data

Cold air

IBO Workshop, Boulder, Colorado, June 201322

Introduction Software environment LLCS assessment Conclusion

LLCS vs VAV under MPC

Operated under MPCwith temperatures allowed to float

between 20 and 25oC during occupied hours

Operated under MPCwith temperatures allowed to float

between 20 and 25oC during occupied hours

Simulating a typical summer week and 22-week period across 16 climates assuming standard internal loads (from people and equipment) for an office.

Fresh air for ventilation and dehumidification

DOAS

Water for cooling

LLCS

Condenser

Eva

pora

tor

Air for cooling, ventilation and dehumidification

Condenser

Eva

pora

tor

VAV

IBO Workshop, Boulder, Colorado, June 201323

Introduction Software environment LLCS assessment Conclusion

Results: zone temperatures and cooling rates for Phoenix climate

Tem

pera

ture

(oC

)

Time (h)

LLCS under MPC VAV under MPC

Tem

pera

ture

(oC

)

Time (h)

Time (h)

The

rmal

load

(W

)

The

rmal

load

(W

)

Time (h)

LLCS vs VAV under MPC

Internal sensible gainTABS cooling rateDOAS cooling rate

Internal sensible gain

VAV cooling rate

Temperature limitsOperative temperature

IBO Workshop, Boulder, Colorado, June 201324

Introduction Software environment LLCS assessment Conclusion

Results: LLCS electricity savings for a typical summer week*E

lect

ricity

sav

ings

(%

)

LLCS vs VAV under MPC

Results: LLCS electricity savings from May 1st – September 30th*

*LLCS assumes simple DOAS (system A)

Ele

ctric

ity s

avin

gs (

%)

IBO Workshop, Boulder, Colorado, June 201325

Introduction Software environment LLCS assessment Conclusion

Split-system

Operated under MPCwith temperatures allowed to float

between 20 and 25oC during occupied hours

Operated under conventional control (only during the operating hours to

maintain constant temperature of 22.5oC)

LLCS vs conventional split-system

Simulating a typical summer week in Atlanta and Phoenix, and taking into account only sensible cooling (no ventilation and dehumidification system).

Water for cooling

LLCS

Condenser

Eva

pora

tor

Lower electricity consumption 33% for Atlanta36% for Phoenix

Condenser

Evaporator

IBO Workshop, Boulder, Colorado, June 201326

Introduction Software environment LLCS assessment Conclusion

Split-system under MPC

Heat pump

Heat pump

Model predictive

control

Heat load predictions

IBO Workshop, Boulder, Colorado, June 201327

Introduction Software environment LLCS assessment Conclusion

Split-system

Operated under MPCwith temperatures allowed to float

between 20 and 25oC during occupied hours

LLCS vs split-system under MPC

Simulating a typical summer week in Atlanta and Phoenix, and taking into account only sensible cooling (no ventilation and dehumidification system).

Operated under MPCwith temperatures allowed to float

between 20 and 25oC during occupied hours

Water for cooling

LLCS

Condenser

Eva

pora

tor

Condenser

Evaporator

Lower electricity consumption 19% for Atlanta11% for Phoenix

IBO Workshop, Boulder, Colorado, June 201328

Introduction Software environment LLCS assessment Conclusion

• LLCS saved up to 50% electricity relative to the VAV system under conventional control and up 23% electricity relative to the VAV system under MPC.

• A split-system under MPC can have lower electricity consumption than LLCS.

• Precooling had important effect for the LLCS. When allowed to precool, LLCS saved up to 20% electricity than otherwise.

• Precooling did not have notable effect on the VAV system electricity consumption.

• Internal loads, pipe spacing, and heat pump sizing have a significant impact on LLCS savings potential.

• A typical DOAS configuration used least amount of electricity.

Summary of main findings

IBO Workshop, Boulder, Colorado, June 2013

Thank you!

IBO Workshop, Boulder, Colorado, June 2013