data-driven model predictive control of low-lift … · 6 pm 12 am6 am 12 pm 6 pm 0 500 1000 1500...

DATA-DRIVEN MODEL PREDICTIVE CONTROL OF LOW-LIFT CHILLERS PRE-COOLING THERMO-ACTIVE BUILDING SYSTEMS

Nick Gayeski, PhD Building Technology, MIT

IBPSA Model Predictive Control Workshop June 2011

Advisors: Dr. Leslie K. Norford, Dr. Peter R. Armstrong

Objective and Topics

Objective: To achieve significant cooling energy savings withdata-driven model-predictive control of low-lift chillers pre-cooling thermo-active building systems (TABS)

1. Low-lift cooling systems (LLCS)

2. Modeling and optimization for LLCS

a. Low-lift chiller performance

b. Data-driven thermal model identification

c. Model-predictive control to pre-cool thermo-active building systems

3. Experimental assessment

4. Ongoing research

Nick Gayeski IBPSA MPC Workshop June, 2011

1. Low lift cooling systems (LLCS)

Low lift cooling systems leverage the following technologies to reduce cooling energy:

� Variable speed compressor

Hydronic distribution with variable flow� Hydronic distribution with variable flow

� Radiant cooling

� Thermal energy storage (TES) e.g. Thermo-active building systems (TABS)

� Model-predictive control (MPC) to pre-cool TABS

� Dedicated outdoor air system (DOAS)


40

Tem

pera

ture

(°C

)

60

200

300

400500600700 psia

Predictive pre-cooling

of TABS and variable

speed fans

Low-lift refers to a lower

temperature difference between

evaporation and condensation

Low lift vapor compression cycle requires less work

0

20

40

T -

Tem

pera

ture

(

1 1.2 1.4

S - Entropy (kJ/kg-K)1.6 1.8

100Radiant cooling and

variable speed

pumping

speed fans

Variable speed

compressor and

load spreading

MPC of LLCS enables lower lift chiller operation

Predict 24-hour optimal chiller control schedule

Load forecasts

Building data

Identify building temperature response models

Active charging

Variable capacity chiller

charging discrete TES

Direct zone cooling

Compressor

Condenser

Evaporator

TXV

Sight

Glass

Outdoor

Air

Outdoor

Air

E1

E2

E3

T5

T6

T4

P1

P2

H3, T7

Chilled Water T2T3

F3

Bypass

BypassCompressor

Condenser

Evaporator

TXV

Sight

Glass

Outdoor

Air

Outdoor

Air

E1

E2

E3

T5

T6

T4

P1

P2

H3, T7

Chilled Water T2T3

F3

Bypass

Bypass

Pre-cooling thermo-active building system

Passive pre-cooling intrinsic TES

Occupied zone

DOAS latent cooling

Simulation studies show significant LLCS cooling energy savings potential

Simulated energy savings: 12 building types in 16 cities relative to the DOE benchmark HVAC systems

Total annual cooling energy savingsTotal annual cooling energy savings� 37 to 84% in standard buildings, on average 60-70%� -9 to 70% in high performance buildings, on average 40-60%

Katipamula S, Armstrong PR, Wang W, Fernandez N, Cho H, Goetzler W,Burgos J, Radhakrishnan R, Ahlfeldt C. 2010.

Cost-Effective Integration of Efficient Low-Lift Baseload Cooling Equipment FY08 Final Report. PNNL-19114. Pacific Northwest National Laboratory. Richland, WA.


Topics

Model-predictive control of low-lift cooling systems toachieve significant cooling energy savings


2. Modeling and optimization for LLCS2. Modeling and optimization for LLCS




3. Experimental assessment

4. Ongoing research


2. Modeling and Optimization for LLCS

Optimize control of a low-lift chiller over a 24-hour look-ahead schedule to minimize daily chiller energy consumption

To pre-cool a thermo-active building system to achieve high chilled water temperatures and space efficient thermal energy chilled water temperatures and space efficient thermal energy storage

Informed by a chiller performance model that predicts chiller power and cooling rate at future conditions for a chosen control schedule

Informed by data-driven zone temperature response models and forecasts of climate conditions and loads


2.a Low lift chiller performance

15

Ou

tdo

or

un

it C

OP

(k

Wth

/kW

e)

EER

51

Typical operationCOP ~ 3.5

Low lift operationCOP ~ 5-10

Experimental testing at 131 steady state conditions

Heat balance < 7 percent error


1 2 3 4 50

5

10

Pressure ratio (kPa/kPa)

Ou

tdo

or

un

it C

OP

(k

Wth

/kW

e)

34

17

4 variable-cubic polynomial models derived from experimental measurement or physics-based simulation

Measured vs Predicted Power Consumption Measured vs Predicted Cooling Capacity

),,T,T(fP fancompressornevaporatiooutdoorair ωω= ),,T,T(gQC fancompressornevaporatiooutdoorair ωω=

Empirical models accurately represent chiller cooling capacity and power

0 500 1000 15000

500

1000

1500

2000Measured vs Predicted Power Consumption

Measure power consumption (W)

Model pre

dic

ted p

ow

er

consum

ption (

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

0.3

0.4

0.5

5/35

th)

1/COP vs Compressor Speed at Fan Speed = 750 RPMT

e/T

o

5/25

10/25Night time

operation

MPC with TABS enables more low-lift operation, resulting in higher chiller COPs

20 30 40 50 60 70 80 900

0.1

0.2

0.3

Compressor Speed (Hz)

1/C

OP

(W

e/W

10/25operation

Radiant

cooling

Te = Evaporating temperature ºC, To = Outdoor air temperature ºC

Load

spreading

2.b Zone temperature model identification

LLCS control requires zone temperature response models to predict temperatures and chiller performance

� Models identified from measured building data

� Zone operative temperature (T )� Zone operative temperature (To)

� The temperature in the TABS concrete-core (Tcc)

� Return water temperature (Tchwr) and chiller evaporating temperature (Te) from which chiller power and cooling rate can be calculated

� 24-hour ahead forecasts of outdoor climate and internal loads


To = operative temperatureTx = outdoor air temperature

∑∑∑∑ ∑−

=

−

=

−

=

−

−=

−

=

++++=Nt

tk

ck

Nt

tk

ik

Nt

tk

ak

Nt

1tk

Nt

tk

xkoko )k(Qe)k(Qd)k(Tc)k(Tb)k(Ta)t(T

Existing data-driven modeling methods can be applied to predict zone temperature

Tx = outdoor air temperatureTa = 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)

� Steady state heat transfer physics constrain CRTF coefficients


� Chiller power and cooling rate depend on TABS thermal state and cooling rate because they determine chilled water return temperature and evaporating temperature

� Predict concrete-core temperature (Tcc) using a CRTF-like transfer

Evaporating temperature must be predicted from TABS temperature response

� Predict concrete-core temperature (Tcc) using a CRTF-like transfer function model

� Predict return water temperature (Tchwr) using a low-order transfer function model in Tcc and cooling rate Qc (or a heat exchanger model)

� Calculate evaporating temperature (Te) as a function of Tchwr

using evaporator parameters and superheat


2.c Pre-cooling control optimization

Optimize chiller operation over 24 hours to minimize energy consumption and maintain thermal comfort

� Employ direct pattern search1 to minimize the objective function by selecting an optimal schedule of 24 compressor speeds, one by selecting an optimal schedule of 24 compressor speeds, one for each hour

� Employ chiller model to calculate cooling rate and power consumption for the next 24 hours

� Employ temperature response models to predict zone temperatures to ensure comfort is maintained over 24 hours

Lewis RM, Torczon V. 1999. Pattern Search Algorithms for Bound Constrained Minimization. SIAM

Journal on Optimization 9 (4): 1082–1099.


where

Pw,t = system power consumption as a function of past compressor speeds and exogenous variables

∑ τ∆ω+ωϕ+ω=ω=

24

1tt,Tet,Tot,w ))(P)(P)(P()(Jvvvv

Optimization minimizes energy, maintains comfort, and avoids freezing the chiller

)(Jminarg ωω

vv

compressor speeds and exogenous variables= weight for operative temperature penalty

PTo,t = operative temperature penalty when OPT exceeds ASHRAE 55 comfort conditions

PTe,t = evaporative temperature penalty for temperatures below freezing

= Vector of 24 compressor speeds, one for each hour of the 24 hours ahead

ϕ

ωv


Pattern search initial guess at current hour

Pattern search algorithm determines optimal compressor speed schedule

24-hour-ahead forecasts of outdoor air temperature,

{ }{ }0,optimal242iinitial →=ω=ω

v

Optimize compressor speeds every hour with updated building data and forecasts

optimal compressor speed schedule for the next 24 hours

Operate chiller for one hour at optimal state

outdoor air temperature, adjacent zone temperatures, and internal loads

{ }optimal241ioptimal →=ω=ω

v

optimal,1ω=ω

)T,T,(ff exoptimal,1optimal,condfancondfan ω=

Nick Gayeski IBPSA MPC Workshop,June, 2011

Topics

Model-predictive control of low-lift cooling systems toachieve significant cooling energy savings


2. Implementing MPC for LLCS2. Implementing MPC for LLCS




3. Experimental assessment of LLCS

4. Ongoing research


3. Experimental assessment of LLCS

Initial scoping study found dramatic savings from LLCS, but

� Assumes idealized thermal storage, not real TES or TABS

� Chiller power is a function of cooling rate but not thermal storage state, as it can be for a TABS systemstate, as it can be for a TABS system

How real are these savings? What practical technical obstacles exist?

� Experimentally implement and test LLCS with MPC on a low-lift chiller pre-cooling TABS

� Compare relative savings of the LLCS to a base case system similar to one of the cases studied by PNNL


IDENTICAL FOR LLCS AND BASE CASE SSAC

Experimental chamber schematic

Climate chamber Test chamber

Temperature sensors measure: Ts, Tx, Ta, Tcc, Tchwr

Power to internal loads: Qi

Radiant concrete floor cooling rate: Qc

Test chamber data-driven CRTF models

Models trained with a few day’s data

Sample training temperature data

Sample training thermal load data

Transfer function models accurately predict zone temperatures 24-hours-ahead


24-hour TABS and chilled water temperature prediction

24 hour operative temperature prediction

Atlanta typical summer week with standard efficiency loads

Phoenix typical summer week with high efficiency loads

� Based on typical meteorological year weather data

� Assume two occupants and ASHRAE 90.1 2004 loads (standard

Tested LLCS for a typical summer week in two climates, Atlanta and Phoenix

Assume two occupants and ASHRAE 90.1 2004 loads (standard efficiency) or 30% lower (high efficiency)

Run LLCS for one week after steady-periodic response is achieved


Model-predictive control optimizes chiller compressor speed at each hour

10

20

30

40

Tem

pera

ture

(C

)

Zone temperature response

5

10

15

20

25

30

Com

pre

ssor

speed (

Hz)

Chiller control schedule

OPT

OAT

RWT

UST

EVT

OPTmax

OPTmin

Occupied

To

To,max

To,min

6 pm 12 am 6 am 12 pm 6 pm0

hour

6 pm 12 am 6 am 12 pm 6 pm0

500

1000

1500

2000

Hour

Cum

ulu

ative e

nerg

y

consum

ption (

Wh)

Chiller energy consumption

6 pm 12 am 6 am 12 pm 6 pm0

50

100

150

200

250

Hour

Chill

er

Pow

er

(W)

Chiller power

6 pm 12 am 6 am 12 pm 6 pm0

hour

Total energy

consumption over

24 hours = 1921 Wh


Pre-cooling redistributes cooling load, allowing lower lift, but maintains comfort

10

20

30

40

Tem

pera

ture

(C

)

Zone temperature response

5

10

15

20

25

30

Com

pre

ssor

speed (

Hz)

Chiller control schedule

OPT

OAT

RWT

UST

EVT

OPTmax

OPTmin

Occupied To

To,max

To,min

Tx

Tcc

Tchwr

6 pm 12 am 6 am 12 pm 6 pm0

hour

6 pm 12 am 6 am 12 pm 6 pm0

500

1000

1500

2000

Hour

Cum

ulu

ative e

nerg

y

consum

ption (

Wh)

Chiller energy consumption

6 pm 12 am 6 am 12 pm 6 pm0

50

100

150

200

250

Hour

Chill

er

Pow

er

(W)

Chiller power

6 pm 12 am 6 am 12 pm 6 pm0

hour

Total energy

consumption over

24 hours = 1921 Wh


Tchwr

Te

Comparing experimental results to PNNL simulation studies

� Select a base-case system as a point of comparison to the PNNL simulation studies.

� Low fan energy split-system air conditioner, SEER 16, with conventional control is most similar to the PNNL case with high efficiency distribution and a variable speed chiller operation


efficiency distribution and a variable speed chiller operation

� Test base-case subject to the same conditions as the LLCS but with thermostatic control achieving same mean temperature

� Compare energy savings in the experimental tests with the PNNL simulations

Comparing savings in experiment to PNNL simulation studies

Atlanta typical summer week results Experimental

Savings (%)

Simulation

Savings (%)

Low-lift cooling system 25 26

VSD chiller w/ radiant cooling -- 0


Phoenix typical summer week results Experimental

Savings (%)

Simulation

Savings (%)

Low-lift cooling system 19 29

VSD chiller w/ radiant cooling -- 0

Split system air conditioner 0 --

Split system air conditioner 0 --

� Improve TABS design by decreasing chilled water pipe spacing permitting higher evaporating temperatures

� Better matching of system capacity and loads using a smaller chiller, adding a false load, or constructing a new test facility

Critiquing the experimental LLCS tests

� Improve chamber insulation to achieve closer to adiabatic boundary conditions

� Comparison to more configurations of systems and control scenarios, comparisons to identical simulations

� Improvements are likely to yield better LLCS performance


4. Ongoing research

Extend to multi-zone TABS systems where multiple zone temperature and TABS responses are predicted simultaneously

Allow 24-hour dispatch of TABS pre-cooling and/or direct cooling by radiant ceiling panels to maintain zone temperature with high radiant ceiling panels to maintain zone temperature with high evaporating temperature during the day-time

Construct a full-scale demonstration project at Masdar City, Abu Dhabi and a location in the United States

Expand simulations using the Building Control Virtual Test Bed by coupling simulation environments required for TABS response and low-lift chiller simulation


Summary

� Developed a method for data-driven MPC of low-lift chillers pre-cooling TABS leveraging curve-fit chiller modeling and CRTF zone and concrete-core temperature modeling

� Implemented these methods in an experimental test chamber to achieve the first working low-lift cooling systemachieve the first working low-lift cooling system

� Compared LLCS performance to a split-system air conditioner, as a predominant technology in developing countries and as a surrogate for PNNL base case systems


Thank you! Questions welcome

Nicholas Gayeski, PhD

Research Affiliate Partner and Co-FounderMassachusetts Institute of Technology KGS Buildings, [email protected] [email protected]

Thank you to my advisors: Prof. Leslie K. Norford, Prof. Peter R. Armstrong, and Prof. Leon Glicksman

Thank you to: Srinivas Katipamula and PNNLMitsubishi Electric Research LaboratoryMartin Family Society of FellowsMassachusetts Institute of TechnologyMasdar Institute of Science and Technology


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