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. Motivation : energy and climate. Addressing energy, climate and development challenges Buildings use 40 % of energy and 70% of electricity 1

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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

Predictive Pre-cooling Control For Low Lift Radiant cooling USING BUILDING THERMAL MASS


Motivation energy and climate

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

USDOE 2006. Building Energy Databook

IPCC 2007. Fourth Assessment Report

Sivak 2009. Energy Policy 37

McNeil and Letschert 2007. ECEEE 2007 Summer Study


Predictive pre cooling control for low lift radiant cooling using building thermal mass

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?


Low lift cooling systems llcs

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


Simulation of llcs shows significant cooling energy savings potential

Simulation of LLCS shows significant cooling energy savings potential

Simulated 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)


Llcs cooling energy savings in atlanta

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)


Predictive pre cooling control for low lift radiant cooling using building thermal mass

700 psia

Low lift vapor compression cycle requires less work

600

500

400

300

Vapor compression cycles shown under typical and low-lift conditions

60

Low-lift refers to a lower temperature difference between evaporation and condensation

200

Predictive pre-cooling of thermal storage and variable speed fans

40

T - Temperature (°C)

Variable-speed compressor adapts exactly and efficiently to conditions

20

100

Radiant cooling and variable speed pump

0

1

1.2

1.4

1.6

1.8

S - Entropy (kJ/kg-K)


Predictive pre cooling control for low lift radiant cooling using building thermal mass

LLCS operates achiller at low lift more of the time

where COP = f(Tx,Tz,Q) [kWth/kWe],

subject to just satisfying the daily load requirement:


Are the simulation results real

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


Low lift chiller performance

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


Measured heat pump performance at many steady state conditions

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


Test results show expected higher cops at low lift conditions

Test results show expected higher COPs at low lift conditions

Low lift operation

COP ~ 5-10

Typical operation

COP ~ 3.5

EER

51

34

17


Empirical models accurately represent chiller cooling capacity power and cop

Empirical models accurately represent chiller cooling capacity, power and COP

4-variable cubic polynomial models


Zone temperature model identification

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


Existing transfer function modeling methods can be applied to predict zone temperature

Existing transfer function modeling methods can be applied to predict zone temperature

OPT = operative temperature

OAT = outdoor air temperature

AAT = adjacent zone air temperature

QI=heat rate from internal loads

QC = cooling rate from mechanical system

a,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


Evaporating temperature is predicted from intermediate temperature response models

Evaporating temperature is predicted from intermediate temperature response models

  • 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)


Data driven models identified for a test chamber with a radiant concrete floor

Data-driven models identified for a test chamber with a radiant concrete floor

Temperature sensors: OPT, OAT, AAT, UST, RWT

Power to internal loads: QI

Radiant concrete floor cooling rate:QC


Predictive pre cooling control for low lift radiant cooling using building thermal mass

Models validated based on accuracy of predicting different data 24-hours-ahead

Sample validation temperature data

Sample validation

thermal load data


Predictive pre cooling control for low lift radiant cooling using building thermal mass

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

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 K

UST RMSE = 0.15 K

RWT RMSE = 0.84 K


Optimization minimizes energy maintains comfort and avoids freezing the chiller

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

  • rt = electric rate at time t, or one for energy optimization

  • Pt = system power consumption as a function of past compressor speeds and exogenous variables

  • = weight for operative temperature penalty

  • PENOPTt = operative temperature penalty when OPT exceeds ASHRAE 55 comfort conditions

  • PENEVTt =evaporative temperature penalty for temperatures below freezing

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


Predictive pre cooling control for low lift radiant cooling using building thermal mass

Perform optimization at every hour with current building data and new forecasts

Pattern search initial guess at current hour

24-hour-ahead forecasts of outdoor air temperature, adjacent zone temperature, and internal loads (OAT, AAT, QI)

Pattern search algorithm determines optimal compressor speed schedule for the next 24 hours

Operate chiller for one hour at optimal state


Predictive pre cooling control for low lift radiant cooling using building thermal mass

Pre-cooling the concrete floor maintains comfort and reduces energy consumption


Predictive pre cooling control for low lift radiant cooling using building thermal mass

Pre-cooling the concrete floor maintains comfort and reduces energy consumption


Experimental assessment of llcs

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?


Building and testing a low lift system

Building and testing a Low-Lift System

  • Build chiller by modifying an inverterheat 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

  • Comparthe LLCS performance to a baseline system - a high efficiency (SEER~16, SCOP~4.7) variable capacity split-system air conditioner (SSAC)


Predictive pre cooling control for low lift radiant cooling using building thermal mass

FROM RADIANT FLOOR

CONDENSER

FROM INDOOR UNIT (CLOSED)

BPHX

TO INDOOR UNIT (CLOSED)

COMPRESSOR

TO RADIANT FLOOR

ELECTRONIC EXPANSION VALVE

TEST CHAMBER

CLIMATE CHAMBER

LLCS and SSAC use the same outdoor unit

IDENTICAL FOR LLCS AND BASE CASE SSAC


Predictive pre cooling control for low lift radiant cooling using building thermal mass

LLCS provides chilled water to a radiant concrete floor (thermal energy storage)

17’

EXPANSION TANK

FILTER

TO CHILLER

RADIANT FLOOR

12’

BPHX

RADIANT MANIFOLD

FROM CHILLER

WATER PUMP

TEST CHAMBER

CLIMATE CHAMBER


Predictive pre cooling control for low lift radiant cooling using building thermal mass

Chiller/heat pump

Radiant concrete floor


Predictive pre cooling control for low lift radiant cooling using building thermal mass

Tested LLCS for a typical summer week in Atlanta subject to standard internal loads

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


Predictive pre cooling control for low lift radiant cooling using building thermal mass

Outdoor climate conditions

Internal loads

Atlanta test

Phoenix test


Predictive pre cooling control for low lift radiant cooling using building thermal mass

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)

  • 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.

  • Assuming no latent cooling by the LLCS


Predictive pre cooling control for low lift radiant cooling using building thermal mass

Predictive pre-cooling control can be applied to other systems to achieve low lift

  • 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 for low lift radiant cooling using building thermal mass

Masdar Test Building


Predictive pre cooling control for low lift radiant cooling using building thermal mass

Slab Temperatures


Predictive pre cooling control for low lift radiant cooling using building thermal mass

Freshly Poured


Predictive pre cooling control for low lift radiant cooling using building thermal mass

Test Building Instrumentation


Masdar city phase 1b demo

Masdar City Phase 1b Demo


Future llcs research

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


  • Future of llcs in real buildings

    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


    Summary

    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.


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