1. PREDICTIVE PRE-COOLING CONTROL FOR LOW LIFT RADIANT COOLING USING BUILDING THERMAL MASS

2. Motivation: energy and climate Addressing energy, climate and development challenges  Buildings use 40% of energy and 70% of electricity 1  Buildings represent low cost carbon emission reduction potential 2  Most rapidly developing cities in cooling-dominated climates 3  Increasing demand for A/C where grids are already stressed 4 1.USDOE 2006. Building Energy Databook 2.IPCC 2007. Fourth Assessment Report 3.Sivak 2009. Energy Policy 37 4.McNeil and Letschert 2007. ECEEE 2007 Summer Study

3. 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?

4. 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

6. 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)

7. 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)

8. 0 20 40 T - Temperature (°C) 60 11.21.4 S - Entropy (kJ/kg-K) 1.61.8 100 200 300 400500600 700 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

9. where COP = f(Tx,Tz,Q) [kWth/kWe], subject to just satisfying the daily load requirement: LLCS operates a chiller at low lift more of the time

10. 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

11. 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

12. Outdoor temp (C) Indoor temp (C) Compressor speed (Hz) Fan speed (RPM) 15 22.5 30 37.5 45 14 24 34 19 30 60 95 300 450 600 750 900 1050 1200 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

13. EER 34 17 51 Typical operation COP ~ 3.5 Low lift operation COP ~ 5-10 Test results show expected higher COPs at low lift conditions

14. 4-variable cubic polynomial models Empirical models accurately represent chiller cooling capacity, power and COP

15. 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

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

17.  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) Evaporating temperature is predicted from intermediate temperature response models

18. 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

19. Models validated based on accuracy of predicting different data 24-hours-ahead Sample validation temperature data Sample validation thermal load data

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

21. r t = electric rate at time t, or one for energy optimization P t = system power consumption as a function of past compressor speeds and exogenous variables = weight for operative temperature penalty PENOPT t = operative temperature penalty when OPT exceeds ASHRAE 55 comfort conditions PENEVT t =evaporative temperature penalty for temperatures below freezing =Vector of 24 compressor speeds, one for each hour of the 24 hours ahead Optimization minimizes energy, maintains comfort, and avoids freezing the chiller

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

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

25. 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?

26.  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

27. CONDENSER ELECTRONIC EXPANSION VALVE COMPRESSOR BPHX TO RADIANT FLOOR FROM RADIANT FLOOR FROM INDOOR UNIT (CLOSED) TO INDOOR UNIT (CLOSED) TEST CHAMBERCLIMATE CHAMBER IDENTICAL FOR LLCS AND BASE CASE SSAC LLCS and SSAC use the same outdoor unit

28. TEST CHAMBERCLIMATE 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)

29. Chiller/heat pumpRadiant concrete floor

30. 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

31. Atlanta test Phoenix test Outdoor climate conditionsInternal loads

32. LLCS energy consumption (Wh) SSAC (SEER~16) energy consumption (Wh) Measured 10,982 Measured14,64525% Deducting latent cooling 1 14,05322% 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)

33. SSAC Thermostatic control SSAC Predictive control Radiant panel predictive control Weekly average COP 4.324.977.46 Cooling delivered (Wh) -47,940-39,920-39,420 Simulated energy (Wh) 11,1108,0385,285 Measured energy (Wh) 14,053n/a Error in simulation 20.9%n/a Savings relative to simulated base case base27.6%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

34. Masdar Test Building

35. Slab Temperatures

36. Freshly Poured

37. Test Building Instrumentation

38. MASDAR CITY PHASE 1B DEMO

39. 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

40. 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 cost 1  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

41. 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.