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Model predictive control for energy efficient cooling and dehumidification Tea Zakula Leslie Norford Peter Armstrong.

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Presentation on theme: "Model predictive control for energy efficient cooling and dehumidification Tea Zakula Leslie Norford Peter Armstrong."— Presentation transcript:

1 Model predictive control for energy efficient cooling and dehumidification Tea Zakula Leslie Norford Peter Armstrong

2 Introduction Software environment LLCS assessment Conclusion Motivation IBO Workshop, Boulder, Colorado, June 2013 Total U.S. energy consumption kg oil equivalent/capita average Commercial buildings Residential buildings Transportation Industrial Primary energy use Source: The World Bank (2010) Source: U.S. Energy Information Administration (2012) 1

3 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

4 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 Occupied Non-occupied Zone temperature Temperature limits Optimized cooling Model predictive control Heat load predictions Cool IBO Workshop, Boulder, Colorado, June

5 Introduction Software environment LLCS assessment Conclusion T s T outisde T fluid With conventional system With low-lift cooling technology Cooling cycle in T-s diagram Thermally 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

6 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

7 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

8 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

9 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: k=3 k=2 k=1 k=0 Time T past T outside,past+present T present Q internal,past+present Q cooling,past+present IBO Workshop, Boulder, Colorado, June

10 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

11 Introduction Software environment LLCS assessment Conclusion Parameters required to achieve the optimal point T outside = 30 o C T w,return = 20 o C T w,return = 17 o C T w,return = 14 o C T w,return = 11 o C Specific power consumption in the optimal point Q cooling /Q cooling,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/COP Results of static optimization are used in software environment to calculate electricity required for cooling. Heat pump optimization results IBO Workshop, Boulder, Colorado, June

12 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

13 Introduction Software environment LLCS assessment Conclusion Model predictive control Q c0 Q c1 Q c2 Q c23 Planning horizon Find the optimal cooling rates for the lowest electricity consumption over the planning horizon. Using heat pump optimization results Using inverse building model Time (h) Cooling rate optimization IBO Workshop, Boulder, Colorado, June

14 Introduction Software environment LLCS assessment Conclusion MATLAB Optimization of cooling rates Building response from inverse model Cooling electricity consumption from heat pump static optimization results TRNSYS Building thermal response Optimal values [Q c0, Q c2, …, Q c23 ] Building response Optimization T z,history, T o,history, T floor,history,T w,history Q c … cooling rate T w … water temperature T o … operative temperature T z … room temperature T floor … floor temperature Model predictive control Execution IBO Workshop, Boulder, Colorado, June

15 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 with LLCS MPC Building with VAV MPC Building with split-system MPC IBO Workshop, Boulder, Colorado, June Zone temperature ( o C) Time (h) Inverse model can adequately replicate results from TRNSYS. Inverse model TRNSYS

16 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

17 Introduction Software environment LLCS assessment Conclusion Proposed DOAS configurations Enthalpy wheel System C Evaporator Condenser Enthalpy wheel System E Condenser Run-around heat pipe Evaporator Enthalpy wheel System B Evaporator Condenser Enthalpy wheel System A Evaporator Condenser Enthalpy wheel System D Evaporator Condenser IBO Workshop, Boulder, Colorado, June

18 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

19 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 MPC with temperatures allowed to float between 20 and 25 o C during occupied hours Operated under conventional control (only during the operating hours to maintain constant temperature of 22.5 o C ) Simulating a typical summer week and 22-week period across 16 climates assuming standard internal loads (from people and equipment) for an office. Condenser Evaporator IBO Workshop, Boulder, Colorado, June

20 Introduction Software environment LLCS assessment Conclusion DOAS configurations analyzed with LLCS Enthalpy wheel System C Evaporator Condenser Enthalpy wheel System E Condenser Run-around heat pipe Evaporator Enthalpy wheel System B Evaporator Condenser Enthalpy wheel System A Evaporator Condenser Enthalpy wheel System D Evaporator Condenser IBO Workshop, Boulder, Colorado, June

21 Introduction Software environment LLCS assessment Conclusion Results: zone temperatures and cooling rates for Phoenix climate Temperature ( o C) Time (h) LLCS under MPC Conventional VAV Internal sensible gain TABS cooling rate DOAS cooling rate Temperature ( o C) Time (h) Thermal load (W) Time (h) Internal sensible gain VAV cooling rate LLCS vs conventional VAV Temperature limits Operative temperature IBO Workshop, Boulder, Colorado, June

22 Introduction Software environment LLCS assessment Conclusion LLCS vs conventional VAV Results: LLCS electricity savings for a typical summer week Electricity savings (%) A LLCS with condenser placed outside C LLCS with parallel condensers, one in supply, the other in return stream D LLCS with parallel condensers, one in supply stream, the other outside E LLCS with condenser placed outside and run-round heat pipe → typical and best performing → second best performing IBO Workshop, Boulder, Colorado, June

23 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

24 Introduction Software environment LLCS assessment Conclusion LLCS vs VAV under MPC Operated under MPC with temperatures allowed to float between 20 and 25 o C during occupied hours Operated under MPC with temperatures allowed to float between 20 and 25 o C 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 Evaporator Air for cooling, ventilation and dehumidification Condenser Evaporator VAV IBO Workshop, Boulder, Colorado, June

25 Introduction Software environment LLCS assessment Conclusion Results: zone temperatures and cooling rates for Phoenix climate Temperature ( o C) Time (h) LLCS under MPC VAV under MPC Temperature ( o C) Time (h) Thermal load (W) Time (h) LLCS vs VAV under MPC Internal sensible gain TABS cooling rate DOAS cooling rate Internal sensible gain VAV cooling rate Temperature limits Operative temperature IBO Workshop, Boulder, Colorado, June

26 Introduction Software environment LLCS assessment Conclusion Results: LLCS electricity savings for a typical summer week* Electricity savings (%) LLCS vs VAV under MPC Results: LLCS electricity savings from May 1 st – September 30 th * *LLCS assumes simple DOAS (system A) Electricity savings (%) IBO Workshop, Boulder, Colorado, June

27 Introduction Software environment LLCS assessment Conclusion Split-system Operated under MPC with temperatures allowed to float between 20 and 25 o C during occupied hours Operated under conventional control (only during the operating hours to maintain constant temperature of 22.5 o C ) 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 Evaporator Lower electricity consumption 33% for Atlanta 36% for Phoenix Lower electricity consumption 33% for Atlanta 36% for Phoenix Condenser Evaporator IBO Workshop, Boulder, Colorado, June

28 Introduction Software environment LLCS assessment Conclusion Split-system under MPC Heat pump Model predictive control Heat load predictions IBO Workshop, Boulder, Colorado, June

29 Introduction Software environment LLCS assessment Conclusion Split-system Operated under MPC with temperatures allowed to float between 20 and 25 o C 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 MPC with temperatures allowed to float between 20 and 25 o C during occupied hours Water for cooling LLCS Condenser Evaporator Condenser Evaporator Lower electricity consumption 19% for Atlanta 11% for Phoenix Lower electricity consumption 19% for Atlanta 11% for Phoenix IBO Workshop, Boulder, Colorado, June

30 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

31 Thank you! IBO Workshop, Boulder, Colorado, June 2013


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