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

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

LLCS description 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 2 IBO Workshop, Boulder, Colorado, June 2013

Model predictive control LLCS description 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. Model predictive control Heat load predictions Zone temperature Temperature limits Cool Non-occupied Occupied Non-occupied Optimized cooling 3 IBO Workshop, Boulder, Colorado, June 2013

LLCS savings strategies Cooling cycle in T-s diagram T 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. Toutisde Tfluid s With conventional system With low-lift cooling technology 4 IBO Workshop, Boulder, Colorado, June 2013

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%. 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. 5 IBO Workshop, Boulder, Colorado, June 2013

Software environment components Heat load predictions Model predictive control Building data Building with TABS and thermal storage Building model Heat pump Cold water Dedicated outdoor air system (DOAS) Ventilation and dehumidification air 6 IBO Workshop, Boulder, Colorado, June 2013

Data-driven (inverse) model Building model 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 7 IBO Workshop, Boulder, Colorado, June 2013

Inverse building model Inverse model of the experimental room – proposed by Armstrong (2009) For zone, operative and floor temperature: 𝑇= 𝑘=1 𝑛 𝒂 𝑘 𝑇 𝑘 + 𝑘=0 𝑛 𝒃 𝑘 𝑇𝑜𝑢𝑡𝑖𝑠𝑑𝑒 𝑘 + 𝑘=0 𝑛 𝒄 𝑘 𝑄𝑖𝑛𝑡𝑒𝑟𝑛𝑎𝑙 𝑘 + 𝑘=0 𝑛 𝒅 𝑘 𝑄𝑐𝑜𝑜𝑙𝑖𝑛𝑔 𝑘 For water return temperature: 𝑇𝑤,𝑟𝑒𝑡𝑢𝑟𝑛= 𝑘=1 𝑛 𝒆 𝑘 𝑇𝑤,𝑟𝑒𝑡𝑢𝑟𝑛 𝑘 + 𝑘=0 𝑛 𝒇 𝑘 𝑇𝑓𝑙𝑜𝑜𝑟 𝑘 + 𝑘=0 𝑛 𝒈 𝑘 𝑄𝑐𝑜𝑜𝑙𝑖𝑛𝑔 𝑘 Tpresent Tpast Coefficients a … g are found using linear regression to TRNSYS data. Time k=3 k=2 k=1 k=0 Toutside,past+present Qinternal,past+present Qcooling,past+present 8 IBO Workshop, Boulder, Colorado, June 2013

Software environment components Heat load predictions Model predictive control Building data Heat pump optimization Building with TABS and thermal storage Heat pump Cold water Dedicated outdoor air system (DOAS) Ventilation and dehumidification air 9 IBO Workshop, Boulder, Colorado, June 2013

Heat pump optimization results 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. Specific power consumption in the optimal point Parameters required to achieve the optimal point Evaporator airflow Condenser airflow Compressor frequency Subcooling on condenser 1/COP Qcooling/Qcooling,max Results of static optimization are used in software environment to calculate electricity required for cooling. Toutside = 30 oC Tw,return = 20 oC Tw,return = 17 oC Tw,return = 14 oC Tw,return = 11 oC 10 IBO Workshop, Boulder, Colorado, June 2013

Software environment components Model predictive control Heat load predictions Model predictive control Building data Building with TABS and thermal storage Heat pump Cold water Dedicated outdoor air system (DOAS) Ventilation and dehumidification air 11 IBO Workshop, Boulder, Colorado, June 2013

Model predictive control Find the optimal cooling rates for the lowest electricity consumption over the planning horizon. Planning horizon Qc0 Qc1 Qc2 Qc23 Cooling rate optimization 0 1 2 23 Time (h) 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 12 IBO Workshop, Boulder, Colorado, June 2013

Model predictive control Qc … cooling rate Tw … water temperature To … operative temperature Tz … room temperature Tfloor … floor temperature Optimization MATLAB Optimization of cooling rates Building response from inverse model Cooling electricity consumption from heat pump static optimization results Building response Tz,history, To,history, Tfloor,history,Tw,history Execution TRNSYS Building thermal response Optimal values [Qc0, Qc2, …, Qc23] 13 IBO Workshop, Boulder, Colorado, June 2013

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 VAV split-system Zone temperature (oC) Time (h) Inverse model can adequately replicate results from TRNSYS. Inverse model TRNSYS Model is fast enough for implementation in a real building (computational time to optimize one week is 5 – 10 min). 14 IBO Workshop, Boulder, Colorado, June 2013

Software environment components Heat load predictions Model predictive control Building data Building with TABS and thermal storage Heat pump DOAS configurations Cold water Dedicated outdoor air system (DOAS) Ventilation and dehumidification air 15 IBO Workshop, Boulder, Colorado, June 2013

Proposed DOAS configurations Enthalpy wheel System A Evaporator Condenser Enthalpy wheel System B Evaporator Condenser Enthalpy wheel System C Evaporator Condenser Enthalpy wheel System D Evaporator Condenser Enthalpy wheel System E Condenser Run-around heat pipe Evaporator 16 IBO Workshop, Boulder, Colorado, June 2013

17 LLCS vs conventional VAV LLCS vs VAV with MPC LLCS vs conventional split-system LLCS vs split-system with MPC 17 IBO Workshop, Boulder, Colorado, June 2013

LLCS vs conventional VAV Simulating a typical summer week and 22-week period across 16 climates assuming standard internal loads (from people and equipment) for an office. LLCS VAV Condenser Condenser DOAS Fresh air for ventilation and dehumidification Evaporator Evaporator Air for cooling, ventilation and dehumidification Water for cooling Operated under MPC with 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) 18 IBO Workshop, Boulder, Colorado, June 2013

DOAS configurations analyzed with LLCS Enthalpy wheel System A Evaporator Condenser Enthalpy wheel System B Evaporator Condenser Enthalpy wheel System C Evaporator Condenser Enthalpy wheel System D Evaporator Condenser Enthalpy wheel System E Condenser Run-around heat pipe Evaporator 19 IBO Workshop, Boulder, Colorado, June 2013

LLCS vs conventional VAV Results: zone temperatures and cooling rates for Phoenix climate LLCS under MPC Conventional VAV Temperature (oC) Time (h) Temperature (oC) Time (h) Temperature limits Operative temperature Thermal load (W) Thermal load (W) Time (h) Time (h) Internal sensible gain Internal sensible gain TABS cooling rate VAV cooling rate DOAS cooling rate 20 IBO Workshop, Boulder, Colorado, June 2013

LLCS vs conventional VAV Results: LLCS electricity savings for a typical summer week Electricity savings (%) → typical and best performing 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 → second best performing 21 IBO Workshop, Boulder, Colorado, June 2013

Model predictive control VAV under MPC Heat load predictions Model predictive control Building data Heat pump Cold air 22 IBO Workshop, Boulder, Colorado, June 2013

LLCS vs VAV under MPC Simulating a typical summer week and 22-week period across 16 climates assuming standard internal loads (from people and equipment) for an office. LLCS VAV Condenser Condenser DOAS Fresh air for ventilation and dehumidification Evaporator Evaporator Air for cooling, ventilation and dehumidification Water for cooling Operated under MPC with temperatures allowed to float between 20 and 25oC during occupied hours Operated under MPC with temperatures allowed to float between 20 and 25oC during occupied hours 23 IBO Workshop, Boulder, Colorado, June 2013

LLCS vs VAV under MPC Results: zone temperatures and cooling rates for Phoenix climate LLCS under MPC VAV under MPC Temperature (oC) Time (h) Temperature (oC) Time (h) Temperature limits Operative temperature Thermal load (W) Thermal load (W) Time (h) Time (h) Internal sensible gain Internal sensible gain TABS cooling rate VAV cooling rate DOAS cooling rate 24 IBO Workshop, Boulder, Colorado, June 2013

LLCS vs VAV under MPC Results: LLCS electricity savings for a typical summer week* Electricity savings (%) Results: LLCS electricity savings from May 1st – September 30th* Electricity savings (%) *LLCS assumes simple DOAS (system A) 25 IBO Workshop, Boulder, Colorado, June 2013

Lower electricity consumption Operated under conventional control 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). LLCS Split-system Condenser Condenser Evaporator Lower electricity consumption 33% for Atlanta 36% for Phoenix Evaporator Water for cooling Operated under MPC with 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) 26 IBO Workshop, Boulder, Colorado, June 2013

Model predictive control Split-system under MPC Model predictive control Heat load predictions Heat pump Heat pump 27 IBO Workshop, Boulder, Colorado, June 2013

Lower electricity consumption 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). LLCS Split-system Condenser Condenser Evaporator Lower electricity consumption 19% for Atlanta 11% for Phoenix Evaporator Water for cooling Operated under MPC with temperatures allowed to float between 20 and 25oC during occupied hours Operated under MPC with temperatures allowed to float between 20 and 25oC during occupied hours 28 IBO Workshop, Boulder, Colorado, June 2013

Summary of main findings 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. IBO Workshop, Boulder, Colorado, June 2013

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