Optimizing Room Temperature Based on Chiller Efficiency – BuildingIQ

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Optimizing Room Temperature Based on Chiller Efficiency

In this blog, we explore the response of room temperature optimization to variation in chiller Coefficient of Performance (COP) at different time of day.

We continue to explore the capability of this optimization in simulated examples. For context, read:

→ Simulation – A Safe Playground for Smart HVAC Control
→ Towards a Zero HVAC-Energy Building and Beyond
→ Optimizing Room Temperature to Utilize Heat Transfer

 

Example 1, Temperature Derating of Chiller COP

The Coefficient of Performance (COP) changes with outside air temperature. High outside air temperature usually means low COP, i.e. the “cost” of cooling is high and the chiller is less efficient in turning electrical energy into cooling energy. From a thermodynamics point of view, it takes more energy to move the same amount of heat over a larger temperature gradient. In these simulations, we shift the outside air temperature and the lowest “cost” of cooling shifts with it. We can see how the optimization responds to this shift and always tries to move the cooling load to time-of-day of high COP (as allowed by the comfort range).

Figure 1.1 Two days of simulation showing various temperatures (degree C) and thermal loads. “zt sp” is the zone temperature setpoint, which is the result of the optimization. “hvac” is the HVAC load required to deliver this “zt sp”. The animation shows the change in “zt sp” and hvac load in response to an artificial shift in outside air temperature as it affects the chiller COP.

 

Figure 1.2 This shows the simulated power profile of the simulation shown in Figure 1.1. The cooling load and hence cooling power is shifted to high COP of the day, i.e. low outside air temperature of the day.

 

Example 2, Humidity Derating of Chiller COP

The Coefficient of Performance (COP) also changes with outside air humidity. Humidity affects the efficiency of evaporative cooling in the cooling tower. High humidity usually means low COP as it is harder for the water in the cooling tower to evaporate and takes the heat away. High humidity also means we have to cool and condense water content in the fresh outside air in addition to just cooling the air itself. In these simulations, we shift the outside air humidity and the highest COP shifts with it. We can see how the optimization responds to this shift and always tries to move the cooling load to time-of-day of high COP (as allowed by the comfort range).

Figure 2.1 Two days of simulation showing various temperatures (degree C) and thermal loads. “zt sp” is the zone temperature setpoint, which is the result of the optimization. “hvac” is the HVAC load required to deliver this “zt sp”. The animation shows the change in “zt sp” and hvac load in response to an artificial shift in outside air humidity as it affects the chiller COP.

 

Figure 2.2 This shows the simulated power profile of the simulation shown in Figure 2.1. The cooling load and hence cooling power is again shifted to high COP of the day, i.e., low outside air humidity of the day.

 

In summary, so far, across a few blogs, we have looked at how room temperature optimization responds to the thermodynamics of the building. This is estimated in terms of the ability of the building to keep the room temperature constant, without HVAC input. We also looked at how the room temperature optimization responds to variations in cooling and heating ‘cost’. The TOU (time of use) tariff, the change in COP —with respect to outside air temperature and humidity— are all in this category. In addition, we also looked at the effect of ‘free’ cooling in terms of air-side economic cooling. Renewable energy sources can also be categorized as ‘free’ cooling or heating. In the next blog, we are going to take a look at optimizing for demand ceiling and demand response events.



Dr. Rui (Ray) Xu is Data Scientist at BuildingIQ. During his career, he has encountered many challenges in the transfer of human knowledge into machines, the interpretation of results of algorithms into human intuition, and the verification of evolving strategies. This experience has helped him to solve some of the most difficult and interesting problems in the industry. He describes himself as a bit quiet but also very energetic and assertive when there is a mathematical, simulated and/or experimental proof behind the scenes.