In this final blog of the series about room temperature optimization, we show how, by adjusting the room temperature setpoint, the power demand can be kept in check. We continue to explore the capability of this optimization in simulated examples. For context, read the complete series:
→ Simulation — A Safe Playground for Smart HVAC Control
→ Towards a Zero HVAC-Energy Building and Beyond
→ Optimizing Room Temperature to Utilize Heat Transfer
→ Optimize Room Temperature to Based on Chiller Efficiency
In this example, when the room temperature is optimized without demand ceiling, the peak power is in the afternoon when the Outside Air Temperature (OAT) is highest. Taking demand ceiling into account, the optimization tries to keep the peak power below 70 kW. This is achieved by precooling the building and then drifting the room temperature during the time of peak power. Note that the simulated peak power is actually slightly above 70 kW. This is due to the difference in sampling rate between simulation and optimization.
In this example, when the room temperature is optimized without demand ceiling, the peak power is in the morning —this is the result of optimizing for the Time of Use (TOU) tariff, (see Towards a Zero HVAC-Energy Building and Beyond). Taking demand ceiling into account, i.e., trying to keep the peak power below 70kW, the optimized room temperature setpoint is similar to Example 1. Both, examples 1 and 2, are successful in keeping the power demand in check. The morning cooling power in this example 2 is more than that in example 1 due to the additional TOU tariff. Note that the very sharp power peak at the start of the second day, which is not reduced, is an artifact of this simulation.
In this example, there is a Demand Response (DR) event from 1pm to 3pm. Taking this into account, the optimization precools the building just before the DR event. This is done in order to drift the room temperature and drop power consumption during the DR event.
This blog concludes our series on Room Temperature Optimization. Here are a few take away points:
During testing and demonstrations, like we do here, we can go through each goal of our control strategy, verifying or showing that they are being achieved. In a real application, the optimization will try to achieve all these goals at the same time or balance the relative importance between them through the formulation of the cost function. For instance, taking example 3 further, the optimization can automatically combine a demand ceiling and a DR event, in which case, the precooling before the DR event can be done more gradually.
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.