Cooling Unit Staging Thresholds for Data Center Energy Control
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Solution Overview
Problem
Existing cooling delivery unit deployment methods in data centers are not energy efficient in maintaining temperature requirements within enclosed environments.
Innovation Solution
A thermal management system with a supervisory system that determines optimal switching thresholds for activating, deactivating, and ramping cooling delivery units based on ambient air temperature, airflow, and energy consumption models to minimize energy usage while maintaining temperature requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If cooling delivery units operate in groups based on standard group operation mode, then temperature requirements are maintained, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts the operation state of cooling delivery units based on real-time ambient air temperature measurements. Instead of static group operation modes, the system continuously optimizes which units are active and at what capacity levels, transitioning between different operational configurations as environmental conditions change.
Solution Approach 2:
The system changes operational parameters including the number of active cooling units, their capacity output levels, and switching thresholds. By adjusting these parameters based on ambient temperature conditions and pre-generated models, the system achieves optimal energy efficiency while maintaining required temperature control.
2Reliability
If more cooling delivery units are activated to handle increased cooling load, then temperature requirements are maintained, but energy consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-generating cooling capacity models and energy consumption models for various ambient temperature conditions and unit configurations. This advance preparation allows the supervisory system to quickly determine optimal unit staging without real-time computation delays, ensuring reliable temperature maintenance while minimizing energy waste.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring ambient air temperature and comparing it against model predictions. The supervisory system uses this feedback to dynamically adjust the staging of cooling units, ensuring that the right number of units are activated at the right capacity levels to meet cooling demands efficiently.
3Use of energy by moving object
If dynamic unit staging is implemented with multiple models and supervisory systems, then energy efficiency improves, but system complexity increases
Solution Approach 1:
The system segments the complexity by dividing it into distinct components: individual cooling unit models, group cooling capacity models, energy consumption models, and a supervisory control system. Each component has a specific function, and their modular structure allows for independent development, testing, and maintenance while working together to achieve energy efficiency.
4Loss of energy
If optimal switching thresholds are used for unit activation and deactivation, then energy consumption is minimized, but control precision requirements increase
Solution Approach 1:
The system creates simplified representations (models) of the complex thermal dynamics and energy consumption characteristics. These models serve as copies that capture the essential behavior patterns, allowing the supervisory system to determine optimal switching thresholds without requiring extremely precise real-time measurements and control of all underlying physical parameters.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Optimizes energy consumption by dynamically managing cooling delivery units, ensuring efficient temperature control in data centers while reducing overall energy expenditure.
Implementation Method 1
the heat generated by these servers and devices must be removed from the enclosed environment... by absorbing heat from within the environment and transferring that heat away from the physical location of the servers
Implementation Method 2
air is chilled by cooler water or working fluid introduced into the environment... and the chilled air is directed into the environment... The circulating air absorbs heat before returning to the cooling delivery units
Data Source
AI summary
A system and method for dynamic unit staging of a group of cooling delivery units mathematically models cooling capacity and energy consumption for each individual unit and for the group as a whole. Unit and group cooling capacity and energy consumption models are stored to memory accessible to a supervisory system along with a control profile providing for unit activation thresholds and allowable modification ranges for said thresholds. During each online operating cycle, the supervisory system determines the required cooling capacity (based on ambient air temperature) to maintain a data center within a required temperature range. Based on the cooling capacity requirement and applicable group energy consumption models, the supervisory system identifies a set of optimal switching thresholds within predetermined threshold modification ranges for unit activation, via which the group of cooling delivery units can maintain the required cooling capacity while minimizing overall energy consumption.