Data Center Cooling Control for Zone-Based Energy Optimization
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Solution Overview
Problem
Current data center cooling systems often lead to inefficiencies due to treating the entire room as a homogenous unit, resulting in oversupply of cooling to areas that don't need it and inadequate adaptation to failures in air handling or cooler systems, with limited computational capacity for advanced control.
Innovation Solution
Implementing a model-based cooling control system that uses sensors to gather data and iteratively test different control parameters for air handling devices, optimizing energy usage while maintaining temperature constraints, and utilizing computational resources from the data center or a provider network for real-time control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If air handlers and coolers are controlled as a homogenous unit, then system simplicity is maintained, but cooling efficiency deteriorates due to oversupply to areas needing less cooling
Solution Approach 1:
The data center room is divided into multiple zones with different thermal characteristics. Each zone is equipped with its own air handlers that can be independently controlled based on local cooling demands, rather than treating the entire room as a single homogenous unit. This segmentation allows precise cooling delivery to each zone, reducing energy waste from oversupplying cooling to areas that don't need it.
Solution Approach 2:
Different regions of the data center are provided with different cooling strategies and control parameters according to their specific thermal loads and environmental conditions. Each air handling device operates with locally optimized control parameters that reflect the actual cooling needs of its served zone, improving overall cooling efficiency.
2Device complexity
If traditional cooling control systems are used, then system simplicity is maintained, but adaptability to failures deteriorates
Solution Approach 1:
The cooling control system continuously monitors the operational status of air handlers, coolers, and environmental conditions. When a failure is detected, the system receives feedback signals and automatically adjusts control parameters or redistributes cooling loads to adapt to the failed component, maintaining cooling effectiveness without requiring complex manual intervention.
Solution Approach 2:
The control system dynamically adjusts its operation in response to changing conditions including component failures. Control parameters are not fixed but are continuously modified based on real-time system state, enabling the system to adapt gracefully to failures by redistributing loads and adjusting operational modes of remaining healthy components.
3Loss of energy
If model-based optimization is implemented, then cooling efficiency is improved, but computational requirements increase
Solution Approach 1:
A thermal model of the data center is pre-developed and stored, capturing the thermal characteristics and heat transfer relationships of the facility. This pre-computed model allows the control system to perform rapid optimization calculations by querying the stored model rather than performing full thermal simulations in real-time, reducing computational requirements while maintaining optimization accuracy.
Solution Approach 2:
The system uses simplified control parameter sets and approximate optimization models that require less computational power for real-time control decisions. While these simplified models may be less accurate than full thermal simulations, they provide sufficient accuracy for operational control and can be computed rapidly on available controller hardware, effectively trading minor precision for computational efficiency.
Data Source
AI summary
A computer room includes rack mounted computing devices and multiple air handling systems for cooling the computer room. A model based cooling control system is implemented on resources of a provider network that includes the rack mounted computing devices. The model based cooling control system uses a model of the computer room to iteratively perform calculations to determine a set of control parameters for cooling the computer room. The set of control parameters are selected to reduce or optimize an amount of energy and/or water used to cool the computer room while satisfying one or more conditions for cooling the computer room such as a maximum allowable temperature of the room and/or a maximum allowable temperature of the rack mounted computing devices in the computer room.