Hierarchical Cooling Microgrids for Data Center Energy Optimization
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Solution Overview
Problem
Conventional data centers consume up to 50% of their total energy for cooling, with point cooling solutions applied independently and not optimized to improve the overall coefficient of performance (COP) of the cooling infrastructure, leading to inefficiencies and increased carbon footprints.
Innovation Solution
A method and system for distributing cooling resources using hierarchically identified cooling microgrids, where each level includes resource actuators that can vary the distribution of cooling resources, optimizing performance and reducing redundancies by delegating cooling objectives to various levels, thus improving thermal management and energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cooling solutions are applied independently to different components, then each component can be cooled, but the overall coefficient of performance (COP) of the cooling infrastructure is not optimized and energy consumption increases
Solution Approach 1:
The data center cooling system is divided into multiple cooling microgrids at different hierarchical levels (global, zonal, local, rack-level). Each microgrid manages cooling resources independently within its scope, allowing localized optimization while contributing to overall system efficiency. This segmentation enables coordinated control across hierarchy levels to optimize total energy consumption while maintaining comprehensive cooling coverage.
Solution Approach 2:
The patent introduces a hierarchical dimension to the cooling control system, organizing microgrids into multiple levels (global→zonal→local→rack). This dimensional organization allows cooling resources to be allocated and optimized across different scales simultaneously, resolving the contradiction between comprehensive cooling coverage and energy efficiency by enabling coordinated optimization at each hierarchical level.
2Reliability
If multiple point cooling solutions are deployed throughout the data center, then cooling coverage is improved, but device complexity and redundancy increase
Solution Approach 1:
The cooling system is segmented into hierarchical microgrids with clear boundary definitions. Each microgrid level (global, zonal, local, rack) has specific responsibilities and control authority, reducing overall system complexity through structured decomposition. This segmentation maintains comprehensive cooling coverage while organizing complexity into manageable hierarchical layers rather than a monolithic complex system.
Solution Approach 2:
The hierarchical microgrid structure acts as an intermediary layer between individual cooling components and central control. Each microgrid level coordinates cooling resources within its scope and interfaces with adjacent levels, simplifying control complexity by distributing decision-making authority across hierarchical intermediaries rather than requiring direct centralized control of all components.
3Ease of manufacture
If conventional cooling infrastructure is used without hierarchical organization, then implementation is simpler, but exergy destruction increases and thermodynamics efficiency decreases
Solution Approach 1:
The cooling infrastructure is segmented into hierarchical microgrids that enable localized exergy optimization at each level while maintaining coordination with other levels. This segmentation allows simple implementation of local control strategies that reduce exergy destruction, with each microgrid level implementing efficiency improvements within its scope without requiring complete system redesign.
Solution Approach 2:
The hierarchical microgrid system introduces dynamic coordination between different cooling levels (global, zonal, local, rack). Each level can dynamically adjust cooling resource allocation based on real-time conditions, optimizing exergy utilization and reducing energy loss. This dynamic hierarchical coordination achieves improved thermodynamics efficiency while maintaining relative implementation simplicity through modular architecture.
Data Source
AI summary
In a method for distributing cooling resources to a plurality of locations using a plurality of hierarchically identified cooling microgrids, conditions detected at the plurality of locations are received. Each level of the hierarchically identified cooling microgrids is a plurality of resource actuators configured to vary distribution of the cooling resources. Settings for the plurality of resource actuators in each of the levels in the cooling microgrid hierarchy that substantially maintain conditions at the plurality of locations within predetermined ranges are determined using a processor, while substantially optimizing at least one measure of performance associated with supplying the cooling resources to the plurality of locations.