Data Center Power Balancing Using Grid Load Prediction
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Solution Overview
Problem
Data centers face challenges in managing power grid fluctuations, leading to supply voltage fluctuations, which can result in either surpluses or shortages, affecting their operations and the reliability of the power grid.
Innovation Solution
Implementing a machine learning-based architecture that learns the operational characteristics of the power grid, including past, current, and future events, to predict upcoming load fluctuations, allowing the data center to dynamically adjust its operations, such as consuming more power, storing power, or migrating services to another grid, to balance the grid and ensure service availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data centers rely on power grid connections, then they can access external power supply, but they experience voltage fluctuations and power supply instability
Solution Approach 1:
The patent introduces an intermediary system comprising power quality correction devices and energy storage systems between the power grid and data center equipment. This intermediary layer absorbs voltage fluctuations and stabilizes power supply, preventing harmful grid variations from directly affecting sensitive computing equipment while maintaining the benefits of grid connection.
Solution Approach 2:
The system employs beforehand cushioning by pre-positioning energy storage devices and power quality correction equipment that can immediately respond to and cushion against upcoming voltage fluctuations and power disturbances. This proactive approach ensures continuous stable power supply to data center equipment even when grid conditions deteriorate.
2Reliability
If data centers use their own independent power sources, then they can ensure continuous power supply, but they cannot provide ancillary services to the power grid
Solution Approach 1:
The patent implements a dual-mode power system that can operate independently to ensure continuous power supply to the data center while simultaneously connecting to the power grid to provide ancillary services such as frequency regulation and voltage support. The system universally serves both internal reliability needs and external grid support functions through intelligent power management.
Solution Approach 2:
The data center's power system serves itself by using its own energy storage and generation capabilities to maintain continuous operation during grid disturbances, while also providing these same capabilities as ancillary services to the broader power grid, achieving both self-reliance and grid contribution.
3Ease of operation
If data centers do not predict power grid load fluctuations, then their operations are simpler, but they cannot proactively mitigate power supply issues
Solution Approach 1:
The system incorporates feedback mechanisms through power quality monitoring devices that continuously measure grid conditions and feed this information to control systems. This feedback enables the system to detect voltage fluctuations and load changes, automatically adjust power quality correction devices and energy storage systems, and maintain optimal operation without complex manual intervention.
Solution Approach 2:
The patent implements preliminary action through predictive analytics and real-time monitoring that anticipate power grid load fluctuations before they affect the data center. The system takes preliminary corrective actions by pre-charging energy storage devices or adjusting power quality correction settings in anticipation of upcoming disturbances, preventing issues before they impact operations.
Data Source
AI summary
Improving the operations of a computer system that is located within a power grid and that is associated with its own power sources. Past operational characteristics of the power grid are analyzed to derive learned characteristics for the power grid. Future operational characteristics of the power grid are also monitored. A prediction regarding a future load event associated with the power grid is then generated using the learned characteristics and the monitored characteristics. In response to this prediction, one or more operations are performed to balance the computer system with the power grid during the future load event, and to ensure a determined availability of services associated with the computer system during the future load event.