Data Center Load Control Using Renewable Power Forecasts
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Solution Overview
Problem
Existing dynamic power management systems in data centers are unable to perform continuous and real-time control, leading to inefficiencies in responding to intermittent power generation and power supply and demand imbalances, and are limited by vertical scalability, which increases costs and resource constraints.
Innovation Solution
A system and method for real-time, scalable, and event-based operational value optimization that utilizes real-time data analysis and adaptive control to dynamically adjust power and compute load, incorporating modular logic for optimal operation and feedback loops to manage power consumption and demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If existing dynamic power management systems are used, then data center operations can be controlled, but continuous and real-time control is unable to be performed, leading to inefficiencies in responding to intermittent power generation
Solution Approach 1:
The system dynamically adjusts power consumption targets and control strategies in real-time based on predicted renewable power availability. The control system transitions from static to dynamic operation, continuously adapting to changing power supply conditions through real-time predictions and automated adjustments.
Solution Approach 2:
The system performs preliminary power consumption adjustments before intermittent power generation events occur. By predicting future power availability from renewable sources, the system proactively modifies power consumption targets and curtails loads in advance, ensuring optimal utilization when power becomes available while avoiding over-controlling during fluctuations.
2Adaptability or versatility
If vertical scalability is increased to improve control capability, then system performance can be enhanced, but costs and resource constraints increase
Solution Approach 1:
The control system is segmented into modular components: prediction modules that forecast power availability, optimization modules that determine control strategies, and execution modules that implement adjustments. This segmentation allows the system to achieve high adaptability through specialized functions while maintaining resource efficiency by activating only the necessary modules for each control decision.
Solution Approach 2:
The control system is designed to perform multiple functions using integrated algorithms that handle prediction, optimization, and control execution. This multi-functionality reduces the need for separate specialized systems, thereby lowering overall resource requirements and costs while maintaining high adaptability across different operating conditions.
3Productivity
If real-time control is implemented to optimize power usage, then power supply and demand imbalances can be addressed, but over-controlling may occur in response to real-time fluctuations
Solution Approach 1:
The system performs preliminary control actions based on predicted power availability before real-time fluctuations occur. By anticipating future power supply conditions, the system adjusts power consumption targets in advance, reducing the need for reactive over-controlling during actual power fluctuations and maintaining more stable operations.
Solution Approach 2:
The system implements feedback mechanisms that monitor actual power consumption and renewable power generation in real-time. This feedback is used to validate predictions and adjust control strategies, preventing over-controlling by comparing expected versus actual conditions and making corrective adjustments only when necessary.
Data Source
AI summary
A computer-implemented method may include analyzing a resource site based on a plurality of data inputs associated with operation of the resource site. The method may include predicting availability of power for the resource site from a renewable power generation source based on data from the data inputs and determining an operational target for the resource site for a determined future time period, where the operational target may include a power consumption target determined based on the predicted availability of power for the resource site from a renewable power generation source during the determined future time period. The computer-implemented method may further include performing real-time controls of power consumption at the resource site to meet the determined power consumption target for the determined future time period, where the real-time controls may include at least temporarily curtailing or modifying power usage of the resource site during the determined future time period.