Datacenter Power Management Using Variable Sources
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Solution Overview
Problem
Datacenters face high electricity costs and reliability challenges due to fluctuating power demands, requiring efficient management of both variable and non-variable power sources to optimize power availability, cost, and carbon footprint.
Innovation Solution
A computer-implemented method for datacenter power management that provisions variable and non-variable power sources, calculates dynamic power needs, determines power capabilities, and couples these sources based on evaluation to achieve power management goals, using a power cache to store energy from variable sources for dispatch during peak demand or high-cost periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If variable power sources (renewable energy) are used to power the datacenter, then carbon footprint is reduced and energy costs are lowered, but power availability becomes unpredictable and unreliable
Solution Approach 1:
The system performs preliminary actions by predicting future power generation from variable sources and pre-scheduling their utilization. The optimization engine forecasts renewable energy production and proactively schedules datacenter workloads to align with predicted high-generation periods, ensuring reliable power availability while maximizing renewable energy usage and reducing carbon footprint
Solution Approach 2:
The system dynamically adjusts power source coupling and workload scheduling based on real-time conditions. The optimization engine continuously monitors actual power generation from variable sources, compares it with predictions, and dynamically re schedules workloads to maintain optimal utilization of renewable energy while ensuring continuous reliable power supply to the datacenter
2Loss of energy
If variable power sources are utilized, then energy costs are reduced, but power demand fluctuation management becomes more complex
Solution Approach 1:
The system implements feedback mechanisms where the optimization engine continuously monitors actual power generation from variable sources, compares it with predictions, and uses this feedback to refine future predictions and adjust scheduling decisions. This closed-loop feedback system automatically manages the complexity of coordinating multiple variable power sources with fluctuating datacenter demands, reducing energy costs while simplifying management
Solution Approach 2:
The optimization engine performs self-service by automatically predicting power generation, scheduling workloads, and adjusting power source coupling without manual intervention. The system autonomously manages the complex coordination between variable power sources and datacenter demands, reducing energy costs while eliminating the need for complex manual power management
3Reliability
If power is provided by multiple power grid sources, then reliability is improved, but power management complexity and coordination requirements increase
Solution Approach 1:
The optimization engine serves multiple functions simultaneously: it predicts power generation from variable sources, schedules datacenter workloads, determines optimal power source coupling, and coordinates multiple power grid sources. This multi-functional universal system manages the complexity of coordinating multiple power sources while ensuring improved reliability through diversified power supply
Data Source
AI summary
Embodiments provide techniques for datacenter power management using variable power sources. Power from the variable power sources is stored in a power cache. An optimization engine receives input criteria such as power availability from non-variable and variable power sources, as well as one or more power management goals. The optimization engine implements a dispatch strategy that dispatches stored energy from the power cache and feeds it to the datacenter, resulting in a mixture of non-variable and variable power sources used to achieve the power management goals, such as reduced power cost, increased power availability, and lowered carbon footprint for the datacenter.