Data Center Energy Management via Simulated Baseline Revenue
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Solution Overview
Problem
Current methods for determining revenue in wholesale electricity markets are limited by reliance on historical actual electricity usage, which can lead to inaccurate revenue calculations and discourage participation due to disincentives and incentives for artificially inflated usage.
Innovation Solution
The development of energy management tools and systems that use mathematical optimization processes to determine suggested operating schedules for energy assets, including data centers, based on simulated customer baseline energy profiles and real-time feedback, to facilitate revenue generation from wholesale electricity markets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If revenue is calculated based on historical actual electricity usage, then revenue generation is simplified, but accuracy of revenue calculation deteriorates and participation is discouraged due to disincentives for artificially inflated usage
Solution Approach 1:
The system performs preliminary actions by establishing simulated customer baseline energy profiles before actual operation, using mathematical optimization to predict what usage would occur under normal conditions. This allows revenue to be calculated based on deviations from the simulated baseline rather than historical actuals, preventing artificial inflation while maintaining accuracy.
Solution Approach 2:
The system implements continuous feedback mechanisms where actual electricity usage is compared against the simulated baseline profile, and the energy management system adjusts operating schedules accordingly. This feedback loop ensures accurate revenue calculation by detecting and correcting deviations from normal usage patterns in real-time.
2Ease of operation
If historical actual electricity usage is used for revenue determination, then implementation is straightforward, but it creates disincentives and incentives for artificially inflated usage
Solution Approach 1:
The system establishes simulated customer baseline energy profiles in advance using mathematical optimization, creating a reference framework before operational decisions are made. This preliminary simulation provides a reliable baseline for comparing actual usage, eliminating the need to rely on historical actuals that are susceptible to manipulation.
Solution Approach 2:
The energy management system automatically generates and updates simulated baseline profiles based on real-time data and optimization algorithms, without requiring manual intervention or reliance on historical records. This self-service capability ensures the integrity of revenue generation by continuously adapting to current operational conditions.
3Productivity
If data centers operate based on current electricity prices and grid conditions, then revenue generation from wholesale electricity markets is maximized, but operational flexibility requirements increase
Solution Approach 1:
The system dynamically adjusts data center operating schedules based on real-time electricity prices and grid conditions. The energy management system continuously optimizes CPU utilization and workload distribution across multiple data centers, allowing operations to adapt flexibly to changing market conditions while maximizing revenue generation.
Solution Approach 2:
The system provides multi-functional capabilities by simultaneously managing workload distribution, energy optimization, and revenue maximization across a portfolio of data centers. The energy management system serves multiple purposes: optimizing operational efficiency, responding to price signals, and generating revenue from wholesale electricity markets.
Data Source
AI summary
The disclosure facilitates management data center utilization for generating energy-related revenue from energy markets. Operating schedules are generated, over a time period T, for operation of an energy management system of energy assets of data center sites. Since CPU utilization (or computing load) can be correlated to energy consumption, the operating schedules can cause the energy management system to modulate the CPU utilization (or computing load) of energy assets within a data center, or to indicate shifting of CPU utilization (or computing load) from one data center site in a certain energy market price region to another data center site in a different energy market price region. When implemented, the generated operating schedules facilitates derivation of the energy-related revenue, over a time period T, associated with operation of the energy assets according to the generated operating schedule.