Data Center Power Mode Control for Cost-Aware DER Switching
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Solution Overview
Problem
Data centers face challenges in optimizing power generation and consumption to minimize costs, as they rely on various power sources including utility grids and distributed energy resources, with existing systems failing to efficiently select operating modes based on real-time and forecasted costs, leading to inefficiencies and increased operational expenses.
Innovation Solution
A data center control system that receives substation meter data, DER meter data, and data center meter data to select operating modes, adjusting power consumption by toggling loads in the distribution network, and making decisions based on cost thresholds and availability of power sources, including renewable energy sources, to minimize costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data centers rely on multiple power sources including utility grids and distributed energy resources, then power supply flexibility is improved, but system complexity increases
Solution Approach 1:
A control system acts as an intermediary between multiple power sources (utility grids, DERs) and the data center loads. This intermediary receives data from various sources, processes it centrally, and makes coordinated decisions about power allocation, thereby managing the complexity of integrating multiple power sources while maintaining supply flexibility.
Solution Approach 2:
The control system performs multiple functions: it receives data from substation meters, DER meters, and data center meters; it processes real-time and forecasted cost data; it selects operating modes; and it adjusts power consumption. This multi-functional approach consolidates what would otherwise require separate systems for each function.
2Loss of energy
If existing systems fail to efficiently select operating modes based on real-time and forecasted costs, then operational costs increase, but system simplicity is maintained
Solution Approach 1:
The control system receives and processes forecasted cost data in advance of actual power consumption decisions. By having forecasted costs available beforehand, the system can proactively select optimal operating modes before costly periods occur, rather than reacting after costs have already increased.
Solution Approach 2:
The system dynamically adjusts operating modes based on real-time conditions and forecasted costs. Rather than using fixed, static operating modes, the control system continuously adapts its decisions based on current power source availability, real-time costs, and forecasted future costs, optimizing operational efficiency throughout the day.
3Productivity
If the control system adjusts power consumption by toggling loads, then power consumption optimization is improved, but operational complexity increases
Solution Approach 1:
The control system automatically monitors data from multiple meters, processes cost information, selects appropriate operating modes, and adjusts power consumption without requiring manual intervention. The system serves itself by making autonomous decisions about when to toggle loads based on the data it collects and processes.
Solution Approach 2:
The control system receives continuous feedback from substation meters, DER meters, and data center meters about actual power consumption and costs. This feedback loop allows the system to verify whether its operating mode selections are achieving the desired cost optimization and to adjust its strategy accordingly.
Data Source
AI summary
A data center has a distribution network and a control system. The control system receives substation meter data, distributed energy resource (DER) meter data, and receive data center meter data. The control system selects a data center operating mode using the substation meter data, the DER meter data, and the data center meter data. The control system then adjusts power consumption of the distribution network based on the selected the data center operating mode.