Data Center Energy Flow Control With Battery Peak Shaving
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Solution Overview
Problem
Data centers face challenges in efficiently managing energy sources to reduce reliance on the electrical power grid, minimize costs, and ensure reliable power supply, especially during grid failures or peak demand periods.
Innovation Solution
An energy management system that intelligently distributes energy using a combination of power generation systems, battery storage, and thermal energy, optimizing energy flows to balance supply and demand, and utilizing diverse power generation technologies to store and manage energy efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional power grid reliance is used, then power availability is maintained, but cost savings and reduced equipment costs are limited
Solution Approach 1:
The power supply system is segmented into multiple independent sources (power generation system, battery storage system, power grid) rather than relying on a single centralized source. This allows the data center to selectively use different power sources based on cost and availability, reducing overall equipment costs while maintaining reliability through redundancy.
Solution Approach 2:
The system dynamically changes the parameter of power source selection based on real-time conditions such as energy costs, availability, and demand. The energy management system optimizes which power source to use by continuously monitoring and adjusting parameters like cost per kWh, system availability, and load requirements, thereby achieving cost savings without compromising power availability.
2Ease of manufacture
If battery storage system is used to store energy, then cost savings during peak periods are achieved, but system complexity increases
Solution Approach 1:
The battery storage system is integrated with the energy management system to automatically charge during low-cost periods and discharge during high-cost periods without requiring manual intervention. The system self-regulates energy flow based on pre-configured optimization criteria, achieving cost savings while minimizing the operational complexity burden on users.
Solution Approach 2:
The battery storage system serves multiple functions: it provides cost savings by arbitraging energy prices, enhances power availability during grid failures, and supports power quality stabilization. This multi-functionality justifies the added complexity by delivering multiple benefits from a single integrated component.
3Ease of manufacture
If power generation system capacity is reduced, then equipment costs are lowered, but ability to satisfy peak energy needs is compromised
Solution Approach 1:
Energy is stored in advance in the battery storage system during periods when the power generation system can produce surplus power or when grid power is available and inexpensive. This preliminary energy accumulation ensures that peak energy needs can be met without requiring the power generation system to be oversized for peak conditions, thereby reducing equipment costs while maintaining peak supply capability.
Solution Approach 2:
The battery storage system acts as an intermediary between the reduced-capacity power generation system and the data center's peak energy demands. It buffers the mismatch between generation capacity and demand by storing excess power when available and releasing it during peaks, enabling cost reduction without sacrificing productivity.
4Reliability
If multiple power sources are integrated, then reliability and resilience are enhanced, but energy flow management complexity increases
Solution Approach 1:
The energy management system continuously monitors the status of all power sources (availability, cost, load conditions) and dynamically adjusts energy flow allocation in real-time. This feedback mechanism automates the complex decision-making process of managing multiple power sources, enhancing reliability through adaptive optimization while minimizing the operational complexity for users.
Solution Approach 2:
The system dynamically reconfigures energy flow paths based on real-time conditions rather than using fixed routing. Power sources and loads are connected through controllable switches that adapt their configuration according to current system state, enabling the system to handle multiple power sources efficiently and resolve management complexity through automated dynamic optimization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Reduces reliance on the electrical power grid, lowers equipment costs, enhances reliability and resilience, and optimizes energy usage, enabling cost-effective and sustainable power management for data centers.
Implementation Method 1
storing energy in a battery storage system
Implementation Method 2
thermal energy storage
Data Source
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AI summary
This disclosure describes techniques that include managing flows of energy within a system that includes a data center and using at least some of the energy flows to provide power to the data center. In some examples, this disclosure describes a system comprising a power generation system, a battery storage system having a state of charge attribute, and processing circuitry having access to an electrical power grid, the power generation system, and the battery storage system. In one example, the processing circuitry is configured to: determine an energy utilization forecast for a data center; monitor energy availability factors; and determine, based on the energy utilization forecast and the monitored energy availability factors, an energy flow configuration defining energy flows involving with the electrical power grid, the power generation system, the battery storage system, and the data center.