Liquid Hydrogen Storage Planning for Data Center Backup Power
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Data centers face challenges in optimizing liquid hydrogen storage for backup power, as excessive storage wastes resources while insufficient storage leads to emergency power failures, particularly when transitioning from diesel fuels to less energy-dense greener fuels like liquid hydrogen.
Innovation Solution
An optimization model is developed to determine the minimum amount of liquid hydrogen to store based on capacity constraints, vendor refueling constraints, and logistical considerations, using a multi-objective, mixed-integer, linear optimization program to adjust storage and refueling rates, ensuring sufficient backup power within constrained spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If liquid hydrogen storage capacity is increased to ensure sufficient backup power, then reliability of emergency power supply is improved, but resource waste increases due to excessive storage
Solution Approach 1:
The patent implements dynamic adjustment of storage capacity based on real-time fuel consumption rates, refueling rates, and operational requirements. The system continuously monitors these parameters and adjusts the liquid hydrogen storage level dynamically, transitioning from static to adaptive management that matches actual needs and prevents both over-storage and under-storage conditions.
Solution Approach 2:
The optimization model adjusts key parameters including storage capacity, refueling rates, and consumption rates to find the optimal balance. By changing these parameters based on vendor refueling constraints and data center fuel consumption patterns, the system determines the minimum necessary storage capacity that ensures reliability without excessive waste.
2Loss of substance
If liquid hydrogen storage capacity is decreased to reduce resource waste, then resource utilization efficiency is improved, but reliability of emergency power supply deteriorates
Solution Approach 1:
The system performs preliminary calculations using the optimization model to determine the minimum required storage capacity before emergencies occur. By pre-calculating optimal storage levels based on vendor response times and refueling rates, the system ensures sufficient backup power is available without over-provisioning, balancing reliability and resource efficiency in advance.
Solution Approach 2:
The patent implements a feedback mechanism that continuously monitors fuel levels, consumption rates, and vendor refueling status. This real-time feedback allows the system to adjust storage decisions dynamically, ensuring that minimum reliable storage is maintained while avoiding excessive accumulation, thereby preventing both reliability failures and resource waste.
3Productivity
If vendor refueling response time is reduced to improve fuel availability, then productivity of refueling operation is improved, but device complexity increases due to logistical constraints
Solution Approach 1:
The patent segments the refueling process into distinct phases: vendor response phase, active refueling phase, and completion phase. By dividing the overall refueling operation into these segments with specific time constraints and capacity limits for each, the system can optimize response time while managing logistical complexity through structured phase management rather than treating refueling as a monolithic process.
Solution Approach 2:
The optimization model incorporates vendor constraints such as maximum refueling rate and simultaneous tank limitations as partial actions. Rather than requiring unlimited refueling capacity, the system accepts these partial constraints and optimizes storage and operations within those bounds, achieving satisfactory productivity improvement without requiring complete removal of logistical constraints.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Aspects of the disclosure are directed to an optimization model for storing liquid hydrogen to power fuel cells in data centers. The optimization model can be based on hydrogen fuel consumption rates in the data center, refueling rates from vendors, refueling response time, storage tank area constraints in the data center, and/or logistical refueling constraints. The optimization model can allow for providing sufficient fuel within a constrained space for backup power in the data center, such as when an emergency arises.