US-12618521-B2 - Hydrogen fueling and storage optimization model

US12618521B2US 12618521 B2US12618521 B2US 12618521B2US-12618521-B2

Abstract

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.

Inventors

Dhruv GUPTA
Hariharan Subramanian
Priya Chhiba
Varun Sakalkar

Assignees

GOOGLE LLC

Dates

Publication Date: 20260505
Application Date: 20230224

Claims (16)

1 . A method for controlling liquid hydrogen storage, comprising: determining, by one or more processors, one or more capacity constraints based on input data associated with a capacity for storing liquid hydrogen; determining, by the one or more processors, one or more vendor refueling constraints based on input data associated with vendor refueling; determining, by the one or more processors, a minimum amount of liquid hydrogen to store based on the one or more capacity constraints and the one or more vendor refueling constraints using an optimization model; outputting, by the one or more processors, instructions for adjusting liquid hydrogen storage based on the minimum amount of liquid hydrogen to store; and automatically adjusting, by the one or more processors, at least one of an amount of liquid hydrogen capable of being stored, fuel inflow, or fuel outflow based on the instructions.
2 . The method of claim 1 , further comprising receiving, by the one or more processors, the input data associated with the capacity for storing the liquid hydrogen and the input data associated with vendor refueling.
3 . The method of claim 1 , wherein the optimization model comprises at least one of a multi-objective, mixed-integer, linear, or constrained optimization model.
4 . The method of claim 1 , wherein the input data associated with a capacity for storing the liquid hydrogen further comprises at least one of a monitored level of liquid hydrogen in a tank at a given time, inflow of liquid hydrogen to the tank, or outflow of liquid hydrogen from the tank.
5 . The method of claim 1 , wherein the input data associated with a capacity for storing liquid hydrogen further comprises at least one of a fuel minimum level of a tank, a fuel maximum level of a tank, or an area available for containing tanks.
6 . The method of claim 1 , wherein the input data associated with vendor refueling further comprises at least one of vendor response time, vendor refueling rate, or a maximum number of tanks the vendor can refuel simultaneously.
7 . The method of claim 1 , wherein determining the minimum amount of liquid hydrogen to store is further based on input data associated with maintenance.
8 . The method of claim 7 , wherein the input data associated with maintenance further comprises at least one of a cost per tank or a cost of liquid hydrogen inventory.
9 . The method of claim 1 , wherein determining the one or more capacity constraints further comprises determining at least one of a constraint associated with volume balance, a constraint associated with fuel minimum or maximum levels of a tank, or a constraint associated with a space limit for containing tanks.
10 . The method of claim 1 , wherein determining the one or more vendor refueling constraints further comprises determining at least one of a constraint associated with inflow based on vendor response time, a constraint associated with inflow based on vendor refueling rate, or a constraint associated with an upper bound based on a number of tanks the vendor can refuel simultaneously.
11 . A system comprising: one or more processors; and one or more storage devices coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations for controlling liquid hydrogen storage, the operations comprising: determining one or more capacity constraints based on input data associated with a capacity for storing liquid hydrogen; determining one or more vendor refueling constraints based on input data associated with vendor refueling; determining a minimum amount of liquid hydrogen to store based on the one or more capacity constraints and the one or more vendor refueling constraints using an optimization model; outputting instructions for adjusting liquid hydrogen storage based on the minimum amount of liquid hydrogen to store; and automatically adjusting at least one of an amount of liquid hydrogen capable of being stored, fuel inflow, or fuel outflow based on the minimum amount of liquid hydrogen to store.
12 . The system of claim 11 , wherein the operations further comprise receiving the input data associated with the capacity for storing the liquid hydrogen and the input data associated with vendor refueling.
13 . The system of claim 11 , wherein determining the minimum amount of liquid hydrogen to store is further based on input data associated with maintenance.
14 . The system of claim 11 , wherein determining the one or more capacity constraints further comprises determining at least one of a constraint associated with volume balance, a constraint associated with fuel minimum or maximum levels of a tank, or a constraint associated with a space limit for containing tanks.
15 . The system of claim 11 , wherein determining the one or more vendor refueling constraints further comprises determining at least one of a constraint associated with inflow based on vendor response time, a constraint associated with inflow based on vendor refueling rate, or a constraint associated with an upper bound based on a number of tanks the vendor can refuel simultaneously.
16 . A non-transitory computer readable medium for storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for controlling liquid hydrogen storage, the operations comprising: determining one or more capacity constraints based on input data associated with a capacity for storing liquid hydrogen; determining one or more vendor refueling constraints based on input data associated with vendor refueling; determining a minimum amount of liquid hydrogen to store based on the one or more capacity constraints and the one or more vendor refueling constraints using an optimization model; outputting instructions for adjusting liquid hydrogen storage based on the minimum amount of liquid hydrogen to store; and automatically adjusting at least one of an amount of liquid hydrogen capable of being stored, a fuel inflow, or a fuel outflow based on the instructions.

Description

BACKGROUND Data centers are facilities that house information technology operations and equipment for an organization and can play a key role in ecommerce, video streaming, government, and various other enterprises. In storing, managing, and disseminating data, data centers consume large amounts of power to operate computers and other associated equipment as well as to operate massive air conditioning units to keep the equipment cool. Utilizing greener energy sources is desirable for consuming such large amounts of power. However, transitioning away from diesel fuels to greener fuels that are less energy dense, such as liquid hydrogen, can create new problems for data centers. Proper sizing of onsite storage of greener fuels can be necessary to deploy cost feasible emergency power backup solutions. For instance, storing too much liquid hydrogen can waste resources while storing too little liquid hydrogen can lead to an inability to keep the data center operating during emergencies. BRIEF 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 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. An aspect of the disclosure provides for a method for controlling liquid hydrogen storage, including: determining, by one or more processors, one or more capacity constraints based on input data associated with a capacity for storing the liquid hydrogen; determining, by the one or more processors, one or more vendor refueling constraints based on input data associated with vendor refueling; determining, by the one or more processors, a minimum amount of liquid hydrogen to store based on the capacity constraints and vendor refueling constraints using an optimization model; and outputting, by the one or more processors, instructions for adjusting liquid hydrogen storage based on the minimum amount of liquid hydrogen to store. In an example, the method further includes receiving, by the one or more processors, the input data associated with the capacity for storing the liquid hydrogen and the input data associated with vendor refueling. In another example, the method further includes automatically adjusting, by the one or more processors, an amount of liquid hydrogen capable of being stored based on the instructions. In yet another example, the method further includes automatically adjusting, by the one or more processors, at least one of fuel inflow or fuel outflow based on the instructions. In yet another example, the optimization model includes at least one of a multi-objective, mixed-integer, linear, or constrained optimization model. In yet another example, the input data associated with a capacity for storing the liquid hydrogen further includes at least one of a monitored level of liquid hydrogen in a tank at a given time, inflow of liquid hydrogen to the tank, or outflow of liquid hydrogen from the tank. In yet another example, the input data associated with a capacity for storing liquid hydrogen further includes at least one of a fuel minimum level of a tank, a fuel maximum level of a tank, or an area available for containing tanks. In yet another example, the input data associated with vendor refueling further includes at least one of vendor response time, vendor refueling rate, or a maximum number of tanks the vendor can refuel simultaneously. In yet another example, determining the minimum amount of liquid hydrogen to store is further based on input data associated with maintenance. In yet another example, the input data associated with maintenance further includes at least one of a cost per tank or a cost of liquid hydrogen inventory. In yet another example, determining the one or more capacity constraints further includes determining at least one of a constraint associated with volume balance, a constraint associated with fuel minimum or maximum levels of a tank, or a constraint associated with a space limit for containing tanks. In yet another example, determining the one or more vendor refueling constraints further includes determining at least one of a constraint associated with inflow based on vendor response time, a constraint associated with inflow based on vendor refueling rate, or a constraint associated with an upper bound based on a number of tanks the vendor can refuel simultaneously. Another aspect of the disclosure provides for a system including: one or more processors; and one or more storage devices coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations for controlling