Hydrogen Refilling Station Simulation for Variable Demand Scenarios
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Solution Overview
Problem
Current simulation tools for hydrogen refilling stations (HRSs) lack flexibility and accuracy in modeling various architectures and demand profiles, leading to inadequate understanding of initial capital and operational costs, and are limited in supporting diverse HRS designs.
Innovation Solution
A method and apparatus for simulating HRSs that generate multiple model instances based on variable parameters in architecture and demand profiles, executed across distributed computing resources to provide detailed performance characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional simulation tools (e.g., HySCapE) are used to model hydrogen refilling stations, then basic hydrogen gas flow modeling is provided, but the tools lack flexibility in supporting diverse HRS architectures and demand profiles, and modeling accuracy is reduced due to overly simple equipment models
Solution Approach 1:
The system segments the HRS modeling into distinct modular components including architecture definition modules, demand profile modules, equipment modeling modules, and simulation execution modules. Each module can be independently configured and modified, allowing diverse architectural configurations while maintaining accurate physical modeling through standardized interfaces between segments.
Solution Approach 2:
The simulation tool employs dynamic modeling approaches where equipment parameters and operational characteristics can vary based on operating conditions. The system dynamically adjusts model parameters based on input architecture specifications and demand profiles, enabling accurate representation of complex equipment behavior across different HRS configurations without requiring separate static models for each scenario.
2Productivity
If multiple simulation scenarios with variable parameters are executed to evaluate different HRS designs, then comprehensive design optimization is achieved, but the simulation time increases significantly
Solution Approach 1:
The system performs preliminary processing of input architecture and demand files to pre-validate configurations, pre-calculate baseline parameters, and pre-organize simulation datasets before actual execution. This preliminary preparation reduces computational overhead during the actual simulation runs, enabling faster execution of multiple scenarios while maintaining comprehensive evaluation capability.
Solution Approach 2:
The simulation tool implements efficient parameter variation strategies where only the necessary parameters are changed between simulation runs based on the specific design scenarios being evaluated. The system intelligently identifies and varies only the relevant parameters while holding others constant, reducing the computational burden of running multiple simulations while still achieving comprehensive design space exploration.
Data Source
AI summary
Providing computer simulations of a hydrogen refilling station (HRS) includes obtaining an HRS architecture file including architecture parameters and a demand profile file including demand parameters. When at least one variable parameter is present in the architecture or demand parameters, each of the variable parameters having a specified range of values and step size, a plurality of model instances is generated for each unique combination of values for the at least one variable parameter. Each of the plurality of model instances is provided to at least one processing device and, at each of the at least one processing device, the model instance is executed and an executed model instance and model results are returned. Upon completion of execution of plurality of model instances, at least a portion of the model results are compiled in an output file.