Hydrogen Supply Network Control for Real-Time Carbon Intensity Compliance
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Solution Overview
Problem
Existing regulatory frameworks and standards for low-carbon hydrogen production and delivery face challenges in managing variability and uncertainty of renewable electricity supply, allocating hydrogen consignments based on GHG emissions intensity, and harmonizing rules across different regions, necessitating more effective control systems to ensure compliance with carbon intensity constraints.
Innovation Solution
A computer-implemented method and system for operating a hydrogen supply network that includes a carbon intensity determination module, allocation mapping module, and production control module to monitor and control production, distribution, and consumption of hydrogen consignments, utilizing computational models for greenhouse gas emissions allocation and optimizing production rates to meet predefined operational constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time control is implemented to manage carbon intensity compliance, then carbon intensity constraint compliance is improved, but system complexity increases
Solution Approach 1:
The control system is segmented into distinct functional modules: a carbon intensity determination module that calculates CI values, an allocation mapping module that determines hydrogen consignment allocations, and a production control module that adjusts production rates. This modular segmentation manages complexity by dividing the overall control function into specialized, manageable components that can operate semi-independently while contributing to real-time carbon intensity compliance.
Solution Approach 2:
The system performs preliminary calculations of carbon intensity values and determines allocation mappings in advance based on predicted renewable electricity supply scenarios. By pre-calculating CI determinations and allocation strategies for multiple possible future states, the system prepares compliance solutions before actual delivery occurs, enabling real-time responsiveness without requiring complex on-the-spot decision-making.
2Reliability
If dynamic allocation of hydrogen consignments is performed to meet carbon intensity constraints, then carbon intensity compliance is improved, but computational requirements increase
Solution Approach 1:
The system calculates carbon intensity values and determines allocation mappings for only the necessary portion of the hydrogen supply network at any given time, rather than performing exhaustive calculations across the entire network. By focusing computational resources on critical allocation decisions and using simplified CI calculation methods where appropriate, the system achieves compliance without requiring excessive computational power.
Solution Approach 2:
The system uses surrogate models or simplified representations of the hydrogen supply network to perform rapid carbon intensity calculations and allocation optimizations. These computational copies or approximations allow the system to evaluate multiple allocation scenarios quickly without requiring full-scale detailed simulations, thereby reducing computational power requirements while maintaining compliance accuracy.
3Reliability
If production rates are adjusted in real-time to optimize carbon intensity, then carbon intensity compliance is improved, but operational stability decreases
Solution Approach 1:
The system implements dynamic adjustment of production rates that adapts to changing renewable electricity supply conditions while maintaining operational stability. By continuously monitoring actual renewable electricity consumption and comparing it against predicted values, the system makes gradual, controlled adjustments to production rates rather than abrupt changes. This dynamic approach allows the system to respond to carbon intensity requirements while preserving production stability through smooth transitions.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor actual carbon intensity values, renewable electricity consumption, and production rates in real-time. This feedback information is used to continuously refine allocation mappings and adjust production rates, creating a closed-loop control system that maintains both carbon intensity compliance and production stability. The feedback loop enables the system to detect deviations and correct them promptly without causing excessive production fluctuations.
Data Source
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AI summary
A method of operating a hydrogen supply network responsive to carbon intensity (CI) requirements comprising: determining the CI for hydrogen produced at the hydrogen production facilities; determining a network flow solution for the hydrogen supply network, the network flow solution defining a network solution space specifying a range of values for production rates of the hydrogen production facilities and a range of values of delivery rates for the hydrogen delivery points which satisfy predefined operational constraints of the hydrogen supply network; allocating production rates from the hydrogen production facilities to each of the plurality of delivery points based on predetermined criteria associated with the delivery points to define an allocation mapping for the hydrogen supply network; generating control variables for controlling the production rates of each of the hydrogen production facilities; and controlling the hydrogen production facilities in accordance with the determined control variables.