Hydrogen Supply Network Carbon Intensity Control Strategy
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Solution Overview
Problem
Current systems for managing carbon intensity (CI) in hydrogen supply networks lack effectiveness in balancing cost, CI, product demand, and availability, and are inflexible to adapt to changing market conditions and regulatory requirements.
Innovation Solution
A computer-implemented method and system that optimizes the production and transportation of low carbon fuels by using an optimization model to generate a control strategy for industrial processing facilities powered by renewable energy, ensuring that hydrogen produced meets defined carbon intensity values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If strict carbon intensity limits are imposed on hydrogen supply networks, then environmental compliance is improved, but system flexibility and adaptability to market conditions deteriorate
Solution Approach 1:
The patent implements dynamic control strategies that allow the hydrogen supply network to adapt its operations in real-time while maintaining carbon intensity compliance. The system uses optimization models that continuously adjust production, storage, and distribution parameters based on changing market conditions, renewable energy availability, and carbon pricing mechanisms, thereby achieving both environmental compliance and system flexibility.
Solution Approach 2:
The system employs parameter changes by adjusting operational variables such as hydrogen production rates, storage levels, and distribution priorities in response to varying carbon intensity requirements and market conditions. This allows the network to maintain compliance with carbon limits while adapting to different economic and environmental scenarios.
2Productivity
If intermediate processing operations (cracking, compressing, liquefying) are intensified to meet demand, then productivity is improved, but carbon intensity increases
Solution Approach 1:
The patent applies periodic action by scheduling intensive processing operations during periods when renewable energy is abundant and carbon intensity limits are less restrictive. The system uses optimization models to determine optimal timing for cracking, compressing, and liquefying operations, thereby achieving high productivity while minimizing carbon footprint through strategic temporal distribution of energy-intensive processes.
Solution Approach 2:
The system maintains continuous hydrogen production and delivery to meet demand while managing carbon intensity through continuous optimization. By keeping processing operations running continuously at optimized levels rather than intermittently at maximum capacity, the system achieves sustained productivity with reduced peak carbon intensity impacts.
3Object-generated harmful factors
If renewable energy sources are used for hydrogen production, then carbon intensity is reduced, but energy availability and reliability worsen
Solution Approach 1:
The patent introduces storage facilities and grid connection interfaces as intermediaries between renewable energy sources and hydrogen production processes. These intermediaries buffer the variability of renewable energy supply, allowing the system to maintain low carbon intensity while ensuring reliable energy availability for continuous hydrogen production and meeting demand requirements.
Solution Approach 2:
The system performs preliminary action by pre-storing hydrogen produced during periods of high renewable energy availability for use during periods when renewable energy is insufficient. This allows the network to maintain low carbon intensity production while ensuring reliable supply during all operating conditions.
Data Source
AI summary
A computer-implemented method of providing hydrogen having a defined carbon intensity (CI) value to an end user location, the process comprising: selecting a total end-to-end maximum CI value for the hydrogen from production to delivery of the hydrogen to an end user location; receiving one or more feedstocks; receiving product CI values associated with each feedstock and/or the produced hydrogen; receiving demand data defining the end user demand for the hydrogen; receiving renewable power data; defining, in an optimization model, a plurality of constraints; generating, using the optimization model, a control strategy for control of the one or more industrial plants; and controlling the industrial plants in accordance with the values of the control variables to process the one or more feedstocks in order to provide a required quantity of hydrogen meeting the selected total end-to-end maximum CI value for use by an end user.