Real-time control system and method for carbon intensity compliance in hydrogen supply network

Abstract

To provide a real-time control system and method for carbon intensity compliance in a hydrogen supply network.SOLUTION: A method of operating a hydrogen supply network in response to carbon intensity (CI) requirements comprises: 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; and allocating production rates from the hydrogen production facilities to each of the 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 control variables.SELECTED DRAWING: None

Description

[Background technology]

[0001] The present invention relates to a real-time method and system for controlling one or more industrial processes in a hydrogen supply network, more particularly to controlling one or more industrial processes in a hydrogen supply network to meet carbon intensity (CI) constraints.

[0002] An industrial gas distribution network includes one or more processes that define the production, conversion, transportation, and distribution of gas for end-user applications.

[0003] Generally, the inputs to an industrial gas distribution network are raw materials (which may include raw materials and / or gas or liquid chemicals for the production of gases or gas precursors) and energy sources to power their production and purification processes. The final outputs of an industrial gas distribution network are gaseous and / or liquefied products that are delivered to end users. In certain applications, industrial gases can be used as fuel gases or liquefied fuel gases for end users.

[0004] Hydrogen distribution networks are critical because they provide a fuel essential for the functioning of economies around the world. However, hydrogen distribution networks are coming under increasing scrutiny because the production, processing, distribution, and end-use of the fuel are often associated with environmental pollutants.

[0005] The technological field of fuel supply and use has undergone significant changes in recent years. Many of these changes are driven by the urgent need to reduce greenhouse gas emissions and mitigate the effects of climate change. As a result, there has been increasing interest in developing low-carbon and renewable fuels that can help reduce the carbon intensity (CI) of transportation and other energy-intensive sectors.

[0006] Governments around the world have imposed strict limits on the carbon dioxide (CI) content of fuels used in a variety of applications. These limits have spurred innovation in the production, transportation, and processing of low-carbon fuels, as well as the development of new technologies and systems for managing those CIs across hydrogen supply networks.

[0007] One area of ​​particular interest in this field is the production of fuels using renewable energy sources such as solar, wind, and hydropower. By leveraging these clean energy sources, it is possible to produce fuels with very low to zero CI at the point of production. Examples of such fuels include green ammonia, green hydrogen, and other low-carbon fuels that can be used in a variety of applications, from powering vehicles to providing energy for industrial processes.

[0008] However, producing low-carbon fuels is only part of the picture. For these fuels to maintain acceptable CI throughout the supply chain, their transportation, intermediate processing, and ultimate delivery to end users must be carefully managed. This involves making a series of complex decisions, including but not limited to factors such as production rates, vessel routing, fuel selection and transportation speed, as well as the choice of land transport route and method for delivery of the fuel to the end user.

[0009] Each of these processes requires energy inputs that can contribute to the CI of the final fuel product, so it is essential to develop efficient and effective control methods to manage the energy consumption and CI of these processing operations.

[0010] Hydrogen is a versatile fuel and energy source that can be used for many applications, including power generation, transportation, industry, and heating. Hydrogen can also contribute to the decarbonization of the global energy supply chain due to the ability to produce hydrogen from renewable sources and to emit no greenhouse gases (GHGs) when used. However, not all hydrogen is produced in a low-carbon or carbon-neutral manner. Depending on the feedstock and production process, hydrogen can result in different levels of GHG emissions and environmental impacts.

[0011] To ensure hydrogen production is in line with the European Union's (EU) and the United Kingdom's (UK) climate goals, both regions are developing regulatory frameworks and standards to define and promote low-carbon hydrogen. These frameworks and standards aim to provide certainty and incentives for investors, producers, and consumers of low-carbon hydrogen, as well as ensure transparency and accountability in hydrogen markets.

[0012] In particular, non-biologically derived renewable fuels (RFNBOs) must achieve at least a 70% reduction in GHG emissions compared to the fossil fuels they replace, calculated over their entire life cycle. Furthermore, RFNBOs must be produced from additional renewable electricity, i.e., the electricity used in their production does not reduce the amount of renewable electricity available on the grid or displace other uses of renewable electricity.

[0013] Additionally, RFNBOs must be tracked through a mass balance chain of custody system, which ensures that the amount of renewable fuel claimed by an economic operator does not exceed the amount of renewable fuel supplied by that operator or other operators in the same supply chain.

[0014] The EU has adopted two delegated acts to provide detailed rules on how to implement these requirements for RFNBOs: The first delegated act defines the conditions under which an RFNBO is considered renewable and clarifies the principle of additionality for renewable electricity.

[0015] The second delegated act prescribes methodologies for calculating the life cycle GHG emissions of RFNBOs and recycled carbon fuels. The EU's mass balance chain of custody system for RFNBOs is based on the International Sustainability Carbon Certification (ISCC) EU203 standard, which requires that mass balance calculations be completed every three months. The ISCC EU203 standard also allows for optional policies on how stocks in the system are disposed of and allocated, meaning that economic operators can choose how to allocate renewable attributes to different shipments in their supply chains.

[0016] The EU REDII Delegated Act also allows any consecutive period to be identified such that the average carbon intensity of the relevant hydrogen production meets the CI threshold. This means that hydrogen producers can choose different time intervals to define their shipments based on their production profile and market conditions.

[0017] In the UK, low-carbon hydrogen is regulated under the UK Low Carbon Hydrogen Standard (LCHS), which sets maximum thresholds for GHG emissions intensity for hydrogen production. The LCHS applies to all hydrogen production technologies and feedstocks, including carbon capture and storage (CCS), biomass, waste, and fossil fuels, including renewable electricity. The LCHS defines low-carbon hydrogen as hydrogen with a GHG emissions intensity of 20gCO2e / MJLHV or less at the point of production.

[0018] The LCHS also develops a methodology for calculating GHG emissions associated with hydrogen production, taking into account various emission categories such as feedstock, process, sequestration, compression, refinery, counterfactual scenarios, and fugitive emissions. Additionally, the LCHS requires hydrogen producers to develop risk mitigation plans for fugitive hydrogen emissions and to report their emissions data to an independent verification organization.

[0019] The LCHS allows producers to define one or more monthly weighted average deliveries, allowing them to "cherry-pick" production intervals so that when periods of high carbon intensity are averaged over periods of low carbon intensity, the weighted average meets the 20gCO2e / MJ threshold. This means that hydrogen producers can optimize their delivery definitions based on their own production history and market demand.

[0020] The LCHS aims to support the implementation of the UK Hydrogen Strategy and Energy Security Strategy, which aims to establish up to 10GW of low-carbon hydrogen production capacity by 2030. The LCHS also sets out the basis for future policy instruments and incentives for the production and consumption of low-carbon hydrogen in the UK.

[0021] However, despite these existing regulatory frameworks and standards in the EU and UK, challenges and gaps remain in ensuring that low-carbon hydrogen is produced and delivered in an efficient and reliable manner. One of these challenges is how to manage the variability and uncertainty in the supply and acceptance of renewable electricity associated with hydrogen production.

[0022] Another challenge is how to allocate and differentiate hydrogen shipments according to their GHG emission intensity and origin in complex and interconnected supply networks. A further challenge is how to harmonize and reconcile different regulations and requirements for low-carbon hydrogen across different regions and markets.

[0023] Therefore, there is a need in the art to provide more effective methods and control systems to address these problems. Summary of the Invention

[0024] The following presents a selection of concepts in a simplified form in order to provide a basic understanding of some aspects of the disclosure. The following is not an extensive overview of the disclosure and is not intended to identify key or critical elements or to delineate the scope of the disclosure. The following merely summarizes some of the concepts of the disclosure as a prelude to the more detailed description provided later.

[0025] Generally, the present disclosure is directed to a process and low-carbon hydrogen supply system that includes multiple production facilities, a distribution network, multiple delivery points, a carbon intensity determination module, an allocation mapping module, and a production control module.

[0026] The system monitors and controls the production, distribution, and consumption of hydrogen shipments according to their carbon intensity and origin. The system ensures that hydrogen shipments delivered to different delivery points comply with their respective carbon intensity constraints when post-calculation and optimization are performed.

[0027] The plurality of production facilities comprise means for producing hydrogen at variable rates, which may include any suitable technology or process capable of producing low-carbon hydrogen from renewable or fossil resources, such as electrolysis, gasification, steam methane reforming with carbon capture, utilization, and storage (CCUS), or any combination thereof.

[0028] Some preferred aspects of the method and system according to the present invention are outlined below.

[0029] Aspect 1: A computer-implemented method for operating a hydrogen distribution network according to carbon intensity (CI) requirements, the hydrogen distribution network including a plurality of hydrogen production facilities and a plurality of hydrogen delivery points, the method being executed by at least one hardware processor and comprising: a) using the computer system to determine the carbon intensity of hydrogen produced at the plurality of hydrogen production facilities; b) using the computer system to determine a network flow solution for the hydrogen distribution network, the network flow solution defining a network solution space specifying ranges of values ​​for production rates of the plurality of hydrogen production facilities in the network and ranges of values ​​for delivery rates of a plurality of hydrogen delivery points in the network that satisfy a plurality of predefined operational constraints of the hydrogen distribution network; c) using the computer system to define an allocation mapping for the hydrogen distribution network, and allocating production rates from each of the plurality of hydrogen production facilities to each of a plurality of delivery points based on predetermined criteria associated with the delivery points within the determined network solution space; d) using the computer system and based on the allocation mapping, generating control variables for controlling the production rate of each of the plurality of hydrogen production facilities; and e) controlling the plurality of hydrogen production facilities in accordance with the generated control variables.

[0030] Aspect 2: The computer-implemented method of aspect 1, wherein step a) includes utilizing one or more computational models configured to allocate greenhouse gas emissions to by-products by one or more of mass allocation, molar allocation, energy-based allocation, and economic allocation.

[0031] Aspect 2A: The computer-implemented method of aspect 2, wherein the one or more computational models include at least one surrogate model.

[0032] Aspect 2B: The computer-implemented method of aspect 2A, wherein the surrogate model includes an equation for carbon intensity that depends on one or more operating parameters of the at least one hydrogen production facility.

[0033] Aspect 3: The computer-implemented method of aspect 2, wherein the one or more computational models include at least one surrogate model that includes an equation for carbon intensity that depends on one or more operating parameters of the at least one hydrogen production facility.

[0034] Aspect 4: The computer-implemented method of aspect 3, wherein the one or more operating parameters include efficiency as a function of production rate.

[0035] Aspect 5: The computer-implemented method of aspect 1, wherein step b) includes determining a production rate of the hydrogen production facility to satisfy a plurality of operational constraints, including one or both of customer demand and network hydraulic constraints.

[0036] Aspect 5A: The computer-implemented method of aspect 5, wherein satisfying the constraints of the network hydraulic constraints includes restricting flow rates on edges in a directed graph representing pipe segments of the hydrogen distribution network.

[0037] Aspect 5B: The computer-implemented method of aspect 5A, wherein if the restricted flow range does not include a value of zero, determining the network flow solution includes implementing a convex constraint such that the predicted pressure drop of the pipe segment is bounded below by a piecewise linear constraint.

[0038] Aspect 6: The computer-implemented method of any one of aspects 1 to 5, wherein step b) comprises utilizing mixed integer quadratic analysis.

[0039] Aspect 7: The computer-implemented method of any one of aspects 1 to 6, wherein steps b) and c) are performed simultaneously in a coupled optimization process.

[0040] Aspect 7A: The coupled optimization process is i q ij d j =p i wherein d j is the delivery rate to delivery point j, and p i is the production rate at production facility i, and the variable p i is calculated as part of the network flow solution.

[0041] Aspect 8: The computer-implemented method of any one of Aspects 1 to 7, wherein step c) further comprises determining a proportion of low-carbon or renewable feedstock for one or more of the hydrogen production facilities.

[0042] Aspect 9: The computer-implemented method of any one of aspects 1 to 8, wherein step c) further comprises assigning an inventory depletion rate to each hydrogen delivery point based on the amount of hydrogen stored and transported between the hydrogen production facility and a, and assigning an inventory growth rate to each hydrogen production facility based on its respective hydrogen production rate.

[0043] Aspect 10: The computer-implemented method of aspect 9, wherein step c) further comprises assigning a production rate such that the sum of the production rate and one or more inventory depletion rates assigned to the delivery points is equal to the hydrogen acceptance rate at the delivery points.

[0044] Aspect 11: The computer-implemented method of any one of aspects 1 to 10, wherein in step c), the predetermined criteria consist of one or more of a current or projected hydrogen demand at the delivery point, a sustainability metric of the hydrogen produced by the production facility, a carbon intensity of hydrogen production by the production facility, or a carbon intensity limit of the delivery point.

[0045] Aspect 11A: The computer-implemented method of any one of the preceding aspects, wherein the hydrogen supply network further comprises a delivery network comprising one or more hydrogen storage and hydrogen transport elements disposed in the network between the hydrogen production facility and the hydrogen delivery point.

[0046] Aspect 11B: The computer-implemented method of any one of aspects 9, 10, or 11A, further comprising assigning an inventory depletion rate to the delivery points based on the amount of hydrogen stored and transported, and assigning an inventory growth rate to each hydrogen production facility based on its respective hydrogen production rate.

[0047] Aspect 11C: The computer-implemented method of any one of aspects 9, 10, or 11A, wherein each inventory depletion rate has an associated carbon intensity.

[0048] Aspect 11D: The computer-implemented method of any one of the preceding aspects, wherein step c) further includes assigning a production rate to the first group of delivery points only if the carbon intensity of the corresponding production facility is less than or equal to the carbon intensity limit of a delivery point in the first group of delivery points.

[0049] Aspect 11E: The computer-implemented method of aspect 11D, wherein step c) further includes assigning an inventory depletion rate to a delivery point in the first group of delivery points only if the associated carbon intensity is less than or equal to the carbon intensity limit for the delivery point.

[0050] Aspect 11F: The computer-implemented method of Aspect 11D or 11E, wherein step c) further includes assigning production rates to the second group of delivery points such that a weighted average carbon intensity of the production rates and inventory depletion rates allocated to the delivery points in the second group of delivery points is less than or equal to the carbon intensity limit of the delivery point.

[0051] Aspect 11G: The computer-implemented method of any one of aspects 9, 10, or 11A-11F, wherein the associated carbon intensity of the inventory depletion rate is a weighted historical average of the carbon intensity of the production rate allocated to inventory growth.

[0052] Aspect 11H: The computer-implemented method of any one of the preceding aspects, wherein the at least one delivery point is operable to provide hydrogen as a feedstock or fuel to the at least one production facility.

[0053] Aspect 11I: The computer-implemented method of aspect 11H, wherein hydrogen is provided to a delivery point operable to provide hydrogen as a feedstock or fuel to the production facility only if the carbon intensity of the inventory depletion rate is greater than the carbon intensity limits of all other delivery points.

[0054] Aspect 11J: A computer-implemented method described in any one of the preceding aspects, wherein if in step c) it is determined that at least some of the specified criteria cannot be met, the method further includes: f) generating a solution that minimizes the violation of the criteria and / or a solution that indicates that the violation of the criteria cannot be met.

[0055] Aspect 11K: The computer-implemented method of any one of the preceding aspects, wherein step c) further includes incorporating a predefined constraint on the greenhouse gas intensity of the hydrogen assigned to the delivery point.

[0056] Aspect 11L: The computer-implemented method of aspect 11K, wherein if it is determined that one or more constraints on the greenhouse gas intensity of the hydrogen assigned to the delivery points cannot be satisfied, the method further includes determining an allocation mapping by minimizing a weighted norm of violations of the constraints on the greenhouse gas intensity of the hydrogen across all delivery points.

[0057] Embodiment 11M: The computer-implemented method of embodiment 11L, wherein the weighted norm is a 2-norm.

[0058] Aspect 11N: The computer-implemented method of any one of the preceding aspects, wherein step b) includes utilizing an optimization process to determine the network flow solution.

[0059] Embodiment 11O: The computer-implemented method of embodiment 11N, wherein in step b), if a network flow solution cannot be determined based on a plurality of predefined operational constraints of the hydrogen supply network, the method further includes adjusting one or more parameters of the optimization process.

[0060] Aspect 11P: The computer-implemented method of aspect 11O, wherein adjusting one or more parameters of the optimization process includes adjusting the ratio of low-carbon or renewable feedstocks to one or more of the hydrogen production facilities to satisfy multiple predefined operational constraints of the hydrogen supply network.

[0061] Aspect 11Q: The computer-implemented method of any one of the preceding aspects, wherein step c) further includes defining one or more allocation mappings using a set of variables q_i_j representing the proportion of the total hydrogen output allocated from production facility / delivered to delivery point j, wherein for a given delivery point j, the sum of q_i_j across production facilities i equals 1.

[0062] Aspect 11R: The computer-implemented method of any one of the preceding aspects, wherein the allocation mapping determined in step c) satisfies the attribute constraint that, for at least one hydrogen delivery point j, the sum of the hydrogen output of attribute terms a_i and q_i_j across hydrogen production facility i is less than constraint limit c_j.

[0063] Aspect 11S: The computer-implemented method of aspect 11R, wherein attribute term a_i includes a greenhouse gas intensity of hydrogen produced at hydrogen production facility i, and c_j includes an upper limit on the greenhouse gas intensity of hydrogen supplied to delivery point j.

[0064] Embodiment 11T: The computer-implemented method of embodiment 11R or 11S, wherein the at least two hydrogen delivery points are not restricted by attribute constraints.

[0065] Aspect 11U: A computer-implemented method described in aspect 11T, wherein for any two delivery points k and m that are not restricted by attribute constraints, the allocation mapping determined in step c) satisfies q_i_k=q_i_m for all production facilities i such that the attributes of hydrogen delivered to delivery points k and m are equal.

[0066] Aspect 11V: The computer-implemented method of any one of the preceding aspects, wherein steps b) and c) include three coupled optimizations: 1) calculating a network flow solution, 2) an allocation mapping of production rates and consumption rates, and 3) determining a delivery rate of low-carbon or renewable feedstock to one or more of the hydrogen production facilities.

[0067] and a process controller configured to control the hydrogen production facilities according to the generated control variables. The hydrogen distribution network includes a plurality of hydrogen production facilities and a plurality of hydrogen delivery points ...

[0068] Aspect 13: The system described in aspect 12, wherein the CI determination module is configured to utilize one or more computational models configured to allocate greenhouse gas emissions to by-products by one or more of mass allocation, molar allocation, energy-based allocation, and economic allocation.

[0069] Aspect 14: The system of aspect 12 or 13, wherein the allocation mapping module is configured to determine the production rate of the hydrogen production facility to satisfy a set of constraints including one or both of customer demand and network hydraulic constraints.

[0070] Aspect 14A: The system of aspect 14, wherein satisfying the constraints on the hydraulic constraints of the network includes restricting flow rates on edges in a directed graph representing pipe segments of the hydrogen distribution network.

[0071] Aspect 14B: The system of aspect 14A, wherein if the restricted flow range does not include a value of zero, determining the network flow solution includes implementing a convex constraint such that the predicted pressure drop of the pipe segment is bounded below by a piecewise linear constraint.

[0072] Aspect 15: The system of aspect 12, 13, or 14, wherein the allocation mapping module is configured to determine a network flow solution and simultaneously allocate production rates and delivery rates in a coupled optimization process.

[0073] Aspect 15A: A coupled optimization process is performed using constraints Σ i q ij d j =p i d j is the delivery rate to delivery point j, and p i is the production rate at production facility i, and the variable p i 16. The system of embodiment 15, wherein σ is calculated as part of the network flow solution.

[0074] Aspect 16: The system of Aspects 12, 13, 14, or 15, wherein the allocation mapping module is further configured to determine a ratio of low-carbon or renewable feedstock to one or more of the hydrogen production facilities.

[0075] Aspect 17: The system of any one of aspects 12-16, wherein the allocation mapping module is further configured to assign an inventory depletion rate to each hydrogen delivery point based on the amount of hydrogen stored and transported, and to assign an inventory growth rate to each hydrogen production facility based on its respective hydrogen production rate.

[0076] Aspect 18: The system of aspect 17, wherein the allocation mapping module is further configured to allocate a production rate such that the sum of the production rate and one or more inventory depletion rates assigned to the delivery point equals the hydrogen acceptance rate at the delivery point.

[0077] Aspect 19: The system described in any one of clauses 12 to 18, wherein the predetermined criteria consist of one or more of current or projected hydrogen demand at the delivery point, a sustainability metric for the hydrogen produced by the production facility, a carbon intensity of hydrogen production by the production facility, or a carbon intensity limit for the delivery point.

[0078] Embodiment 19A: The system of any one of the preceding embodiments, wherein the hydrogen supply network further comprises a delivery network comprising one or more hydrogen storage and hydrogen transport elements disposed in the network between the hydrogen production facility and the hydrogen delivery point.

[0079] Aspect 19B: The system of any one of Aspects 17, 18, and 19A, wherein the allocation mapping module is further configured to assign an inventory depletion rate to the delivery points based on the amount of hydrogen stored and transported, and to assign an inventory growth rate to each hydrogen production facility based on its respective hydrogen production rate.

[0080] Aspect 19C: The system of any one of aspects 17, 18, 19A, and 19B, wherein each inventory depletion rate has an associated carbon intensity.

[0081] Aspect 19D: The system of any one of the preceding aspects, wherein the allocation mapping module is further configured to allocate a production rate to the first group of delivery points only if the carbon intensity of the corresponding production facility is less than or equal to the carbon intensity limit of a delivery point in the first group of delivery points.

[0082] Aspect 19E: The system described in aspect 19D, wherein the allocation mapping module is further configured to assign an inventory depletion rate to a delivery point within the first group of delivery points only if the associated carbon intensity is less than or equal to a carbon intensity limit for the delivery point.

[0083] Aspect 19F: The system of aspect 19D or 19E, wherein the allocation mapping module is further configured to allocate production rates to the second group of delivery points to ensure that a weighted average carbon intensity of the production rates and inventory depletion rates allocated to the delivery points in the second group of delivery points is less than or equal to a carbon intensity limit for the delivery points.

[0084] Aspect 19G: The system of any one of aspects 9, 10, or 19A-19F, wherein the associated carbon intensity of the inventory depletion rate is a weighted historical average of the carbon intensity of the production rate allocated to inventory growth.

[0085] Aspect 19H: The system of any one of the preceding aspects, wherein the at least one delivery point is operable to provide hydrogen as a feedstock or fuel to the at least one production facility.

[0086] Aspect 19I: The system of aspect 19H, wherein the hydrogen is provided to a delivery point operable to provide hydrogen as a feedstock or fuel to the production facility only if the carbon intensity of the inventory depletion rate is greater than the carbon intensity limits of all other delivery points.

[0087] Aspect 19J: A system described in any one of the preceding aspects, wherein if it is determined that at least some of the predetermined criteria cannot be met, the assignment mapping module is further configured to generate a solution that minimizes the violation of the criteria and / or a solution that indicates that the criteria cannot be met.

[0088] Embodiment 19K: The system of any one of the preceding embodiments, wherein the allocation mapping module is further configured to incorporate predefined constraints on the greenhouse gas intensity of hydrogen allocated to the delivery points.

[0089] Aspect 19L: The system of aspect 19K, wherein if it is determined that one or more constraints on the greenhouse gas intensity of the hydrogen assigned to the delivery points cannot be satisfied, the allocation mapping module is further configured to determine the allocation mapping by minimizing a weighted norm of violations of the constraints on the greenhouse gas intensity of the hydrogen across all delivery points.

[0090] Embodiment 19M: The system described in embodiment 19L, wherein the weighted norm is a 2-norm.

[0091] Aspect 19N: The system of any one of the preceding aspects, wherein the assignment mapping module is further configured to utilize an optimization process to determine the network flow solution.

[0092] Aspect 19O: The system of aspect 19N, wherein if the allocation mapping module is unable to determine a network flow solution based on a plurality of predefined operational constraints of the hydrogen supply network, the allocation mapping module is further configured to adjust one or more parameters of the optimization process.

[0093] Embodiment 19P: The system of embodiment 19O, wherein adjusting one or more parameters of the optimization process includes adjusting a ratio of low-carbon or renewable feedstock to one or more of the hydrogen production facilities to satisfy a plurality of predefined operational constraints of the hydrogen supply network.

[0094] Aspect 19Q: The system of any one of the preceding aspects, wherein the allocation mapping module is further configured to define one or more allocation mappings using a set of variables q_i_j representing a proportion of the total hydrogen output delivered to delivery point j that is allocated from production facility I, wherein for a given delivery point j, the sum of q_i_j across all production facilities equals 1.

[0095] Aspect 19R: The system of any one of the preceding aspects, wherein the allocation mapping determined in step c) satisfies the attribute constraint that, for at least one hydrogen delivery point j, the sum of the hydrogen output of attribute terms a_i and q_i_j across the hydrogen production facility is less than the constraint limit c_j.

[0096] Aspect 19S: The system of aspect 19R, wherein attribute term a_i includes a greenhouse gas intensity of hydrogen produced at hydrogen production facility i, and c_j includes an upper limit on the greenhouse gas intensity of hydrogen supplied to delivery point j.

[0097] Embodiment 19T: The system of embodiment 19R or 19S, wherein the at least two hydrogen delivery points are not limited by attribute constraints.

[0098] Aspect 19U: The system of aspect 19T, wherein for any two delivery points k and m that are not restricted by attribute constraints, the allocation mapping determined in step c) satisfies q_i_k=q_i_m for all production facilities i such that the attributes of the hydrogen delivered to delivery points k and m are equal.

[0099] Aspect 19V: The system of any one of the preceding aspects, wherein the allocation mapping module is further configured to perform three coupled optimizations: 1) calculating a network flow solution, 2) allocation mapping of production rates and consumption rates, and 3) determining delivery rates of low-carbon or renewable feedstock to one or more of the hydrogen production facilities.

[0100] Aspect 20: A non-transitory computer-readable storage medium storing a program of instructions executable by a machine to implement a method for operating a hydrogen distribution network according to carbon intensity (CI) requirements, the hydrogen distribution network including a plurality of hydrogen production facilities and a plurality of hydrogen delivery points, the method being executed by at least one hardware processor and comprising: a) using a computer system to determine the carbon intensity of hydrogen produced at the plurality of hydrogen production facilities; and b) using the computer system to determine a network flow solution for the hydrogen distribution network, the network flow solution being based on a range of values ​​for production rates of a plurality of hydrogen production facilities in the network and a plurality of predefined CI requirements for the hydrogen distribution network. a) determining a network solution space that specifies a range of values ​​for delivery rates of a plurality of hydrogen delivery points in a network that satisfy a set of operational constraints; b) using a computer system to define an allocation mapping for a hydrogen supply network, and allocating a production rate from each of a plurality of hydrogen production facilities to each of a plurality of delivery points within the determined network solution space based on predetermined criteria associated with the delivery points; c) using a computer system to define an allocation mapping for a hydrogen supply network, and allocating a production rate from each of a plurality of hydrogen production facilities to each of a plurality of delivery points based on predetermined criteria associated with the delivery points; d) using the computer system to generate control variables for controlling the production rate of each of the plurality of hydrogen production facilities based on the allocation mapping; and e) controlling the plurality of hydrogen production facilities in accordance with the generated control variables.

[0101] Aspect 21: The non-transitory computer-readable storage medium of aspect 20, further configured to execute any one of the methods described in aspects 1, 2, 2A, 2B, 3-5, 5A, 5B, 6, 7, 7A, 8-11, and 11A-11V. [Brief explanation of the drawings]

[0102] Embodiments of the present invention will now be described, by way of example only, with reference to the following drawings in which:

[0103] [Figure 1]FIG. 1 is a schematic diagram of a generalized hydrogen supply network. [Figure 2] FIG. 1 is a schematic diagram of a control system according to an embodiment. [Figure 3] 1 is a schematic node-arc diagram representing a pipe segment. [Figure 4] FIG. 1 is a schematic diagram of a piecewise linear constraint function. [Figure 5] FIG. 1 is a flow diagram of a method according to an embodiment. [Figure 6] 1 is a schematic diagram of a specific exemplary hydrogen supply network. [Figure 7] 7 is a calculation methodology of the present invention applied to the specific example of FIG. 6.

[0104] Embodiments of the present disclosure and their advantages are best understood by reference to the following detailed description, wherein like reference numerals are used to identify like elements illustrated in one or more of the figures, it being understood that the illustrations are for purposes of illustrating embodiments of the present disclosure and not for purposes of limiting them. DETAILED DESCRIPTION OF THE INVENTION

[0105] Various examples and embodiments of the present disclosure are described below. The following description provides specific details to enable a thorough understanding and description of these examples. However, those skilled in the relevant art will understand that one or more embodiments described herein may be practiced without many of these details. Likewise, those skilled in the relevant art will also understand that one or more embodiments of the present disclosure may include other features and / or functions not described in detail herein. Additionally, some well-known structures or functions may not be shown or described in detail below to avoid unnecessarily obscuring the relevant description.

[0106] The present invention is directed to the technical field of control systems for hydrogen supply networks to meet greenhouse gas intensity constraints.

[0107] Modern regulatory incentives for the production of low-carbon hydrogen allow for the application of mass balance chain of custody methodologies. However, the regulatory incentives require that the mass balance of the hydrogen supply network be closed and adjusted only after a relatively long period of time, typically three months. Furthermore, the regulatory incentives provide for broad flexibility in how hydrogen production shipments can be identified, and this process of identifying hydrogen production shipments only needs to be finalized after a relatively long period of time, typically one month.

[0108] Mass balance logistics coordination and hydrogen production and shipping definitions require long execution times and require ex post calculations and optimizations that are not suited to the real-time control needs of a low-carbon hydrogen supply network.

[0109] Therefore, a need exists for a control system for a hydrogen supply network to ensure that production shipments and associated mass balance shipments meet the different carbon intensity constraints associated with the delivery points. Further, a need exists for a control system that takes into account the fact that there is more flexibility in averaging and batching shipments at hydrogen production facilities than at end-use delivery points of hydrogen.

[0110] The technology described herein provides a technical improvement over existing controls of variables associated with industrial fuel production, transportation, and downstream processing to produce and deliver hydrogen to end users. The technical improvement enables control of one or more production facilities to manage technical considerations and constraints related to hydrogen production and delivery. One such technical constraint is the need for delivered hydrogen to meet CI requirements to be classified as low-carbon hydrogen.

[0111] The present invention defines a control system that manages the real-time operation of a hydrogen supply network to ensure that hydrogen shipments supplied to delivery points comply with carbon intensity constraints when post-buffering and mass balance calculations and optimizations are performed.

[0112] Overview of the hydrogen supply network

[0113] 1 shows, for purposes of illustration, a general schematic diagram of an exemplary hydrogen supply network N. The schematic diagram depicts the supply chain of the hydrogen supply network N divided into three categories: production facilities 10, transportation and storage infrastructure 20, and delivery points 30.

[0114] It will be understood that any suitable permutation of one or more individual elements of each of the production facilities 10, transportation and storage infrastructure 20, and delivery points 30 may form a hydrogen supply route that forms part of the network N, as described below.

[0115] In Figure 1, production facilities 10 of a hydrogen supply network N are operable to produce hydrogen from a feedstock. Each of the production facilities is configured to produce hydrogen at a production rate that is selectable and can be varied as a function of time between a maximum level and a minimum level. In other words, each of the production facilities can produce hydrogen at a selected variable rate. The hydrogen produced in the production step can be, but is not necessarily limited to, "green" hydrogen produced using renewable energy.

[0116] The group of production facilities 10 may comprise any suitable number and type of production facilities operable to produce hydrogen at variable rates. The means for producing hydrogen may include any suitable technology or process capable of producing low carbon hydrogen from renewable or fossil resources.

[0117] For example, industrial gas production facility 10 may comprise a hydrogen production plant including an electrolyzer 10-1 operable to produce hydrogen by electrolysis of a feedstock such as water, brine, or steam.

[0118] Alternatively, the industrial gas production plant 10 may comprise a gasification plant 10-2 or reformer 10-3 operable to use methane as a feedstock and produce liquid or gaseous hydrogen by steam reforming.

[0119] Alternatively, hydrogen may be produced from a hydrogen production plant forming part of ammonia production plant 10-4, which may, for example, comprise a hydrogen production plant, an air separation unit (ASU), and an ammonia synthesis plant. Storage for the produced hydrogen may also be provided.

[0120] To reduce the carbon intensity of the production process, the electricity to power one or more industrial gas production plants may be generated, at least in part, by renewable energy sources such as wind and / or solar power sources, although other sources may optionally be utilized. Green fuels produced using renewable power sources have a very low to zero CI at the point of production.

[0121] Each of the production facilities 10-1, 10-2, 10-3, 10-4 may have different capacity, efficiency, cost, and environmental impact.

[0122] The transportation and network infrastructure 20 may comprise any suitable elements operable to transport hydrogen from the production facility 10 to respective delivery points 30 and / or store hydrogen as part of the transportation process from the production facility 10 to the delivery points 30.

[0123] Any suitable infrastructure element may be provided, non-limiting examples of which may include transportation as liquid or gaseous hydrogen by ships and tankers 20-1, trucks 20-2, 20-3, pipelines 20-4, or compression and storage elements 20-5.

[0124] The storage elements may store liquid hydrogen and / or compressed gaseous hydrogen. A typical storage system may include pressure vessels and / or pipe segments connected to a common inlet / outlet pressure-regulated tank. The pressure vessels may be, for example, spheres up to about 25 m in diameter, or "bullets," which are horizontal vessels with large L / D ratios (typically up to about 12:1) up to about 12 m in diameter. In certain regions, underground caverns may also be included as storage systems.

[0125] However, this is not intended to be limiting, and any other suitable infrastructure operable to safely and efficiently store hydrogen may be used.

[0126] Some of these elements may be used in a typical route as part of network N; for example, hydrogen may be transported from production facility 10 to delivery point 30 by a combination of ships 20-1, trucks 20-2, and pipelines 20-4.

[0127] Delivery point 30 may comprise any suitable infrastructure for receiving and utilizing hydrogen produced at one or more of production facilities 10 .

[0128] The hydrogen may be utilized in hydrogen storage, the hydrogen may be used directly (e.g., as part of an industrial process outside the hydrogen supply network N), or the hydrogen may be supplied to third party customers or users at end-user locations (e.g., fuel stations or depots).

[0129] Some possible examples of delivery points 30 are shown in Figure 1. In the case of hydrogen as a low-carbon output, delivery points in the form of end-user locations may include, in non-limiting examples, industrial processing sites or facilities 30-1.

[0130] For hydrogen as a low-carbon fuel, end-user locations may include hydrogen fueling stations or hydrogen fuel depots 30-2. Hydrogen may also be used as a fuel or energy resource for power generation (e.g., power plants 30-3), transportation (e.g., public, commercial, or personal transportation 30-4), or domestic / commercial uses (e.g., domestic or commercial heating 30-5).

[0131] Each delivery point 30 may have different requirements, preferences, prices, and CI constraints. For example, if hydrogen is sold as a green fuel, there are strict CI requirements for that fuel. These are governed by regulations such as REDII in Europe.

[0132] In an embodiment, at least one delivery point 30 may be operable to provide hydrogen as a feedstock or fuel to at least one production facility 10. For example, delivery point 30 may be a power plant that uses hydrogen for electricity generation and provides excess hydrogen to a production facility that uses the hydrogen for gasification or reforming.

[0133] In such cases, hydrogen may be provided to a delivery point operable to provide hydrogen as a feedstock or fuel to a production facility only if the carbon intensity of the stock depletion rate is above the carbon intensity limits of all other delivery points. This may ensure that low-carbon hydrogen is prioritized over other delivery points with more stringent carbon intensity constraints.

[0134] Control System 100

[0135] FIG. 2 shows a schematic diagram of a control system 100 for controlling the elements of a hydrogen supply network N.

[0136] In an embodiment, the present invention provides a method and system for controlling processes in a hydrogen supply network N. In an embodiment, a control system 100 is operable to receive inputs from technical sources and determine optimal operating parameters for one or more production facilities 10, as described below.

[0137] The control system 100 comprises a controller 110. The control system 100 further comprises a process controller 130 enabling the control of the production facility, and a data input block 140 for obtaining and transmitting data from the network N.

[0138] The controller 110 includes at least one hardware processor 112. The controller 110 further includes a carbon intensity determination module 114, an allocation mapping module 116, a production control module 118, a data acquisition module 120, and a data communication module 122.

[0139] Carbon Intensity Determination Module 114

[0140] The carbon intensity determination module 114 may be configured to determine the carbon intensity of the hydrogen produced at the production facilities 10-1, 10-2, 10-3, and 10-4. The carbon intensity determination module 114 may use any suitable methodology or calculation operable to estimate greenhouse gas (GHG) emissions associated with the production of hydrogen from these facilities 10-1, 10-2, 10-3, and 10-4.

[0141] This may be a net value of CI, in an embodiment, and the carbon intensity determination module 114 may also take into account any credits or offsets that may be applied to the production of low-carbon hydrogen.

[0142] In embodiments, the carbon intensity determination module 114 may use several approaches to determine the carbon intensity of the hydrogen produced at the production facilities 10-1, 10-2, 10-3, 10-4.

[0143] In a specific embodiment, the carbon intensity determination module 114 determines the carbon intensity of the hydrogen produced at the production facilities 10-1, 10-2, 10-3, 10-4 using one or more computational models configured to allocate greenhouse gas emissions to by-products using one or more of the following: 1) mass allocation, 2) molar allocation, 3) energy basis allocation, or 4) economic allocation.

[0144] In particular embodiments, one or more life cycle analysis (LCA) models may be utilized. LCA includes analytical methods for estimating the total amount of greenhouse gases emitted during a complete fuel life cycle. The carbon intensity determined from one or more production facilities 10-1, 10-2, 10-3, 10-4 may include, for example, direct impacts resulting from producing and using the fuel, as well as any indirect impacts associated with the fuel.

[0145] In non-limiting embodiments, direct effects can include the production or extraction of feedstocks, the conversion of feedstocks into final fuels or fuel blends, distribution, storage, delivery, and end use of the final fuels by end users.

[0146] As previously mentioned, the Low Carbon Fuel Standard (LCFS) carbon intensity represents the combined atmospheric heat capture effect of five GHGs: CO2, methane (CH4), nitrous oxide (N2O), volatile organic compounds (VOCs), and carbon monoxide (CO). Because these gases are not equivalent in their ability to capture atmospheric heat, they are normalized to the heat capture capacity of CO2.

[0147] The LCA model may incorporate any suitable allocation methodology and may be parameterized using values ​​obtained from regulatory or external models.

[0148] Alternatively or additionally, one or more surrogate models may be used in place of or in addition to the LCA model. The surrogate models include approximate mathematical models for generating CI values ​​that correspond to CI values ​​generated from the LCA model. In other words, the surrogate models are operable to accurately reproduce CI results generated from the LCA model, but are more computationally efficient and, in embodiments, may be integrated with software systems (such as operating systems and control systems) in a manner not possible with the LCA model.

[0149] In embodiments, these models may include computationally feasible surrogate models for modeling the digital CI. Any suitable form or combination of forms of surrogate models may be used. In embodiments, these may include one or more of linear, rational, or bilinear functions of the decision variables of the production facilities 10-1, 10-2, 10-3, and 10-4. In embodiments, the decision variables of the production facilities 10-1, 10-2, 10-3, and 10-4 may include any suitable control variables, such as production rate, steam rate, low-carbon feedstock utilization rate, etc.

[0150] In an embodiment, the CI value may depend on control variables such as the ratio of the production facilities 10-1, 10-2, 10-3, and 10-4 because the efficiency of the production facilities 10-1, 10-2, 10-3, and 10-4 may depend on the ratio of the production facilities 10-1, 10-2, 10-3, and 10-4. In other words, the carbon intensity model may include variables and / or parameters that capture the effect of the operating conditions of the hydrogen production facilities on the carbon intensity of the hydrogen.

[0151] Thus, in embodiments, the impact of the ratios of production facilities 10-1, 10-2, 10-3, and 10-4 on efficiency may need to be further modeled. In embodiments, this may be accomplished through the use of one or more of a linear model, a Box-Jenkins (BJ) model, a piecewise linear model, and a multivariate model. In other words, the carbon intensity model may include carbon intensity equations derived from other models that capture the efficiency of production facilities 10-1, 10-2, 10-3, and 10-4 as a function of the operating conditions of production facilities 10-1, 10-2, 10-3, and 10-4.

[0152] Allocation Mapping Module 116

[0153] The allocation mapping module 116 may be configured to allocate hydrogen production rates from the production facility 10 to the delivery points 20 based on one or more predetermined parameters associated with the delivery points 20 .

[0154] The defined parameters for each delivery point 20 may include one or more of the current or projected hydrogen demand at the delivery point, a sustainability metric for the hydrogen produced by the production facility, the carbon intensity of the hydrogen production by the production facility, or the carbon intensity limit for the delivery point.

[0155] Additionally, other predefined parameters may be set for a delivery point 20, as needed. For example, a delivery point 20 may specify that a particular percentage or percentage range of output originates from a particular production facility 10. The predefined parameters may set particular constraints on the resulting allocation mapping by the allocation mapping module 116.

[0156] The allocation mapping module 116 may also assign inventory depletion rates to delivery points 20 based on the amount of hydrogen stored and transported, and inventory growth rates to production facilities 10 based on their production rates. The allocation mapping module 116 may also allocate different types of shipments to these different groups based on the carbon intensity constraints of the delivery points.

[0157] In an embodiment, the allocation mapping module 116 may accomplish this function by first determining a network flow solution for the hydrogen distribution network, where the network flow solution specifies overall production rates for multiple hydrogen production facilities in the network and delivery rates for multiple hydrogen delivery points in the network. This determines a network flow solution space that satisfies one or more specified constraints. However, as described below, this is non-limiting and other configurations or solutions are possible. For example, the allocation mapping module 116 may perform a coupled optimization to determine the network flow solution and the allocation mapping, as described below.

[0158] In an embodiment, the one or more predefined constraints are selected from the group of customer demand constraints and hydraulic constraints.

[0159] Once the network flow solution is determined, the allocation mapping module 116 is operable to allocate production rates from each of the plurality of 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 distribution network. In an embodiment, this may involve two optimizations: customer allocation mapping and feedstock allocation mapping.

[0160] Alternatively, the assignment mapping and network flow solution steps can be computed by solving a coupled optimization problem. When the assignment mapping and network flow solution are computed simultaneously, the problem becomes Σ i q ij d j =p i where d j is the delivery rate to delivery point j, and p i is the production rate at production facility i, and the variable p i is computed as part of the network flow solution.

[0161] In an embodiment, the allocation mapping module 116 is operable to create an allocation mapping using a set of variables q_i_j that represent the proportion of the total output delivered from production facility i to the allocated delivery point j such that for a given delivery point j, the sum of q_i_j across production facility i equals one.

[0162] In an embodiment, for at least one delivery point j, the determination of the allocation mapping satisfies the constraint that the sum of the attribute terms a_i and q_i_j of the output of the production facility i is less than the constraint limit c_j.

[0163] In a specific embodiment, attribute term a_i includes the greenhouse gas intensity of hydrogen produced at production facility I, and c_j includes an upper limit on the greenhouse gas intensity of hydrogen delivered to delivery point j.

[0164] In embodiments, delivery may be to delivery points that do not require the above constraints. For example, there may be existing hydrogen supply networks where delivery points, such as oil refineries, do not need to meet specific carbon intensity requirements. Therefore, in these cases, hydrogen with a greenhouse gas intensity below the indicated threshold does not need to be allocated to these delivery points.

[0165] However, such an unrestricted scenario can be problematic in the optimization problem. Additionally, when considering fairness across these types of delivery points, it is desirable that the assigned attributes of the hydrogen delivered to these delivery points do not differ substantially from each other and as a function of time.

[0166] As a result, in an embodiment, at least two delivery points are free from the constraint that the sum of the attribute terms a_i and q_i_j of the output of production facility i is less than the constraint limit c_j. This situation applies to many delivery points, such as the oil refineries described above.

[0167] In these situations, it is desirable that the assigned attributes be relatively stable over time and not vary substantially from one delivery point to another. To increase attribute stability and ensure fair allocation of attributes, in embodiments, an alternative fairness constraint is applied such that for any delivery points k and m that have no attribute constraints, the allocation mapping satisfies q_i_k=q_i_m for all production facilities i, ensuring that the attributes of the hydrogen delivered to delivery points k and m are equal.

[0168] This has many advantages. For example, in the absence of the constraint q_i_k=q_i_m to promote stability, the q variables are not constrained by any specified constraint. As a result, the mathematical problem for computing the allocation mapping will be underdetermined. It is well known that solutions to underdetermined mathematical optimization problems can be numerically unstable, and therefore the carbon intensity attributes assigned to customers can vary significantly from calculation period to calculation period. In embodiments, this aspect of the invention avoids these problems.

[0169] A specific embodiment of the configuration and operation of the allocation mapping module 114 is described below.

[0170] Particular embodiments utilize a mixed integer quadratic programming methodology based on a node-arc product model. An exemplary formulation for a simulated pipe segment is shown in Figure 3.

[0171] Figure 3 shows a pipe segment modeled as two nodes i and j connected by an edge (or arc) to form a directed graph configuration. In the model of Figure 3, the flow into a node minus the flow from the node equals the net demand for the node, and the flow along the edge

number

number

[0172] In an embodiment, the flow rate is constrained to the edges in a directed graph representing the pipe segment. The pressure drop across the pipe segment then needs to be modeled. If the flow is bidirectional, then the pressure drop relationship is linearized for computational efficiency.

[0173] However, if the represented pipe segment has flow along corresponding edges that can only flow in a single direction, the pressure drop can be modeled more comprehensively. In an embodiment, this is achieved by bounding the pressure drop by a piecewise linear constraint. In an embodiment, this is defined and solved as a set of piecewise linear convex inequalities.

[0174] Figure 4 shows a schematic diagram of pressure drop P(x) as a function of distance x along a pipe segment. The function P(x) is divided into multiple layers or segments that can be modeled individually to derive the overall function to be represented. This is shown in Equation 1) below.

[0175] 1)

number

[0176] Given that the slope at each level is known, the total cost is the sum of the costs at each level, which can then be converted into a typical piecewise linear function as shown in Equation 2) below.

[0177]

number

[0178] This then leads to equation 3).

number

[0179] In the formula, λ i is a continuous variable.

[0180] Alternatively, the problem can be solved using mixed integer linear programming (MILP), as shown in the following formulation:

[0181] u0: lower limit x u n :upper limit x l i :

number

number

[0182] introduction:

number

number

number

[0183] The model is also used by the production facility 10 and other components of the system as part of determining the network flow solution.

[0184] Once the network flow solution is determined, the allocation mapping module 116 is then operable to allocate a production rate from each of the plurality of 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.

[0185] In an embodiment, this may involve two optimizations: customer production rate allocation mapping and raw material rate allocation mapping to production facility 10.

[0186] In embodiments, the allocation mapping module 116 may also apply additional constraints to the allocation mapping. For example, the allocation mapping module 116 may incorporate constraints on the greenhouse gas intensity of the hydrogen allocated to a particular delivery point.

[0187] Additionally, allocation mapping module 116 may apply relaxed bounds to the constraint problem in particular situations. In an embodiment, if allocation mapping module 116 determines that one or more constraints on the greenhouse gas intensity of the hydrogen allocated to a delivery point cannot be feasibly met, allocation mapping module 116 may apply an alternative approach to address the constraints.

[0188] In particular embodiments, if the constraints set by the original network flow solution are not feasible, the assignment mapping module 116 may derive a new optimization that includes different parameters. In embodiments, the different parameters may include relaxing the original constraints of the determined network flow solution.

[0189] In an embodiment, the new optimization may include generating a different objective function to minimize a function of constraint violations. In an embodiment, the new objective function may be configured to minimize a weighted norm of violations of constraints on the greenhouse gas intensity of hydrogen across all delivery points. In an embodiment, the weighted norm may be a 1-norm or a 2-norm. In an embodiment, the 2-norm is preferred because it spreads the cost of a CI constraint violation across multiple customers, while the 1-norm concentrates the cost of the violation in one or a few customers.

[0190] In addition to production rates, the allocation mapping module 116 is further configured to determine the proportion of low-carbon or renewable feedstocks to one or more of the production facilities 10 .

[0191] In an embodiment, low carbon or renewable feedstocks include landfill gas, renewable natural gas from wastewater treatment, renewable natural gas from municipal food waste, methane captured from coal mines, renewable electricity produced from solar energy, renewable electricity produced from wind energy, or renewable hydroelectric power.

[0192] As a result, in an embodiment, the allocation mapping module 116 may handle three coupled optimizations: 1) calculating a network flow solution, 2) allocation mapping of production rates to consumption rates, and 3) determining the proportion of low-carbon or renewable feedstocks to one or more of the hydrogen production facilities. In an embodiment, these optimizations may be handled in a single coupled process, where the network flow solution and allocation mapping are performed in the coupled process.

[0193] Production Control Module

[0194] 118 The production control module 118 is configured to generate signals operable to enable control of the production rate of the production facility 10 based on the allocation mapping defined by the allocation mapping module 116 .

[0195] The production control module 118 may use any suitable algorithms or techniques to generate control set points to allow real-time adjustments to the production rate of the production facility 10 in response to changing network conditions and demand. The production control module 118 may also communicate with the production facility 10 and the delivery point 20 to coordinate and synchronize their operations.

[0196] In an embodiment, the production control module 118 may further comprise a control element operable to provide a control signal (e.g., a control set point) to the process controller 130 for controlling one or more control elements 10-1C, 10-2C, 10-3C, 10-4C of the process controller 130. In an embodiment, the production control module 118 is operable to generate ratios for the production facilities 10-1, 10-2, 10-3, 10-4 that are communicated to the process controller 130.

[0197] The production control module 118 is operable to communicate with a process controller 130, as described below.

[0198] The data acquisition module 120 is configured to collect data regarding the production, distribution, and consumption of hydrogen from various sources within the network N, including from the sensor block 140, as described below.

[0199] The data acquisition module 120 may use any suitable sensors, meters, or devices capable of measuring and transmitting relevant data regarding network operation and performance. The data acquisition module 120 may also communicate with the carbon intensity determination module 114, the allocation mapping module 116, and the production control module 118 to provide them with the data necessary for their functions.

[0200] The data communications module 122 is configured to communicate the optimized shipping information to relevant parties within the network N. The data communications module 122 may use any suitable communications protocol or channel capable of securely and reliably transmitting and receiving information. The data communications module 122 may also communicate with external entities, such as regulators, verifiers, or customers, to report and verify the carbon intensity of the hydrogen shipments.

[0201] Finally, in an embodiment, the controller 110 may include a data input module 138 operable to receive and process sensor data and other data inputs from the data input block 120 .

[0202] The control system 100 comprises a process controller 130 comprising a plurality of control elements 10-1C, 10-2C, 10-3C, 10-4C...10-nC associated with at least some of the production facilities 10-1, 10-2, 10-3, 10-4...10-n.

[0203] In an embodiment, the parameters controlled by the process controller 130 may include the rate of operation of the hydrogen production process in question, the type of power source, the amount of power, the ramp rate, and the throughput rate of materials (e.g., feedstocks, intermediates, and fuels for producing hydrogen). These parameters are regulated by control elements / control systems 10-1C, 10-2C, 10-3C, 10-4C...10-nC associated with at least some of the production facilities 10-1, 10-2, 10-3, 10-4...10-n.

[0204] Control systems are typically associated with one or more sensors (e.g., flow meters, pressure sensors, temperature sensors, etc., as non-limiting examples) and actuators (e.g., pumps, valves, compressors, or blowers, as non-limiting examples) to maintain a control setpoint (e.g., a throughput or flow setpoint) and regulate the throughput of material through a process. These systems may comprise any suitable controller, for example, a proportional-integral-derivative (PID) controller.

[0205] Each production process in the production facilities 10-1, 10-2, 10-3, 10-4 actually has a maximum and minimum operating capacity. Generally, in dynamic operation, a maximum rate of change applies (which corresponds to the ramp rate). These constraints are typically set by safety, mechanical, electronic, material, or other physical constraints within the equipment.

[0206] The difference between the maximum and minimum operating points defines the operating range. Process constraints impose constraints on the maximum and minimum capacity of each process, along with constraints on the rate of change of production capacity in response to changes in controller setpoints (i.e., ramp rate). Physical equipment limits, quality, and / or safety parameters may also apply.

[0207] The above limits may be determined by the process controller 130 and the process may be controlled by a throughput setpoint either determined locally or provided by the module 118 of the controller 110 .

[0208] In an embodiment, the ratio values ​​for the production facilities 10-1, 10-2, 10-3, 10-4 determined by the process control module 118 are provided to the process controller 130, which is operable to dynamically control the production facilities 10-1, 10-2, 10-3, 10-4 (via control elements 10-1C, 10-2C, 10-3C, 10-4C...10-nC) in response to this data.

[0209] In particular embodiments, the process controller 130 includes a model predictive control (MPC) system. In embodiments, the MPC system includes a multivariable control algorithm that utilizes internal dynamic models of the production facility 10-1, 10-2, 10-3, 10-4 components, an appropriate cost function, and an optimization algorithm. In embodiments, the optimization algorithm is operable to minimize the cost function using multiple control inputs to the control elements 10-1C, 10-2C, 10-3C, 10-4C...10-nC.

[0210] However, in embodiments, alternative functions may be used, which may involve, for example, a similarity function that is maximized.

[0211] The process controller 130 is configured to receive the ratio values ​​of the production facilities 10-1, 10-2, 10-3, 10-4 determined by the process control module 118 and derive operating policies for the production facilities 10-1, 10-2, 10-3, 10-4, including setpoint operating parameters over a predetermined future time horizon. These are then provided to the control elements 10-1C, 10-2C, 10-3C, 10-4C...10-nC to control the associated processes controlled thereby. In an embodiment, it may utilize linear empirical models obtained by system identification of the various processes.

[0212] Alternatively or additionally, embodiments may utilize nonlinear high-fidelity models or nonlinear models created from machine learning algorithms. Because the process controller 130 knows the desired production facility 10-1, 10-2, 10-3, 10-4 ratios, it may be possible to utilize an MPC system to optimize the control set points and process for the current time period while also adapting to future time periods. In embodiments, this is accomplished by optimizing a finite time horizon of the process while implementing the current time period. Then, at the next time period, the optimization is performed again for another finite time horizon.

[0213] The control system 100 may, in embodiments, further comprise a data input block 140 arranged to receive inputs from particular sensors or other components within the hydrogen supply network N.

[0214] In an embodiment, the production facilities 10-1, 10-2, 10-3, 10-4 are equipped with one or more sensors 10-1S, 10-2S, 10-3S, 10-4S of the sensor block 140. These sensors 10-1S, 10-2S, 10-3S enable reporting of process parameters, for example, in an embodiment, hydrogen production rates for the various production facilities 10-1, 10-2, 10-3, 10-4.

[0215] In addition, other data input elements 10-nS may be provided as needed, including but not limited to sensors and sensor data, and may include reported or determined technical values ​​relating to additional elements of the network N.

[0216] In an embodiment, this may include, among other things, technical information related to production information, fuel and process availability or CI values. In general, data input elements 10-1S, 10-2S, 10-3S, 10-4S may also include a report of the carbon intensity output from a given process or step within the supply network N.

[0217] In an embodiment, the present invention provides a control system 100 operable to enable selective control of processes within a hydrogen supply network N to achieve a predetermined result when operating the hydrogen supply network N.

[0218] It should be understood that the above terms "module," "block," and "element" are non-limiting terms and do not necessarily imply any interconnection or grouping between components of systems 100, 110, 130, 140, which may be illustrated with common groupings for purposes of clarity only.

[0219] The low-carbon hydrogen supply system may further comprise a network including means for storing and transporting hydrogen between the production facility and the delivery point. The means for storing and transporting hydrogen may include any suitable infrastructure or equipment capable of safely and efficiently storing and transporting low-carbon hydrogen, such as pipelines, tanks, trucks, ships, or any combination thereof.

[0220] In some embodiments, at least one delivery point 120 may be operable to provide hydrogen as a feedstock or fuel to at least one production facility 110. For example, a delivery point may be a power plant that uses hydrogen for electricity generation and provides excess hydrogen to a production facility that uses the hydrogen for gasification or reforming. In such cases, hydrogen may be provided to a delivery point operable to provide hydrogen as a feedstock or fuel to a production facility only if the carbon intensity of the inventory depletion rate is greater than the carbon intensity limits of all other delivery points. This may ensure that low-carbon hydrogen is prioritized over other delivery points with more stringent carbon intensity constraints.

[0221] A low-carbon hydrogen supply system according to the present invention offers several advantages over conventional systems: In an embodiment, the system is operable to monitor and control the real-time production, distribution, and consumption of hydrogen shipments according to their carbon intensity and source.

[0222] Additionally, the systems and methods may facilitate delivery of hydrogen cargo shipments to different delivery points and compliance with respective carbon intensity constraints when post-hoc (resultant) calculations and optimizations are performed.

[0223] Additionally, the system may optimize the allocation and differentiation of hydrogen shipments based on various criteria such as demand, sustainability, cost, and carbon intensity.

[0224] The system can dynamically adjust to changing network conditions and demand. The system can harmonize and reconcile different regulations and requirements for low-carbon hydrogen across different regions and markets. This can reduce GHG emissions and environmental impacts associated with hydrogen production and consumption.

[0225] This can increase the efficiency and reliability of low-carbon hydrogen supply and provide transparency and accountability in the low-carbon hydrogen market.

[0226] In an embodiment, other outcomes may be ensuring that CI values ​​are met while meeting consumer demand, ensuring efficient and safe operation of plant processes and facilities, and avoiding the need for shutdowns and / or operation outside normal parameters.

[0227] method

[0228] 3 illustrates a method 200 according to an embodiment. In an embodiment, a method is provided that is operable to provide real-time control of one or more industrial processes in a hydrogen supply network. More specifically, the present invention relates to a method for control of one or more industrial processes in a hydrogen supply network to meet carbon intensity (CI) constraints.

[0229] The method is implemented on a computer system utilizing at least one hardware processor 112 and receives input from, among other things, a data input block 140 .

[0230] Step 210: Determine the GHG intensity of the production facility 10

[0231] In step 210, the control system determines the relationship between plant production rates and greenhouse gas intensities of hydrogen produced at one or more production facilities 10-1, 10-2, 10-3, 10-4.

[0232] In particular embodiments, the carbon intensity determination module 114 determines the carbon intensity of the hydrogen produced at the production facilities 10-1, 10-2, 10-3, 10-4 using one or more computational models configured to allocate greenhouse gas emissions to by-products using one or more of the following: 1) mass allocation, 2) molar allocation, 3) energy basis allocation, or 4) economic allocation. In embodiments, the parameters of the computational models may be mandated by regulation.

[0233] In particular embodiments, one or more life cycle analysis (LCA) models may be utilized. LCA includes analytical methods for estimating the total amount of greenhouse gases emitted during a complete fuel life cycle. The carbon intensity determined from one or more production facilities 10-1, 10-2, 10-3, 10-4 may include, for example, direct impacts resulting from producing and using the fuel along with any indirect impacts associated with the fuel.

[0234] In non-limiting embodiments, direct effects can include the production or extraction of feedstocks, the conversion of feedstocks into final fuels or fuel blends, distribution, storage, delivery, and end use of the final fuels by end users.

[0235] As previously mentioned, the Low Carbon Fuel Standard (LCFS) carbon intensity represents the combined atmospheric heat capture effect of five GHGs: CO2, methane (CH4), nitrous oxide (N2O), volatile organic compounds (VOCs), and carbon monoxide (CO). Because these gases are not equivalent in their ability to capture atmospheric heat, they are normalized to the heat capture capacity of CO2.

[0236] The LCA model may incorporate any suitable allocation methodology and may be parameterized using values ​​obtained from regulatory or external models.

[0237] Alternatively or additionally, the determining step may utilize an LCA model including a surrogate model. The surrogate model includes an approximate mathematical model for generating CI values ​​from the LCA model. These may include any suitable surrogate model (e.g., Open LCA, etc.). In a specific embodiment, the surrogate LCA model may be Microsoft Excel-based.

[0238] In an embodiment, the determining step may include utilizing a model that includes one or more of a linear, rational, or bilinear function as a function of a plant decision variable, which may include any suitable control variable, such as production rate, steam rate, low carbon feedstock utilization rate, etc.

[0239] In particular embodiments, the determining step may include utilizing a computational model or models that express the carbon intensity as a bilinear function of variables that represent plant operating conditions, which in particular embodiments include hydrogen production rate, steam production rate, and feedstock (e.g., low-carbon feedstock) consumption rate.

[0240] In an embodiment, the determining step may include utilizing a model to derive an equation for the carbon intensity that is dependent on one or more operating parameters of the production facilities 10-1, 10-2, 10-3, 10-4.

[0241] In an embodiment, the CI value may depend on control variables such as plant rate, since plant efficiency may depend on plant rate. In other words, the carbon intensity model may include variables and / or parameters that capture the effect of the operating conditions of the hydrogen production facility on the carbon intensity of the hydrogen.

[0242] In embodiments, the determining step may further include modeling the impact of plant rate on efficiency via model parameters. In embodiments, this may be accomplished through the use of one or more of a linear model, a Box-Jenkins (BJ) model, a piecewise linear model, and a multivariate model. In other words, the carbon intensity model may include a carbon intensity equation derived from other models that capture the efficiency of production facilities 10-1, 10-2, 10-3, and 10-4 as a function of the operating conditions of production facilities 10-1, 10-2, 10-3, and 10-4.

[0243] A functional schematic of the carbon intensity determination module 114 is shown in Figure 6. Module 114 illustrates typical exemplary inputs, outputs, and data processing paths required to determine the relationship between carbon intensity and production rate of the production facility 10.

[0244] In an embodiment, the carbon intensity determination module 114 receives real-time operating data from the production facility 10 as input.

[0245] In an embodiment, the real-time operating data may include one or more of the following: a raw material input rate to the production facility 10; an energy input rate to the production facility 10; a hydrogen output rate from the production facility 10; and a carbon dioxide capture and sequestered rate from the production facility 10.

[0246] In a specific non-limiting embodiment, consider the example of a blue hydrogen plant. In this scenario, real-time operating data for greenhouse gas intensity calculations may include one or more of the flow rate of natural gas supplied to the production facility, the flow rate of hydrogen produced by the facility, the rate of electricity supplied to the facility, and the rate of carbon dioxide captured and sequestered from the facility.

[0247] Additionally, additional empirical and regulatory data may be utilized in the calculation, including, but not limited to, distribution data such as trucking and shipping, LCA model parameters, regulatory data and contracts including feedstock and / or energy volumes and pricing.

[0248] Once the carbon intensity relationship is determined in step 210, the method proceeds to step 210.

[0249] Step 220: Determine the network flow solution

[0250] In step 220, the controller 110 determines a network flow solution. Determining a network flow solution for a hydrogen distribution network includes determining a network flow solution that specifies overall production rates for multiple hydrogen production facilities in the network and delivery rates for multiple hydrogen delivery points in the network. This determines a network flow solution space that satisfies one or more specified constraints.

[0251] The network flow solution includes one or more of production rates of production facilities, raw material consumption rates, delivery rates to delivery points, inventory depletion rates, inventory growth rates, and allocation mappings between production facilities and delivery points.

[0252] In an embodiment, the network flow solution is computed by formulating a mathematical optimization problem, which, in a non-limiting specific embodiment, may be implemented in Python and Gurobi.

[0253] The input parameters may include the relationship between the carbon intensity and production rate of the production facility 10 determined in step 210 .

[0254] The input parameters of the mathematical optimization problem may further include one or more upper and lower bounds of specific conditions that must be satisfied in the solution. In an embodiment, the one or more predefined constraints are selected from the group of customer demand constraints and hydraulic constraints.

[0255] These may include upper and lower limits on production rate at the plant, lower and upper limits on delivery rate to the delivery point, upper limits on carbon intensity at the delivery point, upper limits on inventory withdrawal rate, and upper limits on inventory growth rate.

[0256] Further inputs may include a mathematical determination of whether certain parameters are met or a mathematical function related to network N. These may include one or more of the following: a Boolean indicator of whether the hydrogen produced at each production facility qualifies as RFNBO, a Boolean indicator of whether the hydrogen delivered to each delivery point qualifies as RFNBO, a long-term weighted moving average of the greenhouse gas intensity assigned to each delivery point, and a long-term weighted moving average of the greenhouse gas intensity of the hydrogen inventory assigned to the inventory increase.

[0257] A mathematical optimization problem is formulated and solved to produce a flow solution that satisfies all of the lower and upper bounds defined above. In an embodiment, determining the network flow solution includes restricting one or more flow rates to edges in a directed graph representing the pipe segments.

[0258] The pressure drop across the pipe segment then needs to be modeled. If the flow is bidirectional, then the pressure drop relationship is linearized for computational efficiency.

[0259] However, if the represented pipe segment has flow along corresponding edges that can only flow in a single direction, the pressure drop can be modeled more comprehensively. In an embodiment, this is achieved by bounding the pressure drop by a piecewise linear constraint. In an embodiment, this is defined and solved as a set of piecewise linear convex inequalities.

[0260] The detailed mathematical optimization of pipe segments is illustrated above in connection with the above description of the allocation mapping module 116 .

[0261] Once the flow solution is generated, the method proceeds to step 230 .

[0262] Step 230: Set allocation mapping

[0263] In step 230, the data generated in the network flow solution of step 220 is utilized by the allocation mapping module 116 to define one or more allocation mappings. This step involves allocating a production rate from each of a plurality of hydrogen production facilities to each of a plurality of delivery points based on predetermined criteria associated with the delivery points to define an allocation mapping for the hydrogen distribution network.

[0264] In an embodiment, the step of allocating mapping may include two optimizations: customer output rate allocation mapping and raw material allocation mapping to production facilities.

[0265] In embodiments, allocating production rates may also include applying additional constraints to the allocation mapping. For example, the allocation mapping module 116 may incorporate constraints on the greenhouse gas intensity of the hydrogen allocated to a particular delivery point.

[0266] Additionally, the allocating step may, in embodiments, include applying relaxed restrictions to the constraint problem in a particular situation. In embodiments, if allocation mapping module 116 determines that one or more constraints on the greenhouse gas intensity of the hydrogen allocated to a delivery point cannot be feasibly met, allocation mapping module 116 may apply an alternative approach to address the constraints.

[0267] In particular embodiments, if the assigning step determines that a constraint determined as part of the original network flow solution is not feasible, the method may further include deriving a new optimization that includes different parameters. In embodiments, the different parameters may include a relaxation of the original constraints of the determined network flow solution.

[0268] In an embodiment, the allocating step may include deriving a new optimization that includes a different objective function to minimize a function of constraint violations. In an embodiment, the new objective function may be configured to minimize a weighted norm of violations of constraints on the greenhouse gas intensity of hydrogen across all delivery points. In an embodiment, the weighted norm may be a 1-norm or a 2-norm. In an embodiment, a 2-norm is preferred because it spreads the cost of violations across multiple customers, while a 1-norm concentrates the cost of violations in one or a few customers.

[0269] In addition to allocating production rates, the allocating step may further include determining a proportion of low-carbon or renewable feedstock for each of one or more of the production facilities 10 .

[0270] In an embodiment, low carbon or renewable feedstocks include landfill gas, renewable natural gas from wastewater treatment, renewable natural gas from municipal food waste, methane captured from coal mines, renewable electricity produced from solar energy, renewable electricity produced from wind energy, or renewable hydroelectric power.

[0271] As a result, in an embodiment, the determining and allocating steps 220, 230 may include three coupled optimizations: 1) calculating a network flow solution, 2) allocating mapping of production rates to consumption rates, and 3) determining the proportion of low-carbon or renewable feedstock to one or more of the hydrogen production facilities.

[0272] In a particular embodiment, the allocation mapping defined in step 230 satisfies four criteria as described below.

[0273] First, the allocation mapping and inventory depletion for a given delivery point summed across all of the production facilities 10 must equal the set point of hydrogen supplied to the delivery point.

[0274] For the above determination, all allocation mappings for a given delivery point must be non-negative, and inventory depletion is determined as the maximum of zero and the sum of the delivery rate setpoints at the delivery point minus the sum of the production rate setpoints at the production facilities.

[0275] Second, the sum of the allocation mappings for a given production facility 10 across all delivery points 30 and the inventory increase must be less than or equal to the rate of hydrogen produced by the facility, and the inventory increase is calculated as the maximum of zero and the sum of the production rates of the production facilities minus the sum of the delivery rates of the delivery points.

[0276] Third, for each delivery point 30 that is characterized as a hydrogen end-use point (and not characterized as a production point), the only allocation mapping that is non-zero is that in which the associated production point is designated as having a carbon intensity lower than the carbon intensity limit associated with the delivery point 30.

[0277] This condition ensures that if hydrogen shipments are confirmed at the end of the regulatory period, each shipment will meet the greenhouse gas intensity cap to meet the requirements of the low-carbon hydrogen incentive.

[0278] Fourth, for each delivery point characterized as a production point, any allocation mapping can be non-negative, provided that exactly one of the following conditions holds:

[0279] 1) the weighted average greenhouse gas intensity of hydrogen in the delivery point allocation mapping is less than the greenhouse gas intensity associated with the delivery point, or

[0280] 2) The weighted average greenhouse gas intensity of hydrogen in the delivery point allocation mapping is above the greenhouse gas intensity limit associated with the delivery point, but the long-term weighted average of the delivery point allocation mapping is below the greenhouse gas intensity limit.

[0281] This condition ensures that if hydrogen shipments are confirmed at the end of the regulatory period, each shipment will meet the greenhouse gas intensity cap to meet the requirements of the low-carbon hydrogen incentive.

[0282] In a real-time system, it is possible that no allocation mapping exists that satisfies all of the above constraints. In this situation, the allocation mapping module 116 provides a mapping that minimizes violations of the above constraints and indicates which constraints could not be met.

[0283] In an embodiment, steps 220 and 230 may be computed simultaneously by solving a coupled optimization problem. When the assignment mapping and network flow solution are computed simultaneously, the problem becomes Σ i q ij d j =p i where d j is the delivery rate to delivery point j, and p i is the production rate at production facility i, and the variable p i is computed as part of the network flow solution.

[0284] In an embodiment, the allocation mapping module 116 is operable to create an allocation mapping using a set of variables q_i_j that represent the proportion of the total output delivered from production facility i to the allocated delivery point j such that for a given delivery point j, the sum of q_i_j across production facility i equals one.

[0285] In an embodiment, for at least one delivery point j, the determination of the allocation mapping satisfies the constraint that the sum of the attribute terms a_i and q_i_j of the output of the production facility i is less than the constraint limit c_j.

[0286] In a specific embodiment, attribute term a_i includes the greenhouse gas intensity of hydrogen produced at production facility I, and c_j includes an upper limit on the greenhouse gas intensity of hydrogen delivered to delivery point j.

[0287] In embodiments, delivery may be to delivery points that do not require the above constraints. For example, there may be existing hydrogen supply networks where delivery points, such as oil refineries, do not need to meet specific carbon intensity requirements. Therefore, in these cases, there is no need to allocate hydrogen with greenhouse gas intensities below the indicated thresholds to these delivery points.

[0288] However, such an unrestricted scenario can be problematic in the optimization problem. Additionally, when considering fairness across these types of delivery points, it is desirable that the assigned attributes of the hydrogen delivered to these delivery points do not differ substantially from each other and as a function of time.

[0289] As a result, in an embodiment, at least two delivery points are free from the constraint that the sum of the attribute terms a_i and q_i_j of the output of production facility i is less than the constraint limit c_j. This situation applies to many delivery points, such as the oil refineries described above.

[0290] In these situations, it is desirable that the assigned attributes be relatively stable over time and not vary substantially from one delivery point to another. To increase attribute stability and ensure fair allocation of attributes, in embodiments, an alternative fairness constraint is applied such that for any delivery points k and m that have no attribute constraints, the allocation mapping satisfies q_i_k=q_i_m for all production facilities i, ensuring that the attributes of the hydrogen delivered to delivery points k and m are equal.

[0291] This has many advantages. For example, in the absence of the constraint q_i_k=q_i_m to promote stability, the q variables are not constrained by any specified constraint. As a result, the mathematical problem for computing the allocation mapping will be underdetermined. It is well known that solutions to underdetermined mathematical optimization problems can be numerically unstable, and therefore the carbon intensity attributes assigned to customers can vary significantly from calculation period to calculation period. In embodiments, this aspect of the invention avoids these problems.

[0292] Step 240: Adjust production rate

[0293] In step 240, the production rate of the production facility 10 may be adjusted to control production according to the determined allocation mapping.

[0294] The control system 110 is operable to regulate the production rate at each production facility 10 by generating a production rate setpoint for each production facility 10 and communicating the setpoint to each respective production facility 10 .

[0295] The setpoints are calculated by the network flow solution to ensure consistency with the determined allocation mapping and greenhouse gas intensity constraints.

[0296] In an embodiment, step 240 includes generating control variables for controlling the production rate of each of the plurality of hydrogen production facilities based on the network flow solution and the allocation mapping.

[0297] Production rate values ​​may be determined by the process control module 118 and provided to the process controller 130, which is operable to dynamically control the production facilities 10-1, 10-2, 10-3, 10-4 (via control elements 10-1C, 10-2C, 10-3C, 10-4C, ... 10-nC) in response to this data.

[0298] In particular embodiments, the generated control variables for production rate control may be utilized in a model predictive control (MPC) system. In embodiments, the MPC system includes a multivariable control algorithm that utilizes internal dynamic models of the production facility 10-1, 10-2, 10-3, 10-4 components, an appropriate cost function, and an optimization algorithm. In embodiments, the optimization algorithm is operable to minimize the cost function using multiple control inputs to the control elements 10-1C, 10-2C, 10-3C, 10-4C...10-nC.

[0299] However, in embodiments, alternative functions may be used, which may involve, for example, a similarity function that is maximized.

[0300] In an embodiment, controlling the multiple hydrogen production facilities according to the values ​​of the control variables may include, by the process controller 130, receiving ratio values ​​for the production facilities 10-1, 10-2, 10-3, 10-4 determined by the process control module 118 and deriving an operating policy for the production facilities 10-1, 10-2, 10-3, 10-4 including setpoint operating parameters over a predetermined future time horizon. Control is then exercised via the control elements 10-1C, 10-2C, 10-3C, 10-4C...10-nC, thereby controlling the associated processes being controlled. In an embodiment, this process may utilize linear empirical models obtained by system identification of the various processes.

[0301] Alternatively or additionally, in embodiments, the process may utilize a nonlinear high-fidelity model or a nonlinear model created from a machine learning algorithm. Because the process controller 130 knows the desired production facility 10-1, 10-2, 10-3, 10-4 ratios, it may be possible to utilize an MPC system to optimize the control set points and process for the current time period while also adapting to future time periods. In embodiments, this is accomplished by optimizing a finite time horizon of the process while implementing the current time period. Then, at the next time period, the optimization is performed again for another finite time horizon.

[0302] Step 250: Adjust the rate of delivery of hydrogen to the delivery point

[0303] In step 250, the delivery rate of each delivery point 30 may be adjusted to control delivery according to the determined allocation mapping.

[0304] The control system 110 is operable to adjust the delivery rate, the target delivery rate being calculated by the network flow solution in a manner consistent with the allocation mapping and greenhouse gas intensity constraints, which may be done in accordance with the plant production rate control outlined above in step 240.

[0305] The setpoints are calculated by the network flow solution to ensure consistency with the determined allocation mapping and greenhouse gas intensity constraints.

[0306] Step 260: Calculate the long-term weighted moving average of the hydrogen allocated to the delivery point

[0307] The control system 110 functions to calculate a long-term weighted moving average of the hydrogen allocated to each delivery point 30 .

[0308] In a non-limiting embodiment, this long-term weighted moving average of hydrogen allocated to a delivery point 30 is calculated as the sum of each production point in the allocation mapping between the production point and the delivery point, summed over each time increment, multiplied by the greenhouse gas intensity of the production point.

[0309] As described above, the long-term weighted moving average of hydrogen allocated to a delivery point may be used in certain circumstances to determine whether the control system can find a network flow solution such that the weighted average greenhouse gas intensity of hydrogen in the delivery point allocation mapping is greater than the greenhouse gas intensity limit value associated with the delivery point.

[0310] Step 270: Calculate the moving average weight of hydrogen allocated to the inventory

[0311] The control system is operative to calculate a weighted moving average of hydrogen allocated to inventory. In an exemplary embodiment of the invention, this long-term weighted moving average of hydrogen allocated to inventory is calculated as the sum for each time increment of the sum for each production point of the allocation mapping between production points and inventory increases multiplied by the greenhouse gas intensity of the production point divided by the sum for each time increment of the sum for each production point of the allocation mapping between production points and inventory increases.

[0312] Example of how it works

[0313] Figure 6 illustrates aspects of another exemplary embodiment of the present invention, depicting a hydrogen supply network N in the UK. Network N comprises a hydrogen pipeline distribution network P supplied by three hydrogen production facilities H1, H2, H3, two of which are operable to receive and use recycled hydrogen from the pipeline network P. Hydrogen from network P is supplied to four distribution points D1, D2, D3, D4.

[0314] In the exemplary embodiment, there are three hydrogen production facilities H1, H2, H3 that supply hydrogen to a pipeline distribution network P.

[0315] The first hydrogen production facility, H1, comprises a blue hydrogen plant that produces hydrogen from natural gas, with a greenhouse gas intensity typically in the range of 15-20 gCO2e / MJ. The blue hydrogen plant operates to receive recycled hydrogen from the pipeline distribution network.

[0316] When used, the recycled hydrogen is combusted to generate steam for the autothermal reforming process used to produce hydrogen. The hydrogen produced at the facility meets the criteria of the UK Low Carbon Hydrogen Standard and is eligible for financial incentives under the UK Hydrogen Production Business Model.

[0317] The second hydrogen production facility, H2, is a chlor-alkali plant that produces hydrogen as a by-product. The greenhouse gas intensity of this hydrogen typically ranges from 5 to 35 g CO2e / MJ, which varies based on the percentage of renewable electricity provided to the facility.

[0318] Shipments of hydrogen produced in chlor-alkali plants with a greenhouse gas intensity of less than 20gCO2e / MJ meet the criteria of the UK Low Carbon Hydrogen Standard and are eligible for financial incentives under the UK Hydrogen Production Business Model.

[0319] The third hydrogen production facility, H3, produces hydrogen by dissociating renewableally produced ("green") ammonia. The greenhouse gas intensity of this hydrogen varies based on the % of grid electricity versus the % of renewable electricity used to produce the upstream green hydrogen and green ammonia, and is typically in the range of 5-10 g CO2e / MJ. The ammonia dissociation facility functions to accept recycled hydrogen from the pipeline distribution network.

[0320] Once used, the recycled hydrogen is burned in a furnace to provide heat to drive an endothermic chemical reaction that dissociates ammonia into hydrogen and nitrogen. Hydrogen produced at the facility meets the criteria of the UK's Low Carbon Hydrogen Standard and is eligible for financial incentives under the UK's hydrogen production business model.

[0321] Delivery point

[0322] An exemplary UK network has six delivery points D1-D6. The first delivery point D1 is for supplying low carbon hydrogen to a direct fired furnace at a UK oil refinery. The direct fired furnace can accept a blend of natural gas, refinery fuel gas, and hydrogen in varying proportions.

[0323] The use of low-carbon hydrogen from the hydrogen supply network reduces the amount of natural gas that needs to be fed to the direct-fired furnace, thereby reducing the refinery's direct greenhouse gas emissions and thereby reducing the refinery's tax liability under the UK Emissions Trading Scheme (ETS). These tax benefits accrue regardless of the greenhouse gas intensity of the hydrogen fed to the direct-fired furnace and regardless of whether the hydrogen qualifies as a non-biologically derived renewable fuel (RFNBO).

[0324] The second delivery point, D2, is for the supply of hydrogen for use in the hydrolysis unit of a UK oil refinery. If the hydrogen supplied to the hydrolysis unit has a greenhouse gas intensity of less than 32.9gCO2e / MJ and qualifies as a non-biologically derived renewable fuel (RFNBO), the hydrogen supplied to the refinery applies to meet the refinery operator's obligations under the UK Renewable Transport Fuels Obligation (RTFO).

[0325] The third delivery point, D3, is for the supply of hydrogen for distribution via truck to fuel stations for fuel cell electric vehicles (FCEVs) in the UK. The additional greenhouse gas intensity associated with compressing the hydrogen into tube trailers, dispensing via truck, and station compression and cooling is 5.6 g CO2e / MJ. To be eligible for credits under the UK RTFO, the total greenhouse gas intensity of the hydrogen supplied to FCEVs must be less than 32.9 g CO2e / MJ. Therefore, the upper limit on the greenhouse gas intensity of hydrogen that can be supplied to tube trailers is 32.9 - 5.6 g CO2e / MJ = 27.3 g CO2e / MJ.

[0326] The fourth delivery point, D4, is for the supply of hydrogen to the liquefaction unit. Once liquefied, the hydrogen is transported via truck to France and supplied to FCEV fueling stations. The hydrogen distributed to qualify FCEVs must be a non-biological renewable fuel (RFNBO) with a greenhouse gas intensity of less than 28.2 g CO2e / MJ to meet obligations under the European Union's Renewable Energy Directive II regulations.

[0327] Renewable electricity is used to liquefy hydrogen and distribute it at fueling stations. However, there are incremental greenhouse gas emissions of 3gCO2e / MJ associated with trucking liquid hydrogen. Therefore, the greenhouse gas intensity of the hydrogen supplied to the liquefaction plant must be less than 28.2gCO2e / MJ = 25.2gCO2e / MJ, and the hydrogen must be produced from non-biological renewable energy sources.

[0328] The fifth delivery point, D5, is for the recycling of hydrogen to the Blue Hydrogen Production Facility, H1, where it is used to produce steam for supply to the autothermal reforming reactor. The hydrogen used in this way reduces the greenhouse gas intensity of the hydrogen produced by the Blue Hydrogen Facility and offsets the natural gas that would otherwise need to be used, thereby reducing the Blue Hydrogen Plant's obligations under the UK Emissions Trading Scheme.

[0329] The sixth delivery point, D6, is for recycling the hydrogen back to the Green Ammonia Dissociation Facility, where it is combusted to provide the heat required for the endothermic ammonia dissociation reaction. Hydrogen used in this way reduces the greenhouse gas intensity of the hydrogen produced by the Green Ammonia Dissociation Facility and offsets the natural gas that would otherwise need to be used, thereby reducing the Green Hydrogen Plant's obligations under the UK Emissions Plan.

[0330] Distribution Network

[0331] In an exemplary embodiment of the invention, there is a pipeline network P operable to distribute hydrogen from any of three production facilities H1, H2, H3 to any of six delivery points D1-D6.

[0332] Additionally, the pipeline network P has a substantial inventory of hydrogen. If the control system 100 sets the delivery rates to the delivery points such that the sum of the delivery rates is less than the sum of the production rates from the production facilities, hydrogen can be drawn from the inventory, thereby depleting the inventory and reducing the average pressure of hydrogen in the pipeline network.

[0333] If the control system 100 sets the delivery rates to delivery points D1-D6 such that the sum of the delivery rates exceeds the sum of the production rates from production facilities H1, H2, and H3, hydrogen can be added to the inventory, thereby building up the inventory and increasing the average pressure of hydrogen in the pipeline network P.

[0334] Control System

[0335] In a non-limiting embodiment of the present invention, the hydrogen supply network N comprises a control system 100 operable to:

[0336] 1) Calculate the greenhouse gas intensity of hydrogen produced at three hydrogen production facilities: H1, H2, and H3.

[0337] 2) Adjust the production rates of the three hydrogen production facilities H1, H2, and H3.

[0338] 3) Adjust the hydrogen delivery rate to the six delivery points D1 to D6.

[0339] 4) Calculate the allocation mapping between the production rates at the three hydrogen production facilities H1, H2, and H3, the inventory depletion and delivery rates to the six delivery points D1 to D6, and the inventory increase amount, as follows:

[0340] 5) For a given hydrogen production facility H1, H2, H3, the sum of all delivery points D1-D6 of the allocation mapping of that hydrogen production facility H1, H2, H3 is equal to the production rate of the specific hydrogen production facility H1, H2, H3.

[0341] 6) For a given delivery point D1-D6, the sum of all hydrogen production facilities H1, H2, H3 in the allocation mapping of that delivery point is equal to the delivery rate of that delivery point D1-D6.

[0342] 7) For delivery points D1-D6 that do not qualify as hydrogen production facilities H1, H2, H3, the only non-zero allocation mappings are those that correspond to production facilities with greenhouse gas intensity less than the maximum greenhouse gas intensity constraint for the hydrogen allocated to that delivery point D1-D6.

[0343] Delivery points D1 to D6 are as follows:

[0344] 1) Calculate a weighted moving average of the hydrogen assigned to delivery points D1 to D6;

[0345] 2) Calculate the weighted moving average of the hydrogen allocated to the inventory.

[0346] 7 illustrates an example operation according to an embodiment. In this example, hydrogen is produced in three hydrogen production facilities H1, H2, H3 and supplied to four delivery points D1-D4.

[0347] In the first functional step of the control system, the greenhouse gas intensity is calculated for each of the three production facilities H1, H2, and H3. For the blue hydrogen plant H1, the greenhouse gas intensity is calculated using the natural gas flow rate, electricity consumption rate, hydrogen production rate, and carbon dioxide capture and sequestration rate. The calculated greenhouse gas intensity for the blue hydrogen plant is 19gCO2e / MJ.

[0348] For the chlor-alkali process H2, the greenhouse gas intensity is calculated using the electricity consumption rate and hydrogen production rate. The greenhouse gas intensity of the chlor-alkali plant is 30gCO2e / MJ.

[0349] For green ammonia dissociation plant H3, the carbon intensity is calculated using the natural gas flow rate to the dissociation furnace, the electricity consumption rate, the hydrogen flow rate to the dissociation furnace, and the flow rate of the produced hydrogen. In exemplary embodiment Case #1, the greenhouse gas intensity of the hydrogen produced from the dissociation of green ammonia is 5 g CO2e / MJ.

[0350] In a second functional step of the control system, a network flow solution is calculated using Python and Gurobi that satisfies the production rate constraints at each of the hydrogen production facilities H1, H2, and H3, the hydrogen delivery rate constraints to each of the delivery points D1-D4, and the allocation mapping constraints.

[0351] First, the sum of the allocation mappings of a given delivery point D1-D4 for all hydrogen production facilities H1, H2, H3 and inventory depletion is equal to the setpoint of hydrogen supplied to the delivery point, all allocation mappings of a given delivery point D1-D4 are non-negative, and inventory depletion is calculated as the maximum of zero and the sum of the setpoints of delivery rates at delivery points D1-D4 minus the sum of the setpoints of production rates at hydrogen production facilities H1, H2, H3.

[0352] For example, for liquefied hydrogen delivery point D4 for shipment to France, the allocation mapping is 0 MMSCFD from blue hydrogen plant H1, 25 MMSCFD from chlor-alkali plant H3, 10 MMSCFD from dissociated green ammonia plant H2, and 0 MMSCFD from inventory depletion.

[0353] The setpoint for the delivery rate of hydrogen for liquefied transport to France is 35 MMSCFD. In accordance with the present invention, all allocation mappings for this delivery point are non-negative. Furthermore, the sum of the allocation mappings for this delivery point is 0 + 25 + 10 + 0 = 35 MMSCFD, which is equal to the 35 MMSCFD setpoint for the delivery rate of hydrogen to delivery point D4.

[0354] Second, the sum of the allocation mappings for all delivery points D1-D4 and inventory increase for a given hydrogen production facility H1, H2, H3 is less than or equal to the rate of hydrogen produced by the facility, and inventory increase is calculated as the maximum of zero and the sum of the production rates of the production facilities minus the sum of the delivery rates of the delivery points.

[0355] As an example, for blue hydrogen plant H1, the allocation mapping is 50 MMSCFD to the oil refinery furnace D1, 0 MMSCFD to the oil refinery hydrolysis unit D2, 0 MMSCFD to the tube trailer D3, 0 MMSCFD for liquefaction transport to France D4, 0 MMSCFD for recycle to blue hydrogen facility D5, 0 MMSCFD for recycle to green ammonia dissociation facility D6, and 10 MMSCFD for inventory increase. Therefore, it is clear that the total allocation mapping for the blue hydrogen plant is 50 + 0 + 0 + 0 + 0 + 10 = 60 MMSCFD, which is equal to the setpoint production at the blue hydrogen facility.

[0356] Third, for each delivery point D1-D4 that is characterized as a hydrogen end-use point and not as a production point, the allocation mapping is only for cases where the greenhouse gas intensity of the associated production point is less than the greenhouse gas intensity limit associated with the delivery point.

[0357] This condition ensures that if hydrogen shipments are confirmed at the end of the regulatory period, each shipment will meet the greenhouse gas intensity cap to meet the requirements for the low-carbon hydrogen incentive. As an example, hydrogen delivered to hydrolysis unit D2 of an oil refinery would not qualify as a hydrogen production point.

[0358] Hydrogen delivered to this point must have a greenhouse gas intensity less than 32.9 gCO2e / MJ. As shown in Figure 7, the only non-zero allocation mapping is from the chlor-alkali plant, which has a greenhouse gas intensity of 30.0 gCO2e / MJ, which is below the threshold of 32.9 gCO2e / MJ.

[0359] Fourth, for each delivery point D5, D6 characterized as a production point, any allocation mapping can be non-negative, provided that exactly one of the following conditions holds:

[0360] the weighted average greenhouse gas intensity of the hydrogen in the delivery point allocation mapping is less than the greenhouse gas intensity associated with the delivery point, or

[0361] The weighted average greenhouse gas intensity of the hydrogen in the allocation mapping of the delivery point is above the greenhouse gas intensity limit value associated with the delivery point, but the long-term weighted average of the allocation mapping of the delivery point is below the greenhouse gas intensity limit value.

[0362] As an example of the fourth condition, consider delivery point D4 for producing liquefied hydrogen for transport to France. Because the hydrogen changes state (from gas to liquid), this delivery point also qualifies as a production point. Note that the greenhouse gas intensity limit for this delivery point is 25.2 g CO2e / MJ.

[0363] The non-zero allocation mapping for liquefied hydrogen is 25 MMSCFD from the chlor-alkali plant, which has a greenhouse gas intensity of 30 gCO2e / MJ, and 10 MMSCFD from the dissociated green ammonia plant, which has a greenhouse gas intensity of 5 gCO2e / MJ. Although the allocated output from the chlor-alkali plant has a carbon intensity above the 25.2 gCO2e / MJ upper limit, the weighted average carbon intensity is [(30 gCO2e / MJ) * (25 MMSCFD) + (5 gCO2e / MJ) * (10 MMSCFD)] / (25 + 10 MMSCFD) = 22.9 gCO2e / MJ, which is below the greenhouse gas intensity threshold of 25.2 gCO2e / MJ.

[0364] In a third functional step of the control system 100, allocation mappings are set based on the network flow solution. In this case, there are allocation mappings of 50 MMSCFD from the Blue Hydrogen Plant to the Oil Refinery Furnace, 20 MMSCFD from the Chlor-Alkali Plant to the Oil Refinery Hydrolyzer, 30 MMSCFD from the Dissociated Green Ammonia Plant to the Hydrogen Liquefaction Unit, 25 MMSCFD from the Chlor-Alkali Plant to the Hydrogen Liquefaction Unit, 10 MMSCFD from the Dissociated Green Ammonia Plant to the Hydrogen Liquefaction Unit, and 10 MMSCFD from the Blue Hydrogen Plant to Inventory Increase.

[0365] These allocation mappings are written to a database so that they can be used to calculate a long-term running average of the greenhouse gas intensity of the accreted inventory and a long-term running average of the allocated carbon intensity for each of the delivery points.

[0366] In the fourth functional step of the control system, production rate setpoints are sent to the hydrogen production facilities H1, H2, and H3. Specifically, the blue hydrogen plant H1 is provided with a production setpoint of 60 MMSCFD, the chlor-alkali plant H2 is provided with a production rate of 45 MMSCFD, and the dissociated green ammonia plant H3 is provided with a production rate of 40 MMSCFD.

[0367] In a fifth functional step of the control system, a delivery rate set point is provided.

[0368] Those skilled in the art will appreciate that various modifications can be made to the examples described above without departing from the scope of the present invention, which is defined by the appended claims.

[0369] Although the invention has been described with reference to the preferred embodiments illustrated in the drawings, it will be understood that various modifications can be made within the spirit and scope of the invention as defined in the following claims.

[0370] For example, although some of the above exemplary embodiments are described in the context of a hydrogen distribution network for supplying hydrogen fuel, the invention is not so limited. The invention is equally applicable to processes for providing hydrogen having a defined carbon intensity value to end-user locations for purposes other than as a fuel. In other words, in embodiments, a hydrogen distribution network may be considered to be a hydrogen distribution network for the supply of hydrogen for any suitable purpose.

[0371] It will be understood that the term "control," as used herein, may refer, in embodiments, to a systematic plan or series of actions designed to manage and optimize the operation of one or more hydrogen production facilities to produce hydrogen having a defined carbon intensity value, taking into account factors such as feedstock carbon intensity, demand data, and process constraints.

[0372] It will be understood that the term "control" as used herein may refer to the management and regulation of the operation of a processing plant to ensure that the production of hydrogen and / or hydrogen fuel complies with defined carbon intensity values ​​and other constraints set by the optimization model.

[0373] It will be understood that the term "fuel" as used herein may refer to any type of fuel used to power a process (including industrial processes) for converting stored fuel energy into useful work. The term "fuel" as used herein may include, but is not limited to, "transportation fuels" used to power vehicles for the purpose of facilitating the movement of people or goods.

[0374] It will be understood that the term "defined carbon intensity (CI) value" as used herein refers to a predetermined or specified value, expressed in mass of carbon dioxide equivalent per unit of energy, for greenhouse gas emissions associated with the production, processing, and distribution of a product such as hydrogen or hydrogen fuel, and may be used as a target or constraint in an optimization process for producing fuel in an environmentally sustainable manner.

[0375] In this specification, unless expressly indicated otherwise, the word "or" is used in the sense of an operator that returns a value of true when either or both of the stated conditions are met, as opposed to the operator "exclusive or," which requires only one of the conditions to be met. The word "comprising" is used in the sense of "including," rather than "consisting of."

[0376] Where applicable, various embodiments provided by the present disclosure may be implemented using hardware, software, or a combination of hardware and software. Also, where applicable, various hardware and / or software components described herein may be combined into composite components including software, hardware, and / or both without departing from the spirit of the present disclosure. Where applicable, various hardware and / or software components described herein may be separated into subcomponents including software, hardware, or both without departing from the scope of the present disclosure. Additionally, where applicable, it is contemplated that software components may be implemented as hardware components, and vice versa.

[0377] Software according to the present disclosure, such as program code and / or data, may be stored on one or more computer-readable media. It is also contemplated that the software identified herein may be networked and / or otherwise implemented using one or more general-purpose or special-purpose computers and / or computer systems. Where applicable, the order of various steps described herein may be changed, combined into composite steps, and / or separated into substeps to provide the features described herein.

[0378] Although various operations are described herein in terms of "modules," "units," or "components," these terms should not be limited to a single unit or function. In addition, functionality attributed to some of the modules or components described herein may be combined and attributed to fewer modules or components.

[0379] It will be apparent to those skilled in the art that modifications, additions, or deletions may be made to the disclosed embodiments without departing from the spirit and scope of the invention. For example, one or more parts of the methods described above may be performed in a different order (or simultaneously) and still achieve desirable results.

Claims

1. 1. A computer-implemented method for operating a hydrogen distribution network according to carbon intensity (CI) requirements, the hydrogen distribution network including a plurality of hydrogen production facilities and a plurality of hydrogen delivery points, the method being executed by at least one hardware processor; a) determining the CI of hydrogen produced at the plurality of hydrogen production facilities using a computer system; b) using a computer system, determining a network flow solution for the hydrogen distribution network, the network flow solution defining a network solution space specifying a range of production rate values ​​for the plurality of hydrogen production facilities in the network and a range of delivery rate values ​​for the plurality of hydrogen delivery points in the network that satisfy a plurality of predefined operational constraints of the hydrogen distribution network; c) using a computer system to define an allocation mapping for the hydrogen distribution network and allocating a production rate from each of the plurality of hydrogen production facilities to each of the plurality of delivery points within the determined network solution space based on predefined criteria associated with the delivery points; d) generating, using a computer system and based on the allocation mapping, control variables for controlling the production rate of each of the plurality of hydrogen production facilities; e) controlling the plurality of hydrogen production facilities according to the generated control variables.

2. 10. The computer-implemented method of claim 1, wherein step a) comprises utilizing one or more computational models configured to allocate greenhouse gas emissions to by-products by one or more of mass allocation, molar allocation, energy basis allocation, and economic allocation.

3. 3. The computer-implemented method of claim 2, wherein the one or more computational models include at least one surrogate model that includes an equation for CI that depends on one or more operating parameters of at least one hydrogen production facility.

4. The computer-implemented method of claim 3 , wherein the one or more operating parameters include efficiency as a function of production rate.

5. 2. The computer-implemented method of claim 1, wherein step b) comprises determining a production rate of the hydrogen production facility to meet a plurality of operational constraints including one or both of customer demand and network hydraulic constraints.

6. The computer-implemented method of claim 1 , wherein step b) comprises utilizing mixed integer quadratic analysis.

7. The computer-implemented method of claim 1 , wherein steps b) and c) are performed simultaneously in a coupled optimization process.

8. The computer-implemented method of claim 1 , wherein step c) further comprises determining a proportion of low-carbon or renewable feedstock for one or more of the hydrogen production facilities.

9. 10. The computer-implemented method of claim 1, wherein step c) further comprises assigning an inventory depletion rate to each hydrogen delivery point based on the amount of hydrogen stored and transported, and assigning an inventory growth rate to each hydrogen production facility based on its respective hydrogen production rate.

10. 10. The computer-implemented method of claim 9, wherein step c) further comprises assigning a production rate such that the sum of the production rate and one or more inventory depletion rates assigned to a delivery point equals a hydrogen acceptance rate at the delivery point.

11. 2. The computer-implemented method of claim 1, wherein in step c), the predetermined criteria comprise one or more of a current or projected hydrogen demand at the delivery point, a sustainability metric of the hydrogen produced by the production facility, a carbon intensity of the hydrogen production by the production facility, or a carbon intensity limit of the delivery point.

12. 1. A system for operating a hydrogen supply network according to carbon intensity (CI) requirements, the hydrogen supply network including a plurality of hydrogen production facilities and a plurality of hydrogen delivery points, the system comprising: at least one hardware processor; a CI determination module configured to determine the CI of hydrogen produced at the plurality of hydrogen production facilities; an assignment mapping module, determining a network flow solution for the hydrogen distribution network, the network flow solution defining a network solution space specifying a range of production rate values ​​for the plurality of hydrogen production facilities in the network and a range of delivery rate values ​​for the plurality of hydrogen delivery points in the network that satisfy a plurality of predefined operational constraints of the hydrogen distribution network; an allocation mapping module configured to allocate a production rate from each of the plurality of hydrogen production facilities to each of the plurality of delivery points within the determined network solution space based on predefined criteria associated with the delivery points to define an allocation mapping for the hydrogen distribution network; a production control module configured to generate control variables for controlling the production rate of each of the plurality of hydrogen production facilities based on the allocation mapping; a process controller configured to control the plurality of hydrogen production facilities according to the generated control variables.

13. 13. The system of claim 12, wherein the CI determination module is configured to utilize one or more computational models configured to allocate greenhouse gas emissions to by-products by one or more of mass allocation, molar allocation, energy basis allocation, and economic allocation.

14. 13. The system of claim 12, wherein the allocation mapping module is configured to determine production rates of the hydrogen production facilities to satisfy a plurality of operational constraints including one or both of customer demand and network hydraulic constraints.

15. The system of claim 12 , wherein the allocation mapping module is configured to determine a network flow solution and simultaneously allocate production rates and delivery rates in a coupled optimization process.

16. The system of claim 12 , wherein the allocation mapping module is further configured to determine a proportion of low-carbon or renewable feedstock to one or more of the hydrogen production facilities.

17. 13. The system of claim 12, wherein the allocation mapping module is further configured to assign an inventory depletion rate to each hydrogen delivery point based on the amount of hydrogen stored and transported, and to assign an inventory growth rate to each hydrogen production facility based on its respective hydrogen production rate.

18. 18. The system of claim 17, wherein the allocation mapping module is further configured to allocate a production rate such that the sum of the production rate and one or more inventory depletion rates allocated to a delivery point equals a hydrogen acceptance rate at the delivery point.

19. 13. The system of claim 12, wherein the predetermined criteria comprise one or more of a current or projected hydrogen demand at the delivery point, a sustainability metric of the hydrogen produced by the production facility, a carbon intensity of the hydrogen production by the production facility, or a carbon intensity limit of the delivery point.

20. 1. A non-transitory computer-readable storage medium storing a program of machine-executable instructions for performing a method for operating a hydrogen supply network according to carbon intensity (CI) requirements, the hydrogen supply network including a plurality of hydrogen production facilities and a plurality of hydrogen delivery points, the method being executed by at least one hardware processor; a) using a computer system to determine the carbon intensity of hydrogen produced at said plurality of hydrogen production facilities; b) using a computer system, determining a network flow solution for the hydrogen distribution network, the network flow solution defining a network solution space specifying a range of production rate values ​​for the plurality of hydrogen production facilities in the network and a range of delivery rate values ​​for the plurality of hydrogen delivery points in the network that satisfy a plurality of predefined operational constraints of the hydrogen distribution network; c) using a computer system to define an allocation mapping for the hydrogen distribution network and allocating, within the determined network solution space, a production rate from each of the plurality of hydrogen production facilities to each of the plurality of delivery points based on predefined criteria associated with the delivery points; d) generating, using a computer system and based on the allocation mapping, control variables for controlling the production rate of each of the plurality of hydrogen production facilities; e) controlling the plurality of hydrogen production facilities according to the generated control variables.

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