Method for controlling operation of hydrogen production plant

Abstract

The invention provides a computer-implemented method for controlling operation of a hydrogen production facility, the method comprising: determining a maximum available amount of energy of a predetermined energy category in a current time interval; determining a target minimum amount of energy of a predetermined energy category for hydrogen production in a current time interval; and determining a hydrogen setpoint for the current time interval using the maximum available amount and the target minimum amount as constraints.

Description

Technical Field

[0001] The present invention relates to a computer-implemented method, system, computer program product, and computer-readable medium for controlling the operation of a hydrogen production facility. Background Technology

[0002] An electrolyzer facility comprises one or more electrolyzer modules. Improving the operation of an electrolyzer facility, particularly at a suitable overall operating setpoint, can reduce the costs associated with operating the facility. However, facility operation is constrained by numerous factors that make determining a suitable operating setpoint challenging.

[0003] In the context of any production facility, including hydrogen production facilities, the growing awareness of environmental footprint is even more challenging. Specifically, in this context, the goal may be to produce hydrogen using specific types of energy or energy blends, particularly using so-called green energy that is at least partially obtained from renewable sources.

[0004] Such objectives present difficulties when it comes to facility operation, as aiming to use a particular type of energy can directly conflict with the setpoint determined to be suitable for facility operation. As an example only, renewable energy is unstable, and optimized setpoints often tend to exhibit behavior with small fluctuations. In this challenging situation, current setpoint determination methods provide unsatisfactory results.

[0005] Therefore, one object of the present invention is to provide a method for controlling the operation of an electrolyzer facility that allows for determining an operational setpoint in more challenging scenarios, such as hydrogen production intended to use a predetermined type of energy, as described above. Summary of the Invention

[0006] This objective is achieved by the present invention. The present invention provides methods, systems, computer program products, and computer-readable media according to the independent claims. Preferred embodiments are set forth in the dependent claims.

[0007] This invention provides a computer-implemented method for controlling the operation of a hydrogen production facility, the method comprising: determining a maximum available amount of energy of a predetermined energy class in a current time interval; determining a target minimum amount of energy of the predetermined energy class for hydrogen production in the current time interval (in other words, the expected minimum amount of energy of the predetermined energy class); and determining a hydrogen setpoint for the current time interval using the maximum available amount and the target minimum amount as constraints (i.e., using the maximum available amount as a constraint and the target minimum amount as a constraint). Specifically, these steps can be performed for each of a plurality of subsequent time intervals.

[0008] Optionally, during the current time interval, the hydrogen setpoint for the upcoming time interval can also be determined, for example, in a predictive manner.

[0009] Using the method disclosed herein, it is possible to ensure that the hydrogen setpoint is determined in such a manner that criteria regarding the use of a predetermined category of energy are taken into account.

[0010] As an example, a goal could be to not use more energy than is available in a predetermined energy category. For example, not using non-green energy could be a goal. The method disclosed herein allows this to be considered when determining the setpoint. As another example, a target minimum for the use of energy in a predetermined energy category could be a goal. For example, after a period of time, energy may lose its classification as an energy in a predetermined category. Thus, a target minimum for the use of energy in a predetermined energy category could be aimed at reducing energy waste in said category due to expiration.

[0011] Furthermore, by using the aforementioned quantities as (soft or hard) constraints, the setpoint can be optimized towards objectives such as low volatility, and these quantities are also taken into account. Additionally, for example, the constraints can be easily adjusted when the criteria for a predetermined energy category change.

[0012] As an example, energy categorized under a specific energy class can be green energy. Energy can be classified as green energy when one or more criteria are met, such as when it is obtained from a renewable source, used within a given timeframe after energy production (time standard), or used within a given region. The time standard is particularly challenging in the context of hydrogen production because energy supply can be highly volatile and may not necessarily match hydrogen demand or production capacity. The method described in this application is particularly well-suited to address this challenge.

[0013] The maximum amount of available energy for a given energy category is referred to herein as Pmax. Pmax can be the sum of newly received and remaining energy in the given energy category (e.g., the energy remaining after past consumption and deducting the amount of energy due in the given energy category, i.e., energy due due due to time standards).

[0014] The target minimum for a predetermined energy category is referred to as Pmin in this paper. Pmin can be determined, for example, based on a time criterion, in order to avoid the expiration of the energy of the predetermined energy category, i.e., the energy loss of the predetermined energy category.

[0015] The length of the time interval is freely selectable. As an example, the specific application or scenario at hand may suggest a time interval, such as one related to execution on a digital controller or to a transaction time window. For example, a typical time interval can range from a few minutes to several hours. A specific but non-limiting example is a 15-minute time interval. However, this disclosure is not limited to a particular time interval.

[0016] The method disclosed herein may include determining, for multiple time intervals, Pmin and Pmax for corresponding intervals. This may be referred to herein as energy tracking (or simply tracking). Optionally, tracking may include determining the amount of energy remaining from a previous time interval for a predetermined energy category, and / or determining the amount of energy supplied for a predetermined energy category during that time interval, and / or determining the amount of energy due or due for predetermined energy. Tracking may also include determining, for each time interval, the amount of energy required during that time interval (energy demand) and / or the amount of energy actually used during that time interval.

[0017] Hydrogen production facilities can also be referred to as hydrogen production facilities or electrolyzer facilities.

[0018] Hydrogen production facilities may include multiple electrolyzer modules. Each electrolyzer module may include multiple stacks. Each stack may include multiple cells. In addition to one or more stacks, an electrolyzer module may include other components such as separation tanks, cooling units, pumps, rectifiers, and / or filters.

[0019] In this disclosure, unless otherwise stated, the terms energy and power are used interchangeably.

[0020] This invention allows for coordinated control of electrolyzer modules, taking into account and utilizing the flexibility of requirements or standards such as green energy / green hydrogen classifications to optimize overall facility operation, particularly production performance.

[0021] This can be applied, for example, to the production of green hydrogen using green energy, where time standards between energy production and consumption, as well as potentially other standards, are applicable. Potentially, this method can utilize exception standards. That is, energy that does not inherently meet the green energy standards (e.g., from the power grid) can still be classified as green energy based on exception standards.

[0022] The method disclosed herein allows for maximizing the production of green hydrogen by minimizing wasted green energy. This method also allows for optimization of asset size, for example, due to optimized use of assets such as power and hydrogen storage, production facilities, and grid constraints. Therefore, a smaller asset size and reduced CAPEX (capital expenditure) can be achieved during the planning phase. This also reduces LCOH (levelized cost of hydrogen) and the investment payback period.

[0023] This disclosure provides a method for tracking currently available energy of a predetermined category (by means of a defined standard classification), such as green energy, and this method can be incorporated into existing energy management methods (e.g., optimization methods) to extend existing models in energy management systems. As an example, the extended model can maximize the benefits of hydrogen production within constraints given by standards (e.g., "when to use energy in the next hour" or "from which energy source to purchase it").

[0024] Extended models can be parameterized. For example, standards can be modeled in this way, meaning that changes to the standard can be implemented in the model through changes in parameters. Therefore, extended models can adapt to changes in standards, such as changes in regulations.

[0025] The extended model can be used to optimize for different objectives, such as "minimizing peak power" or "maximizing the time required to meet hydrogen demand".

[0026] Compared to conventional control algorithms, the extended model can optimize several objectives simultaneously.

[0027] Specifically, as an example, unused / remaining available energy of a predetermined category can be incorporated into the optimization problem. This incorporation allows for optimization for different objectives.

[0028] The method disclosed herein allows for maximizing the use of energy in a predetermined category because it takes into account the maximum available energy of the predetermined category (e.g., in each time interval).

[0029] The methods disclosed herein may include tracking a predetermined category of energy, such as green energy, which is available at every point in decision-making, and taking into account information obtained through tracking, such as soft and / or hard boundary conditions for the optimized energy management system, when optimizing the operation of a hydrogen production facility. Such optimizers may, for example, include forward-looking time series-based optimization algorithms.

[0030] This method can utilize regulatory definitions of predetermined energy categories, such as green energy or green hydrogen, to automatically derive standards for characterizing energy or hydrogen. The method can also utilize market data, particularly the energy price of grid energy. Grid energy is generally an energy mix, and the energy price of this mix can, in some cases, be a good indicator or approximation of the energy composition. For example, the price can be a good indicator of the share of renewable sources. According to this disclosure, the composition of grid energy (the price of grid energy can be used as an indicator of this composition) can be one of the standards used to characterize energy as a predetermined category of energy. For example, based on said standard, grid energy can be characterized as energy of a predetermined category, such as green energy, even if it does not meet other standards for the predetermined category of energy, such as timing standards, which will be explained in more detail below.

[0031] The hydrogen setpoint of a hydrogen production facility determines its hydrogen output. The hydrogen setpoint of a hydrogen production facility will depend at least on the hydrogen setpoint of the electrolyzer module, and therefore on the power setpoint of the electrolyzer module's operation. Thus, the hydrogen setpoint of a hydrogen production facility is related to the power consumed.

[0032] According to this disclosure, energy can be classified into a predetermined energy category based on one or more criteria, including at least one of time criteria, local criteria, and additivity criteria.

[0033] A time standard can specify a period of time that begins with or includes the energy production time, wherein if the energy is consumed within said time period, it is classified only as energy of a predetermined energy category; otherwise, it expires. Specifically, the consumption of energy includes using the energy to produce hydrogen and / or storing the energy for later use in hydrogen production. This time period can be one hour, one day, one month, or even one year, or have any other length. For example, energy may have to be consumed within one hour of production, or within the same full hour that production occurs (hourly).

[0034] A local standard can specify a region where energy is only classified as a predetermined energy category if it is consumed within that region.

[0035] Additivity criteria can stipulate that if an energy production facility is built for the purpose of producing hydrogen, then the energy is only classified as energy in a predetermined energy category.

[0036] According to this disclosure, the energy of the predetermined energy category can be green energy, pink energy, or a predetermined mixture of energy types.

[0037] As an example, green hydrogen can be produced using green energy. Green energy can be energy that meets certain criteria, which may include that it is produced from a renewable source and meets one or more of the criteria outlined above, particularly the time criterion.

[0038] Energy classified into a predetermined energy category can be based on exception criteria, which allow energy that does not meet other mandatory criteria to be classified as energy in the predetermined energy category (by exception). For example, exception criteria may include criteria associated with the composition of grid energy, such as those derived from grid energy prices. If, for example, based on a predefined indicator like energy prices or another suitable indicator, it is determined that the composition of grid energy is considered to include a sufficient amount of energy in the predetermined energy category, then grid energy can be classified as energy in the predetermined energy category.

[0039] For example, such exceptions could allow the use of grid power that might otherwise not be considered green energy, by classifying it as green energy through exceptions. This could alleviate the potential shortage of renewable energy from unstable sources. This concept could be called "green grid power."

[0040] One advantage of using anomaly standards is that it allows for better utilization of available energy and reduces stress on the power grid and / or hydrogen production facilities.

[0041] According to this disclosure, determining a hydrogen setpoint may include using a model that models the operation of a hydrogen facility, taking into account the criteria for classifying energy into a predetermined energy category.

[0042] As an example, standards can also be modeled and incorporated into models that do not consider such standards. Alternatively, operational models that do not consider such standards can be modified to incorporate the standard.

[0043] The advantage of using the model described above is that this type of modeling allows for easy adaptation to changing criteria, such as parameter adaptation.

[0044] According to this disclosure, determining the hydrogen setpoint may include optimization and / or rule-based determination, each aimed at achieving one or more primary objectives and constrained by a maximum available quantity and a target minimum quantity.

[0045] As an example, optimization could be performed on objectives unrelated to the energy classification criteria described above. Instead, the criteria could be considered in the optimization as hard or soft constraints. This can be similarly applied to rule-based determination, such as by applying heuristics that consider the energy classification criteria. For example, a heuristic could prioritize older energies, such as those nearing their expiration date, based on a time criterion.

[0046] According to this disclosure, the main objectives may include at least one of the following: minimizing hydrogen production volatility; minimizing production peak; minimizing production schedule deviation; minimizing hydrogen storage requirements; minimizing power storage requirements; and minimizing degradation of one or more components of a hydrogen production facility.

[0047] According to this disclosure, determining the hydrogen setpoint may include prioritizing the use of older energy sources over the use of newer energy sources. Therefore, when energy is classified into predetermined categories using time standards, the expiration of those predetermined categories can be reduced or avoided.

[0048] According to this disclosure, determining the hydrogen setpoint may take into account the available hydrogen storage capacity and / or available energy storage capacity.

[0049] As an example, it can be considered that, where a time standard applies—that is, where energy must be consumed within a specific time frame to still be classified as a predetermined energy category—energy consumption can also include stored energy. Therefore, when determining the hydrogen setpoint, energy storage, particularly the available storage capacity, can be considered.

[0050] As an example, when it comes to electricity storage, excess electrical energy can be stored, for example, in a battery energy storage system (BESS) during periods of high green power supply, for use when green (e.g., renewable) energy supply is low. Criteria for classifying energy as green energy can allow energy stored for a predetermined period (e.g., one hour) to permanently retain that classification (i.e., the "green" label can be permanently applied to that energy).

[0051] Therefore, the optimization burden in terms of time standards for energy use that require timely availability can be reduced, and energy loss / expiration for predetermined energy categories can be avoided.

[0052] As another example, to consume energy in a timely manner, excess hydrogen can be produced and stored relative to hydrogen demand, based on available hydrogen reserves. This can be taken into account when determining the hydrogen setpoint.

[0053] Similar to electrical storage, this also reduces the burden on timing standards.

[0054] For example, depending on the available hydrogen storage capacity, hydrogen produced from excess green energy can be stored.

[0055] As an example, when it comes to hydrogen storage, where production capacity can handle excess green energy (relative to hydrogen demand and corresponding energy requirements), the excess (green) hydrogen can be stored, for example, in tanks. The stored hydrogen can permanently retain its green hydrogen classification (“green” label).

[0056] Including one or both of the energy and hydrogen storage in the optimization model (which represents a facility with said energy and / or hydrogen storage) gives additional degrees of freedom for achieving the optimization objective, such as the maximum smoothness of meeting demand or the maximum production time.

[0057] According to this disclosure, the maximum available energy of a predetermined energy category can be determined for the current time slot and can include the remaining energy of the predetermined energy category from one or more previous time slots and the energy received by the predetermined energy category.

[0058] According to this disclosure, a target minimum amount of energy for a predetermined energy category can be determined to minimize the expiration of the energy in the predetermined energy category, since the time standard specifies a time period that begins with or includes the energy's production time, wherein if the energy is consumed within the time period, the energy can only be classified as energy in the predetermined energy category, otherwise it will expire.

[0059] According to this disclosure, the method may include tracking the availability and impending expiration of energy for a predetermined energy category, particularly based on the criteria outlined above, especially the time criterion, and taking into account the results of tracking used to determine the hydrogen setpoint.

[0060] Energy tracking for predetermined energy categories, as described above, can be used with various algorithms that can be used to determine the hydrogen setpoint, such as heuristic or rule-based algorithms and optimization-based algorithms. For heuristic algorithms, decisions are made based on a set of rules. This set of rules may include one or more rules that prioritize the use of energy in predetermined categories that are nearing their expiration date. Accordingly, always using the oldest remaining green energy could be a heuristic approach to determining how to meet current needs. For optimization-based algorithms, optimization is typically subject to a set of constraints. This set of constraints may include one or more constraints, which can be hard or soft constraints that logically model the standard for green energy and / or green hydrogen.

[0061] According to this disclosure, the method may include: recording the use of energy of a predetermined energy category for the operation of a hydrogen production facility, particularly to allow for the determination of one or more criteria on which the energy classification as the predetermined energy category is based. For example, this can be used for auditing purposes, such as providing evidence of operation within a regulatory framework.

[0062] The method disclosed herein may include recording any data input to the determination of the hydrogen setpoint and any data output from the determination of the hydrogen setpoint, such as optimizations, and storing them. This may allow for appropriate recording of hydrogen production from energy of a predetermined energy category (e.g., green hydrogen), for example, to meet recording requirements and / or for audit purposes, such as providing evidence of operation within a regulatory framework.

[0063] The standards used for energy classification of a predetermined energy category can be adapted to standards associated with regulatory requirements to be met (e.g., classifying energy as green energy), for example, by adjusting the values ​​of the standards. The standards used to track energy within a predetermined energy category are based on regulatory standards. Therefore, compliance with regulations can be easily established even as regulations change / time-varying, because no hard-coded rules in the control code need to be altered. However, from a technical perspective, there is no difference whether the standards are adapted to regulatory requirements, as the technical challenges and impacts do not depend on these standards.

[0064] In other words, this invention allows for the capture, establishment, and recording of all time-varying standards (e.g., those associated with regulations) within a single tool (e.g., an energy management system). Therefore, this tool can be used, among other things, to demonstrate compliance with regulations and as a source of certification examples and data for future audits.

[0065] The present invention also provides a system including a processing system configured to perform any of the methods disclosed herein.

[0066] The system may also include one or more electrolyzer modules of an electrolyzer facility, and the processing system is configured to control the operation of the one or more electrolyzer modules to operate at a defined target module setpoint. The system may be or includes an electrolyzer facility.

[0067] The system may include hydrogen storage systems and / or energy storage systems, such as BESS (Battery Energy Storage System).

[0068] The present invention also provides a computer program product including instructions that, when executed by a computer, cause the computer to perform any of the methods disclosed herein.

[0069] The present invention also provides a computer-readable medium including instructions that, when executed by a computer, cause the computer to perform any of the methods disclosed herein.

[0070] The features and advantages outlined above in the context of the method also apply to the systems, computer program products, and computer-readable media described herein.

[0071] Further features, examples, and advantages will become apparent from the detailed description with reference to the accompanying drawings. Attached Figure Description

[0072] In the attached diagram,

[0073] Figure 1a , Figure 1b The diagram illustrates a schematic representation of an electrolyzer facility;

[0074] Figure 2 This is a flowchart illustrating the method according to the present disclosure;

[0075] Figure 3 This is a flowchart illustrating the method according to the present disclosure;

[0076] Figure 4 The method according to this disclosure is illustrated schematically;

[0077] Figure 5 The hydrogen production curve of a known method is illustrated schematically;

[0078] Figure 6 A schematic diagram illustrates a hydrogen production curve according to this disclosure;

[0079] Figure 7 A schematic diagram illustrates a hydrogen production curve according to this disclosure;

[0080] Figure 8 A schematic diagram illustrates a hydrogen production curve according to this disclosure. Detailed Implementation

[0081] System 1 of this disclosure includes a processing system 3 (also referred to as a computing system), which is configured to perform the methods according to this disclosure, for example in Figures 2 to 4 The method is outlined in the context of one of the methods described above. Optionally, the system may also be an electrolyzer facility or include an electrolyzer facility comprising multiple electrolyzer modules 2. Such systems include... Figure 1a and Figure 1b As shown.

[0082] Figure 1a and Figure 1b The system is shown as including optional monitoring facility 4, optional electrical storage 5, optional hydrogen storage 6, and optional oxygen storage 7. Furthermore, arrow 8 indicates the power input to the electrolyzer facility, arrow 9 indicates the hydrogen output from the electrolyzer facility, arrow 10 indicates the oxygen output from the electrolyzer facility, arrow 11 indicates the heat output from the facility (or the heat input to the facility), and arrow 12 indicates the water input to the electrolyzer facility.

[0083] For the purpose of illustration rather than for the purposes of this example, power grid 13 and network 14 are shown, to which hydrogen, oxygen and heat are supplied.

[0084] In addition, an optional hydrogen and oxygen separator 15 is shown.

[0085] Notice, Figure 1a The diagram illustrates the ratio Figure 1b The details of individual components of the facility are shown in the illustration only, and are intended to illustrate, in an exemplary manner, that different levels of detail may be considered when viewing facility operation.

[0086] The method disclosed herein can be used as follows: Figure 1a and Figure 1b The method steps are performed in the system shown, for example by processing system 3 or any other suitable system, particularly the system according to this disclosure.

[0087] Figure 2 This is a flowchart illustrating the method according to the present disclosure.

[0088] This disclosure provides a computer-implemented method for controlling the operation of a hydrogen production facility.

[0089] The method includes determining, in step S11, the maximum available energy of a predetermined energy category (e.g., green energy) in the current time interval.

[0090] The maximum available energy for a predetermined energy category can be used as input to determine the hydrogen setpoint in step S13 described below.

[0091] Determining whether an energy belongs to a predetermined energy category may include checking whether the energy meets certain criteria, including positive criteria and exception criteria that allow for violations of one or more positive criteria.

[0092] For example, in optional step S11a, it can be determined whether new energy of a predetermined energy category has been received or has been received, referred to as newly received energy of the predetermined energy category. Alternatively or additionally, in optional step S11b, it can be determined whether energy of the predetermined energy category is still available from previous time intervals. This may include determining whether the remaining energy from previous time intervals still meets the energy criteria of the predetermined energy category, particularly timing criteria. The maximum available amount of energy of the predetermined energy category may, for example, be the sum of the newly received energy of the predetermined energy category determined in step S11a and the energy of the predetermined energy category still available from previous time intervals determined in step S11b.

[0093] To perform this determination, the method may include accessing a standard for classifying energy into a predetermined energy category, matching data related to newly received energy against the standard, and matching data related to remaining energy against the standard.

[0094] As an example, for newly received energy, all standards can be checked, especially the positive and exceptional standards. For remaining energy, optionally, only some standards can be checked, particularly the timing standard. For other standards, such as local standards, checking the standard once when new energy is received is sufficient.

[0095] The method also includes determining, in step S12, a target minimum (desired) amount of energy for a predetermined energy category used for hydrogen production in the current time interval. The target minimum amount of energy for the predetermined energy category can be used as input to determine the hydrogen setpoint in step S13, described below.

[0096] For example, it can be determined that the amount of energy in a predetermined energy category that no longer meets timing criteria in the upcoming time interval is the target minimum amount of energy in that predetermined energy category to be used. In other words, a target minimum amount of energy in a predetermined energy category can be selected to avoid time-related losses of energy in that predetermined energy category due to the reclassification of surplus energy as energy that does not belong to that predetermined energy category.

[0097] The steps described above can be considered as steps for tracking energy of a predetermined energy category. An exemplary tracking method is further described below.

[0098] In optional step S10, inputs for determining the hydrogen setpoint may be identified or received, such as optimization objectives and / or optimization constraints for the operation of the hydrogen facility. Other potential inputs may include environmental information and / or facility status information and / or weather information and / or market information. Other inputs are also possible.

[0099] The method includes using the maximum available quantity and the target minimum quantity as (soft or hard) constraints in step S13 to determine the hydrogen setpoint for the current time interval. As an example, step S13 may include performing optimization of the optimization objective, for example, as input to part of step S10, which yields the hydrogen setpoint. This optimization may be based on an operational model that models the operation of the hydrogen production facility.

[0100] Therefore, it is concluded that the optimization of the hydrogen setpoint can be carried out in any desired manner, with any desired optimization objectives and constraints, while also taking into account constraints to ensure the correct use of energy of the predetermined energy class.

[0101] In particular, the optimization can be based on forward-looking time series, that is, the optimization can include predicted components.

[0102] For example, the above steps can be repeated for each of multiple time slots. Note that the remaining energy of the predetermined energy category between time slots will decrease not only due to actual energy consumption, but also because the previous energy of the predetermined energy category no longer meets the timing criteria; this can be referred to as the loss or expiration of energy in the predetermined energy category.

[0103] In optional step S14, a control signal may be generated and optionally output, which is configured to operate the hydrogen production facility at a defined hydrogen setpoint.

[0104] In optional step S15, the hydrogen production facility can be operated based on control signals.

[0105] The above tracking of energy for a predetermined energy category can be used with different algorithms that can be used to determine the hydrogen setpoint, such as heuristic or rule-based algorithms and optimization-based algorithms.

[0106] For heuristic algorithms, decisions are made based on a set of rules. This set of rules may include one or more rules that prioritize the use of energy in a predetermined energy class that is nearing its expiration date. For optimization-based algorithms, the optimization is typically constrained by a set of constraints. This set of constraints may include one or more constraints, which can be hard or soft constraints, and they logically model the energy criteria of the predetermined energy class.

[0107] Figure 3 The diagram illustrates a flowchart of a method according to this disclosure, in which a database of green power standards is accessed and used as input to track green power, specifically tracking whether the input power is green and whether the remaining power is (still) green. The tracking results are input into an energy management system. Furthermore, market information such as power availability is input into the energy management system.

[0108] In addition, information from other data sources, such as weather forecasts or production schedules, can be provided to the energy management system. This type of information can be used when predictively modeling hydrogen production, as anticipated demand or available energy can depend on it. The energy management system may include a predictive engine that processes this type of information.

[0109] Based on tracked green power, market information, and additional data, the optimization engine can determine an optimized hydrogen setpoint, which can then be used to control the operation of hydrogen production facilities. The determination by the optimization engine includes rules based on green power standards, particularly in the form of constraints.

[0110] Examples of methods according to this disclosure

[0111] In the following example, green energy is used as an example energy for the predetermined energy category. However, the energy for the predetermined energy category can also be pink energy, mixed energy, or any other specific type of energy.

[0112] Hydrogen produced using green energy is referred to as "green hydrogen." As explained above, the production of "green hydrogen" can be a constraint or boundary condition associated with optimizing the operational setpoint of a hydrogen production facility, and to optimize this objective, it is necessary to quantify the environmental footprint or classify production parameters as "green." Criteria for classifying hydrogen as green hydrogen can include the use of green energy, i.e., renewable energy that meets various criteria, including, for example, one or more local criteria, additive criteria, and time criteria, such as the timely use of renewable energy.

[0113] One way to define standards in a relatively simple way is to use regulatory definitions or frameworks to define when hydrogen is considered “green.” This type of quantification has the added benefit of allowing green hydrogen production to be easily tracked and reported to authorities and consumers in the context of incentives, benefits, demand for green hydrogen, and so on.

[0114] A more specific set of standards for green hydrogen production is shown below.

[0115] (1) Local standards. Local standards may include standards regarding where the renewable energy used for hydrogen production comes from. For example, this could be based on bidding zones, as these are already available and provide suitable methods for implementing the quantification of local standards.

[0116] (2) Additivity criteria. For example, an additivity criterion could stipulate that renewable power facilities providing energy for hydrogen production must be built specifically for the production of green hydrogen. Other potential criteria include the existence of long-term power purchase agreements (PPAs).

[0117] (3) Time standard. The time standard may include, for example, energy used for hydrogen production coming from energy production within the same predetermined time period, such as from the same hour, which is referred to as “hourly”. In this context, optionally, a modified storage standard (3b) may also be applied. That is, within the same predetermined time period defined in (3), such as within one hour of production, energy stored (rather than immediately used for hydrogen production) may still conform to the time standard and be used for hydrogen production at any later point in time.

[0118] As an example, in order to classify the produced hydrogen as green hydrogen, the energy used for hydrogen production may have to meet one or a subset of the definitions of three criteria (1), (2), and (3).

[0119] Exceptions can be defined by exception criteria, for example:

[0120] (4) Hydrogen can still be classified as green hydrogen even if grid energy does not meet the requirements of criteria (1) to (3), for example, if it is determined that grid energy may include a sufficient amount of energy from renewable sources. As explained above, the price of grid energy can be used as a good approximation or indicator, especially in reward tier systems.

[0121] However, time standards are particularly challenging in the context of hydrogen production, as energy supply can be highly volatile and may not necessarily match hydrogen demand or production capacity.

[0122] It should be understood that the method of this disclosure allows this challenge in the context of green hydrogen production to be addressed by determining the maximum amount of green energy available in the current time interval and the target / desired minimum amount of green energy to be used in the current time interval, and by inputting said values, for example, as (soft or hard) constraints, when determining the hydrogen setpoint.

[0123] The method disclosed herein may include recording and storing any data inputs and outputs optimized for recording purposes. This can allow for proper recording of green hydrogen production, for example, for managing recording requirements.

[0124] The classification standards for green hydrogen or green energy can be adjusted to align with the regulatory requirements to be met, for example, by adjusting the values ​​of the standards. When green energy tracking standards are based on regulatory standards, compliance can be easily established even as regulations change / time-varying, because no hard-coded rules in the control code need to be altered. However, from a technical perspective, there is no difference between adjusting the standards according to regulatory requirements, as the technical challenges and impacts do not depend on these standards.

[0125] In other words, this invention allows for the capture, establishment, and recording of all time-varying standards (e.g., those associated with regulations) within a single tool (e.g., an energy management system). Therefore, this tool can be used, among other things, to demonstrate compliance with regulations and as a source of certification examples and data for future audits.

[0126] This invention allows consideration of green hydrogen production objectives as well as other potential objectives, such as minimizing production volatility to minimize battery stack degradation, minimizing production peaks to minimize peak power costs, or minimizing production schedule deviations to minimize storage requirements.

[0127] The following examples illustrate in more detail how to track or consider the classification of green hydrogen, for example, using the criteria mentioned above. References Figure 4 .

[0128] For the sake of illustration and simplicity, the method is described based on examples with specific numbers. However, this disclosure is, of course, not limited to any of the exemplary numbers stated.

[0129] Choose a time interval Δt. As an example, a specific application or scenario at hand may suggest a time interval, such as one related to execution on a digital controller or to a transaction time window. However, this disclosure is not limited to a specific time interval.

[0130] In this non-restrictive example, a time interval Δt = 15 minutes is chosen. This choice is merely an example and may reflect the energy trading time in the spot market.

[0131] For each time interval, the method of this disclosure can be regarded as the sum of new energy supply and remaining energy (e.g., energy remaining after past consumption and energy lost / expired due to time standards).

[0132] An exemplary heuristic for meeting current needs is to always use the "oldest remaining energy". This mechanism is... Figure 4 The example shown is for one hour (hourly) and 4 time slots with Δt = 15 minutes.

[0133] exist Figure 4 In the diagram, E0, E1, E2, and E3 each represent an energy slot, each associated with a 15-minute time interval. From left to right, the diagram illustrates the energy slots at four different times T. The numbers in the circles indicate the available energy at the corresponding time T in arbitrary units. The arrows indicate that the shown state is at the end of a time slot Δt = 15 minutes.

[0134] The following text will describe the four times shown in the figure: T=15:00, T=15:15, T=15:30, and T=15:45.

[0135] T=15:00: Between 14:45 and 15:00, an additional 7 units are placed into energy slot E0. There is a load of 9 units in the same time interval. In this example, the method attempts to satisfy the demand from the oldest slot (E3) as much as possible. In this case, this is possible because E3 holds 10 units. Therefore, E3 has 1 unit remaining (10-9). However, at 15:00, slot E3 is emptied because the time criterion is no longer met, as its available time exceeds one hour, resulting in a loss of one energy unit. The existing energy units in the other slots remain unchanged because only E3 satisfies the demand. Therefore, the availability of 7 units in E0, 3 units in E1, and 0 units in E2 will be passed to the next time step. As mentioned before, E3 will not pass anything. Therefore, the total availability is 10 units (7+3), and the next energy should be consumed from E2 (to avoid expiration); however, in this example, E2 remains at 0 units, so there is no upcoming expiration.

[0136] Now, the time periods are reordered (rotated), and new 15-minute time periods begin. The order is as follows: the previous E0 becomes E1; the previous E1 becomes E2; the previous E2 becomes E3; and the previous E3 becomes E0.

[0137] T=15:15: Between 15:00 and 15:15, the additional 3 units have been supplied and placed into the (new) energy slot E0 (formerly E3, in numbered order). Since this energy slot was empty at 15:00, the load cannot be supplied by any units in E3 (formerly E2). Therefore, the load of 11 units is supplied by E2(3), E1(7), and E0(1). This leaves 2 units in E0 as availability propagates to the next time step, with 0 “urgent” units in E2.

[0138] Now, as described above, the time slots are reordered (rotated), and a new 15-minute time slot begins.

[0139] T=15:30: Between 15:15 and 15:30, the additional 4 units have been supplied and placed into the (new) energy slot E0. E3 and E2 are empty. Since the load is 1 unit, it can be supplied from the energy slot E1 (formerly E0). This makes E1 have 1 unit and E0 have 4 units.

[0140] Now, as described above, the time slots are reordered (rotated), and a new 15-minute time slot begins.

[0141] T=15:45: Between 15:30 and 15:45, two additional units were supplied and placed into the (new) energy tank E0. The load is 8 units. All available energy was consumed: E2 consumed 1 unit, E1 consumed 4 units, and E0 consumed 2 units. This leaves one unit unsupplied, with 0 units of available "green energy" remaining. In this situation, only a smaller amount of green hydrogen than required can be produced. This could result in supplying less green hydrogen than needed by using previously produced and stored green hydrogen, or by using non-green energy used for hydrogen production, thus supplying less green hydrogen for that period.

[0142] As explained above, green energy tracking can employ different algorithms, such as heuristic or rule-based algorithms and optimization-based algorithms, to determine hydrogen setpoints.

[0143] For heuristic / rule-based algorithms, this set of rules can be extended by a single rule that, at any decision time, considers the energy intake within the total currently available "legal green" energy. P max (The total at 15:00 in the example above is 74-3+0+1=11) plus the green energy allocated to the oldest time slot. P min (In the example above, between the energy of 1 in the oldest time slot E3) energy intake is prioritized. Using less than... P min We should avoid using more energy, as this would waste green energy. Instead, we should use more... P max The energy is also undesirable, because in this situation, energy and... P max The difference corresponds to non-green energy. This is achieved by prioritizing energy intake. P min and P max The specific implementation of the rule-based algorithm, which extends the rule-based algorithm, can depend on the corresponding rule-based algorithm. However, since the rule can be logically formulated, it can also be included in the context of values. P min and P max In the combination of logical AND, OR, IF / ELSE statements.

[0144] The situation is similar for algorithms based on mathematical optimization. In this case, the current set of constraints can be extended by a separate set of constraints (hard or soft) that logically model the standards for green energy / hydrogen, such as regulatory constraints. Constraints can be formulated using combinations of logical AND, OR, and IF / ELSE statements. It is well known that these types of constraints can be modeled using binary and integer variables. Therefore, constraints can be easily integrated into mathematical optimization models based on mixed-integer linear programming.

[0145] Figure 5 The figure illustrates a hydrogen production curve based on a traditional control algorithm that makes decisions online, i.e., making decisions immediately without considering (predicted) future information.

[0146] For this type of traditional control algorithm, a possible (and probably best) solution that satisfies the time standard (in this example, the "hourly" standard) could be as follows, where one hour is divided into four 15-minute time slots:

[0147] 1) Try using all the energy produced in the first time slot: E used1 = E produced1 If not all produced energy can be used, the remaining energy > 0 is calculated as E. rest1 = E produced1 - E used1 .

[0148] 2) Try using all available energy: E used2 = E rest1 + E produced2 If that's not possible, then use E as much as possible first. rest1 And then continue with E produced2 Calculate the remaining E rest1’ and E rest2 Both remaining energies are now greater than or equal to 0.

[0149] 3) Try using all available energy: E used3 = E rest1’ + E rest2 + E produced3 If that's not possible, then use E as much as possible first. rest1’ And then continue using E rest2 and E produced3 Calculate the remaining E rest1’’ and E rest2’ and E rest3 All remaining energy is greater than or equal to 0.

[0150] 4) Try to use all available energy: E used4= E rest1’’ + E rest2’ + E rest3 + E produced4 If that's not possible, then use E as much as possible first. rest1’’ And then continue using E rest2’ E rest3 and E produced4 Calculate the remaining E rest1’’’ E rest2’’ E rest3’ and E rest4 If E rest1’’’ If it is still greater than 0, the energy is lost because it cannot be used in the next time slot since it was produced more than an hour ago.

[0151] 5) Continue as above, but always ignore energy that is "older" than an hour.

[0152] exist Figure 5 In the diagram, the dashed curve represents renewable energy production, and the dashed curve with white rectangles represents the hydrogen production curve, specifically based on the aforementioned control algorithm. The hydrogen production curve plateaus at its maximum capacity, which in this embodiment is 5.5.

[0153] Although the maximum possible energy can be used when operating the facility as described above, such operations will also result in a very volatile production curve with significant spiking (which is detrimental to battery stack degradation) and potentially unwanted spikes (which are detrimental to operating costs).

[0154] As an example, this solution does not allow less than the maximum possible energy to be used in one time step in order to save energy for the next time step.

[0155] Figure 6 The illustration shows a hydrogen production curve obtained by an exemplary method according to this disclosure (Example 1). A goal of "maximizing smoothness" can be implemented here. Specifically, such a goal can be aimed at minimizing the maximum (peak) power intake, for example, taking into account grid constraints. Figure 6 The example in the diagram uses dashed lines to show the electrical energy produced (per 15-minute time slot). Rectangular diagrams illustrate the optimization of the actual power intake for hydrogen production to meet hourly standards.

[0156] As can be seen from the graph, a minimum peak power of 4.8 was achieved, instead of 8.0 (as shown in the power supply curve) or 5.5 (as shown in the curve). Figure 5The results of the control algorithm in the process improved smoothness. The latter minimized the degradation of the electrolyzer assembly. The difference between minimum and maximum power input was only 0.95 (4.65 - 3.7), while in power production, this difference was 8 (8 - 0). Figure 5 The difference in the control algorithm is 5.5 (5.5 - 0).

[0157] Figure 7 The illustration shows a hydrogen production curve obtained using an exemplary method according to this disclosure (Example 2). A "maximum demand fulfillment" objective can be implemented here. Specifically, such an objective may aim to meet a specific hydrogen production demand for as long as possible. Figure 7 In the example, the hydrogen production demand is shown as a constant thick black line with a value of 4. Power generation is represented by a gray dashed line. Considering the hourly standard, it is possible to meet the demand (4) 78% of the time (gray rectangle). Traditional algorithm ( Figure 5 This is simply impossible to achieve because the constraint must be greater than 4 to eliminate overproduction. Note that hydrogen production in this example can also be optimized for minimizing peak power, such as... Figure 6 As described in the context.

[0158] In addition, with Figure 5 Unlike the control examples of the conventional methods shown, Examples 1 and 2 can each be configured to avoid complete shutdown and are limited to the frequency band between 2.0 and 5.5.

[0159] Examples 1 and 2 illustrate how the implicit flexibility of hourly rules can be used for great benefits when using the methods of this disclosure.

[0160] The above example works well when the fluctuation frequency is less than the time when the energy is no longer classified as green energy (e.g., one hour).

[0161] In some of the following aspects, volatility can be addressed on larger time scales, such as daily scales (like PV), which are difficult to address through hourly regulations, as mentioned above.

[0162] When determining the hydrogen setpoint, electrical storage systems and / or hydrogen storage systems can be considered. The storage capacity of the discussed facilities can be taken into account during modeling or optimization. Therefore, as explained in detail above, excess green energy or excess green hydrogen can be stored to avoid green energy loss due to timing standards, or to reduce the burden of optimization that would otherwise require minimizing losses. Thus, such methods can mitigate volatility.

[0163] Alternatively or additionally, this disclosure may employ exception criteria to allow for the classification of energy as green energy, provided that the required criteria are not met. Specifically, such exceptions may allow the use of grid power that would otherwise not be considered green energy, by exceptionally classifying it as green power. This can alleviate the potential shortage of renewable energy from unstable sources. This concept may be referred to as “green grid power.”

[0164] Figure 8 The illustration shows a hydrogen production curve obtained by an exemplary method according to this disclosure (Example 3).

[0165] In this example, the concept of classifying power as green power, as described above, is incorporated. This type of power may come from local power sources or the power grid (“green grid”).

[0166] This can be done Figure 6 and Figure 7 The steps are performed outside the context shown.

[0167] In other words, under certain conditions, even grid power supplied by hybrid generators can be considered "green." This can be derived, for example, from measurable parameters related to power prices. If the power price is below a certain limit (e.g., €20 / MWh), it means that there is a sufficient amount of green energy in the grid.

[0168] Figure 8 An example is shown where power from this type of (remaining) time is used to minimize deviation from production target 4. For determination purposes, this additional green power can be added to the existing Pmax. There are 4 instances where “green power” (grey line > 0) can be withdrawn from the grid.

[0169] In the graph, the benefits are evident compared to the previous example, as production around 11:00 increases (and thus is closer to demand), and the interval duration around 23:00 is reduced by 15 minutes. The remaining production gap is also reduced. This was achieved using "green grid power" at 7:30 and 19:00 respectively.

[0170] While the invention has been detailed and described in the accompanying drawings and the foregoing description, such description and description are to be regarded as exemplary rather than limiting. The invention is not limited to the disclosed embodiments. In view of the foregoing description and drawings, it will be apparent to those skilled in the art that various modifications can be made within the scope of the invention as defined by the claims.

Claims

1. A computer-implemented method for controlling the operation of a hydrogen production facility, the method comprising: Determine (S11) the maximum available amount of energy for the predetermined energy category in the current time interval; Determine (S12) the target minimum amount of energy for the predetermined energy category used for hydrogen production in the current time interval; as well as The hydrogen setpoint for the current time interval is determined using the maximum available quantity and the target minimum quantity as constraints (S13).

2. The method according to claim 1, The classification of energy into the predetermined energy category is based on one or more criteria, including at least one of the following: A time standard for a specified time period, the time period starting with or including the energy production time, wherein if the energy is consumed within the time period, the energy is only classified as energy of the predetermined energy category, otherwise it will expire; in particular, the consumption of energy includes using the energy to produce hydrogen and / or storing the energy for later use in the production of hydrogen. A local standard for a specified area, wherein if the energy is consumed within the area, the energy is only classified as energy of the predetermined energy category; The additivity criterion stipulates that if the energy production facility is built for the purpose of producing hydrogen, then the energy is only classified as energy within the predetermined energy category. and / or The energy in the predetermined energy category is green energy, pink energy, or a predetermined mixture of energy types.

3. The method according to claim 1 or 2, wherein the energy classified into the predetermined energy category is based on an exception criterion, wherein the exception criterion allows energy that does not meet other mandatory criteria to be exceptionally classified into the predetermined energy category, particularly considering the criteria for energy pr as an indicator of the renewable content of grid power.

4. The method according to any one of the preceding claims, wherein determining the hydrogen setpoint comprises using a model for modeling the operation of a hydrogen facility, the model taking into account the criteria for classifying energy into the predetermined energy category.

5. The method according to any one of the preceding claims, wherein determining the hydrogen setpoint comprises optimization and / or rule-based determination, each aimed at achieving one or more primary objectives and constrained by the maximum available quantity and the minimum quantity of the objectives.

6. The method of claim 5, wherein the primary objective comprises at least one of the following: Minimize hydrogen production volatility; Minimum production peak; Minimum production schedule deviation; Minimum hydrogen storage requirements; Minimum energy storage requirements; Minimal degradation of one or more hydrogen facility components.

7. The method according to any one of the preceding claims, wherein determining the hydrogen setpoint includes using old energy over new energy.

8. The method according to any one of the preceding claims, wherein the determination takes into account the available hydrogen storage capacity and / or available energy storage capacity.

9. The method according to any one of the preceding claims, The maximum available energy of the predetermined energy category is determined for the current time slot and includes the remaining energy of the predetermined energy category from one or more previous time slots and the energy received by the predetermined energy category.

10. The method according to any one of the preceding claims, wherein the target minimum amount of energy of the predetermined energy category is determined to minimize the expiration of energy of the predetermined energy category, since the time standard specifies a time period that begins with or includes the production time of the energy, wherein if the energy is consumed within the time period, the energy is classified only as energy of the predetermined energy category, otherwise it will expire.

11. The method according to any one of the preceding claims, wherein the method comprises tracking the availability and impending expiration of the energy of the predetermined energy category, particularly based on the criteria described in claims 2 and / or 3, and taking into account the results of the tracking used to determine the hydrogen setpoint.

12. The method according to any one of the preceding claims, the method comprising: For the operation of the hydrogen production facility, the use of energy of the predetermined energy category is recorded, in particular to allow for the determination of one or more criteria on which the energy classification of the predetermined energy category is based.

13. A system (1) comprising a processing system (3) configured to perform the method according to any one of claims 1 to 12, particularly further comprising one or more electrolyzer modules (2) of an electrolyzer facility, the processing system being configured to control the operation of the one or more electrolyzer modules (2) to operate at a determined gas setpoint, and optionally further comprising a hydrogen storage system and / or an energy storage system.

14. A computer-readable medium comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 12.

15. A computer program product comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 12.

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