A method for controlling the operation of a hydrogen production plant.

Abstract

The present invention provides a computer implementation method for use in controlling the operation of a hydrogen production plant, the method comprising: determining the maximum available amount of energy of a given energy category in the current time interval; determining the target minimum amount of energy of a given energy category to be used for hydrogen production in the current time interval; and determining the 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 implementation method, system, computer program product, and computer-readable medium for use in controlling the operation of a hydrogen production plant. [Background technology]

[0002] An electrolytic cell plant comprises one or more electrolytic cell modules. Improving the operation of an electrolytic cell plant, particularly operating it at an appropriate overall operating setpoint, can reduce the costs incurred by operating the plant. However, the operation of the plant is constrained by many factors that make it difficult to determine what the appropriate operating setpoint is.

[0003] Adding to the challenge is the growing need to be environmentally conscious in the context of any manufacturing facility, including hydrogen production plants. Specifically, in this context, the goal may be the use of certain types of energy or energy blends for hydrogen production, in particular the use of so-called green energy derived at least partially from renewable resources.

[0004] Such goals present difficulties in terms of plant operation, because attempting to use a particular type of energy may not directly coincide with the setpoints determined to be suitable for the plant's operation. For example, renewable energy is unstable, while optimized setpoints often prefer behavior with minimal instability. Current setpoint determination methods yield unsatisfactory results in this challenging situation.

[0005] Therefore, an object of the present invention is to provide a method for use in controlling the operation of an electrolytic cell plant that enables the determination of operating setpoints in more challenging scenarios, such as hydrogen production aimed at using a predetermined type of energy, as described above. [Overview of the project]

[0006] This objective is achieved by the present invention. The present invention provides a method, system, computer program product, and computer-readable medium as described in the independent claims. Preferred embodiments are provided in the dependent claims.

[0007] The present invention provides a computer implementation method for use in controlling the operation of a hydrogen production plant, the method comprising: determining the maximum available amount of energy of a given energy category in the current time interval; determining the target minimum amount of energy of the given energy category to be used for hydrogen production in the current time interval (in other words, the desired minimum amount of energy of the given energy category); and determining the 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). In particular, these steps may be performed for each of a plurality of consecutive time intervals.

[0008] Optionally, during the current time interval, the hydrogen setpoint for the next time interval may also be determined, for example, by a predictive method.

[0009] By using the method of this disclosure, it is possible to ensure that the hydrogen setpoint is determined in a manner that takes into account criteria for the use of a given category of energy.

[0010] As an example, it may be a goal not to use more energy than the available amount of energy in a given energy category. For example, it may be a goal not to use energy that is not green energy. The method of the present disclosure makes it possible to take this goal into account when determining the setpoint. As another example, the target minimum usage amount of energy in a given energy category may be the goal. For example, energy may lose its category classification as energy in a given category after a while. Thus, the target minimum usage amount of energy in a given energy category may aim to reduce waste of energy in the above category due to expiration.

[0011] Furthermore, by using the above-mentioned amounts as (soft or hard) constraints, the setpoint can be optimized towards goals such as low instability while taking these amounts into account as well. Additionally, easy adjustment of the constraints can be made, for example, when the criteria for a given energy category change.

[0012] As an example, the energy in a given energy category may be green energy. Energy can be categorized as green energy when one or more criteria are met, for example, when the energy is obtained from renewable resources, used within a given amount of time (time criterion) after energy production, or used within a given region. In particular, the time criterion is an issue in the context of hydrogen production because the energy supply can be very unstable and may not necessarily match the hydrogen demand or production capacity. The method of the present application is particularly suitable for addressing this issue. [[ID=X]]

[0013] [[ID=X]] The maximum amount of available energy in a given energy category is referred to herein as Pmax. Pmax can be the sum of the newly received energy in a given energy category and the remaining energy (e.g., the difference between past consumption and the amount of expired energy in a given energy category, i.e., the energy expired according to the time criterion).

[0014] The target minimum amount of energy for a given energy category is referred to herein as Pmin. Pmin can be determined, for example, based on a time criterion so as to avoid the expiration of the energy of a given energy category, i.e., the loss of the energy of a given energy category.

[0015] The length of the time interval can be freely selected. As an example, the current specific application or scenario can propose a time interval related to, for example, execution on a digital control device or related to trading time - windows. Typical time intervals can be, for example, on the order of minutes to hours. One specific but non - limiting example is a 15 - minute time interval. However, the present disclosure is not limited to a specific time interval.

[0016] The method of the present disclosure can comprise determining Pmin and Pmax for each of a plurality of time intervals. This can be referred to herein as the tracking (or, briefly, tracking) of the energy of a given energy. Optionally, the tracking can involve determining the amount of energy of a given energy category remaining from a preceding time interval, and / or determining the amount of energy of a given energy category supplied within the time interval, and / or determining the amount of energy of a given energy that has expired or is about to expire. This tracking can also comprise determining the amount of energy (energy demand) required during the time interval and / or the amount of energy actually used during the time interval for each time interval.

[0017] The hydrogen production plant can also be referred to as a hydrogen plant or an electrolyzer plant.

[0018] A hydrogen production plant may comprise multiple electrolytic cell modules. Each electrolytic cell module may comprise multiple stacks. Each stack may comprise multiple cells. In addition to one or more stacks, an electrolytic cell module may comprise other components, such as separation tanks, cooling units, pumps, rectifiers, and / or filters.

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

[0020] The present invention enables coordinated control of electrolytic cell modules, for example, by leveraging and considering flexibility in requirements or criteria for the classification of green energy / green hydrogen, in order to optimize the overall plant operation, particularly production performance.

[0021] This could be applied, for example, to green hydrogen production using green energy, where time criteria between energy production and consumption and other potential criteria are applicable. Potentially, this method can leverage exception criteria; that is, energy that does not itself meet the criteria for green energy (e.g., from the grid) may nevertheless be categorized as green energy based on exception criteria.

[0022] The method described herein enables the maximization of green hydrogen production by minimizing wasted green energy. The method also enables optimized asset sizing through optimized use of assets such as electricity and hydrogen storage, production plants, and grid limits. This may allow for smaller asset sizes and reduced CAPEX during the planning phase. This may also reduce LCOH (equalized cost of hydrogen) and the payback period.

[0023] This disclosure provides a method that can be incorporated into existing energy management methods (e.g., optimization methods) to enable tracking of currently available energy in a given category (categorized by defined criteria), such as green energy, and to extend existing models in energy management systems. As an example, the extended model may maximize the benefits of hydrogen production within constraints given by criteria (e.g., "when should energy be used in the next hour" or "from which energy source should it be purchased").

[0024] Extended models can be parameterized. For example, criteria can be modeled in such a way that changes in the criteria can be implemented within the model by changing parameters. Thus, extended models can be adaptable to changing criteria, such as those resulting from modified regulations.

[0025] Extended models can be used to optimize for different objectives, such as minimizing peak power demand or maximizing the time hydrogen demand is met.

[0026] In contrast to conventional control algorithms, extended models can optimize several objectives simultaneously.

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

[0028] The method disclosed herein takes into account the maximum available energy of a given category (for example, in each time interval), thus enabling the maximization of the use of energy in that given category.

[0029] The method of this disclosure comprises tracking a predetermined category of energy available at each point in time for decision-making, such as green energy, and may consider the information obtained by tracking when optimizing the operation of a hydrogen plant, for example, as a soft and / or hard boundary condition for optimizing an energy management system. Such an optimizer may include, for example, an optimization algorithm based on a forward-looking time series.

[0030] This method may utilize a predetermined category of energy, such as a regulatory definition of green energy or a definition of green hydrogen, to automatically derive criteria for characterizing energy or hydrogen. The method may also utilize market data, particularly the energy price of grid energy. The energy price of grid energy, which is generally an energy blend, 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 energy from renewable resources. According to this disclosure, the composition of grid energy, for which the price of grid energy can be used as an indicator, can be one of the criteria for characterizing energy as a predetermined category of energy. For example, based on the above criteria, grid energy may be characterized as a predetermined category of energy, such as green energy, even though it does not meet other criteria for a predetermined category of energy, such as the time criterion, which will be described in more detail below.

[0031] The hydrogen setpoint of a hydrogen production plant determines its hydrogen output. The hydrogen setpoint of a hydrogen production plant depends at least on the hydrogen setpoint of the electrolytic cell module, and therefore on the power setpoint under which the electrolytic cell module operates. Thus, the hydrogen setpoint of a hydrogen production plant correlates with power consumption.

[0032] According to this disclosure, the categorization of energy into a given energy category may be based on one or more criteria comprising at least one of the following: time-based criteria, local criteria, and additivity criteria.

[0033] The time criterion may specify a time period beginning with or encompassing the time of energy production, where energy is categorized as energy of a given energy category only if consumed within the time period, otherwise it expires, and in particular, consuming energy includes using energy to produce hydrogen and / or storing energy for later use in the production of hydrogen. The time period may be one hour, one day, one month, or even one year, or may have any other length. For example, energy may have to be consumed within one hour of production or within the same full hour in which production took place (hour-by-hour).

[0034] Local criteria may specify a region, and energy is categorized as energy of a given energy category only if it is consumed within that region.

[0035] The additivity criterion may specify that energy is categorized as belonging to a given energy category only if the energy production facility is built specifically for hydrogen production.

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

[0037] For example, green hydrogen may be produced using green energy. Green energy can be energy that meets certain criteria, including being produced from renewable resources and meeting one or more of the criteria outlined above, particularly the time criterion.

[0038] The categorization of energy as energy of a given energy category is obtained based on exception criteria, which allow energy that does not meet other essential criteria to be (exceptionally) categorized as energy of a given energy category. For example, exception criteria may include criteria associated with the composition of grid energy, which can be derived from grid energy prices, for example. For example, if it is determined that the composition of grid energy is considered to contain a sufficient amount of energy of a given energy category based on a predefined indicator such as energy prices or another appropriate indicator, then grid energy may be categorized as energy of a given energy category.

[0039] For example, such exceptions could allow the use of grid electricity that would otherwise not be considered green energy by exceptionally categorizing grid energy as green energy. This could mitigate the potential shortage of renewable energy from unstable resources. This concept could be called "green grid electricity."

[0040] The advantages of using exception criteria are that it allows for better use of available energy and reduces the burden on the grid and / or hydrogen production plants.

[0041] According to this disclosure, determining the hydrogen setpoint involves using a model that models the operation of a hydrogen plant, the model may take into account the (above) criteria for categorizing energy as energy for a given energy category.

[0042] For example, criteria can also be modeled and incorporated into models that do not take such criteria into account. Alternatively, behavioral models that do not take such criteria into account can be modified to incorporate the criteria.

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

[0044] According to this disclosure, determining the hydrogen setpoint may involve an optimization and / or rule-based decision, each targeting the achievement of one or more primary objectives, and constrained by the maximum available amount and the minimum target amount.

[0045] As an example, optimization may be performed on objectives not associated with the criteria described above for categorizing energy. Instead, the criteria may be taken into account in the form of hard or soft constraints in the optimization. This can be similarly applied to rule-based decisions, for example, by applying a heuristic that takes into account the criteria for categorizing energy. The heuristic may, for example, prioritize the use of older energy, such as energy that is nearing its expiration date based on a time criterion.

[0046] According to this disclosure, the primary objectives may include at least one of the following: minimal hydrogen production instability, minimal production peak, minimal production schedule deviation, minimal hydrogen storage requirements, minimal power storage requirements, and minimal degradation of one or more hydrogen plant components.

[0047] According to this disclosure, determining the hydrogen setpoint may involve prioritizing the use of older energy over the use of newer energy. Therefore, if a time criterion is applied to categorize energy as belonging to a given category of energy, the expiration of energy in a given category may be reduced or avoided.

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

[0049] For example, when a time criterion is applied, i.e., when energy must be consumed within a certain time frame in order to still be categorized as a given energy category, it can be considered that consuming energy may also include storing energy. Therefore, energy storage, in particular the available storage capacity, can be taken into consideration when determining the hydrogen setpoint.

[0050] For example, with regard to electricity storage, during periods of high green electricity supply, excess electrical energy can be stored in a battery energy storage system (BESS) to be used, for example, when green, or renewable, energy supply is low. Criteria for categorizing energy as green energy may allow energy stored within a given time (e.g., one hour) to permanently maintain its green energy classification (i.e., the "green" label can be permanently applied to this energy).

[0051] Therefore, the burden of optimization in terms of time criteria, which requires the timely use of energy, can be reduced, and the loss / expiration of energy in a given energy category can be avoided.

[0052] As another example, in order to consume energy in a timely manner, an excess amount of hydrogen compared to hydrogen demand may be produced and stored in accordance with available hydrogen storage. This can be taken into consideration when determining the hydrogen set point.

[0053] Similar to electricity storage, this reduces the burden in terms of time.

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

[0055] For example, with respect to hydrogen storage, if production capacity can meet the surplus green energy (relative to hydrogen demand and corresponding energy demand), then the surplus (green) hydrogen can be stored, for example, in tanks. Stored hydrogen can permanently maintain its category classification as green hydrogen ("green" label).

[0056] Including either or both energy and / or hydrogen storage in the optimization model (representing a plant having the above energy and / or hydrogen storage) provides additional degrees of freedom for optimizing the achievement of the optimization objective, such as the maximum production time to meet demand or maximum smoothness.

[0057] According to this disclosure, the maximum available energy of a given energy category may be determined for the current time slot and may comprise surplus energy of the given energy category from one or more preceding time slots and received energy of the given energy category.

[0058] According to this disclosure, the target minimum amount of energy in a given energy category may be determined to minimize the expiration of energy in that given energy category based on a time criterion (as described above) that specifies a time period beginning with or including the time of production of the energy, and energy may be categorized as energy in the given energy category only if it is consumed within the time period, otherwise it expires.

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

[0060] The tracking of energy in a given energy category, as described above, can be used in conjunction with different algorithms that may 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. The set of rules may include one or more rules that prioritize the use of energy in a given category that is nearing its expiration date. Accordingly, always using the oldest remaining green energy may be a heuristic used to determine how current demand is met. For optimization-based algorithms, optimization is typically constrained by a set of constraints. The set of constraints may include one or more constraints, which may be hard or soft constraints, that logically model the criteria for green energy and / or green hydrogen.

[0061] According to this disclosure, the method may include logging the use of energy of a given energy category for the operation of a hydrogen production plant, in particular, to enable the determination of one or more criteria (as described above) on which the categorization of energy as energy of a given energy category is based. This may be useful, for example, for audit purposes, to provide evidence that the plant is operating within a regulatory framework.

[0062] The method of this disclosure may include logging any data that is input to, for example, the determination of a hydrogen setpoint, e.g., optimization, and output from this determination of a hydrogen setpoint, and storing such data. This may enable proper documentation of hydrogen production from a given energy category (e.g., green hydrogen) for, for example, to meet documentation requirements and / or for audit purposes, e.g., to provide evidence that it is operating within a regulatory framework.

[0063] Criteria for categorizing energy within a given energy category can be adjusted, for example, by adjusting the value of the criteria, to align with the criteria associated with the regulatory requirements that must be met (for example, for energy to be considered green energy). Here, the criteria for tracking energy within a given energy category are based on regulatory criteria. Therefore, compliance with the regulation can be easily established, even with changing / time-changing regulations, as it does not require changing any hardcoded rules within the control code. However, from a technical standpoint, whether the criteria are adjusted to the regulatory requirements is not a problem, as the technical challenges and effects do not depend on the regulatory requirements.

[0064] In other words, the present invention enables a single tool, such as an energy management system, to capture, set, and document standards that change over time (e.g., those associated with regulations). Thus, this tool can, among other things, be used to prove compliance with regulations and can function as proof instances and data storage for subsequent audits.

[0065] The present invention also provides a system comprising a processing system configured to perform any of the methods of the present disclosure.

[0066] This system may further comprise one or more electrolytic cell modules of an electrolytic cell plant, and the processing system is configured to control the operation of one or more electrolytic cell modules to operate at predetermined target module setting points. This system may be an electrolytic cell plant or may comprise an electrolytic cell plant.

[0067] This system may include a hydrogen storage system and / or an energy storage system such as a BESS (Battery Energy Storage System).

[0068] The present invention also provides a computer program product that, when executed by a computer, includes instructions causing the computer to perform any of the methods of the present disclosure.

[0069] The present invention also provides a computer-readable medium that, when executed by a computer, comprises instructions causing the computer to perform any of the methods of the present disclosure.

[0070] In the context of this method, the features and advantages outlined above apply equally 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. [Brief explanation of the drawing]

[0072] [Figure 1a] Figure 1a shows a schematic diagram of an electrolytic cell plant. [Figure 1b] Figure 1b illustrates a schematic diagram of an electrolytic cell plant. [Figure 2] Figure 2 is a flowchart illustrating the method according to this disclosure. [Figure 3] Figure 3 is a flowchart illustrating the method according to this disclosure. [Figure 4] Figure 4 schematically illustrates the method described herein. [Figure 5] Figure 5 schematically illustrates hydrogen production curves for known methods. [Figure 6] Figure 6 schematically illustrates the hydrogen production curve according to this disclosure. [Figure 7] Figure 7 schematically illustrates the hydrogen production curve according to this disclosure. [Figure 8] Figure 8 schematically illustrates the hydrogen production curve according to this disclosure. [Modes for carrying out the invention]

[0073] System 1 of the present disclosure comprises a processing system 3 (also called a computing system) configured to perform the method according to the present disclosure, for example, the method outlined in one of the contexts of Figures 2 to 4. Optionally, the system may also be an electrolytic cell plant comprising multiple electrolytic cell modules 2, or may comprise an electrolytic cell plant. Such a system is illustrated in Figures 1a and 1b.

[0074] The systems in Figures 1a and 1b are shown to include an optional monitoring device 4, an optional electrical storage unit 5, an optional hydrogen storage unit 6, and an optional oxygen storage unit 7. Furthermore, arrow 8 indicates electrical input to the electrolytic cell plant, arrow 9 indicates hydrogen output from the electrolytic cell plant, arrow 10 indicates oxygen output from the electrolytic cell plant, arrow 11 indicates thermal output from the plant (or thermal input to the plant), and arrow 12 indicates water input to the electrolytic cell plant.

[0075] For illustrative purposes only, a power grid 13 and a network 14 into which hydrogen, oxygen, and heat are supplied are shown, although they are not part of the system of this embodiment.

[0076] Furthermore, an optional hydrogen and oxygen separation tank 15 is shown.

[0077] Note that Figure 1a illustrates a plant with less detail in its individual components than Figure 1b, in order to illustrate in an exemplary way that different levels of detail may be considered when simply looking at the operation of the plant.

[0078] The method disclosed herein may be performed in the system shown in Figures 1a and 1b, and the steps of the method may be performed, for example, by processing system 3, or any other suitable system, in particular the system disclosed herein.

[0079] Figure 2 is a flowchart illustrating the method according to this disclosure.

[0080] This disclosure provides a computer implementation method for use in controlling the operation of a hydrogen production plant.

[0081] This method comprises determining the maximum available amount of energy for a given energy category (e.g., green energy) in the current time interval in step S11. The maximum available amount of energy for a given energy category can be used as an input for determining the hydrogen setpoint in step S13, which is described below.

[0082] Determining whether energy belongs to a given energy category may involve checking whether the energy meets certain criteria, including positive criteria and exception criteria that allow for violations of one or more of the positive criteria.

[0083] For example, in an optional step S11a, it may be determined whether or not new energy of a given energy category is received, which is referred to as the newly received energy of the given energy category. Alternatively or additionally, in an optional step S11b, it may be determined whether energy of a given energy category is still available from a previous time interval. This may involve determining whether the remaining energy from the previous time interval still meets the energy criteria, particularly the time criterion, for the given energy category. The maximum available amount of energy of a given energy category may be, for example, the sum of the newly received energy of the given energy category determined in step S11a and the still available energy of the given energy category from the previous time interval determined in step S11b.

[0084] To make this decision, the method may include accessing criteria for categorizing energy as energy of a given energy category, matching data related to newly received energy with the criteria, and matching data related to the remaining energy with the criteria.

[0085] For example, for newly received energy, all criteria, especially positive and exception criteria, may be checked. For the remaining energy, only a few criteria, especially the time criterion, may be checked. For other criteria, such as local criteria, it may suffice to check the criterion once when the energy is newly received.

[0086] The method also comprises, in step S12, determining a target (desired) minimum amount of energy in a given energy category to be used for hydrogen production in the current time interval. The target minimum amount of energy in the given energy category may be used as an input for determining the hydrogen setpoint in step S13, which is described below.

[0087] For example, the target minimum amount of energy of a given energy category used can be determined to be the amount of energy of that given energy category that no longer meets the time criterion in at least the next time interval. In other words, the target minimum amount of energy of a given energy category can be chosen in a way that avoids the time-related loss of energy of that given energy category by reclassifying the remaining energy as energy that is not of that given energy category.

[0088] The steps described above can be considered steps for tracking energy in a given energy category. An exemplary tracking method is described further below.

[0089] In the optional step S10, inputs for determining the hydrogen setpoint, such as optimization targets and / or optimization constraints for the operation of the hydrogen plant, may be determined or received. Other potential inputs may include environmental information and / or plant condition information and / or weather information and / or market information. Other inputs are possible.

[0090] This method comprises determining a hydrogen setpoint for the current time interval in step S13, using the maximum available amount and the target minimum amount as (soft or hard) constraints. As an example, step S13 may comprise performing an optimization for an optimization target input as part of step S10, for example, which results in the hydrogen setpoint. The optimization may be based on an operating model that models the operation of the hydrogen plant.

[0091] Therefore, the optimization that leads to the hydrogen setpoint can be carried out in any desired way using any desired optimization objectives and constraints, and constraints that ensure the appropriate use of energy in a given energy category may also be taken into consideration.

[0092] Optimization can be achieved, in particular, based on forward time series, meaning that optimization may include a predictive component.

[0093] The above steps may be repeated, for example, for each of multiple time slots. Note that the remaining energy of a given energy category between time slots may be reduced not only by the actual consumption of energy, but also by the fact that the previous energy of the given energy category no longer meets the time criterion, which may be called the loss or expiration of energy of the given energy category.

[0094] In the optional step S14, a control signal configured to operate the hydrogen production plant at the determined hydrogen setpoint may be generated and optionally output.

[0095] In the optional step S15, the hydrogen production plant may be operated based on the control signal.

[0096] The tracking of energy in the aforementioned predetermined energy categories can be used in conjunction with different algorithms that can be used to determine hydrogen setpoints, such as heuristic or rule-based algorithms and optimization-based algorithms.

[0097] For heuristic algorithms, decisions are made based on a set of rules. This set may include one or more rules prioritizing the use of energy in a given energy category that is nearing its expiration date. For optimization-based algorithms, optimization is typically constrained by a set of constraints. This set may include one or more constraints, which may be hard or soft constraints, that logically model the energy criteria for a given energy category.

[0098] Figure 3 is a flowchart illustrating the method according to this disclosure, in which a database with green power standards is accessed and used as input for tracking green power, in particular, whether incoming power is green and whether remaining power is (still) green. The results of this tracking are input into an energy management system. Furthermore, market information such as power availability is input into the energy management system.

[0099] Additionally, information from other data sources, such as weather forecasts or production schedules, may be provided to the energy management system. Such information can be used when predictively modeling hydrogen production, as expected demand or available energy may depend on it. The energy management system may include a predictive engine for processing such information.

[0100] Based on tracked green electricity, market information, and additional information, the optimization engine can determine an optimized hydrogen setpoint, which can then be used to control the operation of the hydrogen production plant. The determination by the optimization engine includes rules based on green electricity criteria, particularly in the form of constraints.

[0101] [Examples of the method according to this disclosure] In the following examples, green energy is used as an example of energy in a given energy category. However, energy in a given energy category can also be pink energy, energy mixtures, or any other specific type of energy.

[0102] Hydrogen produced from green energy is called "green hydrogen." As mentioned above, the production of "green hydrogen" may be a constraint or boundary condition associated with optimizing the operating setpoint of a hydrogen production plant, and in order to optimize such a goal, it is necessary to quantify the environmental footprint or classify the production parameters as "green." Criteria for categorizing hydrogen as green hydrogen may include the use of green energy, i.e., renewable energy that meets different criteria, including one or more of the following: local criteria, additive criteria, and time criteria, for example, the timely use of renewable energy.

[0103] One relatively easy way to define the criteria is to use a regulatory definition or framework to define when hydrogen is considered "green." This type of quantification has the added benefit of also making it easier to track and report to authorities and customers the production of green hydrogen in context such as incentives, benefits, demand for green hydrogen, and so on.

[0104] A more specific example of a set of standards for green hydrogen production is shown below.

[0105] (1) Local criteria. Local criteria may include criteria regarding the origin of the renewable energy used for hydrogen production. This may be based on bidding zones, for example, as these bidding zones are already available and provide a suitable way to implement the quantification of local criteria.

[0106] (2) Additivity criteria. Additivity criteria may specify, for example, that renewable power plants providing energy for hydrogen production must be specifically constructed for the production of green hydrogen. Another potential criterion is the presence or absence of a long-term power purchase agreement (PPA).

[0107] (3) Time criterion. The time criterion may include, for example, that the energy used for hydrogen production arises from energy production within the same predefined time period from the same time, for example, called a “time unit”. In this context, optionally, a modified storage criterion (3b) may also apply. That is, energy may still conform to the time criterion even when it is stored (rather than used immediately for hydrogen production) within the same predefined time period as defined in (3), for example, within the production time, and used for hydrogen production at any later point in time.

[0108] For example, in order to categorize manufactured hydrogen as green hydrogen, the energy used for hydrogen production may have to meet a defined subset or all of the three criteria (1), (2), and (3).

[0109] An exception may be defined by an exception criterion, for example:

[0110] (4) Hydrogen may still be categorized as green hydrogen even if grid energy does not meet the required criteria among criteria (1) to (3), for example, if it is determined that grid energy is likely to contain a sufficient amount of energy from renewable resources. As mentioned above, the price of grid energy can be used as a good approximation or indicator, especially in a merit order system.

[0111] However, the time-based approach presents challenges in the context of hydrogen production, particularly because energy supply can be highly unstable and may not always coincide with hydrogen demand or production capacity.

[0112] The method disclosed herein will be understood to enable addressing this challenge in the context of green hydrogen production by determining the maximum available amount of green energy 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 these values ​​as, for example, (soft or hard) constraints in determining the hydrogen setpoint.

[0113] The method of this disclosure may include logging and storing any data that is input to and output from the optimization process. This could enable proper documentation of green hydrogen production, for example, for regulatory documentation requirements.

[0114] Criteria for categorizing green hydrogen or green energy can be adjusted, for example, by adjusting the value of the criteria, to align with the criteria associated with the regulatory requirements that must be met. If the criteria for green energy tracking are based on regulatory criteria, compliance with the regulations can be easily established, even with changing / time-changing regulations, because it does not require changing hardcoded rules in the control code. However, from a technical standpoint, whether the criteria are adjusted to regulatory requirements is not a problem, as the technical challenges and effects are independent of the requirements.

[0115] In other words, the present invention enables a single tool, such as an energy management system, to capture, set, and document standards that change over time (e.g., those associated with regulations). Thus, this tool can, among other things, be used to prove compliance with regulations and can function as proof instances and data storage for subsequent audits.

[0116] The present invention makes it possible to take into account the goals of green hydrogen production, as well as other potential goals such as minimal production instability to minimize stack degradation, minimal production peak to minimize peak power costs, or minimal production schedule deviation to minimize storage requirements.

[0117] The following examples illustrate in more detail how the categorization of green hydrogen can be tracked or considered using the criteria described above. See Figure 4.

[0118] For illustrative and simplified purposes, the method will be described using examples with specific figures. However, this disclosure is not limited to any of the above-described exemplary figures.

[0119] A time interval Δt is selected. As an example, the specific application or scenario described below may propose a time interval related to execution on a digital control device or related to a trading time window. However, this disclosure is not limited to a specific time interval.

[0120] In this non-restrictive example, a time interval Δt = 15 minutes is chosen. As a mere example, this choice could reflect the trading time of energy in the spot market.

[0121] The method of this disclosure may consider, for each time interval, the amount of available energy to be the sum of new energy supply and remaining energy, for example, energy that has been consumed in the past and energy that has been lost / expired based on time.

[0122] An exemplary heuristic to meet current demand is to always use the "oldest remaining energy." This mechanism is illustrated in Figure 4 for an example of one hour (in time units) and four time slots of Δt = 15 minutes.

[0123] In Figure 4, E0, E1, E2, and E3 each represent an energy slot, each associated with a 15-minute time interval. From left to right, the energy slots are shown for four different time intervals T. The numbers within the circles indicate the energy available for each time interval T in any given unit. The arrows indicate that the shown state is at the end of each time interval Δt = 15 minutes.

[0124] Below, we will explain the situation at four different times, T=15:00, T=15:15, T=15:30, and T=15:45, as illustrated in the diagram.

[0125] T=15:00: Between 14:45 and 15:00, an additional 7 units were placed in energy slot E0. During the same time interval, there was a load of 9 units. In this example, the method attempts to meet the demand starting from the oldest slot (E3) whenever possible. This is possible because E3 holds 10 units. Thus, E3 is left with 1 unit (10-9). However, at 15:00, slot E3 was available for more than 1 hour, so the time criterion is no longer met and it is emptied, resulting in a loss of 1 energy unit. Energy units in the other energy slots remain as their demand is met only by E3. Therefore, the availability that will be passed on to the next time step is 7 units in E0, 3 units in E1, and 0 units in E2. E3 passes nothing, as explained. Therefore, the total availability is 10 units (7+3), and the next energy should be consumed from E2 (to avoid expiration), but in this example, E2 has 0 units and therefore is not about to expire.

[0126] Here, the slots are rearranged (spun), and a new 15-minute time slot begins. 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.

[0127] T=15:15: Between 15:00 and 15:15, an additional 3 units were supplied and placed in the (new) energy slot E0 (which was previously E3 as the numbering had progressed). The load could not be supplied by any unit in E3 (formerly E2) because this energy slot was already empty at 15:00. Therefore, the load of 11 units is supplied from E2(3), E1(7), and E0(1). This leaves 2 units in E0, along with 0 "emergency" units in E2, which are passed on to the next time step as available.

[0128] Here, the slots are reordered (spun) again as described above, and a new 15-minute time slot begins.

[0129] T=15:30: Between 15:15 and 15:30, an additional 4 units are supplied and placed in the (new) energy slot E0. E3 and E2 are empty. The load is 1 unit, so it can be supplied from energy slot E1 (formerly E0). This leaves 1 unit in E1 and 4 units in E0.

[0130] Here, the slots are rearranged (spun) again as described above, and a new 15-minute time slot begins.

[0131] T=15:45: Between 15:30 and 15:45, an additional 2 units are supplied and placed in the (new) energy slot E0. The load is 8 units. All available energy is consumed, i.e., 1 unit from E2, 4 units from E1, and 2 units from E0. This leaves one unit unsupplied, and the remaining available "green energy" becomes 0. In this case, less green hydrogen can be produced than indicated by demand. This could result in supplying less green hydrogen than the demand by using previously produced and stored green hydrogen to meet the demand, or using non-green energy for hydrogen production to meet the demand, thereby resulting in the hydrogen produced for this slot not being green hydrogen.

[0132] As mentioned above, tracking green energy can be used in conjunction with different algorithms that may be used to determine hydrogen setpoints, such as heuristic or rule-based algorithms and optimization-based algorithms.

[0133] For a heuristic / rule-based algorithm, the set of rules can, at any point in the decision-making, amount to the sum P of the currently available "legally green" energy max (in the above example, the total 7 + 3 + 0 + 1 = 11 at 15:00) and the green energy P allocated to the oldest time slot min (in the above example, the energy 1 in the oldest time slot E3), can simply be extended by the rule of prioritizing energy uptake when it lies between them. P min Using less energy is undesirable as it would waste green energy. Conversely, max using more energy is also undesirable, as in this case the energy difference to max P would correspond to non-green energy. P min and P max The specific implementation of the rule-based algorithm's extension by the rule of prioritizing energy uptake such that it lies between min and P max can depend on each rule-based algorithm. However, since this rule can be logically formulated, it can also be included by a combination of logical AND, OR, IF / ELSE statements involving the values P

[0134] For algorithms based on mathematical optimization, the situation is similar. In this case, the current set of constraints can be extended by additional constraints (hard or soft) that logically model the green energy / hydrogen criteria, for example, a set of regulatory constraints. The constraints can be formulated using a combination of logical AND, OR, IF / ELSE statements. It is generally known that these types of constraints can be modeled using binary and integer variables. Therefore, the constraints can be easily integrated into a mathematical optimization model based on mixed integer linear programming.

[0135] Figure 5 illustrates a hydrogen production curve based on a conventional control algorithm where decisions are made online, meaning decisions are made immediately without taking future (predicted) information into account.

[0136] For such conventional control algorithms, a possible (and potentially best) solution to satisfy the time criterion (in this example, the "time unit" criterion) is as follows, where one hour is divided into four 15-minute time slots: 1) Attempt to use all the energy produced in the first time slot: E used1 =E produced1 If not all of the energy produced can be used, the rest > 0, E rest1 =E produced1 -E used1 Calculate so that it becomes this. 2) Try to use all available energy: E used2 =E rest1 +E produced2 If this is not possible, first, as many E as possible rest1 Use E produced2 Continue. The remaining E rest1’ and E rest2 Calculate the remaining energy for both. The remaining energy for both is greater than or equal to 0 here. 3) Try to use all available energy: E used3 =E rest1’ +E rest2 +E produced3 If this is not possible, first as much E as possible rest1’ Use E rest2 and E produced3 Continue. The remaining E rest1’’ and E rest2’ and E rest3 Calculate the remaining energy. All remaining energies are greater than or equal to 0. 4) Try to use all available energy: E used4 =E rest1’’ +E rest2’ +E rest3 +E produced4If this is not possible, first as much E as possible rest1’’ Use E rest2’ , E rest3 and E produced4 Continue. The remaining E rest1’’ , E rest2’’ , E rest3’ , and E rest4 Calculate E rest1’’’ However, if it is still greater than 0, this energy is lost because it was generated more than an hour ago and can no longer be used in the next time slot. 5) Continue as described above, but always ignore any energy that is "older" than one hour.

[0137] In Figure 5, the dotted curve represents the production of renewable energy, and the dashed curve with a white rectangle specifically represents the hydrogen production curve based on the control algorithm described above. The hydrogen production curve plateaus at the maximum capacity, which in this example is 5.5.

[0138] While the maximum possible energy can be used to operate the plant as described above, such operation also results in a highly unstable manufacturing profile with high ramping (bad for stack degradation) and potentially unnecessary peaks (bad for operating costs).

[0139] For example, this solution does not allow using less energy than the maximum possible energy in one time step in order to save energy for the next time step.

[0140] Figure 6 illustrates a hydrogen production curve obtained by an exemplary method (Example 1) according to the present disclosure. Here, a “smoothness maximization” objective may be implemented. Specifically, such an objective may aim to minimize the maximum (peak) power intake, for example, to take grid limitations into account. In the example in Figure 6, the produced electrical energy (per 15-minute time slot) is shown by a dotted line. The optimization of actual power intake to produce hydrogen to meet the time-unit criterion is illustrated by rectangles.

[0141] As can be seen from the figure, a minimum power peak of 4.8 is achieved instead of 8.0 (as a power supply curve) or 5.5 (as a result of the control algorithm in Figure 5), and smoothness is improved. The latter minimizes degradation of the electrolytic cell stack. The difference between the minimum and maximum power intake is only 0.95 (4.65 - 3.7), but the difference in power production is 8 (8 - 0), and the difference using the control algorithm in Figure 5 is 5.5 (5.5 - 0).

[0142] Figure 7 illustrates a hydrogen production curve obtained by an exemplary method (Example 2) according to the present disclosure. Here, the goal of "meeting maximum demand" can be implemented. Specifically, such a goal may aim to meet a certain hydrogen production demand for as long as possible. In the example in Figure 7, the hydrogen production demand is shown as a constant thick black line with a value of 4. Power generation is shown as a gray dotted line. Considering the time unit criterion, it is possible to meet the demand (of 4) for 78% of the time (gray rectangle). Conventional algorithms (Figure 5) cannot meet the demand at all because the limit must be greater than 4 in order to eliminate overproduction. Note that hydrogen production in this example can also be optimized for peak power minimization, as explained in the context of Figure 6.

[0143] Additionally, Examples 1 and 2 may be configured to avoid switching completely off, unlike the conventional control examples shown in Figure 5, and are limited to a bandwidth between 2.0 and 5.5.

[0144] Examples 1 and 2 demonstrate that the implicit flexibility of time-based rules can be used for greater benefit by using the methods of the present disclosure.

[0145] The above example works well when the instability frequency is less than the time after which the energy is no longer categorized as green energy, for example, one hour.

[0146] In some of the following aspects, it becomes possible to address instability on larger time scales, such as daily scales (like PV), which are difficult to address by the time-unit restrictions described above.

[0147] Electrical storage systems and / or hydrogen storage systems may be taken into consideration when determining the hydrogen setpoint. The storage capacity of the plant in question may be taken into consideration for modeling or optimization. Thus, as described in detail above, excess green energy or excess green hydrogen may be stored to avoid time-based green energy loss or to reduce the burden on optimization that would otherwise need to reduce losses. Thus, such methods allow for the mitigation of instability.

[0148] Alternatively or in addition, this disclosure may use exception criteria to allow exceptions to the criteria that energy must meet to be categorized as green energy. Specifically, such exceptions may allow the use of grid electricity that would otherwise not be considered green energy by exceptionally categorizing it as green electricity. This would allow for mitigating potential, unstable shortages of renewable energy resources. This concept may be referred to as “green grid electricity.”

[0149] Figure 8 illustrates a hydrogen production curve obtained by an exemplary method (Example 3) according to this disclosure.

[0150] This example incorporates the concept of categorizing electricity as green electricity, as described above. Such electricity may come from local power sources or from the grid ("green grid electricity").

[0151] This may be done in addition to the steps illustrated in the context of Figures 6 and 7.

[0152] In other words, even grid-supplied electricity coming from a mix of generators can be considered "green" under certain conditions. This can be derived, for example, from a measurable parameter: electricity prices. If electricity prices fall below a certain limit (e.g., 20 euros / MWh), this means a sufficient amount of green energy in the grid energy.

[0153] Figure 8 shows an example where power from such (surplus) time is used to minimize deviation from the production target of 4. Regarding the decision, this additional green power can be added to the existing Pmax. There are 4 times when "green power" can be obtained from the grid (gray line > 0).

[0154] The diagram shows that production increases around 11:00 (and therefore approaches demand), and the duration of the gap around 23:00 decreases by only 15 minutes, resulting in a noticeable increase in profit compared to the previous example. The remaining production gap is also reduced. This was achieved by using "green grid power" at 7:30 and 19:00, respectively.

[0155] Although the present invention has been illustrated and described in detail in the drawings and the foregoing description, such illustrations and descriptions should be considered illustrative and not limiting. The present invention is not limited to the disclosed embodiments. In consideration 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 present invention as defined by the claims.

Claims

1. A computer implementation method for use in controlling the operation of a hydrogen production plant, wherein the method is Determining the maximum available amount of energy of a predetermined energy category in the current time interval (S11), (S12) Determining the target minimum amount of energy of the predetermined energy category to be used for hydrogen production in the current time interval, Using the maximum available amount and the target minimum amount as constraints, the hydrogen setpoint for the current time interval is determined (S13), A method that includes [a certain feature].

2. The categorization of energy into the predetermined energy category is as follows: A time criterion specifying a time period that begins with or includes the time required to produce the energy, wherein the energy is categorized as energy of the predetermined energy category only if it is consumed within the time period, otherwise it expires, and in particular, the consumption of the energy comprises using the energy to produce hydrogen and / or storing the energy for later use in producing hydrogen. Local criteria for specifying a region, wherein the energy is categorized as energy of the predetermined energy category only when consumed within the region. Adaptivity criteria, herein, the energy is categorized as energy of the predetermined energy category only if the energy production facility is built for the purpose of hydrogen production. Based on one or more criteria comprising at least one of the following, and / or The method according to claim 1, wherein the energy of 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 categorization of energy as energy in the predetermined energy category is based on an exception criterion, the exception criterion allows energy that does not meet other essential criteria to be exceptionally categorized as energy in the predetermined energy category, and in particular is a criterion that considers energy price as an indicator of the renewable content of grid power.

4. The method according to any one of claims 1 to 3, wherein determining the hydrogen setpoint comprises using a model that models the operation of a hydrogen plant, the model taking into account the criteria for categorizing energy as energy of the predetermined energy category.

5. The method according to any one of claims 1 to 4, wherein determining the hydrogen setpoints comprises an optimization and / or rule-based determination, each targeting the achievement of one or more primary objectives and constrained by the maximum available amount and the minimum target amount.

6. The aforementioned main objectives are: Minimal hydrogen production instability, Minimum production peak, Minimum manufacturing schedule deviation, Minimum hydrogen storage requirements, Minimum energy storage requirements, Minimum degradation of one or more hydrogen plant components The method according to claim 5, comprising at least one of the following.

7. The method according to any one of claims 1 to 6, wherein determining the hydrogen setpoint is to prioritize the use of older energy over the use of newer energy.

8. The method according to any one of claims 1 to 7, wherein the determination takes into account the available hydrogen storage capacity and / or the available energy storage capacity.

9. The method according to any one of claims 1 to 8, wherein the maximum available energy of the predetermined energy category is determined for the current time slot and comprises surplus energy of the predetermined energy category from one or more preceding time slots and energy received for the predetermined energy category.

10. The method according to any one of claims 1 to 9, 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 by a time criterion specifying a time period / time period that begins with or includes the production time of the energy, and the energy is categorized as energy of the predetermined energy category only if it is consumed within the time period, and otherwise expires.

11. The method according to any one of claims 1 to 10, further comprising tracking the availability and expiration of energy in the predetermined energy category, particularly based on the criteria described in claim 2 and / or 3, and taking into account the results of the tracking to determine the hydrogen set point.

12. The method according to any one of claims 1 to 11, further comprising logging the use of energy of the predetermined energy category for the operation of the hydrogen production plant, in order to enable the determination of one or more criteria on which the categorization of energy as energy of the predetermined energy category is based.

13. System (1) comprising a processing system (3) configured to perform the method according to any one of claims 1 to 12, further comprising one or more electrolytic cell modules (2) of an electrolytic cell plant, wherein the processing system is configured to control the operation of the one or more electrolytic cell modules (2) to operate at the determined hydrogen set point, and optionally further comprising a hydrogen storage system and / or an energy storage system.

14. A computer-readable medium comprising, when executed by a computer, an instruction causing the computer to perform the method according to any one of claims 1 to 12.

15. A computer program product comprising, when executed by a computer, an instruction causing the computer to perform the method described in any one of claims 1 to 12.

Images