Alkaline electrolysis cluster power working medium two-level cooperative regulation and control method

Abstract

The invention discloses an alkaline electrolysis cluster power working medium two-level cooperative regulation and control method which comprises the following steps: step 1, establishing a full response surface model among hydrogen yield, temperature and power based on related parameters of an alkaline electrolysis cell; 2, solving the optimal power distribution of the alkaline electrolysis cluster by taking the maximum hydrogen production as a target and taking the safe operation domain as a constraint condition; 3, evaluating the working medium regulation and control condition of the system by taking the distributed power as a constraint condition and taking temperature stability, hydrogen concentration minimization in oxygen and working medium use minimization as targets; 4, whether the system temperature obtained in the step 3 meets the convergence condition of the temperature used in the step 2 or not is judged, and if yes, a regulation and control strategy is output; and if not, substituting the temperature obtained in the step 3 into the step 2 again for calculation. In conclusion, the hydrogen production efficiency and the operation stability are both considered through the cooperative scheduling architecture of the upper layer and the lower layer, support is provided for maintaining the system temperature and the hydrogen concentration in oxygen to be stable while the operation efficiency of the alkaline electrolysis cluster is ensured, and more practical operation guidance is provided for optimal scheduling of the alkaline electrolysis cluster.

Description

Technical Field

[0001] This invention belongs to the field of power system operation and control, specifically relating to a two-level coordinated control method for the power working fluid in an alkaline electrolysis cluster. Background Technology

[0002] With the increasing global emphasis on renewable energy, hydrogen energy, as a clean and efficient energy carrier, has received widespread attention. Among hydrogen production methods, alkaline electrolyzers have become a research hotspot due to their mature technology and high energy conversion efficiency. Alkaline electrolyzers produce hydrogen by electrolyzing water and exhibit good operational stability, particularly in high current density and large-scale production. However, in practical applications, the efficiency of alkaline electrolyzers is affected by multiple factors, including operating temperature, electrolysis power, and gas purity.

[0003] Traditional alkaline electrolyzer control methods primarily focus on power and the start-up and shutdown of the electrolyzer, while neglecting the control of gas purity, temperature stability, and working fluid usage. This singular control strategy easily leads to energy efficiency losses and may even affect the safety and quality of hydrogen production. Therefore, a more comprehensive and systematic control method is needed to achieve efficient operation of alkaline electrolyzers.

[0004] In recent years, Model Predictive Control (MPC) methods have been increasingly applied to power system regulation, predicting and optimizing future states, and have shown good results. However, existing technologies still have the following shortcomings: on the one hand, most regulation methods do not take into account thermodynamic dynamics and the dynamics of hydrogen concentration in oxygen; on the other hand, regulation strategies often lack real-time performance and are difficult to adapt to constantly changing operating environments. Therefore, there is an urgent need for an innovative regulation method that effectively coordinates power allocation and working fluid regulation in alkaline electrolysis clusters, and takes into account the dynamic characteristics of the system to optimize the hydrogen production process and improve overall energy efficiency. Summary of the Invention

[0005] This invention provides a two-tiered synergistic control method for power and working fluid in alkaline electrolysis clusters, aiming to improve hydrogen production efficiency and electrolyzer operational stability by optimizing power allocation and working fluid control. Specifically, the upper tier establishes a full response surface methodology to analyze the relationship between hydrogen production, electrolysis power, and temperature, thereby formulating an optimal power allocation strategy to maximize hydrogen production and ensure the electrolyzer operates within a safe operating range. The lower tier focuses on optimizing working fluid parameters, including improving temperature stability, minimizing hydrogen concentration in oxygen, and reducing working fluid consumption to maintain system dynamic balance. This innovative tiered control mechanism not only improves the overall performance of the electrolyzer but also provides effective technical support for the development of the hydrogen economy.

[0006] To achieve the above-mentioned technical objectives, the present invention will adopt the following technical solution:

[0007] A two-level coordinated control method for power and working fluid in an alkaline electrolysis cluster is proposed. This method decomposes the control problem of the alkaline electrolysis cluster into two sub-control problems: the upper sub-control problem is responsible for the optimal allocation of electrolysis power in the cluster, while the lower sub-control problem controls the working fluid parameters. Optimal coordinated control of electrolysis power and working fluid parameters is achieved through iteration between the upper and lower levels. The method specifically includes the following steps:

[0008] Step 1: Establish a full response surface model of the relationship between hydrogen production capacity and electrolyzer temperature and electrolysis power based on the relevant parameters of the alkaline electrolyzer;

[0009] Step 2: Construct an optimization model for the upper-level power allocation of the electrolysis cluster to address the upper-level sub-control problem; the objective function of the optimization model for the upper-level power allocation of the electrolysis cluster is to maximize the hydrogen production in the full response surface model, and the optimal electrolysis power allocation of the alkaline electrolysis cluster is solved with the safe operating domain as a constraint.

[0010] Step 3: Construct a lower-level working fluid control model for the electrolysis cluster to address the lower-level sub-control problem; the objective function of the lower-level working fluid control model is constrained by the optimal electrolysis power obtained in Step 2, optimizing the electrolyzer temperature stability, minimizing the hydrogen concentration in oxygen, and minimizing the working fluid usage, and solving for the working fluid control situation of the system.

[0011] Step 4: Determine whether the electrolytic cell temperature obtained in Step 3 and the electrolytic cell temperature used in Step 2 meet the convergence condition. If they do, output the control strategy; if they do not, substitute the electrolytic cell temperature obtained in Step 3 into the upper-level power allocation optimization model of the electrolytic cluster in Step 2 and re-execute Step 2.

[0012] As a further improvement to the above technical solution, in step 4, a model predictive control approach is adopted to generate the control strategy. Specifically, when generating the control strategy, the future [0, N]... p The state within ] is used after the control strategy is generated, taking the first n values ​​as an example. p State simulation is performed at each time point, and then a time interval [n] is generated. p N p +n p The control strategy within [ ] is applied sequentially and incrementally until the operational requirements are met; where: N p Represents the prediction time domain, n p Represents the control time domain, n p <N p .

[0013] As a further improvement to the above technical solution, the full response surface model constructed in step 1 is as follows:

[0014]

[0015] In the formula, (β0,β1,β2,β) 11 ,β 12 ,β 22 ) represents the fitting coefficient, n represents the hydrogen production rate, and p ele Indicates electrolysis power; T sta This indicates the temperature of the electrolytic cell.

[0016] As a further improvement to the above technical solution, in step 2, the objective function of the upper-level power allocation optimization model of the electrolytic cluster is expressed as:

[0017]

[0018] In the formula, m is the number of electrolytic cells in the alkaline electrolysis cluster. N represents the hydrogen production of the i-th electrolyzer at time t, where t represents the time point. p P represents the prediction time domain. ele,i,t p represents the electrolytic power of the i-th electrolytic cell at time t. w，t Let λ represent wind power output, and λ be the wind curtailment penalty.

[0019] The safe operating domain of an alkaline electrolysis cluster is the operating domain containing the electrolysis power p that ensures the temperature and hydrogen concentration in oxygen do not exceed limits when adjusting the parameters of the lower working fluid, satisfying the following:

[0020] p∈R;

[0021] In the formula, R represents the operating range of electrolysis power.

[0022] As a further improvement to the above technical solution, in step 3, the objective function of the lower-level working fluid control model of the electrolytic cluster can be expressed as:

[0023]

[0024] δx t =[HTO,T t -T ref ];

[0025] Δu t =[Δv lye ,Δv c ];

[0026] In the formula, δx t Let Δu be the change in state. i The change in the controlled quantity is HTO, where HTO is the hydrogen concentration in oxygen; T t T represents the temperature of the electrolytic cell; t represents the time point; T ref The set temperature is Q; Q is the weight matrix of the state variables, Δvlye Let Δv be the flow rate of the alkali solution. c is the coolant flow rate; M is the penalty matrix for changes in the control quantity;

[0027] The constraints of the lower-level working fluid control model of the electrolytic cluster are that the control quantity and the change of the control quantity will not exceed the limit, that is:

[0028]

[0029] In the formula, and These refer to the range of the control quantity and the range of changes in the control quantity, respectively.

[0030] As a further improvement to the above technical solution, in step 4, the convergence condition for temperature can be expressed as:

[0031]

[0032] In the formula, E represents the temperature error, k represents the number of iterations, and ε represents the convergence threshold.

[0033] As a further improvement to the above technical solution, the proposed upper and lower layer iterative control algorithm uses the traditional optimization solver Gurobi for the upper layer and the nonlinear solver CasaADi for the lower layer.

[0034] Another technical objective of this invention is to provide a storage medium in which a computer program stored in the storage medium executes the above-described two-level coordinated control method for the power working fluid of an alkaline electrolysis cluster when it is running.

[0035] Another technical objective of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that the computer program executes the above-described two-level coordinated control method for the power working fluid of an alkaline electrolysis cluster.

[0036] The advantages of this invention compared to existing technologies are as follows:

[0037] 1. The two-level synergistic regulation method for power and working fluid in alkaline electrolysis clusters provided by this invention effectively improves hydrogen production efficiency by systematically optimizing the power distribution and working fluid regulation of the electrolyzer. Compared with traditional methods, this invention establishes a full response surface model to analyze in detail the relationship between hydrogen production, electrolysis power, and temperature. This new optimization strategy maximizes hydrogen production, and through scientific and reasonable power distribution, the electrolyzer can operate efficiently under various operating conditions, laying the foundation for the sustainable utilization of hydrogen energy.

[0038] 2. The innovative layered control mechanism of this invention significantly enhances the operational stability of the electrolyzer. In actual operation, the system can monitor the state of the electrolyzer in real time and quickly adjust the power and working fluid parameters. This dynamic adjustment capability ensures that the electrolysis process is unaffected by temperature fluctuations and power mismatches, effectively reducing the risk of system failure. Especially in large-scale electrolysis production environments, this invention can achieve higher safety and reaction stability, thus providing strong support for industrial applications.

[0039] 3. The lower-level control strategy has been further optimized for working fluid parameters, including enhanced temperature control and reduced hydrogen concentration in oxygen. This improvement not only increases hydrogen purity but also ensures the safe operation of the entire electrolysis process. Simultaneously, the use of model predictive control enables the system to respond promptly to external conditions such as changes in grid load, thereby achieving a highly adaptable control strategy and ensuring the electrolysis cluster maintains optimal operating conditions under constantly changing circumstances.

[0040] 4. This invention significantly reduces the production cost of hydrogen by improving the efficiency of hydrogen production and reducing working fluid consumption. This is of great significance for promoting the economic feasibility of the hydrogen energy industry and further promotes the widespread application of renewable energy and the development of the hydrogen economy. In summary, this invention not only improves the economics and safety of hydrogen production but also effectively promotes the transformation to clean energy and sustainable development, making a significant contribution to building a low-carbon future. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of the two-level coordinated control method of power working fluid in alkaline electrolysis clusters described in this invention;

[0043] Figure 2 This is the flowchart of the model prediction rolling control described in this invention;

[0044] Figure 3 The results of the upper-level power allocation are shown; (a) a comparison between the total electrolysis power and the wind power generation power is shown; (b) the power allocation among the three stacks is depicted; and (c) a comparison of the trend changes between the hydrogen production rate curve (solid line) and the stack power curve is shown.

[0045] Figure 4The lower-level scheduling results are shown. Among them: (a) shows the scheduling results of coolant and alkali solution, and (b) shows the temperature of the three electrolyzers and the hydrogen concentration in the oxygen in the system. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Example 1

[0048] The two-level coordinated control method for power and working fluid in alkaline electrolysis clusters described in this invention decomposes the control problem of alkaline electrolysis clusters into two sub-control problems: the upper sub-control problem is responsible for the optimal electrolysis power allocation of the alkaline electrolysis cluster, and the lower sub-control problem controls the working fluid parameters of the alkaline electrolysis cluster. Optimal coordinated control of electrolysis power and working fluid parameters is achieved through iteration between the upper and lower levels (the upper level can be solved by the traditional optimization solver Gurobi, and the lower level can be solved by the nonlinear solver CasaADi). Specifically, it includes the following steps:

[0049] Step 1: Based on the relevant parameters of the alkaline electrolyzer, establish a full response surface model relating hydrogen production capacity to electrolyzer temperature and electrolysis power.

[0050] Specifically, the full response surface model described in this invention can be expressed as:

[0051]

[0052] In the formula: β is the fitting coefficient, For the amount of hydrogen produced, f = [p ele T sta ] is the input variable vector, consisting of electrolysis power and electrolytic cell temperature.

[0053] Expanding the above full response surface model, we get:

[0054]

[0055] In the above formula, (β0,β1,β2,β) 11 ,β 12 ,β 22 ) represents the fitting coefficient. For hydrogen production capacity, p ele Indicates electrolysis power; T sta This indicates the temperature of the electrolytic cell.

[0056] Step 2: Construct an optimization model for the upper-level power allocation of the electrolysis cluster to address the upper-level sub-control problem; the objective function of the optimization model for the upper-level power allocation of the electrolysis cluster is to maximize the hydrogen production in the full response surface model, and the optimal electrolysis power allocation of the alkaline electrolysis cluster is solved with the safe operating domain as a constraint.

[0057] In this step, the objective function of the upper-level power allocation optimization model of the electrolytic cluster can be expressed as:

[0058]

[0059] In the formula, m is the number of electrolytic cells in the alkaline electrolysis cluster. N represents the hydrogen production rate of the i-th electrolyzer at time t, where t represents time. p The prediction time domain refers to the time scale considered by MPC during the optimization strategy generation phase; P ele,i,t p represents the electrolytic power of the i-th electrolytic cell at time t. w，t Let λ represent wind power output, and λ represent the wind curtailment penalty.

[0060] The safe operating domain of an alkaline electrolysis cluster is the operating domain of electrolysis power p that ensures that the temperature and hydrogen concentration in oxygen will not exceed the limits when adjusting the parameters of the lower working fluid, satisfying: p∈R; where R represents the operating domain of electrolysis power, that is, the upper and lower limits of electrolysis power of each electrolytic cell in the electrolysis cluster under the current electrolysis state (temperature, hydrogen concentration in oxygen).

[0061] Step 3: Construct a lower-level working fluid control model for the electrolysis cluster to address the lower-level sub-control problem; the objective function of the lower-level working fluid control model is constrained by the optimal electrolysis power obtained in Step 2, optimizing the electrolyzer temperature stability, minimizing the hydrogen concentration in oxygen, and minimizing the working fluid usage, and solving for the working fluid control situation of the system.

[0062] The objective function of the working fluid control model in the lower layer of the electrolytic cluster can be expressed as:

[0063]

[0064] δx i =[HTO, T] i -T ref ];

[0065] Δu i =[Δv lye Δv c ];

[0066] In the formula, δx i Let Δu be the change in state. i The change in the controlled quantity is HTO, where HTO is the hydrogen concentration in oxygen; T i T represents the temperature of the electrolytic cell; t represents the time point; Tref The set temperature is Q; Q is the weight matrix of the state variables, Δv lye Let Δv be the flow rate of the alkali solution. c is the coolant flow rate; M is the penalty matrix for changes in the control quantity.

[0067] The constraints of the lower-level working fluid control model of the electrolytic cluster are that the control quantity and its change will not exceed the limit, i.e.: u t ∈u, In the formula, and The preset range for the control quantity and the change in the control quantity.

[0068] Step 4: Determine whether the electrolytic cell temperature obtained in Step 3 and the electrolytic cell temperature used in Step 2 meet the convergence condition. If they do, output the control strategy; if they do not, substitute the electrolytic cell temperature obtained in Step 3 into the upper-level power allocation optimization model of the electrolytic cluster in Step 2 and re-execute Step 2.

[0069] In this step, a model predictive control approach is used to generate the control strategy. Specifically, when generating the control strategy, the future [0, N]... p The state within ] is used after the control strategy is generated, taking the first n values ​​as an example. p State simulation is performed at each time point, and then a time interval [n] is generated. p N p +n p The control strategy within [ ] is applied sequentially and incrementally until the operational requirements are met; where: N p Represents the prediction time domain, n p Represents the control time domain, n p <N p .

[0070] Furthermore, in this step, the convergence condition for the electrolytic cell temperature can be expressed as:

[0071]

[0072] In the formula, E represents the temperature error, k represents the number of iterations, and ε represents the convergence threshold.

[0073] Example 2

[0074] The present invention also provides a storage medium, wherein the computer program stored in the storage medium executes the above-described two-level coordinated control method of power working fluid in alkaline electrolysis clusters when running.

[0075] Example 3

[0076] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-described two-level coordinated control method for the power working fluid of the alkaline electrolysis cluster through the computer program.

[0077] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0078] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0080] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0081] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0082] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0083] Application examples

[0084] This application example details the implementation process of the two-level synergistic control method for power and working fluid proposed in three industrial-grade alkaline electrolyzer demonstrations, as follows:

[0085] Step 1: Construct a full response surface model of an alkaline electrolyzer—the three electrolyzers have the same operating parameters, but differ in their degradation levels of 0%, 5%, and 10%, respectively.

[0086] The current was increased from 10% of the rated current in 1% increments to 120% of the rated current, and the temperature was increased from 70°C in 1% increments to 90°C. The hydrogen production rate at different power levels and temperatures was recorded, and the full response surface model was established as follows:

[0087] n1=1.086+1.140·P1+1.191·T1+17.215·P1 2 +0.0000122·T1 2 +0.0137·P1·T1

[0088] n2=16.639+16.098·P2+(-0.00474)·T2+(-1.400)·P2 2 +0.0000136·T2 2 +0.0135·P2·T2

[0089] n3=-0.00477+(-0.00478)·P3+(-1.337)·T3+(-1.276)·P3 2 +0.0000150·T3 2 +0.0133·P3·T3

[0090] Step 2: Upper-level power allocation regulation—With the goal of maximizing hydrogen production in the full response surface model and constrained by the safe operating domain, the following optimization model is established:

[0091]

[0092] The optimization model is constrained to ensure that the electrolysis power p remains within its operating domain, so as to prevent the temperature and hydrogen concentration in oxygen from exceeding limits during the regulation of the lower working fluid.

[0093] p∈R

[0094] In this embodiment, N p Taking 15, that is, MPC considers the optimization strategy within 15 time steps to solve the optimal power allocation of the alkaline electrolysis cluster;

[0095] Step 3: Lower-level working fluid control – Using the allocated power as a constraint, optimize temperature stability, minimize hydrogen concentration in oxygen, and minimize working fluid usage. The optimization model is as follows:

[0096]

[0097] In this embodiment, Q is [100, 100] and M is [20, 10, 10], and the working fluid control strategy of the alkaline electrolysis cluster is solved;

[0098] The constraints of the optimization model are the control amount and the fact that the change in the control amount will not exceed the limit, that is:

[0099]

[0100] In the formula, and Given the range of the control quantity and its change, the system state is governed by thermodynamic dynamics and the dynamics of hydrogen in oxygen. Its differential equation can be discretized into the following constraints:

[0101] x t+1 =f(P t ,x t ,u t )

[0102] Step 4: Upper and Lower Layer Iteration and Model Prediction Rolling – See Attachment Figure 1 The process shown involves iterative iterations at each level, and the convergence criteria are as follows:

[0103]

[0104] In the formula, ε is taken as 0.1%. (See attached...) Figure 2 The process shown demonstrates model predictive rolling control, with a total simulation time of 720 minutes. MPC considers a duration of N. P The time is 15 minutes, and the time for each scroll is optimized to n.p It took 5 minutes, and the result is as follows. Figure 3-4 The results of power-working fluid regulation between the upper and lower layers are shown.

[0105] Figure 3 The results of the upper-layer power allocation are shown. For example... Figure 3 As shown in (a), the total electrolysis power successfully tracked the wind power generation for most of the operating time. The main deviations between the electrolyzer power and the wind power generation occurred in the intervals [11,271] and [416,626], where the available wind power generation exceeded the steady-state operating limits of the system. During these periods, the electrolyzer operated at the lower or upper limit of its feasible power range, constrained by steady-state performance.

[0106] Furthermore, since the operating region based on the dynamic model incorporates real-time temperature and hydrogen concentration margin in oxygen, the system can temporarily exceed steady-state limits (e.g., [1,11] and [406,416]), thereby increasing the duration of full wind power consumption from 230 minutes to 250 minutes.

[0107] Figure 3 (b) depicts the power distribution among the three stacks. The results show that, in order to increase hydrogen production and optimize overall efficiency, more power is allocated to stack 1, which has the lowest degradation level. Quantitatively, stack 1 has an average electrolysis power that is 0.0752 MW higher than stack 2 (maximum difference of 0.1897 MW) and 0.1495 MW higher than stack 3 (maximum difference of 0.3404 MW).

[0108] Figure 3 The hydrogen production rate curve (solid line) in (c) shows a similar trend to the stack power curve. Over the 720-minute time period, the average hydrogen production rates of stacks 1-3 were 35.99 kg / h, 34.21 kg / h, and 32.53 kg / h, respectively. Since power and efficiency in an electrolyzer are generally inversely proportional, the efficiency curve (dashed line) starts at a higher value and decreases as power increases. It is noteworthy that although stack 1 operates at higher power than stacks 2 and 3, it also maintains higher efficiency, with an average efficiency 1.17% higher than stack 2 and 2.26% higher than stack 3.

[0109] Figure 4 The lower-level scheduling results are displayed. For example... Figure 4 As shown in (a), during low power ([1,286]) and high power ([406,626]), the flow rates of the alkali and coolant are fixed at their minimum and maximum values, respectively, to maintain the system temperature and hydrogen concentration in oxygen within allowable ranges. Outside these ranges, the flow rates are dynamically adjusted with the electrolysis power to keep the temperature near the 80°C setpoint. Figure 4As shown in (b), the system temperature remained stable at 80 degrees Celsius during [291,396] and [641,720]. Importantly, the maximum hydrogen concentration in oxygen (1.48%) and the temperature range of 75.01 to 84.98 degrees Celsius remained within acceptable limits even without explicit state constraints, demonstrating the effectiveness of the proposed two-layer scheduling strategy based on region-driven operation.

[0110] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0111] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A two-level synergistic control method for the power working fluid in an alkaline electrolysis cluster, characterized in that, The problem of alkaline electrolysis cluster regulation is divided into two sub-regulation problems: the upper sub-regulation problem is responsible for the optimal electrolysis power allocation of the alkaline electrolysis cluster, and the lower sub-regulation problem is responsible for the regulation of the working fluid parameters of the alkaline electrolysis cluster. The optimal coordinated regulation of electrolysis power and working fluid parameters is achieved through iteration between the upper and lower layers, specifically including the following steps: Step 1: Establish a full response surface model of the relationship between hydrogen production capacity and electrolyzer temperature and electrolysis power based on the relevant parameters of the alkaline electrolyzer; Step 2: Construct an optimization model for the upper-level power allocation of the electrolysis cluster to address the upper-level sub-control problem; the objective function of the optimization model for the upper-level power allocation of the electrolysis cluster is to maximize the hydrogen production in the full response surface model, and the optimal electrolysis power allocation of the alkaline electrolysis cluster is solved with the safe operating domain as a constraint. Step 3: Construct a lower-level working fluid control model for the electrolysis cluster to address the lower-level sub-control problem; the objective function of the lower-level working fluid control model is constrained by the optimal electrolysis power obtained in Step 2, optimizing the electrolyzer temperature stability, minimizing the hydrogen concentration in oxygen, and minimizing the working fluid usage, and solving for the working fluid control situation of the system. Step 4: Determine whether the electrolytic cell temperature obtained in Step 3 and the electrolytic cell temperature used in Step 2 meet the convergence condition. If they do, output the control strategy; if they do not, substitute the electrolytic cell temperature obtained in Step 3 into the upper-level power allocation optimization model of the electrolytic cluster in Step 2 and re-execute Step 2.

2. The two-level synergistic regulation method for the power working fluid of an alkaline electrolysis cluster according to claim 1, characterized in that, In step 4, a model predictive control approach is used to generate the control strategy. Specifically, when generating the control strategy, the future [0, N]... p The state within ] is used after the control strategy is generated, taking the first n values ​​as an example. p State simulation is performed at each time point, and then a time interval [n] is generated. p N p +n p The control strategy within [ ] is applied sequentially and incrementally until the operational requirements are met; where: N p Represents the prediction time domain, n p Represents the control time domain, n p <N p .

3. The two-level coordinated control method for power working fluid in alkaline electrolysis clusters as described in claim 1, characterized in that, In step 1, the constructed full response surface model is as follows: In the formula, (β0,β1,β2,β) 11 ,β 12 ,β 22 ) represents the fitting coefficient, n represents the hydrogen production rate, and p ele Indicates electrolysis power; T sta This indicates the temperature of the electrolytic cell.

4. The two-level coordinated control method for power working fluid in alkaline electrolysis clusters as described in claim 1, characterized in that, In step 2, the objective function of the upper-level power allocation optimization model of the electrolysis cluster is expressed as: In the formula, m is the number of electrolytic cells in the alkaline electrolysis cluster. N represents the hydrogen production of the i-th electrolyzer at time t, where t represents the time point. p P represents the prediction time domain. ele,i,t p represents the electrolytic power of the i-th electrolytic cell at time t. w，t Let λ represent wind power output, and λ be the wind curtailment penalty. The safe operating domain of an alkaline electrolysis cluster is the operating domain containing the electrolysis power p that ensures the temperature and hydrogen concentration in oxygen do not exceed limits when adjusting the parameters of the lower working fluid, satisfying the following: p∈R; In the formula, R represents the operating range of electrolysis power.

5. The two-level synergistic regulation method for the power working fluid of an alkaline electrolysis cluster as described in claim 1, characterized in that, In step 3, the objective function of the lower-level working fluid control model of the electrolytic cluster can be expressed as: δx t =[HTO,T t -T ref ]; Δu t =[Δv lye ,Δv c ]; In the formula, δx t Let Δu be the change in state. i The change in the controlled quantity is HTO, where HTO is the hydrogen concentration in oxygen; T t T represents the temperature of the electrolytic cell; t represents the time point; T ref The set temperature is Q; Q is the weight matrix of the state variables, Δv lye Let Δv be the flow rate of the alkali solution. c is the coolant flow rate; M is the penalty matrix for changes in the control quantity; The constraints of the lower-level working fluid control model of the electrolytic cluster are that the control quantity and the change of the control quantity will not exceed the limit, that is: In the formula, and These refer to the range of the control quantity and the range of changes in the control quantity, respectively.

6. The two-level coordinated control method for power working fluid in alkaline electrolysis clusters as described in claim 1, characterized in that, In step 4, the convergence condition for temperature can be expressed as: In the formula, E represents the temperature error, k represents the number of iterations, and ε represents the convergence threshold.

7. The two-level synergistic regulation method for the power working fluid of an alkaline electrolysis cluster according to claim 1, characterized in that, The proposed upper and lower layer iterative control algorithm uses the traditional optimization solver Gurobi for the upper layer and the nonlinear solver CasaADi for the lower layer.

8. A storage medium, characterized in that, When the computer program stored in the storage medium is run, it executes the two-level coordinated control method of the alkaline electrolysis cluster power working fluid as described in any one of claims 1 to 7.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The computer program executes the two-level coordinated control method of power working fluid in alkaline electrolysis clusters as described in any one of claims 1 to 7.

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