Alkaline electrolytic cell time-varying operation domain calculation method and system based on dynamic model

Abstract

The invention discloses an alkaline electrolytic cell time-varying operation domain calculation method and system based on a dynamic model, and belongs to the field of power system operation regulation and control. The dynamic model-based alkaline electrolytic cell time-varying operation domain calculation method comprises the following steps: establishing an alkaline electrolytic cell operation domain model according to an electrochemical steady-state model, a thermal dynamic model and an oxygen-in-hydrogen dynamic model of an alkaline electrolytic cell; converting the operation domain model of the alkaline electrolytic cell into an optimization model; traversing and solving the optimization model under different initial temperatures and hydrogen concentrations in oxygen to obtain an operation domain; an electrochemical steady state model, a thermodynamic dynamic model and a hydrogen-in-oxygen dynamic model are introduced, so that a comprehensive time-varying operation domain model is established; therefore, the operation domain can reflect the time-varying characteristics of the electrolytic cell more accurately, a reliable boundary is provided for optimization and regulation of the system under different environmental conditions, and the production efficiency and the safety are improved; and a polyhedron pruning algorithm and a geometric constraint decoupling algorithm are adopted, so that the solving efficiency of the operation domain is remarkably improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of power system operation and regulation, and particularly relates to a dynamic model-based time-varying operation domain calculation method and system for an alkaline electrolyzer. BACKGROUND

[0002] With the continuous growth of global energy demand and the emphasis on environmental protection, the development and utilization of clean energy have become an important issue faced by countries. Alkaline electrolysis, as one of the important technologies for hydrogen production, has attracted widespread attention due to its high efficiency and economy. This technology converts electrical energy into chemical energy by electrolyzing water or other hydrogen-containing compounds, opening up new paths for the utilization of renewable energy.

[0003] In the application of alkaline electrolysis, accurate operation domain calculation is crucial as it directly affects the performance and safety of the system. The operation domain defines the safe operation range of the electrolyzer under different operating conditions, providing necessary boundary conditions for the development of control strategies. However, traditional operation domain calculation algorithms usually ignore the time-varying dynamic characteristics of the electrolysis system, mainly relying on static models or empirical methods, failing to fully exploit the potential of fine scheduling of the system, thus making it difficult to guide the fine operation of the electrolysis system.

[0004] Therefore, a dynamic model-based time-varying operation domain calculation method for an alkaline electrolyzer is proposed. SUMMARY

[0005] To overcome the shortcomings of the prior art, the purpose of the present application is to provide a dynamic model-based time-varying operation domain calculation method and system for an alkaline electrolyzer, which solves the problems in the prior art.

[0006] The purpose of the present application can be achieved by the following technical solutions:

[0007] The dynamic model-based time-varying operation domain calculation method for an alkaline electrolyzer includes the following steps:

[0008] An operation domain model for the alkaline electrolyzer is established based on an electrochemical steady-state model, a thermodynamic dynamic model, and an oxygen hydrogen dynamic model of the alkaline electrolyzer;

[0009] The operation domain model for the alkaline electrolyzer is converted into an optimization model by taking the constraints on the state in the operation domain model as the constraints of the optimization model and taking the maximum and minimum of the integral of the operation domain elements as the optimization objectives;

[0010] The optimization model under different initial temperatures and oxygen hydrogen concentrations is iteratively solved to obtain the operation domain.

[0011] Further, the electrochemical steady-state model is:

[0012] U = U rev + U act+U Ω

[0013]

[0014] wherein U rev , U act and U Ω are the inverse voltage, the activation voltage and the ohmic voltage respectively; U rev0 is the inverse voltage fixed value, U is the electrolysis voltage, R0 is the gas constant, T is the temperature, F is the Faraday constant, P is the gas pressure, P0 is the standard atmospheric pressure, s, t1, t2, t3, r1 and r2 are all empirical fitting parameters, A is the electrode plate area, and I is the current;

[0015] The thermal dynamic model is:

[0016]

[0017] Q loss = (T - T amb ) / R

[0018] Q lye = c lye ρ lye v lye (T t - T ch,t-τ )

[0019]

[0020] wherein C cell , C ch and C cool are the heat capacities of the electrolytic cell, the heat exchanger and the cooling liquid respectively; Q ele , Q loss and Q lye are the heat generated by electrolysis, the natural heat dissipation and the heat taken away by the alkali solution respectively; P ele is the total power of electrolysis, is the hydrogen production rate, H is the hydrogen heat value, T amb is the external temperature, R is the thermal resistance, c lye , ρ lye and v lye are the specific heat capacity, the density and the flow rate of the alkali solution respectively; c cool , ρ cool and v cool are the specific heat capacity, the density and the flow rate of the cooling liquid respectively; T ch is the temperature of the heat exchanger, ΔT is the logarithmic mean temperature difference of the heat exchanger, and kA is the heat exchange coefficient of the heat exchanger; T cool is the temperature of the cooling liquid, and T t is the temperature of the electrolytic cell at time t, and T ch,t-τT is the temperature of the exchanger at time t - τ cell,t T is the temperature of the cell at time t ch,t T is the temperature of the exchanger at time t cool,in,t T is the temperature of the incoming coolant at time t cool,t T is the temperature of the coolant at time t

[0021] The hydrogen dynamic model is:

[0022]

[0023] τ an = 2V an / v lye

[0024]

[0025] where n im represents the hydrogen impurity flow rate; represents the hydrogen flow rate entering through gas diffusion; represents the hydrogen flow rate entering through pressure difference; represents the hydrogen flow rate entering through lye mixing; ξ diff is the diffusion coefficient; ξ conv is the convection coefficient; S in is the solubility of hydrogen in lye; γ lye is the lye-related time constant; P ele is the cell pressure; P0 is the reference pressure; a1 is the quadratic fit parameter between solubility and temperature; a2 is the linear fit parameter between solubility and temperature; a3 is the constant fit parameter between solubility and temperature; T is the temperature; HTO(s) is the hydrogen transfer function; is the oxygen production flow rate; τ an is the anode time constant; τ sep is the gas-liquid separation time constant; τ out is the hydrogen discharge time constant; V an is the anode volume; v lye is the lye fluid parameter; P is the pressure; V sep,g is the separator gas phase volume; R g is the gas constant; s is the Laplace transform variable.

[0026] Further, the alkaline cell operating domain model is:

[0027]

[0028] where P represents the system power, x t = [HTO t , T tis the state variable of the alkaline electrolyzer at time t, and is the hydrogen concentration in oxygen HTO t and temperature T t , u t ={v lye ,v cool} is the control variable of the alkaline electrolyzer at time t, and is composed of cooling liquid flow rate v cool and alkali flow rate v lye , f(*) represents the discrete state transition equation of the alkaline electrolyzer, Δu t represents the state variable change amount of the alkaline electrolyzer control variable, represent the allowable range of the state variable, control variable, power and state variable change amount of the alkaline electrolyzer; is the operating domain of the alkaline electrolyzer with initial state x0 and considering time length ε, ε is the considering time length, P t is the power of the alkaline electrolyzer at time t, x t is the state of the alkaline electrolyzer at time t, and x0 is the initial state of the alkaline electrolyzer.

[0029] Further, the optimization model is:

[0030]

[0031] s.t.g(x t ,u t ,P t ,Δu t )≤0

[0032] h(x t ,x t+1 ,u t ,P t )=0

[0033] In the formula, min and max represent the upper and lower operating domains respectively, g(*) represents the constraint on the state variable, control variable, power and state variable change amount of the alkaline electrolyzer in the operating domain model, and h(*) represents the state transition equation in the operating domain model; J is the objective function.

[0034] Further, in solving the optimization model, a polyhedral pruning algorithm and a geometric constraint decoupling algorithm are used to accelerate the solving process.

[0035] Further, when the geometric constraint decoupling algorithm is used to solve the optimization model, the optimization model is split into an optimization model with only temperature constraints and an optimization model with only gas purity constraints, the operating domain results of the two models are solved respectively, and the intersection of the obtained results is taken to obtain the final operating domain.

[0036] The alkaline electrolyzer time-varying operating domain calculation system based on the dynamic model comprises:

[0037] Running domain model construction module: according to the electrochemical steady-state model, the thermodynamic dynamic model and the oxygen hydrogen dynamic model of the alkaline electrolytic cell, the running domain model of the alkaline electrolytic cell is established;

[0038] Model conversion module: taking the constraint of the state in the running domain model as the optimization model constraint, taking the maximum and minimum of the running domain element integral as the optimization target, and converting the running domain model of the alkaline electrolytic cell into an optimization model;

[0039] Model solving module: traversing the optimization model under different initial temperatures and oxygen hydrogen concentrations to obtain the running domain.

[0040] A computer storage medium stores a readable program, when the program runs, the program can instruct a computing device to execute the above-mentioned dynamic model-based time-varying running domain calculation method of the alkaline electrolytic cell.

[0041] An electronic device comprises a processor, a memory, a communication interface and a communication bus, the processor, the memory and the communication interface complete the communication among each other through the communication bus;

[0042] The memory is used to store at least one executable instruction, and the executable instruction makes the processor execute the operation corresponding to the above-mentioned dynamic model-based time-varying running domain calculation method of the alkaline electrolytic cell.

[0043] A computer program product comprises computer instructions, and the computer instructions instruct a computing device to execute the operation corresponding to the above-mentioned dynamic model-based time-varying running domain calculation method of the alkaline electrolytic cell.

[0044] The beneficial effects of the present application are:

[0045] 1、The present application introduces electrochemical steady-state, thermodynamic dynamic and oxygen hydrogen dynamic models to establish a comprehensive time-varying running domain model; this makes the running domain more accurately reflect the time-varying characteristics of the electrolytic cell, provides reliable boundaries for system optimization and regulation under different environmental conditions, and thus improves production efficiency and safety.

[0046] 2、The present application adopts a polyhedral pruning algorithm and a geometric constraint decoupling algorithm, which significantly improves the solving efficiency of the running domain; by accelerating the calculation process, the required computing resources and time are significantly reduced, so that the electrolytic cell can obtain more accurate running boundaries under complex working conditions; this efficient computing capability provides strong support for the optimization scheduling of the electrolytic cell, so that the operating personnel can make scientific decisions based on real-time data to ensure that the operation of the electrolytic cell is carried out in a safe and efficient state.

[0047] 3、The application uses accurate operation boundaries to provide scientific basis for fine scheduling of the electrolytic cell; through optimization of the operation strategy, the operator can make more reasonable decisions under complex working conditions, reduce energy consumption and reduce production cost; this series of innovations brings higher economic benefits to hydrogen production and promotes the goal of sustainable development. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without creative labor.

[0049] Figure 1 is a flow chart of the time-varying operation domain calculation method of the alkaline electrolytic cell of the present application;

[0050] Figure 2 is an operation upper domain analysis graph when only considering temperature constraints when the initial temperature is 77, 77.5, 80, 82.5, and 85 in Example 2;

[0051] Figure 3 is an operation lower domain analysis graph when only considering temperature constraints when the initial temperature is 75 to 85 in Example 2;

[0052] Figure 4 is an operation lower domain analysis graph when only considering gas purity constraints when the initial temperature is 82-85 and the initial hydrogen concentration in oxygen is 0.018 in Example 2;

[0053] Figure 5 is a running domain result graph obtained in Example 2. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0055] Example 1

[0056] As shown in Figure 1 , the time-varying operation domain calculation method of the alkaline electrolytic cell based on the dynamic model comprises the following steps:

[0057] S1, according to the electrochemical steady-state model, the thermodynamic dynamic model and the gas purity dynamic model of the alkaline electrolytic cell, an operation domain model of the alkaline electrolytic cell is established;

[0058] The electrochemical steady-state model is:

[0059] U = U rev + U act + U Ω

[0060]

[0061] wherein U rev , U act and U Ω are the overvoltage, activation overvoltage and ohmic overvoltage respectively; U rec0 is the overvoltage constant, R0 is the gas constant, T is the temperature, F is the Faraday constant, P is the gas pressure, P0 is the standard atmospheric pressure, s, t1, t2, t3, r1 and r2 are all empirical fitting parameters, A is the electrode area, and I is the current;

[0062] The thermal dynamic model is:

[0063]

[0064] Q loss = (T - T amb ) / R

[0065] Q lye = c lye ρ lye v lye (T t - T ch,t-τ )

[0066]

[0067] wherein C cell , C ch and C cool are the heat capacities of the electrolytic cell, heat exchanger and cooling liquid respectively; Q ele , Q loss and Q lye are the heat generated by electrolysis, natural heat dissipation and heat taken away by the alkali solution respectively; P ele is the total power of electrolysis, is the hydrogen production rate, H is the hydrogen heat value, T amb is the ambient temperature, R is the thermal resistance, c lye , ρ lye and v lye are the specific heat capacity, density and flow rate of the alkali solution respectively; c cool , ρ cool and v cool are the specific heat capacity, density and flow rate of the cooling liquid respectively; T ch is the temperature of the heat exchanger, ΔT is the logarithmic mean temperature difference of the heat exchanger, and kA is the heat exchange coefficient of the heat exchanger; Tcool Tc(t) is the temperature of the coolant at time t t Tc(t) is the temperature of the coolant at time t ch,t-τ Tc(t) is the temperature of the coolant at time t cell,t Tc(t) is the temperature of the coolant at time t ch,t Tc(t) is the temperature of the coolant at time t cool,in,t Tc(t) is the temperature of the coolant at time t cool,t Tc(t) is the temperature of the coolant at time t

[0068] The hydrogen-in-oxygen (HTO) dynamic model is:

[0069]

[0070] τ an = 2V an / v lye

[0071]

[0072] where n im represents the hydrogen impurity flow rate; represents the hydrogen flow rate entering through gas diffusion; represents the hydrogen flow rate entering through pressure differential; represents the hydrogen flow rate entering through lye mixing; ξ diff is the diffusion coefficient; ξ conv is the convection coefficient; S in is the solubility of hydrogen in lye; γ lye is the lye-related time constant; P ele is the cell pressure; P0 is the reference pressure; a1 is the quadratic fit parameter between solubility and temperature; a2 is the linear fit parameter between solubility and temperature; a3 is the constant fit parameter between solubility and temperature; T is the temperature; HTO(s) is the hydrogen transfer function; is the oxygen generation flow rate; τ an is the anode time constant; τ sep is the gas-liquid separation time constant; τ out is the hydrogen exhaust time constant; V an is the anode volume; v lye is the lye fluid parameter; P is the pressure; V sep,g is the separator gas phase volume; R g is the gas constant; s is the Laplace transform variable.

[0073] In establishing the operation domain model of the alkaline electrolyzer, the correlation between the states of the alkaline electrolyzer at different time points needs to be established. Therefore, the differential equations in the thermal dynamic model and the oxygen hydrogen dynamic model of the alkaline electrolyzer need to be discretized by using the finite difference method based on the trapezoidal format.

[0074] The operation domain model of the alkaline electrolyzer is:

[0075]

[0076] In the formula, P represents the system power, x t = [HTO t , T t ] is the state variable of the alkaline electrolyzer at time t, which is composed of the oxygen hydrogen concentration HTO t and the temperature T t , u t = {v lye , v cool} is the control variable of the alkaline electrolyzer at time t, which is composed of the cooling liquid flow v cool and the alkali flow v lye , f(*) represents the state transition equation of the discretized alkaline electrolyzer, Δu t represents the state variable change amount of the control variable of the alkaline electrolyzer, represents the allowable range of the state variable, the control variable, the power and the state variable change amount of the alkaline electrolyzer; is the operation domain of the alkaline electrolyzer with the initial state x0 and the consideration time ε, ∈ is the consideration time, P t is the power of the alkaline electrolyzer at time t, x t is the state of the alkaline electrolyzer at time t, and x0 is the initial state of the alkaline electrolyzer.

[0077] S2, the constraints on the state in the operation domain model are used as the optimization model constraints, the maximum and minimum of the integral of the operation domain elements are used as the optimization objectives, and the operation domain model of the alkaline electrolyzer is converted into an optimization model;

[0078] The optimization model is:

[0079]

[0080] s.t.g(x t ,u t ,P t ,Δu t )≤0

[0081] h(x t ,x t+1 ,u t ,P t )=0

[0082] min, max respectively represent the upper and lower domains of operation, g(*) represents the constraints of the state variables, control variables, power and state variable changes in the operation domain model, h(*) represents the state transition equation in the operation domain model; J is the objective function.

[0083] S3, traversing the optimization model under different initial temperatures and hydrogen concentrations in oxygen to obtain the operation domain.

[0084] The operation domain of the alkaline electrolyzer is affected by the initial temperature and the initial hydrogen concentration in oxygen, so when solving the optimization model, different initial temperatures and different initial hydrogen concentrations in oxygen need to be traversed to obtain the operation domain under different initial states.

[0085] In this embodiment, a polyhedral pruning algorithm and a geometric constraint decoupling algorithm are used to accelerate the solution of the optimization model.

[0086] The content involved in solving the optimization model using the geometric constraint decoupling algorithm is: the optimization model is divided into an optimization model with only temperature constraints and an optimization model with only gas purity constraints, the operation domain results of the two models are solved respectively, and the intersection of the obtained results is taken to obtain the final operation domain.

[0087] The content involved in solving the optimization model using the polyhedral pruning algorithm is: based on the monotonicity of the constraint conditions, when traversing the multi-dimensional parameter space, the optimal solution of the uncalculated region is inferred from the results of the calculated region, thereby avoiding repeated calculation of parameter regions with the same optimal solution. Specifically, the algorithm uses the monotonicity of the temperature constraint and the gas purity constraint under different parameter conditions to significantly reduce the number of parameter combinations that need to be solved through pruning strategies.

[0088] Under the temperature constraint condition, the algorithm uses the monotonicity of the system operating power with temperature change to prune: when it is found that the system can continuously operate at the extreme power at a certain critical temperature, according to the monotonicity principle, the optimal solution of other temperature points in the temperature monotonicity interval can be directly determined without calculating one by one.

[0089] Under the gas purity constraint condition, the algorithm uses the monotonicity of the system operating power with purity and temperature change to prune two-dimensionally: after determining the optimal solution under a certain critical purity and temperature combination, according to the two-parameter monotonicity rule, the optimal solution of all points in the parameter region satisfying the monotonicity condition can be directly inferred.

[0090] Through the above pruning strategies, the algorithm significantly reduces the computational complexity and improves the optimization solving efficiency on the premise of ensuring the solving accuracy.

[0091] In the solving process: when only considering the temperature constraint of the alkaline electrolytic cell operation upper limit, if the alkaline electrolytic cell can always operate at the maximum power at an initial temperature, the alkaline electrolytic cell operation upper limit at a temperature lower than the initial temperature does not need to be calculated, and is directly considered as the maximum power of the alkaline electrolytic cell; when only considering the temperature constraint of the system operation lower limit, if the alkaline electrolytic cell can always operate at the minimum power at an initial temperature, the alkaline electrolytic cell operation lower limit at a temperature higher than the initial temperature does not need to be calculated, and is directly considered as the minimum power of the alkaline electrolytic cell; in the alkaline electrolytic cell operation lower limit considering only the gas purity constraint, if the system can always operate at the minimum power at an initial gas purity and an initial temperature, the alkaline electrolytic cell operation lower limit at an initial state with an initial temperature higher than or equal to the temperature and an initial gas purity lower than or equal to the purity does not need to be solved, and is considered as the minimum power of the alkaline electrolytic cell; finally, the gas purity has no effect on the alkaline electrolytic cell operation upper limit, that is, the alkaline electrolytic cell operation upper limit at different gas purities does not need to be calculated.

[0092] Based on the similar inventive concept, the embodiment of the present application also provides a computer storage medium, which stores a readable program, when the program is run by a processor, the program can execute the above-mentioned alkaline electrolytic cell time-varying operation domain calculation method based on a dynamic model.

[0093] Based on the similar inventive concept, the embodiment of the present application provides an electronic device, which comprises a processor, a memory, a communication interface and a communication bus, the processor, the memory and the communication interface complete the communication among each other through the communication bus;

[0094] The memory is used to store at least one executable instruction, and the executable instruction makes the processor execute the operation corresponding to the above-mentioned alkaline electrolytic cell time-varying operation domain calculation method based on a dynamic model.

[0095] Based on the similar inventive concept, the embodiment of the present application also provides a computer program product, which comprises computer instructions, and the computer instructions instruct a computing device to execute the operation corresponding to the above-mentioned alkaline electrolytic cell time-varying operation domain calculation method based on a dynamic model.

[0096] Embodiment 2

[0097] This embodiment takes a typical alkaline electrolytic cell as an example to illustrate the specific calculation steps of the proposed alkaline electrolytic cell time-varying operation domain calculation method based on a dynamic model, and the content is as follows:

[0098] Step 1: Constructing an electrolytic cell operation domain model, based on the steady-state electrochemistry, thermodynamic dynamics and gas purity dynamics model of the alkaline electrolytic cell, an operation domain model is established. The selected typical alkaline electrolytic cell parameters are shown in the following table:

[0099] Table 1: Typical alkaline electrolytic cell parameters

[0100] Parameter Value Parameter Value Parameter Value A 0.3325 [ t3 ] 247.3 f1 25000 s 0.12 [r1] 8.05*1e-5 f2 0.9564 ​ 1.002 [r2] -0.00000025 d e ]]> 500*1e-6 [ t2 ] 8.424

[0078] N ele ]]> 300 K 50.2 m 0.1 γ 1.4 P sto ]]> 3000000 κ 108 com ]]> ​ 0.8 [P0] 101325 J 99750 [CAT amb ]]> 10 C awe ]]> 55*1e06 U rev,0 ]]> 1.219 [R sta ]]> 0.001 C ch ]]> 0.5*1e06 C c ]]> 0.1*1e06 kA 11000 V an ]]> 0.005 [CAT c,in ]]> 10 V sep,g ]]> 1 sep ]]> ​ 5

[0101] Secondly, the electrochemical model is established:

[0102] U = U rev + U act + U Ω

[0103]

[0104] Thermal model:

[0105]

[0106] Q loss = (T - T amb ) / R

[0107] Q lye = c lye p lye v lye (T t - T ch,t-τ )

[0108]

[0109] Oxygen hydrogen model:

[0110]

[0111] T an = 2V an / v lye

[0112]

[0113] Based on the trapezoidal format, the differential equations of the thermal model and the oxygen hydrogen model are converted into algebraic equations, and then the operating domain model is constructed:

[0114]

[0115] Step 2: Operating domain model conversion, establish the corresponding optimization model:

[0116]

[0117] s.t.g(x t , u t , P t , Delta u t ) <= 0

[0118] h(x t , x t+1 , u t , Pt ) = 0

[0119] Step 3: Solve the operating domain. The initial temperature is traversed from 75℃ to 85℃ in steps of 0.5℃ to obtain the lower operating domain with only temperature constraints (see appendix). Figure 2 The calculation results are shown for initial temperatures of 77, 77.5, 80, 82.5, and 85°C. The initial temperature is iterated from 85°C down to 75°C in steps of 0.5°C to obtain the operating upper domain with only temperature constraints (see attached). Figure 3 (as shown); and the initial temperature was traversed from 75℃ to 85℃ in steps of 0.5℃, and the initial hydrogen concentration in oxygen was traversed from 0.018 to 0, resulting in the lower operating domain constrained only by gas purity (see attached). Figure 4 The calculation results are shown when the initial temperature is 82-85°C and the initial hydrogen concentration in oxygen is 0.018. Finally, the upper operating domain with only temperature constraints is taken as the final operating domain, and the intersection of the lower operating domain with only temperature constraints and the lower operating domain with only gas purity constraints is taken to obtain the final operating domain of the alkaline electrolyzer (as shown in the attached figure). Figure 5 (As shown).

[0120] from Figures 2-5 As can be seen, when the initial temperature reaches 77.5℃, the upper operating domain constrained only by temperature is the system's maximum power; therefore, it is unnecessary to calculate the operating domain with temperatures below 77.5℃. When the initial temperature is 82℃, the lower operating domain constrained only by temperature is the minimum power; therefore, it is unnecessary to calculate scenarios with initial temperatures above 82℃. When the initial temperature is 82℃ and the initial hydrogen concentration in oxygen is 0.018, the lower operating domain constrained only by gas purity is the system's minimum power; therefore, it is no longer necessary to consider scenarios with initial temperatures below 82℃.

[0121] The table below compares the computational efficiency of the proposed algorithm with that of an algorithm that does not decouple from geometric constraints but considers both temperature and HTO constraints (hereinafter referred to as the general algorithm):

[0122] Table 2 Comparison of Domain Calculation During Operation

[0123] Algorithm Number of computations Single computation time (s) Computational market (h) General algorithm 21 1532.2 8.94 Proposed algorithm 16 1530.1 6.8

[0124] Table 3 Comparison of Domain Calculation under Running Conditions

[0125] Algorithm Number of computations Single computation time (s) Computational market (h) General algorithm 21*19 2140.8 237.272 Proposed algorithm 15+7*19 1541.2 63.36

[0126] As can be seen from Tables 2 and 3, thanks to the polyhedral pruning algorithm, the number of computations in the running domain is reduced from 420 to 164, a reduction of 60.95%. Furthermore, by decoupling the temperature constraint from the gas purity constraint (geometric constraint decoupling), the proposed algorithm reduces the computation time per run by 28.0% compared to the general algorithm.

[0127] Example 3

[0128] The dynamic model-based time-varying operation domain calculation system of the alkaline electrolyzer specifically comprises:

[0129] An operation domain model construction module: according to an electrochemical steady-state model, a thermodynamic dynamic model and an oxygen hydrogen dynamic model of the alkaline electrolyzer, an operation domain model of the alkaline electrolyzer is established;

[0130] A model conversion module: taking the constraint on the state in the operation domain model as an optimization model constraint, taking the maximum and minimum of the integral of the operation domain element as an optimization objective, and converting the operation domain model of the alkaline electrolyzer into an optimization model;

[0131] A model solving module: traversing and solving the optimization model under different initial temperatures and oxygen hydrogen concentrations to obtain the operation domain.

[0132] The methods described herein can be stored on a recording medium, such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk, as software or computer code, or by computer code originally stored in a remote recording medium or non-transitory machine-readable medium downloaded over a network and to be stored in a local recording medium, so that the methods described herein can be processed by such software on a recording medium using a general-purpose computer, a special-purpose processor or programmable or special-purpose hardware, such as an ASIC or FPGA. It can be understood that the computer, processor, microprocessor controller or programmable hardware includes a storage component (for example, RAM, ROM, flash memory, etc.) that can store or receive software or computer code, when the software or computer code is accessed and executed by the computer, processor or hardware, the methods described herein are implemented. In addition, when a general-purpose computer accesses code for implementing the methods shown herein, the execution of the code will convert the general-purpose computer into a special-purpose computer for executing the methods shown herein.

[0133] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application.

Claims

1. A method for computing the time-varying operating domain of an alkaline electrolyzer based on a dynamic model, characterized in that, The method comprises the following steps: According to an electrochemical steady-state model, a thermodynamic dynamic model and an oxygen hydrogen dynamic model of the alkaline electrolyzer, an alkaline electrolyzer operation domain model is established; The operation domain model is converted into an optimization model by taking the constraints on the state in the operation domain model as the constraints of the optimization model and taking the maximum and minimum of the operation domain element integral as the optimization objectives; The optimization model under different initial temperatures and oxygen hydrogen concentrations is solved to obtain the operation domain.

2. The dynamic model-based caustic electrolyzer time-varying operating domain calculation method of claim 1, wherein, The electrochemical steady-state model is: U = U rev + U act + U Ω wherein U rev , U act , and U Ω are the inverse, activation, and ohmic voltages, respectively; U rev0 is the inverse voltage fixed value, U is the electrolysis voltage, R0 is the gas constant, T is the temperature, F is the Faraday constant, P is the gas pressure, P0 is the standard atmospheric pressure, s, t1, t2, t3, r1, and r2 are all empirical fitting parameters, A is the electrode area, and I is the current; The thermodynamic dynamic model is: Q loss = (T - T amb ) / R Q lye = c lye p lye v lye (T t -T ch,t-τ ) In the formula, C cell C ch and C cool These are the heat capacities of the electrolysis chamber, heat exchanger, and coolant, respectively; Q ele Q loss Q lye These are the heat generated by electrolysis, the heat dissipated naturally, and the heat carried away by the alkali solution, respectively; p ele The total power of electrolysis, T represents the hydrogen production rate, H represents the calorific value of hydrogen, and T represents the hydrogen production rate. amb Where R is the ambient temperature, C is the thermal resistance, and R is the thermal resistance. lye ρ lye v lye These are the specific heat capacity, density, and flow rate of the alkaline solution, respectively; c cool ρ cool v cool These are the specific heat capacity, density, and flow rate of the coolant, respectively; T ch Here, T represents the heat exchanger temperature, ΔT represents the logarithmic mean temperature difference of the heat exchanger, and kA represents the heat transfer coefficient of the heat exchanger. cool T represents the coolant temperature. t Let T be the temperature of the electrolytic cell at time t. ch,t-τ Let T be the temperature of the exchanger at time t-τ. cell,t Let T be the temperature of the electrolytic cell at time t. ch,t Let T be the temperature of the exchanger at time t. cool,in,t Let T be the temperature of the coolant flowing in at time t. cool,t Let t be the temperature of the coolant at time t; The oxygen hydrogen dynamic model is: where n im represents hydrogen impurity flow rate; represents hydrogen flow rate through gas diffusion; represents hydrogen flow rate through pressure differential; represents hydrogen flow rate through caustic mixing; ξ diff is the diffusion coefficient; ξ conv is the convection coefficient; S in is the solubility of hydrogen in caustic; γ lye is the caustic related time constant; P ele is the cell pressure; P0 is the reference pressure; a1 is the quadratic fit parameter between solubility and temperature; a2 is the linear fit parameter between solubility and temperature; a3 is the constant fit parameter between solubility and temperature; T is the temperature; HTO(s) is the hydrogen transfer function; is the oxygen generation flow rate; τ an is the anode time constant; τ sep is the gas-liquid separation time constant; τ out is the hydrogen exhaust time constant; V an is the anode volume; v lye is the caustic fluid parameter; P is the pressure; V sep,g is the separator gas phase volume; R g is the gas constant; s is the Laplace transform variable.

3. The dynamic model-based caustic electrolyzer time-varying operating domain calculation method of claim 2, wherein, The alkaline electrolyzer operation domain model is: wherein P represents the system power, x t = [HTO t , T t ] is the state variable of the alkaline electrolyzer at time t, composed of the hydrogen concentration HTO t in oxygen and the temperature T t , u t = {v lye , v cool} is the control variable of the alkaline electrolyzer at time t, composed of the coolant flow rate v cool and the alkali flow rate v lye , f(*) represents the discrete state transition equation of the alkaline electrolyzer, Δu t represents the state variable change amount of the control variable of the alkaline electrolyzer, represents the allowable range of the state variable, the control variable, the power and the state variable change amount of the alkaline electrolyzer; is the operating domain of the alkaline electrolyzer with the initial state x0 and the consideration time length ε, ε is the consideration time length, P t is the power of the alkaline electrolyzer at time t, x t is the state of the alkaline electrolyzer at time t, and x0 is the initial state of the alkaline electrolyzer.

4. The dynamic model-based caustic electrolyzer time-varying operating domain calculation method of claim 3, wherein, The optimization model is: In the formula, min and max represent the upper and lower domains of operation, g(*) represents the constraints on the state variables, control variables, power and state variable changes in the operation domain model, h(*) represents the state transition equation in the operation domain model, and J is the objective function.

5. The dynamic model-based caustic electrolyzer time-varying operating domain calculation method of claim 1, wherein, In solving the optimization model, a polyhedral pruning algorithm and a geometric constraint decoupling algorithm are used to accelerate the solving process.

6. The dynamic model-based caustic electrolyzer time-varying operating domain calculation method of claim 5, wherein, In solving the optimization model using the geometric constraint decoupling algorithm, the optimization model is divided into an optimization model with only temperature constraints and an optimization model with only gas purity constraints, the operation domain results of the two models are solved respectively, and the intersection of the obtained results is taken to obtain the final operation domain.

7. A system for computing the time-varying operating domain of an alkaline electrolyzer based on a dynamic model, characterized in that, The method comprises: An operation domain model construction module: according to an electrochemical steady-state model, a thermodynamic dynamic model and an oxygen hydrogen dynamic model of the alkaline electrolyzer, an alkaline electrolyzer operation domain model is established; A model conversion module: the operation domain model is converted into an optimization model by taking the constraints on the state in the operation domain model as the constraints of the optimization model and taking the maximum and minimum of the operation domain element integral as the optimization objectives; A model solving module: the optimization model under different initial temperatures and oxygen hydrogen concentrations is solved to obtain the operation domain.

8. A computer storage medium storing a readable program, characterized in that, When the program is running, the program can instruct the computing device to perform the dynamic model-based time-varying operation domain calculation method of the alkaline electrolyzer as claimed in any one of claims 1-6.

9. An electronic device, comprising: The method comprises: A processor, a memory, a communication interface and a communication bus, the processor, the memory and the communication interface complete communication with each other through the communication bus; The memory is used to store at least one executable instruction, and the executable instruction makes the processor perform the corresponding operation of the dynamic model-based time-varying operation domain calculation method of the alkaline electrolyzer as claimed in any one of claims 1-6.

10. A computer program product comprising computer instructions, characterized in that, The computer instructions instruct the computing device to perform the corresponding operation of the dynamic model-based time-varying operation domain calculation method of the alkaline electrolyzer as claimed in any one of claims 1-6.

Images