A method and system for optimizing the capacity of multi-stage electrolyzers based on a temperature control model

Abstract

This invention discloses a multi-stage electrolyzer capacity optimization method and system based on a temperature control model. The method includes: S1, constructing a start-up discrimination model for the electrolyzers; S2, constructing a temperature correction function based on cumulative deviation propagation; S3, building an electrolyzer operating power allocation model and obtaining the unused power of the corresponding electrolyzers; S4, using the input power obtained in S3 to sequentially drive the electrolyzers and transfer the unused power to the next electrolyzer, and constructing an optimization objective function with levelized hydrogen production cost; S5, initializing the capacity and number of electrolyzers, using the minimum value of the optimization objective function constructed in S4 as the objective, and using the optimal capacity of each electrolyzer and the number of electrolyzers as decision variables to solve the optimization objective function, thereby obtaining the optimal electrolyzer configuration. This invention can significantly reduce the levelized hydrogen production cost of the system and improve the wind power absorption rate and overall system economy while ensuring hydrogen production volume.

Description

Technical Field

[0001] This invention relates to the fields of renewable energy and hydrogen energy technology, and in particular to a method and system for optimizing the capacity of a multi-stage electrolyzer based on a temperature control model. Background Technology

[0002] One of the core challenges in hydrogen production through water electrolysis, especially wind power coupled with water electrolysis, is improving the compatibility of water electrolysis hydrogen production devices with renewable energy sources. Although alkaline electrolyzers have significant economic advantages and have been widely used in engineering practice, their inherent limitations, such as slow dynamic response speed and frequent start-stop cycles affecting efficiency and lifespan, still bring new challenges at the system level: how to achieve the best balance between energy consumption and efficiency between power distribution and frequent start-stop cycles.

[0003] Domestic and international research shows that while multi-reactor alkaline electrolyzer systems can improve utilization and hydrogen production efficiency under fluctuating conditions through multi-reactor collaboration and power tiered allocation, their minimum load is still limited by the characteristics of a single reactor. Performance evaluation is closely related to the state transition mechanism, involving factors such as load range, ramp-up capability, and start-up / shutdown losses. Current research has conducted extensive studies on this issue, designing a series of electrolyzer scheduling methods based on the dynamic operating characteristics and temperature variation characteristics of the electrolyzers, thereby improving their compatibility with renewable energy sources.

[0004] However, existing technologies have the following limitations:

[0005] (1) At fine time resolution, there is a lack of a cold and hot start discrimination model based on real temperature dynamics;

[0006] (2) Temperature time-series correlation, ramp-up limitation and power distribution in the process of multi-electrolyte collaborative operation result in a high-dimensional and computationally long problem of heterogeneous capacity rolling optimization of multi-electrolytes, making it difficult to complete high-precision optimization on an annual scale, and making it difficult to fully evaluate the advantages and disadvantages of the strategy.

[0007] (3) The various constraints and temperature dynamics in the coordinated operation of multiple electrolytic cells have not yet been systematically analyzed.

[0008] The aforementioned shortcomings limit the flexibility potential of multi-electrolyzer systems in high-proportion renewable energy systems, and there is an urgent need to develop temperature-driven cold and hot start-up determination and capacity-strategy co-optimization methods. Summary of the Invention

[0009] Purpose of the invention: To address the above-mentioned shortcomings, this invention provides a multi-stage electrolyzer capacity optimization method and system based on a temperature control model. This not only provides an efficient operation and control strategy for large-scale wind power hydrogen production systems, but also helps to achieve higher energy utilization efficiency and economy, and promotes the high-quality development of the green hydrogen industry.

[0010] Technical solution: This invention provides a method for optimizing the capacity of a multi-stage electrolyzer based on a temperature control model, comprising:

[0011] S1. Based on the input power and the temperature state of the electrolytic cell, construct a start-up discrimination model for the electrolytic cell;

[0012] S2. Based on the electrolytic cell start-up discrimination model constructed in S1, a temperature correction function based on cumulative deviation propagation is constructed to constrain the operating temperature sequence of the electrolytic cell within a set temperature range.

[0013] S3. Obtain the input power, combine it with the electrolytic cell start-up discrimination model constructed in S1, build an electrolytic cell operation power allocation model, and obtain the unused power of the corresponding electrolytic cell.

[0014] S4. The input power obtained through S3 drives the electrolyzers sequentially, and the unused power is transferred to the next electrolyzer. An optimization objective function is constructed based on the levelized hydrogen production cost.

[0015] S5. Initialize the capacity and number of electrolytic cells. With the minimum value of the optimization objective function constructed in S4 as the objective, and the optimal capacity and number of electrolytic cells as decision variables, solve the optimization objective function to obtain the optimal electrolytic cell configuration.

[0016] Specifically, in S1, a start-up discrimination model for the electrolyzer is constructed based on the input power, the set standby heating power and hydrogen production power of the electrolyzer, and the temperature state and operating temperature of the electrolyzer.

[0017] More specifically, the start-up discrimination model of the electrolytic cell is as follows:

[0018] When the input power is less than the set standby heating power of the electrolytic cell, the electrolytic cell is in a shutdown state;

[0019] When the input power is greater than the set standby heating power of the electrolytic cell and the temperature of the electrolytic cell is lower than its working temperature, the electrolytic cell is in standby heating state. The electrolytic cell starts heating until the temperature of the electrolytic cell reaches the working temperature.

[0020] When the input power is greater than the set hydrogen production power of the electrolyzer and the temperature of the electrolyzer reaches its operating temperature, the electrolyzer is in hydrogen production mode and begins to produce hydrogen.

[0021] Furthermore, in step S2, the cooling step size when the electrolytic cell is in the shutdown state and the heating step size when it is in the standby heating state are obtained respectively, thereby obtaining the original cumulative temperature sequence of the electrolytic cell. The original cumulative temperature sequence of the electrolytic cell is then corrected by a temperature correction function based on cumulative deviation propagation, so as to constrain the operating temperature sequence of the electrolytic cell within a set temperature range.

[0022] Specifically, in step S2, a temperature correction function based on cumulative deviation propagation is constructed to constrain the operating temperature sequence of the electrolyzer within a set temperature range, as follows:

[0023] The operating temperature of the electrolytic cell at the current moment is obtained and calculated as a temperature deviation from the set temperature range. The operating temperature of the electrolytic cell at the current moment and all subsequent moments is corrected based on this temperature deviation. At the same time, if the temperature deviation at the current moment is greater than the temperature deviation at the previous moment, the difference between the temperature deviations at the two moments is calculated and corrected based on the aforementioned correction.

[0024] If the temperature deviation at the current moment is less than the temperature deviation at the previous moment, then the correction is made based on the temperature deviation at the previous moment.

[0025] More specifically, after completing a round of high-temperature or low-temperature correction, the operating temperature sequence of the corrected electrolyzer is rechecked to see if there are still any out-of-bounds moments; if so, a new round of corresponding correction is started until the operating temperature of the electrolyzer at all times is within the set temperature range.

[0026] Specifically, in step S3, an electrolytic cell operating power allocation model is built, and the unused power of the corresponding electrolytic cell is obtained, as follows:

[0027] The power balance relationship of the water electrolysis hydrogen production system is as follows:

[0028] ;

[0029] in, Let c be the input power. Let be the discarded power at time c. Let c be the heating power loss. Let c be the final hydrogen production power. Let c be the power loss during the uphill climb. Let c be the overload power loss.

[0030] Therefore, the unused power of the electrolytic cell can be calculated. ,as follows:

[0031] .

[0032] More specifically, the climbing loss power at time c. The calculation is as follows:

[0033] ;

[0034] in:

[0035] Let be the initial hydrogen production power at time c. This power can be calculated when the electrolyzer has reached the specified operating temperature range and the input power meets the hydrogen start-up conditions, as follows:

[0036] ;

[0037] in, The rated power of the electrolytic cell, Hydrogen production power of the electrolyzer; This represents the temperature change between the current time and the previous time in the corrected temperature sequence.

[0038] Final hydrogen production power at time c The calculation is as follows:

[0039] ;

[0040] in, This represents the difference in hydrogen production power between time c and the previous time.

[0041] Specifically, in step S4, the objective function for constructing the levelized hydrogen production cost is as follows:

[0042] The total cost of the power supply equipment and all electrolyzers and the total hydrogen production of all electrolyzers are calculated separately. The difference between the two is the levelized cost of hydrogen production, which is used as the objective function for optimization.

[0043] The present invention also provides a multi-stage electrolyzer capacity optimization system that applies the aforementioned multi-stage electrolyzer capacity optimization method based on a temperature control model, comprising:

[0044] The start-up discrimination unit constructs a start-up discrimination model for the electrolytic cell based on the input power and the temperature state of the electrolytic cell, and controls the electrolytic cell accordingly.

[0045] The temperature control unit constructs a temperature control model based on the cumulative deviation propagation of the electrolyzer, according to the start-up discrimination model of the electrolyzer constructed by the start-up discrimination unit, so as to constrain the operating temperature sequence of the electrolyzer within the set temperature range.

[0046] The power allocation unit builds an electrolytic cell operation power allocation model based on the electrolytic cell start-up discrimination model constructed by the start-up discrimination unit, and obtains the unused power of the corresponding electrolytic cell based on the input power.

[0047] The capacity optimization unit constructs an optimization objective function based on the levelized cost of hydrogen production, initializes the capacity and number of electrolyzers, aims to minimize the constructed optimization objective function, and uses the optimal capacity and number of electrolyzers for each electrolyzer as decision variables to solve the optimization objective function, thereby obtaining the optimal electrolyzer configuration.

[0048] Beneficial effects: Compared with traditional fixed capacity configuration methods, this invention can significantly reduce the levelized cost of hydrogen production while ensuring the amount of hydrogen produced, improve the wind power absorption rate and the overall economic efficiency of the system, and is suitable for the design and operation optimization of green hydrogen production systems driven by a high proportion of renewable energy. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in this invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely embodiments of this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart of the multi-stage electrolyzer capacity optimization method based on a temperature control model according to the present invention.

[0051] Figure 2 This is a schematic diagram illustrating the temperature correction method of the present invention through deviation accumulation;

[0052] Figure 3 This is an example graph showing the real-time electrolyzer temperature results output by the electrolyzer temperature discrimination and correction model. The gradient color from green to red represents the temperature increasing from low to high. T cor This represents the real-time temperature of the electrolytic cell.

[0053] Figure 4 This is an example diagram comparing the economic efficiency of the configuration scheme under the present invention and the traditional average power allocation strategy. The numbers in the stacked bar chart represent the capacity of a single electrolytic cell. The data of a single electrolytic cell in the first column represent the economic optimization results of the system under the traditional power allocation strategy, and the remaining columns 2 to 10 represent the optimization results of cascading utilization of multi-stage electrolytic cells.

[0054] Figure 5 This is an example diagram comparing the energy flow of the configuration scheme under the present invention and the traditional average power allocation strategy. In the diagram, the data of a single electrolyzer in the first column (Case 1) represents the energy flow optimization result of the system under the traditional power allocation strategy. The remaining columns (Case 2-10) represent the optimization results of multi-stage electrolyzer cascade utilization. Here, Ediscard represents waste energy consumption, calculated from waste power; Eheat represents heating energy consumption, calculated from heating power; Eheatloss represents heating loss energy consumption, calculated from heating loss power; EH2,final represents final hydrogen production energy consumption, calculated from final hydrogen production power; Elamploss represents ramp-up loss energy consumption, calculated from ramp-up loss power; and Eoverload represents overload loss energy consumption, calculated from overload loss power. Detailed Implementation

[0055] To make the objectives, technical solutions and advantages of the present invention clearer, the present application will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0056] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of the present invention should have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0057] The flowchart of the multi-stage electrolyzer capacity optimization method based on a temperature control model of the present invention is as follows: Figure 1 As shown, it includes:

[0058] S1. Based on the input power and the temperature state of the electrolytic cell, construct a start-up discrimination model for the electrolytic cell.

[0059] In this invention, in S1, a start-up discrimination model for the electrolyzer can be constructed based on the input power, the set standby heating power and hydrogen production power of the electrolyzer, and the temperature state and operating temperature of the electrolyzer.

[0060] Specifically, the start-up discrimination model for the electrolyzer is as follows:

[0061] When the input power is less than the set standby heating power of the electrolytic cell, the electrolytic cell is in a shutdown state, and the temperature of the electrolyte inside will gradually decrease to the ambient temperature, and its cooling step size can be obtained. In this invention, the temperature of the electrolyte inside the electrolytic cell is generally defined as the temperature of the electrolytic cell, and this will be used for the following description.

[0062] When the input power is greater than the set standby heating power of the electrolytic cell, but the temperature of the electrolytic cell is lower than its working temperature, the electrolytic cell is in standby heating state. The electrolytic cell starts heating until the temperature of the electrolytic cell reaches the working temperature, and its heating step can be obtained. In this embodiment, the standby heating power of the electrolytic cell can be set according to the specific electrolytic cell. In this embodiment, it can be set to 12% of the rated power of the electrolytic cell.

[0063] When the input power is greater than the set hydrogen production power of the electrolyzer, and at the same time, the temperature of the electrolyzer reaches its operating temperature, the electrolyzer is in hydrogen production mode and begins to produce hydrogen.

[0064] In this invention, as the temperature of the electrolytic cell gradually decreases to the ambient temperature, the cooling step size ΔT1 at a set time interval can be calculated, as follows:

[0065] ;

[0066] ;

[0067] Among them, Q loss This refers to heat loss during the natural cooling process of the electrolyte; TALK The operating temperature of the electrolytic cell can be set according to the specific electrolytic cell; in this embodiment, it can be set to 80℃. a The ambient temperature is 20°C in this embodiment; R ALK For thermal resistance, this embodiment uses 0.0005℃ / W; C ALK For the electrolytic cell hot melting, this embodiment uses 19.286 MJ / ℃; Δt is the set time interval, which is 300s in this embodiment.

[0068] In this embodiment, the cooling step size calculated at the set time interval using the aforementioned values ​​is 1.86℃.

[0069] In this invention, assuming that the time for the electrolytic cell to heat from ambient temperature to operating temperature is one hour, the heating step size ΔT2 can be calculated during the standby heating process of the electrolytic cell, as follows:

[0070] .

[0071] In this embodiment, the calculated heating step size at the set time interval is 5°C, based on the aforementioned values.

[0072] In this invention, under stable operating conditions, the hydrogen production rate of the electrolyzer exhibits an approximately monotonically increasing linear relationship with the percentage of input power to rated power. This characteristic is primarily based on the stability of the Faraday efficiency. To accurately quantify this relationship, linear fitting can be performed on different input power-hydrogen production characteristics, and the operating results of a 1MW rated power electrolyzer under variable power operating conditions can be calculated based on the aforementioned electrolyzer start-up discrimination model. This includes the percentage of input power to rated power and its corresponding instantaneous hydrogen production rate, such as... Figure 3 As shown, linear fitting analysis was performed on the simulated data of the electrolyzer based on the aforementioned start-up discrimination model. The results show that within the rated power range, there is a good linear correlation between the electrolyzer power and the hydrogen production rate, with fitting coefficients all exceeding 0.99. This indicates that controlling the electrolyzer using the aforementioned start-up discrimination model has high reliability. In this embodiment, under 100% rated load, the instantaneous hydrogen production rate of the electrolyzer is 18.08 kg / h, corresponding to a unit energy consumption of 55.29 kWh / kg H2.

[0073] S2. Based on the electrolytic cell start-up discrimination model constructed in S1, a temperature control model based on cumulative deviation propagation is constructed to constrain the operating temperature sequence of the electrolytic cell within a set temperature range.

[0074] In this invention, without considering the lower and upper limits of the electrolytic cell's temperature, the temperature change of the electrolytic cell is determined based on the input power and the heating threshold. The temperature change of the electrolytic cell is calculated according to the following relationship: when the input power is lower than the set standby heating power of the electrolytic cell, the electrolytic cell is in a shutdown state, and the temperature of the electrolytic cell decreases according to the cooling step size obtained in S1; when the input power exceeds the set standby heating power of the electrolytic cell, the electrolytic cell is in a standby heating state, and the temperature of the electrolytic cell increases according to the heating step size obtained in S1. Thus, the original cumulative temperature time series T can be obtained. raw :

[0075] .

[0076] However, due to the lack of consideration for the temperature constraints of the electrolyzer, the operating temperature of the electrolyzer often exceeds the set temperature range (e.g., 20-80℃). Therefore, further correction of the electrolyzer's operating temperature is required. Based on this, this invention constructs a temperature correction function based on the propagation of cumulative deviation, thereby constructing a temperature control model to constrain the electrolyzer's operating temperature sequence within the set temperature range. Specifically, by identifying high-temperature or low-temperature deviations in the original cumulative temperature sequence, the deviations are continuously propagated to subsequent time points through deviation accumulation, thus avoiding hourly calculations. The entire correction process does not alter the normal fluctuations in the temperature change process and can be embedded in the rolling optimization model for capacity optimization configuration and power optimization allocation of the electrolyzer.

[0077] Specifically, the temperature deviation between the current operating temperature of the electrolytic cell and the set temperature range is obtained and calculated. This temperature deviation is used to correct the operating temperature of the electrolytic cell at the current time and all subsequent times. If the temperature deviation at the current time is greater than the temperature deviation at the previous time, the difference between the temperature deviations at the two times is calculated, and further correction is performed based on the aforementioned correction, i.e., temperature correction based on cumulative deviation propagation. If the temperature deviation at the current time is less than the temperature deviation at the previous time, the aforementioned correction method is still used. Thus, a temperature correction function based on cumulative deviation propagation can be constructed, thereby obtaining the operating temperature sequence of the electrolytic cell.

[0078] In this invention, the temperature deviation between the current operating temperature of the electrolytic cell and the set temperature range includes a low-temperature deviation and a high-temperature deviation, which can be obtained based on the current operating temperature of the electrolytic cell and the minimum and maximum values ​​of the set temperature range. Specifically, if the current operating temperature of the electrolytic cell is lower than the minimum value of the set temperature range, the difference between the two is the corresponding low-temperature deviation; if the current operating temperature of the electrolytic cell is higher than the maximum value of the set temperature range, the difference between the two is the corresponding high-temperature deviation.

[0079] For example, the correction process is as follows:

[0080] When the operating temperature T of the electrolytic cell in (c) Below the minimum temperature threshold T of the set temperature range min At this time, it is necessary to compensate for and correct the deficiencies; specifically, calculate the current temperature exceedance amount ΔT. low (c), as follows:

[0081] ;

[0082] Therefore, the low-temperature correction sequence C can be calculated. low (c), the low-temperature correction sequence C low In (c), the low-temperature correction amount corresponding to the current moment is the maximum value of the low-temperature out-of-bounds amounts at all moments prior to the current moment. Furthermore, it needs to exclude the influence of normal temperature rise based on the low-temperature out-of-bounds amounts, because it has a monotonically non-decreasing characteristic in the time series. The resulting low-temperature correction sequence C... low (c) as follows:

[0083] ;

[0084] Where τ represents a time before time c;

[0085] Therefore, the corrected temperature sequence T can be calculated. low (c), as follows:

[0086] .

[0087] Similarly, when the operating temperature T of the electrolytic cell in (c) Temperature threshold T above the set temperature range max When this happens, the excess portion needs to be corrected, specifically: calculate the current high-temperature excess amount ΔT. high (c), as follows:

[0088] ;

[0089] Therefore, the high-temperature correction sequence C can be calculated. high (c), the high-temperature correction sequence C high In (c), the high-temperature correction amount corresponding to the current moment is the maximum value of the high-temperature out-of-bounds amounts at all moments before the current moment. Furthermore, it needs to exclude the influence of normal temperature decreases based on the high-temperature out-of-bounds amounts, because it has a monotonically non-increasing characteristic in the time series. The resulting high-temperature correction sequence C... high (c) as follows:

[0090] ;

[0091] Therefore, the corrected temperature sequence T can be calculated. low (c), as follows:

[0092] .

[0093] For specific examples, please refer to... Figure 2 .

[0094] Figure 2 (a) is an example diagram for high-temperature deviation correction. The temperature at time 2 is 120℃, and the temperature to be corrected is 120-80=40℃. The temperature at time 3 is 170℃, and the temperature to be corrected is 90℃. However, since a correction of 40℃ has already been made at time 2, the temperature to be corrected at time 3 is 50℃. The temperatures to be corrected at times 4-6 do not exceed 90℃, so a correction of 90℃ is used. The temperature at time 7 is 310℃, and the temperature to be corrected is 230℃, but... The previous maximum correction temperature was 90℃, so the temperature to be corrected at time 7 is 140℃; the temperatures to be corrected at times 8 and 10 do not exceed 230℃, so the correction is made at 230℃; the temperature corresponding to time 11 is 390℃, and the temperature to be corrected is 310℃, but the previous maximum correction temperature was 230℃, so the temperature to be corrected at time 11 is 80℃; the temperatures to be corrected at time 12 do not exceed 310℃, so the correction is made at 310℃.

[0095] Figure 2 (b) is an example diagram of low temperature deviation correction. The temperature corresponding to time 9 is -20℃, and the temperature to be corrected is -20-20=-40℃. The temperature corresponding to time 10 is -40℃, and the temperature to be corrected is -60℃. However, since time 9 has already been corrected to -40℃, the temperature to be corrected at time 10 is -20℃. The temperature to be corrected at times 11-12 does not exceed -60℃, so it is corrected at -60℃.

[0096] In this invention, after completing the aforementioned round of high-temperature or low-temperature correction, the system re-checks whether there are still out-of-bounds moments in the corrected electrolytic cell operating temperature sequence; if so, a new round of corresponding correction operation is started until the operating temperature of the electrolytic cell at all times meets the temperature constraint and the iteration is exited.

[0097] S3. Obtain the input power, combine it with the electrolytic cell start-up discrimination model constructed in S1, build an electrolytic cell operation power allocation model, and obtain the unused power of the corresponding electrolytic cell.

[0098] In this invention, wind power is used as the input power, but other energy sources, such as other green energy sources, can also be used. This invention uses wind power as an example, so the input power, such as wind power P, with the same time series length as the temperature series can be calculated by calling the wind turbine power model. w The details are as follows:

[0099] ;

[0100] Among them, P wr denoted as , where is the rated output power of the wind turbine, which can be taken as 2MW in this embodiment; v is the actual wind speed at the nacelle height; v ci For the cutoff wind speed, 2.5 m / s can be used in this embodiment; v co To determine the cutoff wind speed, it can be set to 25 m / s in this embodiment; v r The rated wind speed can be taken as 14 m / s in this embodiment.

[0101] In this invention, based on the wind power obtained above, and combined with the electrolyzer start-up discrimination model constructed in S1, the temperature sequence of the electrolyzer is distinguished into different states, and the total input wind power is allocated into power components in different states to reflect the energy flow direction of the electrolyzer in different states.

[0102] Specifically, the power balance relationship of the entire water electrolysis hydrogen production system is as follows:

[0103] ;

[0104] in:

[0105] Let c be the input power, such as wind power.

[0106] The waste power at time c refers to the power that cannot be utilized when the input power, such as wind power, is less than the set standby heating power of the electrolytic cell. The calculation formula is as follows:

[0107] ;

[0108] Let be the heating power at time c, calculated using the following formula:

[0109] ;

[0110] in, , which is the temperature change between the current time and the previous time in the corrected temperature sequence; Hydrogen production power of the electrolyzer;

[0111] Let be the heating power loss at time c. This is because the heating power of the electrolyzer has a physical upper limit. Electricity exceeding the heating power is neither able to further increase the electrolyte temperature nor sufficient to meet the hydrogen production load requirements, thus forming an unusable heating power loss, calculated as follows:

[0112] ;

[0113] Let be the final hydrogen production power at time c. According to the ramp-up characteristics of the electrolyzer, the rate of change of hydrogen production power is constrained by the maximum slope. Within 5 minutes, the ramp-up power will not exceed 30% of the electrolyzer's rated power. Therefore, its calculation is as follows:

[0114] ;

[0115] in, This refers to the rated power of the electrolytic cell;

[0116] Let be the initial hydrogen production power at time c. This power can be calculated when the electrolyzer's temperature has reached the specified operating temperature range and the wind power input meets the hydrogen start-up conditions, and the electrolyzer enters hydrogen production. The calculation is as follows:

[0117] ;

[0118] This represents the difference in hydrogen production power between time c and the previous time.

[0119] However, there is a ramp-up loss in the initial hydrogen production power. Combining this with the final hydrogen production power obtained above, the ramp-up loss power at time c can be calculated. The calculation is as follows:

[0120] ;

[0121] in, Let be the overload loss power at time c. After the electrolyzer enters the hydrogen production stage, its hydrogen production power is strictly limited by its rated capacity. The power exceeding the rated capacity cannot be absorbed by the electrolyzer, thus forming an overload loss power that cannot be used for hydrogen production. The calculation is as follows:

[0122] ;

[0123] Therefore, the unused power of the electrolyzer at time c can be calculated. ,as follows:

[0124] .

[0125] S4. The input power obtained through S3 drives the electrolyzers sequentially, and the unused power is transferred to the next electrolyzer. An optimization objective function is constructed using the levelized cost of hydrogen production (LCOH).

[0126] In this invention, the input power of S3, such as wind power, sequentially drives the electrolytic cells, and the unused power is transferred to the next electrolytic cell, specifically as follows:

[0127] ;

[0128] In the formula, P input,i Let P be the input power of the i-th electrolytic cell. unused,i-1 Let be the input power of the (i-1)th electrolytic cell.

[0129] In this invention, the levelized cost of hydrogen production (LCOH) is used to construct the optimization objective function. Specifically, the total cost of power supply equipment (such as a fan) and all electrolyzers and the total hydrogen production of each electrolyzer are calculated separately. The difference between the two is the levelized cost of hydrogen production, which is used as the optimization objective function.

[0130] For example, the detailed calculation process is as follows:

[0131] Hydrogen production curves Y of each electrolyzer H2，i The calculation is as follows:

[0132] ;

[0133] Among them, P H2 , final,i (t) represents the hydrogen production power of the i-th electrolyzer at time t;

[0134] The total hydrogen production Y H2，total The sum of hydrogen production from each electrolyzer is as follows:

[0135] ;

[0136] The levelized cost of hydrogen production (LCOH) model is as follows:

[0137] ;

[0138] Among them, A𝐶 WT For the annualized cost of the wind power system, A0 ALK,i Let be the annualized investment cost of the i-th electrolytic cell; CAPEX i OPEX represents the investment cost of wind turbines and electrolyzers. i This represents the operating and maintenance costs of wind turbines and electrolytic cells, where N represents the total number of electrolytic cells. The annual value discount factor, n represents the equipment lifecycle; r represents the discount rate, which is set to 5.75% in this embodiment; the investment cost, operation and maintenance cost, and lifecycle parameters of the electrolyzer and wind turbine are determined according to the actual application. Examples provided in this embodiment are shown in Table 1.

[0139]

[0140] Table 1

[0141] S5. Initialize the capacity and number of electrolytic cells. With the minimum value of the optimization objective function constructed in S4 as the objective, and the optimal capacity and number of electrolytic cells as decision variables, solve the optimization objective function to obtain the optimal electrolytic cell configuration.

[0142] In this invention, the DE-best-1 algorithm from the geatpy library in Python can be used as a solver to solve the optimization objective function.

[0143] The present invention also provides a multi-stage electrolyzer capacity optimization system that applies the aforementioned multi-stage electrolyzer capacity optimization method based on a temperature control model, comprising:

[0144] The start-up discrimination unit constructs a start-up discrimination model for the electrolytic cell based on the input power and the temperature state of the electrolytic cell, and controls the electrolytic cell accordingly.

[0145] The temperature control unit constructs a temperature control model based on the cumulative deviation propagation of the electrolyzer, according to the start-up discrimination model of the electrolyzer constructed by the start-up discrimination unit, so as to constrain the operating temperature sequence of the electrolyzer within the set temperature range.

[0146] The power allocation unit builds an electrolytic cell operation power allocation model based on the electrolytic cell start-up discrimination model constructed by the start-up discrimination unit, and obtains the unused power of the corresponding electrolytic cell based on the input power.

[0147] The capacity optimization unit constructs an optimization objective function based on the levelized cost of hydrogen production, initializes the capacity and number of electrolyzers, aims to minimize the constructed optimization objective function, and uses the optimal capacity and number of electrolyzers for each electrolyzer as decision variables to solve the optimization objective function, thereby obtaining the optimal electrolyzer configuration.

[0148] This invention provides three embodiments of electrolytic cells, wherein the temperature output results, economic efficiency, and energy flow distribution optimization results of each electrolytic cell are as follows: Figure 3 , Figure 4 and Figure 5 As shown. From Figure 3 It is evident that the temperature of the electrolyzer varies throughout the year with changes in wind power output. This provides a more accurate calculation method for calculating dynamic hydrogen production in the electrolyzer, a function that traditional optimization methods do not possess.

[0149] from Figure 4 As can be seen, when the number of electrolyzers is 10, the system's LCOH reaches the lowest value of 29.77 Yuan / kg, which is 23.27% lower than the single electrolyzer scheme, demonstrating the significant improvement in economic efficiency of the multi-electrolyzer collaborative operation strategy.

[0150] from Figure 5As can be seen, with the increase of total electrolyzer capacity, the overload energy consumption (Eoverload) shows a significant decreasing trend, with a change much larger than that of other energy consumption items. With the expansion of capacity, the system can absorb wind power during more high-power periods, reducing excess wind energy loss. Compared with the traditional configuration method, the system's energy absorption rate has increased by 47.92%.

[0151] Compared with traditional fixed-capacity configuration methods, this invention can significantly reduce the levelized cost of hydrogen production (LCOH) while ensuring hydrogen production capacity, improve the absorption rate of input power such as wind power, and enhance the overall economic efficiency of the system. It is applicable to the design and operation optimization of green hydrogen production systems driven by a high proportion of renewable energy. It not only provides efficient operation and control strategies for large-scale hydrogen production systems, but also helps to achieve higher energy utilization efficiency and economic efficiency, thus promoting the high-quality development of the green hydrogen industry.

[0152] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of the invention as described above, which are not provided in the details for the sake of brevity.

[0153] The embodiments of this invention are intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this invention should be included within the protection scope of this invention.

Claims

1. A method for optimizing the capacity of a multi-stage electrolyzer based on a temperature control model, characterized in that, include: S1. Based on the input power and the temperature state of the electrolytic cell, construct a start-up discrimination model for the electrolytic cell; S2. Based on the electrolytic cell start-up discrimination model constructed in S1, a temperature control model based on cumulative deviation propagation is constructed to constrain the operating temperature sequence of the electrolytic cell within a set temperature range. S3. Obtain the input power, combine it with the electrolytic cell start-up discrimination model constructed in S1, build an electrolytic cell operation power allocation model, and obtain the unused power of the corresponding electrolytic cell. S4. The input power obtained through S3 drives the electrolyzers sequentially, and the unused power is transferred to the next electrolyzer. An optimization objective function is constructed based on the levelized hydrogen production cost. S5. Initialize the capacity and number of electrolytic cells. With the minimum value of the optimization objective function constructed in S4 as the objective, and the optimal capacity and number of electrolytic cells as decision variables, solve the optimization objective function to obtain the optimal electrolytic cell configuration.

2. The multi-stage electrolytic cell capacity optimization method based on a temperature control model according to claim 1, characterized in that, In step S1, a start-up discrimination model for the electrolyzer is constructed based on the input power, the set standby heating power and hydrogen production power of the electrolyzer, and the temperature state and operating temperature of the electrolyzer.

3. The multi-stage electrolytic cell capacity optimization method based on a temperature control model according to claim 2, characterized in that, The start-up discrimination model for the electrolytic cell is as follows: When the input power is less than the set standby heating power of the electrolytic cell, the electrolytic cell is in a shutdown state; When the input power is greater than the set standby heating power of the electrolytic cell and the temperature of the electrolytic cell is lower than its working temperature, the electrolytic cell is in standby heating state. The electrolytic cell starts heating until the temperature of the electrolytic cell reaches the working temperature. When the input power is greater than the set hydrogen production power of the electrolyzer and the temperature of the electrolyzer reaches its operating temperature, the electrolyzer is in hydrogen production mode and begins to produce hydrogen.

4. The multi-stage electrolytic cell capacity optimization method based on a temperature control model according to claim 3, characterized in that, In step S2, the cooling step size when the electrolytic cell is in the shutdown state and the heating step size when it is in the standby heating state are obtained respectively, thereby obtaining the original cumulative temperature time sequence of the electrolytic cell. The original cumulative temperature time sequence of the electrolytic cell is then corrected by a temperature correction function based on cumulative deviation propagation, so as to constrain the operating temperature sequence of the electrolytic cell within a set temperature range.

5. The multi-stage electrolytic cell capacity optimization method based on a temperature control model according to claim 4, characterized in that, In step S2, a temperature control model based on cumulative deviation propagation is constructed to constrain the operating temperature sequence of the electrolyzer within a set temperature range, as detailed below: The operating temperature of the electrolytic cell at the current moment is obtained and calculated as a temperature deviation from the set temperature range. The operating temperature of the electrolytic cell at the current moment and all subsequent moments is corrected based on this temperature deviation. At the same time, if the temperature deviation at the current moment is greater than the temperature deviation at the previous moment, the difference between the temperature deviations at the two moments is calculated and corrected based on the aforementioned correction. If the temperature deviation at the current moment is less than the temperature deviation at the previous moment, then the correction is made based on the temperature deviation at the previous moment.

6. The multi-stage electrolyzer capacity optimization method based on a temperature control model according to claim 5, characterized in that, After completing a round of high or low temperature correction, recheck whether there are still out-of-bounds moments in the corrected electrolytic cell operating temperature sequence; if so, start a new round of corresponding correction operations until the operating temperature of the electrolytic cell at all times is within the set temperature range.

7. The multi-stage electrolytic cell capacity optimization method based on a temperature control model according to claim 1, characterized in that, In step S3, an electrolytic cell operating power allocation model is built, and the unused power of the corresponding electrolytic cell is obtained, as follows: The power balance relationship of the water electrolysis hydrogen production system is as follows: ； in, Let c be the input power. Let be the discarded power at time c. Let c be the heating power loss. Let c be the final hydrogen production power. Let c be the power loss during the uphill climb. Let c be the overload power loss. Therefore, the unused power of the electrolytic cell can be calculated. ,as follows: 。 8. The multi-stage electrolytic cell capacity optimization method based on a temperature control model according to claim 7, characterized in that, The climbing loss power at time c The calculation is as follows: ； in: Let be the initial hydrogen production power at time c. This power can be calculated when the electrolyzer has reached the specified operating temperature range and the input power meets the hydrogen start-up conditions, as follows: ； in, The rated power of the electrolytic cell, Hydrogen production power of the electrolyzer; This represents the temperature change between the current time and the previous time in the corrected temperature sequence. Final hydrogen production power at time c The calculation is as follows: ； in, This represents the difference in hydrogen production power between time c and the previous time.

9. The multi-stage electrolytic cell capacity optimization method based on a temperature control model according to claim 1, characterized in that, In step S4, the objective function for constructing the levelized hydrogen production cost is as follows: The total cost of the power supply equipment and all electrolyzers and the total hydrogen production of all electrolyzers are calculated separately. The difference between the two is the levelized cost of hydrogen production, which is used as the objective function for optimization.

10. A multi-stage electrolytic cell capacity optimization system applying the multi-stage electrolytic cell capacity optimization method based on a temperature control model as described in any one of claims 1 to 9, characterized in that, include: The start-up discrimination unit constructs a start-up discrimination model for the electrolytic cell based on the input power and the temperature state of the electrolytic cell, and controls the electrolytic cell accordingly. The temperature control unit constructs a temperature control model based on the cumulative deviation propagation of the electrolyzer, according to the start-up discrimination model of the electrolyzer constructed by the start-up discrimination unit, so as to constrain the operating temperature sequence of the electrolyzer within the set temperature range. The power allocation unit builds an electrolytic cell operation power allocation model based on the electrolytic cell start-up discrimination model constructed by the start-up discrimination unit, and obtains the unused power of the corresponding electrolytic cell based on the input power. The capacity optimization unit constructs an optimization objective function based on the levelized cost of hydrogen production, initializes the capacity and number of electrolyzers, aims to minimize the constructed optimization objective function, and uses the optimal capacity and number of electrolyzers for each electrolyzer as decision variables to solve the optimization objective function, thereby obtaining the optimal electrolyzer configuration.

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