An operating optimization method and device for an electrolyzer array of an off-grid energy system

Abstract

The present invention provides an operation optimization method and device for an electrolytic cell array of an off-grid energy system, relating to the technical field of operation scheduling of energy systems. The method includes: obtaining the operation data of the off-grid energy system; wherein the operation data includes the power data of the off-grid energy system and the configuration data of the electrolytic cell array; establishing a first constraint model of the off-grid energy system based on the power data; establishing a second constraint model of the electrolytic cell array based on the power data and the configuration data; updating the configuration data of the electrolytic cell array based on the first constraint model and the second constraint model to obtain an optimal configuration scheme; and determining an optimal allocation scheme for the operation power of the electrolytic cell array based on the optimal configuration scheme. This solution can reduce the problems of frequent start-stop of the electrolytic cell array and low hydrogen production efficiency, and improve the operation stability of the electrolytic cell array and the economic benefits of hydrogen production.

Description

Technical Field

[0001] The present invention relates to the technical field of operation scheduling of energy systems, and particularly to an operation optimization method and device for an electrolytic cell array of an off-grid energy system. Background Art

[0002] Under the background of "dual carbon", China is vigorously building a new power system based on renewable energy. As a green energy recognized by countries around the world, hydrogen energy, an off-grid energy system established with electrolytic water hydrogen production as an important energy conversion unit, can not only enhance the local renewable energy consumption capacity to reduce the burden of power grid construction, but also meet the growing demand for hydrogen energy.

[0003] In related technologies, affected by the capacity configuration of different types of electrolytic cells in the electrolytic cell array, when the energy supply equipment increases, the operating power of the electrolytic cell array is prone to fluctuate, resulting in large-area shutdowns of the electrolytic cell array and reducing the operating stability of the array.

[0004] Based on this, there is an urgent need for an operation optimization method and device for an electrolytic cell array of an off-grid energy system to solve the above technical problems. Summary of the Invention

[0005] Embodiments of the present invention provide an operation optimization method and device for an electrolytic cell array of an off-grid energy system, which can effectively improve the operating stability of the electrolytic cell array of the off-grid energy system.

[0006] In a first aspect, embodiments of the present invention provide an operation optimization method for an electrolytic cell array of an off-grid energy system, including:

[0007] Obtain the operation data of the off-grid energy system; wherein, the operation data includes the power data of the off-grid energy system and the configuration data of the electrolytic cell array;

[0008] Based on the power data, establish a first constraint model of the off-grid energy system;

[0009] Based on the power data and the configuration data, establish a second constraint model of the electrolytic cell array;

[0010] Based on the first constraint model and the second constraint model, update the configuration data of the electrolytic cell array to obtain an optimal configuration plan;

[0011] Based on the optimal configuration plan, determine an optimal allocation plan for the operating power of the electrolytic cell array.

[0012] In a second aspect, embodiments of the present invention further provide an operation optimization device for an electrolytic cell array of an off-grid energy system, including:

[0013] An acquisition unit for acquiring the operation data of the off-grid energy system; wherein, the operation data includes the power data of the off-grid energy system and the configuration data of the electrolyzer array;

[0014] A first modeling unit for establishing a first constraint model of the off-grid energy system based on the power data;

[0015] A second modeling unit for establishing a second constraint model of the electrolyzer array based on the power data and the configuration data;

[0016] An update unit for updating the configuration data of the electrolyzer array based on the first constraint model and the second constraint model to obtain an optimal configuration scheme;

[0017] A determination unit for determining an optimal allocation scheme for the operating power of the electrolyzer array based on the optimal configuration scheme.

[0018] In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the method described in any embodiment of this specification is implemented.

[0019] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed on a computer, the computer is made to execute the method described in any embodiment of this specification.

[0020] An embodiment of the present invention provides a method and device for optimizing the operation of an electrolyzer array in an off-grid energy system. The operation data of the off-grid energy system is acquired, a constraint model of the system is established according to the operation data, then the configuration data of the electrolyzer array is updated by using this model to obtain the optimal configuration data, and finally the optimal allocation scheme for the operating power of the electrolyzer array is determined according to the optimal configuration data. By this method, problems such as frequent start-stop of the electrolyzer array and low hydrogen production efficiency can be reduced, and the operation stability of the electrolyzer array and the economic benefits of hydrogen production can be improved. Description of the Drawings

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 It is a flowchart of the method for optimizing the operation of an electrolyzer array in an off-grid energy system provided by an embodiment of the present invention;

[0023] Figure 2 is the hardware architecture diagram of an electronic device provided by an embodiment of the present invention;

[0024] Figure 3 is the structural diagram of an operation optimization device for an electrolytic cell array of an off-grid energy system provided by an embodiment of the present invention;

[0025] Figure 4 is the schematic diagram of the structure of an off-grid energy system provided by an embodiment of the present invention;

[0026] Figure 5 is the schematic diagram of the operating conditions of an electrolytic cell provided by an embodiment of the present invention;

[0027] Figure 6 is the schematic diagram of the combined operation of electrolytic cells provided by an embodiment of the present invention;

[0028] Figure 7 is the schematic diagram of a power distribution scheme provided by an embodiment of the present invention;

[0029] Figure 8 is the schematic diagram of a photovoltaic power generation and load curve provided by an embodiment of the present invention;

[0030] Figure 9 is the schematic diagram of the photovoltaic power generation and electrolytic cell electric power curves for typical days in four seasons provided by an embodiment of the present invention;

[0031] Figure 10 is the schematic diagram of a hydrogen production efficiency curve provided by an embodiment of the present invention;

[0032] Figure 11 is the schematic diagram of a hydrogen production volatility curve provided by an embodiment of the present invention;

[0033] Figure 12 is the schematic diagram of the hydrogen production cost and photovoltaic power generation power consumption ratio under different ratios provided by an embodiment of the present invention;

[0034] Figure 13 is the schematic diagram of a power balance curve provided by an embodiment of the present invention;

[0035] Figure 14 is the schematic diagram of the electrolytic cell power change curve under the balanced operation mode / flexible operation mode provided by an embodiment of the present invention;

[0036] Figure 15 is the schematic diagram of the comparison of the operating conditions of electrolytic cells under the balanced operation mode / flexible operation mode provided by an embodiment of the present invention;

[0037] Figure 16 is the schematic diagram of the hydrogen production amount curve of different hydrogen production methods provided by an embodiment of the present invention. Detailed implementation manners

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0039] As mentioned above, when the existing electrolytic cell arrays face a large energy supply, power fluctuations will occur, resulting in frequent start-stop situations for some electrolytic cells, which not only affects the service life of the electrolytic cells but also affects the stability of the entire off-grid energy system.

[0040] Based on this, the concept of the present invention is to adjust the configuration scheme of the electrolytic cell arrays by using the constraint equations of the system itself and the configuration constraint equations of the electrolytic cells, and select the corresponding power distribution according to different configuration schemes, thereby avoiding the occurrence of frequent start-stop situations.

[0041] The following describes the specific implementation methods of the above concept.

[0042] Please refer to Figure 1 , the embodiments of the present invention provide an operation optimization method for an electrolytic cell array of an off-grid energy system, and the method includes:

[0043] Step 100, obtaining the operation data of the off-grid energy system; wherein, the operation data includes the power data of the off-grid energy system and the configuration data of the electrolytic cell array;

[0044] Step 102, establishing a first constraint model of the off-grid energy system based on the power data;

[0045] Step 104, establishing a second constraint model of the electrolytic cell array based on the power data and the configuration data;

[0046] Step 106, updating the configuration data of the electrolytic cell array based on the first constraint model and the second constraint model to obtain an optimal configuration scheme;

[0047] Step 108, determining an optimal power distribution scheme for the operation power of the electrolytic cell array based on the optimal configuration scheme.

[0048] In an embodiment of the present invention, the operation data of the off-grid energy system is acquired, a constraint model of the system is established according to the operation data, then the configuration data of the electrolyzer array is updated by using the model to obtain the optimal configuration data, and finally the optimal allocation scheme of the operation power of the electrolyzer array is determined according to the optimal configuration data. By this method, the problems of frequent start-stop of the electrolyzer array and low hydrogen production efficiency can be reduced, the operation stability of the electrolyzer array and the economic benefits of hydrogen production can be improved. At the same time, considering the working characteristics of different types of electrolyzers and the electrolyzer array, a flexible mode of combined operation of multiple electrolyzers is proposed, which can increase the number of electrolyzers operating in the high-efficiency operation condition and the rated operation condition.

[0049] The execution manner of each step described below Figure 1 is shown.

[0050] First, for step 100, the operation data of the off-grid energy system is acquired.

[0051] In an embodiment of the present invention, as Figure 4 shown, the off-grid energy system includes an energy supply subsystem, an energy conversion subsystem and an energy storage subsystem. The energy conversion subsystem includes a fuel cell, a heat pump and an electrolyzer array. The energy storage subsystem includes electrical, hydrogen and thermal energy storage devices. The electrolyzer array includes an alkaline electrolyzer and a proton exchange membrane electrolyzer.

[0052] Specifically, the energy supply subsystem is a renewable energy power generation device. The energy conversion subsystem and the energy storage subsystem inside the system can meet the load demands of electricity, heat, hydrogen, etc. Hydrogen production by electrolyzing water is an important energy conversion subsystem, which can be completed by an electrolyzer array composed of multiple electrolyzers connected in parallel. In addition to meeting the hydrogen load demand, the produced hydrogen can also be used for fuel cell power generation. The energy storage subsystem can alleviate the impact of the volatility of renewable energy power generation on the system operation. Reasonably recycling the waste heat of the electrolyzer and the fuel cell can reduce the power consumption of the heat pump, which helps to improve the economic efficiency of the system operation.

[0053] In an embodiment of the present invention, the operation data of the off-grid energy system includes the power data of each subsystem device and the configuration data of the electrolyzer array. Among them, the power data is the basic data for the normal operation of the off-grid energy system. In addition, since the electrolyzer array is composed of different numbers of alkaline electrolyzers and proton exchange membrane electrolyzers, in order to optimize the operation conditions of the electrolyzer array, it is also necessary to additionally acquire the configuration data of the electrolyzer array, so as to facilitate the subsequent adjustment of the electrolyzer array configuration scheme.

[0054] Specifically, the power data in the off-grid energy system can be obtained by sequentially performing modeling calculations on different devices:

[0055] Renewable energy power generation device modeling

[0056] The renewable energy power generation equipment of the system consists of a wind power generation set and a photovoltaic power generation set, and its power generation power constraint is shown in the following equation.

[0057]

[0058] Where: P t RE is the renewable energy power generation power at time t; P t RE,cur is the wind and light curtailment; is the maximum renewable energy power generation power; PV and WT represent photovoltaic and wind power respectively.

[0059] Fuel cell

[0060]

[0061] Where, Q t FC represents the hydrogen consumption of the fuel cell at time t; HV H2 is the calorific value of hydrogen; P t FC represents the electric power of the fuel cell at time t; H t FC represents the waste heat of the fuel cell at time t; η FC is the working efficiency of the fuel cell.

[0062] Heat pump

[0063] The heat pump can convert electrical energy into heat energy to meet the heat load demand or store it in the heat energy storage device. The expression of the heat power output at time t is shown in the following equation:

[0064] H t HP = η HP P t HP

[0065] Where: H t HP is the heat power of the heat pump; η HP is the electro-thermal conversion coefficient of the heat pump; P t HP is the electric power of the heat pump at time t.

[0066] Electric energy storage

[0067] The electric energy storage can suppress the fluctuation of the renewable energy power generation power through the charge and discharge of electric energy. Considering the self-discharge loss of the electric energy storage, its mathematical model is shown in the following equation. The remaining capacity of the electric energy storage at the beginning and end of its scheduling cycle needs to be kept consistent.

[0068]

[0069] Where, E t EES is the remaining capacity of the electrical energy storage at time t; are the charge and discharge efficiencies of the electrical energy storage respectively; P t EES,ch 、P t EES,dis are the charging and discharging powers of the electrical energy storage at time t respectively; are the remaining capacities of the electrical energy storage at the beginning and end of each scheduling period respectively; is a 0-1 variable representing the charging and discharging states of the electrical energy storage at time t; is the upper limit of the charging and discharging powers of the electrical energy storage; are the upper and lower limits of the remaining capacity of the electrical energy storage respectively.

[0070] Thermal energy storage

[0071] Thermal energy storage can assist in achieving the balance between thermal supply and demand, and its mathematical model is shown in the following equation:

[0072]

[0073] Where, is the remaining capacity of the thermal energy storage at time t; are the heat storage and heat release efficiencies of the thermal energy storage respectively; are the heat storage and heat release powers of the thermal energy storage at time t respectively; are the remaining capacities of the thermal energy storage at the beginning and end of each scheduling period respectively; is a 0-1 variable representing the heat storage and heat release states of the thermal energy storage at time t; is the upper limit of the heat storage and heat release powers of the thermal energy storage; are the upper and lower limits of the remaining capacity of the thermal energy storage respectively.

[0074] Hydrogen energy storage

[0075] Hydrogen energy storage can be used to store hydrogen, and its charging and discharging rates should meet the limiting conditions. The mathematical model is shown in the following equation. Since hydrogen can be stored across seasons, the remaining capacities of the hydrogen energy storage at the beginning and end of each scheduling period do not have to be the same.

[0076]

[0077] Where, is the remaining capacity of the hydrogen energy storage at time t; are the charging and discharging rates of the hydrogen energy storage at time t respectively; are the charging and discharging efficiencies of the hydrogen energy storage respectively; is the upper limit of the charging and discharging rates of the hydrogen energy storage; is the upper limit of the remaining capacity of the hydrogen energy storage.

[0078] Electrolyzer

[0079] The operating conditions of a single electrolyzer are divided into the following five types: rated operating condition, high-efficiency operating condition, fluctuating operating condition, standby condition, and shutdown state. In actual production, the electrolyzer can operate with short-term overload, but this operating condition will cause damage to the electrolyzer and affect the safe operation of the system. Therefore, the overload operating condition of the electrolyzer is not considered in the present invention. Specifically, as shown in the following equation:

[0080]

[0081] Wherein, is the electric power of electrolyzer i at time t; is the standby power of electrolyzer i; is the electric power of electrolyzer i corresponding to the maximum hydrogen production efficiency; is the rated power of electrolyzer i; is the fluctuating power of electrolyzer i; is the safe operating power of electrolyzer i. A brief description of each operating condition is as follows:

[0082] 1) Under the rated operating condition, high-efficiency operating condition, and fluctuating operating condition, the electrolyzer can produce hydrogen. The fluctuating operating condition means that the electrolyzer dynamically responds to absorb the renewable energy power generation. However, the long-term operation of the electrolyzer under the fluctuating operating condition will affect its service life.

[0083] 2) Under the standby condition, the electrolyzer stops producing hydrogen, but continuously consumes electric power to maintain the cell temperature and pressure, and is ready to quickly switch to the condition where hydrogen can be produced.

[0084] 3) Under the shutdown state, the electrolyzer does not consume electric power, but needs to meet the start-stop time constraint to start.

[0085] Then, for step 102, based on the power data, a first constraint model of the off-grid energy system is established.

[0086] In the embodiment of the present invention, the first constraint model is established through the following steps: calculate the power data of a single electrolyzer in the electrolyzer array to obtain the first power data of the electrolyzer array under multiple working modes; based on the first power data and the power data of the remaining devices, establish the first constraint model.

[0087] Specifically, in the balanced operation mode, each electrolyzer prepares hydrogen with the same electric power. Since the flexible combination of various working conditions of the electrolyzer is not considered, the balanced operation mode cannot fully utilize the hydrogen production characteristics of the electrolyzer to improve economic benefits. When the output of renewable energy power generation fluctuates greatly, large-area shutdowns will occur when using a single type of electrolyzer. To ensure the safety and efficiency of the water electrolysis hydrogen production system, the flexible operation method of combining electrolyzers with multiple working conditions is adopted in this embodiment.

[0088] As Figure 5 shown, Figure 5 is the electrolyzer operation condition switching logic diagram provided by the embodiment of the present invention. The constraint conditions that need to be satisfied for the condition transformation of the electrolyzer are shown in the following equation:

[0089]

[0090] In the formula, is the rated operation condition of the electrolyzer; when the electrolyzer i is in the rated operation condition at time t, otherwise The same applies to the rest. is the high-efficiency operation condition of the electrolyzer; is the fluctuating operation condition of the electrolyzer; is the standby state of the electrolyzer; is the shutdown state of the electrolyzer. are 0-1 variables, representing the startup action and shutdown action of the electrolyzer respectively. When the electrolyzer starts, otherwise The same applies.

[0091] This equation reveals the coupling relationship between the condition state of the electrolyzer and the startup and shutdown actions, the uniqueness of the electrolyzer operation condition, and the start-stop time constraint that needs to be satisfied after the electrolyzer shuts down.

[0092] The effects of the combined operation of the above-mentioned electrolyzers with multiple working conditions mainly include the following two points: on the one hand, it broadens the operation range of the hydrogen production system; on the other hand, it increases the number of electrolyzers operating in the high-efficiency condition and the rated condition, and extends the service life of the electrolyzer array. The schematic diagram of the combined operation of multiple electrolyzers is as Figure 6 shown, where each rectangle represents an electrolyzer, and different colors represent different working conditions.

[0093] After determining the combined operation of the electrolyzer array, the operating power of the entire electrolyzer array can be calculated according to the operating power of a single electrolyzer. The specific calculation formula is as follows:

[0094]

[0095]

[0096] Where, N EL is the total number of individual electrolyzers in the electrolyzer array; P t Array is the electric power of the electrolyzer array.

[0097] Since hydrogen production by the electrolyzer only occurs under high-efficiency operating conditions, rated operating conditions, and fluctuating operating conditions, the hydrogen production rate expression of the electrolyzer is as shown in the following equation:

[0098]

[0099] Where: is the hydrogen production rate of electrolyzer i at time t; HV H2 is the calorific value of hydrogen; is the thermal power of electrolyzer i at time t; is the hydrogen production amount of electrolyzer i at time t; η EL,el is the operating efficiency of the electrolyzer.

[0100] Therefore, combining the above power calculation equation, a power balance model of the entire off-grid energy system can be established, that is, the first constraint model:

[0101] P t RE +P t FC +P t EES,dis =P t EES,ch +P t Array +P t HP +P t eld

[0102]

[0103]

[0104] Where, P t eld and are the electric load, thermal load, and hydrogen load of the off-grid electric-hydrogen energy system, respectively.

[0105] For step 104, based on the power data and configuration data, a second constraint model of the electrolyzer array is established.

[0106] When the installed capacity of alkaline electrolyzers accounts for a relatively high proportion, the cost of various electrolyzer hydrogen production systems is relatively low, but the renewable energy consumption capacity and volatility adaptability are poor, resulting in a relatively large hydrogen production volatility. When the installed capacity of proton exchange membrane electrolyzers accounts for a relatively high proportion, the renewable energy consumption capacity and hydrogen production volume of the system can be effectively improved. However, the cost of proton exchange membrane electrolyzers is relatively high, which will increase the equipment investment cost of the system and affect the economic benefits.

[0107] Therefore, in the embodiments of the present invention, in combination with the operating power of the electrolyzers, while considering the hydrogen production cost, the average value of hydrogen production volatility, and the penalty cost of the electrolyzer array, a second constraint equation for the capacity configuration of different types of electrolyzers is established:

[0108]

[0109] In the formula, C EL is the hydrogen production cost; c ael , c pemel are the purchase cost coefficients of alkaline electrolyzers and proton exchange membrane electrolyzers in sequence; E AEL , E PEMEL are the installed capacities of alkaline electrolyzers and proton exchange membrane electrolyzers in sequence; C EL,res is the electrolyzer recovery cost; C EL,op is the electrolyzer operation and maintenance cost; L is the service life of the electrolyzer; Δt is the scheduling time interval; T is the total number of time periods in the scheduling cycle; c el , c su , c sd are the electrolyzer usage cost coefficient, start-up and shutdown cost coefficient in sequence; are the electrolyzer start-up action and shutdown action in sequence; W EL is the average value of hydrogen production volatility; C CUR is the penalty cost; c re , c eld , c hld are the curtailment cost coefficients of wind and solar power, electricity load curtailment cost coefficient, and heat load curtailment cost coefficient in sequence; P t RE,cur , P t eld,cur , are the curtailment amounts of wind and solar power, electricity load curtailment amount, and heat load curtailment amount in sequence.

[0110] For step 106, based on the first constraint model and the second constraint model, the configuration data of the electrolyzer array is updated to obtain the optimal configuration scheme.

[0111] In the embodiment of the present invention, the update process includes: inputting the counterweight data into a pre-trained iterative optimization model, and outputting a configuration result that satisfies the objective function; wherein, the iterative optimization model is obtained by training an initial genetic algorithm model, the objective function of the iterative optimization model is the first constraint model and the second constraint model, and the configuration result is the installed capacity ratio of the alkaline electrolyzer and the proton exchange membrane electrolyzer in the electrolyzer array; screening the configuration result to obtain an optimal configuration plan for the installed capacity ratio of the alkaline electrolyzer and the proton exchange membrane electrolyzer.

[0112] Specifically, in this embodiment, a multi-objective particle swarm optimization algorithm is used to solve the multi-objective optimization problem of hydrogen production cost, mean hydrogen production volatility, and penalty cost, and obtain the optimal capacity configuration result of the alkaline electrolyzer and the proton exchange membrane electrolyzer, so as to improve the system economy, operation stability, and renewable energy consumption capacity.

[0113] First, initialize the genetic iterative algorithm according to the existing configuration data of the electrolyzer array. The initialization includes population size, number of iterations, particle latitude, etc., and generate an initial population. Then, use the first constraint equation and the second constraint as the objective function to iteratively update the initial population. The update content includes the position and velocity of the particles, the function values of each particle's objective. According to the particle dominance relationship, use the adaptive grid method to update the particle density, and use the roulette method to update the Pareto optimal solution, etc., until the number of update iterations reaches the preset maximum value, and output the updated configuration result; finally, use the entropy weight method to select the optimal solution to obtain an optimal configuration plan for the installed capacity ratio of the alkaline electrolyzer and the proton exchange membrane electrolyzer.

[0114] For step 108, based on the optimal configuration plan, determine the optimal power distribution plan for the operation of the electrolyzer array.

[0115] The above screening process can give full play to the economic and dynamic response capabilities of each device by coordinately controlling the operation modes of the alkaline electrolyzer and the proton exchange membrane electrolyzer. The combined operation of multiple electrolyzers can combine the advantages of low cost of the alkaline electrolyzer and strong adaptability to fluctuations of the proton exchange membrane electrolyzer. However, when the electrolyzer operates at a low power, there are still problems such as poor hydrogen production effect and easy impact on the service life of the electrolyzer due to frequent start and stop.

[0116] Therefore, the embodiment of the present invention proposes a hydrogen production equipment power distribution method considering capacity configuration to reduce the problems of frequent start and stop of the alkaline electrolyzer and the proton exchange membrane electrolyzer and low hydrogen production efficiency, and improve the hydrogen production effect and hydrogen production economic benefits.

[0117] Such as Figure 7As shown, in the embodiment of the present invention, by comparing the installed capacity of the electrolyzers, the electrolyzers with larger installed capacity are preferentially started while ensuring the efficient operation or rated operation of the electrolyzers with a small installed capacity ratio, so as to avoid the phenomenon of frequent start-stop of the electrolyzers with a small installed capacity ratio and large-area standby of the electrolyzers. When P t Array = E AEL + E PEMEL , the proton exchange membrane electrolyzer and the alkaline electrolyzer operate at rated power simultaneously. When P t Array ≤ min(E PEMEL , E AEL ), the proton exchange membrane electrolyzer and the alkaline electrolyzer distribute power according to the installed capacity ratio; when min(E PEMEL , E AEL ) ≤ P t NET ≤ E AEL + E PEMEL , the electrolyzers with a small installed capacity ratio obtain equalized power, and the electrolyzers with a large installed capacity ratio consume the remaining power, so as to ensure the stable operation of the electrolyzers with a small installed capacity ratio and avoid the occurrence of large-area shutdown.

[0118] Next, the off-grid electrolytic water hydrogen production demonstration project in Zhangye, China is selected as an example for simulation analysis to prove the effectiveness of the method mentioned in the above embodiment.

[0119] The existing equipment of the demonstration project includes: a 1.6 MW PV unit, a 0.35 MW fuel cell, 4 MWh of electrical energy storage, a 1 MW heat pump, 1 MWh of thermal energy storage, and 2.5 MWh of hydrogen energy storage. The electrical load, thermal load, and hydrogen load curves are as Figure 8 (a) shown. By using the K-means algorithm to cluster the annual photovoltaic power generation data, four seasonal photovoltaic power generation power scenarios can be obtained, as Figure 8 (b) shown

[0120] Combined with the typical daily photovoltaic power generation data in the four seasons, taking the three indicators of electrical load, thermal load, and hydrogen load as the evaluation criteria, the operation optimization of the electrolyzer array mentioned in the above embodiment is adopted, as shown in Table 1.

[0121] Table 1 Comparison of results of single electrolyzer and multiple electrolyzer combinations

[0122]

[0123] The photovoltaic power generation and electrolyzer electrical power curves for typical days in the four seasons are as Figure 9 shown. The hydrogen production efficiency and hydrogen production volatility of multiple electrolyzers are respectively as Figure 10 and Figure 11 shown.

[0124] From Figure 9 and Table 1 analysis, it can be seen that as the output of photovoltaic power generation changes, the optimal configuration ratio of the capacities of multiple electrolyzers is alkaline electrolyzer: proton exchange membrane electrolyzer = 2:1. Since the installation cost of the alkaline electrolyzer is relatively low, compared with a single proton exchange membrane electrolyzer, the hydrogen production cost of multiple electrolyzers is reduced by 14.16%, while the hydrogen production amount is only reduced by 6.14%. Since the proton exchange membrane electrolyzer has a fast dynamic response speed, compared with a single alkaline electrolyzer, the hydrogen production amount of multiple electrolyzers is increased by 9.74%, the renewable energy consumption capacity is increased by 2.22%, and the penalty cost is reduced by 7.74%.

[0125] The reasonable allocation of the installed capacities of multiple electrolyzers can effectively improve the hydrogen production efficiency of multiple electrolyzers and their stable operation ability. From Figure 10 it can be seen that the efficiency of the alkaline electrolyzer in the combined mode of multiple electrolyzers is stable at 68.2%, and the electrolysis efficiency of the proton exchange membrane electrolyzer is stable at 71.2%. From Figure 11 it can be seen that the proton exchange membrane electrolyzer adjusts its output continuously to make the hydrogen production volatility of the alkaline electrolyzer change slightly, ensuring the safety and stability of the operation of the hydrogen production equipment. The capacity of a single type of alkaline electrolyzer is 800 kW as a control group, as Figure 11 shown. The peak value of the hydrogen production volatility of a single type of alkaline electrolyzer on a typical spring day is as high as 80%, and the peak value of the hydrogen production volatility of the alkaline electrolyzer in the combined mode of multiple electrolyzers drops to 50%. The average hydrogen production volatility of a single type of alkaline electrolyzer is 13.2%, while the average hydrogen production volatility of the alkaline electrolyzers in the array is only 7.5%, with a reduction of 43.2%, effectively ensuring the safe operation of the alkaline electrolyzer. Therefore, the reasonable configuration of the alkaline electrolyzer and the proton exchange membrane electrolyzer can improve the renewable energy consumption capacity and operation economic benefits of the system, and can improve the system dynamic response ability while ensuring the hydrogen production efficiency.

[0126] When the capacity configuration ratio of the alkaline electrolyzer and the proton exchange membrane electrolyzer changes, the hydrogen production cost and curtailment rate curves of multiple electrolyzers are as Figure 12 shown. As the capacity of the proton exchange membrane electrolyzer increases, the renewable energy consumption capacity of the system gradually increases, but the hydrogen production cost also increases continuously.

[0127] Taking Figure 8 the summer photovoltaic power generation curve in (b) as an example, the operating status of the off-grid power-to-hydrogen energy system equipment considering the combination of multiple electrolyzers is described, as Figure 13 shown. Other scenarios are similar and will not be elaborated here.

[0128] From Figure 13(a) It can be seen that photovoltaic power generation is the main energy source of the off-grid power-to-hydrogen energy system. During the periods when photovoltaic power generation is unavailable, the system's electricity load demand is fully met by fuel cells and electrical energy storage. During the periods of photovoltaic power generation, the system exchanges power with the electrical energy storage to mitigate the volatility of photovoltaic power generation. The electrical energy storage mainly charges during the periods from 9 to 16 and discharges during the periods from 17 to 23 to maintain the balance of the system's electrical power.

[0129] It can be seen from Figure 13 (b) that the heat load demand is mainly supplied by heat pumps. In addition, the waste heat collected by the waste heat recovery systems of the electrolyzers and fuel cells is 2.44 MW, accounting for 36.26% of the heat power supply, which can significantly reduce power consumption.

[0130] It can be seen from Figure 13 (c) that the electrolyzers, as the hydrogen production equipment of the system, operate during the periods from 7 to 18 to meet the peak demand of the hydrogen load, and the remaining hydrogen can be stored in the hydrogen storage tank. When the electrolyzers are not working, the hydrogen energy storage can meet the hydrogen load and the hydrogen consumption demand of the fuel cells. Therefore, the coordinated operation of the electrolyzers and the hydrogen energy storage can effectively improve the consumption capacity of renewable energy and the operating flexibility of the system.

[0131] To improve the hydrogen production efficiency of the electrolyzer array and achieve the reasonable distribution of the electrolyzer array power, based on the optimized configuration results of the electrolyzer array obtained from the above process, through the simulation analysis of the balanced operation mode and the flexible operation mode, the effectiveness of the method proposed in the above embodiments is illustrated.

[0132] The duration of the fluctuating operating conditions is an important factor affecting the service life of the electrolyzers. Taking Figure 8 (b)'s summer photovoltaic power generation curve as an example, by comparing the balanced operation mode and the flexible operation mode, the protective effect of the method proposed in the present invention on the electrolyzers is illustrated. Similar situations in other scenarios are not elaborated here. The comparison of the electrolyzer electrical power under the balanced operation mode and the flexible operation mode is as shown in Figure 14 . The operating conditions of each electrolyzer at each time period are as shown in Figure 15 .

[0133] It can be seen from Figure 14 that each electrolyzer in the balanced operation mode operates at the same power. It can be seen from Figure 15 that the volatility of renewable energy power generation causes the electrolyzers to operate in fluctuating operating conditions most of the time. Only during the peak period of renewable energy power generation, the electrolyzers operate at the rated conditions for a short time.

[0134] The 4 electrolyzers in the flexible operation mode can flexibly switch between multiple operating conditions. It can be seen from Figure 15It can be seen that, compared with the balanced operation mode, the proportion of the rated operation and high-efficiency operation duration of the electrolyzer in the flexible operation mode is relatively high. The alkaline electrolyzer 3 can standby for 1 hour. At most only 1 electrolyzer is in the fluctuating working condition in each time period, and the fluctuating operation duration of the proton exchange membrane electrolyzer is 7 hours to meet the system power regulation requirements.

[0135] Long-term fluctuating operation will damage the function of the electrolyzer and reduce its service life. Therefore, Table 2 counts the operation duration of various working conditions of the electrolyzer under the two operation modes. As can be seen from Table 2, the proportion of the fluctuating operation duration of the electrolyzer in the balanced operation mode is 37.5%. The proportion of the fluctuating operation duration of each electrolyzer in the flexible operation mode is at most 29.17% and at least 0.00%, with an average of 7.29%. Compared with the balanced mode, the average value of the proportion of the fluctuating operation duration of the electrolyzer in the flexible operation mode is reduced by 80.56%. Therefore, adopting the flexible operation mode for the electrolyzer array can effectively alleviate the function damage caused by fluctuating operation, extend the service life of the electrolyzer, and indirectly reduce the hydrogen production cost.

[0136] Table 2 Statistical table of the operation conditions of the electrolyzer

[0137]

[0138] Considering the typical daily photovoltaic power generation in four seasons, the hydrogen production amounts under the balanced operation mode and the flexible operation mode are as Figure 16 shown. The hydrogen production amount in the balanced operation mode is 3.98 tons, and the hydrogen production amount in the flexible operation mode can reach 4.28 tons, with a 7.54% increase. Therefore, the flexible operation mode fully considers various operation conditions of the electrolyzer, can realize the optimal power distribution among the electrolyzer arrays, improve the hydrogen production amount, and verifies the effectiveness of the strategy proposed in the embodiment of the present invention.

[0139] As Figure 2 , Figure 3 shown, the embodiment of the present invention provides an operation optimization device for an electrolyzer array of an off-grid energy system. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, as Figure 2 shown, it is a hardware architecture diagram of an electronic device where the operation optimization device for an electrolyzer array of an off-grid energy system provided by the embodiment of the present invention is located. In addition to Figure 2 the shown processor, memory, network interface, and non-volatile memory, the electronic device where the device in the embodiment is located usually may also include other hardware, such as a forwarding chip responsible for processing packets, etc. Taking the software implementation as an example, as Figure 3 shown, as a logically meaningful device, it is formed by the CPU of its corresponding electronic device reading the computer program in the non-volatile memory into the memory for operation. An operation optimization device for an electrolyzer array of an off-grid energy system provided in this embodiment includes:

[0140] An acquisition unit 300 for acquiring the operation data of the off-grid energy system; wherein the operation data includes the power data of the off-grid energy system and the configuration data of the electrolyzer array.

[0141] A first modeling unit 302 for establishing a first constraint model of the off-grid energy system based on the power data.

[0142] A second modeling unit 304 for establishing a second constraint model of the electrolyzer array based on the power data and the configuration data.

[0143] An update unit 306 for updating the configuration data of the electrolyzer array based on the first constraint model and the second constraint model to obtain an optimal configuration scheme.

[0144] A determination unit 308 for determining an optimal allocation scheme for the operating power of the electrolyzer array based on the optimal configuration scheme.

[0145] In an embodiment of the present invention, the off-grid energy system includes an energy supply subsystem, an energy conversion subsystem, and an energy storage subsystem. The energy conversion subsystem includes a fuel cell, a heat pump, and an electrolyzer array. The energy storage subsystem includes electrical, hydrogen, and thermal energy storage devices. The electrolyzer array includes alkaline electrolyzers and proton exchange membrane electrolyzers.

[0146] In an embodiment of the present invention, when the first modeling unit 302 executes the operation of establishing the first constraint model of the off-grid energy system based on the power data, it is specifically used to perform the following operations: calculating the power data of a single electrolyzer in the electrolyzer array to obtain the first power data of the electrolyzer array in multiple operating modes; establishing the first constraint model based on the first power data and the power data of the remaining devices.

[0147] In an embodiment of the present invention, the first constraint model is established by the following formula:

[0148]

[0149] P t RE +P t FC +P t EES,dis =P t EES,ch +P t Array +P t HP +P t eld

[0150]

[0151]

[0152] In the formula, is the electric power of electrolyzer i at time t; is the standby power of electrolyzer i; is the electric power of electrolyzer i corresponding to the maximum hydrogen production efficiency; is the rated power of electrolyzer i; is the fluctuating power of electrolyzer i; is the rated operating condition of the electrolyzer; is the high-efficiency operating condition of the electrolyzer; is the fluctuating operating condition of the electrolyzer; is the standby state of the electrolyzer; P t RE is the power generation power of the energy supply subsystem at time t; P t FC represents the electric power of the fuel cell at time t; P t EES,dis is the discharge power of the electrical energy storage at time t; P t EES,ch is the power generation power of the electrical energy storage at time t; P t Array is the electric power of the electrolyzer array; P t HP is the electric power of the heat pump at time t; P t eld is the electrical load of the off-grid energy system; N EL is the total number of electrolyzer monomers in the electrolyzer array; is the thermal power of electrolyzer i at time t; is the waste heat of the fuel cell at time t; is the thermal power of the heat pump; is the heat release state of the thermal energy storage at time t; is the heat storage state of the thermal energy storage at time t; is the thermal load of the off-grid energy system; is the hydrogen production amount of electrolyzer i at time t; is the gas release rate of the hydrogen energy storage at time t; is the gas filling rate of the hydrogen energy storage at time t; is the hydrogen consumption of the fuel cell at time t; is the hydrogen load of the off-grid energy system.

[0153] In the embodiments of the present invention, the second constraint model is established by the following formula:

[0154]

[0155] In the formula, C ELis the hydrogen production cost; c ael , c pemel are the purchase cost coefficients of the alkaline electrolyzer and the proton exchange membrane electrolyzer in sequence; E AEL , E PEMEL are the installed capacities of the alkaline electrolyzer and the proton exchange membrane electrolyzer in sequence; C EL,res is the electrolyzer recovery cost; C EL,op is the operation and maintenance cost of the electrolyzer; L is the service life of the electrolyzer; Δt is the scheduling time interval; T is the total number of periods in the scheduling cycle; c el , c su , c sd are the electrolyzer usage cost coefficient, start-up and shutdown cost coefficient in sequence; are the electrolyzer startup action and shutdown action in sequence; W EL is the average value of hydrogen production volatility; C CUR is the penalty cost; c re , c eld , c hld are the curtailment cost coefficient of wind and solar power, the curtailment cost coefficient of electricity load, and the curtailment cost coefficient of heat load in sequence; P t RE,cur , P t eld,cur , are the curtailment amount of wind and solar power, the curtailment amount of electricity load, and the curtailment amount of heat load in sequence.

[0156] In the embodiment of the present invention, when the updating unit 306 performs updating the configuration data of the electrolyzer array based on the first constraint model and the second constraint model to obtain the optimal configuration scheme, it is specifically used to perform the following operations: input the counterweight data into the pre-trained iterative optimization model, and output the configuration result that satisfies the objective function; wherein, the iterative optimization model is obtained by training the initial genetic algorithm model, the objective function of the iterative optimization model is the first constraint model and the second constraint model, and the configuration result is the installed capacity ratio of the alkaline electrolyzer and the proton exchange membrane electrolyzer in the electrolyzer array; perform screening processing on the configuration result to obtain the optimal configuration scheme with the optimal installed capacity ratio of the alkaline electrolyzer and the proton exchange membrane electrolyzer.

[0157] In the embodiment of the present invention, when the determining unit 308 performs determining the optimal allocation scheme of the operating power of the electrolyzer array based on the optimal configuration scheme, it is specifically used to perform the following operations: when P t Array =E AEL +E PEMEL , the proton exchange membrane electrolyzer and the alkaline electrolyzer operate at the rated power simultaneously; when P t Array ≤min(E PEMEL , E AEL) When, based on the installed capacity ratio, the operating power of the proton exchange membrane electrolyzer and the alkaline electrolyzer is allocated; when min(E PEMEL ,E AEL ) ≤ P t NET ≤ E AEL + E PEMEL When, the electrolyzer with a smaller installed capacity ratio obtains the evenly divided power, and the electrolyzer with a larger installed capacity ratio absorbs the remaining power, so that the electrolyzer with a smaller installed capacity ratio is in a stable operating state.

[0158] It can be understood that the structure illustrated in the embodiments of the present invention does not constitute a specific limitation on an operating optimization device for an electrolyzer array of an off-grid energy system. In some other embodiments of the present invention, an operating optimization device for an electrolyzer array of an off-grid energy system may include more or fewer components than those illustrated, or combine certain components, or split certain components, or have different component arrangements. The illustrated components can be implemented in hardware, software, or a combination of software and hardware.

[0159] For the information interaction, execution process, etc. between the various modules within the above device, since they are based on the same concept as the method embodiments of the present invention, the specific content can be referred to the description in the method embodiments of the present invention, and will not be elaborated here.

[0160] The embodiments of the present invention also provide an electronic device, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, an operating optimization method for an electrolyzer array of an off-grid energy system in any embodiment of the present invention is implemented.

[0161] The embodiments of the present invention also provide a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by the processor, the processor is enabled to execute an operating optimization method for an electrolyzer array of an off-grid energy system in any embodiment of the present invention.

[0162] Specifically, a system or device equipped with a storage medium can be provided. On the storage medium, software program code for implementing the functions in any one of the above embodiments is stored, and the computer (or CPU or MPU) of the system or device is enabled to read and execute the program code stored in the storage medium.

[0163] In this case, the program code read from the storage medium itself can implement the functions in any one of the above embodiments. Therefore, the program code and the storage medium storing the program code constitute a part of the present invention.

[0164] Examples of storage media for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROM. Optionally, the program code can be downloaded from a server computer via a communication network.

[0165] Furthermore, it should be clear that not only can the functions of any one of the above embodiments be realized by executing the program code read by a computer, but also by causing an operating system or the like operating on the computer to perform part or all of the actual operations based on the instructions of the program code.

[0166] In addition, it can be understood that the program code read from the storage medium is written into the memory provided in the expansion board inserted into the computer or into the memory provided in the expansion module connected to the computer, and then the CPU or the like installed on the expansion board or the expansion module is caused to perform part and all of the actual operations based on the instructions of the program code, thereby realizing the functions of any one of the above embodiments.

[0167] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0168] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes various media such as ROM, RAM, magnetic disks, or optical disks that can store program code.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for optimizing the operation of an electrolyzer array in an off-grid energy system, characterized in that: include: Acquiring operation data of the off-grid energy system; wherein the operation data includes power data of the off-grid energy system and configuration data of the electrolyzer array; Based on the power data, establishing a first constraint model of the off-grid energy system; establishing a second constraint model of the electrolytic cell array based on the power data and the configuration data; Based on the first constraint model and the second constraint model, the configuration data of the electrolytic cell array is updated to obtain an optimal configuration solution; Based on the optimal configuration scheme, determining an optimal allocation scheme for the operating power of the electrolyzer array; The off-grid energy system includes an energy supply subsystem, an energy conversion subsystem and an energy storage subsystem, wherein the energy supply subsystem is a renewable energy power generation device; the energy conversion subsystem includes a fuel cell, a heat pump and an electrolyzer array; the energy storage subsystem includes electricity, hydrogen and heat energy storage devices; the electrolyzer array includes an alkaline electrolyzer and a proton exchange membrane electrolyzer; The step of establishing a first constraint model of the off-grid energy system based on the power data comprises: Calculating power data of a single electrolytic cell in the electrolytic cell array to obtain first power data of the electrolytic cell array in a multi-working mode; The electrolyzer array includes five operating conditions: in, is the electric power of electrolytic cell i at time t; is the standby power of electrolytic cell i; is the electric power of electrolyzer i corresponding to the maximum hydrogen production efficiency; is the rated power of electrolytic cell i; is the fluctuating power of electrolytic cell i; is the safe operating power of electrolytic cell i; The operating switching conditions of the electrolytic cell array are: In the formula, is the rated operating condition of the electrolytic cell; when the electrolytic cell i is in the rated operating condition at time t, otherwise The rest is similar; For efficient operation of the electrolyzer; The fluctuating operating condition of the electrolyzer; The electrolytic cell is in standby state; The electrolytic cell is in shutdown state; is a 0-1 variable, representing the electrolytic cell startup and shutdown actions respectively; The first power data is calculated by the following formula: Where N EL is the total amount of electrolytic cell monomers in the electrolytic cell array; P t Array is the electrical power of the electrolyzer array; Establishing the first constraint model based on the first power data and power data of other devices; The updating of the configuration data of the electrolytic cell array based on the first constraint model and the second constraint model to obtain an optimal configuration scheme includes: Input the configuration data into a pre-trained iterative optimization model, and output a configuration result that satisfies the objective function; wherein the iterative optimization model is obtained by training the initial genetic algorithm model, the objective function of the iterative optimization model is the first constraint model and the second constraint model, and the configuration result is the installed capacity ratio of the alkaline electrolyzer and the proton exchange membrane electrolyzer in the electrolyzer array; Screening the configuration results to obtain a configuration scheme with an optimal installed capacity ratio of the alkaline electrolyzer and the proton exchange membrane electrolyzer; The first constraint model is established by the following formula: In the formula, is the electric power of electrolytic cell i at time t; is the standby power of electrolytic cell i; is the electric power of electrolyzer i corresponding to the maximum hydrogen production efficiency; is the rated power of electrolytic cell i; is the fluctuating power of electrolytic cell i; is the rated operating condition of the electrolyzer; For efficient operation of the electrolyzer; The fluctuating operating condition of the electrolyzer; is the standby state of the electrolytic cell; P t RE is the power generation power of the energy supply subsystem at time t; P t FC represents the fuel cell power at time t; P t EES,dis is the discharge power of the energy storage at time t; P t EES,ch P is the power generation of the energy storage at time t; t Array is the electrical power of the electrolyzer array; P t HP is the electrical power of the heat pump at time t; P t eld is the electrical load of the off-grid energy system; N EL is the total number of electrolyzer cells in the electrolyzer array; is the thermal power of electrolytic cell i at time t; is the waste heat of the fuel cell at time t; is the thermal power of the heat pump; is the heat release state of thermal energy storage at time t; is the heat storage state of the thermal energy storage at time t; The heat load for off-grid energy systems; is the amount of hydrogen produced by electrolyzer i at time t; is the degassing rate of hydrogen energy storage at time t; is the filling rate of hydrogen energy storage at time t; is the hydrogen consumption of the fuel cell at time t; Hydrogen load for off-grid energy systems.

2. The method according to claim 1, characterized in that The second constraint model is established by the following formula: In the formula, C EL is the cost of hydrogen production; c ael 、c pemel They are the purchase cost coefficients of alkaline electrolyzer and proton exchange membrane electrolyzer respectively; E AEL 、E PEMEL They are the installed capacity of alkaline electrolyzer and proton exchange membrane electrolyzer respectively; C EL,res Cost recovery for electrolyzer; C EL,op is the operation and maintenance cost of the electrolytic cell; L is the service life of the electrolytic cell; Δt is the scheduling time interval; T is the total number of time periods in the scheduling cycle; c el 、c su 、c sd They are the electrolytic cell use cost coefficient, startup and shutdown cost coefficient; The sequence is the electrolytic cell startup and shutdown actions; W EL is the mean fluctuation rate of hydrogen production; C CUR is the penalty cost; c re 、c eld 、c hld They are wind and solar power abandonment cost coefficient, electricity load reduction cost coefficient, and heat load reduction cost coefficient, respectively; P t RE,cur , P t eld,cur , They are the amount of wind and solar power curtailment, the amount of electricity load reduction, and the amount of heat load reduction.

3. The method according to claim 2, characterized in that The step of determining an optimal allocation scheme for the operating power of the electrolyzer array based on the optimal configuration scheme includes: when When the proton exchange membrane electrolyzer and the alkaline electrolyzer are operated at rated power at the same time; when When the operating power of the proton exchange membrane electrolyzer and the alkaline electrolyzer is allocated based on the installed capacity ratio; when When the installed capacity ratio is small, the electrolytic cells with a small installed capacity ratio obtain an evenly distributed power, and the electrolytic cells with a large installed capacity ratio consume the remaining power, so that the electrolytic cells with a small installed capacity ratio are in a stable operating state.

4. An operation optimization device for an electrolyzer array of an off-grid energy system, characterized in that: The method as claimed in any one of claims 1 to 3 comprises: An acquisition unit, used to acquire operation data of the off-grid energy system; wherein the operation data includes power data of the off-grid energy system and configuration data of the electrolyzer array; A first modeling unit, configured to establish a first constraint model of the off-grid energy system based on the power data; A second modeling unit, configured to establish a second constraint model of the electrolytic cell array based on the power data and the configuration data; An updating unit, configured to update the configuration data of the electrolytic cell array based on the first constraint model and the second constraint model to obtain an optimal configuration solution; A determination unit is used to determine an optimal allocation scheme for the operating power of the electrolyzer array based on the optimal configuration scheme.

5. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 3 is implemented.

6. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the method according to any one of claims 1 to 3.

Images