Optimization Configuration Method of Multiple Electrolyzers with Different Power Levels in an Electric-Hydrogen Coupling System

Abstract

The present invention relates to electric energy storage technology, aiming to provide a method for optimizing the configuration of multiple electrolyzers with different power levels in an electric-hydrogen coupling system. The method includes: in a microgrid system, an electric-hydrogen coupling system is composed of a hydrogen production electrolyzer, an energy storage battery, and a hydrogen storage tank; determining the renewable energy access scenario for using the energy storage system and constructing the constraint conditions of the energy storage system; taking the optimal daily average cost as the optimization objective, and establishing an optimization configuration model for multiple electrolyzers with different power levels according to the constraint conditions; solving the optimization configuration model to obtain the optimal configuration scheme for multiple electrolyzers with different power levels. The present invention does not require a multi-tank configuration technology for pre-setting the total power of the electrolyzers. By using mathematical modeling and optimization means, it directly calculates the optimal combination configuration result of the electrolyzers, and at the same time realizes economy and configuration flexibility; the result simulation in the system optimization process is closer to the actual working process of the electrolyzers, which can improve the credibility of the optimization configuration result.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric energy storage, specifically relates to the optimization configuration technology of the electric-hydrogen coupling system, and particularly relates to an optimization configuration method for multiple electrolyzers with different power levels in the electric-hydrogen coupling system. Background Art

[0002] With the transformation of the energy structure and the wide application of renewable energy, new energy electrolytic water hydrogen production, as a green and sustainable hydrogen production method, has attracted much attention. However, with the increase in hydrogen production demand and load fluctuations, electrolyzers with too high power levels gradually show limitations in terms of flexibility, reliability, economy, and energy efficiency. In contrast, the method of jointly producing hydrogen using multiple power level electrolyzers can improve the flexibility of the system, achieve faster start-stop and a larger operating range. In the scenario of renewable energy access, the multi-tank system has a stronger ability to adapt to load fluctuations, and the coordinated operation of electrolyzers with different power levels improves the system stability and the consumption capacity of renewable energy.

[0003] However, there are significant differences between the multi-tank optimization configuration technology and the single-tank optimization configuration technology. Currently, most of the multi-tank optimization configuration methods proposed by researchers are based on the premise of a given total power of the electrolyzers, and use the enumeration method to compare the economy of various combination strategies, such as the public literature "Study on multi-power-level configuration scheme and scheduling strategy for multi-stack alkaline water electrolysis system in off-grid wind power scenario" and the public literature "Off-grid wind / hydrogen systems with multi-electrolyzers: Optimized operational strategies". However, this configuration method pre-determines the total power of the electrolyzers, resulting in artificial judgment errors, so the optimal configuration combination cannot be obtained. At the same time, the enumeration method increases the computational amount and is not suitable for the electric-hydrogen coupling system with a large number of configurable combinations of electrolyzers.

[0004] Therefore, it is necessary to propose a new solution to achieve the optimization configuration of multiple electrolyzers to solve the above key problems. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the deficiencies in the prior art and provide an optimization configuration method for multiple electrolyzers with different power levels in the electric-hydrogen coupling system.

[0006] To solve the technical problem, the solution of the present invention is:

[0007] Provide an optimization configuration method for multi-electrolyzers with different power levels in an electric-hydrogen coupling system, including: in a microgrid system, an electric-hydrogen coupling system is composed of a hydrogen production electrolyzer, an energy storage battery, and a hydrogen storage tank; determine the renewable energy access scenario using this energy storage system, and construct the constraint conditions of the energy storage system; with the optimal daily cost as the optimization goal, establish an optimization configuration model for multi-electrolyzers with different power levels according to the constraint conditions; solve the optimization configuration model to obtain the optimal configuration scheme for multi-electrolyzers with different power levels;

[0008] The constraint conditions of the energy storage system include electrolyzer operation characteristic constraints, energy storage range constraints, and hydrogen production-power balance constraints;

[0009] The objective function of the optimization configuration model includes two parts: operation cost and fixed investment cost, which are specifically as follows:

[0010]

[0011] Among them, Z is the number of typical scenarios of renewable energy access, is the daily operation cost of the z-th typical scenario; is the average annual cost of the fixed investment cost of the electrolyzer group; is the variable to be optimized, including the binary variable representing the electrolyzer configuration, the equipment power variable in the system operation process, the cell temperature variable, the hydrogen production amount, and the binary variable representing the start-stop and operation status of the electrolyzer.

[0012] As a preferred solution of the present invention, assume that the capacity set of available electrolyzer models is and its corresponding unit cost is The configuration quantity of each type of electrolyzer is and n is the total number of models; then,

[0013] The expression of the fixed investment cost is specifically:

[0014]

[0015]

[0016] Among them, is the reciprocal of the present value factor of an annuity; is the annual interest rate, is the electrolyzer life.

[0017] As a preferred solution of the present invention, calculate the fixed investment cost when configuring multiple types of electrolyzers by establishing a unit cost model for electrolyzers with different power levels; specifically including:

[0018] First, analyze the quantitative relationship between the unit cost of the electrolyzer and the equipment capacity; then, based on the investigated equipment capacity and price data, use the least squares method for fitting to obtain the function related to the unit cost and equipment capacity; and calculate the fixed investment cost using this fitting function.

[0019] As a preferred embodiment of the present invention, the expression of the daily operating cost is specifically:

[0020]

[0021] Wherein, is the cold start and hot start flag variable of the th electrolyzer of the th model; and represent the single cost of cold and hot starts; is the amount of discarded electricity at time, is the unit discarded electricity penalty cost; and respectively represent the amount of electricity purchased / sold from the power grid at time, and represent the unit electricity purchase price or electricity sale price; is the hydrogen demand at time, that is, the hydrogen sales volume, is the hydrogen sales price at time; is the total number of electrolyzer models.

[0022] As a preferred embodiment of the present invention, the expression of the variable to be optimized is specifically:

[0023]

[0024] Wherein, represents the temperature of the th electrolyzer of the th type at time represents the number of configurations of the th type of electrolyzer; represents the operating power of the th electrolyzer of the th type at time t; represents the cold start flag variable of the th electrolyzer of the represents the hot start flag variable of the th electrolyzer of the represents the amount of discarded electricity at Purchase electricity from the power grid at time denote Sell electricity to the power grid at time denote Hydrogen demand at time, i.e., hydrogen sales volume denote the binary state variable of the operating state of the th electrolyzer of the i-th type at time t denote the binary state variable of the shutdown state of the th electrolyzer of the i-th type at time t denote the binary state variable of the standby state of the th electrolyzer of the i-th type at time t denote the maximum configured number of the i-th type of electrolyzers; i represents the code corresponding to the electrolyzer model

[0025] As a preferred solution of the present invention, the operating characteristic constraints of the electrolyzer at least include the upper and lower power limits of the electrolyzer and the upper and lower temperature limits of the electrolyzer; where

[0026] The upper and lower power limits of the electrolyzer are expressed as:

[0027]

[0028] The upper and lower temperature limits of the electrolyzer are expressed as:

[0029]

[0030] In the formula denote the capacity of the i-th type of electrolyzer denote the operating power of the th electrolyzer of the i-th type at time t denote the upper limit of the operating power related to temperature of the th electrolyzer of the i-th type denote the lower limit of the operating temperature of the th electrolyzer of the i-th type denote the temperature of the th electrolyzer of the i-th type at time t denote the upper limit of the operating temperature of the th electrolyzer of the i-th type; i represents the code corresponding to the electrolyzer model denote the electrolyzer serial number

[0031] and represent the binary state variables of the operating and shutdown states of the th electrolyzer of the i-th type at time t respectively; when the electrolyzer is in the operating state ; when the electrolyzer is in the shutdown state .

[0032] As a preferred embodiment of the present invention, the operating characteristic constraints of the electrolytic cell further include any one or more of the following constraint conditions:

[0033] (1) Standby power constraint of the electrolytic cell:

[0034]

[0035] wherein, represents the standby power of the th electrolytic cell of the i-th type; represents the temperature of the th electrolytic cell of the i-th type at time t; represents the ambient temperature at time t; represents the binary state variable of the th electrolytic cell of the i-th type in the standby state at time t; represents the thermal resistance of the th electrolytic cell of the i-th type;

[0036] (2) Operating state variable constraints of the electrolytic cell:

[0037]

[0038] wherein, , , respectively represent the binary state variables of the operating state, shutdown state, and standby state of the electrolytic cell at time t;

[0039] (3) Cold start process constraints of the electrolytic cell:

[0040]

[0041] wherein, represents the lower limit of the operating temperature of the th electrolytic cell of the i-th type;

[0042] (4) Cold start constraints of the electrolytic cell:

[0043]

[0044] wherein, is the cold start flag variable;

[0045] (5) Temperature change constraints of the electrolytic cell:

[0046]

[0047] wherein, represents the temperature of the electrolytic cell at adjacent time points ; is the heat generation rate of the electrolyzer; is the heat transfer power transferred from the external heater to the electrolyzer; is the heat absorption rate of the refrigeration system; represents the heat exchange rate between the electrolyzer and the environment; represents the heat carried away by hydrogen and oxygen; represents the heat capacity of the electrolyzer; represents the time interval;

[0048] (6) Heat generation rate constraint of the electrolyzer:

[0049]

[0050] In the formula, represents the operating power of the th electrolyzer of the i-th type; represents the electrolyzer efficiency;

[0051] (7) Heat loss constraint of the external heating source:

[0052]

[0053] In the formula, represents the actual heating power of the external heater of the th electrolyzer of the i-th type; represents the heating power provided by the external heater of the th electrolyzer of the i-th type; is the heating efficiency of the external heater;

[0054] (8) Heat absorption rate constraint of the electrolyzer cooling system:

[0055]

[0056] In the formula, represents the actual absorption power of the cooling device of the th electrolyzer of the i-th type; represents the power used for cooling of the cooling device of the th electrolyzer of the i-th type; represents the coefficient of performance of the cooling system;

[0057] (9) Heat exchange loss constraint between the electrolyzer and the environment:

[0058]

[0059] In the formula, represents the power of heat exchange between the th electrolyzer of the i-th type and the environment; represents the environmental temperature at time t, Represents the thermal resistance of the electrolytic cell;

[0060] (10) Electrothermal operation characteristic constraints of the electrolytic cell:

[0061] The electrothermal operation characteristics of the electrolytic cell need to meet the requirements of the electrothermal characteristic model of the electrolytic cell. The relationship between the upper limit of the electrolysis power and the temperature, and the relationship between the electrolysis efficiency and the upper limit of the power and the temperature are fitted according to the experimental results, so as to establish an electrothermal characteristic model related to the power level, which is used to characterize the influence of the temperature and power of the electrolytic cell on the electrolysis efficiency; including:

[0062] (1) By inputting the rated voltage to the electrolytic cell at different temperatures, obtain its power upper limit; according to the obtained temperature-power data pairs, fit a function related to the power upper limit and the temperature; then normalize the function to obtain the normalized power upper limit function, and finally summarize to obtain the power upper limit functions corresponding to electrolytic cells of different power levels;

[0063] (2) At different temperatures, measure the electrolysis efficiency of the electrolytic cell at different powers; according to the obtained temperature-power-efficiency data pairs, fit a function related to the efficiency, power and temperature; then normalize the function based on the capacity of the electrolytic cell to obtain the normalized efficiency function, and finally summarize to obtain the electrolysis efficiency functions corresponding to electrolytic cells of different power levels;

[0064] For electrolytic cells of different power levels, their maximum power constraints are different, and separate modeling is required in the multi-cell optimization configuration.

[0065] As a preferred solution of the present invention, all constraints need to be multiplied by the binary variable Specifically expressed as:

[0066]

[0067] Among them, Represents the set of inequality constraints; Represents the set of equality constraints; Represents the th binary variable indicating whether the

[0068] th electrolytic cell of the

[0069] (1) State of charge constraint of the energy storage battery:

[0070]

[0071] In the formula, Represents the state of charge of the battery at moment; Represents the dissipation rate of the battery; Represents the state of charge of the battery at the initial moment; and Represents the charging and discharging efficiency of the battery; Represents the storage capacity of the battery; and Represents the charging power and discharging power of the battery at time ;

[0072] (2) Constraints on the charging and discharging power range of the energy storage battery:

[0073]

[0074]

[0075] In the formula, Is the upper limit of the battery charging power, Is the upper limit of the battery discharging power; Represents the battery charging power at time t; Represents the battery discharging power at time t;

[0076] (3) Constraints on the safety and stability of the energy storage battery:

[0077]

[0078] The above formula means that the battery cannot be charged and discharged simultaneously;

[0079] (4) Constraints on the hydrogen storage state of the hydrogen storage tank:

[0080]

[0081] In the formula, Represents the hydrogen storage amount state at time ; Represents the hydrogen storage amount state at the initial moment, Represents the dissipation rate of the hydrogen storage tank; and Represents the hydrogen charging efficiency and hydrogen discharging efficiency of the hydrogen storage tank; Represents The hydrogen energy demand at time; Represents the hydrogen storage capacity of the hydrogen storage tank; Represents The hydrogen production rate at time; Represents The hydrogen energy demand at time; Represents the dissipation rate of the hydrogen storage tank;

[0082] (4) Storage range constraints of lithium batteries and hydrogen storage tanks:

[0083]

[0084]

[0085] In the formula, represents the lower limit of the SOC of the lithium battery; represents the SOC of the lithium battery at time t; represents the upper limit of the SOC of the lithium battery; represents the lower limit of the hydrogen storage state of the hydrogen storage tank; represents the hydrogen storage state of the hydrogen storage tank at time t; represents the upper limit of the hydrogen storage state of the hydrogen storage tank.

[0086] As a preferred solution of the present invention, the hydrogen production-power balance constraint includes any one or more of the following constraint conditions:

[0087] (1) Power balance constraint of the electric-hydrogen coupling system:

[0088]

[0089] In the formula, represents the total electrolysis power at time t; represents the total cooling power at time t; represents the total heating power; represents the total pump power at time t; represents the total purification power at time t; represents the battery charging power at time t; represents the battery discharging power at time t; represents the total compression power at time t; represents the electric load at time t; represents the curtailed power at time t; represents the power sold to the power grid at time t; represents the power purchased from the power grid at time t; represents the wind power at time t; represents the photovoltaic power generation at time t;

[0090] (2) New energy curtailment rate constraint:

[0091]

[0092] In the formula, represents the new energy curtailment rate; represents the upper limit of the allowable curtailment rate; represents the time period to be optimized;

[0093] (3) Power constraints of the water pump, hydrogen purification device and compressor;

[0094]

[0095]

[0096]

[0097] In the formula, 、 、 are the powers of the water pump, hydrogen purification device, and compressor at time t, respectively; represents the hydrogen energy production; 、 、 represent the proportionality coefficients related to the power of the water pump, purification device, and compressor and the hydrogen production rate, respectively.

[0098] As a preferred embodiment of the present invention, the electrolytic cell is an alkaline electrolytic cell; the energy storage battery is a single lithium battery or a battery pack composed of single lithium batteries.

[0099] As a preferred embodiment of the present invention, any one of the following solvers is used to solve the optimization configuration model to obtain the optimal solution for the multi-electrolytic cell configuration with different power levels: Gurobi, IBM CPLEX Optimizer, FICO Xpress, MOSEK, BARON, Lingo, Shanshu COPT, or Alibaba MindOPT.

[0100] The present invention further provides a computing device, including:

[0101] a memory configured to store instructions; and

[0102] a processor configured to call the instructions from the memory and capable of implementing the method for optimizing the configuration of multi-electrolytic cells with different power levels in the aforementioned electro-hydrogen coupling system when executing the instructions.

[0103] The present invention further provides a computer-readable storage medium, on which instructions are stored, and the instructions are used to cause a computer to execute the method for optimizing the configuration of multi-electrolytic cells with different power levels in the aforementioned electro-hydrogen coupling system.

[0104] Description of the invention principle:

[0105] In an electro-hydrogen coupling storage system, it is usually necessary to use a multi-cell coupling configuration with different power levels, and the operating characteristics and costs of these electrolytic cells are related to their power levels. The present invention abandons the multi-cell configuration technology that requires presetting the total power of the electrolytic cells, and uses mathematical modeling and optimization means to model the operating characteristics and cost characteristics of electrolytic cells with different power levels, and directly calculates the optimal combination configuration result of the electrolytic cells.

[0106] Based on this innovative idea, the present invention first proposes an operating characteristic model and a unit cost model for electrolyzers with different power levels. Among them, the operating characteristic model fully considers the influence of temperature on the operation of alkaline electrolyzers, and the unit cost model is the relationship between the unit cost of alkaline electrolyzers and the rated power fitted based on the research data. Based on these two models, there is no need to preset the total power of the electrolyzers in advance. Instead, with the goal of optimal economy, mathematical modeling and optimization means are used, and the optimal combination configuration result of the electrolyzers is directly calculated by a solver.

[0107] In the optimization configuration requirements for the multi-cell scenario targeted by the present invention, the electrolyzer capacity is no longer one of the optimization variables. Instead, it is the configuration quantity of each type of electrolyzer, which is more in line with the actual engineering requirements. In addition, the electrothermal characteristics and unit cost of each configured electrolyzer with a certain power level are independently considered, making the optimization result more accurate and reasonable. This multi-cell optimization configuration method can be directly solved by commercial solvers such as Gurobi.

[0108] Compared with the prior art, the beneficial effects of the present invention at least include:

[0109] 1. The multi-cell configuration technology of the electrolytic hydrogen production system multi-cell optimization configuration method proposed by the present invention does not require presetting the total power of the electrolyzers in advance. By using mathematical modeling and optimization means, the optimal combination configuration result of the electrolyzers is directly calculated, realizing economy and configuration flexibility at the same time.

[0110] 2. The electrothermal characteristic model and the unit capacity cost model of the alkaline electrolyzer proposed by the present invention play an important role in the multi-cell configuration of the alkaline electrolyzer, making the result simulation in the system optimization process closer to the actual working process of the electrolyzer, improving the credibility of the optimization configuration result, and having strong practical significance and engineering application value. Description of the Drawings

[0111] Figure 1 It is the microgrid system structure of the electric-hydrogen hybrid energy storage system containing electrolyzers.

[0112] Figure 2 It is the fitting diagram of the relationship between the unit cost and capacity of the alkaline electrolyzer in the optimization configuration model proposed by the present invention.

[0113] Figure 3 、 Figure 4 、 Figure 5 They are the wind-solar and electrical load curve diagrams of three typical days described in the examples of the present invention respectively.

[0114] Figure 6 It is the 24-hour electricity price curve described in the examples of the present invention.

[0115] Figure 7The start-up and shut-down results of the optimized three typical daily caustic soda electrolyzers described in the embodiments of the present invention.

[0116] Figure 8 , Figure 9 , Figure 10 They are respectively the operation power diagrams of the optimized three typical daily 1MW caustic soda electrolyzers described in the embodiments of the present invention.

[0117] Figure 11 , Figure 12 , Figure 13 They are respectively the operation power diagrams of the optimized three typical daily 3MW caustic soda electrolyzers described in the embodiments of the present invention.

[0118] Figure 14 , Figure 15 , Figure 16 They are respectively the operation power diagrams of the optimized three typical daily 5MW caustic soda electrolyzers described in the embodiments of the present invention.

[0119] Figure 17 , Figure 18 , Figure 19 They are respectively the temperature diagrams of the optimized three typical daily 1MW caustic soda electrolyzers described in the embodiments of the present invention.

[0120] Figure 20 , Figure 21 , Figure 22 They are respectively the temperature diagrams of the optimized three typical daily 3MW caustic soda electrolyzers described in the embodiments of the present invention.

[0121] Figure 23 , Figure 24 , Figure 25 They are respectively the temperature diagrams of the optimized three typical daily 5MW caustic soda electrolyzers described in the embodiments of the present invention. Detailed implementation manners

[0122] The method for optimizing the configuration of multiple electrolyzers with different power levels in the electro-hydrogen coupling system described in the present invention includes:

[0123] (1) In the microgrid system, an electro-hydrogen coupling system is composed of a hydrogen production electrolyzer, a energy storage battery and a hydrogen storage tank; determine the renewable energy access scenario for using the energy storage system, and construct the constraint conditions of the energy storage system;

[0124] (2) Taking the optimal daily average cost as the optimization goal, establish an optimization configuration model for multiple electrolyzers with different power levels according to the constraint conditions; the constraint conditions of the energy storage system include the operation characteristic constraints of the electrolyzer, the energy storage range constraints and the hydrogen production-power balance constraints; the objective function of the optimization configuration model includes two parts: operation cost and fixed investment cost.

[0125] (3) Solve the optimization configuration model to obtain the optimal configuration scheme of multiple electrolyzers with different power levels.

[0126] To more clearly illustrate the technical solutions in the embodiments of the present invention, the implementation manners of the present invention will be introduced below in conjunction with the accompanying drawings.

[0127] 1. The present invention is applicable to various renewable energy access scenarios, such as a stand-alone new wind and solar power microgrid, or a new energy microgrid with coupled wind and solar power.

[0128] Figure 1 The figure shows a microgrid system structure of a hydrogen and electricity hybrid energy storage system as an example, which mainly consists of components such as a photovoltaic array, energy storage batteries (such as lithium batteries), a hydrogen production electrolyzer, a hydrogen storage tank, a load, and a DC-DC converter. The energy storage system includes an electrical energy storage system mainly based on storage batteries and a hydrogen energy storage system mainly based on an electrolyzer and a hydrogen storage tank. Among them, the hydrogen production electrolyzer and the energy storage battery are used as the electrical-hydrogen hybrid energy storage coupling components. In the present invention, the electrolyzer is an alkaline electrolyzer; the energy storage battery is a single lithium battery cell or a battery pack composed of single lithium battery cells.

[0129] 2. The present invention proposes a brand-new fixed investment cost calculation method, which uses a unit cost model to calculate the fixed investment cost, and ensures the accuracy and optimality of the configuration result by clarifying the relationship between the unit cost and the capacity of the electrolyzer. Considering that the fixed investment cost and the operating cost of the electrolyzer together constitute the total cost, the optimization objectives of the electrolyzer optimal configuration in the present invention are divided into the optimization of the fixed investment cost and the operating cost.

[0130] For the above reasons, the objective function of the optimal configuration model described in the present invention is specifically shown in formula (1), which includes two parts: the operating cost and the fixed investment cost. Among them, the operating cost is the average daily cost of the selected typical day, and the fixed investment cost is converted into the average daily cost through the present value factor of an annuity.

[0131] (1)

[0132] In the formula, Z is the number of typical scenarios for the access of the selected renewable energy, is the daily operating cost of the z-th typical scenario; is the average annual cost of the fixed investment cost of the electrolyzer group; is the variable to be optimized, which includes a binary variable representing the electrolyzer configuration, the equipment power variable during the system operation process, the cell temperature variable, the hydrogen production amount, and a binary variable representing the start-stop and operation status of the electrolyzer, and is specifically expressed as formula (2):

[0133] (2)

[0134] Among them, represents the i-th type and the th electrolyzer at time The temperature; Indicates the number of configurations of the i-th type of electrolyzer; Indicates the Operating power of the Indicates the Cold start flag variable of the Indicates the Hot start flag variable of the Indicates Amount of electricity discarded at the moment; Indicates Amount of electricity purchased from the grid at the moment; Indicates Amount of electricity sold to the grid at the moment; Indicates Hydrogen demand at the moment, i.e., hydrogen sales volume; Indicates the binary state variable of the operating state of the -th electrolyzer of the i-th type at time t; Indicates the binary state variable of the shutdown state of the -th electrolyzer of the i-th type at time t; Indicates the binary state variable of the standby state of the -th electrolyzer of the i-th type at time t; Indicates the maximum number of configured units of the i-th type of electrolyzer; i represents the code corresponding to the electrolyzer model.

[0135] 3. Construct a cost model for fixed investment.

[0136] (1) For a multi-electrolyzer hydrogen production system, assume that the set of capacities of available electrolyzer models is , where, the total number of electrolyzer models, and the corresponding unit cost is , and the number of configurations of each type of electrolyzer is , and n is the total number of models.

[0137] The average annual cost of the fixed investment cost of the electrolyzer group can be expressed by formula (3).

[0138] (3)

[0139] Among them, Is the reciprocal of the present value factor of the annuity, and the specific calculation method is formula (4).

[0140] (4)

[0141] Among them, Is the annual interest rate, Is the electrolyzer life.

[0142] (2) By establishing a unit cost model for electrolyzers of different power levels, the fixed investment cost when configuring multiple types of electrolyzers is calculated.

[0143] First, analyze the quantitative relationship between the unit cost of the alkaline electrolyzer and the equipment capacity between them. According to the surveyed equipment capacity and price data, the least squares method is used for fitting to obtain the correlation function between the unit cost and the equipment capacity, and the fixed investment cost is calculated based on this.

[0144] For example, the fitting function in this example can be expressed as formula (5), and the power function curve of the surveyed data and the fitting result is as Figure 2 shown.

[0145] (5)

[0146] It can be seen from the figure that the power function curve fits well with the collected data points, and the determination coefficient of the fitting result . The unit cost of the alkaline electrolyzer decreases rapidly around the 0 point as its capacity increases and starts to decrease slowly from 1.5 MW. Therefore, when configuring electrolyzers, although the combination of small-capacity electrolyzers can improve the system operation flexibility and increase the operation range, it may increase the configuration cost; therefore, using the Figure 2 shown unit cost function to more accurately represent the configuration cost of multiple types of alkaline electrolyzers is of great significance.

[0147] In the optimization configuration requirements for the multi-electrolyzer scenario targeted by the present invention, the electrolyzer capacity is no longer one of the optimization variables. Instead, the configuration quantity of each type of electrolyzer is used, which is more in line with the actual engineering requirements; in addition, the electrothermal characteristics and unit cost of each configured electrolyzer of each power level are independently considered, making the optimization result more accurate and reasonable.

[0148] 4. Different electrolyzer configurations will have a significant impact on the system operation. Therefore, when performing multi-electrolyzer configuration, the operation cost also needs to be considered in the optimization objective.

[0149] Using the typical day's wind-solar and load curves, the average daily cost of the system operation can be estimated, which includes: the cold and hot start costs of the electrolyzer, the cost of purchasing electricity from the grid, the penalty cost for abandoning electricity. At the same time, the revenue from selling electricity and hydrogen is also considered as a negative cost, and the daily operation cost is as shown in formula (6).

[0150] (6)

[0151] Among them, is the th type of electrolyzer's The cold start and hot start flag variables of an electrolyzer. When this variable is 1, it indicates that this electrolyzer has cold start / hot start behavior at moment. Since there are some types of electrolyzers that are not configured, the summation starts from 0. And represents the single cost of cold and hot start. is the abandoned power at moment, is the unit abandoned power penalty cost, and respectively represent the power purchased / sold from the power grid at moment, and represents the unit power purchase price or power sale price, is the hydrogen demand at moment, that is, the hydrogen sales volume; is the hydrogen sales price at moment.

[0152] 5. Constraints of the optimization configuration model

[0153] The constraints involved in this example mainly include: electrolyzer operation characteristic constraints, energy storage range constraints, hydrogen production-power balance constraints.

[0154] (1)Electrolyzer operation characteristic constraints

[0155] In this example, the maximum power constraint of the th electrolyzer of the normalized model is shown in (7).

[0156] (7)

[0157] Then the power constraint of the th electrolyzer of the model can be expressed as (8).

[0158] (8)

[0159] In the formula, represents the capacity of the i-th type of electrolyzer; represents the operating power of the th electrolyzer of the i-th type at time t; represents the upper limit of the operating power related to temperature of the th electrolyzer of the i-th type; i represents the corresponding code of the electrolyzer model; represents the electrolyzer serial number;

[0160] The fitted and normalized efficiency formula is shown in (9).

[0161] (9)

[0162] In the formula, represents the electrolyzer efficiency. At the same time, the temperature in the above formula is the cell temperature. To ensure the safe and stable operation of the electrolyzer, its temperature needs to be within an appropriate range, and the temperature constraint is related to the operating state of the electrolyzer.

[0163] The state of the alkaline electrolyzer is generally divided into three categories: shutdown state, standby state, and operating state. In this example, they are represented by , and respectively. The temperature constraint can be expressed as (10).

[0164] (10)

[0165] Among them, represents the lower limit of the operating temperature of the th electrolyzer of the i-th type; represents the temperature of the th electrolyzer of the i-th type at time t; represents the upper limit of the operating temperature of the th electrolyzer of the i-th type; and are binary state variables representing the operating and shutdown states of the th electrolyzer of the i-th type at time t. When the electrolyzer is in the operating state, , and when the electrolyzer is in the shutdown state, .

[0166] Based on this, the power constraint of the electrolyzer is rewritten as (11).

[0167] (11)

[0168] That is, in the shutdown state, the power of the electrolyzer is 0, and its power upper and lower limit constraints only take effect in the operating state.

[0169] When the electrolyzer is in the standby state, it is necessary to maintain the standby temperature. Therefore, the electrolyzer still requires power supply but does not produce hydrogen. Therefore, the power of the electrolyzer at this time is set to the power value that can maintain the current temperature, represented by , that is:

[0170] (12)

[0171] In the formula, represents the standby power of the th electrolyzer of the i-th type; represents the temperature of the th electrolyzer of the i-th type at time t; represents the ambient temperature at time t; represents the binary state variable of the standby state of the ith electrolyzer at the ith electrolyzer at the thermal resistance of the

[0172] At the same time, constraints are imposed on the operating state variables of the electrolyzer, i.e.:

[0173] (13)

[0174] In the formula, , , respectively represent the binary state variables of the operating state, shutdown state and standby state of the electrolyzer at time t.

[0175] In addition, since the hydrogen purity is low in the early stage of the cold start process of the electrolyzer, the hydrogen generated during this period cannot be utilized and is usually vented. Therefore, it is assumed that the electrolyzer is defined to be in the operating state only when the temperature of the alkaline electrolyzer exceeds the set value, that is, the cold start process is basically completed and the generated hydrogen can be collected. The resulting constraint is shown in Equation (14).

[0176] (14)

[0177] In the formula, represents the lower limit of the operating temperature of the ith electrolyzer.

[0178] The cold start flag variable in the objective function is determined by the state variables at time and time. After the electrolyzer experiences a certain moment, the operating state variable changes from 0 to 1, and the shutdown state variable changes from 1 to 0. Then it is defined that the electrolyzer completes the cold start behavior at this moment, and the cold start flag variable at this moment is set to 1. This behavior requires a certain economic cost; otherwise, the cold start flag variable at this moment is set to 0. The resulting constraint is more clearly expressed as shown in Equation (15).

[0179] (15)

[0180] For a multi-electrolyzer system, the temperature conditions of each electrolyzer are different. The temperature change constraint of the th electrolyzer of model is shown in Equation (16).

[0181] (16)

[0182] Wherein and represent the temperatures of the alkaline electrolyzer at time and the adjacent time point respectively, is the heat generation rate of the alkaline electrolyzer, is the heat transfer power transferred from the external heater to the alkaline electrolyzer, is the heat absorption rate of the cooling system, represents the heat exchange rate between the alkaline electrolyzer and the environment, represents the heat carried away by hydrogen and oxygen, represents the heat capacity of the alkaline electrolyzer; represents the time interval.

[0183] The electrolyzer converts electrical energy into chemical energy to produce hydrogen with an efficiency of , and the remaining energy is converted into heat, which affects the temperature of the electrolyzer. The heat generation rate of the alkaline electrolyzer can be expressed as (17).

[0184] (17)

[0185] In the formula, represents the operating power of the th electrolyzer of the i-th type; represents the electrolyzer efficiency.

[0186] There are losses in the process of heat transfer from the external heat source to the alkaline electrolyzer, resulting in a heat transfer efficiency less than 1, as shown in formula (18).

[0187] (18)

[0188] In the formula, represents the actual heating power of the external heater of the th electrolyzer of the i-th type; represents the heating power provided by the external heater of the th electrolyzer of the i-th type; is the heating efficiency of the external heater.

[0189] The cooling system absorbs heat from the alkaline electrolyzer through the cooling circulation loop. The heat absorption rate of the cooling system is related to the electrolyte flow rate, the electrolyte heat capacity, the electrolyte inlet temperature, and the temperature of the AWE. The ratio of the heat absorption rate to the power consumption of the cooling system can be expressed as the coefficient of performance (COP) of the cooling system, , that is:

[0190] (19)

[0191] Wherein, represents the actual absorption power of the cooling device of the th electrolytic cell of the i-th category; represents the power used for cooling of the cooling device of the th electrolytic cell of the i-th category; represents the performance coefficient of the cooling system.

[0192] The above external heat source and cooling system can assist in adjusting the temperature of the alkaline electrolytic cell, so that the temperature of the electrolytic cell can vary flexibly. However, both of these temperature control methods rely on power supply. In previous studies, the power consumption of these two devices has not been comprehensively considered, and this oversight is likely to cause deviations in energy demand estimation, which in turn has an adverse impact on the system operation efficiency. In the optimization configuration model considering the electrothermal characteristics of the electrolytic cell proposed in this example, the power consumption of these two processes has been considered in detail, fully taking into account the overall energy consumption of the entire system, and striving to build a more accurate and practical model.

[0193] According to the second law of thermodynamics, if there is a temperature difference between the alkaline electrolytic cell and the environment, there is always heat exchange, and the heat exchange equation is shown in Equation (20).

[0194] (20)

[0195] Wherein, represents the power of heat exchange between the th electrolytic cell of the i-th category and the environment; represents the environmental temperature at time t, represents the thermal resistance of the electrolytic cell.

[0196] (2) Electrothermal operation characteristic constraints of the electrolytic cell:

[0197] In addition to the various electrolytic cell operation characteristic constraints described above, the electrothermal characteristic model is also an important part of the optimization constraints of the present invention. Considering the electrothermal operation characteristic constraints of the electrolytic cell, the final configuration scheme will be more in line with the actual situation. According to the experimental results, the relationship between the upper limit of electrolysis power and temperature, and the relationship between electrolysis efficiency and the upper limit of power and temperature are fitted, so as to establish an electrothermal model of the electrolytic cell related to the power level, which is used to characterize the influence of the temperature and power of the electrolytic cell on the electrolysis efficiency. Specifically, it includes the following steps:

[0198] A. At different temperatures, apply the rated voltage to the experimental electrolytic cell to obtain its power, which is the upper limit of its power. Based on the obtained temperature-power data pairs, fit a function relating the upper limit of power to temperature. Then, normalize this function to obtain the normalized upper limit of power function. Finally, the upper limit of power functions corresponding to alkaline electrolytic cells of different power levels can be obtained.

[0199] In this example, the maximum power constraint after normalization is shown in (21).

[0200] (21)

[0201] The above maximum power constraint reflects the influence of the temperature and rated power of the alkaline electrolytic cell on the maximum power. Therefore, for electrolytic cells of different power levels, their maximum power constraints are different and need to be modeled separately in the multi-cell optimization configuration.

[0202] B. At different temperatures, measure the electrolysis efficiency at different powers. Existing methods such as the water displacement method can be used for measurement. Based on the obtained temperature-power-efficiency data pairs, fit a function relating the efficiency to power and temperature. Then, normalize this function based on the capacity of the electrolytic cell to obtain the normalized efficiency function. Finally, the electrolysis efficiency functions corresponding to alkaline electrolytic cells of different power levels can be obtained.

[0203] In this example, the fitted and normalized efficiency formula is shown in Equation (22).

[0204] (22)

[0205] The above efficiency formula reflects the influence of the temperature, real-time power, and rated power of the alkaline electrolytic cell on the efficiency. For electrolytic cells of different power levels, their corresponding constraints are different and also need to be modeled separately in the multi-cell optimization configuration.

[0206] Combining the constraint conditions described in parts (1) and (2) above, for the th alkaline electrolytic cell of the model in this example, the relevant constraints can be expressed as:

[0207] (23)

[0208] Based on the method for configuring the number of alkaline electrolytic cells of different power levels using the direct optimization method proposed in the present invention, all the above constraints related to the alkaline electrolytic cell need to be multiplied by the binary variable Let the above equality constraint be modified as:

[0209] (24)

[0210] Among them, represents the set of inequality constraints; represents the set of equality constraints; represents the th binary variable indicating whether the

[0211] (3) Energy storage range constraint

[0212] The state of charge (SoC) of the energy storage battery changes with the charging / discharging power, and the battery deteriorates over time, as shown in Equation (25).

[0213] (25)

[0214] In the formula, represents the state of charge of the battery at moment; represents the dissipation rate of the battery; represents the state of charge of the battery at the initial moment; represents the data point time interval; and represent the charging and discharging efficiencies of the battery; represents the storage capacity of the battery; and represent the charging power and discharging power of the battery at time t'.

[0215] For the safe operation and lifespan of the battery, the charging and discharging power ranges of the electrolyzer are:

[0216] (26)

[0217] (27)

[0218] In the formula, is the upper limit of the battery charging power, is the upper limit of the battery discharging power; represents the battery charging power at time t; represents the battery discharging power at time t;

[0219] In addition, the battery is not allowed to charge and discharge simultaneously because this behavior will cause energy loss and have an adverse effect on the battery lifespan.

[0220] (28)

[0221] In addition, the change in the hydrogen storage capacity of the hydrogen storage tank is affected by both hydrogen demand and the hydrogen production of the electrolyzer, and the stored hydrogen dissipates over time. Then, the hydrogen storage state of the hydrogen storage tank is as shown in Equation (29).

[0222] (29)

[0223] In the formula, represents the hydrogen storage amount state at time ; represents the hydrogen storage amount state at the initial time, represents the dissipation rate of the hydrogen storage tank; and represent the hydrogen charging efficiency and hydrogen discharging efficiency of the hydrogen storage tank; represents the hydrogen energy demand at time represents the hydrogen storage capacity of the hydrogen storage tank; represents the hydrogen production rate at time represents the hydrogen energy demand at time represents the dissipation rate of the hydrogen storage tank.

[0224] Finally, there are upper and lower bound constraints on the storage ranges of the lithium battery and the hydrogen storage tank, as shown in Eqs. (30) and (31).

[0225] (30)

[0226] (31)

[0227] In the formula, represents the lower limit of the SOC of the lithium battery; represents the SOC of the lithium battery at time t; represents the upper limit of the SOC of the lithium battery; represents the lower limit of the hydrogen storage state of the hydrogen storage tank; represents the hydrogen storage state of the hydrogen storage tank at time t; represents the upper limit of the hydrogen storage state of the hydrogen storage tank.

[0228] (4) Hydrogen production - power balance constraint

[0229] There are obvious differences in the hydrogen production and power balance constraints between the optimization of the multi - cell system and the single - cell system. The total hydrogen production of the system should be expressed as the sum of the hydrogen production amounts of all electrolyzers in the operating state. The total power consumption during the electrolysis process is the sum of the power consumption of all electrolyzers. The total heating power consumption and the total cooling system power consumption are also the sum of the corresponding power consumptions of all alkaline electrolyzers, as shown in Eqs. (32) - (35).

[0230] (32)

[0231] (33)

[0232] (34)

[0233] (35)

[0234] The power balance constraint of the overall system is shown in Equation (36).

[0235] (36)

[0236] In the formula, represents the hydrogen production rate of the th electrolyzer of the i-th type at time t; represents the total electrolysis power at time t; represents the total cooling power at time t; represents the total heating power; represents the total pump power at time t; represents the total purification power at time t; represents the battery charging power at time t; represents the battery discharging power at time t; represents the total compression power at time t; represents the electrical load at time t; represents the curtailed power at time t; represents the power sold to the power grid at time t; represents the power purchased from the power grid at time t; represents the wind power at time t; represents the photovoltaic power generation at time t.

[0237] In order to ensure the full utilization of new energy electric energy, the constraint shown in Equation (37) needs to be satisfied.

[0238] (37)

[0239] In the formula, represents the new energy curtailment rate; represents the upper limit of the allowable curtailment rate; represents the length of the time period to be optimized.

[0240] According to the experimental results of existing literature, in the present invention, the powers of the water pump, the hydrogen purification device, and the compressor are set to be proportional to the hydrogen production amount, and the power constraints are shown in Equations (38)-(40).

[0241] (38)

[0242] (39)

[0243] (40)

[0244] In the formula, , , are respectively the powers of the water pump, the hydrogen purification device, and the compressor at time t; represents the hydrogen production amount; , , respectively represent the proportionality coefficients related to the power of the water pump, the purification device, the compressor and the hydrogen production rate.

[0245] 6. After establishing the optimization configuration model of multi-electrolyzers with different power levels according to the constraint conditions, solve the optimization configuration model to obtain the optimal configuration scheme of multi-electrolyzers with different power levels.

[0246] In the present invention, any one of the following commercial solvers can be used to solve the optimization configuration model to obtain the optimal scheme of the configuration of multi-electrolyzers with different power levels: Gurobi, IBM CPLEX Optimizer, FICO Xpress, MOSEK, BARON, Lingo, Shanshu COPT or Alibaba MindOPT.

[0247] The application method of the solver belongs to the prior art, and the present invention will not elaborate.

[0248] 7. As common knowledge that can be understood by those skilled in the art, a computing device is required to implement the method of the present invention. The computing device includes: a memory configured to store instructions; and a processor configured to call the instructions from the memory and be able to implement the aforementioned power distribution method of the electric-hydrogen hybrid energy storage when executing the instructions.

[0249] At the same time, a computer-readable storage medium is also required to implement the method of the present invention. Instructions are stored on the computer-readable storage medium, and the instructions are used to cause a computer to execute the method. The computing device can be a personal computer, a server, or a network device, etc. The computer storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0250] 8. This example is based on the annual photovoltaic, wind power and power load data of a new energy plant area and uses the K-means method for clustering. Three typical days are clustered, and the data of the three typical days are as Figure 3 , 4 , and shown in Figure 5. The hydrogen energy load comes from a methane field, and the stable demand per hour is 50 kg.

[0251] The electricity purchase price curve from the power grid is asFigure 6 As shown, the electricity selling price to the power grid is set at 50% of the electricity purchasing price. The electrolyzer capacity grades and thermal energy parameters, as well as other system parameters, are shown in Table 1 and Table 2 respectively. The ambient temperature is set at 20 degrees Celsius, and the upper limit of the configuration for each grade of electrolyzer is three units.

[0252] According to the multi-cell optimization configuration method of the alkaline electrolyzer proposed by the present invention, the configuration results for five grades of electrolyzers are as follows: 0 units of 0.25MW electrolyzers, 3 units of 1MW electrolyzers, 0 units of 2MW electrolyzers, 3 units of 3MW electrolyzers, and 3 units of 5MW electrolyzers. A total of 9 electrolyzers are configured, and the total configured power is 27MW.

[0253] The start-stop results of all optimized electrolyzers are as Figure 7 shown. The details of the operating power of electrolyzers with different power grades (1MW, 3MW, 5MW) are as Figures 8 to 16 shown. The details of the operating temperature of electrolyzers with different power grades (1MW, 3MW, 5MW) are as Figures 17 to 25 shown.

[0254] Table 1 Electrolyzer capacity grades and thermal energy parameters

[0255] ;

[0256] Table 2 Other system parameters

[0257] ;

[0258] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various deformations or modifications within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A method for optimizing the configuration of multiple electrolyzers with different power levels in an electric-hydrogen coupling system, characterized in that Including: In a microgrid system, a hydrogen production electrolyzer, an energy storage battery, and a hydrogen storage tank are used to form an electric-hydrogen coupling system; Determine the renewable energy access scenarios for using the electric-hydrogen coupling system, and construct the constraint conditions for the energy storage system; taking the optimal daily average cost as the optimization objective, establish an optimization configuration model for multi-electrolyzers with different power levels according to the constraint conditions; solve the optimization configuration model to obtain the optimal configuration scheme for multi-electrolyzers with different power levels; The constraint conditions of the energy storage system include the electrolyzer operation characteristic constraints, the energy storage range constraints, and the hydrogen production-power balance constraints; The objective function of the optimization configuration model includes two parts: the operation cost and the fixed investment cost, which are specifically as follows: Among them, Z is the number of typical scenarios for renewable energy access, is the daily operating cost of the z-th typical scenario; is the average annual cost of the fixed investment cost of the electrolyzer stack; X is the variable to be optimized, including binary variables representing the electrolyzer configuration, equipment power variables during system operation, cell temperature variables, hydrogen production, and binary variables representing the start-stop and operating states of the electrolyzer. Assume that the set of capacities of available electrolyzer models is and their corresponding unit costs are The number of configurations for each type of electrolyzer is N = {k1, k2, k3,..., k n}, where n is the total number of models; then, The expression of the fixed investment cost is specifically: Among them, is the reciprocal of the present value factor of the annuity; r is the annual interest rate, and y is the service life of the electrolytic cell.

2. The method according to claim 1, wherein By establishing a unit cost model for electrolyzers with different power levels, calculate the fixed investment cost when configuring multiple types of electrolyzers; specifically including: First, analyze the quantitative relationship between the unit cost of the electrolyzer and the equipment capacity; then, according to the investigated equipment capacity and price data, use the least squares method for fitting to obtain the fitting function related to the unit cost and the equipment capacity; use this fitting function to calculate the fixed investment cost.

3. The method according to claim 1, characterized in that The expression of the daily operation cost is specifically: Among them, and are the cold start and hot start flag variables of the κ-th electrolyzer of the i-th type of electrolyzer; e cs and e hs represent the single cost of cold and hot starts; p a (t) is the amount of curtailed power at time t, e a is the unit cost of curtailed power penalty; p buy (t) and p sell (t) respectively represent the amount of power purchased / sold from the power grid at time t, e buy (t) and e sell (t) represent the unit power purchase price or power sale price; is the hydrogen demand at time t, that is, the amount of hydrogen sold, is the hydrogen selling price at time t; n is the total number of electrolyzer types.

4. The method according to claim 1, wherein The expression of the variable X to be optimized is specifically: Among them, T i,κ (t) represents the temperature of the κ-th electrolyzer of the i-th type at time t; k i represents the number of configurations of the i-th type of electrolyzer; represents the operating power of the κ-th electrolyzer of the i-th type at time t; represents the cold start flag variable of the κ-th electrolyzer of the i-th type; represents the hot start flag variable of the κ-th electrolyzer of the i-th type; p a (t) represents the curtailed power at time t; p buy (t) represents the power purchased from the grid at time t; p sell (t) represents the power sold to the grid at time t; represents the hydrogen demand at time t, i.e., the hydrogen sales volume; represents the binary state variable of the operating state of the κ-th electrolyzer of the i-th type at time t; represents the binary state variable of the shutdown state of the κ-th electrolyzer of the i-th type at time t; represents the binary state variable of the standby state of the κ-th electrolyzer of the i-th type at time t; Ins i represents the maximum number of configured units of the i-th type of electrolyzer; i represents the code corresponding to the electrolyzer model.

5. The method according to claim 1, wherein The electrolyzer operation characteristic constraints at least include the upper and lower power limits of the electrolyzer and the upper and lower temperature limits of the electrolyzer; among them, The upper and lower power limits of the electrolyzer are expressed as: The upper and lower temperature limits of the electrolyzer are expressed as: In the formula, represents the capacity of the i-th type of electrolytic cell; represents the operating power of the κ-th electrolytic cell of the i-th type at time t; represents the upper limit of the operating power related to temperature of the κ-th electrolytic cell of the i-th type; represents the lower limit of the operating temperature of the κ-th electrolytic cell of the i-th type; T i,κ (t) represents the temperature of the κ-th electrolytic cell of the i-th type at time t; represents the upper limit of the operating temperature of the κ-th electrolytic cell of the i-th type; i represents the corresponding code of the electrolytic cell model; κ represents the serial number of the electrolytic cell; and are binary state variables representing the operating and shutdown states of the κ-th electrolytic cell of the i-th type at time t, respectively; when the electrolytic cell is in the operating state, when the electrolytic cell is in the shutdown state, 6. The method according to claim 1, characterized in that, The electrolyzer operation characteristic constraints also include any one or more of the following constraint conditions: (1) Electrolyzer standby power constraint: In the formula, represents the standby power of the κ-th electrolytic cell of the i-th type; T i,κ (t) represents the temperature of the κ-th electrolytic cell of the i-th type at time t; T a (t) represents the ambient temperature at time t; represents the binary state variable of the standby state of the κ-th electrolytic cell of the i-th type at time t; represents the thermal resistance of the κ-th electrolytic cell of the i-th type; (2) Electrolyzer operation state variable constraint: In the formula, are binary state variables representing the operating state, shutdown state, and standby state of the electrolytic cell at time t, respectively; (3) Electrolyzer cold start process constraint: In the formula, represents the lower limit of the operating temperature of the κ-th electrolytic cell of the i-th type; (4) Electrolyzer cold start constraint: In the formula, is the cold start flag variable; (5) Electrolyzer temperature change constraint: Where T i,κ (t + δt) represents the temperature of the electrolytic cell at adjacent time points t + δt; is the heat generation rate of the electrolytic cell; is the heat transfer power transferred from the external heater to the electrolytic cell; is the heat absorption rate of the thermal energy of the refrigeration system; represents the heat exchange rate between the electrolytic cell and the environment; represents the heat carried away by hydrogen and oxygen; represents the heat capacity of the electrolytic cell; δt represents the time interval; (6) Electrolyzer heat production rate constraint: In the formula, represents the operating power of the κ-th electrolyzer of the i-th type; represents the electrolyzer efficiency; (7) Heat loss constraint of the external heating source: In the formula, represents the actual heating power supplied by the external heater of the κ-th electrolytic cell of the i-th type; represents the heating power provided by the external heater of the κ-th electrolytic cell of the i-th type; η Heat is the heating efficiency of the external heater; (8) Heat absorption rate constraint of the electrolyzer cooling system: In the formula, represents the actual absorption power of the cooling device of the κ-th electrolytic cell of the i-th type; represents the power used for cooling of the cooling device of the κ-th electrolytic cell of the i-th type; cop cool represents the coefficient of performance of the cooling system; (9) Heat exchange loss constraint between the electrolyzer and the environment: In the formula, represents the power of heat exchange between the κ-th electrolyzer of the i-th type and the environment; T a (t) represents the environmental temperature at time t, represents the thermal resistance of the electrolyzer; (10) Electrolyzer electrothermal operation characteristic constraint: The electrothermal operation characteristics of the electrolyzer need to meet the requirements of the electrolyzer electrothermal characteristic model, which is to fit the relationship between the electrolysis power upper limit and the temperature, and the relationship between the electrolysis efficiency and the power upper limit and the temperature based on the experimental results, so as to establish an electrolyzer electrothermal characteristic model related to the power level, which is used to characterize the influence of the electrolyzer temperature and power on the electrolysis efficiency; including: (1) At different temperatures, input the rated voltage to the electrolyzer to obtain its power upper limit; according to the obtained temperature-power data pairs, fit the function related to the power upper limit and the temperature; then normalize this function to obtain the normalized power upper limit function, and finally summarize the power upper limit functions corresponding to electrolyzers with different power levels; (2) At different temperatures, measure the electrolysis efficiency of the electrolyzer at different powers; according to the obtained temperature-power-efficiency data pairs, fit the function related to the efficiency, power, and temperature; then normalize this function based on the electrolyzer capacity to obtain the normalized efficiency function, and finally summarize the electrolysis efficiency functions corresponding to electrolyzers with different power levels; For electrolyzers with different power levels, their maximum power constraints are different, and separate modeling is required in the multi-cell optimization configuration.

7. The method according to claim 1, wherein All the operating characteristic constraints of the electrolyzers need to be multiplied by the binary variable Ins n as specifically expressed as: Among them, F i,κ (X) represents the set of inequality constraints; G i,κ (X) represents the set of equality constraints; Ins i (κ) represents a binary variable indicating whether the k-th electrolytic cell of the i-th type is configured. If it is 1, it is configured; if it is 0, it is not configured.

8. The method according to claim 1, characterized in that, The energy storage range constraints include any one or more of the following constraint conditions: (1) State of charge constraint of the energy storage battery: Where, SoC Be (t) represents the state of charge of the battery at time t; ξ Be,dsp represents the dissipation rate of the battery; SoC Be,ini represents the state of charge of the battery at the initial moment; and represent the charging and discharging efficiencies of the battery; represents the storage capacity of the battery; and represent the charging power and discharging power of the battery at time t'; (2) Charge and discharge power range constraint of the energy storage battery: In the formula, is the upper limit of the battery charging power, is the upper limit of the battery discharging power; represents the battery charging power at time t; represents the battery discharging power at time t; (3) Safety and stability constraint of the energy storage battery: The above formula indicates that the battery cannot charge and discharge simultaneously; (4) Hydrogen storage state constraint of the hydrogen storage tank: where LoH Hs (t) represents the hydrogen storage amount state at time t; LoH Hs,ini represents the hydrogen storage amount state at the initial time, ξ Hs,dsp represents the dissipation rate of the hydrogen storage tank; and represent the hydrogen charging efficiency and hydrogen discharging efficiency of the hydrogen storage tank; m Ld (t′) represents the hydrogen energy demand at time t′; represents the hydrogen storage capacity of the hydrogen storage tank; m H2 (t′) represents the hydrogen production rate at time t′; m Ld (t′) represents the hydrogen energy demand at time t′; ξ Hs,dsp represents the dissipation rate of the hydrogen storage tank; (4) Storage range constraints of lithium batteries and hydrogen storage tanks: In the formula, represents the lower limit of the SOC of the lithium battery; SoC Be (t) represents the SOC of the lithium battery at time t; represents the upper limit of the SOC of the lithium battery; represents the lower limit of the hydrogen storage state of the hydrogen storage tank; LoH Hs (t) represents the hydrogen storage state of the hydrogen storage tank at time t; represents the upper limit of the hydrogen storage state of the hydrogen storage tank.

9. The method according to claim 1, characterized in that, The hydrogen production-power balance constraints include any one or more of the following constraint conditions: (1) Power balance constraint of the electrolysis-hydrogen coupling system: Wherein, p ele (t) represents the total electrolysis power at time t; p Cool (t) represents the total cooling power at time t; p Heat (t) represents the total heating power; p Pump (t) represents the total pump power at time t; p Puri (t) represents the total purification power at time t; represents the battery charging power at time t; represents the battery discharging power at time t; p Cmp (t) represents the total compression power at time t; p Ld (t) represents the electrical load at time t; p a (t) represents the curtailed power at time t; p sell (t) represents the power sold to the grid at time t; p buy (t) represents the power purchased from the grid at time t; p Wind (t) represents the wind power at time t; p Pv (t) represents the photovoltaic power at time t; (2) New energy curtailment rate constraint: where p a (t) represents the curtailment rate of new energy; β A represents the upper limit of the allowable curtailment rate; τ represents the time period to be optimized; (3) Power constraints of water pumps, hydrogen purification devices, and compressors; where p Pump (t), p Puri (t), p Cmp (t) are the powers of the water pump, hydrogen purification device, and compressor at time t, respectively; represents the hydrogen production quantity α Pump , α Puri , α Cmp represent the proportionality coefficients related to the hydrogen production rate of the water pump, purification device, and compressor powers, respectively.

10. The method according to any one of claims 1 to 9, characterized in that, The electrolyzer is an alkaline electrolyzer; the energy storage battery is a single lithium battery cell or a battery pack composed of single lithium battery cells.

11. The method according to any one of claims 1 to 9, characterized in that, Use any one of the following solvers to solve the optimization configuration model to obtain the optimal solutions for multi-electrolyzer configurations with different power levels: Gurobi, IBM CPLEX Optimizer, FICO Xpress, MOSEK, BARON, Lingo, Shanshu COPT, or Alibaba MindOPT.

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