Optimal configuration method for multiple electrolytic baths with different power levels in electro-hydrogen coupling system

Abstract

The invention relates to an electric energy storage technology, and aims to provide an optimal configuration method for multiple electrolytic baths with different power levels in an electricity-hydrogen coupling system. The method comprises the following steps: in a micro-grid system, forming an electricity-hydrogen coupling system by a hydrogen production electrolytic bath, an energy storage battery and a hydrogen storage tank; determining a renewable energy access scene using the energy storage system, and constructing constraint conditions of the energy storage system; taking the optimal daily average cost as an optimization target, and establishing a multi-electrolytic cell optimization configuration model of different power levels according to the constraint conditions; and the optimal configuration model is solved, and the optimal configuration scheme of the multiple electrolytic cells of different power levels is obtained. According to the method, a multi-cell configuration technology that the total power of the electrolytic cells does not need to be preset is not needed, mathematical modeling and optimization means are adopted, the optimal electrolytic cell combination configuration result is directly calculated, and meanwhile economical efficiency and configuration flexibility are achieved; the result simulation in the system optimization process is closer to the actual working process of the electrolytic cell, and the credibility of the optimization configuration result can be improved.

Description

Technical Field

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

[0002] With the transformation of energy structure and the widespread application of renewable energy, new energy water electrolysis hydrogen production has attracted much attention as a green and sustainable way of hydrogen production. However, with the increase in hydrogen production demand and load fluctuations, electrolyzers with excessively high power levels have gradually shown limitations in terms of flexibility, reliability, economy, and energy efficiency. In contrast, the use of multi-power-level electrolyzers to jointly produce hydrogen can improve the flexibility of the system, achieve faster start and stop, and a larger operating range. In the renewable energy access scenario, the multi-tank system has a stronger ability to adapt to load fluctuations, and the coordinated operation of electrolyzers of different power levels improves the system stability and the ability to absorb renewable energy.

[0003] However, there are significant differences between multi-tank optimization configuration technology and single-tank optimization configuration technology. At present, the multi-tank optimization configuration methods proposed by researchers are mostly based on the premise of the total power of the established electrolyzer, and the enumeration method is used to compare the economic efficiency 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 windpower scenario" and the public literature "Off-grid wind / hydrogen systems with multi-electrolyzers: Optimized operational strategies". However, this configuration method has a fixed total power of the electrolyzer, and there are human judgment errors, so the optimal configuration combination cannot be obtained. At the same time, the enumeration method increases the amount of calculation and is not suitable for electric-hydrogen coupling systems with a large number of electrolyzer configuration combinations.

[0004] Therefore, it is necessary to propose new solutions to achieve the optimal 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 a method for optimizing the configuration of multiple electrolyzers with different power levels in an electric-hydrogen coupling system.

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

[0007] Provided is a method for optimizing the configuration of multiple electrolyzers with different power levels in an electric-hydrogen coupling system, comprising: in a microgrid system, forming an electric-hydrogen coupling system with a hydrogen production electrolyzer, an energy storage battery and a hydrogen storage tank; determining a renewable energy access scenario for using the energy storage system, and constructing constraints for the energy storage system; taking the optimal daily average cost as an optimization target, and establishing an optimization configuration model for multiple electrolyzers with different power levels according to the constraints; solving the optimization configuration model to obtain an optimal configuration scheme for multiple electrolyzers with different power levels;

[0008] The constraints 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: operating cost and fixed investment cost, which are as follows:

[0010]

[0011] Where Z is the number of typical scenarios for renewable energy access, is the daily operating cost of the zth typical scenario; is the average annual cost of the fixed investment cost of the electrolytic cell group; are the variables to be optimized, including binary variables representing the configuration of the electrolyzer, equipment power variables during the system operation process, cell temperature variables, hydrogen production, and binary variables representing the start, stop and operation status of the electrolyzer.

[0012] As a preferred embodiment of the present invention, it is assumed that the capacity set of the available electrolytic cell models is , and its corresponding unit cost is , the number of configurations for each type of electrolyzer is , n is the total number of models; then,

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

[0014]

[0015]

[0016] in, is the inverse of the annuity present value coefficient; is the annual interest rate, The life of the electrolytic cell.

[0017] As a preferred solution of the present invention, a unit cost model of electrolytic cells of different power levels is established to calculate the fixed investment cost when configuring multiple types of electrolytic cells; specifically, the method includes:

[0018] Firstly, the quantitative relationship between the unit cost and equipment capacity of the electrolytic cell is analyzed. Then, according to the equipment capacity and price data of the survey, the least squares method is used to fit the correlation function between the unit cost and equipment capacity. The fixed investment cost is calculated using the fitting function.

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

[0020]

[0021] in, For the Type of electrolyzer Cold start and hot start flag variables for each electrolyzer; and Indicates the single charge for hot and cold starts; for Discarding electricity at all times, Penalty cost for unit power abandonment; and Respectively Purchase / sell electricity from the power grid at all times, and Indicates the unit's electricity purchase price or electricity sales price; for The hydrogen demand at any given moment, i.e. the amount of hydrogen sold, for The price of hydrogen sold at any given moment; is the total number of electrolytic cell models.

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

[0023]

[0024] in, Indicates the i-th category The electrolytic cell at time Temperature; Indicates the number of configurations of the i-th type of electrolytic cell; Indicates the i-th category The operating power of an electrolytic cell at time t; Indicates the i-th category An electrolyzer cold start flag variable; Indicates the i-th category An electrolytic cell hot start flag variable; express Abandoning electricity at all times; express Purchase electricity from the power grid at all times; express Sell ​​electricity from the grid at all times; express The hydrogen demand at any given moment, i.e. the amount of hydrogen sold; represents the i-th category at time t A binary state variable for the operating state of an electrolytic cell; represents the i-th category at time t A binary state variable for the shutdown state of an electrolyzer; represents the i-th category at time t A binary state variable for the standby state of an electrolyzer; Indicates the maximum number of electrolytic cells of the i-th type; i represents the code corresponding to the electrolytic cell model.

[0025] As a preferred embodiment of the present invention, the electrolytic cell operating characteristic constraints include at least the upper and lower limit constraints of the electrolytic cell power and the upper and lower limit constraints of the electrolytic cell temperature; wherein,

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

[0027]

[0028] The upper and lower temperature constraints of the electrolytic cell are expressed as:

[0029]

[0030] In the formula, represents the capacity of the i-th type electrolytic cell; Indicates the i-th category The operating power of an electrolytic cell at time t; Indicates the i-th category The upper limit of the operating power of each electrolyzer related to temperature; Indicates the i-th category The lower limit of the operating temperature of each electrolyzer; Indicates the i-th category The temperature of an electrolytic cell at time t; Indicates the i-th category The upper limit of the operating temperature of each electrolytic cell; i represents the code corresponding to the electrolytic cell model; Indicates the electrolytic cell serial number;

[0031] and are the i-th category at time t. The binary state variable of the running and shutdown state of each electrolytic cell; when the electrolytic cell is in the running state, ; When the electrolyzer is in shutdown state, .

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

[0033] (1) Electrolyzer standby power constraints:

[0034]

[0035] In the formula, Indicates the i-th category Standby power of each electrolyzer; Indicates the i-th category The temperature of an electrolytic cell at time t; represents the ambient temperature at time t; represents the i-th category at time t A binary state variable for the standby state of an electrolyzer; Indicates the i-th category Thermal resistance of an electrolytic cell;

[0036] (2) Constraints on electrolytic cell operating state variables:

[0037]

[0038] In the formula, , , The binary state variables represent the running state, shutdown state and standby state of the electrolyzer at time t respectively;

[0039] (3) Constraints on the cold start process of the electrolyzer:

[0040]

[0041] In the formula, Indicates the i-th category The lower limit of the operating temperature of each electrolyzer;

[0042] (4) Cold start constraints of electrolyzer:

[0043]

[0044] In the formula, It is the cold start flag variable;

[0045] (5) Constraints on electrolytic cell temperature changes:

[0046]

[0047] In the formula, Indicates that the electrolytic cell is at adjacent time points Temperature; is the heat generation rate of the electrolytic cell; The heat transfer power delivered to the electrolyzer by the external heater; is the heat absorption rate of the refrigeration system; Indicates 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 electrolytic cell; Indicates a time interval;

[0048] (6) Constraints on the heat generation rate of the electrolytic cell:

[0049]

[0050] In the formula, Indicates the i-th category The operating power of each electrolyzer; represents the efficiency of the electrolyzer;

[0051] (7) Heat loss constraints of external heating sources:

[0052]

[0053] In the formula, Indicates the i-th category Actual heating power of the external heater of each electrolytic cell; Indicates the i-th category The heating power provided by the external heater of each electrolyzer; is the heating efficiency of the external heater;

[0054] (8) Constraints on the heat absorption rate of the electrolytic cell cooling system:

[0055]

[0056] In the formula, Indicates the i-th category The actual absorbed power of the cooling device of each electrolyzer; Indicates the i-th category The power of the cooling device of each electrolytic cell used for cooling; Indicates the coefficient of performance of the cooling system;

[0057] (9) Constraints on heat exchange losses between the electrolyzer and the environment:

[0058]

[0059] In the formula, Indicates the i-th category The power of heat exchange between an electrolyzer and the environment; represents the ambient temperature at time t, represents the thermal resistance of the electrolytic cell;

[0060] (10) Constraints on the electrothermal operation characteristics of the electrolyzer:

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

[0062] (1) By inputting the rated voltage into the electrolytic cell at different temperatures, its power upper limit is obtained; based on the obtained temperature-power data pairs, a function related to the power upper limit and temperature is fitted; the function is then normalized to obtain the normalized power upper limit function, and finally the power upper limit functions corresponding to electrolytic cells of different power levels are summarized;

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

[0064] The maximum power constraints of electrolytic cells of different power levels are different and need to be modeled separately in the multi-cell optimization configuration.

[0065] As a preferred solution of the present invention, all constraints must be consistent with the binary variables Multiplication, specifically expressed as:

[0066]

[0067] in, represents a set of inequality constraints; represents a set of equality constraints; Indicates Class A binary variable indicating whether an electrolyzer is configured, which is 1 if configured and 0 if not;

[0068] As a preferred solution of the present invention, the energy storage range constraint includes any one or more of the following constraint conditions:

[0069] (1) Energy storage battery state of charge constraints:

[0070]

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

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

[0073]

[0074]

[0075] In the formula, The upper limit of battery charging power. The upper limit of battery discharge power; Indicates the battery charging power at time t; Indicates the battery discharge power at time t;

[0076] (3) Safety and stability constraints of energy storage batteries:

[0077]

[0078] The above formula shows that the battery cannot be charged and discharged at the same time;

[0079] (4) Constraints on hydrogen storage status of hydrogen storage tanks:

[0080]

[0081] In the formula, Indicates time The hydrogen storage state; Indicates the hydrogen storage state at the initial moment, represents the dissipation rate of the hydrogen storage tank; and Indicates the hydrogen filling efficiency and hydrogen discharge efficiency of the hydrogen storage tank; express The demand for hydrogen energy at all times; Indicates the hydrogen storage capacity of the hydrogen storage tank; express The hydrogen production rate at the time; express The demand for hydrogen energy at all times; represents the dissipation rate of the hydrogen storage tank;

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

[0083]

[0084]

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

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

[0087] (1) Power balance constraints 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; Indicates the total heating power; represents the total pump power at time t; represents the total purification power at time t; Indicates the battery charging power at time t; Indicates the battery discharge power at time t; represents the total compression power at time t; represents the power load at time t; represents the abandoned power at time t; Indicates the power sold to the grid at time t; Indicates the power purchased from the grid at time t; represents the wind power at time t; represents the photovoltaic power generation power at time t;

[0090] (2) Constraints on the rate of abandoned electricity from new energy sources:

[0091]

[0092] In the formula, Represents the rate of abandoned electricity from renewable energy sources; Indicates the upper limit of the allowable power abandonment rate; Indicates the time period to be optimized;

[0093] (3) Power constraints of water pumps, hydrogen purification devices and compressors;

[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 amount of hydrogen energy produced; , , They respectively represent the proportional coefficients of the water pump, purification device, compressor power and hydrogen production rate.

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

[0099] As a preferred embodiment of the present invention, the optimization configuration model is solved using any one of the following solvers to obtain the optimal solution for the configuration of multiple electrolytic cells with different power levels: Gurobi, IBM CPLEX Optimizer, FICOXpress, MOSEK, BARON, Lingo, Shanshu COPT or Ali MindOPT.

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

[0101] a memory configured to store instructions; and

[0102] The processor is configured to call the instructions from the memory and implement the aforementioned method for optimizing configuration of multiple electrolyzers with different power levels in the electric-hydrogen coupling system when executing the instructions.

[0103] The present invention further provides a computer-readable storage medium having instructions stored thereon, the instructions being used to enable a computer to execute the aforementioned method for optimizing configuration of multiple electrolyzers of different power levels in an electric-hydrogen coupling system.

[0104] Description of the invention principle:

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

[0106] Based on this innovative idea, the present invention first proposes an operation characteristic model and a unit cost model for electrolytic cells of different power levels. Among them, the operation characteristic model fully considers the influence of temperature on the operation of the alkaline solution electrolytic cell, and the unit cost model is the relationship between the unit cost and the rated power of the alkaline solution electrolytic cell based on the survey data. Based on these two models, it is no longer necessary to pre-set the total power of the electrolytic cell. Instead, with the optimal economy as the goal, mathematical modeling and optimization methods are used to directly calculate the optimal electrolytic cell combination configuration result with a solver.

[0107] In the optimization configuration requirements of the multi-tank scenario targeted by the present invention, the capacity of the electrolyzer is no longer one of the optimization variables, but the number of electrolyzers of each type is used instead, which is more in line with actual engineering requirements; in addition, the electrothermal characteristics and unit cost of each power level of the electrolyzer are independently considered, making the optimization result more accurate and reasonable. The multi-tank optimization configuration method can be directly solved using commercial solvers such as Gurobi.

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

[0109] 1. The multi-tank optimization configuration method for the electrolytic hydrogen production system proposed in the present invention does not require the multi-tank configuration technology of the total power of the electrolyzer to be pre-set. It adopts mathematical modeling and optimization means to directly calculate the optimal electrolyzer combination configuration result, while achieving economy and configuration flexibility.

[0110] 2. The electrothermal characteristic model and unit capacity cost model of the alkaline solution electrolytic cell proposed in the present invention play an important role in the multi-cell configuration of the alkaline solution electrolytic cell, making the simulation results in the system optimization process closer to the actual working process of the electrolytic cell, improving the credibility of the optimization configuration results, and having strong practical significance and engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] Figure 1 The microgrid system structure is an electric-hydrogen hybrid energy storage system containing an electrolyzer.

[0112] Figure 2 A fitting diagram of the relationship between the unit cost and capacity of the alkali solution electrolytic cell in the optimization configuration model proposed for the present invention.

[0113] Figure 3 , Figure 4 , Figure 5 They are respectively the wind, solar and electric energy load curves of three typical days described in the examples of the present invention.

[0114] Figure 6 This is the 24h electricity price curve described in the example of the present invention.

[0115] Figure 7These are the optimized start-up and shutdown results of three typical days of alkali solution electrolysis cells described in the examples of the present invention.

[0116] Figure 8 , Fig. 9 , Fig.10 They are respectively the optimized operating power diagrams of three typical days of 1MW alkaline solution electrolyzer described in the examples of the present invention.

[0117] Fig.11 , Fig.12 , Fig.13 They are respectively the three typical daily operating power diagrams of a 3MW alkaline solution electrolyzer after optimization described in the examples of the present invention.

[0118] Fig.14 , Fig.15 , Fig.16 They are respectively the optimized operating power diagrams of three typical days of 5MW alkaline solution electrolyzers described in the examples of the present invention.

[0119] Fig.17 , Fig.18 , Fig.19 They are temperature diagrams of three typical days of 1MW alkaline solution electrolyzer after optimization described in the examples of the present invention.

[0120] Fig. 20 , Fig.21 , Fig. 22 They are temperature diagrams of three typical days of 3MW alkaline solution electrolyzer after optimization described in the examples of the present invention.

[0121] Fig.23 , Fig.24 , Fig.25 They are temperature diagrams of three typical days of a 5MW alkaline solution electrolyzer after optimization described in the examples of the present invention. DETAILED DESCRIPTION

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

[0123] (1) In the microgrid system, a hydrogen production electrolyzer, energy storage batteries and hydrogen storage tanks are used to form an electric-hydrogen coupling system; the renewable energy access scenario using the energy storage system is determined, and the constraints of the energy storage system are constructed;

[0124] (2) Taking the optimal daily average cost as the optimization target, an optimization configuration model of multiple electrolyzers with different power levels is established according to the constraints; the constraints of the energy storage system include the operating characteristics constraints of the electrolyzers, the energy storage range constraints and the hydrogen production-power balance constraints; the objective function of the optimization configuration model includes two parts: operating cost and fixed investment cost.

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

[0126] In order to more clearly illustrate the technical solution in the embodiment of the present invention, the implementation mode of the present invention will be introduced below with reference to the accompanying drawings.

[0127] 1. The present invention can be applied to a variety of renewable energy access scenarios, such as a separate wind-solar-photovoltaic new energy microgrid, or a wind-solar-photovoltaic coupled new energy microgrid.

[0128] Figure 1 The structure of a microgrid system containing an electric-hydrogen hybrid energy storage system is shown as an example, which is mainly composed of photovoltaic arrays, energy storage batteries (such as lithium batteries), hydrogen production electrolyzers, hydrogen storage tanks, loads, DC-DC converters and other components. The energy storage system includes an electric energy storage system based on batteries and a hydrogen energy storage system based on electrolyzers and hydrogen storage tanks, in which the hydrogen production electrolyzer and the energy storage battery serve as electric-hydrogen hybrid energy storage coupling elements. In the present invention, the electrolyzer is an alkaline solution electrolyzer; the energy storage battery is a lithium battery cell, or a battery pack composed of lithium battery cells.

[0129] 2. The present invention proposes a new method for calculating fixed investment costs, which uses the unit cost model to calculate the fixed investment costs, and clarifies the relationship between the unit cost and capacity of the electrolytic cell to ensure that the configuration result is accurate and optimal. Considering that the fixed investment cost and operating cost of the electrolytic cell together constitute the total cost, the present invention divides the optimization goal of the optimal configuration of the electrolytic cell into the optimization of the fixed investment cost and the operating cost.

[0130] Based on the above reasons, the objective function of the optimization configuration model of the present invention is specifically shown in formula (1), which includes two parts: operating cost and 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 annuity present value coefficient.

[0131] (1)

[0132] In the formula, Z is the number of typical scenarios for the selected renewable energy access. is the daily operating cost of the zth typical scenario; is the average annual cost of the fixed investment cost of the electrolytic cell group; are the variables to be optimized, including the binary variables representing the electrolyzer configuration, the equipment power variables during the system operation, the tank temperature variables, the hydrogen production, and the binary variables representing the start and stop and operation status of the electrolyzer, which are specifically expressed as formula (2):

[0133] (2)

[0134] in, Indicates the i-th category The electrolytic cell at time Temperature; Indicates the number of configurations of the i-th type of electrolytic cell; Indicates the i-th category The operating power of an electrolytic cell at time t; Indicates the i-th category An electrolyzer cold start flag variable; Indicates the i-th category An electrolytic cell hot start flag variable; express Abandoning electricity at all times; express Purchase electricity from the power grid at all times; express Sell ​​electricity from the grid at all times; express The hydrogen demand at any given moment, i.e. the amount of hydrogen sold; represents the i-th category at time t A binary state variable for the operating state of an electrolytic cell; represents the i-th category at time t A binary state variable for the shutdown state of an electrolyzer; represents the i-th category at time t A binary state variable for the standby state of an electrolyzer; Indicates the maximum number of electrolytic cells of the i-th type; i represents the code corresponding to the electrolytic cell model.

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

[0136] (1) For a multi-electrolyzer hydrogen production system, it is assumed that the capacity set of available electrolyzer models is , where the total number of electrolytic cell models and the corresponding unit cost are , the number of configurations for each type of electrolyzer is , n is the total number of models.

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

[0138] (3)

[0139] in, It is the inverse of the annuity present value coefficient, and its specific calculation method is formula (4).

[0140] (4)

[0141] in, is the annual interest rate, The life of the electrolytic cell.

[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 unit cost and equipment capacity of the alkali electrolyzer The quantitative relationship between them is determined by fitting the least square method based on the equipment capacity and price data of the survey, and the correlation function between unit cost and equipment capacity is obtained to calculate the fixed investment cost.

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

[0145] (5)

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

[0147] In the optimization configuration requirements of the multi-tank scenario targeted by the present invention, the electrolytic cell capacity is no longer one of the optimization variables, but is replaced by the configuration quantity of each type of electrolytic cells, which is more in line with actual engineering needs; in addition, the electrothermal characteristics and unit costs of the electrolytic cells of each power level configured are independently considered, making the optimization results more accurate and reasonable.

[0148] 4. Different electrolytic cell configurations will have a significant impact on system operation. Therefore, when configuring multiple electrolytic cells, the operating cost must also be considered as an optimization goal.

[0149] Using the wind, solar and load curves of a typical day, the average daily cost of system operation can be estimated, including: the cold and hot start-up costs of the electrolyzer, the cost of purchasing electricity from the grid, the penalty cost for power abandonment, and the income from electricity and hydrogen sales as negative costs are also taken into account. The daily operating cost As shown in formula (6).

[0150] (6)

[0151] in, For the Type of electrolyzer The cold start and hot start flag variable of the electrolytic cell. When the variable is 1, it means that the electrolytic cell is There is always cold start / hot start behavior. Because some models of electrolyzers are not configured, The summation starts from 0. and Indicates the single charge for hot and cold starts. for Discarding electricity at all times, is the penalty cost for unit power abandonment, and Respectively Purchase / sell electricity from the power grid at all times, and Indicates the unit's electricity purchase price or electricity sales price. for The hydrogen demand at any given moment, i.e. the amount of hydrogen sold; for The price of hydrogen sold at any time.

[0152] 5. Optimize the constraints of the configuration model

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

[0154] (1) Constraints on electrolytic cell operating characteristics

[0155] In this example, the model after normalization is No. The maximum power constraint of each electrolyzer is shown in (7).

[0156] (7)

[0157] Model No. The power constraint of an electrolyzer can be expressed as (8).

[0158] (8)

[0159] In the formula, represents the capacity of the i-th type electrolytic cell; Indicates the i-th category The operating power of an electrolytic cell at time t; Indicates the i-th category The upper limit of the operating power of each electrolytic cell related to the temperature; i represents the code corresponding to the electrolytic cell model; Indicates the electrolytic cell serial number;

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

[0161] (9)

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

[0163] The states of alkali electrolytic cells are generally divided into three categories: shutdown state, standby state and running state. , and It means that the temperature constraint can be expressed as (10).

[0164] (10)

[0165] in, Indicates the i-th category The lower limit of the operating temperature of each electrolyzer; Indicates the i-th category The temperature of an electrolytic cell at time t; Indicates the i-th category The upper limit of the operating temperature of each electrolyzer; and are the i-th category at time t. The binary state variable of the running and shutdown state of each electrolyzer. When the electrolyzer is in the running state, , when the electrolyzer is in shutdown state, .

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

[0167] (11)

[0168] That is, in the shutdown state, the electrolytic cell power is 0, and its upper and lower power limits are only effective in the running state.

[0169] When the electrolyzer is in standby mode, it needs to maintain the standby temperature, so the electrolyzer still needs power supply, but no hydrogen will be produced. Therefore, the electrolyzer power is set to a power value that can maintain the current temperature. Indicates that:

[0170] (12)

[0171] In the formula, Indicates the i-th category Standby power of each electrolyzer; Indicates the i-th category The temperature of an electrolytic cell at time t; represents the ambient temperature at time t; represents the i-th category at time t A binary state variable for the standby state of an electrolyzer; Indicates the i-th category Thermal resistance of an electrolytic cell.

[0172] At the same time, the operating state variables of the electrolyzer are constrained, namely:

[0173] (13)

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

[0175] In addition, due to the low purity of hydrogen in the early stage of the cold start process of the electrolyzer, the hydrogen produced during this period is often not used and is discharged. Therefore, it is assumed that the alkali liquid electrolyzer is in operation only when the temperature 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 formula (14).

[0176] (14)

[0177] In the formula, Indicates the i-th category The lower limit of the operating temperature of the electrolyzer.

[0178] Cold start flag variable in the objective function Depend on Moment and The state variables at the moment determine the decision. After the electrolytic cell has gone through a certain moment, the operating state variables From 0 to 1, the shutdown state variable If it changes from 1 to 0, it means that the electrolytic cell has completed 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 formula (15).

[0179] (15)

[0180] For a multi-electrolyzer system, the temperature of each electrolyzer is different. No. The temperature variation constraint of an electrolytic cell is shown in equation (16).

[0181] (16)

[0182] in and Indicates the time of the alkali electrolyte electrolysis cell and adjacent time points The temperature, is the heat generation rate of the alkali solution electrolyzer, is the heat transfer power delivered to the lye electrolyser by the external heater, is the heat absorption rate of the refrigeration system, Indicates the heat exchange rate between the alkali electrolysis cell and the environment, represents the heat carried away by hydrogen and oxygen, It indicates the heat capacity of the alkali electrolytic cell; Indicates a time interval.

[0183] Electrolyzer efficiency Electrical energy is converted into chemical energy to produce hydrogen, and the remaining energy is converted into thermal energy, which affects the temperature of the electrolytic cell. The heat generation rate of the alkaline solution electrolytic cell can be expressed as (17).

[0184] (17)

[0185] In the formula, Indicates the i-th category The operating power of each electrolyzer; Represents the efficiency of the electrolyzer.

[0186] There is a loss in the process of transferring heat generated by the external heating source to the alkali solution electrolysis cell, resulting in a heat transfer efficiency less than 1, as shown in formula (18).

[0187] (18)

[0188] In the formula, Indicates the i-th category Actual heating power of the external heater of each electrolytic cell; Indicates the i-th category The heating power provided by the external heater of each electrolyzer; is the heating efficiency of the external heater.

[0189] The cooling system absorbs heat from the alkaline electrolyte cell through a cooling cycle. The heat absorption rate of the cooling system is related to the electrolyte flow rate, electrolyte heat capacity, electrolyte inlet temperature, and AWE temperature. 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. ,Right now:

[0190] (19)

[0191] In the formula, Indicates the i-th category The actual absorbed power of the cooling device of each electrolyzer; Indicates the i-th category The power of the cooling device of each electrolytic cell used for cooling; Indicates the coefficient of performance of the cooling system.

[0192] The above-mentioned external heating source and cooling system can help adjust the temperature of the alkaline solution electrolyzer, so that the temperature of the electrolyzer can be flexibly changed. However, both of these temperature control methods rely on the supply of electricity. In previous studies, the power consumption of these two devices has not been fully considered. This negligence can easily lead to deviations in energy demand estimation, which in turn has an adverse effect on the system's operating efficiency. In the optimization configuration model considering the electrothermal characteristics of the electrolyzer proposed in this example, the power consumption of these two processes is carefully considered, 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 alkali solution electrolysis cell and the environment, there will always be heat exchange. The heat exchange equation is shown in equation (20).

[0194] (20)

[0195] In the formula, Indicates the i-th category The power of heat exchange between an electrolyzer and the environment; represents the ambient temperature at time t, Represents the thermal resistance of the electrolytic cell.

[0196] (2) Constraints on the electrothermal operation characteristics of the electrolyzer:

[0197] In addition to the various electrolytic cell operating characteristic constraints described above, the electrothermal characteristic model is also an important component of the optimization constraints of the present invention. Considering the electrothermal operating characteristic constraints of the electrolytic cell, the final configuration scheme will be more practical. According to the experimental results, the relationship between the upper limit of electrolysis power and temperature, as well as the relationship between electrolysis efficiency and power upper limit 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 electrolytic cell temperature and power on electrolysis efficiency. Specifically comprising the following steps:

[0198] A. At different temperatures, the rated voltage is input into the experimental electrolytic cell to obtain its power, which is its power upper limit. According to the obtained temperature-power data pair, a function related to the power upper limit and temperature is fitted, and then the function is normalized to obtain the normalized power upper limit function. Finally, the power upper limit function corresponding to the alkali solution electrolytic cells of different power levels can be obtained.

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

[0200] (twenty one)

[0201] The above maximum power constraint reflects the influence of the temperature and rated power of the alkaline solution 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. The electrolysis efficiency at different powers at different temperatures can be measured by existing methods such as the drainage method. Based on the obtained temperature-power-efficiency data pairs, a function related to efficiency, power and temperature is fitted, and then the function is normalized based on the electrolytic cell capacity to obtain the normalized efficiency function. Finally, the electrolysis efficiency function corresponding to the alkali solution electrolytic cells of different power levels can be obtained.

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

[0204] (twenty two)

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

[0206] Based on the constraints described in parts (1) and (2), for the model No. For a caustic soda electrolyzer, the relevant constraints can be expressed as:

[0207] (twenty three)

[0208] Based on the method for configuring the number of alkali solution electrolyzers of different power levels using a direct optimization method proposed in the present invention, all the above constraints related to the alkali solution electrolyzer need to be related to the binary variable Multiply, and modify the above equality constraint to:

[0209] (twenty four)

[0210] in, represents a set of inequality constraints; represents a set of equality constraints; Indicates Class A binary variable indicating whether an electrolyzer is configured. If it is 1, it is configured, and if it is 0, it is not configured.

[0211] (3) Energy storage range constraints

[0212] The state of charge (SoC) of the energy storage battery changes with the charge / discharge power, and the battery loses power over time, as shown in equation (25).

[0213] (25)

[0214] In the formula, Indicates that the battery is The state of charge at the moment; Indicates the dissipation rate of the battery; Indicates the state of charge of the battery at the initial moment; Indicates the time interval between data points; and Indicates battery charging and discharging efficiency; Indicates the storage capacity of the battery; and represents the charging power and discharging power of the battery at time t'.

[0215] For safe operation and life of the battery, the charging and discharging power range of the electrolyzer is:

[0216] (26)

[0217] (27)

[0218] In the formula, The upper limit of battery charging power. The upper limit of battery discharge power; Indicates the battery charging power at time t; Indicates the battery discharge power at time t;

[0219] Furthermore, batteries cannot be charged or discharged simultaneously, as this behavior would result in energy loss and have a negative impact on battery life.

[0220] (28)

[0221] In addition, the change in the hydrogen storage capacity of the hydrogen storage tank is affected by both the hydrogen demand and the hydrogen production of the electrolyzer, and the stored hydrogen will dissipate over time. The hydrogen storage state of the hydrogen storage tank is shown in formula (29).

[0222] (29)

[0223] In the formula, Indicates time The hydrogen storage state; Indicates the hydrogen storage state at the initial moment, represents the dissipation rate of the hydrogen storage tank; and Indicates the hydrogen filling efficiency and hydrogen discharge efficiency of the hydrogen storage tank; express The demand for hydrogen energy at all times; Indicates the hydrogen storage capacity of the hydrogen storage tank; express The hydrogen production rate at the time; express The demand for hydrogen energy at all times; Represents the dissipation rate of the hydrogen storage tank.

[0224] Finally, the storage ranges of lithium batteries and hydrogen storage tanks are subject to upper and lower limit constraints, as shown in equations (30) and (31).

[0225] (30)

[0226] (31)

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

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

[0229] The optimization of the multi-tank system is significantly different from that of the single-tank system in terms of hydrogen production and power balance constraints. The total hydrogen production of the system should be expressed as the sum of the hydrogen production of all electrolyzers in operation. The total power consumption of 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 consumption of all alkali solution electrolyzers, as shown in formulas (32)-(35).

[0230] (32)

[0231] (33)

[0232] (34)

[0233] (35)

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

[0235] (36)

[0236] In the formula, Indicates the i-th category The hydrogen production rate of each electrolyzer at time t; represents the total electrolysis power at time t; represents the total cooling power at time t; Indicates the total heating power; represents the total pump power at time t; represents the total purification power at time t; Indicates the battery charging power at time t; Indicates the battery discharge power at time t; represents the total compression power at time t; represents the power load at time t; represents the abandoned power at time t; Indicates the power sold to the grid at time t; Indicates the power purchased from the grid at time t; represents the wind power at time t; Represents the photovoltaic power generation power at time t.

[0237] In order to ensure full utilization of renewable energy, the constraints shown in formula (37) need to be met.

[0238] (37)

[0239] In the formula, Represents the rate of abandoned electricity from renewable energy sources; Indicates the upper limit of the allowable power abandonment rate; Indicates the length of the time period to be optimized.

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

[0241] (38)

[0242] (39)

[0243] (40)

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

[0245] 6. After establishing the optimization configuration model of multiple electrolyzers with different power levels according to the constraints, solve the optimization configuration model to obtain the optimal configuration plan of multiple 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 solution for the configuration of multiple electrolytic cells with different power levels: Gurobi, IBM CPLEX Optimizer, FICOXpress, MOSEK, BARON, Lingo, Sunshu COPT or Ali MindOPT.

[0247] The application method of the solver belongs to the prior art and will not be described in detail in the present invention.

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

[0249] At the same time, in order to implement the method described in the present invention, a computer-readable storage medium is also required, and the computer-readable storage medium stores instructions, and the instructions are used to enable the 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: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes.

[0250] 8. This example uses the K-means method to cluster the photovoltaic wind power and electric energy load data of a new energy plant for one year, and clusters three typical days. The data of the three typical days are as follows: Figure 3 , 4 As shown in Figure 5, the hydrogen energy load comes from a methane field, and the stable demand per hour is 50kg.

[0251] From the power grid electricity price curve Figure 6 As shown in Table 1, the electricity price sold to the grid is set to 50% of the electricity purchase price. The electrolyzer capacity level and thermal parameters and other system parameters are shown in Table 1 and Table 2 respectively. The ambient temperature is set to 20 degrees Celsius, and the upper limit of the configuration of each level of electrolyzer is three.

[0252] According to the multi-tank optimization configuration method of alkali solution electrolyzers proposed in the present invention, the configuration results of five levels of electrolyzers are obtained: 0 0.25MW electrolyzers, 3 1MW electrolyzers, 0 2MW electrolyzers, 3 3MW electrolyzers, and 3 5MW electrolyzers. A total of 9 electrolyzers are configured, with a total configuration power of 27MW.

[0253] The start and stop results of all electrolyzers after optimization are as follows Figure 7 The operating power details of electrolyzers of different power levels (1MW, 3MW, 5MW) are shown in Figures 8 to 16 The operating temperature details of electrolyzers with different power levels (1MW, 3MW, 5MW) are shown in Figures 17 to 25 shown.

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

[0255] ;

[0256] Table 2 Other system parameters

[0257] ;

[0258] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art may make various modifications or variations 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: include: In the microgrid system, the hydrogen production electrolyzer, energy storage battery and hydrogen storage tank form an electric-hydrogen coupling system; Determine the renewable energy access scenario using the energy storage system and construct the constraints of the energy storage system; take the optimal daily average cost as the optimization target and establish an optimization configuration model for multiple electrolyzers with different power levels according to the constraints; solve the optimization configuration model to obtain the optimal configuration scheme for multiple electrolyzers with different power levels; The constraints of the energy storage system include electrolyzer operation characteristic constraints, energy storage range constraints and hydrogen production-power balance constraints; The objective function of the optimization configuration model includes two parts: operating cost and fixed investment cost, which are as follows: ； Where Z is the number of typical scenarios for renewable energy access, is the daily operating cost of the zth typical scenario; is the average annual cost of the fixed investment cost of the electrolytic cell group; are the variables to be optimized, including binary variables representing the configuration of the electrolyzer, equipment power variables during the system operation process, cell temperature variables, hydrogen production, and binary variables representing the start, stop and operation status of the electrolyzer.

2. The method according to claim 1, characterized in that Assume that the capacity set of available electrolyzer models is , and its corresponding unit cost is , the number of configurations for each type of electrolyzer is , n is the total number of models; then, The expression of the fixed investment cost is specifically: ； ； in, is the inverse of the annuity present value coefficient; is the annual interest rate, The life of the electrolytic cell.

3. The method according to claim 1, characterized in that By establishing a unit cost model for electrolyzers of different power levels, the fixed investment cost when configuring multiple types of electrolyzers is calculated; specifically, the following are included: Firstly, the quantitative relationship between the unit cost and equipment capacity of the electrolytic cell is analyzed. Then, according to the equipment capacity and price data of the survey, the least squares method is used to fit the correlation function between the unit cost and equipment capacity. The fixed investment cost is calculated using the fitting function.

4. The method according to claim 1, characterized in that The expression of the daily operating cost is specifically: ； in, For the Type of electrolyzer Cold start and hot start flag variables for each electrolyzer; and Indicates the single charge for hot and cold starts; for Discarding electricity at all times, Penalty cost for unit power abandonment; and Respectively Purchase / sell electricity from the power grid at all times, and Indicates the unit's electricity purchase price or electricity sales price; for The hydrogen demand at any given moment, i.e. the amount of hydrogen sold, for The price of hydrogen sold at any given moment; is the total number of electrolytic cell models.

5. The method according to claim 1, characterized in that The variable to be optimized The specific expression is: ； in, Indicates the i-th category The electrolytic cell at time Temperature; Indicates the number of configurations of the i-th type of electrolytic cell; Indicates the i-th category The operating power of an electrolyzer at time t; Indicates the i-th category An electrolyzer cold start flag variable; Indicates the i-th category An electrolytic cell hot start flag variable; express Abandoning electricity at all times; express Purchase electricity from the power grid at all times; express Sell ​​electricity from the grid at all times; express The hydrogen demand at any given moment, i.e. the amount of hydrogen sold; represents the i-th category at time t A binary state variable for the operating state of an electrolytic cell; represents the i-th category at time t A binary state variable for the shutdown state of an electrolyzer; represents the i-th category at time t A binary state variable for the standby state of an electrolyzer; Indicates the maximum number of electrolytic cells of the i-th type; i represents the code corresponding to the electrolytic cell model.

6. The method according to claim 1, characterized in that The electrolytic cell operation characteristic constraints include at least the upper and lower limits of the electrolytic cell power and the upper and lower limits of the electrolytic cell temperature; wherein, The upper and lower limits of the electrolyzer power are expressed as: ； The upper and lower temperature constraints of the electrolytic cell are expressed as: ； In the formula, represents the capacity of the i-th type electrolytic cell; Indicates the i-th category The operating power of an electrolyzer at time t; Indicates the i-th category The upper limit of the operating power of each electrolyzer related to temperature; Indicates the i-th category The lower limit of the operating temperature of each electrolyzer; Indicates the i-th category The temperature of an electrolytic cell at time t; Indicates the i-th category The upper limit of the operating temperature of each electrolytic cell; i represents the code corresponding to the electrolytic cell model; Indicates the electrolytic cell serial number; and are the i-th category at time t. The binary state variable of the running and shutdown state of each electrolytic cell; when the electrolytic cell is in the running state, ; When the electrolyzer is in shutdown state, .

7. The method according to claim 1, characterized in that The electrolytic cell operation characteristic constraints also include any one or more of the following constraints: (1) Electrolyzer standby power constraints: ； In the formula, Indicates the i-th category Standby power of each electrolyzer; Indicates the i-th category The temperature of an electrolytic cell at time t; represents the ambient temperature at time t; represents the i-th category at time t A binary state variable for the standby state of an electrolyzer; Indicates the i-th category Thermal resistance of an electrolytic cell; (2) Constraints on electrolytic cell operating state variables: ； In the formula, , , The binary state variables represent the running state, shutdown state and standby state of the electrolyzer at time t respectively; (3) Constraints on the cold start process of the electrolyzer: ； In the formula, Indicates the i-th category The lower limit of the operating temperature of each electrolyzer; (4) Cold start constraints of electrolyzer: ； In the formula, It is the cold start flag variable; (5) Constraints on electrolytic cell temperature changes: ； In the formula, Indicates that the electrolytic cell is at adjacent time points Temperature; is the heat generation rate of the electrolytic cell; The heat transfer power delivered to the electrolyzer by the external heater; is the heat absorption rate of the refrigeration system; Indicates 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 electrolytic cell; Indicates a time interval; (6) Constraints on the heat generation rate of the electrolytic cell: ； In the formula, Indicates the i-th category The operating power of each electrolyzer; represents the electrolyzer efficiency; (7) Heat loss constraints of external heating sources: ； In the formula, Indicates the i-th category Actual heating power of the external heater of each electrolytic cell; Indicates the i-th category The heating power provided by the external heater of each electrolyzer; is the heating efficiency of the external heater; (8) Constraints on the heat absorption rate of the electrolytic cell cooling system: ； In the formula, Indicates the i-th category The actual absorbed power of the cooling device of each electrolyzer; Indicates the i-th category The power of the cooling device of each electrolytic cell used for cooling; Indicates the coefficient of performance of the cooling system; (9) Constraints on heat exchange losses between the electrolyzer and the environment: ； In the formula, Indicates the i-th category The power of heat exchange between an electrolyzer and the environment; represents the ambient temperature at time t, represents the thermal resistance of the electrolytic cell; (10) Constraints on the electrothermal operation characteristics of the electrolyzer: The electrothermal operation characteristics of the electrolytic cell must meet the requirements of the electrothermal characteristic model of the electrolytic cell. The relationship between the upper limit of electrolysis power and temperature, as well as the relationship between electrolysis efficiency and the upper limit of power and temperature are fitted according to the experimental results, so as to establish an electrothermal characteristic model of the electrolytic cell related to the power level, which is used to characterize the influence of electrolytic cell temperature and power on electrolysis efficiency; including: (1) By inputting the rated voltage into the electrolytic cell at different temperatures, its power upper limit is obtained; based on the obtained temperature-power data pairs, a function related to the power upper limit and temperature is fitted; the function is then normalized to obtain the normalized power upper limit function, and finally the power upper limit functions corresponding to electrolytic cells of different power levels are summarized; (2) measuring the electrolysis efficiency of the electrolytic cell at different powers at different temperatures; fitting a function related to efficiency, power and temperature based on the obtained temperature-power-efficiency data pairs; then normalizing the function based on the electrolytic cell capacity to obtain the normalized efficiency function, and finally summarizing the electrolysis efficiency functions corresponding to electrolytic cells of different power levels; The maximum power constraints of electrolytic cells of different power levels are different and need to be modeled separately in the multi-cell optimization configuration.

8. The method according to claim 1, characterized in that All electrolyzer operating characteristic constraints must be associated with binary variables Multiplication, specifically expressed as: ； in, represents a set of inequality constraints; represents a set of equality constraints; Indicates Class A binary variable indicating whether an electrolyzer is configured. If it is 1, it is configured, and if it is 0, it is not configured.

9. The method according to claim 1, characterized in that: The energy storage range constraint includes any one or more of the following constraints: (1) Energy storage battery state of charge constraints: ； In the formula, Indicates that the battery is The state of charge at the moment; Indicates the dissipation rate of the battery; Indicates the state of charge of the battery at the initial moment; and Indicates battery charging and discharging efficiency; Indicates the storage capacity of the battery; and Indicates the battery time The charging power and discharging power of (2) Constraints on the charging and discharging power range of energy storage batteries: ； ； In the formula, The upper limit of battery charging power. The upper limit of battery discharge power; Indicates the battery charging power at time t; Indicates the battery discharge power at time t; (3) Safety and stability constraints of energy storage batteries: ； The above formula shows that the battery cannot be charged and discharged at the same time; (4) Constraints on hydrogen storage status of hydrogen storage tanks: ； In the formula, Indicates time The hydrogen storage state; Indicates the hydrogen storage state at the initial moment, represents the dissipation rate of the hydrogen storage tank; and Indicates the hydrogen filling efficiency and hydrogen discharge efficiency of the hydrogen storage tank; express The demand for hydrogen energy at all times; Indicates the hydrogen storage capacity of the hydrogen storage tank; express The hydrogen production rate at the time; express The demand for hydrogen energy at all times; represents the dissipation rate of the hydrogen storage tank; (4) Storage range constraints for lithium batteries and hydrogen storage tanks: ； ； In the formula, Indicates the lower limit of SOC of lithium battery; Represents the SOC of the lithium battery at time t; Indicates the upper limit of SOC of lithium battery; Indicates the lower limit of the hydrogen storage state of the hydrogen storage tank; Indicates the hydrogen storage state of the hydrogen storage tank at time t; Indicates the upper limit of the hydrogen storage status of the hydrogen storage tank.

10. The method according to claim 1, characterized in that The hydrogen production-power balance constraint includes any one or more of the following constraints: (1) Power balance constraints of the electric-hydrogen coupling system: ； In the formula, represents the total electrolysis power at time t; represents the total cooling power at time t; Indicates the total heating power; represents the total pump power at time t; represents the total purification power at time t; Indicates the battery charging power at time t; Indicates the battery discharge power at time t; represents the total compression power at time t; represents the power load at time t; represents the abandoned power at time t; Indicates the power sold to the grid at time t; Indicates the power purchased from the grid at time t; represents the wind power at time t; represents the photovoltaic power generation power at time t; (2) Constraints on the rate of abandoned electricity from new energy sources: ； In the formula, Represents the rate of abandoned electricity from renewable energy sources; Indicates the upper limit of the allowable power abandonment rate; Indicates the time period to be optimized; (3) Power constraints of water pumps, hydrogen purification devices and compressors; ； ； ； In the formula, , , are the powers of the water pump, hydrogen purification device and compressor at time t respectively; Indicates hydrogen production , , They respectively represent the proportional coefficients of the water pump, purification device, compressor power and hydrogen production rate.

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

12. The method according to any one of claims 1 to 10, characterized in that The optimization configuration model is solved using any of the following solvers to obtain the optimal solution for the configuration of multiple electrolyzers with different power levels: Gurobi, IBM CPLEX Optimizer, FICO Xpress, MOSEK, BARON, Lingo, Shanshu COPT or Ali MindOPT.

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