A method for scheduling a photo-hydrogen-storage microgrid considering dynamic security constraints of electrolytic cells

Abstract

The present application relates to a kind of light-hydrogen-storage microgrid scheduling method considering electrolytic cell dynamic safety constraint, belong to microgrid scheduling technical field.A kind of light-hydrogen-storage microgrid scheduling method considering electrolytic cell dynamic safety constraint, comprising the following steps: dynamic operation experiment is carried out to AEL electrolytic cell and PEM electrolytic cell in microgrid, obtains hydrogen-oxygen impurity response characteristic, obtains the hydrogen-oxygen impurity proportion dynamic model of electrolytic cell;According to the operating characteristics of microgrid, construct scheduling model.The present application avoids the safety problem caused by hydrogen-oxygen impurity exceeding standard due to the lower limit of electrolytic cell power being given too low, and the problem that electrolytic cell power lower limit is given too high when electrolytic cell response capacity cannot be fully utilized;Avoid the power lower limit of AEL electrolytic cell and PEM electrolytic cell is constantly debugged;The hydrogen-oxygen impurity proportion of system can be limited according to different levels of safety demand, and the control degree of system safety is higher.

Description

Technical Field

[0001] This invention relates to a power grid dispatching method, belonging to the field of microgrid dispatching technology. Background Technology

[0002] Electrogenation for hydrogen production is an effective way to reduce carbon emissions in industries such as power, transportation, and chemicals. To avoid carbon emissions during the electrogenation process, the electrolyzer for hydrogen production needs to be directly coupled to renewable energy generators, which are characterized by fluctuations, randomness, and intermittency. Unlike traditional constant-power operation, when the electrolyzer is directly coupled to the renewable energy generator through a microgrid, the microgrid needs to develop its operating strategy based on the output of the renewable energy source. This aims to improve the utilization rate of renewable energy, reduce the impact of renewable energy fluctuations on the upstream grid and the electrolyzer, and maximize economic benefits.

[0003] Because the diaphragm in the electrolyzer cannot completely isolate the gases generated at the anode and cathode, impurities are present in the generated gases. When the proportion of gas impurities reaches the explosive limit, it can lead to a system explosion, with the impurity concentration being most pronounced in the anode-side gas-liquid separator. The proportion of gas impurities increases as the electrolyzer's power decreases. Therefore, to ensure the safety of the electrolyzer, it is necessary to limit its lower operating power when scheduling the microgrid.

[0004] Existing technical documents:

[0005] The team led by Kong Lingguo from Northeast Electric Power University addressed the problem of optimizing the distribution and centralized interaction of the low-carbon energy system of electricity-hydrogen-heat in the park. With the goal of minimizing operating costs, they solved the problem of optimizing the output of centralized energy equipment in the park by measuring the interaction energy between buildings, thus achieving precise and optimal point-to-point energy interaction among multiple energy entities.

[0006] The team led by Yuan Tiejiang at Dalian University of Technology has established a day-ahead power dispatch model to take into account real-time electricity price changes, the operating status of multiple electrolyzers, and the intermittency of new energy sources. This model aims to ensure the safe and stable operation of the system, avoid large-scale electricity purchases during peak periods and repeated start-ups and shutdowns of alkaline electrolyzers, and reduce system operating costs.

[0007] Yang Ping's team at South China University of Technology determined the upper and lower limits of electrolyzer power by using the annual grid sales rate and the levelized electricity price, and proposed a capacity optimization method for wind-solar-hydrogen-storage microgrids to reduce wind and solar curtailment.

[0008] The current level of technology and its existing problems (defects):

[0009] 1. Currently, in the dispatching of microgrids containing electrolyzers, the lower limit of the electrolyzer's power is usually a given constant value. If the given constant power lower limit is too large, the response capability of the electrolyzer cannot be fully utilized, resulting in low system benefits; if the given constant power lower limit is too small, system safety cannot be guaranteed.

[0010] 2. Microgrids may contain both proton exchange membrane (PEM) and alkaline electrolysis (AEL) cells. Their hydrogen-oxygen impurity ratios differ at the same power, thus requiring different constant power limits. Selecting an appropriate constant power limit to ensure the safe operation of both PEM and AEL electrolyzers while fully utilizing their responsiveness is extremely difficult.

[0011] Therefore, there is an urgent need to propose a photovoltaic-hydrogen-storage microgrid scheduling method that considers the dynamic safety constraints of the electrolyzer to solve the above-mentioned technical problems. Summary of the Invention

[0012] To address the aforementioned technical problems, a photovoltaic-hydrogen-storage microgrid scheduling method considering dynamic security constraints of electrolyzers is provided. A brief overview of the invention is given below to provide a basic understanding of certain aspects of the invention. It should be understood that this overview is not an exhaustive summary of the invention. It is not intended to identify key or essential parts of the invention, nor is it intended to limit the scope of the invention.

[0013] The technical solution of the present invention:

[0014] A photovoltaic-hydrogen-storage microgrid scheduling method considering dynamic security constraints of electrolyzers includes the following steps:

[0015] Step 1: Conduct dynamic operation experiments on AEL and PEM electrolyzers in the microgrid to obtain the response characteristics of hydrogen and oxygen impurities and derive the dynamic model of the hydrogen and oxygen impurity ratio of the electrolyzers.

[0016] Step 2: Construct a scheduling model based on the operating characteristics of the microgrid.

[0017] Preferred: Step one includes the following steps:

[0018] Step 1.1: Derive the power-current curve of the electrolyzer based on the voltage-current curve; combine the gas impurity model and the power-current curve, eliminate the electrolysis current, and the relationship between the gas impurity ratio and the power of the electrolyzer can be obtained.

[0019] Step 1.2: Based on the step size discretization of the scheduling problem, a dynamic security constraint model suitable for microgrid scheduling is derived.

[0020] Preferably, in step 1.1, the dynamic model of the hydrogen-oxygen impurity ratio in the AEL electrolyzer can be expressed as:

[0021]

[0022] Among them, HTO AEL(t) and These represent the proportion and molar number of impurities in the anode-side gas-liquid separator of the AEL electrolyzer at time t; p AEL , and T AEL Here, represents the pressure, volume, and temperature of the gas in the anode-side gas-liquid separator of the AEL electrolyzer, respectively; R is the ideal gas constant, and the number of moles of gaseous impurities in the anode-side gas-liquid separator is related to the number of gaseous impurities in the anode-side half-cell of the electrolyzer.

[0023]

[0024] in, and These are the impurity molar flow rates in the anode-side half-cell of the AEL electrolyzer caused by concentration diffusion, pressure differential osmosis, and alkali circulation, respectively. The molar flow rate of gaseous impurities exiting the anode-side gas-liquid separator of the AEL electrolyzer is related to the electrolytic current of the AEL electrolyzer:

[0025]

[0026] Among them, I AEL is the electrolytic current of the AEL electrolytic cell; F is the Faraday constant;

[0027] Based on (1)-(3), the relationship between the impurity ratio and the electrolysis current in the anode-side gas-liquid separator of the AEL electrolyzer can be derived; the power of the AEL electrolyzer is the product of the electrolysis current and the voltage:

[0028] P AEL (t)=U AEL (t)I AEL (t) (4)

[0029] Among them, P AEL and U AEL These represent the power and voltage of the AEL electrolyzer, respectively; the relationship between voltage and current is determined by the polarization curve:

[0030]

[0031] in, The reversible voltage of the AEL electrolyzer; r 1,AEL r 2,AEL s AEL t 1,AEL t 2,AEL and t 3,AELThe empirical coefficient can be obtained from the dynamic operation experiment of the AEL electrolyzer; the power-current curve of the AEL electrolyzer can be obtained based on the voltage-current curve; by combining the gas impurity model (1)-(3) and the power-current curve (4)-(5) and eliminating the electrolysis current, the relationship between the gas impurity ratio and the power of the AEL electrolyzer can be obtained:

[0032]

[0033] Among them, a AEL and b AEL The constant for the gas impurity response of the AEL electrolyzer can be obtained from equations (1)-(5) and the dynamic operation experiment of the AEL electrolyzer.

[0034] The difference between the gaseous impurity response of a PEM electrolyzer and an AEL electrolyzer lies in the fact that the gaseous impurities in the anode-side half-cell of a PEM electrolyzer consist only of two parts: gaseous impurities caused by pressure differential permeation and concentration gradient diffusion.

[0035]

[0036] in, This indicates the number of moles of impurities in the anode-side gas-liquid separator of the PEM electrolyzer; These represent the impurity molar flow rates caused by concentration diffusion and pressure difference penetration in the anode-side half-cell of the PEM electrolyzer, respectively. The molar flow rate of gaseous impurities flowing out of the gas-liquid separator on the anode side of the PEM electrolyzer;

[0037] Modeling a PEM electrolyzer allows us to determine the relationship between the proportion of gaseous impurities and the power output:

[0038]

[0039] Among them, HTO PEM The proportion of gaseous impurities in the PEM electrolyzer; P PEM The power of the PEM electrolyzer; a PEM and b PEM The constant representing the response of the gas impurities in the PEM electrolyzer can be obtained from dynamic operation experiments of the PEM electrolyzer.

[0040] Preferred: In step 1.2, discretizing the dynamic gas impurity models (6) and (8) of the AEL electrolyzer and PEM electrolyzer can yield a dynamic security constraint model suitable for microgrid dispatch:

[0041]

[0042] Where Δt is the day-ahead scheduling step size; t endThis represents the number of time steps scheduled in the previous day or the dimension of each variable, and is related to the scheduling step size.

[0043]

[0044] Preferred: Step two includes the following steps:

[0045] Step 2.1: Microgrid operating characteristic model;

[0046] Step 2.2: Considering the operating characteristics of the microgrid, the optimal scheduling strategy is obtained by finding the maximum and minimum values ​​of the objective function.

[0047] Preferably, in step 2.1, the source and load power of the microgrid must be kept balanced.

[0048] P fuel (t)+P PV (t)=P AEL (t)+P PEM (t)+P load (t)+P sto (t)+P grid (t) (12)

[0049] Among them, P fuel and P PV P represents the output power of the fuel cell and photovoltaic, respectively; AEL and P PEM P represents the power consumed in hydrogen production by the AEL electrolyzer and the PEM electrolyzer, respectively; load P represents the power of the electrical load in the microgrid; sto This represents the power of electrochemical energy storage; a positive value indicates charging, and a negative value indicates discharging. (P) grid This represents the amount of electricity exchanged between the microgrid and the power system. A positive value indicates that the microgrid sells electricity to the upstream grid, while a negative value indicates that the microgrid purchases electricity from the upstream grid. P PV and P load Given the known daily forecast value, and P grid P sto P AEL P PEM and P fuel These are optimization variables that need to be solved using a scheduling model;

[0050] In a microgrid, power and hydrogen flow are directly coupled; the electrolyzer is the sole source of hydrogen in the microgrid, and the relationship between its hydrogen production and power can be expressed as a quadratic function:

[0051]

[0052] in, and The molar flow rates of hydrogen produced in the AEL electrolyzer and PEM electrolyzer, respectively; h AEL,1 h AEL,2 h AEL,3 h PEM,1 h PEM,2 and h PEM,3 The hydrogen production coefficient of the electrolyzer;

[0053] Fuel cells consume hydrogen to generate electricity, and supply power to microgrids when electricity is insufficient. The ratio of electricity supplied to hydrogen consumption can be expressed as follows:

[0054]

[0055] in, η is the molar flow rate of hydrogen consumed by the fuel cell. cell The hydrogen-to-electricity conversion coefficient for fuel cells;

[0056] In a microgrid, there is a balance between the hydrogen produced by the electrolyzer, the hydrogen consumed by the fuel cell, and the hydrogen stored in the hydrogen storage tank:

[0057]

[0058] in, Let be the molar flow rate of hydrogen entering the hydrogen storage tank; since the amount of hydrogen in the storage tank is never less than 0, then:

[0059]

[0060] in, This indicates the amount of hydrogen stored in the hydrogen storage tank at the initial moment;

[0061] Meanwhile, the hydrogen produced by the microgrid needs to meet the given day-ahead hydrogen production demand:

[0062]

[0063] in, The day-ahead hydrogen production demand of the microgrid is a known value.

[0064] Preferred method: In step 2.2, considering the operating characteristics of the microgrid in actual scheduling, the optimal scheduling strategy is obtained by finding the maximum and minimum values ​​of the objective function, with economy as the objective; the economic objective function can be expressed as:

[0065]

[0066] Among them, w power and w H2 The prices for selling electricity and hydrogen;

[0067] In summary, the microgrid scheduling model considering the dynamic security constraints of the electrolyzer can be expressed as:

[0068]

[0069] The beneficial effects of this invention are:

[0070] 1. This invention avoids the safety problems caused by excessive hydrogen and oxygen impurities due to the low lower limit of the electrolyzer power setting in traditional scheduling methods, as well as the problem of not being able to fully utilize the response capability of the electrolyzer when the lower limit of the electrolyzer power setting is too high.

[0071] 2. This invention avoids the need for continuous adjustment of the power lower limit of AEL and PEM electrolyzers. The proposed scheduling method can automatically allocate power to electrolyzers with different hydrogen and oxygen impurity response characteristics through dynamic safety constraints, ensuring the safety of all electrolyzers while making full use of the power response capability of the electrolyzers;

[0072] 3. This invention can limit the ratio of hydrogen and oxygen impurities in the system according to different levels of safety requirements, and compared with traditional scheduling methods, it provides a higher degree of control over system safety. Attached Figure Description

[0073] Figure 1 This is a flowchart of a photovoltaic-hydrogen-storage microgrid scheduling method considering dynamic safety constraints of electrolyzers according to the present invention;

[0074] Figure 2 This is a power load diagram for a photovoltaic-hydrogen-storage microgrid;

[0075] Figure 3 This is a photovoltaic output diagram of a photovoltaic-hydrogen-storage microgrid;

[0076] Figure 4 This is a comparison chart of the electrolytic cell power distribution results of the proposed method and the traditional method;

[0077] Figure 5 This is a comparison chart of the hydrogen-oxygen impurity ratio in the electrolyzer of the proposed method and the traditional method;

[0078] Figure 6 This is a diagram showing the power distribution of the electrolyzer when the upper limit of hydrogen and oxygen impurities is 1.8%.

[0079] Figure 7 This is a graph showing the response of the electrolyzer to hydrogen and oxygen impurities when the upper limit of hydrogen and oxygen impurities is 1.8%. Detailed Implementation

[0080] To make the objectives, technical solutions, and advantages of this invention clearer, the invention is described below with reference to specific embodiments shown in the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0081] Specific implementation method one: Combining Figure 1 This embodiment describes a photovoltaic-hydrogen-storage microgrid dispatching method considering dynamic safety constraints of electrolyzers, comprising the following steps:

[0082] Step 1: Conduct dynamic operation experiments on AEL and PEM electrolyzers in the microgrid to obtain the response characteristics of hydrogen and oxygen impurities and derive the dynamic model of the hydrogen and oxygen impurity ratio of the electrolyzers.

[0083] Step one includes the following steps:

[0084] Step 1.1: Based on the voltage-current curve, derive the power-current curve of the electrolyzer; combine the electrolyzer current-hydrogen-oxygen impurity ratio model and the electrolyzer current-power curve, and eliminate the electrolysis current to obtain the electrolyzer power-hydrogen-oxygen impurity ratio model.

[0085] In step 1.1, the dynamic model of the hydrogen-oxygen impurity ratio in the AEL electrolyzer can be expressed as:

[0086]

[0087] Among them, HTO AEL (t) and These represent the proportion and molar number of impurities in the anode-side gas-liquid separator of the AEL electrolyzer at time t; p AEL , and T AEL Here, represents the pressure, volume, and temperature of the gas in the anode-side gas-liquid separator of the AEL electrolyzer, respectively; R is the ideal gas constant, and the number of moles of gaseous impurities in the anode-side gas-liquid separator is related to the number of gaseous impurities in the anode-side half-cell of the electrolyzer.

[0088]

[0089] in, and These are the impurity molar flow rates in the anode-side half-cell of the AEL electrolyzer caused by concentration diffusion, pressure differential osmosis, and alkali circulation, respectively. The molar flow rate of gaseous impurities exiting the anode-side gas-liquid separator of the AEL electrolyzer is related to the electrolytic current of the AEL electrolyzer:

[0090]

[0091] Among them, I AEL is the electrolytic current of the AEL electrolytic cell; F is the Faraday constant;

[0092] Based on (1)-(3), the relationship between the impurity ratio and the electrolysis current in the anode-side gas-liquid separator of the AEL electrolyzer can be derived; the power of the AEL electrolyzer is the product of the electrolysis current and the voltage:

[0093] P AEL (t)=U AEL (t)I AEL (t) (4)

[0094] Among them, P AEL and U AEL These represent the power and voltage of the AEL electrolyzer, respectively; the relationship between voltage and current is determined by the polarization curve:

[0095]

[0096] in, The reversible voltage of the AEL electrolyzer; r 1,AEL r 2,AEL s AEL t 1,AEL t 2,AEL and t 3,AEL The empirical coefficient can be obtained from the dynamic operation experiment of the AEL electrolyzer; the power-current curve of the AEL electrolyzer can be obtained based on the voltage-current curve; by combining the gas impurity model (1)-(3) and the power-current curve (4)-(5) and eliminating the electrolysis current, the relationship between the gas impurity ratio and the power of the AEL electrolyzer can be obtained:

[0097]

[0098] Among them, a AEL and b AEL The constant for the gas impurity response of the AEL electrolyzer can be obtained from equations (1)-(5) and the dynamic operation experiment of the AEL electrolyzer.

[0099] The gaseous impurity response of the PEM electrolyzer is similar to that of the AEL electrolyzer. The only difference is that the gaseous impurities in the anode-side half-cell of the PEM electrolyzer consist only of two parts: gaseous impurities caused by pressure gradient permeation and concentration gradient diffusion.

[0100]

[0101] in, This indicates the number of moles of impurities in the anode-side gas-liquid separator of the PEM electrolyzer; These represent the impurity molar flow rates caused by concentration diffusion and pressure difference penetration in the anode-side half-cell of the PEM electrolyzer, respectively. The molar flow rate of gaseous impurities flowing out of the gas-liquid separator on the anode side of the PEM electrolyzer;

[0102] By applying the same modeling method to the PEM electrolyzer, the relationship between its gaseous impurity ratio and power can be derived:

[0103]

[0104] Among them, HTO PEM The proportion of gaseous impurities in the PEM electrolyzer; P PEM The power of the PEM electrolyzer; a PEM and b PEM Similarly, the constant representing the gas impurity response of the PEM electrolyzer can be obtained from the dynamic operation experiment of the PEM electrolyzer.

[0105] Step 1.2: Based on the step size discretization of the scheduling problem, a dynamic constraint model for electrolytic cell safety suitable for microgrid scheduling is derived;

[0106] In step 1.2, discretizing the dynamic gas impurity models (6) and (8) of the AEL electrolyzer and PEM electrolyzer yields a dynamic security constraint model suitable for microgrid dispatch:

[0107]

[0108] Where Δt is the day-ahead scheduling step size; t end This represents the number of time steps scheduled in the previous day or the dimension of each variable, and is related to the scheduling step size.

[0109]

[0110] Step 2: After obtaining the dynamic safety constraint models of AEL electrolyzers and PEM electrolyzers, a scheduling model is constructed based on the operating characteristics of the microgrid;

[0111] Step two includes the following steps:

[0112] Step 2.1: Microgrid operating characteristic model;

[0113] In step 2.1, the source and load power of the microgrid must be kept balanced:

[0114] P fuel (t)+P PV (t)=P AEL (t)+P PEM (t)+P load (t)+P sto (t)+P grid (t) (12)

[0115] Among them, P fuel and P PV P represents the output power of the fuel cell and photovoltaic, respectively; AEL and PPEM P represents the power consumed in hydrogen production by the AEL electrolyzer and the PEM electrolyzer, respectively; load P represents the power of the electrical load in the microgrid; sto This represents the power of electrochemical energy storage; a positive value indicates charging, and a negative value indicates discharging. (P) grid This represents the amount of electricity exchanged between the microgrid and the power system. A positive value indicates that the microgrid sells electricity to the upstream grid, while a negative value indicates that the microgrid purchases electricity from the upstream grid. P PV and P load Given the known daily forecast value, and P grid P sto P AEL P PEM and P fuel These are optimization variables that need to be solved using a scheduling model;

[0116] In a microgrid, power and hydrogen flow are directly coupled; the electrolyzer is the sole source of hydrogen in the microgrid, and the relationship between its hydrogen production and power can be expressed as a quadratic function:

[0117]

[0118] in, and The molar flow rates of hydrogen produced in the AEL electrolyzer and PEM electrolyzer, respectively; h AEL,1 h AEL,2 h AEL,3 h PEM,1 h PEM,2 and h PEM,3 The hydrogen production coefficient of the electrolyzer;

[0119] Fuel cells consume hydrogen to generate electricity, and supply power to microgrids when electricity is insufficient. The ratio of electricity supplied to hydrogen consumption can be expressed as follows:

[0120]

[0121] in, η is the molar flow rate of hydrogen consumed by the fuel cell. cell The hydrogen-to-electricity conversion coefficient for fuel cells;

[0122] In a microgrid, there is a balance between the hydrogen produced by the electrolyzer, the hydrogen consumed by the fuel cell, and the hydrogen stored in the hydrogen storage tank:

[0123]

[0124] in, Let be the molar flow rate of hydrogen entering the hydrogen storage tank; since the amount of hydrogen in the storage tank is never less than 0, then:

[0125]

[0126] in, This indicates the amount of hydrogen stored in the hydrogen storage tank at the initial moment;

[0127] Meanwhile, the hydrogen produced by the microgrid needs to meet the given day-ahead hydrogen production demand:

[0128]

[0129] in, The day-ahead hydrogen production demand of the microgrid is a known value;

[0130] Models (12)-(18) are models based on the operating characteristics of microgrids;

[0131] Step 2.2: Considering the operating characteristics of the microgrid, obtain the optimal scheduling strategy by finding the maximum and minimum values ​​of the objective function;

[0132] In step 2.2, in actual dispatching, the operating characteristics of the microgrid are usually considered. The optimal dispatching strategy is obtained by finding the maximum and minimum values ​​of the objective function, generally with economic efficiency as the objective. The economic objective function can be expressed as:

[0133]

[0134] Among them, w power and w H2 The prices for selling electricity and hydrogen;

[0135] In summary, the microgrid scheduling model considering the dynamic security constraints of the electrolyzer can be expressed as:

[0136]

[0137] This invention is mainly aimed at day-ahead optimization scheduling of microgrids containing electrolyzers. It takes into account the dynamic hydrogen and oxygen impurity response of the electrolyzers to ensure the safety of the electrolyzers, while making full use of the response capability of the electrolyzers.

[0138] Example 1:

[0139] The proposed scheduling method was simulated using a photovoltaic-hydrogen-storage microgrid as an example, and compared with the traditional scheduling method based on a given power lower limit. The system's power load curve and photovoltaic output curve are shown below. Figure 2 and Figure 3 As shown;

[0140] The power allocation results of the electrolyzer in the photovoltaic-hydrogen-storage microgrid are as follows: Figure 4As shown, in traditional methods, PEM and AEL electrolyzers have the same power lower limit, and since their hydrogen production efficiencies are similar, the power allocation results for PEM and AEL electrolyzers are basically the same when maximizing profits. However, in the proposed method, there is no need to pre-define the power lower limit; the scheduling algorithm can automatically optimize the power allocation through dynamic safety constraints. Due to the different dynamic responses of hydrogen and oxygen impurities in PEM and AEL electrolyzers, the allocated power lower limits for the electrolyzers differ significantly.

[0141] The dynamic response of hydrogen and oxygen impurities corresponding to the electrolyzer power is as follows: Figure 5 As shown, in the traditional method, PEM and AEL electrolyzers are allocated the same power. However, because the cross-contamination of hydrogen and oxygen impurities in the AEL electrolyzer is more severe than that in the PEM electrolyzer, the proportion of hydrogen and oxygen impurities in the AEL electrolyzer is higher, always exceeding the safety limit of 2%. For the PEM electrolyzer, although its proportion of hydrogen and oxygen impurities is below the safety limit for a long time, it still exceeds the limit in some periods, endangering the safety of the system.

[0142] Regarding the proposed method, whether it is a PEM electrolyzer or an AEL electrolyzer, the proportion of hydrogen and oxygen impurities is kept below the safety limit of 2% throughout the entire time period, ensuring system safety. At the same time, during certain time periods, the proportion of hydrogen and oxygen impurities in the electrolyzer is equal to 2%, and the system is in a state of operation at the edge, making full use of the response capability of the electrolyzer.

[0143] In addition, such as Figure 6 and Figure 7 As shown, when the upper limit of the hydrogen and oxygen impurity ratio in the dynamic safety constraint is changed to 1.8%, the proposed method can still ensure that the hydrogen and oxygen impurity ratio of the system meets the requirements and operates at the edge during some periods to make full use of the electrolyzer's response capability; this shows that the proposed method can flexibly control the safety of the electrolyzer in the microgrid.

[0144] It should be noted that in the above embodiments, as long as the technical solutions are not contradictory, they can be permuted and combined. Those skilled in the art can exhaust all possibilities based on the mathematical knowledge of permutation and combination. Therefore, the present invention will not describe the technical solutions after permutation and combination one by one, but it should be understood that the technical solutions after permutation and combination have been disclosed by the present invention.

[0145] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A photovoltaic-hydrogen-storage microgrid scheduling method considering dynamic security constraints of electrolyzers, characterized in that: Includes the following steps: Step 1: Conduct dynamic operation experiments on AEL and PEM electrolyzers in the microgrid to obtain the response characteristics of hydrogen and oxygen impurities and derive the dynamic model of the hydrogen and oxygen impurity ratio of the electrolyzers. Step one includes the following steps: Step 1.1: Based on the voltage-current curve, derive the power-current curve of the electrolyzer; combine the gas impurity model and the power-current curve, eliminate the electrolysis current, and derive the relationship between the gas impurity ratio and the power of the electrolyzer. Step 1.2: Based on the step size discretization of the scheduling problem, a dynamic security constraint model suitable for microgrid scheduling is derived; In step 1.2, the dynamic gas impurity models of the AEL electrolyzer and PEM electrolyzer are discretized to derive a dynamic security constraint model suitable for microgrid dispatch: （9） （10） in, This refers to the step size of the current day's scheduling; This represents the number of time steps scheduled in the previous day or the dimension of each variable, and is related to the scheduling step size. （11）； in, for t The proportion of impurities in the anode-side gas-liquid separator of the AEL electrolyzer at any given time; and is a constant representing the gas impurity response of the AEL electrolyzer; This indicates the power of the AEL electrolytic cell; The proportion of gaseous impurities in the PEM electrolyzer; The power of the PEM electrolyzer; and A constant representing the response of a PEM electrolyzer to gas impurities; Step 2: Construct a scheduling model based on the operating characteristics of the microgrid.

2. The photovoltaic-hydrogen-storage microgrid scheduling method considering dynamic safety constraints of electrolyzers according to claim 1, characterized in that: In step 1.1, the dynamic model of the hydrogen-oxygen impurity ratio in the AEL electrolyzer is expressed as follows: （1） in, for t The number of moles of impurities in the anode-side gas-liquid separator of the AEL electrolyzer at any given time; , and These represent the pressure, volume, and temperature of the gas in the anode-side gas-liquid separator of the AEL electrolyzer; Assuming an ideal gas constant, the number of moles of gaseous impurities in the anode-side gas-liquid separator is related to the number of gaseous impurities in the anode-side half-cell of the electrolyzer: （2） in, , and These are the impurity molar flow rates in the anode-side half-cell of the AEL electrolyzer caused by concentration diffusion, pressure differential osmosis, and alkali circulation, respectively. The molar flow rate of gaseous impurities exiting the anode-side gas-liquid separator of the AEL electrolyzer is related to the electrolytic current of the AEL electrolyzer: （3） in, This refers to the electrolytic current of the AEL electrolytic cell; It is Faraday's constant; Based on (1)-(3), the relationship between the impurity ratio and the electrolysis current in the anode-side gas-liquid separator of the AEL electrolyzer is obtained; the power of the AEL electrolyzer is the product of the electrolysis current and the voltage: （4） in, This represents the voltage of the AEL electrolytic cell; the relationship between voltage and current is determined by the polarization curve: （5） in, The reversible voltage of the AEL electrolytic cell; , , , , and The empirical coefficients are derived from the dynamic operation experiments of the AEL electrolyzer; the power-current curve of the AEL electrolyzer is derived based on the voltage-current curve; by combining the gas impurity model (1)-(3) and the power-current curve (4)-(5), the electrolysis current is eliminated, and the relationship between the gas impurity ratio and the power of the AEL electrolyzer is obtained: （6） in, and The results were obtained from equations (1)-(5) and the dynamic operation experiment of the AEL electrolytic cell; The difference between the gaseous impurity response of a PEM electrolyzer and an AEL electrolyzer lies in the fact that the gaseous impurities in the anode-side half-cell of a PEM electrolyzer consist only of two parts: gaseous impurities caused by pressure differential permeation and concentration gradient diffusion. （7） in, This indicates the number of moles of impurities in the anode-side gas-liquid separator of the PEM electrolyzer; , These represent the impurity molar flow rates caused by concentration diffusion and pressure difference penetration in the anode-side half-cell of the PEM electrolyzer, respectively. The molar flow rate of gaseous impurities flowing out of the gas-liquid separator on the anode side of the PEM electrolyzer; Modeling a PEM electrolyzer reveals the relationship between its gaseous impurity ratio and power: （8） in, and The results were obtained from the dynamic operation experiment of the PEM electrolyzer. The dynamic gas impurity models (6) and (8) of the AEL electrolyzer and PEM electrolyzer are discretized to obtain a dynamic security constraint model suitable for microgrid dispatch.

3. The photovoltaic-hydrogen-storage microgrid scheduling method considering dynamic safety constraints of electrolyzers according to claim 2, characterized in that: Step two includes the following steps: Step 2.1: Microgrid operating characteristic model; Step 2.2: Considering the operating characteristics of the microgrid, the optimal scheduling strategy is obtained by finding the maximum and minimum values ​​of the objective function.

4. The photovoltaic-hydrogen-storage microgrid scheduling method considering dynamic safety constraints of electrolyzers according to claim 3, characterized in that: In step 2.1, the source and load power of the microgrid must be kept balanced: （12） in, and These represent the output power of the fuel cell and the photovoltaic system, respectively. and These represent the power consumed in hydrogen production by the AEL electrolyzer and the PEM electrolyzer, respectively. This represents the power of the electrical load in the microgrid; This represents the power of electrochemical energy storage; a positive value indicates charging, and a negative value indicates discharging. This represents the amount of electricity exchanged between the microgrid and the power system. A positive value indicates that the microgrid sells electricity to the upstream grid, while a negative value indicates that the microgrid purchases electricity from the upstream grid. and Given the known daily forecast value, and , , , and These are optimization variables that need to be solved using a scheduling model; In a microgrid, power and hydrogen flow are directly coupled; the electrolyzer is the sole source of hydrogen in the microgrid, and the relationship between its hydrogen production and power is expressed as a quadratic function: （13） （14） in, and The molar flow rates of hydrogen produced in the AEL electrolyzer and PEM electrolyzer are respectively. , , , , and The hydrogen production coefficient of the electrolyzer; Fuel cells consume hydrogen to generate electricity, supplying power to microgrids when electricity is insufficient. The ratio of electricity supplied to hydrogen consumption is expressed as follows: （15） in, The molar flow rate of hydrogen consumed by the fuel cell; The hydrogen-to-electricity conversion coefficient for fuel cells; In a microgrid, there is a balance between the hydrogen produced by the electrolyzer, the hydrogen consumed by the fuel cell, and the hydrogen stored in the hydrogen storage tank: （16） in, Let be the molar flow rate of hydrogen entering the hydrogen storage tank; since the amount of hydrogen in the storage tank is never less than 0, then: （17） in, This indicates the amount of hydrogen stored in the hydrogen storage tank at the initial moment; Meanwhile, the hydrogen produced by the microgrid needs to meet the given day-ahead hydrogen production demand: （18） in, The day-ahead hydrogen production demand of the microgrid is a known value.

5. A photovoltaic-hydrogen-storage microgrid scheduling method considering dynamic safety constraints of electrolyzers according to claim 4, characterized in that: In step 2.2, considering the operating characteristics of the microgrid in actual scheduling, the optimal scheduling strategy is obtained by finding the maximum and minimum values ​​of the objective function, with economy as the objective; the economic objective function is expressed as: （19） in, and The prices for selling electricity and hydrogen; In summary, the microgrid scheduling model considering the dynamic security constraints of the electrolyzer is expressed as: （20）。

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