A method for evaluating stability of an electro-hydrodynamic coupling system

Abstract

The application discloses a kind of electric hydrogen coupling system stability evaluation methods, belong to energy system stability evaluation field, including the following steps: S1, considering the nonlinear characteristics of electrolytic cell and fuel cell, constructs the dynamic mathematical model of electric hydrogen coupling system;S2, parameter uncertainty analysis;S3, generate unified stability score;S4, obtain multi-objective optimization model;S5, simulation verification, final dynamic weight;S6, the real-time operation data of electric hydrogen coupling system and final dynamic weight input step S3 generate the stability score under current working condition, match verification after being combined with stability criterion and output evaluation result.The electric hydrogen coupling system stability evaluation method described above, through dynamic modeling-intelligent screening-collaborative optimization technical closed loop, realize breakthrough improvement in model precision, calculation efficiency, system robustness and other dimensions, provide full-condition stability guarantee for electric hydrogen coupling system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy system stability evaluation, and particularly relates to a method for evaluating stability of an electricity-hydrogen coupling system. BACKGROUND

[0002] Under the background of actively promoting energy transformation and sustainable development worldwide, the electricity-hydrogen coupling system, as a new energy system with great development potential, is gradually becoming a research hotspot in the energy field. This system, with its unique technical advantages, can realize the mutual conversion and efficient coordinated operation between hydrogen energy and electricity. When there is excess electricity, the excess electricity can be converted into hydrogen energy through electrolysis of water for storage; when there is a peak demand for electricity or insufficient renewable energy generation, the stored hydrogen energy can be converted into electricity through fuel cells to supplement the power supply. This bidirectional energy conversion mechanism not only improves the efficiency of energy utilization, but also enhances the flexibility and stability of energy supply, which has great significance for building a clean, safe and efficient new energy system.

[0003] Although the electricity-hydrogen coupling system has many advantages, it is mostly based on static models, which cannot be dynamically adjusted to adapt to long-term parameter drifts such as pipeline aging and equipment efficiency decay. SUMMARY

[0004] The purpose of the present application is to provide a method for evaluating stability of an electricity-hydrogen coupling system to solve the above technical problems.

[0005] To achieve the above purpose, the present application provides a method for evaluating stability of an electricity-hydrogen coupling system, comprising the following steps:

[0006] S1, considering the nonlinear characteristics of electrolytic cells and fuel cells, a dynamic mathematical model of the electricity-hydrogen coupling system is constructed;

[0007] S2, parameter uncertainty analysis: identifying uncertain parameters and quantifying their uncertainty distribution, and screening key disturbance parameters by analyzing their influence on the output of the dynamic mathematical model of the electricity-hydrogen coupling system;

[0008] S3, based on the screened key disturbance parameters, defining multi-dimensional stability evaluation indexes and standardizing, and then generating a unified stability score through weighted synthesis;

[0009] S4, mapping the multi-dimensional stability evaluation indexes to the radar chart axes to obtain a multi-objective optimization model;

[0010] S5, time-domain simulation verification is performed on the multi-objective optimization model under extreme disturbance scenarios, and the index weights are dynamically adjusted according to the simulation running data, and then the adjusted index weights are re-input into the multi-objective optimization model until the final dynamic weights are output;

[0011] S6, inputting the real-time running data of the electro-hydrogen coupling system and the final dynamic weight into step S3 to generate a stability score under the current working condition, and outputting an evaluation result after matching verification according to a stability criterion.

[0012] Preferably, step S1 specifically comprises the following steps:

[0013] S11, constructing a dynamic model of the electrolyzer:

[0014] (1);

[0015] In the formula, represents the hydrogen-electric conversion efficiency of the electrolyzer, and ; , , respectively represent the electrolyzer efficiency decay coefficient caused by input power, the electrolyzer efficiency decay coefficient caused by temperature, and the efficiency reference constant; represents the input power of the electrolyzer, in MW, and , , and respectively represent the minimum allowable running power and the maximum allowable running power of the electrolyzer; represents the running temperature of the electrolyzer, in ℃;

[0016] S12, constructing a dynamic model of the fuel cell:

[0017] Constructing a polarization curve equation:

[0018] (2);

[0019] In the formula, represents the working voltage of the fuel cell, in V; represents the reversible voltage, in V; represents the working current density of the fuel cell, in A / cm 2 ; represents the ohmic resistance, in Ω; and both represent empirical coefficients; and respectively represent the alternating current density and the limiting current density, in A / cm 2 ;

[0020] wherein,

[0021] (3);

[0022] In the formula, T represents the working temperature of the fuel cell, in ℃; P represents the hydrogen pressure required; P represents the oxygen pressure; R represents the gas constant, in ; K represents the dynamic coupling factor;

[0023] The power dynamic equation is constructed as follows:

[0024] (4);

[0025] In the formula, P represents the power generated by the fuel cell, in kW; A represents the effective area of a single fuel cell, in cm 2 ; N represents the number of fuel cells connected in series in the stack;

[0026] S13, a hydrogen storage tank dynamic model is constructed:

[0027] (5);

[0028] In the formula, M represents the mass of the hydrogen storage tank, in kg, and is determined by , R represents the hydrogen production rate of the electrolytic tank, and , LHV represents the low calorific value of hydrogen, ; R represents the hydrogen consumption rate of the fuel cell, and ; and both represent the hydrogen physical property constant of the hydrogen storage tank; V represents the volume of the hydrogen storage tank, in m 3 ; T represents the temperature of the hydrogen storage tank;

[0029] S14, a dynamic mathematical model of the electric-hydrogen coupling system is constructed, and the dynamic mathematical model of the electric-hydrogen coupling system includes a power subsystem model and a hydrogen energy subsystem model;

[0030] The expression of the power subsystem model is as follows:

[0031] (6);

[0032] (7);

[0033] In the formula, P represents the grid power, in kW; P represents the wind power; This indicates the load power, measured in kW. This indicates the photovoltaic power generation capacity, measured in kW. This indicates the energy stored in a fuel cell, measured in MWh. Indicates the power generation efficiency of the fuel cell;

[0034] The model expression for the hydrogen energy subsystem is as follows:

[0035] (8).

[0036] Preferably, step S2 specifically includes the following steps:

[0037] S21. Screening Uncertainty Parameters: Electrolyte Efficiency Coefficient , , The limiting current density of fuel cells Hydrogen storage tank hydrogen physical property constants and ;

[0038] S22. Quantifying the distribution range of uncertainty parameters: Assuming the efficiency coefficient of the electrolyzer , , Follows a normal distribution ,and , , The value range is 0.10-0.14;

[0039] Assuming the limiting current density of a fuel cell Follows uniform distribution ;

[0040] Hydrogen property constants of hydrogen in hydrogen storage tank and The value of the component is ;

[0041] S23. Uncertainty Propagation Simulation: Evaluate the impact of uncertainty parameter fluctuations on the output through Monte Carlo simulation;

[0042] S24. Use the Sobol exponent method to quantify the contribution of uncertainty parameters to the output:

[0043] (9);

[0044] In the formula, Indicates the first The Sobol exponent with several uncertain parameters, and defined as follows: The uncertainty parameters are the key perturbation parameters.

[0045] Preferably, step S23 specifically comprises the following steps:

[0046] S231, each uncertainty parameter is generated according to a distribution Group random sample;

[0047] S232, each sample is respectively substituted into the dynamic mathematical model of the electro-hydrogen coupling system, and a multi-time scale simulation is run;

[0048] S233, record the output: maximum deviation of power grid frequency , peak value of hydrogen storage tank pressure and system efficiency .

[0049] Preferably, the second-level dynamic of the multi-time scale simulation in step S232 adopts the power grid frequency response: ; In the formula, represents the inertia constant;

[0050] The minute-level dynamic adopts the hydrogen storage pressure change: .

[0051] Preferably, step S3 specifically comprises the following steps:

[0052] S31, determine the stability evaluation index: voltage deviation rate ‌, hydrogen pressure fluctuation index and energy storage life loss ; wherein the voltage deviation rate is expressed as follows:

[0053] (10);

[0054] In the formula, represents the rated voltage of the power grid, with the unit of V; represents the real-time voltage, with the unit of V; represents the length of the time window;

[0055] The hydrogen pressure fluctuation index is expressed as follows:

[0056] (11);

[0057] In the formula, represents the real-time hydrogen storage pressure, with the unit of bar; represents the reference hydrogen storage pressure, with the unit of bar;

[0058] The energy storage life loss is expressed as follows:

[0059] (12);

[0060] wherein, represents the maximum discharge depth; represents the real-time charge-discharge power of the fuel cell, in units of kW; represents the total capacity of the fuel cell, in units of kWh;

[0061] S32, standardization processing:

[0062] (13);

[0063] wherein, , and respectively represent the original index value, the historical minimum value and the historical maximum value of the index;

[0064] S33, weighted synthesis:

[0065] (14);

[0066] wherein, represents the dynamic weight; , and respectively represent the weight coefficients of the voltage deviation rate , the hydrogen pressure fluctuation index , and the energy storage life loss , and ;

[0067] S34, calculating the stability score:

[0068] (15);

[0069] wherein, represents the stability score result.

[0070] Preferably, step S4 specifically comprises the following steps:

[0071] S41, mapping the multi-dimensional stability evaluation index to the radar chart axis, and quantifying the comprehensive instability index through geometric characteristics, wherein the geometric characteristics are the area of the radar chart;

[0072] S42, constructing a multi-objective optimization model:

[0073] (16);

[0074] wherein, represents the state voltage of the fuel cell; and respectively represent the minimum value and the maximum value of the state voltage of the fuel cell;

[0075] Hybrid PSO-C robust control is used as the solution algorithm to determine the control input. The particle swarm update formula is as follows:

[0076] (17);

[0077] In the formula, and They represent the first The particle in the first The velocity vector of the nth iteration and the nth iteration The particle in the first The velocity vector of the next iteration; Indicates inertia weight; and These represent individual learning factors and social learning factors, respectively. and All are random numbers, and their values ​​range from 0 to 1; This represents the global optimal position among all particles; Indicates the first The particle in the first The position at the next iteration;

[0078] The robust control expression is as follows:

[0079] (18);

[0080] In the formula, Indicates control input, and; Represents the state vector; Indicates system output; , , , All represent robust controller parameters;

[0081] The expression for the PSO-C hybrid coupling mechanism is as follows:

[0082] (19);

[0083] In the formula, Represents the cost function; , and These represent the tracking error weights, control input suppression weights, and [other weights]. Robustness weights.

[0084] Preferably, step S5 specifically includes the following steps:

[0085] S51. Construct an extreme perturbation model:

[0086] Grid-side disturbances:

[0087] (20);

[0088] wherein, denotes the disturbance power on the grid side; denotes the system rated power; denotes the unit step function;

[0089] Load side disturbance:

[0090] (21);

[0091] wherein, denotes the disturbance power on the load side; denotes the impulse function;

[0092] S52, build a time domain simulation platform:

[0093] (22);

[0094] wherein, denotes the dynamic equation of the system, wherein denotes the state variable; denotes the time;

[0095] S53, update the dynamic weight based on the real-time data of the electric-hydrogen coupling system:

[0096] (23);

[0097] wherein, and denote the dynamic weight at time and time, respectively; denotes the learning rate; denotes the target deviation; denotes the penalty coefficient; denotes the deviation threshold;

[0098] S54, re-input the updated dynamic weight into the multi-objective optimization model, and use step S52 to judge whether the updated multi-objective optimization model converges, if not, return to step S33 for weight redistribution, if yes, output the final multi-objective optimization model and the final dynamic weight .

[0099] Preferably, the matching verification calculation formula in step S6 is as follows:

[0100] (24);

[0101] wherein, represents a comprehensive stability index; represents the measured value of the stability evaluation index of the and respectively represent the upper limit and the lower limit in the stability criterion;

[0102] The stability criterion includes a Lyapunov stability criterion and an H∞ robustness index, wherein the Lyapunov stability criterion is expressed as follows:

[0103] (25);

[0104] wherein, represents a Lyapunov function; represents the gradient of the Lyapunov function;

[0105] The H∞ robustness index is as follows:

[0106] , and (26);

[0107] wherein, represents the norm of the transfer function ; and represents the maximum value of the maximum singular value of the transfer function at all frequencies .

[0108] Preferably, in step S6, the multi-objective optimization result is displayed through a dynamic radar chart.

[0109] Therefore, the present application has the beneficial effects that:

[0110] 1. Comprehensive consideration of nonlinear characteristics: By constructing dynamic models of electrolytic cells and fuel cells, the nonlinear characteristics in the electric-hydrogen coupling system are comprehensively considered, improving the accuracy of the evaluation;

[0111] 2. Multi-dimensional stability evaluation: Multi-dimensional stability evaluation indexes such as voltage deviation rate, hydrogen pressure fluctuation index, and energy storage life loss are defined, which can comprehensively reflect the stability condition of the system;

[0112] 3. Dynamic weight distribution: A unified stability score is generated by weighted synthesis, and the weight can be dynamically adjusted according to real-time data and simulation results, improving the flexibility and adaptability of the evaluation;

[0113] ​4. Extreme disturbance scenario verification: time-domain simulation verification of the multi-objective optimization model under extreme disturbance scenarios to ensure the reliability of the evaluation method in actual application;

[0114] 5. Geometric feature quantification: mapping the multi-dimensional stability evaluation index to the radar chart axis, quantifying the comprehensive instability index through geometric features (such as the area of the radar chart), making the evaluation results more intuitive and easy to understand.

[0115] The technical solutions of the present application will be further described in detail below with the aid of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0116] Figure 1 A flowchart of a stability evaluation method for an electric-hydrogen coupling system according to the present application. DETAILED DESCRIPTION

[0117] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the embodiments of the present application will be further described in detail below with the aid of the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present application and do not limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions throughout.

[0118] It should be noted that the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server comprising a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0119] The embodiments of the present application will be described in detail below with the aid of the accompanying drawings.

[0120] As shown in Figure 1 a stability evaluation method for an electric-hydrogen coupling system, comprising the following steps:

[0121] S1, considering the nonlinear characteristics of the electrolyzer and the fuel cell, a dynamic mathematical model of the electric-hydrogen coupling system is constructed;

[0122] Step S1 specifically comprises the following steps:

[0123] S11, constructing an electrolyzer dynamic model:

[0124] (1);

[0125] wherein, represents the hydrogen electricity conversion efficiency of the electrolyzer, and ; , , respectively represent the input power caused electrolyzer efficiency decay coefficient, the temperature caused electrolyzer efficiency decay coefficient and the efficiency reference constant; represents the input power of the electrolyzer, in MW, and , and respectively represent the minimum allowable operating power and the maximum allowable operating power of the electrolyzer; represents the operating temperature of the electrolyzer, in ℃;

[0126] S12, constructing a fuel cell dynamic model:

[0127] constructing a polarization curve equation:

[0128] (2);

[0129] wherein, represents the working voltage of the fuel cell, in V; represents the reversible voltage, in V; represents the working current density of the fuel cell, in A / cm 2 ; represents the ohmic resistance, in Ω; and both represent empirical coefficients; and respectively represent the alternating current density and the limiting current density, in A / cm 2 ;

[0130] wherein,

[0131] (3);

[0132] wherein, represents the working temperature of the fuel cell, in ℃; represents the required hydrogen pressure; represents the oxygen pressure; represents the gas constant, in ; represents the dynamic coupling factor;

[0133] constructing a power dynamic equation:

[0134] (4);

[0135] wherein, Pfuel represents fuel cell power generation, in units of kW; Aeff represents the effective area of a single fuel cell, in units of cm 2 ; N represents the number of fuel cells connected in series in the stack;

[0136] S13, a dynamic model of the hydrogen storage tank is constructed:

[0137] (5);

[0138] In the formula, m represents the mass of the hydrogen storage tank, in units of kg, and is determined by , r represents the hydrogen production rate of the electrolytic tank, and , LHV represents the low heat value of hydrogen, ; r represents the hydrogen consumption rate of the fuel cell, and ; and both represent hydrogen physical property constants of the hydrogen storage tank; V represents the volume of the hydrogen storage tank, in units of m 3 ; T represents the temperature of the hydrogen storage tank;

[0139] S14, a dynamic mathematical model of the electricity-hydrogen coupling system is constructed, and the dynamic mathematical model of the electricity-hydrogen coupling system includes a power subsystem model and a hydrogen energy subsystem model;

[0140] The expression of the power subsystem model is as follows:

[0141] (6);

[0142] (7);

[0143] In the formula, Pgrid represents the grid power, in units of kW; Pwind represents the wind power; Pload represents the load power, in units of kW; Ppv represents the photovoltaic power generation, in units of kW; Efc represents the fuel cell energy storage energy, in units of MWh; η represents the fuel cell power generation efficiency;

[0144] The expression of the hydrogen energy subsystem model is as follows:

[0145] (8).

[0146] S2, parameter uncertainty analysis: identify uncertainty parameters and quantify their uncertainty distribution, and screen key disturbance parameters by analyzing their influence on the output of the dynamic mathematical model of the electricity-hydrogen coupling system;

[0147] Step S2 specifically comprises the following steps:

[0148] S21, screen uncertainty parameters: electrolyzer efficiency coefficient 、 、 , current density limit of fuel cell , hydrogen physical property constant of hydrogen storage tank and ;

[0149] S22, quantify the distribution range of uncertainty parameters: assume that the electrolyzer efficiency coefficient 、 、 obeys normal distribution , and 、 、 The value range is 0.10-0.14;

[0150] Assume that the current density limit of fuel cell obeys uniform distribution ;

[0151] The value of the hydrogen physical property constant of the hydrogen storage tank and is ;

[0152] S23, uncertainty propagation simulation: evaluate the influence of uncertainty parameter fluctuation on the output through Monte Carlo simulation;

[0153] Step S23 specifically comprises the following steps:

[0154] S231, generate group random samples for each uncertainty parameter according to the distribution;

[0155] S232, substitute each group of samples into the dynamic mathematical model of the electricity-hydrogen coupling system respectively, and run multi-time scale simulation;

[0156] The second-level dynamic of the multi-time scale simulation in step S232 adopts the power grid frequency response: ; In the formula, represents the inertia constant;

[0157] The minute-level dynamic adopts the change of hydrogen storage pressure: .

[0158] S233, record output: maximum deviation of grid frequency , hydrogen storage tank pressure peak , and system efficiency .

[0159] S24, Sobol index method is used to quantify the contribution of uncertainty parameters to the output:

[0160] (9);

[0161] In the formula, Sobol index of the first uncertainty parameter, and the definition The uncertainty parameter is a key disturbance parameter.

[0162] S3, based on the key disturbance parameters screened, define multi-dimensional stability evaluation indexes and standardize, and then generate a unified stability score through weighted synthesis;

[0163] Step S3 specifically includes the following steps:

[0164] S31, determine the stability evaluation index: voltage deviation rate , hydrogen pressure fluctuation index , and energy storage life loss ; wherein the voltage deviation rate The expression is as follows:

[0165] (10);

[0166] In the formula, The rated voltage of the grid is represented by V; The real-time voltage is represented by V; The length of the time window is represented by T;

[0167] The hydrogen pressure fluctuation index The expression is as follows:

[0168] (11);

[0169] In the formula, The real-time hydrogen storage pressure is represented by bar; The reference hydrogen storage pressure is represented by bar;

[0170] The energy storage life loss The expression is as follows:

[0171] (12);

[0172] In the formula, The maximum discharge depth is represented by D represents the real-time charge-discharge power of the fuel cell, with the unit of kW; represents the total capacity of the fuel cell, with the unit of kWh;

[0173] S32, standardization processing:

[0174] (13);

[0175] In the formula, , and respectively represent the original index value, the historical minimum value and the historical maximum value of the index;

[0176] S33, weighted synthesis:

[0177] (14);

[0178] In the formula, represents the dynamic weight; , and respectively represent the weight coefficients of the voltage deviation rate , the hydrogen pressure fluctuation index and the energy storage life loss , and ;

[0179] S34, calculating the stability score:

[0180] (15);

[0181] In the formula, represents the stability score result.

[0182] S4, mapping the multi-dimensional stability evaluation index to the radar chart axis to obtain a multi-objective optimization model;

[0183] Step S4 specifically includes the following steps:

[0184] S41, mapping the multi-dimensional stability evaluation index to the radar chart axis, and quantifying the comprehensive instability index through geometric characteristics, wherein the geometric characteristics are the area of the radar chart;

[0185] S42, constructing a multi-objective optimization model:

[0186] (16);

[0187] In the formula, represents the state voltage of the fuel cell; and respectively represent the minimum value and the maximum value of the state voltage of the fuel cell;

[0188] The hybrid PSO-C robust control is used as a solution algorithm to determine the control input where the particle swarm update formula is as follows:

[0189] (17);

[0190] where, and respectively represent the velocity vector of the i-th particle in the j-th iteration and the velocity vector of the i-th particle in the j-th iteration; represents an inertia weight; and respectively represent an individual learning factor and a social learning factor; and are random numbers, and the value range is 0-1; represents a global optimal position in all particles; represents the position of the i-th particle in the j-th iteration; The robust control expression is as follows:

[0191]

[0192] (18);

[0193] where, represents the control input, and; represents a state vector; represents a system output; , , , all represent robust controller parameters;

[0194] The PSO-C hybrid coupling mechanism expression is as follows:

[0195] (19);

[0196] where, represents a cost function; , and respectively represent a tracking error weight, a control input suppression weight, and a robustness weight.

[0197] ​​​​​​​S5. Perform time-domain simulation verification of the multi-objective optimization model under extreme perturbation scenarios, dynamically adjust the index weights based on the simulation data, and then re-input the adjusted index weights into the multi-objective optimization model until convergence and the final dynamic weights are output.

[0198] Step S5 specifically includes the following steps:

[0199] S51. Construct an extreme perturbation model:

[0200] Grid-side disturbances:

[0201] (20);

[0202] In the formula, This indicates the disturbance power on the grid side; Indicates the system's rated power; Represents the unit step function;

[0203] Load-side disturbance:

[0204] (twenty one);

[0205] In the formula, Indicates the load-side disturbance power; Represents the impulse function;

[0206] S52. Building a time-domain simulation platform:

[0207] (twenty two);

[0208] In the formula, Represent the dynamic equations of the system, where Represents state variables; Indicates time;

[0209] S53. Real-time data update of dynamic weights based on the electro-hydrogen coupling system:

[0210] (twenty three);

[0211] In the formula, and They represent Time and Dynamic weights at any given time; Indicates the learning rate; Indicates target deviation; Indicates the penalty coefficient; Indicates the deviation threshold;

[0212] S54. Update the dynamic weights The multi-objective optimization model is re-input, and it is judged whether the updated multi-objective optimization model converges or not by using step S52, if not, returning to step S33 for weight redistribution, if yes, outputting the final multi-objective optimization model and the final dynamic weight .

[0213] S6, input the real-time operation data of the electric-hydrogen coupling system and the final dynamic weight into step S3 to generate a stability score under the current working condition, and output the evaluation result after matching verification combined with the stability criterion.

[0214] The matching verification calculation formula in step S6 is as follows:

[0215] (24);

[0216] In the formula, represents the comprehensive stability index; represents the measured value of the stability evaluation index; and respectively represent the upper limit and the lower limit in the stability criterion;

[0217] The stability criterion includes Lyapunov stability criterion and H∞ robustness index, wherein the expression of Lyapunov stability criterion is as follows:

[0218] (25);

[0219] In the formula, represents the Lyapunov function; represents the gradient of the Lyapunov function;

[0220] The H∞ robustness index is as follows:

[0221] , and (26);

[0222] In the formula, represents the norm of the transfer function ; represents the maximum value of the maximum singular value of the transfer function at all frequencies .

[0223] Simulation experiment:

[0224] In this simulation, the following simulation experiments are carried out based on the electric-hydrogen coupling system described in the application.

[0225] ​Parameter setting and model construction: According to the actual system parameters and operating conditions, the electrolytic tank efficiency coefficient is determined , , , the fuel cell limiting current density ; hydrogen storage tank hydrogen physical property constant and ; system rated power ; grid rated voltage ; reference hydrogen storage pressure ; maximum discharge depth ; total capacity of fuel cell .

[0226] Based on the above conditions, multiple simulation experiments are carried out, and the following stability evaluation index results are obtained under different disturbance conditions:

[0227] Voltage deviation rate: The voltage deviation rate shows different values. In normal operating conditions, the voltage deviation rate maintains at a low level, with an average value of about 0.02. When the grid side appears positive disturbance such as , the voltage deviation rate instantaneously rises, but under the regulation of the system, it quickly falls and stabilizes at about 0.05. This shows that the system can control the voltage deviation within a certain range through its own regulation mechanism when facing a certain degree of grid power fluctuation, ensuring the stability of power supply.

[0228] Hydrogen pressure fluctuation index: In the stable operation stage, the hydrogen pressure fluctuation index is small, about 0.03. When the system is subjected to a larger disturbance, such as a pulse disturbance on the load side, , the hydrogen pressure fluctuation index will rise. In a typical simulation, the index rises to 0.08, but with the adjustment of the system, the pressure of the hydrogen storage tank gradually recovers to stability, and the hydrogen pressure fluctuation index also decreases. This shows that the system can better maintain the stability of hydrogen pressure when dealing with load changes and other disturbances, ensuring the normal operation of the hydrogen energy subsystem.

[0229] Energy storage life consumption: The energy storage life consumption is closely related to the charge and discharge frequency and depth of the battery energy storage in the system. During the simulation process, when the system frequently adjusts power, i.e. the battery energy storage frequently charges and discharges, the energy storage life consumption increases. Under a certain continuous disturbance condition, after a period of operation, the energy storage life consumption index reaches 0.05. Through the calculation and analysis of energy storage life consumption under different conditions, reference can be provided for the maintenance and replacement of battery energy storage.

[0230] Stability score and comprehensive stability index: the stability score calculated by weighted synthesis presents obvious changes under different working conditions. When the system is not disturbed greatly, the stability score is higher, close to 0.9. When the system is disturbed greatly on both the grid side and the load side, the stability score drops to about 0.7. After dynamic adjustment of the index weight, the stability score rises to about 0.8 after re-simulation calculation, which shows that dynamic weight adjustment has a positive effect on system stability evaluation and can more accurately reflect the actual stability of the system. The comprehensive stability index is also different under different experimental conditions. Under ideal conditions, the comprehensive stability index is close to 1, indicating that the stability of the system in all aspects performs well. When disturbed by complex disturbances, the index will decrease, such as in a simulation containing multiple disturbances at the same time, the comprehensive stability index drops to 0.85. But with the adaptive adjustment of the system, the index gradually rises, which further verifies the stability of the system and the effectiveness of the evaluation method.

[0231] System response: assuming that in the grid frequency mutation scenario, the calculation of the system response time starts from the time when the frequency mutation occurs and ends when the key indicators of the system reach a stable state. Assuming that the time when the frequency mutation occurs is , the time when the key indicators of the system reach a stable state is , the response time is , and the two time points are accurately recorded by high-precision monitoring equipment, so that the response time of the system is 12.7s.

[0232] Confidence evaluation calculation: assuming that independent tests are performed, of which times meet the stability requirements (such as response time within the specified range, and various stability evaluation indicators meet the standards), then the confidence calculation formula is ; the test calculation result confidence reaches 98.3%.

[0233] In summary, the evaluation response time under the grid frequency mutation scenario is shortened to 12.7s, the evaluation result confidence reaches 98.3%, and the requirements of IEEE2030.7 standard are met.

[0234] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application and not to limit them, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that: it can still modify or equivalently replace the technical solutions of the present application, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for stability assessment of an electrical-hydrogen coupling system, characterized in that: Comprise the following steps: S1, considering the nonlinear characteristics of electrolytic tank and fuel cell, a dynamic mathematical model of the electric-hydrogen coupling system is constructed; Step S1 specifically comprises the following steps: S11, a dynamic model of the electrolytic tank is constructed: （1）； In the formula, represents the hydrogen electricity conversion efficiency of the electrolyzer, and ; , , respectively represent the input power resulting electrolyzer efficiency decay coefficient, the temperature resulting electrolyzer efficiency decay coefficient, and the efficiency reference constant; represents the input power of the electrolyzer, in MW, and , and respectively represent the minimum allowable operating power and the maximum allowable operating power of the electrolyzer; represents the operating temperature of the electrolyzer, in °C; S12, a dynamic model of the fuel cell is constructed: A polarization curve equation is constructed: （2）； wherein V represents the operating voltage of the fuel cell, in V; V represents the reversible voltage, in V; A / cm2represents the operating current density of the fuel cell, in A / cm2 2 ; Ω represents the ohmic resistance, in Ω; and both represent an empirical coefficient; and represent the alternating current density and the limiting current density, respectively, in A / cm2 2 ; Wherein, （3）； wherein T represents the operating temperature of the fuel cell in °C; P H2 represents the hydrogen pressure; P O2 represents the oxygen pressure; R represents the gas constant in ; K represents the dynamic coupling factor; A power dynamic equation is constructed: （4）； In the formula, Pfuel represents fuel cell power generation, in kW; Aeff represents the effective area of a single fuel cell, in cm2 2 ; N represents the number of fuel cells connected in series in the stack. S13, a dynamic model of the hydrogen storage tank is constructed: （5）； wherein, represents the hydrogen storage tank mass in kg, and is determined by , represents the hydrogen production rate of the electrolyzer, and , represents the hydrogen lower heating value, ; represents the hydrogen consumption rate of the fuel cell, and ; and both represent the hydrogen gas property constant of the hydrogen storage tank; represents the hydrogen storage tank volume in m 3 ; represents the hydrogen storage tank temperature; S14, a dynamic mathematical model of the electric-hydrogen coupling system is constructed, which includes a power subsystem model and a hydrogen energy subsystem model; The expression of the power subsystem model is as follows: （6）； （7）； wherein represents the grid power in kW; represents the wind power; represents the load power in kW; represents the photovoltaic power in kW; represents the fuel cell storage energy in MWh; represents the fuel cell power generation efficiency; The expression of the hydrogen energy subsystem model is as follows: （8）； S2, parameter uncertainty analysis: identify uncertain parameters and quantify their uncertainty distribution, and screen key disturbance parameters by analyzing their influence on the output of the dynamic mathematical model of the electric-hydrogen coupling system; S3, based on the screened key disturbance parameters, define multi-dimensional stability evaluation indexes and standardize them, and then generate a unified stability score through weighted synthesis; S4, map the multi-dimensional stability evaluation indexes to the radar chart axis to obtain a multi-objective optimization model; S5, time domain simulation verification is performed on the multi-objective optimization model under extreme disturbance scenarios, and the index weight is dynamically adjusted according to the simulation running data, then the adjusted index weight is input into the multi-objective optimization model again until the final dynamic weight is output; S6, input the real-time running data of the electric-hydrogen coupling system and the final dynamic weight into step S3 to generate a stability score under the current working condition, and output the evaluation result after matching verification according to the stability criterion.

2. The method of claim 1, wherein: Step S2 specifically comprises the following steps: S21, screening uncertainty parameters: electrolyzer efficiency coefficient , , , current density limit of fuel cell , hydrogen physical property constants of hydrogen storage tank and ; S22, quantifying the distribution range of the uncertainty parameter: assuming the electrolytic cell efficiency coefficient 、 、 obeys normal distribution , and 、 、 The value range of is 0.10-0.14; Assuming fuel cell limiting current density Subject to uniform distribution ; Hydrogen property constants for hydrogen storage tank and the value range of ; S23, uncertainty propagation simulation: evaluate the influence of uncertain parameter fluctuation on the output through Monte Carlo simulation; S24, Sobol index method is used to quantify the contribution of uncertain parameters to the output: （9）； wherein Sobol indices representing the first uncertainty parameter, and the uncertainty parameter defined as the key perturbation parameter.

3. The method of claim 2, wherein: Step S23 specifically comprises the following steps: S231, each uncertainty parameter is generated according to a distribution group random sample; S232, each group of samples is respectively substituted into the dynamic mathematical model of the electric-hydrogen coupling system, and multi-time scale simulation is run; S233, record output: maximum deviation of grid frequency , peak pressure of hydrogen storage tank , and system efficiency .

4. The method of claim 3, wherein: The second-level dynamic of step S232 adopts the power grid frequency response of the multi-time scale simulation: ; wherein, represents the inertia constant; The minute level dynamic employs a change in hydrogen storage pressure: .

5. The method of claim 4, wherein: Step S3 specifically comprises the following steps: S31, determining stability evaluation index: voltage deviation rate ‌, hydrogen pressure fluctuation index and energy storage life loss ; wherein the voltage deviation rate The expression is as follows: （10）； wherein represents the grid rated voltage in V; represents the real-time voltage in V; represents the length of the time window; hydrogen pressure fluctuation index The expression is as follows: （11）； In the formula, represents the real-time hydrogen storage pressure, in bar; represents the reference hydrogen storage pressure, in bar; energy storage life loss The expression is as follows: （12）； In the formula, represents the maximum discharge depth; represents the real-time charge-discharge power of the fuel cell, in units of kW; represents the total capacity of the fuel cell, in units of kWh; S32, standardization processing: （13）； wherein, , and respectively represent the original index value, the historical minimum value of the index, and the historical maximum value of the index; S33, weighted synthesis: （14）； wherein, represents a dynamic weight; , and respectively represent weight coefficients of voltage deviation rate ‌, hydrogen pressure fluctuation index and energy storage life loss , and ; S34, calculate the stability score: （15）； In the formula, The stability score results are shown.

6. The method of claim 5, wherein: Step S4 specifically comprises the following steps: S41, map the multi-dimensional stability evaluation indexes to the radar chart axis, and quantify the comprehensive instability index through geometric characteristics, wherein the geometric characteristics are the area of the radar chart; S42, construct a multi-objective optimization model: （16）； In the formula, represents a state voltage of the fuel cell; and respectively represent a minimum value and a maximum value of the state voltage of the fuel cell. The mixed PSO-C robust control is used as a solving algorithm to determine the control input wherein the particle swarm updating formula is as follows: （17）； In the formula, and They represent the first The particle in the first The velocity vector of the nth iteration and the nth iteration The particle in the first The velocity vector of the next iteration; Indicates inertia weight; and These represent individual learning factors and social learning factors, respectively. and All are random numbers, and their values ​​range from 0 to 1; This represents the globally optimal position among all particles; Indicates the first The particle in the first The position at the next iteration; The expression of robust control is as follows: （18）； wherein denotes a control input; denotes a state vector; denotes a system output; , , , all denote robust controller parameters; The expression of PSO-C hybrid coupling mechanism is as follows: （19）； wherein represents a cost function; , and represent a tracking error weight, a control input suppression weight, and an H∞ robustness weight, respectively.

7. The method of claim 6, wherein: Step S5 specifically comprises the following steps: S51, construct an extreme disturbance model: Grid side disturbance: （20）； wherein represents the disturbance power at the grid side; represents the system rated power; represents the unit step function; Load side disturbance: （21）； In the formula, represents the load-side disturbance power; represents the impulse function; S52, build a time domain simulation platform: （22）； wherein represents the dynamic equation of the system, wherein represents the state variable; represents the time; S53, update the dynamic weight based on the real-time data of the electric-hydrogen coupling system: （23）； In the formula, and respectively represent the dynamic weight at the moment and the moment; represents the learning rate; represents the target deviation; represents the penalty coefficient; represents the deviation threshold; S54, output the updated dynamic weight re-input the multi-objective optimization model, and determine whether the updated multi-objective optimization model converges by using step S52, if not, return to step S33 to perform weight redistribution, if yes, output the final multi-objective optimization model and the final dynamic weight .

8. The method of claim 7, wherein: The matching verification calculation formula in step S6 is as follows: （24）； In the formula, represents the comprehensive stability index; represents the first measured value of the stability evaluation index; and respectively represent the upper limit and the lower limit in the stability criterion; The stability criterion includes Lyapunov stability criterion and H∞ robustness index, wherein the expression of Lyapunov stability criterion is as follows: （25）； wherein denotes a Lyapunov function; denotes the gradient of the Lyapunov function; The H∞ robustness index is as follows: , and (26); wherein denotes the H∞ norm of the transfer function denotes the maximum value of the maximum singular value of the transfer function at all frequencies​​ 9. The method of claim 8, wherein: In step S6, the multi-objective optimization results are displayed through dynamic radar chart.

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