Electro-hydrogen coupling system stability evaluation method

Abstract

The invention discloses an electricity-hydrogen coupling system stability evaluation method, which belongs to the field of energy system stability evaluation, and comprises the following steps: S1, considering nonlinear characteristics of an electrolytic bath and a fuel cell, and constructing a dynamic mathematical model of an electricity-hydrogen coupling system; s2, parameter uncertainty analysis is carried out; s3, generating a unified stability score; s4, obtaining a multi-objective optimization model; s5, performing simulation verification to obtain a final dynamic weight; and S6, inputting the real-time operation data of the electricity-hydrogen coupling system and the final dynamic weight into the step S3 to generate a stability score under the current working condition, performing matching verification in combination with a stability criterion, and outputting an evaluation result. By the adoption of the stability evaluation method for the electricity-hydrogen coupling system, through a technical closed loop of dynamic modeling-intelligent screening-collaborative optimization, breakthrough improvement is achieved in the dimensions of model precision, calculation efficiency, system robustness and the like, and full-working-condition stability guarantee is provided for the electricity-hydrogen coupling system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy system stability assessment, and particularly to a method for assessing the stability of an electric-hydrogen coupling system. Background Art

[0002] Under the background of the global active promotion of energy transformation and sustainable development, as a new energy system with great development potential, the electric-hydrogen coupling system is gradually becoming a research hotspot in the energy field. With its unique technical advantages, this system can achieve the mutual conversion and efficient coordinated operation between hydrogen energy and electric energy. When there is an excess of electric energy, the redundant electric energy can be converted into hydrogen energy through electrolytic water hydrogen production for storage; when the peak demand for electric energy occurs or the renewable energy power generation is insufficient, the stored hydrogen energy can be converted into electric energy through a fuel cell to supplement the power supply. This two-way energy conversion mechanism not only improves the energy utilization efficiency but also enhances the flexibility and stability of energy supply, which is of great significance for building a clean, safe, and efficient new energy system.

[0003] Although the electric-hydrogen coupling system has many advantages, due to mostly using static models, it cannot be dynamically adjusted to adapt to long-term parameter drifts, such as pipeline aging and equipment efficiency decay. Summary of the Invention

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

[0005] To achieve the above purpose, the present invention provides a method for assessing the stability of an electric-hydrogen coupling system, including the following steps: S1. Considering the nonlinear characteristics of the electrolyzer and fuel cell, construct a dynamic mathematical model of the electric-hydrogen coupling system; S2. Parameter uncertainty analysis: Identify the uncertainty parameters and quantify their uncertainty distributions, and screen the key perturbation 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 perturbation parameters, define multi-dimensional stability evaluation indicators and standardize them, and then generate a unified stability score through weighted synthesis; S4. Map the multi-dimensional stability evaluation indicators to the radar chart axis to obtain a multi-objective optimization model; S5. Conduct time-domain simulation verification on the multi-objective optimization model under extreme perturbation scenarios, dynamically adjust the index weights according to the simulation operation data, and then re-enter the adjusted index weights into the multi-objective optimization model until the final dynamic weights are convergently output; S6. Input the real-time operation data of the electric-hydrogen coupling system and the final dynamic weights into step S3 to generate the stability score under the current working condition, and output the evaluation result after matching and verifying with the stability criterion.

[0006] Preferably, step S1 specifically includes the following steps: S11. Construct a dynamic model of the electrolyzer: (1); In the formula, represents the hydrogen-electric conversion efficiency of the electrolyzer, and ; , , respectively represent the electrolyzer efficiency decay coefficient caused by the input power, the electrolyzer efficiency decay coefficient caused by the temperature, and the efficiency reference constant; represents the input power of the electrolyzer, with the unit of 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, with the unit of °C; S12. Construct a dynamic model of the fuel cell: Construct a polarization curve equation: (2); In the formula, represents the working voltage of the fuel cell, with the unit of V; represents the reversible voltage, with the unit of V; represents the working current density of the fuel cell, with the unit of A / cm 2 ; represents the ohmic impedance, with the unit of Ω; and both represent empirical coefficients; and respectively represent the alternating current density and the limiting current density, with the unit of A / cm 2 ; Among them, (3); In the formula, represents the working temperature of the fuel cell, with the unit of °C; represents the required hydrogen pressure; represents the oxygen pressure; represents the gas constant, with the unit of ; represents the dynamic coupling factor; Construct a power dynamic equation: (4); In the formula, represents the power generation power of the fuel cell, with the unit of kW; Represents the effective area of a single fuel cell, with the unit of cm 2 ; Represents the number of fuel cells connected in series in the stack; S13. Construct a dynamic model of the hydrogen storage tank: (5); In the formula, Represents the mass of the hydrogen storage tank, with the unit of kg, and Is determined by Decided, Represents the hydrogen production rate of the electrolyzer, and , Represents the lower heating value of hydrogen, ; Represents the hydrogen consumption rate of the fuel cell, and ; And Both represent the hydrogen physical property constants of the hydrogen storage tank; Represents the volume of the hydrogen storage tank, with the unit of m 3 ; Represents the temperature of the hydrogen storage tank; S14. Construct a dynamic mathematical model of the power-to-hydrogen coupling system. The dynamic mathematical model of the power-to-hydrogen coupling system includes a power subsystem model and a hydrogen energy subsystem model; Among them, the expression of the power subsystem model is as follows: (6); (7); In the formula, Represents the grid power, with the unit of kW; Represents the wind power; Represents the load power, with the unit of kW; Represents the photovoltaic power generation power, with the unit of kW; Represents the energy storage energy of the fuel cell, with the unit of MWh; Represents the power generation efficiency of the fuel cell; The expression of the hydrogen energy subsystem model is as follows: (8).

[0007] Preferably, step S2 specifically includes the following steps: S21. Screen the uncertainty parameters: the electrolyzer efficiency coefficient , , , the limiting current density of the fuel cell , the hydrogen physical property constants of the hydrogen storage tank And ; S22. Quantify the distribution range of uncertainty parameters: Assume that the electrolyzer efficiency coefficient , , follow a normal distribution , and , , have a value range of 0.10 - 0.14; Assume that the limiting current density of the fuel cell follows a uniform distribution ; Physical properties constants of hydrogen in the hydrogen storage tank and have a value range of ; S23. Uncertainty propagation simulation: Evaluate the impact of uncertainty parameter fluctuations on the output through Monte Carlo simulation; S24. Use the Sobol index method to quantify the contribution degree of uncertainty parameters to the output: (9); In the formula, represents the Sobol index of the -th uncertainty parameter, and the uncertainty parameter defined as is the key perturbation parameter.

[0008] Preferably, step S23 specifically includes the following steps: S231. Generate groups of random samples for each uncertainty parameter according to its distribution; S232. Substitute each group of samples into the dynamic mathematical model of the electric-hydrogen coupling system and run multi-time scale simulation; S233. Record the output: The maximum deviation of the grid frequency , the peak pressure of the hydrogen storage tank and the system efficiency .

[0009] Preferably, the second-level dynamics of the multi-time scale simulation described in step S232 adopt the grid frequency response: ; In the formula, represents the inertia constant; The minute-level dynamics adopt the change of hydrogen storage pressure: .

[0010] Preferably, step S3 specifically includes the following steps: S31. Determine the stability evaluation indexes: Voltage deviation rate , hydrogen pressure fluctuation index and energy storage life loss ; Among them, the voltage deviation rate The expression is as follows: (10); 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 time window length; Hydrogen pressure fluctuation index The expression is as follows: (11); 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; Energy storage life loss The expression is as follows: (12); In the formula, represents the maximum depth of discharge; represents the real-time charge and discharge power of the fuel cell, with the unit of kW; represents the total capacity of the fuel cell, with the unit of kWh; S32. Standardization processing: (13); In the formula, , and respectively represent the original index value, the historical minimum value and the historical maximum value of the index; S33. Weighted synthesis: (14); In the formula, represents the dynamic weight; , and respectively represent the voltage deviation rate , the hydrogen pressure fluctuation index and the energy storage life loss of the weight coefficients, and ; S34. Calculate the stability score: (15); In the formula, represents the stability score result.

[0011] Preferably, step S4 specifically includes the following steps: S41. Map the multi-dimensional stability evaluation index to the radar chart axis and quantify the comprehensive instability index through geometric features, where the geometric feature is the area of the radar chart; S42. Construct a multi-objective optimization model: (16); In the formula, represents the state voltage of the fuel cell; and represent the minimum and maximum values of the fuel cell state voltage respectively; Adopt the hybrid PSO-C robust control as the solution algorithm to determine the control input , where the particle swarm update formula is as follows: (17); In the formula, and represent the velocity vectors of the th particle at the th iteration and the velocity vector of the th particle at the th iteration respectively; represents the inertia weight; and represent the individual learning factor and the social learning factor respectively; and are both random numbers, and the value range is 0 - 1; represents the global optimal position among all particles; represents the position of the th particle at the th iteration; The robust control expression is as follows: (18); In the formula, represents the control input, and; represents the state vector; represents the system output; , , , all represent the robust controller parameters; The PSO-C hybrid coupling mechanism expression is as follows: (19); In the formula, represents the cost function; , and represent the tracking error weight, the control input suppression weight, and the robustness weight respectively.

[0012] Preferably, step S5 specifically includes the following steps: S51. Construct an extreme disturbance model: Grid-side disturbance: (20); In the formula, represents the disturbance power on the grid side; represents the rated power of the system; represents the unit step function; Load-side disturbance: (21); In the formula, represents the disturbance power on the load side; represents the impulse function; S52. Build a time-domain simulation platform: (22); In the formula, represents the dynamic equation of the system, where 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 moment of and the dynamic weight at the moment of represents the learning rate; represents the target deviation; represents the penalty coefficient; represents the deviation threshold; S54. Re-enter the updated dynamic weight into the multi-objective optimization model, and use step S52 to determine whether the updated multi-objective optimization model converges. If not, return to step S33 for weight reallocation. If so, output the final multi-objective optimization model and the final dynamic weight .

[0013] Preferably, the matching verification calculation formula described in step S6 is as follows: (24); In the formula, represents the comprehensive stability index; represents the measured value of the th stability evaluation index; and respectively represent the upper and lower limits in the stability criterion; The stability criterion includes the Lyapunov stability criterion and the H∞ robustness index. The expression of the Lyapunov stability criterion is as follows: (25); In the formula, represents the Lyapunov function; represents the gradient of the Lyapunov function; The H∞ robustness index is as follows: , and (26); In the formula, represents the transfer function of norm; represents at all frequencies the transfer function the maximum value of the maximum singular value.

[0014] Preferably, in step S6, the multi-objective optimization result is displayed through a dynamic radar chart.

[0015] Therefore, the present invention adopts the above-mentioned method for evaluating the stability of an electro-hydrogen coupling system, and the beneficial effects are as follows: 1. Comprehensively consider the nonlinear characteristics: By constructing the dynamic models of the electrolyzer and the fuel cell, the nonlinear characteristics in the electro-hydrogen coupling system are comprehensively considered, improving the accuracy of the evaluation; 2. Multi-dimensional stability evaluation: Define multi-dimensional stability evaluation indicators such as the voltage deviation rate, hydrogen pressure fluctuation index, and energy storage life loss, which can comprehensively reflect the stability status of the system; 3. Dynamic weight allocation: Generate a unified stability score through weighted synthesis, and the weights can be dynamically adjusted according to real-time data and simulation results, improving the flexibility and adaptability of the evaluation; 4. Verification of extreme disturbance scenarios: Conduct time-domain simulation verification on the multi-objective optimization model under extreme disturbance scenarios to ensure the reliability of the evaluation method in practical applications; 5. Quantification of geometric features: Map the multi-dimensional stability evaluation indicators to the radar chart axis, and quantify the comprehensive instability index through geometric features (such as the area of the radar chart), making the evaluation result more intuitive and easy to understand.

[0016] Next, through the attached drawings and embodiments, the technical solutions of the present invention will be further described in detail. Description of the Drawings

[0017] Figure 1Flowchart of a method for evaluating the stability of an electric-hydrogen coupling system according to the present invention. Detailed implementation manners

[0018] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the following further describes the embodiments of the present invention in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of this application. The examples of the embodiments are shown in the accompanying drawings, in which the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end.

[0019] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

[0020] The following further describes the embodiments of the present invention in detail with reference to the accompanying drawings.

[0021] As Figure 1 shown, a method for evaluating the stability of an electric-hydrogen coupling system includes the following steps: S1. Considering the non-linear characteristics of the electrolyzer and the fuel cell, construct a dynamic mathematical model of the electric-hydrogen coupling system; Step S1 specifically includes the following steps: S11. Construct a dynamic model of the electrolyzer: (1); In the formula, represents the hydrogen-electric conversion efficiency of the electrolyzer, and ; , , respectively represent the electrolyzer efficiency decay coefficient caused by the input power, the electrolyzer efficiency decay coefficient caused by the temperature, and the efficiency reference constant; represents the input power of the electrolyzer, with the unit of 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, with the unit of °C; S12. Construct a dynamic model of the fuel cell: Construct a polarization curve equation: (2); In the formula, represents the working voltage of the fuel cell, with the unit of V; represents the reversible voltage, with the unit of V; represents the working current density of the fuel cell, with the unit of A / cm 2 ; represents the ohmic impedance, with the unit of Ω; and both represent empirical coefficients; and represent the alternating current density and the limiting current density respectively, with the unit of A / cm 2 ; Among them, (3); In the formula, represents the working temperature of the fuel cell, with the unit of °C; represents the required hydrogen pressure; represents the oxygen pressure; represents the gas constant, with the unit of ; represents the dynamic coupling factor; Construct the power dynamic equation: (4); In the formula, represents the power generation power of the fuel cell, with the unit of kW; represents the effective area of a single fuel cell, with the unit of cm 2 ; represents the number of fuel cells connected in series in the stack; S13. Construct the dynamic model of the hydrogen storage tank: (5); In the formula, represents the mass of the hydrogen storage tank, with the unit of kg, and is determined by ; represents the hydrogen production rate of the electrolyzer, and , represents the lower heating value of hydrogen, ; represents the hydrogen consumption rate of the fuel cell, and ; and both represent the hydrogen physical property constants of the hydrogen storage tank; represents the volume of the hydrogen storage tank, with the unit of m 3 ; represents the temperature of the hydrogen storage tank; S14. Build a dynamic mathematical model of the power-to-hydrogen coupling system. The dynamic mathematical model of the power-to-hydrogen coupling system includes a power subsystem model and a hydrogen energy subsystem model; Among them, the expression of the power subsystem model is as follows: (6); (7); In the formula, represents the grid power, with the unit of kW; represents the wind power; represents the load power, with the unit of kW; represents the photovoltaic power generation, with the unit of kW; represents the energy storage of the fuel cell, with the unit of MWh; represents the power generation efficiency of the fuel cell; The expression of the hydrogen energy subsystem model is as follows: (8).

[0022] S2. Parameter uncertainty analysis: Identify the uncertainty parameters, quantify their uncertainty distributions, and screen the key perturbation parameters by analyzing their influence on the output of the dynamic mathematical model of the power-to-hydrogen coupling system; Step S2 specifically includes the following steps: S21. Screen the uncertainty parameters: the electrolyzer efficiency coefficient , , , the limiting current density of the fuel cell , the physical property constants of hydrogen in the hydrogen storage tank and ; S22. Quantify the distribution range of the uncertainty parameters: Assume that the electrolyzer efficiency coefficients , , follow a normal distribution , and , , have a value range of 0.10 - 0.14; Assume that the limiting current density of the fuel cell follows a uniform distribution ; The physical property constants of hydrogen in the hydrogen storage tank and take values within the range of ; S23. Uncertainty propagation simulation: Evaluate the influence of the uncertainty parameter fluctuations on the output through Monte Carlo simulation; Step S23 specifically includes the following steps: ​​S231. Each uncertainty parameter is generated according to a distribution to obtain a group of random samples; S232. Substitute each group of samples into the dynamic mathematical model of the electricity-hydrogen coupling system and run multi-time scale simulations; For the second-level dynamics of the multi-time scale simulation described in step S232, the power grid frequency response is adopted: ; where represents the inertia constant; For the minute-level dynamics, the change in hydrogen storage pressure is adopted: .

[0023] S233. Record the outputs: the maximum deviation of the power grid frequency , the peak value of the hydrogen storage tank pressure and the system efficiency .

[0024] S24. Use the Sobol index method to quantify the contribution of uncertainty parameters to the outputs: (9); where represents the Sobol index of the th uncertainty parameter, and the uncertainty parameter with is defined as the key perturbation parameter.

[0025] S3. Based on the selected key perturbation parameters, define a multi-dimensional stability evaluation index and standardize it, and then generate a unified stability score through weighted synthesis; Step S3 specifically includes the following steps: S31. Determine the stability evaluation indices: the voltage deviation rate , the hydrogen pressure fluctuation index and the energy storage life loss ; where the expression of the voltage deviation rate is as follows: (10); where represents the rated voltage of the power grid, in V; represents the real-time voltage, in V; represents the time window length; The expression of the hydrogen pressure fluctuation index is as follows: (11); where represents the real-time hydrogen storage pressure, in bar; represents the reference hydrogen storage pressure, in bar; The energy storage life loss The expression is as follows: (12); Wherein, represents the maximum depth of discharge; represents the real-time charge and discharge power of the fuel cell, with the unit of kW; represents the total capacity of the fuel cell, with the unit of kWh; S32. Standardization processing: (13); Wherein, , and respectively represent the original index value, the historical minimum value and the historical maximum value of the index; S33. Weighted synthesis: (14); Wherein, represents the dynamic weight; , and respectively represent the voltage deviation rate , the hydrogen pressure fluctuation index and the energy storage life loss weight coefficients, and ; S34. Calculate the stability score: (15); Wherein, represents the stability score result.

[0026] S4. Map the multi-dimensional stability evaluation index to the radar chart axis to obtain a multi-objective optimization model; Step S4 specifically includes the following steps: S41. Map the multi-dimensional stability evaluation index to the radar chart axis, and quantify the comprehensive instability index through geometric features, where the geometric feature is the area of the radar chart; S42. Construct a multi-objective optimization model: (16); 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; Adopt a hybrid PSO-C robust control as the solution algorithm to determine the control input , where the particle swarm update formula is as follows: (17); Wherein, and respectively represent the velocity vectors of the th particle at the th iteration and the velocity vector of the th particle at the th iteration; represents the inertia weight; and respectively represent the individual learning factor and the social learning factor; and are both random numbers, and their value ranges are from 0 to 1; represents the global optimal position among all particles; represents the position of the th particle at the th iteration; The robust control expression is as follows: (18); In the formula, represents the control input, and; represents the state vector; represents the system output; , , , all represent the robust controller parameters; The PSO-C hybrid coupling mechanism expression is as follows: (19); In the formula, represents the cost function; , and respectively represent the tracking error weight, the control input suppression weight, and the robustness weight.

[0027] S5. Conduct a time-domain simulation verification on the multi-objective optimization model under extreme disturbance scenarios, dynamically adjust the index weights according to the simulation operation data, and then re-enter the adjusted index weights into the multi-objective optimization model until convergence to output the final dynamic weights; Step S5 specifically includes the following steps: S51. Construct an extreme disturbance model: Grid-side disturbance: (20); In the formula, represents the disturbance power on the grid side; represents the rated power of the system; represents the unit step function; Load-side disturbance: (21); Wherein, represents the disturbance power on the load side; represents the impulse function; S52. Build a time-domain simulation platform: (22); Wherein, represents the dynamic equation of the system, where 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); Wherein, and respectively represent the moment of and the dynamic weights at the moment of represents the learning rate; represents the target deviation; represents the penalty coefficient; represents the deviation threshold; S54. Re-enter the updated dynamic weight into the multi-objective optimization model, and use step S52 to determine whether the updated multi-objective optimization model converges. If not, return to step S33 for weight reallocation. If so, output the final multi-objective optimization model and the final dynamic weight .

[0028] S6. Input the real-time operation data of the electric-hydrogen coupling system and the final dynamic weight into step S3 to generate the stability score under the current working condition, and output the evaluation result after matching and verification in combination with the stability criterion.

[0029] The calculation formula for the matching verification described in step S6 is as follows: (24); Wherein, represents the comprehensive stability index; represents the measured value of the th stability evaluation index; and respectively represent the upper and lower limits in the stability criterion; The stability criterion includes the Lyapunov stability criterion and the H∞ robustness index, and the expression of the Lyapunov stability criterion is as follows: (25); Wherein, represents the Lyapunov function; represents the gradient of the Lyapunov function; The H∞ robustness index is as follows: , and (26); wherein, represents the transfer function of norm; represents at all frequencies the maximum value of the maximum singular value of the transfer function under.

[0030] Simulation experiment: In this simulation, the following simulation experiments are carried out based on the electro-hydrogen coupling system described in the present invention.

[0031] Parameter setting and model construction: According to the actual system parameters and operating conditions, determine the electrolyzer efficiency coefficient , , , the fuel cell limiting current density ; the physical properties constants of hydrogen in the hydrogen storage tank and ; the system rated power ; the grid rated voltage ; the reference hydrogen storage pressure ; the maximum depth of discharge ; the total capacity of the fuel cell .

[0032] Based on the above conditions, multiple simulation experiments are carried out, and the following stability evaluation index results are obtained under different disturbance conditions: Voltage deviation rate: The voltage deviation rate shows different values. Under normal operating conditions, the voltage deviation rate remains at a low level, with an average value of about 0.02. When a positive disturbance such as occurs on the grid side, the voltage deviation rate rises instantaneously, but under the regulation of the system, it quickly drops back and stabilizes at about 0.05. This shows that when the system faces a certain degree of grid power fluctuation, it can control the voltage deviation within a certain range through its own regulation mechanism to ensure the stability of power supply.

[0033] Hydrogen pressure fluctuation index: During the stable operation stage, the hydrogen pressure fluctuation index is small, about 0.03. When the system is subjected to a large disturbance, such as When there is a pulsed disturbance, the hydrogen pressure fluctuation index will increase to some extent. In a typical simulation, this index rises to 0.08. However, as the system is adjusted, the pressure in the hydrogen storage tank gradually returns to stability, and the hydrogen pressure fluctuation index also decreases accordingly. This indicates that the system can maintain the stability of the hydrogen pressure well when dealing with disturbances such as load changes, ensuring the normal operation of the hydrogen energy subsystem.

[0034] Energy storage life loss: The energy storage life loss is closely related to the charge-discharge frequency and depth of the battery energy storage in the system. During the simulation, when the system frequently performs power regulation, that is, when the battery energy storage charges and discharges frequently, the energy storage life loss increases. Under a certain working condition of continuous disturbance, after running for a period of time, the energy storage life loss index reaches 0.05. By calculating and analyzing the energy storage life loss under different working conditions, it can provide a reference basis for the maintenance and replacement of the battery energy storage.

[0035] Stability score and comprehensive stability index: The stability score obtained through weighted comprehensive calculation shows obvious changes under different working conditions. When the system is not subjected to large disturbances, the stability score is relatively high, close to 0.9. When the system is simultaneously subjected to large disturbances on both the grid side and the load side, the stability score drops to about 0.7. After dynamically adjusting the index weights and re-performing the simulation calculation, the stability score rebounds to about 0.8, indicating that the dynamic weight adjustment has a positive effect on the system stability assessment and can more accurately reflect the actual stability status of the system. The comprehensive stability index also varies under different experimental conditions. Under ideal working conditions, the comprehensive stability index is close to 1, indicating that the stability performance of all aspects of the system is good. When subjected to complex disturbances, the index will decrease. For example, in a simulation that includes multiple disturbances at the same time, the comprehensive stability index drops to 0.85. However, as the system adapts and adjusts, the index gradually rebounds, further verifying the stability of the system and the effectiveness of the evaluation method.

[0036] System response: Assume that in the scenario of sudden change in grid frequency, the calculation of the system response time starts from the moment of sudden frequency change and ends at the moment when the key indicators of the system reach the stable state. Set the moment of sudden frequency change as and the moment when the key indicators of the system reach the stable state as Then the response time By accurately recording these two moments with high-precision monitoring equipment, the response time of the system is obtained as 12.7 s.

[0037] Confidence level assessment calculation: Assume that independent tests are carried out, and the number of times the system meets the stability requirements (such as the response time is within the specified range and all stability assessment indicators meet the standards) is Then the calculation formula for the confidence level is ;The confidence level of the result obtained through test calculation reaches 98.3%.

[0038] In summary, in the scenario of sudden change in power grid frequency, the evaluation response time of the present invention is shortened to 12.7 s, and the confidence level of the evaluation result reaches 98.3%, meeting the requirements of the IEEE2030.7 standard.

[0039] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, 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 invention.

Claims

1. A method for evaluating the stability of an electric-hydrogen coupling system, characterized in that: The following steps are involved: S1. Considering the nonlinear characteristics of electrolyzer and fuel cell, a dynamic mathematical model of the electric-hydrogen coupling system is constructed; S2. Parameter uncertainty analysis: Identify uncertainty parameters and quantify their uncertainty distribution, and select key disturbance parameters by analyzing their impact on the output of the dynamic mathematical model of the electric-hydrogen coupling system; S3. Based on the selected key disturbance parameters, define and standardize multi-dimensional stability evaluation indicators, and then generate a unified stability score through weighted synthesis; S4, mapping the multi-dimensional stability evaluation index to the radar chart axis to obtain a multi-objective optimization model; S5. Perform time-domain simulation verification on the multi-objective optimization model under extreme disturbance scenarios, dynamically adjust the indicator weights according to the simulation operation data, and then re-input the adjusted indicator weights into the multi-objective optimization model until the final dynamic weights are output after convergence; S6. Input the real-time operating 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 and verification in combination with the stability criterion.

2. The method for evaluating the stability of an electric-hydrogen coupling system according to claim 1, characterized in that: Step S1 specifically includes the following steps: S11. Constructing the dynamic model of electrolytic cell: （1）； In the formula, represents the hydrogen-to-electricity conversion efficiency of the electrolyzer, and ; , , They respectively represent the electrolytic cell efficiency attenuation coefficient caused by input power, the electrolytic cell efficiency attenuation coefficient caused by temperature, and the efficiency reference constant; represents the input power of the electrolyzer in MW, and , and They represent the minimum allowable operating power and the maximum allowable operating power of the electrolyzer respectively; Indicates the operating temperature of the electrolytic cell, in °C; S12. Constructing a dynamic model of fuel cell: Construct the polarization curve equation: （2）； In the formula, Indicates the operating voltage of the fuel cell, in V; Represents the reversible voltage, in V; Indicates the operating current density of the fuel cell, in A / cm 2 ; Represents ohmic impedance, in Ω; and All represent empirical coefficients; and Represents the AC current density and limiting current density respectively, in units of A / cm 2 ; in, （3）； In the formula, Indicates the operating temperature of the fuel cell, in °C; Indicates the required hydrogen pressure; Indicates oxygen pressure; Represents the gas constant in units of ; represents the dynamic coupling factor; Construct the power dynamic equation: （4）； In the formula, Indicates the power generated by the fuel cell, in kW; Represents the effective area of ​​a single fuel cell, in cm 2 ; Indicates the number of fuel cells in the stack connected in series; S13. Constructing a dynamic model of hydrogen storage tank: （5）； In the formula, Indicates the mass of the hydrogen storage tank in kg, and Depend on Decide, represents the hydrogen production rate of the electrolyzer, and , Indicates the lower heating value of hydrogen. ; represents the fuel cell hydrogen consumption rate, and ; and All represent the physical property constants of hydrogen in hydrogen storage tanks; Indicates the volume of the hydrogen storage tank, in m 3 ; Indicates the temperature of the hydrogen storage tank; S14. construct a dynamic mathematical model of the electric-hydrogen coupling system, wherein the dynamic mathematical model of the electric-hydrogen coupling system includes a power subsystem model and a hydrogen energy subsystem model; The power subsystem model expression is as follows: （6）； （7）； In the formula, Indicates the power of the power grid, in kW; Indicates wind power; Indicates load power in kW; Represents photovoltaic power generation, in kW; Represents the energy storage capacity of the fuel cell in MWh; Indicates the power generation efficiency of fuel cells; The hydrogen energy subsystem model expression is as follows: （8）。 3. The method for evaluating the stability of an electric-hydrogen coupling system according to claim 2, characterized in that: Step S2 specifically includes the following steps: S21. Screening uncertainty parameters: electrolytic cell efficiency coefficient , , , the current density limit of the fuel cell , Hydrogen physical properties constants of hydrogen storage tank and ; S22. Quantify the distribution range of uncertainty parameters: Assuming that the electrolyzer efficiency coefficient , , Normal distribution ,and , , The value range of is 0.10-0.14; Assuming the current density of the fuel cell limit Uniform distribution ; Hydrogen storage tank hydrogen physical constants and The value of is ; S23, Uncertainty Propagation Simulation: Evaluate the impact of uncertainty parameter fluctuations on output through Monte Carlo simulation; S24. Use the Sobol index method to quantify the contribution of uncertainty parameters to the output: （9）； In the formula, Indicates The Sobol index of the uncertainty parameter is defined as The uncertainty parameter is the key disturbance parameter.

4. The method for evaluating the stability of an electric-hydrogen coupling system according to claim 3, characterized in that: Step S23 specifically includes the following steps: S231, each uncertainty parameter is generated according to the distribution Group random sample; S232, respectively substituting each group of samples into a dynamic mathematical model of the electric-hydrogen coupling system, and running a multi-time scale simulation; S233, record output: maximum deviation of grid frequency , Peak pressure of hydrogen storage tank and system efficiency .

5. The method for evaluating the stability of an electric-hydrogen coupling system according to claim 4, characterized in that: The second-level dynamics of the multi-time-scale simulation described in step S232 adopt the power grid frequency response: ; In the formula, represents the inertia constant; Dynamic hydrogen storage pressure changes at minute level: .

6. The method for evaluating the stability of an electric-hydrogen coupling system according to claim 5, characterized in that: Step S3 specifically includes the following steps: S31. Determine the stability evaluation index: voltage deviation rate ‌, Hydrogen pressure fluctuation index and energy storage life loss ; Among them, the voltage deviation rate The expression is as follows: （10）； In the formula, Indicates the rated voltage of the power grid, in V; Indicates the real-time voltage in V; Indicates the length of the time window; Hydrogen pressure fluctuation index The expression is as follows: （11）； In the formula, Indicates the real-time hydrogen storage pressure in bar; Indicates the reference hydrogen storage pressure in bar; Energy storage life loss The expression is as follows: （12）； In the formula, Indicates the maximum discharge depth; Indicates the real-time charging and discharging power of the fuel cell, in kW; Indicates the total capacity of the fuel cell in kWh; S32, Standardization Processing: （13）； In the formula, , and Respectively represent the original indicator value, the historical minimum value and the historical maximum value of the indicator; S33, weighted synthesis: （14）； In the formula, Represents dynamic weight; , and Respectively represent the voltage deviation rate ‌, Hydrogen pressure fluctuation index and energy storage life loss The weight coefficient of ; S34. Calculate the stability score: （15）； In the formula, Represents the stability score result.

7. The method for evaluating the stability of an electric-hydrogen coupling system according to claim 6, characterized in that: Step S4 specifically includes the following steps: S41, mapping the multi-dimensional stability evaluation index onto the radar chart axis, and quantifying the comprehensive instability index through geometric features, where the geometric feature is the area of ​​the radar chart; S42. Constructing a multi-objective optimization model: （16）； In the formula, Indicates the status voltage of the fuel cell; and Respectively represent the minimum and maximum values ​​of the fuel cell state voltage; Hybrid PSO-C robust control is used as the solution algorithm to determine the control input , where the particle swarm update formula is as follows: （17）； In the formula, and Respectively represent The particle in The velocity vector of the first iteration and the The particle in The velocity vector of the iteration; represents the inertia weight; and They represent individual learning factor and social learning factor respectively; and They are all random numbers, and the value range is 0-1; represents the global optimal position among all particles; Indicates The particle in The position at the iteration; The robust control expression is as follows: （18）； In the formula, represents the control input, and; represents the state vector; Indicates system output; , , , All represent robust controller parameters; The PSO-C hybrid coupling mechanism expression is as follows: （19）； In the formula, represents the cost function; , and denote the tracking error weight, control input suppression weight and Robustness weights.

8. The method for evaluating the stability of an electric-hydrogen coupling system according to claim 7, characterized in that: Step S5 specifically The following steps are involved: S51. Constructing extreme disturbance model: Grid side disturbance: （20）； In the formula, Represents the disturbance power on the grid side; Indicates 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）； In the formula, represents the dynamic equation of the system, where Represents state variables; Indicates time; S53. Update dynamic weights based on real-time data of the electric-hydrogen coupling system: （23）； In the formula, and Respectively Moment and Dynamic weight of the moment; represents the learning rate; Indicates target deviation; represents the penalty coefficient; represents the deviation threshold; S54, the updated dynamic weight Re-enter the multi-objective optimization model, and use step S52 to determine whether the updated multi-objective optimization model converges. If not, return to step S33 to redistribute the weights. If yes, output the final multi-objective optimization model and the final dynamic weights. .

9. The method for evaluating the stability of an electric-hydrogen coupling system according to claim 8, characterized in that: The matching verification calculation formula described in step S6 is as follows: （24）； In the formula, represents the comprehensive stability index; Indicates The measured values ​​of the stability evaluation indicators; and Respectively represent the upper and lower limits of the stability criterion; The stability criteria include the Lyapunov stability criterion and Robustness index, where the Lyapunov stability criterion expression is as follows: （25）； In the formula, represents the Lyapunov function; represents the gradient of the Lyapunov function; The ‌H∞ robustness index is as follows‌: ,and (26); In the formula, Represents the transfer function of norm; Indicates that at all frequencies Lower transfer function The maximum value of the largest singular value of .

10. The method for evaluating the stability of an electric-hydrogen coupling system according to claim 9, characterized in that: In step S6, the multi-objective optimization results are displayed through a dynamic radar chart.

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