Salt cavern hydrogen storage safety and light hydrogen joint control system

Abstract

The application discloses a salt cavern hydrogen storage safety and light hydrogen joint control system and relates to the technical field of industrial automation control. The salt cavern hydrogen storage safety risk control and hydrogen injection operation optimization problem are solved. First, biological electrochemical sensors are used to collect microbial metabolic activity data in the salt cavern, and multi-source information fusion analysis is performed to evaluate the microbial corrosion risk level. In combination with strain field and temperature field data obtained by distributed optical fiber sensing, the seepage diffusion characteristics of hydrogen in the salt rock fracture and the corrosion synergistic effect are analyzed in real time, and a dynamic safety operation boundary is generated. Further, by using a multi-objective particle swarm optimization algorithm with constraints, electrolytic cell power distribution instructions and hydrogen injection and production safety operation values are generated under the condition of meeting the safety boundary constraint, so that economic efficiency and risk balance are realized. Finally, a fuzzy PID control algorithm is used to coordinate the control of the electrolytic cell power, the compressor speed, the hydrogen injection valve opening degree and the cooling system power, so that the safety linkage and efficient operation of the light hydrogen system are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial automation control, in particular to a salt cavern hydrogen storage safety and light-hydrogen joint control system. BACKGROUND

[0002] Under the background of global energy structure transformation and large-scale application of renewable energy, hydrogen energy as a clean and efficient secondary energy carrier has become an important path to promote the low-carbon development of the energy system. Especially with the rapid popularization of photovoltaic power generation technology, how to efficiently and safely realize the "light-hydrogen" coordinated conversion and storage has become a key technical challenge to improve the renewable energy consumption capacity and ensure the safety of energy supply. Salt cavern underground hydrogen storage is considered as an important solution for large-scale long-time energy storage due to its huge storage capacity, high safety and low cost. The coordinated operation and control between photovoltaic power generation, electrolytic hydrogen production and salt cavern hydrogen storage is the core link to build an efficient hydrogen energy system.

[0003] The existing light-hydrogen coordination and salt cavern hydrogen storage control method still has several substantial technical defects. First, in the aspect of salt cavern hydrogen storage safety monitoring, the existing system mostly relies on single parameter or lagging detection means, which is difficult to accurately perceive and early warn potential risks such as microbial corrosion and hydrogen leakage, resulting in insufficient comprehensiveness and real-time of safety state evaluation. Secondly, in the aspect of system coordinated control, the existing strategy usually optimizes electrolysis, hydrogen storage and hydrogen use as independent subsystems, which cannot globally and dynamically coordinate the multi-energy flow coupling relationship of electricity, hydrogen and gas, and cannot simultaneously consider operation economy, grid regulation demand and hydrogen storage safety constraints. In addition, most of the existing control systems have limited optimization ability for complex working conditions with multiple objectives and multiple constraints, and the adaptability of the control algorithm to source and load uncertainties is poor, which often leads to low system operation efficiency and slow regulation response, and cannot meet the requirements of control quality and reliability for large-scale engineering application. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a salt cavern hydrogen storage safety and light-hydrogen joint control system, which solves the problems in the above background art.

[0005] In order to achieve the above object, the present application is realized by the following technical scheme: the salt cavern hydrogen storage safety and light hydrogen joint control system comprises the following modules: a salt cavern hydrogen storage monitoring module, a hydrogen migration sensing module, a risk constraint optimization module and a light hydrogen coordinated control module; the salt cavern hydrogen storage monitoring module is used for monitoring and collecting microbial metabolic activity data in the salt cavern through a bioelectrochemical sensor, including redox potential, electron transfer rate and metabolite concentration, and performing multi-source information fusion analysis to evaluate the microbial corrosion risk level; the hydrogen migration sensing module is used for real-time analysis of the seepage diffusion characteristics of hydrogen in salt rock cracks and the synergistic effect of biological corrosion according to the microbial corrosion risk level, combined with the strain field and temperature field data obtained by the distributed optical fiber sensor arranged in the salt cavern surrounding rock, to generate a dynamic safety operation boundary; the risk constraint optimization module is used for receiving power grid dispatching requirements, and through a multi-objective particle swarm optimization algorithm with constraints, the electrolytic cell power distribution instruction and the injection and extraction hydrogen safety operation value are generated by taking the system operation economy optimization and corrosion risk minimization as the target under the condition of meeting the dynamic safety operation boundary constraint; the light hydrogen coordinated control module is used for coordinating and controlling the electrolytic cell operating power, compressor speed, hydrogen injection regulating valve opening degree and cooling system power through a fuzzy PID control algorithm according to the electrolytic cell power distribution instruction and the injection and extraction hydrogen safety operation value.

[0006] Further, the specific process of multi-source information fusion analysis on the microbial metabolic activity data in the salt cavern is as follows: the microbial metabolic activity data is standardized pretreated, the characteristic parameters representing the metabolic states of each bacterial species are extracted from the pretreated microbial metabolic activity data through principal component analysis, the characteristic parameters are fused based on the multi-source information fusion framework of Bayesian fusion, the mutual support degree of the metabolic activity of each bacterial species is quantified, and the comprehensive evaluation value of the fused microbial activity is output; the microorganisms in the salt cavern include sulfate-reducing bacteria, acid-producing bacteria and methanogens.

[0007] Further, the specific process of evaluating the microbial corrosion risk level is as follows: based on the microbial activity comprehensive evaluation value, a quantitative mapping relationship between microbial activity and corrosion rate is established, the synergistic corrosion mechanism of metabolic products of sulfate-reducing bacteria, acid-producing bacteria and methanogens is analyzed, and the combined effect of chemical corrosion and biological corrosion of microbial metabolism on salt rock materials is quantified; according to the corrosion effect degree, the microbial corrosion risk level is divided, and a risk warning mechanism based on the dynamic change of bacterial activity is established, and the microbial corrosion risk level is output.

[0008] Further, according to the microbial corrosion risk level, combined with the strain field and temperature field data obtained by the distributed optical fiber sensor arranged in the surrounding rock of the salt cavern, the specific process of real-time analysis of the hydrogen seepage and diffusion characteristics in the salt rock fracture and the specific process of the synergistic effect of biological corrosion are as follows: based on the microbial corrosion risk level, the active area of biological corrosion is determined, the strain field data obtained by the distributed optical fiber sensor is combined to identify the development characteristics of the salt rock fracture, and the thermodynamic effect of hydrogen seepage is analyzed through the temperature field data; a multi-field coupling analysis framework is established to quantify the enhancement effect of microbial corrosion on the permeability of the fracture, analyze the seepage path and diffusion rate of hydrogen in the corrosion-modified fracture network, evaluate the influence of the chemical reaction between the biological corrosion products and hydrogen on the seepage characteristics, and calculate the synergistic action intensity index of microbial corrosion and hydrogen seepage.

[0009] Further, the specific process of generating a dynamic safety operation boundary is as follows: based on the synergistic action intensity index of microbial corrosion and hydrogen seepage, the maximum allowable hydrogen injection pressure and hydrogen injection rate threshold values of different corrosion risk areas are determined; according to the fracture development characteristics identified by the real-time strain field data, the pressure change rate limit in the hydrogen injection process is dynamically adjusted, and combined with the temperature field monitoring data, the upper limit threshold of the hydrogen injection temperature is set; a dynamic safety operation boundary parameter set including the maximum hydrogen injection pressure, the maximum hydrogen injection rate, the pressure change rate limit, and the hydrogen injection temperature limit is formed.

[0010] Further, in the risk constraint optimization module, the specific process of optimization calculation by the multi-objective particle swarm optimization algorithm with constraints is as follows: the particle swarm position and velocity are initialized, the total power of the electrolytic cell, the hydrogen injection rate set value, the hydrogen injection pressure set value, and the hydrogen injection temperature set value are encoded into the particle position vector; the system operation economic indicators composed of the electricity purchase cost, the equipment loss cost, and the hydrogen sales revenue, and the microbial corrosion risk level are taken as the optimization objectives, and the dynamic safety operation boundary parameter set is taken as the constraint condition to calculate the fitness value of each particle; the particle position and velocity are updated by comparing the individual optimal and global optimal solutions, and the iteration optimization is performed until the convergence condition is met, and the Pareto optimal solution set is output.

[0011] Further, under the condition of meeting the dynamic safety operation boundary constraint, the specific process of rolling generation of electrolytic cell power distribution instructions and injection and production hydrogen safety operation values is as follows: the solution with the optimal comprehensive economic benefit is selected from the Pareto optimal solution set, and the power distribution proportion of each electrolytic cell, the hydrogen injection rate set value, the hydrogen injection pressure set value, and the hydrogen injection temperature set value are decoded; the injection and production hydrogen safety operation values include the hydrogen injection rate set value, the hydrogen injection pressure set value, and the hydrogen injection temperature set value; it is verified whether the generated power distribution instructions exceed the electrolytic cell operation capability range and whether the injection and production hydrogen safety operation values meet the constraint condition of the dynamic safety operation boundary.

[0012] Further, according to the electrolytic tank power distribution instruction and the injection and extraction hydrogen safety operation value, the specific process of coordinating and controlling the electrolytic tank running power, the compressor rotating speed, the injection hydrogen regulating valve opening degree and the cooling system power through the fuzzy PID control algorithm is as follows: taking the total power instruction in the electrolytic tank power distribution instruction and the single unit power distribution proportion as the electrolytic tank power control set value, taking the injection hydrogen pressure set value, the injection hydrogen rate set value and the injection hydrogen temperature set value in the injection and extraction hydrogen safety operation value as the control set values of the compressor rotating speed, the injection hydrogen regulating valve opening degree and the cooling system power respectively; real-time acquisition of the power grid frequency signal, calculation of the deviation and the deviation change rate of the signal from the standard frequency, real-time acquisition of the hydrogen pipe network pressure, the injection hydrogen pipeline flow and the injection hydrogen temperature signal, and calculation of the deviation and the deviation change rate of each signal from the corresponding set value respectively; inputting the power grid frequency deviation and the deviation change rate into the fuzzy reasoning system, adjusting the PID parameters of the power control loop, outputting the electrolytic tank total power adjustment amount, and dynamically distributing to each electrolytic tank unit according to the power distribution proportion; inputting the hydrogen pipe network pressure, the injection hydrogen rate and the injection hydrogen temperature deviation and the deviation change rate into the fuzzy reasoning system respectively, adjusting the PID parameters of the corresponding control loop, and corresponding outputting the compressor rotating speed adjustment amount, the injection hydrogen regulating valve opening degree adjustment amount and the cooling system power adjustment amount.

[0013] The present application has the following advantages:

[0014] (1) The salt cavern hydrogen storage safety and photo-hydrogen joint control system realizes accurate monitoring and dynamic evaluation of the metabolic activity and corrosion effect of microorganisms in the salt cavern through the synergistic effect of the salt cavern hydrogen storage monitoring module and the hydrogen migration sensing module. Through multi-source fusion of bioelectrochemical sensing and distributed optical fiber sensing, multi-dimensional data such as redox potential, electron transfer rate, crack strain and temperature field can be obtained simultaneously, and a coupling mechanism of microbial corrosion and hydrogen seepage diffusion is established, thereby generating a dynamic safety operation boundary. Overcomes the defects of the prior art that only relies on a single signal and cannot reflect the multi-field coupling state of the salt cavern in real time, effectively improves the risk identification and early warning capability of the salt cavern hydrogen storage process.

[0015] (2) The salt cavern hydrogen storage safety and photo-hydrogen joint control system realizes dynamic optimization and fine control of electrolytic tank power distribution and injection and extraction hydrogen operation parameters through the risk constraint optimization module and the photo-hydrogen coordinated control module. Using a multi-objective particle swarm optimization algorithm with constraints, a balance between economy and safety can be achieved, and power and operation instructions that meet dynamic boundary conditions are generated automatically. Through the fuzzy PID control algorithm, the electrolytic tank power, the compressor rotating speed, the injection hydrogen valve opening degree and the cooling system are coordinated and adjusted, thereby improving the real-time adaptive ability of the system under complex working conditions and ensuring the safety, stability and efficiency of the salt cavern hydrogen storage operation.

[0016] Of course, implementing any product of the present application does not necessarily require all the advantages described above to be achieved simultaneously. Attached Figure Description

[0017] Figure 1 This is a flowchart of the salt cavern hydrogen storage safety and photovoltaic-hydrogen co-regulation control system of the present invention. Detailed Implementation

[0018] This application's embodiments solve the problems in the prior art of achieving multi-source information fusion and dynamic risk assessment in the salt cavern hydrogen storage process, as well as the problems of lag in the response of the hydrogen storage system and lack of safety constraint optimization under conditions of photovoltaic power fluctuations, through the salt cavern hydrogen storage safety and photovoltaic-hydrogen co-regulation control system.

[0019] The overall concept of the solution in this application embodiment is as follows:

[0020] By using a salt cavern hydrogen storage monitoring module and a hydrogen migration sensing module, data on microbial metabolic activity within the salt cavern, as well as multi-dimensional information such as surrounding rock strain and temperature field, are acquired. A coupled model of microbial corrosion and hydrogen permeation diffusion is established to form a dynamic safe operating boundary. Then, through a risk constraint optimization module and a photovoltaic-hydrogen coordinated control module, based on a constrained multi-objective optimization algorithm and a fuzzy PID control method, rolling optimization and real-time coordination of electrolyzer power allocation and hydrogen injection and production operations are completed. This balances the system's safety, economy, and adaptability, enabling safe joint operation of salt cavern hydrogen storage and photovoltaic hydrogen production.

[0021] Please see Figure 1 This invention provides a technical solution: a salt cavern hydrogen storage safety and photo-hydrogen co-regulation control system, comprising the following modules: a salt cavern hydrogen storage monitoring module, a hydrogen migration sensing module, a risk constraint optimization module, and a photo-hydrogen coordination control module. The salt cavern hydrogen storage monitoring module is used to monitor and collect microbial metabolic activity data within the salt cavern using bioelectrochemical sensors, including redox potential, electron transfer rate, and metabolite concentration, and to perform multi-source information fusion analysis to assess the microbial corrosion risk level. The hydrogen migration sensing module is used to, based on the microbial corrosion risk level and combined with strain and temperature field data obtained from distributed fiber optic sensors deployed in the surrounding rock of the salt cavern, implement... The system analyzes the seepage and diffusion characteristics of hydrogen in salt rock fissures and the synergistic effect of biocorrosion to generate a dynamic safe operating boundary. The risk constraint optimization module receives grid dispatch requirements and uses a constrained multi-objective particle swarm optimization algorithm to generate electrolyzer power allocation instructions and safe operating values ​​for hydrogen injection and production on a rolling basis, with the objectives of optimizing system operating economy and minimizing corrosion risk, while meeting the constraints of the dynamic safe operating boundary. The photohydrogen coordination control module coordinates and controls the electrolyzer operating power, compressor speed, hydrogen injection regulating valve opening, and cooling system power according to the electrolyzer power allocation instructions and safe operating values ​​for hydrogen injection and production, using a fuzzy PID control algorithm.

[0022] In this embodiment, the salt cavern hydrogen storage monitoring module is used to monitor the safety status inside the salt cavern in real time. This module obtains metabolic activity data of microorganisms in the salt cavern through bioelectrochemical sensors. A "bioelectrochemical sensor" refers to a sensing device that can detect the electrochemical signals (such as redox potential, electron transfer rate) produced by microorganisms during metabolism. The principle is to capture the electron transfer activity of microorganisms through an electrode system. The data collected by this module include redox potential (reflecting the activity of microbial metabolism), electron transfer rate (characterizing the intensity of microbial energy metabolism), and metabolite concentration (such as hydrogen sulfide, organic acids), and multi-source information fusion analysis is performed. Through fusion analysis, the corrosion effect of different bacterial groups on salt rock materials can be quantified, and the corrosion risk level can be evaluated, providing input conditions for subsequent safety boundary modeling. The hydrogen migration perception module is used to analyze the seepage and diffusion process of hydrogen in salt rock fractures and its synergistic effect with microbial corrosion. This module will receive data from distributed optical fiber sensing devices arranged in the surrounding rock of the salt cavern. Distributed optical fiber sensing is a sensing technology that uses optical fibers as sensing media to obtain strain and temperature distribution by detecting changes in scattered signals in the optical fiber. Its advantage is that it can achieve continuous monitoring of a large range and multiple points. Through strain field data, the development degree and trend of salt rock internal fractures can be identified; through temperature field data, the thermal effect of hydrogen during seepage can be analyzed. Combined with the microbial corrosion risk level, this module establishes a coupling model of hydrogen seepage and corrosion effect, thereby generating a dynamic safety operating boundary, i.e., determining the maximum allowable hydrogen injection pressure, rate, and temperature range in real time. The risk constraint optimization module is mainly responsible for scheduling and optimization calculation at the system level. This module receives scheduling requirements from the power grid (such as the amount of excess photovoltaic power that needs to be absorbed) and performs calculations using a multi-objective particle swarm optimization algorithm. The multi-objective particle swarm optimization algorithm is a swarm intelligence-based optimization method that can handle multiple optimization objectives simultaneously. Its objectives include optimal operating economy (reducing costs and increasing benefits) and minimizing corrosion risk. The algorithm takes electrolyzer power, hydrogen injection rate, hydrogen injection pressure, and hydrogen injection temperature as optimization variables, and the dynamic safety operating boundary as a constraint condition. Through rolling optimization (i.e., periodic repeated optimization calculation), the module can dynamically output power distribution instructions for the electrolyzer and safe operation values for hydrogen injection and extraction, enabling the system to operate both safely and economically. The photovoltaic-hydrogen coordinated control module is used to perform specific control actions and achieve the linkage of photovoltaic power generation, electrolytic hydrogen production, and salt cavern hydrogen storage. This module uses a fuzzy PID control algorithm. Among them, PID control (proportional-integral-derivative control) is the most common feedback control method in industrial control, while fuzzy PID introduces fuzzy logic reasoning based on traditional PID, enabling the controller to adaptively adjust parameters according to error size and change rate, suitable for complex systems with nonlinearity and multiple variable coupling.The module takes the electrolytic tank power distribution instruction as the power setting value of the electrolytic tank, and takes the setting values of the hydrogen injection pressure, hydrogen injection rate and hydrogen injection temperature as the control targets of the compressor rotating speed, hydrogen injection valve opening degree and cooling system power respectively. The control module collects signals such as power grid frequency, hydrogen pipe network pressure, hydrogen injection flow and hydrogen injection temperature in real time, calculates the deviation and deviation rate of these signals from the setting values, and inputs them into the fuzzy reasoning system for adjustment. In this way, the running state can be quickly adjusted when the power grid frequency fluctuates, the hydrogen pipe network pressure changes or the temperature fluctuates, ensuring stable and safe operation of the system.

[0023] Specifically, the specific process of multi-source information fusion analysis on the microbial metabolic activity data in the salt cave is as follows: standardizing preprocessing of the microbial metabolic activity data, extracting feature parameters representing the metabolic states of each bacterial species from the preprocessed microbial metabolic activity data through principal component analysis, fusing the feature parameters based on a multi-source information fusion framework of Bayesian fusion, quantifying the mutual support degree of the metabolic activities of each bacterial species, and outputting a comprehensive evaluation value of the fused microbial activity; the microorganisms in the salt cave include sulfate-reducing bacteria, acid-producing bacteria and methanogens.

[0024] In the embodiment, the original metabolic activity data matrix is denoted as ; wherein: : represents the original measurement value of the th microbial species under the th metabolic index (redox potential, electron transfer rate, metabolite concentration); : the number of bacterial species, which is 3 in this embodiment, i.e., sulfate-reducing bacteria, acid-producing bacteria and methanogens; : the number of metabolic indicators. The mean-variance normalization method is used to convert the original data into standardized data: ; wherein: : the standardized value; : the mean value of the th metabolic index; : the standard deviation of the th metabolic index. Principal component analysis is used to extract feature parameters to construct a covariance matrix: ; wherein: : the standardized data matrix; : the covariance matrix of the metabolic indicators. The covariance matrix is decomposed: ; wherein: : the th principal component feature vector; : the corresponding eigenvalue, representing the importance of the principal component; : the number of selected principal components, satisfying that the cumulative variance contribution rate exceeds a threshold . The determination method is as follows: based on the cumulative variance contribution rate curve, when adding a principal component, the increase in contribution rate is less than the empirical difference. Stop adding principal components at this point. Obtain the principal component score for each type of microorganism: ;in: :No. microorganisms in the first Scores on each principal component; : Elements in the principal component loading matrix, representing the th The first indicator in the The weights on each principal component. Bayesian fusion calculations determine the overall evaluation value by assigning principal component score vectors to each bacterial species. As input, a Bayesian fusion model is established: ;in: A set of hypotheses about microbial activity, such as "high activity", "medium activity", and "low activity"; Assume a total number; : under the assumption Inoculation Principal component distribution likelihood; The assumed prior probability can be estimated using historical statistical data. The method for determining the fusion weight coefficients is as follows: ;in: strains The fusion weight coefficient; strains The variance of the principal component scores; the smaller the variance, the higher the stability and the greater the weight. Output the overall evaluation value. The formula for calculating the overall evaluation value is: ;in: : Comprehensive evaluation value of microbial activity after fusion; strains The weighted principal component score mean is calculated using the following formula: ;in The eigenvalues ​​of the principal components are used to reflect the importance of those principal components. Ultimately, As an input index for evaluating the risk of microbial corrosion in salt caves, it is provided for subsequent modules. The final output is used as a comprehensive evaluation value of microbial activity in salt caves, which provides input for subsequent corrosion risk level evaluation and dynamic safety boundary calculation. In the actual salt hydrogen storage scene, the system collects the metabolic activity data of sulfate-reducing bacteria, acid-producing bacteria and methanogens in the salt cave in real time through bioelectrochemical sensors, including redox potential, electron transfer rate and metabolic product concentration. The collected data is standardized and pretreated and principal component analysis is performed to extract characteristic parameters representing the metabolic state of each species, thereby eliminating data dimension redundancy and highlighting key metabolic information. Subsequently, based on the multi-source information fusion framework of Bayesian fusion, the characteristic parameters of each species are weighted and fused to generate a comprehensive evaluation value of microbial activity. This comprehensive evaluation value can reflect the activity level and mutual support relationship of different bacteria in the salt cave. For example, when the activity of sulfate-reducing bacteria increases significantly and the activity of acid-producing bacteria remains at a moderate level, the system can accurately reflect the trend of increasing overall microbial activity through the fusion algorithm, thereby prompting potential local corrosion risk. This method can effectively reduce the errors caused by single sensing or single index analysis and improve the evaluation accuracy of microbial metabolic state.

[0025] Specifically, the specific process of evaluating the microbial corrosion risk level is as follows: based on the comprehensive evaluation value of microbial activity, a quantitative mapping relationship between microbial activity and corrosion rate is established, the synergistic corrosion mechanism of metabolic products of sulfate-reducing bacteria, acid-producing bacteria and methanogens is analyzed, and the combined effect of microbial metabolism on chemical corrosion and biological corrosion of salt rock materials is quantified; according to the degree of corrosion effect, the risk level of microbial corrosion is divided, and a risk warning mechanism based on the dynamic change of microbial activity is established, and the risk level of microbial corrosion is output.

[0026] In this embodiment, specifically, the specific process of evaluating the microbial corrosion risk level is as follows: first, based on the comprehensive evaluation value of microbial activity , a quantitative mapping relationship between microbial activity and corrosion rate is established, a nonlinear regression model is used to calculate the corrosion rate estimate, and the calculation formula is: ; wherein: : the comprehensive corrosion rate estimate of salt rock materials; : the comprehensive evaluation value of microbial activity, output by the previous step; : the activity corrosion rate mapping function based on a nonlinear function (logarithmic function); : the activity mapping weight coefficient, determined by least squares fitting; : the quantitative contribution parameter of the first metabolic product, derived from metabolic product concentration feature extraction; : the metabolic product effect weight coefficient, determined based on Bayesian posterior probability estimation; : Metabolic product synergy regulation coefficient, determined by regression fitting based on historical corrosion experiment data; : Metabolic product category number, including sulfide produced by sulfate-reducing bacteria, organic acid produced by acid-producing bacteria, and metabolic byproducts of methanogens. Second, the synergistic corrosion mechanism of metabolic products of sulfate-reducing bacteria, acid-producing bacteria and methanogens is analyzed, and the combined effects of chemical corrosion and biological corrosion are comprehensively considered. The corrosion effect index is calculated by using the superposition coupling model, and the formula is: ; Wherein: : Microbial corrosion effect index; : Corrosion rate estimate value, calculated by the above formula; : Salt rock material chemical corrosion effect intensity, derived based on ion exchange reaction rate; : Salt rock material biological corrosion effect intensity, derived based on distribution of metabolic byproducts of bacterial flora; : Effect weight coefficient, determined by using entropy method to reduce subjectivity. Third, according to the microbial corrosion effect index , a risk classification threshold set is set, and a dynamic threshold updating mechanism is used. When the fluctuation of bacterial flora activity is detected to be beyond the mean square deviation range of historical fluctuation interval, the threshold is automatically adjusted to enhance the warning sensitivity. The microbial corrosion risk level is divided as follows: when , the low risk level is output; when , the medium risk level is output; when , the high risk level is output; and when , the extremely high risk level is output. Finally, a risk warning mechanism based on dynamic changes of bacterial flora activity is established, and the activity trends of sulfate-reducing bacteria, acid-producing bacteria and methanogens are tracked in real time. When the comprehensive evaluation value of activity the rate of change of the microorganism corrosion risk level exceeds the dynamically set threshold value, triggering adjustment of the risk level and output of the corresponding microorganism corrosion risk level result. In an actual salt cavern hydrogen storage scenario, the system dynamically evaluates the corrosion risk of the salt rock material based on the comprehensive evaluation value of microorganism activity obtained through the preceding steps. For example, when the activity of sulfate-reducing bacteria is significantly enhanced, while the activity of acid-producing bacteria and methanogenic bacteria is at a medium-high level, the system can accurately calculate the comprehensive corrosion effect of the salt rock material and identify the superimposed action area of chemical corrosion and biological corrosion by quantitatively mapping the activity and corrosion rate. Based on the corrosion effect index, the system automatically classifies the risk level: the low-risk area can continue normal operation, the medium-high-risk area triggers local hydrogen injection rate or pressure adjustment, and the extremely high-risk area starts an early warning, requiring immediate adjustment of hydrogen storage operation or restriction of electrolyzer power. In actual operation, this risk evaluation process realizes forward-looking monitoring of microorganism corrosion. For example, when the activity of the microbial community in a certain salt cavern area rapidly rises in a short period of time, the system timely adjusts the risk level through the dynamic threshold updating mechanism, realizing early warning of potential corrosion events. By tracking the changes in microbial community activity in real time, the system can provide accurate basis for subsequent hydrogen injection operation optimization, while ensuring the safety and continuous operation capability of the salt cavern hydrogen storage. The system can comprehensively and dynamically quantify the combined effect of microorganisms on salt rock corrosion, take into account the synergistic action of different bacterial metabolisms, improve the accuracy and sensitivity of corrosion risk level determination, and realize fine management of salt cavern hydrogen storage safety, providing reliable safety decision support for the photovoltaic-hydrogen combined control system.

[0027] Specifically, according to the microorganism corrosion risk level, in combination with the strain field and temperature field data obtained by the distributed optical fiber sensing arranged in the salt cavern surrounding rock, the specific process of real-time analysis of the seepage and diffusion characteristics of hydrogen in salt rock fractures and the specific process of the synergistic effect of biological corrosion is as follows: based on the microorganism corrosion risk level, the biological corrosion active area is determined, the strain field data obtained by the distributed optical fiber sensing is used to identify the fracture development characteristics of the salt rock, and the thermodynamic effect in the hydrogen seepage process is analyzed through the temperature field data; a multi-field coupling analysis framework is established to quantify the enhancement effect of microorganism corrosion on the fracture permeability, analyze the seepage path and diffusion rate of hydrogen in the corrosion-modified fracture network, evaluate the influence of the chemical reaction between the biological corrosion products and hydrogen on the seepage characteristics, and calculate the synergistic action strength index of microorganism corrosion and hydrogen seepage.

[0028] In the present embodiment, the biological corrosion active area is determined: according to the result output by the microorganism corrosion risk level, the area with high microorganism activity inside the salt rock is determined; the area with high risk level is regarded as the biological corrosion active area, which is used for subsequent fracture seepage analysis. Fracture feature identification and seepage thermodynamic analysis: in combination with the strain field data obtained by the distributed optical fiber sensing , the spatial distribution and development degree of the salt rock fractures are identified; the temperature field data The thermodynamic effects in hydrogen gas seepage process, such as the influence of local temperature rise on gas viscosity and diffusion rate, are analyzed; the strain-temperature coupling mapping relationship is established to provide geometric and physical constraints for hydrogen gas seepage modeling. The formula for multi-field coupled seepage analysis and calculation: ; Parameter description: : Effective permeability of corrosion-modified fracture; : Reference permeability of uncorroded fracture; : The first : The coverage or concentration of microbially influenced corrosion products on the surface of the fracture; : Corrosion enhancement effect coefficient, determined by historical corrosion experiment data regression fitting; : Number of microbially influenced corrosion product species, including metabolic products of sulfate-reducing bacteria, acid-producing bacteria and methanogens. This formula quantifies the enhancement effect of microbially influenced corrosion on fracture permeability; for subsequent hydrogen seepage path and rate analysis. Hydrogen seepage path and diffusion rate calculation formula (based on the extended form of Darcy's law): ; Parameter description: : Hydrogen seepage velocity vector in the fracture network; : Effective permeability of corrosion-modified fracture, calculated by the above formula; : Hydrogen dynamic viscosity; : Hydrogen pressure field; : Fracture temperature field, from distributed optical fiber sensing; : Thermodynamic diffusion coupling coefficient, calibrated by experimental data. This formula considers the influence of corrosion-enhanced permeability and temperature field on seepage rate; the diffusion rate of hydrogen can be calculated by the velocity vector ; Parameter description: : Microbially influenced corrosion and hydrogen seepage synergy strength index; : Microbially influenced corrosion effect index, output from the corrosion risk level above; : Hydrogen seepage velocity vector, calculated by the above formula; : Microbially influenced corrosion active area volume; , : Synergy weight coefficient, determined based on historical experimental data and sensitivity analysis. ​The system reflects the mutual coupling effect of microbial corrosion and hydrogen gas seepage, and can be used for dynamic safety boundary calculation and control strategy optimization. In the actual salt cavern hydrogen storage scene, the system first identifies the active area of microorganisms inside the salt rock according to the previous microbial corrosion risk level analysis results. For example, when the activity of sulfate-reducing bacteria and acid-producing bacteria in a certain salt cavern area is significantly enhanced, the area is marked as an active area of biological corrosion, which is used for subsequent hydrogen gas seepage analysis. Combined with the strain field and temperature field data obtained in real time by the distributed optical fiber sensor arranged in the surrounding rock, the system can accurately identify the spatial distribution, development degree and thermodynamic changes that may occur in the hydrogen gas seepage process of the fracture, such as the influence of local temperature rise on gas viscosity and diffusion rate. Through multi-field coupling analysis, the system evaluates the enhancement effect of microbial corrosion on the permeability of the fracture, and calculates the seepage path and diffusion rate of hydrogen gas in the corrosion-modified fracture network. For example, in the area where microbial corrosion is active and the fracture development is sufficient, the hydrogen gas seepage speed is significantly accelerated, but the system can quantify this change in real time and output the seepage distribution characteristics. Further, the system calculates the synergistic effect intensity index of hydrogen gas seepage and microbial corrosion, which is used to dynamically adjust the hydrogen storage operation strategy. The system can identify the active area of microorganisms and the characteristics of fracture seepage in real time and dynamically, quantify the enhancement effect of microbial corrosion on hydrogen gas seepage, and realize fine monitoring of the hydrogen gas distribution and pressure state in the salt cavern, thereby providing accurate basis for subsequent electrolytic cell power distribution, hydrogen injection rate adjustment and safety boundary calculation of hydrogen storage, thereby significantly improving the safety and operation reliability of salt cavern hydrogen storage.

[0029] Specifically, the specific process of generating a dynamic safety operation boundary is as follows: based on the synergistic effect intensity index of microbial corrosion and hydrogen gas seepage, the maximum allowable hydrogen injection pressure and hydrogen injection rate threshold values of different corrosion risk areas are determined; according to the fracture development characteristics identified by the real-time strain field data, the pressure change rate limit in the hydrogen injection process is dynamically adjusted, and the upper limit threshold of the hydrogen injection temperature is set in combination with the temperature field monitoring data; and a dynamic safety operation boundary parameter set including the maximum hydrogen injection pressure, the maximum hydrogen injection rate, the pressure change rate limit and the hydrogen injection temperature limit is formed.

[0030] In the embodiment, the calculation formula of the maximum allowable hydrogen injection pressure and hydrogen injection rate threshold value is: , ; parameter description: : the maximum allowable hydrogen injection pressure of the current corrosion risk area; : the maximum allowable hydrogen injection rate of the current corrosion risk area; : the reference maximum hydrogen injection pressure in the uncorroded state of the fracture; : the reference maximum hydrogen injection rate in the uncorroded state of the fracture; : the synergistic effect intensity index of microbial corrosion and hydrogen gas seepage, which is obtained from the foregoing calculation; , : Pressure and flow regulation weight coefficients determined by historical experimental data regression fitting to ensure hydrogen injection safety in active corrosion areas; This formula dynamically adjusts the maximum pressure and hydrogen injection rate threshold according to the synergy index, reflecting the constraint of corrosion enhancement on hydrogen injection safety. Pressure change rate limit setting calculation formula: ; Parameter description: : Pressure change rate limit during hydrogen injection; : Real-time average crack strain obtained from distributed optical fiber sensing; : Maximum allowable crack strain threshold determined based on material mechanics experiments; : Pressure change rate reference coefficient calibrated by historical experimental data; This formula converts crack development characteristics into hydrogen injection pressure change rate constraints to prevent rapid crack expansion or leakage. Hydrogen injection temperature upper limit threshold setting calculation formula: ; Parameter description: : Temperature upper limit threshold during hydrogen injection; : Reference hydrogen injection temperature upper limit under non-corrosion state of salt rock; : Real-time temperature field change amplitude obtained from distributed optical fiber sensing data; : Temperature regulation coefficient determined by experimental data to ensure that temperature changes do not cause crack expansion or intensify hydrogen reaction. Combining temperature field dynamic adjustment of hydrogen injection temperature upper limit ensures safe operation of hydrogen injection. Forming a set of dynamic safety operation boundary parameters, calculating 、 、 and The complete dynamic safety operation boundary parameter set is formed; the boundary parameter set is used to constrain the hydrogen injection control strategy, and the safety operation of the salt cavern hydrogen storage process is realized; the parameters are dynamically adjusted according to historical experimental data, crack strain characteristics and temperature monitoring data, and meet the real-time safety requirements of the system. In the actual salt cavern hydrogen storage scene, the system first identifies different corrosion risk areas inside the salt cavern according to the synergistic action intensity index of microbial corrosion and hydrogen seepage calculated in the previous calculation. For example, when the activity of sulfate-reducing bacteria and acid-producing bacteria is concentrated and the crack development is obvious in a certain area, the area is determined as a high-risk area, and the system dynamically determines the maximum allowable hydrogen injection pressure and hydrogen injection rate threshold value of the area to prevent excessive hydrogen injection from causing crack expansion or hydrogen leakage. Combined with the strain field data obtained by the distributed optical fiber sensing, the system can monitor the development change of the crack in real time, and convert the crack strain characteristics into the injection pressure change rate constraint. For example, when the strain of a crack rises rapidly and approaches the maximum value allowed by the material, the system automatically reduces the pressure change rate of the area to prevent the crack from expanding suddenly. The temperature field monitoring data is used to adjust the upper limit of the hydrogen injection temperature. When the local temperature rise may cause hydrogen expansion or accelerate chemical reaction, the system automatically reduces the hydrogen injection temperature threshold value to ensure the safety of operation. Through the generation of the dynamic safety operation boundary, the system can constrain the hydrogen injection pressure, rate and temperature on the basis of real-time monitoring of microbial corrosion activity, crack strain and temperature change, and realize the fine control of the salt cavern hydrogen storage process. The scheme can effectively reduce the hydrogen injection risk in the active corrosion area, prevent the crack from expanding too fast or hydrogen from diffusing abnormally, thereby significantly improving the safety and controllability of the salt cavern hydrogen storage, and providing a reliable basis for subsequent hydrogen injection strategy optimization.

[0031] Specifically, in the risk constraint optimization module, the specific process of optimization calculation by the multi-objective particle swarm optimization algorithm with constraints is as follows: initialize the particle swarm position and velocity, encode the total power of the electrolytic cell, the hydrogen injection rate set value, the hydrogen injection pressure set value and the hydrogen injection temperature set value into the particle position vector; the system operation economic indicators and the microbial corrosion risk level composed of the purchase power cost, the equipment loss cost and the hydrogen sales revenue are taken as the optimization objectives, and the dynamic safety operation boundary parameter set is taken as the constraint condition to calculate the fitness value of each particle; the particle position and velocity are updated by comparing the individual optimal and global optimal solutions, and the iteration optimization is performed until the convergence condition is met, and the Pareto optimal solution set is output.

[0032] In the embodiment, the particle swarm initialization encodes the total power of the electrolytic cell, the hydrogen injection rate set value, the hydrogen injection pressure set value and the hydrogen injection temperature set value into the particle position vector : ; parameter description: : the position vector of the th particle; : the total power set value of the electrolytic cell corresponding to the particle; : hydrogen injection rate set value corresponding to the particle; : hydrogen injection pressure set value corresponding to the particle; : hydrogen injection temperature set value corresponding to the particle; , : particle swarm size. Initialize velocity vector : small random disturbance for particle search. Fitness function calculation takes system operation economy index and microbial corrosion risk level as optimization objectives: ; ; parameter description: : economy index, including electricity purchase cost, equipment wear cost and hydrogen sales revenue; : electricity purchase cost; : equipment wear cost; : hydrogen sales revenue; : economy weight coefficient, determined by historical economic data regression fitting; : microbial corrosion risk index; : microbial corrosion effect index, output by previous step; : corrosion risk weight coefficient, determined by expert experience and material corrosion resistance. Constraint condition: particle position vector must meet the dynamic safe operation boundary parameter set. Particle update is realized by comparing individual optimal solution and global optimal solution update particle position and velocity: ; ; parameter description: : velocity vector of the th particle at iteration ; represents the moving direction and speed of the particle in the search space. : position vector of the th particle at iteration ; represents the coordinates of the current solution of the particle in the search space. : historical optimal position vector of the th particle; that is, the optimal solution of the particle so far. : global optimal position vector of the group; that is, the optimal solution of all particles so far. : inertia weight coefficient; used to balance the global search and local search ability. The value is determined by linear decreasing method: ; and are the initial and minimum inertia weights, respectively; is the maximum number of iterations. : learning factor; used to control the influence strength of individual cognition and group cognition on particle update. : random coefficient, uniformly distributed introducing search randomness to prevent falling into local optimum. : current iteration index; . : particle index; , where is the total number of particles. : updated velocity vector of the th particle, used for position calculation in the next iteration. : updated position vector of the th particle, i.e., the new candidate solution. Convergence criterion and output iteration to meet the convergence condition (maximum iteration number or the fitness change is less than the threshold ), output the Pareto optimal solution set . The output Pareto optimal solution set takes into account both economy and safety; each solution can correspond to specific electrolyzer power, hydrogen injection rate, pressure and temperature set value, providing input for the light hydrogen coordination control module. In the actual salt cavern hydrogen storage and photovoltaic water electrolysis collaborative operation scene, the risk constraint optimization module realizes the coordinated optimization of economy and safety through the multi-objective particle swarm optimization algorithm with constraints. The system first encodes the total power of the electrolyzer, the hydrogen injection rate, the hydrogen injection pressure and the hydrogen injection temperature into the particle position vector, and each particle represents a feasible operation strategy. The optimization target considers the purchase cost of electricity, equipment loss, hydrogen sales revenue and salt cavern microbial corrosion risk level, and through the dynamic safety operation boundary constraint, it ensures that all candidate strategies will not exceed the safety threshold of the corrosion active area. In the iteration process, the particles constantly update their positions and velocities according to the individual historical optimum and the global optimum. For example, when a strategy causes the hydrogen injection pressure to approach the safety upper limit of the corrosion active area, the particle swarm automatically adjusts the strategy to reduce the hydrogen injection rate or power output, thereby reducing the potential risk. At the same time, the system avoids falling into local optimum through inertia weight and random search mechanism, ensuring that the Pareto optimal solution set covers the balance interval of economy and safety. The system can automatically generate multiple hydrogen injection strategies that balance economy and safety under the premise of real-time monitoring of salt cavern microbial corrosion risk and crack strain characteristics, with each strategy explicitly corresponding to electrolyzer power, hydrogen injection rate, pressure and temperature set value. Through this optimization calculation, salt cavern hydrogen storage operation can maximize economic benefits while effectively controlling microbial corrosion risk and crack propagation, significantly improving the safety and controllability of the hydrogen storage process, and providing an operable real-time control scheme for the coordinated scheduling of photovoltaic water electrolysis and hydrogen storage systems.

[0033] Specifically, under the condition of meeting the dynamic safe operation boundary constraint, the specific process of rolling generation of electrolytic cell power distribution instruction and injection and extraction hydrogen safety operation value is as follows: selecting the optimal solution with optimal comprehensive economic benefit from the Pareto optimal solution set, decoding to obtain the power distribution proportion of each electrolytic cell, injection hydrogen rate set value, injection hydrogen pressure set value and injection hydrogen temperature set value; the injection and extraction hydrogen safety operation value includes injection hydrogen rate set value, injection hydrogen pressure set value and injection hydrogen temperature set value; verifying whether the generated power distribution instruction exceeds the electrolytic cell operation capability range and whether the injection and extraction hydrogen safety operation value meets the constraint condition of dynamic safe operation boundary.

[0034] In the embodiment, the rolling generation of electrolytic cell power distribution instruction and injection and extraction hydrogen safety operation value selects the optimal solution with economic benefit from the Pareto optimal solution set In the embodiment, the economic index The smallest solution is selected as the reference solution of the current rolling time step: ; parameter description: : the reference solution position vector selected by the current rolling time step; : the mthPareto optimal solution vector; : the economic fitness value, calculated by the formula defined in the previous step; : the solution that makes The smallest solution is selected. By selecting the optimal solution with economic benefit from the Pareto optimal solution set, the system is ensured to run in a state that takes into account safety and benefit. Decoding the reference solution obtains the power distribution proportion of each electrolytic cell and the injection and extraction hydrogen safety operation value, ; parameter description: : the power distribution proportion of the mth electrolytic cell; : the total number of electrolytic cells; : the injection hydrogen rate set value; : the injection hydrogen pressure set value; : the injection hydrogen temperature set value. Among them , , , The injection and extraction hydrogen safety operation values constitute the hydrogen injection process constraints. The generated electrolyzer power distribution instructions are checked one by one to see if the power of each electrolyzer unit exceeds the maximum power range allowed by the design; the injection and extraction hydrogen safety operation values, including the injection rate, injection pressure and injection temperature, are checked one by one to see if they fall within the upper and lower limit ranges specified by the dynamic safety operation boundary; if any power instruction or injection and extraction hydrogen operation value exceeds the safety range, a correction or restriction mechanism is triggered to adjust the out-of-limit value to the allowed range to ensure safe operation of the electrolyzer and injection and extraction hydrogen operation without causing material damage or hydrogen leakage risk; this verification process is part of the rolling control loop and can be linked with real-time monitoring data to ensure that the operation instructions generated at each time step meet the safety constraint requirements. The rolling generation means repeating this process at each control period to dynamically update the control instructions based on real-time safety boundaries and economic feedback. In the actual salt cavern hydrogen storage and photovoltaic water electrolysis collaborative operation scenario, the rolling generation of electrolyzer power distribution instructions and injection and extraction hydrogen safety operation value modules realizes the dynamic balance of economy and safety. The system selects the most economical solution from the Pareto optimal solution set obtained by multi-objective particle swarm optimization as the reference strategy for the current rolling time step. By decoding the strategy, the electrolyzer power distribution ratio and the safety operation values of the injection rate, injection pressure and injection temperature are obtained, which are directly related to the salt cavern corrosion risk and crack strain characteristics. During execution, the system checks whether the electrolyzer power exceeds the design allowable range to ensure the safety of each electrolyzer operation. At the same time, the injection rate, pressure and temperature are verified to see if they fall within the upper and lower limit ranges specified by the dynamic safety operation boundary. If the limit is exceeded, the system will automatically trigger a correction mechanism to adjust the out-of-limit value to the allowed range, thereby preventing overpressure or overcurrent in the active microbial corrosion zone from causing salt rock damage or hydrogen leakage risk. The verification and correction process is closely linked with real-time monitoring data to achieve dynamic rolling update at each control period.

[0035] Specifically, according to the electrolytic tank power distribution instruction and the injection and extraction hydrogen safety operation value, the specific process of coordinating and controlling the electrolytic tank operating power, the compressor rotating speed, the injection hydrogen regulating valve opening degree and the cooling system power by the fuzzy PID control algorithm is as follows: taking the total power instruction in the electrolytic tank power distribution instruction and the unit power distribution proportion as the electrolytic tank power control set value, and taking the injection hydrogen pressure set value, the injection hydrogen rate set value and the injection hydrogen temperature set value in the injection and extraction hydrogen safety operation value as the control set value of the compressor rotating speed, the injection hydrogen regulating valve opening degree and the cooling system power respectively; the grid frequency signal is collected in real time, the deviation and the deviation change rate of the signal from the standard frequency are calculated, the hydrogen pipe network pressure, the injection hydrogen pipeline flow and the injection hydrogen temperature signals are collected in real time, and the deviation and the deviation change rate of each signal from the corresponding set value are calculated respectively; the grid frequency deviation and the deviation change rate are input into the fuzzy reasoning system, the PID parameters of the power control loop are adjusted, the electrolytic tank total power adjusting amount is output, and the power distribution proportion is dynamically distributed to each electrolytic tank unit; the hydrogen pipe network pressure, the injection hydrogen rate and the injection hydrogen temperature deviation and the deviation change rate are input into the fuzzy reasoning system respectively, the PID parameters of the corresponding control loop are adjusted, and the compressor rotating speed adjusting amount, the injection hydrogen regulating valve opening degree adjusting amount and the cooling system power adjusting amount are output correspondingly.

[0036] In the embodiment, the light hydrogen coordination control module determines the electrolytic tank power control set value based on the control process of the fuzzy PID: ; parameter description: : the kth electrolytic tank power set value; : the total power instruction in the electrolytic tank power distribution instruction; : the power distribution proportion of the kth electrolytic tank, which is output by rolling optimization; , wherein r is the total number of electrolytic tanks. The compressor rotating speed, the injection hydrogen valve opening degree and the cooling system power set value are: ; parameter description: : the compressor rotating speed set value; : the injection hydrogen regulating valve opening degree set value; : the cooling system power set value; : the pressure, rate and temperature set values in the injection and extraction hydrogen safety operation value. Signal acquisition and deviation calculation, grid frequency deviation and deviation change rate: ; parameter description: : the grid frequency deviation; : the real-time collected grid frequency; : the standard grid frequency; : the grid frequency deviation change rate. Hydrogen pipe network pressure, injection hydrogen flow and temperature deviation and change rate: ; parameter description: : the real-time collected hydrogen pipe network pressure, injection hydrogen flow and injection hydrogen temperature; : Corresponding set value of hydrogen pipeline network pressure, hydrogen injection flow rate, hydrogen injection temperature; : Corresponding deviation of hydrogen pipeline network pressure, hydrogen injection flow rate, hydrogen injection temperature; : Deviation change rate of hydrogen pipeline network pressure, hydrogen injection flow rate, hydrogen injection temperature. Fuzzy PID adjustment, total power adjustment of electrolytic cell: ; Parameter description: : Total power adjustment; : PID parameter of electrolytic cell power loop, dynamically adjusted by fuzzy inference system; : Fuzzy function output of power grid frequency deviation. Fuzzy method: divide the deviation into five levels (negative large, negative small, zero, positive small, positive large), and the weight coefficient is calibrated through historical experimental data. Electrolytic cell unit power distribution: ; Parameter description: : Power adjustment of the th electrolytic cell; : Power distribution ratio. Compressor speed, hydrogen injection valve opening, cooling power adjustment: ; Parameter description: : Compressor speed adjustment, hydrogen injection valve opening adjustment, cooling power adjustment; : PID parameter of compressor loop; : PID parameter of hydrogen injection valve loop; : PID parameter of cooling loop; : Each deviation fuzzification function output; PID parameters are dynamically adjusted through fuzzy inference rules, and the rules are calibrated according to historical experimental data and system response performance. The output control quantity and the electrolytic tank power regulation quantity are proportionally distributed to each unit, and the compressor, hydrogen injection valve and cooling system are executed according to their respective regulation quantities; the system real-time feedback deviation and deviation rate form a closed-loop rolling control, realizing the joint debugging operation of the electrolytic tank and the hydrogen system. In the actual salt cave hydrogen storage and photovoltaic water electrolysis collaborative operation scene, the hydrogen coordination control module realizes the coordinated adjustment of electrolytic tank power, compressor speed, hydrogen injection valve opening and cooling system power through the fuzzy PID algorithm. In each control cycle, the system first decodes the electrolytic tank power distribution instruction obtained by rolling optimization and the hydrogen injection and extraction safety operation value to determine the power set value of each electrolytic tank and the control set value of the compressor, hydrogen injection valve and cooling system. The system real-time collects the grid frequency, hydrogen pipe network pressure, hydrogen injection flow and temperature signals, and calculates the deviation and rate of change of each signal. By fuzzing the deviation, the system dynamically adjusts the PID parameters based on historical experience data and response characteristics to realize fine adjustment of the total power of the electrolytic tank, and rolls to each unit according to the power distribution proportion. At the same time, the regulation quantity of the compressor speed, hydrogen injection valve opening and cooling power is output through the corresponding fuzzy PID loop to ensure that the pressure, rate and temperature of the hydrogen injection process are strictly controlled within the dynamic safe operation boundary range. The system can real-time coordinate the electrolytic tank and hydrogen injection operation under the conditions of photovoltaic power fluctuation and uncertainty existing in the active area of salt cave microbial corrosion, and realize fast response and fine adjustment. The electrolytic tank power is dynamically adjusted according to the optimized distribution proportion, the compressor and hydrogen injection valve closed-loop control ensures the stability of hydrogen pressure and flow, and the cooling system is self-adaptively adjusted according to the change of hydrogen injection temperature, thereby effectively preventing overpressure, overcurrent or overtemperature.

[0037] In summary, the present application has at least the following effects:

[0038] The salt cave hydrogen storage safety and light hydrogen joint control system realizes quantitative evaluation and dynamic early warning of the risk level of microbial corrosion in the salt cave by analyzing microbial metabolic activity through multi-source information fusion and establishing an activity-corrosion mapping relationship. In combination with strain field and temperature field data of the distributed optical fiber sensor, real-time analysis is performed on the seepage and diffusion characteristics of hydrogen in the salt rock fracture, and the synergistic effect of microbial corrosion is quantified, so that a dynamic safe operation boundary is generated to ensure that the hydrogen injection operation is carried out within the safe range. Based on the risk constraint, a multi-objective particle swarm optimization algorithm is used to optimize the electrolytic cell power distribution and the safe operation value of hydrogen injection and extraction, so as to realize the safe and controllable hydrogen injection rate, pressure and temperature. Through the fuzzy PID control algorithm, the electrolytic cell power, compressor speed, hydrogen injection regulating valve opening and cooling system power are dynamically adjusted to realize the linkage control of the electrolytic cell and the hydrogen injection and extraction system, and the economy and safety are taken into account. The whole system forms a closed loop control from microbial activity monitoring to risk assessment, and then to hydrogen injection optimization and light hydrogen coordination control, so as to realize the all-round safety guarantee and operation efficiency improvement of the salt cave hydrogen storage process.

[0039] Those skilled in the art will appreciate that embodiments of the application can be provided as methods, systems or computer program products. Accordingly, the application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can be embodied in the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage media, etc.) having computer usable program code embodied thereon.

[0040] The present application is described in reference to flowchart and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowchart and / or block diagrams, as well as combinations of flows and / or blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions, which are executed via the processor of the computer or other programmable data processing apparatus, generate a means for implementing the functions specified in the flowchart and / or block diagrams. Figure 1 The functions specified in the flow or flows and / or blocks Figure 1 The functions specified in the flow or flows and / or blocks

[0041] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including instruction means, which implement the functions specified in the flowchart and / or block diagrams. Figure 1 The functions specified in the flow or flows and / or blocks Figure 1 The functions specified in the flow or flows and / or blocks

[0042] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operations steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable data processing devices provide processes for implementing the functions specified in the flowchart Figure 1 one flowchart or multiple flowcharts and / or blocks Figure 1 one block or multiple blocks.

[0043] Although the preferred embodiments of the application have been described, those skilled in the art will be able to make additional modifications and variations to these embodiments without departing from the spirit and scope of the application. Accordingly, it is intended that the appended claims be construed to include all such modifications and variations as fall within the scope of the application.

[0044] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A safety and photohydrogen co-regulation control system for salt cavern hydrogen storage, characterized in that, It includes the following modules: salt cavern hydrogen storage monitoring module, hydrogen migration sensing module, risk constraint optimization module, and photohydrogen coordination control module; The salt cavern hydrogen storage monitoring module is used to monitor and collect data on the metabolic activity of microorganisms in the salt cavern through bioelectrochemical sensors, including redox potential, electron transfer rate and metabolite concentration, and to perform multi-source information fusion analysis to assess the level of microbial corrosion risk. The hydrogen migration sensing module is used to analyze the seepage and diffusion characteristics of hydrogen in salt rock fissures and the synergistic effect of bio-corrosion in real time, based on the microbial corrosion risk level and the strain field and temperature field data obtained by the distributed optical fiber sensors deployed in the salt cavern surrounding rock, and to generate a dynamic safe operation boundary. The risk constraint optimization module is used to receive grid dispatch requirements and, through a constrained multi-objective particle swarm optimization algorithm, aims to optimize system operation economy and minimize corrosion risk. Under the condition of satisfying dynamic safe operation boundary constraints, it continuously generates electrolyzer power allocation instructions and hydrogen injection and production safety operation values. The specific process of optimization calculation using a constrained multi-objective particle swarm optimization algorithm is as follows: Initialize the particle swarm position and velocity, and encode the total power of the electrolyzer, the hydrogen injection rate setpoint, the hydrogen injection pressure setpoint, and the hydrogen injection temperature setpoint into particle position vectors; Using the system operation economic indicators consisting of electricity purchase cost, equipment loss cost, and hydrogen sales revenue, and the microbial corrosion risk level as optimization objectives, and the dynamic safe operation boundary parameter set as constraints, the fitness value of each particle is calculated. The particle position and velocity are updated by comparing the individual optimal and the global optimal solutions. The optimization is iterated until the convergence condition is met, and the Pareto optimal solution set is output. Under the condition of satisfying the dynamic safety operation boundary constraints, the specific process of rolling out the electrolyzer power allocation command and the safe operating values ​​for hydrogen injection and production is as follows: The solution with the best overall economic benefits is selected from the Pareto optimal solution set, and the power allocation ratio, hydrogen injection rate setting, hydrogen injection pressure setting and hydrogen injection temperature setting of each electrolyzer are obtained by decoding. The safe operating values ​​for hydrogen injection and production include the hydrogen injection rate setting, the hydrogen injection pressure setting, and the hydrogen injection temperature setting. Verify whether the generated power allocation command exceeds the electrolyzer's operating capacity and whether the safe operation values ​​for hydrogen injection and production meet the constraints of the dynamic safe operation boundary. The photohydrogen coordination control module is used to coordinate and control the electrolyzer operating power, compressor speed, hydrogen injection regulating valve opening and cooling system power according to the electrolyzer power allocation command and hydrogen injection and extraction safety operation value through a fuzzy PID control algorithm.

2. The salt cavern hydrogen storage safety and photohydrogen co-regulation control system according to claim 1, characterized in that: The specific process of multi-source information fusion analysis of microbial metabolic activity data within salt caverns is as follows: The microbial metabolic activity data were standardized and preprocessed. Principal component analysis was used to extract characteristic parameters representing the metabolic state of each species from the preprocessed microbial metabolic activity data. The characteristic parameters were fused based on a Bayesian fusion multi-source information fusion framework to quantify the mutual support of the metabolic activities of each species and output the fused comprehensive evaluation value of microbial activity. The microorganisms in the salt caverns include sulfate-reducing bacteria, acid-producing bacteria, and methanogens.

3. The salt cavern hydrogen storage safety and photohydrogen co-regulation control system according to claim 2, characterized in that: The specific process for assessing the risk level of microbial corrosion is as follows: Based on the comprehensive evaluation value of microbial activity, a quantitative mapping relationship between microbial activity and corrosion rate was established. The synergistic corrosion mechanism of sulfate-reducing bacteria, acid-producing bacteria and methanogens was analyzed, and the combined effect of microbial metabolism on chemical corrosion and biological corrosion of salt rock materials was quantified. Microbial corrosion risk levels are classified according to the degree of corrosion effect, and a risk early warning mechanism based on the dynamic changes in microbial community activity is established to output the microbial corrosion risk level.

4. The salt cavern hydrogen storage safety and photohydrogen co-regulation control system according to claim 1, characterized in that: Based on the risk level of microbial corrosion, and combined with strain and temperature field data obtained from distributed fiber optic sensors deployed in the surrounding rock of the salt cavern, the specific process of real-time analysis of the seepage diffusion characteristics of hydrogen in salt rock fissures and the synergistic effect of bio-corrosion is as follows: Based on the risk level of microbial corrosion, active areas of biocorrosion are identified. Strain field data acquired by distributed optical fiber sensing is used to identify the development characteristics of salt rock fractures. The thermodynamic effects of hydrogen seepage process are analyzed through temperature field data. A multi-field coupling analysis framework was established to quantify the enhancing effect of microbial corrosion on fracture permeability and to analyze the seepage path and diffusion rate of hydrogen in the corrosion-modified fracture network. The influence of the chemical reaction between biocorrosion products and hydrogen on seepage characteristics was assessed, and the synergistic effect intensity index of microbial corrosion and hydrogen seepage was calculated.

5. The salt cavern hydrogen storage safety and photohydrogen co-regulation control system according to claim 4, characterized in that: The specific process for generating dynamic safe operating boundaries is as follows: Based on the synergistic effect intensity index of microbial corrosion and hydrogen permeation, the maximum allowable hydrogen injection pressure and hydrogen injection rate thresholds for different corrosion risk areas were determined. Based on the fracture development characteristics identified by real-time strain field data, the pressure change rate limit during hydrogen injection is dynamically adjusted, and combined with temperature field monitoring data, an upper limit threshold for hydrogen injection temperature is set. A dynamic set of safe operating boundary parameters is formed, including the maximum hydrogen injection pressure, maximum hydrogen injection rate, pressure change rate limit, and hydrogen injection temperature limit.

6. The salt cavern hydrogen storage safety and photohydrogen co-regulation control system according to claim 1, characterized in that: Based on the electrolyzer power allocation command and hydrogen injection / production safety operating values, the specific process of coordinating and controlling the electrolyzer operating power, compressor speed, hydrogen injection regulating valve opening, and cooling system power through a fuzzy PID control algorithm is as follows: The total power command and the power allocation ratio of each unit in the electrolyzer power allocation command are used as the electrolyzer power control setting values. The hydrogen injection pressure setting value, hydrogen injection rate setting value and hydrogen injection temperature setting value in the hydrogen injection and production safety operation value are used as the control setting values ​​for compressor speed, hydrogen injection regulating valve opening and cooling system power, respectively. Real-time acquisition of power grid frequency signals, calculation of the deviation of the signal from the standard frequency and the rate of change of the deviation, real-time acquisition of hydrogen pipeline pressure, hydrogen injection pipeline flow and hydrogen injection temperature signals, and calculation of the deviation of each signal from the corresponding set value and the rate of change of the deviation. The grid frequency deviation and deviation change rate are input into the fuzzy inference system to adjust the PID parameters of the power control loop, output the total power adjustment of the electrolytic cell, and dynamically distribute it to each electrolytic cell unit according to the power distribution ratio. The deviations and rates of change of hydrogen pipeline pressure, hydrogen injection rate, and hydrogen injection temperature are input into the fuzzy inference system, and the PID parameters of the corresponding control loops are adjusted to output the compressor speed regulation, hydrogen injection regulating valve opening regulation, and cooling system power regulation.

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