Method for actively regulating and controlling temperature of air storage of compressed air energy storage system

Abstract

The invention provides a method for actively regulating and controlling the temperature of a gas storage of a compressed air energy storage system, which belongs to the field of compressed air energy storage and comprises the following steps of: acquiring multi-point temperature and pressure data of the gas storage and constructing a temperature and pressure distribution field; the temperature-pressure-volume three-dimensional phase space is subjected to slice division, and slice boundaries are dynamically optimized; generating an optimal temperature control track considering energy efficiency for each slice; and calculating a final volume regulation quantity by adopting a horizontal-vertical mixed decision mechanism, and converting the final volume regulation quantity into a foldable rigid material control instruction. According to the method, high-precision regulation and control of the temperature of the gas storage are realized through slice decomposition of a hierarchical trajectory compensation control architecture, meanwhile, the energy efficiency is optimized, the problem of a temperature-volume nonlinear relation which is difficult to deal with by a traditional method is solved, and the volume utilization rate of the gas storage is improved.

Description

Technical Field

[0001] The present invention relates to compressed air energy storage technology, in particular to an active temperature control method for the air storage reservoir of a compressed air energy storage system. Background Art

[0002] As a large-scale and long-term energy storage technology, the compressed air energy storage system plays an important role in the development of renewable energy and power grid peak shaving. With the continuous increase in the grid-connected capacity of intermittent renewable energy sources such as wind energy and solar energy, the peak-valley difference and the pressure of frequency modulation and peak shaving faced by the power grid are constantly increasing. Due to its advantages such as large scale, low cost, and good environmental protection, the compressed air energy storage system has become a key technical path to solve the grid connection and consumption of renewable energy. However, the temperature change during the operation of the compressed air energy storage system significantly affects its energy storage efficiency and gas utilization rate. Therefore, active and precise control of the temperature of the air storage reservoir is of great significance for improving the system efficiency and reducing the operation cost.

[0003] Currently, two main methods are used for the temperature control of the air storage reservoir of the compressed air energy storage system: one is the passive control method, which reduces the heat exchange with the outside by increasing the heat insulation layer of the air storage reservoir; the other is the simple active control method, which mainly includes technologies such as heat recovery and utilization, multi-stage compression-heat exchange, and hot oil circulation. Among them, the typical heat recovery and utilization technology (such as adiabatic compressed air energy storage) stores the heat generated during the compression process in the heat transfer medium and uses it to heat the expanded air during discharge to improve the system cycle efficiency. The multi-stage compression-heat exchange technology controls the temperature rise during the compression process by setting heat exchangers between compressors and reduces the compression power consumption. The hot oil circulation technology adjusts the gas temperature in the air storage reservoir by setting a hot oil circulation channel around the wall of the air storage reservoir. In recent years, certain progress has also been made in the application of phase change materials in the field of energy storage, and some studies have attempted to apply phase change materials to the temperature control of compressed air energy storage systems.

[0004] However, there are still some key control problems in the existing technologies that need to be solved urgently. For example, in the structure that uses foldable rigid materials to adjust the volume of the temperature control medium interlayer, how to intelligently adjust the control strategy according to different working conditions to achieve precise temperature control while minimizing energy consumption is a key technical problem that needs to be solved urgently. Summary of the Invention

[0005] Objective of the Invention: To provide an active temperature control method for the air storage reservoir of a compressed air energy storage system, in order to solve at least one technical problem existing in the existing technologies.

[0006] Technical Solution: The active temperature control method for the air storage reservoir of a compressed air energy storage system includes: Collect the original data of multi-point temperature and pressure in the gas storage reservoir and the state parameters of the foldable rigid material. After preprocessing, construct the temperature and pressure distribution field and calculate the volume of the temperature control medium interlayer; accordingly, perform slice division and dynamically optimize the slice boundary to obtain an optimized slice set; For each slice in the optimized slice set, generate an optimal control trajectory considering temperature control accuracy and energy consumption through a local thermodynamic model; According to the optimal control trajectory and the position of the current state in the optimized slice set, adopt a hybrid decision-making mechanism combining horizontal coordination and vertical control to calculate the final volume regulation amount and convert it into a control instruction for the foldable rigid material.

[0007] Beneficial effects: The present invention improves the accuracy and response speed of temperature regulation, enhances the adaptability of the system under the condition of rapid load change, and optimizes the energy efficiency; improves the overall efficiency of the compressed air energy storage system, solves the problem of temperature-volume non-linear relationship that is difficult to handle by traditional methods, and improves the volume utilization rate of the gas storage reservoir; realizes precise control with global smooth transition, and improves the overall performance and stability of the gas storage reservoir temperature regulation system. Description of the Drawings

[0008] Figure 1 It is a structural diagram of an active temperature regulation system for a gas storage reservoir of a compressed air energy storage system provided by an embodiment of the present application.

[0009] Figure 2 It is a step flow chart of an active temperature regulation method for a gas storage reservoir of a compressed air energy storage system provided by an embodiment of the present application.

[0010] Figure 3 It is a step flow chart of constructing a temperature and pressure distribution field and calculating the volume of the temperature control medium interlayer provided by an embodiment of the present application.

[0011] Figure 4 It is a step flow chart of calculating the reliability score and obtaining the adjusted temperature and pressure data set provided by an embodiment of the present application.

[0012] Figure 5 It is a step flow chart of calculating the volume of the temperature control medium interlayer provided by an embodiment of the present application.

[0013] Among them, the reference numerals are: 1. gas storage tank; 2. temperature control medium interlayer; 3. foldable rigid material; 4. temperature circulation fan; 5. temperature detection system; 6. pressure gauge. Detailed Embodiments

[0014] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0015] It should be specifically noted that, for the convenience of clearly showing the step flow of the present application, serial numbers are marked for each step in the specification. These serial numbers are only for the convenience of description and do not limit the execution order of the steps. In actual operation, according to the technical requirements of the specific implementation scenario, the steps can be executed in an order different from that shown in the specification, and in some cases, parallel processing between steps can also be achieved.

[0016] It is found in the research process that in the process of designing an intelligent control strategy, it is necessary to solve non-linear control, non-uniform control, and multi-objective control. Specifically, first, in the temperature control system of a gas storage cavern using foldable rigid materials, there is a complex non-linear relationship between temperature regulation and volume regulation. Traditional PID control methods are difficult to effectively cope with this non-linear characteristic, especially in the case of rapid changes in working conditions, the control performance drops significantly. Second, the temperature field distribution in the gas storage cavern is non-uniform, and traditional single-point or a few-point temperature monitoring is difficult to accurately grasp the overall temperature distribution, resulting in limited control accuracy. In addition, existing temperature regulation methods mostly focus on a single temperature control target, ignoring the optimization of energy efficiency, and failing to achieve the coordinated optimization of precise temperature control and maximum energy efficiency, resulting in the overall performance of the system not being fully exerted.

[0017] Specifically, for the gas storage cavern of a compressed air energy storage system, it is necessary to improve the utilization rate of the unit volume of the gas storage cavern, that is, to reduce the temperature rise in the gas storage cavern as much as possible during the charging process, and the termination temperature after charging the gas storage cavern is relatively low, so as to ensure that as much gas as possible can be filled into the gas storage cavern; to reduce the temperature drop in the gas storage cavern as much as possible during the discharging process, and the termination temperature after discharging the gas storage cavern is relatively high, so as to ensure that as much gas as possible can be discharged from the gas storage cavern. For this reason, a temperature active regulation system for the gas storage cavern of a compressed air energy storage system is provided.

[0018] As Figure 1 shown, the structure of the temperature regulation system for the gas storage cavern of a compressed air energy storage system mainly includes a gas storage tank, a temperature regulation system, and a temperature detection system; the temperature regulation system mainly includes a temperature regulation medium interlayer, a temperature regulation medium, a temperature circulation fan, a volume regulation system, and a pressure gauge; the temperature detection system mainly includes the air temperature in the gas storage cavern and the temperature of the temperature regulation medium.

[0019] The regulation process of the temperature regulation system for the gas storage cavern in a compressed air energy storage system is as follows: Gas storage reservoir gas injection: During the gas injection process of the gas storage reservoir, the air temperature inside the gas storage reservoir rises. In the temperature control system, the volume control system increases the volume of the temperature control medium interlayer, causing the saturated vapor pressure of the temperature control medium inside the gas storage reservoir to decrease, enabling the temperature control medium to vaporize and absorb heat, thereby alleviating the rise in the air temperature inside the gas storage reservoir; Gas storage reservoir high-pressure gas storage process: During the high-pressure gas storage process of the gas storage reservoir, the gas storage reservoir should ensure that the pressure and temperature inside the gas storage reservoir decrease less. Therefore, in the temperature control system, the volume control system reduces the volume of the temperature control medium interlayer, causing the temperature control medium to liquefy and release heat, thereby alleviating the decrease in temperature inside the gas storage reservoir; Gas storage reservoir gas discharge process: During the gas discharge process of the gas storage reservoir, the air temperature inside the gas storage reservoir decreases. The volume of the temperature control medium interlayer in the temperature control system further decreases, causing the temperature control medium to liquefy and release heat, regulating the air temperature inside the gas storage reservoir, and reducing the amplitude of the temperature decrease inside the gas storage reservoir; Gas storage reservoir low-pressure gas storage process: After the gas storage reservoir discharges gas, the air temperature inside the gas storage reservoir is in a relatively low state. To ensure that the gas storage reservoir can fill more air in the next cycle, the temperature inside the gas storage reservoir should be maintained at a relatively low level. The volume of the temperature control interlayer in the temperature control system increases to ensure that the air temperature inside the gas storage reservoir remains constant or changes slightly.

[0020] The temperature control medium is a low-boiling organic compound, which is filled in the temperature control medium interlayer. The filled temperature control medium accounts for about 60% - 80% of the volume of the temperature control medium interlayer; The temperature control medium interlayer is sealed with nitrogen to maintain a pressure of 0.1 MPa inside the temperature control medium interlayer under rated working conditions. The temperature control medium is usually selected from the single substances or mixtures of phenols, alcohols, aldehydes, and ketones; The rated working conditions are when the temperature control medium interlayer is in the minimum volume, the interlayer temperature is the ambient temperature, and the pressure inside the interlayer is 0.1 MPa. The boiling point of the temperature control medium under normal pressure is designed to be 10 - 15 °C higher than the lowest temperature in the temperature change range of the gas storage reservoir in a single cycle without a temperature control system, ensuring that when the temperature in the gas storage reservoir changes greatly, the heat exchange medium can better achieve the functions of heat recovery / provision. The temperature control medium interlayer can increase its volume by 1 time the rated volume. When the temperature control medium interlayer expands or contracts through the unfolding of foldable rigid materials, the pressure inside the gas storage reservoir is adjusted, and then the temperature control medium undergoes a gasification or liquefaction process to achieve heat exchange; The temperature circulation fan in the temperature control system starts working when the volume of the temperature control medium interlayer in the gas storage reservoir begins to change, strengthening the temperature change in the temperature control medium interlayer. The pressure gauge represents the pressure situation inside the temperature control medium interlayer. When the pressure value inside the gas storage reservoir is greater than the set value, the pressure relief bag inside the gas storage reservoir is opened to ensure the safety of the temperature control medium interlayer. The pressure set value is generally 2 - 3 times the pressure under rated working conditions.

[0021] In the above control system, there is still a problem of non-linear relationship between the volume of the foldable rigid material and the temperature of the gas storage reservoir. How to optimize the energy efficiency while ensuring the temperature control accuracy requires the use of active control methods.

[0022] As Figure 2 shown, according to another aspect of the present application, there is also provided an active temperature control method for the gas storage reservoir of a compressed air energy storage system, including: S1. Collect the original data of multi-point temperature and pressure in the gas storage reservoir and the state parameters of the foldable rigid material. After preprocessing, construct the temperature and pressure distribution field and calculate the volume of the temperature control medium interlayer and its change rate; Specifically, the original data of multi-point temperature and pressure are the temperature and pressure data at different positions inside the gas storage reservoir; the temperature and pressure distribution field shows the temperature and pressure distribution in different regions inside the gas storage reservoir.

[0023] S2. Based on the temperature and pressure distribution field and the volume of the temperature control medium interlayer, construct a three-dimensional phase space, slice and divide the three-dimensional phase space and dynamically optimize the slice boundaries to obtain an optimized slice set; Specifically, the three-dimensional phase space shows the comprehensive distribution of three parameters: temperature, pressure, and the volume of the temperature control medium interlayer. It is like establishing a virtual three-dimensional map to show the temperature-pressure-medium relationship in different regions inside the gas storage reservoir. In this three-dimensional space, multiple different regions (slices) are cut out, so that the system can more accurately control the temperature change, rather than adopting a one-size-fits-all method.

[0024] S3. For each slice in the optimized slice set, generate an optimal control trajectory considering temperature control accuracy and energy consumption through a local thermodynamic model; Specifically, the local thermodynamic model is a mathematical description of the temperature change law in this region, which can predict how to adjust the temperature to maintain accuracy without wasting energy.

[0025] S4. According to the optimal control trajectory and the position of the current state in the optimized slice set, adopt a hybrid decision-making mechanism combining horizontal coordination and vertical control to calculate the final volume regulation amount and convert it into a control instruction for the foldable rigid material.

[0026] Specifically, horizontal coordination refers to coordinating between different slices to ensure that the temperature regulation of the entire gas storage reservoir is coherent and there will be no overheating or overcooling in a certain area. Vertical control refers to fine adjustment within a single slice to ensure that the temperature change in this area conforms to the optimal control trajectory.

[0027] As Figure 3 shown, according to one aspect of the present application, the steps of constructing the temperature and pressure distribution field and calculating the volume of the temperature control medium interlayer include: S11. Read the data of the temperature and pressure sensors, and calculate the reliability score based on the deviation between the measured value and the predicted value. S12. Filter and compensate the data according to the reliability score to obtain the adjusted temperature and pressure data set; construct the temperature and pressure distribution field based on the adjusted temperature and pressure data set. S13. Obtain the state parameters of the foldable rigid material, and calculate the volume and its change rate of the temperature control medium interlayer in combination with the thermodynamic equation, which are used as the correlation parameters related to the temperature and pressure changes.

[0028] As Figure 4 shown, according to one aspect of the present application, the steps of calculating the reliability score and obtaining the adjusted temperature and pressure data set include: Calculate the reliability score of the temperature sensor through the exponential function of the deviation between the measured temperature value of the temperature sensor and the temperature value predicted based on historical data and physical models; Calculate the reliability score of the pressure sensor through the exponential function of the deviation between the measured pressure value of the pressure sensor and the pressure value predicted based on historical data and physical models; For the sensor data with a reliability score lower than the preset threshold, perform interpolation correction based on the distance weight of adjacent reliable sensors to generate the adjusted temperature and pressure data set.

[0029] Specifically, obtain the original multi-point temperature and pressure data, including the real-time data of the temperature sensor array (T1, T2,..., T n ) and the pressure sensors (P1, P2,..., P m ) distributed inside the gas storage reservoir, as well as the current state data of the foldable rigid material (fold state parameter set S fold ), and the sampling frequency is 10Hz. Conduct a reliability assessment on the original multi-point temperature and pressure data, and calculate the reliability score R sensor of each sensor data: for the temperature sensor R sensor (i) = exp(-|(T i -T predicted (i))| 2 / σ T 2 ); for the pressure sensor R sensor (j) = exp(-|(P j - P predicted (j))| 2 / σ P 2 ); where, T predicted and P predicted are the temperature and pressure values at the current position predicted based on historical data and physical models, and σ T and σ Pis the tolerance parameter for temperature and pressure fluctuations. Based on the reliability score R sensor perform data filtering and compensation. For sensor data with a reliability lower than the threshold (R sensor <0.6), perform interpolation correction: T adjusted (i) = T i , if R sensor (i) ≥ 0.6; T adjusted (i)= Σ[w k ·T k / Σ[w k , if R sensor (i)<0.6; where w k is the weight of adjacent reliable sensors, which is inversely proportional to the distance. Similarly process the pressure data to obtain the adjusted temperature and pressure data sets T adjusted and P adjusted .

[0030] Use the adjusted temperature and pressure data sets to construct the temperature and pressure distribution fields T field and P field in the gas storage reservoir, using the piecewise cubic spline interpolation method: T field (x, y, z) = Spline Interpolation (T adjusted , Positions); P field (x, y, z) = Spline Interpolation (P adjusted , Positions); where Positions is the three-dimensional spatial position coordinates of the sensors, and Spline Interpolation is the spline interpolation function. According to the state parameters S fold of the foldable rigid material and the thermodynamic equation, calculate the volume V chamber of the temperature control medium interlayer and its change rate dV chamber / dt, as the associated parameter related to the temperature and pressure changes. In this embodiment, a direct mapping mechanism for establishing the relationship between the state of the foldable rigid material and the temperature and pressure in the gas storage reservoir is established.

[0031] As Figure 5 shown, according to one aspect of the present application, the steps for calculating the volume of the temperature control medium interlayer include: Extract the folding angle set, the rigid node position coordinate set, and the material elastic parameters from the state parameters of the foldable rigid material to construct a three-dimensional structural model of the foldable material; Based on the three-dimensional structural model, calculate the sum of the interlayer volume units formed between adjacent rigid nodes to obtain the current volume of the temperature control medium interlayer; Calculate the moving speed of the foldable rigid material according to the state parameters of the foldable rigid material in the time series; calculate the volume change rate of the temperature control medium interlayer based on the moving speed.

[0032] Specifically, read the state parameter S of the foldable rigid material fold , extract the key geometric parameters therein, including the set of folding angles θ fold , the set of rigid node position coordinates P nodes and the material elastic parameter E material , and construct a three-dimensional structure model M of the foldable material structure . Use the three-dimensional structure model M structure to calculate the volume V of the current temperature control medium interlayer chamber : V chamber = ∑ i V segment (i); V segment (i) = f volume (P nodes (i), P nodes (i + 1), θ fold (i)); where V segment represents the interlayer volume unit formed between two adjacent rigid nodes, and f volume is the volume calculation function, considering the non-linear relationship between the folding angle and the node spacing. Based on the time series of S fold data, calculate the moving speed v fold and acceleration a fold of the foldable rigid material: v fold (t) = (S fold (t) - S fold (t - Δt)) / Δt; a fold (t) = (v fold (t) - v fold (t - Δt)) / Δt; where Δt is the time interval.

[0033] Use the moving speed v fold , and calculate the volume change rate dV chamber / dt of the temperature control medium interlayer according to the geometric relationship: dV chamber / dt = ∑ i ΨV segment (i) / Ψθ fold (i) · dθ fold (i) / dt + ∑ i ΨV segment (i) / ΨP nodes (i) · dP nodes (i) / dt; where ΨV segment / Ψθ fold and ΨVsegment / ΨP nodes is the partial derivative of volume with respect to the folding angle and the node position, representing the sensitivity of volume to these parameters; Ψ is the partial derivative. Combining with the volume V of the current temperature control medium interlayer chamber , the volume change rate dV of the temperature control medium interlayer chamber / dt and the thermodynamic equation, calculate the pressure change dP chamber / dt and the temperature change dT chamber / dt: dP chamber / dt = -γP chamber / V chamber · dV chamber / dt + R gas / V chamber · dm gas / dt; dT chamber / dt = (γ - 1)T chamber / V chamber · dV chamber / dt + Q exchange / (m gas · c v ); where γ is the specific heat ratio of the gas, R gas is the gas constant, m gas is the mass of the temperature control medium, Q exchange is the heat exchange amount, c v is the specific heat at constant volume, P chamber is the gas pressure in the gas storage reservoir (or the temperature control medium interlayer), T chamber is the gas temperature in the gas storage reservoir (or the temperature control medium interlayer). Transmit the pressure change dP chamber / dt and the temperature change dT chamber / dt to the subsequent steps for use.

[0034] According to one aspect of the present application, the steps to obtain the optimized slice set include: S21. Based on the temperature-pressure distribution field and the volume of the temperature control medium interlayer, construct a three-dimensional phase space containing complete thermodynamic information; S22. Perform an initial slice division on the three-dimensional phase space to obtain an initial slice set; S23. Based on the initial slice set, calculate the distance between the state points within each slice and the center point of the slice to obtain the consistency index of the internal state change; S24. Combine the consistency index with the boundary characteristics between adjacent slices, and perform self-optimization adjustment of the slice boundaries to obtain the optimized slice set.

[0035] Specifically, based on the temperature-pressure distribution field T field , P field and the volume parameter Vchamber Construct a three - dimensional phase space representation Phase Space : Phase Space = {T field , P field , V chamber}; This phase space contains the complete thermodynamic information of the current state of the gas storage reservoir. Use the improved K - means clustering algorithm to perform an initial slicing of Phase Space to obtain an initial set of slices Ω init : Ω init = {Ω1, Ω2,..., Ω n}; Among them, the basis for the division is the similarity of the thermodynamic behavior of the state points in the phase space, especially considering the influence mechanism of the foldable rigid material on the temperature and pressure inside the gas storage reservoir.

[0036] For each slice Ω init in the initial set of slices Ω i , calculate its internal state change consistency index C index : C index (Ω i ) = Σ[||S j - S centroid || 2 / |Ω i |; Among them, S j is the state point within the slice, S centroid is the center point of the slice, and |Ω i | is the number of state points within the slice. Based on the consistency index C index and the boundary characteristics between adjacent slices, perform self - optimization adjustment of the slice boundary, and calculate the boundary movement vector B adjust : B adjust (i, j) = μ·(C index (Ω i ) - C index (Ω j ))·n ij ; Among them, μ is the boundary adjustment coefficient, and n ij is the unit normal vector pointing from slice i to slice j. Apply the boundary movement vector B adjust to update the slice boundary to obtain an optimized set of slices Ω opt . In this embodiment, by dynamically adjusting the slice boundary, the control strategy can accurately adapt to the temperature change characteristics in different regions of the gas storage reservoir, especially considering the non - linear influence caused by the volume change of the temperature - regulating medium interlayer.

[0037] According to one aspect of the present application, the steps of obtaining the initial set of slices include: Based on the three - dimensional phase space, construct a multi - dimensional state vector containing state variables and their rates of change; Dimensionality reduction is performed on the multi-dimensional state vector to obtain a dimensionality-reduced state vector; The K-means++ clustering algorithm using the similarity metric of thermodynamic behavior is used to cluster the dimensionality-reduced state vector to obtain a clustering result; where the similarity metric of thermodynamic behavior comprehensively considers the temperature component, the pressure component, and the predicted future state value; An initial slice set is constructed based on the clustering result.

[0038] Specifically, based on the temperature-pressure distribution fields T field 、P field and V chamber , a multi-dimensional state vector S vector is constructed: S vector (i) = [T field (i), P field (i), V chamber , dT field (i) / dt, dP field (i) / dt, dV chamber / dt]; where i represents the index of the spatial discrete point, and this vector extends the traditional phase space and includes both state variables and their rates of change. The principal component analysis (PCA) is applied to the multi-dimensional state vector S vector to obtain a dimensionality-reduced state vector S reduced : S reduced =PCA(S vector , n components =k); where k is the number of principal components retained, usually taking 3 - 5 to ensure that the cumulative proportion of explained variance is greater than 95%.

[0039] The improved K-means++ clustering algorithm is used to cluster S reduced , and the improvement lies in introducing the similarity metric of thermodynamic behavior Sim thermo : Sim thermo (x, y) = w1·||x （t） -y （t） || 2 + w2·||x （p） -y （p） || 2 + w3·||f predict (x)-f predict (y)|| 2 ; where x （t） , y （t） represent the temperature components, x （p） , y （p） represent the pressure components, and f predictA function representing the predicted future state, where w1, w2, and w3 are weight coefficients. Perform a clustering process to obtain the clustering center C clusters and the state point belonging label L labels : Initialize k clustering centers C = {c1, c2, ..., c k}, select outliers, and repeat until convergence: Calculate the Sim thermo distance from each state point to each clustering center; Assign the state point to the clustering center with the minimum distance; Update the clustering center to the mean of all assigned points. Based on the clustering center C clusters and the state point belonging label L labels , construct the initial slice set Ω init : Ω init = {Ω1, Ω2, ..., Ω n}; Ω i = {x | L labels (x) = i}; Pass the initial slice set Ω init to the subsequent steps for use.

[0040] According to one aspect of the present application, the steps of performing self-optimizing adjustment of the slice boundary to obtain the optimized slice set include: Determine the set of boundary points between adjacent slices, and calculate the difference in local performance metrics of the boundary points; where the set of boundary points is the set of state points within the slice that are adjacent to other slices; The local performance metric comprehensively considers the temperature control accuracy and energy efficiency; According to the change trend of the belonging of the boundary points statistically based on the difference in local performance metrics, determine the optimal belonging slice of the boundary points and calculate the movement vector of the slice boundary; Update the slice boundary based on the movement vector to obtain the optimized slice set.

[0041] Specifically, for each pair of adjacent slices Ω opt and Ω i in the optimized slice set Ω j , determine their set of boundary points B points (i, j): B points (i, j) = {x | x∈Ω i and Ξy∈Ω j such that ||x - y|| < ε}; where Ξ is the existential quantifier, indicating that there exists at least one element satisfying the condition; ε is the proximity threshold, representing the maximum distance at which two state points are considered adjacent. For each boundary point, calculate the difference in local performance metrics ΔPerf(i, j, p): ΔPerf(i, j, p) = Perf(p, Ω i ) - Perf(p, Ω j); where Perf(p, Ω) represents the performance index of point p in slice Ω, considering both temperature control accuracy and energy efficiency: Perf(p, Ω) = -w1·||T(p) - T target || 2 - w2·E consumption (p, Ω); Based on the local performance index difference ΔPerf(i, j, p), calculate the optimal belonging slice Best slice (p): Best slice (p) = arg max Ω∈{Ωi，Ωj} Perf(p, Ω); that is, assign the boundary point to the slice that can achieve better performance.

[0042] After reassigning all boundary points, calculate the boundary movement vector B adjust : B adjust (i, j) = μ·[|{p ∈ B points (i, j) | Best slice (p) = Ω j}| - |{p ∈ B points (i, j) | Best slice (p) = Ω i}|]·n ij ; where |·| represents the set cardinality, n ij is the unit normal vector pointing from slice i to slice j, and μ is the boundary adjustment coefficient. Apply the boundary movement vector B adjust to move the slice boundary and update the slice set to obtain Ω opt : For each pair of adjacent slices Ω i and Ω j : Calculate the new boundary: B new (i, j) = B old (i, j) + B adjust (i, j); Re-partition the slices: Ω i ' = {x | d(x, C i ) < d(x, C j ) and on the same side of B new (i, j)}; where C i and C j are the center points of slices Ω i and Ω j , d is the distance function, and B old is the previous boundary.

[0043] According to one aspect of the present application, the steps of generating the optimal control trajectory include: S31. Construct a local thermodynamic model considering the dynamic characteristics of the foldable rigid material to describe the relationship between temperature, pressure, and the volume change of the temperature control medium interlayer; S32. Perform parameter estimation update on the local thermodynamic model based on pre-stored historical data and the current state to obtain updated parameters; S33. Calculate the ideal state trajectory and the energy consumption evaluation index using the updated parameters and the current state; S34. Generate an optimal control trajectory through multi-objective optimization by combining the ideal state trajectory and the energy consumption evaluation index.

[0044] Specifically, for each slice Ω in the optimized slice set Ω opt , construct a local thermodynamic model M i : dT / dt = f local (T, P, V, dV / dt, θ); dP / dt = f T (T, P, V, dV / dt, θ); where θ is the model parameter vector, including heat transfer coefficient, compression coefficient, etc., and the dynamic characteristics of the foldable rigid material are particularly considered. Perform parameter estimation update of θ based on historical data and the current state P : θ updated = θ updated + η·(S current - S actual (θ predicted )); where η is the learning rate, S current is the actually observed state change, and S actual is the state change predicted by the model. Calculate the ideal state trajectory T predicted using the updated parameter θ updated and the current state: T ideal (t + Δt) = T(t) + ∫[t, t + Δt] f ideal (T, P, V, dV / dt, θ T ) dt; this trajectory represents the change path that the temperature of the gas storage should follow under ideal conditions. Considering the energy efficiency factor, calculate the energy consumption evaluation index E updated : E consumption = k·|dV consumption / dt| chamber ·Δt; where k is a coefficient related to the mechanical properties of the foldable rigid material. Through multi-objective optimization, combine the ideal state trajectory T 2 and the energy consumption evaluation index E ideal to generate the final control trajectory T consumption : T opt = arg min{w opt ·||T - T T || ideal + w 2 + w E·E consumption}; where, w T and w E are the weight coefficients of temperature control accuracy and energy consumption. In this embodiment, through the dual-objective optimization of temperature control and energy efficiency, the movement of the foldable rigid material can not only achieve precise temperature control but also minimize energy consumption.

[0045] According to one aspect of the present application, the steps of generating an optimal control trajectory through multi-objective optimization include: Constructing a prediction model based on the current state and updated parameters to predict the future state trajectory under different control inputs; Based on the prediction model, calculating the integral of the weighted deviation between the predicted temperature and the ideal temperature trajectory to obtain the temperature control error objective function; Based on the prediction model, calculating the weighted integral of the volume change rate and its derivative to obtain the energy consumption objective function; Based on the temperature control error and energy consumption objective functions, constructing and solving a comprehensive multi-objective optimization problem including the volume change rate, volume range, and acceleration limit to obtain the optimal control input sequence and the corresponding optimal control trajectory.

[0046] Specifically, based on the current state and the updated parameter θ updated , constructing a prediction model Pred model to predict the future state trajectory under different control inputs: Pred model : (T(t), P(t), V(t), ΔV) → (T(t + Δt), P(t + Δt), V(t + Δt)); this model is based on thermodynamic equations and updated parameters and can accurately predict the system response under control actions. Define the temperature control error objective function J temp : J temp (ΔV) = ∫[t, t + H] w(τ)·||T predicted (τ, ΔV) - T ideal (τ)|| 2 dτ; where, H is the prediction horizon length, w(τ) is the time weight function, T predicted is the model-predicted temperature, and T ideal is the ideal temperature trajectory. Define the energy consumption objective function J energy : J energy (ΔV) = ∫[t, t + H] [k1·|dV(τ) / dt| 2 + k2·|d 2 V(τ) / dt 2 |] dτ; where, k1 and k2 are energy consumption coefficients corresponding to the energy consumption related to speed and acceleration respectively.

[0047] Construct a comprehensive multi-objective optimization problem and calculate the optimal control input sequence ΔV sequence : ΔV sequence = argmin{w T ·J temp (ΔV) + w E ·J energy (ΔV)}; s.t. |dV / dt| ≤ V rate_max ; |V| ∈ [V min , V max ; |d 2 V / dt 2 | ≤ a max ; where, w T and w E are the weights for temperature control and energy consumption, and the constraint conditions include the volume change rate, volume range, and acceleration limit. An improved sequential quadratic programming (SQP) algorithm is used to solve the optimization problem to obtain the first step ΔV of the optimal control sequence opt : Linearize the non-linear problem at the current point; construct a quadratic programming sub-problem; solve the sub-problem to obtain the search direction; perform a line search to determine the step size; update the current point; check for convergence, and if not converged, repeat the above steps. According to the first step ΔV of the optimal control sequence opt and the prediction model Pred model , generate the optimal temperature control trajectory T opt : T opt (t+Δt) = Pred model (T(t), P(t), V(t), ΔV opt ).

[0048] According to one aspect of the present application, the steps for calculating the final volume regulation amount include: S41. Based on the position of the current state in the set of optimization slices, determine the dominant slice and the secondary slice of the control decision, and calculate the membership weight; S42. In the horizontal coordination layer, based on the membership weight, calculate the weighted sum of the differences between the optimal control trajectories of each slice and the current state to obtain the coordination compensation term between slices; S43. In the vertical control layer, according to the optimal control trajectory within the dominant slice, calculate the basic volume regulation amount including the proportional and differential control components; S44. Combine the coordination compensation term and the basic volume regulation amount to calculate the final volume regulation amount.

[0049] Specifically, based on the position of the current state in the set of optimization slices Ω opt , determine the dominant slice Ω dominant and the secondary slice Ω secondary, calculate the membership weight W belongs : W belongs (i) = exp(-d(S current , Ω i ) / σ d ); where d is the distance from the state point S current to the slice Ω i , and σ d is the distance normalization parameter. In the horizontal coordination layer, calculate the coordination compensation term Δ coordination : Δ coordination = Σ[W belongs (i)·(T opt (i) - T current )]; This can ensure a smooth control transition in the slice boundary region. In the vertical control layer, according to the optimal control trajectory T dominant within the dominant slice Ω opt , calculate the basic volume regulation amount ΔV base : ΔV base = K P ·(T opt - T current ) + K D ·d(T current ) / dt; where K P and K D are the proportional and differential control coefficients. Combining the horizontal layer coordination compensation term Δ coordination and the vertical layer basic regulation amount ΔV base , calculate the final volume regulation amount ΔV final : ΔV final = ΔV base + α·Δ coordination ; where α is the coordination compensation weight coefficient. Convert the final volume regulation amount ΔV final into the control instruction Ctrl command for the foldable rigid material, including the stretching / shrinking direction and rate, complete the core control algorithm of this scheme, and achieve the precise control of the temperature control medium interlayer, so as to achieve the purpose of actively regulating the temperature of the gas storage reservoir: Ctrl command = {Direction(ΔV final ), Magnitude(ΔV final ), Rate(ΔV final )}; where Direction represents the volume change direction, Magnitude represents the change amplitude, and Rate represents the change rate. This control instruction directly acts on the driving mechanism of the foldable rigid material to achieve precise regulation of the volume of the temperature control medium interlayer.

[0050] This embodiment realizes high-precision active control of the temperature of the air storage reservoir in a compressed air energy storage system. In particular, through a slicing and decomposition hierarchical control architecture, it solves the problem of the non-linear relationship between the volume adjustment of the foldable rigid material and the temperature of the air storage reservoir, while optimizing the energy efficiency and improving the overall performance of the system.

[0051] According to one aspect of the present application, the step of converting the final volume regulation amount into a control instruction for the foldable rigid material includes: Decompose the final volume regulation amount into a direction component and an amplitude component; Based on the amplitude component, considering the mechanical properties of the foldable rigid material, calculate the optimal driving rate. The optimal driving rate uses a piecewise function to ensure high precision during small adjustments and high response speed during large adjustments; Adjust the driving parameters according to the current state and volume change rate of the temperature control medium sandwich to avoid oscillation; Based on the material properties and mechanical actuator parameters, convert the direction component, amplitude component, and driving rate into a control instruction including execution timing information.

[0052] Specifically, decompose the final volume regulation amount ΔV final into a direction component Dir v and an amplitude component Mag v : Dir v = sign(ΔV final ); Mag v = |ΔV final |; Based on the amplitude component Mag v , considering the mechanical properties of the foldable rigid material, calculate the optimal driving rate Rate v : if Mag v < V threshold_small : Rate v = Rate min + (Rate max -Rate min )·(Mag v / V threshold_small ) 0.5 ; else: Rate v = Rate max ; This piecewise function ensures a lower rate for small adjustments to improve precision and a higher rate for large adjustments to improve response speed. Considering the current state V chamber and the change rate dV chamber / dt of the temperature control medium sandwich, adjust the driving parameters to avoid oscillation: if Dir v ·dV chamber / dt < 0 && |dV chamber / dt| > Oscillationthreshold : Rate v = Rate v · Damping factor ; where Oscillation threshold is the oscillation judgment threshold, and Damping factor is the damping coefficient (usually taken as 0.6 - 0.8).

[0053] Based on the material properties and mechanical actuator parameters, convert the direction component Dir v , amplitude component Mag v and the optimal drive rate Rate v into a specific drive signal Drive signals : Drive signals = f convert (Dir v , Mag v , Rate v , Actuator params ); where f convert is the conversion function, and Actuator params is the actuator parameter set. Generate the final control instruction Ctrl command , including execution timing information: Ctrl command = {Direction: Dir v , Magnitude: Mag v , Rate: Rate v , Timing: Current time , Execution window : [Current time , Current time + Execution duration , Drive signals : Drive signals}; where Execution duration is the execution duration, calculated based on Mag v and Rate v ; Current time is the current time; Timing represents time; Execution window is the execution time window. The final control instruction Ctrl command is used as the final output and sent to the drive system of the foldable rigid material.

[0054] In a specific embodiment of the present application, taking the active temperature control of the air storage reservoir in a compressed air energy storage system as the object, the specific implementation process of the proposed method is described in detail in this embodiment. The volume of the air storage reservoir is 80,000 cubic meters, and 84 temperature sensors and 36 pressure sensors are arranged inside. The adjustable range of the temperature control medium interlayer composed of foldable rigid materials is 5,000 - 9,000 cubic meters. The specific steps are as follows: Step 1: Multi-point temperature and pressure data acquisition and preprocessing.

[0055] In practical applications, real-time data of the temperature sensor array and pressure sensors distributed in the air storage reservoir, as well as the current state parameters of the foldable rigid materials, are obtained at a sampling frequency of 10 Hz.

[0056] 1.1 Calculation of data reliability score.

[0057] For the temperature sensor data, calculate the reliability score R sensor (i), the formula is: R sensor (i) = exp(-‖(T i - T predicted (i))‖ 2 / σ T 2 );where T i is the measured value of the i-th temperature sensor, in °C; T predicted (i) is the predicted temperature value at the i-th position based on historical data and physical models, in °C; σ T is the temperature fluctuation tolerance parameter, with a value of 1.2 °C; i is the sensor index, ranging from 1 to 84; exp is the natural exponential function. For example, if the measured value of a temperature sensor T3 = 35.6 °C and the predicted value T predicted (3) = 36.1 °C, then: R sensor (3) = exp(-‖(35.6 - 36.1)‖ 2 / 1.2 2 ) =exp(-0.25 / 1.44) = exp(-0.174) = 0.840; Similarly, for the pressure sensor data, calculate the reliability score R sensor (j), the formula is: R sensor (j) = exp(-‖(P j - P predicted (j))‖ 2 / σ P 2 );where P j is the measured value of the j-th pressure sensor, in MPa; P predicted(j) is the predicted pressure value at the j-th position based on historical data and physical models, with the unit of MPa; σ P is the pressure fluctuation tolerance parameter, with a value of 0.06 MPa; j is the sensor index, ranging from 1 to 36. For example, if the measured value of a pressure sensor P5 = 7.82 MPa and the predicted value P predicted (5) = 7.85 MPa, then: R sensor (5) = exp(-‖(7.82 - 7.85)‖ 2 / 0.06 2 ) = exp(-0.0009 / 0.0036) = exp(-0.25) = 0.779.

[0058] 1.2. Data filtering and compensation.

[0059] Interpolate and correct the sensor data with a reliability score lower than the threshold of 0.6: T adjusted (i) = T i , if R sensor (i) ≥ 0.6; T adjusted (i) = Σ[w k ·T k / Σ[w k , if R sensor (i)<0.6; where w k is the weight of adjacent reliable sensors, which is inversely proportional to the distance d k between sensors. The calculation formula is w k = 1 / d k 2 ; d k is the Euclidean distance between the i-th sensor and the k-th sensor, with the unit of meter; T k is the measured value of the k-th sensor, with the unit of °C. For example, if the reliability score of sensor T 12 R sensor (12) = 0.45 < 0.6, and the measured values of its three adjacent reliable sensors T 11 , T 13 , T 14 are 34.8 °C, 35.2 °C, and 34.6 °C respectively, and the distances from T 12 are 1.5 m, 2.0 m, and 2.5 m respectively, then: w 11 = 1 / 1.5 2 =0.444; w 13 = 1 / 2.0 2 = 0.25; w 14 = 1 / 2.5 2 = 0.16 T adjusted(12) = (0.444×34.8 + 0.25×35.2 + 0.16×34.6) / (0.444 + 0.25 + 0.16) = 34.88 °C. Similarly, process the pressure data to obtain the adjusted temperature-pressure dataset T adjusted and P adjusted .

[0060] 1.3 Calculation of the volume of the temperature-regulating medium interlayer

[0061] Extract the set of folding angles θ fold from the state parameters S of the foldable rigid material fold = {θ1, θ2, ..., θ n}, the set of rigid node position coordinates P nodes = {P1, P2, ..., P n+1}, and the material elastic parameter E material , and construct a three-dimensional structure model M of the foldable material structure . Based on this model, calculate the volume V of the current temperature-regulating medium interlayer chamber : V chamber = Σ i V segment (i); where V segment (i) is the interlayer volume unit formed between two adjacent rigid nodes i and i + 1, and the calculation formula is V segment (i) = f volume (P nodes (i), P nodes (i + 1), θ fold (i)); f volume is the volume calculation function, which considers the non-linear relationship between the folding angle and the node spacing; i is the node index, ranging from 1 to n. In specific implementation, assume there are 50 rigid nodes, the average node spacing is 1.2 m, and the average current folding angle is 48°, and calculate to get V chamber = 7,350 cubic meters

[0062] According to the S data in the time series fold , calculate the volume change rate dV of the temperature-regulating medium interlayer chamber / dt: dV chamber / dt = Σ i ΨV segment (i) / Ψθ fold (i) · dθ fold (i) / dt + Σ i ΨV segment (i) / ΨP nodes (i) · dP nodes ; where ΨVsegment (i) / Ψθ fold (i) is the partial derivative of volume with respect to the folding angle; ΨV segment (i) / ΨP nodes (i) is the partial derivative of volume with respect to the node position; dθ fold (i) / dt is the change rate of the folding angle, with the unit of ° / s; dP nodes (i) / dt is the change rate of the node position, with the unit of m / s. In this example, the current dV chamber / dt = -8.5 cubic meters per second.

[0063] Step Two: Dynamic partitioning and boundary determination of the phase space slices.

[0064] 2.1 Construction of the three-dimensional phase space.

[0065] Based on the temperature-pressure distribution fields T field , P field and the volume parameter V chamber construct a three-dimensional phase space representation Phase Space : Phase Space = {T field , P field , V chamber}; where T field is the temperature distribution field, constructed by interpolation from the adjusted temperature data T adjusted ; P field is the pressure distribution field, constructed by interpolation from the adjusted pressure data P adjusted ; V chamber is the volume of the temperature-regulating medium interlayer.

[0066] 2.2 Initial slicing of the phase space.

[0067] Based on Phase Space construct a multi-dimensional state vector S vector : S vector (i) = [T field (i), P field (i), V chamber , dT field (i) / dt, dP field (i) / dt, dV chamber / dt]; where i is the index of the spatial discrete points; T field (i) is the temperature value at the i-th spatial point, with the unit of °C; P field (i) is the pressure value at the i-th spatial point, with the unit of MPa; dT field (i) / dt is the temperature change rate, with the unit of °C / s; dP field(i) / dt is the pressure change rate, with the unit of MPa / s; dV chamber / dt is the volume change rate, with the unit of cubic meters / s.

[0068] For S vector perform dimensionality reduction using principal component analysis (PCA): S reduced = PCA(S vector , n components = 4); where n components is the number of principal components to be retained, taking the value of 4; PCA is the principal component analysis function; S reduced is the state vector after dimensionality reduction. In this example, 4 principal components are retained, and the cumulative explained variance ratio reaches 97.3%.

[0069] Cluster S reduced using the improved K-means++ clustering algorithm, and introduce the thermodynamic behavior similarity metric Sim thermo : Sim thermo (x, y) = w1·‖x （t） - y （t） ‖ 2 + w2·‖x （p） - y （p） ‖ 2 + w3·‖f predict (x) - f predict (y)‖ 2 ; where x （t） , y （t） represent the temperature components of the state vectors x and y; x （p） , y （p） represent the pressure components of the state vectors x and y; f predict represents the function for predicting the future state; w1, w2, w3 are weight coefficients, taking the values of 0.4, 0.3, 0.3 respectively; ‖·‖ 2 represents the squared Euclidean distance. Execute the clustering process, select k = 5 clusters, and obtain the cluster centers C clusters and the state point membership labels L labels , and construct the initial slice set Ω init = {Ω1, Ω2,..., Ω5}.

[0070] 2.3. Self-optimizing adjustment of slice boundaries.

[0071] For each pair of adjacent slices Ω init in the initial slice set Ω i and Ω j , determine the boundary point set B points (i, j): B points (i, j) = {x | x ∈ Ω iand Ξy∈Ω j such that ‖x - y‖ < ε}; where x is the state point in the slice Ω i ; y is the state point in the slice Ω j ; ε is the proximity threshold with a value of 0.2; ‖x - y‖ is the distance between state points. Calculate the difference in local performance metrics ΔPerf(i, j, p) of the boundary points: ΔPerf(i, j, p) = Perf(p, Ω i ) - Perf(p, Ω j ); where Perf(p, Ω) represents the performance metric of point p in the slice Ω, and the calculation formula is Perf(p, Ω) = -w1·‖T(p) - T target ‖ 2 - w2·E consumption (p, Ω); w1 and w2 are weight coefficients with values of 0.7 and 0.3 respectively; T(p) is the temperature of point p in °C; T target is the target temperature in °C; E consumption (p, Ω) is the energy consumption evaluation metric.

[0072] Based on the difference in local performance metrics ΔPerf(i, j, p), determine the optimal belonging slice Best slice (p) of the boundary point: Best slice (p) = arg max Ω∈{Ωi，Ωj} Perf(p, Ω); where arg max represents the slice Ω that maximizes the performance metric Perf(p, Ω). Calculate the boundary movement vector B adjust (i, j): B adjust (i, j) = μ·[|{p∈B points (i, j) | Best slice (p)=Ω j}| - |{p∈B points (i, j) | Best slice (p)=Ω i}|]·n ij ; where μ is the boundary adjustment coefficient with a value of 0.15; |·| represents the set cardinality; n ij is the unit normal vector pointing from slice i to slice j. Apply the boundary movement vector B adjust to move the slice boundary and update the slice set to obtain Ω opt = {Ω1', Ω2', ..., Ω5'}.

[0073] Step Three: Generate the optimal temperature control trajectory within the slice.

[0074] 3.1. Construct the local thermodynamic model.

[0075] For each slice Ω in the optimized slice set Ω opt a local thermodynamic model M i is constructed as follows local : dT / dt = f T (T, P, V, dV / dt, θ); dP / dt = f P (T, P, V, dV / dt, θ); where T is the temperature in °C; P is the pressure in MPa; V is the volume in cubic meters; dV / dt is the volume change rate in cubic meters per second; θ is the model parameter vector including the heat transfer coefficient, compression coefficient, etc.; f T and f P are functions describing the temperature and pressure changes. Based on historical data and the current state, parameter estimation is performed to update θ updated : θ updated = θ current + η·(S actual - S predicted (θ current )); where η is the learning rate with a value of 0.1; θ current is the current parameter vector; S actual is the actually observed state change; S predicted is the state change predicted by the model.

[0076] 3.2. Multi-objective optimization to generate the control trajectory.

[0077] Based on the current state and the updated parameter θ updated a prediction model Pred model is constructed to predict the future state trajectory under different control inputs.

[0078] Define the temperature control error objective function J temp : J temp (ΔV) = ∫[t, t + H] w(τ)·‖T predicted (τ, ΔV) - T ideal (τ)‖ 2 dτ; where H is the prediction horizon length with a value of 60 seconds; w(τ) is the time weight function that decreases as τ increases; T predicted is the model-predicted temperature in °C; T ideal is the ideal temperature trajectory in °C; ΔV is the control input sequence.

[0079] Define the energy consumption objective function J energy : J energy (ΔV) = ∫[t, t + H] [k1·|dV(τ) / dt| 2 +k2·|d2 V(τ) / dt 2 |] dτ; where k1 and k2 are energy consumption coefficients, with values of 0.05 and 0.02 respectively; dV(τ) / dt is the volume change rate, in cubic meters per second; d 2 V(τ) / dt 2 is the volume change acceleration, in cubic meters per second 2 .

[0080] Construct a comprehensive multi-objective optimization problem: ΔV sequence = arg min{w T ·J temp (ΔV) + w E ·J energy (ΔV)}; s.t. |dV / dt| ≤ V rate_max ; |V| ∈ [V min , V max ; |d 2 V / dt 2 | ≤ a max ; where w T 、w E are the weights of temperature control and energy consumption, with values of 0.8 and 0.2 respectively; V rate_max is the maximum volume change rate, with a value of 20 cubic meters per second; V min 、V max are the volume range limits, with values of 5,000 cubic meters and 9,000 cubic meters respectively; a max is the maximum acceleration limit, with a value of 5 cubic meters per second 2 ; arg min represents the control input sequence that minimizes the objective function. Use the improved sequential quadratic programming (SQP) algorithm to solve the optimization problem and obtain the first step ΔV opt = -12.5 cubic meters. According to the first step ΔV opt of the optimal control sequence and the prediction model Pred model , generate the optimal temperature control trajectory T opt .

[0081] Step 4. Horizontal-vertical hybrid decision-making and control quantity calculation.

[0082] 4.1. Hybrid decision-making calculation.

[0083] Based on the position of the current state in Ω opt , determine the dominant slice Ω dominant = Ω3' and the secondary slices Ω secondary = {Ω2', Ω4'}, and calculate the membership weight W belongs : Wbelongs (i) = exp(-d(S current , Ω i ) / σ d ); where d is the distance from the state point S current to the slice Ω i ; σ d is the distance normalization parameter with a value of 0.5; exp is the natural exponential function. It is calculated that W belongs = {0.02, 0.28, 0.61, 0.09, 0.00}.

[0084] At the horizontal coordination layer, calculate the coordination compensation term Δ coordination : Δ coordination = Σ[W belongs (i)·(T opt (i) - T current )]; where T opt (i) is the optimal control trajectory within the slice Ω i ; T current is the current temperature in °C. It is calculated that Δ coordination = 0.8 °C.

[0085] At the vertical control layer, according to the optimal control trajectory T dominant within the dominant slice Ω opt , calculate the basic volume regulation amount ΔV base : ΔV base = K P ·(T opt - T current ) + K D ·d(T current ) / dt; where K P , K D are the proportional and differential control coefficients with values of 15 m³ / °C and 30 m³·s / °C respectively; d(T current ) / dt is the current temperature change rate in °C / s. Assume T opt = 36.2 °C, T current = 35.8 °C, d(T current ) / dt = 0.04 °C / s, then: ΔV base = 15×(36.2 - 35.8) + 30×0.04 = 6.0 + 1.2 = 7.2 m³.

[0086] 4.2. Control instruction conversion.

[0087] Combine the horizontal layer coordination compensation term Δ coordination and the vertical layer basic regulation amount ΔV base, calculate the final volume regulation amount ΔV final : ΔV final = ΔV base + α·Δ coordination ; where α is the coordination compensation weight coefficient, with a value of 10 cubic meters / °C. Calculate ΔV final = 7.2 + 10×0.8 = 15.2 cubic meters. Decompose ΔV final into the direction component Dir v and the amplitude component Mag v : Dir v = sign(ΔV final ) = +1; Mag v = |ΔV final | = 15.2 cubic meters. Based on the amplitude component Mag v , considering the mechanical properties of the foldable rigid material, calculate the optimal driving rate Rate v : if Mag v <V threshold_small : Rate v = Rate min + (Rate max -Rate min )·(Mag v / V threshold_small ) 0.5 ; else: Rate v = Rate max ; where V threshold_small is the small adjustment threshold, with a value of 10 cubic meters; Rate min , Rate max are the minimum and maximum driving rates, with values of 2 cubic meters / s and 15 cubic meters / s respectively. Since Mag v = 15.2>V threshold_small = 10, so Rate v = Rate max =15 cubic meters / s.

[0088] Considering the current state V chamber and dV chamber / dt of the temperature regulating medium interlayer, adjust the driving parameters to avoid oscillation: ifDir v ·dV chamber / dt<0&&|dV chamber / dt|>Oscillation threshold : Rate v = Rate v ·Damping factor ; where Oscillationthreshold is the oscillation judgment threshold, with a value of 5 m³ / s; Damping factor is the damping coefficient, with a value of 0.7. Since Dir v = +1, dV chamber / dt = -8.5 m³ / s, Dir v ·dV chamber / dt = -8.5 < 0, and |dV chamber / dt| = 8.5 > 5, so damping needs to be applied: Rate v = 15 × 0.7 = 10.5 m³ / s. Finally, the control instruction Ctrl command is generated, which contains direction, amplitude, rate, and execution timing information, and is directly sent to the drive system of the foldable rigid material.

[0089] The application of this embodiment in the active regulation of the temperature of the gas storage reservoir in a compressed air energy storage system achieves the following technical effects: The temperature fluctuation in the gas storage reservoir is controlled within the range of ±0.5°C, while the fluctuation range of the traditional method is ±1.8°C; compared with the traditional PID control method, the energy consumption is reduced by 32.5%; in the case of rapid changes in working conditions, the response time is shortened by 45.3%; the volume utilization rate of the gas storage reservoir is increased by 15.2%, and the overall energy efficiency level of the system is improved by 8.7%. This embodiment successfully solves the problem of the non-linear relationship between the volume adjustment of the foldable rigid material and the temperature of the gas storage reservoir through a slice decomposition hierarchical trajectory compensation control architecture, optimizes the energy efficiency while ensuring the temperature control accuracy, and improves the system performance.

[0090] The multi-point temperature and pressure data filtering and compensation method based on reliability scoring of the present invention calculates the deviation between each sensor data and the predicted value in real time, assigns a reliability score to each sensor, and performs data filtering and compensation according to the score, effectively solving the problem that it is difficult to accurately grasp the overall temperature distribution in single-point monitoring by traditional methods. This makes the construction of the temperature and pressure field more accurate and reliable. The measurement error is reduced from ±2.5°C of the traditional method to ±0.8°C, and the accuracy is improved by more than 68%. It enables the system to maintain high-precision temperature monitoring even when individual sensors fail or abnormal readings occur, laying a solid foundation for subsequent precise control, and is especially suitable for the working condition environment with uneven temperature distribution in large spaces such as gas storage caverns. By constructing a mathematical model of the geometric parameters of the material and the volume of the temperature-regulating medium sandwich layer, a direct correlation between the state of the foldable material and the temperature control effect is established. This enables the system to accurately calculate the volume of the temperature-regulating medium sandwich layer and its change rate. The response time is shortened from 1.8 seconds of the traditional method to 0.6 seconds, an increase of approximately 67%. By establishing a mapping relationship between geometric parameters such as the folding angle and node position of the foldable rigid material and the sandwich layer volume, the system can quickly determine a suitable material state adjustment scheme according to the required temperature change, avoiding blind trial and error and over-adjustment in traditional methods, improving the accuracy and response speed of temperature control, and enhancing the adaptability of the system under rapidly changing load conditions. By introducing a measure of thermodynamic behavior similarity to intelligently divide the three-dimensional temperature-pressure-volume phase space, the problem that traditional single control strategies are difficult to adapt to different working conditions is solved. First, a multi-dimensional state vector containing state variables and their change rates is constructed, and then the phase space is divided through principal component analysis dimensionality reduction and an improved clustering algorithm. The control accuracy is improved by 46%, from ±0.92°C to ±0.5°C. Different from traditional experience-based division methods, by predicting the future state of the system and considering the similarity of thermodynamic behavior, grouping of states with similar thermodynamic behavior is achieved, enabling the control strategy to be optimized for regions with different thermodynamic characteristics, improving the control accuracy and adaptability. By calculating the difference in performance indicators of boundary points in different slices and dynamically adjusting the slice boundaries, the problem of unstable control in the boundary region of fixed slice division is solved. First, the set of boundary points of adjacent slices is determined, and then based on the comprehensive performance indicators of temperature control accuracy and energy efficiency, the performance differences of boundary points in different slices are calculated and the boundaries are dynamically adjusted. Compared with the fixed division method, the control stability is improved by 58%, and the temperature fluctuation in the boundary region is reduced from ±1.2°C to ±0.5°C. This enables the system to automatically optimize the slice structure according to the actual working conditions, avoiding control jitter in the slice boundary region of traditional methods, and achieving global smooth control, which is especially suitable for the actual application scenario where the temperature distribution in the gas storage cavern changes dynamically over time.By simultaneously considering two objectives of temperature control error and energy consumption, while ensuring control accuracy, minimizing energy consumption, it solves the problem that traditional methods only focus on a single temperature control objective and ignore energy efficiency; constructs a prediction model, defines two objective functions of temperature control error and energy consumption, and under the consideration of various physical constraints, uses an improved sequential quadratic programming algorithm to solve the optimal control sequence. In practical applications, on the premise of maintaining temperature control accuracy, the energy consumption is reduced by 32.5%. It not only solves the contradiction between temperature control and energy efficiency, but also makes the control strategy meet the actual engineering requirements and achieve economical and efficient operation by comprehensively considering physical constraints such as volume change rate, volume range, and acceleration limit, improving the overall efficiency of the compressed air energy storage system. Through the combination of coordination between slices and control within slices, the unity of global consistency and local accuracy is achieved, and the limitations of traditional single-level control strategies in complex systems are solved; first, determine the dominant slice and secondary slice of the current state, calculate the membership degree weight, then calculate the coordination compensation term between slices in the horizontal layer, calculate the basic volume regulation amount in the vertical layer, and finally combine the two to obtain the final regulation amount. The system response time is reduced by 45.3%, and the control smoothness is improved by 53.7%. It fully utilizes the advantages of different slice control strategies, avoids control jumps at slice boundaries, and realizes precise control with global smooth transition, which is especially suitable for the temperature precise regulation requirements of large and complex systems such as gas storage caverns. By distinguishing between small adjustment and large adjustment scenarios, different rate calculation strategies are adopted respectively, and combined with an anti-oscillation mechanism, it solves the problem that it is difficult to balance accuracy and speed in traditional fixed-rate control under different adjustment amplitudes; decomposes the volume regulation amount into direction and amplitude components, calculates the optimal driving rate using a piecewise function according to the amplitude size, and adjusts the driving parameters according to the current volume change state to avoid oscillation. The accuracy of small adjustments is improved by 63.2%, the response time of large adjustments is shortened by 42.7%, and the system oscillation is reduced by 78.6%. It enables the driving of foldable rigid materials to ensure both high accuracy of small adjustments and fast response requirements of large adjustments, while effectively suppressing system oscillation, improving the overall performance and stability of the gas storage cavern temperature regulation system.

[0091] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all belong to the protection scope of the present invention.

Claims

1. An active temperature control method for the gas storage reservoir of a compressed air energy storage system, characterized in that It includes: Collect the original data of multi-point temperature and pressure in the gas storage cavern and the state parameters of the foldable rigid material. After preprocessing, construct the temperature and pressure distribution field and calculate the volume of the temperature control medium interlayer; accordingly, perform slice division and dynamically optimize the slice boundary to obtain an optimized slice set; For each slice in the optimized slice set, generate an optimal control trajectory considering temperature control accuracy and energy consumption through a local thermodynamic model; According to the optimal control trajectory and the position of the current state in the optimized slice set, adopt a hybrid decision-making mechanism combining horizontal coordination and vertical control to calculate the final volume regulation amount and convert it into a control instruction for the foldable rigid material.

2. The method according to claim 1, characterized in that, The steps of constructing the temperature and pressure distribution field and calculating the volume of the temperature control medium interlayer include: Read the data of temperature and pressure sensors, and calculate the reliability score based on the deviation between the measured value and the predicted value; Filter and compensate the data according to the reliability score to obtain an adjusted temperature and pressure data set, and construct the temperature and pressure distribution field based on this; Obtain the state parameters of the foldable rigid material, and calculate the volume of the temperature control medium interlayer in combination with the thermodynamic equation.

3. The method according to claim 2, characterized in that, The steps of calculating the reliability score and obtaining the adjusted temperature and pressure data set include: Calculate the reliability score of the temperature sensor through the exponential function of the deviation between the measured temperature value of the temperature sensor and the temperature value predicted based on historical data and physical models; Calculate the reliability score of the pressure sensor through the exponential function of the deviation between the measured pressure value of the pressure sensor and the pressure value predicted based on historical data and physical models; For the sensor data with a reliability score lower than the preset threshold, perform interpolation correction based on the distance weight of adjacent reliable sensors to generate an adjusted temperature and pressure data set.

4. The method according to claim 2, wherein The steps of calculating the volume of the temperature control medium interlayer include: Extract the folding angle set, the rigid node position coordinate set, and the material elastic parameters from the state parameters of the foldable rigid material to construct a three-dimensional structure model of the foldable material; accordingly, calculate the sum of the interlayer volume units formed between adjacent rigid nodes to obtain the current volume of the temperature control medium interlayer; According to the state parameters of the foldable rigid material in the time series, calculate the movement speed of the foldable rigid material; and accordingly calculate the volume change rate of the temperature control medium interlayer.

5. The method according to claim 1, wherein The steps of obtaining the optimized slice set include: Based on the temperature and pressure distribution field and the volume of the temperature control medium interlayer, construct a three-dimensional phase space containing complete thermodynamic information; perform initial slice division on it to obtain an initial slice set; Based on the initial slice set, calculate the distance between the state points in each slice and the center point of the slice to obtain the consistency index of the internal state change; combine it with the boundary characteristics between adjacent slices, and perform self-optimization adjustment of the slice boundary to obtain the optimized slice set.

6. The method according to claim 5, characterized in that, The steps of obtaining the initial slice set include: Based on the three-dimensional phase space, construct a multi-dimensional state vector containing state variables and their change rates and perform dimensionality reduction processing to obtain a reduced-dimensional state vector; Use the K-means++ clustering algorithm for measuring thermodynamic behavior similarity to cluster the reduced-dimensional state vector to obtain a clustering result; accordingly construct the initial slice set; where the thermodynamic behavior similarity measurement comprehensively considers the temperature component, the pressure component, and the predicted value of the future state.

7. The method according to claim 5, wherein The steps of performing self-optimizing adjustment of the slicing boundary to obtain the optimized slice set include: Determine the set of boundary points between adjacent slices and calculate the difference in local performance indicators of the boundary points; where the set of boundary points are the state points within the slice that are close to other slices; the local performance indicators comprehensively consider the temperature control accuracy and energy efficiency; According to the change trend of the belonging of the boundary points statistically based on the difference in local performance indicators, determine the optimal belonging slice of the boundary points and calculate the movement vector of the slicing boundary; accordingly update the slicing boundary to obtain the optimized slice set.

8. The method according to claim 1, characterized in that The steps of generating the optimal control trajectory include: Construct a local thermodynamic model considering the dynamic characteristics of the foldable rigid material to describe the relationship between temperature, pressure and the volume change of the temperature control medium interlayer; based on the pre-stored historical data and the current state, perform parameter estimation and update on it to obtain the updated parameters; Use the updated parameters and the current state to calculate the ideal state trajectory and the energy consumption evaluation index; accordingly, generate the optimal control trajectory through multi-objective optimization.

9. The method according to claim 8, wherein The steps of generating the optimal control trajectory through multi-objective optimization include: Construct a prediction model based on the current state and the updated parameters; accordingly calculate the integral of the weighted deviation between the predicted temperature and the ideal temperature trajectory to obtain the temperature control error objective function; Based on the prediction model, calculate the weighted integral of the volume change rate and its derivative to obtain the energy consumption objective function; Based on the temperature control error and the energy consumption objective function, construct a comprehensive multi-objective optimization problem including the volume change rate, volume range and acceleration limit and solve it to obtain the optimal control input sequence and the corresponding optimal control trajectory.

10. The method according to claim 1, wherein The steps of calculating the final volume regulation amount include: Based on the position of the current state in the optimized slice set, determine the dominant slice and the secondary slice of the control decision and calculate the belonging degree weight; In the horizontal coordination layer, based on the belonging degree weight, calculate the weighted sum of the differences between the optimal control trajectories of each slice and the current state to obtain the coordination compensation term between slices; In the vertical control layer, according to the optimal control trajectory within the dominant slice, calculate the basic volume regulation amount including proportional and differential control components; Combine the coordination compensation term and the basic volume regulation amount to calculate the final volume regulation amount.

Images