Active temperature control method for compressed air energy storage system gas storage reservoir

Abstract

This invention provides a method for actively controlling the temperature of a compressed air energy storage system's gas reservoir. This method, which belongs to the field of compressed air energy storage, includes the following steps: collecting temperature and pressure data from multiple points in the gas reservoir and constructing a temperature and pressure distribution field; slicing the temperature-pressure-volume three-dimensional phase space and dynamically optimizing the slice boundaries; generating an optimal temperature control trajectory for each slice that takes energy efficiency into account; and employing a horizontal-vertical hybrid decision mechanism to calculate the final volume control value and convert it into control instructions for a foldable rigid material. This invention achieves high-precision gas reservoir temperature control through a slice-decomposition hierarchical trajectory compensation control architecture, while simultaneously optimizing energy efficiency. This solves the temperature-volume nonlinearity problem, which is difficult to address with traditional methods, and improves the gas reservoir's volume utilization.

Description

Technical Field

[0001] The present invention relates to a compressed air energy storage technology, and in particular to a method for actively controlling the temperature of a gas storage reservoir of a compressed air energy storage system. Background Art

[0002] Compressed air energy storage systems, as a large-scale, long-term energy storage technology, play an important role in the development of renewable energy and grid peak regulation. With the continuous increase in the grid-connected capacity of intermittent renewable energy sources such as wind and solar power, the peak-to-valley difference and frequency regulation and peak-shaving pressure faced by the power grid are increasing. Compressed air energy storage systems, due to their advantages such as large scale, low cost, and good environmental performance, have become a key technical path for solving the problem of renewable energy grid connection and absorption. However, temperature changes during the operation of compressed air energy storage systems significantly affect their energy storage efficiency and gas utilization rate. Therefore, active and precise control of the gas storage temperature is of great significance to improving system efficiency and reducing operating costs.

[0003] Currently, compressed air energy storage (CAES) reservoir temperature control primarily employs two methods: passive control, which reduces heat exchange with the outside world by adding insulation to the reservoir; and simple active control, which primarily includes technologies such as heat recovery, multi-stage compression-heat exchange, and hot oil circulation. Typical heat recovery technologies (such as adiabatic CAES) store the heat generated during the compression process in a heat transfer medium, which is then used to heat the expanding air during discharge, improving system cycle efficiency. Multi-stage compression-heat exchange technology, by placing heat exchangers between the compressors, controls the temperature rise during the compression process and reduces compression power consumption. Hot oil circulation technology regulates the gas temperature within the reservoir by providing hot oil circulation channels around the reservoir walls. In recent years, the application of phase change materials in energy storage has also made progress, with some studies attempting to apply phase change materials to temperature control in CAES systems.

[0004] However, existing technologies still face some key control challenges that need to be addressed. For example, in structures that use foldable rigid materials to adjust the volume of the temperature-regulating medium interlayer, intelligently adjusting the control strategy based on different operating conditions to achieve precise temperature control while minimizing energy consumption is a key technical challenge that needs to be addressed. Summary of the Invention

[0005] Purpose of the invention: To provide a method for actively controlling the temperature of a compressed air energy storage system gas storage reservoir, in order to solve at least one technical problem existing in the prior art.

[0006] Technical solution: A method for actively controlling the temperature of a compressed air energy storage system gas storage reservoir, including:

[0007] The original temperature and pressure data at multiple points in the gas storage reservoir and the state parameters of the foldable rigid material are collected. After preprocessing, the temperature and pressure distribution field is constructed and the volume of the temperature-regulating medium interlayer is calculated. Based on this data, slice division is performed and the slice boundaries are dynamically optimized to obtain an optimized slice set.

[0008] For each slice in the optimized slice set, the optimal control trajectory considering temperature control accuracy and energy consumption is generated through the local thermodynamic model;

[0009] According to the optimal control trajectory and the position of the current state in the optimized slice set, a hybrid decision-making mechanism combining horizontal coordination and vertical control is adopted to calculate the final volume control amount and convert it into control instructions for the foldable rigid material.

[0010] Beneficial effects: The present invention improves the accuracy and response speed of temperature control, enhances the system's adaptability under conditions of rapid load changes, and optimizes energy efficiency; improves the overall efficiency of the compressed air energy storage system, solves the problem of temperature-volume nonlinear relationship that is difficult to deal with by traditional methods, and improves the volume utilization rate of the gas storage reservoir; realizes precise control of smooth transition across the entire region, and improves the overall performance and stability of the gas storage reservoir temperature control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A structural diagram of a system for actively controlling the temperature of a compressed air energy storage reservoir in an embodiment of the present application.

[0012] Figure 2 A flowchart of the steps of a method for actively controlling the temperature of a compressed air energy storage system gas storage reservoir provided in an embodiment of the present application.

[0013] Figure 3 A flowchart of the steps for constructing a temperature and pressure distribution field and calculating the volume of a temperature-regulating medium interlayer provided in an embodiment of the present application.

[0014] Figure 4 A flowchart of the steps for calculating the reliability score and obtaining the adjusted temperature and pressure data set provided in an embodiment of the present application.

[0015] Figure 5 A flow chart of the steps for calculating the volume of the temperature regulating medium interlayer provided in an embodiment of the present application.

[0016] The accompanying drawings are marked as follows: 1. gas storage tank; 2. temperature regulating medium interlayer; 3. foldable rigid material; 4. temperature circulation fan; 5. temperature detection system; 6. pressure gauge. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0018] It should be noted that to clearly illustrate the steps of this application, serial numbers are assigned to each step in the specification. These serial numbers are for illustrative purposes only and do not limit the order in which the steps must be executed. In actual operation, depending on the technical requirements of the specific implementation scenario, the steps may be executed in a different order than shown in the specification, and in some cases, parallel processing between steps may be implemented.

[0019] During the research process, it was found that in the process of designing intelligent control strategies, it is necessary to solve nonlinear control, non-uniform control and multi-objective control. Specifically, first, in the temperature control system of the gas storage reservoir using foldable rigid materials, there is a complex nonlinear relationship between temperature control and volume control. The traditional PID control method is difficult to effectively deal with this nonlinear characteristic, especially when the working conditions change rapidly, the control performance is significantly reduced. Secondly, the temperature field distribution in the gas storage reservoir is uneven, 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, the existing temperature control methods focus more on a single temperature control target, ignore the optimization of energy efficiency, and fail to achieve the coordinated optimization of precise temperature control and maximum energy efficiency, resulting in the failure to fully utilize the overall performance of the system.

[0020] Specifically, for compressed air energy storage systems, it's necessary to improve the utilization rate per unit volume of the gas reservoir. Specifically, the temperature rise during the inflation process should be minimized, and the final inflation temperature should be low, ensuring that the maximum amount of gas can be charged into the reservoir. Furthermore, the temperature drop during the deflation process should be minimized, and the final deflation temperature should be high, ensuring that the maximum amount of gas can be discharged. To this end, a system for actively controlling the temperature of the gas reservoir in a compressed air energy storage system is provided.

[0021] like Figure 1 As shown in the figure, the structure of the temperature control system of the compressed air energy storage system gas storage reservoir mainly includes the gas storage tank, the temperature control system, and the temperature detection system; the temperature control system mainly includes the temperature control medium interlayer, the temperature control medium, the temperature circulation fan, the volume control system, and the pressure gauge; the temperature detection system mainly includes the air temperature of the gas storage reservoir and the temperature of the temperature control medium.

[0022] The control process of the gas storage temperature control system in the compressed air energy storage system is as follows:

[0023] Gas storage inflation: During the gas storage inflation process, the air temperature inside the gas storage rises. The volume control system in the temperature 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 to drop, causing the temperature control medium to vaporize and absorb heat, thereby alleviating the rise in air temperature inside the gas storage;

[0024] High-pressure gas storage process in gas storage: During the high-pressure gas storage process, the gas storage should ensure that the pressure and temperature drop in the gas storage are minimal. Therefore, the volume control system in the temperature control system reduces the volume of the temperature control medium interlayer, causing the temperature control medium to liquefy and release heat, thereby alleviating the temperature drop in the gas storage;

[0025] Gas storage deflation process: During the deflation process, the air temperature inside the gas storage decreases, and the volume of the temperature control medium interlayer of the temperature control system is further reduced, causing the temperature control medium to liquefy and release heat, thereby regulating the air temperature inside the gas storage and reducing the temperature drop inside the gas storage;

[0026] During low-pressure gas storage, the air temperature inside the gas storage is relatively low after the gas storage is deflated. To ensure that more air can be added to the gas storage during the next cycle, the temperature inside the gas storage must be kept relatively low. The volume of the temperature-regulating interlayer in the temperature control system is increased to ensure that the air temperature inside the gas storage remains constant or fluctuates minimally.

[0027] The temperature regulating medium is a low boiling point organic matter, which is filled in the temperature regulating medium interlayer. The filled temperature regulating medium accounts for about

[0028] The interlayer is nitrogen-sealed to maintain a pressure of 0.1 MPa under rated operating conditions. The temperature-regulating medium is typically a single substance or mixture of phenols, alcohols, aldehydes, and ketones. Rated operating conditions are when the interlayer is at its minimum volume, the interlayer temperature is at ambient temperature, and the pressure within the interlayer is 0.1 MPa. The boiling point of the temperature-regulating medium under normal pressure is designed to increase by 10-15°C from the lowest temperature within the gas storage temperature range during a single cycle without a temperature control system. This ensures that the heat exchange medium can effectively recover and provide heat even when the gas storage temperature fluctuates significantly. The temperature-regulating medium interlayer can increase in volume to 1 times its rated volume. When the interlayer expands or contracts through the foldable rigid material, the pressure within the gas storage reservoir is adjusted, causing the temperature-regulating medium to vaporize or liquefy, achieving heat exchange. The temperature circulation fan in the temperature control system activates when the volume of the interlayer begins to change, intensifying the temperature change within the interlayer. A pressure gauge indicates the pressure within the interlayer. When the pressure within the gas storage reservoir exceeds the set value, a pressure relief bag is opened to ensure the safety of the interlayer. The set pressure value is generally 2 to 3 times the rated operating pressure.

[0029] In the above control system, there is still a problem of nonlinear relationship between the adjustable volume of the foldable rigid material and the temperature of the gas storage reservoir. In order to optimize energy efficiency while ensuring temperature control accuracy, an active control method needs to be adopted.

[0030] like Figure 2 As shown, according to another aspect of the present application, a method for actively controlling the temperature of a gas storage reservoir based on a compressed air energy storage system is also provided, comprising:

[0031] S1. Collecting raw temperature and pressure data at multiple points within the gas storage reservoir and state parameters of the foldable rigid material, constructing the temperature and pressure distribution field after preprocessing and calculating the volume of the temperature-regulating medium interlayer and its rate of change;

[0032] Specifically, the multi-point temperature and pressure raw data are the temperature and pressure data at different locations inside the gas storage; the temperature and pressure distribution field shows the temperature and pressure distribution in different areas inside the gas storage.

[0033] S2. Construct a three-dimensional phase space based on the temperature and pressure distribution field and the volume of the temperature-regulating medium interlayer, slice the three-dimensional phase space, and dynamically optimize the slice boundaries to obtain an optimized slice set;

[0034] Specifically, the three-dimensional phase space displays the comprehensive distribution of three parameters: temperature, pressure, and the volume of the temperature-regulating medium interlayer. This is like building a virtual 3D map, showing the temperature-pressure-medium relationship in different regions within the gas storage reservoir. By cutting out multiple distinct regions (slices) within this three-dimensional space, the system can more precisely control temperature changes, rather than using a one-size-fits-all approach.

[0035] S3, for each slice in the optimized slice set, generating an optimal control trajectory considering temperature control accuracy and energy consumption through a local thermodynamic model;

[0036] Specifically, the local thermodynamic model uses mathematical methods to describe the temperature variation pattern of the area, and can predict how to adjust the temperature to maintain accuracy without wasting energy.

[0037] S4. Based on the optimal control trajectory and the position of the current state in the optimized slice set, a hybrid decision-making mechanism combining horizontal coordination and vertical control is adopted to calculate the final volume control amount and convert it into control instructions for the foldable rigid material.

[0038] Specifically, horizontal coordination involves coordinating across different slices to ensure consistent temperature regulation across the entire gas storage facility, preventing any particular area from overheating or overcooling. Vertical control involves fine-tuning within a single slice to ensure that the temperature changes in that area adhere to the optimal control trajectory.

[0039] like Figure 3As shown, according to one aspect of the present application, the steps of constructing a temperature and pressure distribution field and calculating the volume of the temperature regulating medium interlayer include:

[0040] S11, reading temperature and pressure sensor data, and calculating a reliability score based on the deviation between the measured value and the predicted value;

[0041] S12. Filter and compensate the data according to the reliability score to obtain an adjusted temperature and pressure data set; construct a temperature and pressure distribution field based on the adjusted temperature and pressure data set;

[0042] S13. Obtain the state parameters of the foldable rigid material, and calculate the volume of the temperature regulating medium interlayer and its change rate in combination with thermodynamic equations as parameters associated with temperature and pressure changes.

[0043] like Figure 4 As 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:

[0044] Calculate the reliability score of the temperature sensor using an exponential function of the deviation between the actual temperature value measured by the temperature sensor and the temperature value predicted based on historical data and physical models;

[0045] Calculate the reliability score of the pressure sensor using an 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;

[0046] For sensor data with reliability scores lower than the preset threshold, interpolation correction is performed based on the distance weights of adjacent reliable sensors to generate an adjusted temperature and pressure dataset.

[0047] Specifically, the original data of temperature and pressure at multiple points are obtained, including the temperature sensor array distributed inside the gas storage (T1, T2, ..., T n ) and pressure sensors (P1, P2, ..., P m ) real-time data, as well as the current state data of the foldable rigid material (folding state parameter set S fold ), the sampling frequency is 10Hz. Reliability evaluation is performed on the original data of multi-point temperature and pressure, and the reliability score R of each sensor data is calculated. sensor :Temperature sensor R sensor (i) = exp(-|(T i -T predicted (i))| 2 / σ T 2 ); Pressure sensor R sensor (j) = exp(-|(P j -P predicted (j))|2 / σ P 2 ); where T predicted and P predicted is the temperature and pressure value of the current location predicted based on historical data and physical models, σ T and σ P is the fluctuation tolerance parameter of temperature and pressure. Based on the reliability score R sensor Perform data filtering and compensation, and treat sensor data with reliability below the threshold (R sensor <0.6) for 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 the adjacent reliable sensor, which is inversely proportional to the distance. Similarly, the pressure data is processed to obtain the adjusted temperature and pressure data set T adjusted and P adjusted .

[0048] The temperature and pressure distribution field T in the gas storage is constructed using the adjusted temperature and pressure data set. field and P field , using 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); Positions is the three-dimensional spatial coordinates of the sensor, Spline Interpolation is the spline interpolation function. According to the state parameter S of the foldable rigid material fold Using thermodynamic equations, calculate the volume V of the temperature regulating medium interlayer chamber and its rate of change dV chamber / dt, as a parameter associated with temperature and pressure changes. This embodiment establishes a direct mapping mechanism between the state of the foldable rigid material and the temperature and pressure of the gas storage reservoir.

[0049] like Figure 5 As shown, according to one aspect of the present application, the step of calculating the volume of the temperature regulating medium interlayer includes:

[0050] Extracting 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;

[0051] Based on the three-dimensional structural model, the sum of the sandwich volume units formed between adjacent rigid nodes is calculated to obtain the current temperature-regulating medium sandwich volume;

[0052] The movement speed of the foldable rigid material is calculated according to the state parameters of the foldable rigid material in a time series; and the volume change rate of the temperature regulating medium interlayer is calculated based on the movement speed.

[0053] Specifically, read the foldable rigid material state parameter S fold , extract the key geometric parameters, including the folding angle set θ fold , rigid node position coordinate set P nodes and material elastic parameter E material , constructing a three-dimensional structural model of foldable materials M structure . Using the three-dimensional structure model M structure 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 sandwich volume element formed between two adjacent rigid nodes, f volume is the volume calculation function, taking into account the nonlinear relationship between the folding angle and the node spacing. fold Data, calculate the movement speed v of the foldable rigid material fold and acceleration a fold :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.

[0054] Using the movement speed v fold , calculate the volume change rate dV of the temperature regulating medium interlayer according to the geometric relationship chamber / dt:dV chamber / dt=∑ i ΨV segment (i) / Ψθfold (i) · dθ fold (i) / dt +∑ i ΨV segment (i) / ΨP nodes (i) dP nodes (i) / dt; where ΨV segment / Ψθ fold and ΨV segment / ΨP nodes is the partial derivative of the volume with respect to the folding angle and node position, indicating the sensitivity of the volume to these parameters; Ψ is the partial derivative. Combined with the volume V of the current temperature regulating medium interlayer chamber , the volume change rate of the temperature regulating medium interlayer dV chamber / dt and thermodynamic equations to calculate the pressure change dP in the medium interlayer chamber / dt and 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 gas specific heat ratio, R gas is the gas constant, m gas is the mass of the temperature control medium, Q exchange is the heat exchange capacity, c v is the specific heat at constant volume, P chamber is the gas pressure in the gas storage (or temperature regulating medium interlayer), T chamber is the gas temperature in the gas storage (or temperature regulating medium interlayer). chamber / dt and temperature change dT chamber / dt is passed to subsequent steps for use.

[0055] According to one aspect of the present application, the step of obtaining an optimized slice set includes:

[0056] S21. Construct a three-dimensional phase space containing complete thermodynamic information based on the temperature and pressure distribution field and the volume of the temperature-regulating medium interlayer;

[0057] S22, performing initial slice division on the three-dimensional phase space to obtain an initial slice set;

[0058] S23. Based on the initial slice set, calculate the distance between the state point in each slice and the slice center point to obtain a consistency index of the internal state change;

[0059] S24. Combining the consistency index with the boundary characteristics between adjacent slices, performing slice boundary self-optimization adjustment to obtain an optimized slice set.

[0060] Specifically, based on the temperature and pressure distribution field T field 、P field And the volume parameter V chamber Constructing a three-dimensional phase space representation 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. The improved K-means clustering algorithm is used to cluster the Phase Space Perform initial slice division to obtain the initial slice set Ω init :Ω init = {Ω1, Ω2, ..., Ω n The division is based on the similarity of the thermodynamic behaviors of state points in the phase space, with special consideration given to the impact mechanism of foldable rigid materials on the temperature and pressure in the gas storage reservoir.

[0061] For the initial slice set Ω init Each slice 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 in the slice, S centroid is the center point of the slice, |Ω i | is the number of state points in the slice. Based on the consistency index C index and the boundary characteristics between adjacent slices, perform slice boundary self-optimization adjustment, and calculate the boundary movement vector B adjust :B adjust (i, j) = μ·(C index (Ω i ) - C index (Ω j ))·n ij ; where μ is the boundary adjustment coefficient, n ij is the unit normal vector pointing from slice i to slice j. Apply the boundary motion vector Badjust Update the slice boundary to get the optimized slice set Ω opt This embodiment dynamically adjusts the slice boundaries so that the control strategy can accurately adapt to the temperature variation characteristics of different areas within the gas storage reservoir, especially considering the nonlinear effect caused by the volume change of the temperature regulating medium interlayer.

[0062] According to one aspect of the present application, the step of obtaining an initial slice set includes:

[0063] Based on the three-dimensional phase space, a multi-dimensional state vector including state quantities and their rates of change is constructed;

[0064] Performing dimensionality reduction processing on the multi-dimensional state vector to obtain a reduced-dimensional state vector;

[0065] The K-means++ clustering algorithm based on thermodynamic behavior similarity metric is used to cluster the reduced-dimensional state vectors and obtain clustering results. The thermodynamic behavior similarity metric comprehensively considers the temperature component, pressure component, and future state prediction value.

[0066] Construct an initial set of slices based on the clustering results.

[0067] Specifically, based on the temperature and pressure distribution field T field 、P field and V chamber , construct a multidimensional 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 represents the index of a discrete point in space. This vector expands the traditional phase space and includes both the state quantity and its rate of change. vector Apply principal component analysis (PCA) to reduce the dimension and obtain the reduced dimension state vector S reduced :S reduced =PCA(S vector , n components =k); where k is the number of principal components retained, usually 3-5, to ensure that the cumulative explained variance ratio is greater than 95%.

[0068] Using the improved K-means++ clustering algorithm to reduced Clustering is performed, and the improvement lies in the introduction of thermodynamic behavior similarity measurement 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） represents the temperature component, x （p） 、y （p） represents the pressure component, f predict Represents the function of predicting future state, w1, w2, w3 are weight coefficients. Execute the clustering process and get the cluster center C clusters And the state point attribute label L labels :Initialize k cluster centers C = {c1, c2, ..., c k}, select outliers, repeat until convergence: calculate the Sim from each state point to each cluster center thermo Distance; assign the state point to the cluster center with the smallest distance; update the cluster center to the mean of all assigned points. Based on the cluster center C clusters And the state point attribute label L labels , construct the initial slice set Ω init :Ω init = {Ω1, Ω2, ..., Ω n};Ω i = {x | L labels (x) = i}; the initial slice set Ω init Passed to subsequent steps for use.

[0069] According to one aspect of the present application, the steps of performing slice boundary self-optimization adjustment to obtain an optimized slice set include:

[0070] Determine the set of boundary points between adjacent slices and calculate the difference in local performance indicators of the boundary points; the boundary points are state points within a slice that are close to other slices; the local performance indicators comprehensively consider temperature control accuracy and energy efficiency;

[0071] According to the difference of local performance indicators, the trend of the boundary points' attribution change is statistically analyzed, the optimal slice to which the boundary points belong is determined, and the moving vector of the slice boundary is calculated;

[0072] The slice boundaries are updated based on the motion vector to obtain an optimized slice set.

[0073] Specifically, for the optimized slice set Ω opt Each pair of adjacent slices in Ω i and Ω j , determine their boundary point set Bpoints (i, j): B points (i, j) = {x | x∈Ω i And Ξy∈Ω j Let ||xy|| < ε}; where Ξ is an existential quantifier, indicating that there is at least one element that satisfies the condition; ε is the proximity threshold, indicating the maximum distance between two state points that are considered to be adjacent. For each boundary point, calculate the local performance index difference 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, taking into account 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 slice Best of the boundary point slice (p): Best slice (p) =arg max Ω∈{Ωi，Ωj} Perf(p,Ω); that is, assigning boundary points to slices that can achieve better performance.

[0074] After redistributing 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 cardinality of the set, n ij is the unit normal vector from slice i to slice j, μ is the boundary adjustment coefficient. Apply boundary movement vector B adjust Move the slice boundary and update the slice set to get Ω opt : For each pair of adjacent slices Ω i and Ω j : Calculate new boundary: B new (i, j) = B old (i, j) + B adjust (i, j); re-divide the slice: Ω i ' = {x | d(x,C i ) <d(x,C j) and in B new (i, j) same side}; where C i and C j Slice Ω i and Ω j The center point of , d is the distance function, B old The previous boundary.

[0075] According to one aspect of the present application, the step of generating an optimal control trajectory includes:

[0076] S31. Construct a local thermodynamic model considering the dynamic characteristics of foldable rigid materials to describe the relationship between temperature, pressure and the volume change of the thermostatic medium interlayer;

[0077] S32. Based on pre-stored historical data and current state, perform parameter estimation and update on the local thermodynamic model to obtain updated parameters;

[0078] S33, using the updated parameters and the current state, calculating the ideal state trajectory and energy consumption evaluation index;

[0079] S34. Combine the ideal state trajectory and energy consumption evaluation index to generate the optimal control trajectory through multi-objective optimization.

[0080] Specifically, for the optimized slice set Ω opt Each slice Ω in i , construct the local thermodynamic model M local :dT / dt= f T (T, P, V, dV / dt, θ); dP / dt = f P (T, P, V, dV / dt, θ); where θ is the model parameter vector, including heat transfer coefficient, compression coefficient, etc., especially considering the dynamic characteristics of foldable rigid materials. Based on historical data and 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, S actual is the actual observed state change, S predicted is the state change predicted by the model. Using the updated parameters θ updated and the current state, calculate the ideal state trajectory T ideal :T ideal (t+Δt) = T(t) + ∫[t, t+Δt] f T (T, P, V, dV / dt, θ updated) dt; This trajectory represents the change path that the gas storage temperature should follow under ideal conditions. Considering the energy efficiency factor, calculate the energy consumption evaluation index E consumption :E consumption = k·|dV chamber / dt| 2 ·Δt; where k is a coefficient related to the mechanical properties of the foldable rigid material. Through multi-objective optimization, combined with the ideal state trajectory T ideal and energy consumption evaluation index E consumption Generate the final control trajectory T opt :T opt = arg min{w T ||T - T ideal || 2 +w E ·E consumption}; where w T and w E is the weight coefficient 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 achieve both precise temperature control and minimize energy consumption.

[0081] According to one aspect of the present application, the step of generating an optimal control trajectory through multi-objective optimization includes:

[0082] Build a prediction model based on the current state and updated parameters to predict the future state trajectory under different control inputs;

[0083] Based on the prediction model, the integral of the weighted deviation between the predicted temperature and the ideal temperature trajectory is calculated to obtain the temperature control error objective function;

[0084] Based on the prediction model, the weighted integral of the volume change rate and its derivative is calculated to obtain the energy consumption objective function;

[0085] Based on the temperature control error and energy consumption objective functions, a comprehensive multi-objective optimization problem including volume change rate, volume range and acceleration limit is constructed and solved to obtain the optimal control input sequence and the corresponding optimal control trajectory.

[0086] Specifically, based on the current state and the updated parameters θ updated , build the prediction model Pred model , used to predict future state trajectories 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. Define the temperature control error objective function Jtemp :J temp (ΔV) = ∫[t, t+H] w(τ)·||T predicted (τ, ΔV) - T ideal (τ)|| 2 dτ; where H is the prediction time domain length, w(τ) is the time weight function, and T predicted For the model to predict temperature, 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 velocity and acceleration, respectively.

[0087] 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)}; st |dV / dt| ≤ V rate_max ;|V| ∈ [V min , V max ];|d 2 V / dt 2 | ≤ a max ; Among them, w T and w E is the weight of temperature control and energy consumption, and the constraints include volume change rate, volume range and acceleration limit. The improved sequential quadratic programming (SQP) algorithm is used to solve the optimization problem and obtain the first step ΔV of the optimal control sequence. opt :Linearize the nonlinear problem at the current point; construct a quadratic programming subproblem; solve the subproblem to obtain the search direction; perform line search to determine the step size; update the current point; check convergence, and repeat the above steps if it does not converge. 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 ).

[0088] According to one aspect of the present application, the step of calculating the final volume control amount includes:

[0089] S41. Based on the position of the current state in the optimized slice set, determine the dominant slice and the secondary slice for the control decision, and calculate the attribution weight;

[0090] S42. At the horizontal coordination layer, based on the attribution weight, calculate the weighted sum of the difference between the optimal control trajectory of each slice and the current state to obtain the coordination compensation term between the slices;

[0091] S43. In the vertical control layer, according to the optimal control trajectory in the dominant slice, a basic volume control variable including proportional and differential control components is calculated;

[0092] S44. Calculate the final volume control amount by combining the coordination compensation item and the basic volume control amount.

[0093] Specifically, based on the current state in the optimized slice set Ω opt The position in the control decision slice Ω is determined dominant and secondary slice Ω secondary , calculate the attribution weight W belongs :W belongs (i) = exp(-d(S current ,Ω i ) / σ d ); where d is the state point S current To slice Ω i The distance, σ d is the distance normalization parameter. In the horizontal coordination layer, the coordination compensation term Δ between slices is calculated coordination :Δ coordination = Σ[W belongs (i)·(T opt (i) - T current )]; This ensures smooth control transition in the slice boundary area. In the vertical control layer, according to the dominant slice Ω dominant The optimal control trajectory T opt , calculate the basic volume control 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. Combined with the horizontal layer coordination compensation term Δ coordination and vertical layer basic control quantity ΔV base , calculate the final volume control amount ΔV final:ΔV final = ΔV base + α·Δ coordination ; where α is the coordination compensation weight coefficient. The final volume control amount ΔV final Control command Ctrl to convert to foldable rigid material command , including the direction and rate of expansion / contraction, completes the core control algorithm of this solution, realizes precise control of the temperature regulating medium interlayer, and thus achieves the purpose of active regulation of the gas storage temperature: Ctrl command = {Direction(ΔV final ), Magnitude(ΔV final ), Rate(ΔV final )}; where Direction represents the direction of volume change, Magnitude represents the magnitude of the change, and Rate represents the rate of change. This control instruction directly acts on the drive mechanism of the foldable rigid material, achieving precise control of the volume of the temperature-regulating medium interlayer.

[0094] This embodiment achieves high-precision active regulation of the compressed air energy storage system's gas storage reservoir temperature. In particular, by slicing and decomposing the hierarchical control architecture, it solves the problem of the nonlinear relationship between the adjustable volume of the foldable rigid material and the gas storage reservoir temperature, while optimizing energy efficiency and improving the overall system performance.

[0095] According to one aspect of the present application, the step of converting the final volume control amount into a control instruction for the foldable rigid material includes:

[0096] Decompose the final volume control quantity into direction component and amplitude component;

[0097] Based on the amplitude component, the optimal driving rate is calculated taking into account the mechanical properties of the foldable rigid material. The optimal driving rate uses a piecewise function to ensure high accuracy in small adjustments and high response speed in large adjustments.

[0098] According to the current state and volume change rate of the temperature-regulating medium interlayer, the driving parameters are adjusted to avoid oscillation;

[0099] Based on material properties and mechanical actuator parameters, the direction component, amplitude component and driving rate are converted into control instructions containing execution timing information.

[0100] Specifically, the final volume control amount ΔV final Decomposed into direction component Dir v and the 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 foldable rigid materials, 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 that a lower rate is used to improve accuracy when making small adjustments, and a higher rate is used to improve response speed when making large adjustments. Considering the current state V of the temperature-controlled medium interlayer chamber and the rate of change dV chamber / dt, adjust the drive parameters to avoid oscillation: if Dir v ·dV chamber / dt<0&&|dV chamber / dt|>Oscillation threshold :Rate v = Rate v Damping factor Among them, Oscillation threshold is the oscillation judgment threshold, Damping factor is the damping coefficient (usually 0.6-0.8).

[0101] Based on the material properties and mechanical actuator parameters, the directional component Dir v , amplitude component Mag v and optimal drive rate v Converted into a specific drive signal Drive signals :Drive signals = f convert (Dir v , Mag v , Rate v , Actuator params ); where f convert Actuator is the conversion function. params Generates the final control instruction Ctrl for the actuator parameter set. command , containing 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 For execution time, according to Mag v and Rate v Calculated; Current time is the current time; Timing indicates time; Execution window The final control instruction Ctrl is the execution time window. command As the final output, it is sent to the driving system of the foldable rigid material.

[0102] In a specific embodiment of this application, the proposed method is described in detail for active temperature control of a compressed air energy storage system. The reservoir has a capacity of 80,000 cubic meters and is equipped with 84 temperature sensors and 36 pressure sensors. The temperature-regulating medium layer, constructed of foldable rigid material, has an adjustable range of 5,000 to 9,000 cubic meters. The specific steps are as follows:

[0103] Step 1: Multi-point temperature and pressure data collection and preprocessing.

[0104] In practical applications, real-time data from the temperature sensor array and pressure sensors distributed in the gas storage reservoir, as well as the current state parameters of the foldable rigid material, are obtained at a sampling frequency of 10 Hz.

[0105] 1.1. Calculation of data reliability score.

[0106] For 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 measurement value of the i-th temperature sensor, in °C; T predicted (i) is the temperature value of the i-th location predicted based on historical data and physical models, in °C; σ Tis the temperature fluctuation tolerance parameter, which is 1.2℃; i is the sensor index, which ranges from 1 to 84; exp is the natural exponential function. For example, if the temperature sensor measures T3 = 35.6℃, the predicted value T predicted (3) = 36.1℃, then: R sensor (3) = exp(-‖(35.6 - 36.1)‖ 2 / 1.2 2 ) =exp(-0.25 / 1.44) = exp(-0.174) = 0.840;

[0107] Similarly, for 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 jth pressure sensor, in MPa; P predicted (j) is the pressure value at the jth position predicted based on historical data and physical models, in MPa; σ P is the pressure fluctuation tolerance parameter, which is 0.06MPa; j is the sensor index, which ranges from 1 to 36. For example, the measured value of a pressure sensor P5 = 7.82MPa, and the predicted value P predicted (5) = 7.85MPa, then: R sensor (5) = exp(-‖(7.82 - 7.85)‖ 2 / 0.06 2 ) = exp(-0.0009 / 0.0036) = exp(-0.25) = 0.779.

[0108] 1.2. Data filtering and compensation.

[0109] For sensor data with reliability scores lower than the threshold of 0.6, interpolation correction is performed: 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 the adjacent reliable sensor, and the distance d between the sensor k Inversely proportional, the calculation formula is wk = 1 / d k 2 ;d k is the Euclidean distance between the i-th sensor and the k-th sensor, in meters; T k is the measurement value of the kth sensor, in °C. For example, if sensor T 12 Reliability score R sensor (12) = 0.45<0.6, the three adjacent reliable sensors T 11 、T 13 、T 14 The measured values ​​were 34.8℃, 35.2℃, and 34.6℃, respectively, which were consistent with T 12 The distances are 1.5m, 2.0m, and 2.5m respectively, then: 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℃. The pressure data is processed in the same way to obtain the adjusted temperature and pressure data set T adjusted and P adjusted .

[0110] 1.3. Calculation of the volume of the temperature-regulating medium interlayer.

[0111] From the foldable rigid material state parameter S fold Extract the folding angle set θ from fold = {θ1, θ2, ..., θ n}、Rigid node position coordinate set P nodes = {P1, P2, ..., P n+1} and material elastic parameter E material , constructing a three-dimensional structural model of foldable materials M structure Based on this model, the volume V of the current temperature-regulating medium interlayer is calculated. chamber :V chamber = Σ i V segment (i); where V segment (i) is the sandwich 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), Pnodes (i+1), θ fold (i)); f volume is the volume calculation function, which takes into account the nonlinear relationship between the folding angle and the node spacing; i is the node index, ranging from 1 to n. In the specific implementation, assuming there are 50 rigid nodes, the average node spacing is 1.2m, and the current average folding angle is 48°, the calculated V chamber = 7,350 cubic meters.

[0112] According to the time series S fold Data, 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 (i) / dt; where ΨV segment (i) / Ψθ fold (i) is the partial derivative of volume with respect to folding angle; ΨV segment (i) / ΨP nodes (i) is the partial derivative of the volume with respect to the node position; dθ fold (i) / dt is the rate of change of the folding angle, in degrees / s; dP nodes (i) / dt is the rate of change of the node position, in m / s. In this example, the current dV chamber / dt = -8.5 cubic meters per second.

[0113] Step 2: Dynamic division of phase space slices and boundary determination.

[0114] 2.1. Construction of three-dimensional phase space.

[0115] Based on the temperature and pressure distribution field T field 、P field And the volume parameter V chamber Constructing a three-dimensional phase space representation Space :Phase Space = {T field , P field , V chamber}; where T field is the temperature distribution field, which is obtained by adjusting the temperature data T adjusted Constructed by interpolation; P field is the pressure distribution field, which is obtained by adjusting the pressure data P adjustedConstructed by interpolation; V chamber is the volume of the temperature regulating medium interlayer.

[0116] 2.2. Initial slicing of phase space.

[0117] Based on Phase Space Construct a multidimensional 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 a discrete point in space; T field (i) is the temperature value of the i-th spatial point, in °C; P field (i) is the pressure value of the i-th spatial point, in MPa; dT field (i) / dt is the temperature change rate, in °C / s; dP field (i) / dt is the pressure change rate, in MPa / s; dV chamber / dt is the volume change rate, in cubic meters per second.

[0118] To S vector Apply principal component analysis (PCA) to perform dimensionality reduction: S reduced = PCA(S vector , n components =4); where n components The number of principal components retained is 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%.

[0119] Using the improved K-means++ clustering algorithm to reduced Clustering is performed and the thermodynamic behavior similarity measure Sim is introduced 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） represents the temperature component of the state vectors x and y; x（p） 、y （p） represents the pressure components of the state vectors x and y; f predict Represents the function of predicting future states; w1, w2, and w3 are weight coefficients, which are 0.4, 0.3, and 0.3 respectively; ‖·‖ 2 Represents the squared Euclidean distance. Execute the clustering process, select k=5 clusters, and get the cluster center C clusters And the state point attribute label L labels , construct the initial slice set Ω init = {Ω1, Ω2, ..., Ω5}.

[0120] 2.3. Self-optimization adjustment of slice boundaries.

[0121] For the initial slice set Ω init Each pair of adjacent slices in Ω i and Ω j , determine the boundary point set B points (i, j): B points (i, j) = {x | x∈Ω i And Ξy∈Ω j So that ‖xy‖<ε}; where x is the slice Ω i The state point in ; y is the slice Ω j The state points in ; ε is the proximity threshold, which is set to 0.2; ‖xy‖ is the distance between the state points. Calculate the local performance index difference 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 index of point p in 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, which are 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 index.

[0122] Based on the local performance index difference ΔPerf(i, j, p), determine the best slice Best for the boundary point slice (p): Best slice (p) = arg max Ω∈{Ωi，Ωj} Perf(p,Ω); arg max represents the slice Ω that maximizes the performance index Perf(p,Ω). Calculate the boundary movement vector Badjust (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, which is 0.15; |·| represents the cardinality of the set; n ij is the unit normal vector pointing from slice i to slice j. Apply the boundary motion vector B adjust Move the slice boundary and update the slice set to get Ω opt = {Ω1', Ω2', ..., Ω5'}.

[0123] Step 3: Generate the optimal temperature control trajectory within the slice.

[0124] 3.1. Construction of local thermodynamic model.

[0125] For the optimized slice set Ω opt Each slice Ω in i , construct the local thermodynamic model M local : dT / dt = f T (T, P, V, dV / dt, θ); dP / dt = f P (T, P, V, dV / dt, θ); where T is temperature in °C; P is pressure in MPa; V is volume in cubic meters; dV / dt is the volume change rate in cubic meters per second; θ is the model parameter vector, including heat transfer coefficient, compressibility, etc.; f T and f P is a function that describes the change of temperature and pressure. Based on historical data and 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, which is 0.1; θ current is the current parameter vector; S actual is the actual observed state change; S predicted The state change predicted by the model.

[0126] 3.2. Multi-objective optimization to generate control trajectory.

[0127] Based on the current state and the updated parameters θupdated , build the prediction model Pred model Used to predict future state trajectories under different control inputs.

[0128] 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 time domain length, which is 60 seconds; w(τ) is the time weight function, which decreases as τ increases; T predicted T is the temperature predicted by the model, in °C; ideal is the ideal temperature trajectory in °C; ΔV is the control input sequence.

[0129] 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; k1 and k2 are energy consumption coefficients, which are 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 .

[0130] Constructing a comprehensive multi-objective optimization problem: ΔV sequence = arg min{w T ·J temp (ΔV) + w E ·J energy (ΔV)}; st |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, which are 0.8 and 0.2 respectively; V rate_max is the maximum volume change rate, which is 20 cubic meters / s; V min 、V max For volume range restrictions, the values ​​are 5,000 cubic meters and 9,000 cubic meters respectively; a maxThe maximum acceleration limit is 5 cubic meters / s 2 arg min represents the control input sequence that minimizes the objective function. The improved sequential quadratic programming (SQP) algorithm is used to solve the optimization problem and obtain the first step ΔV of the optimal control sequence. opt = -12.5 cubic meters. According to the first step of the optimal control sequence, ΔV opt and the prediction model Pred model , generate the optimal temperature control trajectory T opt .

[0131] Step 4: Horizontal-vertical hybrid decision-making and control quantity calculation.

[0132] 4.1. Hybrid decision computing.

[0133] Based on the current state in Ω opt The position in the control decision slice Ω is determined dominant = Ω3' and secondary slice Ω secondary = {Ω2', Ω4'}, calculate the attribution weight W belongs :W belongs (i) = exp(-d(S current ,Ω i ) / σ d ); where d is the state point S current To slice Ω i distance; σ d is the distance normalization parameter, which takes a value of 0.5; exp is the natural exponential function. belongs = {0.02, 0.28, 0.61, 0.09, 0.00}.

[0134] At the horizontal coordination layer, the coordination compensation term Δ between slices is calculated coordination :Δ coordination = Σ[W belongs (i)·(T opt (i) - T current )]; where T opt (i) is the slice Ω i The optimal control trajectory within T current is the current temperature in °C. Calculate Δ coordination = 0.8℃.

[0135] In the vertical control layer, according to the dominant slice Ω dominant The optimal control trajectory T opt , calculate the basic volume control amount ΔV base :ΔV base = K P ·(T opt - Tcurrent ) + K D ·d(T current ) / dt; where K P , K D are the proportional and differential control coefficients, which are 15 m³ / °C and 30 m³·s / °C respectively; d(T current ) / dt is the current temperature change rate, in ℃ / s. Assume T opt = 36.2℃, T current = 35.8℃, d(T current ) / dt = 0.04℃ / s, then: ΔV base = 15×(36.2 - 35.8) + 30×0.04 = 6.0 + 1.2 = 7.2 cubic meters.

[0136] 4.2. Control instruction conversion.

[0137] Combined with the horizontal layer coordination compensation term Δ coordination and vertical layer basic control quantity ΔV base , calculate the final volume control amount ΔV final :ΔV final = ΔV base + α·Δ coordination ; where α is the coordination compensation weight coefficient, which is 10 cubic meters / ℃. Calculate ΔV final = 7.2 + 10 × 0.8 = 15.2 cubic meters. final Decomposed into 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 foldable rigid materials, 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_smallTo slightly adjust the threshold, the value is 10 cubic meters; Rate min 、Rate max are the minimum and maximum driving rates, which are 2 cubic meters / s and 15 cubic meters / s respectively. v = 15.2>V threshold_small = 10, so Rate v = Rate max =15 cubic meters / s.

[0138] Consider the current state V of the temperature regulating medium interlayer chamber and dV chamber / dt, adjust the drive parameters to avoid oscillation: ifDir v ·dV chamber / dt<0&&|dV chamber / dt|>Oscillation threshold : Rate v = Rate v Damping factor Oscillation threshold is the oscillation judgment threshold, which is 5 cubic meters / s; Damping factor is the damping coefficient, which is 0.7. v = +1,dV chamber / dt = -8.5 cubic meters / 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 cubic meters / s. The control command Ctrl is finally generated. command , containing direction, amplitude, rate and execution timing information, which is sent directly to the driving system of the foldable rigid material.

[0139] This embodiment, applied to the active temperature control of the compressed air energy storage system's gas reservoir, achieves the following technical benefits: Temperature fluctuations within the gas reservoir are controlled within a range of ±0.5°C, compared to ±1.8°C for traditional methods; energy consumption is reduced by 32.5% compared to traditional PID control methods; response time is shortened by 45.3% under rapidly changing operating conditions; gas reservoir volume utilization is increased by 15.2%, and overall system energy efficiency is improved by 8.7%. This embodiment successfully addresses the nonlinear relationship between the adjustable volume of foldable rigid materials and gas reservoir temperature by employing a slice-decomposition hierarchical trajectory compensation control architecture. This approach optimizes energy efficiency while ensuring temperature control accuracy, improving system performance.

[0140] The present invention's multi-point temperature and pressure data filtering and compensation method, based on reliability scoring, assigns a reliability score to each sensor by calculating the deviation between each sensor's data and the predicted value in real time. Data filtering and compensation are then performed based on the score, effectively resolving the problem of single-point monitoring in traditional methods that makes it difficult to accurately grasp the overall temperature distribution. This makes the construction of the temperature and pressure field more accurate and reliable, reducing the measurement error from ±2.5°C in traditional methods to ±0.8°C, an improvement of over 68%. This enables the system to maintain high-precision temperature monitoring even when individual sensors fail or produce abnormal readings, laying a solid foundation for subsequent precise control. This method is particularly suitable for working conditions such as gas storage facilities, where temperature distribution is uneven within large spaces. By constructing a mathematical model of material geometric parameters and the volume of the temperature-regulating medium interlayer, a direct correlation is established between the state of the foldable material and the temperature control effect. This enables the system to accurately calculate the volume of the temperature-regulating medium interlayer and its rate of change, shortening the response time from 1.8 seconds in traditional methods to 0.6 seconds, an improvement of approximately 67%. By establishing a mapping relationship between the folding angle, node position and other geometric parameters of the foldable rigid material and the interlayer volume, the system can quickly determine the appropriate material state adjustment plan based on the required temperature change, avoiding the blind trial and error and over-adjustment in traditional methods, improving the accuracy and response speed of temperature control, and enhancing the system's adaptability under conditions of rapid load changes. By introducing a thermodynamic behavior similarity measure, the temperature-pressure-volume three-dimensional phase space is intelligently divided, solving the problem that traditional single control strategies are difficult to adapt to different working conditions. First, a multidimensional state vector containing state quantities and their rates of change 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℃ to ±0.5℃. Unlike traditional experience-based division methods, by considering the similarity of thermodynamic behavior by predicting the future state of the system, states with similar thermodynamic behavior are grouped, allowing the control strategy to be optimized for areas with different thermodynamic characteristics, improving control accuracy and adaptability. By calculating the performance index differences of boundary points in different slices and dynamically adjusting the slice boundaries, the problem of unstable control in the boundary area of ​​fixed slice division is solved. First, the boundary point set 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 area is reduced from ±1.2℃ to ±0.5℃. This enables the system to automatically optimize the slice structure according to the actual working conditions, avoids the control jitter of the traditional method in the slice boundary area, and realizes full-domain smooth control. It is particularly suitable for actual application scenarios where the temperature distribution in the gas storage reservoir changes dynamically over time.By simultaneously considering both temperature control error and energy consumption, this approach minimizes energy consumption while ensuring control accuracy. This approach addresses the problem of traditional methods that focus solely on temperature control while ignoring energy efficiency. A predictive model is constructed, defining both temperature control error and energy consumption as objective functions. Taking into account various physical constraints, an improved sequential quadratic programming algorithm is used to solve the optimal control sequence. In practice, energy consumption was reduced by 32.5% while maintaining temperature control accuracy. This approach not only resolves the conflict between temperature control and energy efficiency, but also comprehensively considers physical constraints such as volume change rate, volume range, and acceleration limits, enabling the control strategy to meet practical engineering requirements while achieving cost-effective operation and improving the overall efficiency of the compressed air energy storage system. By combining inter-slice coordination with intra-slice control, this approach achieves a balance between global consistency and local accuracy, overcoming the limitations of traditional single-level control strategies in complex systems. First, the dominant and secondary slices in the current state are determined, and degree weights are calculated. Then, inter-slice coordination compensation terms are calculated at the horizontal level, and basic volume control variables are calculated at the vertical level. These two factors are combined to produce the final control variable. System response time is reduced by 45.3%, and control smoothness is improved by 53.7%. The advantages of different slice control strategies are fully utilized, control jumps at slice boundaries are avoided, and precise control with smooth transitions across the entire domain is achieved. This is particularly suitable for the precise temperature control needs of large and complex systems such as gas storage facilities. By distinguishing between small and large adjustment scenarios, different rate calculation strategies are adopted, and combined with an anti-oscillation mechanism, the problem of traditional fixed-rate control that is difficult to balance accuracy and speed under different adjustment amplitudes is solved; the volume control amount is decomposed into direction and amplitude components, and the optimal drive rate is calculated using a piecewise function based on the amplitude. The drive parameters are adjusted 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 system oscillation is reduced by 78.6%. This allows the drive of foldable rigid materials to ensure high precision for small adjustments while meeting the rapid response requirements of large adjustments. At the same time, it effectively suppresses system oscillations, improving the overall performance and stability of the gas storage temperature control system.

[0141] The preferred embodiments of the present invention are described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within 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 fall within the scope of protection of the present invention.

Claims

1. A method for actively controlling the temperature of a compressed air energy storage system gas storage reservoir, characterized in that: include: The original temperature and pressure data at multiple points in the gas storage reservoir and the state parameters of the foldable rigid material are collected. After preprocessing, the temperature and pressure distribution field is constructed and the volume of the temperature-regulating medium interlayer is calculated. Based on this data, slice division is performed and the slice boundaries are dynamically optimized to obtain an optimized slice set. For each slice in the optimized slice set, the optimal control trajectory considering temperature control accuracy and energy consumption is generated through the local thermodynamic model; According to the optimal control trajectory and the position of the current state in the optimized slice set, a hybrid decision-making mechanism combining horizontal coordination and vertical control is adopted to calculate the final volume control amount and convert it into control instructions for the foldable rigid material; The steps to obtain the optimized slice set include: Based on the temperature and pressure distribution field and the volume of the temperature-regulating medium interlayer, a three-dimensional phase space containing complete thermodynamic information is constructed; an initial slice division is performed on the phase space to obtain an initial slice set; Based on the initial slice set, the distance between the state point in each slice and the slice center point is calculated to obtain the consistency index of the internal state change; this is combined with the boundary characteristics between adjacent slices to perform slice boundary self-optimization adjustment to obtain the optimized slice set; The steps to generate the optimal control trajectory include: A local thermodynamic model that considers the dynamic characteristics of foldable rigid materials is constructed to describe the relationship between temperature, pressure, and the volume change of the thermostatic medium interlayer. Based on pre-stored historical data and the current state, parameter estimation and update are performed to obtain updated parameters. Using the updated parameters and current state, the ideal state trajectory and energy consumption evaluation index are calculated; based on this, the optimal control trajectory is generated through multi-objective optimization; The steps to calculate the final volume control amount include: Based on the position of the current state in the optimized slice set, the dominant slice and the secondary slice of the control decision are determined, and the attribution weight is calculated; At the horizontal coordination layer, based on the attribution weight, the weighted sum of the difference between the optimal control trajectory of each slice and the current state is calculated to obtain the coordination compensation term between slices; In the vertical control layer, the basic volume control quantity including proportional and differential control components is calculated according to the optimal control trajectory in the dominant slice; The final volume control amount is calculated by combining the coordination compensation term and the basic volume control amount.

2. The method according to claim 1, characterized in that The steps for constructing the temperature and pressure distribution field and calculating the volume of the temperature-regulating medium interlayer include: Read temperature and pressure sensor data and calculate reliability scores based on the deviation between measured and predicted values; Data filtering and compensation are performed based on the reliability score to obtain the adjusted temperature and pressure data set, and the temperature and pressure distribution field is constructed based on this data; The state parameters of the foldable rigid material are obtained and the volume of the temperature regulating medium interlayer is calculated using thermodynamic equations.

3. The method according to claim 2, characterized in that The steps for calculating the reliability score and obtaining the adjusted temperature and pressure data set include: Calculate the reliability score of the temperature sensor using an exponential function of the deviation between the actual temperature value measured by the temperature sensor and the temperature value predicted based on historical data and physical models; Calculate the reliability score of the pressure sensor using an 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 sensor data with reliability scores lower than the preset threshold, interpolation correction is performed based on the distance weights of adjacent reliable sensors to generate an adjusted temperature and pressure dataset.

4. The method according to claim 2, characterized in that The steps for calculating the volume of the temperature control medium interlayer include: The folding angle set, rigid node position coordinate set, and material elastic parameters are extracted from the state parameters of the foldable rigid material to construct a three-dimensional structural model of the foldable material. Based on this, the sum of the interlayer volume units formed between adjacent rigid nodes is calculated to obtain the current temperature-regulating medium interlayer volume. The movement speed of the foldable rigid material is calculated according to the state parameters of the foldable rigid material in time series; and the volume change rate of the temperature regulating medium interlayer is calculated accordingly.

5. The method according to claim 1, wherein The steps to obtain the initial slice set include: Based on the three-dimensional phase space, a multi-dimensional state vector including the state quantity and its rate of change is constructed and dimensionality reduction is performed to obtain a reduced-dimensional state vector; The K-means++ clustering algorithm based on thermodynamic behavior similarity measure is used to cluster the reduced-dimensional state vectors to obtain clustering results; based on this, an initial slice set is constructed; the thermodynamic behavior similarity measure comprehensively considers the temperature component, pressure component and future state prediction value.

6. The method according to claim 1, characterized in that The steps of performing slice boundary self-optimization adjustment to obtain an 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; the boundary points are state points within a slice that are close to other slices; the local performance indicators comprehensively consider temperature control accuracy and energy efficiency; According to the difference in local performance indicators, the ownership change trend of the boundary points is statistically analyzed, the optimal slice to which the boundary points belong is determined, and the movement vector of the slice boundary is calculated; accordingly, the slice boundary is updated to obtain the optimized slice set.

7. The method according to claim 1, characterized in that Through multi-objective optimization, the steps to generate the optimal control trajectory include: A prediction model is constructed based on the current state and updated parameters. Based on this model, the integral of the weighted deviation between the predicted temperature and the ideal temperature trajectory is calculated to obtain the temperature control error objective function. Based on the prediction model, the weighted integral of the volume change rate and its derivative is calculated to obtain the energy consumption objective function; Based on the temperature control error and energy consumption objective functions, a comprehensive multi-objective optimization problem including volume change rate, volume range and acceleration limit is constructed and solved to obtain the optimal control input sequence and the corresponding optimal control trajectory.

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