Decentralized compensation active disturbance rejection control method for isobaric compressed air energy storage system

Abstract

The application provides a decentralized compensation active disturbance rejection control method for an isobaric compressed air energy storage system, and is characterized by comprising the following steps: constructing an input-output relationship model based on a system transfer function matrix for a high-order multivariable isobaric compressed air energy storage system; determining a control structure based on different working stages; designing a compensation ADRC controller for a diagonal transfer function corresponding to each loop of the isobaric compressed air energy storage system; parameter tuning of the decentralized compensation ADRC controller is carried out based on a bandwidth parameterization method, gain parameters of an extended state observer and a state feedback control law in each loop are determined, and a designed decentralized compensation ADRC controller is obtained; and the designed decentralized compensation ADRC controller is applied to control of a compression stage and an expansion stage of the isobaric compressed air energy storage system. The application improves a traditional decentralized ADRC structure by introducing a high-order compensation link, and realizes effective control of a low-order controller on a high-order multivariable system.

Description

Technical Field

[0001] This invention relates to the field of energy system control technology, specifically to a distributed compensation active disturbance rejection control method for isobaric compressed air energy storage systems. Background Technology

[0002] With the global energy structure transformation, renewable energy sources such as wind and solar power have been vigorously developed. However, renewable energy power exhibits significant randomness and volatility, causing its grid connection to impact grid frequency and voltage. Isobaric compressed air energy storage, as an advanced energy storage technology, eliminates the need for a bottom gas chamber, significantly reducing the storage volume. Simultaneously, the constant pressure within the storage chamber ensures stable operation during the energy storage and release phases, resulting in higher energy storage efficiency. This effectively mitigates grid power fluctuations and improves energy utilization efficiency.

[0003] Isobaric compressed air energy storage systems have the following technical characteristics:

[0004] The system comprises a compression energy storage stage and an expansion energy release stage, each of which is a multiple-input multiple-output (MIMO) structure. The system contains high-order inertial components such as heat exchangers, gas storage chambers, and compressors / expanders. There are strong coupling characteristics between the input and output loops. The system operates under a wide range of conditions, exhibiting strong nonlinear characteristics.

[0005] Traditional control methods have the following technical problems:

[0006] Low-order extended state observers (ESOs) cannot accurately observe all state information, leading to degraded control performance. Distributed ADRC treats inter-loop coupling as disturbances, but its decoupling effect on strongly coupled systems is limited. Centralized control strategies are computationally complex and have poor engineering practicality. Existing methods struggle to quantitatively evaluate and optimize the stability region of multivariable control systems. Traditional controllers struggle to maintain stable operation when system parameters fluctuate and operating conditions change. Summary of the Invention

[0007] The purpose of this invention is to overcome the above-mentioned technical deficiencies and provide a distributed compensation active disturbance rejection control method for isobaric compressed air energy storage systems. By introducing a high-order compensation element, the traditional distributed ADRC structure is improved, enabling the low-order controller to effectively control the high-order multivariable system.

[0008] To achieve the above technical objectives, the present invention provides a distributed compensation active disturbance rejection control method for isobaric compressed air energy storage systems, comprising the following steps:

[0009] For a high-order multivariable isobaric compressed air energy storage system, an input-output relationship model is constructed based on the system transfer function matrix. The input-output relationship model includes: compressor / expander modeling, heat exchanger modeling, cavity module modeling, air storage tank modeling, heat transfer oil tank modeling, and motor / generator modeling.

[0010] The control structure is determined based on different working stages. In the compression stage, the control variables include the inlet guide vane angle and the flow rate of cold heat transfer oil, while the controlled variables include the total power consumption and the inlet pressure of the gas storage tank. In the expansion stage, the control variables include the inlet air flow rate and the flow rate of hot heat transfer oil, while the controlled variables include the power generation and the outlet heat transfer oil temperature.

[0011] A separate compensated ADRC controller is designed for the diagonal transfer function corresponding to each loop of the isobaric compressed air energy storage system. The loop includes: power consumption loop, gas storage pressure loop, power generation loop and heat transfer oil temperature loop.

[0012] Based on the bandwidth parameterization method, the distributed compensation ADRC controller is parameter-tuned to determine the gain parameters of the extended state observer and the state feedback control law in each loop, thus obtaining the designed distributed compensation ADRC controller.

[0013] The designed distributed compensation ADRC controller is applied to the compression and expansion phases of an isobaric compressed air energy storage system.

[0014] Compared with the prior art, the beneficial effects of the present invention include:

[0015] This invention presents a distributed compensation active disturbance rejection control method for isobaric compressed air energy storage systems. The distributed structure eliminates the need for complex decoupling networks, allowing each loop controller to be independently designed and debugged, facilitating maintenance. It is not only applicable to energy systems but can also be extended to multivariable process control in fields such as chemical engineering, power generation, and machinery. By improving control performance, this invention can increase the energy cycle efficiency of isobaric compressed air energy storage systems by 3%–5%, resulting in an annual increase in power generation of several million yuan. The strong robustness of this invention ensures stable system operation under parameter perturbations and changes in operating conditions, reducing downtime due to faults.

[0016] According to some embodiments of the present invention, the ADRC controller includes a compensation element, an extended state observer, and a state feedback control law.

[0017] According to some embodiments of the present invention, parameter tuning of a distributed compensation ADRC controller is performed based on a bandwidth parameterization method, including the following steps:

[0018] Based on a distributed compensation ADRC controller of a set order, the ESO state equation is established, the ESO gain is determined by bandwidth parameterization, and the two-degree-of-freedom system closed-loop transfer function is derived by combining the state feedback control law.

[0019] The controlled process is approximated by simulation modeling or system identification. The controller order is selected and the compensation element is designed. Based on the bandwidth parameterization method, the ESO gain and the controller feedback gain are set respectively to achieve parameter co-optimization and obtain the designed distributed compensation ADRC controller.

[0020] According to some embodiments of the present invention, the distributed compensation ADRC controller includes a compensation element, an extended state observer, and a state feedback control law, wherein the compensation element is used to reduce the higher-order system object to match the order of the ADRC controller, thereby improving the observation accuracy of the extended state observer;

[0021] Each loop is equipped with an independent extended state observer to observe the state variables and total disturbance of the loop;

[0022] The state feedback control law performs disturbance compensation and feedback control based on the observations of the extended state observer.

[0023] According to some embodiments of the present invention, when the distributed compensation ADRC controller is applied to the compression stage, the flow rate of the cold heat transfer oil and the inlet guide vane angle of the first-stage compressor are used as control variables, and the outlet pressure of compressed air and the total power consumption of compression are used as controlled variables. The control objective of the compression stage is to maintain the inlet pressure of the air storage tank to meet the underwater storage pressure requirements, while matching the total power consumption of compression with the power grid deficit.

[0024] According to some embodiments of the present invention, when the distributed compensation ADRC controller is applied to the expansion stage, the compressed air flow rate and the heat transfer oil flow rate are the control variables, and the generator output power and the outlet heat transfer oil temperature are the controlled variables. The control objective of the expansion stage is to make the generator output power track the user load demand, while maintaining the outlet heat transfer oil temperature within a preset safety range.

[0025] According to some embodiments of the present invention, the control process of the distributed compensation ADRC controller includes the following steps:

[0026] By adjusting and adapting the dynamic characteristics of the high-order system through the diagonal transfer function of the corresponding loop of the compensation link, the system order is kept consistent with the preset order of the controller.

[0027] The operating state variables of the loop are captured in real time by a dedicated extended state observer, and various influencing factors such as coupling interference between loops and external environmental fluctuations are integrated into a total disturbance and observed synchronously.

[0028] The state feedback control law receives observation data from the extended state observer. On the one hand, it provides targeted compensation for the identified total disturbance. On the other hand, it executes feedback adjustment logic according to the preset control target, ultimately achieving precise control of a single loop and multi-loop coordinated action to complete the control task of the entire multivariable system.

[0029] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0030] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, wherein the abstract drawings are to be completely consistent with one of the drawings in the specification:

[0031] Figure 1 A schematic diagram of a distributed compensation ADRC controller provided in one embodiment of the present invention;

[0032] Figure 2 This is a structural diagram of an isobaric compressed air energy storage system provided in one embodiment of the present invention;

[0033] Figure 3 This is a block diagram of a distributed compensation ADRC control system provided in one embodiment of the present invention;

[0034] Figure 4 A comparison diagram of the stable regions during the expansion phase of an isobaric compressed air energy storage system provided in one embodiment of the present invention;

[0035] Figure 5 A comparison diagram of the heat transfer oil temperature control effect during the expansion stage of an isobaric compressed air energy storage system, provided as an embodiment of the present invention;

[0036] Figure 6 A comparison diagram of the total power generation control effect during the expansion stage of an isobaric compressed air energy storage system provided in one embodiment of the present invention;

[0037] Figure 7 A comparison diagram of the inlet pressure control effect of the air storage tank during the compression stage of an isobaric compressed air energy storage system is provided as an embodiment of the present invention.

[0038] Figure 8 A comparison chart of the total power consumption control effect during the compression stage of an isobaric compressed air energy storage system provided in an embodiment of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0040] It should be noted that although functional modules are divided in the system diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than the module division in the system or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned figures are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0041] Reference Figures 1 to 7 , Figure 1 A schematic diagram of a distributed compensation ADRC controller provided in one embodiment of the present invention; Figure 2 This is a structural diagram of an isobaric compressed air energy storage system provided in one embodiment of the present invention; Figure 3 This is a block diagram of a distributed compensation ADRC control system provided in one embodiment of the present invention; Figure 4 A comparison diagram of the stable regions during the expansion phase of an isobaric compressed air energy storage system provided in one embodiment of the present invention; Figure 5 A comparison diagram of the heat transfer oil temperature control effect during the expansion stage of an isobaric compressed air energy storage system, provided as an embodiment of the present invention; Figure 6 A comparison diagram of the total power generation control effect during the expansion stage of an isobaric compressed air energy storage system provided in one embodiment of the present invention; Figure 7 A comparison diagram of the inlet pressure control effect of the air storage tank during the compression stage of an isobaric compressed air energy storage system is provided as an embodiment of the present invention. Figure 8 A comparison chart of the total power consumption control effect during the compression stage of an isobaric compressed air energy storage system provided in an embodiment of the present invention.

[0042] In one embodiment, a distributed compensation active disturbance rejection control method for isobaric compressed air energy storage systems includes the following steps: For a high-order multivariable isobaric compressed air energy storage system, constructing an input-output relationship model based on the system transfer function matrix. This input-output relationship model includes: compressor / expander modeling, heat exchanger modeling, cavity module modeling, air reservoir modeling, heat transfer oil tank modeling, and motor / generator modeling. Determining the control structure based on different operating stages, wherein the control variables during the compression stage include: inlet guide vane angle and cold heat transfer oil flow rate, and the controlled variables include: total power consumption and air reservoir inlet pressure; the control variables during the expansion stage include: inlet air flow rate and hot heat transfer oil flow rate. The controlled variables include: power generation and outlet heat transfer oil temperature; a separate compensated ADRC controller is designed for the diagonal transfer function corresponding to each loop of the isobaric compressed air energy storage system, the loops including: power consumption loop, gas storage pressure loop, power generation loop, and heat transfer oil temperature loop; based on the bandwidth parameterization method, the distributed compensated ADRC controller is parameter-tuned to determine the gain parameters of the expanded state observer and state feedback control law in each loop, thus obtaining the designed distributed compensated ADRC controller; the designed distributed compensated ADRC controller is applied to the compression and expansion phase control of the isobaric compressed air energy storage system.

[0043] Step 1: Establish the controlled object model

[0044] For a high-order multivariable system of order n×n, its input-output relationship can be described as follows:

[0045]

[0046] Where Y=[Y1,Y2,…,Y] n ] T Let R be the system output vector, where R = [R1, R2, ..., R]. n ] T Given the system input vector, the diagonal transfer function G in the transfer function matrix G is... ii All are high-order inertial elements:

[0047]

[0048] In the formula, K is the gain, T is the time constant, and n is the order.

[0049] Step 2: Design a distributed compensation ADRC structure

[0050] The core innovation of this invention lies in combining the compensation element with distributed Active Disturbance Rejection Controller (ADRC) to form a distributed compensated ADRC. The distributed compensated ADRC treats the coupling between loops in a multivariable system as a disturbance and designs and observes the compensation ESO for each loop individually. Specifically, it ignores the off-diagonal transfer function in the system transfer function matrix and focuses on the diagonal transfer function G corresponding to loop i. ii After designing the ADRC controller for each loop, the distributed compensation ADRC design for the multivariable system is complete. Taking a 2×2 system as an example, for the transfer function matrix:

[0051]

[0052] The distributed compensation ADRC includes:

[0053] Independent loop design: Ignoring off-diagonal elements in the transfer function matrix, for each loop i, the diagonal transfer function G... ii Design a separate compensation ADRC controller

[0054] Compensation Link G cpi For the i-th loop, a compensation stage is designed. To compensate for higher-order information, the observed system order needs to be compensated to match that of ADRC. The transfer function of the compensation stage is:

[0055]

[0056] Where m is the order of the ADRC controller and n is the order of the controlled object. The compensation mechanism reduces the order of the higher-order system object to match the ESO order, thereby improving the observation accuracy of the ESO.

[0057] Extended State Observer (ESO): Each loop is configured with an independent ESO to observe the state variables and total disturbances of its loop;

[0058] State Feedback Control Law (SFCL): Based on ESO observations, disturbance compensation and feedback control are performed.

[0059] Step 3: Derive the closed-loop transfer function of the system

[0060] For example, the closed-loop transfer function of the m-th order distributed compensation ADRC is:

[0061] 1. ESO equation of state:

[0062]

[0063] ESO gain can be determined through bandwidth parameterization:

[0064]

[0065] in Let be the observer bandwidth, and:

[0066]

[0067] 2. In equation (1-5), u0 is the generalized control variable. For an m-order controller, the state feedback control law can be:

[0068]

[0069] Then, a state feedback control law with a compensation component is added:

[0070]

[0071] 3. Order:

[0072]

[0073] The closed-loop transfer function of the system can then be transformed into a two-degree-of-freedom form:

[0074]

[0075] in:

[0076]

[0077] The molecular polynomial is related to the ESO gain, and its order does not exceed m.

[0078] For example, when m=1

[0079]

[0080] Substituting this into equation (3-7), we get:

[0081]

[0082] 4. Closed-loop transfer function of a multivariable system:

[0083]

[0084] Step 4: Controller parameter tuning

[0085] Distributed compensation ADRC parameter tuning method:

[0086] The controlled process can be approximated as G(s) = K / (T) using simulation modeling or system identification. s +1)n form

[0087] Select the controller order m and design the compensation circuit G. cp (s)=1 / (T s +1) n-m

[0088] Calculate controller parameters:

[0089]

[0090] Setting ESO gain based on bandwidth parameterization method:

[0091]

[0092] Set the controller feedback gain:

[0093]

[0094] Step 5: Application of Isobaric Compressed Air Energy Storage System

[0095] Applying distributed adaptive regenerative braking (ADRC) to the multivariable operation control of isobaric compressed air energy storage systems specifically includes:

[0096] Compression stage control design:

[0097] Controlled variable: Compressed air outlet pressure P ainas Total power consumption of the compressor (W) c ;

[0098] Controlled quantity: Cold heat transfer oil flow rate G o,c The first-stage compressor inlet guide vane angle θ IGV ;

[0099] Control objective: Maintain the inlet pressure of the gas storage tank to meet underwater storage requirements, while ensuring that power consumption matches the grid power deficit.

[0100] Expansion stage control design:

[0101] Controlled variable: Generator output power (W) e Outlet heat transfer oil temperature T o,e ;

[0102] Controlled quantity: Compressed air flow rate G a,e , Heat transfer oil flow rate Go,e;

[0103] Control objective: The power generation capacity should track the user load demand while maintaining the heat transfer oil temperature within a safe range.

[0104] This invention solves the order mismatch problem by reducing the order of higher-order objects through a compensation stage, enabling lower-order ESOs to accurately observe the system state and improve control accuracy. It offers excellent decoupling performance; the distributed structure treats loop coupling as a total disturbance for observation and compensation, and the compensation stage improves frequency characteristics, achieving an approximate inverse decoupling effect. Quantitative stability analysis, based on the Ostrowski band stability region determination method, allows for quantitative comparison of the stability margins of different controllers, providing theoretical guidance for parameter optimization. Strong robustness is demonstrated; within a parameter perturbation range of ±10%, the distributed compensation ADRC performance exhibits a more concentrated scatter distribution, demonstrating stronger robustness. It is suitable for complex energy systems and has been successfully applied to offshore wind power-subsea compressed air energy storage systems, achieving multi-variable coordinated control during the compression / expansion phase and improving system energy cycle efficiency.

[0105] The present invention exhibits superior dynamic performance. Simulation results show that, compared with traditional PID and distributed ADRC, the method of the present invention reduces the settling time by 30%-50% and the overshoot by more than 50%.

[0106] Example: Isobaric Compressed Air Energy Storage System

[0107] System Description: This embodiment describes an underwater isobaric compressed air energy storage system with a rated power of 3MW. The system structure is as follows: Figure 4 As shown, the system mainly includes a three-stage compression / expansion unit, six plate heat exchangers, a hot and cold thermal oil storage tank, a flexible underwater gas storage tank, and an oil pump motor unit. The system operation is divided into three stages:

[0108] Compression and energy storage stage (0-300s): Excess electricity is used to drive a compressor to compress air into an air storage tank at a depth of 300m underwater.

[0109] Storage phase (300-400s): The system is in standby mode, and the gas storage tank maintains a constant pressure.

[0110] Expansion and energy release stage (400-500s): Compressed air is released to drive the expander to generate electricity, meeting the electricity demand.

[0111] 1. System dynamic model construction:

[0112] Compressor / expander modeling: A semi-empirical model is used to describe the variable operating condition characteristics, with a rated pressure ratio of π. c,0 =3.3924, rated isentropic efficiency η c,0 =0.9, rated speed 670 rad / s. The inlet guide vane angle θIGV of the first-stage compressor is adjustable from -20° to 60°.

[0113] Heat exchanger modeling: A quasi-one-dimensional dynamic model is adopted, decomposing each heat exchanger into N=6 sub-units in spatial dimension, with the total air-side conductivity KA. a =9.4 kJ / (K·s), total thermal conductivity KA on the heat transfer oil side o =6.8 kJ / (K·s).

[0114] Cavity module modeling: Considering the total volume of compressor / expander, air duct, and heat exchanger, V vol =2m³, pressure loss coefficient k vol =0.02, pressure recovery coefficient σ ap =(1-k vol )³=0.941

[0115] Gas storage tank modeling: The flexible gas storage tank is located at a depth of 300m underwater, with a constant gas storage pressure P. as =ρ sw gH + Pa = 3099 kPa, gas storage temperature equals seawater temperature T. as =T sw =288.15 K. The dynamics of the gas storage mass are governed by the mass conservation equation. describe

[0116] Modeling of heat transfer oil tanks: The hot oil tank is an insulating material (heat dissipation coefficient 0), and the cold oil tank is a heat transfer material (heat dissipation coefficient λ). ot =0.651 kW / K), the total tank volume satisfies the mass conservation of heat transfer oil.

[0117] Motor / generator modeling: Motor efficiency η m =0.94, generator efficiency η g =0.94, oil pump mechanical efficiency η p =0.9, considering the moment of inertia J and frequency damping coefficient D p Impact

[0118] 2. Determination of rated operating conditions:

[0119] The system's rated parameters were determined through performance index optimization.

[0120] When G o / G a When the efficiency η increases from 1.0 to 3.0, the system cycle efficiency η sIt first increases and then decreases, reaching its maximum value of 54.2% at point 2.1.

[0121] Inlet pressure P of the gas storage tank ainas With G o / G a Increasing monotonically as G increases o / G a When P > 1.8, ainas It exceeds the minimum pressure requirement of 3100 kPa for underwater gas storage.

[0122] Considering both efficiency and pressure requirements, the rated heat transfer oil-to-air flow ratio is determined to be G. o / G a =2.1.

[0123] The final rated operating parameters are shown in Table 1:

[0124] Table 1 Final Rated Operating Condition Parameters

[0125] parameter Compression stage expansion phase Airflow rate (kg / s) 6.178 18.535 (3x compression) Heat transfer oil flow rate (kg / s) 12.974 38.923 Compressor / expander speed (rad / s) 670 670 Pressure ratio / expansion ratio at each level 3.3924 2.8929 Runtime (s) 300 100

[0126] 3. Multivariable controller design:

[0127] Control structure determined: The system is a dual-input dual-output structure.

[0128] Compression stage: Control variable θ IGV (Inlet guide vane angle) and G o,c (Cold heat transfer oil flow rate); Controlled quantity W c (Total power consumption) and P ainas (Gas storage tank inlet pressure).

[0129] Expansion phase: Control variable G a,e (Inlet airflow) and G o,e (Heat transfer oil flow rate); Controlled quantity W e (Power generation) and T o,e (Outlet temperature of heat transfer oil).

[0130] Nonlinear analysis: The Vinnicombe spacing is used to measure the differences between models under different operating conditions. In the compression stage, the pressure loop has Vg > 0.6 below 40% operating condition, and in the expansion stage, the temperature loop has Vg > 0.5 below 70% operating condition, indicating that the system exhibits strong nonlinearity.

[0131] Coupling Analysis: Through Gershgorin band and RGA analysis, the system transfer function matrix is ​​a non-diagonal dominant matrix, and the low-frequency coupling exponent λ is... 11 The range of 0.6-0.9 confirms the existence of strong coupling between the loops.

[0132] 4. Controller parameter tuning and compensation design:

[0133] Based on the identified 100% operating condition transfer function, the controller parameters are tuned according to the aforementioned formula.

[0134] Controller I (Power Consumption Loop): Main Transfer Function G IGV -W c (s) = -23.45 / (0.68s+1)³, design compensation element G cp1 (s) = 1 / (0.68s + 1)², parameter b0 = -34.49, ω c =1.47, ω o =14.71

[0135] Controller II (Gas Storage Pressure Loop): Main Transfer Function G o -P(s)=40.83 / (2.64s+1) 5 Design compensation stage G cp2 (s)=1 / (2.64s+1) 4 Parameter b0=15.46, ω c =0.38, ω o =3.79

[0136] Controller III (Power Generation Loop): Main Transfer Function G a -W e (s) = 242.28 / (0.59s+1)³, design compensation element G cp3 (s) = 1 / (0.59s + 1)², parameter b0 = 410.64, ω c =1.69, ω o =16.95

[0137] Controller IV (Heat Transfer Oil Temperature Loop): Main Transfer Function G o -T(s)=1.52 / (2.24s+1) 4 Design compensation stage G cp4 (s) = 1 / (2.24s + 1)³, parameter b0 = 0.68, ω c =0.45, ω o =4.46

[0138] Compared with traditional PID controller parameters, ADRC parameters have clear physical meaning and a systematic tuning method.

[0139] 5. Control performance verification:

[0140] Step response performance: The power consumption setpoint is reduced from 3000kW to 2500kW in 100s, and the gas storage pressure setpoint is increased from 3100kPa to 3300kPa in 200s. The distributed ADRC compensation reduces the settling time by 47%, overshoot by 90%, and disturbance rejection recovery time by 45%.

[0141] Compression stage performance: The total power consumption setpoint decreased from 3000kW to 2500kW in 100s, while the gas reservoir inlet pressure setpoint increased from 3100kPa to 3300kPa in 200s. For the power consumption control loop, the system under distributed compensation ADRC control returned to a stable state more quickly. At 100s, distributed compensation ADRC showed better tracking performance than PID control, with smaller settling time and overshoot. Furthermore, the change in inlet heat transfer oil flow rate caused by the change in gas reservoir pressure setpoint was relatively small, resulting in less coupling effect in this loop. For the gas reservoir pressure control loop, distributed compensation ADRC reached the 3100kPa setpoint more quickly in the initial stage. Facing disturbances from changes in the inlet guide vanes at 100s and changes in the gas reservoir inlet pressure setpoint at 200s, distributed compensation ADRC exhibited superior tracking and disturbance rejection performance.

[0142] Expansion Phase Performance: The setpoint for total power generation decreases from 5000kW to 4500kW in 80 seconds, and the setpoint for outlet heat transfer oil temperature decreases from 330K to 320K in 140 seconds. Under this condition, the main loop variation range is significantly larger than the fluctuations under the influence of coupled loop disturbances, with overshoot not exceeding 2%, resulting in zero adjustment time for both disturbances. For the total power generation control loop, the distributed compensation ADRC can reach a stable state faster and more accurately under both the 80-second setpoint change and the 140-second inlet heat transfer oil disturbance. For the outlet heat transfer oil temperature control loop, the distributed compensation ADRC can reach the initial setpoint in approximately 30 seconds with a small overshoot and a fast adjustment time. When the airflow disturbance occurs at 80 seconds, the PID controller has not yet fully stabilized, but the distributed compensation ADRC can quickly counteract the disturbance. Furthermore, the distributed compensation ADRC exhibits better tracking performance when the outlet heat transfer oil temperature setpoint decreases.

[0143] Stable Region Comparison: Ostrowski band analysis shows that the stable range of the distributed compensation ADRC during the compression phase is [0, 1.3] and [0, 12], while that of the PID is only [0, 9] (the power consumption loop is unstable); the stable range during the expansion phase is [0, 7.5] and [0, 15], significantly larger than that of the PID ([0, 6] and [0, 12]).

[0144] 7. Summary of Key Technological Advantages:

[0145] Effectiveness of the compensation mechanism: The compensation mechanism matches the ESO observation bandwidth with the dynamic characteristics of the object, reducing the observation error from 15% to less than 3%.

[0146] Decoupling mechanism: Treating loop coupling as an expanded state and performing real-time observation compensation, the RGA exponent drops from 0.85 to below 0.3, achieving approximate decoupling.

[0147] Robustness: It has a strong ability to adapt to parameter perturbations and changes in operating conditions, and can remain stable within ±20% of the identification model error.

[0148] Industrial applicability:

[0149] This invention has been successfully applied to the simulation and multivariable operation control of underwater isobaric compressed air energy storage systems, and has the following industrial practical value:

[0150] The project is easy to implement: the distributed structure does not require a complex decoupling network, each loop controller can be designed and debugged independently, and maintenance is convenient.

[0151] Wide range of applications: It is not only applicable to energy systems, but can also be extended to multivariable process control in fields such as chemical, power, and machinery.

[0152] Significant economic benefits: By improving control performance, the energy cycle efficiency of the isobaric compressed air energy storage system can be increased by 3%-5%, and the annual power generation can reach several million yuan.

[0153] High reliability: Strong robustness ensures stable operation of the system under parameter perturbations and changes in operating conditions, reducing downtime due to failures.

[0154] Low computational resource requirements: Low-order controllers have a simple structure and are suitable for embedded controllers and real-time control systems.

[0155] The control execution module includes a compression stage control unit and an expansion stage control unit. The compression stage control unit is used to adjust the flow rate of cold heat transfer oil and the inlet guide vane angle of the first-stage compressor. The expansion stage control unit is used to adjust the flow rate of compressed air and the flow rate of heat transfer oil.

[0156] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

[0157] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A distributed compensation active disturbance rejection control method for isobaric compressed air energy storage systems, characterized in that, Including the following steps: For a high-order multivariable isobaric compressed air energy storage system, an input-output relationship model is constructed based on the system transfer function matrix. The input-output relationship model includes: compressor / expander modeling, heat exchanger modeling, cavity module modeling, air storage tank modeling, heat transfer oil tank modeling, and motor / generator modeling. The control structure is determined based on different working stages. In the compression stage, the control variables include the inlet guide vane angle and the flow rate of cold heat transfer oil, while the controlled variables include the total power consumption and the inlet pressure of the gas storage tank. In the expansion stage, the control variables include the inlet air flow rate and the flow rate of hot heat transfer oil, while the controlled variables include the power generation and the outlet heat transfer oil temperature. A separate compensated ADRC controller is designed for the diagonal transfer function corresponding to each loop of the isobaric compressed air energy storage system. The loop includes: power consumption loop, gas storage pressure loop, power generation loop and heat transfer oil temperature loop. Based on the bandwidth parameterization method, the distributed compensation ADRC controller is parameter-tuned to determine the gain parameters of the extended state observer and the state feedback control law in each loop, thus obtaining the designed distributed compensation ADRC controller. The designed distributed compensation ADRC controller is applied to the compression and expansion phases of an isobaric compressed air energy storage system.

2. The distributed compensation active disturbance rejection control method for isobaric compressed air energy storage systems according to claim 1, characterized in that, The ADRC controller includes a compensation element, an extended state observer, and a state feedback control law.

3. The distributed compensation active disturbance rejection control method for isobaric compressed air energy storage systems according to claim 2, characterized in that, Based on the bandwidth parameterization method, parameter tuning of the distributed compensation ADRC controller is performed, including the following steps: Based on a distributed compensation ADRC controller of a set order, the ESO state equation is established, the ESO gain is determined by bandwidth parameterization, and the two-degree-of-freedom system closed-loop transfer function is derived by combining the state feedback control law. The controlled process is approximated by simulation modeling or system identification. The controller order is selected and the compensation element is designed. Based on the bandwidth parameterization method, the ESO gain and the controller feedback gain are set respectively to achieve parameter co-optimization and obtain the designed distributed compensation ADRC controller.

4. The distributed compensation active disturbance rejection control method for isobaric compressed air energy storage systems according to claim 3, characterized in that, The distributed compensation ADRC controller includes a compensation element, an extended state observer, and a state feedback control law, wherein the compensation element is used to reduce the higher-order system object to match the order of the ADRC controller, thereby improving the observation accuracy of the extended state observer. Each loop is equipped with an independent extended state observer to observe the state variables and total disturbance of the loop; The state feedback control law performs disturbance compensation and feedback control based on the observations of the extended state observer.

5. The distributed compensation active disturbance rejection control method for isobaric compressed air energy storage systems according to claim 4, characterized in that, When the distributed compensation ADRC controller is applied to the compression stage, the flow rate of the cold heat transfer oil and the angle of the guide vane at the inlet of the first-stage compressor are used as control variables, and the outlet pressure of compressed air and the total power consumption of compression are used as controlled variables. The control objective of the compression stage is to maintain the inlet pressure of the air storage tank to meet the underwater storage pressure requirements, while matching the total power consumption of compression with the power grid deficit.

6. The distributed compensation active disturbance rejection control method for isobaric compressed air energy storage systems according to claim 5, characterized in that, When the distributed compensation ADRC controller is applied to the expansion stage, the compressed air flow rate and the heat transfer oil flow rate are the controlled variables, and the generator output power and the outlet heat transfer oil temperature are the controlled variables. The control objective during the expansion phase is to ensure that the generator output power tracks the user load demand while maintaining the outlet heat transfer oil temperature within a preset safe range.

7. The distributed compensation active disturbance rejection control method for isobaric compressed air energy storage systems according to claim 3, characterized in that, The control process of the distributed compensation ADRC controller includes the following steps: By adjusting and adapting the dynamic characteristics of the high-order system through the diagonal transfer function of the corresponding loop of the compensation link, the system order is kept consistent with the preset order of the controller. The operating state variables of the loop are captured in real time by a dedicated extended state observer, and various influencing factors such as coupling interference between loops and external environmental fluctuations are integrated into a total disturbance and observed synchronously. The state feedback control law receives observation data from the extended state observer. On the one hand, it provides targeted compensation for the identified total disturbance. On the other hand, it executes feedback adjustment logic according to the preset control target, ultimately achieving precise control of a single loop and multi-loop coordinated action to complete the control task of the entire multivariable system.

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