Analog PID-based control instruction quick decomposition method for industrial load

Abstract

The application provides a kind of based on simulation PID industrial load-oriented control instruction quick decomposition method, it is related to control engineering technical field.Establish energy storage-thermal power-industrial load combined system model, propose control instruction decomposition method, design fuzzy PID controller, to realize the control of industrial load, thermal power unit and energy storage system respectively, establish control instruction decomposition model, simulation analysis and introduce the frequency evaluation index under step load disturbance, verify the effectiveness of control instruction decomposition model, and assess the frequency stability of system when facing load disturbance.The application introduces fuzzy PID control algorithm on the basis of traditional low wave filter algorithm, divides the primary control instruction after frequency division into low-frequency component PL of thermal power unit and industrial load and high-frequency component PH of energy storage, better stimulates industrial load to actively participate in system regulation, improves the stability and regulation precision of system frequency, and promotes efficient operation of new power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of control engineering technology, and particularly relates to a control instruction quick decomposition method for industrial load based on analog PID. BACKGROUND

[0002] There are some significant challenges in the field of industrial load regulation. First, due to the variety of industrial load equipment types, different regulation mechanisms, and the sensitivity restrictions of production information, there are obstacles in the information exchange between industrial load and power grid in the two-way regulation. This situation makes industrial load unable to fully integrate the potential value of its adjustable load into its production and operation strategy, thereby affecting the operation efficiency and flexibility of the entire system.

[0003] Secondly, there is a general demand for control decisions to be transferred from the system level to the local level in current industrial load regulation. This change is to improve control speed and make the system respond and adapt to frequent changes in actual application conditions more quickly. However, it is not easy to achieve this change, and many technical and management challenges need to be addressed, including how to ensure the accuracy, stability and reliability of local control, and how to coordinate the relationship between local control and overall system scheduling.

[0004] In addition, the research on control instruction at home and abroad mainly focuses on the fields of unmanned aerial vehicle formation, multi-agent system and power system voltage control, while the research on large-capacity industrial load regulation is relatively less. This leads to the relative lag of technical accumulation and research progress in the field of industrial load regulation, and there is a research gap and deficiency for specific problems of industrial load regulation.

[0005] In summary, current industrial load regulation faces many challenges and deficiencies, and innovative research and technological breakthroughs are needed to solve these problems, thereby promoting the progress and application of industrial load regulation technology.

[0006] At present, the research on the use of analog PID algorithm for industrial load regulation, China patent "CN202310887388.0 an industrial flexible load regulation method and system suitable for power grid regulation" put forward an industrial flexible load regulation method suitable for power grid regulation, this technology relates to the field of industrial flexible load regulation, specifically, it is an industrial flexible load regulation method and system suitable for power grid regulation. The method includes the following key steps: obtaining the total load data of the typical industrial load in the target area in N operation period, dividing the total load in N operation period to obtain the optimal segmentation with the minimum load fluctuation, determining the adjustable capacity range Q of the total load according to the optimal segmentation, determining the constraint condition for regulating the typical industrial flexible load based on the adjustable capacity Q, and finally regulating the typical industrial load according to the constraint condition. The patent is relatively traditional in the way of load regulation, and it fails to fully utilize the fuzzy PID control algorithm, thus there is a defect in the balance between real-time and stability. In addition, the adaptive adjustment strategy of the patent in dealing with frequency change and stability is relatively simple, and it cannot fully respond to the complex industrial load regulation demand, and lacks flexibility for different load conditions. SUMMARY

[0007] The technical problem to be solved by the present application is to overcome the shortcomings of the prior art. The present application provides a control instruction rapid decomposition method for industrial load based on analog PID, which introduces fuzzy PID control algorithm on the basis of traditional low wave filter algorithm, and divides the control instruction after frequency division into low frequency component PL of thermal power unit and industrial load and high frequency component PH of energy storage, so as to better stimulate industrial load to actively participate in system regulation, improve the stability and regulation accuracy of system frequency, and promote the efficient operation of new power system.

[0008] To solve the above technical problems, the technical solution adopted by the present application is:

[0009] A control instruction rapid decomposition method for industrial load based on analog PID, comprising the following steps:

[0010] Step 1: establish a combined system model of energy storage-thermal power-industrial load; respectively construct energy storage element model, thermal power unit model and generator-load model, and then integrate them together to form a primary frequency regulation model of the combined system, which will consider the interaction and influence among energy storage element, thermal power unit and industrial load, and realize real-time regulation of energy storage element and thermal power unit through monitoring the change of system frequency, so as to realize stable control of system frequency and balance of supply and demand;

[0011] Step 2: propose a control instruction decomposition method; adaptively adjust the cutoff frequency of low-pass filter, based on the adjusted system frequency signal, use frequency division technology to decompose the control instruction into control instructions of different frequency components;

[0012] Step 3: Design fuzzy PID controllers to control industrial loads, thermal power units, and energy storage systems separately.

[0013] Step 4: Establish a control instruction decomposition model;

[0014] Step 5: Perform simulation analysis and introduce frequency evaluation indicators under step load disturbance to verify the effectiveness of the control instruction decomposition model and evaluate the frequency stability of the system in the face of load disturbance.

[0015] Furthermore, in step 1, the energy storage-thermal power-industrial load combined system is composed of a total of M energy storage elements, multiple thermal power equivalent groups, and industrial loads. The energy storage element model, thermal power unit model, and generator-load model are specifically constructed as follows:

[0016] Steam turbine transfer function G fn (s) is:

[0017]

[0018] Among them, T CH represents the time constant of the steam turbine, T RH Represents the time constant of the reheater, F HP Represents the gain of the reheater; the transfer function of the thermal power unit speed regulator G F (s) is:

[0019]

[0020] Among them, T G is the time constant of the thermal power unit speed governor;

[0021] The generator-load model transfer function G(s) is:

[0022]

[0023] Where H represents the inertia constant of the generator, and D represents the load damping coefficient of the generator.

[0024] Further, in the integration of energy storage, thermal power and industrial load models in step 1, a state space model is used to describe the charging and discharging process of the energy storage system, considering its response rate, efficiency, maximum and minimum energy storage capacity, to ensure a fast response to frequency changes; the thermal power unit model should include its inertia response, regulation rate and time constant, considering the regulation characteristics of the thermal power unit and the dynamic characteristics of start-up and shutdown, the industrial load is regarded as a controllable load, which is adjusted through demand response strategy, the model includes the dynamic controllability of the load and its sensitivity to frequency changes; local energy storage controllers, steam turbine governors, demand response controllers, distributed control of energy storage subsystems, thermal power units and auxiliary control and detection equipment are used to coordinate the power distribution of different resources to optimize the global performance indicators; at the same time, real-time monitoring of system frequency changes is carried out through a sensor network to obtain real-time data, and the output power of energy storage and thermal power units is dynamically adjusted according to real-time data; the mutual influence between energy storage, thermal power and load is analyzed, and dynamic behavior simulation and sensitivity analysis are carried out using simulation tools to ensure that the response of each part is coordinated and will not cause self-contradictory operation instructions; finally, the effectiveness of each scheme is tested using the simulation platform MATLAB, and interactive testing is carried out in the simulation environment to identify potential system dynamic problems.

[0025] Further, in step 2, the method of adaptively adjusting the cutoff frequency of the low-pass filter is proposed for the change in system frequency and its rate of change, aiming to dynamically adjust the characteristics of the filter according to the change in system frequency and its rate of change to better respond to frequency fluctuations; the command decomposition is realized using a first-order low-pass filter, which dynamically adjusts the cutoff frequency of the low-pass filter by real-time monitoring of the change in system frequency;

[0026] The specific expression of the low-pass first-order filter is as follows:

[0027]

[0028] where T1 is the filter time constant;

[0029] When the control command is decomposed, the frequency signal is divided into a low-frequency component P L and a high-frequency component P H , where the low-frequency component P L is used as the control command for industrial load, and the high-frequency component P H is used as the control command for energy storage elements.

[0030] Further, the specific method of step 3 is as follows:

[0031] Step 3.1: Design a two-input, three-output fuzzy PID controller, with frequency and frequency rate as inputs, and basic domains of input and output variables determined according to actual system conditions;

[0032] wherein the frequency f is set d (t) is the difference between the set frequency f and the actual output frequency f(t), as shown in the following equation:

[0033] Δf(t) = f d (t) - f(t)

[0034] The PID control equation u(t) with frequency and frequency rate as input is shown as follows:

[0035]

[0036] wherein k p represents the proportional coefficient; k i represents the integral time constant; and k d represents the differential time constant;

[0037] Step 3.2: Data acquisition and frequency domain analysis

[0038] Periodically acquire power data using a data acquisition device, and ensure that the data includes a time stamp; perform spectrum conversion using the acquired data to convert the power signal from the time domain to the frequency domain; generate PID control oscillation data based on the results of the frequency domain analysis to describe the oscillation of the control system; determine whether the gain of the fuzzy PID controller needs to be adjusted based on the PID control oscillation data, and select to increase or decrease the gain of the PID controller according to the nature of the oscillation to suppress or alleviate the oscillation; generate new PID controller gain parameter data according to the results of the gain adjustment for application in actual control;

[0039] Step 3.3: Adjust the gain of the PID and generate a fuzzy rule base

[0040] Acquire control instruction data and import it into a normal PID controller for defuzzification to obtain reasoning result data; integrate the reasoning result data to form a fuzzy rule base; use the fuzzy rule base to set the simulation step size of the PID controller to determine the sampling time of the PID control system; and perform fuzzy level division according to the sampling time to generate PID adaptive fuzzy level data.

[0041] The fuzzy universe of the frequency rate is [-1, 1], and the fuzzy subsets include negative big NB, negative small NS, negative zero NZ, positive zero PZ, positive small PS, and positive big PB.

[0042] The fuzzy universe of the frequency is [0, 1], and the fuzzy subsets include negative big NB, negative small NS, zero ZE, positive small PS, and positive big PB.

[0043] The normalized fuzzy domain of the filtering time constant T1 is [0,1], and the fuzzy subsets include negative large NB, negative medium NM, negative small NS, negative zero NZ, positive zero PZ, positive small PS, positive medium PM, and positive large PB.

[0044] Furthermore, the specific method of step 4 is:

[0045] Step 4.1: Build a simulation model;

[0046] Establish a simulation model, including energy storage components, thermal power units, industrial loads, and a control instruction decomposition model. When setting simulation conditions, comprehensively consider the system initial state, load size, thermal power unit response speed, energy storage component response rate, efficiency, maximum and minimum energy storage capacity, thermal power unit regulation time constant and unit regulation power, the dynamic controllability of the industrial load, and its sensitivity to frequency changes.

[0047] Step 4.2: Determine safety performance constraints and pre-process the frequency signal;

[0048] Based on comprehensive consideration of energy storage capacity, thermal power unit regulation capabilities, and industrial load response limitations, various technical and operational constraints are mapped to executable instruction indicators to meet safety and performance requirements, thereby performing frequency signal limiting preprocessing;

[0049] Step 4.3: Control instruction decomposition;

[0050] After preprocessing, the system frequency signal is decomposed into control instructions suitable for industrial loads, thermal power units and energy storage systems. This includes dividing the frequency signal into low-frequency and high-frequency components, and generating corresponding control instructions based on the needs of different subsystems.

[0051] For industrial loads, design corresponding control strategies to meet their needs and characteristics, involving the control of the following operations: turning on and off or adjusting power output of industrial loads, overload protection and current limiting control, peak cutting and peak shifting, demand response and energy-saving scheduling;

[0052] For energy storage systems, the charging and discharging status is controlled and flexibly adjusted to participate in the electricity market to ensure the energy balance and frequency stability of the system.

[0053] Continuously monitor system frequency changes, adjust control instructions in real time, and perform feedback control; by combining with the frequency feedback information of the actual system, continuously optimize and adjust control instructions to ensure stable control and coordinated operation of the system frequency.

[0054] Furthermore, the step 5 specifically includes:

[0055] Step 5.1: Introduce a step load disturbance in a steady state to simulate the situation where the system faces a sudden change in external load.

[0056] Under the step load disturbance, the response capability of the system when facing a sudden change in load can be observed, including the change in frequency, system stability, dynamic response speed, and steady-state error performance. Under the continuous load disturbance, the response of the system under the condition of a continuously changing load over a long period of time can be observed to evaluate the dynamic performance and stability of the system.

[0057] Step 5.2: Introduce frequency evaluation indicators, including maximum frequency deviation Δf max and steady-state frequency deviation Δf, to evaluate the frequency stability of the system under load disturbance; the maximum frequency deviation Δf max and the steady-state frequency deviation Δf are as follows:

[0058] Δf max = max | f 测量 -f 额定 |

[0059]

[0060] where f 测量 is the actual measured frequency; f 额定 is the rated frequency of the system; and f 平均 is the average value of the measured frequency over a period of time.

[0061] The maximum frequency deviation focuses on the response capability of the system to instantaneous or sudden disturbances, while the steady-state frequency deviation focuses on the frequency deviation of the system in a long-term stable operation state.

[0062] Step 5.3: Run the simulation model and record the results

[0063] Run the simulation model in Simulink, record the frequency change of the system under load disturbance, and calculate the frequency evaluation indicators. By analyzing the results of the indicators, evaluate the performance of the control instruction decomposition model under different load conditions, and verify its effectiveness and reliability in improving the frequency stability of the system.

[0064] The beneficial effects produced by the above technical scheme are that the control instruction quick decomposition method for industrial load based on analog PID provided by the application combines the traditional low-pass filter algorithm and the fuzzy control algorithm, retains the simplicity of the traditional algorithm, fully gives play to the adaptability and flexibility of the fuzzy PID control algorithm, can achieve a good balance between real-time and stability, and ensures stable operation of the system frequency; the method of introducing adaptive adjustment of the low-pass filter cutoff frequency and the adaptive control strategy of the frequency change rate are introduced, the application realizes intelligent response to system frequency change, effectively balances the energy regulation between industrial load and energy storage system, improves the frequency regulation accuracy and stability of the system, saves energy resources, meets the requirements of sustainable development, and verifies the effectiveness of the control instruction decomposition model and evaluates the frequency stability of the system when facing load disturbance. This helps to further optimize system design and regulation strategy, ensures the stability and reliability of the system under various working conditions. In summary, the application has significant technical advantages and application prospects in improving industrial load regulation efficiency, realizing control instruction quick decomposition, improving system stability and improving energy utilization efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 The control instruction quick decomposition method for industrial load based on analog PID provided by the application is provided.

[0066] Figure 2 The frequency membership function curve provided by the application is provided.

[0067] Figure 3 The frequency membership function curve provided by the application is provided.

[0068] Figure 4 The frequency change rate membership function curve provided by the application is provided.

[0069] Figure 5 The filter time constant membership function curve provided by the application is provided.

[0070] Figure 6 The primary frequency coordination control strategy provided by the application is provided.

[0071] Figure 7 The simulation model provided by the application is provided.

[0072] Figure 8 The random load disturbance waveform provided by the application is provided.

[0073] Figure 9A system total output power comparison chart provided for the embodiment of the present application;

[0074] Figure 10 A frequency deviation comparison chart provided for the embodiment of the present application;

[0075] Figure 11 A steady-state frequency deviation comparison chart provided for the embodiment of the present application. DETAILED DESCRIPTION

[0076] The specific embodiments of the present application are described in further detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application, but are not used to limit the scope of the present application.

[0077] The present application integrates energy storage, thermal power units and industrial loads into a combined system, considers the interaction and influence between them, and realizes stable control of system frequency and balance of supply and demand; combined with fuzzy control algorithm and PID control algorithm, a fuzzy PID controller is designed for the respective control of industrial load, thermal power unit and energy storage system, the system frequency signal is decomposed into control instructions suitable for industrial load and energy storage system to ensure system stability and meet different control requirements; the frequency evaluation index is introduced to evaluate the frequency stability of the system and verify the effectiveness of the control instruction decomposition model. Taking the load of a certain factory as an example, the control instruction of industrial load at the regulation node is quickly decomposed as shown in Figure 1 , and the specific steps are described as follows.

[0078] Step 1: Establish a combined system model of energy storage-thermal power-industrial load.

[0079] The two-way regulation between industrial load and power grid faces information exchange obstacles, resulting in the failure of industrial load to fully utilize its potential adjustable value. In order to solve this problem, the present embodiment establishes a combined system including energy storage element model, thermal power unit model and generator-load model, which are then integrated together to realize primary frequency regulation model. Through this model, the system frequency can be stably controlled and the supply and demand can be balanced. This system design helps to fully utilize the potential value of adjustable load, thereby improving the stability and efficiency of the power grid.

[0080] The combined system model of energy storage-thermal power-industrial load integrates energy storage elements, thermal power units and industrial loads into a combined system, and fully considers the interaction and influence between them. By considering the charging and discharging characteristics, energy conversion efficiency of energy storage elements, power generation efficiency, start-stop response speed of thermal power units, and the dynamic characteristics and variation law of industrial load, the change of system frequency can be monitored in real time, and the charging and discharging state of energy storage elements and the power generation of thermal power units can be adjusted accordingly. At the same time, the industrial load is dynamically adjusted according to the demand, so as to achieve stable control of system frequency and balance of supply and demand.

[0081] In the integration of energy storage, thermal power and industrial load models, effectively handling the dynamic interactions between them is crucial to ensure the stability of system frequency and the balance of supply and demand. For energy storage systems, a state-space model is used to describe their charging and discharging process, taking into account their response rate, efficiency, maximum and minimum storage capacity, to ensure they can respond quickly to frequency changes. The thermal power unit model should include its inertia response, regulation rate and time constant, taking into account the regulation characteristics of thermal power units and the dynamic characteristics of start-up and shutdown. Industrial loads are considered controllable loads that can be adjusted through demand response strategies. The model includes the dynamic controllability of the load and its sensitivity to frequency changes. Local energy storage controllers, steam turbine governors, demand response controllers, distributed control of energy storage subsystems, thermal power units, and auxiliary control and detection equipment are used to coordinate power distribution among different resources to optimize global performance indicators. At the same time, real-time monitoring of system frequency changes is carried out through a sensor network to obtain real-time data, and the output power of energy storage and thermal power units is dynamically adjusted based on real-time data. The mutual influence between energy storage, thermal power and load is analyzed, and dynamic behavior simulation and sensitivity analysis are carried out using simulation tools to ensure that the responses of each part are coordinated and do not result in self-contradictory operation instructions. Finally, the effectiveness of each scheme is tested using the simulation platform MATLAB, and interactive testing is carried out in the simulation environment to identify potential system dynamic problems.

[0082] The energy storage-thermal power-industrial load combined system model is shown in Figure 2 , where s is the Laplace operator, ΔP L is the load disturbance, Δf is the system frequency deviation, K G is the unit regulation power of industrial load, K H is the unit regulation power of thermal power unit, K D is the unit regulation power of energy storage element, P j，ref is the rated power of the jth energy storage element, P j is the actual output power of the jth energy storage element, G(s) is the generator-load model transfer function, G F (s) is the thermal power unit governor transfer function, G fn (s) is the steam turbine transfer function.

[0083] The steam turbine transfer function is:

[0084]

[0085] where T CH represents the time constant of the steam turbine, T RH represents the time constant of the reheater, F HP represents the gain of the reheater.

[0086] The thermal power unit governor transfer function is:

[0087]

[0088] where T is the time constant of the thermal power unit governor. G is the time constant of the thermal power unit governor.

[0089] The transfer function of the generator-load model is:

[0090]

[0091] where H represents the inertia constant of the generator, and D represents the load damping coefficient of the generator.

[0092] Step 2: Propose control command decomposition method

[0093] This embodiment adopts the combination of traditional low-pass filter algorithm and fuzzy control algorithm. By combining these two algorithms, the self-adaptability and flexibility of fuzzy PID control algorithm are fully utilized on the basis of retaining the simplicity of traditional algorithm, providing effective methods and technical support for the coordinated operation of industrial load and energy storage system.

[0094] Firstly, an adaptive method of adjusting the cutoff frequency of the low-pass filter is proposed according to the characteristics of system frequency variation. By monitoring the system frequency variation in real time, the cutoff frequency of the low-pass filter is dynamically adjusted to ensure the effectiveness of the filtering effect and at the same time maintain the sensitivity to system frequency variation. This method can optimize the performance of the filter under different frequency disturbance conditions, ensuring the stability and response speed of the system.

[0095] The command decomposition is realized by a first-order low-pass filter, and the specific expression of the low-pass first-order filter is as follows:

[0096]

[0097] where T1 is the filter time constant.

[0098] Finally, according to the adjusted system frequency signal, the control command is decomposed into different frequency components by using frequency division technology. Specifically, the frequency signal is divided into low-frequency component P L and high-frequency component P H , where the low-frequency component P L is used to control the industrial load, and the high-frequency component P H is used to control the energy storage element.

[0099] Step 3: Fuzzy PID controller design.

[0100] The embodiment adopts a fuzzy PID controller design to realize the respective control of industrial load, thermal power generating unit and energy storage system. The fuzzy PID controller combines fuzzy control algorithm and PID control algorithm, has strong adaptability and flexibility, can guarantee system stability, and dynamically adjusts according to actual conditions to meet the control requirements of different system components.

[0101] The embodiment designs a two-input, three-output fuzzy PID controller, and frequency and frequency change rate are input. The basic domain of input and output variables is determined according to actual conditions of the system, and is as follows:

[0102] Δf(t)=f d (t)-f(t)

[0103]

[0104] Wherein, Δf(t) represents the difference between the set temperature f d (t) and the actual output temperature f(t); u(t) represents the PID control equation; k p represents the proportional coefficient; k i represents the integral time constant; and k d represents the differential time constant.

[0105] The data acquisition device is used to regularly obtain the running power data, and the data record contains a time stamp to facilitate subsequent time series analysis. The sampling frequency and sampling point number are determined, the power signal data is preprocessed, and the direct current component is removed. Usually, it is necessary to ensure that the data length is a power of 2, which can be realized by zero padding. These data use the built-in function fft of MATLAB to apply the FFT algorithm, and convert the power signal from the time domain to the frequency domain. Through the sampling frequency and the length of the FFT result, the frequency value on the frequency axis can be calculated, and the extracted time delay periodicity characteristic data is analyzed in the frequency domain to detect the oscillation of the controller, including finding the peak value or specific frequency component in the frequency spectrum. According to the result of the frequency domain analysis, the PID control oscillation data describing the oscillation of the control system is generated. According to these PID control oscillation data, it is determined whether the gain of the fuzzy PID controller needs to be adjusted. According to the nature of the oscillation, the gain of the PID controller is increased or decreased to suppress or alleviate the oscillation. After adjusting the gain, new PID controller gain parameter data is generated to be applied to the actual control.

[0106] On the other hand, the control instruction data is collected and introduced into the normal PID controller for anti-fuzzy processing to obtain reasoning result data. Then, the reasoning result data is integrated to form a fuzzy rule base, and the simulation step of the PID controller is set by using the rule base to determine the sampling time of the PID control system. According to the determined sampling time of the PID control system, fuzzy grade data of the PID adaptive fuzzy grade is generated. These processes aim to improve the stability and performance of the control system, so that it can better adapt to changes in the dynamic environment.

[0107] The fuzzy domain of the frequency change rate is [-1, 1], and the fuzzy subsets include: NB (negative big), NS (negative small), NZ (negative zero), PZ (positive zero), PS (positive small), and PB (positive big).

[0108] The fuzzy domain of the frequency is [0, 1], and the fuzzy subsets include: NB (negative big), NS (negative small), ZE (zero), PS (positive small), and PB (positive big).

[0109] The normalized fuzzy domain of the filter time constant T1 is [0, 1], and the fuzzy subsets include: NB (negative big), NM (negative medium), NS (negative small), NZ (negative zero), PZ (positive zero), PS (positive small), PM (positive medium), and PB (positive big). The fuzzy rules are shown in Table 1.

[0110] Table 1 Fuzzy rule table

[0111]

[0112] The membership functions of the input and output quantities are shown in Figure 3 , Figure 4 and Figure 5 . Specifically, when the absolute value of the frequency change rate increases, it indicates that the system frequency changes more dramatically, at which time the cutoff frequency of the low-pass filter should be moderately increased to speed up the response of the filter, smooth the output fluctuations of the thermal power unit and industrial load, and ensure the stability of the system frequency. Conversely, when the absolute value of the frequency change rate is small, it indicates that the system frequency changes slowly, at which time the cutoff frequency of the low-pass filter should be moderately reduced to reduce the response speed of the filter and reduce the output fluctuations of the energy storage element, saving energy resources.

[0113] By adaptively adjusting the cutoff frequency of the low-pass filter, different situations of system frequency change can be more flexibly handled, the energy regulation between industrial load and energy storage system can be effectively balanced, the frequency regulation accuracy and stability of the system can be improved, energy resources can be saved, and more intelligent and sustainable power regulation control can be achieved.

[0114] Step 4: Control instruction decomposition model modeling.

[0115] The main purpose of establishing the control instruction decomposition model is to convert the system frequency change signal into control instructions suitable for industrial loads and energy storage systems, thereby facilitating the coordinated operation of the system. This model includes energy storage elements, thermal power units, industrial loads, and control instruction decomposition modules in the simulation, as shown in Figure 6

[0116] When establishing the simulation model, various simulation conditions need to be considered, including the initial state of the system, the size of the load, the response speed of the thermal power unit, the response rate, efficiency, maximum and minimum energy storage capacity of the energy storage element, the adjustment time constant and unit adjustment power of the thermal power unit, the dynamic controllability of the industrial load, and the sensitivity parameters of the frequency change. On the basis of comprehensively considering the energy storage capacity, the adjustment capacity of the thermal power unit, and the response limit of the industrial load, the technical and operational constraints should be mapped to executable instruction indicators (such as power, energy, and adjustment rate) to meet the safety and performance requirements, thereby performing amplitude preprocessing on the frequency signal.

[0117] On the basis of comprehensively considering the characteristics of various devices and the preprocessing of the grid frequency signal, the state of charge (SOC) of the energy storage system, the maximum charge and discharge rate, and the temperature of the energy storage system should be combined with the current scheduling requirements to map the upper and lower limits of the charge and discharge instructions. At the same time, the thermal power unit should be based on the minimum output, the maximum ramp rate, the standby capacity, and the start-stop characteristics to determine the safe adjustment range it can provide in the current operating environment. For industrial loads, the key degree of demand for electrical energy should be combined with the process to set specific execution limits, response times, and time boundaries for the impact on important process links, and to ensure that there is no significant risk to important process links when executing the scheduling instructions. The control indicators of all the above devices should be connected with the preprocessing results of the frequency signal to ensure that fine scheduling and stability are achieved while considering the safety and economic benefits of operation.

[0118] After preprocessing, the system frequency signal is decomposed into control instructions suitable for industrial loads, thermal power units, and energy storage systems. These control instructions are generated based on the low-frequency and high-frequency components obtained by decomposition, and appropriate control strategies are designed for industrial loads and energy storage systems to meet their requirements and operating conditions. For industrial loads, appropriate control strategies are designed to meet their requirements and characteristics, involving the following operations: starting, stopping, or adjusting the power output of industrial loads, overload protection and current limiting control, peak cutting and peak shifting, demand response and energy-saving scheduling. For energy storage systems, the state of charge and flexible power market participation are controlled to ensure energy balance and frequency stability of the system.

[0119] ​During the simulation process, the system frequency changes are continuously monitored, and the control instructions are adjusted in real time, and feedback control is performed. By combining the frequency feedback information of the actual system, the control instructions are continuously optimized and adjusted to ensure stable control and coordinated operation of the system frequency. This process is a key link in the operation of the system, which can effectively improve the efficiency and stability of the system, and then realize the reliability and sustainability of the system. As shown in Figure 7 P G represents the primary frequency modulation instruction of the thermal power unit, P G，ref represents the theoretical power of the thermal power unit, P H represents the high-frequency component of the primary frequency modulation instruction, P L represents the low-frequency component of the primary frequency modulation instruction, P b represents the high-frequency component compensation value of the thermal power unit, P f represents the energy storage control power instruction, P a1 represents the output of the output constraint module 1, P r2 represents the theoretical power of the energy storage, P a2 represents the reference power of the energy storage element queue, P fess represents the output power of the energy storage element, P F，act represents the actual output power of the thermal power unit, and T1 represents the filter time constant.

[0120] Step 5: Simulation analysis and introduction of frequency evaluation index under step load disturbance.

[0121] Simulation analysis and introduction of frequency evaluation index under step load disturbance are carried out to verify the effectiveness of the control instruction decomposition model and evaluate the frequency stability of the system when facing load disturbance. First, in the stable state, step load disturbance is introduced to simulate the situation that the system faces sudden changes in external load. Load disturbance can include sudden increase or decrease of load, so as to observe the response ability of the system to load disturbance, as shown in Figure 8 .

[0122] The pure thermal power system, droop control and the joint system proposed in this embodiment are built in Simulink, and simulation research is carried out for two typical cases of step load and continuous load. By comparing and analyzing the simulation results of the three systems under different load disturbances, the effectiveness of the new strategy proposed in this embodiment in improving system performance and stability is verified.

[0123] Under step load disturbance, the response ability of the system to sudden changes in load can be observed, including the performance of frequency changes, system stability, dynamic response speed, steady-state error, etc. Under continuous load disturbance, the response of the system to the continuously changing load over a long period of time can be observed to evaluate the dynamic performance and stability of the system.

[0124] By comparing the simulation results of the pure thermal power system, droop control, and the combined system of the present embodiment under different operating conditions, the effectiveness of the proposed strategy can be verified, including its effects on improving system frequency stability, reducing the impact of load disturbances, etc.

[0125] Subsequently, frequency evaluation indicators such as the maximum frequency deviation |Δf max | and the steady-state frequency deviation Δf s are introduced to assess the frequency stability of the system under load disturbances. The maximum frequency deviation focuses on the system's response capability to instantaneous or sudden disturbances, while the steady-state frequency deviation focuses on the frequency deviation of the system in the long-term stable operation state.

[0126] The calculation formula of the maximum frequency deviation Δf max is as follows:

[0127] Δf max = max |f 测量 -f 额定 |

[0128] Where f 测量 is the actual measured frequency, and f 额定 is the rated frequency of the system.

[0129] The calculation formula of the steady-state frequency deviation Δf s is as follows:

[0130]

[0131] Where f 平均 is the average value of the measured frequency over a period of time.

[0132] Finally, the simulation model is run, and the frequency variation of the system under load disturbance is recorded, as shown in Figure 9 , and the frequency evaluation indicators such as Figure 10 and Figure 11 are calculated. By analyzing the results of these indicators, the performance of the control instruction decomposition model under different load conditions is evaluated, and its effectiveness and reliability in improving system frequency stability are verified.

[0133] In summary, the application provides a control instruction quick decomposition method for industrial load based on analog PID control. By establishing a joint system model of comprehensive energy storage, thermal power and industrial load, the stability control and supply-demand balance of the system frequency are realized. The fuzzy PID control algorithm is adopted, which not only retains the simplicity of the traditional control algorithm, but also enhances the adaptability and flexibility of the system, and provides an effective technical means to realize the coordinated operation of industrial load and energy storage system. On this basis, the application introduces a low-pass filter and frequency division technology, and adjusts the cutoff frequency of the filter through an adaptive method, realizing the quick and accurate decomposition of the control instruction. Combined with the design of the fuzzy PID controller, the application allows accurate real-time control of various types of loads, thereby improving the dynamic performance and stability of the entire system. Through the establishment of the simulation model and the introduction of the frequency evaluation index under the step load disturbance, the effectiveness of the control instruction decomposition model is further verified, ensuring the performance and stable operation of the system under various load conditions.

[0134] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the application, and not to limit them; although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope defined by the claims of the application.

Claims

1. A method for rapid decomposition of control instructions for industrial loads based on simulated PID, characterized by: The method comprises the following steps: Step 1: Establish a combined energy storage-thermal power-industrial load system model. Separately construct models for the energy storage component, thermal power unit, and generator-load. These models are then integrated to form a primary frequency regulation model for the combined system. This model considers the interactions and influences among the energy storage component, thermal power unit, and industrial load. By monitoring changes in system frequency, it enables real-time regulation of the energy storage component and thermal power unit, achieving stable control of system frequency and supply-demand balance. Step 2: Propose a control instruction decomposition method; adaptively adjust the low-pass filter cutoff frequency, and use frequency division technology to decompose the control instruction into control instructions of different frequency components based on the adjusted system frequency signal; Step 3: Design fuzzy PID controllers to control industrial loads, thermal power units, and energy storage systems separately. Step 4: Establish a control instruction decomposition model; the specific method is: Step 4.1: Build a simulation model; Establish a simulation model, including energy storage components, thermal power units, industrial loads, and a control instruction decomposition model. When setting simulation conditions, comprehensively consider the system initial state, load size, thermal power unit response speed, energy storage component response rate, efficiency, maximum and minimum energy storage capacity, thermal power unit regulation time constant and unit regulation power, the dynamic controllability of the industrial load, and its sensitivity to frequency changes. Step 4.2: Determine safety performance constraints and pre-process the frequency signal; Based on comprehensive consideration of energy storage capacity, thermal power unit regulation capabilities, and industrial load response limitations, various technical and operational constraints are mapped to executable instruction indicators to meet safety and performance requirements, thereby performing frequency signal limiting preprocessing; Step 4.3: Control instruction decomposition; After preprocessing, the system frequency signal is decomposed into control instructions suitable for industrial loads, thermal power units and energy storage systems. This includes dividing the frequency signal into low-frequency and high-frequency components, and generating corresponding control instructions based on the needs of different subsystems. For industrial loads, design corresponding control strategies to meet their needs and characteristics, involving the control of the following operations: turning on and off industrial loads or adjusting power output, overload protection and current limiting control, peak cutting and peak shifting, demand response and energy-saving scheduling; For energy storage systems, the charging and discharging status is controlled and flexibly adjusted to participate in the electricity market to ensure the energy balance and frequency stability of the system. Continuously monitor system frequency changes, adjust control instructions in real time, and perform feedback control; by combining with the actual system frequency feedback information, continuously optimize and adjust control instructions to ensure stable system frequency control and coordinated operation; Step 5: Perform simulation analysis and introduce frequency evaluation indicators under step load disturbance to verify the effectiveness of the control instruction decomposition model and evaluate the frequency stability of the system in the face of load disturbance.

2. The method for rapid decomposition of control instructions for industrial loads based on simulated PID according to claim 1, characterized in that: In step 1, the energy storage-thermal power-industrial load combined system is composed of M energy storage elements, multiple thermal power equivalent groups, and industrial loads. The energy storage element model, thermal power unit model, and generator-load model are constructed as follows: Steam turbine transfer function for: ； Among them, T CH represents the time constant of the steam turbine, T RH Represents the time constant of the reheater, F HP represents the gain of the reheater; Transfer function of thermal power unit speed regulator for: ； Among them, T G is the time constant of the thermal power unit speed governor; Generator-load model transfer function for: ； Where H represents the inertia constant of the generator, and D represents the load damping coefficient of the generator.

3. The method for rapid decomposition of control instructions for industrial loads based on simulated PID according to claim 2, characterized in that: In the integration of the energy storage, thermal power, and industrial load models in step 1, a state-space model is used to describe the charging and discharging process of the energy storage system, taking into account its response rate, efficiency, maximum and minimum energy storage capacity to ensure rapid response to frequency changes. The thermal power unit model should include its inertial response, regulation rate, and time constant, taking into account the regulation characteristics of the thermal power unit and the dynamic characteristics of startup and shutdown. The industrial load is regarded as a controllable load and adjusted through a demand response strategy. The model includes the dynamic controllability of the load and its sensitivity to frequency changes. A local energy storage controller, a steam turbine speed governor, and a demand response controller are used to distribute and control the energy storage subsystem, the thermal power unit, and auxiliary control and detection equipment to coordinate the power allocation of different resources to optimize global performance indicators. At the same time, system frequency changes are monitored in real time, and real-time data is obtained through a sensor network. The output power of the energy storage and thermal power units is dynamically adjusted based on the real-time data. The mutual influence between the energy storage, thermal power, and load is analyzed, and dynamic behavior simulation and sensitivity analysis are performed using simulation tools to ensure that the responses of each part are coordinated and do not cause contradictory operating instructions. Finally, the simulation platform MATLAB is used to test the effectiveness of each solution, and interactive testing is performed in the simulation environment to identify potential system dynamic problems.

4. The method for rapid decomposition of control instructions for industrial loads based on simulated PID according to claim 1 is characterized by: In step 2, the method for adaptively adjusting the cutoff frequency of the low-pass filter is proposed based on the change of the system frequency and its rate of change, aiming to dynamically adjust the characteristics of the filter according to the change of the system frequency and its rate of change to better respond to frequency fluctuations; instruction decomposition is achieved using a first-order low-pass filter, and the cutoff frequency of the low-pass filter is dynamically adjusted by monitoring the change of the system frequency in real time; The specific expression of the low-pass first-order filter is as follows: ； Among them, T1 is the filtering time constant; When the control instruction is decomposed, the frequency signal is divided into low frequency components P L and high frequency component P H , where the low-frequency component P L Used as control instruction for industrial load, and the high frequency component P H It serves as the control instruction of the energy storage element.

5. The method for rapid decomposition of control instructions for industrial loads based on simulated PID according to claim 1, characterized in that: The specific method of step 3 is: Step 3.1: Design a two-input, three-output fuzzy PID controller with frequency and frequency rate as inputs. The basic domain of input and output variables is determined according to the actual system conditions. Among them, the set frequency f d The difference between (t) and the actual output frequency f(t) , as shown below: ； The PID control equation u(t) with frequency and frequency change rate as input is expressed as follows: ； Among them, k p represents the proportional coefficient; k i represents the integral time constant; k d represents the differential time constant; Step 3.2: Data acquisition and frequency domain analysis; Use data acquisition equipment to regularly acquire power data, ensuring that the data includes timestamps. Use the acquired data to perform spectrum conversion, converting the power signal from the time domain to the frequency domain. Based on the results of the frequency domain analysis, generate PID control oscillation data to describe the oscillation of the control system. Based on the PID control oscillation data, determine whether the gain of the fuzzy PID controller needs to be adjusted. Depending on the nature of the oscillation, increase or decrease the gain of the PID controller to suppress or reduce the oscillation. Based on the results of the gain adjustment, generate new PID controller gain parameter data for actual control. Step 3.3: Adjust the PID gain and generate the fuzzy rule base; Collect control instruction data, import it into a normal PID controller for defuzzification processing, and obtain inference result data; integrate the inference result data to form a fuzzy rule base; use the fuzzy rule base to set the simulation step size of the PID controller, thereby determining the sampling time of the PID control system; perform fuzzy level classification based on the sampling time, and generate PID adaptive fuzzy level data; The fuzzy domain of the frequency change rate is [-1, 1], and the fuzzy subsets include negative large NB, negative small NS, negative zero NZ, positive zero PZ, positive small PS, and positive large PB; The fuzzy domain of frequency is [0, 1], and the fuzzy subsets include negative large NB, negative small NS, zero ZE, positive small PS, and positive large PB; The normalized fuzzy domain of the filtering time constant T1 is [0, 1], and the fuzzy subsets include negative large NB, negative medium NM, negative small NS, negative zero NZ, positive zero PZ, positive small PS, positive medium PM, and positive large PB.

6. The method for rapid decomposition of control instructions for industrial loads based on simulated PID according to claim 1, characterized in that: The step 5 specifically includes: Step 5.1: Introduce a step load disturbance in the steady state to simulate the system facing a sudden change in external load; Under step load disturbances, the system's response to sudden changes in load can be observed, including frequency changes, system stability, dynamic response speed, and steady-state error. Under continuous load disturbances, the system's response to load changes over a long period of time can be observed to evaluate the system's dynamic performance and stability. Step 5.2: Introduce frequency evaluation indicators, including maximum frequency deviation f max and steady-state frequency deviation f, used to evaluate the frequency stability of the system under load disturbance; maximum frequency deviation f max and steady-state frequency deviation f are as follows: ； ； Among them, f 测量 is the actual measured frequency; f 额定 is the rated frequency of the system; f 平均 is the average value of the frequencies measured over a period of time; The maximum frequency deviation focuses on the system's response to instantaneous or sudden disturbances, while the steady-state frequency deviation focuses on the system's frequency deviation under long-term stable operation; Step 5.3: Run the simulation model and record the results The simulation model was run in Simulink to record the frequency changes of the system under load disturbances and calculate the frequency evaluation index. By analyzing the index results, the performance of the control instruction decomposition model under different load conditions was evaluated to verify its effectiveness and reliability in improving the system frequency stability.

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