Industrial load-oriented control instruction rapid decomposition method based on simulation PID (Proportion Integration Differentiation)

Abstract

The invention provides an industrial load-oriented control instruction rapid decomposition method based on simulation PID, and relates to the technical field of control engineering. The method comprises the following steps: establishing an energy storage-thermal power-industrial load combined system model, proposing a control instruction decomposition method, designing a fuzzy PID (Proportion Integration Differentiation) controller to respectively control an industrial load, a thermal power generating unit and an energy storage system, establishing a control instruction decomposition model, performing simulation analysis, and introducing a frequency evaluation index under step load disturbance. And verifying the effectiveness of the control instruction decomposition model, and evaluating the frequency stability of the system when facing load disturbance. On the basis of a traditional low-wave filter-through algorithm, a fuzzy PID control algorithm is introduced, a primary control instruction after frequency division is divided into a low-frequency component PL of a thermal power generating unit and an industrial load and a high-frequency component PH of energy storage, the industrial load is better excited to actively participate in system adjustment, the stability and regulation and control precision of the system frequency are improved, and the control efficiency is improved. And efficient operation of a novel power system is promoted.

Description

Technical Field

[0001] The invention relates to the technical field of control engineering, and in particular to a method for quickly decomposing control instructions for industrial loads based on simulated PID. Background Art

[0002] There are some significant challenges in the field of industrial load regulation. First, due to the wide variety of industrial load equipment types, different regulation mechanisms, and the sensitivity of production information, there are obstacles to information exchange between industrial loads and the power grid. This situation prevents industrial loads from fully incorporating the potential value of their adjustable loads into their production and operating strategies, thus affecting the operating efficiency and flexibility of the entire system.

[0003] Secondly, there is a general demand to transfer control decisions from the system level to the local level in current industrial load regulation. This shift is to increase the control speed so that the system can respond more quickly and adapt to frequently changing actual application situations. However, it is not easy to achieve this shift, 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 command issues at home and abroad is mainly concentrated in the fields of drone formation, multi-agent system and power system voltage control, while there are relatively few studies on large-capacity industrial load regulation. This has led to the relatively lagging technical accumulation and research progress in the field of industrial load regulation, and there are gaps and deficiencies in the research of specific issues of industrial load regulation.

[0005] In summary, the current industrial load control faces many challenges and shortcomings, which need to be solved through innovative research and technological breakthroughs to promote the advancement and application of industrial load control technology.

[0006] At present, regarding the research on industrial load regulation using analog PID algorithm, the Chinese patent "CN202310887388.0 A method and system for industrial flexible load regulation suitable for power grid regulation" proposes an industrial flexible load regulation method suitable for power grid regulation. This technology involves the field of industrial flexible load regulation, specifically, 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 typical industrial loads in the target area in N operating periods, dividing the total load in N operating periods to obtain the optimal segmentation with the smallest load fluctuation, determining the adjustable capacity range Q of the total load based on the optimal segmentation, determining the constraint conditions for regulating the typical industrial flexible load based on the adjustable capacity Q, and finally regulating the typical industrial load according to the constraint conditions. The patent is relatively traditional in its load regulation method, and fails to make full use of the fuzzy PID control algorithm, thus having defects in the balance between real-time and stability. In addition, the patent's adaptive regulation strategy in dealing with frequency changes and stability is relatively simple, and it cannot fully respond to complex industrial load regulation needs, and lacks flexibility for different load conditions. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a method for quickly decomposing control instructions for industrial loads based on analog PID in view of the deficiencies of the above-mentioned prior art. On the basis of the traditional low-wave filtering algorithm, a fuzzy PID control algorithm is introduced to divide the primary control instructions after frequency division into a low-frequency component PL of thermal power units and industrial loads and a high-frequency component PH of energy storage, so as to better stimulate the 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 systems.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0009] A method for quickly decomposing control instructions for industrial loads based on simulated PID includes the following steps:

[0010] Step 1: Establish a joint system model of energy storage, thermal power and industrial loads; construct the energy storage component model, thermal power unit model and generator-load model respectively, and then integrate them together to form a primary frequency regulation model of the joint system. This model will consider the interaction and influence between energy storage components, thermal power units and industrial loads, and realize real-time regulation of energy storage components and thermal power units by monitoring changes in system frequency, so as to achieve stable control of system frequency and balance supply and demand;

[0011] Step 2: Propose a control instruction decomposition method; adaptively adjust the low-pass filter cutoff frequency, and 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 a fuzzy PID controller to control industrial loads, thermal power units and energy storage systems respectively;

[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 when facing load disturbance.

[0015] Furthermore, in step 1, the energy storage-thermal power-industrial load combined system is composed of a combined system primary frequency regulation model consisting 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] Among them, H represents the inertia constant of the generator, and D represents the load damping coefficient of the generator.

[0024] Furthermore, in the integration of the energy storage, thermal power and industrial load models in step 1, for the energy storage system, a state space model is used to describe its charging and discharging process, taking into account its response rate, efficiency, maximum and minimum energy storage capacity, to ensure that it can respond quickly 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 turbine speed governor, and a demand response controller are distributed. The energy storage subsystem, thermal power units, and auxiliary control and detection equipment are controlled in a coordinated manner to coordinate the power allocation of different resources to optimize the global performance indicators; at the same time, the system frequency changes are monitored in real time, real-time data is obtained through the sensor network, and the output power of the energy storage and thermal power units is dynamically adjusted according to the real-time data; the mutual influence between 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 tests are performed in the simulation environment to identify potential system dynamic problems.

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

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

[0027]

[0028] Wherein, T1 is the filtering time constant;

[0029] When the control instruction is decomposed, the frequency signal is divided into a low-frequency component 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.

[0030] Furthermore, the specific method of step 3 is:

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

[0032] Among them, the setting frequency f d The difference Δf(t) between the actual output frequency f(t) and the input frequency f(t) is as follows:

[0033] Δf(t)=f d (t)-f(t)

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

[0035]

[0036] Among them, k p Indicates the proportionality coefficient; k i represents the integral time constant; k d represents the differential time constant;

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

[0038] Use data acquisition equipment to regularly acquire power data, ensuring that the data includes timestamps; use the acquired data to perform spectrum conversion to convert the power signal from the time domain to the frequency domain; generate PID control oscillation data based on the results of frequency domain analysis to describe the oscillation of the control system; based on the PID control oscillation data, decide whether to adjust the gain of the fuzzy PID controller, and choose to increase or decrease the gain of the PID controller according to the nature of the oscillation to suppress or reduce the oscillation; based on the results of the gain adjustment, generate new PID controller gain parameter data for application in actual control;

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

[0040] Collect control instruction data, import it into a normal PID controller for anti-fuzzy 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 of the PID controller, so as to determine the sampling time of the PID control system; perform fuzzy level division according to the sampling time, and generate PID adaptive fuzzy level data;

[0041] 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;

[0042] 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;

[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: Establish a simulation model;

[0046] Establish a simulation model, including energy storage elements, thermal power units, industrial loads, and control instruction decomposition models; when setting simulation conditions, comprehensively consider the system initial state, load size, response speed of thermal power units, response rate of energy storage elements, efficiency, maximum and minimum energy storage capacity, regulation time constant and unit regulation power of thermal power units, dynamic controllability of industrial loads, and their sensitivity parameters 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 capability and industrial load response limitations, various technical and operational constraints are mapped to executable instruction indicators to meet safety and performance requirements, thereby limiting the frequency signal for 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, including dividing the frequency signal into low-frequency components and high-frequency components, and generating corresponding control instructions according to 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: opening, closing 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;

[0052] For energy storage systems, they are regulated by controlling the charging and discharging status and participating in the flexible 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 system frequency control and coordinated operation.

[0054] Furthermore, the step 5 specifically includes:

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

[0056] Under step load disturbance, the system's response capability to sudden load changes can be observed, including frequency changes, system stability, dynamic response speed, and steady-state error performance. Under continuous load disturbance, the system's response to load changes that continue over a long period of time can be observed to evaluate the system's dynamic performance and stability.

[0057] Step 5.2: Introduce frequency evaluation indicators, including the maximum frequency deviation Δf max and steady-state frequency deviation Δf, which are used 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] 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;

[0061] The maximum frequency deviation focuses on the system's ability to respond to instantaneous or sudden disturbances, while the steady-state frequency deviation focuses on the frequency deviation of the system under long-term stable operation;

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

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

[0064] The beneficial effects of the above technical scheme are: the control instruction rapid decomposition method based on analog PID for industrial load provided by the present invention, the fuzzy PID controller design combines the traditional low-pass filtering algorithm and the fuzzy control algorithm, while retaining the simplicity of the traditional algorithm, gives full play to the adaptability and flexibility of the fuzzy PID control algorithm, can achieve a good balance between real-time and stability, and ensure the stable operation of the system frequency; by introducing the method of adaptively adjusting the cutoff frequency of the low-pass filter and the adaptive control strategy of the frequency change rate, the present invention realizes the intelligent response to the system frequency change, effectively balances the energy regulation between the industrial load and the energy storage system, improves the frequency modulation accuracy and stability of the system, and also saves energy resources, which meets the requirements of sustainable development; simulation analysis and the introduction of frequency evaluation indicators under step load disturbance verify the effectiveness of the control instruction decomposition model, and evaluate the frequency stability of the system in the face of load disturbance. This helps to further optimize the system design and adjustment strategy, and ensure the stability and reliability of the system under various working conditions. In summary, the present invention has significant technical advantages and application prospects in improving the efficiency of industrial load regulation, realizing rapid decomposition of control instructions, improving system stability and improving energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A flow chart of a method for rapid decomposition of control instructions for industrial loads based on simulated PID provided in an embodiment of the present invention;

[0066] Figure 2 A schematic diagram of a frequency regulation model of an energy storage-thermal power-industrial load combined system provided in an embodiment of the present invention;

[0067] Figure 3 A frequency membership function curve diagram provided by an embodiment of the present invention;

[0068] Figure 4 A frequency change rate membership function curve diagram provided by an embodiment of the present invention;

[0069] Figure 5 A graph showing a membership function curve of a filtering time constant provided by an embodiment of the present invention;

[0070] Figure 6 A schematic diagram of a primary frequency modulation coordination control strategy provided by an embodiment of the present invention;

[0071] Figure 7 A schematic diagram of a simulation model provided by an embodiment of the present invention;

[0072] Figure 8 A random load disturbance waveform diagram provided by an embodiment of the present invention;

[0073] Fig. 9A comparison diagram of the total output power of the system provided by the embodiment of the present invention;

[0074] Fig.10 A frequency deviation comparison diagram provided by an embodiment of the present invention;

[0075] Fig.11 A comparison diagram of steady-state frequency deviation provided by an embodiment of the present invention. DETAILED DESCRIPTION

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

[0077] The present invention integrates energy storage, thermal power units and industrial loads into a joint system, taking into account the interactions and influences between them, to achieve stable control of system frequency and balance of supply and demand; combining fuzzy control algorithm and PID control algorithm, designing fuzzy PID controller for separate control of industrial loads, thermal power units and energy storage systems, decomposing system frequency signals into control instructions suitable for industrial loads and energy storage systems to ensure system stability and meet different control requirements; introducing frequency evaluation indicators to evaluate the frequency stability of the system, verifying the effectiveness of the control instruction decomposition model. Taking the load of a factory in a certain place as an example, the control instructions of the industrial load at the control node are quickly decomposed as follows: Figure 1 As shown, the specific steps are described as follows.

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

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

[0080] The energy storage-thermal power-industrial load joint system model integrates energy storage components, thermal power units and industrial loads into a joint system, and fully considers the interactions and influences between them. By considering the charging and discharging characteristics and energy conversion efficiency of energy storage components, as well as the power generation efficiency and start-stop response speed of thermal power units, combined with the dynamic characteristics and change laws of industrial loads, the changes in system frequency can be monitored in real time, and the charging and discharging status of energy storage components and the power generation power of thermal power units can be adjusted accordingly. At the same time, dynamic adjustments are made according to the needs of industrial loads to achieve the purpose of stable control of system frequency and balance of supply and demand.

[0081] In the integration of energy storage, thermal power and industrial load models, it is key to effectively handle the dynamic interaction between them 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, considering their response rate, efficiency, maximum and minimum energy storage capacity to ensure that they can respond quickly to frequency changes. The model of thermal power units should include their inertial response, regulation rate and time constant, considering the regulation characteristics of thermal power units and the dynamic characteristics of startup and shutdown. Industrial loads are regarded as controllable loads and can be adjusted through demand response strategies. The model includes the dynamic controllability of loads and their sensitivity to frequency changes. Local energy storage controllers, turbine speed governors, demand response controllers are used to distribute control of energy storage subsystems, thermal power units, and auxiliary control and detection equipment to coordinate the power allocation of different resources to optimize global performance indicators. At the same time, the system frequency changes are monitored in real time, real-time data is obtained through the sensor network, and the output power of energy storage and thermal power units is dynamically adjusted according to the real-time data. The mutual influence between 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 tests are performed in the simulation environment to identify potential system dynamic problems.

[0082] The energy storage-thermal power-industrial load combined system model is as follows: Figure 2 As shown, where s is the Laplace operator, ΔP L is the load disturbance, Δf is the system frequency deviation, K G Regulate power for industrial load units, K H is the unit adjustment power of the thermal power unit, K D is the unit regulation power of the 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 transfer function of the thermal power unit speed governor, G fn (s) is the turbine transfer function.

[0083] The steam turbine transfer function is:

[0084]

[0085] 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.

[0086] The transfer function of the thermal power unit speed regulator is:

[0087]

[0088] Among them, T G is the time constant of the thermal power unit speed governor.

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

[0090]

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

[0092] Step 2: Propose a control instruction decomposition method;

[0093] This embodiment adopts a combination of a traditional low-pass filtering algorithm and a fuzzy control algorithm. By combining these two algorithms, the adaptability and flexibility of the fuzzy PID control algorithm are fully utilized while retaining the simplicity of the traditional algorithm, providing effective methods and technical support for the coordinated operation of industrial loads and energy storage systems.

[0094] First, according to the characteristics of system frequency changes, a method for adaptively adjusting the cutoff frequency of the low-pass filter is proposed. By monitoring the system frequency changes in real time, the cutoff frequency of the low-pass filter is dynamically adjusted to ensure the effectiveness of the filtering effect while maintaining sensitivity to system frequency changes. This method can optimize the performance of the filter under different frequency disturbance conditions and ensure the stability and response speed of the system.

[0095] Instruction decomposition is achieved using a first-order low-pass filter. The specific expression of the low-pass first-order filter is as follows:

[0096]

[0097] Among them, T1 is the filtering time constant.

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

[0099] Step 3: Fuzzy PID controller design.

[0100] This embodiment adopts fuzzy PID controller design to realize the separate control of industrial load, thermal power unit and energy storage system. Fuzzy PID controller combines fuzzy control algorithm and PID control algorithm, has strong adaptability and flexibility, can ensure system stability, and dynamically adjust according to actual conditions to meet the control requirements of different system components.

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

[0102] Δf(t)=f d (t)-f(t)

[0103]

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

[0105] Use data acquisition equipment to regularly obtain running power data, and ensure that the data record contains a timestamp for subsequent timing analysis. Determine the sampling frequency and number of sampling points, pre-process the power signal data, and remove the DC component. It is usually necessary to ensure that the data length is a power of 2, which can be achieved by zero padding. These data use MATLAB's built-in function fft to apply the FFT algorithm to convert the power signal from the time domain to the frequency domain. The frequency value on the frequency axis can be calculated by the sampling frequency and the length of the FFT result. The extracted time-delay periodic characteristic data is analyzed in the frequency domain to detect the oscillation of the controller, including finding peaks or specific frequency components in the spectrum. According to the results of the frequency domain analysis, 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 reduce the oscillation. After adjusting the gain, new PID controller gain parameter data is generated for actual control.

[0106] On the other hand, the control instruction data is collected and imported into the normal PID controller for anti-fuzzy processing to obtain the inference result data. Subsequently, these inference result data are integrated to form a fuzzy rule base, which is used to set the simulation step size of the PID controller to determine the sampling time of the PID control system. Fuzzy level division is performed according to the determined sampling time of the PID control system to generate PID adaptive fuzzy level data. These processes are aimed at improving the stability and performance of the control system so that it can better adapt to changes in a dynamic environment.

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

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

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

[0110] Table 1 Fuzzy rules table

[0111]

[0112] The membership function of input and output is as follows: Figure 3 , Figure 4 and Figure 5 As shown. Specifically, when the absolute value of the frequency change rate increases, it indicates that the system frequency changes more drastically. At this time, the cutoff frequency of the low-pass filter should be appropriately increased to speed up the response speed of the filter, smooth the output fluctuations of thermal power units and industrial loads, and ensure the stability of the system frequency. On the contrary, when the absolute value of the frequency change rate is small, it indicates that the system frequency changes more slowly. At this time, the cutoff frequency of the low-pass filter should be appropriately reduced to reduce the response speed of the filter, reduce the output fluctuations of the energy storage element, and save energy resources.

[0113] By adaptively adjusting the cutoff frequency of the low-pass filter, we can more flexibly respond to different situations of system frequency changes, effectively balance the energy regulation between industrial loads and energy storage systems, improve the frequency regulation accuracy and stability of the system, save energy resources, and achieve more intelligent and sustainable power regulation control.

[0114] Step 4: Model the control instruction decomposition model.

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

[0116] When establishing a simulation model, various simulation conditions need to be considered, including the initial state of the system, load size, response speed of thermal power units, response rate of energy storage elements, efficiency, maximum and minimum energy storage capacity, regulation time constant and unit regulation power of thermal power units, dynamic controllability of industrial loads and their sensitivity parameters to frequency changes. Based on comprehensive consideration of energy storage capacity, regulation capability of thermal power units and response limitations of industrial loads, various technical and operational constraints should be mapped to executable instruction indicators (such as power, energy, regulation rate) to meet safety and performance requirements, thereby performing amplitude limiting preprocessing on frequency signals.

[0117] On the basis of comprehensive consideration of the characteristics of various equipment and the preprocessing of the grid frequency signal, it is necessary to combine the technical indicators of the energy storage system such as the state of charge (SOC), maximum charge and discharge rate, and temperature with the current dispatching needs, and map them into the upper and lower limits of the charge and discharge instructions; at the same time, the thermal power unit should clarify the safe adjustment range that it can provide under the current operating environment based on the minimum output, maximum climbing rate, spare capacity, and start-stop characteristics; for industrial loads, it is necessary to combine the criticality of the process flow to the power demand, set the interruptible, peak-shifting or adjustable loads as specific execution limits, response time, and the duration of the impact on production, and ensure that no major risks are caused to important process links when executing dispatching instructions. The control indicators of all the above equipment should be connected with the preprocessing results of the frequency signal to ensure that the safety and economic benefits of operation are taken into account while achieving refined dispatching and stability.

[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 corresponding control strategies are designed according to the characteristics of industrial loads and energy storage systems to meet their needs and operating conditions. For industrial loads, corresponding control strategies are designed to meet their needs and characteristics, involving the control of the following operations: turning on, 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. For energy storage systems, they are adjusted by controlling the charging and discharging status and flexible electricity market participation to ensure the energy balance and frequency stability of the system.

[0119] During the simulation process, the system frequency changes are continuously monitored, 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 the 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 operating efficiency and stability of the system, thereby achieving the reliability and sustainable operation of the system. Figure 7 As shown, P G Indicates the primary frequency regulation instruction of the thermal power unit, P G，ref Indicates the theoretical power output of the thermal power unit, P H Represents the high frequency component of a frequency modulation instruction, P L Represents the low-frequency component of a frequency modulation instruction, P b Indicates 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 output constraint module 1, P r2 Indicates the theoretical power that the energy storage should generate, 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 It represents the actual output power of the thermal power unit, and T1 represents the filtering time constant.

[0120] Step 5: Simulate and analyze and introduce frequency evaluation indicators under step load disturbance.

[0121] The simulation analysis and the introduction of frequency evaluation index under step load disturbance are aimed at verifying the effectiveness of the control instruction decomposition model and evaluating the frequency stability of the system in the face of load disturbance. First, in the stable state, a step load disturbance is introduced to simulate the situation where the system faces a sudden change in external load. Load disturbances can include sudden increases or decreases in load, so as to observe the system's response ability to load disturbances, such as Figure 8 shown.

[0122] The pure thermal power system, droop control and the combined system proposed in this embodiment built in Simulink are simulated for two typical cases: 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 system's response capability to sudden load changes can be observed, including frequency changes, system stability, dynamic response speed, steady-state error, etc. Under continuous load disturbance, 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.

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

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

[0126] Maximum frequency deviation Δf max The calculation formula is as follows:

[0127] Δf max =max|f 测量 -f 额定 |

[0128] Among them, f 测量 is the actual measured frequency; f 额定 is the rated frequency of the system.

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

[0130]

[0131] Among them, f 平均 is the average value of the frequencies measured over a period of time.

[0132] Finally, run the simulation model and record the frequency changes of the system under load disturbance, such as Fig. 9 As shown, the frequency evaluation index is calculated as Fig.10 and Fig.11 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 present invention provides a method for rapid decomposition of control instructions for industrial loads based on analog PID control. By establishing a joint system model of comprehensive energy storage, thermal power and industrial loads, stable control of system frequency and supply and demand balance are achieved. By adopting a fuzzy PID control algorithm, this method not only retains the simplicity of traditional control algorithms, but also enhances the adaptability and flexibility of the system, and provides an effective technical means to achieve coordinated operation of industrial loads and energy storage systems. On this basis, the present invention achieves rapid and accurate decomposition of control instructions by introducing low-pass filters and frequency division technology, and adjusting the cutoff frequency of the filter by an adaptive method. Combined with the design of fuzzy PID controller, the present invention 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 a simulation model and the introduction of frequency evaluation indicators under step load disturbances, 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 present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

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 joint system model of energy storage, thermal power and industrial loads; construct the energy storage component model, thermal power unit model and generator-load model respectively, and then integrate them together to form a primary frequency regulation model of the joint system. This model will consider the interaction and influence between energy storage components, thermal power units and industrial loads, and realize real-time regulation of energy storage components and thermal power units by monitoring changes in system frequency, so as to achieve stable control of system frequency and balance supply and demand; Step 2: Propose a control instruction decomposition method; adaptively adjust the low-pass filter cutoff frequency, and based on the adjusted system frequency signal, use frequency division technology to decompose the control instruction into control instructions of different frequency components; Step 3: Design a fuzzy PID controller to realize the separate control of industrial loads, thermal power units and energy storage systems; Step 4: Establish a control instruction decomposition model; 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 when facing load disturbance.

2. The method for rapid decomposition of control instructions for industrial loads based on simulated PID according to claim 1 is 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 combined system primary frequency regulation model is constructed; the energy storage element model, the thermal power unit model and the generator-load model are constructed as follows: Steam turbine transfer function G fn (s) is: 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 G of thermal power unit speed governor F (s) is: Among them, T G is the time constant of the thermal power unit speed governor; The generator-load model transfer function G(s) is: Among them, 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 is characterized in that: In the integration of energy storage, thermal power and industrial load models in step 1, for the energy storage system, a state space model is used to describe its charging and discharging process, taking into account its response rate, efficiency, maximum and minimum energy storage capacity to ensure that it can respond quickly 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, and the industrial load is regarded as a controllable load, which is adjusted through the 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, a demand response controller, a distributed control energy storage subsystem, a thermal power unit and auxiliary control and detection equipment are used to coordinate the power allocation of different resources to optimize the global performance indicators; at the same time, the system frequency changes are monitored in real time, real-time data is obtained through the sensor network, and the output power of the energy storage and thermal power units is dynamically adjusted according to the real-time data; the mutual influence between 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 tests are 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 in that: In step 2, the method for adaptively adjusting the cutoff frequency of the low-pass filter is proposed for the change of the system frequency and its change rate, aiming to dynamically adjust the characteristics of the filter according to the change of the system frequency and its change rate, so as to better respond to the frequency fluctuation; the instruction decomposition is realized by using a first-order low-pass filter, and the cutoff frequency of the low-pass filter is dynamically adjusted by real-time monitoring of the change of the system frequency; The specific expression of the low-pass first-order filter is as follows: Wherein, T1 is the filtering time constant; When the control instruction is decomposed, the frequency signal is divided into a low-frequency component 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 is 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 change rate as inputs. The basic domain of input and output variables is determined according to the actual system conditions. Among them, the setting frequency f d The difference Δf(t) between the actual output frequency f(t) and the input frequency f(t) is as follows: Δf(t)=f d (t)-f(t); The PID control equation u(t) with frequency and frequency change rate as input is expressed as follows: Among them, k p Indicates the proportionality 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 to convert the power signal from the time domain to the frequency domain; generate PID control oscillation data based on the results of frequency domain analysis to describe the oscillation of the control system; based on the PID control oscillation data, decide whether to adjust the gain of the fuzzy PID controller, and choose to increase or decrease the gain of the PID controller according to the nature of the oscillation to suppress or reduce the oscillation; based on the results of the gain adjustment, generate new PID controller gain parameter data for application in actual control; Step 3.3: Adjust the gain of PID and generate the fuzzy rule base; Collect control instruction data, import it into a normal PID controller for anti-fuzzy 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 of the PID controller, so as to determine the sampling time of the PID control system; perform fuzzy level division according to 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 specific method of step 4 is: Step 4.1: Establish a simulation model; Establish a simulation model, including energy storage elements, thermal power units, industrial loads, and control instruction decomposition models; when setting simulation conditions, comprehensively consider the system initial state, load size, response speed of thermal power units, response rate of energy storage elements, efficiency, maximum and minimum energy storage capacity, regulation time constant and unit regulation power of thermal power units, dynamic controllability of industrial loads, and their sensitivity parameters 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 capability and industrial load response limitations, various technical and operational constraints are mapped to executable instruction indicators to meet safety and performance requirements, thereby limiting the frequency signal for 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, including dividing the frequency signal into low-frequency components and high-frequency components, and generating corresponding control instructions according to 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: opening, closing 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, they are regulated by controlling the charging and discharging status and participating in the flexible 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 frequency feedback information of the actual system, continuously optimize and adjust control instructions to ensure stable system frequency control and coordinated operation.

7. 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 stable state to simulate the situation where the system faces a sudden change in external load; Under step load disturbance, the system's response capability to sudden load changes can be observed, including frequency changes, system stability, dynamic response speed, and steady-state error performance. Under continuous load disturbance, the system's response to load changes that continue 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 the maximum frequency deviation Δf max and steady-state frequency deviation Δf, which are used 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: Δf max =max|f 测量 -f 额定 |; 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 ability to respond to instantaneous or sudden disturbances, while the steady-state frequency deviation focuses on the frequency deviation of the system under long-term stable operation; Step 5.3: Run the simulation model and record the results Run the simulation model in Simulink, record the frequency changes of the system under load disturbance, and calculate the frequency evaluation index; by analyzing the results of the index, evaluate the performance of the control instruction decomposition model under different load conditions, and verify its effectiveness and reliability in improving the system frequency stability.

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