Quantitative evaluation method, device and equipment for industrial electrolytic metal load response capability, storage medium and program product

Abstract

The invention relates to a quantitative evaluation method, device and equipment for industrial electrolytic metal load response capability, a storage medium and a program product, and relates to the technical field of load response capability evaluation. The method can improve the accuracy of quantitative evaluation. The method comprises the following steps: constructing a PI controller steady-current control system through an industrial electrolytic metal load model by adopting a proportional-integral control algorithm; according to the current constraint dimension, performing integration through a multiple-input-multiple-output system to obtain a target dynamic structure; according to the target dynamic structure, a multi-dimensional dynamic boundary model is constructed through a PI controller current stabilization control system, and the maximum adjustable power boundary of the electrolytic load is calculated according to the state variable of the current constraint dimension; constructing an operation scene set for different load gap types in combination with related historical data, and generating a demand response excitation price curve; and according to the maximum adjustable power boundary, the physical safety boundary and the demand response excitation price curve, quantizing an industrial electrolytic metal load response capability value.

Description

Technical Field

[0001] The present application relates to the technical field of load response ability evaluation, and particularly to a method, device, computer equipment, computer-readable storage medium, and computer program product for quantitatively evaluating the load response ability of industrial electrolytic metal. Background Art

[0002] Industrial electrolytic metal load is a typical high-energy-consuming industrial load, whose load characteristics present multi-dimensional advantages. It has a large thermal energy storage potential in the thermodynamics dimension, and at the same time, it also has the characteristics of high power density, large thermal inertia, and good control characteristics in electrical characteristics. Under normal operating conditions, the high-energy-consuming electrolytic metal load realizes constant power operation through a constant current control method; adjusting the power of the electrolytic metal load within a short period of time will not affect the quality of the industrial output metal products, and the output will only be slightly affected. Therefore, it is crucial to evaluate the response ability of industrial electrolytic metal load.

[0003] In traditional technologies, the response ability of industrial electrolytic metal load is mainly evaluated by a method of modeling through a certain specific dimension. However, it is difficult to comprehensively and accurately evaluate the response ability of industrial electrolytic metal load only through single-dimensional modeling, resulting in a problem of low accuracy in the methods of traditional technologies. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium, and computer program product for quantitatively evaluating the load response ability of industrial electrolytic metal in view of the above technical problems.

[0005] In a first aspect, the present application provides a method for quantitatively evaluating the load response ability of industrial electrolytic metal, including:

[0006] Adopting a proportional-integral control algorithm, through a preset industrial electrolytic metal load model, to construct a PI (Proportional-Integral) controller constant current control system; the PI controller constant current control system is used to reduce the fluctuation amplitude of the electrolytic current;

[0007] Determine a plurality of current constraint dimensions, and integrate them through a multi-input multi-output system according to the current constraint dimensions to obtain a target dynamic structure;

[0008] According to the target dynamic structure, through the PI controller constant current control system, construct a multi-dimensional dynamic boundary model, and calculate the maximum adjustable power boundary of the electrolytic load according to the state variables of the current constraint dimensions; the multi-dimensional dynamic boundary model is used to describe the physical safety boundary of the operation of the metal load.

[0009] Combined with relevant historical data, construct a set of operation scenarios for different load gap types, and generate a demand response incentive price curve based on the set of operation scenarios;

[0010] Quantify the load response ability value of industrial electrolytic metal loads according to the maximum adjustable power boundary, the physical safety boundary, and the demand response incentive price curve.

[0011] In one embodiment, the determining a plurality of current constraint dimensions and integrating them through a multi-input multi-output system according to the current constraint dimensions to obtain a target dynamic structure includes:

[0012] Determine the thermodynamic constraint dimension, the electrochemical constraint dimension, and the mechanical structure constraint dimension as the current constraint dimensions; construct a thermodynamic constraint model corresponding to the thermodynamic constraint dimension, an electrochemical constraint model corresponding to the electrochemical constraint dimension, and a mechanical structure constraint model corresponding to the mechanical structure constraint dimension; integrate the thermodynamic constraint model, the electrochemical constraint model, and the mechanical structure constraint model into the same dynamic structure through a multi-input multi-output system to obtain the target dynamic structure.

[0013] In one embodiment, the method further includes:

[0014] In the case where it is identified that the electrolytic cell resistance suddenly increases and the electrolytic current deviates from the reference value, the PI controller steady current control system detects the current deviation value between the actual electrolytic current and the preset electrolytic current, and generates a voltage adjustment amount according to the current deviation value; adjusts the rectifier output voltage according to the voltage adjustment amount to correct the actual electrolytic current to the preset electrolytic current.

[0015] In one embodiment, the combining relevant historical data to construct a set of operation scenarios for different load gap types includes:

[0016] Generate the first operation scenario information of thermal power units, the second operation scenario information of wind power generation, and the third operation scenario information of photovoltaic power generation according to the relevant historical data; generate the regional network active power deficit information under various types of load gaps according to the first operation scenario information, the second operation scenario information, and the third operation scenario information; perform fusion processing on the regional network active power deficit information to obtain the set of operation scenarios.

[0017] In one embodiment, the generating a demand response incentive price curve based on the set of operation scenarios includes:

[0018] Based on the set of operating scenarios, obtain the regulation power, the revenue of participating dispatching units, and the time step under the current type of load gap; based on the regulation power, the revenue of participating dispatching units, and the time step, generate the objective function of the electrolytic metal load regulation model for different types of load gaps; based on the objective function of the electrolytic metal load regulation model, generate the demand response incentive price curve.

[0019] In one embodiment, the method further includes:

[0020] Analyze the maximum adjustable power boundary, the physical safety boundary, and the demand response incentive price curve, and generate the optimal regulation decision under the incentive according to the analysis result; in response to the received response capacity regulation request, adjust the industrial electrolytic metal load response capacity value under the incentive according to the optimal regulation decision.

[0021] In a second aspect, the present application also provides a quantization evaluation device for the response capacity of industrial electrolytic metal loads, including:

[0022] A system construction module for constructing a PI controller steady current control system through a preset industrial electrolytic metal load model by using a proportional-integral control algorithm; the PI controller steady current control system is used to reduce the fluctuation amplitude of the electrolysis current;

[0023] A dimension integration module for determining a plurality of current constraint dimensions and integrating them through a multi-input multi-output system according to the current constraint dimensions to obtain a target dynamic structure;

[0024] A power calculation module for constructing a multi-dimensional dynamic boundary model through the PI controller steady current control system according to the target dynamic structure, and calculating the maximum adjustable power boundary of the electrolytic load according to the state variables of the current constraint dimensions; the multi-dimensional dynamic boundary model is used to describe the physical safety boundary of the metal load operation;

[0025] A curve generation module for constructing a set of operating scenarios for different types of load gaps in combination with relevant historical data, and generating a demand response incentive price curve based on the set of operating scenarios;

[0026] A capacity quantization module for quantifying the response capacity value of the industrial electrolytic metal load according to the maximum adjustable power boundary, the physical safety boundary, and the demand response incentive price curve.

[0027] In a third aspect, the present application also provides a computer device, including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0028] Adopt the proportional-integral control algorithm to construct a PI controller steady current control system through a preset industrial electrolytic metal load model; the PI controller steady current control system is used to reduce the fluctuation amplitude of the electrolytic current; determine multiple current constraint dimensions, and integrate them through a multi-input multi-output system according to the current constraint dimensions to obtain a target dynamic structure; construct a multi-dimensional dynamic boundary model through the PI controller steady current control system according to the target dynamic structure, and calculate the maximum adjustable power boundary of the electrolytic load according to the state variables of the current constraint dimensions; the multi-dimensional dynamic boundary model is used to describe the physical safety boundary of the metal load operation; combine relevant historical data to construct an operation scenario set for different load gap types, and generate a demand response incentive price curve based on the operation scenario set; quantify the response ability value of the industrial electrolytic metal load according to the maximum adjustable power boundary, the physical safety boundary, and the demand response incentive price curve.

[0029] Fourthly, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0030] Adopt the proportional-integral control algorithm to construct a PI controller steady current control system through a preset industrial electrolytic metal load model; the PI controller steady current control system is used to reduce the fluctuation amplitude of the electrolytic current; determine multiple current constraint dimensions, and integrate them through a multi-input multi-output system according to the current constraint dimensions to obtain a target dynamic structure; construct a multi-dimensional dynamic boundary model through the PI controller steady current control system according to the target dynamic structure, and calculate the maximum adjustable power boundary of the electrolytic load according to the state variables of the current constraint dimensions; the multi-dimensional dynamic boundary model is used to describe the physical safety boundary of the metal load operation; combine relevant historical data to construct an operation scenario set for different load gap types, and generate a demand response incentive price curve based on the operation scenario set; quantify the response ability value of the industrial electrolytic metal load according to the maximum adjustable power boundary, the physical safety boundary, and the demand response incentive price curve.

[0031] Fifthly, the present application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0032] Adopt a proportional-integral control algorithm to construct a PI controller steady current control system through a preset industrial electrolytic metal load model; the PI controller steady current control system is used to reduce the fluctuation amplitude of the electrolysis current; determine multiple current constraint dimensions, and integrate them through a multi-input multi-output system according to the current constraint dimensions to obtain a target dynamic structure; according to the target dynamic structure, construct a multi-dimensional dynamic boundary model through the PI controller steady current control system, and calculate the maximum adjustable power boundary of the electrolysis load according to the state variables of the current constraint dimensions; the multi-dimensional dynamic boundary model is used to describe the physical safety boundary of the metal load operation; combine relevant historical data to construct an operation scenario set for different load gap types, and generate a demand response incentive price curve based on the operation scenario set; quantify the industrial electrolytic metal load response ability value according to the maximum adjustable power boundary, the physical safety boundary, and the demand response incentive price curve.

[0033] The above-mentioned method, device, computer device, computer-readable storage medium, and computer program product for quantifying and evaluating the response ability of industrial electrolytic metal loads determine multiple current constraint dimensions, integrate them through a multi-input multi-output system according to the current constraint dimensions to obtain a target dynamic structure, and then construct a multi-dimensional dynamic boundary model through the PI controller steady current control system according to the target dynamic structure, realizing fine modeling of the load dynamic behavior; in order to provide a unified data basis and mathematical expression for control and evaluation, design a steady current control system based on a PI controller and integrate it into the rectifier control loop to achieve rapid identification and precise adjustment of the electrolysis current under interference conditions such as load impact phenomena, avoid large current offsets, improve the stability and continuity of the electrolytic metal system operation, introduce multiple constraint dimensions and multi-dimensional physical boundary conditions, and establish a multi-dimensional dynamic boundary model to realize the quantitative evaluation of the industrial electrolytic metal load response ability value, and at the same time improve the accuracy of the quantitative evaluation, providing a reliable basis for its participation in power system scheduling. Brief Description of the Drawings

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0035] Figure 1 It is an application environment diagram of the method for quantifying and evaluating the response ability of industrial electrolytic metal loads in an embodiment;

[0036] Figure 2Schematic diagram of the process for the quantitative evaluation method of the load response ability of industrial electrolytic metals in an embodiment;

[0037] Figure 3 Schematic diagram of the process for the constraint dimension integration step in an embodiment;

[0038] Figure 4 Schematic diagram of the process for the quantitative evaluation method of the load response ability of industrial electrolytic metals in a specific embodiment;

[0039] Figure 5 Structural block diagram of the quantitative evaluation device for the load response ability of industrial electrolytic metals in an embodiment;

[0040] Figure 6 Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0041] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0042] The quantitative evaluation method for the load response ability of industrial electrolytic metals provided by the embodiments of the present application can be applied to an application environment as shown in Figure 1 In the shown application environment, the terminal communicates with the server through a network. The data storage system can store the data that the server needs to process. The data storage system can be integrated on the server, or can be placed in the cloud or other network servers. In the application environment as shown in Figure 1 In the shown application environment, the terminal can be, but is not limited to, various personal computers, laptop computers, smart phones and tablet computers. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0043] In one embodiment, as shown in Figure 2 In the shown, a quantitative evaluation method for the load response ability of industrial electrolytic metals is provided. Taking the method applied to the terminal in Figure 1 as an example, the method includes the following steps:

[0044] Step S201, constructing a PI controller steady current control system through a preset industrial electrolytic metal load model by using a proportional-integral control algorithm; the PI controller steady current control system is used to reduce the fluctuation amplitude of the electrolytic current.

[0045] Among them, the PI controller steady current control system is expressed as:

[0046]

[0047] In the above formula, is the inductor change amount output by the controller, is the proportional gain of the controller, is the integral gain of the controller, is the current reference value.

[0048] Specifically, in response to the quantization evaluation instruction of the industrial electrolytic metal load response ability, the terminal adopts a proportional-integral control algorithm to construct a PI controller steady current control system through a preset industrial electrolytic metal load model, and reduces the fluctuation amplitude of the electrolysis current by using the PI controller steady current control system.

[0049] Step S202: Determine multiple current constraint dimensions, and integrate them through a multi-input multi-output system according to the current constraint dimensions to obtain a target dynamic structure.

[0050] Among them, the current constraint dimensions include but are not limited to the thermodynamic constraint dimension, the electrochemical constraint dimension, and the mechanical structure constraint dimension, etc.

[0051] Among them, the multi-input multi-output system can be expressed as:

[0052]

[0053]

[0054]

[0055]

[0056] In the above formula, is the state variable at moment, is the state variable at is the input variable at is the output variable at is the load power of the electrolytic cell, is the change amount of the temperature inside the electrolytic cell, is the concentration of the target metal electrolyte in the electrolytic cell.

[0057] Specifically, the terminal determines multiple current constraint dimensions, and integrates the thermodynamic constraint dimension, the electrochemical constraint dimension, and the mechanical structure constraint dimension into the same dynamic structure through a multi-input multi-output system to obtain a target dynamic structure.

[0058] Step S203: Based on the target dynamic structure, stabilize the current control system through a PI controller, construct a multi-dimensional dynamic boundary model, and calculate the maximum adjustable power boundary of the electrolysis load according to the state variables of the current constraint dimension; the multi-dimensional dynamic boundary model is used to describe the physical safety boundary of the operation of metal loads.

[0059] Among them, with the multi-dimensional dynamic state variables as the input, the upper limit of the adjustable power boundary at a unit time obtained by taking the minimum value under multi-dimensional dynamic constraints is expressed as:

[0060]

[0061] Among them, is the maximum adjustable power of the industrial electrolytic metal load, is the maximum allowable temperature change of the electrolytic cell, is the maximum value of the direct current, is the upper limit of the concentration constraint of the target metal electrolyte in the electrolytic cell, is the lower limit of the anode distance constraint, is the average value of the direct current flowing through the electrolytic cell.

[0062] Specifically, the terminal constructs a multi-dimensional dynamic boundary model covering thermal, electrochemical concentration, and anode distance changes based on the target dynamic structure through the PI controller current stabilization control system, calculates the maximum adjustable power boundary according to the state variables of the current constraint dimension, and outputs the adjustment upper limit of the electrolysis load; the multi-dimensional dynamic boundary model is used to describe the physical safety boundary of the operation of metal loads.

[0063] It should be noted that after the present application completes the modeling and state mapping of the multi-dimensional physical processes of industrial electrolytic metal loads, a maximum adjustable power boundary model is further established to quantify the instantaneous adjustment ability limit of the load under three types of dynamic constraint conditions: heat, electrochemistry, and mechanics. By inputting multi-dimensional dynamic state variables and selecting the minimum value of the critical power under various constraint conditions, the upper limit of the maximum adjustable power of the load system at each moment is obtained, forming a real-time response ability evaluation method driven by constraints. The power boundary model considers the following three types of limiting factors: Heat constraint: Based on the maximum temperature rise that the cell can withstand per unit time, the upper limit of power adjustment under the temperature rise limit is derived to ensure that the electrolytic cell temperature does not exceed the standard and prevent equipment damage or abnormal reactions caused by overheating; Electrochemical concentration constraint: Considering the difference between the consumption rate of industrial electrolytic metals brought about by current changes and the concentration of the electrolyte containing the target metal element at present, the maximum adjustment range of the system is limited on the premise of ensuring the continuity of the reaction, and the reaction interruption caused by too low concentration is avoided; Mechanical structure constraint: Based on the initial anode-to-cathode distance and the real-time anode-to-cathode distance change function, the relationship between the anode consumption rate and the square of the current is introduced, and the minimum anode-to-cathode distance boundary allowed by the anode structure is limited to prevent electrode sinking or mechanical structure instability.

[0064] The calculation expressions of the upper limits of the three power adjustments are finally combined in the form of the minimum value as the maximum adjustable power output value of the overall system. It not only has a clear physical meaning and engineering constraint background, but also can dynamically update the boundary results according to real-time state parameters, realizing continuous, safe, and predictable control of the adjustment ability of industrial electrolytic metal loads, and significantly improving the reliability and scheduling friendliness of the system's external response. This model plays a core role in evaluating the response ability in the present application and is the basic support for the precise participation of industrial electrolytic metal systems in the flexible regulation of the power system.

[0065] Step S204: Combine relevant historical data to construct a set of operation scenarios for different load gap types, and generate a demand response incentive price curve based on the set of operation scenarios.

[0066] Among them, the relevant historical data can be the historical data of new energy wind and solar power generation output, the historical data of traditional thermal power unit output, and the historical data of the demand response market incentive curve.

[0067] Among them, the set of operation scenarios for different load gap types can be expressed as:

[0068]

[0069] In the above formula, is the set of typical operation scenarios containing regions, is the active power deficit situation of the regional network under the th load gap;

[0070] The system state of the regional system on the th typical operating day can be expressed as:

[0071]

[0072] In the above formula, is the operating scenario of the thermal power unit, is the operating scenario of wind power generation, is the operating scenario of photovoltaic power generation.

[0073] Specifically, the terminal combines relevant historical data to construct an operating scenario set for different types of load gaps, and generates a demand response incentive price curve based on the operating scenario set.

[0074] Step S205: Quantify the load response ability value of industrial electrolytic metal loads according to the maximum adjustable power boundary, physical safety boundary, and demand response incentive price curve.

[0075] Specifically, the terminal quantitatively evaluates the load response ability value of the industrial electrolytic metal load to be measured according to the maximum adjustable power boundary, physical safety boundary, and demand response incentive price curve, and obtains the corresponding quantitative evaluation result.

[0076] In the above method for quantitatively evaluating the load response ability of industrial electrolytic metal loads, by determining multiple current constraint dimensions, integrating through a multi-input multi-output system according to the current constraint dimensions to obtain a target dynamic structure, and then constructing a multi-dimensional dynamic boundary model through a PI controller steady-flow control system based on the target dynamic structure to achieve fine modeling of the load dynamic behavior; in order to provide a unified data basis and mathematical expression for control and evaluation, by designing a steady-flow control system based on a PI controller and integrating it into the rectifier control loop, rapid identification and precise adjustment of the electrolysis current are achieved under interference conditions such as load impact phenomena, avoiding large current offsets, improving the stability and continuity of the operation of the electrolytic metal system, by introducing multiple constraint dimensions and multi-dimensional physical boundary conditions, and establishing a multi-dimensional dynamic boundary model, the quantitative evaluation of the load response ability value of industrial electrolytic metal loads is realized, and at the same time, the accuracy of the quantitative evaluation is improved, providing a reliable basis for its participation in power system scheduling.

[0077] In one embodiment, as Figure 3 shown, in the above step S202, determining multiple current constraint dimensions, integrating through a multi-input multi-output system according to the current constraint dimensions to obtain a target dynamic structure specifically includes the following steps:

[0078] Step S301, determine the thermodynamic constraint dimension, the electrochemical constraint dimension, and the mechanical structure constraint dimension as the current constraint dimensions.

[0079] Step S302, construct a thermodynamic constraint model corresponding to the thermodynamic constraint dimension, an electrochemical constraint model corresponding to the electrochemical constraint dimension, and a mechanical structure constraint model corresponding to the mechanical structure constraint dimension.

[0080] Step S303, integrate the thermodynamic constraint model, the electrochemical constraint model, and the mechanical structure constraint model into the same dynamic structure through a multi-input multi-output system to obtain the target dynamic structure.

[0081] Specifically, establishing an evaluation model for the load response ability of electrolytic metal based on multi-dimensional dynamic constraints includes constructing an industrial electrolytic metal thermodynamic constraint model expressed as:

[0082]

[0083] In the above formula, is the direct current flowing through the electrolytic cell at the th electrolytic cell, is the equivalent resistance of the th electrolytic cell, is a calculation step length, is the heat absorption of the chemical reaction in the electrolytic cell, is the heat dissipation to the environment, is the wind speed around the electrolytic cell, is the specific heat capacity of the electrolyte in the electrolytic cell, is the mass of the electrolyte in the electrolytic cell.

[0084] Construct an electrochemical constraint model for the load of industrial electrolytic metal expressed as:

[0085]

[0086]

[0087] In the above formula, is the concentration of the electrolyte containing the target metal element, is the feeding rate of the target metal element, is the volume of the electrolyte in the electrolytic cell, is the electrochemical consumption rate, is the minimum concentration of the electrolyte containing the target metal element in the electrolytic cell, is the maximum concentration of the electrolyte containing the target metal element in the electrolytic cell.

[0088] Construct a mechanical structure constraint model for the load of industrial electrolytic metal expressed as:

[0089]

[0090]

[0091] In the above formula, is the distance between the anode and the molten metal layer, is the initial distance, is the anode consumption rate coefficient, is the minimum value of the distance.

[0092] Through a multi-input multi-output system, the thermodynamic constraint model, the electrochemical constraint model, and the mechanical structure constraint model are integrated into the same dynamic structure to obtain the target dynamic structure.

[0093] For further explanation, the single-cell power of the industrial electrolytic metal load electrical characteristic model can be expressed as:

[0094]

[0095] In the above formula, represents the load power of the th electrolytic cell; represents the direct current flowing through the th electrolytic cell; The th electrolytic cell's equivalent resistance.

[0096] Regarding the dynamic boundary problem caused by the changes of multiple physical factors involved in the operation of the industrial electrolytic metal system, this application proposes a multi-dimensional dynamic constraint modeling scheme. Specifically, a thermodynamic constraint model, an electrochemical constraint model, and a mechanical structure constraint model are constructed, and finally they are unified and integrated into the same dynamic system structure to form an evaluation framework with controllable response boundaries and clear variable logic. In terms of thermodynamic constraint modeling, by considering the heat balance relationship between the Joule heat caused by the current, the endothermic electrolysis reaction, and the heat dissipation to the environment, a model for the rate of change of the electrolytic cell temperature is established. The model not only considers basic parameters such as the specific heat capacity in the cell and the mass of the electrolyte, but also introduces the environmental temperature and ventilation speed into the heat dissipation function, so as to be able to dynamically reflect the temperature rise process under different operating environments and ensure that the temperature does not exceed the safety upper limit during the evaluation process. In terms of electrochemical concentration constraint modeling, by introducing a model for the change of the electrolyte concentration containing the target metal element, a functional relationship is established among the feeding rate, reaction consumption rate, and current density of the industrial electrolytic metal, so that the concentration boundary becomes a dynamic limiting condition that must be satisfied during the current regulation process, which can prevent electrolysis interruption or reaction abnormalities caused by too low concentration. In terms of mechanical structure constraint modeling, a model for the decreasing anode distance under the cumulative action of the current is constructed. By introducing the initial anode distance and the anode consumption coefficient, the integral of the square of the current is used as the driving factor for the shortening of the anode distance, realizing the modeling expression of the physical structure limit of the anode, and thus introducing a structural safety boundary when evaluating the maximum load response ability.

[0097] By constructing thermodynamic constraint, electrochemical concentration constraint and mechanical structure constraint models, not only can the real physical state during the operation of industrial electrolytic metals be comprehensively reflected, but also a dynamic foundation with a complete structure and clear variables is provided for subsequently integrating it into a multi-input multi-output system, significantly improving the accuracy, systematicness and practicality of the load regulation ability assessment.

[0098] In one embodiment, the method of the present application further includes the following steps:

[0099] When it is recognized that the resistance of the electrolytic cell suddenly increases and the electrolytic current deviates from the reference value, the PI controller steady current control system detects the current deviation value between the actual electrolytic current and the preset electrolytic current, and generates a voltage adjustment amount according to the current deviation value; adjusts the rectifier output voltage according to the voltage adjustment amount to correct the actual electrolytic current to the preset electrolytic current.

[0100] Specifically, when the terminal recognizes that the resistance of the electrolytic cell suddenly increases and the electrolytic current deviates from the reference value, the PI controller steady current control system actively adjusts the rectifier output voltage by detecting the error between the actual current and the set current, and corrects the electrolytic current to the set value.

[0101] Further explanation: During the production of industrial electrolytic metal loads, the occurrence of a load impact phenomenon in the electrolytic cell will cause an increase in the cell resistance, manifested as an increase in the back electromotive force, resulting in the electrolytic current deviating from the set value. A steady current control system is constructed to avoid large fluctuations in the current. Under normal conditions, the electrolytic current is equal to the electrolytic current reference value and operates under normal working conditions; when the load impact phenomenon just occurs and the steady current system has not yet acted, under the influence of the load impact phenomenon, the cell resistance increases and the rectifier output voltage remains unchanged, so the electrolytic current decreases and deviates from its reference value. At this time, the load operating state is the working condition when there is no action in the series current control after the load impact phenomenon occurs. After the steady current system recognizes the error signal, it intervenes and adjusts the output voltage to adjust the electrolytic current back to its reference value. At this time, the load operates under the working condition after the series current control is completed.

[0102] It should also be noted that a discrete system is constructed for the self-saturated reactor steady current control system, and the corresponding critical state quantity is solved according to the coefficients in the discrete system. , and then solve the optimized PID (Proportional-Integral-Derivative) parameters of the self-saturated reactor current stabilization control system based on the Ziegler-Nichols method. For the open-loop system formed by connecting the controller in series with the self-saturated reactor current stabilization control system, add unit negative feedback to form a closed-loop control system, and use the step response to test and verify the control climbing ability. The PI controller parameters based on the Ziegler-Nichols law PID self-tuning can be expressed as:

[0103]

[0104] Among them, represents the proportional gain of the controller; represents the integral gain of the controller; and respectively represent the critical state quantities of the discrete system. Then, aiming at the problem of excessive overshoot under the Ziegler-Nichols method tuning, iterative parameter tuning is carried out according to the step climbing test results to form a test system for complete PID self-tuning calculation and climbing verification. Among them, the difference between the first peak value and the step constant value is greater than 0, it decreases, the difference is less than 0, it increases, represents the number of iterative adjustments, and stops until the overshoot is less than the target overshoot percentage; then, represents the number of iterative adjustments. If the time from the first peak value to the steady-state error of the system is less than 2% is greater than the target time , it decreases, represents the number of decreases; and respectively represent the iterative PI parameters after iterative adjustment. Record and output each set of overshoot and during the iteration, and select the current optimal PI value through the optimization algorithm.

[0105]

[0106] To address the phenomena of sudden increase in resistance and deviation of electrolysis current from the set value caused by factors such as load impact in industrial electrolytic metal systems, the present application further constructs a current stabilization control system based on a PI controller to achieve dynamic suppression and automatic regulation of current fluctuations. The current stabilization control system continuously monitors the error between the actual electrolysis current and the preset reference current, and real-time calculates the inductor adjustment amount based on the proportional (P) and integral (I) algorithms, thereby controlling the output voltage of the rectifier bridge. In specific implementation, the system takes the PI controller as the core and combines a self-saturable reactor to participate in voltage regulation, and can actively adjust the DC output voltage according to the error signal, so that the electrolysis current deviating from the set value gradually returns to the reference level. This technical solution does not rely on external manual intervention, has good response speed and regulation stability, and is particularly suitable for dealing with sudden disturbances caused by phenomena such as load impact. Through the self-saturable reactor current stabilization control system, the present application significantly improves the self-recovery ability of the system to abnormal states and the consistency of steady-state operation, providing stable support for the controllability of electrolytic metal loads to participate in the power system.

[0107] In one embodiment, in the above step S204, in combination with relevant historical data, an operation scenario set for different types of load gaps is constructed, specifically including the following steps:

[0108] According to relevant historical data, generate the first operation scenario information of thermal power units, the second operation scenario information of wind power generation, and the third operation scenario information of photovoltaic power generation; according to the first operation scenario information, the second operation scenario information, and the third operation scenario information, generate the regional network active power deficit information under various types of load gaps; fuse and process the regional network active power deficit information to obtain the operation scenario set.

[0109] Specifically, the operation scenarios of different load gaps include but are not limited to the first operation scenario of thermal power units, the second operation scenario of wind power generation, and the third operation scenario of photovoltaic power generation. The operation scenario set for different types of load gaps is expressed as:

[0110]

[0111] In the above formula, is the typical operation scenario set including regions, is the active power deficit situation of the regional network under the th type of load gap.

[0112] Based on the historical data of new energy wind and solar power generation output, the historical data of traditional thermal power unit output, and the historical data of the demand response market incentive curve, while calculating the probability distributions of new energy output and traditional unit load, a demand incentive price curve under the operating scenario is generated. On this basis, the Latin hypercube simulation method is used to simulate and generate operating scenarios under different types of load gaps in the distribution of new energy output scenarios and traditional unit load scenarios.

[0113] The system state of the regional system on the th typical operating day is expressed as:

[0114]

[0115] In the above formula, is the operating scenario of the thermal power unit, is the operating scenario of wind power generation, is the operating scenario of photovoltaic power generation.

[0116] In this embodiment, for the uncertain factors of the load cluster in the regional network, including the wind-solar uncertainty model and the thermal power unit uncertainty model, it is necessary to determine the probability distributions of wind-solar output and thermal power unit output under the operating scenario of the

[0117] In one of the embodiments, in the above step S204, based on the operating scenario set, generating a demand response incentive price curve specifically includes the following steps:

[0118] According to the operating scenario set, obtain the regulation power, the income of the participating dispatching unit, and the time step under the current type of load gap; according to the regulation power, the income of the participating dispatching unit, and the time step, generate the objective function of the electrolytic metal load regulation model for different types of load gaps; based on the objective function of the electrolytic metal load regulation model, generate a demand response incentive price curve.

[0119] Specifically, the objective function of the electrolytic metal load regulation model for the same type of load gap can be expressed as:

[0120]

[0121]

[0122] In the above formula, is the objective function, is the income of the electrolytic metal load participating in the demand response at the th moment under the operating scenario of the th type of load gap, is the Under the operating scenario of the class load gap, the regulated power of the electrolytic metal load at time , is the revenue of the participating dispatching unit under the th class load gap operating scenario, and

[0123] is the time step. This application is based on typical operating scenarios, considering a multi-dimensional dynamic constraint evaluation model for the response ability of electrolytic metal loads. It runs an optimization model for the regulation of electrolytic metal loads considering different types of load gaps, and obtains an optimal operating dispatch method for electrolytic metal loads to participate in system dispatch responses based on the demand incentive curve under different load gap types. The objective function of the electrolytic metal load regulation model considering different types of load gaps is to maximize the revenue of industrial electrolytic metal loads participating in the regional system demand dispatch.

[0124] The power conservation equation constraint of the regional system is expressed as:

[0125]

[0126] In the above formula, represents the total load gap power under the th class load gap operating scenario, represents the adjustable output power provided by the industrial electrolytic metal load under the th class load gap operating scenario, is the output power of the thermal power unit under the th class load gap operating scenario, is the output power of the wind power under the th class load gap operating scenario, is the output power of the photovoltaic power under the th class load gap operating scenario.

[0127] The electrolytic metal load regulation model of this application considering different types of load gaps simultaneously satisfies the operating constraints of electrolytic metal loads, including power conservation equation constraints of the regional system, transmission line power flow constraints, and phase angle constraints, etc.

[0128] In one embodiment, the method of this application further includes the following steps:

[0129] Analyze the maximum adjustable power boundary, physical safety boundary, and demand response incentive price curve, and generate an optimal regulation decision under the incentive according to the analysis results; in response to the received response ability regulation request, adjust the response ability value of the industrial electrolytic metal load under the incentive according to the optimal regulation decision.

[0130] Specifically, the terminal analyzes the maximum adjustable power boundary, physical safety boundary, and demand response incentive price curve, generates an optimal adjustment decision under the incentive according to the analysis result, and in response to the received response capacity adjustment request, adjusts the response capacity value of the industrial electrolytic metal load under the incentive, thereby increasing the scientificity and flexibility of the solution.

[0131] In one embodiment, as Figure 4 shown, a method for quantitatively evaluating the response capacity of an industrial electrolytic metal load in a specific embodiment is provided, which specifically includes the following steps:

[0132] Step S401, a PI controller steady current control system is constructed by using a proportional-integral control algorithm through a preset industrial electrolytic metal load model; the PI controller steady current control system is used to reduce the fluctuation amplitude of the electrolysis current.

[0133] Step S402, the thermodynamic constraint dimension, electrochemical constraint dimension, and mechanical structure constraint dimension are determined as the current constraint dimensions; a thermodynamic constraint model corresponding to the thermodynamic constraint dimension, an electrochemical constraint model corresponding to the electrochemical constraint dimension, and a mechanical structure constraint model corresponding to the mechanical structure constraint dimension are constructed; the thermodynamic constraint model, electrochemical constraint model, and mechanical structure constraint model are integrated into the same dynamic structure through a multi-input multi-output system to obtain a target dynamic structure.

[0134] Step S403, a multi-dimensional dynamic boundary model is constructed according to the target dynamic structure through the PI controller steady current control system, and the maximum adjustable power boundary of the electrolysis load is calculated according to the state variables of the current constraint dimension; the multi-dimensional dynamic boundary model is used to describe the physical safety boundary of the metal load operation.

[0135] Step S404, according to relevant historical data, the first operation scenario information of the thermal power unit, the second operation scenario information of the wind power generation, and the third operation scenario information of the photovoltaic power generation are generated; according to the first operation scenario information, the second operation scenario information, and the third operation scenario information, the regional network active power deficit information under various types of load gaps is generated; the regional network active power deficit information is fused to obtain an operation scenario set.

[0136] Step S405, according to the operation scenario set, the adjustment power, the income of the participating dispatching unit, and the time step under the current type of load gap are obtained; according to the adjustment power, the income of the participating dispatching unit, and the time step, the objective function of the electrolytic metal load regulation model for different types of load gaps is generated; based on the objective function of the electrolytic metal load regulation model, a demand response incentive price curve is generated.

[0137] In step S406, analyze the maximum adjustable power boundary, physical safety boundary, and demand response incentive price curve, and generate an optimal adjustment decision under the incentive according to the analysis results; in response to the received response capacity adjustment request, adjust the industrial electrolytic metal load response capacity value under the incentive according to the optimal adjustment decision.

[0138] The beneficial effects brought by the above embodiments are as follows:

[0139] In this embodiment, by constructing an industrial electrolytic metal load model, the operating state of the electrolytic metal load under multiple physical processes such as electrical, thermal, electrochemical, and mechanical can be comprehensively characterized, realizing fine modeling of the load dynamic behavior, providing a unified data basis and mathematical expression for control and evaluation. By designing a constant-current regulation system based on a PI controller and integrating it into the rectifier control loop, rapid identification and precise regulation of the electrolytic current can be achieved under interference conditions such as load impact phenomena, avoiding large current offsets, and improving the stability and continuity of the operation of the industrial electrolytic metal system. By introducing multi-dimensional physical boundary conditions such as thermal constraints, electrochemical constraints, and mechanical structure constraints, and establishing a power boundary model, a quantitative evaluation of the adjustment ability of the electrolytic metal load is realized, providing a reliable basis for its participation in power system dispatching.

[0140] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps in other steps.

[0141] Based on the same inventive concept, an embodiment of the present application also provides a quantitative evaluation device for the industrial electrolytic metal load response capacity for implementing the above-mentioned quantitative evaluation method of the industrial electrolytic metal load response capacity. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the quantitative evaluation device for the industrial electrolytic metal load response capacity provided below can refer to the limitations on the quantitative evaluation method of the industrial electrolytic metal load response capacity in the above text, and will not be repeated here.

[0142] In an exemplary embodiment, such as Figure 5As shown, a quantitative evaluation device for the load response ability of industrial electrolytic metals is provided, including:

[0143] A system construction module 501, which is used to construct a PI controller steady current control system through a preset industrial electrolytic metal load model by using a proportional-integral control algorithm; the PI controller steady current control system is used to reduce the fluctuation amplitude of the electrolysis current;

[0144] A dimension integration module 502, which is used to determine multiple current constraint dimensions, and integrate them through a multi-input multi-output system according to the current constraint dimensions to obtain a target dynamic structure;

[0145] A power calculation module 503, which is used to construct a multi-dimensional dynamic boundary model through the PI controller steady current control system according to the target dynamic structure, and calculate the maximum adjustable power boundary of the electrolysis load according to the state variables of the current constraint dimensions; the multi-dimensional dynamic boundary model is used to describe the physical safety boundary of the metal load operation;

[0146] A curve generation module 504, which is used to construct a set of operation scenarios for different load gap types in combination with relevant historical data, and generate a demand response incentive price curve based on the set of operation scenarios;

[0147] An ability quantification module 505, which is used to quantify the industrial electrolytic metal load response ability value according to the maximum adjustable power boundary, the physical safety boundary, and the demand response incentive price curve.

[0148] In one embodiment, the dimension integration module 502 is further used to determine the thermodynamic constraint dimension, the electrochemical constraint dimension, and the mechanical structure constraint dimension as the current constraint dimensions; construct a thermodynamic constraint model corresponding to the thermodynamic constraint dimension, an electrochemical constraint model corresponding to the electrochemical constraint dimension, and a mechanical structure constraint model corresponding to the mechanical structure constraint dimension; integrate the thermodynamic constraint model, the electrochemical constraint model, and the mechanical structure constraint model into the same dynamic structure through a multi-input multi-output system to obtain a target dynamic structure.

[0149] In one embodiment, the quantitative evaluation device for the load response ability of industrial electrolytic metals further includes a current correction module, which is used to detect the current deviation value between the actual electrolysis current and the preset electrolysis current by the PI controller steady current control system when it is recognized that the electrolytic cell resistance suddenly increases and the electrolysis current deviates from the reference value, and generate a voltage adjustment amount according to the current deviation value; adjust the rectifier output voltage according to the voltage adjustment amount to correct the actual electrolysis current to the preset electrolysis current.

[0150] In one embodiment, the curve generation module 504 is further configured to generate first operating scenario information of a thermal power unit, second operating scenario information of wind power generation, and third operating scenario information of photovoltaic power generation according to relevant historical data; generate regional network active power deficit information under various types of load gaps according to the first operating scenario information, the second operating scenario information, and the third operating scenario information; perform fusion processing on the regional network active power deficit information to obtain an operating scenario set.

[0151] In one embodiment, the curve generation module 504 is further configured to obtain the regulation power, the revenue of the participating dispatching units, and the time step under the current type of load gap according to the operating scenario set; generate an objective function of an electrolytic metal load regulation model for different types of load gaps according to the regulation power, the revenue of the participating dispatching units, and the time step; and generate a demand response incentive price curve based on the objective function of the electrolytic metal load regulation model.

[0152] In one embodiment, the quantization evaluation device for the industrial electrolytic metal load response ability further includes an incentive regulation module, configured to analyze the maximum adjustable power boundary, the physical safety boundary, and the demand response incentive price curve, and generate an optimal regulation decision under the incentive according to the analysis result; and in response to a received response ability regulation request, adjust the industrial electrolytic metal load response ability value under the incentive according to the optimal regulation decision.

[0153] Each module in the above quantization evaluation device for the industrial electrolytic metal load response ability can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or stored in a memory in the computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to the above respective modules.

[0154] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structural diagram may be as Figure 6As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for quantitatively evaluating the load response ability of industrial electrolytic metals. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0155] Those skilled in the art can understand that Figure 6 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0156] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0157] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0158] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0159] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0160] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0161] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this application.

[0162] The above-described embodiments merely represent several implementation manners of this application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of this application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of this application shall be subject to the appended claims.

Claims

1. A method for quantitatively evaluating the load response ability of industrial electrolytic metals, characterized in that The method includes: Adopting a proportional-integral control algorithm to construct a PI controller current stabilization control system through a preset industrial electrolytic metal load model; the PI controller current stabilization control system is used to reduce the fluctuation amplitude of the electrolytic current; Determining a plurality of current constraint dimensions, and integrating them through a multi-input multi-output system according to the current constraint dimensions to obtain a target dynamic structure; Constructing a multi-dimensional dynamic boundary model according to the target dynamic structure through the PI controller current stabilization control system, and calculating the maximum adjustable power boundary of the electrolytic load according to the state variables of the current constraint dimensions; the multi-dimensional dynamic boundary model is used to describe the physical safety boundary of the metal load operation; Combining relevant historical data to construct an operation scenario set for different load gap types, and generating a demand response incentive price curve based on the operation scenario set; Quantifying the response ability value of the industrial electrolytic metal load according to the maximum adjustable power boundary, the physical safety boundary, and the demand response incentive price curve.

2. The method according to claim 1, wherein The determining a plurality of current constraint dimensions and integrating them through a multi-input multi-output system according to the current constraint dimensions to obtain a target dynamic structure includes: Determining the thermodynamic constraint dimension, the electrochemical constraint dimension, and the mechanical structure constraint dimension as the current constraint dimensions; Constructing a thermodynamic constraint model corresponding to the thermodynamic constraint dimension, an electrochemical constraint model corresponding to the electrochemical constraint dimension, and a mechanical structure constraint model corresponding to the mechanical structure constraint dimension; Integrating the thermodynamic constraint model, the electrochemical constraint model, and the mechanical structure constraint model into the same dynamic structure through a multi-input multi-output system to obtain the target dynamic structure.

3. The method according to claim 1, wherein The method further includes: When it is recognized that the electrolytic cell resistance suddenly increases and the electrolytic current deviates from the reference value, the PI controller current stabilization control system detects the current deviation value between the actual electrolytic current and the preset electrolytic current, and generates a voltage adjustment amount according to the current deviation value; Adjusting the rectifier output voltage according to the voltage adjustment amount to correct the actual electrolytic current to the preset electrolytic current.

4. The method according to claim 1, wherein The combining relevant historical data to construct an operation scenario set for different load gap types includes: Generating first operation scenario information of thermal power units, second operation scenario information of wind power generation, and third operation scenario information of photovoltaic power generation according to the relevant historical data; Generating regional network active power deficit information under various load gaps according to the first operation scenario information, the second operation scenario information, and the third operation scenario information; Performing fusion processing on the regional network active power deficit information to obtain the operation scenario set.

5. The method according to claim 1, wherein The generating a demand response incentive price curve based on the operation scenario set includes: Obtaining the regulation power, the revenue of the participating dispatching unit, and the time step under the current type of load gap according to the operation scenario set; Generating an objective function of the electrolytic metal load regulation model for different types of load gaps according to the regulation power, the revenue of the participating dispatching unit, and the time step; Generate the demand response incentive price curve based on the objective function of the electrolytic metal load regulation model.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Analyze the maximum adjustable power boundary, the physical safety boundary, and the demand response incentive price curve, and generate an optimal regulation decision under the incentive according to the analysis results; In response to the received response capacity regulation request, adjust the industrial electrolytic metal load response capacity value under the incentive according to the optimal regulation decision.

7. A quantitative evaluation device for the load response ability of industrial electrolytic metals, characterized in that, The device includes: A system construction module, configured to construct a PI controller steady current control system through a preset industrial electrolytic metal load model by using a proportional-integral control algorithm; the PI controller steady current control system is used to reduce the fluctuation amplitude of the electrolytic current; A dimension integration module, configured to determine multiple current constraint dimensions, and integrate them through a multi-input multi-output system according to the current constraint dimensions to obtain a target dynamic structure; A power calculation module, configured to construct a multi-dimensional dynamic boundary model through the PI controller steady current control system according to the target dynamic structure, and calculate the maximum adjustable power boundary of the electrolytic load according to the state variables of the current constraint dimensions; the multi-dimensional dynamic boundary model is used to describe the physical safety boundary of the metal load operation; A curve generation module, configured to construct a set of operation scenarios for different load gap types in combination with relevant historical data, and generate a demand response incentive price curve based on the set of operation scenarios; A capacity quantification module, configured to quantify the industrial electrolytic metal load response capacity value according to the maximum adjustable power boundary, the physical safety boundary, and the demand response incentive price curve.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

Images