Evaluation Method for Frequency Regulation Control Capability of Supercapacitor Coupled with Thermal Power Unit

Abstract

This application discloses a method for evaluating the frequency regulation control capability of supercapacitors coupled with thermal power units, relating to the field of electric-thermal coupling frequency regulation technology. By introducing dynamic weight updates and factor correlation analysis, this application adaptively captures key factors affecting allocation accuracy and coordinated control performance, making the evaluation results more closely aligned with actual operating conditions and significantly improving the accuracy and timeliness of the evaluation. It organically integrates unit-level capability evaluation, coordinated control evaluation, and comprehensive benefit evaluation, achieving a multi-dimensional comprehensive analysis across the entire chain from equipment performance to strategy and economic benefits. By constructing a closed-loop optimization mechanism that feeds back the final conclusion to the initial weight parameters, the evaluation model can be continuously corrected, thus maintaining the high efficiency and advanced nature of the evaluation system over the long term. This effectively improves the dynamic response speed, control stability, and overall economic benefits of the combined system, possessing extremely high practical application value.

Description

Technical Field

[0001] This application relates to the field of electric-thermal coupling frequency modulation technology, specifically to a method for evaluating the frequency modulation control capability of supercapacitors coupled with thermal power units. Background Technology

[0002] Against the backdrop of the accelerated construction of new power systems, the high proportion of renewable energy connected to the grid poses severe challenges to the flexibility, stability, and economy of system operation. Traditional assessment methods for coordinated control of thermal power and energy storage systems often have significant limitations: on the one hand, the assessment process is mostly static; on the other hand, existing methods usually separate unit capacity assessment, coordinated control strategy analysis, and economic benefit calculation, making it difficult to form a closed-loop optimization.

[0003] Existing technologies, such as the invention patent application with publication number CN120150187A, disclose a method and system for evaluating the frequency regulation capability of energy storage thermal power units. This method includes the following steps: based on thermal power energy storage operation data, identifying node indicators and status records, extracting fluctuations and frequencies, evaluating frequency response adaptability, analyzing regulation rate and coordination ratio, screening risk units, and outputting the frequency regulation capability evaluation range. In this invention, by integrating and identifying the operation data of energy storage thermal power units, real-time monitoring of power fluctuations, energy storage status, and load changes is achieved. The response cycle of energy storage units is optimized to ensure the stability of the frequency regulation process. Comprehensive monitoring of battery capacity, temperature rise rate, and cycle frequency avoids overload and efficiency degradation, thereby ensuring stable grid dispatch. Through health risk identification and frequency regulation response space delineation, the synergy between energy storage and thermal power units is realized, enhancing the grid's ability to regulate fluctuating energy sources and its long-term reliability, avoiding response lag or efficiency problems in traditional methods.

[0004] Regarding the above-mentioned solutions, the inventors of this application have discovered at least the following technical problems: 1. Currently, there is a lack of a dynamic weight update mechanism based on pre-acquired allocation data, which fails to improve allocation accuracy and reliability; it cannot reduce the bias caused by traditional static weights, making allocation decisions more precise, improving resource utilization efficiency, and providing a higher-quality data foundation for subsequent steps. 2. Currently, there is a lack of unit-based evaluation methods, and the ability to independently evaluate thermal power units and supercapacitors is lacking; there is a lack of detailed evaluation at the unit level, making it impossible to accurately identify the strengths and weaknesses of each unit, and thus failing to provide a basis for targeted optimization.

[0005] 2. Currently, there is a lack of integrated coordination and control data and allocation capability assessment results, making it impossible to calculate factor correlations and dynamically adjust weights to generate multi-dimensional indicators; it is impossible to comprehensively assess the overall performance of coordination and control, and cannot improve the response speed and strategy adaptability to emergencies; this cannot enhance system stability, reduce control delays, or ensure the safe and efficient operation of power systems or other application scenarios. Summary of the Invention

[0006] To address the aforementioned technical shortcomings, the purpose of this application is to provide a method for evaluating the frequency regulation control capability of supercapacitors coupled with thermal power units.

[0007] To solve the above-mentioned technical problems, this application adopts the following technical solution: This application provides a method for evaluating the frequency regulation control capability of supercapacitors coupled with thermal power units. The method includes the following steps: Step 1, allocation capability evaluation: Based on the pre-acquired allocation data, the influencing factors are analyzed and obtained, and then the weights are dynamically updated to generate weighted allocation accuracy data, thereby analyzing and obtaining the allocation capability evaluation result.

[0008] Step 2, Unit-level Capability Assessment: The capacity of the thermal power units is assessed based on the pre-acquired thermal power unit data to obtain the thermal power unit capacity assessment result, and the capacity of the supercapacitors is assessed based on the pre-acquired supercapacitor data to obtain the supercapacitor capacity assessment result. The thermal power unit capacity assessment result and the supercapacitor capacity assessment result are recorded as the unit capability assessment result.

[0009] Step 3: Coordination and Control Evaluation: Based on the pre-acquired coordination and control data and the allocation capability evaluation results, the correlation between factors is calculated, and the weights are dynamically adjusted to analyze and obtain dynamic response performance indicators, coordination and control strategy effectiveness indicators, and stability data indicators, thereby generating coordination and control evaluation results.

[0010] Step 4: Comprehensive benefit assessment: Based on the unit capability assessment results, coordination and control assessment results, and pre-acquired economic data, perform dynamic benefit prediction and generate comprehensive benefit assessment results.

[0011] Step 5, Dynamic Optimization and Output: Based on all evaluation results, calculate the comprehensive evaluation index, output the final evaluation conclusion, and feed it back to Step 1 for dynamically updating the weight parameters in the allocation capability evaluation.

[0012] Preferably, the step of analyzing and deriving influencing factors based on pre-acquired allocation data, and then dynamically updating the weights to generate weighted allocation accuracy data, thereby analyzing and deriving allocation capability assessment results, includes: calculating the correlation between allocation instructions and actual output using a dynamic weight adjustment algorithm based on pre-acquired allocation data to generate influencing factors; dynamically updating the weights based on the influencing factors to generate weighted allocation accuracy data; and evaluating the rationality of the allocation strategy based on the weighted allocation accuracy data to generate allocation capability assessment results.

[0013] Preferably, the step of calculating the correlation between allocation instructions and actual output using a dynamic weight adjustment algorithm based on pre-acquired allocation data to generate an influencing factor includes: calculating the correlation using a formula. Determine the impact factor ,in Represented as Correlation values ​​within a time period Represented as the initial weight coefficients, Represented as the first The weight coefficients corresponding to each allocation factor This is represented by the number corresponding to the allocation factor. , This represents the total number of distribution factors. This is the normalization coefficient.

[0014] Preferably, the step of evaluating the rationality of the allocation strategy and generating an allocation capability evaluation result includes: comparing the weighted allocation accuracy data with a weighted allocation accuracy data threshold; when the weighted allocation accuracy data is less than or equal to the weighted allocation accuracy data threshold, the allocation capability evaluation result is reasonable; when the weighted allocation accuracy data is greater than the weighted allocation accuracy data threshold, the allocation capability evaluation result is unreasonable.

[0015] Preferably, the thermal power unit data includes the thermal power unit's response delay time, regulation rate, regulation accuracy, continuous frequency regulation time, and unit life loss; the supercapacitor data includes the supercapacitor's response time, power density, energy density, cycle efficiency, and state-of-charge operating range.

[0016] Preferably, the step of evaluating the capacity of the thermal power unit based on pre-acquired thermal power unit data to obtain a thermal power unit capacity evaluation result, and evaluating the capacity of the supercapacitor based on pre-acquired supercapacitor data to obtain a supercapacitor capacity evaluation result, includes: A1, through calculation formula... The thermal power unit capacity index was obtained. , This represents the response delay duration. This represents the maximum allowable response delay. This is expressed as the adjustment rate. This is expressed as the maximum adjustment rate. This is expressed as adjustment precision. This is expressed as the duration of sustainable frequency modulation. This is expressed as the maximum sustainable frequency modulation duration. This represents the loss of the unit's lifespan. This is expressed as the maximum permissible lifespan loss. , , , and These are respectively represented as the weighting factor corresponding to the response delay time of the thermal power unit, the weighting factor corresponding to the regulation rate, the weighting factor corresponding to the regulation accuracy, the weighting factor corresponding to the sustainable frequency regulation duration, and the weighting factor corresponding to the unit's life loss.

[0017] A2. Through calculation formula The supercapacitor capability index was derived. , This represents the response time. This represents the maximum allowed response time. Expressed as power density, Expressed as maximum power density, Expressed as energy density, Expressed as maximum energy density, Expressed as cycle efficiency. This indicates the operating range under charged conditions. , , , and These represent the weighting factors corresponding to the response time, power density, energy density, cycle efficiency, and state-of-charge operating range of the supercapacitor, respectively.

[0018] A3. Compare the thermal power unit capacity index with the thermal power unit capacity index threshold. When the thermal power unit capacity index is greater than or equal to the thermal power unit capacity index threshold, the thermal power unit capacity assessment result is recorded as high; otherwise, the thermal power unit capacity assessment result is recorded as low.

[0019] The supercapacitor capability index is compared with the supercapacitor capability index threshold. When the supercapacitor capability index is greater than or equal to the supercapacitor capability index threshold, the supercapacitor capability assessment result is recorded as high; otherwise, the supercapacitor capability assessment result is recorded as low.

[0020] Preferably, the step of calculating the factor correlation based on the pre-acquired coordination and control data and the allocation capability assessment results, and then dynamically adjusting the weights, includes: calculating the correlation using a formula. Determine the correlation between factors ,in This is represented as the result of the allocation capacity assessment. Represented as the first The change value of each coordinated control data, This is represented by the number corresponding to each coordinated control data. , This represents the total number of coordinated control data. Represented as the first The weighting factor corresponding to each coordinated control data point.

[0021] Preferably, the analysis yields dynamic response performance indicators, coordinated control strategy effectiveness indicators, and stability data indicators, generating a coordinated control evaluation result, including: when the dynamic response performance indicator is greater than or equal to the dynamic response performance indicator threshold, the coordinated control strategy effectiveness indicator is greater than or equal to the coordinated control strategy effectiveness indicator threshold, and the stability data indicator is greater than or equal to the stability data indicator threshold, the coordinated control evaluation result is judged to be high; otherwise, the coordinated control evaluation result is judged to be low.

[0022] Preferably, the step of dynamically predicting benefits based on the unit capability assessment results, coordination and control assessment results, and pre-acquired economic data to generate a comprehensive benefit assessment result includes: using a dynamic benefit prediction model and influencing factors to predict long-term benefits based on the unit capability assessment results, coordination and control assessment results, and pre-acquired economic data, generating long-term benefit prediction values; quantifying technical benefits based on the long-term benefit prediction values, including the reduction rate of computer group regulation mileage and the wear reduction rate of key components; quantifying economic benefits based on the long-term benefit prediction values, including calculating frequency regulation compensation revenue and operation and maintenance cost savings; and generating a comprehensive benefit assessment result based on the quantified results of the technical and economic benefits.

[0023] Preferably, the generation of a comprehensive benefit assessment result based on the quantitative results of the technical and economic benefits includes: the comprehensive benefit assessment result equal to the product of the technical benefit weight and the technical benefit index, plus the product of the economic benefit weight and the economic benefit index.

[0024] The beneficial effects of this application are as follows: 1. The method for evaluating the frequency regulation control capability of supercapacitors and thermal power units provided in this application, by introducing dynamic weight updates and factor correlation analysis, adaptively captures key factors affecting the accuracy of allocation and coordinated control performance, making the evaluation results more consistent with actual operating conditions and significantly improving the accuracy and timeliness of the evaluation; it organically integrates unit-level capability evaluation, coordinated control evaluation, and comprehensive benefit evaluation, realizing a full-chain, multi-dimensional comprehensive analysis from equipment performance to strategy and economic benefits; by constructing a closed-loop optimization mechanism from the final conclusion feedback to the initial weight parameters, the evaluation model can be continuously corrected, thereby maintaining the efficiency and advancement of the evaluation system in the long term; it effectively improves the dynamic response speed, control stability, and overall economic benefits of the joint system, and has extremely high practical application value.

[0025] 2. This application introduces a dynamic weight update mechanism based on pre-acquired allocation data. Dynamic weight updates enable the evaluation process to adapt to real-time changing environmental conditions, significantly improving allocation accuracy and reliability. It reduces the bias caused by traditional static weights, making allocation decisions more precise, improving resource utilization efficiency, and providing a higher-quality data foundation for subsequent steps. A unit-based evaluation method is adopted, independently evaluating the capabilities of thermal power units and supercapacitors based on pre-acquired thermal power unit data and supercapacitor data, respectively. Through detailed evaluation at the unit level, the strengths and weaknesses of each unit are accurately identified, providing a basis for targeted optimization.

[0026] 3. This application integrates coordinated control data and allocation capability assessment results, calculates factor correlations, and dynamically adjusts weights to generate multi-dimensional indicators; it can comprehensively evaluate the overall performance of coordinated control, improve response speed and strategy adaptability to emergencies; this enhances system stability, reduces control latency, and ensures the safe and efficient operation of power systems or other application scenarios. Multi-source data integration and dynamic forecasting capabilities enable the system to accurately assess overall economic benefits, support strategic decision-making and resource planning; improve return on investment, reduce operational risks, and promote sustainable development, optimizing resource allocation through real-time benefit insights. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart illustrating the implementation steps of the method described in this application. Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] Please see Figure 1 As shown, this application provides a method for evaluating the frequency regulation control capability of supercapacitors coupled with thermal power units, including: Step 1, allocation capability evaluation: Based on pre-acquired allocation data, the influencing factors are analyzed and derived, and then the weights are dynamically updated to generate weighted allocation accuracy data, thereby analyzing and obtaining the allocation capability evaluation result.

[0031] In a specific example, the process of analyzing and deriving influencing factors based on pre-acquired allocation data, and then dynamically updating the weights to generate weighted allocation accuracy data, thereby analyzing and deriving allocation capability assessment results, includes: calculating the correlation between allocation instructions and actual output using a dynamic weight adjustment algorithm based on pre-acquired allocation data to generate influencing factors; dynamically updating the weights based on the influencing factors to generate weighted allocation accuracy data; and evaluating the rationality of the allocation strategy based on the weighted allocation accuracy data to generate allocation capability assessment results.

[0032] In a specific example, the step of calculating the correlation between allocation instructions and actual output using a dynamic weight adjustment algorithm based on pre-acquired allocation data, and generating an influencing factor, includes: calculating the correlation between allocation instructions and actual output using a formula. Determine the impact factor ,in Represented as Correlation values ​​within a time period Represented as the initial weight coefficients, Represented as the first The weight coefficients corresponding to each allocation factor This is represented by the number corresponding to the allocation factor. , This represents the total number of distribution factors. This is the normalization coefficient.

[0033] It should be noted that the impact factor is a dynamic scalar value used to measure the strength of the influence of correlation on weight adjustment; a larger value indicates a greater contribution of the current correlation to weight updates. The initial weight coefficient represents the pre-set base weight coefficients of the allocation factors, usually set based on historical data. The weight coefficients corresponding to the allocation factors are used for normalization in multi-factor scenarios. The weight coefficients corresponding to the allocation factors represent the number of variables influencing the allocation strategy, such as economic factors and technical factors. The normalization coefficients ensure that the impact factor... Within the range, excessive fluctuations in weight updates are prevented; furthermore, the influence factor is obtained by performing weighted normalization operations through a hardware divider unit, mapping the correlation to a proportional coefficient of adjustable weights. Its value dynamically reflects the degree of influence of the matching degree between the allocation instruction and the actual output on the system weights, and avoids division-to-zero errors through a caching mechanism.

[0034] It should be noted that the above The correlation value within a time period is calculated using the formula. The conclusion is Correlation values ​​within a time period , Represented as The corresponding numbers for each time point within the time period. , Represented as The total number of time points within the time period Represented as the first The allocation instructions for each time point Represented as the first Actual output at each point in time This represents the average value of the allocation instruction at various time points. It represents the average of the actual output at each time point.

[0035] Furthermore, The correlation value within the time period represents the value within the time period. The degree of linear correlation between the allocation instructions and the actual output within a time period, with a value range of [value missing]. The closer the value is to 1, the stronger the positive correlation; the closer it is to -1, the stronger the negative correlation; and the closer it is to 0, the weaker the correlation. The total number of samples is set according to the system sampling frequency. The unit of the allocation command is usually megawatt, representing the power allocation value issued by the dispatch system. The unit of the actual output is usually megawatt, representing the actual power output value of the thermal power unit or supercapacitor. The average values ​​of the allocation command at each time point and the average values ​​of the actual output at each time point are used for centralization to eliminate bias.

[0036] It should be noted that the dynamic updating of the influence factor weights is as follows: the updated weight equals the current weight multiplied by the adjustment factor, where the adjustment factor equals 1 plus the product of the learning rate coefficient and the difference between the influence factor and the benchmark influence factor. Furthermore, the learning rate coefficient controls the speed of weight updates, the influence factor represents the strength of the current correlation's impact on weight adjustment, and the benchmark influence factor serves as a preset threshold to determine whether the influence factor deviates from its normal range, thereby dynamically adjusting the weights to achieve adaptive optimization. Simultaneously, the physical essence of the weight update algorithm is to perform a linear transformation using a digital signal processor, dynamically adjusting the weights based on the influence factor deviation to achieve adaptive learning, and using a limiter to prevent weight overflow from the system boundary, ensuring stability.

[0037] It should be noted that the specific process for generating weighted allocation accuracy data is as follows: through the calculation formula... Obtain weighted allocation accuracy data ,in Represented as the updated weights; For the first The absolute error at each time point.

[0038] Furthermore, the weighted allocation accuracy data is a scalar value that represents the weighted average absolute error between the allocation command and the actual output. The smaller the value, the higher the accuracy. The absolute error represents the deviation between the allocation command and the actual output. The weighted allocation accuracy data is calculated by performing weighted average calculation through the accumulator unit, which combines the weights and errors to generate a comprehensive accuracy index. This index is used to quantify the execution effect of the allocation strategy and ensures accuracy through floating-point operations.

[0039] In a specific example, the evaluation of the rationality of the allocation strategy and the generation of an allocation capability evaluation result includes: comparing the weighted allocation accuracy data with a weighted allocation accuracy data threshold; when the weighted allocation accuracy data is less than or equal to the weighted allocation accuracy data threshold, the allocation capability evaluation result is reasonable; when the weighted allocation accuracy data is greater than the weighted allocation accuracy data threshold, the allocation capability evaluation result is unreasonable.

[0040] It should be noted that the historical data setting of the weighted allocation accuracy data threshold is used to determine whether the accuracy is acceptable; the allocation capability assessment result is a binary output, that is, reasonable is 1, unreasonable is 0; the assessment process performs threshold judgment through comparator circuit, compares the weighted allocation accuracy data with the preset standard, generates a binary status flag, and outputs the assessment result through the status register to achieve rapid decision-making.

[0041] Step 2, Unit-level Capability Assessment: The capacity of the thermal power units is assessed based on the pre-acquired thermal power unit data to obtain the thermal power unit capacity assessment result, and the capacity of the supercapacitors is assessed based on the pre-acquired supercapacitor data to obtain the supercapacitor capacity assessment result. The thermal power unit capacity assessment result and the supercapacitor capacity assessment result are recorded as the unit capability assessment result.

[0042] In a specific example, the thermal power unit data includes the thermal power unit's response delay time, regulation rate, regulation accuracy, sustainable frequency regulation duration, and unit life loss; the supercapacitor data includes the supercapacitor's response time, power density, energy density, cycle efficiency, and state-of-charge operating range.

[0043] In a specific example, the process of evaluating the capacity of thermal power units based on pre-acquired thermal power unit data to obtain a thermal power unit capacity evaluation result, and evaluating the capacity of supercapacitors based on pre-acquired supercapacitor data to obtain a supercapacitor capacity evaluation result, includes: A1, through calculation formulas... The thermal power unit capacity index was obtained. , This represents the response delay duration. This represents the maximum allowable response delay. This is expressed as the adjustment rate. This is expressed as the maximum adjustment rate. This is expressed as adjustment precision. This is expressed as the duration of sustainable frequency modulation. This is expressed as the maximum sustainable frequency modulation duration. This represents the loss of the unit's lifespan. This is expressed as the maximum permissible lifespan loss. , , , and These are respectively represented as the weighting factor corresponding to the response delay time of the thermal power unit, the weighting factor corresponding to the regulation rate, the weighting factor corresponding to the regulation accuracy, the weighting factor corresponding to the sustainable frequency regulation duration, and the weighting factor corresponding to the unit's life loss.

[0044] It should be noted that the thermal power unit capability index is a scalar value used to quantify the comprehensive frequency regulation capability of the thermal power unit; a higher value indicates a stronger capability. The response delay time is in seconds, referring to the delay time from receiving a frequency regulation command to starting a response. The maximum permissible response delay time is in seconds and is set based on historical data. The regulation rate is in megawatts per second, referring to the change in power regulation of the thermal power unit per unit time. The maximum regulation rate is in megawatts per second. Regulation accuracy is a dimensionless proportional value, representing the percentage deviation between the actual output power and the commanded power, ranging from 0 to 1. The sustainable frequency regulation duration is in seconds, referring to the duration for which the thermal power unit can continuously perform frequency regulation operations. The maximum sustainable frequency regulation duration is in seconds. Unit life loss is a dimensionless proportional value, referring to the proportion of unit life degradation caused by frequency regulation operations, ranging from 0 to 1. The maximum permissible life loss is a dimensionless proportional value.

[0045] It should be noted that, , , , , , The weighting factors corresponding to the response delay duration, regulation rate, regulation accuracy, sustainable frequency regulation duration, and unit life loss of thermal power units were obtained by factor analysis. First, the information of response delay duration, regulation rate, regulation accuracy, sustainable frequency regulation duration, and unit life loss of thermal power units was condensed. Then, the variance explained after rotation was obtained, and the weights were obtained by dividing the cumulative variance explained.

[0046] It should be noted that factor analysis is a well-known technique. It is a multivariate statistical analysis method that starts by studying the internal dependencies of variables and reduces some variables with complex relationships to a few comprehensive factors. Information condensation is expressed as the calculation of the median. The variance explained rate is the amount of information extracted by the factors. Variance explained rate = eigenvalues ​​ / total number of analysis terms. The rotated variance explained rate is expressed as the variance explained by the factors after maximum variance rotation.

[0047] Furthermore, the thermal power unit capacity assessment calculation performs weighted normalization operations through an embedded processor, mapping multiple parameters into a unified capacity index. The weight coefficients are dynamically configured based on an expert system or historical operating data, and the calculation accuracy is ensured through a floating-point arithmetic unit to avoid overflow errors.

[0048] A2. Through calculation formula The supercapacitor capability index was derived. , This represents the response time. This represents the maximum allowed response time. Expressed as power density, Expressed as maximum power density, Expressed as energy density, Expressed as maximum energy density, Expressed as cycle efficiency. This indicates the operating range under charged conditions. , , , and These represent the weighting factors corresponding to the response time, power density, energy density, cycle efficiency, and state-of-charge operating range of the supercapacitor, respectively.

[0049] It should be noted that the supercapacitor capability index is a scalar value used to quantify the comprehensive frequency modulation capability of a supercapacitor; a higher value indicates a stronger capability. Response time is measured in seconds, referring to the delay time from receiving a command to starting to output power. The maximum permissible response time is measured in seconds. Power density is measured in kilowatts per kilogram, referring to the power that a supercapacitor can provide per unit mass. The maximum power density is measured in kilowatts per kilogram. Energy density is measured in watt-hours per kilogram, referring to the energy that a supercapacitor can store per unit mass. The maximum energy density is measured in watt-hours per kilogram. Cycle efficiency is a dimensionless proportional value, referring to the energy efficiency during charge-discharge cycles, with a value ranging from 0 to 1. The state-of-charge (SOC) range is measured as a percentage, referring to the width of the SOC range within which the supercapacitor can effectively operate.

[0050] It should be noted that, , , , , , We obtained the weighting factors corresponding to the response time, power density, energy density, cycle efficiency, and state of charge (SOC) range of the supercapacitor through factor analysis. First, we condensed the information of the supercapacitor's response time, power density, energy density, cycle efficiency, and SOC range. Then, we obtained the variance explained after rotation and obtained the weights by dividing the cumulative variance explained.

[0051] Furthermore, the supercapacitor capability assessment calculation is performed by a digital signal processor to perform linear combination calculations, converting physical parameters into capability indicators. The weighting coefficients are optimized based on the application scenario, and intermediate results are stored through a caching mechanism to improve computational efficiency.

[0052] A3. Compare the thermal power unit capacity index with the thermal power unit capacity index threshold. When the thermal power unit capacity index is greater than or equal to the thermal power unit capacity index threshold, the thermal power unit capacity assessment result is recorded as high; otherwise, the thermal power unit capacity assessment result is recorded as low.

[0053] The supercapacitor capability index is compared with the supercapacitor capability index threshold. When the supercapacitor capability index is greater than or equal to the supercapacitor capability index threshold, the supercapacitor capability assessment result is recorded as high; otherwise, the supercapacitor capability assessment result is recorded as low.

[0054] This application introduces a dynamic weight update mechanism based on pre-acquired allocation data. Dynamic weight updates enable the evaluation process to adapt to real-time changing environmental conditions, significantly improving allocation accuracy and reliability. It reduces the bias caused by traditional static weights, making allocation decisions more precise, improving resource utilization efficiency, and providing a higher-quality data foundation for subsequent steps. A unit-based evaluation method is adopted, independently evaluating the capabilities of thermal power units and supercapacitors based on pre-acquired thermal power unit data and supercapacitor data, respectively. Through detailed evaluation at the unit level, the strengths and weaknesses of each unit are accurately identified, providing a basis for targeted optimization.

[0055] Step 3: Coordination and Control Evaluation: Based on the pre-acquired coordination and control data and the allocation capability evaluation results, the correlation between factors is calculated, and the weights are dynamically adjusted to analyze and obtain dynamic response performance indicators, coordination and control strategy effectiveness indicators, and stability data indicators, thereby generating coordination and control evaluation results.

[0056] In a specific example, the calculation of factor correlation based on pre-acquired coordination and control data and the allocation capacity assessment results, and the subsequent dynamic adjustment of weights, includes: calculating the correlation using a formula. Determine the correlation between factors ,in This is represented as the result of the allocation capacity assessment. Represented as the first The change value of each coordinated control data, This is represented by the number corresponding to each coordinated control data. , This represents the total number of coordinated control data. Represented as the first The weighting factor corresponding to each coordinated control data point.

[0057] It should be noted that factor correlation is a scalar value used to quantify the degree of linear correlation between coordinated control data and allocation capacity assessment results, with a value range of [range missing]. The closer the value is to 1, the stronger the positive correlation; the closer it is to -1, the stronger the negative correlation; and the closer it is to 0, the less positive correlation there is. When the allocation capability assessment result is 0, the factor correlation is 0, indicating no correlation. The unit of change value of coordinated control data is percentage, which represents the magnitude of change of coordinated control parameters relative to the baseline state at the monitoring time point.

[0058] Furthermore, the correlation calculation is performed by an embedded processor to calculate the Pearson correlation coefficient. The changes in the coordinated control data and the allocation capability assessment results are weighted and normalized to generate a factor correlation index, which is used to quantify the dynamic correlation strength between the two. The calculation accuracy is ensured by a floating-point unit to avoid division by zero errors.

[0059] It should be noted that, through the calculation formula Derive the updated weights is a dimensionless coefficient, representing the adjusted . The weight factor value corresponding to each coordinated control data; The current weight is a dimensionless coefficient, representing the weight before adjustment. The weight factor value corresponding to each coordinated control data; The learning rate coefficient is a dimensionless scalar, preset based on the system's adaptive requirements, used to control the weight update speed, and its value range is [value range missing]. , The baseline factor correlation is a scalar value set based on historical data. It is used to determine whether the factor correlation deviates from the normal range, thereby triggering a weight adjustment.

[0060] Furthermore, the dynamic adjustment of weights is achieved by performing a linear transformation through a digital signal processor, using the deviation between the factor correlation and the benchmark correlation to dynamically adjust the weights, thus realizing adaptive learning. A limiter is used to prevent the weights from overflowing the system boundary, ensuring stability.

[0061] In a specific example, the analysis yields dynamic response performance indicators, coordinated control strategy effectiveness indicators, and stability data indicators, generating a coordinated control evaluation result. This includes: when the dynamic response performance indicator is greater than or equal to the dynamic response performance indicator threshold, the coordinated control strategy effectiveness indicator is greater than or equal to the coordinated control strategy effectiveness indicator threshold, and the stability data indicator is greater than or equal to the stability data indicator threshold, the coordinated control evaluation result is judged to be high; otherwise, the coordinated control evaluation result is judged to be low.

[0062] It should be noted that the threshold values ​​for dynamic response performance indicators, the effectiveness indicators of coordinated control strategies, and the stability data indicators are set based on historical data. Furthermore, the generation of coordinated control evaluation results involves a comparator circuit performing multi-condition judgments, comparing the indicators with preset thresholds, generating binary status flags, and outputting the evaluation results through a status register to achieve rapid decision-making.

[0063] It should be noted that the specific process for calculating dynamic response performance indicators, coordinated control strategy effectiveness indicators, and stability data indicators through adaptive multi-dimensional fusion evaluation is as follows: The dynamic response performance indicator is obtained by multiplying the values ​​of each dynamic response performance function by their corresponding updated weights and then summing the results. The input data for the dynamic response performance function includes response time and adjustment rate, etc. The coordinated control strategy effectiveness indicator is obtained by multiplying the values ​​of each coordinated control strategy effectiveness function by their corresponding updated weights and then summing the results. The input data for the coordinated control strategy effectiveness function includes strategy execution success rate and error rate, etc. The stability data indicator is obtained by multiplying the values ​​of each stability function by their corresponding updated weights and then summing the results. The input data for the stability function includes fluctuation amplitude and recovery time, etc.

[0064] Furthermore, the dynamic response performance function, the coordinated control strategy effectiveness function, and the stability function are mathematical functions based on the corresponding input data, used to quantify the performance, effectiveness, and stability of each dimension. The physical essence of adaptive multi-dimensional fusion evaluation is to map multi-dimensional data to a unified index by performing weighted fusion calculations in parallel using multi-core processors. The weights are dynamically adjusted based on factor correlations, and intermediate results are stored through a caching mechanism to improve evaluation efficiency. Adaptive multi-dimensional fusion evaluation maps multi-dimensional data to a unified index by performing weighted fusion calculations in parallel using multi-core processors. The weights are dynamically adjusted based on factor correlations, and intermediate results are stored through a caching mechanism to improve evaluation efficiency.

[0065] Step 4: Comprehensive benefit assessment: Based on the unit capability assessment results, coordination and control assessment results, and pre-acquired economic data, perform dynamic benefit prediction and generate comprehensive benefit assessment results.

[0066] In a specific example, the step of dynamically predicting benefits based on the unit capability assessment results, coordination and control assessment results, and pre-acquired economic data to generate a comprehensive benefit assessment result includes: using a dynamic benefit prediction model and influencing factors to predict long-term benefits based on the unit capability assessment results, coordination and control assessment results, and pre-acquired economic data, generating long-term benefit prediction values; quantifying technical benefits based on the long-term benefit prediction values, including the reduction rate of computer group regulation mileage and the wear reduction rate of key components; quantifying economic benefits based on the long-term benefit prediction values, including calculating frequency regulation compensation revenue and operation and maintenance cost savings; and generating a comprehensive benefit assessment result based on the quantified results of the technical and economic benefits.

[0067] It should be noted that, based on the unit capability assessment results, coordination and control assessment results, and pre-acquired economic data, a dynamic benefit prediction model and influencing factors are used to predict long-term benefits, generating predicted long-term benefit values; these are then calculated using the formula... Determine the long-term benefit forecast ,in This is represented as an impact factor on efficiency. , This represents the total number of benefit-influencing factors. Represented as the first The weight coefficients corresponding to each benefit influencing factor Represented as the first The values ​​of the benefit influencing factors, This is represented as a correction factor.

[0068] It should be noted that the long-term benefit forecast value is a scalar value used to quantify the predicted long-term comprehensive benefits; a higher value indicates better benefits. The weight coefficients corresponding to the benefit influencing factors are dimensionless coefficients, representing the relative importance of each influencing factor in the forecast, and are set based on historical data. The value of each influencing factor is a dimensionless scalar, representing the quantified value of a specific factor extracted from unit capability assessment results, coordination and control assessment results, and economic data, such as system efficiency and market conditions. The total number of benefit influencing factors is a positive integer, representing the number of influencing factors used in the forecast. The model adjustment coefficient is a dimensionless scalar used to calibrate the model output, dynamically adjusted based on the system operating environment. Furthermore, the dynamic benefit forecasting model performs weighted linear combination calculations through an embedded processor, mapping multi-dimensional input data to long-term benefit indicators. The weight coefficients are dynamically updated based on an adaptive learning algorithm, and the floating-point arithmetic unit ensures calculation accuracy and avoids overflow errors.

[0069] It should be noted that, based on the aforementioned long-term benefit forecasts, the quantification of technological benefits, including the reduction rate of mileage adjustment by the computer group and the rate of wear reduction of key components, specifically includes: through calculation formulas. The reduction rate of unit regulation mileage was obtained. , This indicates the base adjustment mileage. This is represented as the current adjustment mileage. It is calculated using the formula... Determine the wear reduction rate of key components ,in This is expressed as the baseline wear rate. This represents the current wear rate.

[0070] It should be noted that the unit regulation mileage reduction rate is a percentage value used to quantify the degree of reduction in regulation distance during frequency regulation. A higher value indicates better technical benefits. The benchmark regulation mileage is in kilometers, representing the typical regulation distance of the unit under unoptimized conditions. The current regulation mileage is in kilometers, representing the actual regulation distance of the unit under the optimized strategy. Furthermore, the calculation of the unit regulation mileage reduction rate involves performing a division operation using a digital signal processor, comparing the benchmark and the current value to generate a reduction rate index, which is used to evaluate the technical effectiveness of the frequency regulation strategy. Intermediate results are stored through a caching mechanism to improve computational efficiency.

[0071] It should be noted that the critical component wear mitigation rate is a percentage value used to quantify the degree of wear reduction in critical components (such as turbine blades); a higher value indicates better technical benefits. The baseline wear rate is a dimensionless proportional value, representing the typical wear ratio of the component in the unoptimized state. The current wear rate is a dimensionless proportional value, representing the actual wear ratio of the component under the optimized strategy. Furthermore, the calculation of the critical component wear mitigation rate is performed by subtraction and division operations through an embedded processor, mapping wear rate changes to mitigation indicators, and ensuring accuracy through floating-point units to support maintenance decisions.

[0072] It should be noted that, based on the aforementioned long-term benefit forecast, the economic benefits are quantified, including the calculation of frequency regulation compensation revenue and operation and maintenance cost savings. Specifically, the frequency regulation compensation revenue equals the frequency regulation capacity multiplied by the compensation unit price multiplied by the operating time; the operation and maintenance cost savings equal the baseline operation and maintenance cost minus the current operation and maintenance cost.

[0073] It should be noted that the unit of frequency regulation compensation revenue is monetary (e.g., yuan), used to quantify the direct economic benefits obtained from the frequency regulation market; the unit of frequency regulation capacity is megawatts, representing the frequency regulation power capacity provided by the system; the unit of compensation price is monetary per megawatt-hour, representing the unit compensation from the market for frequency regulation services; and the unit of operating time is hours, representing the cumulative time the system has participated in frequency regulation. Furthermore, the calculation of frequency regulation compensation revenue involves performing linear operations through a multiplier unit, multiplying the capacity, unit price, and time to generate the revenue value, and then aggregating multi-period data through an accumulator for economic benefit assessment.

[0074] It should be noted that the unit for operational cost savings is monetary (e.g., yuan), used to quantify the reduction in operational costs resulting from the optimization strategy; the baseline operational cost represents the typical operational expenditure under the unoptimized state; and the current operational cost represents the actual operational expenditure under the optimization strategy. Furthermore, the calculation of operational cost savings uses a subtractor unit to perform difference operations, quantifying cost changes into savings indicators, and employs floating-point processing to ensure accuracy, thus supporting economic decision-making.

[0075] In a specific example, the generation of a comprehensive benefit assessment result based on the quantitative results of the technical and economic benefits includes: the comprehensive benefit assessment result equals the product of the technical benefit weight and the technical benefit index, plus the product of the economic benefit weight and the economic benefit index.

[0076] It should be noted that the comprehensive benefit assessment result is a scalar value used to quantify the overall benefit level; a higher value indicates better comprehensive benefits. The technical benefit weight is a dimensionless coefficient representing the relative importance of technical benefits in the comprehensive assessment, based on system target settings. The technical benefit index is a dimensionless scalar, calculated by weighting the reduction rate of unit regulation mileage and the wear mitigation rate of key components. The economic benefit weight is a dimensionless coefficient representing the relative importance of economic benefits in the comprehensive assessment; the economic benefit index is a dimensionless scalar, calculated by weighting the frequency regulation compensation revenue and maintenance cost savings. Furthermore, weighted fusion uses a multi-core processor to perform linear weighted calculations, merging technical and economic benefit indicators into a unified output. The weighting coefficients are dynamically adjusted based on historical data or real-time demand, and intermediate values ​​are stored through a caching mechanism to improve assessment efficiency.

[0077] This application integrates coordinated control data and allocation capacity assessment results, calculates factor correlations, and dynamically adjusts weights to generate multi-dimensional indicators. It comprehensively evaluates the overall performance of coordinated control, improving response speed and strategy adaptability to emergencies. This enhances system stability, reduces control latency, and ensures the safe and efficient operation of power systems or other application scenarios. Multi-source data integration and dynamic forecasting capabilities enable the system to accurately assess overall economic benefits, supporting strategic decision-making and resource planning; it improves return on investment, reduces operational risks, and promotes sustainable development by optimizing resource allocation through real-time benefit insights.

[0078] Step 5, Dynamic Optimization and Output: Based on all evaluation results, calculate the comprehensive evaluation index, output the final evaluation conclusion, and feed it back to Step 1 for dynamically updating the weight parameters in the allocation capability evaluation.

[0079] It should be noted that the comprehensive evaluation index is a weighted fusion of the allocation capacity evaluation results, thermal power unit capacity evaluation results, supercapacitor capacity evaluation results, coordinated control evaluation results, and comprehensive benefit evaluation results. It is a scalar value used to quantify the overall system performance; the higher the value, the better the performance. Based on the comprehensive evaluation index, the optimization parameter set is dynamically optimized to generate adaptive adjustment parameters. Based on the optimization parameter set, an optimization suggestion report is generated to output system improvement suggestions. Based on the optimization suggestion report, the final evaluation conclusion is output to obtain a system state summary. Based on the final evaluation conclusion, the weight parameters in step one are updated to form a closed-loop adaptive system.

[0080] It should be noted that the allocation capacity assessment result is a binary variable, with a value of 1 when it is reasonable and 0 when it is unreasonable; the thermal power unit capacity assessment result is a binary variable, with a value of 1 when it is high and 0 when it is low; the supercapacitor capacity assessment result is a binary variable, with a value of 1 when it is high and 0 when it is low; the coordination and control assessment result is a binary variable, with a value of 1 when it is high and 0 when it is low; and the comprehensive benefit assessment result is a scalar value. Furthermore, the calculation of the comprehensive assessment index is performed by weighted fusion operation through an embedded processor, which maps the multi-dimensional assessment results into a unified performance index, and the calculation accuracy is ensured by a floating-point arithmetic unit to avoid overflow errors.

[0081] It should be noted that the process of generating an optimization parameter set by executing a dynamic optimization algorithm based on the comprehensive evaluation index is as follows: Through the dynamic optimization algorithm... Optimize the parameter set, where Represented as the updated set of optimized parameters, it is a vector containing the learning rate coefficient and the baseline impact factor; The current set of optimization parameters is a vector. Represented as the optimization step size coefficient, it is a dimensionless scalar, based on the system's adaptive requirements, used to control the parameter update speed, and its value range is... ; It is represented as a comprehensive evaluation index for the target, and is a scalar value set based on historical best performance. For comprehensive evaluation indicators; The gradient of the comprehensive evaluation index on the optimized parameter set is a vector representing the direction of the influence of parameter changes on the index. Furthermore, the dynamic optimization algorithm performs gradient descent operations through a digital signal processor, dynamically adjusts the parameters using the deviation between the comprehensive evaluation index and the target value to achieve adaptive optimization, and prevents parameters from overflowing the system boundary through a limiter to ensure stability.

[0082] It should be noted that the specific process for generating an optimization suggestion report based on the aforementioned optimization parameter set is as follows: The optimization parameter set is parsed to extract the changing trends of the learning rate coefficient and the benchmark influence factor; based on these trends, a text-formatted optimization suggestion report is generated, including weight adjustment suggestions, parameter optimization directions, and expected performance improvements; the optimization suggestion report is output through the report generation module and stored in the system database; furthermore, the generation of the optimization suggestion report involves performing a formatting operation through an embedded text engine to convert the parameter data into a readable report, and verifying the report's integrity through a memory verifier to prevent data loss.

[0083] It should be noted that, based on the optimization suggestion report, the final evaluation conclusion is output in the following specific process: key optimization suggestions are read from the optimization suggestion report; based on the key optimization suggestions, the final evaluation conclusion is generated through the decision logic unit, and the conclusion includes "performance optimization is feasible" or "further adjustments are needed"; the final evaluation conclusion is transmitted to the user interface through the output interface; furthermore, the final evaluation conclusion is output, the conclusion status is stored in the status register, and the data is transmitted in real time through the communication module to ensure timely feedback of the conclusion.

[0084] It should be noted that the final evaluation conclusion is fed back to step one to dynamically update the weight parameters in the allocation capability assessment. The specific process is as follows: extract feedback signals from the final evaluation conclusion; based on the feedback signals, adjust the weight parameters in step one, including the impact factor and the benchmark impact factor, through a weight update algorithm; the updated weight parameters are used for the next round of allocation capability assessment in step one; furthermore, the feedback process realizes data circulation through a closed-loop control unit, dynamically corrects the initial parameters using the final evaluation conclusion, forms an adaptive system, and stores historical feedback through a caching mechanism.

[0085] The method for evaluating the frequency regulation control capability of supercapacitors coupled with thermal power units provided in this application adaptively captures key factors affecting the accuracy of allocation and coordinated control performance by introducing dynamic weight updates and factor correlation analysis. This makes the evaluation results more consistent with actual operating conditions, significantly improving the accuracy and timeliness of the evaluation. It organically integrates unit-level capability evaluation, coordinated control evaluation, and comprehensive benefit evaluation, realizing a multi-dimensional comprehensive analysis across the entire chain from equipment performance to strategy and economic benefits. By constructing a closed-loop optimization mechanism that feeds back the final conclusion to the initial weight parameters, the evaluation model can be continuously corrected, thus maintaining the high efficiency and advanced nature of the evaluation system in the long term. This method effectively improves the dynamic response speed, control stability, and overall economic benefits of the combined system, and has extremely high practical application value.

[0086] The above content is merely an example and illustration of the concept of this application. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the inventive concept or exceed the scope defined in this specification, they should all fall within the protection scope of this application.

Claims

1. A method for evaluating the frequency modulation control capability of a super capacitor coupled to a thermal power unit, characterized in that, Comprise: Step one, allocation ability evaluation: based on the pre-acquired allocation data, the influencing factors are analyzed, and the weight is dynamically updated to generate weighted allocation accuracy data, so as to analyze the allocation ability evaluation result; Step two, unit layer ability evaluation: based on the pre-acquired thermal power unit data, the thermal power unit ability is evaluated to obtain the thermal power unit ability evaluation result, and based on the pre-acquired super capacitor data, the super capacitor ability is evaluated to obtain the super capacitor ability evaluation result, and the thermal power unit ability evaluation result and the super capacitor ability evaluation result are recorded as the unit ability evaluation result; The thermal power unit ability is evaluated based on the pre-acquired thermal power unit data to obtain the thermal power unit ability evaluation result, and the super capacitor ability is evaluated based on the pre-acquired super capacitor data to obtain the super capacitor ability evaluation result, comprising: A1, by a calculation formula A1, by a calculation formula , A1, by a calculation formula A1, by a calculation formula A1, by a calculation formula A1, by a calculation formula A1, by a calculation formula A1, by a calculation formula A1, by a calculation formula A1, by a calculation formula A1, by a calculation formula , , , and respectively represent a weight factor corresponding to the response delay time length of the thermal power generating unit, a weight factor corresponding to the regulation speed, a weight factor corresponding to the regulation accuracy, a weight factor corresponding to the sustainable frequency modulation time length, and a weight factor corresponding to the unit life loss. A2, by the calculation formula The supercapacitor capability index is obtained , is expressed as the response time, is expressed as the maximum allowed response time, is expressed as the power density, is expressed as the maximum power density, is expressed as the energy density, is expressed as the maximum energy density, is expressed as the cycle efficiency, is expressed as the state of charge operating range, , , , and respectively represent the weight factor corresponding to the response time of the supercapacitor, the weight factor corresponding to the power density, the weight factor corresponding to the energy density, the weight factor corresponding to the cycle efficiency, and the weight factor corresponding to the state of charge operating range. A3, compare the thermal power unit ability index with the thermal power unit ability index threshold value, when the thermal power unit ability index is greater than or equal to the thermal power unit ability index threshold value, the thermal power unit ability evaluation result is recorded as high, otherwise the thermal power unit ability evaluation result is recorded as low; Compare the super capacitor ability index with the super capacitor ability index threshold value, when the super capacitor ability index is greater than or equal to the super capacitor ability index threshold value, the super capacitor ability evaluation result is recorded as high, otherwise the super capacitor ability evaluation result is recorded as low; Step three, coordinated control evaluation: based on the pre-acquired coordinated control data and the allocation ability evaluation result, the factor correlation is calculated, and then the weight is dynamically adjusted, so as to analyze the dynamic response performance index, the coordinated control strategy effectiveness index and the stability data index, and generate the coordinated control evaluation result; Step four, comprehensive benefit evaluation: based on the unit ability evaluation result, the coordinated control evaluation result and the pre-acquired economic data, the benefit dynamic prediction is carried out, and the comprehensive benefit evaluation result is generated; Step five, dynamic optimization and output: based on all the evaluation results, the comprehensive evaluation index is calculated, the final evaluation conclusion is output, and fed back to step one for dynamic updating of the weight parameter in the allocation ability evaluation.

2. The method of claim 1, wherein, Based on the pre-acquired allocation data, the influencing factors are analyzed, and the weight is dynamically updated to generate weighted allocation accuracy data, so as to analyze the allocation ability evaluation result, comprising: Based on the pre-acquired allocation data, the dynamic weight adjustment algorithm is used to calculate the correlation between the allocation instruction and the actual output, generate the influencing factor; based on the influencing factor, the weight is dynamically updated to generate the weighted allocation accuracy data; based on the weighted allocation accuracy data, the rationality of the allocation strategy is evaluated to generate the allocation ability evaluation result.

3. The method of claim 2, wherein, The dynamic weight adjustment algorithm is used to calculate the correlation between the allocation instruction and the actual output based on the pre-acquired allocation data to generate the influencing factor, comprising: The influence factor is obtained by a calculation formula The influence factor is obtained by a calculation formula wherein is represented as the relevance value in the time period, is represented as an initial weight coefficient, is represented as a weight coefficient corresponding to the nth distribution factor, is represented as a number corresponding to the distribution factor, , , is represented as the total number of distribution factors, is a normalization coefficient.

4. The method of claim 2, wherein, The rationality of the allocation strategy is evaluated to generate the allocation ability evaluation result, comprising: The weighted allocation accuracy data is compared with a weighted allocation accuracy data threshold, and when the weighted allocation accuracy data is less than or equal to the weighted allocation accuracy data threshold, the allocation capability evaluation result is reasonable; and when the weighted allocation accuracy data is greater than the weighted allocation accuracy data threshold, the allocation capability evaluation result is unreasonable.

5. The method of claim 1, wherein, The thermal power unit data includes response delay duration, regulation speed, regulation accuracy, sustainable frequency modulation duration and unit life loss of the thermal power unit; and the super capacitor data includes response duration, power density, energy density, cycle efficiency and state of charge operation range of the super capacitor.

6. The method of claim 1, wherein, The factor correlation is calculated based on the pre-acquired coordination control data and the allocation capability evaluation result, and the weight is dynamically adjusted, including: By the calculation formula derived factor relevance wherein is expressed as the allocation ability evaluation result, is expressed as the change value of the th coordination control data, is expressed as the number corresponding to each coordination control data, , is expressed as the total number of coordination control data, is expressed as the weight factor corresponding to the th coordination control data.

7. The method of claim 1, wherein, The dynamic response performance index, the coordination control strategy effectiveness index and the stability data index are analyzed to generate the coordination control evaluation result, including: When the dynamic response performance index is greater than or equal to a dynamic response performance index threshold, the coordination control strategy effectiveness index is greater than or equal to a coordination control strategy effectiveness index threshold, and the stability data index is greater than or equal to a stability data index threshold, it is judged that the coordination control evaluation result is high; otherwise, it is judged that the coordination control evaluation result is low.

8. The method of claim 1, wherein, The benefit dynamic prediction is performed based on the unit capability evaluation result, the coordination control evaluation result and the pre-acquired economic data to generate a comprehensive benefit evaluation result, including: Based on the unit capability evaluation result, the coordination control evaluation result and the pre-acquired economic data, a long-term benefit prediction value is generated by using a dynamic benefit prediction model and an influence factor; the technical benefit is quantified based on the long-term benefit prediction value, including calculating a unit regulation mileage reduction rate and a key component wear reduction rate; the economic benefit is quantified based on the long-term benefit prediction value, including calculating a frequency modulation compensation income and an operation and maintenance cost saving; and the comprehensive benefit evaluation result is generated based on the quantification results of the technical benefit and the economic benefit.

9. The method of claim 8, wherein, The comprehensive benefit evaluation result is generated based on the quantification results of the technical benefit and the economic benefit, including: The comprehensive benefit evaluation result is equal to the multiplication result of a technical benefit weight and a technical benefit index, plus the multiplication result of an economic benefit weight and an economic benefit index.

Images