Method and device for evaluating frequency regulation capability of active equipment

Abstract

The invention discloses an active equipment frequency regulation capability evaluation method and device, and the method comprises the steps: constructing an evaluation index system for the power grid frequency regulation capability demands of active equipment based on the all-working-condition scenes of power grid steady-state operation, fault ride-through and fault recovery; calculating each index value according to the participation of the active equipment in the power grid frequency modulation process; an improved analytic hierarchy process-index correlation combination weighting method is adopted to determine the weight of each index, correction factors such as regional influence and control mode compatibility are introduced, and scene self-adaptive dynamic weight adjustment is realized; and constructing a fuzzy evaluation matrix to obtain an equipment all-condition frequency modulation capability comprehensive evaluation result. According to the method, a frequency modulation whole-process index system and scene self-adaptive weighting are taken as the core, subjective and objective combined fuzzy comprehensive evaluation is utilized, the frequency regulation capability evaluation method suitable for multiple types of active equipment is established, and the frequency modulation performance of the equipment can be dynamically and objectively evaluated under different power grid strength and resource fluctuation scenes.

Description

Technical Field

[0001] This invention relates to the field of operation and control technology of new energy equipment in power systems, specifically to a method and device for evaluating the frequency regulation capability of active power equipment. Background Technology

[0002] With the large-scale development of new energy power generation technologies, the proportion of various types of active power equipment such as wind power, photovoltaic power, and energy storage in the power system continues to increase, and their ability to participate in grid frequency regulation directly affects the safe and stable operation of the power system. The grid operation process covers three core stages: steady-state operation, fault ride-through, and fault recovery. The demand for active power equipment to participate in grid frequency regulation varies significantly in different stages.

[0003] Existing frequency regulation capability assessment methods mostly focus on single operating states or specific types of equipment, lacking systematic coverage of the entire metastable state process. Furthermore, the indicator systems are not comprehensive enough to fully reflect the frequency regulation performance of equipment under different operating conditions. Meanwhile, traditional weighting methods often employ single subjective or objective weighting, failing to fully consider the dynamic changes in power grid operating scenarios and the influence of regional characteristics and control methods. This results in insufficient objectivity, adaptability, and reliability of the assessment results, failing to provide a scientific basis for power grid dispatch optimization and equipment selection and configuration. Therefore, it is necessary to construct an evaluation indicator system covering the entire metastable state process, combined with scenario-adaptive weighting methods, to achieve a comprehensive, objective, and dynamic assessment of the frequency regulation capabilities of various types of active power equipment. Summary of the Invention

[0004] The technical problem to be solved by this invention is to overcome the shortcomings of the prior art and provide a method and device for evaluating the frequency regulation capability of active power equipment in the entire process of the transient steady-state operation of various types of active power equipment participating in grid frequency regulation, specifically for wind power equipment, photovoltaic power equipment, DC and converter equipment, grid-connected energy storage equipment and grid-connected energy storage equipment, so as to solve the problem that the existing technology cannot accurately quantify and evaluate the frequency regulation capability of active power equipment under all operating conditions.

[0005] To solve the above technical problems, the present invention adopts the following technical solution:

[0006] First, this invention proposes a method for evaluating the frequency regulation capability of active power equipment, comprising:

[0007] Based on the full operating conditions of power grid steady-state operation, fault ride-through and fault recovery, an evaluation index system is constructed to meet the requirements of active power equipment's participation in power grid frequency regulation, including steady-state operation index, fault ride-through index and fault recovery index.

[0008] Based on the actual process data of active power equipment participating in power grid frequency regulation, calculate the specific values ​​of each indicator in the evaluation index system.

[0009] The initial weights of each indicator are determined by a combination of analytic hierarchy process (AHP) and indicator correlation. Scene characteristics, regional influence, and compatibility of control methods are introduced as correction factors to dynamically adjust the initial weights, thereby obtaining the final weights of each indicator in a specific scenario.

[0010] Based on the specific values ​​and final weights of each indicator, a fuzzy evaluation matrix is ​​constructed, and the comprehensive evaluation result of the frequency regulation capability of the active equipment under all operating conditions is calculated.

[0011] Furthermore, the evaluation index framework for the frequency regulation capability of active power equipment includes steady-state operation indicators, fault ride-through indicators, and fault recovery indicators:

[0012] The steady-state operating indicators include steady-state frequency deviation and steady-state power deviation.

[0013] The indicators during fault ride-through include equivalent inertia coefficient, equivalent droop coefficient, inertia response time, inertia support duration, maximum frequency deviation, frequency change rate, primary frequency modulation response time, and primary frequency modulation rise time.

[0014] The fault recovery indicators include primary frequency modulation adjustment time, primary frequency modulation duration, frequency recovery speed, fault recovery frequency deviation, and grid-type steady-state power deviation.

[0015] Furthermore, the method for calculating the steady-state operating index is as follows:

[0016] The steady-state frequency deviation Δf is calculated using the following method. steady :

[0017] ,

[0018] Where f steady f is the actual frequency after the equipment reaches steady state. N This is the rated frequency of the power grid.

[0019] The steady-state power deviation ΔP is calculated using the following method. steady :

[0020] ,

[0021] Where P steady P represents the actual active power of the equipment or system after it reaches steady state. N This refers to the reference value of active power for equipment or systems.

[0022] The equivalent inertia coefficient and equivalent droop coefficient during fault ride are properties of the active equipment itself.

[0023] The inertia response time index characterizes the time required for the power grid frequency deviation (Δf) to exceed the dead zone until the active power equipment provides inertia support power to reach 10% of its theoretical target value. The inertia response time index value t is calculated using the following method. jr ,

[0024] ,

[0025] The inertia support duration characterizes the time interval from when the grid frequency deviation (Δf) exceeds the dead zone to when the frequency reaches its maximum deviation value. The inertia support duration index value t is calculated using the following method. js ,

[0026] ,

[0027] The maximum frequency deviation Δf is calculated using the following method. max :

[0028] ,

[0029] The rate of change of frequency (ROCOF) is calculated using the following method:

[0030] ,

[0031] The primary frequency regulation response time characterizes the time required from the grid frequency deviation (Δf) exceeding the dead zone to the primary frequency regulation power provided by the active power equipment reaching 10% of its theoretical target value. The primary frequency regulation response time index value t is calculated using the following method. p ,

[0032] ,

[0033] The primary frequency regulation rise time characterizes the time required from the grid frequency deviation (Δf) exceeding the dead zone until the primary frequency regulation power provided by the active power equipment reaches 90% of its theoretical target value. The primary frequency regulation rise time index value t is calculated using the following method. r ,

[0034] ,

[0035] Where ∆f is the system frequency deviation; t h10 This refers to the moment when the power provided by the active equipment to support inertia reaches 10% of its theoretical target value; t f1 f1 is the time when the grid frequency deviation reaches its maximum; f1 is the frequency value when the frequency deviation is at its maximum during frequency regulation; t0 is the time when the grid frequency deviation exceeds the dead zone; t p10 This is the moment when the primary frequency regulation power provided by the active power equipment reaches 10% of its theoretical target value; t p90 This is the moment when the primary frequency modulation power provided by the active equipment reaches 90% of its theoretical target value.

[0036] The primary frequency regulation settling time characterizes the moment from when the grid frequency deviation (Δf) exceeds the dead zone until the primary frequency regulation power provided by the active power equipment continuously operates within ±5% of the target power value. The primary frequency regulation settling time index value t is calculated using the following method. s ,

[0037] ,

[0038] The duration of primary frequency regulation characterizes the time from when the grid frequency deviation (Δf) exceeds the dead zone to when the grid frequency deviation recovers to within the dead zone. The primary frequency regulation duration index value t is calculated using the following method. d ,

[0039] ,

[0040] The frequency recovery rate v is calculated using the following method. f ,

[0041] ,

[0042] The fault recovery frequency deviation Δf is calculated using the following method. rec ,

[0043] ,

[0044] Among them, t p0 f2 is the moment when the primary frequency regulation power provided by the active power equipment continuously operates within ±5% of the target power value; t is the starting point of the operation. f2 Frequency value at time t f2 The grid frequency deviation recovers to the start of the dead zone.

[0045] Considering the impact of switching between control modes such as grid connection, grid construction, steady-state, and transient control, the deviation between the actual power and the rated power of the active power equipment before and after the control mode switching should not exceed 2%P. n P n This refers to the rated active power of the equipment.

[0046] Furthermore, an improved AHP (Analytical Hierarchy Process)-CRITIC (Criterion Importance Through Intercriteria Correlation) combined weighting method is used to determine the weights of each indicator, and a correction factor is introduced to achieve dynamic weight adjustment that adapts to different scenarios. A fuzzy evaluation matrix is ​​then constructed to obtain a comprehensive evaluation result of the equipment's frequency regulation capability under all operating conditions, including:

[0047] The steps of the AHP method are as follows:

[0048] Step 1: Construct the judgment matrix A. For each evaluation factor under the same criterion level, use the 1-9 scaling method to compare their pairwise importance. The scale value is positively correlated with the relative importance of the factor (the higher the scale value, the stronger the relative importance of the corresponding factor). Based on the above comparison results, construct the judgment matrix A=(a) between the criterion level and the indicator level. ij ) n×n (i,j=1,2,…n), where n is the number of evaluation indicators P.

[0049] ,

[0050] Among them, a ij For P i For indicator P j The relative importance of a ji For P j For indicator P i The relative importance of each indicator. When i=j, the relative importance of the same evaluation indicator is equal, so the diagonal elements of the judgment matrix are always 1. This judgment matrix satisfies a ij >0 and a ij a ji =1, which is a positive reciprocal matrix.

[0051] Step 2: Solve for the largest eigenvalue λ of the judgment matrix. max and eigenvectors.

[0052] Step 3, Consistency Check. First, calculate the consistency index:

[0053] ,

[0054] Then, based on the number of indicators n, the random consistency index RI is obtained from a table. The RI values ​​corresponding to orders 3 to 9 are [0.58, 1.12, 1.24, 1.36, 1.41, 1.45, 1.49], respectively. The consistency index is then calculated.

[0055] ,

[0056] When CR < 0.1, the consistency test passes; otherwise, the judgment matrix needs to be adjusted. The eigenvector corresponding to the largest eigenvalue that passes the consistency test is the weight vector obtained by the AHP method, denoted as w. AHP .

[0057] The steps of the CRITIC method are as follows:

[0058] Step 1: Construct the original data matrix for the evaluation indicators. Let there be m evaluation objects and n evaluation indicators. Obtain the original observations of each evaluation object under different indicators through data collection, and construct the original data matrix X = (x... ij ) m×n (i=1,2,…m,j=1,2,…n), x ij This represents the original data of the i-th evaluation object under the j-th indicator.

[0059] ,

[0060] Step 2: Standardization of indicator data. Since the dimensions and orders of magnitude of each evaluation indicator may differ, the original data needs to be standardized to eliminate the influence of dimensions. For positive indicators (the larger the indicator value, the better), the standardization formula is used:

[0061] ,

[0062] For negative indicators (the smaller the indicator value, the better), a standardized formula is used:

[0063] ,

[0064] Where max(x) j ), min(x j ) are the maximum and minimum values ​​of the j-th indicator, respectively. ij The value is the standardized indicator value, and the standardized data range is [0,1].

[0065] Step 3, calculate the index variability measure σ j Based on the standardized indicator data, the standard deviation σ of each indicator is calculated. j :

[0066] ,

[0067] in Let be the standardized mean of the j-th indicator. The standard deviation σ is... j σ reflects the dispersion of the index values. j The larger the value, the stronger the indicator's ability to distinguish between evaluation objects, and the greater its objective weight should be.

[0068] Step 4: Calculate the conflict measure (correlation coefficient) between indicators. Calculate the Pearson correlation coefficient r between any two indicators j and k. jk :

[0069] ,

[0070] Correlation coefficient r jkThe closer the absolute value is to 1, the stronger the correlation between the two indicators, the higher the degree of information overlap, and the weaker the conflict; conversely, the closer the absolute value is to 1, the stronger the conflict.

[0071] Step 5: Calculate the objective weights of the indicators. First, calculate the information content C of each indicator. j Taking into account the variability and conflict of indicators, C j The calculation formula is:

[0072] ,

[0073] Subsequently, regarding C j After normalization, the CRITIC weight vector w of each index is obtained. CRITIC .

[0074] A correction factor is introduced to achieve dynamic weight adjustment that adapts to the scenario. The following method is used to construct a fuzzy evaluation matrix to complete index calculation and comprehensive scoring:

[0075] Step 1: Introduce scene features for different scenarios. The three indicators are system equivalent inertia, wind and solar power output fluctuation, and grid stress level. They determine whether the weights rely more on expert subjective experience (AHP) or objective data characteristics (CRITIC) under different operating conditions. The most commonly used adaptive expression is the sigmoid function:

[0076] ,

[0077] Among them, a i The coefficients to be calibrated can be fitted using historical perturbation samples or a small-scale network simulation set; f i This is the index normalization function.

[0078] Step 2: Project the influence of each region on each indicator as t. Divide the overall power grid A into z different regional sets. The weight matrix of the influence of each regional power grid on the active support capability of the overall power grid is ρ. Then project the influence of each region on each indicator as t:

[0079] ,

[0080] Step 3: Calculate the control mode compatibility c(M). M describes the active power equipment control mode. Define γ as a safety factor (0.05~0.1) to prevent weights from being completely zeroed out. The executable domain score q for each index i is... i (M), then mapped to c(M):

[0081] ,

[0082] Step 4, obtain the final weight vector. Using the formula:

[0083] ,

[0084] The final weight vector is calculated, where ⊙ represents the Hadamard multiplication, i.e., a correction for each indicator; Norm represents the normalization calculation. The final weight vector is:

[0085] ,

[0086] Step 5, construct the fuzzy evaluation matrix. Let the evaluation index for frequency regulation capability be U=(u i ) 1×m The set of comments is V=(v j ) 1×n The normalized index values ​​are converted into membership degrees to form a fuzzy evaluation matrix R, and r is used as the membership degree. ij Indicator u i For level v j The degree of membership.

[0087] ,

[0088] Step 6, Comprehensive Scoring. A weighted average fuzzy algorithm B=W·R is used to obtain the comprehensive evaluation result vector B=(b i ) 1×n , where b i This indicates that the evaluated object belongs to level v. j The overall membership degree.

[0089] Furthermore, the frequency regulation capability of active power equipment under all operating conditions is quantitatively evaluated by comprehensively calculating the evaluation index values ​​and corresponding index weights:

[0090] Active equipment frequency regulation capability S:

[0091] ,

[0092] Comparison of frequency regulation capabilities of different active power equipment (wind, solar, energy storage, flexible DC).

[0093] On the other hand, this application also provides an active power equipment frequency regulation capability assessment device, comprising:

[0094] The module is built based on the full operating conditions of power grid steady-state operation, fault ride-through and fault recovery, and an evaluation index system is constructed to meet the requirements of active power equipment to participate in power grid frequency regulation.

[0095] The calculation module calculates the values ​​of various indicators based on the participation of active equipment in the power grid frequency regulation process;

[0096] The evaluation module uses an improved analytic hierarchy process (AHP)-index correlation combination weighting method to determine the weight of each index and introduces a correction factor to achieve dynamic weight adjustment that adapts to the scenario.

[0097] The display module constructs a fuzzy evaluation matrix to obtain a comprehensive evaluation result of the equipment's frequency regulation capability under all operating conditions; the frequency regulation capability is used to quantitatively evaluate the frequency regulation capability of various types of active equipment under all operating conditions.

[0098] Meanwhile, this application provides a computer device, including: a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of any of the above-described active equipment frequency regulation capability evaluation methods.

[0099] The present invention adopts the above technical solution and has the following technical effects compared with the prior art:

[0100] This invention first constructs a framework of evaluation indicators covering frequency regulation capabilities at various stages, based on the full-condition requirements of active power equipment participating in power grid frequency regulation, and calculates the values ​​of each indicator. An improved hierarchical analysis-indicator correlation combination weighting method is used to introduce correction factors to determine the weights of each indicator, and a fuzzy evaluation matrix is ​​constructed to complete the indicator calculation and comprehensive evaluation results. The indicator system and comprehensive evaluation method constructed through the technical solution of this invention make the evaluation results more reliable. Furthermore, the final evaluation results of this invention are presented as evaluation values, making the evaluation results more accurate and intuitive. Attached Figure Description

[0101] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0102] Figure 1 This is a schematic diagram illustrating the steps of the active power equipment frequency regulation capability evaluation method of the present invention.

[0103] Figure 2 This invention relates to a topology diagram of active power equipment participating in power grid frequency regulation.

[0104] Figure 3 This is a diagram of the evaluation index system for the frequency regulation capability of active power equipment in this invention.

[0105] Figure 4 This is a schematic diagram of the active power equipment frequency regulation capability evaluation device of the present invention.

[0106] Figure 5 This is a schematic diagram of the computer equipment involved in the active equipment frequency regulation capability evaluation method of the present invention. Detailed Implementation

[0107] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0108] The following describes, with reference to the accompanying drawings, a method and apparatus for evaluating the frequency regulation capability of active equipment provided in the embodiments of this application.

[0109] like Figure 1 As shown, this paper addresses the full-condition operation scenario of multiple types of active power equipment participating in grid frequency regulation, focusing on wind power generation equipment, photovoltaic power generation equipment, DC and converter equipment, and energy storage equipment.

[0110] like Figure 2 As shown, this embodiment provides a method for evaluating the frequency regulation capability of active power equipment, including:

[0111] S101, based on the full operating conditions of power grid steady-state operation, fault ride-through and fault recovery, constructs an evaluation index system for the requirements of active power equipment to participate in power grid frequency regulation.

[0112] S102, calculate the values ​​of each index based on the participation of active equipment in the power grid frequency regulation process;

[0113] S103 adopts an improved hierarchical analysis-index correlation combination weighting method to determine the weight of each index, and introduces a correction factor to achieve dynamic weight adjustment that adapts to the scenario.

[0114] S104. Construct a fuzzy evaluation matrix to obtain a comprehensive evaluation result of the equipment's frequency regulation capability under all operating conditions, and achieve overall evaluation through an evaluation device.

[0115] In this embodiment, as Figure 3 As shown, the evaluation index system for the full-condition frequency regulation capability of active power equipment includes steady-state operation indexes, fault ride-through indexes, and fault recovery indexes:

[0116] The steady-state operating indicators include steady-state frequency deviation and steady-state power deviation.

[0117] The indicators during fault ride-through include equivalent inertia coefficient, equivalent droop coefficient, inertia response time, inertia support duration, maximum frequency deviation, frequency change rate, primary frequency modulation response time, and primary frequency modulation rise time.

[0118] The fault recovery indicators include primary frequency modulation adjustment time, primary frequency modulation duration, frequency recovery speed, fault recovery frequency deviation, and grid-type steady-state power deviation.

[0119] In this embodiment, the steady-state operation index calculation method is as follows:

[0120] The steady-state frequency deviation Δf is calculated using the following method. steady ,

[0121] ,

[0122] Where f steady f is the actual frequency after the equipment reaches steady state. N This is the rated frequency of the power grid.

[0123] The steady-state power deviation ΔP is calculated using the following method. steady ,

[0124] ,

[0125] Where P steady P represents the actual active power of the equipment or system after it reaches steady state. N This refers to the reference value of active power for equipment or systems.

[0126] The equivalent inertia coefficient and equivalent droop coefficient during fault ride are properties of the active equipment itself.

[0127] The inertia response time index characterizes the time required for the power grid frequency deviation (Δf) to exceed the dead zone until the active power equipment provides inertia support power to reach 10% of its theoretical target value. The inertia response time index value t is calculated using the following method. jr ,

[0128] ,

[0129] The inertia support duration characterizes the time interval from when the grid frequency deviation (Δf) exceeds the dead zone to when the frequency reaches its maximum deviation value. The inertia support duration index value t is calculated using the following method. js ,

[0130] ,

[0131] The maximum frequency deviation Δf is calculated using the following method. max :

[0132] ,

[0133] The rate of change of frequency (ROCOF) is calculated using the following method:

[0134] ,

[0135] The primary frequency regulation response time characterizes the time required from the grid frequency deviation (Δf) exceeding the dead zone until the primary frequency regulation power provided by the active power equipment reaches 10% of its theoretical target value. The primary frequency regulation response time index value t is calculated using the following method. p ,

[0136] ,

[0137] The primary frequency regulation rise time characterizes the time required from the grid frequency deviation (Δf) exceeding the dead zone until the primary frequency regulation power provided by the active power equipment reaches 90% of its theoretical target value. The primary frequency regulation rise time index value t is calculated using the following method. r ,

[0138]

[0139] Where ∆f is the system frequency deviation; t h10 This refers to the moment when the power provided by the active equipment to support inertia reaches 10% of its theoretical target value; t f1 f1 is the time when the grid frequency deviation reaches its maximum; f1 is the frequency value when the frequency deviation is at its maximum during frequency regulation; t0 is the time when the grid frequency deviation exceeds the dead zone; t p10 This is the moment when the primary frequency regulation power provided by the active power equipment reaches 10% of its theoretical target value; t p90 This is the moment when the primary frequency modulation power provided by the active equipment reaches 90% of its theoretical target value.

[0140] The primary frequency regulation settling time characterizes the moment from when the grid frequency deviation (Δf) exceeds the dead zone until the primary frequency regulation power provided by the active power equipment continuously operates within ±5% of the target power value. The primary frequency regulation settling time index value t is calculated using the following method. s ,

[0141] ,

[0142] The duration of primary frequency regulation characterizes the time from when the grid frequency deviation (Δf) exceeds the dead zone to when the grid frequency deviation recovers to within the dead zone. The primary frequency regulation duration index value t is calculated using the following method. d ,

[0143] ,

[0144] The frequency recovery rate v is calculated using the following method. f ,

[0145] ,

[0146] The fault recovery frequency deviation Δf is calculated using the following method. rec ,

[0147] ,

[0148] Among them, t p0 f2 is the moment when the primary frequency regulation power provided by the active power equipment continuously operates within ±5% of the target power value; t is the starting point of the operation. f2 Frequency value at time t f2 The grid frequency deviation recovers to the start of the dead zone.

[0149] Considering the impact of switching between control modes such as grid connection, grid construction, steady-state, and transient control, the deviation between the actual power and the rated power of the active power equipment before and after the control mode switching should not exceed 2%P. n .

[0150] In this embodiment, an improved AHP (Analytical Hierarchy Process)-CRITIC (Criterion Importance Through Intercriteria Correlation) combined weighting method is used to determine the weights of each indicator. A correction factor is introduced to achieve dynamic weight adjustment that adapts to different scenarios. A fuzzy evaluation matrix is ​​then constructed to obtain a comprehensive evaluation result of the equipment's frequency regulation capability under all operating conditions, including:

[0151] The steps of the AHP method are as follows:

[0152] Step 1: Construct the judgment matrix A. For each evaluation factor under the same criterion level, use the 1-9 scale to compare their pairwise importance. The scale value is positively correlated with the relative importance of the factor (the higher the scale value, the stronger the relative importance of the corresponding factor). Based on the above comparison results, construct the judgment matrix A=(a) between the criterion level and the indicator level. ij ) n×n (i,j=1,2,…n), where n is the number of evaluation indicators P.

[0153] ,

[0154] Among them, a ij For P i For indicator P j The relative importance of a ji For P j For indicator P i The relative importance of each indicator. When i=j, the relative importance of the same evaluation indicator is equal, so the diagonal elements of the judgment matrix are always 1. This judgment matrix satisfies a ij >0 and a ij a ji =1, which is a positive reciprocal matrix.

[0155] Step 2: Solve for the largest eigenvalue λ of the judgment matrix. max and eigenvectors.

[0156] Step 3, Consistency Check. First, calculate the consistency index:

[0157] ,

[0158] Then, based on the number of indicators n, the random consistency index RI is obtained from a table. The RI values ​​corresponding to orders 3 to 9 are [0.58, 1.12, 1.24, 1.36, 1.41, 1.45, 1.49], respectively. The consistency index is then calculated.

[0159] ,

[0160] When CR < 0.1, the consistency test passes; otherwise, the judgment matrix needs to be adjusted. The eigenvector corresponding to the largest eigenvalue that passes the consistency test is the weight vector obtained by the AHP method, denoted as w. AHP .

[0161] The steps of the CRITIC method are as follows:

[0162] Step 1: Construct the original data matrix for the evaluation indicators. Let there be m evaluation objects and n evaluation indicators. Obtain the original observations of each evaluation object under different indicators through data collection, and construct the original data matrix X = (x... ij ) m×n (i=1,2,…m,j=1,2,…n), x ij This represents the original data of the i-th evaluation object under the j-th indicator.

[0163] ,

[0164] Step 2: Standardization of indicator data. Since the dimensions and orders of magnitude of each evaluation indicator may differ, the original data needs to be standardized to eliminate the influence of dimensions. For positive indicators (the larger the indicator value, the better), the standardization formula is used:

[0165] ,

[0166] For negative indicators (the smaller the indicator value, the better), a standardized formula is used:

[0167] ,

[0168] Where max(x) j ), min(x) j ) are the maximum and minimum values ​​of the j-th indicator, respectively. ijThe value is the standardized indicator value, and the standardized data range is [0,1].

[0169] Step 3, calculate the index variability measure σ j Based on the standardized indicator data, the standard deviation σ of each indicator is calculated. j :

[0170] ,

[0171] in Let be the standardized mean of the j-th indicator. The standard deviation σ is... j σ reflects the dispersion of the index values. j The larger the value, the stronger the indicator's ability to distinguish between evaluation objects, and the greater its objective weight should be.

[0172] Step 4: Calculate the conflict measure (correlation coefficient) between indicators. Calculate the Pearson correlation coefficient r between any two indicators j and k. jk :

[0173] ,

[0174] Correlation coefficient r jk The closer the absolute value is to 1, the stronger the correlation between the two indicators, the higher the degree of information overlap, and the weaker the conflict; conversely, the closer the absolute value is to 1, the stronger the conflict.

[0175] Step 5: Calculate the objective weights of the indicators. First, calculate the information content C of each indicator. j Taking into account the variability and conflict of indicators, C j The calculation formula is:

[0176] ,

[0177] Subsequently, regarding C j After normalization, the CRITIC weight vector w of each index is obtained. CRITIC .

[0178] A correction factor is introduced to achieve dynamic weight adjustment that adapts to the scenario. The following method is used to construct a fuzzy evaluation matrix to complete index calculation and comprehensive scoring:

[0179] Step 1: Introduce scene features for different scenarios. The three indicators are system equivalent inertia, wind and solar power output fluctuation, and grid stress level. They determine whether the weights rely more on expert subjective experience (AHP) or objective data characteristics (CRITIC) under different operating conditions. The most commonly used adaptive expression is the sigmoid function:

[0180] ,

[0181] Among them, a i The coefficients to be calibrated can be fitted using historical perturbation samples or a small-scale network simulation set; f i This is the index normalization function.

[0182] Step 2: Project the influence of each region on each indicator as t. Divide the overall power grid A into z different regional sets. The weight matrix of the influence of each regional power grid on the active support capability of the overall power grid is ρ. Then project the influence of each region on each indicator as t:

[0183] ,

[0184] Step 3: Calculate the control mode compatibility c(M). M describes the active power equipment control mode. Define γ as a safety factor (0.05~0.1) to prevent weights from being completely zeroed out. The executable domain score q for each index i is... i (M), then mapped to c(M):

[0185] ,

[0186] Step 4, obtain the final weight vector. The final weight vector is calculated using the formula:

[0187] ,

[0188] Where ⊙ represents the Hadamard multiplication, i.e., adjusting each indicator item by item; Norm is the normalization calculation, and the final weight vector is:

[0189] ,

[0190] Step 5, construct the fuzzy evaluation matrix. Let the evaluation index for frequency regulation capability be U=(u i ) 1×m The set of comments is V=(v j ) 1×n The normalized index values ​​are converted into membership degrees to form a fuzzy evaluation matrix R, and r is used as the membership degree. ij Indicator u i For level v j The degree of membership.

[0191] ,

[0192] Step 6, Comprehensive Scoring. A weighted average fuzzy calculation B=W·R is used to obtain the comprehensive evaluation result vector B=(b i ) 1×n , where b i This indicates that the evaluated object belongs to level v. j The overall membership degree.

[0193] In this embodiment, the frequency regulation capability of active power equipment under all operating conditions is quantitatively evaluated by comprehensively calculating the frequency regulation capability assessment index value and the corresponding index weight:

[0194] Active equipment frequency regulation capability S:

[0195] ,

[0196] Comparison of frequency regulation capabilities of different active power equipment (wind, solar, energy storage, flexible DC).

[0197] Example 2: Figure 4 As shown in the figure, this application provides a device for evaluating the frequency regulation capability of active power equipment, including:

[0198] Module 201 is constructed to build an evaluation index system based on the full operating conditions of power grid steady-state operation, fault ride-through and fault recovery, and to meet the requirements of active power equipment to participate in power grid frequency regulation.

[0199] Calculation module 202 calculates the values ​​of various indicators based on the active power equipment's participation in the power grid frequency regulation process;

[0200] Evaluation module 203 uses an improved analytic hierarchy process (AHP)-index correlation combination weighting method to determine the weight of each index, and introduces a correction factor to achieve dynamic weight adjustment that is adaptive to the scenario.

[0201] The display module 204 constructs a fuzzy evaluation matrix to obtain a comprehensive evaluation result of the frequency regulation capability of the equipment under all operating conditions; the frequency regulation capability is used to quantitatively evaluate the frequency regulation capability of various types of active equipment under all operating conditions.

[0202] Example 3: This application provides a computer device, including a memory 1 and a processor 2, and may further include a network interface 3. The memory stores a computer program. The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The computer device stores an operating system 4. The memory is an example of a computer-readable medium. When the computer program is executed by the processor, it causes the processor to execute a method for evaluating the frequency regulation capability of active power equipment. Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0203] In this embodiment, the active power equipment frequency regulation capability evaluation method provided in this application can be implemented as a computer program, and the computer program can be implemented in the form of, for example, Figure 5 It runs on the computer device shown.

[0204] Example 4: This example proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the control method as described in this invention.

[0205] Example 5: The present invention proposes a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the method described in the present invention.

[0206] It should be noted that the processing flow of embodiments 2-5 corresponds to the specific steps of the method provided in embodiment 1 of the present invention, and has the corresponding functional modules and beneficial effects of the method. Technical details not described in detail in this embodiment can be found in the method provided in embodiment 1 of the present invention.

[0207] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0208] In summary, this invention provides a method and apparatus for evaluating the frequency regulation capability of active power equipment. Considering the frequency regulation capability requirements of active power equipment in power grid operation, this application constructs a three-state evaluation index system encompassing steady-state operation, fault ride-through, and fault recovery. An improved analytic hierarchy process (AHP)-index correlation combination weighting method is used to determine the weights of each index, and correction factors such as regional influence and control mode compatibility are introduced to achieve dynamic weight adjustment adaptively to different scenarios. A fuzzy evaluation matrix is ​​constructed to complete index calculations and comprehensive scoring, fully considering the role of expert experience and objective data, thereby yielding more accurate decision-making results. The final evaluation results are presented in the form of evaluation values ​​and graphs, providing high intuitiveness.

[0209] The specific implementation schemes described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific implementation schemes of the present invention and are not intended to limit the scope of the present invention. Any equivalent changes and modifications made by those skilled in the art without departing from the concept and principles of the present invention should fall within the scope of protection of the present invention.

Claims

1. A method for evaluating the frequency regulation capability of active power equipment, characterized in that, include: Based on the full operating conditions of power grid steady-state operation, fault ride-through and fault recovery, an evaluation index system is constructed to meet the requirements of active power equipment's participation in power grid frequency regulation, including steady-state operation index, fault ride-through index and fault recovery index. Based on the actual process data of active power equipment participating in power grid frequency regulation, calculate the specific values ​​of each indicator in the evaluation index system. The initial weights of each indicator are determined by a combination of analytic hierarchy process (AHP) and indicator correlation. Scene characteristics, regional influence, and compatibility of control methods are introduced as correction factors to dynamically adjust the initial weights, thereby obtaining the final weights of each indicator in a specific scenario. Based on the specific values ​​and final weights of each indicator, a fuzzy evaluation matrix is ​​constructed, and the comprehensive evaluation result of the frequency regulation capability of the active equipment under all operating conditions is calculated.

2. The method according to claim 1, characterized in that, The steady-state operating indicators include steady-state frequency deviation and steady-state power deviation. The indicators during fault ride-through include equivalent inertia coefficient, equivalent droop coefficient, inertia response time, inertia support duration, maximum frequency deviation, frequency change rate, primary frequency modulation response time, and primary frequency modulation rise time. The fault recovery indicators include primary frequency modulation adjustment time, primary frequency modulation duration, frequency recovery speed, fault recovery frequency deviation, and grid-type steady-state power deviation.

3. The method according to claim 2, characterized in that, The specific calculation method for steady-state operation evaluation indicators is as follows: steady-state frequency deviation Δf steady The following method is used for calculation: ， Where f steady f is the actual frequency after the equipment reaches steady state. N The rated frequency of the power grid; Steady-state power deviation ΔP steady The following method is used for calculation: ， Where P steady P represents the actual active power of the equipment or system after it reaches steady state. ref This refers to the reference value of active power for equipment or systems.

4. The method according to claim 3, characterized in that, The equivalent inertia coefficient and equivalent droop coefficient in the fault ride-through indicators are attributes of the active equipment itself. Inertia response time is a metric that characterizes the time required for the power grid frequency deviation Δf to exceed the dead zone until the active power equipment provides inertia support power to reach 10% of its theoretical target value. The inertia response time metric value is t. jr Calculate using the following method: ， Inertia support duration characterizes the time interval from when the grid frequency deviation Δf exceeds the dead zone to when the frequency reaches its maximum deviation value. The inertia support duration index value t js Calculate using the following method: ， Maximum frequency deviation Δf max The following method is used for calculation: ， The rate of change of frequency (ROCOF) is calculated using the following method: ， Primary frequency regulation response time characterizes the time required from the grid frequency deviation Δf exceeding the dead zone until the primary frequency regulation power provided by the active power equipment reaches 10% of its theoretical target value. The primary frequency regulation response time index value t p Calculate using the following method: ， The primary frequency regulation rise time characterizes the time required from the grid frequency deviation Δf exceeding the dead zone until the primary frequency regulation power provided by the active power equipment reaches 90% of its theoretical target value. The primary frequency regulation rise time index value t r Calculate using the following method: ， Where ∆f is the system frequency deviation; t h10 This refers to the moment when the power provided by the active equipment to support inertia reaches 10% of its theoretical target value; t f1 f1 is the time when the grid frequency deviation reaches its maximum; f1 is the frequency value when the frequency deviation is at its maximum during frequency regulation; t0 is the time when the grid frequency deviation exceeds the dead zone; t p10 This is the moment when the primary frequency regulation power provided by the active power equipment reaches 10% of its theoretical target value; t p90 This is the moment when the primary frequency modulation power provided by the active equipment reaches 90% of its theoretical target value.

5. The method according to claim 4, characterized in that, During fault recovery, the primary frequency regulation adjustment time characterizes the moment from when the grid frequency deviation Δf exceeds the dead zone to when the primary frequency regulation power provided by the active power equipment continues to operate within ±5% of the power target value. The primary frequency regulation adjustment time index value t s Calculate using the following method: ， The duration of primary frequency regulation characterizes the time from when the grid frequency deviation Δf exceeds the dead zone to when the grid frequency deviation recovers to within the dead zone. The index value of the duration of primary frequency regulation is t. d Calculate using the following method: ， Frequency recovery speed v f Calculate using the following method: ， Fault recovery frequency deviation Δf rec Calculate using the following method: ， Among them, t p0 f2 is the moment when the primary frequency regulation power provided by the active power equipment continuously operates within ±5% of the target power value; t is the starting point of the operation. f2 Frequency value at time t f2 It is the moment when the power grid frequency deviation begins to recover to the dead zone; Grid-type steady-state power deviation index: The deviation between the actual power and the rated power before and after the switching of the active power equipment control mode is no greater than 2%P. n P n This refers to the rated active power of the equipment.

6. The method according to claim 1, characterized in that, An improved Analytic Hierarchy Process (AHP)-Indicator Correlation CRITIC combined weighting method is adopted to determine the weights of each indicator and a correction factor is introduced to achieve dynamic weight adjustment that is adaptive to the scenario. A fuzzy evaluation matrix is ​​constructed to obtain a comprehensive evaluation result of the equipment's frequency regulation capability under all operating conditions, including: The steps of the AHP method are as follows: Step 1: Construct the judgment matrix A; for each evaluation factor under the same criterion level, use the 1-9 scale method to compare their pairwise importance. The scale value is positively correlated with the relative importance of the factor. Based on the above comparison results, construct the judgment matrix A=(a) between the criterion level and the indicator level. ij ) n×n Let i,j=1,2,…n, where n is the number of evaluation indicators P. ， Among them, a ij For P i For indicator P j The relative importance of a ji For P j For indicator P i The relative importance of each evaluation indicator is equal when i=j, so the diagonal elements of the judgment matrix are always 1. This judgment matrix satisfies a ij >0 and a ij a ji =1, which is a positive reciprocal matrix; Step 2: Solve for the largest eigenvalue λ of the judgment matrix. max and eigenvectors; Step 3, Consistency check: First, calculate the consistency index. ， Then, based on the number of indicators n, the random consistency index RI is obtained from a table. The RI values ​​corresponding to orders 3 to 9 are [0.58, 1.12, 1.24, 1.36, 1.41, 1.45, 1.49], respectively. The consistency index is then calculated. ， When CR < 0.1, the consistency test is passed; otherwise, the judgment matrix needs to be adjusted. The eigenvector corresponding to the largest eigenvalue that passes the consistency test is the weight vector obtained by the AHP method, denoted as w. AHP .

7. The method according to claim 6, characterized in that, The steps of the CRITIC method are as follows: Step 1: Construct the original data matrix for evaluation indicators: Assume there are m evaluation objects and n evaluation indicators. Obtain the original observations of each evaluation object under different indicators through data collection, and construct the original data matrix X = (x... ij ) m×n x ij This represents the original data of the i-th evaluation object under the j-th indicator; ， Where i = 1, 2, ..., m, j = 1, 2, ..., n; Step 2, Standardization of Indicator Data: For positive indicators, a standardization formula is used: ， For negative indicators, a standardized formula is used: ， Where max(x) j ), min(x j ) are the maximum and minimum values ​​of the j-th indicator, respectively. ij These are the standardized indicator values, and the standardized data ranges from [0,1]. Step 3: Calculate the index variability measure σ j Based on the standardized indicator data, calculate the standard deviation σ for each indicator. j : ， in Let be the standardized mean and standard deviation σ of the j-th indicator. j σ reflects the dispersion of the index values. j The larger the value, the stronger the indicator's ability to distinguish between evaluation objects, and its objective weight should be greater. Step 4: Calculate the conflict measure between indicators: Calculate the Pearson correlation coefficient r between any two indicators j and k. jk : ， Correlation coefficient r jk The closer the absolute value is to 1, the stronger the correlation between the two indicators, the higher the information overlap, and the weaker the conflict; conversely, the closer the absolute value is to 1, the stronger the conflict. Step 5: Calculate the objective weights of the indicators: First, calculate the information content C of each indicator. j Taking into account the variability and conflict of indicators, C j The calculation formula is: ， Subsequently, regarding C j After normalization, the CRITIC weight vector w of each index is obtained. CRITIC .

8. The method according to claim 6, characterized in that, A correction factor is introduced to achieve dynamic weight adjustment that adapts to the scenario. The following method is used to construct a fuzzy evaluation matrix to complete index calculation and comprehensive scoring: Step 1: Introduce scene features for different scenarios Three indicators These are the system equivalent inertia, wind and solar power output fluctuation, and grid stress level indicators, respectively. They determine whether the weights rely more on expert subjective experience (AHP) or objective data characteristics (CRITIC) under different operating conditions, using an sigmoid function. ， Among them, a i For the coefficients to be calibrated, fit them using historical perturbation samples or a small-scale network simulation set; f i For index normalization function; Step 2: Project the influence of each region on each indicator as t: Divide the overall power grid A into z different regional sets. The weight matrix of the influence of each regional power grid on the active support capability of the overall power grid is ρ. Then project the influence of each region on each indicator as t: ； Step 3: Calculate the control mode compatibility c(M): Let M describe the active power equipment control mode, define γ as the minimum protection coefficient (0.05~0.1), and the executable domain score q for each index i. i (M), then mapped to c(M): ， Step 4: Calculate the final weight vector: ， Where ⊙ represents the Hadamard multiplication, i.e., adjusting each indicator item by item; Norm is the normalization calculation, and the final weight vector is: ， Step 5: Construct the fuzzy evaluation matrix: Let the evaluation index for frequency regulation capability be U=(u i ) 1×m The set of comments is V=(v j ) 1×n The normalized index values ​​are converted into membership degrees to form a fuzzy evaluation matrix R, and r is used as the membership degree. ij Indicator u i For level v j Membership degree: ， Step 6, Comprehensive Scoring: A weighted average fuzzy algorithm B=W*R ​​is used to obtain the comprehensive evaluation result vector B=(b i ) 1×n , where b i This indicates that the evaluated object belongs to level v. j The overall membership degree.

9. The method according to claim 8, characterized in that, The frequency regulation capability of active power equipment under all operating conditions is quantitatively evaluated based on the frequency regulation capability assessment index values ​​and their corresponding index weights. The frequency regulation capability S of active power equipment is used to compare the frequency regulation capabilities of different active power equipment. 。 10. A computer device, characterized in that, include: A memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform an assessment of the frequency regulation capability of active equipment as described in any one of claims 1 to 9.

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