Method and device for evaluating transient supporting capability of energy storage power station to power grid

Abstract

The invention provides a method and a device for evaluating the transient support capability of an energy storage power station to a power grid, and the method comprises the steps: calculating the weight of an index value after the dimensionalization processing through different weighting methods, and optimizing the weight of the index value after the dimensionalization processing, and the transient support capability of the energy storage power station on the power grid is evaluated according to the obtained combination weight of each evaluation index. It can be seen that different weights obtained by different weighting methods are comprehensively considered, the upper layer adopts at least two weighting methods to calculate the weight of the index value after the dimensionalization processing, and the lower layer optimizes the weight of the index value after the dimensionalization processing to obtain the combined weight of each evaluation index. That is to say, the combined weight of each evaluation index is obtained by adopting the double-layer combined weighting, the precision of the evaluation result is improved, and the reliability of the energy storage power station for evaluating the transient support capability of the power grid can be further improved.

Description

Technical Field

[0001] The present application relates to the field of energy storage technology, and in particular to a method and device for evaluating the transient support capability of an energy storage power station for a power grid. Background Art

[0002] With the significant increase in the scale of installed capacity of new energy, the gradual transformation of the power structure to clean and low-carbon energy has become an inevitable trend in the development of the power industry. Most wind and solar resources are concentrated in the northwest, northeast, and north China, which are far away from the main grid and are distributed inversely to the load center. They mostly adopt the development model of "large-scale centralized development and high-voltage direct current long-distance transmission". In areas with relatively rich wind and solar resources, a weakly interconnected power grid with a high proportion of new energy has gradually formed. However, for a weakly interconnected power grid with a high proportion of new energy, the main grid support capacity is insufficient, the system inertia level is low, and there are few flexible adjustment resources. In addition, the weak anti-interference characteristics of a high proportion of new energy under AC and DC faults in the power grid make the system present a complex characteristic of frequency and voltage coupling, and the safe and stable operation of the regional power grid faces severe challenges.

[0003] With the rapid development of energy storage technology, new energy storage represented by electrochemical energy storage has flexible bidirectional regulation and millisecond-level rapid response capabilities. Among them, the grid-type energy storage proposed by drawing on the physical mechanism of synchronous generators has better stability in scenarios such as weak power grids. It is an effective means to solve the problem of insufficient flexible regulation resources in the power system and improve the transient support capacity of the system. Most of the related technologies use subjective weighting methods represented by the analytic hierarchy process and / or objective weighting methods represented by the entropy weight method to evaluate the transient support capacity of energy storage power stations participating in the frequency and voltage regulation process of weakly interconnected power grids. However, although the subjective weighting method has good flexibility and adaptability and is suitable for more complex and fuzzy problems, the weight assignment is affected by subjective factors such as personal preferences and experience, and the results are difficult to quantify well. The objective weighting method is good in data objectivity and quantitative accuracy, but data acquisition is difficult, over-reliance on historical data, and completely ignoring subjective factors will also lead to a lack of comprehensiveness in the results. Therefore, the evaluation method provided by the relevant technology is less reliable. Summary of the invention

[0004] In order to solve the problem of low reliability in the prior art, the present application provides a method for evaluating the transient support capability of an energy storage power station for a power grid, which may include: de-dimensionalizing the index values ​​of various evaluation indicators. Using at least two weighting methods to calculate the weights of the de-dimensionalized index values, and optimizing the weights of the de-dimensionalized index values ​​to obtain the combined weights of various evaluation indicators. Evaluate the transient support capability of the energy storage power station for the power grid based on the combined weights of various evaluation indicators.

[0005] Optionally, the evaluation index includes a first evaluation index for evaluating the power grid and a second evaluation index for evaluating the energy storage power station. The first evaluation index may also be called a power grid layer evaluation index. The second evaluation index may be called a power station layer evaluation index.

[0006] The first evaluation index includes bus voltage limit margin, voltage qualification rate, average voltage deviation energy, second-level apparent power deviation, voltage deviation limit margin, maximum frequency deviation, frequency fluctuation variance and frequency stability margin. The second evaluation index includes regulation speed, regulation accuracy, response time, battery voltage extreme difference, battery state of charge and battery health status.

[0007] In some possible implementations, the indicator values ​​of each evaluation indicator are de-dimensionalized, including:

[0008] The indicator value of each evaluation indicator is divided into multiple intervals according to the preset indicator limit.

[0009] The probability of each evaluation indicator in each interval is counted, and the indicator value after de-dimensionalization is calculated based on the probability of each evaluation indicator in each interval.

[0010] For example, the indicator value after dedimensionalization satisfies:

[0011]

[0012] Where T(j) represents the dedimensionalized value of the jth evaluation index, P i (j) represents the probability of the jth evaluation indicator in the i-th interval, and n represents the number of intervals.

[0013] In some other possible implementations, at least two weighting methods are used to calculate the weights of the dedimensionalized index values, and the weights of the dedimensionalized index values ​​are optimized to obtain the combined weights of the evaluation indicators, including:

[0014] At least two weighting methods among the analytic hierarchy process, paired comparison method, CRITIC method or entropy weight method are used to calculate the weight of the index value after de-dimensionalization. Among them, the analytic hierarchy process and the paired comparison method are subjective weighting methods, and the CRITIC method and the entropy weight method are objective weighting methods. Of course, other subjective weighting methods or objective weighting methods can also be used to calculate the weight of the index value after de-dimensionalization, which is not limited in this application.

[0015] The weight matrix is ​​constructed according to the weights of the dedimensionalized indicator values.

[0016] The goal is to minimize the deviation between the weights of the dedimensionalized indicator values ​​calculated by different weighting methods, and build an evaluation model based on the elements of the weight matrix.

[0017] The mixed integer linear programming algorithm is used to solve the evaluation model and obtain the combined weights of various evaluation indicators.

[0018] Exemplarily, the evaluation model satisfies:

[0019]

[0020] Among them, F represents the deviation between the weights of the dedimensionalized index values ​​calculated by different weighting methods, r represents the total number of weighting methods, m represents the total number of evaluation indicators, and w lj is the element in the lth row and jth column of the weight matrix, which represents the weight of the index value of the jth evaluation index after dedimensionalization calculated by the lth weighting method. j represents the combined weight of the j-th evaluation indicator.

[0021] In another possible implementation, the transient support capability of the energy storage power station for the power grid is evaluated according to the combined weights of various evaluation indicators, including:

[0022] The comprehensive evaluation results are calculated based on the combined weights of various evaluation indicators and the indicator values ​​after dimensionless processing.

[0023] Evaluate the transient support capability of the energy storage power station to the power grid based on the comprehensive evaluation results.

[0024] Optional, the comprehensive evaluation results meet the following requirements:

[0025]

[0026] Among them, T represents the comprehensive evaluation result, W j represents the combined weight of the j-th evaluation indicator, T(j) represents the indicator value after dedimensionalization of the j-th evaluation indicator, and m represents the total number of evaluation indicators.

[0027] It is conceivable that the comprehensive evaluation result can reflect the transient support capability of the energy storage power station for the power grid. Therefore, the evaluation of the transient support capability of the energy storage power station for the power grid is realized based on the comprehensive evaluation result.

[0028] On the other hand, the present application also provides a device for evaluating the transient support capability of an energy storage power station for a power grid, comprising:

[0029] The processing module is used to perform dimensionless processing on the indicator values ​​of various evaluation indicators.

[0030] The optimization module is used to calculate the weight of the indicator value after de-dimensionalization by using at least two weighting methods, and optimize the weight of the indicator value after de-dimensionalization to obtain the combined weight of each evaluation indicator.

[0031] The evaluation module is used to evaluate the transient support capability of the energy storage power station to the power grid according to the combined weights of various evaluation indicators.

[0032] Optionally, the evaluation index includes a first evaluation index for evaluating a power grid and a second evaluation index for evaluating an energy storage power station.

[0033] The first evaluation index includes bus voltage over-limit margin, voltage qualification rate, average voltage deviation energy, second-level apparent power deviation, voltage deviation over-limit margin, maximum frequency deviation, frequency fluctuation variance and frequency stability margin.

[0034] The second evaluation index includes regulation speed, regulation accuracy, response time, battery voltage extreme difference, battery state of charge and battery health status.

[0035] In some possible implementations, the processing module is used to:

[0036] The indicator value of each evaluation indicator is divided into multiple intervals according to the preset indicator limit.

[0037] The probability of each evaluation indicator in each interval is counted, and the indicator value after de-dimensionalization is calculated based on the probability of each evaluation indicator in each interval.

[0038] Optionally, the indicator value after dimensionless processing satisfies:

[0039]

[0040] Where T(j) represents the dedimensionalized value of the jth evaluation index, P i (j) represents the probability of the jth evaluation indicator in the i-th interval, and n represents the number of intervals.

[0041] In some other possible implementations, the optimization module is used to:

[0042] At least two weighting methods among the analytic hierarchy process, paired comparison method, CRITIC method or entropy weight method are used to calculate the weight of the index value after de-dimensionalization. Among them, the analytic hierarchy process and the paired comparison method are subjective weighting methods, and the CRITIC method and the entropy weight method are objective weighting methods. Of course, other subjective weighting methods or objective weighting methods can also be used to calculate the weight of the index value after de-dimensionalization, which is not limited in this application.

[0043] The weight matrix is ​​constructed according to the weights of the dedimensionalized indicator values.

[0044] The goal is to minimize the deviation between the weights of the dedimensionalized indicator values ​​calculated by different weighting methods, and build an evaluation model based on the elements of the weight matrix.

[0045] The mixed integer linear programming algorithm is used to solve the evaluation model and obtain the combined weights of various evaluation indicators.

[0046] Optionally, the evaluation model satisfies:

[0047]

[0048] Among them, F represents the deviation between the weights of the dedimensionalized index values ​​calculated by different weighting methods, r represents the total number of weighting methods, m represents the total number of evaluation indicators, and w lj is the element in the lth row and jth column of the weight matrix, which represents the weight of the index value of the jth evaluation index after dedimensionalization calculated by the lth weighting method. j represents the combined weight of the j-th evaluation indicator.

[0049] In some further possible implementations, the evaluation module is used to:

[0050] The comprehensive evaluation results are calculated based on the combined weights of various evaluation indicators and the indicator values ​​after dimensionless processing.

[0051] Evaluate the transient support capability of the energy storage power station to the power grid based on the comprehensive evaluation results.

[0052] Optional, the comprehensive evaluation results meet the following requirements:

[0053]

[0054] Among them, T represents the comprehensive evaluation result, W j represents the combined weight of the j-th evaluation indicator, T(j) represents the indicator value after dedimensionalization of the j-th evaluation indicator, and m represents the total number of evaluation indicators.

[0055] It is conceivable that the comprehensive evaluation result can reflect the transient support capability of the energy storage power station for the power grid. Therefore, the evaluation of the transient support capability of the energy storage power station for the power grid is realized based on the comprehensive evaluation result.

[0056] On the other hand, the present application also provides a computer device, including: one or more processors.

[0057] A processor is used to execute one or more programs.

[0058] When one or more programs are executed by one or more processors, the above-mentioned evaluation method is implemented.

[0059] In another aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, the above-mentioned evaluation method is implemented.

[0060] Compared with the prior art, the beneficial effects of this application are:

[0061] The method for evaluating the transient support capacity of the power grid by the energy storage power station provided in the present application calculates the weights of the index values ​​after de-dimensionalization through different weighting methods, and optimizes the weights of the index values ​​after de-dimensionalization, and then realizes the evaluation of the transient support capacity of the power grid by the energy storage power station according to the combined weights of the various evaluation indicators obtained. It can be seen that the present application comprehensively considers the different weights obtained by different weighting methods, and optimizes the different weights, which can improve the accuracy of the comprehensive evaluation results, and thus can improve the reliability of the evaluation of the transient support capacity of the power grid by the energy storage power station.

[0062] The upper layer of the present application uses at least two weighting methods to calculate the weights of the indicator values ​​after de-dimensionalization, and the lower layer optimizes the weights of the indicator values ​​after de-dimensionalization to obtain the combined weights of various evaluation indicators. In other words, the present application uses a double-layer combined weighting to obtain the combined weights of various evaluation indicators, further improving the accuracy of the evaluation results.

[0063] This application performs dimensionless processing on the indicator values ​​of various evaluation indicators, that is, the dimensionless indicator values ​​are obtained using probability statistical quantitative analysis, which solves the differences between the indicator values ​​and reduces the difficulty of calculating the double-layer comprehensive weights and comprehensive evaluation results.

[0064] This application constructs an evaluation model with the goal of minimizing the deviation between the weights of dedimensionalized indicator values ​​calculated by different weighting methods, and uses a mixed integer linear programming algorithm to solve the evaluation model to obtain the combined weights of various evaluation indicators, thereby optimizing the weights of dedimensionalized indicator values ​​and improving the accuracy of the combined weights. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0066] Figure 1 A schematic flow chart of a method for evaluating the transient support capability of an energy storage power station for a power grid in an embodiment of the present application;

[0067] Figure 2 This is a schematic structural diagram of a comprehensive evaluation system for the transient support capability of an energy storage power station to a power grid in an embodiment of the present application;

[0068] Figure 3 A schematic structural diagram of a device for evaluating the transient support capability of an energy storage power station for a power grid in an embodiment of the present application. DETAILED DESCRIPTION

[0069] The technical solution in this application will be described below in conjunction with the accompanying drawings.

[0070] The terms "first", "second", etc. in the specification embodiments, claims, and drawings of the present application are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, including a series of steps or units. The method, system, product, or device is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0071] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0072] Embodiment 1:

[0073] The present application embodiment provides a method for evaluating the transient support capability of an energy storage power station to a power grid, such as Figure 1 The evaluation method 100 comprises the following steps:

[0074] Step S1: De-dimensionalize the index values ​​of various evaluation indexes to obtain the de-dimensionalized index values.

[0075] Step S2: using at least two weighting methods to calculate the weights of the dedimensionalized index values, and optimizing the weights of the dedimensionalized index values ​​to obtain the combined weights of various evaluation indexes.

[0076] Step S3: Evaluate the transient support capability of the energy storage power station to the power grid according to the combined weights of various evaluation indicators.

[0077] Optionally, the evaluation indicators can constitute a comprehensive evaluation system for the transient support capability of energy storage power stations for the power grid. Figure 2As shown, the evaluation system may include a target layer, a criterion layer, and an indicator layer. Among them, the target layer may be the transient support capability of the energy storage power station for the power grid. The criterion layer may include power grid layer evaluation indicators (i.e., the first evaluation indicator) and power station layer evaluation indicators (i.e., the second evaluation indicator). The indicator layer includes multiple evaluation indicators. The multiple evaluation indicators may include the bus voltage limit margin, voltage qualification rate, average voltage deviation energy, second-level apparent power deviation, voltage deviation limit margin, maximum frequency deviation, frequency fluctuation variance, and frequency stability margin in the power grid layer evaluation indicators, and also include the adjustment speed, adjustment accuracy, response time, battery voltage extreme difference, battery state of charge, and battery health status in the power station layer evaluation indicators.

[0078] The above evaluation indicators are introduced below.

[0079] Bus voltage limit margin: refers to the ratio of the difference between the bus voltage upper limit and the operating voltage to the limit range, which is used to measure the ability of the bus voltage to ensure that it does not exceed the limit when a disturbance occurs in the power grid. It can meet the following requirements:

[0080]

[0081] In the formula, A1 represents the bus voltage over-limit margin, V represents the bus operating voltage, Indicates the upper limit of the busbar operating voltage. V Indicates the lower limit of the bus operating voltage.

[0082] Voltage qualification rate: refers to the proportion of the total time that the voltage of the power grid is within the qualified range in an operation cycle to the total assessment time, which is used to measure the power supply qualification rate of the busbar in the operation cycle. It can meet:

[0083]

[0084] Where: A2 represents the voltage qualification rate, L represents the number of buses involved in the assessment, t l It indicates the voltage exceeding limit time of bus 1, and T indicates the operation cycle.

[0085] Average voltage deviation energy: used to measure the energy loss caused by voltage deviation, and is used to describe the statistics of the size and duration of voltage deviation in the power system. It reflects the product of the degree to which the voltage deviates from the nominal value in a certain period of time and the duration. Satisfies:

[0086]

[0087] Where: A3 represents the average voltage deviation energy. T represents the evaluation period in hours. ξ represents the number of voltage deviations, V t It indicates the bus operating voltage at time t. V0 indicates the bus reference voltage.

[0088] Second-level apparent power deviation: used to measure the power deviation caused by emergency accidents. The management of second-level apparent power deviation is crucial to maintaining the stable operation of the power system and the normal operation of the equipment.

[0089]

[0090] Where: A4 represents the apparent power deviation in seconds, S represents the apparent power of the power grid, and S0 represents the apparent power reference value of the power grid. It should be noted that the time scale of the apparent power deviation is in seconds.

[0091] Voltage deviation margin:

[0092] Voltage deviation refers to the relative deviation of the actual operating voltage at a node from the system nominal voltage. When the grid voltage deviation index exceeds the limit, it will cause the operating performance of electrical equipment such as motors and lighting fixtures to deteriorate or even be damaged. Therefore, the grid voltage deviation margin is considered to meet the following requirements:

[0093]

[0094] Where: A5 represents the voltage deviation margin, δU represents the voltage deviation, U i represents the actual operating voltage of node i; U N Indicates the nominal voltage, 7% indicates the over-limit value, and 4.2% indicates the warning value.

[0095] Maximum frequency deviation: It is used to characterize the limit value of frequency deviation and is one of the important indicators for measuring the stability and quality of power system operation.

[0096] A6=max(f-f0|)

[0097] Where: A6 represents the maximum frequency deviation. f represents the rated frequency in Hz. f0 represents the reference power in Hz.

[0098] Frequency fluctuation variance: It is used to characterize the stability of frequency deviation and is a statistic that measures the degree of frequency fluctuation in the power system. It reflects the amplitude and volatility of frequency changes in the power grid. It satisfies:

[0099]

[0100] Where: A7 represents the frequency fluctuation variance. t It represents the frequency of the power grid at time t, in Hz. It represents the average frequency value during the evaluation period, in Hz.

[0101] Frequency stability margin: It is used to indicate that the frequency of the power grid is in a safe state, reflecting the ability and stability of frequency fluctuations in the power grid.

[0102]

[0103] Where A8 represents the frequency stability margin, f cr It indicates the frequency value that makes the given frequency curve just critically safe.

[0104] Regulation speed: used to measure how quickly the energy storage power station responds to regulation instructions. Generally speaking, the faster the energy storage regulation speed, the faster it can meet the requirements of the regulation power instruction, and the better the frequency and voltage recovery effect. Satisfy:

[0105]

[0106] In the formula, B1 represents the regulation speed, v represents the regulation speed of the energy storage power station, and v N Indicates standard regulation speed.

[0107] Regulation accuracy: used to measure the difference between the final output of the energy storage power station and the regulation command value. The smaller the difference, the higher the regulation accuracy.

[0108]

[0109] In the formula, B2 represents the regulation accuracy. ΔP represents the regulation error of the energy storage power station, ΔP N Indicates the standard adjustment error.

[0110] Response time: It is used to measure the time required for the energy storage power station to start adjusting after receiving the adjustment command. The shorter the response time, the better the response performance. Generally, the ratio of the actual adjustment time to the standard adjustment time is used to reflect the response time of the energy storage power station. Satisfy:

[0111]

[0112] In the formula, B3 represents the response time, t represents the response time of the energy storage power station, and t N Indicates the standard response time.

[0113] Battery voltage range: refers to the maximum difference in battery voltage, reflecting the performance difference between the maximum and minimum voltage cells and the attenuation of the cells.

[0114] B4=U max -U min

[0115] In the formula, B4 represents the battery voltage extreme difference, U max Indicates the maximum value of the single battery voltage, U minIndicates the minimum value of the single cell voltage.

[0116] Battery charge state: Indicates the ratio of the remaining power of a single battery to the capacity of a fully charged state. It reflects whether the battery is overcharged or over-discharged. The present invention characterizes the average level of the energy storage power station. Satisfies:

[0117]

[0118] In the formula, B5 represents the battery charge state, C m Represents the remaining capacity of the energy storage power station, C N Indicates the rated capacity of the energy storage power station.

[0119] Battery health status: used to reflect the aging degree of energy storage batteries. Meet:

[0120]

[0121] In the formula, B6 represents the battery health status, C M Indicates the current maximum discharge capacity of the energy storage power station.

[0122] In some embodiments, the de-dimensionalization of the indicator values ​​of each evaluation indicator in step S1 includes:

[0123] The indicator value of each evaluation indicator is divided into multiple intervals according to the preset indicator limit.

[0124] The probability of each evaluation indicator in each interval is counted, and the indicator value after de-dimensionalization is calculated based on the probability of each evaluation indicator in each interval.

[0125] For example, the indicator value after dedimensionalization satisfies:

[0126]

[0127] Where T(j) represents the dedimensionalized value of the jth evaluation indicator. n represents the number of intervals. i (j) represents the probability of the jth evaluation indicator in the i-th interval, P i (j) may be the element in the i-th row and j-th column in the data matrix Y, and the data matrix Y may be composed of all probability values ​​of m indicators in n intervals.

[0128] In some other embodiments, in step S2, at least two weighting methods are used to calculate the weights of the dedimensionalized index values, and the weights of the dedimensionalized index values ​​are optimized to obtain the combined weights of the evaluation indicators, including:

[0129] Step S2a: Use at least two weighting methods among the Improved Analytical Hierarchy Process (IAHP), the paired comparison method, the CRITIC method or the Entropy Weight (EP) method to calculate the weight of the index value after de-dimensionalization. Among them, the analytic hierarchy process and the paired comparison method are subjective weighting methods, and the CRITIC method and the entropy weight method are objective weighting methods. Of course, other subjective weighting methods or objective weighting methods can also be used to calculate the weight of the index value after de-dimensionalization, which is not limited in this application.

[0130] Step S2b: construct a weight matrix according to the weights of the dedimensionalized index values.

[0131] Step S2c: The goal is to minimize the deviation between the weights of the dedimensionalized indicator values ​​calculated by different weighting methods, and to construct an evaluation model based on the elements of the weight matrix.

[0132] Step S2d: Using a mixed integer linear programming (MILP) algorithm to solve the evaluation model, and obtain the combined weights of various evaluation indicators.

[0133] In the above step S2a, the embodiment of the present application uses the analytic hierarchy process, the paired comparison method, the CRITIC method and the entropy weight method to respectively calculate the weights of the index values ​​after dedimensionalization. Specifically, as follows:

[0134] (1) Analytical Hierarchy Process

[0135] The hierarchical analysis method has the characteristic of optimal matrix consistency, which can effectively improve the accuracy of weight calculation and reduce the influence of subjective components to a certain extent. The specific process is as follows:

[0136] First, the importance of the evaluation indicators with respect to a certain indicator of the previous level is compared pairwise, so as to construct the judgment matrix Z of the indicators at each level, satisfying:

[0137]

[0138] In the present application embodiment, z pp Represents the element in the p-th row and p-th column of the judgment matrix Z.

[0139] Secondly, the consistency test is performed on the judgment matrix Z. Specifically, the consistency ratio Z can be calculated b :

[0140]

[0141] Among them, C1 represents the average random consistency test index, Z1 represents the consistency index, and satisfies:

[0142] λ max represents the maximum eigenvalue, and p represents the matrix order.

[0143] When the consistency ratio Z b When it is less than 0.1, it can be determined that the judgment matrix Z passes the consistency test, otherwise the judgment matrix Z is reconstructed.

[0144] Calculate the weight of the indicator value after de-dimensionalization using the hierarchical analysis method:

[0145]

[0146] Among them, w 1j It represents the weight of the indicator value after de-dimensionalization calculated by the hierarchical analysis method, q j represents the jth element in the eigenvector Q, which satisfies: Q = [q1, q2, ..., q m ] T . T stands for transpose.

[0147] (2) Paired comparison method

[0148] The use of paired comparison method requires that the indicators are comparable and the comparison results are transitive. The specific process is as follows:

[0149] First, a comparison set of evaluation indicators is constructed, and m+1 elements are added as virtual evaluation indicators to represent the least important evaluation indicators.

[0150] Secondly, compare each evaluation indicator in the evaluation indicator comparison set one by one. For the kth evaluation indicator x k and the jth evaluation metric x j , if x k X j Important, then V kj =1; otherwise V kj =0. If the two evaluation indicators are equally important, then V kj =0.5. Among them, V kj Represents a comparison element, which is a Boolean value.

[0151] Finally, according to the comparison element V kj Obtain the weight of the indicator value after de-dimensionalization by using the paired comparison method:

[0152]

[0153] Among them, w 2jIt indicates the weight of the indicator value after dimensionless processing calculated by the paired comparison method.

[0154] (3) CRITIC method

[0155] The CRITIC method takes into account the possible discreteness between data. From the three aspects of correlation, contrast intensity and discreteness, by introducing the concepts of correlation coefficient, information entropy and coefficient of variation, the data information carrying capacity D of the jth evaluation index is finally obtained. j , so according to the data information carrying capacity D j The weight of the indicator value after de-dimensionalization is obtained by using the paired comparison method:

[0156]

[0157] Among them, w 3j It indicates the weight of the indicator value after dimensionless processing calculated by the paired comparison method.

[0158] (4) Entropy Weight Method

[0159] By standardizing the data of each evaluation index and using the definition of information entropy in information theory, the information entropy E of the jth evaluation index is obtained. j , and finally use the information entropy E j Calculate the weight of the indicator value after de-dimensionalization using the entropy weight method:

[0160]

[0161] Among them, w 4j It indicates the weight of the indicator value after de-dimensionalization calculated by the entropy weight method.

[0162] In the embodiment of the present application, the above step S202 constructs a weight matrix according to the weights of the dedimensionalized index values. Specifically, the weight matrix W can be obtained according to the above four weighting methods:

[0163]

[0164] It can be seen that in the embodiment of the present application, the weight matrix W can be a matrix with 4 rows and m columns.

[0165] Exemplarily, in the above step S203, the evaluation model satisfies:

[0166]

[0167] Among them, F represents the deviation between the weights of the dedimensionalized index values ​​calculated by different weighting methods, r represents the total number of weighting methods, m represents the total number of evaluation indicators, and w ljis the element in the lth row and jth column of the weight matrix, which represents the weight of the index value of the jth evaluation index after dedimensionalization calculated by the lth weighting method. j represents the combined weight of the j-th evaluation indicator.

[0168] In some other embodiments, in step S3, the transient support capability of the energy storage power station for the power grid is evaluated according to the combined weights of various evaluation indicators, including:

[0169] The comprehensive evaluation results are calculated based on the combined weights of various evaluation indicators and the indicator values ​​after dimensionless processing.

[0170] Evaluate the transient support capability of the energy storage power station to the power grid based on the comprehensive evaluation results.

[0171] Optional, the comprehensive evaluation results meet the following requirements:

[0172]

[0173] Among them, T represents the comprehensive evaluation result, W j represents the combined weight of the j-th evaluation indicator, T(j) represents the indicator value after dedimensionalization of the j-th evaluation indicator, and m represents the total number of evaluation indicators.

[0174] It is conceivable that the comprehensive evaluation result can reflect the transient support capability of the energy storage power station for the power grid. Therefore, the evaluation of the transient support capability of the energy storage power station for the power grid is realized based on the comprehensive evaluation result.

[0175] Embodiment 2:

[0176] Based on the same inventive concept, the embodiment of the present application also provides a device for evaluating the transient support capability of an energy storage power station to a power grid, such as Figure 3 The evaluation device 200 comprises:

[0177] The processing module 201 is used to perform dimensionless processing on the index values ​​of various evaluation indexes.

[0178] The optimization module 202 is used to calculate the weight of the indicator value after the dimensionless processing by using at least two weighting methods, and optimize the weight of the indicator value after the dimensionless processing to obtain the combined weight of each evaluation indicator.

[0179] The evaluation module 203 is used to evaluate the transient support capability of the energy storage power station to the power grid according to the combined weights of various evaluation indicators.

[0180] Optionally, the evaluation index includes a first evaluation index for evaluating a power grid and a second evaluation index for evaluating an energy storage power station.

[0181] The first evaluation index includes bus voltage over-limit margin, voltage qualification rate, average voltage deviation energy, second-level apparent power deviation, voltage deviation over-limit margin, maximum frequency deviation, frequency fluctuation variance and frequency stability margin.

[0182] The second evaluation index includes regulation speed, regulation accuracy, response time, battery voltage extreme difference, battery state of charge and battery health status.

[0183] In some possible implementations, the processing module 201 is used to:

[0184] The indicator value of each evaluation indicator is divided into multiple intervals according to the preset indicator limit.

[0185] The probability of each evaluation indicator in each interval is counted, and the indicator value after de-dimensionalization is calculated based on the probability of each evaluation indicator in each interval.

[0186] Optionally, the indicator value after dimensionless processing satisfies:

[0187]

[0188] Where T(j) represents the dedimensionalized value of the jth evaluation index, P i (j) represents the probability of the jth evaluation indicator in the i-th interval, and n represents the number of intervals.

[0189] In some other possible implementations, the optimization module 202 is used to:

[0190] At least two weighting methods among the analytic hierarchy process, paired comparison method, CRITIC method or entropy weight method are used to calculate the weight of the index value after de-dimensionalization. Among them, the analytic hierarchy process and the paired comparison method are subjective weighting methods, and the CRITIC method and the entropy weight method are objective weighting methods. Of course, other subjective weighting methods or objective weighting methods can also be used to calculate the weight of the index value after de-dimensionalization, which is not limited in this application.

[0191] The weight matrix is ​​constructed according to the weights of the dedimensionalized indicator values.

[0192] The goal is to minimize the deviation between the weights of the dedimensionalized indicator values ​​calculated by different weighting methods, and build an evaluation model based on the elements of the weight matrix.

[0193] The mixed integer linear programming algorithm is used to solve the evaluation model and obtain the combined weights of various evaluation indicators.

[0194] Optionally, the evaluation model satisfies:

[0195]

[0196] Among them, F represents the deviation between the weights of the dedimensionalized index values ​​calculated by different weighting methods, r represents the total number of weighting methods, m represents the total number of evaluation indicators, and w lj is the element in the lth row and jth column of the weight matrix, which represents the weight of the index value of the jth evaluation index after dedimensionalization calculated by the lth weighting method. j represents the combined weight of the j-th evaluation indicator.

[0197] In some further possible implementations, the evaluation module 203 is used to:

[0198] The comprehensive evaluation results are calculated based on the combined weights of various evaluation indicators and the indicator values ​​after dimensionless processing.

[0199] Evaluate the transient support capability of the energy storage power station to the power grid based on the comprehensive evaluation results.

[0200] Optional, the comprehensive evaluation results meet the following requirements:

[0201]

[0202] Among them, T represents the comprehensive evaluation result, W j represents the combined weight of the j-th evaluation indicator, T(j) represents the indicator value after dedimensionalization of the j-th evaluation indicator, and m represents the total number of evaluation indicators.

[0203] It is conceivable that the comprehensive evaluation result can reflect the transient support capability of the energy storage power station for the power grid. Therefore, the evaluation of the transient support capability of the energy storage power station for the power grid is realized based on the comprehensive evaluation result.

[0204] Embodiment 3:

[0205] Based on the same inventive concept, the embodiment of the present application also provides a computer device, which includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of the evaluation method provided in the above embodiment.

[0206] Embodiment 4:

[0207] Based on the same inventive concept, the embodiment of the present application also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both a built-in storage medium in a computer device and an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the evaluation method provided in the above embodiment.

[0208] Those skilled in the art will appreciate that the embodiments of the application may be provided as methods, systems, or computer program products. Therefore, the application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0209] The application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0210] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0211] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0212] The above are merely embodiments of the application and are not intended to limit the application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the application are included in the scope of the claims of the pending application.

Claims

1. A method for evaluating the transient support capability of an energy storage power station for a power grid, characterized in that: include: De-dimensionalize the indicator values ​​of each evaluation indicator; At least two weighting methods are used to calculate the weights of the dedimensionalized index values, and the weights of the dedimensionalized index values ​​are optimized to obtain the combined weights of the evaluation indicators. The transient support capability of the energy storage power station for the power grid is evaluated according to the combined weights of the various evaluation indicators.

2. The evaluation method according to claim 1, characterized in that: The evaluation index includes a first evaluation index for evaluating the power grid and a second evaluation index for evaluating the energy storage power station; The first evaluation index includes bus voltage over-limit margin, voltage qualification rate, average voltage deviation energy, second-level apparent power deviation, voltage deviation over-limit margin, maximum frequency deviation, frequency fluctuation variance and frequency stability margin; The second evaluation index includes adjustment speed, adjustment accuracy, response time, battery voltage extreme difference, battery state of charge and battery health state.

3. The evaluation method according to claim 1, characterized in that: The de-dimensionalization of the indicator values ​​of each evaluation indicator includes: Dividing the index values ​​of the evaluation indicators into multiple intervals according to the preset index limits; The probability of each evaluation index in each interval is counted, and the index value after de-dimensionalization is calculated according to the probability of each evaluation index in each interval.

4. The evaluation method according to claim 3, characterized in that: The index value after dedimensionalization satisfies: Where T(j) represents the dedimensionalized value of the jth evaluation indicator, P i (j) represents the probability of the j-th evaluation indicator in the i-th interval, and n represents the number of intervals.

5. The evaluation method according to claim 1, characterized in that: The method of using at least two weighting methods to calculate the weights of the dedimensionalized index values ​​and optimizing the weights of the dedimensionalized index values ​​to obtain the combined weights of the evaluation indexes includes: At least two weighting methods, including the analytic hierarchy process, the paired comparison method, the CRITIC method, or the entropy weight method, are used to calculate the weights of the dedimensionalized index values; Constructing a weight matrix according to the weights of the dedimensionalized index values; The goal is to minimize the deviation between the weights of the dedimensionalized index values ​​calculated by different weighting methods, and to construct an evaluation model based on the elements of the weight matrix; The evaluation model is solved using a mixed integer linear programming algorithm to obtain the combined weights of the various evaluation indicators.

6. The evaluation method according to claim 5, characterized in that: The evaluation model satisfies: Where F represents the deviation between the weights of the dedimensionalized index values ​​calculated by different weighting methods, r represents the total number of weighting methods, m represents the total number of evaluation indicators, and w lj is the element in the lth row and jth column of the weight matrix, representing the weight of the index value of the jth evaluation index after dedimensionalization calculated by the lth weighting method, W j represents the combined weight of the j-th evaluation indicator.

7. The evaluation method according to claim 1, characterized in that: The transient support capability of the energy storage power station for the power grid is evaluated according to the combined weight of each evaluation index, including: Calculate the comprehensive evaluation result according to the combined weight of each evaluation index and the index value after de-dimensionalization; The transient support capability of the energy storage power station for the power grid is evaluated according to the comprehensive evaluation result.

8. The evaluation method according to claim 7, characterized in that: The comprehensive evaluation results meet the following requirements: Wherein, T represents the comprehensive evaluation result, W j represents the combined weight of the j-th evaluation indicator, T(j) represents the indicator value after dedimensionalization of the j-th evaluation indicator, and m represents the total number of the evaluation indicators.

9. An evaluation device for the transient support capability of an energy storage power station to a power grid, characterized in that: include: A processing module is used to perform dimensionless processing on the indicator values ​​of various evaluation indicators; An optimization module, used to calculate the weight of the indicator value after de-dimensionalization by using at least two weighting methods, and optimize the weight of the indicator value after de-dimensionalization to obtain the combined weight of each evaluation indicator; An evaluation module is used to evaluate the transient support capability of the energy storage power station to the power grid according to the combined weight of the various evaluation indicators.

10. The evaluation device according to claim 9, characterized in that The evaluation index includes a first evaluation index for evaluating the power grid and a second evaluation index for evaluating the energy storage power station; The first evaluation index includes bus voltage over-limit margin, voltage qualification rate, average voltage deviation energy, second-level apparent power deviation, voltage deviation over-limit margin, maximum frequency deviation, frequency fluctuation variance and frequency stability margin; The second evaluation index includes adjustment speed, adjustment accuracy, response time, battery voltage extreme difference, battery state of charge and battery health state.

11. The evaluation device according to claim 9, characterized in that The processing module is used for: Dividing the index values ​​of the evaluation indicators into multiple intervals according to the preset index limits; The probability of each evaluation index in each interval is counted, and the index value after de-dimensionalization is calculated according to the probability of each evaluation index in each interval.

12. The evaluation device according to claim 11, characterized in that The index value after dedimensionalization satisfies: Where T(j) represents the dedimensionalized value of the jth evaluation indicator, P i (j) represents the probability of the j-th evaluation indicator in the i-th interval, and n represents the number of intervals.

13. The evaluation device according to claim 9, characterized in that The optimization module is used to: At least two weighting methods, including the analytic hierarchy process, the paired comparison method, the CRITIC method, or the entropy weight method, are used to calculate the weights of the dedimensionalized index values; Constructing a weight matrix according to the weights of the de-dimensionalized index values; The goal is to minimize the deviation between the weights of the dedimensionalized index values ​​calculated by different weighting methods, and to construct an evaluation model based on the elements of the weight matrix; The evaluation model is solved using a mixed integer linear programming algorithm to obtain the combined weights of the various evaluation indicators.

14. The evaluation device according to claim 13, characterized in that The evaluation model satisfies: Where F represents the deviation between the weights of the dedimensionalized index values ​​calculated by different weighting methods, r represents the total number of weighting methods, m represents the total number of evaluation indicators, and w lj is the element in the lth row and jth column of the weight matrix, representing the weight of the index value of the jth evaluation index after dedimensionalization calculated by the lth weighting method, W j represents the combined weight of the j-th evaluation indicator.

15. The evaluation device according to claim 9, characterized in that The evaluation module is used to: Calculate the comprehensive evaluation result according to the combined weight of each evaluation index and the index value after de-dimensionalization; The transient support capability of the energy storage power station for the power grid is evaluated according to the comprehensive evaluation result.

16. The evaluation device according to claim 15, characterized in that The comprehensive evaluation results meet the following requirements: Wherein, T represents the comprehensive evaluation result, W j represents the combined weight of the j-th evaluation indicator, T(j) represents the indicator value after dedimensionalization of the j-th evaluation indicator, and m represents the total number of the evaluation indicators.

17. A computer device, characterized in that: include: one or more processors; The processor is used to store one or more programs; When the one or more programs are executed by the one or more processors, the evaluation method according to any one of claims 1 to 8 is implemented.

18. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the evaluation method according to any one of claims 1 to 8 is implemented.

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