Distributed energy storage comprehensive evaluation method and system for distributed new energy consumption

Abstract

The invention discloses a distributed energy storage comprehensive evaluation method and system for distributed new energy consumption, and relates to the technical field of distributed energy storage comprehensive evaluation, and the method comprises the steps: building a distributed energy storage comprehensive evaluation index system and an influence factor set for distributed energy storage multi-application scene influence characteristics under target distributed new energy consumption; using an entropy weight method to calculate second-level index weights of the second-level indexes of the distributed energy storage comprehensive evaluation system in the corresponding first-level indexes; calculating a corresponding first-level index weight for the first-level index of the distributed energy storage comprehensive evaluation system by using an analytic hierarchy process; the comprehensive weight of the influence factors is obtained through the second-level index weight and the first-level index weight, and an evaluation index quantification result is obtained through fuzzy comprehensive evaluation; and according to an evaluation result, obtaining quantitative evaluation of distributed energy storage deployed by the target power system. The micro-grid operation environment is quantitatively evaluated, the safety and stability of the micro-grid operation environment are maintained, and the practical application requirements are met.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed energy storage comprehensive evaluation, and in particular to a distributed energy storage comprehensive evaluation method and system for distributed new energy consumption. Background Art

[0002] Renewable energy power generation can alleviate environmental problems and energy crises, but due to the limitations of natural conditions, renewable energy power generation has great uncertainty and has serious impacts on the safe and stable operation of the power system. The current conventional means is to deploy energy storage in the power system containing renewable energy power generation. On the one hand, it can improve the level of renewable energy power generation and consumption, and on the other hand, it can provide support when disturbances occur in the power system.

[0003] When there is abundant renewable energy generation, the power system may face insufficient absorption, resulting in "wind abandonment" and "solar abandonment" phenomena, resulting in energy waste. At this time, deploying appropriate distributed energy storage can effectively improve the absorption level of renewable energy. When there is a sudden increase in load or frequency stability problems in the power system, the stored energy can be used for peak load regulation and frequency regulation. It can not only avoid energy waste, but also promote the safety and stability of the power system. Distributed energy storage is flexible to install and fast to charge and discharge. At present, the optimal configuration of distributed energy storage has become a research hotspot.

[0004] Existing research on the optimal configuration scenarios of distributed energy storage lacks methods for quantitatively evaluating the regulation capabilities of distributed energy storage systems. It is impossible to quantitatively evaluate the regulation capabilities of different types of distributed energy storage in different configurations and combinations. Therefore, quantitative evaluation of distributed energy storage has become a difficult point. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is: how to solve the problem that the existing methods lack the quantitative evaluation of the regulation capability of the distributed energy storage system.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a distributed energy storage comprehensive evaluation method for distributed new energy consumption, comprising: establishing a distributed energy storage comprehensive evaluation index system and a set of influencing factors according to the impact characteristics of distributed energy storage in multiple application scenarios under the target distributed new energy consumption; using the entropy weight method to calculate the secondary index weights of the secondary indexes of the distributed energy storage comprehensive evaluation system that account for the corresponding primary indexes; using the hierarchical analysis method to calculate the corresponding primary index weights of the primary indexes of the distributed energy storage comprehensive evaluation system; obtaining the comprehensive weights of the influencing factors from the secondary index weights and the primary index weights and using fuzzy comprehensive evaluation to obtain the quantitative results of the evaluation indicators; and obtaining a quantitative evaluation of the distributed energy storage deployed in the target power system based on the evaluation results.

[0008] As a preferred solution of the distributed energy storage comprehensive evaluation method for distributed new energy consumption described in the present invention, the distributed energy storage comprehensive evaluation index system includes primary indicators and secondary indicators; the primary indicators include secondary indicators of different types and quantities.

[0009] As a preferred solution of the distributed energy storage comprehensive evaluation method for distributed new energy consumption of the present invention, the influencing factor set refers to the primary indicator set of the indicator system, which is expressed as:

[0010] U={u1,u2,…,u m}

[0011] Among them, u i (i=1, 2, ..., m) is a set of influencing factors, and m is the number of first-level indicator elements.

[0012] As a preferred solution of the distributed energy storage comprehensive evaluation method for distributed new energy consumption described in the present invention, wherein: the secondary indicator weight includes constructing a secondary indicator matrix, normalizing the secondary indicator elements, and determining the secondary indicator weight of the primary indicator; the constructed secondary indicator matrix includes m distributed energy storage elements to be evaluated, i.e., primary indicators, and the matrix composed of the secondary indicators of the m elements is expressed as:

[0013]

[0014] R=(r ij ) m×n ,i=1,2,…,m,j=1,2,…,n

[0015] Among them, R is the matrix composed of secondary indicators, r ij is the jth secondary indicator element under the ith primary indicator, and n is the number of secondary indicator elements; the secondary indicator element normalization includes normalizing each secondary indicator element so that the magnitudes of the secondary indicators are the same, and the normalized secondary matrix is:

[0016] X=(x ij ) m×n

[0017] Among them, X is the normalized secondary matrix, x ij is the normalized secondary indicator element; the normalized secondary indicator element is expressed as:

[0018]

[0019] The determination of the weight of the secondary index in the primary index refers to the weight of the secondary index. According to the definition of entropy, the entropy value b of the jth secondary index is j It is expressed as:

[0020]

[0021] If and only if x ij = 0, let x ij lnx ij =0; through b j Determine the weight of each secondary indicator to the primary indicatorω j , expressed as:

[0022]

[0023] Among them, ω j It is the weight of the secondary indicator.

[0024] As a preferred solution of the distributed energy storage comprehensive evaluation method for distributed new energy consumption described in the present invention, wherein: the first-level indicator weight includes using the hierarchical analysis method to calculate the corresponding weight of the first-level indicator of the distributed energy storage comprehensive evaluation system, establishing an importance numerical meaning table, and evaluating the influencing factors u i The importance between each other is assigned, and the discriminant matrix of microgrid operation influencing factors is established. The weight vector is calculated using the sum method to check the consistency. The discriminant matrix of influencing factors is expressed as:

[0025]

[0026] Among them, A is the discriminant matrix of factors affecting microgrid operation, a iq is the importance degree between two different primary indicators, q is the qth primary indicator; the use of the sum method to calculate the weight vector includes normalizing the discriminant matrix by column to form a normalized matrix B, wherein the elements in the matrix B are expressed as:

[0027]

[0028] Among them, b ij are the normalized elements; sum the rows of matrix B to obtain vector V, where the elements in vector V are expressed as:

[0029]

[0030] Among them, v i is the element after row summation; the vector V is normalized to obtain the first-level indicator weight vector W, and the elements in the first-level indicator weight vector W are expressed as:

[0031]

[0032] Among them, w iis an element of the first-level indicator weight vector; the verification consistency includes comparing the average random consistency index RI table to determine whether the consistency ratio CR meets the consistency requirements, and the calculated consistency index is expressed as:

[0033]

[0034] Among them, CI is the consistency index, λ max is the maximum characteristic root of the influencing factor discriminant matrix A; the average random consistency index RI of the corresponding order matrix is ​​determined by looking up the table, the consistency ratio is calculated, and whether it is consistent is determined, which is expressed as:

[0035]

[0036] Among them, CR is the consistency ratio.

[0037] As a preferred solution of the distributed energy storage comprehensive evaluation method for distributed new energy consumption described in the present invention, the quantified results of the evaluation indicators include calculating the comprehensive weight, establishing an evaluation set, constructing the degree of membership of the influencing factors to the evaluation factors, solving the indicator result vector, and normalizing the indicator result vector; the calculated comprehensive weight is expressed as:

[0038] W′=w i *∑ω j,i

[0039] Among them, W′ is the comprehensive weight, ω j,i is the secondary indicator weight of the jth secondary indicator of the ith primary indicator; the establishment of the evaluation set is expressed as:

[0040] Y={y1,y2,…,y N}

[0041] Among them, y is various possible evaluation results.

[0042] As a preferred solution of the distributed energy storage comprehensive evaluation method for distributed new energy consumption described in the present invention, wherein: the construction of the membership of the influencing factors to the evaluation factors includes the i-th factor u in the influencing factor set i To judge, i The factor corresponds to the gth element y in the evaluation set g The degree of membership is h ig , then according to the i-th factor u i The evaluation results are:

[0043] H i =(r i1 ,r i2 ,…,r iN ),i=1,2,…,m

[0044] The membership matrix is ​​expressed as:

[0045]

[0046] The solution index result vector is expressed as:

[0047] F=W′*H

[0048] Wherein, F is the indicator result vector; the normalized indicator result vector is expressed as:

[0049]

[0050] Among them, F' g is the normalized indicator result vector.

[0051] Another object of the present invention is to provide a system for a distributed energy storage comprehensive evaluation method for distributed new energy consumption, which can solve the problem of distributed energy storage comprehensive evaluation for distributed new energy consumption by constructing a distributed energy storage comprehensive evaluation system.

[0052] To solve the above technical problems, the present invention provides the following technical solutions: a distributed energy storage comprehensive evaluation system for distributed new energy consumption, comprising a data acquisition module, a secondary indicator weight calculation module, a primary indicator weight calculation module, an evaluation indicator quantification module and an evaluation module; the data acquisition module is used to establish a distributed energy storage comprehensive evaluation index system and a set of influencing factors according to the impact characteristics of distributed energy storage in multiple application scenarios under the target distributed new energy consumption; the secondary indicator weight calculation module is used to use the entropy weight method to calculate the secondary indicator weights of the corresponding primary indicators for the secondary indicators of the distributed energy storage comprehensive evaluation system; the primary indicator weight calculation module is used to use the hierarchical analysis method to calculate the corresponding primary indicator weights for the primary indicators of the distributed energy storage comprehensive evaluation system; the evaluation indicator quantification module is used to obtain the comprehensive weights of the influencing factors from the secondary indicator weights and the primary indicator weights and use fuzzy comprehensive judgment to obtain the evaluation indicator quantification results; the evaluation module is used to obtain a quantitative evaluation of the distributed energy storage deployed in the target power system according to the evaluation results.

[0053] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the distributed energy storage comprehensive evaluation method for distributed new energy consumption as described above are implemented.

[0054] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the distributed energy storage comprehensive evaluation method for distributed new energy consumption as described above.

[0055] The beneficial effects of the present invention are as follows: the distributed energy storage comprehensive evaluation method for distributed new energy consumption provided by the present invention performs quantitative evaluation on the microgrid operating environment based on the fuzzy comprehensive evaluation method, thereby maintaining the safety and stability of the microgrid operating environment, which meets the actual application requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0057] Figure 1 A flow chart of a distributed energy storage comprehensive evaluation method for distributed new energy consumption provided in the first embodiment of the present invention.

[0058] Figure 2 A structural diagram of a distributed energy storage comprehensive evaluation system for distributed new energy consumption provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0060] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0061] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a distributed energy storage comprehensive evaluation method for distributed new energy consumption, including: establishing a distributed energy storage comprehensive evaluation index system and a set of influencing factors according to the impact characteristics of distributed energy storage in multiple application scenarios under the target distributed new energy consumption; using the entropy weight method to calculate the secondary index weights of the secondary indexes of the distributed energy storage comprehensive evaluation system that account for the corresponding primary indexes; using the hierarchical analysis method to calculate the corresponding primary index weights of the primary indexes of the distributed energy storage comprehensive evaluation system; obtaining the comprehensive weights of the influencing factors from the secondary index weights and the primary index weights and using fuzzy comprehensive evaluation to obtain the quantitative results of the evaluation index; and obtaining a quantitative evaluation of the distributed energy storage deployed in the target power system according to the evaluation results.

[0062] S1. Based on the impact characteristics of distributed energy storage in multiple application scenarios under the target distributed new energy consumption, a comprehensive evaluation index system and a set of influencing factors for distributed energy storage are established.

[0063] The established distributed energy storage comprehensive evaluation index system includes primary indicators and secondary indicators. The distributed energy storage comprehensive evaluation index system is shown in Table 1:

[0064] Table 1 Comprehensive evaluation index system for distributed energy storage

[0065]

[0066]

[0067] The influencing factor set refers to the first-level indicator set of the indicator system. The influencing factor is the first-level indicator, which is expressed as:

[0068] U={u1,u2,…,u m}

[0069] Among them, u i (i=1, 2, ..., m) is a set of influencing factors, and m is the number of first-level indicator elements.

[0070] S2. Use the entropy weight method to calculate the weight of the secondary indicators of the distributed energy storage comprehensive evaluation system, which accounts for the corresponding primary indicators.

[0071] S2.1. Constructing a secondary indicator matrix: Suppose there are m distributed energy storage elements to be evaluated, then the matrix composed of the secondary indicators of these m elements is as follows:

[0072]

[0073] R=(r ij ) m×n ,i=1,2,…,m,j=1,2,…,n

[0074] Among them, R is the matrix composed of secondary indicators, r ij is the jth secondary indicator element under the i-th primary indicator, and n is the number of secondary indicator elements.

[0075] S2.2. Normalization of secondary indicator elements: Since the magnitudes of the secondary indicators are different, it is necessary to normalize the secondary indicator elements. The normalized secondary matrix is ​​expressed as:

[0076] X=(x ij ) m×n

[0077] Among them, X is the normalized secondary matrix, x ij It is the normalized secondary indicator element.

[0078] x ij The normalized expression is:

[0079]

[0080] S2.3. Determine the weight of the secondary index to the primary index: According to the definition of entropy, the entropy value b of the jth secondary index j for:

[0081]

[0082] If and only if x ij = 0, let x ij lnx ij =0; here if and only if x ij When it is 0, it causes lnx ij An assignment that is meaningless. Under normal circumstances, x ij A number that is not 0.

[0083] By b j Determine the weight of each secondary indicator to the primary indicatorω j , expressed as:

[0084]

[0085] Among them, ω j It is the weight of the secondary indicator.

[0086] S3. Use the analytic hierarchy process to calculate the corresponding first-level indicator weights for the first-level indicators of the comprehensive evaluation system of distributed energy storage.

[0087] The analytic hierarchy process is used to calculate the corresponding weights of the first-level indicators of the distributed energy storage comprehensive evaluation system, which includes the following steps:

[0088] S3.1. First, establish a table of the meaning of importance values.

[0089] Table 2 Importance numerical meaning table

[0090]

[0091]

[0092] S3.2. Influencing factors u i The importance between each other is assigned, and the discriminant matrix of factors affecting the operation of the microgrid is established as follows:

[0093]

[0094] The discriminant matrix A has the following properties:

[0095]

[0096] Among them, A is the discriminant matrix of factors affecting microgrid operation, a iq is the importance degree between two different first-level indicators, and q is the qth first-level indicator.

[0097] S3.3. Use the sum method to calculate the weight vector.

[0098] S3.3.1. First, the discriminant matrix is ​​normalized by column to form a normalized matrix B, where the elements in the matrix B are:

[0099]

[0100] Among them, b ij are the normalized elements.

[0101] S3.3.2. Then sum the rows of matrix B to obtain vector V, where the elements of vector V are:

[0102]

[0103] Among them, v i The elements are summed row by row.

[0104] S3.3.3. Then normalize the vector V to obtain the first-level indicator weight vector W. The elements in the first-level indicator weight vector W are:

[0105]

[0106] Among them, w i is the element of the first-level indicator weight vector.

[0107] S3.4. Check consistency and compare the average random consistency index RI table to determine whether the consistency ratio CR meets the consistency requirements, where:

[0108] S3.4.1. Calculate the consistency index CI as follows:

[0109]

[0110] Among them, CI is the consistency index, λ max is the maximum eigenvalue of the discriminant matrix A of the influencing factors.

[0111] S3.4.2. Look up the table to determine the average random consistency index RI of the corresponding order matrix:

[0112] Table 3 Average random consistency index RI table

[0113]

[0114] S3.4.3. Calculate the consistency ratio CR and determine whether it is consistent, expressed as:

[0115]

[0116] Among them, CR is the consistency ratio.

[0117] When CR=0, it is considered that the influencing factor discriminant matrix A has complete consistency. When CR<0.1, it is considered that the consistency of the influencing factor discriminant matrix A is acceptable. When CR>0.1, it is considered that the influencing factor discriminant matrix A does not meet the consistency requirements and needs to be reconstructed.

[0118] S4. The comprehensive weight of the influencing factors is obtained by the weight of the secondary indicators and the weight of the primary indicators, and the quantitative results of the evaluation indicators are obtained by using fuzzy comprehensive evaluation.

[0119] S4.1. Calculate the comprehensive weight: The calculation method is as follows:

[0120] W′=w i *∑ω j,i

[0121] Among them, W′ is the comprehensive weight, ω j,i is the secondary indicator weight of the jth secondary indicator of the ith first-level indicator, w i is the weight of the i-th first-level indicator.

[0122] S4.2. Establish the evaluation set represented as:

[0123] Y={y1,y2,…,y N}

[0124] Among them, y is various possible evaluation results, and y1 is the first possible evaluation result.

[0125] S4.3. Construct the membership of the influencing factors to the evaluation factors. i To judge, i The factor corresponds to the gth element y in the evaluation set g The degree of membership is h ig , then according to the i-th factor u i The evaluation results are:

[0126] H i =(h i1 ,h i2 ,…,h iN ),i=1,2,…,m

[0127] The membership matrix is ​​expressed as:

[0128]

[0129] S4.4, the solution index result vector is expressed as:

[0130] F=W′*H

[0131] Among them, F is the indicator result vector, and the operation "*" adopts The model is called the weighted average model and is a commonly used model in practice.

[0132] S4.5. The normalized index result vector is expressed as:

[0133]

[0134] Among them, F' g is the normalized indicator result vector.

[0135] S5. Based on the evaluation results, a quantitative evaluation of the distributed energy storage deployed in the target power system is obtained.

[0136] According to the indicator result vector, the quantitative evaluation results of the distributed energy storage deployed in the target power system are obtained, which makes an important contribution to the safe and stable operation of the target distributed new energy.

[0137] Example 2, reference Figure 2 , which is the second embodiment of the present invention, which is different from the previous embodiment in that it provides a distributed energy storage comprehensive evaluation system for distributed new energy consumption, including: a data acquisition module 100, a secondary indicator weight calculation module 200, a primary indicator weight calculation module 300, an evaluation indicator quantification module 400 and an evaluation module 500;

[0138] The data acquisition module 100 is used to establish a distributed energy storage comprehensive evaluation index system and a set of influencing factors based on the impact characteristics of distributed energy storage in multiple application scenarios under the target distributed new energy consumption.

[0139] The secondary indicator weight calculation module 200 is used to calculate the secondary indicator weights of the corresponding primary indicators for the secondary indicators of the distributed energy storage comprehensive evaluation system using the entropy weight method.

[0140] The first-level indicator weight calculation module 300 is used to calculate the corresponding first-level indicator weights for the first-level indicators of the distributed energy storage comprehensive evaluation system using the hierarchical analysis method.

[0141] The evaluation index quantification module 400 is used to obtain the comprehensive weight of the influencing factors from the secondary index weight and the primary index weight and to obtain the evaluation index quantification result by using fuzzy comprehensive evaluation.

[0142] The evaluation module 500 is used to obtain a quantitative evaluation of the distributed energy storage deployed in the target power system according to the evaluation results.

[0143] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0144] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0145] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0146] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0147] Embodiment 3 is the third embodiment of the present invention. It is different from the first two embodiments in that it is used to verify the technical effects adopted in the present invention so as to verify the real effects of the method.

[0148] First, the comprehensive evaluation index system of distributed energy storage established includes primary indicators and secondary indicators. The comprehensive evaluation index system of distributed energy storage is shown in the table:

[0149] Table 4 Comprehensive evaluation index system of distributed energy storage

[0150]

[0151] Then the influencing factor set established is the first-level indicator of the indicator system, which is expressed as:

[0152] U={reliability, economy, environmental protection, performance}

[0153] The target distributed energy storage system including distributed power system includes lead-acid batteries, lithium batteries and supercapacitors. The secondary index parameters of these three types of distributed energy storage are shown in the following table:

[0154] Table 5 Secondary index parameters of three types of distributed energy storage

[0155]

[0156]

[0157] The display results are displayed with different decimal places. The specific data is used for solution. According to the secondary index parameters, the weight of the secondary index to the primary index can be obtained. j It is {0.1244, 0.1136, 0.0478, 0.0712, 0.0537, 0.0575, 0.1112, 0.0575, 0.1312, 0.0354, 0.064, 0.0841, 0.0484}.

[0158] The influencing factor discriminant matrix A is:

[0159]

[0160] The normalized discriminant matrix B is:

[0161]

[0162] According to the discriminant matrix B, the first-level indicator weight vector W can be obtained as {0.47, 0.28, 0.16, 0.09}.

[0163] The maximum eigenvalue of the discriminant matrix A is 4.031, and the CR value can be obtained as 0.0103. Therefore, the discriminant matrix A is consistent and meets the evaluation requirements.

[0164] According to the obtained first-level indicator weights and second-level indicator weights, the comprehensive weight W′ can be calculated as {0.134, 0.082, 0.036, 0.018}.

[0165] Establish an evaluation set Y: Y = {excellent operating condition, good operating condition, inspection required, maintenance required}.

[0166] The membership matrix is ​​obtained according to the target system parameters:

[0167]

[0168] Combining the membership matrix and the comprehensive weight index, the final evaluation index result vector F′ is: {0.767, 0.1021, 0.0670, 0.0639}.

[0169] It can be seen from the evaluation results that the distributed energy storage of the target power system containing distributed power sources operates well.

[0170] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A comprehensive evaluation method for distributed energy storage for distributed renewable energy consumption, characterized by: include, According to the impact characteristics of distributed energy storage in multiple application scenarios under the target distributed new energy consumption, a comprehensive evaluation index system and a set of influencing factors for distributed energy storage are established; The entropy weight method is used to calculate the weight of the secondary indicators of the distributed energy storage comprehensive evaluation system, which accounts for the corresponding primary indicators; The analytic hierarchy process is used to calculate the corresponding first-level indicator weights of the first-level indicators of the distributed energy storage comprehensive evaluation system; The comprehensive weight of the influencing factors is obtained by the weight of the secondary indicators and the weight of the primary indicators, and the quantitative results of the evaluation indicators are obtained by using fuzzy comprehensive evaluation; Based on the evaluation results, a quantitative assessment of the distributed energy storage deployed in the target power system is obtained.

2. The distributed energy storage comprehensive evaluation method for distributed new energy consumption as claimed in claim 1 is characterized by: The distributed energy storage comprehensive evaluation index system includes primary indicators and secondary indicators; The first-level index includes second-level indexes of different types and quantities.

3. The distributed energy storage comprehensive evaluation method for distributed new energy consumption as claimed in claim 2 is characterized by: The influencing factor set refers to the first-level indicator set of the indicator system, which is expressed as: U={u1,u2,…,u m } Among them, u i (i=1, 2, ..., m) is a set of influencing factors, and m is the number of first-level indicator elements.

4. The distributed energy storage comprehensive evaluation method for distributed new energy consumption as claimed in claim 3 is characterized by: The secondary indicator weights include constructing a secondary indicator matrix, normalizing the secondary indicator elements, and determining the secondary indicator weights for the primary indicators; The construction of the secondary indicator matrix includes m distributed energy storage elements to be evaluated, i.e., primary indicators. The matrix composed of the secondary indicators of the m elements is expressed as: R=(r ij ) m×n ,i=1,2,…,m,j=1,2,…,n Among them, R is the matrix composed of secondary indicators, r ij is the jth secondary indicator element under the i-th primary indicator, and n is the number of secondary indicator elements; The secondary index element normalization includes normalizing each secondary index element so that the magnitudes of the secondary indexes are the same. The normalized secondary matrix is: X=(x ij ) m×n Among them, X is the normalized secondary matrix, x ij It is the normalized secondary indicator element; The normalized secondary index elements are expressed as, The determination of the weight of the secondary index in the primary index refers to the weight of the secondary index. According to the definition of entropy, the entropy value b of the jth secondary index is j It is expressed as, If and only if x ij = 0, let x ij lnx ij =0; By b j Determine the weight of each secondary indicator to the primary indicatorω j , expressed as, Among them, ω is the weight of the secondary indicator.

5. The distributed energy storage comprehensive evaluation method for distributed new energy consumption as claimed in claim 4 is characterized by: The first-level indicator weights include using the hierarchical analysis method to calculate the corresponding weights of the first-level indicators of the distributed energy storage comprehensive evaluation system, establishing an importance numerical meaning table, and i The importance between each other is assigned, the discriminant matrix of microgrid operation influencing factors is established, the weight vector is calculated using the sum method, and the consistency is tested; The influencing factor discriminant matrix is ​​expressed as: Among them, A is the discriminant matrix of factors affecting microgrid operation, a iq is the importance degree between two different first-level indicators, q is the qth first-level indicator; The method of calculating the weight vector by using the sum method includes normalizing the discriminant matrix by column to form a normalized matrix B, wherein the elements in the matrix B are expressed as: Among them, b ij is the normalized element; Sum the rows of matrix B to get vector V, where the elements in vector V are expressed as, Among them, v i is the element after row summation; The vector V is normalized to obtain the first-level indicator weight vector W. The elements in the first-level indicator weight vector W are expressed as: Among them, w i is the element of the first-level indicator weight vector; The consistency check includes comparing the average random consistency index RI table, judging whether the consistency ratio CR meets the consistency requirements, and calculating the consistency index expressed as: Among them, CI is the consistency index, λ max is the maximum eigenvalue of the discriminant matrix A of the influencing factors; Look up the table to determine the average random consistency index RI of the corresponding order matrix, calculate the consistency ratio, and judge whether it is consistent, expressed as, Among them, CR is the consistency ratio.

6. The distributed energy storage comprehensive evaluation method for distributed new energy consumption as claimed in claim 5, characterized in that: The quantitative results of the evaluation indicators include calculating comprehensive weights, establishing evaluation sets, constructing the degree of membership of influencing factors to evaluation factors, solving the indicator result vector, and normalizing the indicator result vector; The calculation comprehensive weight is expressed as, In′=w i *∑ω j，i Among them, W′ is the comprehensive weight, ω j,i is the secondary indicator weight of the jth secondary indicator of the ith first-level indicator; The establishment of the evaluation set is expressed as: Y={y1,y2,…,y N } Among them, y is various possible evaluation results.

7. The distributed energy storage comprehensive evaluation method for distributed new energy consumption as claimed in claim 6, characterized in that: The construction of the membership of the influencing factors to the evaluation factors includes the i-th factor u in the influencing factor set i To judge, i The factor corresponds to the gth element y in the evaluation set g The degree of membership is h ig , then according to the i-th factor u i The evaluation results are: H i =(r i1 ,r i2 ,…,r iN ),i=1,2,…,m The membership matrix is ​​expressed as: The solution index result vector is expressed as: F=W′*H Among them, F is the indicator result vector; The normalized processing of the indicator result vector is expressed as: Among them, F' g is the normalized indicator result vector.

8. A system using the distributed energy storage comprehensive evaluation method for distributed new energy consumption as described in any one of claims 1 to 7, characterized in that: It includes a data collection module (100), a secondary index weight calculation module (200), a primary index weight calculation module (300), an evaluation index quantification module (400) and an assessment module (500); The data acquisition module (100) is used to establish a distributed energy storage comprehensive evaluation index system and an influencing factor set based on the impact characteristics of distributed energy storage in multiple application scenarios under the target distributed new energy consumption; The secondary index weight calculation module (200) is used to calculate the secondary index weight of the corresponding primary index for the secondary index of the distributed energy storage comprehensive evaluation system using an entropy weight method; The first-level indicator weight calculation module (300) is used to calculate the corresponding first-level indicator weight for the first-level indicator of the distributed energy storage comprehensive evaluation system using the hierarchical analysis method; The evaluation index quantification module (400) is used to obtain the comprehensive weight of the influencing factors from the secondary index weight and the primary index weight and to obtain the evaluation index quantification result using fuzzy comprehensive evaluation; The evaluation module (500) is used to obtain a quantitative evaluation of the distributed energy storage deployed in the target power system according to the evaluation results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the distributed energy storage comprehensive evaluation method for distributed new energy consumption described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distributed energy storage comprehensive evaluation method for distributed new energy consumption described in any one of claims 1 to 7 are implemented.

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