User side energy storage grid-connected risk grade evaluation and safe operation evaluation method

Abstract

The invention discloses a user-side energy storage grid-connected risk level evaluation and safe operation evaluation method, which comprises the following steps of: firstly, constructing a risk level index system by considering limiting factors of a user-side energy storage risk level; and then, an improved entropy weight method is adopted to calculate the index weight of a risk index system of the user-side energy storage system, risk grade division is carried out, and a risk grade evaluation process of user-side energy storage is constructed. And finally, evaluating the safe operation of the user side energy storage system on the distribution network side based on an AHP-entropy weight method. According to the method, a user-side energy storage grid-connected risk level evaluation method and a user-side energy storage grid-connected safe operation evaluation method are improved, the grid-connected risk level can be evaluated more accurately, safe operation evaluation is carried out on the basis of risk evaluation, the safety and reliability of user-side energy storage grid connection are further enhanced, and the user-side energy storage grid-connected risk level evaluation method and the user-side energy storage grid-connected safe operation evaluation method are improved. And an effective guarantee is provided for user-side energy storage grid connection.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage grid connection, and in particular to a method for evaluating the risk level and safe operation of energy storage grid connection at a user side. Background Art

[0002] New energy storage is an important technology and basic equipment supporting the new power system. It is of great significance to promote green energy transformation, respond to extreme events, ensure energy security, promote high-quality energy development, and support the realization of climate change goals. As an important part of new energy storage, user-side energy storage has become increasingly prominent with the expansion of user-side energy storage. At present, there is a lack of user-side energy storage grid-connected safety evaluation methods and tools, which makes it impossible to identify and troubleshoot user-side energy storage projects that are "connected to the grid with problems" and to clarify the safety responsibilities of all parties, which also brings great challenges to grid connection safety. Summary of the invention

[0003] The present invention proposes a method for evaluating the risk level of user-side energy storage grid connection and assessing safe operation, which can at least solve one of the technical problems in the background technology.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A method for evaluating the risk level and safe operation of energy storage grid connection at the user side, the specific steps are as follows:

[0006] S100. Consider the limiting factors of the user-side energy storage risk level and build a user-side energy storage risk level indicator system;

[0007] S200, using an improved entropy weight method to calculate the indicator weights of the risk indicator system of the user-side energy storage system, and to divide the risk levels, to construct a risk level assessment model for the user-side energy storage, and to obtain the indicator weights of the assessment indicators;

[0008] S300. Based on the evaluation index weights, the AHP-entropy weight method is used to evaluate the safe operation of the user-side energy storage system on the distribution network side.

[0009] Furthermore, the method for building a risk level assessment model for the user-side energy storage system in step S200 of the present invention is as follows:

[0010] S210, using the TOPSIS method to normalize the original data of the user-side energy storage risk level index;

[0011] S220, setting the weight of the user-side energy storage risk level evaluation index based on the entropy weight method;

[0012] S230, classifying the risk level of the safety performance of the user-side energy storage system according to the indicator weight;

[0013] S240, construct a risk level assessment process for user-side energy storage;

[0014] Furthermore, the specific steps of the normalization process in S210 of the present invention are as follows:

[0015] S211, user-side energy storage system risk indicator attribute convergence processing;

[0016] When using TOPSIS evaluation, all indicators are required to change in the same direction. First, high-quality indicators are converted into low-quality indicators or low-quality indicators are converted into high-quality indicators. Low-quality indicators are optimized. The reciprocal method (1 / X) is used for absolute low-quality indicators, and the difference method (1-X) is used for relative low-quality indicators.

[0017] S212, dimensionless processing of the convergence data;

[0018] Suppose a decision problem, whose decision matrix is ​​A, can form a standardized decision matrix Z', whose elements are Z' ij , and there is

[0019]

[0020] In the formula, f ij is the data matrix of the indicator, given by the decision matrix

[0021]

[0022] S213, constructing a weighted decision matrix according to the decision matrix;

[0023] Construct a normalized weighted decision matrix Z whose elements Z ij

[0024] Z ij =W j Z' ij i=1,…,n;j=1,…,m

[0025] Among them, W j is the indicator weight;

[0026] S214, determining an ideal solution and a negative ideal solution according to a weighted decision matrix Z;

[0027] Determine the ideal solution and negative ideal solution among the finite solutions if the element Z in the decision matrix Z ij The larger the value, the better the solution.

[0028]

[0029] S215. Calculate the distance from each solution to the ideal point and the distance to the negative ideal point ;

[0030]

[0031] S216. Calculate the relative closeness C between the ideal solution and the negative ideal solution i ;

[0032] After finding the distance to the ideal solution and the distance to the negative ideal operation index, the relative closeness C can be calculated. i , sort by this value, the larger the value, the better;

[0033]

[0034] Furthermore, the method for calculating the weight of the user-side energy storage risk level evaluation index in step S220 of the present invention is as follows:

[0035] S221, establishing an original matrix;

[0036] According to the number of districts and counties n and the number of indicators m, the original matrix is ​​constructed:

[0037] X=(x ij ) nxm (i=1,2,3,…,n,j=1,2,3,…,m)

[0038] Among them, x ij Data for indicators;

[0039] S222, normalize and standardize the original matrix data;

[0040] Normalize the selected indicators to preliminarily eliminate the differences caused by the dimensions:

[0041] For positive indicators:

[0042] For negative indicators:

[0043] In the formula, max(X ij )、min(X ij ) represent the maximum and minimum values ​​of the evaluation index respectively;

[0044] All evaluation index calculation results are converted to [0,1]. In order to facilitate the subsequent comprehensive evaluation of the distribution network carrying capacity, they are further converted into percentage calculation results.

[0045] G c =b1x′ c +b2

[0046] In the formula, G cis the percentage calculation result of index c; b1 and b2 are constant values;

[0047] S223, calculating the entropy value according to the normalization processing result;

[0048]

[0049] Where P ij This is the result after the indicator data is standardized;

[0050] S224. Calculate the indicator weight according to the entropy value;

[0051]

[0052] 5. The method for evaluating the risk level of energy storage grid connection and safe operation of user-side energy storage according to claim 4 is characterized in that: the risk level evaluation process of energy storage on the user side in S240 is specifically as follows:

[0053] S241. Data preparation: Conduct risk level assessment of user-side energy storage and make full use of existing measurement data to implement risk level assessment of user-side energy storage;

[0054] S242, indicator parameter calculation: Combined with the actual data prepared, the corresponding influencing factor indicator calculation method is used to calculate, so as to obtain the specific value x1, x2, ... x of each indicator. C ;

[0055] S243, indicator observability processing: perform observability improvement processing, perform weight processing and dimensionless processing on indicators, and realize quantification and comparability of each evaluation indicator;

[0056] S244. Comprehensive evaluation and calculation of user-side energy storage risk level: Perform comprehensive evaluation and calculation of user-side energy storage risk level, assess the user-side energy storage risk level, and analyze the evaluation results.

[0057] Furthermore, the method for evaluating the safe operation of energy storage on the user side in S300 of the present invention comprises the following specific steps:

[0058] S310, collecting evaluation index values ​​of different user-side energy storage power stations;

[0059] S320, through the entropy weight method, obtain the weight coefficient w of each indicator j ;

[0060] S330, selecting the optimal value of each indicator in different schemes to form a reference series in grey relational analysis;

[0061] S340, compare the reference series according to the grey correlation coefficient;

[0062] The grey correlation coefficient ζ between each series and the optimal series is obtained by calculation i (k);

[0063] S305, comparing and ranking grey correlation degrees;

[0064] Among them, the grey correlation degree γ between the i-th solution and the optimal solution i for:

[0065]

[0066] The degree of correlation between each plan and the optimal plan is not simply reflected by the size of the correlation, but is described by the ranking of the correlation sizes; the higher the correlation, the higher the ranking, indicating that the plan is closer to the ideal plan, and the plan with the highest ranking is the optimal user-side energy storage safe operation state.

[0067] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0068] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0069] It can be seen from the above technical scheme that the user-side energy storage grid-connected risk level evaluation and safe operation assessment method of the present invention first considers the limiting factors of the user-side energy storage risk level to construct a risk level index system. Then, the improved entropy weight method is used to calculate the indicator weights of the user-side energy storage system risk index system, and the risk level is divided to construct a risk level assessment process for user-side energy storage. Finally, the safe operation of the user-side energy storage system on the distribution network side is evaluated based on the AHP-entropy weight method.

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] This method improves the user-side energy storage grid-connected risk level evaluation method and the household-side energy storage grid-connected safe operation evaluation method, considers the limiting factors of the user-side energy storage risk level to construct the user-side energy storage risk level index system, combines the TOPSIS method with the entropy weight method, and can more accurately evaluate the grid-connected risk level. It is suitable for multi-dimensional, multi-indicator large system data, with simple calculations, quantitative, objective, true and reliable results, and scientifically uses inherent information to judge the utility value of indicators, effectively improving the authenticity of the evaluation results and making the evaluation division results more objective and reasonable. Based on the AHP-entropy weight method, the safe operation of the user-side energy storage system on the distribution network side is evaluated, and the safety and reliability of the user-side energy storage grid connection are further enhanced, making the results more accurate and objective, and providing effective safety guarantees for the user-side energy storage grid connection. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 is a flow chart of the method of the present invention;

[0073] Figure 2 It is an evaluation index system for the safety risk level of the user-side energy storage system;

[0074] Figure 3 This is the risk level assessment process for energy storage on the user side of the distribution network. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0076] like Figure 1 As shown, the user-side energy storage grid-connected risk level evaluation and safe operation assessment method described in this embodiment includes the following steps:

[0077] S100. Consider the limiting factors of the user-side energy storage risk level and build a user-side energy storage risk level indicator system;

[0078] S200, using an improved entropy weight method to calculate the indicator weights of the risk indicator system of the user-side energy storage system, and to divide the risk levels, to construct a risk level assessment model for the user-side energy storage, and to obtain the indicator weights of the assessment indicators;

[0079] S300. Based on the evaluation index weights, the AHP-entropy weight method is used to evaluate the safe operation of the user-side energy storage system on the distribution network side.

[0080] The following are specific instructions:

[0081] S100. Consider the limiting factors of the user-side energy storage risk level (such as intermittent voltage, internal resistance, current, etc.) and build a user-side energy storage risk level indicator system;

[0082] The limiting factors of the distributed generation user-side energy storage risk level are sorted out and summarized, and a distribution network distributed generation user-side energy storage risk level indicator system is constructed, such as Figure 2 The following is the system structure of the user-side energy storage risk level evaluation index system, which specifically includes early discharge characteristics, final discharge characteristics, sub-transient process characteristics, transient process characteristics, and steady-state process characteristics;

[0083] The early discharge characteristics include the current I bf , voltage V bf , the final discharge characteristics include the current I at cutoff off , the duration of the current when it is cut off T dur The sub-transient process characteristics include internal resistance R0, diffusion resistance coefficient k R , transient process characteristics include transient index τ, and steady-state process characteristics include intermittent voltage V in .

[0084] S200, using an improved entropy weight method to calculate the indicator weights of the risk indicator system of the user-side energy storage system, and to divide the risk levels, to construct a risk level assessment model for the user-side energy storage, and to obtain the indicator weights of the assessment indicators;

[0085] The method for building the risk level assessment model of the user-side energy storage system is as follows:

[0086] S210, using the TOPSIS method to normalize the original data of the user-side energy storage risk level index;

[0087] The specific steps of the standardization process are as follows:

[0088] S211, user-side energy storage system risk indicator attribute convergence processing;

[0089] During TOPSIS evaluation, all indicators are required to change in the same direction. For the original indicators of the user-side energy storage risk level indicator, the user-side energy storage operation indicators (internal resistance, diffusion resistance coefficient, transient index, intermittent voltage) are high-quality indicators, and the indicators before and after operation (voltage and current before operation, cut-off current and cut-off current duration) are low-quality indicators. First, the high-quality indicators must be converted into low-quality indicators or the low-quality indicators must be converted into high-quality indicators. Usually, the low-quality indicators are optimized, and the reciprocal method (1 / X) is used for the absolute low-quality indicators, and the difference method (1-X) is used for the relative low-quality indicators.

[0090] S212, dimensionless processing of the convergence data;

[0091] Since the measurement units of evaluation indicators are different, in order to eliminate the influence of the measurement units of indicators, the indicator data values ​​must be normalized, that is, dimensionless.

[0092] Suppose a decision problem, whose decision matrix is ​​A, can form a standardized decision matrix Z', whose elements are Z' ij , and there is

[0093]

[0094] In the formula, f ij is the data matrix of the indicator, given by the decision matrix

[0095]

[0096] S213, constructing a weighted decision matrix according to the decision matrix;

[0097] Construct a normalized weighted decision matrix Z whose elements Z ij

[0098] Z ij =W j Z' ij i=1,…,n;j=1,…,m

[0099] Among them, W j is the indicator weight;

[0100] S214, determining an ideal solution and a negative ideal solution according to a weighted decision matrix Z;

[0101] Determine the ideal solution and negative ideal solution among the finite solutions if the element Z in the decision matrix Z ij The larger the value, the better the solution.

[0102]

[0103] The above is the definition of the ideal solution and the negative ideal solution by the classic TOPSIS algorithm. In the construction of the evaluation system of the present invention, the ideal solution and the negative ideal solution need to be redesigned:

[0104] The high value (90% quantile), the middle value (median), and the low value (10% quantile) of each indicator are calculated according to the quantile principle.

[0105] S215. Calculate the distance from each solution to the ideal point and the distance to the negative ideal point

[0106]

[0107] S216. Calculate the relative closeness C between the ideal solution and the negative ideal solutioni ;

[0108] After finding the distance to the ideal solution and the distance to the negative ideal operation index, the relative closeness C can be calculated. i , sort by this value, the larger the value, the better;

[0109]

[0110] S220, setting the weight of the user-side energy storage risk level evaluation index based on the entropy weight method;

[0111] The method for calculating the weight of the user-side energy storage risk level evaluation index is as follows:

[0112] S221, establishing an original matrix;

[0113] According to the number of districts and counties n and the number of indicators m, the original matrix is ​​constructed:

[0114] X=(x ij ) nxm (i=1,2,3,…,n,j=1,2,3,…,m)

[0115] Among them, x ij Data for indicators;

[0116] S222, normalize and standardize the original matrix data;

[0117] Normalize the selected indicators to preliminarily eliminate the differences caused by the dimensions:

[0118] For positive indicators:

[0119] For negative indicators:

[0120] In the formula, max(X ij )、min(X ij ) represent the maximum and minimum values ​​of the evaluation index respectively;

[0121] All evaluation index calculation results are converted to [0,1]. In order to facilitate the subsequent comprehensive evaluation of the distribution network carrying capacity, they are further converted into percentage calculation results.

[0122] G c =b1x′ c +b2

[0123] In the formula, G c It is the percentage calculation result of index c; b1 and b2 are constants.

[0124] S223, calculating the entropy value based on the normalized standardization processing result

[0125]

[0126] Where P ij This is the result after the indicator data is standardized.

[0127] S224. Calculate the index weight based on the entropy value

[0128]

[0129] S230, classifying the risk level of the safety performance of the user-side energy storage system according to the indicator weight;

[0130] According to the degree to which user-side energy storage is affected by the safe operation of the distribution network, the risk levels are divided into four categories from low to high: green, yellow, orange, and red, as shown in Table 1.

[0131] Table 1 Risk level assessment criteria

[0132]

[0133] S240, construct a risk level assessment process for user-side energy storage;

[0134] like Figure 3 The following is the risk level assessment process for user-side energy storage. The specific method is as follows:

[0135] S241. Data preparation: Conduct risk level assessment of user-side energy storage and make full use of existing measurement data to implement risk level assessment of user-side energy storage;

[0136] S242, indicator parameter calculation: Combined with the actual data prepared, the corresponding influencing factor indicator calculation method is used to calculate, so as to obtain the specific value x1, x2, ... x of each indicator. C ;

[0137] S243, indicator observability processing: perform observability improvement processing, perform weight processing and dimensionless processing on indicators, and realize quantification and comparability of each evaluation indicator;

[0138] S244. Comprehensive evaluation and calculation of user-side energy storage risk level: Perform comprehensive evaluation and calculation of user-side energy storage risk level, assess the user-side energy storage risk level, and analyze the evaluation results.

[0139] S300, based on the evaluation index weights, the AHP-entropy weight method is used to evaluate the safe operation of the user-side energy storage system on the distribution network side;

[0140] On the basis of improving the grey correlation analysis and combining the entropy weight method, the index weight w is introduced. jThe grey correlation degree is calculated. A comprehensive evaluation method is used to evaluate the safe operation of user-side energy storage. The specific steps are as follows:

[0141] S310, collecting evaluation index values ​​of different user-side energy storage power stations;

[0142] S320, through the entropy weight method, obtain the weight coefficient w of each indicator j ;

[0143] S330, selecting the optimal value of each indicator in different schemes to form a reference series in grey relational analysis;

[0144] S340, compare the reference series according to the grey correlation coefficient;

[0145] The grey correlation coefficient ζ between each series and the optimal series is obtained by calculation i (k), the specific formula is as follows:

[0146]

[0147] Among them, a is the minimum difference between the two poles, b is the maximum difference between the two poles, ρ is the resolution coefficient (usually 0.5), x0(k) is the optimal sequence, x i (k) is the kth sequence of the ith solution.

[0148] The formula for calculating the minimum difference and maximum difference is as follows

[0149]

[0150] S305, comparing and ranking grey correlation degrees;

[0151] Among them, the grey correlation degree γ between the i-th solution and the optimal solution i for:

[0152]

[0153] The degree of correlation between each solution and the optimal solution is not simply reflected by the size of the correlation, but is described by the ranking of the correlation. The higher the correlation, the higher the ranking, indicating that the solution is closer to the ideal solution, and the solution with the highest ranking is the optimal user-side energy storage safe operation state.

[0154] In summary, the user-side energy storage grid-connected risk level evaluation and safe operation assessment method of the present invention first considers the limiting factors of the user-side energy storage risk level to construct a risk level index system. Then, the improved entropy weight method is used to calculate the index weights of the user-side energy storage system risk index system, and the risk level is divided to construct a risk level assessment process for user-side energy storage. Finally, the safe operation of the user-side energy storage system on the distribution network side is evaluated based on the AHP-entropy weight method.

[0155] Compared with the prior art, the present invention has the following beneficial effects:

[0156] This method improves the user-side energy storage grid-connected risk level evaluation method and the user-side energy storage grid-connected safe operation evaluation method. It combines the TOPSIS method with the entropy weight method, and can more accurately evaluate the grid-connected risk level. It is suitable for large-scale system data with multiple dimensions and multiple indicators. The calculation is simple, and the results are quantitative, objective, true, and reliable. It scientifically uses inherent information to judge the utility value of indicators, effectively improves the authenticity of the evaluation results, and makes the evaluation division results more objective and reasonable. Based on the AHP-entropy weight method, the safe operation of the user-side energy storage system on the distribution network side is evaluated, and the safety and reliability of the user-side energy storage grid connection are further enhanced, making the results more accurate and objective, and providing effective safety guarantees for the user-side energy storage grid connection.

[0157] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0158] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0159] In another embodiment provided in the present application, a computer program product comprising instructions is also provided, which, when executed on a computer, enables the computer to execute any of the user-side energy storage grid-connected risk level evaluation and safe operation assessment methods in the above embodiments.

[0160] It is understandable that the system, device and storage medium provided in the embodiments of the present invention correspond to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above methods.

[0161] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0162] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0163] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0164] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the risk level and safe operation of energy storage grid connection at the user side, characterized in that: The specific steps are as follows: S100. Consider the limiting factors of the user-side energy storage risk level and build a user-side energy storage risk level indicator system; S200, using an improved entropy weight method to calculate the indicator weights of the risk indicator system of the user-side energy storage system, and to divide the risk levels, to construct a risk level assessment model for the user-side energy storage, and to obtain the indicator weights of the assessment indicators; S300. Based on the evaluation index weights, the AHP-entropy weight method is used to evaluate the safe operation of the user-side energy storage system on the distribution network side.

2. The method for evaluating the risk level of energy storage grid connection and safe operation of the user side according to claim 1 is characterized by: The method for building a risk level assessment model for the user-side energy storage system in step S200 is as follows: S210, using the TOPSIS method to normalize the original data of the user-side energy storage risk level index; S220, setting the weight of the user-side energy storage risk level evaluation index based on the entropy weight method; S230, classifying the risk level of the safety performance of the user-side energy storage system according to the indicator weight; S240. Construct a risk level assessment process for user-side energy storage.

3. The method for evaluating the risk level and safe operation of energy storage grid connection at the user side according to claim 2 is characterized in that: The specific steps of the normalization process in S210 are as follows: S211, user-side energy storage system risk indicator attribute convergence processing; When using TOPSIS evaluation, all indicators are required to change in the same direction. First, high-quality indicators are converted into low-quality indicators or low-quality indicators are converted into high-quality indicators. Low-quality indicators are optimized. The reciprocal method (1 / X) is used for absolute low-quality indicators, and the difference method (1-X) is used for relative low-quality indicators. S212, dimensionless processing of the convergence data; Suppose a decision problem, whose decision matrix is ​​A, can form a standardized decision matrix Z', whose elements are Z' ij , and there is In the formula, f ij is the data matrix of the indicator, given by the decision matrix S213, constructing a weighted decision matrix according to the decision matrix; Construct a normalized weighted decision matrix Z whose elements Z ij Z ij =W j Z' ij i=1,…,n;j=1,…,m Among them, W j is the indicator weight; S214, determining an ideal solution and a negative ideal solution according to a weighted decision matrix Z; Determine the ideal solution and negative ideal solution among the finite solutions if the element Z in the decision matrix Z ij The larger the value, the better the solution. S215. Calculate the distance from each solution to the ideal point and the distance to the negative ideal point S216. Calculate the relative closeness C between the ideal solution and the negative ideal solution i ; After finding the distance to the ideal solution and the distance to the negative ideal operation index, the relative closeness C can be calculated. i , sort by this value, the larger the value, the better; 4. The method for evaluating the risk level of energy storage grid connection and safe operation of the user side according to claim 3 is characterized by: The method for calculating the weight of the user-side energy storage risk level evaluation index in step S220 is as follows: S221, establishing an original matrix; According to the number of districts and counties n and the number of indicators m, the original matrix is ​​constructed: X=(x ij ) nxm (i=1,2,3,…,n,j=1,2,3,…,m) Among them, x ij Data for indicators; S222, normalizing and standardizing the original matrix data; Normalize the selected indicators to preliminarily eliminate the differences caused by the dimensions: For positive indicators: For negative indicators: In the formula, max(X ij )、min(X ij ) represent the maximum and minimum values ​​of the evaluation index respectively; All evaluation index calculation results are converted to [0,1]. In order to facilitate the subsequent comprehensive evaluation of the distribution network carrying capacity, they are further converted into percentage calculation results. G c =b1x c ′+b2 In the formula, G c is the percentage calculation result of index c; b1 and b2 are constant values; S223, calculating the entropy value according to the normalization processing result; Where P ij This is the result after the indicator data is standardized; S224. Calculate the indicator weight according to the entropy value; 5. The method for evaluating the risk level of energy storage grid connection and safe operation of the user side according to claim 4 is characterized by: The risk level assessment process of the user-side energy storage in S240 is specifically as follows: S241. Data preparation: Conduct risk level assessment of user-side energy storage and make full use of existing measurement data to implement risk level assessment of user-side energy storage; S242, indicator parameter calculation: Combined with the actual data prepared, the corresponding influencing factor indicator calculation method is used to calculate, so as to obtain the specific value x1, x2, ... x of each indicator. C ; S243, indicator observability processing: perform observability improvement processing, perform weight processing and dimensionless processing on indicators, and realize quantification and comparability of each evaluation indicator; S244. Comprehensive evaluation and calculation of user-side energy storage risk level: Perform comprehensive evaluation and calculation of user-side energy storage risk level, assess the user-side energy storage risk level, and analyze the evaluation results.

6. The method for evaluating the risk level of energy storage grid connection and safe operation of the user side according to claim 1, characterized in that: The method for evaluating the safe operation of energy storage on the user side in S300 comprises the following specific steps: S310, collecting evaluation index values ​​of different user-side energy storage power stations; S320, through the entropy weight method, obtain the weight coefficient w of each indicator j ; S330, selecting the optimal value of each indicator in different schemes to form a reference series in grey relational analysis; S340, compare the reference series according to the grey correlation coefficient; The grey correlation coefficient ζ between each series and the optimal series is obtained by calculation i (k); S305, comparing and ranking grey correlation degrees; Among them, the grey correlation degree γ between the i-th solution and the optimal solution i for: The degree of correlation between each plan and the optimal plan is not simply reflected by the size of the correlation, but is described by the ranking of the correlation sizes; the higher the correlation, the higher the ranking, indicating that the plan is closer to the ideal plan, and the plan with the highest ranking is the optimal user-side energy storage safe operation state.

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