Energy storage site selection method and device considering emergency scene based on extension cloud model

Abstract

The invention discloses an energy storage site selection method and device considering an emergency scene based on an extension cloud model, and relates to the technical field of power grid energy storage configuration, and the method comprises the steps: building a regional power grid node emergency energy storage demand evaluation index system, and calculating the index value of each index of each node in a regional power grid; establishing an evaluation index grade boundary cloud model based on the extension cloud model, and calculating an expected value, an entropy value and a hyper-entropy value of each index in the regional power grid; generating a correlation degree between each index and the evaluation index grade boundary cloud model according to the basic parameters, and forming a membership matrix; calculating a comprehensive evaluation score of each node according to the membership matrix and the index weight of each index; the index weight is determined by the index value of each index of each node in the regional power grid; and according to the comprehensive evaluation scores of all the nodes, determining energy storage to-be-configured nodes in consideration of the emergency scene. The application can comprehensively reflect the demand of each node of the regional power grid for emergency power supply and energy storage.

Description

Technical Field

[0001] The present application relates to the technical field of power grid energy storage configuration, and in particular to a method and device for energy storage site selection in emergency scenarios based on a topological cloud model. Background Art

[0002] With the complexity of power systems and the evolving energy mix, regional power grids are facing increasing demands for reliability and flexibility in emergency power supply situations. Traditional power dispatch and emergency response models often rely on traditional power generation facilities. In the face of sudden power demand or natural disasters, they may not be able to provide sufficient power in a timely manner, leading to power outages and shortages. Therefore, improving power grid reliability in emergency power supply situations has become a key issue in power system optimization and planning. With the rapid development of distributed energy and energy storage technologies, energy storage systems have become a crucial tool for addressing power supply and demand imbalances and improving the grid's emergency response capabilities. In emergency power supply scenarios, the appropriate configuration of energy storage devices can rapidly adjust grid frequency, provide backup power, and alleviate power supply constraints in the event of grid failures or load fluctuations. Therefore, effectively assessing the energy storage requirements at each node in the regional power grid is crucial for improving the grid's emergency power supply capabilities. Summary of the Invention

[0003] The purpose of this application is to provide a method and device for energy storage site selection in emergency scenarios based on a topological cloud model, which can fully reflect the demand for emergency power supply and energy storage at each node of the regional power grid.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for energy storage site selection in emergency scenarios based on a topological cloud model, including:

[0006] Establish an emergency energy storage demand assessment index system for regional power grid nodes and calculate the index value of each index for each node in the regional power grid; the regional power grid node emergency energy storage demand assessment index system includes five indicators: the average annual power outage duration of the node, the average annual number of power outages at the node, the proportion of primary load, the unit power outage loss value, and the average annual main transformer overload rate at the node;

[0007] Based on the extension cloud model, an evaluation index level limit cloud model is established to calculate the basic parameters of each indicator in the regional power grid; the basic parameters include expected value, entropy value and super entropy value;

[0008] Generate the correlation between each indicator and the evaluation indicator level limit cloud model according to the basic parameters to form a membership matrix;

[0009] Calculating a comprehensive evaluation score for each node based on the membership matrix and the indicator weights of each indicator; the indicator weights are determined by the indicator values ​​of each indicator of each node in the regional power grid;

[0010] The energy storage nodes to be configured in emergency scenarios are determined based on the comprehensive evaluation scores of all nodes.

[0011] In a second aspect, the present application provides an energy storage site selection device for emergency scenarios based on a topological cloud model, including:

[0012] An evaluation index system establishment module is used to establish an evaluation index system for emergency energy storage demand at regional power grid nodes and calculate the index value of each index at each node in the regional power grid. The evaluation index system for emergency energy storage demand at regional power grid nodes includes five indicators: average annual power outage duration at nodes, average annual number of power outages at nodes, primary load ratio, unit power outage loss value, and average annual main transformer overload rate at nodes.

[0013] An evaluation index level limit cloud model establishment module is used to establish an evaluation index level limit cloud model based on the extension cloud model and calculate the basic parameters of each indicator in the regional power grid; the basic parameters include expected value, entropy value and super entropy value;

[0014] A membership matrix forming module is used to generate the correlation between each indicator and the evaluation indicator level limit cloud model according to the basic parameters to form a membership matrix;

[0015] A comprehensive evaluation score calculation module is used to calculate the comprehensive evaluation score of each node based on the membership matrix and the indicator weight of each indicator; the indicator weight is determined by the indicator value of each indicator of each node in the regional power grid;

[0016] The energy storage node to be configured determination module is used to determine the energy storage nodes to be configured in emergency scenarios based on the comprehensive evaluation scores of all nodes.

[0017] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned energy storage site selection method based on the extension cloud model for emergency scenarios.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned energy storage site selection method based on the extension cloud model considering emergency scenarios.

[0019] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned energy storage site selection method based on the extension cloud model considering emergency scenarios.

[0020] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0021] The present application provides a method and device for energy storage site selection in emergency scenarios based on a topological cloud model. By constructing an emergency energy storage demand evaluation index system for regional power grid nodes including five indicators: the average annual power outage duration of nodes, the average annual number of power outages at nodes, the proportion of primary load, the value of unit power shortage loss, and the average annual main transformer overload rate at nodes, a complete set of evaluation index systems is proposed, covering multi-dimensional factors such as power outage duration, number of power outages, and load proportion, which can comprehensively reflect the demand for emergency power supply and energy storage at various nodes in the regional power grid; the evaluation index level boundary cloud model established by the topological cloud model, combined with the index weight, can effectively evaluate the energy storage demand of various nodes in the regional power grid, thereby improving the emergency power supply capacity of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 This is an application environment diagram of an energy storage site selection method based on a topological cloud model in an emergency scenario in one embodiment of the present application.

[0024] Figure 2 A flowchart of a method for energy storage site selection in emergency scenarios based on a topological cloud model is provided in accordance with an embodiment of the present application.

[0025] Figure 3 A schematic diagram of the specific process of an energy storage site selection method considering emergency scenarios based on an extension cloud model provided in one embodiment of the present application.

[0026] Figure 4 A schematic diagram of the functional modules of an energy storage site selection device considering emergency scenarios based on an extension cloud model provided in one embodiment of the present application.

[0027] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0029] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0030] The energy storage site selection method based on the extension cloud model considering emergency scenarios provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the request to be processed to the server 104. After receiving the request to be processed, the server 104 establishes an emergency energy storage demand evaluation index system for the regional power grid node, calculates the index value of each index of each node in the regional power grid, establishes an evaluation index level limit cloud model based on the extension cloud model, calculates the basic parameters of each index in the regional power grid, generates the correlation between each index and the evaluation index level limit cloud model based on the basic parameters, forms a membership matrix, calculates the comprehensive evaluation score of each node based on the membership matrix and the index weight of each index, and determines the energy storage node to be configured under the emergency scenario based on the comprehensive evaluation score of all nodes. The server 104 can feedback the obtained energy storage node to be configured to the terminal 102. In addition, in some embodiments, the energy storage site selection method for emergency scenarios based on the extension cloud model can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly select the energy storage site for the request to be processed, or the server 104 can obtain the video to be processed from the data storage system and select the energy storage site for the request to be processed.

[0031] Terminal 102 may include, but is not limited to, various desktop computers, laptops, smartphones, tablet computers, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers, or may be a cloud server.

[0032] In an exemplary embodiment, Figure 2As shown, a method for selecting an energy storage site in an emergency scenario based on a topological cloud model is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, and can also be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, including the following steps 201 to 205.

[0033] Step 201: Establish an emergency energy storage demand assessment index system for regional power grid nodes and calculate the index value of each index for each node in the regional power grid; the regional power grid node emergency energy storage demand assessment index system includes five indicators: the average annual power outage duration of the node, the average annual number of power outages at the node, the proportion of primary load, the unit power shortage loss value, and the average annual main transformer overload rate at the node.

[0034] Step 202: Establish an evaluation index level limit cloud model based on the extension cloud model to calculate basic parameters of each index in the regional power grid; the basic parameters include expected value, entropy value and super entropy value.

[0035] Step 203 : generating the correlation between each indicator and the evaluation indicator level limit cloud model according to the basic parameters to form a membership matrix.

[0036] Step 204 , calculating the comprehensive evaluation score of each node based on the membership matrix and the indicator weight of each indicator; the indicator weight is determined by the indicator value of each indicator of each node in the regional power grid.

[0037] Step 205 : Determine energy storage nodes to be configured in emergency scenarios based on the comprehensive evaluation scores of all nodes.

[0038] Implement the above steps 201 to 205, and by constructing a regional power grid node emergency energy storage demand evaluation index system including five indicators: the average annual power outage duration of the node, the average annual number of power outages at the node, the proportion of primary load, the value of unit power shortage loss, and the average annual main transformer overload rate at the node, a complete set of evaluation index systems is proposed, covering multi-dimensional factors such as power outage duration, number of power outages, and load proportion, which can fully reflect the demand of each node in the regional power grid for emergency power storage; the evaluation index level limit cloud model established by the extension cloud model, combined with the index weight, can effectively evaluate the energy storage demand of each node in the regional power grid, thus improving the emergency power supply capacity of the power grid. In addition, this application comprehensively considers multiple key indicators, combines the extension cloud model and the entropy weight method, and can more accurately evaluate the energy storage demand of each node in the regional power grid.

[0039] like Figure 3As shown in the figure, first, an index system for the degree of energy storage demand for each node in the regional power grid is established, including five indicators: the average annual power outage duration of the node, the average annual number of power outages at the node, the proportion of primary load, the unit power shortage loss value, and the average annual main transformer overload rate at the node. Through statistics and calculations based on the actual operating data of the regional power grid throughout the year, specific values ​​are obtained for each node, and the calculated index values ​​are normalized.

[0040] Next, based on pre-defined grading intervals, the cloud model is applied to transform the grade boundaries of each indicator, converting the data for each indicator into the cloud model's expected value, entropy value, and super-entropy value. Random numbers are generated using a normal distribution, and the correlation between each node's indicator data and the corresponding grade is calculated. This process is repeated multiple times to ensure the reliability and stability of the evaluation results, forming a membership matrix.

[0041] Finally, by analyzing the information entropy and discrimination of each indicator, the entropy weight method was used to calculate the weight of each indicator and determine its importance in the comprehensive evaluation. The indicator weights were combined with the membership degree to calculate a comprehensive evaluation vector, and the weighted average method was used to determine the final energy storage demand score for each node. Through multiple calculations, the expected value and credibility of the final comprehensive evaluation score were determined. Ultimately, the energy storage demand priority of each node was determined, and the node with the highest expected value was selected as the node for energy storage installation.

[0042] An evaluation index system for emergency energy storage demand at regional power grid nodes is established, including five indicators: the average annual power outage duration at the node, the average annual number of power outages at the node, the proportion of primary load, the unit power outage loss value, and the average annual main transformer overload rate at the node. The specific calculation formulas are shown in formulas (1)-(5). Based on the actual operating data of the regional power grid throughout the year, the corresponding index values ​​for each node in the regional power grid are counted and calculated.

[0043]

[0044] Where, T avg represents the average power outage duration throughout the year, T total Indicates the total duration of statistics in hours, T outage Indicates the total power outage duration during the statistical period, N outage is the total number of power outages during the statistical period; N avg represents the average annual power outage times of the node; R first is the primary load proportion, P first is the average power of the first-level load under the node during the statistical time, P l is the average total load power of the node during the statistical period; V outage is the unit power loss value of the node, E load is the total electricity consumption during the statistical period at this node, and GDP is the gross domestic product during the statistical period at this node; Roverload is the node annual average main transformer overload rate, T heavy It is the main transformer overload time within the statistical time at this node.

[0045] The energy storage site selection method based on the extension cloud model considering emergency scenarios also includes: normalizing the original value of each indicator of each node in the regional power grid to obtain the indicator value of each indicator of each node in the regional power grid.

[0046] The above normalization operation converts the absolute value of the indicator into a relative value to eliminate the influence of different dimensions. The specific calculation method is as follows:

[0047]

[0048] Where x ij represents the original value of the jth indicator under the i-th node, It means x under the jth index ij The maximum and minimum values ​​of d ij Indicates the index value (relative value) of the jth index under the i-th node.

[0049] The results of the emergency energy storage demand index for each node in the regional power grid are divided into five levels: low, lower, medium, higher, and very high, corresponding to levels 1 to 5, respectively. This is used to determine the degree of demand for energy storage at each node. The level boundaries of the energy storage demand index for each node in the regional power grid are shown in Table 1.

[0050] Table 1 Level limits of energy storage requirements for each node in the regional power grid

[0051]

[0052] Assume that the energy storage demand index A (A = x1, x2, ..., x n ) The upper and lower critical values ​​of the evaluation level corresponding to are x max 、x min , then the classification level boundary of the index can be regarded as a double constraint space [x min , x max The expected value E of the evaluation index level limit cloud model is calculated by the conversion relationship between the level standard interval number (the level standard interval number is the number in the double constraint space) and the normal cloud model. x , entropy value E n , super entropy value H e The calculation formula is as follows:

[0053]

[0054] Where x min is the minimum value of the grade standard interval; xmax is the maximum value of the grade standard interval; s is a constant that can be adjusted based on the fuzziness and randomness of the indicator, and is set to 0.0002 here. The grade boundaries of the energy storage requirement indicators for each node in the regional power grid based on the Extension Cloud model are shown in Table 2.

[0055] Table 2 Level limits of energy storage demand indicators for each node in the regional power grid based on the extension cloud model

[0056]

[0057] The three data in brackets in Table 2 correspond to the expected values ​​E of the indicators in turn. x , entropy value E n 、Super entropy H e .

[0058] In the above step 203 , the correlation between each indicator and the evaluation indicator level limit cloud model is generated according to the basic parameters to form a membership matrix, which specifically includes the following steps 301 to 303 .

[0059] Step 301: The indicator value of each indicator is taken as a cloud droplet to randomly generate a normal distribution random number; the normal distribution random number is a random number whose expected value is the entropy value and the standard deviation is the super entropy value.

[0060] Step 302: Generate the correlation between each indicator and the evaluation indicator level limit cloud model based on the normal distribution random number and the expected value of the indicator.

[0061] Step 303: Repeat the calculation several times of the correlation between each indicator and the evaluation indicator level limit cloud model to form a membership matrix.

[0062] Index value data of each indicator for comprehensive benefit evaluation of energy storage ij As a cloud droplet, the random generation obeys the expected value E n , standard deviation is H e Normally distributed random number E n ′, and then calculate the index value data d of each index ij The correlation degree between the evaluation index level limit cloud model is calculated as follows:

[0063]

[0064] Where k is the indicator data d ij The degree of membership between the cloud model and the corresponding evaluation index level limit.

[0065] In order to improve the credibility of the evaluation results, the cloud membership k is calculated y times and the average value is taken. Here y is 1500, and finally the n matrix K is formed. i(i=1,2...n), K i is a matrix with m rows and p columns, where m is the total number of indicators, m=5, p is the total number of level limits, p=5, K i Indicates the index value d corresponding to the i-th node ij The membership matrix between the corresponding evaluation index level limit cloud model.

[0066] In the above step 204 , the comprehensive evaluation score of each node is calculated according to the membership matrix and the indicator weight of each indicator, which specifically includes the following steps 401 to 403 .

[0067] Step 401: Calculate the indicator weight of each indicator using the entropy weight method.

[0068] In the above step 401 , the entropy weight method is used to calculate the indicator weight of each indicator, which specifically includes the following steps 501 to 504 .

[0069] Step 501: Calculate the weight of the index value of each node under each index based on the index value of each index of each node and the index values ​​of all nodes under the same index.

[0070] Based on the indicator value d ij The entropy weight method is used to calculate the indicator weight. Calculate the proportion p of the indicator value under the i-th node under the j-th indicator ij , the calculation formula is:

[0071]

[0072] Where p ij is the proportion of the index value of the i-th node under the j-th index, and n is the total number of nodes.

[0073] Step 502: Calculate the information entropy value of each indicator according to the proportion of the indicator value of each node under each indicator.

[0074] Calculate the information entropy value e of the jth indicator j , the calculation formula is:

[0075]

[0076] Where, e j is the information entropy value of the j-th indicator. Due to the dimension elimination process, there is p ij = 0, when p ij =0, let ln(p ij )=0 to prevent uncalculated phenomena.

[0077] Step 503: Calculate the discrimination of each indicator based on the information entropy value of each indicator; the discrimination of the indicator is used to characterize the importance of the indicator in the comprehensive evaluation.

[0078] Through analysis, we know that the information entropy value of the indicator is inversely proportional to the weight value of the indicator, so the information entropy discrimination D is introduced. j , D j Proportional to the indicator weight value, calculate the discrimination D of the jth indicator j , the calculation formula is:

[0079] D j =1-e j (11);

[0080] Where D j is the discrimination of the jth indicator. j The larger the value of j The greater its importance in the comprehensive evaluation.

[0081] Step 504: Divide the discrimination of each indicator by the total discrimination to obtain the indicator weight of each indicator; the total discrimination is the sum of the discriminations of all indicators.

[0082] Calculate the indicator weight w of the jth indicator j , the calculation expression is as follows:

[0083]

[0084] Where w j is the weight of the j-th indicator, m is the total number of indicators, and m=5.

[0085] Through the above calculation process, we can get the indicator weight coefficient matrix W, that is,

[0086] W=(w1,w2,...,w m ) (13).

[0087] Step 402: Calculate a comprehensive evaluation vector based on the membership matrix and the indicator weights of each indicator.

[0088] Step 403: Calculate the comprehensive evaluation score of each node according to the comprehensive evaluation vector using the weighted average method.

[0089] The membership matrix K is combined with the index weight coefficient matrix W to obtain the comprehensive evaluation vector B that can reflect the comprehensive performance. The calculation formula is as follows:

[0090] B = WK (14);

[0091] B=[b1,b2,...,b5] (15);

[0092] Where b i is the component corresponding to the comprehensive evaluation vector B; i=1, 2...5.

[0093] Then the weighted average method is used to obtain the comprehensive evaluation score r:

[0094]

[0095] Where b i is the component corresponding to the comprehensive evaluation vector B; f i is the score of evaluation level i, and the scores corresponding to evaluation levels 1 to 5 are 2, 4, 6, 8, and 10 respectively.

[0096] In the above step 205, the energy storage nodes to be configured under consideration of emergency scenarios are determined based on the comprehensive evaluation scores of all nodes, specifically including: for each node, repeating the calculation multiple times to obtain the expected value of the comprehensive evaluation score of each node; sorting the expected values ​​of the comprehensive evaluation scores of all nodes, and determining the node with the largest expected value of the comprehensive evaluation score as the energy storage node to be configured under consideration of emergency scenarios.

[0097] As can be seen from the above formula, in the process of calculating the correlation between the indicator to be evaluated and the evaluation indicator level limit cloud model, the evaluation indicator level limit cloud model normal cloud generator randomly generates normally distributed random numbers. Therefore, it is necessary to calculate multiple times to obtain the expected value E of the comprehensive evaluation score. rx and entropy E rn , the calculation formula is:

[0098]

[0099] Where n is the number of operations; r i (x) is the index value d ij The comprehensive evaluation score obtained by the i-th operation; according to the expected value E of the comprehensive evaluation score rx and entropy E rn Calculate the credibility factor θ. The larger the value of the credibility factor, the smaller the credibility of the comprehensive evaluation result. Conversely, the greater the credibility of the evaluation result.

[0100] Based on the comprehensive evaluation results, which include the expected values ​​of the comprehensive evaluation scores of all nodes, nodes with larger expected values ​​in the comprehensive evaluation are selected as nodes to be configured for energy storage under emergency scenarios.

[0101] The present application also provides an application scenario, which applies the above-mentioned energy storage site selection method based on the extension cloud model in emergency scenarios. Specifically: the energy storage site selection method based on the extension cloud model in emergency scenarios provided in this embodiment can be applied in regional power grid energy storage site selection scenarios. The regional power grid energy storage site selection scenario includes a request sending link, an energy storage site selection link and a comprehensive evaluation result release link; the request to be processed enters the energy storage site selection link from the request sending, obtains the corresponding comprehensive evaluation result through human-machine collaboration, and enters the downstream comprehensive evaluation result release link, and uses the node with a larger expected value in the comprehensive evaluation result as the energy storage node to be configured. The energy storage site selection method based on the extension cloud model in emergency scenarios provided in this embodiment belongs to the machine marking link in the energy storage site selection link. Specifically, in the process of energy storage site selection for videos, an emergency energy storage demand evaluation index system for regional power grid nodes can be established, the index value of each index of each node in the regional power grid can be calculated, and an evaluation index level limit cloud model can be established based on the extension cloud model. The basic parameters of each index in the regional power grid can be calculated, and the correlation between each index and the evaluation index level limit cloud model can be generated based on the basic parameters to form a membership matrix. The comprehensive evaluation score of each node can be calculated based on the membership matrix and the index weight of each index. The energy storage nodes to be configured in emergency scenarios can be determined based on the comprehensive evaluation scores of all nodes.

[0102] This application is a distributed energy storage site selection method based on the extension cloud model in emergency scenarios. It establishes an indicator system to evaluate the energy storage demand level of each node in the regional power grid under emergency power supply demand, including five indicators: annual average power outage duration, annual average number of power outages, primary load ratio, unit power shortage loss value and annual average main transformer overload rate. The calculation is based on the actual operating data of the regional power grid, and then normalized to eliminate the influence of different dimensions and ensure the comparability between the indicators. The normalized values ​​are divided into five energy storage demand levels and classified from low to high according to predetermined thresholds. This method adopts the extension cloud model, represents the level of each indicator through fuzzy and probabilistic methods, calculates the expected value, entropy value and super entropy value of each indicator classification boundary, and evaluates the correlation between the indicator data of each node and the cloud model classification. The results will be repeatedly calculated multiple times to improve the reliability of the results. Applying the entropy weight method, the importance of each indicator is determined based on its information entropy. The final weight calculation reflects the relative importance of each indicator in the overall assessment. By combining these weight values ​​with the cloud model's membership, a comprehensive evaluation vector is derived, which is used to calculate the final energy storage demand score for each node. The point with the highest expected value is selected as the grid node for energy storage deployment. This method provides an evaluation method based on actual operating data for optimizing energy storage deployment locations in regional power grids under emergency conditions, optimizing the allocation of energy storage resources.

[0103] Based on the same inventive concept, an embodiment of the present application further provides a device for energy storage site selection based on an extension cloud model for considering emergency scenarios, which is used to implement the aforementioned method for energy storage site selection based on an extension cloud model for considering emergency scenarios. The implementation solution provided by this device is similar to the implementation solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the device for energy storage site selection based on an extension cloud model for considering emergency scenarios provided below can be found in the above-mentioned limitations of the method for energy storage site selection based on an extension cloud model for considering emergency scenarios, and will not be repeated here.

[0104] In an exemplary embodiment, Figure 4 As shown, a storage site selection device based on the extension cloud model considering emergency scenarios is provided, including the following modules:

[0105] Evaluation index system establishment module T1 is used to establish an evaluation index system for emergency energy storage demand at regional power grid nodes and calculate the index value of each index at each node in the regional power grid. The evaluation index system for emergency energy storage demand at regional power grid nodes includes five indicators: average annual power outage duration at the node, average annual number of power outages at the node, primary load ratio, unit power outage loss value, and average annual main transformer overload rate at the node.

[0106] Evaluation index level limit cloud model establishment module T2 is used to establish an evaluation index level limit cloud model based on the extension cloud model and calculate the basic parameters of each indicator in the regional power grid; the basic parameters include expected value, entropy value and super entropy value;

[0107] A membership matrix forming module T3 is used to generate the correlation between each indicator and the evaluation indicator level limit cloud model according to the basic parameters to form a membership matrix;

[0108] Comprehensive evaluation score calculation module T4, used to calculate the comprehensive evaluation score of each node based on the membership matrix and the indicator weight of each indicator; the indicator weight is determined by the indicator value of each indicator of each node in the regional power grid;

[0109] The energy storage node to be configured determination module T5 is used to determine the energy storage node to be configured in consideration of emergency scenarios based on the comprehensive evaluation scores of all nodes.

[0110] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store energy storage site selection data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes a method for energy storage site selection in emergency scenarios based on a topological cloud model.

[0111] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0112] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0113] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0114] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0115] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0116] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0117] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0118] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0119] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for energy storage site selection in emergency scenarios based on an extension cloud model, characterized in that: The energy storage site selection method based on the extension cloud model considering emergency scenarios includes: Establish an emergency energy storage demand assessment index system for regional power grid nodes and calculate the index value of each index for each node in the regional power grid; the regional power grid node emergency energy storage demand assessment index system includes five indicators: the average annual power outage duration of the node, the average annual number of power outages at the node, the proportion of primary load, the unit power outage loss value, and the average annual main transformer overload rate at the node; Based on the extension cloud model, an evaluation index level limit cloud model is established to calculate the basic parameters of each indicator in the regional power grid; the basic parameters include expected value, entropy value and super entropy value; Generate the correlation between each indicator and the evaluation indicator level limit cloud model according to the basic parameters to form a membership matrix; Calculating a comprehensive evaluation score for each node based on the membership matrix and the indicator weights of each indicator; the indicator weights are determined by the indicator values ​​of each indicator of each node in the regional power grid; The energy storage nodes to be configured in emergency scenarios are determined based on the comprehensive evaluation scores of all nodes.

2. The energy storage site selection method based on the extension cloud model considering emergency scenarios according to claim 1 is characterized in that: The energy storage site selection method based on the extension cloud model considering emergency scenarios also includes: The original value of each indicator of each node in the regional power grid is normalized to obtain the indicator value of each indicator of each node in the regional power grid.

3. The energy storage site selection method based on the extension cloud model considering emergency scenarios according to claim 1 is characterized in that: Generate the correlation between each indicator and the evaluation indicator level limit cloud model based on the basic parameters to form a membership matrix, specifically including: The indicator value of each indicator is used as a cloud droplet to randomly generate normal distribution random numbers; normal distribution random numbers are random numbers whose expected value is entropy value and standard deviation is super entropy value; Generating a correlation degree between each indicator and the evaluation indicator level limit cloud model according to the normally distributed random number and the expected value of the indicator; The correlation between each indicator and the evaluation indicator level limit cloud model is calculated repeatedly several times to form a membership matrix.

4. The energy storage site selection method based on the extension cloud model considering emergency scenarios according to claim 1 is characterized in that: The comprehensive evaluation score of each node is calculated based on the membership matrix and the indicator weight of each indicator, specifically including: The entropy weight method is used to calculate the indicator weight of each indicator; Calculating a comprehensive evaluation vector based on the membership matrix and the indicator weights of each indicator; The weighted average method is used to calculate the comprehensive evaluation score of each node according to the comprehensive evaluation vector.

5. The method for selecting an energy storage site based on an extension cloud model in an emergency scenario according to claim 4 is characterized in that: The entropy weight method is used to calculate the indicator weight of each indicator, including: Based on the index value of each indicator of each node and the index values ​​of all nodes under the same indicator, calculate the proportion of the index value of each node under each indicator; Calculate the information entropy value of each indicator according to the proportion of the indicator value of each node under each indicator; The discrimination of each indicator is calculated based on the information entropy value of each indicator; the discrimination of the indicator is used to characterize the importance of the indicator in the comprehensive evaluation; Divide the discrimination of each indicator by the total discrimination to obtain the indicator weight of each indicator; the total discrimination is the sum of the discriminations of all indicators.

6. The method for selecting an energy storage site based on an extension cloud model in an emergency scenario according to claim 1, characterized in that: The energy storage nodes to be configured in emergency scenarios are determined based on the comprehensive evaluation scores of all nodes, including: For each node, the calculation is repeated multiple times to obtain the expected value of the comprehensive evaluation score of each node; The expected values ​​of the comprehensive evaluation scores of all nodes are sorted, and the node with the largest expected value of the comprehensive evaluation score is determined as the energy storage node to be configured under the emergency scenario.

7. A device for selecting an energy storage site in an emergency scenario based on an extension cloud model, characterized in that: The energy storage site selection device based on the extension cloud model considering emergency scenarios includes: An evaluation index system establishment module is used to establish an evaluation index system for emergency energy storage demand at regional power grid nodes and calculate the index value of each index at each node in the regional power grid. The evaluation index system for emergency energy storage demand at regional power grid nodes includes five indicators: average annual power outage duration at nodes, average annual number of power outages at nodes, primary load ratio, unit power outage loss value, and average annual main transformer overload rate at nodes. An evaluation index level limit cloud model establishment module is used to establish an evaluation index level limit cloud model based on the extension cloud model and calculate the basic parameters of each indicator in the regional power grid; the basic parameters include expected value, entropy value and super entropy value; A membership matrix forming module is used to generate the correlation between each indicator and the evaluation indicator level limit cloud model according to the basic parameters to form a membership matrix; A comprehensive evaluation score calculation module is used to calculate the comprehensive evaluation score of each node based on the membership matrix and the indicator weight of each indicator; the indicator weight is determined by the indicator value of each indicator of each node in the regional power grid; The energy storage node to be configured determination module is used to determine the energy storage nodes to be configured in emergency scenarios based on the comprehensive evaluation scores of all nodes.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the energy storage site selection method based on the extension cloud model in emergency scenarios as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for energy storage site selection based on an extension cloud model and considering emergency scenarios according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for energy storage site selection based on an extension cloud model and considering emergency scenarios according to any one of claims 1 to 6 is implemented.

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