Plateau region insurance supply type power grid scheme selection method

Abstract

The invention discloses a plateau region supply insurance type power grid scheme selection method, and relates to the technical field of electric power engineering, and the method comprises the steps: constructing a negative index system; clustering and dividing the areas according to load characteristics, resource conditions and geographical conditions; constructing a multi-dimensional evaluation index system of the candidate power supply schemes; constructing a multiple linear regression model to obtain a judgment area of the candidate power supply scheme; and obtaining an optimal power supply scheme of the region based on the comprehensive score and the judgment region. According to the technical scheme of the invention, by establishing a four-layer judgment system, including the negative condition, the clustering analysis, the multi-dimensional comprehensive evaluation, the cost regression model and the economic boundary judgment, the scientific, quantitative and interpretable selection of the power supply scheme can be realized, the selected power supply scheme is ensured to have better adaptability and feasibility in the aspects of technology, economy and reliability, and the reliability of the power supply scheme is improved. Therefore, the construction cost can be effectively reduced, the reliability and sustainability of a power supply scheme are improved, and a scientific decision basis is provided for power grid construction in plateau regions.

Description

A method for selecting power grid schemes for high-altitude areas Technical Field

[0001] This application relates to the field of power engineering technology, and in particular to a method for selecting a power grid scheme for high-altitude areas. Background Technology

[0002] High-altitude regions, due to their unique characteristics such as high elevation, complex climate, rugged terrain, and inconvenient transportation, have always faced power supply problems. While extending traditional main grid power systems can provide stable power, it encounters challenges such as massive engineering projects, extremely high construction costs, significant construction difficulties, and environmental restrictions. Furthermore, the maintenance and upgrading of power grids in high-altitude regions are also extremely challenging due to transportation and climate factors. Although microgrid technology has alleviated power supply problems in remote areas to some extent, its long-term operating costs are high, and its reliability and environmental adaptability are poor. More importantly, existing power supply solutions have not fully considered the complex terrain, climate, and environmentally sensitive limitations of high-altitude regions, resulting in many power supply solutions failing to achieve the expected results in practical applications.

[0003] Current methods for selecting power supply schemes generally have significant shortcomings: First, evaluation indicators are often singular or overly reliant on subjective judgment, resulting in many research findings failing to reflect the multi-dimensional advantages and disadvantages of power supply schemes and lacking a quantitative and comprehensive evaluation system. Second, existing methods often lack clear boundaries for power supply modes, failing to scientifically define the applicable areas for different power supply modes. For example, the boundary conditions regarding main grid extension distance and microgrid power supply capacity are unclear, making it impossible to reasonably define the application scope of various power supply modes. Summary of the Invention

[0004] The purpose of this application is to provide a method for selecting power grid schemes for high-altitude areas, aiming to solve the technical problems of a single evaluation system and ambiguous power supply mode boundaries in the existing technology.

[0005] To achieve the above objectives, this application provides a method for selecting a power supply guarantee scheme for plateau regions. The method includes: constructing a veto index system comprising multiple veto indicators; performing preliminary screening of construction conditions within the region based on these multiple veto indicators to obtain preliminary power supply schemes that meet construction conditions; clustering the region according to load characteristics, resource conditions, and geographical conditions based on the preliminary power supply schemes to obtain multiple resource type zones; constructing a multi-dimensional evaluation index system for candidate power supply schemes based on the multiple resource type zones; calculating the weights of the multi-dimensional evaluation indicators using the AHP method; and calculating the comprehensive score of the candidate power supply schemes based on the TOPSIS algorithm; constructing a cost-based multiple linear regression model based on the multi-dimensional evaluation indicators and the comprehensive score to obtain the cost fitting curve of the candidate power supply schemes; obtaining the judgment region of the candidate power supply schemes based on the cost fitting curves; and obtaining the optimal power supply scheme for the region based on the comprehensive score and the judgment region.

[0006] In one embodiment, the expression for the veto index system is: In the formula, Indicates the first One veto indicator in The construction conditions are met; Indicates the first One veto indicator in The construction conditions are not met; Any region within the set of regions; the set of regions is ; The veto index vector is , .

[0007] In one embodiment, the region is clustered according to natural conditions and load characteristics to obtain multiple resource type regions, including: constructing clustering feature vectors based on the region's load characteristics, resource conditions, and geographical conditions. ;in, This represents the maximum load and is used to measure peak power supply capacity requirements. This is the average load value, used to measure average power demand. This is the daily load fluctuation coefficient, used to measure the difference between peak and off-peak loads; Light resource level, used to measure the normalized annual average radiation; Wind resource level is used to measure the normalization of annual average wind speed or wind power density. Distance to mainnet nodes; For terrain complexity; The altitude is used to determine equipment efficiency and environmental adaptability; the clustering feature vector is preprocessed using Z-score to obtain a standardized clustering feature vector; based on the standardized clustering feature vector, the Ward minimum variance clustering method is used to divide the region into different resource type areas.

[0008] In one embodiment, a multi-dimensional evaluation index system for candidate power supply schemes is constructed, including: constructing a multi-dimensional evaluation index matrix for candidate power supply schemes. ;in, Indicates the first The candidate power supply schemes are in the first The original evaluation values ​​for each indicator; This indicates the number of candidate power supply schemes participating in the comprehensive comparison; This indicates the number of multi-dimensional evaluation indicators used to evaluate the candidate power supply schemes; the positive and negative indicators in the multi-dimensional candidate power supply scheme indicator matrix are standardized respectively.

[0009] In one embodiment, the standardized expression for the positive index is: The standardized expression for the reverse indicator is: In the formula, For the first The candidate power supply schemes are in the first Standardized data on a positive indicator; Indicates the first The candidate power supply schemes are in the first The original evaluation values ​​for each indicator; For all candidate power supply schemes in the first The maximum value of the original data on each positive indicator; For all candidate power supply schemes in the first The minimum value of the original data on a positive indicator; For the first The candidate power supply schemes are in the first Standardized data on a reverse indicator.

[0010] In one embodiment, the AHP method is used to calculate the weights of multi-dimensional evaluation indicators, including: constructing a judgment matrix. ;in, For the judgment matrix; The number of multi-dimensional evaluation indicators used to evaluate the candidate power supply schemes; To determine the matrix The Middle line, number The elements of the column are used to represent the first... The evaluation indicator is relative to the first The importance of each evaluation indicator is assessed; the principal eigenvalue problem is solved using the eigenvalue method to obtain the normalized principal eigenvector; the expression for the normalized principal eigenvector is: The expression for the principal eigenvalue problem is: , To determine the matrix The largest eigenvalue; To determine the matrix correspond The principal eigenvectors are obtained; based on the normalized principal eigenvectors, the weights of the multi-dimensional evaluation indicators are obtained.

[0011] In one implementation, a judgment matrix is ​​constructed. This includes: constructing a judgment matrix based on the multi-dimensional evaluation index system, wherein the expression of the judgment matrix is ​​as follows: ; calculate the consistency index and consistency ratio respectively; wherein, the expression for the consistency index is: The expression for the consistency ratio is: , Consistency indicators; Consistency ratio; As a random consistency indicator; judgment The relationship with the threshold value, when When the value is less than the threshold, the judgment matrix passes the consistency check; if When the value is greater than or equal to the threshold, the judgment matrix is ​​adjusted, and the consistency index and consistency ratio are calculated again, and a judgment is made. The relationship between the value of the threshold and the judgment matrix is ​​maintained until the judgment matrix meets the consistency requirements.

[0012] In one embodiment, calculating the comprehensive score of the candidate power supply scheme based on the TOPSIS algorithm includes: constructing a weighted normalization matrix, wherein the weighted normalization matrix includes a forward matrix and an inverse matrix; the expression of the forward matrix is ​​as follows: The expression for the inverse matrix is: ,in, It is a positive matrix; For the first The final weight of each evaluation indicator; For the first The candidate power supply schemes are in the first Standardized data on a positive indicator; It is an inverse matrix; For the first The candidate power supply schemes are in the first Standardized data on each inverse index; calculate the positive and negative ideal solutions of the candidate power supply schemes respectively; wherein, the expression for the positive ideal solution is: The expression for the negative ideal solution is: In the formula, The ideal distance; For the positive ideal solution, for The maximum value in the weighted matrix; The ideal distance is negative. For a negative ideal solution, for Find the minimum value in the weighting matrix; calculate the comprehensive score of the candidate power supply scheme; the expression for the comprehensive score is: In the formula, The final score is calculated based on the overall score.

[0013] In one embodiment, a cost-based multiple linear regression model is constructed to obtain the cost fitting curve of the candidate power supply scheme. Based on the cost fitting curve, a determination region for the candidate power supply scheme is obtained, including: constructing sample data, wherein the sample data includes construction cost, operation and maintenance cost, and key feature parameters of regional characteristic variables, denoted as: In the formula, Distance from the main network, For regional load scale, For renewable energy resource levels, For terrain complexity, For regional category labels, To obtain a comprehensive score, based on the sample data, a multiple linear regression method is used to fit the main grid cost function curve and the microgrid cost function curve, respectively. Based on the intersection of the main grid cost function curve and the microgrid cost function curve, the boundary value of the main grid extension economic distance is obtained. Based on the boundary value of the main grid extension economic distance, the judgment area of ​​the candidate power supply scheme is obtained.

[0014] In one embodiment, the plurality of veto indicators include high-risk geological disaster indicators, traffic accessibility indicators, altitude restriction indicators, key environmental and ecological indicators, extreme weather indicators, and policy restriction indicators.

[0015] In one embodiment, the candidate power supply schemes include a main grid extension scheme, a microgrid scheme, and a hybrid scheme.

[0016] In one embodiment, the multi-dimensional evaluation indicators include technical feasibility indicators, economic indicators, operational adaptability indicators, and safety and reliability indicators.

[0017] In one embodiment, the determination area includes the main grid extension economic boundary distance, the microgrid applicable load boundary, and the hybrid scheme applicable range.

[0018] The above-mentioned technical solution of this application has at least the following beneficial technical effects: By establishing a four-layer discrimination system, including rejection conditions, cluster analysis, multi-dimensional comprehensive evaluation, cost regression model and economic boundary determination, the technical solution of this application can realize the scientific, quantitative and interpretable selection of power supply schemes, ensuring that the selected power supply schemes have better adaptability and feasibility in terms of technology, economy and reliability, thereby effectively reducing construction costs, improving the reliability and sustainability of power supply schemes, and providing a scientific decision-making basis for power grid construction in plateau areas. Attached Figure Description

[0019] Figure 1 is a flowchart illustrating an embodiment of the plateau region power grid selection method provided in this application; Figure 2 is a flowchart illustrating an embodiment of step S2 in the plateau region power grid selection method provided in this application; Figure 3 is a flowchart illustrating an embodiment of step S3 in the plateau region power grid selection method provided in this application; Figure 4 is a flowchart illustrating another embodiment of step S3 in the plateau region power grid selection method provided in this application; Figure 5 is a flowchart illustrating yet another embodiment of step S3 in the plateau region power grid selection method provided in this application; Figure 6 is a flowchart illustrating an embodiment of step S4 in the plateau region power grid selection method provided in this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of this application. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0021] The embodiments described in this application are only some, not all, of the embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments described herein without inventive effort are within the scope of protection of this application.

[0022] High-altitude regions, due to their unique characteristics such as high elevation, complex climate, rugged terrain, and inconvenient transportation, have always faced power supply problems. While extending traditional main grid power systems can provide stable power, it encounters challenges such as massive engineering projects, extremely high construction costs, significant construction difficulties, and environmental restrictions. Furthermore, the maintenance and upgrading of power grids in high-altitude regions are also extremely challenging due to transportation and climate factors. Although microgrid technology has alleviated power supply problems in remote areas to some extent, its long-term operating costs are high, and its reliability and environmental adaptability are poor. More importantly, existing power supply solutions have not fully considered the complex terrain, climate, and environmentally sensitive limitations of high-altitude regions, resulting in many power supply solutions failing to achieve the expected results in practical applications.

[0023] Current methods for selecting power supply schemes generally have significant shortcomings: First, evaluation indicators are often singular or overly reliant on subjective judgment, resulting in many research findings failing to reflect the multi-dimensional advantages and disadvantages of power supply schemes and lacking a quantitative and comprehensive evaluation system. Second, existing methods often lack clear boundaries for power supply modes, failing to scientifically define the applicable areas for different power supply modes. For example, the boundary conditions regarding main grid extension distance and microgrid power supply capacity are unclear, making it impossible to reasonably define the application scope of various power supply modes.

[0024] To address the aforementioned technical problems, this application provides a method for selecting a power grid scheme for high-altitude regions. Referring to Figure 1, in one embodiment of this application, the method includes the following steps: Step S1: Constructing a veto indicator system. The veto indicator system includes multiple veto indicators. Based on these multiple veto indicators, preliminary screening of construction conditions within the region is conducted to obtain preliminary power supply schemes that meet construction conditions. These multiple veto indicators include high-risk geological disaster indicators (whether construction is permitted in landslide, debris flow, or permafrost subsidence zones), accessibility indicators (whether equipment transportation channels are available), altitude restriction indicators (whether ultra-high-altitude equipment can operate stably), key environmental and ecological indicators (whether it is located within an ecological red line or protected area), extreme weather indicators (whether storms, freezing rain, or snow depth exceed standards), and policy restriction indicators (whether the construction of new transmission lines or stations is permitted). Furthermore, the expression for the veto indicator system is: In the formula, Indicates the first One veto indicator in The construction conditions are met; Indicates the first One veto indicator in The construction conditions are not met; Any region within the set of regions; the set of regions is ; The veto index vector is , .

[0025] If any indicator is not met, the plan will be eliminated to prevent plans that do not meet the construction conditions from entering further evaluation.

[0026] Step S2: Based on the preliminary power supply scheme, the region is clustered according to load characteristics, resource conditions, and geographical conditions to obtain multiple resource type zones. Please refer to Figure 2. One embodiment of step S2 includes the following specific steps: Step S21: Based on the region's load characteristics, resource conditions, and geographical conditions, construct clustering feature vectors. .in, This represents the maximum load and is used to measure peak power supply capacity requirements. This is the average load value, used to measure average power demand. This is the daily load fluctuation coefficient, used to measure the difference between peak and off-peak loads; Light resource level, used to measure the normalized annual average radiation; Wind resource level is used to measure the normalization of annual average wind speed or wind power density. Distance to mainnet nodes; For terrain complexity; Altitude is used to determine equipment efficiency and environmental adaptability; Step S22: Z-score is used to preprocess the clustering feature vectors to obtain standardized clustering feature vectors. In clustering, in actual samples from plateau regions, the number of typical categories can be set to 4, resulting in four types of regions, namely: , , and .in, Areas with high load and limited resources; Medium load, difficult terrain; Excellent resources; : Extremely high-quality resources or extremely high-altitude areas.

[0027] Step S23: Based on the standardized clustering feature vectors, the Ward minimum variance clustering method is used to divide the region into different resource type areas.

[0028] Step S3: Based on multiple resource type areas, construct a multi-dimensional evaluation index system for candidate power supply schemes. Calculate the weights of the multi-dimensional evaluation indicators using the AHP (Analytic Hierarchy Process) method, and calculate the comprehensive score of the candidate power supply schemes based on the TOPSIS algorithm. Candidate power supply schemes include main grid extension schemes, microgrid schemes, and hybrid schemes. Multi-dimensional evaluation indicators include technical feasibility indicators, economic indicators, operational adaptability indicators, and safety and reliability indicators. Referring to Figure 3, in one embodiment of step S3, constructing the multi-dimensional evaluation index system for candidate power supply schemes includes the following specific steps: Step S31a: Construct a multi-dimensional evaluation index matrix for candidate power supply schemes. ;in, Indicates the first The candidate power supply schemes are in the first The original evaluation values ​​for each indicator; This indicates the number of candidate power supply schemes participating in the comprehensive comparison; This indicates the number of multi-dimensional evaluation indicators used to evaluate candidate power supply schemes; step S31b: standardize the positive and negative indicators in the multi-dimensional candidate power supply scheme indicator matrix. Specifically, the standardization expression for the positive indicators is: The standardized expression for the reverse indicator is: In the formula, For the first The candidate power supply schemes are in the first Standardized data on a positive indicator; Indicates the first The candidate power supply schemes are in the first The original evaluation values ​​for each indicator; For all candidate power supply schemes in the first The maximum value of the original data on each positive indicator; For all candidate power supply schemes in the first The minimum value of the original data on a positive indicator; For the first The candidate power supply schemes are in the first Standardized data on a reverse indicator.

[0029] Furthermore, referring to Figure 4, in another embodiment of step S3, the weights of the multi-dimensional evaluation indicators are calculated using the AHP method, including the following specific steps: Step S32a, constructing the judgment matrix. ;in, For the judgment matrix; The number of multi-dimensional evaluation indicators used to evaluate candidate power supply schemes; To determine the matrix The Middle line, number The elements of the column are used to represent the first... The evaluation indicator is relative to the first The importance of each evaluation indicator. In one embodiment of step S32a, a judgment matrix is ​​constructed. The specific steps include the following: Step S321a: Based on the multi-dimensional evaluation index system, construct a judgment matrix. The expression of the judgment matrix is ​​as follows: Step S322a: Calculate the consistency index and consistency ratio respectively; wherein, the expression for the consistency index is: The expression for the consistency ratio is: , Consistency indicators; Consistency ratio; For random consistency index; Step S323a, judgment The relationship with the threshold value, when If the value is less than the threshold, the judgment matrix passes the consistency check; if... When the value is greater than or equal to the threshold, the judgment matrix is ​​adjusted, and the consistency index and consistency ratio are calculated again. The relationship between the value and the threshold is determined until the judgment matrix meets the consistency requirement. In one specific embodiment, the threshold is 0.1, when... When the matrix passes the consistency test, it is determined that the matrix passes the consistency test. The judgment matrix should be adjusted and the above calculation should be repeated until the consistency requirement is met.

[0030] To enhance the influence of different region categories on the indicator weights, step S323a also proposes a category bias function: ,in, This is the weighting adjustment coefficient; the recommended value range is [value range missing]. ; The class bias kernel function is defined as follows: Based on the four types of regions in step S22, , (For categories with large loads) the power supply capacity reliability index category bias kernel function can be set to 1; , (For the "Resource Excellence" category), the category bias kernel function for the economic resource matching degree index can be set to 1. Then, the weights are adjusted using the following formula: The final weights are normalized according to the following expression: .

[0031] Step S32b: Solve the principal eigenvalue problem using the eigenvalue method to obtain the normalized principal eigenvectors; the expression for the normalized principal eigenvectors is: The expression for the principal eigenvalue problem is: , To determine the matrix The largest eigenvalue; To determine the matrix correspond The main eigenvectors.

[0032] Step S32c: Based on the unitized principal eigenvector, obtain the weights of the multi-dimensional evaluation indicators.

[0033] Furthermore, referring to Figure 5, in another embodiment of step S3, the comprehensive score of the candidate power supply scheme is calculated based on the TOPSIS algorithm, including the following specific steps: Step S33a, constructing a weighted normalization matrix, which includes a forward matrix and an inverse matrix. The expression for the forward matrix is... The expression for the inverse matrix is: ,in, It is a positive matrix; For the first The final weight of each evaluation indicator; For the first The candidate power supply schemes are in the first Standardized data on a positive indicator; It is an inverse matrix; For the first The candidate power supply schemes are in the first Standardized data on each inverse index; Step S33b: Calculate the positive and negative ideal solutions for the candidate power supply schemes respectively; wherein, the expression for the positive ideal solution is: The expression for the negative ideal solution is: In the formula, The ideal distance; For the positive ideal solution, for The maximum value in the weighted matrix; Negative ideal distance; For a negative ideal solution, for Find the minimum value in the weighting matrix; Step S33c: Calculate the comprehensive score of the candidate power supply schemes; The expression for the comprehensive score is: In the formula, The final score is calculated based on the overall score.

[0034] Step S4: Based on multi-dimensional evaluation indicators and comprehensive scores, construct a cost-based multiple linear regression model to obtain the cost fitting curve of the candidate power supply scheme. Based on the cost fitting curve, obtain the judgment region of the candidate power supply scheme. The judgment region includes the main grid extension economic boundary distance, the microgrid applicable load boundary, and the applicable range of the hybrid scheme. Please refer to Figure 6. One embodiment of step S4 includes the following specific steps: Step S41: Construct sample data, which includes construction costs, operation and maintenance costs, and key characteristic parameters of regional characteristic variables, denoted as: In the formula, Distance from the main network, For regional load scale, For renewable energy resource levels, For terrain complexity, For regional category labels, To achieve a comprehensive score; in step S42, based on the sample data, a multiple linear regression method is used to fit the main grid cost function curve and the microgrid cost function curve, respectively. To improve prediction, the four types of regions in step S22 ( , , and Transform the variable into a dummy variable for each category. Construct an indicator function, the expression of which is: In the formula, The regions obtained from clustering (4 categories in total, k=1,2,3,4); For category The indicator function, the first The area belongs to hour, Take 1, otherwise Take 0.

[0035] Adding this dummy variable to the model yields the following expression for the main network cost function curve: In the formula, For the first The cost of the regional mainnet; This is a constant term in the mainnet cost model; In the mainnet cost The corresponding cost coefficient; In the mainnet cost The corresponding cost coefficient; In the mainnet cost The corresponding cost coefficient; In the mainnet cost The corresponding cost coefficient; In the mainnet cost The corresponding cost coefficient.

[0036] The expression for the microgrid cost function curve is: In the formula, For the first The cost of microgrids in the region; This is a constant term in the microgrid cost model; For microgrid costs The corresponding cost coefficient; For microgrid costs The corresponding cost coefficient; For microgrid costs The corresponding cost coefficient; For microgrid costs The corresponding cost coefficient; For microgrid costs The corresponding cost coefficient.

[0037] Step S43: Based on the intersection of the main grid cost function curve and the microgrid cost function curve, obtain the boundary value of the main grid extension economic distance. The intersection of the main grid cost function curve and the microgrid cost function curve satisfies the following relationship: In the formula, Extend the economic distance boundary value of the main network.

[0038] get The expression is: ,when Microgrid solutions are more economical; when Mainnet extension is more economical.

[0039] Step S44: Based on the economic distance boundary value of the main grid extension, the determination area of ​​the candidate power supply scheme is obtained. Specifically, based on the cost function curve of step S41, the main grid-microgrid economic boundary is calculated for each area, and combined with the comprehensive score of step S3 and the resource type area of ​​step S2, a three-segment determination area is obtained: Main grid extension area: low and Microgrid area: high Mixed power supply area: And .

[0040] in, Represents an uncertainty buffer. The range of values ​​is: .

[0041] Step S5: Based on the comprehensive score and the judgment area, obtain the optimal power supply scheme for the region. In this step, based on the comprehensive score and the judgment area, quantitative evaluation and regional matching are performed on each candidate power supply scheme in the plateau area. The comprehensive score and the judgment area are overlaid and analyzed to select the scheme with the highest comprehensive score in the region that meets the judgment area requirements, thus forming the optimal power supply scheme for the region.

[0042] This application aims to protect a method for selecting power supply schemes for high-altitude areas. The technical solution of this application establishes a four-layer discrimination system, including rejection conditions, cluster analysis, multi-dimensional comprehensive evaluation, cost regression model and economic boundary determination. This enables the scientific, quantitative and interpretable selection of power supply schemes, ensuring that the selected power supply schemes have better adaptability and feasibility in terms of technology, economy and reliability. This effectively reduces construction costs, improves the reliability and sustainability of power supply schemes, and provides a scientific decision-making basis for power grid construction in high-altitude areas.

[0043] It should be understood that the specific embodiments described above are merely illustrative or explanatory of the principles of this application and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of this application should be included within the protection scope of this application. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

Claims

1. A method for selecting a power grid scheme for ensuring power supply in plateau areas, characterized in that, include: A veto indicator system is constructed, which includes multiple veto indicators. Based on these multiple veto indicators, the construction conditions in the region are initially screened to obtain a preliminary power supply scheme that meets the construction conditions. Based on the preliminary power supply scheme, the region is clustered according to load characteristics, resource conditions and geographical conditions to obtain multiple resource type regions; Based on the multiple resource type zones, a multi-dimensional evaluation index system for candidate power supply schemes is constructed. The weights of the multi-dimensional evaluation indexes are calculated using the AHP method, and the comprehensive score of the candidate power supply schemes is calculated based on the TOPSIS algorithm. Based on the multi-dimensional evaluation indicators and the comprehensive score, a cost multiple linear regression model is constructed to obtain the cost fitting curve of the candidate power supply scheme. Based on the cost fitting curve, the judgment region of the candidate power supply scheme is obtained. Based on the comprehensive score and the determination area, the optimal power supply scheme for the area is obtained.

2. The method for selecting a power grid scheme for high-altitude areas according to claim 1, characterized in that, The expression for the veto index system is: In the formula, Indicates the first One veto indicator in The construction conditions are met; Indicates the first One veto indicator in The construction conditions are not met; Any region within the set of regions; the set of regions is ； The veto index vector is ， 。 3. The method for selecting a power grid scheme for high-altitude areas according to claim 1, characterized in that, The region is clustered according to natural conditions and load characteristics to obtain multiple resource type regions, including: constructing clustering feature vectors based on the region's load characteristics, resource conditions, and geographical conditions. ;in, This represents the maximum load, used to measure peak power supply capacity demand. This is the average load value, used to measure average power demand. This is the daily load fluctuation coefficient, used to measure the difference between peak and off-peak loads; Light resource level, used to measure the normalized annual average radiation; Wind resource level is used to measure the normalization of annual average wind speed or wind power density. Distance to mainnet nodes; For terrain complexity; The altitude is used to determine equipment efficiency and environmental adaptability; the clustering feature vector is preprocessed using Z-score to obtain a standardized clustering feature vector; based on the standardized clustering feature vector, the Ward minimum variance clustering method is used to divide the region into different resource type areas.

4. The method for selecting a power grid scheme for high-altitude areas according to claim 1, characterized in that, Construct a multi-dimensional evaluation index system for candidate power supply schemes, including: constructing a multi-dimensional evaluation index matrix for candidate power supply schemes. ;in, Indicates the first The candidate power supply schemes are in the first The original evaluation values ​​for each indicator; This indicates the number of candidate power supply schemes participating in the comprehensive comparison; This indicates the number of multi-dimensional evaluation indicators used to evaluate the candidate power supply schemes; the positive and negative indicators in the multi-dimensional candidate power supply scheme indicator matrix are standardized respectively.

5. The method for selecting a power grid scheme for high-altitude areas according to claim 4, characterized in that, The standardized expression for the positive index is: The standardized expression for the reverse indicator is: In the formula, For the first The candidate power supply schemes are in the first Standardized data on a positive indicator; Indicates the first The candidate power supply schemes are in the first The original evaluation values ​​for each indicator; For all candidate power supply schemes in the first The maximum value of the original data on each positive indicator; For all candidate power supply schemes in the first The minimum value of the original data on a positive indicator; For the first The candidate power supply schemes are in the first Standardized data on a reverse indicator.

6. The method for selecting a power grid scheme for high-altitude areas according to claim 5, characterized in that, The Analytic Hierarchy Process (AHP) is used to calculate the weights of multi-dimensional evaluation indicators, including: constructing a judgment matrix. ;in, For the judgment matrix; The number of multi-dimensional evaluation indicators used to evaluate the candidate power supply schemes; To determine the matrix The Middle line, number The elements of the column are used to represent the first... The evaluation indicator is relative to the first The importance of each evaluation indicator is assessed; the principal eigenvalue problem is solved using the eigenvalue method to obtain the normalized principal eigenvector; the expression for the normalized principal eigenvector is: The expression for the principal eigenvalue problem is: ， To determine the matrix The largest eigenvalue; To determine the matrix correspond The principal eigenvectors are obtained; based on the normalized principal eigenvectors, the weights of the multi-dimensional evaluation indicators are obtained.

7. The method for selecting a power grid scheme for high-altitude areas according to claim 6, characterized in that, Construct the judgment matrix This includes: constructing a judgment matrix based on the multi-dimensional evaluation index system, wherein the expression of the judgment matrix is ​​as follows: ; calculate the consistency index and consistency ratio respectively; wherein, the expression for the consistency index is: The expression for the consistency ratio is: ， Consistency indicators; The consistency ratio; As a random consistency indicator; judgment The relationship with the threshold value, when When the value is less than the threshold, the judgment matrix passes the consistency check; if When the value is greater than or equal to the threshold, the judgment matrix is ​​adjusted, and the consistency index and consistency ratio are calculated again, and a judgment is made. The relationship between the value of the threshold and the judgment matrix is ​​maintained until the judgment matrix meets the consistency requirements.

8. The method for selecting a power grid scheme for high-altitude areas according to claim 7, characterized in that, The comprehensive score of the candidate power supply scheme is calculated based on the TOPSIS algorithm, including: constructing a weighted normalization matrix, which includes a forward matrix and an inverse matrix; the expression of the forward matrix is ​​as follows: The expression for the inverse matrix is: ,in, It is a positive matrix; For the first The final weight of each evaluation indicator; For the first The candidate power supply schemes are in the first Standardized data on a positive indicator; It is an inverse matrix; For the first The candidate power supply schemes are in the first Standardized data on each inverse index; calculate the positive and negative ideal solutions of the candidate power supply schemes respectively; wherein, the expression for the positive ideal solution is: The expression for the negative ideal solution is: In the formula, The ideal distance; For the positive ideal solution, for The maximum value in the weighted matrix; Negative ideal distance; For a negative ideal solution, for Find the minimum value in the weighting matrix; calculate the comprehensive score of the candidate power supply scheme; the expression for the comprehensive score is: In the formula, The overall score is calculated based on the total score.

9. The method for selecting a power grid scheme for high-altitude areas according to claim 1, characterized in that, A cost-based multiple linear regression model is constructed to obtain the cost fitting curve of the candidate power supply scheme. Based on the cost fitting curve, the determination region of the candidate power supply scheme is obtained, including: constructing sample data, which includes construction cost, operation and maintenance cost, and key feature parameters of regional characteristic variables, denoted as: In the formula, Distance from the main network, For regional load scale, For renewable energy resource levels, For terrain complexity, For regional category labels, To obtain a comprehensive score, based on the sample data, a multiple linear regression method is used to fit the main grid cost function curve and the microgrid cost function curve, respectively. Based on the intersection of the main grid cost function curve and the microgrid cost function curve, the boundary value of the main grid extension economic distance is obtained. Based on the boundary value of the main grid extension economic distance, the judgment area of ​​the candidate power supply scheme is obtained.

10. The method for selecting a power grid scheme for high-altitude areas according to any one of claims 1 to 9, characterized in that, The multiple veto indicators include high-risk geological disaster indicators, accessibility indicators, altitude restriction indicators, key environmental and ecological indicators, extreme weather indicators, and policy restriction indicators; and / or, the candidate power supply schemes include main grid extension schemes, microgrid schemes, and hybrid schemes; and / or, the multi-dimensional evaluation indicators include technical feasibility indicators, economic indicators, operational adaptability indicators, and safety and reliability indicators; and / or, the judgment area includes the main grid extension economic boundary distance, the microgrid applicable load boundary, and the applicable range of the hybrid scheme.

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