AI-based energy supply and demand accurate matching system and method in virtual power plant

Abstract

The invention discloses an AI-based energy supply and demand accurate matching system and method in a virtual power plant, and relates to the technical field of power planning, and the system comprises a power supply station which is used for supplying power; the equipment access module is used for accessing the electric equipment into the virtual power plant system and acquiring an operation log of the electric equipment; the station analysis module is used for acquiring the operation data of the power supply station and analyzing the power supply state of the power supply station based on the operation data of the power supply station; the power demand analysis module is used for predicting future demand data of various types of electric equipment; and the electric power automatic matching supply module is used for automatically carrying out electric power matching supply based on the demand data of various kinds of electric power equipment and the electric power supply state of the electric power supply station. The system has the advantages that full-chain management of power demand-supply is integrated, and comprehensive monitoring and optimal distribution of power from demand to final supply are realized through intelligent and automatic means.

Description

Technical Field

[0001] The present invention relates to the technical field of power planning, and in particular to an AI-based precise energy supply and demand matching system and method in a virtual power plant. Background Art

[0002] A virtual power plant is a power coordination and management system that uses advanced information and communication technologies and software systems to aggregate and coordinate distributed energy resources (DER) to participate in the electricity market and power grid operation as a special power plant. It achieves autonomous coordination and optimization control by aggregating adjustable loads, energy storage equipment, power supply side and other resources in different spaces.

[0003] The existing virtual power plant system, due to the lack of reasonable and effective comprehensive analysis of both electricity demand and supply, is difficult to achieve overall planning for power supply and cannot allocate the most optimized power supply site for power-consuming equipment, which can easily lead to irrational use of power supply resources and affect the efficiency of automated power supply. Summary of the invention

[0004] In order to solve the above technical problems, a system and method for accurately matching energy supply and demand based on AI in a virtual power plant are provided. This technical solution solves the above problems.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: An AI-based precise energy supply and demand matching system in a virtual power plant, comprising: A power supply site, wherein the power supply site is used to supply power; A device access module, which is used to connect power-consuming devices to the virtual power plant system and collect operation logs of power-consuming devices; a site analysis module, the site analysis module being electrically connected to the power supply site, the site analysis module being used to obtain operation data of the power supply site and to analyze the power supply status of the power supply site based on the operation data of the power supply site; An electric power demand analysis module, which is used to analyze and predict future demand data of various electric power equipment based on the operation logs of the electric power equipment; An electric power automation matching and supply module is electrically connected to the site analysis module and the electric power demand analysis module. The electric power automation matching and supply module is used to determine the corresponding power supply site for each electric device based on the power demand data of each type of electric device and the power supply status of the power supply site, and automatically match and supply electricity.

[0006] Preferably, the site analysis module includes: A data collection unit, the data collection unit is used to collect operation data of the power supply site, the operation data of the power supply site includes the working time of the power supply site, the average workload and the supply voltage level; A specificity analysis unit, the specificity analysis unit is used to determine a specificity index of each power supply site based on the supply voltage level of the power supply site; A risk analysis unit is used to analyze the failure risk value of the power supply site based on the working hours and average workload of the power supply site.

[0007] Preferably, the power automation matching and supply module specifically includes: A plan generating unit, the plan generating unit being used to generate at least one power supply allocation plan based on future demand data of each type of power equipment and power supply efficiency of each power supply site; A scheme evaluation unit, the scheme evaluation unit is used to evaluate and select the optimal power supply allocation scheme; A scheme execution unit is used to automatically match and supply electricity according to the power efficiency supplied by each power supply site in the optimal power supply allocation scheme.

[0008] Furthermore, a method for accurately matching energy supply and demand based on AI in a virtual power plant is proposed, which is applicable to the energy supply and demand accurately matching system based on AI in the virtual power plant as described above, including: Collecting operation data of power supply sites; Determine the specific indicators of each power supply site based on the supply voltage level of the power supply site; Determine the risk index of the power supply site based on the working hours and average workload of the power supply site; Collect historical demand data of various types of electrical equipment; Based on the historical demand data of various types of power equipment, analyze and predict the future demand data of various types of power equipment; generating at least one power supply allocation plan based on the future demand data of each type of power equipment and the power supply efficiency of each power supply site; Evaluate and select the optimal power supply allocation plan; The power supply is automatically matched according to the power efficiency supplied by each power supply site in the optimal power supply allocation plan.

[0009] Optionally, the determining of the specificity index of each power supply site based on the supply voltage level of the power supply site specifically includes: Based on the operation log of the power-consuming equipment, determine the required voltage level of the power-consuming equipment connected to the virtual power plant system, and record it as the matching voltage level of the power-consuming equipment; The matching voltage levels of all electrical equipment are combined to obtain the total matching voltage level set A of the virtual power plant system. ,in, is the e-th matching voltage level of the virtual power plant system, is the total number of matching voltage levels of the virtual power plant system; will be able to supply The power supply station is Matching supply sites; Sure All matching supply sites of The total number of all matching supply sites is recorded as Specific supply indicators; Determine all supply voltage levels that each power supply site can supply, recorded as station voltage level; Filter out the minimum value of the specific supply index of the power station voltage level corresponding to the power supply site, and record it as the specific power supply quantity of the power supply site; Based on the specific power supply quantity of the power supply site, the specificity index of each power supply site is calculated by a normalized formula; The normalization formula is specifically: In the formula, is the specificity index of the ith power supply site, is the maximum value of the benchmark specificity index for all power supply sites, is the minimum value of the benchmark specificity indicator for all electricity supply sites, is the benchmark specificity index of the ith power supply site, and n is the total number of power supply sites.

[0010] Optionally, the determining of the risk index of the power supply site based on the working hours and average workload of the power supply site specifically includes: Taking the working hours and average workload of power supply stations as independent variables, an operation risk regression model of power supply stations is established based on the Logistic regression model. Substituting the actual values ​​of the working hours and average workload of the power supply station into the operation risk regression model of the power supply station, the risk index of the power supply station is obtained; The formula for running the risk regression model is: In the formula, Risk indicators for power supply sites, Working hours for power supply stations, is the average workload of the power supply site, are model coefficients.

[0011] Optionally, the analyzing and predicting the future demand data of various types of power equipment based on the historical demand data of various types of power equipment specifically includes: Based on the historical demand data of various types of electrical equipment, linear or nonlinear regression fitting is used to predict the future demand data of electrical equipment.

[0012] Optionally, generating at least one power supply allocation scheme based on the future demand data of each type of power equipment and the power supply efficiency of each power supply site specifically includes: Determine the matching voltage level of each electrical device, and determine the future demand data of each matching voltage level based on the matching voltage level of each electrical device and the future demand data of each device; Adding order to all matching voltage levels in ascending order of specific supply indicators; According to the additional order, a plurality of distribution power supply sites are selected for each matching voltage level in turn to obtain a power supply allocation scheme, wherein the distribution power supply sites follow a singleness criterion, wherein a power supply site cannot be used as a distribution power supply site for two matching voltage levels at the same time; The generation of the power supply allocation plan is repeated until no new power supply allocation plan can be generated.

[0013] Optionally, the selecting a plurality of distribution power supply sites for each matching voltage level specifically includes: Select at least one power supply site from the matching supply sites of the matching voltage level so that the sum of the total power supply capacity of all power supply sites is greater than or equal to the future demand data of the matching voltage level, and use the selected power supply site as the allocated power supply site of the matching voltage level.

[0014] Optionally, the evaluating and selecting the optimal power supply allocation scheme specifically includes: Based on the specific indicators of each power supply site and the risk indicators of each power supply site, an allocation scheme evaluation model is constructed; Based on the allocation scheme evaluation model, the reasonable index of each power supply allocation scheme is evaluated, and the power supply allocation scheme corresponding to the maximum value of the reasonable index is selected as the optimal power supply allocation scheme; The allocation scheme evaluation model is specifically as follows: In the formula, To provide a reasonable indicator for the power supply allocation plan, is the risk index of the ith power supply site, is the total power supply capacity of the i-th power supply station, The power supply from the ith power supply station to the eth power device corresponding to its matching voltage level, is the total number of all power-consuming devices that can be supplied by the i-th power supply site.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention comprehensively analyzes the power demand-supply dual-end system, comprehensively evaluates the supply status of the power supply site based on the power supply site's ability to supply specific types of power and the operating risks of the power supply site, and predicts changes in future power demand data based on historical demand data of power equipment and current change trends. Based on the supply status of the power supply site combined with future power demand data, an allocation scheme evaluation model is designed based on the concept of load balancing, which fully considers the specific indicators, supply capacity and load balancing of the power supply site, realizes the overall planning of power supply, allocates the most optimized power supply site for power in advance, makes full use of power supply resources, and ensures the efficiency of automated power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is the block diagram of the AI-based energy supply and demand precise matching system in the virtual power plant proposed in this solution; Figure 2 Flowchart of the method for determining the specificity indicators of power supply sites proposed for this scheme; Figure 3 Flowchart of the method for determining risk indicators for power supply sites proposed for this scheme; Figure 4 A flow chart of the method for generating at least one power supply allocation scheme proposed in the present scheme; Figure 5 Flowchart of the method for evaluating and selecting the optimal power supply allocation plan proposed for this solution. DETAILED DESCRIPTION

[0017] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0018] Reference Figure 1 As shown, an AI-based energy supply and demand precise matching system in a virtual power plant includes: A power supply site, wherein the power supply site is used to supply power; A device access module, which is used to connect power-consuming devices to the virtual power plant system and collect operation logs of power-consuming devices; a site analysis module, the site analysis module being electrically connected to the power supply site, the site analysis module being used to obtain operation data of the power supply site and to analyze the power supply status of the power supply site based on the operation data of the power supply site; An electric power demand analysis module, which is used to analyze and predict future demand data of various electric power equipment based on the operation logs of the electric power equipment; An electric power automation matching and supply module is electrically connected to the site analysis module and the electric power demand analysis module. The electric power automation matching and supply module is used to determine the corresponding power supply site for each electric device based on the power demand data of each type of electric device and the power supply status of the power supply site, and automatically match and supply electricity.

[0019] Preferably, the site analysis module includes: A data collection unit, the data collection unit is used to collect operation data of the power supply site, the operation data of the power supply site includes the working time of the power supply site, the average workload and the supply voltage level; A specificity analysis unit, the specificity analysis unit is used to determine a specificity index of each power supply site based on the supply voltage level of the power supply site; A risk analysis unit is used to analyze the failure risk value of the power supply site based on the working hours and average workload of the power supply site.

[0020] Preferably, the power automation matching and supply module specifically includes: A plan generating unit, the plan generating unit being used to generate at least one power supply allocation plan based on future demand data of each type of power equipment and power supply efficiency of each power supply site; A scheme evaluation unit, the scheme evaluation unit is used to evaluate and select the optimal power supply allocation scheme; A scheme execution unit is used to automatically match and supply electricity according to the power efficiency supplied by each power supply site in the optimal power supply allocation scheme.

[0021] The use process of the AI-based energy supply and demand precise matching system in the above virtual power plant is as follows: Step 1: The data collection unit collects the operation data of the power supply site, and the operation data of the power supply site includes the working time, average workload and supply voltage level of the power supply site; Step 2: The specificity analysis unit determines the specificity index of each power supply site based on the supply voltage level of the power supply site; Step 3: The risk analysis unit determines the risk index of the power supply site based on the working hours and average workload of the power supply site; Step 4: The power demand analysis module analyzes and predicts the future demand data of various power-consuming equipment based on the operation logs of the power-consuming equipment; Step 5: The plan generation unit is used to generate at least one power supply allocation plan based on the future demand data of each type of power equipment and the power supply efficiency of each power supply site; Step 6: The scheme evaluation unit evaluates and selects the optimal power supply allocation scheme; Step 7: The plan execution unit automatically matches the supply of electricity according to the power efficiency supplied by each power supply site in the optimal power supply allocation plan.

[0022] See also Figure 2 As shown, based on the supply voltage level of the power supply site, the specific indicators of each power supply site are determined to include: Based on the operation log of the power-consuming equipment, determine the required voltage level of the power-consuming equipment connected to the virtual power plant system, and record it as the matching voltage level of the power-consuming equipment; The matching voltage levels of all electrical equipment are combined to obtain the total matching voltage level set A of the virtual power plant system. ,in, is the e-th matching voltage level of the virtual power plant system, is the total number of matching voltage levels of the virtual power plant system; will be able to supply The power supply station is Matching supply sites; Sure All matching supply sites of The total number of all matching supply sites is recorded as Specific supply indicators; Determine all the supply voltage levels that each power supply site can supply, recorded as the power station voltage level; Filter out the minimum value of the specific supply index of the power station voltage level corresponding to the power supply site, and record it as the specific power supply quantity of the power supply site; Based on the specific power supply quantity of the power supply site, the specificity index of each power supply site is calculated by a normalized formula; The normalization formula is specifically: In the formula, is the specificity index of the ith power supply site, is the maximum value of the benchmark specificity index for all power supply sites, is the minimum value of the benchmark specificity indicator for all electricity supply sites, is the benchmark specificity index of the ith power supply site, and n is the total number of power supply sites.

[0023] It can be understood that, for a power supply site, the smaller the specific supply index of the supply voltage level it can supply, the more unique the supply voltage level it can supply is relative to other power supply sites, and the stronger its supply specificity for certain specific supply voltage levels. Therefore, when planning power supply, the power allocation to power supply sites with strong specificity should be minimized to ensure that there is sufficient supply capacity to cope with the special supply voltage level supply tasks that may be required in the future. Based on this, this scheme designs the minimum value of the specific supply index of all supply voltage levels that each power supply site can supply as the benchmark specificity index of the power supply site, and adopts the forward normalization supply method to obtain the specificity index of the power supply site. The larger the specificity index of the power supply site, the lower the power level requirement of the power supply site. If there are multiple power supply sites that can supply synchronously, the higher the specificity of the power supply site.

[0024] See also Figure 3 As shown in the figure, based on the working hours and average workload of the power supply site, the risk indicators of the power supply site are determined to include: Taking the working hours and average workload of power supply stations as independent variables, an operation risk regression model of power supply stations is established based on the Logistic regression model. Substituting the actual values ​​of the working hours and average workload of the power supply station into the operation risk regression model of the power supply station, the risk index of the power supply station is obtained; The formula for running the risk regression model is: In the formula, Risk indicators for power supply sites, Working hours for power supply stations, is the average workload of the power supply site, are model coefficients.

[0025] The Logistic regression model is a generalized linear regression analysis model, which is often used in data mining, automatic disease diagnosis, economic forecasting and other fields. The Logistic regression model estimates the probability of an event based on a given independent variable data set. Since the result is a probability, the range of the dependent variable is between 0 and 1. In this embodiment, the working hours and average workload of the power supply site are used as independent variables, and the probability of no failure of the power supply site is used as a risk indicator. The Logistic regression model is established with the risk indicator as the dependent variable, which can accurately predict the probability of no failure of the power supply site based on the two-layer factors of the working hours and average workload of the power supply site.

[0026] Based on the historical demand data of various types of power equipment, the future demand data of various types of power equipment are analyzed and predicted, including: Based on the historical demand data of various types of electrical equipment, linear or nonlinear regression fitting is used to predict the future demand data of electrical equipment.

[0027] Reference Figure 4 As shown, the generating of at least one power supply allocation scheme based on the future demand data of each type of power equipment and the power supply efficiency of each power supply site specifically includes: Determine the matching voltage level of each electrical device, and determine the future demand data of each matching voltage level based on the matching voltage level of each electrical device and the future demand data of each device; Adding order to all matching voltage levels in ascending order of specific supply indicators; According to the additional order, a plurality of distribution power supply sites are selected for each matching voltage level in turn to obtain a power supply allocation scheme, wherein the distribution power supply sites follow a singleness criterion, wherein a power supply site cannot be used as a distribution power supply site for two matching voltage levels at the same time; The generation of the power supply allocation plan is repeated until no new power supply allocation plan can be generated.

[0028] The selecting of a plurality of distribution power supply sites for each matching voltage level specifically includes: Select at least one power supply site from the matching supply sites of the matching voltage level so that the sum of the total power supply capacity of all power supply sites is greater than or equal to the future demand data of the matching voltage level, and use the selected power supply site as the allocated power supply site of the matching voltage level.

[0029] Reference Figure 5 As shown, the evaluation and selection of the optimal power supply allocation scheme specifically includes: Based on the specific indicators of each power supply site and the risk indicators of each power supply site, an allocation scheme evaluation model is constructed; Based on the allocation scheme evaluation model, the reasonable index of each power supply allocation scheme is evaluated, and the power supply allocation scheme corresponding to the maximum value of the reasonable index is selected as the optimal power supply allocation scheme; The allocation scheme evaluation model is specifically as follows: In the formula, To provide a reasonable indicator for the power supply allocation plan, is the risk index of the ith power supply site, is the total power supply capacity of the i-th power supply station, The power supply from the ith power supply station to the eth power device corresponding to its matching voltage level, is the total number of all power-consuming devices that can be supplied by the i-th power supply site.

[0030] Through comprehensive analysis of the power demand-supply dual-end system, based on the power supply site's ability to supply specific types of electricity and the operating risks of the power supply site, the supply status of the power supply site is comprehensively evaluated. At the same time, based on the historical demand data of power equipment and current change trends, changes in future power demand data are predicted. Based on the supply status of the power supply site and the future power demand data, an allocation scheme evaluation model is designed based on the concept of load balancing, which fully considers the specific indicators, supply capacity and load balancing of the power supply site to achieve overall planning of power supply.

[0031] In summary, the advantages of the present invention are: integrating the whole chain management of power demand and supply, and realizing comprehensive monitoring and optimized distribution of power from demand to final supply through intelligent and automated means. Based on the specific supply capacity, operation risk, historical power demand data and future forecast data of power supply sites, the system designs a distribution scheme evaluation model through a load balancing algorithm to ensure timely, efficient and reasonable power supply.

[0032] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. An AI-based energy supply and demand precise matching system in a virtual power plant, characterized in that: include: A power supply site, wherein the power supply site is used to supply power; A device access module, which is used to connect power-consuming devices to the virtual power plant system and collect operation logs of power-consuming devices; a site analysis module, the site analysis module being electrically connected to the power supply site, the site analysis module being used to obtain operation data of the power supply site and to analyze the power supply status of the power supply site based on the operation data of the power supply site; An electric power demand analysis module, which is used to analyze and predict future demand data of various electric power equipment based on the operation logs of the electric power equipment; An electric power automation matching and supply module is electrically connected to the site analysis module and the electric power demand analysis module. The electric power automation matching and supply module is used to determine the corresponding power supply site for each electric device based on the power demand data of each type of electric device and the power supply status of the power supply site, and automatically match and supply electricity.

2. According to claim 1, the AI-based energy supply and demand precise matching system in a virtual power plant is characterized in that: The site analysis module includes: A data collection unit, the data collection unit is used to collect operation data of the power supply site, the operation data of the power supply site includes the working time of the power supply site, the average workload and the supply voltage level; A specificity analysis unit, the specificity analysis unit is used to determine a specificity index of each power supply site based on the supply voltage level of the power supply site; A risk analysis unit is used to analyze the failure risk value of the power supply site based on the working hours and average workload of the power supply site.

3. According to claim 2, the AI-based energy supply and demand precise matching system in a virtual power plant is characterized in that: The power automation matching and supply module specifically includes: A plan generating unit, the plan generating unit being used to generate at least one power supply allocation plan based on future demand data of each type of power equipment and power supply efficiency of each power supply site; A scheme evaluation unit, the scheme evaluation unit is used to evaluate and select the optimal power supply allocation scheme; A scheme execution unit is used to automatically match and supply electricity according to the power efficiency supplied by each power supply site in the optimal power supply allocation scheme.

4. A method for accurately matching energy supply and demand based on AI in a virtual power plant, applicable to the accurate matching system for energy supply and demand based on AI in a virtual power plant as claimed in any one of claims 1 to 3, characterized in that: include: Collecting operation data of power supply sites; Determine the specific indicators of each power supply site based on the supply voltage level of the power supply site; Determine the risk index of the power supply site based on the working hours and average workload of the power supply site; Collect historical demand data of various types of electrical equipment; Based on the historical demand data of various types of power equipment, analyze and predict the future demand data of various types of power equipment; generating at least one power supply allocation plan based on the future demand data of each type of power equipment and the power supply efficiency of each power supply site; Evaluate and select the optimal power supply allocation plan; The power supply is automatically matched according to the power efficiency supplied by each power supply site in the optimal power supply allocation plan.

5. The method for accurately matching energy supply and demand based on AI in a virtual power plant according to claim 4 is characterized in that: The specific index of each power supply site determined based on the supply voltage level of the power supply site specifically includes: Based on the operation log of the power-consuming equipment, determine the required voltage level of the power-consuming equipment connected to the virtual power plant system, and record it as the matching voltage level of the power-consuming equipment; The matching voltage levels of all electrical equipment are combined to obtain the total matching voltage level set A of the virtual power plant system. ,in, is the e-th matching voltage level of the virtual power plant system, is the total number of matching voltage levels of the virtual power plant system; will be able to supply The power supply station is Matching supply sites; Sure All matching supply sites of The total number of all matching supply sites is recorded as Specific supply indicators; Determine all supply voltage levels that each power supply site can supply, recorded as station voltage level; Filter out the minimum value of the specific supply index of the power station voltage level corresponding to the power supply site, and record it as the specific power supply quantity of the power supply site; Based on the specific power supply quantity of the power supply site, the specificity index of each power supply site is calculated by a normalized formula; The normalization formula is specifically: In the formula, is the specificity index of the ith power supply site, is the maximum value of the benchmark specificity index for all power supply sites, is the minimum value of the benchmark specificity indicator for all electricity supply sites, is the benchmark specificity index of the ith power supply site, and n is the total number of power supply sites.

6. The method for accurately matching energy supply and demand based on AI in a virtual power plant according to claim 5, characterized in that: Determining the risk index of the power supply site based on the working hours and average workload of the power supply site specifically includes: Taking the working hours and average workload of power supply stations as independent variables, an operation risk regression model of power supply stations is established based on the Logistic regression model. Substituting the actual values ​​of the working hours and average workload of the power supply station into the operation risk regression model of the power supply station, the risk index of the power supply station is obtained; The formula for running the risk regression model is: In the formula, Risk indicators for power supply sites, Working hours for power supply stations, is the average workload of the power supply site, are model coefficients.

7. The method for accurately matching energy supply and demand based on AI in a virtual power plant according to claim 6, characterized in that: The analysis and prediction of future demand data of various types of power equipment based on historical demand data of various types of power equipment specifically includes: Based on the historical demand data of various types of electrical equipment, linear or nonlinear regression fitting is used to predict the future demand data of electrical equipment.

8. The method for accurately matching energy supply and demand based on AI in a virtual power plant according to claim 7, characterized in that: The generating at least one power supply allocation scheme based on the future demand data of each type of power equipment and the power supply efficiency of each power supply site specifically includes: Determine the matching voltage level of each electrical device, and determine the future demand data of each matching voltage level based on the matching voltage level of each electrical device and the future demand data of each device; Adding order to all matching voltage levels in ascending order of specific supply indicators; According to the additional order, a plurality of distribution power supply sites are selected for each matching voltage level in turn to obtain a power supply allocation scheme, wherein the distribution power supply sites follow a singleness criterion, wherein a power supply site cannot be used as a distribution power supply site for two matching voltage levels at the same time; The generation of the power supply allocation plan is repeated until no new power supply allocation plan can be generated.

9. The method for accurately matching energy supply and demand based on AI in a virtual power plant according to claim 8, characterized in that: The selecting of a plurality of distribution power supply sites for each matching voltage level specifically includes: Select at least one power supply site from the matching supply sites of the matching voltage level so that the sum of the total power supply capacity of all power supply sites is greater than or equal to the future demand data of the matching voltage level, and use the selected power supply site as the allocated power supply site of the matching voltage level.

10. The method for accurately matching energy supply and demand based on AI in a virtual power plant according to claim 9, characterized in that: The evaluation and selection of the optimal power supply allocation scheme specifically includes: Based on the specific indicators of each power supply site and the risk indicators of each power supply site, an allocation scheme evaluation model is constructed; Based on the allocation scheme evaluation model, the reasonable index of each power supply allocation scheme is evaluated, and the power supply allocation scheme corresponding to the maximum value of the reasonable index is selected as the optimal power supply allocation scheme; The allocation scheme evaluation model is specifically as follows: In the formula, To provide a reasonable indicator for the power supply allocation plan, is the risk index of the ith power supply site, is the total power supply capacity of the i-th power supply station, The power supply from the ith power supply station to the eth power device corresponding to its matching voltage level, is the total number of all power-consuming devices that can be supplied by the i-th power supply site.

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