Renewable energy utilization rate comprehensive evaluation method and system based on transformer substation carbon footprint

Abstract

The invention discloses a renewable energy utilization rate comprehensive evaluation method and system based on transformer substation carbon footprint, and relates to the technical field of comprehensive evaluation, and the method comprises the following steps: obtaining and preprocessing energy consumption historical data and carbon emission historical data of a transformer substation; constructing a renewable energy utilization rate fuzzy evaluation model based on the historical energy consumption data and the historical carbon emission data of the transformer substation; obtaining a comprehensive evaluation value of the renewable energy utilization rate of the carbon footprint based on the renewable energy utilization rate fuzzy evaluation model; and comprehensively evaluating the renewable energy utilization rate of the transformer substation carbon footprint based on the comprehensive evaluation value, thereby solving the problem that the traditional evaluation method generally depends on direct energy consumption and carbon emission data and lacks comprehensive consideration on the renewable energy utilization efficiency, so that the accuracy and comprehensiveness of comprehensive evaluation are reduced.

Description

Technical Field

[0001] The present invention relates to the field of comprehensive evaluation technology, and more specifically, to a method and system for comprehensive evaluation of renewable energy utilization based on substation carbon footprint. Background Art

[0002] The renewable energy utilization rate of the substation's carbon footprint refers to the proportion of renewable energy (such as solar energy, wind energy, hydropower, etc.) in the energy used by the substation during its operation, and the contribution of these renewable energy sources to reducing the substation's carbon emissions and environmental impact.

[0003] In recent years, with the rapid development of renewable energy, especially the widespread application of clean energy such as solar energy and wind energy, the study of renewable energy utilization rate of substation carbon footprint is gradually attracting attention. However, traditional carbon footprint assessment usually relies on direct energy consumption and carbon emission data, lacks comprehensive consideration of renewable energy utilization efficiency, and reduces the accuracy and comprehensiveness of comprehensive assessment. In view of the above problems, the present invention proposes a solution. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method and system for comprehensive evaluation of renewable energy utilization based on the carbon footprint of a substation. The method and system for comprehensive evaluation of renewable energy utilization based on the carbon footprint of a substation are used to solve the problem that traditional evaluation methods usually rely on direct energy consumption and carbon emission data, lack comprehensive consideration of renewable energy utilization efficiency, thereby reducing the accuracy and comprehensiveness of the comprehensive evaluation.

[0005] To achieve the above object, the present invention provides the following technical solutions: The invention discloses a comprehensive evaluation method for renewable energy utilization rate based on the carbon footprint of a substation, comprising the following steps: obtaining and preprocessing the historical energy consumption data and carbon emission data of the substation; constructing a fuzzy evaluation model for renewable energy utilization rate based on the historical energy consumption data and carbon emission data of the substation; obtaining a comprehensive evaluation value of renewable energy utilization rate of the carbon footprint based on the fuzzy evaluation model for renewable energy utilization rate; and comprehensively evaluating the renewable energy utilization rate of the carbon footprint of the substation based on the comprehensive evaluation value.

[0006] In a preferred embodiment, a fuzzy evaluation model for renewable energy utilization is constructed based on the energy consumption historical data and carbon emission historical data of the substation, specifically: the renewable energy consumption ratio value, carbon emission intensity value and energy efficiency value are calculated based on the energy consumption historical data and carbon emission historical data of the substation; the renewable energy consumption ratio value, carbon emission intensity value and energy efficiency value are calculated based on the energy consumption historical data and carbon emission historical data of the substation as the renewable energy utilization fuzzy evaluation index of the carbon footprint; the renewable energy utilization fuzzy evaluation index of the carbon footprint is fuzzified to obtain a fuzzy set of renewable energy utilization of the carbon footprint; the renewable energy utilization fuzzy set of the carbon footprint is input into a preset neural network model for training to obtain a fuzzy evaluation model of renewable energy utilization.

[0007] In a preferred embodiment, the fuzzy evaluation index of renewable energy utilization rate based on the carbon footprint is fuzzified to obtain a fuzzy set of renewable energy utilization rate of the carbon footprint, specifically: the fuzzy evaluation index of renewable energy utilization rate of the carbon footprint is input into a preset membership function, and the membership of the fuzzy evaluation index of renewable energy utilization rate of each carbon footprint is calculated; and the fuzzy set of the fuzzy evaluation index of renewable energy utilization rate of each carbon footprint is constructed based on the membership of the fuzzy evaluation index of renewable energy utilization rate of each carbon footprint.

[0008] In a preferred embodiment, the fuzzy set of renewable energy utilization rate of the carbon footprint is input into a preset neural network model for training to obtain a fuzzy evaluation model of renewable energy utilization rate, specifically: the membership degree corresponding to the fuzzy set of renewable energy utilization rate of the carbon footprint is input into the preset neural network model, and the model parameters are initialized for iterative training; the loss function is calculated to perform model verification to obtain a fuzzy evaluation model of renewable energy utilization rate.

[0009] In a preferred embodiment, a comprehensive evaluation value of the renewable energy utilization rate of the carbon footprint is obtained based on the fuzzy evaluation model of renewable energy utilization rate, specifically: the fuzzy evaluation model of renewable energy utilization rate is used to obtain fuzzy set data of the fuzzy evaluation index of renewable energy utilization rate of each carbon footprint; the fuzzy set data is input into the defuzzification formula of the renewable energy utilization evaluation index of the carbon footprint to calculate the comprehensive evaluation value.

[0010] In a preferred embodiment, a comprehensive evaluation is performed on the renewable energy utilization rate of the substation carbon footprint based on the comprehensive evaluation value, specifically: when the comprehensive evaluation value is greater than a preset evaluation threshold, it indicates that the renewable energy utilization rate of the substation carbon footprint is normal; when the comprehensive evaluation value is less than or greater than the preset evaluation threshold, an early warning is issued to increase the proportion of renewable energy use and reduce reliance on non-renewable energy.

[0011] Technical effects and advantages of the comprehensive evaluation method and system for renewable energy utilization based on substation carbon footprint of the present invention: 1. The present invention can provide a comprehensive and accurate carbon footprint assessment method by constructing a fuzzy assessment model of renewable energy utilization rate based on the historical data of energy consumption and carbon emissions of the substation. First, by obtaining the historical data of energy consumption and carbon emissions of the substation, the comprehensiveness and authenticity of the data are ensured. These data, as the basis of the assessment model, can accurately reflect the energy use and carbon emissions of the substation; secondly, the fuzzy assessment model can effectively handle the uncertainty and ambiguity in the data, especially when the volatility of energy consumption and carbon emissions is large, fuzzy logic can provide more flexible and adaptive analysis results; by constructing a fuzzy assessment model of renewable energy utilization rate, the actual utilization level of renewable energy in the substation can be effectively evaluated, thereby providing data support for subsequent energy optimization and carbon emission reduction. This model not only takes into account the accuracy of historical data, but also comprehensively evaluates energy utilization rate through fuzzy processing methods, overcoming the data uncertainty and external factors that may be encountered in traditional assessment methods; finally, through the comprehensive assessment value, it can provide a scientific basis for energy management and carbon emission control of substations, and promote the application of renewable energy and the continuous reduction of carbon emissions. This method is highly practical and flexible, and can provide important reference for energy utilization and environmental protection policy formulation in substations, helping to achieve greener and lower-carbon electricity production and consumption. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 The figure is a flow chart of the comprehensive evaluation method of renewable energy utilization rate based on the carbon footprint of a substation according to the present invention.

[0012] Figure 2 It is a structural schematic diagram of the comprehensive evaluation system for renewable energy utilization rate based on the carbon footprint of a substation according to the present invention. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] Embodiment 1, Figure 1 The present invention provides a comprehensive evaluation method for renewable energy utilization based on the carbon footprint of a substation, comprising the following steps: S1, obtain the energy consumption historical data and carbon emission historical data of the substation and perform preprocessing; In this example, the energy consumption history data and carbon emission history data of the substation are obtained, specifically: Energy consumption history data includes the following data: Power consumption data: including total power consumption of substations and individual power consumption records of various equipment (such as transformers, distribution equipment, etc.); Fuel consumption data: If there are backup generators or other equipment that relies on fossil fuels in the substation, the fuel consumption needs to be recorded; Renewable energy consumption data: If the substation uses solar, wind or other renewable energy sources, the production and consumption of these energy sources also need to be recorded.

[0015] Furthermore, the historical carbon emission data includes the following data: Carbon emissions data: Carbon emissions calculated based on energy consumption, usually determined by using standard emission factors for fuel type and energy consumption; Carbon emission factors: Emission factors for different energy types (e.g., the amount of CO2 produced per kilowatt-hour of electricity consumed). These factors vary depending on the source of the energy and how it is used, and may need to be selected based on actual circumstances.

[0016] It should be noted that obtaining and preprocessing the energy consumption and carbon emission historical data of the substation has several significant advantages. First, by comprehensively collecting the energy consumption and carbon emission data of the substation, including the use of electricity, fuel and renewable energy, the energy utilization efficiency and environmental protection level of the substation can be fully reflected. This provides a scientific basis for subsequent evaluation and optimization.

[0017] Secondly, the data preprocessing process ensures the accuracy and consistency of the data. Through cleaning, normalization and time series alignment, noise and inconsistency in the data can be eliminated, thereby improving the reliability of the model. The processing of missing values ​​and outliers can effectively avoid bias in the analysis results and ensure the accuracy of the evaluation results. In addition, by calculating carbon emissions and standardizing energy consumption data, different types of energy consumption and carbon emissions can be uniformly compared and analyzed.

[0018] Furthermore, the use of these pre-processed data can provide important support for the carbon footprint assessment, energy optimization and policy formulation of substations. For example, by evaluating the utilization of renewable energy, substations can find the potential to increase the proportion of renewable energy use and thus reduce carbon emissions. This not only helps substations to achieve economic energy cost optimization, but also promotes the realization of green and low-carbon goals, in line with the requirements of sustainable development.

[0019] S2, constructing a fuzzy evaluation model for renewable energy utilization based on the energy consumption historical data and carbon emission historical data of the substation; In this example, a fuzzy evaluation model for renewable energy utilization is constructed based on the energy consumption history data and carbon emission history data of the substation, specifically: Calculate the renewable energy consumption ratio value, carbon emission intensity value and energy efficiency value based on the energy consumption history data and carbon emission history data of the substation; The renewable energy consumption ratio value and carbon emission intensity are calculated based on the energy consumption history data and carbon emission history data of the substation, and the energy efficiency value and energy efficiency are used as the renewable energy utilization rate fuzzy evaluation index of the carbon footprint; Fuzzy processing is performed on the fuzzy evaluation index of renewable energy utilization rate based on the carbon footprint to obtain a fuzzy set of renewable energy utilization rate of the carbon footprint; The renewable energy utilization rate fuzzy set of the carbon footprint is input into a preset neural network model for training to obtain a renewable energy utilization rate fuzzy evaluation model.

[0020] In this example, the fuzzy evaluation index of renewable energy utilization rate based on the carbon footprint is fuzzified to obtain the fuzzy set of renewable energy utilization rate of carbon footprint, which is specifically: Inputting the renewable energy utilization rate fuzzy evaluation index of the carbon footprint into a preset membership function, and calculating the membership of the renewable energy utilization rate fuzzy evaluation index of each carbon footprint; A fuzzy set of the fuzzy evaluation index of renewable energy utilization rate of each carbon footprint is constructed based on the membership degree of the fuzzy evaluation index of renewable energy utilization rate of each carbon footprint.

[0021] It should be noted that fuzzy sets have many advantages. First, fuzzification can effectively deal with the uncertainty and complexity in carbon footprint assessment. Since carbon footprint and renewable energy utilization are often affected by multiple factors, traditional quantitative assessment methods may find it difficult to fully consider the complex relationship between these factors. By fuzzifying specific indicators through membership functions, the fuzziness and uncertainty in the data can be more flexibly reflected, thereby improving the accuracy and credibility of the assessment results.

[0022] Secondly, the introduction of the membership function enables the renewable energy utilization rate of each carbon footprint to be processed within a unified framework. This not only helps to eliminate the differences between different data dimensions, but also ensures that the evaluation process remains consistent and fair under different energy consumption and carbon emission backgrounds. By calculating the membership function, the different levels of renewable energy utilization of carbon footprints can be described in more detail, providing a more delicate analysis perspective.

[0023] Furthermore, the construction of fuzzy sets further enhances the comprehensiveness and adaptability of the evaluation results. Under the framework of fuzzy sets, the renewable energy utilization rate of each carbon footprint can be evaluated from multiple levels and dimensions, and the membership values ​​of different situations can be integrated into a more comprehensive evaluation result. This provides decision makers with more intuitive and comprehensive data support, helping them make more accurate and scientific decisions in carbon emission control, energy management and the formulation of sustainable development strategies.

[0024] In summary, the carbon footprint renewable energy utilization rate assessment method based on fuzzy processing can not only effectively deal with uncertainty, but also provide strong support for achieving low-carbon goals and optimizing energy structure by enhancing the flexibility of data processing and the accuracy of assessment.

[0025] In this example, the renewable energy consumption ratio value, carbon emission intensity value and energy efficiency value are calculated based on the energy consumption history data and carbon emission history data of the substation. The specific calculation formula is as follows:

[0026] in, is the renewable energy consumption ratio, is the total amount of renewable energy used by the substation during a specific period of time, is the total energy consumption of the substation in a specific period of time, including the consumption of renewable energy and non-renewable energy. , and is the weight coefficient, is the power output of the i-th renewable energy source, is the power output of the i-th non-renewable energy source, is the carbon emission intensity value, is the carbon emission of the i-th energy source, is the power output of the jth non-renewable energy source, is the number of non-renewable energy sources, is the number of renewable energy types, is the energy efficiency value, is the current carbon emission intensity of the substation, is the maximum allowable carbon emission intensity.

[0027] In this example, the renewable energy utilization rate fuzzy set of the carbon footprint is input into a preset neural network model for training to obtain a renewable energy utilization rate fuzzy evaluation model, specifically: Inputting the membership degree corresponding to the renewable energy utilization rate fuzzy set of the carbon footprint into a preset neural network model, initializing the model parameters for iterative training; The loss function is calculated to verify the model and obtain the fuzzy evaluation model of renewable energy utilization.

[0028] It should be noted that inputting the fuzzy set of renewable energy utilization of carbon footprint into the neural network model for training can give full play to the advantages of neural networks in dealing with complex and nonlinear problems. First of all, neural networks have strong learning capabilities and can automatically identify complex patterns and relationships from a large amount of historical data. In the assessment of carbon footprint, the interaction between energy consumption and carbon emissions and the utilization of renewable energy are usually nonlinear and multidimensional, and traditional methods may find it difficult to accurately capture this complexity. Through the training of neural network models, these complex nonlinear relationships can be effectively identified and fitted, improving the accuracy and robustness of the assessment.

[0029] Secondly, using fuzzy sets and membership inputs to neural networks can fully utilize the advantages of fuzzy logic in dealing with uncertainty and ambiguity. In practical applications, the assessment of carbon footprint and renewable energy utilization often involves data uncertainty or ambiguity. Fuzzy sets provide a flexible way to represent this uncertain information. By learning membership values, neural networks can extract effective features from fuzzy information, ensuring that the model can better adapt to different situations.

[0030] In addition, through iterative training and loss function verification, the neural network model can be continuously optimized during the training process and the evaluation accuracy can be gradually improved. The calculation and verification process of the loss function can not only help adjust the model parameters, but also effectively avoid overfitting and underfitting problems, ensuring that the model has good generalization ability, so that it is suitable for the carbon footprint and renewable energy utilization rate evaluation of different substations.

[0031] In this example, the renewable energy utilization rate fuzzy evaluation index of the carbon footprint is input into a preset membership function to calculate the membership of the renewable energy utilization rate fuzzy evaluation index of each carbon footprint. The specific calculation formula is as follows:

[0032] in, is the membership degree of the fuzzy set corresponding to the fuzzy evaluation index of renewable energy utilization rate of carbon footprint, Fuzzy evaluation index of renewable energy utilization rate based on carbon footprint, is the mean of the fuzzy set corresponding to the fuzzy evaluation index of renewable energy utilization rate of carbon footprint, is the standard deviation of the fuzzy set corresponding to the fuzzy evaluation index of renewable energy utilization rate of carbon footprint, and e is the exponential function.

[0033] S3, obtaining a comprehensive evaluation value of renewable energy utilization rate of carbon footprint based on the renewable energy utilization rate fuzzy evaluation model; In this example, the comprehensive evaluation value of the renewable energy utilization rate of the carbon footprint is obtained based on the renewable energy utilization rate fuzzy evaluation model, specifically: The renewable energy utilization rate fuzzy evaluation model is used to obtain fuzzy set data of the renewable energy utilization rate fuzzy evaluation index of each carbon footprint; The fuzzy set data is input into the defuzzification formula of the renewable energy utilization evaluation index of the carbon footprint to calculate the comprehensive evaluation value.

[0034] Furthermore, the comprehensive evaluation value calculation formula is as follows:

[0035] in, The comprehensive assessment value of renewable energy utilization rate for carbon footprint, is the weight of the fuzzy set of renewable energy utilization evaluation indicators for the i-th carbon footprint, is the membership degree of the fuzzy set of renewable energy utilization evaluation indicators of the i-th carbon footprint, is the data corresponding to the fuzzy set of renewable energy utilization evaluation indicators of the i-th carbon footprint, and n is the total number of fuzzy sets.

[0036] It should be noted that the comprehensive evaluation value of renewable energy utilization rate based on the fuzzy evaluation model of renewable energy utilization rate and carbon footprint has several significant advantages. First, by defuzzifying the output of the fuzzy evaluation model of renewable energy utilization rate (i.e., fuzzy set data), the fuzzy data can be effectively converted into specific, easy-to-understand and applicable evaluation results. This defuzzification process can convert complex fuzzy information into an accurate comprehensive evaluation value, allowing decision makers to clearly understand the renewable energy utilization of substations and formulate effective energy optimization and carbon emission control strategies accordingly.

[0037] Secondly, the use of defuzzification formulas for comprehensive evaluation can integrate multiple dimensions and indicators in the fuzzy evaluation model to ensure that the evaluation results can fully reflect the renewable energy utilization rate of the carbon footprint. This integration method not only takes into account the impact of a single evaluation indicator, but also fully reflects the combined effect of multiple factors, avoiding the one-sidedness that may be caused by a single indicator, making the evaluation results more objective and accurate.

[0038] Furthermore, this method can effectively deal with the uncertainty and complexity in carbon footprint assessment by combining fuzzy evaluation and defuzzification techniques. In practical applications, carbon footprint and renewable energy utilization are usually affected by multiple factors, and the uncertainty of the data is high. The fuzzy evaluation model provides a flexible framework to deal with these uncertainties, while defuzzification converts these fuzzy information into practical decision-making basis, which helps to make more reasonable and scientific decisions in complex environments.

[0039] S4, performing a comprehensive evaluation on the renewable energy utilization rate of the substation carbon footprint based on the comprehensive evaluation value.

[0040] In this example, a comprehensive evaluation of the renewable energy utilization rate of the substation carbon footprint is performed based on the comprehensive evaluation value, specifically: When the comprehensive evaluation value is greater than a preset evaluation threshold, it indicates that the renewable energy utilization rate of the substation carbon footprint is normal; When the comprehensive evaluation value is less than a preset evaluation threshold, an early warning is issued to increase the proportion of renewable energy use and reduce dependence on non-renewable energy.

[0041] It should be noted that the comprehensive evaluation of the renewable energy utilization rate of the substation carbon footprint based on the comprehensive evaluation value has significant advantages, especially in terms of energy management and environmental protection. First, by setting the evaluation threshold and judging the renewable energy utilization rate of the substation carbon footprint according to the size of the comprehensive evaluation value, an intuitive and clear performance evaluation can be achieved. This method provides a quantitative standard so that the renewable energy utilization of the carbon footprint can be quickly and clearly evaluated, which is convenient for managers to understand the performance of the substation in terms of energy use in a timely manner.

[0042] Secondly, when the comprehensive evaluation value is lower than the preset threshold, the system can issue an early warning to indicate that the substation is at risk of over-reliance on non-renewable energy. This early warning mechanism can take corrective measures in a timely manner, prompting management to increase the proportion of renewable energy use, thereby reducing carbon emissions. This not only helps optimize the energy structure of the substation, but also effectively supports the implementation of environmental protection policies and promotes the advancement of green and low-carbon development strategies.

[0043] In addition, this evaluation method is highly flexible and adaptable. By setting the evaluation threshold reasonably, the evaluation standard of renewable energy utilization rate can be adjusted according to the actual situation of different substations. For example, substations in different regions or of different sizes may have different energy utilization backgrounds and needs. By adjusting the threshold, the rationality and effectiveness of the evaluation results can be ensured.

[0044] Furthermore, this method simplifies the decision-making process by comparing the comprehensive evaluation value with the preset threshold, helping managers to quickly identify potential problems in energy use and take measures, which helps to optimize energy efficiency, reduce environmental impact, and enhance the sustainable development capabilities of substations.

[0045] In general, this carbon footprint renewable energy utilization rate assessment method based on comprehensive evaluation value provides a simple, intuitive and flexible framework, which helps substations improve energy utilization efficiency, reduce carbon emissions and promote the realization of green and low-carbon goals.

[0046] Example 2,.

[0047] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0048] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0049] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0050] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0051] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0052] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A comprehensive evaluation method for renewable energy utilization based on substation carbon footprint, characterized in that: The following steps are involved: Obtain and pre-process the energy consumption and carbon emission historical data of the substation; Constructing a fuzzy evaluation model for renewable energy utilization based on the energy consumption historical data and carbon emission historical data of the substation; Obtaining a comprehensive evaluation value of renewable energy utilization rate of carbon footprint based on the renewable energy utilization rate fuzzy evaluation model; A comprehensive evaluation of the renewable energy utilization rate of the substation carbon footprint is performed based on the comprehensive evaluation value.

2. The comprehensive evaluation method for renewable energy utilization rate based on substation carbon footprint according to claim 1 is characterized in that: A fuzzy evaluation model for renewable energy utilization is constructed based on the energy consumption historical data and carbon emission historical data of the substation, specifically: Calculate the renewable energy consumption ratio value, carbon emission intensity value and energy efficiency value based on the energy consumption history data and carbon emission history data of the substation; The renewable energy consumption ratio value and carbon emission intensity are calculated based on the energy consumption history data and carbon emission history data of the substation, and the energy efficiency value and energy efficiency are used as the renewable energy utilization rate fuzzy evaluation index of the carbon footprint; Fuzzy processing is performed on the fuzzy evaluation index of renewable energy utilization rate based on the carbon footprint to obtain a fuzzy set of renewable energy utilization rate of the carbon footprint; The renewable energy utilization rate fuzzy set of the carbon footprint is input into a preset neural network model for training to obtain a renewable energy utilization rate fuzzy evaluation model.

3. The comprehensive evaluation method for renewable energy utilization rate based on substation carbon footprint according to claim 2 is characterized in that: The fuzzy evaluation index of renewable energy utilization rate based on the carbon footprint is fuzzified to obtain the fuzzy set of renewable energy utilization rate of carbon footprint, which is specifically: Inputting the renewable energy utilization rate fuzzy evaluation index of the carbon footprint into a preset membership function, and calculating the membership of the renewable energy utilization rate fuzzy evaluation index of each carbon footprint; A fuzzy set of the fuzzy evaluation index of renewable energy utilization rate of each carbon footprint is constructed based on the membership degree of the fuzzy evaluation index of renewable energy utilization rate of each carbon footprint.

4. The comprehensive evaluation method for renewable energy utilization rate based on substation carbon footprint according to claim 3 is characterized in that: The renewable energy utilization rate fuzzy set of the carbon footprint is input into a preset neural network model for training to obtain a renewable energy utilization rate fuzzy evaluation model, specifically: Inputting the membership degree corresponding to the renewable energy utilization rate fuzzy set of the carbon footprint into a preset neural network model, initializing the model parameters for iterative training; The loss function is calculated to verify the model and obtain the fuzzy evaluation model of renewable energy utilization.

5. The comprehensive evaluation method for renewable energy utilization rate based on substation carbon footprint according to claim 4 is characterized in that: Based on the fuzzy evaluation model of renewable energy utilization rate, the comprehensive evaluation value of renewable energy utilization rate of carbon footprint is obtained, which is specifically: The renewable energy utilization rate fuzzy evaluation model is used to obtain fuzzy set data of the renewable energy utilization rate fuzzy evaluation index of each carbon footprint; The fuzzy set data is input into the defuzzification formula of the renewable energy utilization evaluation index of the carbon footprint to calculate the comprehensive evaluation value.

6. The comprehensive evaluation method for renewable energy utilization rate based on substation carbon footprint according to claim 5 is characterized in that: Based on the comprehensive evaluation value, the renewable energy utilization rate of the substation carbon footprint is comprehensively evaluated, specifically: When the comprehensive evaluation value is greater than a preset evaluation threshold, it indicates that the renewable energy utilization rate of the substation carbon footprint is normal; When the comprehensive evaluation value is less than a preset evaluation threshold, an early warning is issued to increase the proportion of renewable energy use and reduce dependence on non-renewable energy.

7. The comprehensive evaluation method for renewable energy utilization rate based on substation carbon footprint according to claim 6 is characterized in that: The renewable energy consumption ratio, carbon emission intensity and energy efficiency are calculated based on the energy consumption history data and carbon emission history data of the substation. The specific calculation formula is as follows: in, is the renewable energy consumption ratio, is the total amount of renewable energy used by the substation during a specific period of time, is the total energy consumption of the substation in a specific period of time, including the consumption of renewable energy and non-renewable energy. , and is the weight coefficient, is the power output of the i-th renewable energy source, is the power output of the i-th non-renewable energy source, is the carbon emission intensity value, is the carbon emission of the i-th energy source, is the power output of the jth non-renewable energy source, is the number of non-renewable energy sources, is the number of renewable energy types, is the energy efficiency value, is the current carbon emission intensity of the substation, is the maximum allowable carbon emission intensity.

8. The comprehensive evaluation method for renewable energy utilization rate based on substation carbon footprint according to claim 7 is characterized in that: The renewable energy utilization rate fuzzy evaluation index of the carbon footprint is input into the preset membership function to calculate the membership of the renewable energy utilization rate fuzzy evaluation index of each carbon footprint. The specific calculation formula is as follows: in, is the membership degree of the fuzzy set corresponding to the fuzzy evaluation index of renewable energy utilization rate of carbon footprint, Fuzzy evaluation index of renewable energy utilization rate based on carbon footprint, is the mean of the fuzzy set corresponding to the fuzzy evaluation index of renewable energy utilization rate of carbon footprint, is the standard deviation of the fuzzy set corresponding to the fuzzy evaluation index of renewable energy utilization rate of carbon footprint, and e is the exponential function.

9. The comprehensive evaluation method for renewable energy utilization rate based on substation carbon footprint according to claim 8 is characterized in that: The fuzzy set data is input into the defuzzification formula of renewable energy utilization evaluation index of carbon footprint to calculate the comprehensive evaluation value. The specific calculation formula is: in, The comprehensive assessment value of renewable energy utilization rate for carbon footprint, is the weight of the fuzzy set of renewable energy utilization evaluation indicators for the i-th carbon footprint, is the membership degree of the fuzzy set of renewable energy utilization evaluation indicators of the i-th carbon footprint, is the data corresponding to the fuzzy set of renewable energy utilization evaluation indicators of the i-th carbon footprint, and n is the total number of fuzzy sets.

10. A system using the comprehensive evaluation method for renewable energy utilization based on substation carbon footprint as described in any one of claims 1 to 9, characterized in that: Including data acquisition module, model building module, fuzzy evaluation module and comprehensive evaluation module: A data acquisition module is used to acquire the energy consumption history data and carbon emission history data of the substation and perform preprocessing; A model building module, used to build a fuzzy evaluation model of renewable energy utilization rate based on the energy consumption historical data and carbon emission historical data of the substation; A fuzzy evaluation module, used to obtain a comprehensive evaluation value of the renewable energy utilization rate of the carbon footprint based on the renewable energy utilization rate fuzzy evaluation model; The comprehensive evaluation module is used to comprehensively evaluate the renewable energy utilization rate of the substation carbon footprint based on the comprehensive evaluation value.

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