Enterprise low-carbon development situation assessment and optimization method and system based on electric power data

Abstract

The invention discloses an enterprise low-carbon development situation assessment and optimization method and system based on electric power data, and the method comprises the steps: constructing an enterprise electric quantity and carbon emission coupling model based on the historical energy data of an enterprise, and calculating the carbon emission and low-carbon development factors of the enterprise in real time according to the enterprise electric quantity and carbon emission coupling model; according to the enterprise low-carbon development situation evaluation method, an enterprise carbon emission load is acquired, a benchmark development situation curve of the enterprise is acquired, enterprise low-carbon development situation evaluation is performed according to the enterprise carbon emission load, the enterprise low-carbon development factors and the benchmark development situation curve of the enterprise, and an enterprise low-carbon development optimization strategy is determined according to an enterprise low-carbon development situation evaluation result through the optimization method. According to the method, the characteristics of high real-time performance and high accuracy of electric power data are utilized, real-time and accurate evaluation of the low-carbon development trend of an enterprise in the development process is achieved, and an enterprise low-carbon development optimization strategy is given.

Description

Technical Field

[0001] The present invention belongs to the field of electric power technology, and specifically relates to an enterprise low-carbon development trend assessment and optimization method and system based on electric power data. Background Art

[0002] At present, the development of a low-carbon economy has become the trend and direction of the world's economic development, and clean and low-carbon transformation has become the key to the sustainable and high-quality development of enterprises. Assessing the low-carbon development status of enterprises and enhancing their ability to adapt to green and low-carbon transformation and upgrading can not only help enterprises seek a reasonable position in low-carbon development, and timely adjust the low-carbon development route, enhance market competitiveness, but also help promote the comprehensive green transformation of economic and social development.

[0003] The existing enterprise low-carbon level evaluation index system is lagging and relatively general. It is not only unable to achieve real-time evaluation of its own low-carbon development trend, but also unable to reflect the green and low-carbon development level of the enterprise in its industry. It affects the practicality and reliability of the enterprise's low-carbon level assessment in transformation decision-making, and is not conducive to enterprises to timely adjust and optimize the low-carbon transformation technology route according to development conditions. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for evaluating and optimizing the low-carbon development trend of an enterprise based on power data in order to address the above-mentioned problems existing in the prior art.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] In a first aspect, the present invention proposes a method for evaluating the low-carbon development trend of an enterprise based on power data, comprising:

[0007] S1. Construct a coupling model of enterprise electricity and carbon emissions based on the enterprise's historical energy data;

[0008] S2. Calculate the carbon emissions and low-carbon development factors of the enterprise in real time based on the coupling model of the enterprise's electricity and carbon emissions, and obtain the enterprise's benchmark development trend curve;

[0009] S3. Evaluate the low-carbon development trend of the enterprise based on its carbon emissions, its low-carbon development factor and its benchmark development trend curve.

[0010] In S2, the low-carbon development factor of the enterprise is calculated based on the following formula:

[0011]

[0012] In the above formula, are the low-carbon development factors of the enterprise when carbon increases and decreases on the jth day of the nth year, respectively. n,j , P n,jare the output and unit price of the enterprise on the jth day of the nth year, CEF n,j is the carbon emissions of the enterprise on the jth day of the nth year;

[0013] The company's baseline development trend curve is:

[0014] f(Q n,j P n,j )=IAC n-1 Q n,j P n,j

[0015] In the above formula, f(Q n,j P n,j ) is the benchmark development trend of the enterprise, IAC n-1 It is the carbon emission intensity of the industry in which the enterprise is located in the n-1th year.

[0016] The S3 includes:

[0017] like And CEF n,j ≥f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and carbon emissions, and its emission level is higher than that of the industry;

[0018] like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and carbon emissions, and its emission level is lower than that of the industry;

[0019] like And CEF n,j ≥f(Q n,j P n,j ), the enterprise is judged to be in a trend of reducing production and increasing carbon emissions, and its emission level is higher than that of the industry;

[0020] like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and increasing carbon emissions, and its emission level is lower than that of the industry;

[0021] like And CEF n,j ≥f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and reducing carbon emissions, and its emission level is higher than that of the industry;

[0022] like And CEF n,j <f(Q n,j P n,j), then the enterprise is judged to be in a trend of increasing production and reducing carbon emissions, and its emission level is lower than that of the industry;

[0023] like And CEF n,j ≥f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and carbon emissions, and its emission level is higher than that of the industry;

[0024] like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and carbon emissions, and its emission level is lower than that of the industry;

[0025] in, are the low-carbon development factors of the enterprise when carbon increases and decreases on the jth day of the nth year, and CEF n,j is the carbon emissions of the enterprise on the jth day of the nth year, f(Q n,j P n,j ) is the benchmark development trend of the enterprise, Q n,j , P n,j are the output and unit price of the product of the enterprise on the jth day of the nth year respectively.

[0026] The S1 includes:

[0027] S11. Calculate the company's historical carbon emissions based on the company's historical energy data:

[0028]

[0029] In the above formula, CEF n is the carbon emissions of the enterprise in the nth year, CF n,m 、CC n,m , OC n,m are the consumption, carbon content, and carbon oxidation rate of the mth fossil fuel of the enterprise in the nth year, EE n EF n are the net purchased electricity and net purchased heat of the enterprise in year n, EI n , EH n are the average electricity carbon emission factor and heat emission factor of the grid node where the enterprise is located in the nth year, CEP n is the carbon emissions of the enterprise in the industrial production process in the nth year, and M is the number of types of fossil fuels;

[0030] S12. Based on the historical carbon emissions of enterprises, the autoregressive distributed lag model is used to construct the following enterprise electricity and carbon emissions coupling model:

[0031]

[0032] In the above formula, c0 is the basic influence constant, α i , β i , γ i is the influence coefficient, r, s, q are the lag orders, EE n-i is the net purchased electricity of the enterprise in year ni, Q n-i The output of the industry in the nith year.

[0033] In a second aspect, the present invention proposes a method for optimizing enterprise low-carbon development based on power data, comprising:

[0034] A. Use the aforementioned assessment method to assess the low-carbon development trend of the enterprise;

[0035] B. According to the results of the enterprise's low-carbon development situation assessment, determine the enterprise's low-carbon development optimization strategy, including:

[0036] If the company is in a trend of increasing carbon emissions, it should adopt an optimization strategy of building new energy to reduce carbon emissions during the production process of products.

[0037] In the optimization strategy of building new energy to reduce carbon emissions in the production process, the minimum capacity of building new energy is G min Calculated based on the following conditions:

[0038]

[0039] In the above formula, CEF n,j is the carbon emissions of the enterprise on the jth day of the nth year, G is the capacity of the enterprise to build new energy, h n,j is the number of hours of use of renewable energy power generation equipment on the jth day of the nth year, EI n,j is the average carbon emission factor of the power grid node where the enterprise is located on the jth day of the nth year, Q n,j , P n,j are the output and unit price of the enterprise on the jth day of the nth year, U n,j is the cost of renewable energy electricity for the enterprise on the jth day of the nth year, f(Q n,j P n,j ) is the benchmark development trend of the enterprise.

[0040] In a third aspect, the present invention proposes an enterprise low-carbon development trend assessment system based on power data, including a coupling model construction module, a low-carbon development factor calculation module, a benchmark development trend curve acquisition module, and an assessment module;

[0041] The coupling model building module is used to build a coupling model of enterprise electricity and carbon emissions based on the enterprise's historical energy data;

[0042] The low-carbon development factor calculation module is used to calculate the carbon emissions and low-carbon development factors of the enterprise in real time according to the coupling model of the enterprise's electricity and carbon emissions;

[0043] The benchmark development trend curve acquisition module is used to obtain the benchmark development trend curve of the enterprise;

[0044] The evaluation module is used to evaluate the low-carbon development trend of the enterprise based on the carbon emissions of the enterprise, the low-carbon development factor of the enterprise and the benchmark development trend curve of the enterprise.

[0045] The low-carbon development factor calculation module calculates the low-carbon development factor of the enterprise based on the following formula:

[0046]

[0047] In the above formula, are the low-carbon development factors of the enterprise when carbon increases and decreases on the jth day of the nth year, respectively. n,j , P n,j are the output and unit price of the enterprise on the jth day of the nth year, CEF n,j is the carbon emissions of the enterprise on the jth day of the nth year;

[0048] The baseline development trend curve of the enterprise is:

[0049] f(Q n,j P n,j )=IAC n-1 Q n,j P n,j

[0050] In the above formula, f(Q n,j P n,j ) is the benchmark development trend of the enterprise, IAC n-1 The carbon emission intensity of the industry in which the enterprise is located in year n-1;

[0051] The evaluation module evaluates the low-carbon development trend of enterprises based on the following principles:

[0052] like And CEF n,j ≥f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and carbon emissions, and its emission level is higher than that of the industry;

[0053] like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and carbon emissions, and its emission level is lower than that of the industry;

[0054] like And CEFn,j ≥f(Q n,j P n,j ), the enterprise is judged to be in a trend of reducing production and increasing carbon emissions, and its emission level is higher than that of the industry;

[0055] like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and increasing carbon emissions, and its emission level is lower than that of the industry;

[0056] like And CEF n,j ≥f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and reducing carbon emissions, and its emission level is higher than that of the industry;

[0057] like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and reducing carbon emissions, and its emission level is lower than that of the industry;

[0058] like And CEF n,j ≥f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and carbon emissions, and its emission level is higher than that of the industry;

[0059] like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and carbon emissions, and its emission level is lower than that of the industry;

[0060] in, are the low-carbon development factors of the enterprise when carbon increases and decreases on the jth day of the nth year, and CEF n,j is the carbon emissions of the enterprise on the jth day of the nth year, f(Q n,j P n,j ) is the benchmark development trend of the enterprise, Q n,j , P n,j are the output and unit price of the product of the enterprise on the jth day of the nth year respectively.

[0061] In a fourth aspect, the present invention proposes an enterprise low-carbon development optimization system based on power data, including the aforementioned enterprise low-carbon development situation assessment system and an optimization strategy determination module;

[0062] The optimization strategy determination module is used to determine the enterprise low-carbon development optimization strategy according to the evaluation results obtained by the enterprise low-carbon development situation evaluation system, specifically including:

[0063] If the company is in a trend of increasing carbon emissions, it should adopt an optimization strategy of building new energy to reduce carbon emissions during the production process of products.

[0064] In the optimization strategy of building new energy to reduce carbon emissions in the production process, the minimum capacity of building new energy is G min Calculated based on the following conditions:

[0065]

[0066] In the above formula, CEF n,j is the carbon emissions of the enterprise on the jth day of the nth year, G is the capacity of the enterprise to build new energy, h n,j is the number of hours of use of renewable energy power generation equipment on the jth day of the nth year, EI n,j is the average carbon emission factor of the power grid node where the enterprise is located on the jth day of the nth year, Q n,j , P n,j are the output and unit price of the enterprise on the jth day of the nth year, U n,j is the cost of renewable energy electricity for the enterprise on the jth day of the nth year, f(Q n,j P n,j ) is the benchmark development trend of the enterprise.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] 1. The present invention proposes a method for evaluating the low-carbon development trend of an enterprise based on electricity data, including constructing a coupling model of enterprise electricity and carbon emissions based on the enterprise's historical energy data, calculating the enterprise's carbon emissions and low-carbon development factor in real time according to the coupling model of enterprise electricity and carbon emissions, and obtaining the enterprise's benchmark development trend curve, and evaluating the enterprise's low-carbon development trend according to the enterprise's carbon emissions, the enterprise's low-carbon development factor and the enterprise's benchmark development trend curve. This method utilizes the characteristics of electricity data with strong real-time and high accuracy to achieve real-time and accurate evaluation of the enterprise's own low-carbon development trend during its development process.

[0069] 2. The present invention proposes an enterprise low-carbon development optimization method based on power data. The method determines the enterprise low-carbon development optimization strategy according to the enterprise low-carbon development situation assessment results, including the optimization strategy of building new energy to reduce carbon emissions of products in the production process when the enterprise is in a trend of increasing carbon, and obtains the minimum capacity for building new energy, and provides the enterprise low-carbon development optimization strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is an overall flow chart of the evaluation method described in Example 1.

[0071] Figure 2This is an overall flow chart of the optimization method described in Example 2.

[0072] Figure 3 This is a structural diagram of the evaluation system described in Example 3.

[0073] Figure 4 This is a structural diagram of the optimization system described in Example 4. DETAILED DESCRIPTION

[0074] The present invention is further described in detail below in conjunction with specific implementations and drawings.

[0075] Embodiment 1:

[0076] like Figure 1 As shown, a method for evaluating the low-carbon development trend of an enterprise based on power data is carried out in the following steps:

[0077] 1. Based on the company's historical energy data, including the company's annual fossil fuel consumption, net purchased electricity and heat data, the company's historical carbon emissions are calculated using the following formula:

[0078]

[0079] In the above formula, CEF n is the carbon emissions of the enterprise in the nth year, CF n,m 、CC n,m , OC n,m are the consumption, carbon content, and carbon oxidation rate of the mth fossil fuel of the enterprise in the nth year, EE n EF n are the net purchased electricity and net purchased heat of the enterprise in year n, EI n , EH n are the average electricity carbon emission factor and heat emission factor of the grid node where the enterprise is located in the nth year, CEP n is the carbon emissions from the enterprise’s industrial production process in the nth year, and M is the number of fossil fuel types.

[0080] Typical energy accounting parameters of this embodiment are shown in Table 1:

[0081] Table 1 Typical energy carbon emission accounting parameters

[0082]

[0083] 2. The autoregressive distributed lag model (ARDL) is used to construct the following enterprise electricity and carbon emissions coupling model, and the coefficients in the model are determined based on the historical carbon emissions of the enterprise:

[0084]

[0085] In the above formula, c0 is the basic influence constant, α i , β i , γ i is the influence coefficient, r, s, q are the lag orders, EE n-i is the net purchased electricity of the enterprise in year ni, Q n-i The output of the industry in the nith year.

[0086] 3. Obtain the enterprise's electricity consumption data in real time, and calculate the enterprise's carbon emissions CEF on the jth day of the nth year in real time based on the following enterprise electricity and carbon emissions coupling model: n,j :

[0087]

[0088] 4. Collect the company’s daily output Q n,j and product price P n,j , forming the enterprise information data set for the jth day of the nth year {(CEF n，j , Q n，j P n，j )}.

[0089] 5. Calculate the low-carbon development factor of the enterprise based on the information data set of the enterprise on the jth day of the nth year:

[0090]

[0091] In the above formula, are the low-carbon development factors of the enterprise when carbon increases and decreases on the jth day of the nth year, respectively. n,j , P n,j are the output and unit price of the enterprise on the jth day of the nth year, CEF n,j is the carbon emissions of the enterprise on the jth day of the nth year.

[0092] 6. Based on the energy and economic data of the industry in which the enterprise is located in the n-1th year, the benchmark development trend curve of the enterprise is obtained:

[0093] f(Q n,j P n,j )=IAC n-1 Q n,j P n,j

[0094]

[0095] In the above formula, f(Q n,j P n,j ) is the benchmark development trend of the enterprise, IAC n-1 IEC n-1 , IGP n-1 They are respectively the carbon emission intensity, total carbon emissions and total output value of the industry in which the enterprise is located in the n-1th year.

[0096] 7. Evaluate the low-carbon development trend of the enterprise based on its carbon emissions, low-carbon development factors and its benchmark development trend curve, including:

[0097] like And CEF n,j ≥f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and carbon emissions, and its emission level is higher than that of the industry;

[0098] like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and carbon emissions, and its emission level is lower than that of the industry;

[0099] like And CEF n,j ≥f(Q n,j P n,j ), the enterprise is judged to be in a trend of reducing production and increasing carbon emissions, and its emission level is higher than that of the industry;

[0100] like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and increasing carbon emissions, and its emission level is lower than that of the industry;

[0101] like And CEF n,j ≥f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and reducing carbon emissions, and its emission level is higher than that of the industry;

[0102] like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and reducing carbon emissions, and its emission level is lower than that of the industry;

[0103] like And CEF n,j ≥f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and carbon emissions, and its emission level is higher than that of the industry;

[0104] like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and carbon emissions, and its emission level is lower than that of the industry;

[0105] in, They are the low-carbon development factors of the enterprise when carbon increases and decreases on the jth day of the nth year.

[0106] Embodiment 2:

[0107] like Figure 2 As shown, a method for optimizing enterprise low-carbon development based on power data includes:

[0108] A. Use steps 1-7 in Example 1 to evaluate the low-carbon development trend of the enterprise.

[0109] B. According to the results of the enterprise's low-carbon development situation assessment, determine the enterprise's low-carbon development optimization strategy, including:

[0110] If the enterprise is in a trend of increasing carbon emissions, it should adopt an optimization strategy of building new energy sources to reduce carbon emissions during the production process of products;

[0111] If the enterprise has a higher emission level than the industry average, it can adopt the development of low-carbon production technology mainly through external learning;

[0112] If the company has lower emissions than the industry average, it can adopt internal innovation to develop low-carbon production technologies;

[0113] If an enterprise is in the process of reducing production, it can enhance the added value of its products.

[0114] Among them, in the optimization strategy of building new energy to reduce carbon emissions in the production process, the minimum capacity of building new energy is G min Calculated based on the following conditions:

[0115]

[0116] In the above formula, CEF n,j is the carbon emissions of the enterprise on the jth day of the nth year, G is the capacity of the enterprise to build new energy, h n,j is the number of hours of use of renewable energy power generation equipment on the jth day of the nth year, EI n,j is the average carbon emission factor of the power grid node where the enterprise is located on the jth day of the nth year, Q n,j , P n,j are the output and unit price of the enterprise on the jth day of the nth year, U n,j is the cost of renewable energy electricity for the enterprise on the jth day of the nth year, f(Q n,j P n,j ) is the benchmark development trend of the enterprise.

[0117] Embodiment 3:

[0118] like Figure 3As shown, a system for evaluating the low-carbon development situation of an enterprise based on power data includes a coupling model building module, a low-carbon development factor calculation module, a benchmark development situation curve acquisition module, and an evaluation module.

[0119] The coupling model building module is used to build the following enterprise electricity and carbon emissions coupling model based on the enterprise's historical energy data:

[0120]

[0121] In the above formula, CEF n is the carbon emissions of the enterprise in the nth year, c0 is the basic impact constant, α i , β i , γ i is the influence coefficient, r, s, q are the lag orders, EE n-i is the net purchased electricity of the enterprise in year ni, Q n-i is the output of the industry in the nith year, CEF n Calculated based on the following formula:

[0122]

[0123] In the above formula, CEF n is the carbon emissions of the enterprise in the nth year, CF n,m 、CC n,m , OC n,m are the consumption, carbon content, and carbon oxidation rate of the mth fossil fuel of the enterprise in the nth year, EE n EF n are the net purchased electricity and net purchased heat of the enterprise in year n, EI n , EH n are the average electricity carbon emission factor and heat emission factor of the grid node where the enterprise is located in the nth year, CEP n is the carbon emissions from the enterprise’s industrial production process in the nth year, and M is the number of fossil fuel types.

[0124] The low-carbon development factor calculation module is used to calculate the carbon emissions and low-carbon development factor of the enterprise in real time according to the coupling model of the enterprise's electricity and carbon emissions, wherein the calculation formula of the low-carbon development factor is as follows:

[0125]

[0126] In the above formula, are the low-carbon development factors of the enterprise when carbon increases and decreases on the jth day of the nth year, respectively. n,j , P n,j are the output and unit price of the enterprise on the jth day of the nth year, CEF n,j is the carbon emissions of the enterprise on the jth day of the nth year.

[0127] The benchmark development trend curve acquisition module is used to obtain the benchmark development trend curve of the enterprise based on the energy and economic data of the industry in which the enterprise is located in the n-1th year:

[0128] f(Q n,j P n,j )=IAC n-1 Q n,j P n,j

[0129]

[0130] In the above formula, f(Q n,j P n,j ) is the benchmark development trend of the enterprise, IAC n-1 IEC n-1 , IGP n-1 They are respectively the carbon emission intensity, total carbon emissions and total output value of the industry in which the enterprise is located in the n-1th year.

[0131] The evaluation module is used to evaluate the low-carbon development trend of the enterprise according to the carbon emissions of the enterprise, the low-carbon development factor of the enterprise and the benchmark development trend curve of the enterprise, including:

[0132] like And CEF n,j ≥f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and carbon emissions, and its emission level is higher than that of the industry;

[0133] like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and carbon emissions, and its emission level is lower than that of the industry;

[0134] like And CEF n,j ≥f(Q n,j P n,j ), the enterprise is judged to be in a trend of reducing production and increasing carbon emissions, and its emission level is higher than that of the industry;

[0135] like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and increasing carbon emissions, and its emission level is lower than that of the industry;

[0136] like And CEF n,j ≥f(Q n,j P n,j), then the enterprise is judged to be in a trend of increasing production and reducing carbon emissions, and its emission level is higher than that of the industry;

[0137] like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and reducing carbon emissions, and its emission level is lower than that of the industry;

[0138] like And CEF n,j ≥f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and carbon emissions, and its emission level is higher than that of the industry;

[0139] like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and carbon emissions, and its emission level is lower than that of the industry.

[0140] Embodiment 4:

[0141] like Figure 4 As shown, a low-carbon development optimization system for enterprises based on power data includes the enterprise low-carbon development situation evaluation system and the optimization strategy determination module described in Example 3.

[0142] The optimization strategy determination module is used to determine the enterprise's low-carbon development optimization strategy based on the evaluation results obtained by the enterprise low-carbon development trend evaluation system, including: if the enterprise is in a trend of increasing carbon emissions, then adopt an optimization strategy of building new energy to reduce carbon emissions in the production process of products. In this strategy, the minimum capacity of building new energy, i.e., G min Calculated based on the following conditions:

[0143]

[0144] In the above formula, CEF n,j is the carbon emissions of the enterprise on the jth day of the nth year, G is the capacity of the enterprise to build new energy, h n,j is the number of hours of use of renewable energy power generation equipment on the jth day of the nth year, EI n,j is the average carbon emission factor of the power grid node where the enterprise is located on the jth day of the nth year, Q n,j , P n,j are the output and unit price of the enterprise on the jth day of the nth year, U n,j is the cost of renewable energy electricity for the enterprise on the jth day of the nth year, f(Q n,j P n,j ) is the benchmark development trend of the enterprise.

Claims

1. A method for evaluating the low-carbon development trend of an enterprise based on power data, characterized in that: The evaluation methods include: S1. Construct a coupling model of enterprise electricity and carbon emissions based on the enterprise's historical energy data; S2. Calculate the carbon emissions and low-carbon development factors of the enterprise in real time based on the coupling model of the enterprise's electricity and carbon emissions, and obtain the enterprise's benchmark development trend curve; S3. Evaluate the low-carbon development trend of the enterprise based on its carbon emissions, its low-carbon development factor and its benchmark development trend curve.

2. According to the method for evaluating the low-carbon development trend of an enterprise based on power data in claim 1, it is characterized in that: In S2, the low-carbon development factor of the enterprise is calculated based on the following formula: In the above formula, are the low-carbon development factors of the enterprise when carbon increases and decreases on the jth day of the nth year, respectively. n,j , P n,j are the output and unit price of the enterprise on the jth day of the nth year, CEF n,j is the carbon emissions of the enterprise on the jth day of the nth year; The company's baseline development trend curve is: f(Q n,j P n,j )=IAC n-1 Q n,j P n,j In the above formula, f(Q n,j P n,j ) is the benchmark development trend of the enterprise, IAC n-1 It is the carbon emission intensity of the industry in which the enterprise is located in the n-1th year.

3. The method for evaluating the low-carbon development trend of an enterprise based on power data according to claim 1 or 2, characterized in that: The S3 includes: like And CEF n,j ≥f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and carbon emissions, and its emission level is higher than that of the industry; like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and carbon emissions, and its emission level is lower than that of the industry; like And CEF n,j ≥f(Q n,j P n,j ), the enterprise is judged to be in a trend of reducing production and increasing carbon emissions, and its emission level is higher than that of the industry; like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and increasing carbon emissions, and its emission level is lower than that of the industry; like And CEF n,j ≥f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and reducing carbon emissions, and its emission level is higher than that of the industry; like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and reducing carbon emissions, and its emission level is lower than that of the industry; like And CEF n,j ≥f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and carbon emissions, and its emission level is higher than that of the industry; like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and carbon emissions, and its emission level is lower than that of the industry; in, are the low-carbon development factors of the enterprise when carbon increases and decreases on the jth day of the nth year, and CEF n,j is the carbon emissions of the enterprise on the jth day of the nth year, f(Q n,j P n,j ) is the benchmark development trend of the enterprise, Q n,j , P n,j are the output and unit price of the product of the enterprise on the jth day of the nth year respectively.

4. A method for evaluating the low-carbon development trend of an enterprise based on power data according to claim 1 or 2, characterized in that: The S1 includes: S11. Calculate the company's historical carbon emissions based on the company's historical energy data: In the above formula, CEF n is the carbon emissions of the enterprise in the nth year, CF n,m 、CC n,m , OC n,m are the consumption, carbon content, and carbon oxidation rate of the mth fossil fuel of the enterprise in the nth year, EE n EF n are the net purchased electricity and net purchased heat of the enterprise in year n, EI n , EH n are the average electricity carbon emission factor and heat emission factor of the grid node where the enterprise is located in the nth year, CEP n is the carbon emissions of the enterprise in the industrial production process in the nth year, and M is the number of types of fossil fuels; S12. Based on the historical carbon emissions of enterprises, the autoregressive distributed lag model is used to construct the following enterprise electricity and carbon emissions coupling model: In the above formula, c0 is the basic influence constant, α i , β i , γ i is the influence coefficient, r, s, q are the lag orders, EE n-i is the net purchased electricity of the enterprise in year ni, Q n-i The output of the industry in the nith year.

5. A method for optimizing enterprise low-carbon development based on power data, characterized in that: The optimization method comprises: A. Use the evaluation method described in any one of claims 1 to 4 to evaluate the low-carbon development trend of the enterprise; B. According to the results of the enterprise's low-carbon development situation assessment, determine the enterprise's low-carbon development optimization strategy, including: If the company is in a trend of increasing carbon emissions, it should adopt an optimization strategy of building new energy to reduce carbon emissions during the production process of products.

6. The enterprise low-carbon development optimization method based on power data according to claim 5 is characterized in that: In the optimization strategy of building new energy to reduce carbon emissions in the production process, the minimum capacity of building new energy is G min Calculated based on the following conditions: In the above formula, CEF n,j is the carbon emissions of the enterprise on the jth day of the nth year, G is the capacity of the enterprise to build new energy, h n,j is the number of hours of utilization of renewable energy power generation equipment on the jth day of the nth year, EI n,j is the average carbon emission factor of the power grid node where the enterprise is located on the jth day of the nth year, Q n,j , P n,j are the output and unit price of the enterprise on the jth day of the nth year, U n,j is the cost of renewable energy electricity for the enterprise on the jth day of the nth year, f(Q n,j P n,j ) is the benchmark development trend of the enterprise.

7. A system for evaluating the low-carbon development trend of enterprises based on power data, characterized in that: The system includes a coupling model building module, a low-carbon development factor calculation module, a benchmark development situation curve acquisition module, and an evaluation module; The coupling model building module is used to build a coupling model of enterprise electricity and carbon emissions based on the enterprise's historical energy data; The low-carbon development factor calculation module is used to calculate the carbon emissions and low-carbon development factors of the enterprise in real time according to the coupling model of the enterprise's electricity and carbon emissions; The benchmark development trend curve acquisition module is used to obtain the benchmark development trend curve of the enterprise; The evaluation module is used to evaluate the low-carbon development trend of the enterprise based on the carbon emissions of the enterprise, the low-carbon development factor of the enterprise and the benchmark development trend curve of the enterprise.

8. The enterprise low-carbon development trend assessment system based on power data according to claim 7 is characterized in that: The low-carbon development factor calculation module calculates the low-carbon development factor of the enterprise based on the following formula: In the above formula, are the low-carbon development factors of the enterprise when carbon increases and decreases on the jth day of the nth year, respectively. n,j , P n,j are the output and unit price of the enterprise on the jth day of the nth year, CEF n,j is the carbon emissions of the enterprise on the jth day of the nth year; The baseline development trend curve of the enterprise is: f(Q n,j P n,j )=IAC n-1 Q n,j P n,j In the above formula, f(Q n,j P n,j ) is the benchmark development trend of the enterprise, IAC n-1 The carbon emission intensity of the industry in which the enterprise is located in year n-1; The evaluation module evaluates the low-carbon development trend of enterprises based on the following principles: like And CEF n,j ≥f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and carbon emissions, and its emission level is higher than that of the industry; like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and carbon emissions, and its emission level is lower than that of the industry; like And CEF n,j ≥f(Q n,j P n,j ), the enterprise is judged to be in a trend of reducing production and increasing carbon emissions, and its emission level is higher than that of the industry; like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and increasing carbon emissions, and its emission level is lower than that of the industry; like And CEF n,j ≥f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and reducing carbon emissions, and its emission level is higher than that of the industry; like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of increasing production and reducing carbon emissions, and its emission level is lower than that of the industry; like And CEF n,j ≥f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and carbon emissions, and its emission level is higher than that of the industry; like And CEF n,j <f(Q n,j P n,j ), then the enterprise is judged to be in a trend of reducing production and carbon emissions, and its emission level is lower than that of the industry; in, are the low-carbon development factors of the enterprise when carbon increases and decreases on the jth day of the nth year, and CEF n,j is the carbon emissions of the enterprise on the jth day of the nth year, f(Q n,j P n,j ) is the benchmark development trend of the enterprise, Q n,j , P n,j are the output and unit price of the product of the enterprise on the jth day of the nth year respectively.

9. An enterprise low-carbon development optimization system based on power data, characterized in that: The system includes the enterprise low-carbon development trend assessment system and optimization strategy determination module as described in claim 7 or 8; The optimization strategy determination module is used to determine the enterprise low-carbon development optimization strategy according to the evaluation results obtained by the enterprise low-carbon development situation evaluation system, specifically including: If the company is in a trend of increasing carbon emissions, it should adopt an optimization strategy of building new energy to reduce carbon emissions during the production process of products.

10. The enterprise low-carbon development optimization system based on power data according to claim 9 is characterized in that: In the optimization strategy of building new energy to reduce carbon emissions in the production process, the minimum capacity of building new energy is G min Calculated based on the following conditions: In the above formula, CEF n,j is the carbon emissions of the enterprise on the jth day of the nth year, G is the capacity of the enterprise to build new energy, h n,j is the number of hours of utilization of renewable energy power generation equipment on the jth day of the nth year, EI n,j is the average carbon emission factor of the power grid node where the enterprise is located on the jth day of the nth year, Q n,j , P n,j are the output and unit price of the enterprise on the jth day of the nth year, U n,j is the cost of renewable energy electricity for the enterprise on the jth day of the nth year, f(Q n,j P n,j ) is the benchmark development trend of the enterprise.

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