Enterprise product electric power carbon footprint measurement and power purchase strategy optimization method and system

Abstract

This invention proposes a method and system for measuring the electricity carbon footprint of enterprise products and optimizing electricity purchase strategies. The method first constructs a grid path mapping relationship for the electricity used in enterprise product production based on the grid node status of the grid nodes accessed. Then, it constructs a clustering model based on the dynamic topology of the grid and the electricity carbon emission characteristics of nodes, dynamically aggregating all electricity-consuming nodes involved in the product production process into multiple virtual grid nodes. The comprehensive carbon emission factor of each virtual grid node is calculated to form the enterprise's electricity carbon footprint, enabling flexible measurement of electricity consumption in multi-dimensional spatiotemporal dimensions. Finally, a mapping model between the product's electricity carbon footprint and electricity carbon emissions is constructed, converting the product carbon footprint constraint into a constraint on the electricity carbon emissions of product production, which is then used in the enterprise's electricity purchase strategy optimization model. Solving this model yields the enterprise's optimized electricity purchase strategy, significantly improving the economy and feasibility of the enterprise's electricity purchase decisions while meeting carbon emission constraints.

Description

Technical Field

[0001] This invention belongs to the field of electricity carbon footprint accounting, specifically involving a method and system for measuring the electricity carbon footprint of enterprise products and optimizing electricity purchase strategies based on dynamic topological carbon clustering of power grids. Background Technology

[0002] With the ongoing global efforts to reduce carbon emissions and the strengthening of carbon constraints in international trade, a company's product carbon footprint has gradually become a crucial factor influencing market access, pricing, and competitiveness. Companies not only need to focus on direct emissions during production but also must meticulously calculate and manage the implicit electricity carbon emissions throughout the product's entire lifecycle. In industrial production, electricity consumption is typically a significant component of a product's carbon footprint. However, electricity carbon emissions are not static but are influenced by multiple factors, including voltage levels, power supply areas, and temporal operating conditions. With the large-scale integration of renewable energy sources, the characteristics of electricity carbon emissions at different nodes and time periods in the power grid exhibit significant spatial differences and temporal fluctuations. This presents companies with technical challenges when calculating their product's electricity carbon footprint, including a large number of power consumption nodes and the strong time-varying nature of carbon factors.

[0003] Existing methods for calculating product carbon footprint typically employ fixed or regionally averaged electricity carbon emission factors, or estimate based on the company's overall electricity purchase structure. These methods fail to reflect the actual grid carbon emission characteristics of a company across different production processes, electricity consumption nodes, and time periods. Furthermore, traditional methods often separate carbon footprint calculation from electricity purchase decisions, failing to collaboratively optimize a company's electricity purchase structure while meeting product carbon footprint constraints. This results in a lack of effective decision-making support when facing carbon constraints and electricity market price fluctuations. Therefore, accurately characterizing the electricity carbon footprint of a company's product production process under complex grid topologies and multi-source, multi-time-period electricity carbon emission characteristics, and further applying this product carbon footprint to electricity purchase decisions to achieve optimal corporate economics while meeting low-carbon requirements, has become a critical technical problem urgently needing to be solved in the field of low-carbon production and energy management. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned problems in the existing technology by providing a method and system for calculating the electricity carbon footprint of enterprise products and optimizing electricity purchase strategies based on dynamic topology carbon clustering of power grids.

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

[0006] In a first aspect, this invention proposes a method for calculating the electricity carbon footprint of enterprise products, including:

[0007] S1. Based on the grid node status of the enterprise's product production access, construct the grid path mapping relationship for the enterprise's product production electricity consumption;

[0008] S2. Based on the power grid path mapping relationship of enterprise product production electricity consumption, construct a clustering model based on power grid dynamic topology and node power carbon emission characteristics, and dynamically aggregate all electricity consumption nodes involved in the product production process into multiple virtual power grid nodes.

[0009] S3. Calculate the comprehensive carbon emission factor of each virtual grid node to form the enterprise's product electricity carbon footprint.

[0010] S2 includes:

[0011] Calculate any two power grid nodes , The electrical carbon similarity is calculated, and if the electrical carbon similarity value is greater than or equal to a set threshold, then the grid node is... , When nodes are assigned to the same virtual power grid, their electrical carbon similarity is calculated using the following formula:

[0012] ;

[0013] ;

[0014] In the above formula, For power grid nodes , The similarity of the electrical carbon, For power grid nodes , In the time window The distance between the carbon and the internal electrode. , , These are the weighting coefficients for carbon intensity variation characteristics, voltage level differences, and power supply area differences, respectively. , They are respectively Time period power grid node , The voltage carbon strength, , Respectively, power grid nodes , voltage level, , Respectively, power grid nodes , The power supply area , These are voltage level indication functions and power supply area indication functions, respectively.

[0015] In S3, the comprehensive carbon emission factor of each virtual power grid node is calculated using the following formula:

[0016] ;

[0017] In the above formula, for Time-based virtual power grid nodes The comprehensive carbon emission factor For virtual power grid nodes The set of power grid nodes within the area, for Time period determined by external nodes Towards power grid nodes The amount of electricity delivered, for external nodes of the time period The carbon emission factor of electricity for Time period power grid node By virtual power grid nodes Internal generator set The amount of electricity generated, For generator sets Carbon emission factors.

[0018] Secondly, this invention proposes a method for optimizing enterprise electricity purchase strategies, including:

[0019] S4. Based on the aforementioned method for calculating the carbon footprint of enterprise products, construct a mapping model between the carbon footprint of products and carbon emissions of electricity, and convert the carbon footprint of products into the carbon emissions of electricity generated during product production.

[0020] S5. Considering the constraints of carbon emissions from electricity production, and with the goal of minimizing the enterprise's electricity purchase cost and carbon cost, construct an optimization model for the enterprise's electricity purchase strategy and solve for the optimal strategy.

[0021] In S4, the mapping model between product electricity carbon footprint and electricity carbon emissions includes:

[0022] ;

[0023] In the above formula, Carbon emissions per unit of electricity produced. For product output, For a set of virtual power grid nodes, For virtual power grid nodes The set of power grid nodes within the area, for Time-based virtual power grid nodes The comprehensive carbon emission factor for Time period power grid node Electricity consumption for product manufacturing For time windows;

[0024] In S5, the objective function of the enterprise electricity purchase strategy optimization model includes:

[0025] ;

[0026] ;

[0027] ;

[0028] In the above formula, For product output, , Time windows Regular electricity prices and green electricity prices within the country , Time windows The amount of conventional electricity purchased and the amount of green electricity purchased within the country. For product carbon footprint, For the time window in the carbon market The average carbon price within, Carbon emissions per unit of electricity produced. This refers to the carbon emissions generated from non-electricity consumption processes during the production of a single product. For production process Non-electricity carbon emission factor per unit output For production process The production response function is obtained by regressing historical electricity-production data. for Time-based production process At virtual grid nodes Electricity consumption For a set of virtual power grid nodes;

[0029] The constraints of the enterprise's electricity purchase strategy optimization model include constraints on the enterprise's electricity consumption for product production, constraints on the carbon emissions from electricity generated during product production, and constraints on virtual node power balance. The constraints on the carbon emissions from electricity generated during product production include:

[0030] ;

[0031] In the above formula, This is the upper limit for the carbon footprint of a product.

[0032] Thirdly, this invention proposes an enterprise product electricity carbon footprint calculation system, including an electricity path mapping module, a virtual power grid node division module, and an electricity carbon footprint accounting module;

[0033] The power consumption path mapping module is used to construct the power grid path mapping relationship for the power consumption of the enterprise's product production based on the status of the power grid nodes accessed by the enterprise's product production.

[0034] The virtual power grid node partitioning module is used to construct a clustering model based on the power grid path mapping relationship of the enterprise's product production electricity consumption, and dynamically aggregate all electricity consumption nodes involved in the product production process into multiple virtual power grid nodes.

[0035] The electricity carbon footprint accounting module is used to calculate the comprehensive carbon emission factor of each virtual power grid node to form the electricity carbon footprint of the enterprise's products.

[0036] The virtual power grid node partitioning module partitions multiple virtual power grid nodes based on the following strategy:

[0037] Calculate any two power grid nodes , The electrical carbon similarity is calculated, and if the electrical carbon similarity value is greater than or equal to a set threshold, then the grid node is... , When nodes are assigned to the same virtual power grid, their electrical carbon similarity is calculated using the following formula:

[0038] ;

[0039] ;

[0040] In the above formula, For power grid nodes , The similarity of the electrical carbon, For power grid nodes , In the time window The distance between the carbon and the internal electrode. , , These are the weighting coefficients for carbon intensity variation characteristics, voltage level differences, and power supply area differences, respectively. , They are respectively Time period power grid node , The voltage carbon strength, , Respectively, power grid nodes , voltage level, , Respectively, power grid nodes , The power supply area , These are voltage level indication functions and power supply area indication functions, respectively.

[0041] The electricity carbon footprint calculation module uses the following formula to calculate the comprehensive carbon emission factor of each virtual power grid node:

[0042] ;

[0043] In the above formula, for Time-based virtual power grid nodes The comprehensive carbon emission factor For virtual power grid nodes The set of power grid nodes within the area, for Time period determined by external nodes Towards power grid nodes The amount of electricity delivered, for external nodes of the time period The carbon emission factor of electricity for Time period power grid node By virtual power grid nodes Internal generator set The amount of electricity generated, For generator sets Carbon emission factors.

[0044] Fourthly, this invention proposes an enterprise electricity purchase strategy optimization system, including an electricity carbon footprint conversion module and an enterprise electricity purchase strategy optimization model construction and solution module;

[0045] The electricity carbon footprint conversion module is used to construct a mapping model between the product electricity carbon footprint and electricity carbon emissions based on the enterprise product electricity carbon footprint output by the aforementioned enterprise product electricity carbon footprint calculation system, and convert the product electricity carbon footprint into the product production electricity carbon emissions.

[0046] The module for constructing and solving the enterprise electricity purchase strategy optimization model is used to consider the constraints of carbon emissions from product production electricity, and to construct an enterprise electricity purchase strategy optimization model with the goal of minimizing enterprise electricity purchase costs and carbon costs, and then solve it to obtain the enterprise electricity purchase optimization strategy.

[0047] The mapping model between the product's electricity carbon footprint and electricity carbon emissions includes:

[0048] ;

[0049] In the above formula, Carbon emissions per unit of electricity produced. For product output, For a set of virtual power grid nodes, For virtual power grid nodes The set of power grid nodes within the area, for Time-based virtual power grid nodes The comprehensive carbon emission factor for Time period power grid node Electricity consumption for product manufacturing For time windows;

[0050] The objective function of the enterprise electricity purchase strategy optimization model includes:

[0051] ;

[0052] ;

[0053] ;

[0054] In the above formula, For product output, , Time windows Regular electricity prices and green electricity prices within the country , Time windows The amount of conventional electricity purchased and the amount of green electricity purchased within the country. For product carbon footprint, For the time window in the carbon market The average carbon price within, Carbon emissions per unit of electricity produced. This refers to the carbon emissions generated from non-electricity consumption processes during the production of a single product. For production process Non-electricity carbon emission factor per unit output For production process The production response function is obtained by regressing historical electricity-production data. for Time-based production process At virtual grid nodes Electricity consumption For a set of virtual power grid nodes;

[0055] The constraints include constraints on electricity consumption for enterprise production, carbon emissions from electricity generated during product production, and virtual node power balance constraints. The carbon emissions from electricity generated during product production constraints include:

[0056] ;

[0057] In the above formula, This is the upper limit for the carbon footprint of a product.

[0058] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0059] 1. This invention proposes a method for calculating the electricity carbon footprint of enterprise products. First, based on the grid node status of the enterprise's product production, a grid path mapping relationship for the electricity used in product production is constructed. Then, based on this grid path mapping relationship, a clustering model based on the dynamic topology of the grid and the carbon emission characteristics of nodes is built. All electricity-consuming nodes involved in the product production process are dynamically aggregated into multiple virtual grid nodes. Finally, the comprehensive carbon emission factor of each virtual grid node is calculated to form the enterprise's electricity carbon footprint. This method, by introducing dynamic topology carbon mapping and clustering of the grid, enables flexible calculation of electricity used in enterprise product production across multiple spatiotemporal dimensions. It solves the problems of high complexity and large cumulative error in traditional enterprise product carbon footprint calculation due to the large number of electricity-consuming nodes and significant temporal dynamic changes.

[0060] 2. The proposed method for optimizing enterprise electricity purchase strategies is based on the calculated carbon footprint of enterprise products' electricity consumption. It constructs a mapping model between the product's carbon footprint and carbon emissions from electricity consumption, converting the product's carbon footprint into carbon emissions from product production. Then, considering the constraint of carbon emissions from product production, and aiming to minimize the enterprise's electricity purchase cost and carbon cost, it constructs an optimization model for the enterprise's electricity purchase strategy and solves it to obtain the optimized strategy. This method, by constructing a mapping model between the product's carbon footprint and carbon emissions from electricity consumption, transforms the product's carbon footprint constraint into a constraint on carbon emissions from product production, forming an executable electricity purchase decision constraint. Therefore, while meeting carbon emission constraints, it significantly improves the economic efficiency and feasibility of the enterprise's electricity purchase decision, effectively supporting the enterprise's green and low-carbon transformation. Attached Figure Description

[0061] Figure 1 This is a flowchart of the method described in Example 1.

[0062] Figure 2 This is a flowchart of the method described in Example 2.

[0063] Figure 3 This is a structural diagram of the system described in Example 3.

[0064] Figure 4This is a structural diagram of the system described in Example 4. Detailed Implementation

[0065] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0066] Example 1:

[0067] This embodiment takes a specific enterprise as the research object and implements the method for calculating the carbon footprint of an enterprise's products based on the present invention. Figure 1 As shown, the specific steps are as follows:

[0068] 1. Based on the power system network topology and the grid connection node locations of each production line of the enterprise's products, construct the grid path mapping relationship for the power consumption of the enterprise's products.

[0069] This step breaks down product electricity consumption from a single main meter into traceable power grid node-level electricity consumption paths, constructing a mapping relationship between the enterprise's product production electricity consumption and the power grid. It clarifies the voltage level, power supply area, and time period information of each power grid node, as well as which power grid nodes each process uses, providing a foundation for subsequent carbon accounting and electricity purchase optimization. Its specific implementation methods include:

[0070] Label the attributes of power grid nodes. For each power grid node, record its voltage carbon intensity. Voltage level Power supply area Time period To form spatiotemporal tags for power grid nodes .

[0071] Identify the production processes and grid connection nodes for the company's products. For each production process, determine the grid connection node, forming a complete set of all nodes involved in the product's production process. .

[0072] Construct a power grid path mapping relationship. Map the electricity consumption of each process in the product manufacturing process to the corresponding node, as follows:

[0073] ;

[0074] In the above formula, for Time period power grid node Electricity consumption for product manufacturing for Time-based production process At power grid nodes Electricity consumption.

[0075] 2. Based on the power grid path mapping relationship of enterprise product production electricity consumption, a clustering model based on the dynamic topology of the power grid and the carbon emission characteristics of node electricity is constructed to dynamically aggregate all electricity consumption nodes involved in the product production process into multiple virtual power grid nodes.

[0076] Because the product's electricity consumption spans different voltage levels, power supply areas, and time periods, the carbon attributes of electricity at each node exhibit significant differences. This can be achieved by analyzing the entire node set. Nodes with similar carbon emission characteristics are aggregated to construct several virtual power grid nodes.

[0077] Grid nodes , The spatiotemporal labels are respectively , Then these two power grid nodes are in the time window Inner carbon distance have:

[0078] ;

[0079] In the above formula, , , These are the weighting coefficients for carbon intensity variation characteristics, voltage level differences, and power supply area differences, respectively. , They are respectively Time period power grid node , The voltage carbon strength, , Respectively, power grid nodes , voltage level, , Respectively, power grid nodes , The power supply area , These are voltage level indication functions and power supply area indication functions, respectively.

[0080] Based on the assessment of electrical carbon similarity, can the carbon attributes of the electricity supplied to products by different grid nodes at a certain time period be equivalently combined? , Electrocarbon similarity have:

[0081] .

[0082] The electrocarbon similarity value ranges from (0,1], with a larger value indicating that the carbon properties of the two nodes are more similar. When the value is greater than or equal to the set threshold, the power grid node , They were assigned to the same virtual power grid node.

[0083] In this embodiment, all power-consuming nodes involved in the product manufacturing process are clustered into three virtual power grid nodes, namely A1, A2, and A3.

[0084] 3. Calculate the comprehensive carbon emission factor of each virtual grid node to form the enterprise's product electricity carbon footprint.

[0085] Virtual nodes are connected to the external power grid via lines and may contain their own power sources or distributed power sources. Assume that the set of virtual power grid nodes is obtained by clustering based on the clustering model in step 2. Any virtual power grid node The set of internal power grid nodes is At any time period Virtual power grid node The sources of carbon emissions from electricity generation include carbon emissions from external power grid inflows and carbon emissions from power generation within virtual nodes. The combined carbon emission factor... have:

[0086] ;

[0087] In the above formula, for Time period determined by external nodes Towards Nodes in The amount of electricity delivered, for external nodes of the time period The carbon emission factor of electricity for Time period nodes By virtual power grid nodes Internal generator set The amount of electricity generated, For generator sets Carbon emission factors.

[0088] For virtual grid nodes A1, A2, and A3, their combined carbon factor and the amount of electricity delivered to enterprises during three specific time periods are shown in Table 1:

[0089] Table 1. Comprehensive carbon factor of virtual grid nodes and electricity delivered to enterprises during a certain period.

[0090] .

[0091] Example 2:

[0092] like Figure 2As shown, a method for optimizing a company's electricity purchasing strategy includes the following steps:

[0093] (1) The carbon footprint of the enterprise's products was calculated using the method described in Example 1.

[0094] (2) Construct a mapping model between product electricity carbon footprint and electricity carbon emissions, and convert product electricity carbon footprint into product production electricity carbon emissions, so as to realize the conversion of product electricity carbon footprint constraints into product production electricity carbon emissions constraints.

[0095] A strict mapping relationship is established between the product's electricity carbon footprint and the electricity carbon emissions generated by the enterprise's production. A mapping model between the product's electricity carbon footprint and electricity carbon emissions is constructed. This model is based on the comprehensive carbon emission factor of each virtual grid node in the virtual grid node set, and the electricity carbon emissions per unit of product production. have:

[0096] ;

[0097] In the above formula, For product output, For a set of virtual power grid nodes, For virtual power grid nodes The set of power grid nodes within the area, for Time-based virtual power grid nodes The comprehensive carbon emission factor for Time period power grid node Electricity consumption for product manufacturing For time windows.

[0098] (3) Under the premise of meeting the constraints of carbon emissions from product production and the demand for production electricity, and taking into account the transaction prices of conventional electricity and green electricity, with the goal of minimizing the enterprise's electricity purchase cost and carbon cost, construct an optimization model for the enterprise's electricity purchase strategy, and solve for the enterprise's electricity purchase optimization strategy that meets the product's carbon footprint requirements, including the optimal electricity purchase combination for the enterprise in each time period and each production process.

[0099] The objective function of the enterprise electricity purchase strategy optimization model includes:

[0100] ;

[0101] In the above formula, For product output, , Time windows Regular electricity prices and green electricity prices within the country , Time windows The amount of conventional electricity purchased and the amount of green electricity purchased within the country. For product carbon footprint, For the time window in the carbon market The average carbon price within the region.

[0102] Among them, product carbon footprint have:

[0103] ;

[0104] In the above formula, It represents the carbon emissions generated by non-electricity consumption processes during the production of a unit of product.

[0105] Based on historical operational data, a response model is constructed to link electricity consumption and product output during the production process. Furthermore, a quantitative correlation is established between product output and non-electricity carbon emissions, thereby realizing the correlation between electricity consumption and non-electricity carbon emissions. The mappings are as follows:

[0106] Establish a mapping relationship between electricity consumption and product output. Time-based production process At virtual grid nodes The electricity consumption is Then the total electricity consumption of this production process during that period is Production process exist Production during a period have:

[0107] .

[0108] Establish a mapping relationship between product output and non-electricity carbon emissions, realizing the mapping from electricity consumption to non-electricity carbon emissions. Production process The non-electricity carbon emission factor per unit output is The non-electricity carbon emissions per unit of product across all production processes and time periods. for:

[0109] ;

[0110] In the above formula, For production process The production response function is obtained by regressing historical electricity consumption-production data.

[0111] The constraints of the enterprise's power purchase strategy optimization model include:

[0112] Electricity consumption constraints for enterprise production:

[0113] ;

[0114] ;

[0115] Carbon emission constraints on electricity production for products:

[0116] When the carbon footprint limit of a product is given according to the requirements of the target market ,have:

[0117] .

[0118] Virtual node power balance constraints:

[0119] For any virtual power grid node ,exist Power balance needs to be maintained during all time periods, including:

[0120] ;

[0121] In the above formula, , , They are respectively Time-based virtual power grid nodes The generating capacity of the internal generator set, the total power of all loads, and the line loss power. for Time periods flow into virtual grid nodes from other nodes The equivalent power, for Time-based virtual power grid nodes The equivalent power flowing out to other nodes.

[0122] In this embodiment, the green electricity purchase price for enterprises is set at 0.50 yuan / kWh, the market purchase price is 0.42 yuan / kWh, and the carbon market price is 100 yuan / tCO2. Enterprises prioritize offsetting carbon emissions by purchasing green electricity at virtual grid nodes with higher comprehensive carbon factors, and gradually expand to virtual grid nodes with lower comprehensive carbon factors. The corresponding electricity purchase strategy is shown in Table 2.

[0123] Table 2 Enterprise Electricity Purchase Strategies

[0124]

[0125] As can be seen, the objective function values ​​of strategy 1, strategy 2, and strategy 3 are 863.9, 866.34, and 893.99, respectively. Therefore, strategy 1 is selected as the optimal electricity purchase strategy for the company.

[0126] Example 3:

[0127] like Figure 3As shown, an enterprise product electricity carbon footprint calculation system includes an electricity path mapping module, a virtual power grid node division module, and an electricity carbon footprint accounting module.

[0128] The power consumption path mapping module is used to construct the power grid path mapping relationship for the power consumption of the enterprise's product production based on the status of the power grid nodes accessed by the enterprise's product production. Its specific implementation method is shown in Example 1.

[0129] The virtual power grid node partitioning module is used to construct a clustering model based on the power grid path mapping relationship of the enterprise's product production electricity consumption, and the dynamic topology of the power grid and the carbon emission characteristics of the nodes. This model dynamically aggregates all electricity-consuming nodes involved in the product production process into multiple virtual power grid nodes. Specific partitioning strategies include:

[0130] Calculate any two power grid nodes , The electrical carbon similarity is calculated, and if the electrical carbon similarity value is greater than or equal to a set threshold, then the grid node is... , When nodes are assigned to the same virtual power grid, their electrical carbon similarity is calculated using the following formula:

[0131] ;

[0132] ;

[0133] In the above formula, For power grid nodes , The similarity of the electrical carbon, For power grid nodes , In the time window The distance between the carbon and the internal electrode. , , These are the weighting coefficients for carbon intensity variation characteristics, voltage level differences, and power supply area differences, respectively. , They are respectively Time period power grid node , The voltage carbon strength, , Respectively, power grid nodes , voltage level, , Respectively, power grid nodes , The power supply area , These are voltage level indication functions and power supply area indication functions, respectively.

[0134] The electricity carbon footprint calculation module is used to calculate the comprehensive carbon emission factor of each virtual grid node using the following formula, thereby forming the enterprise's product electricity carbon footprint:

[0135] ;

[0136] In the above formula, for Time-based virtual power grid nodes The comprehensive carbon emission factor For virtual power grid nodes The set of power grid nodes within the area, for Time period determined by external nodes Towards power grid nodes The amount of electricity delivered, for external nodes of the time period The carbon emission factor of electricity for Time period power grid node By virtual power grid nodes Internal generator set The amount of electricity generated, For generator sets Carbon emission factors.

[0137] Example 4:

[0138] like Figure 4 As shown, an enterprise electricity purchase strategy optimization system includes an electricity path mapping module, a virtual power grid node partitioning module, an electricity carbon footprint accounting module, an electricity carbon footprint conversion module, and an enterprise electricity purchase strategy optimization model construction and solution module.

[0139] The electricity path mapping module, virtual power grid node division module, and electricity carbon footprint accounting module are detailed in Example 3.

[0140] The electricity carbon footprint conversion module is used to construct a mapping model between a company's product electricity carbon footprint and electricity carbon emissions based on the company's product electricity carbon footprint, converting the product electricity carbon footprint into the electricity carbon emissions from product production. The mapping model between the product electricity carbon footprint and electricity carbon emissions includes:

[0141] ;

[0142] In the above formula, Carbon emissions per unit of electricity produced. For product output, For a set of virtual power grid nodes, For virtual power grid nodes The set of power grid nodes within the area, for Time-based virtual power grid nodes The comprehensive carbon emission factor for Time period power grid node Electricity consumption for product manufacturing For time windows.

[0143] The module for constructing and solving the enterprise electricity purchase strategy optimization model is used to consider the constraint of the maximum allowable carbon emissions from electricity production, and to construct an enterprise electricity purchase strategy optimization model with the objective of minimizing enterprise electricity purchase costs and carbon costs, and then solve for the enterprise's optimized electricity purchase strategy. The objective function of the enterprise electricity purchase strategy optimization model includes:

[0144] ;

[0145] ;

[0146] ;

[0147] In the above formula, For product output, , Time windows Regular electricity prices and green electricity prices within the country , Time windows The amount of conventional electricity purchased and the amount of green electricity purchased within the country. For product carbon footprint, For the time window in the carbon market The average carbon price within, Carbon emissions per unit of electricity produced. This refers to the carbon emissions generated from non-electricity consumption processes during the production of a single product. For production process Non-electricity carbon emission factor per unit output For production process The production response function is obtained by regressing historical electricity-production data. for Time-based production process At virtual grid nodes Electricity consumption This is a set of virtual power grid nodes.

[0148] The constraints of the enterprise's power purchase strategy optimization model include:

[0149] Electricity consumption constraints for enterprise production:

[0150] ;

[0151] ;

[0152] Carbon emission constraints on electricity production for products:

[0153] ;

[0154] In the above formula, This is the upper limit for the carbon footprint of a product.

[0155] Virtual node power balance constraints:

[0156] ;

[0157] In the above formula, , , They are respectively Time-based virtual power grid nodes The generating capacity of the internal generator set, the total power of all loads, and the line loss power. for Time periods flow into virtual grid nodes from other nodes The equivalent power, for Time-based virtual power grid nodes The equivalent power flowing out to other nodes.

Claims

1. A method for calculating the carbon footprint of an enterprise's products based on electricity, characterized in that, The method includes: S1. Based on the grid node status of the enterprise's product production access, construct the grid path mapping relationship for the enterprise's product production electricity consumption; S2. Based on the power grid path mapping relationship of enterprise product production electricity consumption, construct a clustering model based on power grid dynamic topology and node power carbon emission characteristics, and dynamically aggregate all electricity consumption nodes involved in the product production process into multiple virtual power grid nodes. S3. Calculate the comprehensive carbon emission factor of each virtual grid node to form the enterprise's product electricity carbon footprint.

2. The method for calculating the carbon footprint of enterprise products based on electricity, as described in claim 1, is characterized in that... S2 includes: Calculate any two power grid nodes , The electrical carbon similarity is calculated, and if the electrical carbon similarity value is greater than or equal to a set threshold, then the grid node is... , When nodes are assigned to the same virtual power grid, their electrical carbon similarity is calculated using the following formula: ； ； In the above formula, For power grid nodes , The similarity of the electrical carbon, For power grid nodes , In the time window The distance between the carbon and the internal electrode. , , These are the weighting coefficients for carbon intensity variation characteristics, voltage level differences, and power supply area differences, respectively. , They are respectively Time period power grid node , The voltage carbon strength, , Respectively, power grid nodes , voltage level, , Respectively, power grid nodes , The power supply area , These are voltage level indication functions and power supply area indication functions, respectively.

3. The method for calculating the carbon footprint of enterprise products based on electricity, as described in claim 1 or 2, is characterized in that... In S3, the comprehensive carbon emission factor of each virtual power grid node is calculated using the following formula: ； In the above formula, for Time-based virtual power grid nodes The comprehensive carbon emission factor For virtual power grid nodes The set of power grid nodes within the area, for Time period determined by external nodes Towards power grid nodes The amount of electricity delivered, for external nodes of the time period The carbon emission factor of electricity for Time period power grid node By virtual power grid nodes Internal generator set The amount of electricity generated, For generator sets Carbon emission factors.

4. A method for optimizing an enterprise's electricity purchase strategy, characterized in that, The method includes: S4. Based on the enterprise product electricity carbon footprint calculated by the method described in claim 1, construct a mapping model between product electricity carbon footprint and electricity carbon emissions, and convert product electricity carbon footprint into product production electricity carbon emissions. S5. Considering the maximum allowable carbon emissions from electricity production, and with the goal of minimizing the enterprise's electricity purchase cost and carbon cost, construct an optimization model for the enterprise's electricity purchase strategy, and solve for the optimal electricity purchase strategy.

5. The method for optimizing enterprise power purchase strategies according to claim 4, characterized in that, In S4, the mapping model between product electricity carbon footprint and electricity carbon emissions includes: ； In the above formula, Carbon emissions per unit of electricity produced. For product output, For a set of virtual power grid nodes, For virtual power grid nodes The set of power grid nodes within the area, for Time-based virtual power grid nodes The comprehensive carbon emission factor for Time period power grid node Electricity consumption for product manufacturing For time windows.

6. The method for optimizing enterprise power purchase strategies according to claim 4 or 5, characterized in that, In S5, the objective function of the enterprise electricity purchase strategy optimization model includes: ； ； ； In the above formula, For product output, , Time windows Regular electricity prices and green electricity prices within the country , Time windows The amount of conventional electricity purchased and the amount of green electricity purchased within the country. For product carbon footprint, For the time window in the carbon market The average carbon price within, Carbon emissions per unit of electricity produced. This refers to the carbon emissions generated from non-electricity consumption processes during the production of a single product. For production process Non-electricity carbon emission factor per unit output For production process The production response function is obtained by regressing historical electricity-production data. for Time-based production process At virtual grid nodes Electricity consumption For a set of virtual power grid nodes; The constraints of the enterprise's electricity purchase strategy optimization model include constraints on the enterprise's electricity consumption for product production, constraints on the carbon emissions from electricity generated during product production, and constraints on virtual node power balance. The constraints on the carbon emissions from electricity generated during product production include: ； In the above formula, This is the upper limit for the carbon footprint of a product.

7. A system for calculating the carbon footprint of an enterprise's products based on electricity consumption, characterized in that, The system includes an electricity path mapping module, a virtual power grid node partitioning module, and an electricity carbon footprint accounting module. The power consumption path mapping module is used to construct the power grid path mapping relationship for the power consumption of the enterprise's product production based on the status of the power grid nodes accessed by the enterprise's product production. The virtual power grid node partitioning module is used to construct a clustering model based on the power grid path mapping relationship of the enterprise's product production electricity consumption, and dynamically aggregate all electricity consumption nodes involved in the product production process into multiple virtual power grid nodes. The electricity carbon footprint accounting module is used to calculate the comprehensive carbon emission factor of each virtual power grid node to form the electricity carbon footprint of the enterprise's products.

8. The enterprise product electricity carbon footprint calculation system according to claim 7, characterized in that, The virtual power grid node partitioning module partitions multiple virtual power grid nodes based on the following strategy: Calculate any two power grid nodes , The electrical carbon similarity is calculated, and if the electrical carbon similarity value is greater than or equal to a set threshold, then the grid node is... , When nodes are assigned to the same virtual power grid, their electrical carbon similarity is calculated using the following formula: ； ； In the above formula, For power grid nodes , The similarity of the electrical carbon, For power grid nodes , In the time window The distance between the carbon and the internal electrode. , , These are the weighting coefficients for carbon intensity variation characteristics, voltage level differences, and power supply area differences, respectively. , They are respectively Time period power grid node , The voltage carbon strength, , Respectively, power grid nodes , voltage level, , Respectively, power grid nodes , The power supply area , These are voltage level indication functions and power supply area indication functions, respectively. The electricity carbon footprint calculation module uses the following formula to calculate the comprehensive carbon emission factor of each virtual power grid node: ； In the above formula, for Time-based virtual power grid nodes The comprehensive carbon emission factor For virtual power grid nodes The set of power grid nodes within the area, for Time period determined by external nodes Towards power grid nodes The amount of electricity delivered, for external nodes of the time period The carbon emission factor of electricity for Time period power grid node By virtual power grid nodes Internal generator set The amount of electricity generated, For generator sets Carbon emission factors.

9. A system for optimizing an enterprise's electricity purchase strategy, characterized in that, The system includes an electricity carbon footprint conversion module and a module for constructing and solving enterprise electricity purchase strategy optimization models. The electricity carbon footprint conversion module is used to construct a mapping model between the product electricity carbon footprint and electricity carbon emissions based on the enterprise product electricity carbon footprint output by the system of claim 7, and convert the product electricity carbon footprint into the product production electricity carbon emissions. The module for constructing and solving the enterprise electricity purchase strategy optimization model is used to consider the constraints of carbon emissions from product production electricity, and to construct an enterprise electricity purchase strategy optimization model with the goal of minimizing enterprise electricity purchase costs and carbon costs, and then solve it to obtain the enterprise electricity purchase optimization strategy.

10. The enterprise power purchase strategy optimization system according to claim 9, characterized in that, The mapping model between the product carbon footprint and electricity carbon emissions includes: ； In the above formula, Carbon emissions per unit of electricity produced. For product output, For a set of virtual power grid nodes, For virtual power grid nodes The set of power grid nodes within the area, for Time-based virtual power grid nodes The comprehensive carbon emission factor for Time period power grid node Electricity consumption for product manufacturing For time windows; The objective function of the enterprise electricity purchase strategy optimization model includes: ； ； ； In the above formula, For product output, , Time windows Regular electricity prices and green electricity prices within the country , Time windows The amount of conventional electricity purchased and the amount of green electricity purchased within the country. For product carbon footprint, For the time window in the carbon market The average carbon price within, Carbon emissions per unit of electricity produced. This refers to the carbon emissions generated from non-electricity consumption processes during the production of a single product. For production process Non-electricity carbon emission factor per unit output For production process The production response function is obtained by regressing historical electricity-production data. for Time-based production process At virtual grid nodes Electricity consumption For a set of virtual power grid nodes; The constraints include constraints on electricity consumption for enterprise production, carbon emissions from electricity generated during product production, and virtual node power balance constraints. The carbon emissions from electricity generated during product production constraints include: ； In the above formula, This is the upper limit for the carbon footprint of a product.

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