Method, system and equipment for calculating and optimizing carbon emission of electric vehicle charging pile and medium

Abstract

The invention relates to the technical field of carbon emission calculation, in particular to an electric vehicle charging pile carbon emission calculation and optimization method, system, equipment and medium, a carbon emission flow matrix is constructed, and a conduction mechanism of carbon emission is determined by establishing carbon emission flow distribution of nodes; based on the carbon emission flow matrix and the conduction mechanism of the carbon emission, tracking the carbon emission condition of the charging pile of the generator set, analyzing the influence of different types of generator sets on the carbon emission of the charging pile, and evaluating the change of the carbon emission; based on a carbon emission change result obtained through evaluation, the power level of the charging pile and the change rule of carbon emission under different charging loads are analyzed, the power requirement of the charging pile is combined with the power flow characteristic of a power system, and an evaluation method for distinguishing the carbon emission characteristics of different units is constructed, so that the influence mechanism of charging pile access on the carbon emission of a power grid is depicted; the limitation that unit differences are not distinguished in a traditional method is overcome, and low-carbon cooperative operation of a power grid and a charging pile system is promoted.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of carbon emission calculation, and in particular to a method, system and device for calculating and optimizing carbon emission of electric vehicle charging piles, and a medium. BACKGROUND

[0002] With the continuous development of new energy vehicles, the construction of charging piles is also expanding. A considerable part of the carbon emissions of electric vehicles comes from the electric energy consumed in the interaction process of electric vehicles and charging piles. These carbon emissions not only depend on whether the power source accessed during charging is clean energy or non-clean energy, but also are closely related to the power flow distribution of the power distribution network. Specifically, the power flow distribution of the power distribution network reflects the load and loss of different lines and different nodes in the transmission and distribution process, directly affecting the actual carbon emission intensity of the power involved in the charging process. For example, during the peak load period, the power grid often needs to call more fossil energy generators with high marginal cost and large carbon emissions to meet the electricity demand. At this time, the power flow of the power distribution network is usually in a heavy load state, the line loss increases, and the unit carbon emission corresponding to the charging behavior also increases accordingly. During the low load period or the high output period of renewable energy (such as the peak output of photovoltaic power during the day or the surplus of wind power at night), the power flow distribution is relatively light, and the clean energy penetration rate is high. At this time, the carbon emission generated by charging is significantly reduced. Therefore, with the change of the power flow distribution of the power distribution network, the carbon emission of the charging pile usually has a trend of "high in peak time and low in valley time". Accurate evaluation and guidance of the charging behavior to match the low-carbon power flow period are crucial to the environmental benefits of electric transportation.

[0003] At present, the existing carbon emission evaluation method of charging piles mainly estimates carbon emission through carbon emission factor accounting or conversion. These methods usually take the official published carbon emission factor as the coefficient and multiply the electricity consumption to estimate the carbon emission. Some methods also use real-time monitoring of carbon emission by equipment. However, these methods ignore the particularity of charging piles as a carrier for new energy vehicles to participate in demand response of the power grid, and regard charging piles as general electricity load, resulting in deviation in the estimation of carbon emission. For example, when an electric vehicle participates in green power consumption through a charging pile, green power is used, and almost no carbon emission is generated, which is significantly different from the carbon emission calculated by the traditional method. Therefore, the existing method cannot accurately reflect the carbon emission of charging piles in the process of green power consumption, and a more accurate calculation method is urgently needed. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the application provides an electric vehicle charging pile carbon emission calculation and optimization method and system, which solves the problem that the existing carbon emission evaluation method cannot accurately reflect the conduction process of the carbon emission of the charging pile, especially when the electric vehicle participates in green power consumption.

[0006] To solve the above technical problems, the application provides the following technical solutions. In a first aspect, the application provides an electric vehicle charging pile carbon emission calculation and optimization method, comprising: A carbon emission flow matrix is constructed to determine the conduction mechanism of carbon emission by establishing the carbon emission flow distribution of nodes; Based on the carbon emission flow matrix and the conduction mechanism of carbon emission, the carbon emission of the charging pile of the generating unit is tracked, the influence of different types of generating units on the carbon emission of the charging pile is analyzed, and the carbon emission change is evaluated; According to the evaluated carbon emission change, the influence of the charging pile power on the carbon emission and the carbon emission change of the charging pile under different charging loads are analyzed, and the influence of the charging pile and the power system power flow on the carbon emission is evaluated.

[0007] As a preferred scheme of the electric vehicle charging pile carbon emission calculation and optimization method, the construction of the carbon emission flow matrix comprises: Based on the power grid topology structure, a power flow distribution matrix is established to describe the active power flow transmission relationship between the generating units, branches and load nodes; Based on the active power flow transmission relationship, a load distribution matrix is introduced to represent the power correlation and energy consumption between different load nodes and system nodes; According to the actual physical relationship, a node active flux matrix is established, and based on the node active flux matrix, the carbon emission intensity of the node is calculated.

[0008] As a preferred scheme of the electric vehicle charging pile carbon emission calculation and optimization method, the determination of the conduction mechanism of carbon emission by establishing the carbon emission flow distribution of nodes comprises: The matrix calculation expression of the node carbon potential is obtained by analyzing the change of the node carbon potential; Based on the matrix calculation expression of the node carbon potential, the conduction relationship of the node carbon emission flow is calculated to quantify the carbon flow coupling effect between nodes and the carbon conduction influence of the power generation side on the load side; The carbon emission conduction path between the nodes of the power system is calculated to determine the distribution of the carbon emission, and the identification and quantitative description of the overall carbon emission conduction characteristics of the power system are performed.

[0009] As a preferred embodiment of the carbon emission calculation and optimization method for electric vehicle charging piles described in this invention, the method for tracking the carbon emission status of charging piles and analyzing the impact of different types of generator sets on the carbon emissions of charging piles includes: Track the impact of clean energy units and non-clean energy units on the carbon emissions of charging piles, and compare the contribution ratio of different generator units to the node carbon potential under different operating conditions. Based on the contribution ratio of different generator sets to the nodal carbon potential under different operating periods, the carbon emission transmission relationship between electric vehicle charging piles and generator sets is analyzed, and the transmission path and proportion of carbon emission flow under different energy structures are obtained. Based on the transmission path and proportion of carbon emission flows under different energy structures, the changes in carbon emissions from charging piles are determined, and a comparative analysis is conducted based on the power output of different units.

[0010] As a preferred embodiment of the carbon emission calculation and optimization method for electric vehicle charging piles described in this invention, the assessment of carbon emission changes includes: The relationship between the carbon emission factor of the generating unit and the power output was analyzed, and the impact of changes in the carbon emission factor of different types of generating units on the carbon emissions of charging piles was assessed. Furthermore, the impact of load fluctuations on carbon emissions was assessed based on the load changes of the power grid nodes where the charging piles are located.

[0011] As a preferred embodiment of the carbon emission calculation and optimization method for electric vehicle charging piles described in this invention, the step of analyzing the impact of charging pile power on carbon emissions and the changes in carbon emissions of charging piles under different charging loads, based on the assessment, includes: The impact of changes in charging pile power on carbon emissions is assessed by examining the relationship between charging pile power and the carbon emission factor of the power system. The changes in carbon emissions from charging piles are calculated based on their power demand under different charging loads, and expressed as follows: in, This represents the amount of carbon emissions generated by the power generation side per unit of time to meet the load demand of nodes. For any th For each node with load access, the carbon emission flow rate corresponding to the load is... , , , , These represent the carbon emissions generated at nodes 1, 2, 3, and Y, respectively, while they are under load. Represents the load distribution matrix. Let T denote the nodal carbon potential vector, and T denote the transpose.

[0012] The beneficial effects of the preferred technical solution are: by introducing the relationship between the charging pile power and the carbon emission factor of the power system, the influence of the charging pile on carbon emission under different charging loads can be evaluated, the carbon emission is dynamically calculated according to the change of the charging pile power demand, the carbon emission fluctuation of the charging pile under different load conditions is reflected in real time by combining the change of the carbon emission factor of the power system, the carbon emission level of the charging pile is reduced, and the green operation ability of the power system is improved.

[0013] As a preferred scheme of the electric vehicle charging pile carbon emission calculation and optimization method, wherein: the evaluation of the influence of the charging pile and the power system power flow on carbon emission comprises: By combining the power flow distribution of the power system and the power demand of the charging pile, the influence of the charging pile and the power system power flow change on carbon emission is analyzed, and the influence of the power system load change on the carbon emission of the charging pile is determined. The unit injection distribution matrix is introduced to describe the active power distribution from the generator unit to the load node and the direction and boundary conditions of the carbon emission flow.

[0014] The beneficial effects of the preferred technical solution are: by combining the power flow distribution of the power system and the power demand of the charging pile, the influence of the charging pile and the power system power flow change on carbon emission can be accurately analyzed, so as to predict the specific influence of the power system load fluctuation on the carbon emission of the charging pile; the influence of the load change of the power system on the increase and decrease of the carbon emission of the charging pile can be evaluated, by introducing the unit injection distribution matrix, the active power distribution from the generator unit to the load node and the direction and boundary conditions of the carbon emission flow can be clearly described, and the carbon emission control ability of the electric vehicle charging pile under different load conditions is improved.

[0015] In a second aspect, the present application provides an electric vehicle charging pile carbon emission calculation and optimization system, comprising: A carbon emission flow matrix module is configured to build a carbon emission flow matrix, establish a carbon emission flow distribution of nodes, and determine the conduction mechanism of carbon emission. A unit carbon emission analysis module is configured to track the charging pile carbon emission of the unit according to the carbon emission flow matrix, analyze the influence of different types of generator units on the charging pile carbon emission, and evaluate the carbon emission change. A power carbon emission analysis module is configured to evaluate the influence of the charging pile power on carbon emission and the carbon emission change of the charging pile under different charging loads, and analyze the influence of the charging pile and the power system power flow on carbon emission.

[0016] In a third aspect, the present application provides an electronic device, comprising: A memory and a processor. The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions, which realize the steps of the electric vehicle charging pile carbon emission calculation and optimization method.

[0017] In a fourth aspect, the present application provides a computer readable storage medium storing computer executable instructions, which realize the steps of the electric vehicle charging pile carbon emission calculation and optimization method when executed by a processor.

[0018] Compared with the prior art, the present application has the following beneficial effects: by constructing a carbon emission flow matrix and analyzing the carbon emission conduction mechanism between the nodes of the power system and the charging piles, the present application can evaluate the carbon emission of the charging piles under different loads and power system flow changes, track the influence of clean energy units and non-clean energy units on the carbon emission of the charging piles by introducing a unit injection distribution matrix, and evaluate the effect of different power generating units on the carbon emission of the charging piles in real time; in combination with the power demand of the charging piles and the power system flow, the present application can dynamically analyze the change of the carbon emission of the charging piles, and overcome the limitation of the traditional method that does not distinguish different units. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0020] Figure 1 The overall flowchart of the electric vehicle charging pile carbon emission calculation and optimization method according to an embodiment of the present application.

[0021] Figure 2 The IEEE14 node system diagram of the electric vehicle charging pile carbon emission calculation and optimization method according to an embodiment of the present application.

[0022] Figure 3 The program flowchart of the electric vehicle charging pile carbon emission calculation and optimization method according to an embodiment of the present application.

[0023] Figure 4 The day-night carbon potential comparison diagram of the electric vehicle charging pile carbon emission calculation and optimization method according to an embodiment of the present application.

[0024] Figure 5 The node carbon potential change diagram of the electric vehicle charging pile carbon emission calculation and optimization method according to an embodiment of the present application when the node 1 is connected to the variable load.

[0025] Figure 6 A node 8 access change load node carbon potential change graph of the electric vehicle charging pile carbon emission calculation and optimization method according to an embodiment of the present application.

[0026] Figure 7 A node 5 access change load node carbon potential change graph of the electric vehicle charging pile carbon emission calculation and optimization method according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0028] Embodiment 1, refer to Figure 1 According to an embodiment of the present application, an electric vehicle charging pile carbon emission calculation and optimization method is provided, comprising: In order to solve the problem that the existing carbon emission evaluation method cannot accurately reflect the conduction process of the carbon emission of the charging pile, especially the large calculation error of the traditional method when the electric vehicle participates in green power consumption, the present application provides an electric vehicle charging pile carbon emission calculation and optimization method.

[0029] S1: Construct a carbon emission flow matrix, determine the conduction mechanism of carbon emission by establishing the carbon emission flow distribution of the node; S2: Based on the carbon emission flow matrix and the conduction mechanism of carbon emission, track the carbon emission of the charging pile of the generating unit, analyze the influence of different types of generating units on the carbon emission of the charging pile, and evaluate the carbon emission change; S3: According to the evaluated carbon emission change, analyze the influence of the charging pile power on the carbon emission and the carbon emission change of the charging pile under different charging loads, and evaluate the influence of the charging pile and the power system flow on the carbon emission.

[0030] Therefore, by constructing the carbon emission flow matrix and combining the carbon emission flow distribution between the nodes and the charging piles of the power system, the present application can describe the conduction mechanism of carbon emission, and ensure the dynamic monitoring and optimization of the carbon emission of the charging pile. By analyzing the influence of different types of generating units on the carbon emission of the charging pile, especially the carbon emission change under different load conditions, the accuracy of carbon emission evaluation is improved.

[0031] Embodiment 2, refer to Figures 2-7 According to an embodiment of the present application, based on the above-mentioned embodiment, an electric vehicle charging pile carbon emission calculation and optimization method is provided.

[0032] In this embodiment of the application, step S1 constructs a carbon emission flow matrix, and determines the carbon emission transmission mechanism by establishing the carbon emission flow distribution of nodes, including: A1: Based on the power grid topology, establish a power flow distribution matrix to describe the active power flow transmission relationship between generator sets, branches, and load nodes.

[0033] A2: Based on the active power flow transmission relationship, a load distribution matrix is ​​introduced to represent the power correlation and energy consumption between different load nodes and system nodes.

[0034] A3: Based on the actual physical relationships, establish the active power flux matrix of the nodes, and calculate the carbon emission intensity of the nodes based on the active power flux matrix of the nodes.

[0035] A4: By analyzing the changes in nodal carbon potential, the matrix calculation expression for nodal carbon potential is obtained.

[0036] A5: Based on the matrix calculation expression of nodal carbon potential, calculate the transmission relationship of nodal carbon emission flow, quantify the carbon flow coupling effect between nodes and the carbon transmission impact from the power generation side to the load side; A6: Calculate the carbon emission transmission paths between power system nodes, determine the distribution of carbon emissions, and identify and quantitatively describe the overall carbon emission transmission characteristics of the power system.

[0037] It should be noted that by constructing a carbon emission flow matrix, the transmission mechanism of carbon emissions in the power system can be accurately described. Based on the establishment of the power grid topology and power flow distribution matrix, combined with the calculation of the active power flux matrix at each node, the carbon emission flux and carbon potential changes of each node can be accurately tracked; the impact of different generator units and load nodes on carbon emissions can be reflected, avoiding the bias in the carbon emission estimation of charging piles in traditional calculation methods; by tracking the changes in carbon potential in real time, a scientific basis can be provided for the optimization of carbon emissions of charging piles, which helps to improve the accuracy of carbon emission calculation and ensure accurate assessment of carbon emissions of the power system and charging piles.

[0038] Specifically, A1~A5 include cases where one is known to have The power distribution network topology with nodes is given. Each node has a generator set connected. Calculate the power flow distribution of the power distribution system if there are load connections at each node. The branch power flow distribution matrix is ​​defined as a Rank matrix, branch power flow distribution matrix Yuan Composition of elements, among which Belongs to the set of inflow node branches j belongs to the set of outflow node branches ; This represents the sum of all active power flows from the starting node i to the destination node j. It represents the sum of all active power flows from the starting node j to the target node i; node With nodes When there are branches connecting them, start from the starting node Flow to target node The sum of all contributing currents is the positive current. ,but The sum of all active power flows from target node j to starting node i is called the reverse power flow. If there is no forward power flow and only reverse power flow exists, the reverse power flow value is... Sometimes, , Branch power flow distribution matrix Represented as: Introducing the load distribution matrix This describes the connection relationships between all electrical loads and the power system, as well as the active power load. The matrix is... An dimensional matrix with elements of order 1. , , From the initial load node Flow to target node The sum of all active power is positive power. ,but From the target node Flow to the starting node The sum of all active power is the reverse power. When only reverse power exists and no forward power exists, we have , , Represented as: According to Kirchhoff's laws, the branches flowing into and out of a node... Since the branch currents are equal, the active power flowing into and out of a node is also equal. Because the node carbon potential depends only on the inflow power and not on the outflow, a matrix describing the sum of all active power flowing into a node at any given time is introduced—the node active flux matrix. The node active flux matrix is: Diagonal matrix of order 1, denoted by the symbol Indicates that the element is represented by Representation; element value It consists of two parts: one is the active power input from adjacent branches, and the other is the active power injected into the node by neighboring generators, expressed as: where, is the element of the node active power flow matrix, is the adjacent branch of the node n, is the active power injected into the node n, is the active power output of the generator group connected to the node n, and is equal to 0 if there is no generator group connected to the node n, and the non-diagonal elements are equal to 0, denotes the set of branches flowing into the node n.

[0039] The diagonal element of the node active power matrix in the row is equal to and the sum of the elements in the column, and let , then we have: where, is the node active power flow matrix, is the order row vector with all elements equal to 1, denotes diagonalization, is the active power column vector, is the branch power flow distribution matrix and is the transpose of the product of the distribution matrices.

[0040] A6 includes the branch carbon flow rate distribution matrix, each matrix element corresponds to the carbon flow rate value of a specific node-branch combination; the carbon flow rate matrix is an order matrix, denoted by : where, is the carbon flow rate matrix, denotes the branch power flow distribution matrix, denotes diagonalization, is the node carbon potential vector.

[0041] The carbon potential of the node n is calculated according to the definition of the node carbon potential : where, is the node carbon potential, denotes the sum of the active power injections of the adjacent branches flowing into the node n; the adjacent branches belong to the set , ​P = ∑Pgi Ci = ∑Pgi Ci = ∑Pgi Ci = ∑Pgi Ci = ∑Pgi Ci = ∑Pgi Ci = ∑Pgi Ci = ∑Pgi where, Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi

[0042] Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi where, Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Pi = ∑Pgi Represents nodes Connecting branch roads The injected power is common. This represents the active power injected by generator Gi connected to node i, where T represents transpose and X represents the active power injected by the generator Gi. Each node is connected to the generator set. From 1 to , From 1 to , Let be the nodal carbon potential vector. Represents the inverse of a matrix. Represents the branch power flow distribution matrix. The active flux matrix of the nodes is represented as Diagonal matrix of order 1 For carbon emission intensity matrix, The distribution matrix, This is the transpose of the distribution matrix. The potential of the carbon node.

[0043] For example, the IEEE 14-bus system is used as the research object, and the power output of the generator set and the power of the electrical load are both in MW. Assuming that there is no line loss, the carbon emissions of the new energy generator set are regarded as zero, and the carbon emission flow of the following implementation case is calculated using the DC power flow method.

[0044] The system is equipped with five generator units, including three thermal power generator units and two new energy generator units. G1 is a coal-fired unit with a rated capacity of 120MW; G2 and G4 are both gas-fired units with rated capacities of 40MW and 21MW respectively; G3 is a photovoltaic generator unit with a rated capacity of 60MW; and G5 is a hydroelectric generator unit with a rated capacity of 20MW. The IEEE 14-node system for unit output, load, and active power flow distribution is as follows: Figure 2 As shown; the model was built using MATLAB, the MATPOWER toolkit was applied, and the Newton-Raphson method was used for power flow calculation. The program flow is as follows. Figure 3 As shown; Assume the carbon emission intensity of all generator sets is expressed in kgCO2 / (kW·h); and consider the carbon emission intensity of new energy generator sets as 0; then the carbon emission intensity matrix is ​​as follows: in, For carbon emission intensity matrix, This is a transpose.

[0045] Define an indicator to characterize the relationship between active power flow and nodal carbon emission flow, assuming... Each node describes a node. The equivalent carbon emissions per unit of electricity consumed, as represented by the generator set, are shown by the symbol. denotes the carbon emission intensity of the generator set, and the unit is kg / MWh, and the formula is as follows: wherein, is the node corresponding to the unit power consumption, and the equivalent carbon emission of the generator set is denotes the branch set of the node flowed into by the power flow and the carbon emission flow, is a branch, is the active power flow of the branch, is the carbon emission flow density of the branch i flowed into the node n from the line i, is the total carbon emission flow on the branch i; the node active flux and the node carbon potential of all nodes can be obtained by calculation, as shown in Table 1.

[0046] Table 1 Node active flux and node carbon potential

[0047] The node carbon potential can be up to 87.5 gCO2 / (kW·h) at most and 0 at least, and the carbon flow presents a clear hierarchical structure; through the carbon flow rate matrix of each branch , since the active power of the node 1 and the node 8 only comes from the generator set of the node, the node carbon potential thereof is equal to the carbon emission intensity of the generator set; the nodes 11 and 12 are only provided with active power by the node 6, so their carbon potential is equal to that of the node 6, while the nodes 2, 3 and 6 have power coming from other branches, so their carbon potential is not consistent with the carbon emission intensity of the node; the result is consistent with the power flow distribution obtained by the direct current power flow calculation method, which can verify its correctness; the carbon emission flow density and the carbon emission flow rate of each branch are calculated, as shown in Table 2. Table 2 Active power flow of branch and carbon flow rate

[0048] The carbon emission flow depends on the power flow of the power system, and the active power flow direction of the power system is closely related to the load side and the generator side unit, and it is also necessary to verify the conservation of the carbon emission flow; according to the generator set output, the load distribution matrix and the carbon emission flow rate distribution matrix, the load carbon emission flow rate and the unit injection carbon emission flow rate can be obtained; as shown in Table 3.

[0049] Table 3 Load carbon flow rate and unit injection carbon flow rate

[0050] The sum of the load carbon emission flow rate is equal to the sum of the unit injection carbon flow rate, and the conservation of the carbon emission flow is verified.

[0051] In an alternative embodiment, the carbon emission flow matrix in step S1 can also consider the dynamic impact of load fluctuations on carbon emission flow by combining time series data analysis; for the time series fluctuations of load in the power system, the load distribution matrix and historical load data are combined to dynamically adjust the calculation of carbon emission flux of each node. By introducing time series data analysis, the calculation accuracy of the carbon emission flow matrix can be further optimized to ensure that the carbon emission changes of charging piles and the power system in different time periods are accurately reflected. This approach helps to analyze the impact of short-term load fluctuations (such as peak load changes in the morning and evening) on carbon emission flow, providing support for real-time carbon emission optimization and ensuring optimal carbon emission management under different load and power generation conditions.

[0052] In another alternative embodiment, the carbon emission flow matrix in step S1 can also be further optimized by introducing a power grid dispatching optimization model; by combining the dispatching optimization model of the power system, the calculation method of the carbon emission flow matrix is adjusted in real time according to the load demand of the power grid and the output of the generator set. For example, when the load demand of the power grid is high, low-carbon emission generator sets (such as hydroelectric or wind power) can be preferentially dispatched by optimizing the dispatching of the power grid, thereby reducing the overall carbon emission of the power system. Through this optimization method, the impact of different dispatching strategies on carbon emission flow can be accurately evaluated, and real-time decision support for carbon emission optimization of charging piles can be provided to promote low-carbon operation.

[0053] In the embodiments of the present application, the change of node carbon potential in step A4 includes: Different generator sets have different carbon emission intensities, such as coal-fired power generation sets using fossil energy such as coal and natural gas, which have high carbon emission intensity, and clean energy such as photovoltaic and water conservancy facilities, which have very low carbon emission intensity, so the carbon emission intensity of the same node is different when different generator sets are connected; The formula of the node carbon emission intensity vector of X generator sets is: wherein, represents the node carbon emission intensity vector of X generator sets, , , represent the node carbon emission intensity of 1, 2, X generator sets, respectively, and T represents transposition.

[0054] The node carbon potential vector describes the equivalent carbon emission amount on the power generation side corresponding to the consumption of a unit of electricity at the node, which is represented by wherein, is the node carbon potential vector, represents the carbon emission intensity of the i-th generator set, and T represents transposition.​​ carbon potential of the i-th node, carbon potential of the i-th node, carbon potential of the i-th node, carbon potential of the i-th node, carbon potential of the i-th node, T represents transpose.

[0055] In an optional embodiment, the analysis of the change of the node carbon potential in step A4 can also be further optimized by introducing a dynamic load adjustment mechanism; the calculation method of the node carbon potential can be dynamically adjusted according to the real-time load demand in the power system; by considering the influence of load fluctuation on carbon emission intensity, the carbon potential of the node not only depends on the type of generator unit, but also should be adjusted according to the real-time load change; for example, when the load demand increases, the conduction path of carbon emission may change, thereby affecting the distribution of carbon potential; in this case, real-time load data can be introduced and combined with a load prediction model (such as a regression analysis model based on historical data or a machine learning prediction model) to dynamically optimize the node carbon potential, ensuring the real-time and accuracy of carbon emission.

[0056] In another optional embodiment, the analysis of the change of the node carbon potential in step A4 can also be further refined by introducing a multi-region collaborative scheduling algorithm; the influence of collaborative scheduling of generator units in different regions of the power system on carbon emission can be considered; by designing an inter-regional collaborative optimization algorithm, the output of the generator unit and the power flow direction are optimized according to the carbon emission intensity and power demand of each region; this algorithm can more accurately simulate and analyze the carbon emission conduction process between regions, especially when the power grid experiences load fluctuations or the penetration rate of new energy increases, the overall carbon emission can be reduced by adjusting the power flow path between regions.

[0057] In the embodiments of the present application, step S2 tracks the carbon emission of the charging pile of the unit based on the carbon emission flow matrix and the conduction mechanism of carbon emission, analyzes the influence of different types of generator units on the carbon emission of the charging pile, and evaluates the change of carbon emission, including: B1: Track the influence of clean energy units and non-clean energy units on the carbon emission of the charging pile, and compare the contribution proportion of different generator units to the node carbon potential in different operation periods.

[0058] B2: Based on the contribution proportion of different generator units to the node carbon potential in different operation periods, analyze the carbon emission conduction relationship between the electric vehicle charging pile and the unit, and obtain the conduction path and proportion of carbon emission flow under different energy structures.

[0059] B3: Based on the conduction path and proportion of carbon emission flow under different energy structures, determine the change of the carbon emission of the charging pile, and compare and analyze based on the power output of different units.

[0060] B4: Evaluate the impact of changes in carbon emission factors of different types of generator sets on the carbon emissions of charging piles.

[0061] B5: According to the load changes of the grid node where the charging pile is located, evaluate the impact of load fluctuations on carbon emissions.

[0062] Specifically, B1-B5 include first analyzing the composition of the generator set, calculating the node carbon potential of each generator set, and then studying the conduction mechanism and influence of charging pile carbon emissions according to the carbon potential of the charging pile. For example, G3 photovoltaic generator set works during the day, while at night it has zero output. Now explore the impact of charging piles on carbon emissions in daytime and nighttime usage scenarios. First, add the function of calculating the carbon potential source proportion to reflect the changes in carbon potential proportion, which can trace the source of node carbon potential; calculate the contribution of each generator set to the node carbon potential source, the calculation formula is: Node carbon potential source proportion (Gi to node n) Where, is the power injected by Gi and ultimately delivered to node n; is the total carbon flow rate of node n; e Gi is the node carbon emission intensity of the generator set, the calculation results are shown in Table 4: Table 4 Node carbon potential source proportion

[0063] The daily usage pattern of electric vehicles is closely related to the user's daily routine, with most commuting time during the day. Usually, there is a large amount of traffic during the day, and electric vehicles are in use. At night, the car owner enters the rest time, and the electric vehicle is in charging state. Therefore, whether the photovoltaic generator set is in operation has an impact on the carbon emissions of the charging pile. The charging scenario at night is different from that during the day. When electric vehicles are charging at night, there is no sunlight, and the photovoltaic generator set does not operate. The distribution network node connected to the charging pile cannot obtain the active power of the photovoltaic generator set, and the node carbon potential will change. Based on the node carbon potential source, change the output of the generator set, make the photovoltaic generator set G3 output zero, simulate the night charging scenario of the charging pile, and explore the impact on the carbon emissions of the charging pile according to the node carbon potential source proportion. The node carbon potential source proportion is shown in Table 5.

[0064] Table 5 Night carbon potential source proportion

[0065] Table 5 shows that, except for node 8, the proportion of carbon potential from generator G1 has increased in all nodes. At night, the proportion of carbon potential from thermal power in the node where the charging pile is located has increased. Further comparison of node carbon potential during the day and at night is shown in Table 6. Table 6. Comparison of Nodal Carbon Potential During Day and Night

[0066] Daytime and nighttime node carbon potential data, such as Figure 4 As shown, the carbon potential (yellow) is lower during the day than at night (blue). The photovoltaic generator is directly connected to node 3. From the perspective of the power system topology, node 3 is the closest, and active power is consumed locally, resulting in the most significant decrease in carbon potential. At node 8, the active power flow comes entirely from the hydroelectric generator, so the carbon potential is zero. The carbon potentials at nodes 1 and 7 remain almost unchanged. The carbon potentials at other nodes increase during the day, and carbon emissions from charging increase at night. This study investigates the impact of carbon emission transmission from charging piles by studying their carbon potential. Figure 2 As shown in the node system diagram, G3 and G5 have a total output of 80MW. This study investigates the impact of the access of new energy units on the carbon emission transmission path to charging piles, how to change the carbon potential of the nodes where charging piles are located, and thus affect carbon emissions, as well as the impact of centralized and distributed construction of new energy units. Four groups were set up for comparison: Group 1: G3 output 60MW, G5 output 20MW; Group 2: G3 20MW, G5 60MW; Group 3: G3 79.9MW, G5 0MW; Group 4: to study the impact of clean energy on nearby nodes. To avoid accuracy issues caused by matrix singularities, Group 3 was set as follows: G3 output 79.9MW, G5 output 0.1MW; Group 4: G3 output 0.1MW; G5 output 79.9MW. Unit G3 is located at node 3, and unit G5 is located at node 8. The shortest path between nodes 3 and 8 is formed through nodes 4 and 7. The carbon potential of nodes 3, 4, 7, and 8 was calculated and summarized, as shown in Table 7. Table 7 Comparison of Carbon Potential at Grouped Nodes

[0067] The impact of new energy generator access on node carbon potential diffuses outward from the access node. Groups 1 and 2, as well as nodes 3 and 8, all have clean energy units. The node carbon potential decreases from node 4 to nodes 3 and 8. In group 3, the carbon potential decreases from node 7 to node 3. In group 4, the carbon potential decreases from node 3 to node 8. Compared with distributed access of generator units, the more concentrated the construction of new energy units at the node, the more significant the reduction in node carbon potential. This indicates that carbon emissions transmitted to charging piles are greatly affected by the power system topology, which is consistent with the boundary conditions for calculating the carbon emission flow of the power system.

[0068] In an optional implementation, the carbon emission status of the charging piles of the tracking unit in step B1 can be further optimized by introducing a diversified load forecasting model to improve the accuracy of carbon emission prediction. Specifically, by combining the commuting patterns and charging behaviors of electric vehicle users, and based on factors such as users' historical charging data, weather conditions, and traffic flow, the charging load demand at different times and locations can be predicted, thereby accurately simulating the actual impact of load fluctuations on carbon emissions. For example, the difference between daytime and nighttime charging loads can be accurately predicted by the model, further assessing the changing trend of grid load fluctuations on carbon emissions.

[0069] In another optional implementation, the carbon emissions of the charging piles tracked by the generator units in step B1 can be further improved through regionally optimized power system dispatching to enhance carbon emission control. Specifically, the dispatching strategy for generator units is optimized based on the differences in carbon emission factors across different regions of the power system. For example, in regions with lower carbon emission factors (such as areas with a high concentration of photovoltaic generator units), more renewable energy generator units can be prioritized for dispatching to provide power to the charging piles; while in regions with higher carbon emission factors (such as areas with a high concentration of thermal power units), the output of units with higher carbon emissions can be reduced through flexible dispatching to minimize the negative impact on the charging piles.

[0070] In this embodiment of the application, step S2, analyzing the carbon emission transmission relationship between electric vehicle charging piles and generator sets, includes: To describe the relationship between carbon emissions from electric vehicle charging stations and generating units, it is necessary to use a carbon emission flow matrix to track the carbon emission transmission paths between different generating units (such as clean energy units and traditional thermal power units) and charging stations; by analyzing the power flow distribution in the power system, the carbon emission situation of charging station nodes can be determined, thereby accurately reflecting the carbon emission transmission relationship of different types of generating units during the charging process.

[0071] In an optional implementation, the carbon emission transmission relationship in step S2 can be further optimized by introducing a load scheduling strategy for electric vehicle charging stations. By combining a power system load forecasting model with the real-time load demand of charging stations, the carbon emission levels of charging stations at different time periods can be accurately predicted. Specifically, the load scheduling strategy can enable charging stations to charge during periods of lower carbon emissions, avoiding overcharging during periods of high carbon emissions.

[0072] In another optional implementation, the carbon emission transmission relationship in step S2 can be further enhanced by coordinating the scheduling of charging piles with new energy generator sets; electric vehicle charging piles can coordinate with the power output fluctuations of clean energy generator sets such as wind power or photovoltaics, and use an intelligent scheduling system to adjust charging time and power, thereby increasing the charging load when wind power and photovoltaic output are high, reducing the load of traditional thermal power units, and improving the carbon emission efficiency of the system.

[0073] It should be noted that by dynamically tracking the contribution ratio of different generator sets to the carbon potential of the grid nodes, and combining the grid topology and power flow distribution, the transmission path and impact of clean energy output, generator set layout and operating sequence on the carbon emissions of charging piles were quantified.

[0074] In this embodiment of the application, step S3, based on the assessment, analyzes the impact of charging pile power on carbon emissions and the changes in carbon emissions of charging piles under different charging loads, and assesses the impact of charging piles and power system flow on carbon emissions, including: C1: Assess the impact of changes in charging pile power on carbon emissions by examining the relationship between charging pile power and the carbon emission factor of the power system.

[0075] C2: Calculate the changes in carbon emissions of charging piles based on their power requirements under different charging loads.

[0076] C3: Combining the power flow distribution of the power system with the power demand of charging piles, analyze the impact of power flow changes on carbon emissions and determine the impact of power system load changes on carbon emissions of charging piles.

[0077] C4: Introduces the unit injection distribution matrix to describe the active power distribution from the generator set to the load node, as well as the direction and boundary conditions of carbon emission flow.

[0078] It should be noted that the carbon emission flow model established in this study, by combining the relationship between charging pile power and the carbon emission factor of the power system, can accurately assess the carbon emission changes of charging piles under different charging loads based on the real-time power flow of the grid, providing a theoretical basis and technical support for the optimization of carbon emissions of charging piles. Specifically, the model can realize the dynamic calculation of carbon emissions. It does not use a fixed regional average carbon emission factor, but takes the load change of charging piles as input, updates the power flow distribution of the system in real time through the model, and solves the corresponding dynamic node carbon potential, thereby reflecting the carbon emission fluctuations of charging piles under different load conditions in real time, reducing carbon emission levels, and improving the green operation capability of the power system. Inside the model, by introducing the unit injection distribution matrix, the power distribution and the direction of carbon emission flow can be clearly described, ensuring the accuracy of carbon emission calculation. The source tracing method based on carbon emission flow theory effectively improves the carbon emission control capability of charging piles and promotes the coordinated development of the power system and new energy, achieving the goals of reducing carbon emissions, improving efficiency, and reducing costs.

[0079] Specifically, C2 is represented as: in, This represents the amount of carbon emissions generated by the power generation side per unit of time to meet the load demand of nodes. For any th For each node with load access, the carbon emission flow rate corresponding to the load is... , , , , These represent the carbon emissions generated at nodes 1, 2, 3, and Y, respectively, while they are under load. Represents the load distribution matrix. Let T denote the nodal carbon potential vector, and T denote the transpose.

[0080] C4 includes the introduction Unit injection distribution matrix of order To clarify the connection relationship between the connected generator sets and the distribution system, and to characterize the distribution of active power flow direction between power system nodes and generator set nodes, as well as the boundary conditions of generator set carbon emission flow, matrix elements are used... It means that among them Similar to the elements of the branch power flow distribution matrix, Characterized from generator node Transmitted to load node The total active power, when the system has a source node To the target node During positive power transmission, then Conversely, if the direction of active power flow is opposite, the active power... , ,but Represented as: in, For introduction The unit injection distribution matrix of order, For matrix elements, .

[0081] For example, a scenario is simulated where a larger load is connected at a node, i.e., a large-scale use of charging piles at a node. The change curve of the node's carbon potential is then analyzed to assess the specific impact of the significant increase in load on carbon emissions transmitted to the charging piles.

[0082] Generator unit G1 increases its output by 120MW, and loads from 0MW to 120MW are added individually at nodes 1, 5, and 8 to simulate the scenario of large-scale use of charging piles at the nodes; node 1 is the point with the maximum carbon potential, and node 8 is the point with the minimum carbon potential; the carbon potential of node 1 varies with load as shown in Table 8 and... Figure 5 As shown; Table 8. Changes in carbon potential at node 1

[0083] The carbon potential at node 8 varies with load as shown in Table 9 and Figure 6 As shown; Table 9. Changes in carbon potential at node 8

[0084] The carbon potential at node 5 varies with load as shown in Table 10 and Figure 7 As shown; Table 10. Carbon potential change at node 5

[0085] Depend on Figure 5 It can be seen that node 1 has the highest carbon potential, and is connected to a 240MW coal-fired power generating unit, resulting in the highest carbon emission intensity. The line graph of the carbon potential change of each node shows that the carbon potential of each node remains unchanged. Since the coal-fired power generating unit connected to node 1 has an output of 240MW, which is much greater than the 120MW load connected to the node, the active power at node 1 is sufficient, and there is no need to draw active power from other branches. Therefore, the carbon potential of each node remains unchanged.

[0086] Depend on Figure 6 It can be seen that the carbon potential of node 8 is the lowest, with a value of zero. When a hydroelectric generator is connected, the carbon emission intensity is zero. Increasing the load at this node causes the carbon potential of other nodes to increase. The carbon potential of node 8 also increases when the connected load is greater than 20MW. This is because the connected load is greater than the output of the G5 hydroelectric generator, resulting in insufficient active power. Active power needs to be drawn from other branches, which causes the change in node carbon potential.

[0087] Depend on Figure 7 It can be seen that when a changing load is connected to node 5, the carbon potential of other nodes shows both an increase and a decrease. Since the thermal power generator at node 1 has a large output, and node 5 is adjacent to node 1, it can obtain active power locally, so the fluctuation of the node carbon potential is not obvious. When the connected load is less than the output of the generator set at the node, the node carbon potential remains unchanged. Guiding consumers on the load side to charge electric vehicles at locations with low node carbon potential is beneficial to reducing carbon dioxide emissions, but it may change the power flow of the power system, leading to an increase in node carbon potential. For example, when the load is greater than 40MW, the carbon potential of node 3 is greater than that of node 8. Guiding users to charge at node 3 is more effective in reducing carbon emissions than charging at node 8.

[0088] In summary, this invention, by introducing a generator injection distribution matrix, can effectively track the impact of different types of generator sets on the carbon emissions of charging piles. Furthermore, by combining the power demand of charging piles with the power flow of the power system, it achieves accurate control of carbon emissions from charging piles. This not only overcomes the limitations of traditional methods but also allows for dynamic optimization based on changes in power system load and charging pile power, thereby improving the carbon emission optimization capability of electric vehicle charging piles and promoting the coordinated operation of the power grid and charging pile system.

[0089] Example 3 illustrates a schematic scheme for calculating and optimizing carbon emissions from electric vehicle charging piles. It should be noted that the technical solution of this system for calculating and optimizing carbon emissions from electric vehicle charging piles is based on the same concept as the technical solution of the method described above. Details not described in detail in this embodiment can be found in the description of the method described above.

[0090] This embodiment also provides a carbon emission calculation and optimization system for electric vehicle charging piles, including: The carbon emission matrix module constructs a carbon emission matrix and determines the transmission mechanism of carbon emissions by establishing the carbon emission distribution of nodes. The unit carbon emission analysis module tracks the carbon emission status of the unit's charging piles based on the carbon emission flow matrix and carbon emission transmission mechanism, analyzes the impact of different types of generator units on the carbon emission of charging piles, and assesses changes in carbon emissions. The power carbon emission analysis module analyzes the impact of charging pile power on carbon emissions based on the assessed changes in carbon emissions, as well as the changes in carbon emissions of charging piles under different charging loads, and assesses the impact of charging piles and power system flow on carbon emissions.

[0091] This embodiment also provides an electronic device applicable to the calculation and optimization of carbon emissions from electric vehicle charging piles, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for calculating and optimizing carbon emissions from electric vehicle charging piles as proposed in the above embodiment.

[0092] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for calculating and optimizing carbon emissions from electric vehicle charging piles as proposed in the above embodiments.

[0093] The storage medium proposed in this embodiment and the method for calculating and optimizing carbon emissions from electric vehicle charging piles proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0094] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0095] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for calculating and optimizing carbon emissions from electric vehicle charging stations, characterized in that, include: Construct a carbon emission flow matrix and determine the carbon emission transmission mechanism by establishing the carbon emission flow distribution of nodes; Based on the carbon emission flow matrix and the carbon emission transmission mechanism, we track the carbon emissions of the charging piles of the generator sets, analyze the impact of different types of generator sets on the carbon emissions of the charging piles, and assess the changes in carbon emissions. Based on the assessed changes in carbon emissions, the impact of charging pile power on carbon emissions and the changes in carbon emissions of charging piles under different charging loads are analyzed, and the impact of charging piles and power system flow on carbon emissions is assessed.

2. The method for calculating and optimizing carbon emissions from electric vehicle charging piles as described in claim 1, characterized in that, The construction of the carbon emission flow matrix includes: Based on the power grid topology, a power flow distribution matrix is ​​established to describe the active power flow transmission relationship between generator sets, branches and load nodes. Based on the active power flow transmission relationship, a load distribution matrix is ​​introduced to represent the power correlation and energy consumption between different load nodes and system nodes; Based on the actual physical relationships, a node active power flux matrix is ​​established, and the node carbon emission intensity is calculated based on the node active power flux matrix.

3. The method for calculating and optimizing carbon emissions from electric vehicle charging stations as described in claim 2, characterized in that, The process of determining the carbon emission transmission mechanism by establishing the carbon emission flow distribution at nodes includes: By analyzing the changes in nodal carbon potential, the matrix calculation expression for nodal carbon potential is obtained; Based on the matrix calculation expression of nodal carbon potential, the transmission relationship of nodal carbon emission flow is calculated, and the carbon flow coupling effect between nodes and the carbon transmission impact from the power generation side to the load side are quantified. Calculate the carbon emission transmission paths between nodes in the power system, determine the distribution of carbon emissions, and identify and quantitatively describe the overall carbon emission transmission characteristics of the power system.

4. The method for calculating and optimizing carbon emissions from electric vehicle charging stations as described in claim 3, characterized in that, The carbon emissions of the charging piles of the tracked generator units were analyzed, and the impact of different types of generator units on the carbon emissions of the charging piles was examined, including: Track the impact of clean energy units and non-clean energy units on the carbon emissions of charging piles, and compare the contribution ratio of different generator units to the node carbon potential under different operating conditions. Based on the contribution ratio of different generator sets to the nodal carbon potential under different operating periods, the carbon emission transmission relationship between electric vehicle charging piles and generator sets is analyzed, and the transmission path and proportion of carbon emission flow under different energy structures are obtained. Based on the transmission path and proportion of carbon emission flows under different energy structures, the changes in carbon emissions from charging piles are determined, and a comparative analysis is conducted based on the power output of different units.

5. The method for calculating and optimizing carbon emissions from electric vehicle charging stations as described in claim 4, characterized in that, The assessment of changes in carbon emissions includes: The relationship between the carbon emission factor of the generating unit and the power output was analyzed, and the impact of changes in the carbon emission factor of different types of generating units on the carbon emissions of charging piles was assessed. Furthermore, the impact of load fluctuations on carbon emissions was assessed based on the load changes of the power grid nodes where the charging piles are located.

6. The method for calculating and optimizing carbon emissions from electric vehicle charging stations as described in claim 5, characterized in that, The analysis of the impact of charging pile power on carbon emissions and the changes in carbon emissions of charging piles under different charging loads, based on the assessed changes in carbon emissions, includes: The impact of changes in charging pile power on carbon emissions is assessed by examining the relationship between charging pile power and the carbon emission factor of the power system. The changes in carbon emissions from charging piles are calculated based on their power demand under different charging loads, and expressed as follows: in, This represents the amount of carbon emissions generated by the power generation side per unit of time to meet the load demand of nodes. For any th For each node with load access, the carbon emission flow rate corresponding to the load is... , , , , These represent the carbon emissions generated at nodes 1, 2, 3, and Y, respectively, while they are under load. Represents the load distribution matrix. Let T denote the nodal carbon potential vector, and T denote the transpose.

7. The method for calculating and optimizing carbon emissions from electric vehicle charging stations as described in claim 6, characterized in that, The assessment of the impact of charging stations and power system flow on carbon emissions includes: By combining the power flow distribution of the power system with the power demand of charging piles, the impact of power system power flow changes on carbon emissions is analyzed, and the impact of power system load changes on carbon emissions of charging piles is determined. A generator injection distribution matrix is ​​introduced to describe the active power distribution from the generator set to the load node, as well as the direction and boundary conditions of carbon emission flow.

8. A carbon emission calculation and optimization system for electric vehicle charging piles, employing the carbon emission calculation and optimization method for electric vehicle charging piles as described in any one of claims 1-7, characterized in that, include: The carbon emission matrix module constructs a carbon emission matrix and determines the transmission mechanism of carbon emissions by establishing the carbon emission distribution of nodes. The unit carbon emission analysis module tracks the carbon emission status of the unit's charging piles based on the carbon emission flow matrix and carbon emission transmission mechanism, analyzes the impact of different types of generator units on the carbon emission of charging piles, and assesses changes in carbon emissions. The power carbon emission analysis module analyzes the impact of charging pile power on carbon emissions based on the assessed changes in carbon emissions, as well as the changes in carbon emissions of charging piles under different charging loads, and assesses the impact of charging piles and power system flow on carbon emissions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for calculating and optimizing carbon emissions from electric vehicle charging piles as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for calculating and optimizing carbon emissions from electric vehicle charging piles as described in any one of claims 1 to 7.

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