Fine measurement method for carbon emission on electricity consumption side based on carbon emission flow theory

Abstract

The application discloses a kind of carbon emission fine metering method of electricity side based on carbon emission flow theory, it is related to distribution network carbon emission flow calculation technical field, the method includes: based on the carbon flow rate of real-time power value in the charging process of energy storage element, the discharge carbon potential of energy storage element is solved, and the discharge carbon potential is the node carbon potential of energy storage element discharge moment;Based on the carbon potential of energy storage element discharge moment and the energy amount probability model of energy storage element initial charging moment, the node carbon potential represented by daily mileage is obtained;Based on the node carbon potential represented by daily mileage, the node carbon potential distribution is predicted.The application can realize the fine metering of distribution network side carbon emission, to analyze the high carbon element of user side accordingly, facilitate relevant department to monitor user side carbon emission situation, and formulate the carbon reduction policy of electric energy consumer.

Description

Technical Field

[0001] This invention relates to the field of carbon emission flow calculation technology for power distribution networks, and specifically to a precise method for measuring carbon emissions on the electricity consumption side based on carbon emission flow theory. Background Technology

[0002] Carbon reduction in the power system requires coordinated efforts across the entire supply chain, from power generation to grid and load. To unlock the potential for carbon reduction in the power sector, it is necessary to guide electricity users to participate in carbon reduction, support the healthy development of the carbon market, and promote the low-carbon transformation of the power economy. Real-time, accurate, and comprehensive statistical accounting of carbon emissions is one of the important bases for understanding the current status and trends of carbon emissions in the power industry, and provides corresponding policy support for carbon reduction.

[0003] In the power sector, the carbon emission factor on the electricity consumption side serves as a crucial bridge between end-user electricity consumption and system carbon emissions. Current calculation systems largely rely on generation-side accounting, with little to no statistical accounting of carbon emissions from the distribution network side. There is an urgent need to conduct carbon emission statistical accounting on the user side to promote the achievement of low-carbon goals. Summary of the Invention

[0004] The technical problem to be solved by this invention is how to achieve precise calculation of carbon emissions on the distribution network side.

[0005] The present invention solves the above-mentioned technical problems through the following technical means:

[0006] This invention proposes a precise method for measuring carbon emissions on the electricity consumption side based on carbon emission flow theory. The method includes:

[0007] Based on the real-time power value carbon flow rate during the charging process of the energy storage element, the discharge carbon potential of the energy storage element is calculated, where the discharge carbon potential is the nodal carbon potential of the energy storage element at the discharge point.

[0008] Based on the carbon potential at the discharge moment of the energy storage element and the probability model of the charge at the start of charging of the energy storage element, the nodal carbon potential represented by the daily driving mileage is obtained.

[0009] Based on the node carbon potential represented by daily mileage, the node carbon potential distribution is predicted.

[0010] Furthermore, the step of solving for the discharge carbon potential of the energy storage element based on the real-time power value carbon flow rate during the charging process includes:

[0011] Let the charging time of the energy storage element be t0-t, and the element be in a discharging state after time t. Then the discharge carbon potential at time t is:

[0012]

[0013] In the formula: e B(t) represents the carbon potential of the energy storage element at time t when it transitions from a charging state to a discharging state; F0 and E0 are the remaining carbon flow rate and charge of the energy storage element when it transitions from a discharging state to a charging state, respectively; R b (t) and P b (t) represents the carbon flow rate and charging power during the charging process of the energy storage element, respectively; η represents the charging and discharging efficiency of the energy storage.

[0014] Furthermore, the calculation process of the probability model of the charge level at the initial charging moment of the energy storage element includes:

[0015] Based on the factors affecting the charging of energy storage components, a probability model of the initial charge level of the energy storage component is calculated. These factors include charging duration, charging power, and battery capacity. The probability model of the charge level is as follows:

[0016]

[0017] In the formula: s is the daily mileage; W 100 The electrical energy consumed by the car per 100 kilometers; η is the charging efficiency; P c E represents the charging power; E represents the battery capacity of the car.

[0018] Furthermore, the nodal carbon potential, expressed as daily mileage, is obtained based on the carbon potential at the discharge moment of the energy storage element and the probability model of the charge level at the initial charging moment of the energy storage element, including:

[0019] Suppose that the linear relationship between the carbon flow rate and the electrical quantity at the start of the energy storage element is F0 = kE0, where E0 is the electrical quantity at the start of charging, F0 is the carbon flow rate at the start of charging, and k is a constant.

[0020] Based on the aforementioned linear relationship, and based on the carbon potential at the discharge moment of the energy storage element and the probability model of the charge level at the initial charging moment of the energy storage element, the nodal carbon potential represented by daily mileage is obtained as follows:

[0021]

[0022] In the formula: e B R is the node carbon potential expressed in terms of daily mileage. b The carbon flow rate.

[0023] Further, the prediction of nodal carbon potential distribution based on the nodal carbon potential represented by daily mileage includes:

[0024] Based on the nodal carbon potential represented by the daily mileage, a normal distribution is performed to obtain the carbon potential probability model of the energy storage element.

[0025] The carbon potential probability model is multiplied by the node carbon potential vector to predict the node carbon potential distribution.

[0026] Furthermore, the calculation of the nodal carbon potential vector includes:

[0027] Based on the branch data and node load of the distribution network, the forward-backward substitution method is used to solve the active power flow of the distribution network.

[0028] Given the carbon potential and active power output of the main grid input point and each distributed generator unit, the main grid access point is calculated as an equivalent generator unit model. The carbon potential of node i is the carbon flow density of all active power flows flowing out of node i. The carbon potential of node i is denoted as the ratio of the carbon flow rate to the active power of node i:

[0029]

[0030] In the formula: e i Let I be the carbon potential at node i; + p is the set of branches of the active power flow flowing into node i; Br For the active power flow of branch r; ρ r p is the carbon flux density of branch r; Gi Active power flow injected into generator i; eG i Let be the carbon potential of generator i;

[0031] Extending the carbon potential of node i to the power grid yields the branch power flow distribution matrix, the node active power flux matrix, the unit injection distribution matrix, and the unit carbon emission intensity vector.

[0032] Based on the branch power flow distribution matrix, node active power flux matrix, unit injection distribution matrix, and generator carbon emission intensity vector, the node carbon potential vector is calculated as follows:

[0033]

[0034] In the formula: E G Let P be the carbon emission intensity vector of the generator set. N Let P be the active flux matrix of the nodes. B Let P be the branch power flow distribution matrix. G Inject the distribution matrix into the unit.

[0035] Furthermore, the method also includes:

[0036] Calculate the node output distribution factor, specifically the active power output distribution factor H from node i to node j. ij for:

[0037]

[0038] In the formula: W ij Let W∑ be the network flow from node i through branch ij to neighboring node j; iH is the sum of network flows into node i; if there are no branch connections between two nodes or no positive network flows into adjacent branches, then H ij =0, when i=j, H ij =1.

[0039] Furthermore, the method also includes:

[0040] Calculate the path output distribution factor. If there are multiple connected paths from node i to node j, calculate the path output distribution factor D from node i to node j. ij for:

[0041]

[0042] Where: H st Output the distribution factor for the node, where Λ is the set of paths.

[0043] Furthermore, the method also includes:

[0044] Based on the elements of the i-th row of the unit-node association matrix, determine the contribution of the i-th unit to the carbon flow of all nodes in the power network. The unit-node association matrix is ​​R. U-N :

[0045]

[0046] Furthermore, the method also includes:

[0047] The carbon flow contribution of a single generator unit to all branches within the network can be calculated based on the unit-branch correlation matrix. for:

[0048]

[0049] The contribution of all generating units in the network to the load carbon flow rate can be obtained based on the unit-load correlation matrix R. U-L for:

[0050]

[0051] The advantages of this invention are as follows: This invention takes into account the time-varying characteristics of carbon potential of energy storage power sources, proposes the moment-to-moment carbon potential of energy storage elements in electric vehicles, constructs a carbon emission flow calculation model on the distribution network side, and can obtain the carbon emission situation on the distribution network side, realize the precise measurement of carbon emissions on the distribution network side, and analyze the high-carbon elements on the user side accordingly, so as to facilitate relevant departments to monitor the carbon emission situation on the user side and formulate carbon reduction policies for electricity consumers.

[0052] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating a precise method for measuring carbon emissions on the electricity consumption side based on carbon emission flow theory, as proposed in one embodiment of the present invention.

[0054] Figure 2 This is a schematic diagram of the topology of an IEEE 33-node distribution network system in one embodiment of the present invention;

[0055] Figure 3 This is a schematic diagram of a 61-node system in a certain area according to an embodiment of the present invention;

[0056] Figure 4 This is a schematic diagram of the carbon potential of 61 nodes in a distribution network system of a certain area at the 100th hour in one embodiment of the present invention;

[0057] Figure 5 This is a schematic diagram of the node carbon potential change in one embodiment of the present invention, wherein (a) is Figure 4 The carbon potential changes at nodes 10 and 29 are shown in (b). Figure 4 A graph showing the carbon potential changes of 61 nodes within the central distribution network system;

[0058] Figure 6 This is a schematic diagram of the energy storage battery charge, carbon flow rate, and carbon potential in a distribution network system of a certain area according to an embodiment of the present invention. Detailed Implementation

[0059] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0060] The following is a reference appendix. Figure 1 This invention describes a method for precise measurement of carbon emissions on the electricity consumption side based on carbon emission flow theory, comprising the following steps:

[0061] S1. Based on the real-time power value carbon flow rate during the charging process of the energy storage element, solve for the discharge carbon potential of the energy storage element, wherein the discharge carbon potential is the nodal carbon potential of the energy storage element at the discharge point.

[0062] S2. Based on the carbon potential at the discharge time of the energy storage element and the probability model of the energy quantity at the start of charging of the energy storage element, the nodal carbon potential represented by the daily driving mileage is obtained.

[0063] S3. Based on the node carbon potential represented by the daily mileage, predict the node carbon potential distribution.

[0064] It should be noted that this embodiment takes into account the time-varying characteristics of the carbon potential of the energy storage power source and proposes the moment-to-moment carbon potential of the electric vehicle energy storage element. This moment-to-moment carbon potential is the node carbon potential expressed by the daily driving mileage. Based on the node carbon potential expressed by the daily driving mileage, the node carbon potential distribution can be predicted, and the carbon emission situation on the distribution network side can be obtained, realizing the precise measurement of carbon emissions on the distribution network side. Based on this, the high carbon elements on the user side can be analyzed, which facilitates relevant departments to monitor the carbon emission situation on the user side and formulate carbon reduction policies for electricity consumers.

[0065] Furthermore, based on the calculated carbon potential at any given time, the power generation of traditional generator sets and new energy generator sets can be rationally allocated to different time periods, thereby reducing the power generation of traditional generator sets and achieving the goal of reducing carbon dioxide emissions.

[0066] In one embodiment, step S1 specifically includes the following steps:

[0067] Let the charging time of the energy storage element be t0-t, and the element be in a discharging state after time t. Then the discharge carbon potential at time t is:

[0068]

[0069] In the formula: e B (t) represents the carbon potential of the energy storage element at time t when it transitions from a charging state to a discharging state; F0 and E0 are the remaining carbon flow rate and charge of the energy storage element when it transitions from a discharging state to a charging state, respectively; R b (t) and P b (t) represents the carbon flow rate and charging power during the charging process of the energy storage element, respectively; η represents the charging and discharging efficiency of the energy storage.

[0070] The capacity constraints for energy storage power sources are as follows:

[0071]

[0072] E min ≤E s (t)≤E max

[0073] In the formula: E s (t) represents the charge of the energy storage element at time t; ε represents the self-discharge efficiency of the battery; P B (t) represents the charging and discharging power of the battery at time t, when P B When (t)≥0, the battery is charged, and when P B When (t)≤0, the battery discharges; β dis and β ch E represents the charge / discharge efficiency of the energy storage element.min and E max These represent the lower and upper limits of the remaining power capacity of the energy storage device, respectively.

[0074] The power constraints for energy storage power sources are as follows:

[0075] P Bmin ≤P B (t)≤P Bmax

[0076] In the formula: P Bmin and P Bmax This defines the lower and upper limits of the charging and discharging power of energy storage power sources. This paper considers the charging power of energy storage power sources as positive load power, and conversely, the discharging power as negative load power.

[0077] It should be noted that, based on distribution network power flow calculations, the target parameter for carbon flow calculation on the distribution network side is the nodal carbon potential. Using the nodal carbon potential as the dependent variable, the flow path of carbon emissions generated by the active power flow of generating units within the system is calculated. For loads on the electricity consumption side, it is necessary to analyze the composition of their carbon emissions; for generating units, it is necessary to understand the ultimate target of their carbon flow. This embodiment considers energy storage power sources, represented by energy storage, and constructs a user-side carbon flow operation model containing energy storage power sources. The carbon potential of energy storage power sources, represented by energy storage, changes with their charge and discharge states; therefore, their carbon potential calculation needs to be remodeled. Since the energy storage power source is connected to the distribution system via nodes, the carbon potential at the moment of discharge can be obtained by solving for the real-time power value and carbon flow rate during the charging process of the energy storage power source by this node.

[0078] Furthermore, based on the different uses and driving characteristics of electric vehicles, a carbon emission measurement model for EVs under different charging modes and the probability model of charging probability and charging load influencing factors is proposed.

[0079] (a) Charging mode

[0080] China's 2015 document, "Electric Vehicle Conductive Charging System Part 1: General Requirements," categorizes EV charging modes into slow charging, conventional charging, and fast charging. Table 1 illustrates these different charging modes. However, charging suppliers vary across regions, leading to variations in power, current, phase voltage, and carbon flow rate during actual operation. Since specific measurements of carbon flow rate for different charging stations are not yet available, corresponding assumptions must be made regarding carbon flow rate.

[0081] Table 1 Comparison of Charging Modes

[0082]

[0083] Currently, my country's power supply is still dominated by coal-fired power, with some regions using renewable energy sources such as photovoltaics and nuclear power. The carbon flow rate values ​​for charging in the table above are relatively high, with only fast charging and conventional charging showing a decrease.

[0084] (b) EV charging mode analysis

[0085] EVs can be categorized into six types based on their usage: buses, taxis, private cars, official vehicles, ride-hailing vehicles, and sanitation vehicles. The following section will determine the charging time periods and charging probabilities for different vehicle types based on their usage patterns. Table 2 shows the charging time periods for different EV types, derived from EV travel data in Hefei City. The corresponding charging modes are determined by the areas where EVs are located during different charging time periods, and the charging probabilities for different time periods are based on probability statistics.

[0086] Table 2 Comparison of charging methods for different vehicle types

[0087]

[0088] (c) Probability model of charge and carbon potential at the start of charging

[0089] (1) Calculate the probability model of the charge level at the initial charging moment of the energy storage element:

[0090] Charging time, charging power, and battery capacity together determine the battery level at the start of charging. Charging time can be calculated from daily mileage; this embodiment utilizes vehicle driving characteristics to obtain its probability distribution, making it more scientific. The charging time is:

[0091]

[0092] In the formula: T c Charging time (h); daily mileage (km); W 100 Electricity consumed by a car per 100 kilometers, expressed in (kW.h) / 100 kilometers; η is the charging efficiency; P c This refers to charging power, measured in kW.

[0093] The battery capacity at the start of charging can be calculated using the formula for charging time, as follows:

[0094]

[0095] In the formula: s is the daily mileage; W 100 The electrical energy consumed by the car per 100 kilometers; η is the charging efficiency; P c E represents the charging power; E represents the battery capacity of the car.

[0096] From the formula for energy consumption, we can see that the initial charging energy E0 is linearly related to the daily mileage s. Therefore, E0 also follows a log-normal distribution, i.e.:

[0097]

[0098] In the formula: Let lnE0 be the mathematical expectation; Let σ be the standard deviation of ln E0. D denoted as lns, which represents the standard deviation.

[0099] (2) Based on the carbon potential at the discharge moment of the energy storage element and the probability model of the charge level at the start of charging of the energy storage element, the nodal carbon potential represented by the daily mileage is obtained:

[0100] Suppose that the linear relationship between the carbon flow rate and the electrical quantity at the start of the energy storage element is F0 = kE0, where E0 is the electrical quantity at the start of charging, F0 is the carbon flow rate at the start of charging, and k is a constant.

[0101] Based on the aforementioned linear relationship, and based on the carbon potential at the discharge moment of the energy storage element and the probability model of the charge level at the initial charging moment of the energy storage element, the nodal carbon potential represented by daily mileage is obtained as follows:

[0102]

[0103] In the formula: e B R is the node carbon potential expressed in terms of daily mileage. b The carbon flow rate.

[0104] Furthermore, the nodal carbon potential e B The daily mileage *s* is linearly correlated with the carbon potential at the nodes. Based on the properties of the normal distribution, the carbon potential at the nodes is *e*. B It conforms to a normal distribution, that is:

[0105]

[0106] In the formula: Let the mathematical expectation of the nodal carbon potential be . denoted as the standard deviation of the node carbon potential.

[0107] In one embodiment, step S3 includes the following steps:

[0108] S31. Based on the node carbon potential represented by the daily mileage, perform normal distribution to obtain the carbon potential probability model of the energy storage element.

[0109] S32. Multiply the carbon potential probability model by the node carbon potential vector to predict the node carbon potential distribution.

[0110] In one embodiment, step S32, the calculation of the nodal carbon potential vector includes the following steps:

[0111] (1) Based on the branch data and node load of the distribution network, the forward-backward substitution method is used to solve the active power flow of the distribution network.

[0112] It should be noted that the forward-backward substitution method includes, but is not limited to, implicit zbus Gaussian method, improved Newton's method, improved fast decoupling method, etc.

[0113] (2) Given the main grid input point and the carbon potential and active power output of each distributed generator unit, the main grid access point is equivalent to a generator unit model for calculation. Assuming the carbon potential of the i-th node is ei (i = 1, 2, ..., n), the nodal carbon potential vector is defined as: E n =[e1,e2,…,e n ] T The carbon potential of node i is the carbon flux density of all active power flows flowing out of node i, and the carbon potential of node i is denoted as the ratio of the carbon flux rate to the active power of node i:

[0114]

[0115] In the formula: e i Let I be the carbon potential at node i; + p is the set of branches of the active power flow flowing into node i; Br For the active power flow of branch r; ρ r p is the carbon flux density of branch r; Gi Active power flow injected into generator i; eG i Let be the carbon potential of generator i;

[0116] Extending the carbon potential of node i to the power grid yields the branch power flow distribution matrix, the node active power flux matrix, the unit injection distribution matrix, and the unit carbon emission intensity vector.

[0117] Based on the branch power flow distribution matrix, node active power flux matrix, unit injection distribution matrix, and generator carbon emission intensity vector, the node carbon potential vector is calculated as follows:

[0118]

[0119] In the formula: E G Let P be the carbon emission intensity vector of the generator set. N Let P be the active flux matrix of the nodes. B Let P be the branch power flow distribution matrix. G Inject the distribution matrix into the unit.

[0120] It should be noted that assessing carbon emissions from power systems based on power flow tracking requires the following assumptions: First, according to the principle of proportional sharing, power flow is distributed to each branch at the node; second, nodes containing distributed generation sources supply power to that node first, with the surplus fed into the grid. Given a power network N containing b branches and n nodes, of which k nodes have generator injection and m nodes are load nodes.

[0121] It should be noted that whether the energy storage element of an electric vehicle is in a working state depends on the magnitude of the discharge carbon potential and the node carbon potential of the power source. When the power source carbon potential is less than the node carbon potential, the power source is in a discharging state; otherwise, it is in a charging state or an off-grid state.

[0122] In this embodiment, the calculation of node carbon potential is jointly determined by the carbon emission flow of the generator set and the carbon emission flow of other nodes. When the carbon potential of all nodes in the system is taken into account, a node carbon potential vector can be obtained. The node carbon potential vector can reflect the carbon potential level of all nodes in the system, and thus can be used to analyze high-carbon elements to determine the access status of new energy units. At the same time, the node carbon potential vector is a calculation of the carbon potential of all nodes in the system.

[0123] In one embodiment, the carbon flow factors considered in this embodiment include: a) carbon emission factor, b) node output distribution factor, and c) path output distribution factor, wherein:

[0124] a) Carbon emission factors

[0125] The average carbon emission factor (CEF) is a coefficient representing the carbon dioxide emissions from a certain energy-consuming process. Its definition is as follows:

[0126]

[0127] In the formula: C ef F represents the carbon emission factor, W represents the carbon emission amount, and F represents the electricity consumption.

[0128] The carbon emission factors on the electricity consumption side are mainly divided into two categories. The first category is used to calculate the carbon emissions generated when consuming a unit of electricity; the second category is used to calculate the carbon emission reduction corresponding to the generation of a unit of electricity by new energy power equipment.

[0129] b) Node output distribution factor

[0130] The node output distribution factor represents the ratio of the network flow (active power flow or carbon emission flow) from the starting node to the adjacent target node at a given instant to the total network flow flowing into that starting node. The node active power output distribution factor H from node i to node j... ij for:

[0131]

[0132] In the formula: W ij Let W∑ be the network flow from node i through branch ij to neighboring node j; i H is the sum of network flows into node i. If there are no branch connections between two nodes or no positive network flows flowing into adjacent branches, then H... ij =0, especially when i=j, H ij =1.

[0133] When the starting and ending nodes are fixed, the numerical values ​​of the carbon emission flow and power flow output distribution factors are equal. In power network analysis, these two distribution factors are collectively referred to as the node output distribution factors, denoted by the symbol H. ij express.

[0134] c) Path output distribution factor

[0135] The path output distribution factor is used to represent the contribution rate of the network flow (active power flow or carbon emission flow) flowing out of a certain starting node on a certain path to the total network flow flowing into the target node.

[0136] Suppose there exists a connected path l between node i and node j in the power grid, and the set of branches of this path is L, H st The node outputs the distribution factor, so the output distribution factor of path l above is... for:

[0137]

[0138] If there are multiple connected paths from node i to node j, assuming the set of these paths is Λ, then the path output distribution factor D from node i to node j is... ij for:

[0139]

[0140] For a connected path with a given starting node and an ending node, its carbon emission flow path output distribution factor is... and active power flow path distribution factor These two distribution factors are equal, and are collectively referred to as the path output distribution factor, denoted by the symbol D. ij express.

[0141] In this implementation, the carbon emission factor can be calculated from either the power generation side or the power consumption side. The node output and path output distribution factors represent the carbon flow contribution rates of relevant nodes and paths within the system (including the power consumption side and the power generation side). The carbon emission factor can yield the average carbon emission of a node, serving as a reference and boundary condition for carbon dioxide emissions. The node output distribution factor represents the distribution relationship between power flow and carbon emission flow during steady-state operation of the system. The path output distribution factor represents the path information of power flow injected into the system by generator sets and carbon emission flow from generator sets to various target nodes.

[0142] In one embodiment, the method further includes:

[0143] 1) Based on the element in the i-th row of the unit-node association matrix, determine the contribution of the i-th unit to the carbon flow of all nodes in the power network:

[0144] Taking a node i in the power grid as an example, the carbon emission flow flowing into this node at any given time is entirely provided by the generator sets in the power grid. The specific location of the generator sets in the system and the carbon flow they inject determine the carbon flow contribution rate of each generator set to this node.

[0145] In a power grid, the carbon flow density of all outflowing power from a node is equal to the carbon potential of that node. Therefore, the... The contribution rate of the generator unit to the carbon flow at node i It can be represented as:

[0146]

[0147] In the formula: For the first The active power output of the generator sets; For the first Carbon emission intensity of a generator set; For the first The path output distribution factor between the generator set and the i-th node.

[0148] Extending the above equation into matrix form, we can obtain the distribution information of the contribution rate of all generating units in the power grid to the carbon flow of all nodes. This can be achieved using the unit-node carbon flow correlation matrix. express.

[0149] Combined with E G P N P B P G Simplifying the formula, we can obtain:

[0150]

[0151] 2) Based on the unit-branch correlation matrix, the carbon flow contribution of a single unit to all branches within the network can be calculated:

[0152] For a branch (i,j) in the power grid, the carbon emission flow supplied to this branch by different generator sets varies. The carbon emission flow supplied to this branch by each generator set is related to the power flow injection of the unit and its location in the network, and can be represented by the node output distribution factor of the carbon flow.

[0153] According to the definition of the node output distribution factor, there is a node with the following characteristics in branch (i,j). The carbon flow provided by the generator set can be expressed as:

[0154]

[0155] Extending the above formula into matrix form, we can obtain the contribution of a certain generator unit to the carbon flow rate of all branches in the power grid, using the unit-branch carbon flow correlation matrix. Based on the properties of the distribution factors of node and path outputs, the formula can be rearranged as follows:

[0156]

[0157] In the formula: It is a row vector with k elements (only the kth element is 1, and all the other elements are 0).

[0158] Given the power flow within the system, the unit-branch correlation matrix can be further simplified using the relevant power flow matrix to obtain:

[0159]

[0160] 3) Based on the unit-load correlation matrix, the contribution of all units in the network to the load carbon flow rate can be obtained:

[0161] Based on the principle of proportional sharing, for a given node in the system, the contribution ratio of all generators in the network to the load carbon flow rate is equal to the combined contribution ratio of the carbon flow rate flowing into that node. Assume that the i-th node has a load P. Li The carbon flow rate corresponding to this load is R. Li Therefore, The carbon flow provided by the generator set is:

[0162]

[0163] Extending the above equation into matrix form, we can obtain the distribution of carbon flow rates among all generator units and the remaining loads within the network. This can be achieved using the generator-load carbon flow correlation matrix. Represented. Combined with the node active flux matrix P nBy defining the properties and adjusting the relevant formulas, we can obtain:

[0164]

[0165] Define P Ln Let P be the node load vector, and let the load of the i-th node be P. Li Then the node load vector can be expressed as:

[0166] P LN =[P L1 ,P L2 ,…P LN ] T .

[0167] In this embodiment, the unit node carbon flow correlation matrix is ​​used to calculate the carbon flow rate injected by all generator units in the system on the carbon flow rate flowing into a certain node, i.e., the contribution of all generator units to the carbon flow distribution of nodes in the system; the unit-branch correlation matrix is ​​the contribution of the k-th generator unit to the system branch; the unit-load correlation matrix is ​​the contribution of the carbon emission flow injected by all generator units to the carbon flow rate corresponding to the load of a certain node.

[0168] The following is a simulation analysis:

[0169] The feasibility and accuracy of the carbon flow calculation model were verified by using the IEEE 33-bus system and a 10kV distribution network with distributed photovoltaic power generation in a certain area of ​​Anhui Province. The results obtained from this calculation model were used to analyze high-carbon users on the distribution network side.

[0170] A) Distribution network system of IEEE 33 nodes

[0171] The topology of the IEEE 33 standard node distribution network system is as follows: Figure 2 As shown in Appendix A, the line parameters and load data of this system are as follows. Node 20 is connected to an energy storage battery with a maximum charge and discharge power of 60kW. The battery has a charge and discharge efficiency of 95%, a self-discharge efficiency of 1%, and a maximum storage capacity of 1400kWh. Initially, the battery has a capacity of 686kWh and a carbon flow rate of 380kg.

[0172] The generators in the distribution network system are treated as a constant power model, and their data are shown in Table 1. The node voltages and branch currents of the distribution network are obtained from the data in Appendix A using a forward-backward substitution algorithm. From this, the branch power flow distribution matrix P can be obtained using relevant calculations. B .

[0173] Table 3 Generator Parameters

[0174]

[0175] The root node at the connection between the main network and the distribution network is equivalent to a generator set, whose output power is denoted as the active power flowing through branch 1-2. Generator set G1 at the root node is equivalent to a coal-fired unit, while G2, G3, and G4 are gas-fired units connected to nodes 7, 24, and 29 respectively. The carbon emission intensity of the generator sets within the distribution network system and the carbon emission vector E of the generator sets are known. G As shown below:

[0176] E G =[0.85 0.55 0.60 0.65] T

[0177] The active flux matrix P obtained using the above data N The matrix has been verified to be invertible, and the carbon potential of all nodes can be obtained from the node carbon potential vector. The results are shown in Table 4.

[0178] Table 4 Comparison of node carbon potential

[0179]

[0180]

[0181] Based on existing conclusions, when there are no distributed generation sources or ring networks in the distribution network system, the carbon potential of all nodes in the system is equal to the carbon potential of the main grid connection point. The data in the second column of Table 4 are all 0.85, thus verifying that when there are no distributed generation units, the carbon potential of all nodes in the system is consistent with that at the main grid connection point. Nodes 7, 24, and 29 are all connected to gas turbine units, which converts the electricity originally supplied by the main grid into energy supplied by the gas turbine units, thereby significantly reducing the carbon potential of each connection point and subsequent nodes that are supplied with electricity by gas turbine units.

[0182] Taking the system running for one hour as an example, based on the initial state of the energy storage battery, it can be seen that the power supply is in a discharging state, and its relevant values ​​are shown in Table 5.

[0183] Table 5. Energy storage battery discharge time (one hour)

[0184]

[0185] The contributions of the generating units on the electricity consumption side to the node and load carbon flow are shown in Tables 6 and 7.

[0186] Table 6. Generator Set Carbon Flow Supply to Nodes

[0187]

[0188]

[0189] Table 7. Carbon Flow Supply from Generator Sets to Load

[0190]

[0191] Tables 6 and 7 above fully illustrate that the carbon flow of each node and load is provided by different generator sets within the system, and the different locations of the generator sets determine the differences in the composition of the carbon flow of different nodes and loads.

[0192] Table 8. Carbon Flow Supply from Generator Sets to Branch Circuits

[0193]

[0194] Table 8 shows the carbon flow supply of each generator unit to all branches within the distribution network. Branches without distributed generator power supply do not have corresponding carbon flow supply.

[0195] Based on the above calculation model, the carbon flow distribution mechanism on the electricity consumption side can be specifically analyzed, providing new ideas for future enterprise carbon flow analysis and government carbon emission policy formulation. This paper calculates the carbon flow distribution of the distribution network system based on a time cross-section. If a similar analysis is performed over a continuous time period, this method can be used to calculate the changes in carbon potential at nodes within the system and the distribution mechanism of carbon flow in branches and loads, providing a reliable calculation model for future distributed generation power supply based on time periods.

[0196] B) A 10kV system with node 61 in a certain transformer substation in Anhui Province

[0197] To verify the feasibility and accuracy of the carbon emission flow model presented in this paper, a 10kV feeder in operation in a transformer substation in Anhui Province was selected as the research object. This system includes 61 nodes, of which nodes 15, 39, and 57 are connected to gas turbine units, node 61 is connected to a hydroelectric unit, and the rest are photovoltaic power generation. Specific data are shown in Appendix B1. The topology diagram and branch parameters of this feeder system are shown in [reference needed]. Figure 3 As shown, node 36 is connected to an energy storage element.

[0198] Because a large number of new energy generators are connected to the system, the carbon potential of each node is significantly reduced. If a distributed power source exists only at the root node of a certain radial branch, then the carbon potential of all nodes in this branch is equal to that node's carbon potential; conversely, if other distributed power sources exist in the branch, the carbon potential of its nodes will change. Figure 4 This figure shows the carbon potential of 61 nodes in the distribution network system at the 100th hour, and further verifies the changes in carbon potential of each node in the system.

[0199] Figure 5-(a) shows the carbon potential changes at nodes 10 and 29, with an operating time of 567 hours and load power varying over time. There are no renewable energy units at the connection point between node 10 and the main grid, therefore its carbon potential remains at 0.85 kg·CO₂ / kWh. There are two photovoltaic units on the branch connecting node 29 to the main grid, located at nodes 18 and 19 respectively. This causes the carbon potential at node 29 to decrease, fluctuating around 0.4 kg·CO₂ / kWh with changes in power. Figure 5 -(b) shows the carbon potential changes of 61 nodes in the distribution network system. Except for the branches connected to the main grid and the corresponding nodes where there are no new energy generating units, the carbon potential of all other nodes with new energy generating units and the carbon potential of their subsequent power supply show a significant decrease and fluctuate with changes in load power.

[0200] Node 36 is connected to an energy storage battery. The battery's charge / discharge state is determined based on its charge level and the node's carbon potential. The specific state changes can be observed from... Figure 6 Observations show that the charge / discharge state of an energy storage battery can be determined by its charge and carbon potential, and the trends of both are in a one-to-one correspondence.

[0201] in accordance with Figure 5 The carbon potential of relevant nodes in the middle is lower than that at the main network connection point and Figure 6 During the discharge state of the energy storage battery, the new energy generating units and energy storage batteries will provide low-carbon electricity to the distribution network. Therefore, it can be concluded that connecting distributed renewable energy generating units and energy storage devices to the distribution network system can significantly reduce carbon emissions from various parts of the system.

[0202] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0203] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0204] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0205] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0206] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A precise method for measuring carbon emissions on the electricity consumption side based on carbon emission flow theory, characterized in that, The method includes: Based on the real-time power value and carbon flow rate during the charging process of the energy storage element, the discharge carbon potential of the energy storage element is calculated. The discharge carbon potential is the nodal carbon potential at the discharge point of the energy storage element. Assuming the charging time of the energy storage element is t0-t, and the element is in a discharging state after time t, the discharge carbon potential at time t is: In the formula: The carbon potential of the power source represents the energy storage element's transition from a charging state to a discharging state at time t. and These are the remaining carbon flow and electrical charge of the energy storage element as it transitions from its previous discharge state to its charging state; and These represent the carbon flow rate and charging power during the charging process of the energy storage element, respectively. Indicates the charging and discharging efficiency of energy storage; Based on the carbon potential at the discharge moment of the energy storage element and the probability model of the charge level at the initial charging moment of the energy storage element, the nodal carbon potential represented by daily mileage is obtained, including: Assume a linear relationship between carbon flow and electrical charge at the initial moment of the energy storage element. , The initial charge level. Carbon flow rate at the start of charging. k It is a constant; Based on the aforementioned linear relationship, and based on the carbon potential at the discharge moment of the energy storage element and the probability model of the charge level at the initial charging moment of the energy storage element, the nodal carbon potential represented by daily mileage is obtained as follows: In the formula: The node carbon potential is expressed in terms of daily mileage. Carbon flow rate; Based on the node carbon potential represented by daily mileage, predict the node carbon potential distribution; The calculation process of the energy storage element's charge probability model at the initial charging moment includes: Based on the factors affecting the charging of energy storage components, a probability model of the initial charge level of the energy storage component is calculated. These factors include charging duration, charging power, and battery capacity. The probability model of the charge level is as follows: In the formula: Daily mileage; The amount of electricity consumed by a car per 100 kilometers; For charging efficiency; This refers to the charging power. E This refers to the car battery charge.

2. The method for precise measurement of carbon emissions on the electricity consumption side based on carbon emission flow theory as described in claim 1, characterized in that, The prediction of nodal carbon potential distribution based on the nodal carbon potential represented by daily mileage includes: Based on the node carbon potential represented by the daily mileage, a normal distribution is performed to obtain the carbon potential probability model of the energy storage element. The carbon potential probability model is multiplied by the node carbon potential vector to predict the node carbon potential distribution.

3. The method for precise measurement of carbon emissions on the electricity consumption side based on carbon emission flow theory as described in claim 2, characterized in that, The calculation of the nodal carbon potential vector includes: Based on the branch data and node load of the distribution network, the forward-backward substitution method is used to solve the active power flow of the distribution network. Given the main grid input points and the carbon potential and active power output of each distributed generator unit, the main grid access points are calculated as equivalent generator unit models. i The carbon potential is the outflow node. i Carbon flow density of all active currents, denoted by nodes i The carbon potential is the node i The ratio of carbon flux to active power is: In the formula: For nodes i carbon potential; For inflow node i A collection of tributaries of the meritorious current; branch road r The meritorious trend; branch road r carbon flux density; For generator i The injected active power flow; For generator i carbon potential; Node i The carbon potential is extended to the power grid to obtain the branch power flow distribution matrix, the node active power flux matrix, the unit injection distribution matrix, and the unit carbon emission intensity vector. Based on the branch power flow distribution matrix, node active power flux matrix, unit injection distribution matrix, and generator carbon emission intensity vector, the node carbon potential vector is calculated as follows: In the formula: This represents the carbon emission intensity vector of the generator set. Let be the active flux matrix of the nodes. This is the branch power flow distribution matrix. Inject the distribution matrix into the unit.

4. The method for precise measurement of carbon emissions on the electricity consumption side based on carbon emission flow theory as described in claim 1, characterized in that, The method further includes: Calculate the node output distribution factor from the node. i To the node j The node active power output distribution factor for: In the formula: For the node i Passing through the side road ij The network flow to the adjacent node j; For inflow node i The sum of network flows; if there are no branch connections between two nodes or no positive network flow flowing into adjacent branches, then ,when i=j hour, .

5. The method for precise measurement of carbon emissions on the electricity consumption side based on carbon emission flow theory as described in claim 1, characterized in that, The method further includes: Calculate the path output distribution factor, if from node i To the node j There are multiple connected paths between nodes. i To the node j Path output distribution factor for: In the formula: Output the distribution factor for the node. It is a set of paths.

6. The method for precise measurement of carbon emissions on the electricity consumption side based on carbon emission flow theory as described in claim 3, characterized in that, The method further includes: Based on the unit-node correlation matrix, the first i row element, determine the first i The contribution of generating units to the carbon flow of all nodes in the power network is given by the unit-node correlation matrix. : 。 7. The method for precise measurement of carbon emissions on the electricity consumption side based on carbon emission flow theory as described in claim 3, characterized in that, The method further includes: The carbon flow contribution of a single generator unit to all branches within the network can be calculated based on the unit-branch correlation matrix. for: in, For the first Carbon emission intensity of a generator set; The contribution of all generating units in the network to the load carbon flow rate can be obtained based on the unit-load correlation matrix. for: in, Indicates the first i The load of each node.

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