Green electricity source tracing accounting method and system considering network loss allocation fairness

Abstract

The invention discloses a green electricity source tracing accounting method and system considering network loss allocation fairness, and the method comprises the following steps: collecting the operation data of a power grid, and constructing a data set; carrying out load flow calculation by adopting a Newton-Raphson method, and calculating the network loss of lines between different nodes; reverse power flow tracking is carried out, and power provided by the power generation node to the load node is calculated; a linguistic variable-based direct influence matrix is constructed for predefined influence factors, a comprehensive relation matrix is obtained through processing, the importance degrees of different influence factors are calculated, and the transmission loss proportion shared by the power generation side and the load side is determined and corrected; calculating the network loss borne by the load side on the corresponding line in combination with the network loss of the line; and calculating the green power consumption amount of the load node based on the power provided by the power generation node to the load node and the network loss borne by the load side. According to the invention, through combination of load flow calculation, network loss reverse tracking and fuzzy comprehensive evaluation, physical accuracy of network loss allocation and fairness and rationality of green power consumption amount accounting are realized.

Description

Technical Field

[0001] This invention belongs to the field of new energy power system technology, specifically relating to a green electricity traceability accounting method and system that considers the fairness of grid loss sharing. Background Technology

[0002] Green electricity, as a crucial component of renewable energy, has gradually become a vital support for the green transformation of the power system and carbon reduction policies due to its clean and low-carbon characteristics. With the continuous increase in my country's installed renewable energy capacity and the rising proportion of green power sources such as wind and solar power in the grid, higher demands are being placed on the use, verification, and disclosure of green electricity. Driven by policies such as corporate carbon information disclosure, green electricity trading, and carbon border adjustment mechanisms, accurately identifying the proportion of green electricity actually consumed by end-load nodes has become a key link in promoting the construction of green electricity mechanisms. However, unlike traditional commodities or information carriers, green electricity has significant homogeneity in its physical attributes. In actual grid operation, electricity generated by various power sources is transmitted in a mixed manner, and meters or measuring devices cannot directly identify the source attributes of the electricity. Therefore, without physical isolation or labeling of the grid, it is difficult to directly determine whether a node has consumed green electricity through path or metering methods. This physical indistinguishability has become a significant bottleneck limiting the accuracy of current green electricity traceability and accounting.

[0003] Currently, several green electricity tracking and allocation methods based on power flow calculations exist. These methods analyze power flow paths using the proportional sharing principle, based on power system power flow results, to identify the electricity consumption composition of load nodes. Among them, the direct tracking method and the proportional sharing method based on node injected power have become the most representative power tracking algorithms. Their basic idea is to deduce the power supply source corresponding to each load node based on the power distribution ratio among branches, based on steady-state power flow calculations, thus achieving a certain degree of physical tracking and green electricity proportion quantification.

[0004] Existing technology CN117094568A discloses a method for allocating carbon reduction contributions from distribution network users based on carbon flow tracing. This invention transforms the actual lossy network into a lossless virtual network by performing bidirectional network loss allocation on the power flow of the distribution network. Subsequently, it uses reverse power flow tracing to obtain the proportion of active power provided by each generator in the load of different nodes. Finally, it combines the carbon emission intensity of each generator to achieve a quantitative allocation of users' carbon reduction contributions. Existing technology CN118316105A discloses a green electricity source tracing method based on regional power grid power flow topology analysis. This invention introduces power flow topology modeling, unifying the modeling of power sources, buses, lines, main transformers, and loads as nodes and establishing an electrical power flow calculation matrix. Based on path tracing of power flow direction and splitting coefficient, it uses depth-first search (DFS) to achieve path-by-path allocation from power source to load. It outputs the user-level green electricity ratio, generates a distribution matrix at each time step, and accumulates it to form the annual result, realizing accurate tracking of green electricity transmission paths under complex power grid conditions and quantifying the calculation of users' green electricity consumption ratio.

[0005] However, existing technologies still have the following shortcomings: current methods only provide a simple processing and allocation of line network losses, failing to differentiate the actual impact of generation and load sides on network losses under different operating factors. Because the roles of the two sides in the formation of network losses differ, their respective responsibilities for network losses also differ, but existing methods fail to reflect this, leading to an unreasonable allocation of network loss responsibility. In the green electricity consumption accounting process, this deficiency directly affects fairness and accuracy, making it difficult to meet the need for a fair quantification of users' carbon reduction contributions. Summary of the Invention

[0006] To address the unequal division of responsibilities between power generation and consumption in existing technologies, this invention provides a green energy traceability and accounting method and system that considers the fairness of network loss allocation. The method includes: collecting power grid operation data and constructing a dataset; performing power flow calculations using the Newton-Raphson method to calculate network losses between different nodes; performing reverse power flow tracing to calculate the power supplied by power generation nodes to load nodes; constructing a direct influence matrix based on linguistic variables for predefined influencing factors, and obtaining a comprehensive relationship matrix through processing; calculating the importance of different influencing factors; determining and correcting the network loss allocation ratio between the power generation and load sides; calculating the network loss borne by the load side on the corresponding line based on the line's network loss; and calculating the green energy consumption of the load node based on the power supplied by the power generation node to the load node and the network loss borne by the load side. This invention achieves both physical accuracy in network loss allocation and fairness and rationality in green energy consumption accounting by combining power flow calculation, reverse network loss tracing, and fuzzy comprehensive evaluation.

[0007] The present invention adopts the following technical solution.

[0008] The first aspect of the present invention provides a green electricity source tracing and accounting method that considers the fairness of network loss sharing, comprising: S1. Collect power grid operation data and construct a dataset; the dataset includes a set of node information, a set of power grid topology, a set of line parameters, a set of node power, and operating times. S2. Using the dataset as input, the Newton-Raphson method is used to calculate the power flow, solve for the voltage magnitude and phase angle of each node, and calculate the network loss of the line between different nodes. S3. Model the network loss of each branch as a virtual load, and perform reverse power flow tracing through the sharing ratio principle to calculate the power provided by the generation node to the load node; S4. Construct a direct influence matrix based on linguistic variables for predefined influencing factors, and obtain a comprehensive relationship matrix through triangular fuzzy number transformation and standardization. Based on the comprehensive relationship matrix, calculate the importance of different influencing factors, determine the proportion of network loss shared by the generation side and the load side, and make corrections. Combined with the network loss of the line, calculate the network loss borne by the load side on the corresponding line. S5. Based on the power supplied by the power generation node to the load node and the network loss borne by the load side, and combined with the node marker of each node, calculate the green electricity consumption of the load node.

[0009] Preferably, in S3, for each node, the total active power injection of the corresponding node is calculated based on the active power flowing into the node from its neighboring nodes and the active power injection of the generators on the node; and the power provided by the generating nodes to the load nodes is calculated based on the sharing ratio principle of the power grid being in an equivalent lossless network state.

[0010] Preferably, the comprehensive relation matrix obtained in S4 through triangular fuzzy number transformation and standardization is as follows: Construct a set of direct influence matrices, which contains multiple direct influence matrices; each direct influence matrix consists of the degree of influence between different predefined influencing factors, and the degree of influence is set according to a predefined set of linguistic variables. For any two influencing factors, the degree of direct influence that appears most frequently in the corresponding degree of direct influence of all direct influence matrices is taken as the comprehensive degree of direct influence between the corresponding influencing factors; based on the predefined linguistic variable-triangular fuzzy number mapping relationship, the linguistic variable of comprehensive degree of direct influence is converted into a triangular fuzzy number to obtain the triangular fuzzy number comprehensive direct influence matrix; For the degree of direct influence of each triangular fuzzy number in the comprehensive direct influence matrix, the average value of the triangular fuzzy numbers is taken as the overall degree of direct influence between the corresponding influencing factors, thus obtaining the overall direct influence matrix; for each row of the overall direct influence matrix, all elements of the corresponding row are summed as the row sum; the maximum value of the row sum of all rows is used as the denominator, and the overall direct influence matrix is ​​used as the numerator, and normalization is performed to obtain the standardized direct influence matrix; Subtract the standardized direct influence matrix from the identity matrix and take the inverse matrix. Multiply the standardized direct influence matrix by the inverse matrix to obtain the comprehensive relationship matrix, which contains the degree of comprehensive relationship between different influencing factors.

[0011] Preferably, the process of calculating the importance of different influencing factors in S4 is as follows: For each influencing factor, the degree of its comprehensive relationship with other influencing factors is summed to obtain the relative importance of the corresponding influencing factor; the degree of comprehensive relationship of all other influencing factors with the corresponding influencing factor is summed to obtain the degree of influence of the corresponding influencing factor; the relative importance and degree of influence of the influencing factor are added together to obtain the importance of the corresponding influencing factor among all influencing factors.

[0012] Preferably, the process of determining the network loss ratio shared by the generation side and the load side in S4 is as follows: For the generation side, the importance of all factors affecting the generation side is summed to obtain the influence weight of the generation side; for the load side, the importance of all factors affecting the load side is summed to obtain the influence weight of the load side; the influence weight of the generation side is used as the numerator, and the sum of the influence weights of the generation side and the load side is used as the denominator to obtain the network loss ratio allocated to the generation side; the influence weight of the load side is divided by the sum of the influence weights of the generation side and the load side to obtain the network loss ratio allocated to the load side.

[0013] Preferably, the process of calculating the network loss borne by the load side on the line in S4 is as follows: For any given time, the network losses of all lines at that time are summed to obtain the total active power loss of the entire network at that time; based on the total active power loss of the entire network at each time and the active power injection of the node, the marginal network loss factor of the corresponding node is calculated. Based on the marginal network loss factors of the generation and load side nodes, and combined with the load power of the load node and the active power injection of the generation node, the network loss ratio shared by the generation and load sides is corrected to obtain the corrected sharing ratio. Multiply the network loss of the line by the load-side adjusted allocation ratio to obtain the network loss borne by the load side on the corresponding line.

[0014] Preferably, the process of correcting the network loss ratio shared by the generation side and the load side is as follows: For the generation side, the absolute value of the product of the marginal network loss factor of all generation nodes and the active power injection is summed to obtain the objective impact factor of the generation side; for the load side, the absolute value of the product of the marginal network loss factor of all load nodes and the load power is summed to obtain the objective impact factor of the load side. The objective influencing factor on the generation side is used as the numerator, and the sum of the objective influencing factors on the generation side and the load side is used as the denominator to calculate the correction coefficient on the generation side; the correction coefficient on the load side is obtained by subtracting the correction coefficient on the generation side from 1. The generation-side network loss ratio and the generation-side correction coefficient are weighted to obtain the generation-side corrected allocation ratio; the load-side network loss ratio and the load-side correction coefficient are weighted to obtain the load-side corrected allocation ratio.

[0015] Preferably, in S5, for each operating time in the set of operating times, based on the power provided by the power generation node to the load node and the network loss borne by the corresponding load node, combined with the node information in the node information set, the power provided by the power generation node marked as green electricity to the load node and the network loss borne by the corresponding load node are accumulated through the node tag bit to obtain the green electricity active power of the load node. The time difference between two adjacent times in the set of operating times is taken as the time width. The green electricity consumption can be obtained by multiplying the time width by the green electricity active power of the load node.

[0016] A second aspect of the present invention provides a green energy traceability accounting system that considers the fairness of grid loss sharing, and uses a green energy traceability accounting method that considers the fairness of grid loss sharing, including: The power grid operation data acquisition module collects power grid operation data and constructs a dataset; the dataset includes a set of node information, a set of power grid topology, a set of line parameters, a set of node power, and operating times. The power flow calculation module takes the dataset as input, uses the Newton-Raphson method to perform power flow calculations, solves for the voltage magnitude and phase angle of each node, and calculates the network loss of the line between different nodes; The power calculation module is injected into the power generation node, which models the network loss of each branch as a virtual load and performs reverse power flow tracing through the sharing ratio principle to calculate the power provided by the power generation node to the load node. The load-side network loss calculation module constructs a direct influence matrix based on linguistic variables for predefined influencing factors, and obtains a comprehensive relationship matrix through triangular fuzzy number transformation and standardization. Based on the comprehensive relationship matrix, it calculates the importance of different influencing factors, determines the network loss ratio shared by the generation side and the load side, and performs corrections. Combined with the network loss of the line, it calculates the network loss borne by the load side on the corresponding line. The green energy consumption module calculates the amount of green electricity absorbed by the load nodes based on the power provided by the power generation nodes to the load nodes and the network losses borne by the load side, combined with the node marker of each node.

[0017] A third aspect of the present invention provides a terminal, including a processor and a storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform steps of a green electricity source tracing and accounting method that takes into account the fairness of network loss sharing.

[0018] A fourth aspect of the invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a green electricity source tracing and accounting method that takes into account the fairness of network loss sharing.

[0019] Compared with the prior art, the beneficial effects of the present invention include at least the following: 1. This invention constructs a direct influence matrix of linguistic variables of influencing factors. Through triangular fuzzy number processing, standardization, and matrix inverse analysis, a comprehensive relationship matrix is ​​obtained, thereby quantifying the importance of influencing factors and determining the network loss allocation ratio between the generation and load sides. Simultaneously, it combines marginal network loss factors with nodal power injection / load power to construct objective influencing factors, achieving a secondary correction of the allocation ratio. This method integrates subjective factor evaluation with objective grid operation characteristics, making network loss allocation both fair and reasonable, and allowing for real-time adjustment according to operating conditions, significantly outperforming existing simple ratio or fixed parameter allocation methods.

[0020] 2. After obtaining the allocated line network losses and the power relationship between generation nodes and load nodes, this invention distinguishes green electricity nodes by combining node marker bits, calculates the green electricity power of load nodes in time periods, and obtains the green electricity consumption through time integration. This method can accurately distinguish the actual network losses borne by green electricity on the transmission path, avoiding over-counting, under-counting, or incorrect allocation of green electricity. Attached Figure Description

[0021] Figure 1 This is a flowchart of a green electricity source tracing and accounting method that considers the fairness of network loss sharing, provided by the present invention; Figure 2 This is a diagram showing the sharing ratio in an embodiment of the present invention; Figure 3 This is a node diagram in the embodiments of the present invention that does not consider network loss; Figure 4 This is a node diagram considering network loss in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0023] Example 1 like Figure 1 As shown, Embodiment 1 of the present invention provides a green electricity source tracing and accounting method that considers the fairness of network loss sharing, including the following steps: S1. Collect power grid operation data and construct a dataset. The data set includes node information set, power grid topology set, line parameter set, node power set, and operating time.

[0024] B is a set of node information, including: node number, node type, node flag bit, region, and voltage level; where, the node number is the identifier of each node in the power system; the node type is divided into PQ nodes (load nodes), PV nodes (generation nodes), and balancing nodes according to the node function; the node flag bit is used to indicate whether it is a green electricity node, with green nodes marked as TRUE and non-green nodes marked as FALSE; M is a set of power grid topologies, which includes: start node, end node, line number and name; Z represents the set of line parameters, including: line resistance, line reactance, and line length; line resistance is typically the resistance per unit length (Ω / km), which affects active power loss; line reactance is typically the reactance per unit length (Ω / km), which affects voltage phase angle; line length is used to calculate total impedance. I represents the set of node power, which includes: node active load, reactive load, node active generation, and reactive generation. T represents the running time: power recording time data.

[0025] S2. Using the dataset as input, the Newton-Raphson method is used to calculate the power flow, solve for the voltage magnitude and phase angle of each node, and calculate the network loss of the line between different nodes.

[0026] For any node j Based on the node information set in the dataset and the corresponding node data in the node power set, the active and reactive power injected into the corresponding node are calculated through the actual power balance of the node: ; In the formula, and For nodes jThe amount of active power injection and reactive power injection; and It is a node j The active and reactive power output of the generator on the platform; and For nodes j The active and reactive power load consumption; Based on the node information set, line electrical parameter set, and power grid topology data in the dataset, the corresponding line admittance is calculated, and the admittance matrix is ​​constructed using the following formula: ; In the formula, For nodes j With nodes k Admittance of the branch between; It is a node j With nodes k The admittance impedance of an intermediate branch describes the ability of a circuit to impede the flow of current. For nodes j With nodes k The resistance of the branch between For nodes j With nodes k Reactance of the branch circuit; i It is the imaginary unit; For nodes j With nodes k The conductance of the branch is the real part of the admittance; For nodes j With nodes k The susceptance of the branch is the imaginary part of the admittance; Admittance matrix Y The value in the j-th row and k-th column; It is all nodes j A set of connected nodes; Based on Kirchhoff's current law and the complex power expression, and combining the active power injection, reactive power injection, admittance matrix, and voltage variables of the node, the complex power of the corresponding net injected power is calculated: ; In the formula, It is a node j The complex power of net injected power; For nodes j Total active power injection; For nodes j Total reactive power injection; For nodes j The conjugate complex number of the current phasor is obtained by inverting the phase angle of the complex form of the current. For nodesj Voltage phasors; It is the admittance matrix Y The elements in the array; n is the number of nodes; Based on the admittance matrix and the complex power expression of the nodes, the nonlinear power flow equations can be obtained by expanding the total active power injection and total reactive power injection of the nodes: ; In the formula, and They are nodes j and nodes k voltage amplitude, and They are nodes j and nodes k The voltage phase angle.

[0027] As can be seen from the power flow equations above, a node contains four operating variables (i.e., active power). P reactive power Q Voltage amplitude V Voltage phase angle θ This corresponds to two equations; in a power system with n nodes, there are 4n variables and 2n equations. If each node is given two variable values, the remaining 2n unknowns can be solved through the equations; since the power flow calculation equation is a nonlinear system of equations, the Newton-Raphson method is used for linearization in the solution.

[0028] The active and reactive power injections at nodes calculated based on the dataset are used as the total active and reactive power injections for the corresponding nodes in the nonlinear power flow equations. The Newton-Raphson method is then used to solve for the voltage magnitude and phase angle of each node. Based on the voltage magnitude and phase angle of each node, the voltage magnitude and phase angle of the nodes are calculated. j Flow to Node k The active power; the specific formula is: ; In the formula, For the node i Flow to Node j The sign of the active power determines the actual direction of power flow; if Then the power is generated by the node. j Flow to Node k Conversely, it is determined by the node. j Flow to Node k ; Based on the node j Flow to Node k Active power and from node k Flow to Node j Active power, calculation nodej and nodes k Network loss of the branch: ; In the formula, For the node k Flow to Node j The active power; For nodes j and nodes k Network loss between the lines.

[0029] S3. Model the network loss of each branch as a virtual load, and perform reverse power flow tracing through the sharing ratio principle to calculate the power provided by the generation node to the load node.

[0030] The principle of the reverse current algorithm in power flow tracing is similar to that of power flow calculation. The power calculation formula for injecting a node is as follows: ; In the formula, For nodes j Total active power injection; From adjacent nodes k Inflow node j The active power; node j The active power output of the generator, that is, the active power injected by the generator; When the power grid is in the state of an equivalent lossless network, that is At that time, the active power injection of a node can be converted into: ; In the formula, Power allocation factor indicates the node k Power of nodes j The proportion of contribution; Inject the total active power into node k; The matrix form corresponding to the formula is: ; In the formula, For the allocation matrix; P This is a vector representing the proportion of total power at each node in the power grid; The power vector injected into each generator node of the network; matrix The elements in are: ; based on The elements of vector P can be obtained as follows: ; Based on the principle of sharing ratio, the formula for countercurrent tracing is: ; In the formula, For load nodes j The load power; Inject active power into generator node k; The power supplied by power generation node k to load node j.

[0031] In this embodiment, the principle of sharing ratio is as follows: Figure 2 In a single-node power flow tracing scenario, nodes have inflow lines 1 and 2 with inflow powers P1 and P2, and outflow lines 3 and 4 with outflow powers P3 and P4. The power of the outflowing branches is proportionally provided by the power of the inflowing branches, and the power of the inflowing branches is proportionally allocated by the power of the outflowing branches. The outflow power P3 is provided by inflow line 1. The power provided by line 2 is .

[0032] We now know how to perform source tracing in a lossless network. However, in actual power grids, line power losses exist, and direct tracing would result in a non-closed source-load power flow. By analyzing power flow calculations, we can determine branch network losses, which share the same attribute as load nodes. Therefore, branch network losses can be treated as virtual loads. In the subsequent loss allocation process, this network can be considered a lossless network for power flow tracing. Figure 2 No virtual load and Figure 3 The virtual load is shown in the diagram.

[0033] S4. Construct a direct influence matrix based on linguistic variables for predefined influencing factors, and obtain a comprehensive relationship matrix through triangular fuzzy number transformation and standardization. Based on the comprehensive relationship matrix, calculate the importance of different influencing factors, determine the proportion of network loss shared by the generation side and the load side, and make corrections. Combined with the network loss of the line, calculate the network loss borne by the load side on the corresponding line.

[0034] Construct a set of directly influential matrices The set contains multiple direct influence matrices; each direct influence matrix consists of the degree of influence between different predefined influencing factors, the degree of influence being set according to a predefined set of linguistic variables; among them, represents the direct impact matrix given by the r-th expert participating in the analysis. The matrix value represents the degree of direct impact between different influencing factors, determined based on the language variable set shown in Table 1; h is the total number of influencing factors; in this embodiment, the influencing factors include: power generation characteristics, load fluctuation, line impedance, voltage level, regional electrical distance, and network loss; power generation characteristics and load fluctuation are the final allocation subjects, while line impedance, voltage level, and regional electrical distance are intermediate transmission factors used to reflect the amplification or weakening effect of the objective attributes of the power grid on the power generation and load sides, and network loss is the outcome factor; Table 1 Set of Language Variables

[0035] For any two influencing factors, the degree of direct influence that appears most frequently in the corresponding degree of direct influence matrix is ​​taken as the comprehensive degree of direct influence between the corresponding influencing factors. Based on the linguistic variable-triangular fuzzy number mapping relationship in Table 1, the comprehensive direct influence degree is converted into triangular fuzzy numbers to obtain the comprehensive direct influence matrix. ;in, This represents the degree of direct influence of influencing factor a on influencing factor b via the triangular fuzzy number. This value is not a fixed value, but rather a fuzzy number; where, This is the lower bound (i.e., the minimum possible value) for the degree of direct influence. The median (i.e., the most likely value) of the degree of direct influence. This is the upper bound of the degree of direct influence (i.e., the maximum possible value). To determine the degree of direct influence for each triangular fuzzy number in the comprehensive direct influence matrix, the average of the triangular fuzzy numbers is taken as the overall degree of direct influence among the corresponding influencing factors, thus obtaining the overall direct influence matrix; the specific formula is as follows: ; In the formula, The degree of direct impact on the whole; The matrix directly affects the whole; To prevent matrix calculation divergence, for each row that directly affects the matrix as a whole, the sum of all elements in that row is accumulated; the maximum sum of all rows is used as the denominator, and the matrix directly affecting the whole is used as the numerator, for normalization processing to obtain the standardized matrix of direct effects; the specific calculation formula is as follows: ; In the formula, The standardization process directly affects the matrix; Indicates taking the matrix The maximum sum of all rows; Subtracting the standardized direct influence matrix from the identity matrix and taking its inverse, then multiplying the standardized direct influence matrix by this inverse matrix yields the comprehensive relationship matrix, which contains the degree of comprehensive relationship between different influencing factors; the specific calculation formula is as follows: ; In the formula, It is the identity matrix; This indicates the degree of comprehensive relationship between influencing factor a and influencing factor b.

[0036] In a preferred implementation, for each influencing factor, its overall relationship with other influencing factors is summed to obtain the relative importance of the corresponding influencing factor; the overall relationship with the corresponding influencing factor from all other influencing factors is summed to obtain the degree of influence of the corresponding influencing factor; the relative importance and degree of influence of the influencing factor are added together to obtain the importance of the corresponding influencing factor among all influencing factors; the degree of influence is subtracted from the relative importance of the influencing factor to obtain the causal strength of the corresponding influencing factor; the specific calculation formula is as follows: ; In the formula, The relative importance of influencing factor a; The degree to which factor a is affected; The value represents the importance of factor a; the larger the value, the more important the factor is among all influencing factors. To determine the causal strength of factor a, If the value is greater than 0, then factor a is a causal factor, indicating that it has a significant impact on other factors; otherwise, it is an outcome factor.

[0037] In this embodiment, The larger the value, the greater the impact on network losses, and the greater the proportion of network losses that should be borne. Since the network loss allocation is between the generation and load sides, other relevant factors are only used in the intermediate causal analysis to improve rationality and are not ultimately considered as the main bearers. Their impact has already been implicitly reflected in the calculation of relative importance and degree of impact. .

[0038] As a preferred implementation, the network loss ratio shared by the generation side and the load side is calculated based on the relative importance of the influencing factors on the generation side and the load side; the specific process is as follows: For the generation side, the importance of all influencing factors is summed to obtain the generation side's influence weight. For the load side, the importance of all influencing factors is summed to obtain the load side's influence weight. The generation side's influence weight is used as the numerator, and the sum of the generation side's and load side's influence weights is used as the denominator to obtain the network loss ratio allocated to the generation side. The load side's influence weight is divided by the sum of the generation side's and load side's influence weights to obtain the network loss ratio allocated to the load side. The specific formula is as follows: ; In the formula, Weights for all power generation-related impacts; Weighting of the impact on all load sides, i.e., electricity consumption-related factors; This represents the set of factors influencing power generation. Internal influencing factors g ; This represents the set of factors influencing power generation. Internal influencing factors l ; The proportion of network losses allocated to the power generation side; The proportion of network loss allocated to the load side.

[0039] As a preferred implementation, for any given time, the network losses of all lines at that time are summed to obtain the total active power loss of the entire network at that time; based on the total active power loss of the entire network at each time and the active power injection of the nodes, the marginal network loss factor of the corresponding node is calculated; the specific formula is as follows: ; In the formula, Let be the marginal network loss factor of node k at time t; The change in active power loss across the entire network at time t is caused by the change in injected power disturbance. Let be the net change in active power injected into node k at time t; As a preferred implementation, the marginal network loss factor can also be calculated by using the partial derivatives of the power flow equations (commonly the transposed Jacobian matrix) to calculate the slight increase in node injection power on total network loss.

[0040] As a preferred implementation, based on the marginal network loss factors of the generation and load sides, and combined with the load power of the load nodes and the active power injection of the generation nodes, the network loss ratio shared by the generation and load sides is corrected to obtain the corrected sharing ratio; the specific process is as follows: For the generation side, the objective impact factor is obtained by summing the absolute values ​​of the products of the marginal network loss factors of all generation nodes and the active power injection. For the load side, the objective impact factor is obtained by summing the absolute values ​​of the products of the marginal network loss factors of all load nodes and the load power. The specific calculation formula is as follows: ; In the formula, Objective influencing factors on the power generation side; These are objective influencing factors on the load side. and These are the sets of nodes on the generation side and the load side, respectively. Let k be the marginal network loss factor of the power generation node k. Let be the marginal network loss factor of node j on the load side; The generation-side objective influence factor is used as the numerator, and the sum of the generation-side and load-side objective influence factors is used as the denominator to calculate the generation-side correction coefficient; the load-side correction coefficient is obtained by subtracting the generation-side correction coefficient from 1; the specific calculation formula is as follows: ; The generation-side network loss allocation ratio and the generation-side correction coefficient are weighted to obtain the generation-side corrected allocation ratio; the load-side network loss allocation ratio and the load-side correction coefficient are weighted to obtain the load-side corrected allocation ratio; the specific calculation formula is as follows: ; In the formula, This is the allocation ratio after correction on the power generation side; This is the allocation ratio after load-side correction; The allocation ratio between power generation and load is influenced by both subjective and objective factors.

[0041] As a preferred implementation method, based on the corrected sharing ratio between the generation side and the load side, and combined with the network loss of the line, all network losses are proportionally allocated to obtain the network losses borne by the generation side and the load side on the corresponding line; the specific process is as follows: Multiplying the network loss of the line by the adjusted allocation ratio on the generation side yields the network loss borne by the generation side on the corresponding line; multiplying the network loss of the line by the adjusted allocation ratio on the load side yields the network loss borne by the load side on the corresponding line; the specific formula is as follows: ; In the formula, The grid loss that should be borne by the power generation side on this line. This refers to the network loss that the load side of the line should bear.

[0042] S5. Based on the power supplied by the power generation node to the load node and the network loss borne by the load side, and combined with the node marker of each node, calculate the green electricity consumption of the load node.

[0043] For each operating time in the set of operating times, based on the power provided by power generation node k to load node j, the network loss that load node j should bear, and combined with the node information in node information set B, the power source is determined to be a green power source by using the node marker bit. The green power ratio and the amount of electricity consumed by the nodes marked as green power are then calculated. Specifically, the power provided by the power generation nodes marked as green power to the load nodes and the network loss borne by the corresponding load nodes are summed to obtain the green power active power of the load nodes. The specific calculation formula is as follows: ; In the formula, For load nodes L j The active power of green electricity; For node B in set B L j The active power contributed by the green electricity generators; For nodes L j The active power loss that should be borne, d is the sum of the power loss and the node. L j All related network losses; The time difference between two adjacent moments in the set of operating moments is taken as the time width. Multiplying the time width by the active power of green electricity at the load node yields the green electricity consumption, as shown in the formula: ; In the formula, For load nodes L j In a certain Δt The amount of green electricity consumed within a given time period; The total green energy consumption within the target period can be obtained by summing up the green energy consumption of different time series.

[0044] Example 2 Embodiment 2 of the present invention provides a green electricity source tracing and accounting system that considers the fairness of network loss sharing, comprising: The power grid operation data acquisition module collects power grid operation data and constructs a dataset; the dataset includes a set of node information, a set of power grid topology, a set of line parameters, a set of node power, and operating times. The power flow calculation module takes the dataset as input, uses the Newton-Raphson method to perform power flow calculations, solves for the voltage magnitude and phase angle of each node, and calculates the network loss of the line between different nodes; The power calculation module is injected into the power generation node, which models the network loss of each branch as a virtual load and performs reverse power flow tracing through the sharing ratio principle to calculate the power provided by the power generation node to the load node. The load-side network loss calculation module constructs a direct influence matrix based on linguistic variables for predefined influencing factors, and obtains a comprehensive relationship matrix through triangular fuzzy number transformation and standardization. Based on the comprehensive relationship matrix, it calculates the importance of different influencing factors, determines the network loss ratio shared by the generation side and the load side, and performs corrections. Combined with the network loss of the line, it calculates the network loss borne by the load side on the corresponding line. The green energy consumption module calculates the amount of green electricity absorbed by the load nodes based on the power provided by the power generation nodes to the load nodes and the network losses borne by the load side, combined with the node marker of each node.

[0045] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0046] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0047] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0048] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0049] Finally, 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 the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A green electricity source tracking and accounting method considering the fairness of network loss sharing, characterized in that, include: S1. Collect power grid operation data and construct a dataset; the dataset includes a set of node information, a set of power grid topology, a set of line parameters, a set of node power, and operating times. S2. Using the dataset as input, the Newton-Raphson method is used to calculate the power flow, solve for the voltage magnitude and phase angle of each node, and calculate the network loss of the line between different nodes. S3. Model the network loss of each branch as a virtual load, and perform reverse power flow tracing through the sharing ratio principle to calculate the power provided by the generation node to the load node; S4. Construct a direct influence matrix based on linguistic variables for predefined influencing factors, and obtain a comprehensive relationship matrix through triangular fuzzy number transformation and standardization. Based on the comprehensive relationship matrix, calculate the importance of different influencing factors, determine the proportion of network loss shared by the generation side and the load side, and make corrections. Combined with the network loss of the line, calculate the network loss borne by the load side on the corresponding line. S5. Based on the power supplied by the power generation node to the load node and the network loss borne by the load side, and combined with the node marker of each node, calculate the green electricity consumption of the load node.

2. The green electricity source tracing and accounting method considering the fairness of network loss sharing according to claim 1, characterized in that: In S3, for each node, the total active power injection of the corresponding node is calculated based on the active power flowing into the node from its neighboring nodes and the active power injection from the generators on the node. Based on the sharing ratio principle when the power grid is in an equivalent lossless network state, the power supplied by the generating nodes to the load nodes is calculated.

3. The green electricity source tracing and accounting method considering the fairness of network loss sharing according to claim 1, characterized in that: The comprehensive relation matrix obtained in S4 through triangular fuzzy number transformation and standardization is as follows: Construct a set of direct influence matrices, which contains multiple direct influence matrices; each direct influence matrix consists of the degree of influence between different predefined influencing factors, and the degree of influence is set according to a predefined set of linguistic variables. For any two influencing factors, the degree of direct influence that appears most frequently in the corresponding degree of direct influence of all direct influence matrices is taken as the comprehensive degree of direct influence between the corresponding influencing factors; based on the predefined linguistic variable-triangular fuzzy number mapping relationship, the linguistic variable of comprehensive degree of direct influence is converted into a triangular fuzzy number to obtain the triangular fuzzy number comprehensive direct influence matrix; For the degree of direct influence of each triangular fuzzy number in the comprehensive direct influence matrix, the average value of the triangular fuzzy numbers is taken as the overall degree of direct influence between the corresponding influencing factors, thus obtaining the overall direct influence matrix; for each row of the overall direct influence matrix, all elements of the corresponding row are summed as the row sum; the maximum value of the row sum of all rows is used as the denominator, and the overall direct influence matrix is ​​used as the numerator, and normalization is performed to obtain the standardized direct influence matrix; Subtract the standardized direct influence matrix from the identity matrix and take the inverse matrix. Multiply the standardized direct influence matrix by the inverse matrix to obtain the comprehensive relationship matrix, which contains the degree of comprehensive relationship between different influencing factors.

4. A green electricity source tracing and accounting method considering the fairness of network loss sharing according to claim 1 or 3, characterized in that: The process for calculating the importance of different influencing factors in S4 is as follows: For each influencing factor, the degree of its comprehensive relationship with other influencing factors is summed to obtain the relative importance of the corresponding influencing factor; the degree of comprehensive relationship of all other influencing factors with the corresponding influencing factor is summed to obtain the degree of influence of the corresponding influencing factor; the relative importance and degree of influence of the influencing factor are added together to obtain the importance of the corresponding influencing factor among all influencing factors.

5. The green electricity source tracing and accounting method considering the fairness of network loss sharing according to claim 1, characterized in that: The process for determining the network loss ratio shared between the generation side and the load side in S4 is as follows: For the generation side, the importance of all factors affecting the generation side is summed to obtain the influence weight of the generation side; for the load side, the importance of all factors affecting the load side is summed to obtain the influence weight of the load side; the influence weight of the generation side is used as the numerator, and the sum of the influence weights of the generation side and the load side is used as the denominator to obtain the network loss ratio allocated to the generation side; the influence weight of the load side is divided by the sum of the influence weights of the generation side and the load side to obtain the network loss ratio allocated to the load side.

6. The green electricity source tracing and accounting method considering the fairness of network loss sharing according to claim 1, characterized in that: The process of calculating the network loss borne by the load side on the line in S4 is as follows: For any given time, the network losses of all lines at that time are summed to obtain the total active power loss of the entire network at that time; based on the total active power loss of the entire network at each time and the active power injection of the node, the marginal network loss factor of the corresponding node is calculated. Based on the marginal network loss factors of the generation and load side nodes, and combined with the load power of the load node and the active power injection of the generation node, the network loss ratio shared by the generation and load sides is corrected to obtain the corrected sharing ratio. Multiply the network loss of the line by the load-side adjusted allocation ratio to obtain the network loss borne by the load side on the corresponding line.

7. A green electricity source tracing and accounting method considering the fairness of network loss sharing according to claim 1 or 6, characterized in that: The process of correcting the network loss ratio shared between the generation side and the load side is as follows: For the generation side, the absolute value of the product of the marginal network loss factor of all generation nodes and the active power injection is summed to obtain the objective impact factor of the generation side; for the load side, the absolute value of the product of the marginal network loss factor of all load nodes and the load power is summed to obtain the objective impact factor of the load side. The objective influencing factor on the generation side is used as the numerator, and the sum of the objective influencing factors on the generation side and the load side is used as the denominator to calculate the correction coefficient on the generation side; the correction coefficient on the load side is obtained by subtracting the correction coefficient on the generation side from 1. The generation-side network loss ratio and the generation-side correction coefficient are weighted to obtain the generation-side corrected allocation ratio; the load-side network loss ratio and the load-side correction coefficient are weighted to obtain the load-side corrected allocation ratio.

8. The green electricity source tracing and accounting method considering the fairness of network loss sharing according to claim 1, characterized in that: In S5, for each operating time in the set of operating times, based on the power provided by the power generation node to the load node and the network loss borne by the corresponding load node, combined with the node information in the node information set, the power provided by the power generation node marked as green electricity to the load node and the network loss borne by the corresponding load node are accumulated through the node tag bit to obtain the green electricity active power of the load node. The time difference between two adjacent times in the set of operating times is taken as the time width. The green electricity consumption can be obtained by multiplying the time width by the green electricity active power of the load node.

9. A green electricity source tracking and accounting system that considers the fairness of network loss sharing, using the method described in any one of claims 1 to 8, characterized in that, include: The power grid operation data acquisition module collects power grid operation data and constructs a dataset; the dataset includes a set of node information, a set of power grid topology, a set of line parameters, a set of node power, and operating times. The power flow calculation module takes the dataset as input, uses the Newton-Raphson method to perform power flow calculations, solves for the voltage magnitude and phase angle of each node, and calculates the network loss of the line between different nodes; The power calculation module is injected into the power generation node, which models the network loss of each branch as a virtual load and performs reverse power flow tracing through the sharing ratio principle to calculate the power provided by the power generation node to the load node. The load-side network loss calculation module constructs a direct influence matrix based on linguistic variables for predefined influencing factors, and obtains a comprehensive relationship matrix through triangular fuzzy number transformation and standardization. Based on the comprehensive relationship matrix, it calculates the importance of different influencing factors, determines the network loss ratio shared by the generation side and the load side, and performs corrections. Combined with the network loss of the line, it calculates the network loss borne by the load side on the corresponding line. The green energy consumption module calculates the amount of green electricity absorbed by the load nodes based on the power provided by the power generation nodes to the load nodes and the network losses borne by the load side, combined with the node marker of each node.

10. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-8.

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