Carbon emission allocation strategy determination method for power distribution network and electronic device

Abstract

This invention discloses a method and electronic device for determining a carbon emission allocation strategy for a distribution network. The method includes: acquiring current operating data of the distribution network; determining the initial carbon emission factors of multiple load nodes in the distribution network for the current time period based on the current operating data, wherein the initial carbon emission factor represents the carbon emission amount corresponding to a unit of electricity used by the corresponding load node; determining an objective function based on the initial carbon emission factors of the multiple load nodes for the current time period; optimizing the initial carbon emission allocation strategy of the distribution network for the current time period with the minimum function value of the objective function as the optimization objective, to obtain the target carbon emission allocation strategy of the distribution network for the current time period. This invention solves the technical problems of insufficient feasibility of carbon emission allocation strategies and poor carbon reduction capacity of distribution networks caused by insufficient consideration of factors in carbon emission assessment of distribution networks in related technologies.

Description

Technical Field

[0001] This invention relates to the field of carbon emission assessment technology for power systems, and more specifically, to a method and electronic device for determining carbon emission allocation strategies for distribution networks. Background Technology

[0002] With the increasing severity of global climate change, the installed capacity of new energy power generation, represented by distributed photovoltaic and wind power, has experienced explosive growth. New power systems are exhibiting new characteristics of high-proportion renewable energy integration and strong volatility on both the source and load sides. While the large-scale grid connection of new energy sources improves the supply of green electricity, it also significantly increases the uncertainty and complexity of distribution network operation, transforming the distribution network from a unidirectional radial network into a complex network with multiple power sources and bidirectional power flow. Against this backdrop, establishing a scientific, accurate, and dynamic carbon emission measurement and assessment system is of great significance for achieving precise carbon emission reduction in the power system, promoting green electricity trading, and driving the coordinated development of the electricity market. Related technologies have significant shortcomings in determining carbon emission allocation strategies for distribution networks, mainly in the following aspects:

[0003] Using static carbon emission factors based on annual or monthly averages, or relying solely on a single fixed value from the main grid injection point, ignores the strong fluctuations in the output of distributed photovoltaic and wind power, as well as the spatiotemporal variations on both the source and load sides. This results in calculated carbon emission factors lagging behind the actual grid operating status, failing to accurately reflect the greenness or carbon intensity of electricity at a specific moment or node. Furthermore, ignoring indirect carbon emissions from line losses leads to a lack of physical basis and fairness in carbon liability allocation, easily triggering disputes on the user side and hindering the synergy between the electricity market and the carbon market. In summary, related technologies, due to insufficient consideration of factors in distribution network carbon emission assessment, result in inadequate feasibility of carbon emission allocation strategies and poor carbon reduction capabilities in distribution networks.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method and electronic device for determining carbon emission allocation strategies in power distribution networks, which at least solves the technical problems of insufficient feasibility of carbon emission allocation strategies and poor carbon emission reduction capabilities of power distribution networks due to insufficient consideration of factors in carbon emission assessment of power distribution networks.

[0006] According to one aspect of the present invention, a method for determining a carbon emission allocation strategy for a distribution network is provided, comprising: acquiring current operating data of the distribution network; determining, based on the current operating data, initial carbon emission factors of multiple load nodes in the distribution network for the current time period, wherein the initial carbon emission factor represents the carbon emission amount corresponding to a unit of electricity used by the corresponding load node; determining an objective function based on the initial carbon emission factors of the multiple load nodes for the current time period, wherein the objective function is used to indicate the total carbon emission amount of the distribution network in the current time period and the dispersion of carbon emission factors among the multiple load nodes; optimizing the initial carbon emission allocation strategy of the distribution network for the current time period with the minimum function value of the objective function as the optimization objective, to obtain a target carbon emission allocation strategy of the distribution network for the current time period, wherein the initial carbon emission allocation strategy includes the initial carbon emission factors and initial carbon emission allocation coefficients of the multiple load nodes for the current time period, and the initial carbon emission allocation coefficients represent the proportion of the line loss carbon emission amount borne by the corresponding load node in the total network loss carbon emission amount of the distribution network.

[0007] According to another aspect of the present invention, a device for determining a carbon emission allocation strategy for a distribution network is also provided, comprising: an operation data acquisition module for acquiring current operation data of the distribution network; an initial carbon emission factor determination module for determining, based on the current operation data, the initial carbon emission factors of multiple load nodes in the distribution network in the current time period, wherein the initial carbon emission factor represents the carbon emission amount corresponding to a unit of electricity used by the corresponding load node; an objective function determination module for determining, based on the initial carbon emission factors of multiple load nodes in the current time period, wherein the objective function indicates the total carbon emission of the distribution network in the current time period and the dispersion of carbon emission factors among multiple load nodes; and a target carbon emission allocation strategy determination module for optimizing the initial carbon emission allocation strategy of the distribution network in the current time period with the minimum function value of the objective function as the optimization objective, to obtain the target carbon emission allocation strategy of the distribution network in the current time period, wherein the initial carbon emission allocation strategy includes the initial carbon emission factors and initial carbon emission allocation coefficients of multiple load nodes in the current time period, and the initial carbon emission allocation coefficients represent the proportion of the line loss carbon emission borne by the corresponding load node in the total network loss carbon emission of the distribution network.

[0008] According to another aspect of the present invention, a non-volatile storage medium is also provided, which stores multiple instructions adapted for a method for determining a carbon emission allocation strategy for a power distribution network, any one of which can be loaded and executed by a processor.

[0009] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any one of the methods for determining carbon emission allocation strategies for a power distribution network.

[0010] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of a method for determining a carbon emission allocation strategy for a power distribution network.

[0011] In this embodiment of the invention, the current operating data of the distribution network is acquired; based on the current operating data, the initial carbon emission factors of multiple load nodes in the distribution network are determined for the current time period, wherein the initial carbon emission factor represents the carbon emission amount corresponding to a unit of electricity used by the corresponding load node; based on the initial carbon emission factors of multiple load nodes for the current time period, an objective function is determined, wherein the objective function is used to indicate the total carbon emission of the distribution network in the current time period and the dispersion of carbon emission factors among multiple load nodes; with the minimum function value of the objective function as the optimization objective, the initial carbon emission allocation strategy of the distribution network in the current time period is optimized to obtain the target carbon emission allocation strategy of the distribution network in the current time period, wherein the initial carbon emission allocation strategy includes multiple The initial carbon emission factor and initial carbon emission allocation coefficient of the load node in the current time period are respectively. The initial carbon emission allocation coefficient represents the proportion of the line loss carbon emission borne by the corresponding load node in the total network loss carbon emission of the distribution network. It achieves the goal of constructing an objective function based on the initial carbon emission factor determined by the current operation data of the distribution network, and optimizing the initial carbon emission allocation strategy with the minimum function value as the optimization objective. This aims to accurately determine the target carbon emission allocation strategy, thereby achieving the technical effect of improving the feasibility of the carbon emission allocation strategy and improving the carbon emission reduction capacity of the distribution network. In turn, it solves the technical problem of insufficient feasibility of carbon emission allocation strategy and poor carbon emission reduction capacity of distribution network caused by insufficient consideration of factors in the carbon emission assessment of distribution network. Attached Figure Description

[0012] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0013] Figure 1 This is a flowchart of a method for determining a carbon emission allocation strategy for a power distribution network according to an embodiment of the present invention;

[0014] Figure 2 This is a flowchart of an optional carbon emission allocation strategy determination method for a power distribution network according to an embodiment of the present invention;

[0015] Figure 3 This is a schematic diagram of an optional improved IEEE 13-node power distribution system topology according to an embodiment of the present invention;

[0016] Figure 4 This is a schematic diagram illustrating the impact of optional photovoltaic access on node voltage according to an embodiment of the present invention;

[0017] Figure 5 This is a schematic diagram comparing carbon emission factors under different optional calculation methods according to an embodiment of the present invention;

[0018] Figure 6 This is a schematic diagram comparing the fitness of different optional genetic algorithms according to an embodiment of the present invention;

[0019] Figure 7 This is a schematic diagram comparing carbon emission factors before and after optimization of an optional objective function according to an embodiment of the present invention;

[0020] Figure 8 This is a schematic diagram comparing the total carbon emissions on the power generation side and the load side according to an embodiment of the present invention;

[0021] Figure 9 This is a schematic diagram of an optional carbon emission factor prediction result according to an embodiment of the present invention;

[0022] Figure 10 This is a schematic diagram of a carbon emission allocation strategy determination device for a power distribution network according to an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] According to an embodiment of the present invention, a method embodiment for determining the carbon emission allocation strategy of a power distribution network is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0026] Figure 1 This is a flowchart of a method for determining a carbon emission allocation strategy for a distribution network according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0027] Step S102: Obtain the current operating data of the power distribution network.

[0028] Optionally, by acquiring multi-source operational data such as the current network topology of the distribution network, electrical parameters of lines and transformers, active power output data, reactive power output data and generation type of distributed power sources (such as photovoltaic and wind power), power data of the upstream main grid injection points, active power load data, reactive power load data of each load node, power factor, and electricity consumption, high-precision, high-spatial-resolution data support can be provided for subsequent power flow calculations, carbon flow tracing, and line loss carbon emission allocation. Specifically, any electrical node in the distribution network satisfies active and reactive power balance at any sampling time, i.e. ,in, This represents the active power of the power sources (including but not limited to distributed power sources and grid injection points) connected to any electrical node at any sampling time. This represents the active power consumed by loads (including but not limited to residential, commercial, and industrial users) connected to any electrical node at any sampling time. This represents the reactive power of a power source connected to any electrical node at any sampling time. This represents the active power consumed by the load connected to any electrical node at any sampling time. This indicates the total number of specified electrical nodes, where the specified electrical nodes are all the remaining electrical nodes except for any one of them. This represents the voltage of any electrical node at any sampling time. This represents the voltage of any specified electrical node at any sampling time. This represents the voltage phase angle difference between any electrical node and any specified electrical node at any sampling time. This represents the voltage phase angle of any electrical node at any sampling time. This represents the voltage phase angle of any specified electrical node at any sampling time. This represents the conductance between any electrical node and any specified electrical node at any sampling time. This represents the susceptance between any electrical node and any specified electrical node at any sampling time. Using voltage and voltage phase angle as state variables, the voltage distribution of the distribution network is obtained by solving the aforementioned nonlinear equations, providing a basis for subsequent calculations.

[0029] Step S104: Based on the current operating data, determine the initial carbon emission factor of multiple load nodes in the distribution network for the current time period, where the initial carbon emission factor represents the carbon emission amount corresponding to the unit electricity used by the corresponding load node.

[0030] Optionally, based on current operating data, the initial carbon potential (i.e., initial carbon emission factor) of each load node is recursively calculated from the main grid injection node along the power flow direction. This initial carbon emission factor can accurately quantify the actual carbon emissions on the generation side corresponding to the unit electricity consumption of each load node, truly reflecting the access of distributed power sources and the dynamic impact of source-load fluctuations on the carbon intensity of the local distribution network. This provides accurate basic data support for subsequent optimization and allocation of line loss carbon emissions based on the principle of fairness, thereby ensuring the physical consistency and real-time accuracy of the carbon emission assessment results.

[0031] In one optional embodiment, determining the initial carbon emission factors of multiple load nodes in the distribution network for the current time period based on current operating data includes: determining, based on current operating data, the average active power between any load node and a target node, and the average active power output of any load node for the current time period, wherein the target node represents an electrical node upstream of any load node among multiple electrical nodes in the distribution network; the multiple electrical nodes include at least multiple load nodes, main grid injection nodes, distributed generation nodes, and generator nodes; determining the initial carbon emission factor of any load node for the current time period based on the average active power between any load node and the target node and the average active power output of any load node; and obtaining the initial carbon emission factors of multiple load nodes for the current time period by using the same method as obtaining the initial carbon emission factor of any load node for the current time period.

[0032] Optionally, based on current operating data, by identifying the electrical topology of the distribution network, including load nodes, main grid injection nodes, distributed generation nodes, and generator nodes, the initial carbon emission factor is recursively calculated from upstream to downstream along the power flow direction. Specifically, for any load node, the power injection from its upstream neighboring target nodes and the active power output of the load node's own distributed generation are extracted. Based on the principle of proportional sharing, the power is weighted and averaged to determine its corresponding carbon emission intensity, thereby constructing an initial carbon emission factor reflecting the local grid carbon flow transmission pattern. This embodiment's method for determining the initial carbon emission factor enables accurate tracking of carbon potential from the source to the load end, dynamically capturing the real-time impact of distributed generation fluctuations and power flow distribution changes on node carbon intensity. It overcomes the deficiency of static carbon emission factors in related technologies, which cannot reflect spatiotemporal differences. This provides physically consistent benchmark data for subsequent refined carbon allocation considering line losses and power source responsibilities, ensuring the real-time nature, accuracy, and physical interpretability of the carbon emission assessment results.

[0033] In an optional embodiment, when there are multiple target nodes and each load node includes multiple generators, the initial carbon emission factor of any load node in the current time period is determined based on the average active power between any load node and the target nodes and the average active power output of any load node. This includes determining the initial carbon emission factor of any load node in the current time period based on the average active power between the multiple target nodes and each load node and the average active power output of each of the multiple generators, in the following manner:

[0034] ;

[0035] in, This represents the initial carbon emission factor of any load node in the current time period. Indicates the index of any load node. This represents a collection of multiple generators. This represents the index of any one of multiple generators. This represents the average active power output of any generator. This represents the preset carbon emission intensity of any generator. Represents a set of multiple target nodes. This represents the index of any one of multiple target nodes. This represents the average active power between any load node and any target node. This represents the initial carbon emission factor of any target node in the current time period.

[0036] Optionally, by constructing a carbon flow tracking mathematical model with a complex topology involving multiple generators and upstream nodes, the above formula is used to weight and sum the output of all distributed generators and their corresponding preset carbon emission intensity within the set of any load node, along with the active power transmitted from all upstream adjacent target nodes and their corresponding initial carbon emission factors. The sum is then normalized using the total injected power as the denominator, thereby accurately calculating the initial carbon emission factor of the load node. This calculation method strictly adheres to the physical principles of carbon flow conservation and proportional sharing, comprehensively covering the carbon contributions of various power sources such as the main grid, distributed photovoltaics, and wind power. It accurately reflects the transmission and mixing process of upstream grid carbon potential to downstream load nodes, effectively solving the problems of difficulty in quantifying carbon responsibility and static factor distortion caused by mixed power supply from multiple sources in scenarios with high proportions of renewable energy access. This provides benchmark data with high physical realism and spatiotemporal resolution for the fair allocation of line loss carbon emissions and dynamic carbon emission factor assessment.

[0037] Step S106: Determine the objective function based on the initial carbon emission factors of multiple load nodes in the current time period. The objective function is used to indicate the total carbon emissions of the distribution network in the current time period and the degree of dispersion of carbon emission factors among multiple load nodes.

[0038] Optionally, based on the initial carbon emission factors of multiple load nodes in the current time period, a multi-objective optimization function is constructed, with minimizing the total carbon emissions of the distribution network as a secondary objective and minimizing the dispersion of carbon emission factors among multiple load nodes as the primary objective. The total carbon emission objective aims to minimize the overall carbon footprint of the distribution network by optimizing the network loss sharing weights, while the dispersion objective, by introducing the mean square error of node carbon emission factors, aims to eliminate the uneven distribution of carbon responsibility caused by differences in distribution network topology and power supply structure, thus physically reflecting the principle that nodes with similar electrical distances should have similar carbon emission factors. The establishment of this objective function can achieve dual optimization of the economic operating efficiency and social equity of the distribution network, satisfying the total control requirements for the low-carbon development of the new power system while avoiding unfair responsibility sharing caused by abrupt changes in carbon emission factors by smoothing the differences in carbon emission factors among adjacent nodes.

[0039] In an optional embodiment, when the distribution network includes multiple load nodes and multiple generator nodes, an objective function is determined based on the initial carbon emission factors of the multiple load nodes in the current time period. This includes: determining the average carbon emission factor of the multiple load nodes in the current time period based on their initial carbon emission factors; determining a first function based on the initial carbon emission factors and average carbon emission factor of the multiple load nodes in the current time period, wherein the first function indicates the dispersion of carbon emission factors among the multiple load nodes in the current time period; determining a second function based on the initial carbon emission factors and average active power load of the multiple load nodes in the current time period, and the average active power output and preset carbon emission intensity of the multiple generator nodes in the current time period, wherein the second function indicates the total carbon emissions of the distribution network in the current time period; and determining the objective function based on the first and second functions.

[0040] Optionally, in complex scenarios where the distribution network includes multiple load nodes and multiple generator nodes, a first function is constructed by calculating the deviation of the initial carbon emission factor of each node from the overall average value. This quantifies and minimizes the dispersion of carbon emission factors among nodes, thereby ensuring fairness in allocation. Simultaneously, a second function is constructed by combining the output of each generator node, the preset carbon intensity, and the characteristics of the load nodes to quantify and minimize the total global carbon emissions of the distribution network to achieve emission reduction targets. Finally, these two functions are weighted and combined to form a comprehensive objective function. This process can realize the transformation from single-dimensional total optimization to multi-objective collaborative optimization that takes into account both the total low carbon emissions of the distribution network and the fairness of node carbon responsibility. It avoids market disputes caused by the disparity in carbon responsibility due to close electrical distances by suppressing drastic fluctuations in the carbon potential of adjacent nodes, and drives collaborative carbon reduction on both the source and load sides by minimizing global carbon emissions, thereby significantly improving the rationality and acceptability of the carbon emission assessment results of the distribution network.

[0041] In one optional embodiment, determining a first function based on the initial carbon emission factors and average carbon emission factors of multiple load nodes in the current time period includes: determining the first function based on the initial carbon emission factors and average carbon emission factors of multiple load nodes in the current time period in the following manner:

[0042] ;

[0043] in, This represents the function value of the first function. This represents the initial carbon emission factor of any one of multiple load nodes in the current time period. Indicates the index of any load node. This represents the total number of load nodes. This represents the average carbon emission factor.

[0044] Optionally, by constructing a dispersion assessment model based on mean square error statistics, the sum of squared deviations between the initial carbon emission factors of each load node and the average carbon emission factor of the distribution network is normalized to obtain a first function value, thereby quantifying the degree of difference in carbon emission intensity among different load nodes in the current period. The average carbon emission factor can be obtained as follows: The method used in this implementation to determine the first function can intuitively reflect the balance of carbon responsibility allocation in spatial distribution. Minimizing the function value of the first function aims to suppress drastic fluctuations in carbon potential caused by differences in network topology or local power characteristics, and prevent abrupt changes in the carbon responsibility of adjacent electrical nodes. This establishes an optimization orientation centered on fairness at the mathematical level, ensuring that the final target carbon emission factor not only conforms to the physical current law, but also reflects the fair principle of synergistic burden reduction on both the source and load sides.

[0045] In one optional embodiment, the second function is determined based on the initial carbon emission factors and average active power load of multiple load nodes in the current time period, and the average active power output and preset carbon emission intensity of multiple generator nodes in the current time period, respectively. This determination includes: based on the initial carbon emission factors and average active power load of multiple load nodes in the current time period, and the average active power output and preset carbon emission intensity of multiple generator nodes in the current time period, the second function is determined in the following manner:

[0046] ;

[0047] in, Let G represent the function value of the second function, and let G represent the set of multiple generator nodes. This represents the index of any generator node among multiple generator nodes. This represents the average active power output of any generator node during the current time period. This represents the preset carbon emission intensity of any generator node in the current time period. This represents the index of any one of multiple load nodes. This represents the total number of load nodes. This represents the initial carbon emission factor of any load node in the current time period. This represents the average active power load of any load node during the current time period.

[0048] Optionally, a carbon emission total calculation model can be constructed that incorporates both generation-side emissions and load-side consumption. This model sums the products of the active power output of each generator node and its preset carbon emission intensity, as well as the products of the initial carbon emission factor of each load node and its active power load, to calculate the total carbon emissions of the distribution network in the current period. This second function strictly adheres to the carbon flow conservation law, enabling full-chain carbon emission quantification from the power source to the user. Minimizing the value of the second function aims to guide the optimization algorithm to prioritize the use of low-carbon power sources and optimize power flow distribution to reduce the overall carbon footprint. This provides a core indicator characterizing the overall environmental benefits of the distribution network for multi-objective optimization, ensuring the accuracy of the assessment results at the macro-level and the effectiveness of the emission reduction orientation.

[0049] In one optional embodiment, determining the target function based on the first function and the second function includes: determining the target function based on the first function and the second function in the following manner:

[0050] ;

[0051] in, This represents the function value of the objective function. This represents the function value of the first function. This represents the function value of the second function. This represents the function value of the first function of the distribution network under ideal operating conditions. This represents the function value of the second function under ideal operating conditions. This represents the first weighting coefficient. This represents the second weighting coefficient.

[0052] Optionally, by constructing a weighted multi-objective optimization function based on ideal operating scenarios, the dispersion index representing the carbon responsibility fairness of load nodes and the total carbon emission index representing the overall emission reduction benefit of the distribution network are standardized, and weighting coefficients are introduced to balance their priorities in the optimization process. The above objective function design can effectively solve the problem that it is difficult to directly compare indicators of different dimensions. At the same time, by introducing function values ​​under ideal operating scenarios for normalization, the numerical magnitude deviation caused by differences in distribution network scale or operating conditions can be eliminated. This allows the optimization algorithm to flexibly adjust the emphasis on local carbon emission factor balance and global carbon emission minimum, thereby guiding the subsequent optimization algorithm to search for the optimal solution that takes into account social fairness and environmental economy, and improving the robustness and engineering applicability of dynamic carbon emission assessment of distribution networks under complex operating scenarios.

[0053] Step S108: With the minimum function value of the objective function as the optimization objective, the initial carbon emission allocation strategy of the distribution network in the current period is optimized to obtain the target carbon emission allocation strategy of the distribution network in the current period. The initial carbon emission allocation strategy includes the initial carbon emission factor and initial carbon emission allocation coefficient of multiple load nodes in the current period. The initial carbon emission allocation coefficient represents the proportion of the line loss carbon emission borne by the corresponding load node in the total network loss carbon emission of the distribution network.

[0054] Optionally, guided by minimizing the objective function, an adaptive genetic algorithm can be used to search and iteratively optimize the initial carbon emission allocation strategy, which includes the initial carbon emission factor and the line loss carbon emission allocation coefficient. By dynamically adjusting the proportion of each load node in the total network loss carbon emission, the optimized carbon emission allocation strategy satisfies both the fairness requirement of minimizing the mean square error of the carbon potential of the load nodes and the economic objective of minimizing the total carbon emission of the distribution network. In the optimization process using the adaptive genetic algorithm, firstly, a penalty function term is introduced to transform the constrained optimization into an unconstrained optimization. A fitness function is then constructed to evaluate the merits of candidate carbon emission allocation strategies, ensuring that individuals with smaller original objective function values ​​and less constraint violation receive higher fitness. The fitness function can be obtained as follows: ,in, This represents a candidate carbon emission allocation strategy in any iteration process. This represents the function value of the objective function corresponding to the candidate carbon emission sharing strategy. This represents the value corresponding to any one of multiple constraints, where m represents the index of any constraint. This represents the preset positive penalty coefficient for any constraint. Furthermore, to avoid premature convergence or getting trapped in local optima in the adaptive genetic algorithm, an adaptive mechanism is used to dynamically adjust the crossover and mutation probabilities. Specifically, when an individual's fitness is high, the crossover probability is reduced to protect superior individuals, and the mutation probability is reduced to retain superior genes. Conversely, when an individual's fitness is low, the crossover probability is increased to increase search diversity, and the mutation probability is increased to introduce new genes. This guides the population towards the optimal solution that minimizes the mean square error of node carbon potential and the total carbon emissions of the distribution network, ultimately obtaining a target carbon emission sharing strategy that balances fairness and economy. The crossover probability of the current individual can be obtained as follows: ,in, Indicates the maximum fitness of the population. Indicates the average fitness of the population. This indicates the higher fitness of the two individuals before the crossover operation. This indicates the upper limit of the preset crossover probability. This represents the preset lower limit of the crossover probability; the mutation probability of the current individual can be obtained as follows. ,in, This indicates the current fitness level of an individual. This represents the preset upper limit of the mutation probability. This represents the preset lower limit of the probability of variation. This optimization process can shift from a static, extensive model where all line losses are borne by the load side to a dynamic bilateral allocation model based on physical carbon flow tracking and multi-objective optimization. It accurately quantifies the contribution of power source fluctuations and load-side characteristics to network loss carbon emissions, solving the technical challenges of ambiguous carbon responsibility attribution and unfair allocation in related technologies. By incentivizing power sources to smooth output and users to improve power factor, it collaboratively reduces network losses from both the source and load sides, providing a scientific, fair, and highly physically interpretable decision-making basis for precise carbon reduction in distribution networks and coordination with the electricity market.

[0055] In an optional embodiment, before optimizing the initial carbon emission allocation strategy of the distribution network in the current time period with the goal of minimizing the function value of the objective function to obtain the target carbon emission allocation strategy of the distribution network in the current time period, the method further includes: determining the line loss carbon emission of multiple load nodes in the current time period based on the average active power load of multiple load nodes in the current time period and the total network loss carbon emission; determining the allocation weight of multiple load nodes in the current time period; and determining the initial carbon emission allocation coefficient of multiple load nodes in the current time period based on the line loss carbon emission and allocation weight of multiple load nodes in the current time period.

[0056] Optionally, before using the adaptive genetic algorithm for optimization, firstly, the basic line loss carbon emissions to be borne by each load node are calculated based on the ratio of the average active power load of each load node to the total network loss carbon emissions. Then, combined with pre-determined allocation weights, the initial carbon emission allocation coefficients for multiple load nodes in the current period are calculated. The line loss carbon emissions of any load node in the current period can be obtained as follows: ,in, Indicates the index of any load node. This represents the total number of load nodes. This represents the average active power load of any load node during the current time period. This represents the total carbon emissions from network losses during the current period. This represents a set of multiple load branches (two load nodes form one load branch) in a distribution network. This represents the carbon flow density of any one of the multiple load branches in the current time period (which is the initial carbon emission allocation coefficient of the first node of that branch). This represents the line loss rate of any load branch in the current time period. The line loss rate indicates the portion of active power that is converted into heat energy due to the resistance of lines and transformers during transmission and distribution. The allocated weight of any load node in the current time period can be obtained as follows: ,in, This represents the node weight of any load node. This represents the preset load importance index for any load node. This represents the preset power factor for any load node. The initial carbon emission allocation factor for any load node in the current time period can be obtained as follows: The above process provides the optimization algorithm with a physically meaningful starting point for a feasible solution space that conforms to the initial fairness principle. This not only avoids the slow convergence or getting stuck in local optima caused by uneven distribution of initial solutions in the early stages of blind search, but also ensures that the initial carbon emission allocation strategy has a logically reasonable allocation of carbon emission responsibility, laying a solid foundation for the rapid optimization of subsequent multi-objective optimization algorithms.

[0057] In one optional embodiment, when the distribution network includes multiple electrical nodes and multiple branches, and the current time period includes multiple sampling times, the initial carbon emission allocation strategy of the distribution network in the current time period is optimized with the minimum function value of the objective function as the optimization objective, to obtain the target carbon emission allocation strategy of the distribution network in the current time period. This includes: determining the constraints of the distribution network in the current time period, wherein the constraints include at least: the voltage of multiple electrical nodes at any sampling time in the current time period is within a preset voltage range; the power of multiple branches at any sampling time is within a preset power range; the bus loss rate of the distribution network does not exceed a preset bus loss rate; the carbon emission factor of the load nodes among the multiple electrical nodes is greater than or equal to zero in the current time period; and the power generation of the distributed power nodes among the multiple electrical nodes does not exceed a preset power generation in the current time period, wherein the power includes active power and reactive power; the multiple electrical nodes include at least load nodes and distributed power nodes; based on the constraints, the initial carbon emission allocation strategy is optimized with the minimum function value of the objective function as the optimization objective, to obtain the target carbon emission allocation strategy.

[0058] Optionally, firstly, based on the physical operating characteristics of the distribution network, a set of constraints is constructed that includes voltage amplitude, branch power flow (active and reactive power), bus loss rate, non-negativity of carbon emission factor, and upper limit of distributed generation output, to ensure that the optimization process strictly follows the safe operation specifications and physical feasibility of the power system. That is, the constraints are: ; ; ; ;in, This represents the voltage of any electrical node among multiple electrical nodes at any sampling time. This represents the lower limit of the preset power range for any electrical node. This represents the upper limit of the preset power range for any electrical node. Represents the index of any electrical node. This represents the active power of any one of multiple branches at any sampling time, where any branch is any electrical node. and designated electrical nodes composition, This represents the upper limit of the preset active power range for any branch. This represents the voltage phase angle difference of any branch at any sampling time. This represents the voltage phase angle of any electrical node at any sampling time. This indicates the voltage phase angle of a specified electrical node at any sampling time. This represents the conductance of any branch at any sampling time. This represents the susceptance of any branch at any sampling time. This represents the reactive power of any branch at any sampling time. This represents the upper limit of the preset reactive power range for any branch. Let N represent the set of multiple branches, and let N represent the set of multiple electrical nodes. Indicates the bus loss rate of the distribution network. Indicates the preset bus loss rate. This represents the total active power supplied by the distribution network. Represents a set of multiple load nodes. This represents the active power load of any load node among multiple load nodes at any sampling time. Indicates the index of any load node. This represents the line loss rate of any branch at any sampling time. This represents the active power from any electrical node to a specified electrical node at any sampling time. This represents the power generation capacity of the distributed power generation nodes in the current time period. This represents the preset power generation capacity. Subsequently, under the premise of satisfying the above constraints, the initial carbon emission allocation strategy is optimized iteratively based on minimizing the objective function to obtain the target carbon emission allocation strategy. This strategy not only enables precise dynamic allocation of carbon emission responsibility in space and time, but also avoids safety hazards such as voltage exceeding limits, line overload, or excessive network losses caused by optimization results. At the same time, by forcing the carbon emission factor to be non-negative and the output constraint, the physical rationality and engineering feasibility of the assessment results can be guaranteed. Ultimately, under the premise of ensuring the safe and stable operation of the distribution network, the optimal balance between minimizing the overall carbon emissions of the distribution network and the fairness of carbon responsibility among nodes is achieved.

[0059] After determining the target carbon emission allocation strategy, to overcome the latency and prediction bias issues of static or ex-post accounting methods in related technologies when dealing with the high proportion of renewable energy access in new distribution networks, a dynamic carbon emission factor rolling prediction framework based on a combination of Long Short-Term Memory (LSTM) networks and exponential smoothing is further introduced. Specifically, firstly, using historical data strongly correlated with the distribution network's carbon emission factor as input features, a multi-dimensional time-series feature vector is constructed, including the total active power of the distribution network load, the total output of renewable energy, the dynamic carbon emission factor, and time characteristics. The multi-dimensional time-series feature vector at any historical sampling moment can be obtained in the following way. ,in, This represents the total active power of the distribution network at any historical sampling time. This represents the total renewable energy output of the distribution network at any historical sampling point. This represents the carbon emission factor of the distribution network at any historical sampling point. This represents the hour code corresponding to any historical sampling time. This represents the weekday code corresponding to any historical sampling time. Subsequently, leveraging the powerful temporal dependency capture capability of the LSTM network, long-term memory and periodic patterns in the evolution of carbon emission factors are extracted, thereby generating predicted carbon emission factors for future prediction times. The predicted carbon emission factor for any prediction time can be obtained as follows: in, This represents a multidimensional temporal feature vector of multiple historical sampling moments preceding any given historical sampling moment, where T represents the total number of historical sampling moments. This represents the time interval between any predicted time and any historical sampling time. This represents the trainable parameters of the LSTM network (including but not limited to the weight matrix and bias). Next, to address the phase lag and amplitude deviation caused by sudden changes in photovoltaic output or drastic load fluctuations in the LSTM network, a first-order exponential smoothing method is introduced as a lightweight correction mechanism. The prediction error of the carbon emission factor at the current moment is calculated using real-time acquired data. ,in, This represents the actual carbon emission factor at the current moment. This represents the predicted carbon emission factor at the current moment, obtained from the LSTM network prediction. This represents the index of the current time. Furthermore, by using the carbon emission factor prediction error at the current time to perform online rolling corrections on the carbon emission factor prediction value for the next prediction time after the current time, the corrected carbon emission factor prediction value for the next prediction time after the current time can be obtained. ,in, Indicates the next predicted time after the current time. This represents the predicted carbon emission factor value for the next prediction time after the current time, obtained by the LSTM network. This represents the preset smoothing coefficient, which determines the strength of the correction. Through a dynamic carbon emission factor rolling prediction framework based on LSTM networks, carbon emission factor assessment can be upgraded from passive static calculation to a forward-looking prediction and real-time correction mode. This allows the prediction results to quickly respond to the strong fluctuations on both the source and load sides, providing highly reliable and low-latency forward-looking carbon potential information for day-ahead low-carbon dispatching of the distribution network, optimization of energy storage charging and discharging, and user-side demand response.

[0060] Through the above steps S102 to S108, an objective function can be constructed based on the initial carbon emission factor determined by the current operating data of the distribution network. The optimization objective is to minimize the function value of the objective function, thereby optimizing the initial carbon emission allocation strategy and accurately determining the target carbon emission allocation strategy. This achieves the technical effect of improving the feasibility of the carbon emission allocation strategy and enhancing the carbon emission reduction capacity of the distribution network. In turn, it solves the technical problem of insufficient feasibility of the carbon emission allocation strategy and poor carbon emission reduction capacity of the distribution network caused by insufficient consideration of factors in the carbon emission assessment of the distribution network.

[0061] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation method. Figure 2 This is a flowchart of an optional carbon emission allocation strategy determination method for a power distribution network according to an embodiment of the present invention, as follows: Figure 2 As shown, the method includes using an improved IEEE 13-node distribution system as a test system to verify the effectiveness of the carbon emission allocation strategy determination method under a typical distribution network structure.

[0062] S1: Collects current operating data of the power distribution system, specifically including: Figure 3 This is a schematic diagram of an optional improved IEEE 13-node distribution system topology according to an embodiment of the present invention. First, the base capacity of the distribution system is set to 1 MVA, the base voltage to 0.4 kV, and the safe operating range of the node voltage is limited to between 0.95 and 1.05 pu. The main grid injection power is set to 225 kW, and the corresponding static carbon emission factor is 0.65 tCO2 / MWh. Next, the carbon emission intensity of renewable energy sources such as photovoltaics is defined as 0 tCO2 / MWh, and a transaction scenario is set where node 3 users purchase 50 kWh of green electricity and node 9 users purchase 30 kWh of green electricity. Further, the specific operating data of each node in the distribution system at 13:00 is collected. Table 1 shows the current operating data acquisition results of an optional improved IEEE 13-node distribution system according to an embodiment of the present invention. Nodes 1 to 8 are mainly conventional loads or have photovoltaic access; nodes 1 to 5 and nodes 7 to 8 have no photovoltaic output; nodes 3 and 6 are connected to 120 kW and 80 kW photovoltaic systems respectively; and node 9 not only carries 100 kW... The dataset includes kW active load and 30 kVar reactive load, and is equipped with energy storage devices that discharge at 30 kW power. This dataset provides the basic input conditions for subsequent power flow calculations, network loss analysis, and dynamic carbon emission factor assessment.

[0063] Table 1

[0064]

[0065] S2: Determine the current voltage, carbon flow density, and line loss rate of the power distribution system. Specifically, this includes: based on the balance equations of active and reactive power at each node, the voltage distribution of the power distribution system is obtained by solving the above balance equations. The specific implementation process is the same as in the aforementioned embodiments, and will not be repeated here.

[0066] Table 2 shows the carbon flow density and line loss rate results for each branch in an optional improved IEEE 13-node distribution system according to an embodiment of the present invention. The data in the table shows that the line loss rates differ among branches. For example, branches 0-1 and 8-9 have higher losses, at 2.1 kW and 2.0 kW respectively, because these branches are close to the power source inlet or carry a large total power flow. Branch 2-3 has the lowest loss, at only 0.8 kW. The table lists the carbon flow density for each branch, which reflects the carbon emissions per unit of active power flowing through that branch. This value varies with the power flow path and power injection conditions. For example, at branch 3-4, the carbon potential flowing through this branch is significantly reduced due to the zero-carbon output photovoltaic power generation (150 kW) injected at node 3, with the carbon flow density decreasing from 0.63 upstream to 0.58. This directly reflects the dilution effect of distributed green power sources on the local grid carbon intensity. Meanwhile, branch 0-1, closer to the main grid inlet, maintains a higher carbon flow density of 0.65, reflecting the high carbon emission background of the main grid power supply. The data in this table not only reveals the spatial distribution characteristics of line losses but also demonstrates the transmission and attenuation patterns of carbon emissions along the grid topology through carbon flow density.

[0067] Table 2

[0068]

[0069] Figure 4This diagram illustrates the impact of optional photovoltaic (PV) access on node voltage according to an embodiment of the present invention. The diagram compares the voltage amplitudes of various nodes in the distribution system under two scenarios: with and without distributed PV access, aiming to verify the impact of PV access on voltage quality and the safety of the distribution system. In the scenario without PV, due to line impedance and the increasing or uneven distribution of load from the beginning to the end, current flows through the lines, causing voltage drops. The voltage gradually decreases with increasing power supply distance. The voltage at node 8 drops to 0.88 pu, exceeding the allowable range of 0.95~1.05 pu for the distribution system, posing a serious risk of voltage exceeding the lower limit, which may lead to malfunction or even damage to user-side equipment. When 150 kWp and 100 kWp PV systems are connected to nodes 3 and 6 respectively, the active power generated locally by the PV power source is directly injected into the local grid, effectively reducing the active power transmitted from the main grid to the end nodes. This reduces the current on the lines and the resulting voltage loss, thus minimizing voltage drops. The orange curve in the figure shows that after all nodes were connected to photovoltaics, the overall voltage level of the distribution system was significantly improved (compared to the case where the nodes on the blue curve were not connected to photovoltaics). The voltage of all nodes was raised to the safe range of 0.96~1.02 pu, fully meeting the voltage constraint condition of 0.95~1.05 pu. This comparison result strongly proves that the reasonable configuration of distributed photovoltaics can not only balance part of the load demand locally, but also effectively improve the voltage quality of the distribution system, avoid power quality accidents caused by low voltage, and thus delay the investment in grid upgrading and transformation. However, the figure also reveals potential risks. For example, due to the installation of a large-capacity photovoltaic system (150 kWp) at node 3, its voltage rose to 1.02 pu. Although it is still within the allowable range, it is very close to the voltage limit of 1.05 pu. This suggests that the risk of voltage exceeding the limit should be closely monitored during periods of high photovoltaic output. It is recommended to use energy storage systems to absorb excess reactive or active power, or to install automatic voltage regulators and reactive power compensation devices for coordinated regulation to ensure the absolute safety of the distribution system operation.

[0070] S3: Determine the current target carbon emission allocation strategy for the power distribution system, specifically including: constructing an optimization objective with minimizing the mean square error of node carbon emission factors as the primary objective and minimizing the total carbon emissions of the power distribution system as the secondary objective; based on the current voltage, carbon flow density, and line loss rate determined in step S2, incorporate power flow balance constraints, node voltage upper and lower limit constraints, branch transmission power limit constraints, line loss rate upper limit constraints, dynamic carbon emission factor non-negativity constraints, and renewable energy output range constraints into the boundary conditions of the optimization model; use an adaptive genetic algorithm to iteratively optimize the initial population, where the crossover probability and mutation probability are dynamically adjusted according to the individual fitness to avoid getting trapped in local optima, and a penalty function is introduced to handle constraint violations until the algorithm converges to obtain a solution that satisfies all physical constraints and has the optimal objective function; finally, combine the target carbon emission factors of each node obtained from the optimization solution and the determined carbon emission allocation coefficients to form the current target carbon emission allocation strategy. The specific implementation process is the same as in the aforementioned embodiments and will not be repeated here.

[0071] Figure 5This diagram illustrates a comparison of carbon emission factors under different optional calculation methods according to an embodiment of the present invention. As can be seen from the diagram, the methods in related technologies use a single static carbon emission factor to perform a one-size-fits-all calculation of all nodes in the entire power distribution system. This extensive approach ignores the differences in the actual carbon responsibility of different users due to variations in power supply structure, self-provided distributed photovoltaic (PV) access, and energy storage charging and discharging behavior. This can easily lead to distortions in carbon reduction incentives. For example, users who install PV systems, although actually consuming clean electricity, are still overestimated as high-carbon users, thus discouraging their enthusiasm for green energy investment. In contrast, this embodiment, based on carbon flow tracking theory, transmits the high carbon emission factor injected into the main grid along the grid topology according to power ratio and accurately integrates the carbon emission factor characteristics of zero-carbon PV output and mixed energy storage discharge. As shown in the blue bars of the diagram, thanks to the local absorption of distributed power sources, the carbon potential of PV nodes 3 and 6 is significantly reduced to 0.205 and 0.210, respectively, while the gate-side node 0 maintains the main grid benchmark of 0.65. This node-level differentiated allocation mechanism can achieve a precise profile of carbon emission responsibility, reflecting not only the user's actual environmental contribution but also effectively incentivizing the local utilization and consumption of clean energy. Table 3 shows the grid loss carbon emission results of an optional allocation target according to an embodiment of the present invention. The table displays the specific allocation results under the bilateral allocation mechanism for grid loss carbon emission determined based on the target carbon emission allocation coefficient, aiming to address the unfairness of methods in related technologies that place all grid loss carbon emission on the load side. Data shows that grid loss carbon emission is reasonably divided into 32% borne by the power supply side and 68% borne by the load side, reflecting the physical fairness principle of sharing grid loss responsibility between the source and load. Within the load side, the allocation is further refined to each user node. Among them, user Node 9, due to its large load and participation in energy storage discharge, bears a 20% share. Users Node 3 and Node 6, due to their high proportion of photovoltaic self-consumption, have significantly reduced allocation ratios of 12% and 8% respectively, while the remaining users bear a total of 28%. This result not only quantifies the actual contribution of different users to grid loss carbon emissions, but also confirms the fairness and effectiveness of the method in this embodiment in incentivizing power sources to smooth output, guiding users to improve power factor, and promoting green electricity consumption through differentiated allocation coefficients. Figure 6This diagram illustrates a comparison of the fitness of different optional genetic algorithms according to an embodiment of the present invention. As shown in the diagram, the adaptive genetic algorithm used in this embodiment solves the optimization objective. Its core improvement lies in using a carbon flow sensitivity matrix to guide the initial population generation, making it closer to the feasible region, thereby improving search efficiency. During the evolutionary process, a fitness function containing the objective function value and constraint violation penalty terms is constructed. Based on the relative magnitude of individual fitness and the average fitness of the population, the crossover probability and mutation probability are dynamically and adaptively adjusted to balance global exploration and local development capabilities. Selection, crossover, and mutation operations are executed sequentially until convergence. Compared with genetic algorithms in related technologies (i.e., traditional genetic algorithms), the adaptive genetic algorithm used in this embodiment exhibits significant superiority. For example, the adaptive genetic algorithm used in this embodiment reaches a high fitness value of 0.91 in only the 30th generation and quickly stabilizes, while genetic algorithms in related technologies only slowly climb to 0.82 in the 65th generation, accompanied by significant oscillations. Data shows that the adaptive genetic algorithm used in this embodiment has a convergence speed that is improved by about 54% and an optimal fitness that is 11% higher, which fully verifies the fast convergence, stability and optimization accuracy of the adaptive genetic algorithm when dealing with complex nonlinear constraints and multi-objective optimization problems. Figure 7 This is a schematic diagram comparing carbon emission factors before and after optimization of an optional objective function according to an embodiment of the present invention. The diagram shows that the carbon emission factor (i.e., carbon potential) before optimization directly originates from the initial population. Because no penalty for differences in carbon potential between adjacent nodes is introduced, the carbon potential of photovoltaic node 3 is abnormally low, while the carbon potential of its electrically adjacent node 4 is excessively high. This drastic jump in carbon potential, while reflecting extreme differences, violates the physical law that closer electrical distances lead to more similar power sources, easily causing unfair carbon responsibility allocation and user disputes. Therefore, this embodiment innovatively introduces constraints into the objective function. After optimization, the carbon potential of node 3 reasonably recovers, while that of node 4 decreases slightly. The maximum carbon potential difference between adjacent nodes is significantly compressed, and the overall carbon potential distribution curve is smoother and more continuous. This improvement not only ensures the fairness of carbon responsibility allocation but also effectively prevents some users from obtaining unreasonably high carbon credits or suffering excessive penalties due to abnormal fluctuations in carbon potential.

[0072] Table 3

[0073]

[0074] S4: Verify the feasibility of the target carbon emission allocation strategy. Specifically, based on the conservation laws of carbon flow tracking theory, perform a physical consistency check on the optimized target carbon emission allocation strategy. This is done by calculating the total carbon emissions on the generation side (i.e., the product of the main grid injected power and the static carbon emission factor potential) and the total carbon emissions on the load side (i.e., the sum of the products of the active load of each node and its corresponding target carbon emission factor), and calculating the absolute value of the difference. Determine if this difference is less than a preset conservation error threshold (e.g., 0.5). If the condition is met, it proves that the grid loss carbon emissions are accurately implied in the load-side carbon potential, strictly adhering to the dual conservation laws of energy and carbon, indicating that the target carbon emission allocation strategy has high credibility and is feasible. If the difference exceeds the preset conservation error threshold, it indicates that the target carbon emission allocation strategy has numerical calculation errors or carbon flow path topology errors, requiring re-solution until the conservation constraints are met. Figure 8 This is a schematic diagram comparing the total carbon emissions on the power generation side and the load side according to an embodiment of the present invention. The diagram shows that the carbon emissions on the power generation side are 146.25 kgCO2 / h, and the carbon emissions on the load side are 146.10 kgCO2 / h. The difference is 0.15 kgCO2 / h, which is much smaller than the preset conservation error threshold of 0.5 kgCO2 / h. This indicates that the carbon flow tracking model, network loss sharing mechanism, and optimization solution process constructed in this embodiment strictly follow the dual conservation laws of energy and carbon, and the evaluation results have high reliability.

[0075] S5: Determine the carbon emission factor prediction results for future prediction times, specifically including: constructing a time-series feature vector containing the total active power of the distribution system load, the total output of renewable energy, historical dynamic carbon emission factors, and time characteristics; using a Long Short-Term Memory (LSTM) network to extract the time-series evolution law and long-term dependence of carbon emission factors, generating predicted carbon emission factor values ​​for future prediction times; calculating the carbon emission factor prediction error for the current time by combining real-time collected data, and using a first-order exponential smoothing method to perform online rolling correction on the predicted carbon emission factor values ​​for the next prediction time, dynamically adjusting the smoothing coefficient to balance the stability and response speed of the prediction; finally outputting the corrected carbon emission factor prediction results for future prediction times with high timeliness and high accuracy. The specific implementation process is the same as the aforementioned embodiments and will not be repeated here.

[0076] Figure 9This diagram illustrates an optional carbon emission factor prediction result according to an embodiment of the present invention. The diagram shows the prediction results for the carbon emission factor (i.e., carbon potential) at node 3 (the photovoltaic user) using only LSTM and after adding exponential smoothing correction. Using LSTM to predict the carbon emission factor can capture the overall trend, but there is phase lag and amplitude deviation during the rapid changes in photovoltaic output in the early morning and evening, due to the inherent time lag of LSTM's response to sudden changes. After adding exponential smoothing correction, the predicted carbon emission factor is dynamically adjusted using the latest measured error, and the correction curve almost coincides with the true value. This diagram verifies that even without complex Kalman filtering, simple first-order exponential smoothing can significantly improve prediction accuracy, meeting the needs of new power systems for dynamic tracking of carbon emission factors.

[0077] This embodiment can achieve at least one of the following effects: (1) A dynamic allocation method for network loss carbon emissions based on bidirectional coupling of "carbon flow-electrical loss" establishes a bidirectional coupling mechanism between power flow calculation and carbon flow tracking: by virtualizing branch network losses as intermediate nodes, the carbon flow can see the network losses; and simultaneously considering the power output fluctuation sensitivity (power supply side responsibility) and load power factor and electricity consumption (load side responsibility), the bilateral allocation coefficient is determined. This mechanism enables the power supply side and the load side to share the network loss carbon emissions, which is more in line with the principle of physical fairness, and can incentivize the power supply to smooth its output and users to improve their power factor, thereby reducing network losses from both the source and load sides. (2) A dynamic carbon potential rolling assessment framework that integrates time-series prediction and rolling correction. A lightweight rolling prediction strategy of "LSTM + exponential smoothing" is proposed: LSTM captures the daily cycle and trend of carbon potential, and exponential smoothing uses real-time collected data to make low-latency corrections to the predicted value at the next moment. Compared with pure LSTM prediction, this framework can reduce the root mean square error, and has a small computational load and fast response, making it suitable for the edge computing environment of the distribution network. More importantly, it elevates carbon potential assessment from static accounting to forward-looking prediction and real-time correction, providing highly reliable dynamic signals for day-ahead low-carbon dispatch, energy storage charging and discharging optimization, and demand response, filling the gaps in the dynamism and practicality of related technologies and methods.

[0078] This embodiment also provides a carbon emission allocation strategy determination device for a power distribution network. This device is used to implement the above embodiments and preferred embodiments, and will not be repeated for details already described. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0079] According to an embodiment of the present invention, an apparatus embodiment for implementing the above-described method for determining carbon emission allocation strategies for power distribution networks is also provided. Figure 10This is a schematic diagram of a carbon emission allocation strategy determination device for a power distribution network according to an embodiment of the present invention, as shown below. Figure 10 As shown, the carbon emission allocation strategy determination device for the aforementioned power distribution network includes: an operation data acquisition module 1000, an initial carbon emission factor determination module 1002, an objective function determination module 1004, and a target carbon emission allocation strategy determination module 1006, wherein:

[0080] The operation data acquisition module 1000 is used to acquire the current operation data of the power distribution network;

[0081] The initial carbon emission factor determination module 1002 is connected to the operation data acquisition module 1000 and is used to determine the initial carbon emission factor of multiple load nodes in the distribution network in the current time period based on the current operation data. The initial carbon emission factor represents the carbon emission amount corresponding to the unit electricity used by the corresponding load node.

[0082] The objective function determination module 1004 is connected to the initial carbon emission factor determination module 1002 and is used to determine the objective function based on the initial carbon emission factors of multiple load nodes in the current time period. The objective function is used to indicate the total carbon emissions of the distribution network in the current time period and the degree of dispersion of carbon emission factors among multiple load nodes.

[0083] The target carbon emission allocation strategy determination module 1006 is connected to the objective function determination module 1004. It is used to optimize the initial carbon emission allocation strategy of the distribution network in the current period with the minimum function value of the objective function as the optimization objective, so as to obtain the target carbon emission allocation strategy of the distribution network in the current period. The initial carbon emission allocation strategy includes the initial carbon emission factor and initial carbon emission allocation coefficient of multiple load nodes in the current period. The initial carbon emission allocation coefficient represents the proportion of the line loss carbon emission borne by the corresponding load node in the total network loss carbon emission of the distribution network.

[0084] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0085] It should be noted that the aforementioned data acquisition module 1000, initial carbon emission factor determination module 1002, objective function determination module 1004, and target carbon emission allocation strategy determination module 1006 correspond to steps S102 to S108 in the embodiments. The instances and application scenarios implemented by these modules and their corresponding steps are the same, but they are not limited to the content disclosed in the above embodiments. It should also be noted that these modules, as part of the device, can run on a computer terminal.

[0086] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0087] The aforementioned carbon emission allocation strategy determination device for the power distribution network may also include a processor and a memory. The aforementioned operating data acquisition module 1000, initial carbon emission factor determination module 1002, objective function determination module 1004, and target carbon emission allocation strategy determination module 1006 are all stored in the memory as program modules, and the processor executes the aforementioned program modules stored in the memory to realize the corresponding functions.

[0088] The processor contains a core that retrieves the corresponding program modules from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0089] According to an embodiment of this application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program runs, it controls the device where the non-volatile storage medium is located to execute any of the above-mentioned methods for determining the carbon emission allocation strategy of the power distribution network.

[0090] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals, and the non-volatile storage medium includes stored programs.

[0091] Optionally, during program execution, the device containing the non-volatile storage medium may be controlled to execute any of the above-mentioned methods for determining the carbon emission allocation strategy of the power distribution network.

[0092] According to an embodiment of this application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any of the above-described methods for determining carbon emission allocation strategies for power distribution networks.

[0093] According to an embodiment of this application, an embodiment of a computer program product is also provided. Optionally, in this embodiment, the computer program product includes a computer program that, when executed by a processor, implements the steps of the method for determining the carbon emission allocation strategy of any of the above-described power distribution networks.

[0094] Optionally, when the aforementioned computer program product is executed on a data processing device, it is suitable for executing any of the aforementioned methods for determining carbon emission allocation strategies for power distribution networks.

[0095] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. The processor executes any of the above-described methods for determining carbon emission allocation strategies for power distribution networks.

[0096] The order of the above embodiments of the present invention is merely for description and does not represent the superiority or inferiority of the embodiments.

[0097] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of modules described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between modules, and may be electrical or other forms.

[0099] The modules described above as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0100] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0101] If the aforementioned integrated modules are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0102] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining a carbon emission allocation strategy of a power distribution network, characterized in that, include: Obtain the current operating data of the power distribution network; Based on the current operating data, the initial carbon emission factors of multiple load nodes in the distribution network are determined for the current time period, wherein the initial carbon emission factor represents the carbon emission amount corresponding to the unit of electricity used by the corresponding load node; Based on the initial carbon emission factors of the multiple load nodes in the current time period, an objective function is determined, wherein the objective function is used to indicate the total carbon emissions of the distribution network in the current time period, and the degree of dispersion of the carbon emission factors among the multiple load nodes; With the goal of minimizing the function value of the objective function, the initial carbon emission allocation strategy of the distribution network in the current time period is optimized to obtain the target carbon emission allocation strategy of the distribution network in the current time period. The initial carbon emission allocation strategy includes the initial carbon emission factor and initial carbon emission allocation coefficient of the multiple load nodes in the current time period. The initial carbon emission allocation coefficient represents the proportion of the line loss carbon emission borne by the corresponding load node in the total network loss carbon emission of the distribution network.

2. The method of claim 1, wherein, The determination of the initial carbon emission factors of multiple load nodes in the distribution network for the current time period based on the current operating data includes: Based on the current operating data, the average active power between any load node and the target node, and the average active power output of any load node are determined during the current time period. The target node refers to the electrical node upstream of any load node among the multiple electrical nodes in the distribution network. The multiple electrical nodes include at least the multiple load nodes, main grid injection nodes, distributed power generation nodes, and generator nodes. Based on the average active power between any load node and the target node and the average active power output of any load node, the initial carbon emission factor of any load node in the current time period is determined. The initial carbon emission factors of the plurality of load nodes in the current time period are obtained by using the method of obtaining the initial carbon emission factor of any one of the load nodes in the current time period.

3. The method of claim 2, wherein, When there are multiple target nodes and each load node includes multiple generators, determining the initial carbon emission factor of each load node in the current time period based on the average active power between the load node and the target node and the average active power output of the load node includes: Based on the average active power between multiple target nodes and any load node, and the average active power output of each of the multiple generators, the initial carbon emission factor of any load node in the current time period is determined as follows: ； in, This represents the initial carbon emission factor of any load node in the current time period. This represents the index of any of the load nodes. This represents a set of the multiple generators. This represents the index of any one of the plurality of generators. This represents the average active power output of any of the generators. This represents the preset carbon emission intensity of any of the generators. This represents the set of the multiple target nodes. This represents the index of any one of the plurality of target nodes. This represents the average active power between any load node and any target node. This represents the initial carbon emission factor of any target node in the current time period.

4. The method of claim 1, wherein, In the case where the distribution network includes the plurality of load nodes and the plurality of generator nodes, determining the objective function based on the initial carbon emission factors of the plurality of load nodes in the current time period includes: Based on the initial carbon emission factors of the multiple load nodes in the current time period, the average carbon emission factor of the multiple load nodes in the current time period is determined. Based on the initial carbon emission factors and the average carbon emission factors of the plurality of load nodes in the current time period, a first function is determined, wherein the first function is used to indicate the degree of dispersion of carbon emission factors among the plurality of load nodes in the current time period; Based on the initial carbon emission factor and average active power load of the multiple load nodes in the current time period, and the average active power output and preset carbon emission intensity of the multiple generator nodes in the current time period, a second function is determined, wherein the second function is used to indicate the total carbon emissions of the distribution network in the current time period; The target function is determined based on the first function and the second function.

5. The method of claim 4, wherein, The determination of the first function based on the initial carbon emission factor and the average carbon emission factor of the multiple load nodes in the current time period includes: Based on the initial carbon emission factor and the average carbon emission factor of the multiple load nodes in the current time period, the first function is determined in the following manner: ； wherein, denotes a function value of the first function, denotes the initial carbon emission factor for any load node of the plurality of load nodes for the current time period, denotes an index of the any load node, denotes a total number of the plurality of load nodes, denotes the average carbon emission factor.

6. The method of claim 4, wherein, The second function is determined based on the initial carbon emission factor and average active power load of the multiple load nodes in the current time period, and the average active power output and preset carbon emission intensity of the multiple generator nodes in the current time period, including: Based on the initial carbon emission factor and average active power load of the multiple load nodes in the current time period, and the average active power output and preset carbon emission intensity of the multiple generator nodes in the current time period, the second function is determined in the following manner: ； in, G represents the function value of the second function, and G represents the set of the plurality of generator nodes. This represents the index of any one of the plurality of generator nodes. This represents the average active power output of any generator node during the current time period. This represents the preset carbon emission intensity of any generator node during the current time period. This represents the index of any one of the plurality of load nodes. This represents the total number of the multiple load nodes. This represents the initial carbon emission factor of any load node in the current time period. This represents the average active power load of any load node during the current time period.

7. The method of claim 4, wherein, Determining the target function based on the first function and the second function includes: Based on the first function and the second function, the target function is determined in the following manner: ； in, This represents the function value of the objective function. This represents the function value of the first function. This represents the function value of the second function. This represents the function value of the first function under ideal operating conditions of the power distribution network. This represents the function value of the second function under the ideal operating scenario. This represents the first weighting coefficient. This represents the second weighting coefficient.

8. The method of claim 1, wherein, Before optimizing the initial carbon emission allocation strategy of the distribution network in the current time period with the objective function value as the optimization objective, and obtaining the target carbon emission allocation strategy of the distribution network in the current time period, the method further includes: Based on the average active power load of the multiple load nodes in the current time period and the total network loss carbon emissions, the line loss carbon emissions of the multiple load nodes in the current time period are determined. Determine the allocation weight of the multiple load nodes in the current time period; Based on the line loss carbon emissions and the allocation weight of the multiple load nodes in the current time period, the initial carbon emission allocation coefficient of the multiple load nodes in the current time period is determined.

9. The method according to any one of claims 1 to 8, characterized in that, Given that the distribution network includes multiple electrical nodes and multiple branches, and the current time period includes multiple sampling times, the initial carbon emission allocation strategy of the distribution network in the current time period is optimized with the minimum function value of the objective function as the optimization objective, resulting in the target carbon emission allocation strategy of the distribution network in the current time period, including: The constraints of the distribution network in the current time period are determined, wherein the constraints include at least the following: the voltage of the plurality of electrical nodes at any sampling time in the current time period is within a preset voltage range; the power of the plurality of branches at any sampling time is within a preset power range; the bus loss rate of the distribution network does not exceed a preset bus loss rate; the carbon emission factor of the load nodes among the plurality of electrical nodes is greater than or equal to zero in the current time period; and the power generation of the distributed power generation nodes among the plurality of electrical nodes does not exceed a preset power generation rate in the current time period, wherein the power includes active power and reactive power; the plurality of electrical nodes include at least the load nodes and the distributed power generation nodes; Based on the constraints, the initial carbon emission allocation strategy is optimized with the goal of minimizing the function value of the objective function, resulting in the target carbon emission allocation strategy.

10. An electronic device, comprising: It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the carbon emission allocation strategy determination method for the distribution network as described in any one of claims 1 to 9.

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