Power distribution network carbon state estimation method and device based on multiple types of charging stations, computer device, and medium

Abstract

The application relates to a power distribution network carbon state estimation method and device based on multiple types of charging stations, computer equipment and a medium. The method comprises the following steps: standardizing collected power distribution network multi-source data to obtain target multi-source data, obtaining power flow data based on the target multi-source data, obtaining the node carbon density of each node according to the power flow data, and mapping the node carbon density to the multiple types of charging stations to obtain the station carbon density of the multiple types of charging stations. The method can improve the carbon state evaluation accuracy.

Description

Technical Field

[0001] This application relates to the field of power distribution network technology, and in particular to a method, apparatus, computer equipment, and medium for estimating the carbon state of power distribution networks based on multiple types of charging stations. Background Technology

[0002] With the introduction of dual carbon targets and the acceleration of transportation electrification, the number of electric vehicles continues to increase, and charging infrastructure in various scenarios and locations is being rapidly deployed. Currently, carbon accounting for the power system mostly uses the annual or monthly average emission factor of the regional power grid, which cannot reflect the differences in carbon density of the distribution network at different nodes and times. Even if users actively charge during green electricity periods, their actual carbon emission reduction contribution is difficult to accurately quantify and reflect. Furthermore, vehicle-grid interaction and orderly charging are mainly based on time-of-use pricing or simplified pricing signals, lacking accurate characterization and feedback mechanisms for user carbon emission behavior. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, apparatus, computer equipment, and medium for estimating the carbon state of a distribution network based on multiple types of charging stations, which can improve the accuracy of carbon state assessment, in response to the above-mentioned technical problems.

[0004] Firstly, this application provides a method for estimating the carbon state of a distribution network based on multiple types of charging stations, including:

[0005] The collected multi-source data of the distribution network is standardized to obtain the target multi-source data; the multi-source data of the distribution network includes the active power output and carbon emission parameters of various power sources, distribution network node data, and power station data; the distribution network node data includes the distribution network topology.

[0006] Power flow data is obtained based on target multi-source data, and the nodal carbon density of each node is obtained based on the power flow data; the power flow data includes the active power flow of each line and the active power injection of each node.

[0007] The node carbon density is mapped to multiple types of charging stations to obtain the station carbon density of multiple types of charging stations.

[0008] In one embodiment, the step of obtaining the nodal carbon density of each node based on the power flow data includes:

[0009] For the current node, obtain the set of upstream nodes that supply active power flow to the current node, and obtain the total upstream carbon flow and total upstream power supplied by all upstream nodes based on the upstream node set;

[0010] Obtain the local power supply corresponding to the current node, and obtain the total amount of newly added carbon flow and the total local output power based on the local power supply;

[0011] The node carbon density of the current node is obtained based on the total upstream carbon flow, the total upstream power, the total locally added carbon flow, and the total local output power.

[0012] In one embodiment, the step of mapping the node carbon density to multiple types of charging stations to obtain the station carbon density of multiple types of charging stations includes:

[0013] Obtain the access nodes of various types of charging stations in the power distribution network, and use the node carbon density corresponding to the access node as the station carbon density of various types of charging stations.

[0014] In one embodiment, the method further includes:

[0015] Based on the carbon density of the charging station and the single-charge power curve, the carbon footprint information of the single-charge behavior of electric vehicles is obtained; the single-charge power curve is used to characterize the start time, end time and charging power of a single charge behavior; the carbon footprint information is used to characterize the actual carbon emissions of a single charge behavior.

[0016] Based on the current baseline carbon intensity and carbon footprint information, obtain the emission reduction corresponding to a single charging behavior;

[0017] User points are awarded based on emission reductions, and user account information is updated based on these points.

[0018] In one embodiment, the step of obtaining the emission reduction corresponding to a single charging behavior based on the current baseline carbon intensity and carbon footprint information includes:

[0019] The total charging power of a single charging action is obtained from the single charging power curve. The product of the total charging power and the current baseline carbon intensity is used as the current baseline emissions.

[0020] The difference between the current baseline emissions and the actual carbon emissions is used as the emission reduction for a single charging activity.

[0021] In one embodiment, the method further includes:

[0022] Based on the updated user account information, obtain the historical charging records of each user and extract the feature vectors corresponding to the historical charging records.

[0023] All users are clustered based on feature vectors, and user profiles are constructed based on the clustering results;

[0024] Based on user profiles, scenario information, and initial carbon incentive signals, incentive budget constraints are constructed, and a carbon incentive model is built based on these constraints.

[0025] In one embodiment, the method further includes:

[0026] The charging strategy is obtained based on the carbon density and carbon incentive model of the power station.

[0027] In the case of authorized escrow, the charging power is adjusted according to the charging strategy to obtain an updated charging power curve, and the user account information is updated based on the updated charging power curve.

[0028] Secondly, this application also provides a distribution network carbon state estimation device based on multiple types of charging stations, comprising:

[0029] The data processing module is used to standardize the collected multi-source data of the distribution network to obtain the target multi-source data. The multi-source data of the distribution network includes the active power output and carbon emission parameters of various power sources, distribution network node data, and power station data. The distribution network node data includes the distribution network topology.

[0030] The carbon density acquisition module is used to acquire power flow data based on target multi-source data, and to acquire the nodal carbon density of each node based on the power flow data; the power flow data includes the active power flow of each line and the active power injection of each node.

[0031] The carbon state estimation module is used to map the node carbon density to multiple types of charging stations to obtain the station carbon density of multiple types of charging stations.

[0032] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method steps of any one of the first aspects.

[0033] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method steps of any one of the first aspects.

[0034] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the method steps of any one of the first aspects.

[0035] The aforementioned method, device, computer equipment, and medium for estimating the carbon state of a distribution network based on multiple types of charging stations standardize the collected multi-source data of the distribution network to obtain target multi-source data. Based on the target multi-source data, power flow data is obtained, and the node carbon density of each node is obtained according to the power flow data. The node carbon density is then mapped to multiple types of charging stations to obtain the station carbon density of multiple types of charging stations. This method can accurately capture the differences in carbon density at different times and locations, achieve high-granularity carbon state perception, ensure that carbon density calculation is synchronized with the actual operating state of the power grid, improve the credibility of emission reduction estimation, and ensure the accuracy of carbon accounting. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is an application environment diagram of a distribution network carbon state estimation method based on multiple types of charging stations in one embodiment;

[0038] Figure 2 This is a flowchart illustrating a distribution network carbon state estimation method based on multiple types of charging stations in one embodiment.

[0039] Figure 3 This is a flowchart illustrating a distribution network carbon state estimation method based on multiple types of charging stations in another embodiment.

[0040] Figure 4 This is a structural block diagram of a power distribution network carbon state estimation device based on multiple types of charging stations in one embodiment;

[0041] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0043] The carbon state estimation method for distribution networks based on multiple types of charging stations provided in this application can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on the cloud or other network servers. Terminal 102 is used to standardize the collected multi-source data of the distribution network to obtain target multi-source data, acquire power flow data based on the target multi-source data, and obtain the node carbon density of each node based on the power flow data. The power flow data includes the active power flow of each line and the active power injection of each node. The node carbon density is mapped to multiple types of charging stations to obtain the station carbon density of multiple types of charging stations. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle devices, projection devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. Headset devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0044] In one exemplary embodiment, such as Figure 2 As shown, a method for estimating the carbon state of a distribution network based on multiple types of charging stations is provided, and this method is applied to... Figure 1 Taking terminal 102 as an example, the explanation includes the following steps 202 to 206. Wherein:

[0045] S202: Standardize the collected multi-source data of the distribution network to obtain the target multi-source data; the multi-source data of the distribution network includes the active power output and carbon emission parameters of various power sources, distribution network node data, and substation data; the distribution network node data includes the distribution network topology.

[0046] Optionally, the carbon status of a distribution network refers to the real-time or near-real-time carbon emission intensity and dynamics corresponding to the production, transmission, and consumption of electricity within a specific time and a specific distribution network area (node ​​or line). To meet the need for high-granularity distribution network carbon status statistics, multi-source data of the distribution network is collected. This multi-source data includes active power output and carbon emission parameters of various power sources (i.e., source-side data), distribution network node data (i.e., grid-side data), and power station data.

[0047] Specifically, source-side data refers to the active power output and carbon emission parameters of various power sources collected at time t, including: power injected from the upstream grid. and the corresponding marginal emission factor Active power output of each photovoltaic power source and its carbon emission factors Active power output of each wind power source and its carbon emission factors The active power output of each type of power source at the node is uniformly denoted as the nodal power source active power output. The carbon emission factor per unit of electricity is denoted as The network-side data includes the node number n of each distribution network and its topological relationship; the resistance of each line. Reactance (Line connects node i and node j); the voltage, current, and power measurements of each node can be used to calculate the active power flow of each line at time t based on the node's power output and load power. and the active power injected into each node. The site data includes the site ID 's', type (fast charging, slow charging, battery swapping), geographical location, and access ID 'n'; and the site's access capacity. Real-time active power of charging piles within the station , where i represents a single charging action. To ensure computational consistency, all data is standardized to a standard time step. If the sampling periods of different data sources are inconsistent, the data can be resampled using a linear interpolation-preservation method.

[0048] S204: Obtain power flow data based on target multi-source data, and obtain the nodal carbon density of each node based on the power flow data; the power flow data includes the active power flow of each line and the active power injection of each node.

[0049] Optionally, based on power flow calculation results and carbon emission flow theory, the carbon density of each node in the distribution network is estimated in real time. Power flow data refers to the active power flow of each line and the active power injection at each node, obtained through power flow algorithms based on target data such as the distribution network topology, node voltage / current / power, and line impedance, reflecting the real-time power flow status of the grid. Node carbon density calculation, based on carbon emission flow theory, treats carbon emissions as carbon flows distributed with the active power flow. Using a weighted average recursive formula, it integrates upstream node carbon flows and local power source carbon emission factors to calculate the carbon density of each node in real time, achieving precise coupling of "power flow-carbon flow".

[0050] S206: Map the node carbon density to multiple types of charging stations to obtain the station carbon density of multiple types of charging stations.

[0051] Optionally, since the charging station is fixedly connected to a specific node of the power distribution network, the real-time carbon density of the node connected to the station is directly assigned as the station's carbon density. At the same time, the carbon density differentiation of different stations (fast charging, slow charging, etc.) due to differences in the access node and operating time is retained, and the node carbon density is mapped to multiple types of electric vehicle charging stations to achieve station-level carbon state estimation.

[0052] In the aforementioned distribution network carbon state estimation method based on multiple types of charging stations, target multi-source data is obtained by standardizing the collected multi-source data of the distribution network. Power flow data is then obtained based on the target multi-source data, and the node carbon density of each node is obtained based on the power flow data. The node carbon density is then mapped to multiple types of charging stations to obtain the station carbon density of multiple types of charging stations. This method can accurately capture the differences in carbon density at different times and locations, achieve high-granularity carbon state perception, ensure that carbon density calculation is synchronized with the actual operating state of the power grid, improve the credibility of emission reduction estimation, and ensure the accuracy of carbon accounting.

[0053] In an exemplary embodiment, the step of obtaining the node carbon density of each node based on power flow data includes: for the current node, obtaining the set of upstream nodes that supply active power flow to the current node, and obtaining the total upstream carbon flow and total upstream power supplied by all upstream nodes based on the upstream node set; obtaining the local power source corresponding to the current node, and obtaining the total local newly added carbon flow and total local output power based on the local power source; and obtaining the node carbon density of the current node based on the total upstream carbon flow, the total upstream power, the total local newly added carbon flow, and the total local output power.

[0054] Optionally, since the carbon density of the current node is affected by the carbon flow input from upstream nodes, it is necessary to first identify the set of upstream nodes supplying active power flow to it, and summarize the total carbon flow and power of all upstream nodes to reflect the contribution weight of upstream carbon flow to the current node. In addition, local power sources (such as photovoltaic and wind power) at the current node will generate additional carbon flow; therefore, it is necessary to calculate the total carbon flow and power output of local power sources to reflect the corrective effect of local power sources on the node's carbon density. The node's carbon density is a comprehensive reflection of upstream input carbon flow and local additional carbon flow, calculated using a weighted average logic of total carbon flow ÷ total power, i.e., (total upstream carbon flow + total local additional carbon flow) ÷ (total upstream power + total local power output), ensuring that the carbon density is strongly bound to the node's actual power flow and power source composition.

[0055] For example, at time t, the active power flow of each line is obtained using a power flow algorithm. and the active power injected at each node For node n, its active power injection satisfies:

[0056]

[0057] in, The total active power of node n is the load, which includes the base load and the total power of the charging stations connected to this node.

[0058] Furthermore, for any node n, its carbon density (Unit: gCO2 / kWh) can be expressed as a weighted average of the carbon flow input from all upstream nodes and the newly added carbon flow from local power sources. At time t, the set of upstream nodes supplying active power to node n is: The active power flow from node k to node n is Then the carbon density at node n satisfies:

[0059]

[0060] in, The carbon density of upstream node k at time t can be expressed as: . Let n be the total active power output of the local power source at node n. Let be the carbon emission factor per unit electricity of the local power source at node n, which can be expressed as: .

[0061] Furthermore, at time t, the carbon density t of all nodes in the network is as follows:

[0062]

[0063] in, For nodal carbon transport coefficients: ; The local power weighting coefficient for node k when it does not supply power to node n: .

[0064] In this embodiment, for the current node, the set of upstream nodes supplying active power flow to the current node is obtained, and the total upstream carbon flow and total upstream power supplied by all upstream nodes are obtained based on the upstream node set. The local power source corresponding to the current node is obtained, and the total local newly added carbon flow and total local output power are obtained based on the local power source. Based on the total upstream carbon flow, total upstream power, total local newly added carbon flow, and total local output power, the node carbon density of the current node is obtained. This can capture the carbon density differences between different nodes caused by differences in upstream power source structure and power flow direction, and can realize accurate real-time carbon density calculation at the node level, providing accurate data support for subsequent site carbon density mapping and single-charge carbon footprint calculation.

[0065] In an exemplary embodiment, the step of mapping node carbon density to multiple types of charging stations to obtain the station carbon density of multiple types of charging stations includes: obtaining the access nodes of multiple types of charging stations in the power distribution network, and using the node carbon density corresponding to the access node as the station carbon density of multiple types of charging stations.

[0066] Optionally, various types of charging stations (fast charging, slow charging, battery swapping stations, etc.) have clearly defined and fixed access nodes in the power distribution network. The carbon density of the access node directly reflects the real-time carbon emission level at that location. Since the power supply of the station comes entirely from the power distribution network node it is connected to, the node carbon density has accurately quantified the carbon emission intensity per unit of electricity at that location. Therefore, there is no need for additional complex calculations; the real-time carbon density of the access node can be directly assigned as the station carbon density of the corresponding station, achieving a precise mapping of the carbon status between "nodes" and "stations".

[0067] For example, for the access node as The carbon density of the s-th charging station at time t is defined as: .in, For nodes The carbon density at time t.

[0068] Different types of charging stations (slow charging stations in residential areas, fast charging stations in office parks, fast charging stations in highway service areas, battery swapping stations, etc.) have different access nodes, operating periods, and load curves, resulting in varying carbon densities. The differences will be significant. This mapping provides fundamental data support for designing carbon footprint capture and incentive strategies for different scenarios.

[0069] In an exemplary embodiment, the method further includes: obtaining carbon footprint information of a single charging behavior of an electric vehicle based on the carbon density of the charging station and the single charging power curve; the single charging power curve is used to characterize the start time, end time, and charging power of a single charging behavior; the carbon footprint information is used to characterize the actual carbon emissions of a single charging behavior; obtaining the emission reduction corresponding to a single charging behavior based on the current baseline carbon intensity and carbon footprint information; obtaining user points based on the emission reduction, and updating user account information based on the user points.

[0070] Optionally, the charging station's carbon density is a dynamic signal that varies over time, representing the amount of carbon emissions indirectly generated by obtaining one kilowatt-hour of electricity from that charging station at a specific moment. The single-charge power curve, recorded by the charging station or vehicle BMS, includes precise start and end times, as well as the charging power (kW) at each moment. This curve defines a timeline profile of electricity consumption behavior. The charging curve and the carbon density curve are convolved at the same time resolution. During charging, if the carbon density is low (more green electricity), the corresponding carbon emissions for that charge are low; conversely, they are high.

[0071] Optionally, baseline carbon intensity typically represents the default emission level without any optimization management. For example, it can be set as the daily average carbon intensity of the regional power grid, or the typical carbon intensity during a fixed peak period. Emission reduction is the difference between baseline emissions and the actual carbon footprint. It quantifies the carbon emissions avoided by a user charging during low-carbon periods compared to charging during average or high-carbon periods. The calculated emission reduction is automatically converted into points according to rules and written to the corresponding user's account database. These points are clearly searchable, traceable, and tamper-proof. Since these points can be redeemed for electricity vouchers, charging service fee discounts, physical goods, or traded in the carbon market, a positive cycle of low-carbon behavior – quantified emission reduction – and incentives is formed.

[0072] In this embodiment, carbon footprint information of electric vehicles for a single charge is obtained based on the carbon density of the charging station and the single charging power curve. Based on the current baseline carbon intensity and carbon footprint information, the emission reduction corresponding to the single charge is obtained. User points are obtained based on the emission reduction, and user account information is updated based on the user points. This enables accurate measurement of personal carbon footprint, adapts to user scenarios and historical behavior, and ensures the accuracy of emission reduction calculation.

[0073] In an exemplary embodiment, the step of obtaining the emission reduction corresponding to a single charging behavior based on the current baseline carbon intensity and carbon footprint information includes: obtaining the total charging power of a single charging behavior based on the single charging power curve; multiplying the total charging power by the current baseline carbon intensity as the current baseline emission; and using the difference between the current baseline emission and the actual carbon emission as the emission reduction corresponding to the single charging behavior.

[0074] For example, the carbon footprint of a single charging activity of an electric vehicle can be calculated using the carbon density of the charging station versus the power output of a single charge. Assume that a user's single charging activity is numbered i, and the start time of this charging activity is... The end time is At time t, the charging power is The total charging amount for this charging action is:

[0075]

[0076] Assume this charging behavior occurs at the station. It occurs at time t, when the carbon density of the station is... Therefore, the actual carbon footprint of this charging activity is:

[0077]

[0078] in, This represents the carbon emissions of this charging activity in its actual carbon state.

[0079] Furthermore, by introducing a dynamic baseline carbon intensity, the emission reduction for each charging action is calculated. For a given user u, its baseline carbon intensity is defined. The baseline carbon intensity is determined by the region's long-term average carbon emission factor and the average carbon density corresponding to the user's historical charging periods. For the user's scenario (e.g., residential area, office area), the average carbon density during charging periods without incentive effects is selected as the baseline.

[0080] For user u's i-th charging behavior, its baseline emissions are defined as:

[0081]

[0082] in, This represents the total amount of electricity charged during this charging session.

[0083] The emission reduction of this charging activity relative to the baseline is defined as:

[0084]

[0085] When users are guided to shift their charging behavior from high-carbon periods to low-carbon periods, then ,thereby This indicates that the charging activity achieved positive emission reduction.

[0086] Furthermore, a carbon account is established for each electric vehicle user and updated in real time after each charging activity. For user u, their carbon account includes the following attributes:

[0087] (1) Cumulative actual carbon emissions:

[0088]

[0089] (2) Cumulative baseline carbon emissions:

[0090]

[0091] in, A collection of all charging behaviors for user u.

[0092] (3) Cumulative emission reductions:

[0093]

[0094] (4) Carbon credit balance .

[0095] Furthermore, points are generated for users based on the emission reduction from a single charging action. For the i-th session, the point is defined as:

[0096]

[0097] Where k is the conversion coefficient between carbon emission reduction and integral. The basic activity rewards are used to encourage users to continue participating in low-carbon charging.

[0098] After each charging session, the actual carbon emissions of that session will be recorded. Baseline emission reductions Emission reduction and points The system writes data to user u's carbon account and updates the accumulated amount and points balance, enabling dynamic recording and visual management of the user's carbon footprint throughout the entire charging process.

[0099] In an exemplary embodiment, the method further includes: obtaining the historical charging records of each user based on the updated user account information, and extracting the feature vectors corresponding to the historical charging records; clustering all users based on the feature vectors, and constructing user profiles based on the clustering results; constructing incentive budget constraints based on user profiles, scenario information and initial carbon incentive signals, and constructing a carbon incentive model based on the incentive budget constraints.

[0100] Optionally, core features strongly correlated with carbon reduction and charging behavior (such as average parking time per charge, percentage of electricity used during low-carbon periods, nighttime charging rate, fast charging session rate, and number of incentive responses) are extracted from the user's historical charging records to form feature vectors representing user behavior patterns, providing a data foundation for user classification. Using clustering algorithms such as K-Means, all users are grouped based on the similarity of their feature vectors. By analyzing the feature differences between cluster centers, each group of users is labeled with tags such as "nighttime-slow charging," "daytime-fast charging," and "low-carbon responsiveness," constructing personalized profiles that match actual user behavior and achieving accurate identification of user needs. Using user profiles (reflecting user carbon sensitivity and charging flexibility), scenario information (such as carbon density differences between residential areas and highway service areas), and initial carbon incentive signals (such as points multipliers and service fee discounts) as input, a total incentive budget constraint is first set (ensuring controllable incentive costs). Then, an optimization model is constructed with the goal of maximizing network-wide emission reduction. The optimal incentive signal is solved using methods such as linear programming to achieve efficient allocation of incentive resources. By analyzing the characteristics of cluster centers, users can be tagged with labels such as "nighttime-slow charging," "daytime-fast charging," and "low-carbon response." Combining charging station type, geographical location, and load characteristics, charging stations are categorized into different application scenarios, such as "long-term parking in residential areas," "office park scenarios," and "fast charging scenarios in highway service areas." User profiles combined with station scenarios are used for differentiated setting of subsequent incentive parameters and baseline carbon intensity.

[0101] For example, historical data is used to construct user behavior profiles and identify multiple charging scenarios. For each user u, a feature vector is constructed based on their historical charging records. :

[0102]

[0103] in, Average parking time for a single user charge; For carbon-sensitive indicators, such as the percentage of electricity generated during low-carbon periods in historical charging data; The percentage of total charging volume that is used for charging at night; The proportion of fast charging sessions in the total number of sessions; The number of times or the average response magnitude in history.

[0104] Furthermore, methods such as K-Means clustering are used to classify users. The clustering objective can be expressed as:

[0105]

[0106] in, Let the cluster center be the cluster center for the k-th type of users. This is the set of users belonging to this class.

[0107] Furthermore, based on user profiles and scenario information, a carbon incentive model is constructed, and the potential for responding to user-side demand is evaluated. At time t, let the carbon incentive signal be... (For example, the points multiplier, the discount factor for charging service fees, etc.), the adjustable amount of user u's charging power can be modeled as:

[0108]

[0109] in, This is the user elasticity coefficient, determined by their category and scenario. For example, users who prioritize low-carbon living and spend long periods of time parking in their residential areas have a higher coefficient, while users with a strong need for high-speed fast charging have a lower coefficient. As a bias term, the user describes the natural adjustment trend under no-stimulus conditions, which can be obtained by fitting historical data.

[0110] Carbon incentive strategy This will affect users' charging schedules and power curves, thereby altering the actual carbon emissions per charge. and emission reduction Within a settlement period, user u's total emission reductions can be expressed as:

[0111]

[0112] in, This refers to the set of charging activities performed by the user within the settlement period. The corresponding points reward can be represented as:

[0113]

[0114] Treating points as incentives, the overall incentive budget constraint is:

[0115]

[0116] in, This is the preset total points or subsidy budget.

[0117] Under the premise of meeting incentive budget constraints, this invention establishes a carbon inclusive incentive optimization model by optimizing the incentive signal It in each time period to maximize the total network emission reduction or comprehensive benefits:

[0118]

[0119] The solution can be found using linear programming.

[0120] In this embodiment, by obtaining the historical charging records of each user based on the updated user account information, extracting the feature vectors corresponding to the historical charging records, clustering all users based on the feature vectors, constructing user profiles based on the clustering results, constructing incentive budget constraints based on user profiles, scenario information and initial carbon incentive signals, and constructing a carbon incentive model based on the incentive budget constraints, it is possible to achieve precise stratification of user needs, improve the gamification of incentive strategies, and reduce incentive costs.

[0121] In an exemplary embodiment, the method further includes: obtaining a charging strategy based on the station's carbon density and a carbon incentive model; adjusting the charging power according to the charging strategy in an authorized hosting scenario to obtain an updated charging power curve, and updating the user account information based on the updated charging power curve.

[0122] Optionally, carbon incentive strategies and carbon account systems can be applied to the actual vehicle-to-grid interaction process and settlement can be completed. Using real-time carbon density at charging stations (reflecting the carbon emission level at charging locations) and a carbon incentive model (integrated with user profiles, scenario information, and budget constraints) as core inputs, future low-carbon periods are predicted, and targeted strategies are generated. Personalized low-carbon charging suggestions are generated for different user categories and scenarios. For example, for slow-charging users in residential areas, suggestions are given for periods with lower carbon density at night; for users in office parks, suggestions are given for midday when photovoltaic output is high; and for users in highway service areas, suggestions are given for relatively low-carbon charging periods while meeting rigid travel needs. For charging users or scenarios that have implemented authorized management, the charging power curve is automatically adjusted without violating user travel needs and battery health status boundaries. This involves shifting part of the load from high-carbon periods to low-carbon periods, or performing discharge during low-carbon periods, resulting in an updated charging power curve. The adjusted charging power curve is re-input into the carbon footprint calculation model to recalculate the actual carbon emissions and emission reductions. Then, the cumulative emissions, emission reductions, and carbon credit balance of the user's carbon account are updated according to the rules to achieve a closed loop of "strategy execution - effect quantification - account feedback".

[0123] In this embodiment, by obtaining the charging strategy based on the carbon density and carbon incentive model of the power station, and adjusting the charging power according to the charging strategy in the authorized management scenario, an updated charging power curve is obtained, and the user account information is updated based on the updated charging power curve. This enables the low-carbon adaptive charging behavior, reduces the pressure on high-carbon power generation, and lowers the overall carbon emission level.

[0124] In one exemplary embodiment, such as Figure 3 As shown, a method for estimating the carbon state of a distribution network based on multiple types of charging stations is provided. This method includes the following steps:

[0125] (1) Multi-source data acquisition and preprocessing: The acquired multi-source data of the distribution network is standardized to obtain the target multi-source data; the multi-source data of the distribution network includes the active power output and carbon emission parameters of various power sources, distribution network node data, and station data; the distribution network node data includes the distribution network topology.

[0126] (2) Real-time estimation of carbon density at distribution network nodes: Based on target multi-source data, obtain power flow data, and for the current node, obtain the set of upstream nodes that transmit active power flow to the current node, and obtain the total upstream carbon flow and the total upstream power transmitted by all upstream nodes according to the upstream node set; obtain the local power source corresponding to the current node, and obtain the total local newly added carbon flow and the total local output power according to the local power source; obtain the node carbon density of the current node based on the total upstream carbon flow, the total upstream power, the total local newly added carbon flow, and the total local output power; the power flow data includes the active power flow of each line and the active power injected into each node.

[0127] (3) Node carbon density charging station carbon state mapping: Obtain the access nodes of multiple types of charging stations in the distribution network, and use the node carbon density corresponding to the access node as the station carbon density of multiple types of charging stations.

[0128] (4) Construct a real-time carbon account system for users: Based on the carbon density of the charging station and the single charging power curve, obtain the carbon footprint information of the single charging behavior of electric vehicles; the single charging power curve is used to characterize the start time, end time and charging power of the single charging behavior; the carbon footprint information is used to characterize the actual carbon emissions of the single charging behavior; obtain the total charging power of the single charging behavior based on the single charging power curve, and multiply the total charging power by the current baseline carbon intensity as the current baseline emissions; use the difference between the current baseline emissions and the actual carbon emissions as the emission reduction corresponding to the single charging behavior; obtain user points based on the emission reduction, and update user account information based on user points.

[0129] (5) Construction of carbon incentive strategy: Based on the updated user account information, obtain the historical charging records of each user and extract the feature vectors corresponding to the historical charging records; cluster all users based on the feature vectors and construct user profiles based on the clustering results; construct incentive budget constraints based on user profiles, scenario information and initial carbon incentive signals, and construct carbon incentive models based on incentive budget constraints.

[0130] (6) Vehicle-to-grid interaction execution and settlement: Obtain the charging strategy based on the station carbon density and carbon incentive model; in the authorized management scenario, adjust the charging power according to the charging strategy to obtain the updated charging power curve, and update the user account information based on the updated charging power curve.

[0131] In this embodiment, target multi-source data is obtained by standardizing the collected multi-source data of the distribution network. Power flow data is obtained based on the target multi-source data, and the node carbon density of each node is obtained according to the power flow data. The node carbon density is mapped to multiple types of charging stations to obtain the station carbon density of multiple types of charging stations. This can accurately capture the carbon density differences at different times and locations, realize high-granularity carbon status perception, ensure that the carbon density calculation is synchronized with the actual operating status of the power grid, improve the credibility of emission reduction estimation, and ensure the accuracy of carbon accounting.

[0132] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0133] Based on the same inventive concept, this application also provides a carbon state estimation device for a distribution network based on multiple types of charging stations, for implementing the aforementioned method for estimating the carbon state of a distribution network based on multiple types of charging stations. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the carbon state estimation device for a distribution network based on multiple types of charging stations provided below can be found in the limitations of the carbon state estimation method for a distribution network based on multiple types of charging stations described above, and will not be repeated here.

[0134] In one exemplary embodiment, such as Figure 4 As shown, a carbon state estimation device for a distribution network based on multiple types of charging stations is provided, including: a data processing module 10, a carbon density acquisition module 20, and a carbon state estimation module 30, wherein:

[0135] The data processing module 10 is used to standardize the collected multi-source data of the distribution network to obtain the target multi-source data. The multi-source data of the distribution network includes the active power output and carbon emission parameters of various power sources, distribution network node data, and substation data. The distribution network node data includes the distribution network topology.

[0136] The carbon density acquisition module 20 is used to acquire power flow data based on target multi-source data, and to acquire the node carbon density of each node based on the power flow data; the power flow data includes the active power flow of each line and the active power injection of each node.

[0137] The carbon state estimation module 30 is used to map the node carbon density to multiple types of charging stations to obtain the station carbon density of multiple types of charging stations.

[0138] In an exemplary embodiment, the carbon density acquisition module 20 is further configured to acquire, for the current node, a set of upstream nodes that supply active power flow to the current node, and acquire the total upstream carbon flow and total upstream power supplied by all upstream nodes based on the upstream node set; acquire the local power source corresponding to the current node, and acquire the total local newly added carbon flow and total local output power based on the local power source; and acquire the node carbon density of the current node based on the total upstream carbon flow, total upstream power, total local newly added carbon flow, and total local output power.

[0139] In an exemplary embodiment, the carbon state estimation module 30 is further configured to obtain the access nodes of multiple types of charging stations in the power distribution network, and use the node carbon density corresponding to the access node as the station carbon density of the multiple types of charging stations.

[0140] In an exemplary embodiment, the carbon state estimation module 30 is further configured to obtain carbon footprint information of a single charging behavior of an electric vehicle based on the carbon density of the charging station and the single charging power curve; the single charging power curve is used to characterize the start time, end time, and charging power of a single charging behavior; the carbon footprint information is used to characterize the actual carbon emissions of a single charging behavior; based on the current baseline carbon intensity and carbon footprint information, the emission reduction corresponding to the single charging behavior is obtained; user points are obtained based on the emission reduction, and user account information is updated based on the user points.

[0141] In an exemplary embodiment, the carbon state estimation module 30 is further configured to obtain the total charging power of a single charging behavior based on the single charging power curve, multiply the total charging power by the current baseline carbon intensity as the current baseline emissions, and use the difference between the current baseline emissions and the actual carbon emissions as the emission reduction corresponding to the single charging behavior.

[0142] In an exemplary embodiment, the carbon state estimation module 30 is further configured to obtain the historical charging records of each user based on the updated user account information, and extract the feature vectors corresponding to the historical charging records; cluster all users based on the feature vectors, and construct user profiles based on the clustering results; construct incentive budget constraints based on user profiles, scenario information and initial carbon incentive signals, and construct a carbon incentive model based on the incentive budget constraints.

[0143] In an exemplary embodiment, the carbon state estimation module 30 is further configured to obtain a charging strategy based on the station's carbon density and carbon incentive model; in the case of authorized hosting, the charging power is adjusted according to the charging strategy to obtain an updated charging power curve, and the user account information is updated based on the updated charging power curve.

[0144] The modules in the aforementioned distribution network carbon state estimation device based on multiple types of charging stations can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0145] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for estimating the carbon state of a power distribution network based on multiple types of charging stations. The display unit is used to generate a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0146] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0147] In one exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps: standardizing the collected multi-source data of the distribution network to obtain target multi-source data; the multi-source data of the distribution network includes active power output and carbon emission parameters of various power sources, distribution network node data, and power station data; the distribution network node data includes the distribution network topology; acquiring power flow data based on the target multi-source data, and acquiring the node carbon density of each node based on the power flow data; the power flow data includes the active power flow of each line and the active power injection of each node; mapping the node carbon density to multiple types of charging stations to obtain the station carbon density of multiple types of charging stations.

[0148] In one embodiment, the process of obtaining the node carbon density of each node based on power flow data when the processor executes the computer program includes: for the current node, obtaining the set of upstream nodes that supply active power flow to the current node, and obtaining the total upstream carbon flow and the total upstream power supplied by all upstream nodes based on the set of upstream nodes; obtaining the local power source corresponding to the current node, and obtaining the total local newly added carbon flow and the total local output power based on the local power source; and obtaining the node carbon density of the current node based on the total upstream carbon flow, the total upstream power, the total local newly added carbon flow, and the total local output power.

[0149] In one embodiment, the process of the processor executing a computer program to map node carbon density to multiple types of charging stations to obtain the station carbon density of the multiple types of charging stations includes: obtaining the access nodes of the multiple types of charging stations in the distribution network, and using the node carbon density corresponding to the access node as the station carbon density of the multiple types of charging stations.

[0150] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining carbon footprint information of a single charging behavior of an electric vehicle based on the carbon density of the charging station and the single charging power curve; the single charging power curve is used to characterize the start time, end time, and charging power of a single charging behavior; the carbon footprint information is used to characterize the actual carbon emissions of a single charging behavior; obtaining the emission reduction corresponding to a single charging behavior based on the current baseline carbon intensity and carbon footprint information; obtaining user points based on the emission reduction, and updating user account information based on the user points.

[0151] In one embodiment, the process of the processor executing a computer program to obtain the emission reduction corresponding to a single charging behavior based on the current baseline carbon intensity and carbon footprint information includes: obtaining the total charging power of a single charging behavior based on the single charging power curve; multiplying the total charging power by the current baseline carbon intensity as the current baseline emission; and using the difference between the current baseline emission and the actual carbon emission as the emission reduction corresponding to the single charging behavior.

[0152] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining the historical charging records of each user based on the updated user account information, and extracting the feature vectors corresponding to the historical charging records; clustering all users based on the feature vectors, and constructing user profiles based on the clustering results; constructing incentive budget constraints based on user profiles, scenario information and initial carbon incentive signals, and constructing a carbon incentive model based on the incentive budget constraints.

[0153] In one embodiment, when the processor executes the computer program, it also performs the following steps: obtaining a charging strategy based on the station's carbon density and carbon incentive model; in the case of authorized hosting, adjusting the charging power according to the charging strategy to obtain an updated charging power curve, and updating the user account information based on the updated charging power curve.

[0154] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0155] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0156] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0157] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0158] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for estimating the carbon state of a distribution network based on multiple types of charging stations, characterized in that, The method includes: The collected multi-source data of the distribution network is standardized to obtain target multi-source data; the multi-source data of the distribution network includes active power output and carbon emission parameters of various power sources, distribution network node data, and power station data; the distribution network node data includes the distribution network topology. Power flow data is obtained based on the target multi-source data, and the node carbon density of each node is obtained based on the power flow data; the power flow data includes the active power flow of each line and the active power injection of each node. The node carbon density is mapped to multiple types of charging stations to obtain the station carbon density of the multiple types of charging stations.

2. The method according to claim 1, characterized in that, The step of obtaining the nodal carbon density of each node based on the current flow data includes: For the current node, obtain the set of upstream nodes that supply active power flow to the current node, and obtain the total upstream carbon flow and total upstream power supplied by all upstream nodes based on the set of upstream nodes; Obtain the local power source corresponding to the current node, and obtain the total amount of newly added carbon flow and the total local output power based on the local power source; The node carbon density of the current node is obtained based on the total upstream carbon flow, the total upstream power, the total locally added carbon flow, and the total local output power.

3. The method according to claim 1, characterized in that, The step of mapping the node carbon density to multiple types of charging stations to obtain the station carbon density of the multiple types of charging stations includes: Obtain the access nodes of various types of charging stations in the power distribution network, and use the node carbon density corresponding to the access node as the station carbon density of the various types of charging stations.

4. The method according to claim 1, characterized in that, The method further includes: Based on the carbon density and single-charge power curve of the charging station, carbon footprint information of a single charging behavior of an electric vehicle is obtained; the single-charge power curve is used to characterize the start time, end time, and charging power of the single charging behavior; the carbon footprint information is used to characterize the actual carbon emissions of the single charging behavior. Based on the current baseline carbon intensity and the carbon footprint information, the emission reduction corresponding to the single charging behavior is obtained; User points are obtained based on the emission reductions, and user account information is updated based on the user points.

5. The method according to claim 4, characterized in that, The step of obtaining the emission reduction corresponding to the single charging behavior based on the current baseline carbon intensity and the carbon footprint information includes: The total charging power of a single charging action is obtained based on the single charging power curve, and the product of the total charging power and the current baseline carbon intensity is used as the current baseline emissions. The difference between the current baseline emissions and the actual carbon emissions is used as the emission reduction corresponding to the single charging action.

6. The method according to claim 4, characterized in that, The method further includes: Based on the updated user account information, obtain the historical charging records of each user, and extract the feature vectors corresponding to the historical charging records; All users are clustered based on the feature vectors, and user profiles are constructed based on the clustering results; Based on the user profile, scenario information, and initial carbon incentive signal, an incentive budget constraint is constructed, and a carbon incentive model is built based on the incentive budget constraint.

7. The method according to claim 6, characterized in that, The method further includes: The charging strategy is obtained based on the carbon density of the station and the carbon incentive model. In the authorized escrow scenario, the charging power is adjusted according to the charging strategy to obtain an updated charging power curve, and the user account information is updated based on the updated charging power curve.

8. A distribution network carbon state estimation device based on multiple types of charging stations, characterized in that, The device includes: The data processing module is used to standardize the collected multi-source data of the distribution network to obtain target multi-source data; the multi-source data of the distribution network includes active power output and carbon emission parameters of various power sources, distribution network node data, and power station data; the distribution network node data includes the distribution network topology. The carbon density acquisition module is used to acquire power flow data based on the target multi-source data, and to acquire the node carbon density of each node according to the power flow data; the power flow data includes the active power flow of each line and the active power injection of each node. The carbon state estimation module is used to map the node carbon density to multiple types of charging stations to obtain the station carbon density of the multiple types of charging stations.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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