Method and system for evaluating influence of vehicle-to-grid interaction based on charging load characteristic image

Abstract

The application discloses a charging load characteristic image-based influence evaluation method and system of vehicle-to-grid interaction on a power distribution network, belongs to the technical field of vehicle-to-grid interaction extended planning of a power distribution network, and has the following technical scheme: a typical charging scene analysis system of a new energy vehicle is constructed; a multi-dimensional charging load characteristic image analysis system is constructed; a simulation analysis system of influences of the new energy vehicle on the power distribution network is established; a quantitative analysis method based on load characteristics superposition is adopted to superpose charging load characteristic curves in each scene and basic load characteristic curves of a region to be evaluated in time sequence, calculate and compare power distribution network evaluation indexes before and after superposition, and realize quantitative evaluation of influences of charging facilities access; and an influence analysis system of vehicle-to-grid interaction on the power distribution network is established: vehicle-to-grid interaction scenes are determined, vehicle-to-grid interaction participation rates are taken as variables, power distribution network evaluation indexes under vehicle-to-grid interaction are analyzed and calculated, and quantitative evaluation of influences of vehicle-to-grid interaction is realized.

Description

Technical Field

[0001] This invention relates to the field of extended planning technology for vehicle-to-grid interaction in power distribution networks, and in particular to a method and system for assessing the impact of vehicle-to-grid interaction on power distribution networks based on charging load characteristic profiles. Background Technology

[0002] With the acceleration of global energy transition and electrification, new energy vehicles have become a vital force driving green and low-carbon development in the transportation sector. According to the International Energy Agency (IEA), the global number of new energy vehicles is projected to reach 350 million by 2030, with China, as the world's largest new energy vehicle market, expected to have over 100 million vehicles. This large-scale integration of new energy vehicles presents unprecedented challenges and opportunities for power distribution networks. Vehicle-to-Grid (V2G) technology, as a novel energy interaction model, can not only address the impact of new energy vehicle charging on the power distribution network but also provide ancillary services such as peak shaving and frequency regulation, improving the flexibility and reliability of grid operation.

[0003] Currently, the most typical methods for analyzing the impact of vehicle-to-grid (V2G) interaction on distribution networks mainly fall into three categories: First, methods considering the spatiotemporal distribution of charging load. The core of this method is to assess the impact on the distribution network by analyzing the spatiotemporal distribution characteristics of new energy vehicle charging load. Its advantage lies in its accurate prediction of the spatiotemporal distribution of charging load, but it lacks sufficient systematic analysis of the diversity of user charging behavior and the characteristics of different types of user charging behavior. Second, methods considering V2G interaction control strategies. The core of this method is to optimize the interaction strategy by analyzing the impact of different control strategies on the distribution network. Its advantage is that the optimization and application of V2G interaction control strategies can improve interaction efficiency, but the method itself is highly dependent on the accuracy of charging load prediction and lacks inclusiveness of distribution network planning results. Third, methods considering the coupling of multiple factors. The core of this method is to consider the coupling scenarios of new energy vehicle charging load, distributed power sources, demand response, and other factors to achieve a comprehensive impact assessment on the distribution network. Its advantage is that it can comprehensively consider the coupling effects of multiple factors, providing a scientific basis for comprehensive distribution network planning, but in practical applications, the computational workload is large, and the application efficiency needs to be optimized.

[0004] Therefore, existing methods for analyzing the impact of vehicle-to-grid interaction on distribution networks generally suffer from problems such as insufficient mining of user charging behavior characteristics, inadequate adaptability to distribution network planning, or low computational efficiency. These methods are insufficient to comprehensively and accurately assess the actual operating status and potential impact of distribution networks under vehicle-to-grid interaction scenarios, and cannot provide scientific and efficient decision support for the optimization planning and operation control of distribution networks. Summary of the Invention

[0005] To address the shortcomings of existing methods for analyzing the impact of vehicle-to-grid interaction on distribution networks, such as insufficient mining of user charging behavior characteristics and inadequate adaptability to distribution network planning, this invention provides a method and system for assessing the impact of vehicle-to-grid interaction on distribution networks based on charging load characteristic profiles. This method and system can comprehensively and accurately assess the impact of vehicle-to-grid interaction on distribution networks, improve the scientific rigor and efficiency of distribution network planning and operation control, and provide decision-making support for the green and low-carbon development of the power grid. The specific technical solution is as follows: The method for assessing the impact of vehicle-to-grid interaction on the power distribution network based on charging load characteristic profiling includes the following steps: S1. Constructing a typical charging scenario analysis system for new energy vehicles, which includes classifying charging scenarios based on at least one factor such as station scale, charging mode, geographical location, land use, and service targets to form differentiated charging scenarios; S2. Constructing a multi-dimensional charging load characteristic profiling analysis system, which includes collecting actual operating data of charging load for each differentiated charging scenario, plotting charging load characteristic curves based on the collected actual operating data, and fitting the probability distribution of charging load using a Gaussian mixture model to obtain the distribution probability of charging load under each scenario; S3. Establishing a simulation analysis system for the impact of new energy vehicles connecting to the power distribution network. The impact simulation analysis system includes a new energy vehicle charging load model and a distribution network topology simulation model built based on a multi-dimensional charging load characteristic profile analysis system. The distribution network topology simulation model is used to analyze the impact on the distribution network by simulating access scenarios under different new energy vehicle penetration rates and / or different charging strategies. S4. A quantitative analysis method based on load characteristic superposition is adopted to superimpose the charging load characteristic curves under each scenario with the basic load characteristic curves of the area to be evaluated in a time series, calculate and compare the distribution network evaluation indicators before and after superposition, and realize the quantitative evaluation of the impact of charging facility access. S5. An analysis system for the impact of vehicle-to-grid interaction on the distribution network is established. This system includes determining vehicle-to-grid interaction scenarios, using the vehicle-to-grid interaction participation rate as a variable, analyzing and calculating the distribution network evaluation indicators under vehicle-to-grid interaction, and realizing the quantitative evaluation of the impact of vehicle-to-grid interaction.

[0006] Preferably, fitting the probability distribution of the charging load using a Gaussian mixture model includes the following steps: calculating the mean and standard deviation parameters of the Gaussian distribution using actual charging load operating data, and then calculating the charging load distribution probability for each charging period using the cumulative distribution function of the Gaussian distribution; wherein, the formula for calculating the mean is expressed as: The formula for calculating the sample standard deviation is as follows: In the formula, For sample data, The number of data points; The charging load distribution probability for each charging period is calculated using the standard normal cumulative distribution function.

[0007] Preferably, the evaluation indicators for the distribution network include at least the peak shaving rate and the valley filling rate.

[0008] Preferably, step S3 further includes identifying weak links in the power distribution network based on simulation results.

[0009] Preferably, the distribution network evaluation indicators used for quantitative evaluation in step S4 include equipment load rate, load factor, or peak-valley difference rate; wherein, the calculation formula for the equipment load rate η is: Where η represents the equipment load rate; S represents the actual operating load of the power supply equipment (kW); R represents the rated capacity of the power supply equipment (kVA); the formula for calculating the load rate is: Where Kp represents the load factor, Indicates average active power load; This represents the maximum active load; the formula for calculating the peak-to-valley difference rate is: ,in, Indicates the peak-to-valley difference rate; Indicates the maximum load; Indicates minimum load; This indicates the difference between peak and valley loads.

[0010] Preferably, step S5 specifically includes: S51, identifying feasible vehicle-to-grid interaction scenarios, and using different vehicle-to-grid interaction participation rates as analysis variables to analyze the load peak shaving capability of vehicle-to-grid interaction behavior on the distribution network under each scenario; S52. Based on a defined interaction scenario, calculate and evaluate the distribution network evaluation indicators under different vehicle-to-grid interaction participation levels. The distribution network evaluation indicators shall include at least the peak shaving rate and valley filling rate.

[0011] The preferred formula for calculating peak reduction rate is: ,in, Indicates peak reduction rate; Indicates the original peak load; This indicates the remaining peak load after peak shaving; This indicates the amount of load reduction.

[0012] The preferred formula for calculating the vehicle-to-everything (V2X) interaction participation rate is: ,in, Indicates the participation rate in vehicle-to-everything (V2X) interaction; This indicates the actual number of new energy vehicles participating in the reverse discharge response; This indicates the total number of new energy vehicles in the region (system).

[0013] The preferred formula for calculating the valley filling rate is: ,in, Indicates the valley filling rate; This represents the original valley load; This indicates the remaining valley load after valley filling; This indicates the load capacity for filling the valley.

[0014] The present invention also provides a vehicle-to-grid interaction impact assessment system based on charging load characteristic profiles, which is applied to the above-mentioned vehicle-to-grid interaction impact assessment method based on charging load characteristic profiles.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: By constructing a typical charging scenario analysis system and a multi-dimensional charging load characteristic profiling system, random and diverse user charging behaviors are transformed into quantifiable and predictable charging load probability models and characteristic curves, achieving systematic and refined mining of charging behavior characteristics. A quantitative analysis method based on load characteristic superposition is adopted to directly compare core planning indicators such as load rate and peak-valley difference rate before and after charging load access. This method has high tolerance for charging load prediction accuracy, overcoming the shortcomings of existing technologies where insufficient prediction accuracy leads to evaluation failure. It can directly serve distribution network planning decisions, and the quantitative analysis method based on load characteristic superposition avoids the complex time-domain iterative solution in traditional multi-factor coupling methods, significantly reducing the computational workload and improving the efficiency of the method's engineering application. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a flowchart of the method for assessing the impact of vehicle-to-grid interaction on the power distribution network based on charging load characteristic profiling, as described in Example 1. Figure 2 This is a diagram of a 110kV / 10kV radial distribution network topology built in the electrical simulation environment of Example 2; Figure 3 This is a model diagram of a single charging device built on the electrical platform of Embodiment 2; Figure 4 This is a simulation model diagram of a new energy vehicle charging system with a single charging device according to Example 2; Figure 5 The figure shows the simulation results of voltage changes when the charging device in Example 2 is connected to different nodes of the power distribution network in sequence. Figure 6 This is a schematic diagram of the harmonic content of a new energy vehicle charging device at different times during an electrical simulation of Example 2. Figure 7 A graph showing the THDi changes during the entire charging process of a single charging device in Example 2; Figure 8 The graph shows the THDi variation of the battery in Example 2 at 0%, 5%, 10%, 15%, and 20% capacities. Figure 9 The graph shows the THDi variation of the battery in Example 2 at 10%, 20%, 30%, 40%, and 50% capacity. Figure 10 The graph shows the THDi variation of the battery in Example 2 at 0%, 20%, 40%, 60%, and 80% capacity. Figure 11 The graph shows the THDi variation of the battery in Example 2 at 0%, 20%, 40%, 60%, and 80% capacity. Figure 12 This is a schematic diagram illustrating the current variation on the 10kV side in the electrical simulation of Example 2; Figure 13 This is a graph showing the changes in the annual load characteristic curves of the distribution network in Area A before and after the addition of new charging load in Example 3. Figure 14 This is a graph showing the changes in the annual load characteristic curves of the distribution network in Area B before and after the addition of new charging load in Example 3. Figure 15 This is a graph showing the changes in the annual load characteristic curves of the C-zone distribution network before and after the addition of new charging load in Example 3. Figure 16 This is a graph showing the changes in the annual load characteristic curves of the D-zone distribution network before and after the addition of new charging load in Example 3. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0020] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0021] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0022] Example 1 Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for assessing the impact of vehicle-to-grid interaction on the distribution network based on charging load characteristic profiling. This embodiment provides a method for assessing the impact of vehicle-to-grid interaction on the distribution network based on charging load characteristic profiling, which includes the following steps: S1. Construct a typical charging scenario analysis system for new energy vehicles. The construction of a typical charging scenario analysis system for new energy vehicles includes the following steps: Based on at least one factor such as station scale, charging mode, geographical location, land use nature, and service target, the charging scenarios are divided to form differentiated charging scenarios.

[0023] The classification of new energy vehicle charging scenarios is based on a multi-dimensional system, primarily grounded in the following core factors: station size, charging mode, geographical location, land use, and target users. Station size, based on the station's floor area, number of charging piles, and service capacity, can be categorized into small (<10 charging piles), medium (10-50 charging piles), large (50-100 charging piles), and super-large (>100 charging piles) charging stations. Charging modes, based on charging speed and method, can be divided into slow charging, fast charging, supercharging, and battery swapping station modes. Geographical location is categorized according to its location, including urban core areas, main urban areas, urban-rural fringe areas, highway service areas, and scenic areas. Location type, based on the nature of the service location, can be categorized into residential communities, commercial centers, office buildings, public parking lots, logistics parks, and bus stations. This multi-dimensional classification allows for a refined categorization of charging scenarios, laying the foundation for subsequent load characteristic analysis.

[0024] S2. Construct a profile of the charging load characteristics of new energy vehicles. The construction of a multi-dimensional charging load characteristic profile analysis system includes the following steps: Construct a multi-dimensional charging load characteristic profile analysis system, collect actual operating data of charging load for each differentiated charging scenario, and draw charging load characteristic curves based on the collected actual operating data of charging load. Then, use a Gaussian mixture model to fit the probability distribution of charging load to obtain the distribution probability of charging load under each scenario.

[0025] Preferably, after collecting actual charging load operation data, the box plot statistical analysis method is used to remove abnormal data, and then the charging load characteristic curve is plotted, thereby minimizing the interference of abnormal and sudden data on the analysis results.

[0026] Furthermore, fitting the probability distribution of the charging load using a Gaussian model includes the following steps: The mean and standard deviation of the actual operating data of the charging load after cleaning were calculated. The formula for calculating the mean is as follows: The formula for calculating the sample standard deviation is expressed as follows: In the formula, For sample data, The number of data points.

[0027] Then, the standard normal cumulative distribution function (=NORM.S.DIST(z,TRUE)) in spreadsheet and other calculation tools was used to calculate and determine the probability distribution of charging load for each charging period.

[0028] Furthermore, the analysis results of typical charging load characteristics and the probability of charging load occurrence in various spatiotemporal scenarios can be expressed using a Gaussian model, and its curve fitting process satisfies the following Gaussian distribution probability density function equation: In the formula, For sample data, For the number of data points, These are the weighting coefficients. Let be the mean of the nth Gaussian component. Let be the standard deviation of the i-th Gaussian component.

[0029] Based on the above equations, the probability of charging load occurrence under various new energy vehicles or typical charging scenarios can be expressed. Under the condition that boundary conditions such as charging technology and charging costs (electricity price, service fees, etc.) do not change significantly, the charging load under the planned scenario can be predicted. The prediction formula is: In the formula: j represents the type of new energy vehicle; This represents the total charging load of all types of new energy vehicles at time i. Let represent the probability of the j-th type of new energy vehicle experiencing a charging load at time i. This represents the total number of the j-th type of new energy vehicles in operation; This represents the charging power of the j-th type of new energy vehicle.

[0030] S3. Based on the multi-dimensional charging load characteristic profile analysis system, establish a simulation analysis system for the impact of new energy vehicles accessing the distribution network. The simulation analysis system for the impact of new energy vehicles accessing the distribution network includes a new energy vehicle charging load model and a simulation model of the distribution network topology. The simulation model of the distribution network topology analyzes the impact on the distribution network by simulating access scenarios under different new energy vehicle penetration rates and / or different charging strategies.

[0031] In this example, the construction of the new energy vehicle charging load model fully considers the type of vehicle power battery, the vehicle charging method, vehicle charging habits, and the spatiotemporal distribution characteristics of the charging load. Specifically, based on the multi-dimensional charging load characteristic profiling analysis system, the construction steps of the new energy vehicle charging load model include: The system collects operational data from charging equipment and user behavior data, and preprocesses the collected data. The charging equipment data includes equipment model, rated power, operating status, and fault records; user charging behavior data includes charging time (start time, end time, duration), charging amount, charging frequency, charging location (e.g., home charging stations, public fast charging stations, battery swapping stations), payment method, and user vehicle information (e.g., battery capacity, driving range, fast charging compatibility). Outliers are removed from the collected data using a box plot method.

[0032] The data is divided into scenarios based on multiple factors, including at least geographical location, user type, charging method and time period characteristics. A load model will be built for each scenario.

[0033] Extract the load characteristic parameters for each scenario, among which key parameters include at least the charging start time, charging duration, charging power, and number of daily charging cycles.

[0034] A Gaussian mixture model was used to fit the key parameters.

[0035] Based on the probability distribution results, calculate the total charging load at any time t: In the formula: j represents the type of new energy vehicle; This represents the total charging load of all types of new energy vehicles at time i. Let represent the probability of the j-th type of new energy vehicle experiencing a charging load at time i. This represents the total number of the j-th type of new energy vehicles in operation; This represents the charging power of the j-th type of new energy vehicle.

[0036] The load curve was generated using an Excel spreadsheet.

[0037] By following the steps above, the charging load model for new energy vehicles can be constructed.

[0038] The simulation model of the distribution network topology is an electromagnetic transient equivalent circuit model constructed by digitally mapping the electrical connections and equipment parameters of the real distribution network. Its core objective is to accurately calculate electrical quantities such as voltage amplitude / phase, total harmonic distortion (THDi) of current, and active / reactive power losses at each node under given charging load injection conditions, thereby assessing the impact of new energy vehicle access on the distribution network. The construction method of the distribution network topology simulation model is as follows: The system topology was built using electromagnetic transient (EMT) simulation software for power systems. An equivalent model was built on a multi-domain simulation modeling platform, and the model parameters were assigned values.

[0039] Based on the active power time-series data (8760 hours or 24 hours of a typical day) derived from the new energy vehicle charging load model, a simulation model of the distribution network topology is run, and the simulation data is recorded. Based on the simulation data, weak links in the distribution network, such as excessive voltage deviation, harmonic pollution, and equipment overload, are identified. The sensitivity relationship between charging facility access and distribution network access is determined, and the degree of impact of different factors on the distribution network is assessed. Finally, a simulation analysis system for charging facility access to the distribution network is constructed.

[0040] Furthermore, step S3 also includes identifying weak links in the distribution network based on simulation results. Specifically, the criteria for judging weak links should be based on current national and industry standards. For example, the national standard "Power Quality - Supply Voltage Deviation GB / T 12325-2008" stipulates that the acceptable range of supply voltage for 10 kV and below distribution networks is ±7%; another example is the national standard "Power Quality - Harmonics in Public Power Grids GB / T 14549 93," which stipulates that the total harmonic distortion (THDu) of 10 kV distribution network voltage should not exceed 4.0%, and the content of each harmonic should not exceed 3.2% (odd order) and 1.6% (even order), etc.

[0041] S4. A quantitative analysis method based on load characteristic superposition is adopted to superimpose the charging load characteristic curves of each scenario with the basic load characteristic curves of the area to be evaluated in a time series, calculate and compare the distribution network evaluation indicators before and after superposition, and realize the quantitative evaluation of the impact of charging facility access.

[0042] In this example, the distribution network evaluation indicators used for quantitative evaluation include equipment load factor, load rate, or peak-valley difference rate. The formula for calculating the equipment load factor η is: Where η represents the equipment load rate; S represents the actual operating load of the power supply equipment (kW); and R represents the rated capacity of the power supply equipment (kVA).

[0043] The formula for calculating the load factor is: In the formula, Kp represents the load factor. Indicates average active power load; Indicates the maximum active load; The formula for calculating the peak-to-valley difference rate is: in, Indicates the peak-to-valley difference rate; Indicates the maximum load; Indicates minimum load; This indicates the difference between peak and valley loads.

[0044] Specifically, in this step, existing load data of the distribution network or power supply equipment in the area to be evaluated is collected, and its load characteristic curve is plotted. Based on the existing load characteristic curve, a comprehensive load characteristic curve is generated by overlaying it over the entire cycle or by focusing on overlaying the charging load during typical periods. By comparing the changes in indicators such as equipment load rate, peak-valley difference rate, and annual load rate before and after the load characteristic curve overlay, a quantitative evaluation of the impact on the grid connection is achieved.

[0045] It should be noted that whether to use full-cycle overlay or typical overlay depends on the available data sample. If full-cycle load data is available, full-cycle overlay should be used; otherwise, typical overlay should be used.

[0046] S5. Establish a system for analyzing the impact of vehicle-to-grid interaction on the distribution network. This system includes: identifying vehicle-to-grid interaction scenarios, using the participation rate of vehicle-to-grid interaction as a variable, analyzing and calculating the distribution network evaluation indicators under vehicle-to-grid interaction, and achieving a quantitative evaluation of the impact of vehicle-to-grid interaction.

[0047] The formula for calculating the vehicle-to-everything (V2X) interaction participation rate is as follows: ,in, Indicates the participation rate in vehicle-to-everything (V2X) interaction; This indicates the actual number of new energy vehicles participating in the reverse discharge response; This indicates the total number of new energy vehicles in the region (system).

[0048] Specifically, step S5 includes the following steps: S51. Identify feasible vehicle-to-grid (V2G) interaction scenarios and use different V2G participation rates as analytical variables to analyze the load shaving capacity of the distribution network under each scenario. The load shaving capacity is evaluated by calculating the peak shaving rate. The higher the peak shaving rate, the higher the peak shaving capacity, and vice versa.

[0049] It should be noted that feasible vehicle-to-grid (V2G) interaction scenarios are judged based on the common charging vehicle types of the charging stations to be evaluated, the interaction feasibility and capabilities of these vehicles, and typical charging scenarios. Considering new energy vehicle types, buses and heavy trucks with large-capacity power batteries have greater interaction potential than sedans and SUVs. Regarding "interaction capability," private passenger vehicles offer superior flexibility, while non-private passenger vehicles, due to operational needs and ownership issues, have lower flexibility in participating in V2G interaction. In terms of interaction capability, the advantages and disadvantages of participating vehicles in terms of interaction frequency, interaction time, and available interaction duration are assessed. Considering typical charging scenarios, long-term parking scenarios are superior to short-term parking scenarios, such as residences and workplaces, where long-term parking is preferred, and V2G interaction flexibility is superior to parking locations such as highway service areas and tourist attractions. A four-quadrant judgment system can be established using the willingness to respond to V2G interaction and the scale of adjustment that can participate in interaction as judgment criteria to systematically assess the feasibility of V2G interaction scenarios.

[0050] In this example, the formula for calculating the peak reduction ratio is: in, Indicates peak reduction rate; Indicates the original peak load; This indicates the remaining peak load after peak shaving. This indicates the amount of load reduction.

[0051] S52. Based on a defined interaction scenario, calculate and evaluate distribution network evaluation indicators under different vehicle-grid interaction participation levels. These evaluation indicators should include at least peak shaving rate and valley filling rate. Preferably, the distribution network evaluation indicators also include equipment load factor, load factor, peak-valley difference rate, and grid interaction participation rate.

[0052] Furthermore, the formula for calculating the peak reduction rate is: ,in, Indicates peak reduction rate; Indicates the original peak load; This indicates the remaining peak load after peak shaving. This indicates the amount of load reduction. The formula for calculating the valley filling rate is: ,in, Indicates the valley filling rate; This represents the original valley load; This indicates the remaining valley load after valley filling; This indicates the load capacity for filling the valley.

[0053] By quantifying the impact of vehicle-to-grid interaction on the distribution network, we can more accurately assess the actual effectiveness of vehicle-to-grid interaction in mitigating load fluctuations and optimizing resource allocation in the distribution network, providing a systematic analysis and decision-making basis for the preparation of distribution network planning schemes and operation management.

[0054] In summary, by constructing a typical charging scenario analysis system and a multi-dimensional charging load characteristic profiling system, random and diverse user charging behaviors are transformed into quantifiable and predictable charging load probability models and characteristic curves, achieving systematic and refined mining of charging behavior characteristics. A quantitative analysis method based on load characteristic superposition is adopted to directly compare core planning indicators such as load rate and peak-valley difference rate before and after charging load access. This method has high tolerance for charging load prediction accuracy, overcoming the shortcomings of existing technologies where insufficient prediction accuracy leads to evaluation failure. It can directly serve distribution network planning decisions, and the quantitative analysis method based on load characteristic superposition avoids the complex time-domain iterative solution in traditional multi-factor coupling methods, significantly reducing the computational workload and improving the efficiency of the method's engineering application.

[0055] Example 2 To clearly demonstrate the effectiveness of the vehicle-to-grid interaction impact assessment method based on charging load characteristic profiles in Example 1 on the power distribution network, this example performs simulation analysis.

[0056] This embodiment takes a real urban power distribution network as the object and uses electrical simulation software to build a simulation model of the charging load of new energy vehicles connected to the power distribution network, in order to verify the effectiveness of the analysis method proposed in this invention.

[0057] Please refer to Figure 2 , Figure 2 This document describes the 110kV / 10kV radial distribution network topology built in the simulation environment. First, based on the actual electrical parameters of the target distribution network, a 110kV / 10kV radial distribution network topology model is established. The distribution network topology model includes: 110kV power supply: line voltage 110kV, frequency 50Hz, short-circuit capacity 1500MVA, X / R=10; 110kV main transformer: rated capacity 50MVA, turns ratio 110 / 10.5kV, short-circuit impedance 12.5%, connection group YNd11; 10kV distribution transformer: rated capacity 630kVA, turns ratio 10 / 0.4kV, short-circuit impedance 4.5%, connection group Dyn11; 10kV feeder: Five access nodes are set up: N1, N2, N3, N4 and N5. Among them, N1 is closest to the 110kV power source and N5 is the farthest.

[0058] Fixed load elements are connected in parallel at each node to represent the existing foundation load of that node.

[0059] Then, a charging load model for new energy vehicles is constructed. Please refer to [the relevant documentation / reference]. Figures 3-4 , Figure 3 This is a model diagram of a single charging device built on a simulation platform. Figure 4 A simulation model diagram of a new energy vehicle charging system with a single charging unit. To simulate the actual operating characteristics of the charging facility, the high-frequency DC / DC power converter in the charging unit is simplified to a nonlinear resistor, thereby improving simulation efficiency while ensuring accuracy.

[0060] To comprehensively assess the impact of charging load access, the following simulation conditions were set: The charging facilities were connected sequentially to nodes N1 to N5 to simulate the impact of the connection point distance on the distribution network. With multiple charging devices operating simultaneously, the initial SOC of each device was set to different combinations such as 0%, 20%, 40%, 60%, and 80%. Simulations of a single charging device and unordered charging of multiple charging devices were run separately.

[0061] Run the simulation model and analyze the simulation results: 1. Impact on node voltage Please refer to Figure 5 , Figure 5 The simulation results show the voltage changes when the charging device is connected to different nodes of the distribution network. The results indicate that the farther the connection point is from the 110kV power source, the more severe the voltage drop at the node, a pattern completely consistent with traditional electrical loads.

[0062] 2. Harmonic Influence Analysis Please refer to Figure 6 , Figure 6 Simulation diagram of the harmonic content of new energy vehicle charging device at different times. The results show that the new energy vehicle charging device mainly generates 5th, 7th, 11th and 13th harmonics, among which the 5th and 7th harmonics are the most serious.

[0063] Please refer to Figure 7 , Figure 7 The graph shows the changes in THDi throughout the charging process of a single charging device. It can be seen that THDi remains high throughout the entire charging process, only gradually decreasing to a reasonable level near the end of the charging process.

[0064] Please refer to Figure 8 Figure 11 , Figure 8 The graph shows the THDi variation at battery capacities of 0%, 5%, 10%, 15%, and 20%. Figure 9 The graph shows the changes in THDi at battery capacities of 10%, 20%, 30%, 40%, and 50%. Figure 10 The graph shows the THDi variation at battery capacities of 0%, 20%, 40%, 60%, and 80%. Figure 11The diagram shows the THDi variation at battery capacities of 0%, 20%, 40%, 60%, and 80%. Simulation results indicate that when multiple charging devices operate simultaneously, there is a mutual cancellation effect between harmonic current phasors. The occurrence time of the THDi peak changes with the SOC combination, but the total harmonic content remains significant and may exceed the access limits specified in relevant standards.

[0065] Please refer to Figure 12 , Figure 12 This is a schematic diagram illustrating the simulation of current changes on the 10kV side. The results show that after the charging load is connected, the current on the 10kV side increases significantly, leading to increased network losses; as the battery gradually fills up, the current gradually decreases, and the losses decrease accordingly; when charging ends, the current returns to 0, and the losses return to the level before charging.

[0066] This embodiment verifies, through platform simulation, that the simulation analysis system described in step S3 can accurately assess the impact of charging load access on distribution network voltage, harmonics, and network losses.

[0067] Example 3 This embodiment further uses four administrative districts (A, B, C, D) and a commercial complex in a city as examples to verify the vehicle-to-grid interaction impact assessment method proposed in this invention.

[0068] Annual distribution network load data were collected for four administrative regions (A, B, C, and D) respectively. Annual load characteristic curves were plotted, and the maximum load, minimum load, peak-to-valley difference rate, and annual load factor for each region were calculated. The results are summarized in Table 1, and the corresponding annual load characteristic curves are shown below. Figures 13-16 As shown in the "current year" curve, Figure 13 This is a graph showing the changes in the annual load characteristic curves of the distribution network in Area A before and after the addition of new charging load. Figure 14 This is a graph showing the changes in the annual load characteristic curves of the distribution network in Zone B before and after the addition of new charging load. Figure 15 This is a graph showing the changes in the annual load characteristic curves of the distribution network in Zone C before and after the addition of new charging load. Figure 16 The graph shows the changes in the annual load characteristic curves of the D area distribution network before and after the addition of new charging loads.

[0069] Table 1. Load Level and Load Characteristic Curves of Distribution Network in the Example Area Based on the planning schemes for newly built charging stations in each administrative region during the "15th Five-Year Plan" period, the number of new charging stations in each region was determined (32 in Region A, 38 in Region B, 15 in Region C, and 11 in Region D). Using the charging load characteristic curve obtained in step S2 of Example 1, the full-cycle charging load of 8760 hours was superimposed onto the base load curve of each region. The superimposed typical daily maximum / minimum load, peak-valley difference rate, annual maximum / average load, and annual load factor were calculated. The results are shown in Tables 2 to 5. The superimposed annual load characteristic curve is shown in Table 5. Figures 13-16 The curve shown is "After the new charging station is connected".

[0070] Table 2. Analysis of the Impact of Newly Added Charging Stations on Distribution Network Load After Integration into Area A (Planned Annual Number) Table 3. Analysis of the Impact of Newly Added Charging Stations on Distribution Network Load After Integration into Area B (Planned Annually) Table 4. Analysis of the Impact of Newly Added Charging Stations on Distribution Network Load After Connection in Planning Year C Table 5. Analysis of the impact on distribution network load after the planned new charging stations in Zone D are connected. Taking Area A as an example, after adding the new charging load, the peak load of the distribution network increased by 70.00 MW, and the annual load factor rose from 48.21% to 48.67%, an increase of 0.46%. In Area C, due to the lower base load, the annual load factor increased from 61.53% to 66.94%, an increase of 5.41%. The results show that the load superposition method proposed in this invention can intuitively and quantitatively reflect the impact of charging facility access on the economic efficiency of distribution network operation and equipment utilization.

[0071] Taking a large commercial complex in Area A as an example, the complex has 50 charging piles with V2G capability. Under disordered charging conditions, the maximum load of the complex is 4.50MW, corresponding to a 10kV line load rate of 47.37% and a load peak-valley difference rate of 57.78%.

[0072] Assuming 6% of new energy vehicle owners respond to the vehicle-to-grid interaction demand, and each participating vehicle discharges in reverse at a power of 40kW (discharge duration constrained by peak load periods in commercial areas), under this setting, the maximum load of commercial areas after interaction decreases to 3.30MW, the line load rate decreases to 34.74%, and the peak-to-valley difference rate narrows to 42.42% (see Table 6 for details). These results quantitatively demonstrate that vehicle-to-grid interaction can effectively achieve peak shaving and valley filling, significantly improving the operating indicators of the distribution network.

[0073] Table 6. Analysis Results of the Impact of Vehicle-to-Network Interaction on the Power Distribution Network in Commercial Scenarios Through case studies of administrative districts and commercial areas, the load superposition method described in step S4 was verified to quantitatively evaluate the impact of charging access on distribution network planning indicators, and the vehicle-to-grid interaction evaluation method described in step S5 was verified to quantitatively analyze the peak shaving effect under different participation rates. All simulation and case study results demonstrate that the analytical method proposed in this invention is scientific and efficient, and can provide reliable decision support for distribution network planning and vehicle-to-grid interaction strategy formulation.

[0074] Example 4 This embodiment provides a system for assessing the impact of vehicle-to-grid interaction on the distribution network based on charging load characteristic profiles, which is applied to the method for assessing the impact of vehicle-to-grid interaction on the distribution network based on charging load characteristic profiles in Embodiment 1.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for assessing the impact of vehicle-to-grid interaction on the distribution network based on charging load characteristic profiles, characterized in that, Includes the following steps: S1. Construct a typical charging scenario analysis system for new energy vehicles. The construction of the typical charging scenario analysis system for new energy vehicles includes: classifying charging scenarios based on at least one factor such as station scale, charging mode, geographical location, land use nature, and service targets to form differentiated charging scenarios. S2. Construct a multi-dimensional charging load characteristic profile analysis system. The construction of the multi-dimensional charging load characteristic profile analysis system includes: collecting actual operating data of charging load for each differentiated charging scenario, drawing charging load characteristic curves based on the collected actual operating data of charging load, and fitting the probability distribution of charging load using a Gaussian mixture model to obtain the distribution probability of charging load under each scenario. S3. Establish a simulation analysis system for the impact of new energy vehicles accessing the power distribution network. The simulation analysis system for the impact of new energy vehicles accessing the power distribution network includes a new energy vehicle charging load model and a simulation model of the power distribution network topology, which are constructed based on a multi-dimensional charging load characteristic profile analysis system. The simulation model of the power distribution network topology is used to analyze the impact on the power distribution network by simulating access scenarios under different new energy vehicle penetration rates and / or different charging strategies. S4. Using a quantitative analysis method based on load characteristic superposition, the charging load characteristic curves under each scenario are superimposed with the basic load characteristic curves of the area to be evaluated in a time series. The distribution network evaluation indicators before and after superposition are calculated and compared to achieve a quantitative evaluation of the impact of charging facility access. S5. Establish a system for analyzing the impact of vehicle-to-grid interaction on the distribution network. This system includes identifying vehicle-to-grid interaction scenarios, using the participation rate in vehicle-to-grid interaction as a variable, analyzing and calculating the distribution network evaluation indicators under vehicle-to-grid interaction, and achieving a quantitative evaluation of the impact of vehicle-to-grid interaction.

2. The method for assessing the impact of vehicle-to-grid interaction on the distribution network based on charging load characteristic profiling as described in claim 1, characterized in that, Fitting the probability distribution of charging load using a Gaussian mixture model includes the following steps: The mean and standard deviation parameters of the Gaussian distribution are calculated using actual charging load operation data. Then, the charging load distribution probability for each charging period is calculated using the cumulative distribution function of the Gaussian distribution. The formula for calculating the mean is as follows: The formula for calculating the sample standard deviation is as follows: In the formula, For sample data, The number of data points; The charging load distribution probability for each charging period is calculated using the standard normal cumulative distribution function.

3. The method for assessing the impact of vehicle-to-grid interaction on the distribution network based on charging load characteristic profiling as described in claim 1, characterized in that, The evaluation indicators for power distribution networks should include at least peak shaving rate and valley filling rate.

4. The method for assessing the impact of vehicle-to-grid interaction on the distribution network based on charging load characteristic profiling as described in claim 1, characterized in that, Step S3 also includes identifying weak links in the power distribution network based on simulation results.

5. The method for assessing the impact of vehicle-to-grid interaction on the distribution network based on charging load characteristic profiling as described in claim 1 or 4, characterized in that, The power distribution network evaluation indicators used for quantitative evaluation in step S4 include equipment load rate, load factor, or peak-valley difference rate; wherein, the calculation formula for the equipment load rate η is: Where η represents the equipment load rate; S represents the actual operating load of the power supply equipment (kW); R represents the rated capacity of the power supply equipment (kVA); the formula for calculating the load rate is: Where Kp represents the load factor, Indicates average active power load; This represents the maximum active load; the formula for calculating the peak-to-valley difference rate is: ,in, Indicates the peak-to-valley difference rate; Indicates the maximum load; Indicates minimum load; This indicates the difference between peak and valley loads.

6. The method for assessing the impact of vehicle-to-grid interaction on the distribution network based on charging load characteristic profiling as described in claim 1, characterized in that, Step S5 specifically includes: S51. Identify feasible vehicle-to-grid (V2G) interaction scenarios and use different V2G participation rates as analytical variables to analyze the load shaving capacity of the distribution network under each scenario. S52. Based on a defined interaction scenario, calculate and evaluate the distribution network evaluation index under different vehicle-to-grid interaction participation levels. The distribution network evaluation index includes at least peak shaving rate and valley filling rate.

7. The method for assessing the impact of vehicle-to-grid interaction on the distribution network based on charging load characteristic profiling as described in claim 6, characterized in that, The formula for calculating the peak reduction rate is: ,in, Indicates peak reduction rate; Indicates the original peak load; This indicates the remaining peak load after peak shaving. This indicates the amount of load reduction.

8. The method for assessing the impact of vehicle-to-grid interaction on the distribution network based on charging load characteristic profiling as described in claim 1, characterized in that, The formula for calculating the vehicle-to-everything (V2X) interaction participation rate is as follows: ,in, Indicates the participation rate in vehicle-to-everything (V2X) interaction; This indicates the actual number of new energy vehicles participating in the reverse discharge response; This indicates the total number of new energy vehicles in the region (system).

9. The method for assessing the impact of vehicle-to-grid interaction on the distribution network based on charging load characteristic profiling as described in claim 6, characterized in that, The formula for calculating the valley filling rate is: ,in, Indicates the valley filling rate; This represents the original valley load; This indicates the remaining valley load after valley filling; This indicates the load capacity for filling the valley.

10. A system for assessing the impact of vehicle-to-grid interaction on the distribution network based on charging load characteristic profiles, characterized in that: The method for assessing the impact of vehicle-to-grid interaction on the distribution network based on charging load characteristic profiles, as described in any one of claims 1 to 9, is applicable.

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