Orderly charging and discharging method and system for electric vehicles based on power line carrier communication

Abstract

The present invention discloses an orderly charging and discharging method and system for electric vehicles based on power line carrier communication, which relates to the technical field of orderly charging and discharging of electric vehicles. The method predicts the predicted adjustable margin of the current charging station on the time section according to the spatiotemporal distribution characteristics of the user's charging and discharging behavior, dynamically matches the charging and discharging power caliber through a dual current negotiation mechanism to avoid overload or underload situations, combines the spatiotemporal distribution characteristic prediction with the closed-loop correction of the disturbance information to make the predicted adjustable margin value dynamically approach the actual working condition, utilizes virtual identity authentication and trickle transmission strategy to ensure the safe and reliable transmission of the disturbance information; corrects the disturbance information based on the channel correction factor to generate an orderly charging and discharging strategy that takes into account both the stability of the power grid and the needs of the user to achieve smooth switching of power expansion / contraction, effectively solves the coupling problem of the spatiotemporal randomness of the user behavior and the defects of the communication environment, and realizes precise dynamic power regulation of the charging station.

Description

Technical Field

[0001] The present invention relates to the technical field of orderly charging and discharging of electric vehicles, and in particular to a method and system for orderly charging and discharging of electric vehicles based on power line carrier communication. Background Art

[0002] With the large-scale access of electric vehicles to the power grid, centralized charging behavior has led to a continuous increase in peak-to-valley load differences. Existing charging and discharging control strategies face two bottlenecks. First, user behavior fluctuates randomly in the temporal dimension due to the influence of vehicle pick-up time and electricity price sensitivity. In the spatial dimension, local load peaks are formed due to the aggregation of charging hotspots, causing the adjustable margin prediction based on historical data to deviate significantly from reality. Second, the strong electromagnetic noise generated by high-power charging and discharging at charging stations poses challenges to traditional wireless communications. The main manifestations are: power fluctuations cause the channel signal-to-noise ratio to drop sharply, and the transmission integrity of key parameters is damaged; fixed frame length protocols cannot adapt to changes in the power fluctuation range, and data congestion is exacerbated during high-load periods; centralized architectures pose the risk of sensitive information interception. Therefore, it can be seen that the spatiotemporal randomness of user charging and discharging behavior will form local load peaks, causing the adjustable margin prediction based on historical data to deviate significantly from reality. Moreover, the real-time and security deficiencies of communication in the complex electromagnetic environment of charging stations further amplify the control deviation. Therefore, it is urgent to build an integrated solution that integrates spatiotemporal behavior prediction, anti-interference communication, and dynamic charging and discharging control.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the application and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0004] The purpose of the present invention is to address the problem of inaccurate dynamic power regulation of charging stations caused by the dual defects of the spatiotemporal randomness of user charging and discharging behavior and the complexity of the communication environment in the existing technology. A method and system for orderly charging and discharging of electric vehicles based on power line carrier communication are proposed. The charging and discharging power caliber is dynamically matched through a dual current negotiation mechanism to avoid overload or underload. The adjustable margin prediction value is dynamically approximated to the actual working condition by combining the spatiotemporal distribution characteristic prediction with the closed-loop correction of the disturbance information. Virtual identity authentication and trickle transmission strategy are used to ensure the safe and reliable transmission of disturbance information. The disturbance information is corrected based on the channel correction factor to generate an orderly charging and discharging strategy that takes into account both grid stability and user needs to achieve smooth switching of power expansion / reduction, effectively solving the coupling problem of the spatiotemporal randomness of user behavior and the defects of the communication environment, and realizing precise dynamic power regulation of the charging station.

[0005] In a first aspect, a technical solution provided in an embodiment of the present invention is: a method for orderly charging and discharging of an electric vehicle based on power line carrier communication, comprising the following steps:

[0006] S1. Predict the adjustable margin of the current charging station in the time section based on the spatiotemporal distribution characteristics of the user's charging and discharging behavior;

[0007] S2. Determine the charging and discharging channels of each charging and discharging cooperative subject through a dual negotiation mechanism, and negotiate the disturbance information of the charging and discharging channels through a preset charging and discharging mode;

[0008] S3. Send disturbance information to the charging station centralized control platform via trickle transmission mode based on power line carrier communication technology;

[0009] S4. The charging station centralized control platform corrects the predicted adjustable margin based on the disturbance information at each time section to obtain the target adjustable margin;

[0010] S5. Determine a channel correction factor for each charging and discharging collaborative entity based on the demand-supply relationship and the target adjustable margin; and determine an orderly charging and discharging strategy for each charging and discharging collaborative entity by correcting the disturbance information using the channel correction factor.

[0011] Preferably, the method of predicting the adjustable margin of the current charging station in a time section according to the spatiotemporal distribution characteristics of the user's charging and discharging behavior comprises the following steps:

[0012] S11, obtaining temporal and spatial distribution characteristic data of household charging and discharging behaviors to construct a charging and discharging feature vector, wherein the charging and discharging feature vector includes: temporal characteristics, spatial characteristics, behavioral characteristics, and environmental characteristics;

[0013] S12. Based on historical electricity price data, pre-divide 24 hours into multiple electricity price intervals, and determine the electricity price elasticity coefficient based on the change in charging and discharging demand and the change in electricity price in combination with a sliding window algorithm;

[0014] S13. Expand or shrink the electricity price range according to the electricity price elasticity coefficient to obtain a set of time sections, and perform cluster analysis on the charge and discharge feature vectors within each time section according to the charging mode to determine a charging mode cluster;

[0015] S14. An improved long short-term memory network fused with attention mechanism is used to construct an adjustable margin prediction model. The historical load sequence and the charging mode cluster corresponding to the section are used as inputs. The predicted power supply and predicted power consumption are output and combined with the reserved safety margin to calculate the predicted adjustable margin corresponding to each section.

[0016] Preferably, the method of determining the charge and discharge channels of each charge and discharge cooperative subject through a dual negotiation mechanism and negotiating the disturbance information of the charge and discharge channels through a preset charge and discharge mode comprises the following steps:

[0017] S21. Select the minimum of the upper limit of the power that the charging pile can supply and the upper limit of the power that the electric vehicle can receive as the upper limit of the power regulation of the charging channel; and select the minimum of the upper limit of the power that the charging pile can receive and the upper limit of the power that the electric vehicle can feed as the lower limit of the power regulation of the discharge channel;

[0018] S22: Using the difference between the power adjustment upper limit of the charging channel and the minimum safe charging power as the power adjustment range for the charging channel; and using the difference between the power adjustment upper limit of the discharging channel and the minimum safe discharging power as the power adjustment range for the discharging channel;

[0019] S23. Obtain a preset charge and discharge mode of the electric vehicle, determine a charge and discharge period and a corresponding target charge and discharge power according to time sensitivity and electricity price sensitivity, and adjust the preset charge and discharge mode according to the charge and discharge period and the corresponding charge and discharge power to determine a target charge and discharge mode;

[0020] S24, determining a power fluctuation range corresponding to the current time section according to a vector difference between the current charge and discharge power corresponding to the preset charge and discharge mode and the target charge and discharge power corresponding to the target charge and discharge mode;

[0021] S25. Construct disturbance information based on the current power consumption status of the electric vehicle, power fluctuation range, current charging and discharging power, power regulation caliber, time sensitivity, and electricity price sensitivity.

[0022] Preferably, the method of determining the charging and discharging period and the corresponding target charging and discharging power according to the time sensitivity and the electricity price sensitivity, and adjusting the preset charging and discharging mode according to the charging and discharging period and the corresponding charging and discharging power to determine the target charging and discharging mode comprises the following steps:

[0023] S231, determining time sensitivity based on the expected charge and discharge start time and end time set by the user and the current time; determining electricity price sensitivity based on the real-time electricity price and the user's psychologically expected electricity price;

[0024] S232. Determine the real-time load rate of the power grid based on the rated capacity of the charging station transformer and the current load capacity; determine the load adjustment coefficient based on whether the real-time load rate of the power grid falls within a preset range; and determine the comprehensive sensitivity based on time sensitivity, electricity price sensitivity, the load adjustment coefficient, and their corresponding weight coefficients.

[0025] S233, sorting the time sections in descending order according to the comprehensive sensitivity, and selecting the first n time sections with high sensitivity as candidate charge and discharge periods;

[0026] S234: Select the minimum of the upper limit of the charging channel power adjustment, the maximum allowable charging power, and the theoretical charging power required for the user's desired state of charge as the target charging power; select the minimum of the lower limit of the discharging channel power adjustment, the maximum allowable discharge power, and the maximum discharge power allowed by the remaining battery capacity as the target discharge power, and construct the target charge and discharge power for the current time section based on the target charging power and target discharge power;

[0027] S235. Compare the candidate charge and discharge time periods and their corresponding target charge and discharge powers with the preset charge and discharge mode. If the charge and discharge power of the candidate time period is within the power adjustment range of the corresponding time section of the preset charge and discharge mode, directly replace the corresponding parameters in the preset charge and discharge mode to determine the target charge and discharge mode; if it exceeds the adjustment range, proportionally adjust the target charge and discharge power according to the upper limit of the power adjustment range to determine the target charge and discharge mode, where the ratio is the ratio of the upper limit of the adjustment range to the candidate target power.

[0028] Preferably, the method of sending the disturbance information to the charging station centralized control platform in a trickle transmission mode based on the power line carrier communication technology comprises the following steps:

[0029] S31. Generate a virtual identity code using the identity codes of the charging pile and electric vehicle corresponding to the charging and discharging collaborative entity;

[0030] S32, packaging the disturbance information into a plurality of trickle information blocks to be encrypted according to the fluctuation range of the charge and discharge power;

[0031] S33, dividing the virtual identity code according to the size and number of the Trickle information blocks to obtain virtual identity subcodes, and configuring the number information of each Trickle information block for each virtual identity subcode;

[0032] S34. Encrypt the corresponding trickle information block using the virtual identity subcode and its corresponding number information, and then send it to the charging station centralized control platform via power line carrier communication technology.

[0033] Preferably, the step of packaging the disturbance information into a plurality of trickle information blocks to be encrypted according to the fluctuation range of the charge and discharge power comprises the following steps:

[0034] S321, mapping the charging and discharging power fluctuation range to a three-dimensional feature space to construct a fluctuation feature space mapping model; constructing a transmission reliability model based on the power line carrier communication characteristics of OFDM modulation;

[0035] S322. Taking maximizing data transmission efficiency as the objective function, constructing a single-packet data volume constraint, a minimum transmission interval constraint, and a reliability constraint for the objective function; solving the objective function using the Lagrange multiplier method to obtain the optimal single-packet data volume and the minimum transmission interval;

[0036] S323: Pack the disturbance information into a plurality of trickle information blocks to be encrypted, taking the optimal single-packet data amount as a unit.

[0037] As a preference, the transmission reliability model The formula form is as follows:

[0038] ,

[0039] Among them, d is the data block size, t is the data sending interval, 、 are the power line channel attenuation coefficients, 、 are the volatility impact factors, is the OFDM subcarrier utilization correction coefficient, is the charge and discharge power fluctuation.

[0040] Preferably, the charging station centralized control platform corrects the predicted adjustable margin according to the disturbance information at each time section to obtain the target adjustable margin; the steps include:

[0041] S41. The charging station centralized control platform generates a virtual identity code based on the handshake information with the charging and discharging collaborative entity, and divides the virtual identity code into several virtual identity sub-codes based on the source and number of the trickle information blocks. The trickle information blocks are decrypted using the virtual identity sub-codes and sequenced according to the numbering information to obtain the disturbance information for each time section.

[0042] S42. Determine a charge-discharge correction factor based on the current charge-discharge power, time sensitivity, and electricity price sensitivity in the disturbance information at each time section, combined with the charge-discharge rated power; determine a real-time load rate adjustment factor based on the current charge-discharge power and the charge-discharge rated power; and calculate a grid safety factor based on the real-time grid frequency deviation and voltage deviation.

[0043] S43. Correct the predicted adjustable margin according to the charge and discharge correction factor, the load rate adjustment factor, the grid safety factor, and the current charge and discharge power to obtain a target adjustable margin.

[0044] Preferably, the method of determining a channel correction factor for each charge-discharge coordination subject according to the demand-supply relationship and the target adjustable margin; and correcting the disturbance information by the channel correction factor to determine an orderly charge-discharge strategy for each charge-discharge coordination subject comprises the following steps:

[0045] S51. Calculate the vector value of the difference between the actual power consumption and the power regulation caliber of the charging and discharging cooperative entities corresponding to the time section to determine candidate entities for pre-regulation that participate in power regulation of the charging station, where the sign of the vector value represents the direction of power flow, with positive indicating charging and negative indicating discharging.

[0046] S52. Determine the grid stability contribution based on the grid frequency deviation and voltage fluctuation value; determine the capacity regulation participation based on the historical charge and discharge regulation response rate and regulation amplitude ratio; determine the grid fluctuation smoothness based on the grid power fluctuation rate and fluctuation amplitude during the charge and discharge period;

[0047] S53, determining the regulation priority weight factor of the corresponding pre-regulation candidate subject by performing weighted summation on the grid stability contribution, capacity regulation participation, and fluctuation smoothness and their corresponding weight factors;

[0048] S54, determining a control amount for each pre-control candidate subject based on the control priority weight factor, the power control caliber, and the actual power consumption; using the control amount as a channel correction factor for the corresponding charge and discharge channel to generate a channel correction factor sequence corresponding to each charge and discharge channel;

[0049] S55. Using the target adjustable margin as a constraint boundary, sequentially extracting the corresponding control amount for each time period in the correction factor sequence to expand or reduce the current charge and discharge power to guide the charge and discharge coordination subject to perform orderly charge and discharge.

[0050] In a second aspect, a technical solution provided in an embodiment of the present invention is: an orderly charging and discharging system for an electric vehicle, comprising:

[0051] Prediction module: predicts the adjustable margin of the current charging station in the time section based on the spatiotemporal distribution characteristics of the user's charging and discharging behavior;

[0052] Negotiation module: Determines the charging and discharging channels of each charging and discharging collaborative entity through a dual negotiation mechanism, and negotiates the disturbance information of the charging and discharging channels through preset charging and discharging modes;

[0053] Interaction module: Based on power line carrier communication technology, it sends disturbance information to the charging station centralized control platform through trickle transmission mode;

[0054] Correction module: Corrects the predicted adjustable margin according to the disturbance information at each time section to obtain the target adjustable margin;

[0055] Execution module: Determine the channel correction factor of each charging and discharging collaborative entity based on the demand-supply relationship and the target adjustable margin; use the channel correction factor to correct the disturbance information to determine the orderly charging and discharging strategy of each charging and discharging collaborative entity.

[0056] In a third aspect, a technical solution provided in an embodiment of the present invention is: an electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps of the orderly charging and discharging method of an electric vehicle based on power line carrier communication are implemented.

[0057] In a fourth aspect, a technical solution provided in an embodiment of the present invention is: a storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are loaded and executed by a processor, the steps of a method for orderly charging and discharging of electric vehicles based on power line carrier communication are implemented.

[0058] Beneficial effects of the present invention:

[0059] (1) To address the problem that the temporal and spatial randomness of user charging and discharging behavior causes the prediction of the adjustable margin to deviate seriously from reality, this application constructs a charging and discharging feature vector covering time, space, behavior and environmental characteristics, optimizes the time section division based on the electricity price elasticity coefficient, and outputs the initial margin in combination with the prediction model of the improved LSTM fusion attention mechanism; then, with real-time disturbance information (such as power fluctuation range, sensitivity parameters) as input, through multi-dimensional dynamic correction of charging and discharging correction factors, load rate adjustment factors and grid safety factors, the predicted adjustable margin is adaptively tracked by user behavior fluctuations and grid state changes, significantly reducing the prediction deviation rate and improving the robustness of grid dispatching decisions;

[0060] (2) To address the problem of communication real-time, integrity and security defects caused by strong electromagnetic noise in charging stations, this application utilizes the inherent anti-electromagnetic interference characteristics of the power line channel and packages the disturbance information in blocks through a virtual identity code segmentation encryption and numbering mechanism; based on the transmission reliability model mapped with the power fluctuation range (including the channel attenuation coefficient and the OFDM subcarrier utilization correction coefficient), the single packet data volume and the sending interval are optimized to maximize data transmission efficiency and meet reliability constraints, ensuring that data maintains transmission integrity in a low signal-to-noise ratio environment caused by power fluctuations. At the same time, the virtual identity authentication mechanism cuts off the leakage path of sensitive information, suppressing data congestion and interception risks from the root;

[0061] (3) To address the problem of dynamic power regulation misalignment caused by the coupling of spatiotemporal randomness and communication defects, this application uses a dual negotiation mechanism to dynamically match the charging and discharging channel caliber and generate disturbance information based on the equipment power upper limit and safety margin. Based on the target adjustable margin, the channel correction factor sequence is calculated by adjusting the priority weight factors (including grid stability contribution, capacity regulation participation, and fluctuation smoothness) to perform vector correction on the disturbance. Driven by the demand-supply relationship, the charging and discharging collaborative entities are guided to perform power reduction / expansion operations in sequence, effectively smoothing local load peaks, reducing grid frequency deviation and voltage fluctuation amplitude, and solving the coupling problem of randomness and communication defects.

[0062] The above content of the invention is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Other features, objects, and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are provided for the purpose of illustrating preferred embodiments only and are not to be construed as limiting the present invention. Like reference characters are used throughout the drawings to designate like parts.

[0064] Figure 1 The flowchart of the method for orderly charging and discharging of electric vehicles based on power line carrier communication of the present invention is shown.

[0065] Figure 2 The present invention provides a flow chart for generating disturbance information.

[0066] Figure 3 This is a flow chart of the trickle transmission of disturbance information of the present invention.

[0067] Figure 4 A flow chart is generated for the ordered charge and discharge strategy of the present invention.

[0068] Figure 5 This is a structural block diagram of the orderly charging and discharging system for electric vehicles of the present invention. DETAILED DESCRIPTION

[0069] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0070] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be performed in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operations are completed, but can also have additional steps not included in the figures; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0071] The field of orderly electric vehicle charging and discharging technology faces two technical bottlenecks: First, user charging and discharging behavior exhibits random fluctuations in time, influenced by factors such as vehicle pickup times and electricity price sensitivity. In the spatial dimension, localized load peaks are formed due to the clustering of charging hotspots. This makes it difficult for traditional adjustable margin prediction methods based on single historical data to effectively capture the dynamic correlations in spatiotemporal distribution characteristics. This results in significant deviations between predicted values and actual operating conditions, seriously impacting the accuracy and reliability of grid dispatch. Second, the strong electromagnetic noise generated by high-power charging and discharging at charging stations presents three core challenges for traditional wireless communication technologies: power fluctuations cause a sharp drop in the channel signal-to-noise ratio, compromising the transmission integrity of key parameters such as disturbance information; fixed frame length protocols cannot adapt to varying power fluctuations, easily causing data congestion during high-load periods; and centralized communication architectures carry the risk of sensitive information being intercepted, making it difficult to meet the stringent communication requirements for real-time, security, and anti-interference performance required for real-time grid control. These intertwined technical bottlenecks make it difficult for existing charging and discharging control strategies to dynamically match user behavior with grid operating conditions, creating a vicious cycle of prediction inaccuracy, communication failure, and control instability.

[0072] Example 1:

[0073] This application addresses the common challenges in the above-mentioned industries by building an integrated solution that integrates spatiotemporal behavior prediction, anti-interference communication, and dynamic charge and discharge control, thereby overcoming the coupling interference between spatiotemporal randomness and communication defects. This provides an innovative technical path for improving the dynamic power regulation accuracy of charging stations and balancing grid stability with user needs. Figure 1 As shown, the orderly charging and discharging method of an electric vehicle based on power line carrier communication includes the following steps:

[0074] S1. Predict the adjustable margin of the current charging station in the time section based on the spatiotemporal distribution characteristics of the user's charging and discharging behavior.

[0075] As an optional embodiment, the method of predicting the predicted adjustable margin of the current charging station on a time section based on the spatiotemporal distribution characteristics of the user's charging and discharging behavior includes the following steps:

[0076] S11. Acquire temporal and spatial distribution characteristic data of household charging and discharging behaviors to construct a charging and discharging feature vector, wherein the charging and discharging feature vector includes: temporal characteristics, spatial characteristics, behavioral characteristics, and environmental characteristics.

[0077] For example, through the sensors and user account systems deployed in the charging facilities, the time characteristics (corresponding to the flat electricity price interval), spatial characteristics (located in the high-load area of the commercial district, with dense office buildings around), behavioral characteristics (users have a short single charging time and a high daily charging frequency, mostly for commuting and recharging needs) and environmental characteristics (battery charging efficiency decreases during high temperatures in summer, resulting in a 10%-15% extension of charging time) of the morning peak period (7:00-9:00) are collected in real time to form a charging and discharging feature vector, thereby characterizing the "high frequency, short duration, and temperature-sensitive" charging behavior pattern during this period.

[0078] S12. Based on historical electricity price data, 24 hours are pre-divided into multiple electricity price intervals, and the electricity price elasticity coefficient is determined according to the change in charging and discharging demand and the change in electricity price in combination with a sliding window algorithm.

[0079] For example, based on the charging station's historical data from the past three months, a 24-hour period is pre-divided into three basic intervals: peak (8:00-12:00, 17:00-22:00), flat (6:00-8:00, 12:00-17:00), and valley (22:00-6:00 the following day). Analysis using a sliding window algorithm revealed that a 5% decrease in electricity prices during valley periods increases charging demand by 8% (with an elasticity coefficient of 1.6), while a 10% increase in electricity prices during peak periods only results in a 3% decrease in demand (with an elasticity coefficient of 0.3). Consequently, the valley period is further expanded into two sub-sections: deep valley (23:00-3:00 the following day) and shallow valley (22:00-23:00, 3:00-6:00) to accommodate users' strong sensitivity to low-priced electricity.

[0080] S13. Expand or shrink the electricity price range according to the electricity price elasticity coefficient to obtain a time section set, and perform cluster analysis on the charge and discharge feature vectors in each time section according to the charging mode to determine a charging mode cluster.

[0081] For example, within the "Deep Valley" section, cluster analysis of charge and discharge feature vectors identified two typical patterns: one is the "household user cluster" (charging start times are concentrated between 11:30 PM and 0:30 AM, single charges reach over 80% of battery capacity, and are highly sensitive to electricity prices), and the other is the "ride-hailing user cluster" (charging times are distributed between 1:00 AM and 4:00 AM, single charges reach approximately 50% of battery capacity, and they prioritize charging speed over electricity price). The behavioral differences between these two clusters provide a detailed input dimension for subsequent predictive models.

[0082] S14. An improved long short-term memory network fused with attention mechanism is used to construct an adjustable margin prediction model. The historical load sequence and the charging mode cluster corresponding to the section are used as inputs. The predicted power supply and predicted power consumption are output and combined with the reserved safety margin to calculate the predicted adjustable margin corresponding to each section.

[0083] For example, the historical load sequence (such as the actual charging power curve during the valley period of the past week) and the above-mentioned charging mode cluster labels are input into the improved LSTM network, and the key features are automatically identified through the attention mechanism. For example, when it is detected that the proportion of "home user cluster" exceeds 60% and the next day is a weekday, the model will strengthen the weight allocation of the "load surge from 23:00 to 0:00 in the evening" feature, output the predicted power supply (the sum of the rated power of the charging piles) and consumption (the sum of the predicted demand of the clusters), and combine it with the reserved safety margin of the power grid (such as 15%) to finally generate the predicted adjustable margin corresponding to the section (such as the upper limit of the available charging power is 85% of the rated power).

[0084] This embodiment collects spatiotemporal distribution data of user charging and discharging behaviors (such as high-frequency charging demand at urban charging stations during the morning peak period and concentrated charging behavior at suburban household charging piles during the evening off-peak period) to construct a charging and discharging feature vector that includes time characteristics (such as peak and off-peak periods of electricity prices, weekday / holiday cycles), spatial characteristics (such as geographical coordinates of charging stations, regional load density), behavioral characteristics (such as single charging duration, average daily charging frequency), and environmental characteristics (such as the impact of temperature on battery charging efficiency and the suppression of travel demand by rainfall). The complex attributes of user behavior are converted into computable digital representations. Based on historical electricity price data, 24 hours are divided into several basic electricity price intervals (such as peak, flat, and off-peak periods). The elastic correlation between the change in charging and discharging demand and electricity price fluctuations in different periods is dynamically calculated through a sliding window algorithm (such as the sensitive response of a 15% drop in charging demand when the electricity price rises by 10% in a certain period, which can be analyzed through historical data). The method then dynamically expands or contracts the electricity price range (e.g., dividing the commuting period, which is sensitive to electricity prices, into multiple sub-sections) based on the data. Cluster analysis is then performed on the charge and discharge feature vectors within each time section to identify clusters of highly elastic charging patterns (e.g., users who charge only when the electricity price is below the capacity threshold) and clusters of low-elastic charging patterns (e.g., users who charge urgently but are insensitive to electricity prices). Furthermore, an improved long short-term memory (LSTM) network is integrated with an attention mechanism to construct a prediction model. This model takes historical load sequences (e.g., the actual charge and discharge power in each period of the past week) and charging pattern cluster labels as inputs. The attention mechanism automatically focuses on historical data segments that are highly relevant to the current section characteristics (e.g., identifying the load suppression patterns of suburban charging stations caused by heavy rain). The model then outputs predicted electricity supply and consumption that takes into account differences in user behavior. Furthermore, the model generates a predicted adjustable margin for each time section, combined with the reserved safety margin required for safe grid operation. This embodiment achieves systematic modeling of the spatiotemporal randomness of user charging and discharging behaviors through the technical design route of feature vector construction, dynamic division of time sections, pattern clustering, and model prediction. It breaks through the limitations of traditional static prediction methods based on single historical data, provides a margin assessment basis that is more in line with actual working conditions for real-time grid scheduling, and significantly improves the dynamic adaptability of charging station load forecasting and the reliability of grid operation.

[0085] S2. Determine the charging and discharging channels of each charging and discharging cooperative subject through a dual negotiation mechanism, and negotiate the disturbance amount information of the charging and discharging channels through a preset charging and discharging mode.

[0086] As an optional embodiment, the charging and discharging channels of each charging and discharging cooperative subject are determined through a dual negotiation mechanism, and the disturbance amount information of the charging and discharging channels is negotiated through a preset charging and discharging mode; Figure 2 As shown, the following steps are included:

[0087] S21. Select the minimum of the upper limit of the power that the charging pile can supply and the upper limit of the power that the electric vehicle can receive as the upper limit of the power regulation of the charging channel; and select the minimum of the upper limit of the power that the charging pile can receive and the upper limit of the power that the electric vehicle can feed as the lower limit of the power regulation of the discharge channel;

[0088] S22: Using the difference between the power adjustment upper limit of the charging channel and the minimum safe charging power as the power adjustment range for the charging channel; and using the difference between the power adjustment upper limit of the discharging channel and the minimum safe discharging power as the power adjustment range for the discharging channel;

[0089] S23. Obtain a preset charge and discharge mode of the electric vehicle, determine a charge and discharge period and a corresponding target charge and discharge power according to time sensitivity and electricity price sensitivity, and adjust the preset charge and discharge mode according to the charge and discharge period and the corresponding charge and discharge power to determine a target charge and discharge mode;

[0090] S24, determining a power fluctuation range corresponding to the current time section according to a vector difference between the current charge and discharge power corresponding to the preset charge and discharge mode and the target charge and discharge power corresponding to the target charge and discharge mode;

[0091] S25. Construct disturbance information based on the current power consumption status of the electric vehicle, power fluctuation range, current charging and discharging power, power regulation caliber, time sensitivity, and electricity price sensitivity.

[0092] It can be understood that this embodiment adopts a dual negotiation mechanism that dynamically couples device power constraints with user needs. In the charging and discharging channel establishment phase, the hardware power limits of the charging pile and the electric vehicle are used as safety boundaries (for example, the upper limit of the power supply of a household charging pile is 7kW, and the upper limit of the power receiving power of the electric vehicle is 6.6kW, then the upper limit of the charging channel power adjustment is 6.6kW). Combined with the minimum safe charging power (such as 2kW), the power adjustment caliber (4.6kW) is calculated to define a physically feasible range for the elastic adjustment of the charging and discharging power; in the charging and discharging mode negotiation phase, for different user types (such as electricity price-sensitive household users and time-sensitive online car-hailing drivers), time sensitivity (time sensitivity = (user-set expected charging end start time - when The system dynamically adjusts the preset charging and discharging modes based on a quantitative assessment of the ratio of the real-time electricity price (the ratio of the previous time) to the preset maximum time difference threshold and electricity price sensitivity (electricity price sensitivity = the ratio of |real-time electricity price - user's psychologically expected electricity price| to the maximum electricity price deviation threshold). For example, the preset mode for users sensitive to electricity prices during off-peak hours can be adjusted to "charging at maximum power from 11:00 PM to 3:00 AM the next day," while the mode for users in urgent need of charging during peak hours prioritizes the time target of "charging to 80% within 30 minutes." The power fluctuation range of the current section is determined by calculating the power vector difference between the preset mode and the target mode (for example, the increment from 3 kW to 6 kW is +3 kW). Disturbance information is then constructed by integrating power usage status (charging / discharging), power adjustment caliber, and sensitivity parameters. Through the technical design route of "hardware security constraints, user demand mapping, mode dynamic calibration, and disturbance information generation", this mechanism has achieved the upgrade of the charging and discharging channel from "fixed power configuration" to "coordinated regulation of users, equipment, and power grids". It not only avoids the risk of equipment overload, but also dynamically generates accurate disturbance information based on user behavior characteristics, providing underlying data support with both security and flexibility for subsequent anti-interference communications and power grid regulation, and effectively solving the industry problem that power regulation lags behind changes in user demand under the traditional single negotiation mode.

[0093] As an optional embodiment, determining the charging and discharging period and the corresponding target charging and discharging power according to the time sensitivity and the electricity price sensitivity, and adjusting the preset charging and discharging mode according to the charging and discharging period and the corresponding charging and discharging power to determine the target charging and discharging mode includes the following steps:

[0094] S231, determining time sensitivity based on the expected charge and discharge start time and end time set by the user and the current time; determining electricity price sensitivity based on the real-time electricity price and the user's psychologically expected electricity price;

[0095] S232. Determine the real-time load rate of the power grid based on the rated capacity of the charging station transformer and the current load capacity; determine the load adjustment coefficient based on whether the real-time load rate of the power grid falls within a preset range; and determine the comprehensive sensitivity based on time sensitivity, electricity price sensitivity, the load adjustment coefficient, and their corresponding weight coefficients.

[0096] S233, sorting the time sections in descending order according to the comprehensive sensitivity, and selecting the first n time sections with high sensitivity as candidate charge and discharge periods;

[0097] S234: Select the minimum of the upper limit of the charging channel power adjustment, the maximum allowable charging power, and the theoretical charging power required for the user's desired state of charge as the target charging power; select the minimum of the lower limit of the discharging channel power adjustment, the maximum allowable discharge power, and the maximum discharge power allowed by the remaining battery capacity as the target discharge power, and construct the target charge and discharge power for the current time section based on the target charging power and target discharge power;

[0098] S235. Compare the candidate charge and discharge time periods and their corresponding target charge and discharge powers with the preset charge and discharge mode. If the charge and discharge power of the candidate time period is within the power adjustment range of the corresponding time section of the preset charge and discharge mode, directly replace the corresponding parameters in the preset charge and discharge mode to determine the target charge and discharge mode; if it exceeds the adjustment range, proportionally adjust the target charge and discharge power according to the upper limit of the power adjustment range to determine the target charge and discharge mode, where the ratio is the ratio of the upper limit of the adjustment range to the candidate target power.

[0099] It is understood that in steps S231-S232, this embodiment uses a time difference algorithm and a price deviation model to convert the user's subjective intention into a computable sensitivity index. For example, if a user sets their electric vehicle to charge between 22:00 and 24:00 (with a desired start time of 22:00), and the current time is 21:30, the 30-minute time difference is normalized and mapped to high time sensitivity (approaching the upper threshold). The deviation between the real-time electricity price of 0.5 yuan / kWh and the user's expected value of 0.45 yuan / kWh is calculated using the absolute value to quantify the price sensitivity. Furthermore, the system uses the operating status of the charging station's transformer (e.g., a rated capacity of 1250kVA, a current load of 900kVA, and a load factor of 72%, falling into the "medium load" category) to match the corresponding load adjustment coefficient (e.g., a coefficient of 0.9 for medium load). A comprehensive sensitivity (e.g., a comprehensive value of 0.82 for a specific section) is calculated using a preset weighting matrix (e.g., a time sensitivity weight of 0.4, a price sensitivity weight of 0.3, and a load adjustment coefficient of 0.3). In S233-S235, the 24-hour time period is sorted based on comprehensive sensitivity, with the top three highly sensitive periods (e.g., 22:00-23:00, 23:00-24:00, and 7:00-8:00) being prioritized as candidate charging and discharging periods. The target charging and discharging power is determined based on the device power limit and the user's state of charge requirements (e.g., a charging channel upper limit of 6.6kW, a user's power requirement of 5kW to charge to 80% in one hour, a maximum allowable charging power of 6kW, and a minimum of 5kW). If the candidate period power (e.g., 5kW) is within the preset mode's regulation range (e.g., 2-6kW), the mode parameters are updated directly. If it exceeds (e.g., the candidate power is 7kW), it is scaled to 6kW based on the ratio of the upper limit of 6kW to the candidate power (6 / 7), ensuring that power regulation remains within the device's safety range.

[0100] This embodiment uses the technical design route of "digitalization of demand characteristics, indexation of grid status, and multi-objective dynamic calibration" to achieve an upgrade of the charging and discharging mode from "fixed preset" to "adaptive adjustment driven by user behavior and grid status." This not only ensures user charging convenience, but also avoids the risk of grid overload through load rate constraints, providing cross-domain collaborative technical support for the precise generation of orderly charging and discharging strategies.

[0101] S3. Based on the power line carrier communication technology, the disturbance information is sent to the charging station centralized control platform through the trickle transmission mode.

[0102] As an optional embodiment, the power line carrier communication technology is used to send the disturbance information to the charging station centralized control platform through the trickle transmission mode; Figure 3 As shown, the following steps are included:

[0103] S31. Generate a virtual identity code using the identity codes of the charging pile and electric vehicle corresponding to the charging and discharging collaborative entity;

[0104] S32, packaging the disturbance information into a plurality of trickle information blocks to be encrypted according to the fluctuation range of the charge and discharge power;

[0105] S33, dividing the virtual identity code according to the size and number of the Trickle information blocks to obtain virtual identity subcodes, and configuring the number information of each Trickle information block for each virtual identity subcode;

[0106] S34. Encrypt the corresponding trickle information block using the virtual identity subcode and its corresponding number information, and then send it to the charging station centralized control platform via power line carrier communication technology.

[0107] It can be understood that this embodiment adopts an anti-interference communication mechanism of virtual identity dynamic encryption and data block transmission. In S31, the unique identification code of the charging pile (such as device ID: CN-CHARGER-001) and the electric vehicle VIN code (such as LVSHC6DF0FC001234) are combined through a hash algorithm to generate a virtual identity code (such as VF-8A3C5D2E), thereby decoupling the physical device and the communication identity and blocking the direct association path of sensitive information. In S32-S33, a transmission reliability model is constructed based on the OFDM (Orthogonal Frequency Division Multiplexing) modulation characteristics, targeting the charging and discharging power fluctuation range (e.g., power fluctuation of ±2kW during a certain period). The Lagrange multiplier method is used to determine the optimal single-packet data size (e.g., splitting the disturbance information into 1024-byte blocks) and the minimum transmission interval (e.g., 50ms), generating a sequence of Trickle information blocks. Simultaneously, the virtual identity code is segmented into 10 subcodes (e.g., VF-8A, VF-3C, etc.) based on the number of information blocks (e.g., 10 blocks). Each subcode is assigned a unique number (1-10), establishing a dynamic binding relationship between subcode, number, and information block. In S34, the AES encryption algorithm is used, using the virtual identity subcode as the key and the number as the vector parameter. Each Trickle information block is encrypted packet by packet (e.g., information block 3 is encrypted using subcode VF-5D and number 3 to generate a dynamic key). The encrypted information is then transmitted to the centralized control platform via the Orthogonal Frequency Division Multiplexing channel of the power line carrier communication (PLC).

[0108] In this embodiment, the designed anti-interference communication mechanism disassembles the complete data frame of traditional centralized communication into micro data blocks adapted to the power fluctuation characteristics through the technical design route of "identity virtualization, data fragmentation, and dynamic encryption". It utilizes the inherent anti-electromagnetic interference characteristics of the power line channel and combines the dual encryption factors of the virtual identity subcode and the data block number to achieve safe and reliable transmission of disturbance quantity information in a strong electromagnetic noise environment, breaking through the transmission bottleneck of the fixed frame length protocol during high-load periods, and avoiding the risk of sensitive information being intercepted and analyzed, providing highly robust communication link support for real-time power grid regulation.

[0109] As an optional embodiment, the step of packaging the disturbance information into a plurality of trickle information blocks to be encrypted according to the fluctuation range of the charge and discharge power includes the following steps:

[0110] S321, mapping the charging and discharging power fluctuation range to a three-dimensional feature space to construct a fluctuation feature space mapping model; constructing a transmission reliability model based on the power line carrier communication characteristics of OFDM modulation;

[0111] S322. Taking maximizing data transmission efficiency as the objective function, constructing a single-packet data volume constraint, a minimum transmission interval constraint, and a reliability constraint for the objective function; solving the objective function using the Lagrange multiplier method to obtain the optimal single-packet data volume and the minimum transmission interval;

[0112] S323: Pack the disturbance information into a plurality of trickle information blocks to be encrypted, taking the optimal single-packet data amount as a unit.

[0113] It can be understood that in S321, the charging and discharging power fluctuation range (e.g., a fluctuation amplitude of ±3kW during peak hours) is mapped to a three-dimensional feature space consisting of "power change rate-fluctuation frequency-duration," constructing a fluctuation feature space mapping model to achieve an abstract representation of power fluctuations from the time domain to the multidimensional feature domain. Simultaneously, based on the characteristics of OFDM-modulated power line carrier communication, a transmission reliability model is established to quantify the relationship between parameters such as data block size, transmission interval, and power fluctuation. In S322, with the goal of maximizing data transmission efficiency, constraints are established, including an upper limit on the single-packet data volume (e.g., 2048 bytes), a lower limit on the minimum transmission interval (e.g., 10ms), and a reliability threshold R (e.g., R ≥ 0.9). The optimal single-packet data volume (e.g., splitting large-scale disturbance information into 1500 bytes / block) and the minimum transmission interval (e.g., 30ms) are solved using the Lagrange multiplier method, achieving dynamic coupling of power fluctuation characteristics and communication transmission parameters. In S323, the disturbance information is divided into several trickle information blocks according to the optimal single-packet data volume (for example, the fluctuation information in a certain period is divided into 8 data blocks) to ensure the transmission integrity of each information block in a strong electromagnetic noise environment.

[0114] In this embodiment, through the technical design route of "multi-dimensional modeling of fluctuation characteristics, mathematical optimization of communication parameters, and adaptive matching of data blocks", the adaptation limitations of traditional fixed-frame length communication protocols to power fluctuations are broken through, and the transmission efficiency and reliability of power line carrier communication in dynamic power scenarios are coordinated and optimized. It provides theoretical and algorithmic support for the real-time and safe transmission of disturbance information in the strong electromagnetic environment of charging stations, and significantly improves the robustness of the communication link to complex working conditions.

[0115] As an optional embodiment, the transmission reliability model The formula form is as follows:

[0116] ,

[0117] Among them, d is the data block size, t is the data sending interval, 、 are the power line channel attenuation coefficients, where Reflects the degree of attenuation of the power line channel on the data block size, Reflects the degree of attenuation of the power line channel on the data transmission interval; 、 are the volatility impact factors, Used to adjust the impact of charging and discharging power fluctuations and related factors on transmission reliability. As a compensation term for the charge and discharge power fluctuation, is the OFDM subcarrier utilization correction coefficient, is the charge and discharge power fluctuation.

[0118] It can be understood that the transmission reliability model uses the Lagrange multiplier method to solve the optimal single-packet data volume and the minimum sending interval, and decomposes the disturbance information into trickle information blocks that adapt to the power fluctuation range (such as automatically optimizing the data block size to 1024 bytes and the sending interval to 50ms in the power fluctuation ±2kW scenario), maintaining transmission integrity in a strong electromagnetic noise environment; by dynamically adjusting the transmission parameters, the coordinated optimization of data transmission efficiency and reliability is achieved, breaking through the data congestion bottleneck of the fixed frame length protocol during high-load periods, and at the same time utilizing the inherent anti-interference characteristics of the power line channel to control the bit error rate within the industry safety threshold, providing underlying communication guarantees for the real-time and accurate transmission of charging and discharging control instructions, and significantly improving the stability of the system in complex electromagnetic environments.

[0119] S4. The charging station centralized control platform corrects the predicted adjustable margin according to the disturbance information at each time section to obtain the target adjustable margin.

[0120] As an optional embodiment, the charging station centralized control platform corrects the predicted adjustable margin according to the disturbance information at each time section to obtain the target adjustable margin; the method includes the following steps:

[0121] S41. The charging station centralized control platform generates a virtual identity code based on the handshake information with the charging and discharging collaborative entity, and divides the virtual identity code into several virtual identity sub-codes based on the source and number of the trickle information blocks. The trickle information blocks are decrypted using the virtual identity sub-codes and sequenced according to the numbering information to obtain the disturbance information for each time section.

[0122] S42. Determine a charge-discharge correction factor based on the current charge-discharge power, time sensitivity, and electricity price sensitivity in the disturbance information at each time section, combined with the charge-discharge rated power; determine a real-time load rate adjustment factor based on the current charge-discharge power and the charge-discharge rated power; and calculate a grid safety factor based on the real-time grid frequency deviation and voltage deviation.

[0123] S43. Correct the predicted adjustable margin according to the charge and discharge correction factor, the load rate adjustment factor, the grid safety factor, and the current charge and discharge power to obtain a target adjustable margin.

[0124] It can be understood that this embodiment adopts a prediction and calibration mechanism of dynamic decryption of virtual identity and coupled correction of multi-dimensional parameters. In S41, the charging station control platform generates a dynamic virtual identity code based on the unique device identifier and timestamp through the initial handshake protocol with the charging and discharging collaborative entity (such as combining the charging pile ID "CP-001" with the electric vehicle VIN code and performing a SHA-256 hash operation), and divides the virtual identity code into a corresponding number of subcodes (each subcode carries specific segmentation information of the original code) according to the number of trickle information blocks received (such as 8 information blocks transmitted in a certain period of time). Through the binding relationship of "subcode-number-information block" (such as the 5th information block corresponds to the 5th subcode), packet-by-packet decryption and sequence reorganization are realized to ensure that the disturbance information received from the strong electromagnetic noise environment (such as the power fluctuation of a section during the evening peak period +2kW, time sensitivity 0.75) is completely restored. In S42, the charge and discharge correction factor is used to reflect the degree to which the actual power is close to the rated power (for example, the charge and discharge correction factor = (current charge and discharge power / charge and discharge rated power) × (1 + time sensitivity × w1 + electricity price sensitivity × w2), where w1 and w2 are weight coefficients, and w1 + w2 = 1). The load rate adjustment factor is determined by combining the preset range of the ratio of the rated capacity of the charging station transformer to the current load capacity (for example, the load rate of the charging station transformer reaches 85%) (for example, when the load is low (for example, the load rate is <50%), the adjustment factor is close to 1, allowing a higher degree of power adjustment freedom; when the load is high (for example, the load rate is ≥80%), the adjustment factor is significantly less than 1, limiting unnecessary charge and discharge power to avoid overload). At the same time, the grid safety factor (for example, the power grid safety factor) is calculated based on the frequency deviation (for example, ±0.15Hz) and voltage deviation (for example, ±3%) monitored in real time by the grid. Grid safety factor = normalized value of frequency deviation × h1 + normalized value of voltage deviation × h2, where h1 and h2 are weight coefficients respectively, and h1 + h2 = 1); in S43, the multi-dimensional parameters are dynamically coupled with the current charge and discharge power through a preset correction model (such as target margin = predicted margin × charge and discharge correction factor × load rate adjustment factor + grid safety factor × rated capacity); for example, if the initial predicted adjustable margin of a section is 200kW, after correction (charge and discharge correction factor 0.92, load rate adjustment factor 0.8, grid safety factor 0.12), it is adjusted to 200×0.92×0.8+0.12×500=191.2kW, forming a target adjustable margin that integrates user real-time behavior (such as temporary adjustment of charging time period), equipment operating status (such as charging pile power utilization rate) and grid operating conditions (such as local voltage fluctuations).

[0125] In this embodiment, through the technical design path of "communication security authentication, real-time data collection, and cross-domain factor calibration", the limitations of the traditional prediction model in responding to dynamic disturbances have been broken through, and the adjustable margin has been upgraded from "static estimation driven by historical data" to "dynamic correction driven by real-time multi-source information". It provides a more timely and accurate margin benchmark for power grid dispatching, and effectively improves the power regulation accuracy of charging stations in complex scenarios and the stability of power grid operation.

[0126] S5. Determine a channel correction factor for each charging and discharging collaborative entity based on the demand-supply relationship and the target adjustable margin; and determine an orderly charging and discharging strategy for each charging and discharging collaborative entity by correcting the disturbance information using the channel correction factor.

[0127] As an optional embodiment, the channel correction factor of each charge-discharge coordination subject is determined according to the demand-supply relationship and the target adjustable margin; the disturbance information is corrected by the channel correction factor to determine the orderly charge-discharge strategy of each charge-discharge coordination subject; Figure 4 As shown, the following steps are included:

[0128] S51. Calculate the vector value of the difference between the actual power consumption and the power regulation caliber of the charging and discharging cooperative entities corresponding to the time section to determine candidate entities for pre-regulation that participate in power regulation of the charging station, where the sign of the vector value represents the direction of power flow, with positive indicating charging and negative indicating discharging.

[0129] S52. Determine the grid stability contribution based on the grid frequency deviation and voltage fluctuation value; determine the capacity regulation participation based on the historical charge and discharge regulation response rate and regulation amplitude ratio; determine the grid fluctuation smoothness based on the grid power fluctuation rate and fluctuation amplitude during the charge and discharge period;

[0130] S53, determining the regulation priority weight factor of the corresponding pre-regulation candidate subject by performing weighted summation on the grid stability contribution, capacity regulation participation, and fluctuation smoothness and their corresponding weight factors;

[0131] S54, determining a control amount for each pre-control candidate subject based on the control priority weight factor, the power control caliber, and the actual power consumption; using the control amount as a channel correction factor for the corresponding charge and discharge channel to generate a channel correction factor sequence corresponding to each charge and discharge channel;

[0132] S55. Using the target adjustable margin as a constraint boundary, sequentially extracting the corresponding control amount for each time period in the correction factor sequence to expand or reduce the current charge and discharge power to guide the charge and discharge coordination subject to perform orderly charge and discharge.

[0133] It can be understood that this embodiment adopts a control mechanism that dynamically coordinates multi-dimensional priority evaluation and power regulation. In S51, by calculating the vector difference between the actual power consumption of the charging and discharging collaborative subjects (such as charging piles and electric vehicles) and the power regulation caliber (for example, the actual charging power of a charging pile is 4kW, the upper limit of the regulation caliber is 6kW, and the vector difference +2kW indicates that there is 2kW of charging expansion space), pre-regulation candidate subjects with power regulation potential are screened out, and the vector symbol clarifies the power flow direction (positive for charging and negative for discharging) to distinguish the control direction. In S52-S53, a three-dimensional evaluation system is constructed, including grid stability contribution (for example, the contribution score of the difference between the real-time frequency deviation and the voltage fluctuation value in the range of 0-1 is used as the grid stability contribution, such as a contribution score of 0.9 when the frequency deviation is ±0.1Hz), capacity regulation participation (capacity regulation participation is calculated based on the product of the historical regulation response rate and the proportion of the regulation amplitude), and fluctuation smoothness (fluctuation smoothness = g1 / (1+power fluctuation rate×fluctuation amplitude)+g2×grid stability contribution+g3×capacity regulation participation, where g1, g2, and g3 are weight coefficients, and g1+g2+g3=1). A weighted regulation priority weight factor (for example, a comprehensive weight of a candidate entity is 0.85) is generated through a preset weight matrix (such as 0.4:0.3:0.3), thereby achieving a cross-dimensional quantitative ranking of "grid stability contribution-user regulation capability-power fluctuation impact". In S54-S55, the control amount (e.g., 2kW×0.85=1.7kW) is calculated based on the weight factor, power regulation caliber (e.g., 6kW) and actual power (4kW), and a channel correction factor sequence is generated (e.g., the control amount in each time period is +1.7kW and +1.5kW, respectively). The current charging and discharging power is dynamically adjusted with the target adjustable margin (e.g., 262kW) as the constraint boundary. For example, when the charging power needs to be reduced at a certain section during peak hours, the subjects with low weight factors (e.g., non-emergency charging users) are given priority to be reduced according to the control amount (-1.2kW), while the subjects with high weight factors (e.g., bus charging stations that contribute highly to grid stability) maintain their original power, ensuring that the control process is smooth and does not exceed the margin boundary.

[0134] This embodiment achieves an intelligent upgrade of the charging and discharging strategy from "disorderly decentralized regulation" to "priority-driven, supply-demand coordination" through the technical design path of "potential subject screening - multi-dimensional indicator evaluation - weight-driven regulation - margin boundary constraint". It effectively smoothes local load peaks while ensuring the stability of grid frequency and voltage, solves the problem of power regulation inaccuracy caused by the coupling of user behavior randomness and communication defects, and significantly improves the global optimization capability of orderly charging and discharging of charging stations and the reliability of grid operation.

[0135] Example 2:

[0136] A technical solution also provided in the embodiment of the present invention is: an orderly charging and discharging system for electric vehicles, such as Figure 5 Shown, including:

[0137] Prediction module 101: predicts the adjustable margin of the current charging station in the time section according to the spatiotemporal distribution characteristics of the user's charging and discharging behavior;

[0138] Negotiation module 102: determines the charging and discharging channels of each charging and discharging cooperative subject through a dual negotiation mechanism, and negotiates the disturbance information of the charging and discharging channels through a preset charging and discharging mode;

[0139] Interaction module 103: Sending disturbance information to the charging station centralized control platform via trickle transmission mode based on power line carrier communication technology;

[0140] Correction module 104: Corrects the predicted adjustable margin according to the disturbance information at each time section to obtain a target adjustable margin;

[0141] Execution module 105: Determine a channel correction factor for each charge-discharge coordination entity based on the demand-supply relationship and the target adjustable margin; and correct the disturbance information using the channel correction factor to determine an orderly charge-discharge strategy for each charge-discharge coordination entity.

[0142] This embodiment has at least the following substantial technical effects: the prediction module accurately generates a time-section prediction with adjustable margin by integrating spatiotemporal behavioral characteristics with an improved LSTM model, addressing the problem of traditional prediction methods' insufficient response to the randomness of user behavior. The negotiation module dynamically matches charging and discharging channels based on device power limits and user sensitivity, ensuring both device safety and improving adaptability to user needs. The interaction module leverages the anti-interference characteristics of power line carrier communication and virtual identity encryption technology to ensure the secure and reliable transmission of disturbance information in strong electromagnetic environments. The correction module uses a multi-dimensional dynamic correction mechanism to enable the prediction margin to track user behavior and grid state changes in real time, significantly improving prediction accuracy. The execution module implements differentiated power control of charging and discharging coordination entities based on priority weight factors and target margin constraints, effectively smoothing load peaks and maintaining grid stability. Through the coordinated operation of these five modules, the system forms an integrated solution of "prediction-negotiation-communication-correction-execution," comprehensively improving the orderliness of electric vehicle charging and discharging, grid compatibility, and user experience.

[0143] Example 3:

[0144] A technical solution provided in an embodiment of the present invention is: an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps of the orderly charging and discharging method of an electric vehicle based on power line carrier communication are implemented.

[0145] Example 4:

[0146] A technical solution provided in an embodiment of the present invention is: a storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are loaded and executed by a processor, the steps of a method for orderly charging and discharging of electric vehicles based on power line carrier communication are implemented.

[0147] Through the description of the above implementation methods, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional modules as needed, that is, the internal structure of the specific device can be divided into different functional modules to complete all or part of the functions described above.

[0148] In the embodiments provided in this application, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the embodiments of the structure described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another structure, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, structure or unit, which can be electrical, mechanical or other forms.

[0149] Units described as separate components may or may not be physically separate, and components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0150] In addition, the functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or software functional units.

[0151] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0152] The specific implementation described above is a preferred implementation of the method and system for orderly charging and discharging of electric vehicles based on power line carrier communication of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation. Any equivalent changes made in accordance with the shape and structure of the present invention are within the scope of protection of the present invention.

Claims

1. An orderly charging and discharging method for electric vehicles based on power line carrier communication, characterized in that: The steps include: S1. Predict the adjustable margin of the current charging station in the time section based on the spatiotemporal distribution characteristics of the user's charging and discharging behavior; S2. Determine the charging and discharging channels of each charging and discharging cooperative subject through a dual negotiation mechanism, and negotiate the disturbance information of the charging and discharging channels through a preset charging and discharging mode; S3. Send disturbance information to the charging station centralized control platform via trickle transmission mode based on power line carrier communication technology; S4. The charging station centralized control platform corrects the predicted adjustable margin based on the disturbance information at each time section to obtain the target adjustable margin; S5. Determine a channel correction factor for each charging and discharging coordination entity based on the demand-supply relationship and the target adjustable margin; and use the channel correction factor to correct the disturbance information to determine an orderly charging and discharging strategy for each charging and discharging coordination entity. The method of predicting the adjustable margin of the current charging station on a time section according to the spatiotemporal distribution characteristics of the user's charging and discharging behavior comprises the following steps: S11, obtaining temporal and spatial distribution characteristic data of household charging and discharging behaviors to construct a charging and discharging feature vector, wherein the charging and discharging feature vector includes: temporal characteristics, spatial characteristics, behavioral characteristics, and environmental characteristics; S12. Based on historical electricity price data, pre-divide 24 hours into multiple electricity price intervals, and determine the electricity price elasticity coefficient based on the change in charging and discharging demand and the change in electricity price in combination with a sliding window algorithm; S13. Expand or shrink the electricity price range according to the electricity price elasticity coefficient to obtain a set of time sections, and perform cluster analysis on the charge and discharge feature vectors within each time section according to the charging mode to determine a charging mode cluster; S14. An improved long short-term memory network fused with attention mechanism is used to construct an adjustable margin prediction model. The historical load sequence and the charging mode cluster corresponding to the section are used as inputs. The predicted power supply and predicted power consumption are output and combined with the reserved safety margin to calculate the predicted adjustable margin corresponding to each section.

2. The method for orderly charging and discharging of electric vehicles based on power line carrier communication according to claim 1, characterized in that: The method of determining the charge and discharge channels of each charge and discharge cooperative subject through a dual negotiation mechanism and negotiating the disturbance information of the charge and discharge channels through a preset charge and discharge mode comprises the following steps: S21. Select the minimum value of the upper limit of the power that the charging pile can supply and the upper limit of the power that the electric vehicle can receive as the power adjustment upper limit of the charging channel; Furthermore, the minimum value of the upper limit of the power that the charging pile can receive and the upper limit of the power that the electric vehicle can feed is selected as the lower limit of the power regulation of the discharge channel; S22: Using the difference between the power adjustment upper limit of the charging channel and the minimum safe charging power as the power adjustment range for the charging channel; and using the difference between the power adjustment upper limit of the discharging channel and the minimum safe discharging power as the power adjustment range for the discharging channel; S23. Obtain a preset charge and discharge mode of the electric vehicle, determine a charge and discharge period and a corresponding target charge and discharge power according to time sensitivity and electricity price sensitivity, and adjust the preset charge and discharge mode according to the charge and discharge period and the corresponding charge and discharge power to determine a target charge and discharge mode; S24, determining a power fluctuation range corresponding to the current time section according to a vector difference between the current charge and discharge power corresponding to the preset charge and discharge mode and the target charge and discharge power corresponding to the target charge and discharge mode; S25. Construct disturbance information based on the current power consumption status of the electric vehicle, power fluctuation range, current charging and discharging power, power regulation caliber, time sensitivity, and electricity price sensitivity.

3. The method for orderly charging and discharging of electric vehicles based on power line carrier communication according to claim 2, characterized in that: The method of determining a charge and discharge period and a corresponding target charge and discharge power according to time sensitivity and electricity price sensitivity, and adjusting a preset charge and discharge mode according to the charge and discharge period and the corresponding charge and discharge power to determine the target charge and discharge mode comprises the following steps: S231, determining time sensitivity based on the expected charge and discharge start time and end time set by the user and the current time; determining electricity price sensitivity based on the real-time electricity price and the user's psychologically expected electricity price; S232. Determine the real-time load rate of the power grid based on the rated capacity of the charging station transformer and the current load capacity; determine the load adjustment coefficient based on whether the real-time load rate of the power grid falls within a preset range; and determine the comprehensive sensitivity based on time sensitivity, electricity price sensitivity, the load adjustment coefficient, and their corresponding weight coefficients. S233, sorting the time sections in descending order according to the comprehensive sensitivity, and selecting the first n time sections with high sensitivity as candidate charge and discharge periods; S234: Select the minimum of the upper limit of the charging channel power adjustment, the maximum allowable charging power, and the theoretical charging power required for the user's desired state of charge as the target charging power; select the minimum of the lower limit of the discharging channel power adjustment, the maximum allowable discharge power, and the maximum discharge power allowed by the remaining battery capacity as the target discharge power, and construct the target charge and discharge power for the current time section based on the target charging power and target discharge power; S235: Compare the candidate charge and discharge time period and its corresponding target charge and discharge power with the preset charge and discharge mode. If the charge and discharge power of the candidate time period is within the power regulation range of the corresponding time section of the preset charge and discharge mode, directly replace the corresponding parameters in the preset charge and discharge mode to determine the target charge and discharge mode. If the regulation range is exceeded, the target charge and discharge power is proportionally adjusted according to the upper limit of the power regulation range to determine the target charge and discharge mode. The ratio is the ratio of the upper limit of the regulation range to the candidate target power.

4. The method for orderly charging and discharging of electric vehicles based on power line carrier communication according to claim 1, characterized in that: The method of sending disturbance information to the charging station centralized control platform in a trickle transmission mode based on power line carrier communication technology includes the following steps: S31. Generate a virtual identity code using the identity codes of the charging pile and electric vehicle corresponding to the charging and discharging collaborative entity; S32, packaging the disturbance information into a plurality of trickle information blocks to be encrypted according to the fluctuation range of the charge and discharge power; S33, dividing the virtual identity code according to the size and number of the Trickle information blocks to obtain virtual identity subcodes, and configuring the number information of each Trickle information block for each virtual identity subcode; S34. Encrypt the corresponding trickle information block using the virtual identity subcode and its corresponding number information, and then send it to the charging station centralized control platform via power line carrier communication technology.

5. The method for orderly charging and discharging of electric vehicles based on power line carrier communication according to claim 4, characterized in that: The method of packaging the disturbance information into a plurality of trickle information blocks to be encrypted according to the fluctuation range of the charge and discharge power includes the following steps: S321, mapping the charging and discharging power fluctuation range to a three-dimensional feature space to construct a fluctuation feature space mapping model; constructing a transmission reliability model based on the power line carrier communication characteristics of OFDM modulation; S322. Taking maximizing data transmission efficiency as the objective function, constructing a single-packet data volume constraint, a minimum transmission interval constraint, and a reliability constraint for the objective function; solving the objective function using the Lagrange multiplier method to obtain the optimal single-packet data volume and the minimum transmission interval; S323: Pack the disturbance information into a plurality of trickle information blocks to be encrypted, taking the optimal single-packet data amount as a unit.

6. The method for orderly charging and discharging of electric vehicles based on power line carrier communication according to claim 5, characterized in that: Transmission reliability model The formula form is as follows: ； Among them, d is the data block size, t is the data sending interval, 、 are the power line channel attenuation coefficients, 、 are the volatility impact factors, is the OFDM subcarrier utilization correction coefficient, is the charge and discharge power fluctuation.

7. The method for orderly charging and discharging of electric vehicles based on power line carrier communication according to claim 1 or 4, characterized in that: The charging station centralized control platform corrects the predicted adjustable margin according to the disturbance information at each time section to obtain the target adjustable margin; the steps include: S41. The charging station centralized control platform generates a virtual identity code based on the handshake information with the charging and discharging collaborative entity, and divides the virtual identity code into several virtual identity sub-codes based on the source and number of the trickle information blocks. The trickle information blocks are decrypted using the virtual identity sub-codes and sequenced according to the numbering information to obtain the disturbance information for each time section. S42. Determine a charge-discharge correction factor based on the current charge-discharge power, time sensitivity, and electricity price sensitivity in the disturbance information at each time section, combined with the charge-discharge rated power; determine a real-time load rate adjustment factor based on the current charge-discharge power and the charge-discharge rated power; and calculate a grid safety factor based on the real-time grid frequency deviation and voltage deviation. S43. Correct the predicted adjustable margin according to the charge and discharge correction factor, the load rate adjustment factor, the grid safety factor, and the current charge and discharge power to obtain a target adjustable margin.

8. The method for orderly charging and discharging of electric vehicles based on power line carrier communication according to claim 1, characterized in that: The method of determining a channel correction factor for each charge-discharge coordination subject based on the demand-supply relationship and the target adjustable margin; and correcting the disturbance information using the channel correction factor to determine an orderly charge-discharge strategy for each charge-discharge coordination subject comprises the following steps: S51. Calculate the vector value of the difference between the actual power consumption and the power regulation caliber of the charging and discharging cooperative entities corresponding to the time section to determine candidate entities for pre-regulation that participate in power regulation of the charging station, where the sign of the vector value represents the direction of power flow, with positive indicating charging and negative indicating discharging. S52. Determine the grid stability contribution based on the grid frequency deviation and voltage fluctuation value; determine the capacity regulation participation based on the historical charge and discharge regulation response rate and regulation amplitude ratio; determine the grid fluctuation smoothness based on the grid power fluctuation rate and fluctuation amplitude during the charge and discharge period; S53, determining the regulation priority weight factor of the corresponding pre-regulation candidate subject by performing weighted summation on the grid stability contribution, capacity regulation participation, and fluctuation smoothness and their corresponding weight factors; S54, determining a control amount for each pre-control candidate subject based on the control priority weight factor, the power control caliber, and the actual power consumption; using the control amount as a channel correction factor for the corresponding charge and discharge channel to generate a channel correction factor sequence corresponding to each charge and discharge channel; S55. Using the target adjustable margin as a constraint boundary, sequentially extracting the corresponding control amount for each time period in the correction factor sequence to expand or reduce the current charge and discharge power to guide the charge and discharge coordination subject to perform orderly charge and discharge.

9. An orderly charging and discharging system for electric vehicles, applicable to the orderly charging and discharging method for electric vehicles based on power line carrier communication according to any one of claims 1 to 8, characterized in that: include: Prediction module: predicts the adjustable margin of the current charging station in the time section based on the spatiotemporal distribution characteristics of the user's charging and discharging behavior; Negotiation module: Determines the charging and discharging channels of each charging and discharging collaborative entity through a dual negotiation mechanism, and negotiates the disturbance information of the charging and discharging channels through preset charging and discharging modes; Interaction module: Based on power line carrier communication technology, it sends disturbance information to the charging station centralized control platform through trickle transmission mode; Correction module: Corrects the predicted adjustable margin according to the disturbance information at each time section to obtain the target adjustable margin; Execution module: Determine the channel correction factor of each charging and discharging collaborative entity based on the demand-supply relationship and the target adjustable margin; use the channel correction factor to correct the disturbance information to determine the orderly charging and discharging strategy of each charging and discharging collaborative entity.

10. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps of the method for orderly charging and discharging of an electric vehicle based on power line carrier communication are implemented as described in any one of claims 1 to 8.

11. A storage medium, characterized in that: The storage medium stores computer-executable instructions, which, when loaded and executed by the processor, implement the steps of the method for orderly charging and discharging of an electric vehicle based on power line carrier communication according to any one of claims 1 to 8.

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