Electric vehicle ordered charging and discharging method and system based on power line carrier communication

Abstract

The invention discloses an electric vehicle ordered charging and discharging method and system based on power line carrier communication, and relates to the technical field of electric vehicle ordered charging and discharging. Charging and discharging power calibers are dynamically matched through a dual-current negotiation mechanism, the overload or underload situation is avoided, and an adjustable margin predicted value dynamically approaches to a real working condition through combination of space-time distribution characteristic prediction and disturbance quantity information closed-loop correction. Safe and reliable transmission of disturbance information transmission is guaranteed by using virtual identity authentication and a trickle transmission strategy; and the disturbance quantity information is corrected based on the channel correction factor so as to generate an ordered charging and discharging strategy giving consideration to the stability of the power grid and user requirements, so that smooth switching of power capacity expansion / capacity reduction is realized, the coupling problem of user behavior space-time randomness and communication environment defects is effectively solved, and accurate adjustment of the dynamic power of the charging station is realized.

Description

Technical Field

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

[0002] With the large-scale access of electric vehicles to the power grid, the peak-valley difference of the load continues to increase due to centralized charging behavior. The existing charging and discharging control strategies face dual bottlenecks. First, user behavior fluctuates randomly in the time dimension affected by the vehicle pick-up time and price sensitivity, and forms local load peaks due to the aggregation of charging hotspots in the space dimension, resulting in a serious deviation between the predicted adjustable margin based on historical data and the actual situation. Second, the strong electromagnetic noise generated by high-power charging and discharging in the charging station poses challenges to traditional wireless communication, mainly manifested in: the power fluctuation causes a sharp drop in the signal-to-noise ratio of the channel, and the transmission integrity of key parameters is damaged; the fixed frame length protocol cannot adapt to the change of the power fluctuation range, and the data congestion is aggravated during high-load periods; the centralized architecture has the risk of interception of sensitive information. It can be seen that the spatio-temporal randomness of user charging and discharging behavior will form local load peaks, resulting in a serious deviation between the predicted adjustable margin based on historical data and the actual situation, and the defects in communication real-time performance and security in the complex electromagnetic environment of the charging station further amplify the control deviation. Therefore, there is an urgent need to construct an integrated solution that combines spatio-temporal behavior prediction, anti-interference communication, and dynamic charging and discharging control.

[0003] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present application, and therefore it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0004] The object of the present invention is to address the problem that the spatio-temporal randomness of user charging and discharging behavior and the complex communication environment in the prior art lead to inaccurate dynamic power adjustment of the charging station. The present invention proposes an orderly charging and discharging method and system for electric vehicles based on power line carrier communication. By means of a dual current negotiation mechanism, the charging and discharging power caliber is dynamically matched to avoid overload or underload situations. Through the combination of spatio-temporal distribution characteristic prediction and closed-loop correction of disturbance amount information, the predicted value of the adjustable margin dynamically approaches the actual working condition. The virtual identity authentication and trickle transmission strategy are used to ensure the secure and reliable transmission of disturbance information. Based on the channel correction factor, the disturbance amount information is corrected to generate an orderly charging and discharging strategy that takes into account both the power grid stability and user needs to achieve a smooth switching of power expansion / contraction, effectively cracking the coupling problem of the spatio-temporal randomness of user behavior and the communication environment defects, and realizing accurate dynamic power adjustment of the charging station.

[0005] In a first aspect, a technical solution provided in an embodiment of the present invention is: an orderly charging and discharging method for electric vehicles based on power line carrier communication, including the following steps: S1. Predict the predicted adjustable margin of the current charging station on the time section according to the spatio-temporal distribution characteristics of users' charging and discharging behaviors; S2. Determine the charging and discharging channels of each charging and discharging collaborative entity through a dual negotiation mechanism, and negotiate the disturbance quantity information of the charging and discharging channels through a preset charging and discharging mode; S3. Send the disturbance quantity information to the charging station centralized control platform through the trickle transmission mode based on the power line carrier communication technology; S4. The charging station centralized control platform corrects the predicted adjustable margin according to the disturbance quantity information on each time section to obtain the target adjustable margin; S5. Determine the channel correction factor of each charging and discharging collaborative entity according to the demand-supply relationship and the target adjustable margin; correct the disturbance quantity information through the channel correction factor to determine the orderly charging and discharging strategy of each charging and discharging collaborative entity.

[0006] Preferably, the step of predicting the predicted adjustable margin of the current charging station on the time section according to the spatio-temporal distribution characteristics of users' charging and discharging behaviors includes the following steps: S11. Obtain the spatio-temporal distribution characteristic data of users' charging and discharging behaviors to construct a charging and discharging feature vector, where the charging and discharging feature vector includes: time feature, space feature, behavior feature, and environmental feature; S12. Pre-divide 24 hours into multiple electricity price intervals based on historical electricity price data, and determine the electricity price elasticity coefficient according to the change in charging and discharging demand and the change in electricity price in combination with the sliding window algorithm; S13. Expand or contract the electricity price interval according to the electricity price elasticity coefficient to obtain a time section set, and perform clustering analysis on the charging and discharging feature vectors within each time section according to the charging mode to determine the charging mode cluster; S14. Use an improved long short-term memory network to fuse the attention mechanism to construct an adjustable margin prediction model, take the historical load sequence and the charging mode cluster corresponding to the section as inputs, output the predicted power supply quantity and the predicted power consumption quantity, and calculate the predicted adjustable margin corresponding to each section in combination with the reserved safety margin.

[0007] Preferably, the step of determining the charging and discharging channels of each charging and discharging collaborative entity through a dual negotiation mechanism, and negotiating the disturbance quantity information of the charging and discharging channels through a preset charging and discharging mode includes the following steps: S21. Select the minimum value of the upper limit of the power supplyable by the charging pile and the upper limit of the power receivable by the electric vehicle as the upper limit of the power adjustment of the charging channel; and select the minimum value of the upper limit of the power receivable by the charging pile and the upper limit of the power feedable by the electric vehicle as the lower limit of the power adjustment of the discharging channel; S22. Use the difference between the upper power adjustment limit and the minimum safe charging power of the charging channel as the power adjustment range of the charging channel; and use the difference between the upper power adjustment limit and the minimum safe discharging power of the discharging channel as the power adjustment range of the discharging channel. S23. Obtain the preset charging and discharging mode of the electric vehicle, determine the charging and discharging time periods and the corresponding target charging and discharging powers according to the time sensitivity and the electricity price sensitivity, and adjust the preset charging and discharging mode according to the charging and discharging time periods and the corresponding charging and discharging powers to determine the target charging and discharging mode. S24. Determine the power fluctuation range corresponding to the current time section according to the vector difference between the current charging and discharging power corresponding to the preset charging and discharging mode and the target charging and discharging power corresponding to the target charging and discharging mode. S25. Construct disturbance quantity information based on the current power consumption state of the electric vehicle, the power fluctuation range, the current charging and discharging power, the power adjustment range, the time sensitivity, and the electricity price sensitivity.

[0008] Preferably, the step of determining the charging and discharging time periods and the corresponding target charging and discharging powers according to the time sensitivity and the electricity price sensitivity, and adjusting the preset charging and discharging mode according to the charging and discharging time periods and the corresponding charging and discharging powers to determine the target charging and discharging mode includes the following steps: S231. Determine the time sensitivity according to the expected charging and discharging start time, charging and discharging end time set by the user and the current time; determine the electricity price sensitivity according to the real-time electricity price and the user's psychological expected electricity price. S232. Determine the real-time grid load rate according to the rated capacity of the charging station transformer and the current load capacity; determine the load adjustment coefficient according to the preset range in which the real-time grid load rate falls; determine the comprehensive sensitivity according to the time sensitivity, the electricity price sensitivity, the load adjustment coefficient and their corresponding weight coefficients. S233. Sort the time sections in descending order according to the comprehensive sensitivity, and select the first n time sections with high sensitivity as the candidate charging and discharging time periods. S234. Select the minimum value among the upper power adjustment limit of the charging channel, 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 value among the lower power adjustment limit of the discharging channel, the maximum allowable discharging power, and the maximum discharging power allowed by the remaining battery power as the target discharging power, and construct the target charging and discharging power under the current time section according to the target charging power and the target discharging power. S235. Compare the candidate charging and discharging time periods and their corresponding target charging and discharging powers with the preset charging and discharging mode. If the charging and discharging power of the candidate period is within the power adjustment range of the corresponding time section of the preset charging and discharging mode, directly replace the corresponding parameters in the preset charging and discharging mode to determine the target charging and discharging mode; if it exceeds the adjustment range, adjust the target charging and discharging power proportionally according to the upper limit of the power adjustment range to determine the target charging and discharging mode, where the proportion is the ratio of the upper limit of the adjustment range to the candidate target power.

[0009] Preferably, the disturbance amount information is sent to the charging station centralized control platform through the trickle transmission mode based on the power line carrier communication technology, including the following steps: S31. Generate a virtual identity code through the identity codes of the charging pile and the electric vehicle corresponding to the charging and discharging coordination entity; S32. Pack the disturbance amount information into several trickle information blocks to be encrypted according to the fluctuation range of the charging and discharging power; S33. Divide the virtual identity code according to the size and quantity of the trickle information blocks to obtain virtual identity sub-codes, and configure the number information of each trickle information block for each virtual identity sub-code; S34. Encrypt the corresponding trickle information block through the virtual identity sub-code and its corresponding number information, and send it to the charging station centralized control platform through the power line carrier communication technology.

[0010] Preferably, packing the disturbance amount information into several trickle information blocks to be encrypted according to the fluctuation range of the charging and discharging power includes the following steps: S321. Map the charging and discharging power fluctuation range to a three-dimensional feature space to construct a fluctuation feature space mapping model; construct a transmission reliability model based on the power line carrier communication characteristics of OFDM modulation; S322. Take maximizing the data transmission efficiency as the objective function, construct the single-packet data volume constraint, minimum sending interval constraint and reliability constraint of the objective function; solve the objective function through the Lagrange multiplier method to obtain the optimal single-packet data volume and the minimum sending interval; S323. Pack the disturbance amount information into several trickle information blocks to be encrypted with the optimal single-packet data volume as the unit.

[0011] Preferably, the transmission reliability model has the following formula form: , where d is the data block size, t is the data sending interval, , are respectively the power line channel attenuation coefficients, , are respectively the fluctuation influence factors, is the OFDM sub - carrier utilization correction coefficient, is the charge - discharge power fluctuation amount.

[0012] Preferably, the charging station centralized control platform corrects the predicted adjustable margin according to the disturbance amount information at each time section to obtain the target adjustable margin; the steps are as follows: S41. The charging station centralized control platform generates a virtual identity code according to the handshake information with the charge - discharge collaborative entity, and divides the virtual identity code into several virtual identity sub - codes according to the source and quantity of the trickle information blocks, decrypts the trickle information blocks through the virtual identity sub - codes, and performs sequence recombination according to the numbering information to obtain the disturbance amount information of each time section; S42. Determine the charge - discharge correction factor according to the current charge - discharge power, time sensitivity, electricity price sensitivity in the disturbance amount information of each time section and the rated charge - discharge power; determine the real - time load rate adjustment factor according to the current charge - discharge power and the rated charge - discharge power; calculate the grid safety factor according to the real - time grid frequency deviation and voltage deviation; S43. Correct the predicted adjustable margin according to the charge - discharge correction factor, load rate adjustment factor, grid safety factor, and current charge - discharge power to obtain the target adjustable margin.

[0013] Preferably, determine the channel correction factor of each charge - discharge collaborative entity according to the demand - supply relationship and the target adjustable margin; correct the disturbance amount information through the channel correction factor to determine the orderly charge - discharge strategy of each charge - discharge collaborative entity; the steps are as follows: S51. Calculate the vector value of the difference between the actual power consumption of the charge - discharge collaborative entity corresponding to the time section and the power adjustment caliber to determine the pre - adjustment candidate entities participating in the charging station power adjustment, where the sign of the vector value represents the power flow direction, positive for charging and negative for discharging; S52. Determine the grid stability contribution degree based on the grid frequency deviation and voltage fluctuation value; determine the capacity adjustment participation degree based on the historical charge - discharge adjustment response rate and the ratio of the adjustment amplitude; determine the grid fluctuation smoothness based on the volatility and fluctuation amplitude of the grid power during the charge - discharge period; S53. Perform weighted summation through the grid stability contribution degree, capacity adjustment participation degree, fluctuation smoothness and their corresponding weight factors to determine the regulation priority weight factor of the corresponding pre - adjustment candidate entity; S54. Determine the regulation amount of each pre - adjustment candidate entity according to the regulation priority weight factor, power adjustment caliber and actual power consumption; use the regulation amount as the channel correction factor of the corresponding charge - discharge channel to generate the channel correction factor sequence corresponding to each charge - discharge channel; S55. Using the target adjustable margin as the constraint boundary, sequentially extract the regulation amounts corresponding to each time period in the correction factor sequence to expand or reduce the current charging and discharging power to guide the charging and discharging collaborative entities to perform orderly charging and discharging.

[0014] In a second aspect, a technical solution provided in an embodiment of the present invention is: an electric vehicle orderly charging and discharging system, including: A prediction module: predicting the predicted adjustable margin of the current charging station on the time section according to the spatio-temporal distribution characteristics of the user's charging and discharging behavior; A negotiation module: determining the charging and discharging channels of each charging and discharging collaborative entity through a dual negotiation mechanism, and negotiating the disturbance amount information of the charging and discharging channels through a preset charging and discharging mode; An interaction module: sending the disturbance amount information to the charging station centralized control platform through a trickle transmission mode based on the power line carrier communication technology; A correction module: correcting the predicted adjustable margin according to the disturbance amount information on each time section to obtain the target adjustable margin; An execution module: determining the channel correction factor of each charging and discharging collaborative entity according to the demand supply relationship and the target adjustable margin; correcting the disturbance amount information through the channel correction factor to determine the orderly charging and discharging strategy of each charging and discharging collaborative entity.

[0015] In a third aspect, 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 electric vehicle orderly charging and discharging method based on power line carrier communication are implemented.

[0016] In a fourth aspect, a technical solution provided in an embodiment of the present invention is: a storage medium, wherein computer-executable instructions are stored in the storage medium, and when the computer-executable instructions are loaded and executed by a processor, the steps of the electric vehicle orderly charging and discharging method based on power line carrier communication are implemented.

[0017] Advantages of the present invention: (1) Aiming at the problem that the prediction of the adjustable margin is seriously deviated from the actual due to the spatio-temporal randomness of the user's charging and discharging behavior, this application constructs a charging and discharging feature vector covering time, space, behavior and environmental characteristics, and optimizes the time section division based on the price elasticity coefficient, and combines the prediction model of the improved LSTM fusion attention mechanism to output the initial margin; then, taking the real-time disturbance amount information (such as the power fluctuation range, sensitivity parameter) as the input, through multi-dimensional dynamic correction of the charging and discharging correction factor, load rate adjustment factor and grid safety factor, the predicted adjustable margin is adaptively tracked with the user behavior fluctuation and the grid state change, significantly reducing the prediction deviation rate and improving the robustness of the grid dispatching decision-making; (2)Regarding the problem of defects in communication real-time performance, integrity, and security caused by strong electromagnetic noise in charging stations, this application utilizes the inherent electromagnetic interference resistance characteristics of the power line channel to block and package the disturbance information through a virtual identity code segmentation encryption and numbering mechanism; based on a transmission reliability model mapped by the power fluctuation range (including the channel attenuation coefficient and the OFDM subcarrier utilization correction coefficient), it optimizes the single-packet data volume and transmission interval to maximize data transmission efficiency and meet the reliability constraints, ensuring the transmission integrity of data in a low signal-to-noise ratio environment caused by power fluctuations. At the same time, the virtual identity authentication mechanism truncates the sensitive information leakage path, suppressing the risks of data congestion and interception at the source; (3)Regarding the problem of inaccurate dynamic power regulation caused by the coupling of spatio-temporal randomness and communication defects, this application uses a dual negotiation mechanism to dynamically match the charging and discharging channel diameters and generate disturbance information with the device power upper limit and safety margin as constraints; based on the target adjustable margin, it calculates the channel correction factor sequence by regulating the priority weight factors (including the power grid stability contribution degree, capacity regulation participation degree, and fluctuation smoothness) to vectorially correct the disturbance. Driven by the demand-supply relationship, it guides the charging and discharging collaborative entities to perform power reduction / expansion operations in sequence, effectively suppressing local load peaks, reducing the power grid frequency deviation and voltage fluctuation amplitude, and solving the coupling problem of randomness and communication defects.

[0018] The above-mentioned invention content 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 according to the content of the specification. And in order to make the above and other purposes, features, and advantages of the present invention more obvious and understandable, the following specifically illustrates the specific implementation manners of the present invention. Brief Description of the Drawings

[0019] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, purposes, and advantages of the present invention will become more obvious. The drawings are only used for the purpose of showing the preferred embodiments and are not considered as a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components.

[0020] Figure 1 It is a flowchart of the method for orderly charging and discharging of electric vehicles based on power line carrier communication of the present invention.

[0021] Figure 2 It is a flowchart of the generation of disturbance information of the present invention.

[0022] Figure 3 It is a flowchart of the trickle transmission of disturbance information of the present invention.

[0023] Figure 4 It is a flowchart of the generation of the orderly charging and discharging strategy of the present invention.

[0024] Figure 5 This is the structural block diagram of the orderly charging and discharging system for electric vehicles of the present invention. Detailed implementation mode

[0025] To make the purpose, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific implementation mode described here is only one of the best embodiments of the present invention, which is only used to explain the present invention and does not limit the protection scope of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.

[0026] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations (or steps) can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. When the operations are completed, the process can be terminated, but there may also be additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0027] In the field of electric vehicle orderly charging and discharging technology, the industry is facing double technical bottlenecks: First, the charging and discharging behaviors of users show random fluctuation characteristics in the time dimension affected by factors such as pick-up time period and electricity price sensitivity. In the space dimension, local load peaks are formed due to the aggregation of charging hotspots, resulting in the difficulty of traditional adjustable margin prediction methods based on single historical data to effectively capture the dynamic correlation characteristics in the spatio-temporal distribution characteristics, leading to a significant deviation between the predicted value and the actual working condition, seriously affecting the accuracy and operation reliability of power grid dispatching. Second, in the high-power charging and discharging scenario of charging stations, the strong electromagnetic noise environment poses three core challenges to traditional wireless communication technologies: the power fluctuation causes a sudden drop in the channel signal-to-noise ratio, resulting in damage to the transmission integrity of key parameters such as disturbance quantity information; the fixed frame length protocol cannot adapt to the change of the power fluctuation range, and data congestion is likely to occur during high-load periods; the centralized communication architecture has the risk of interception of sensitive information and is difficult to meet the strict requirements of the power grid real-time regulation for communication real-time performance, security and anti-interference. The above technical bottlenecks are coupled with each other, resulting in the difficulty of existing charging and discharging control strategies to achieve the dynamic matching of user behavior characteristics and power grid operation status, forming a vicious cycle of prediction inaccuracy -> communication failure -> regulation instability.

[0028] Embodiment 1: In response to the above-mentioned common problems in the industry, this application has cracked the coupled interference of spatio-temporal randomness and communication defects by constructing an integrated solution that combines spatio-temporal behavior prediction, anti-interference communication, and dynamic charging and discharging regulation, providing an innovative technical path for improving the dynamic power regulation accuracy of charging stations and balancing grid stability and user needs; as Figure 1 shown, the method for orderly charging and discharging of electric vehicles based on power line carrier communication includes the following steps: S1. Predict the predicted adjustable margin of the current charging station at the time section according to the spatio-temporal distribution characteristics of the user's charging and discharging behavior.

[0029] As an optional embodiment, the predicting the predicted adjustable margin of the current charging station at the time section according to the spatio-temporal distribution characteristics of the user's charging and discharging behavior; includes the following steps: S11. Obtain the spatio-temporal distribution characteristic data of the user's charging and discharging behavior to construct a charging and discharging feature vector, where the charging and discharging feature vector includes: time feature, space feature, behavior feature, and environmental feature.

[0030] Exemplarily, through sensors and user account systems deployed in charging facilities, the time feature (corresponding to the flat electricity price interval), space feature (located in the high-load area of the business district with dense surrounding office buildings), behavior feature (short single charging duration of users, high daily charging frequency, mostly for commuting energy replenishment needs), and environmental feature (the charging efficiency of the battery decreases in summer high temperature, resulting in a 10%-15% increase in charging duration) during the morning peak period (7:00-9:00) are collected in real time to form a charging and discharging feature vector, so as to depict the charging behavior pattern of "high frequency, short duration, temperature sensitive" during this period.

[0031] S12. Pre-divide 24 hours into multiple electricity price intervals based on historical electricity price data, and determine the electricity price elasticity coefficient according to the change amount of charging and discharging demand and the change amount of electricity price in combination with the sliding window algorithm.

[0032] Exemplarily, based on the historical data of this charging station in the past 3 months, 24 hours are pre-divided into three basic intervals of "peak (8:00-12:00, 17:00-22:00), flat (6:00-8:00, 12:00-17:00), valley (22:00-next day 6:00)". Through analysis by the sliding window algorithm, it is found that when the electricity price in the valley period drops by 5%, the charging demand increases by 8% (elasticity coefficient is 1.6), while when the electricity price in the peak period rises by 10%, the demand only drops by 3% (elasticity coefficient is 0.3). Accordingly, the valley period is further expanded into two sub-sections of "deep valley (23:00-next day 3:00)" and "shallow valley (22:00-23:00, 3:00-6:00)" to match the strong sensitivity of users to low-price electricity.

[0033] S13. Expand or contract the electricity price range according to the electricity price elasticity coefficient to obtain a set of time sections, and perform clustering analysis on the charging and discharging feature vectors within each time section according to the charging mode to determine the charging mode clusters.

[0034] Exemplarily, within the "deep valley" section, perform clustering analysis on the charging and discharging feature vectors to identify two typical modes: one is the "household user cluster" (the charging start time is concentrated between 23:30 and 0:30, the single charging amount reaches more than 80% of the battery capacity, and the electricity price sensitivity is high), and the other is the "online car-hailing user cluster" (the charging time is distributed between 1:00 and 4:00, the single charging amount is about 50%, and the charging speed is emphasized rather than the electricity price). The behavioral differences between the two clusters provide a sub-input dimension for the subsequent prediction model.

[0035] S14. Construct an adjustable margin prediction model by using an improved long short-term memory network integrated with an attention mechanism. Take the historical load sequence and the charging mode cluster corresponding to the section as inputs, output the predicted power supply and the predicted power consumption, and calculate the predicted adjustable margin corresponding to each section in combination with the reserved safety margin.

[0036] Exemplarily, input the historical load sequence (such as the actual charging power curve during the deep valley period in the past week) and the above-mentioned charging mode cluster labels into the improved LSTM network. Automatically identify key features through the attention mechanism. For example, when it is detected that the proportion of the "household user cluster" exceeds 60% and the next day is a working day, the model will strengthen the weight assignment of the feature of "sudden increase in load from 23:00 to 0:00 at night", output the predicted power supply (the total rated power of the charging piles) and the consumption (the total predicted demand of the cluster), and combine the safety margin reserved by the power grid (such as 15%) to finally generate the predicted adjustable margin corresponding to this section (such as the upper limit of the available charging power is 85% of the rated power).

[0037] In this embodiment, by collecting the spatio-temporal distribution data of users' charging and discharging behaviors (such as the high-frequency charging demand of urban charging stations during the morning rush hour and the concentrated charging behaviors of suburban home chargers during the late off-peak hour), a charging and discharging feature vector is constructed, which includes time features (such as peak-valley periods of electricity prices, working day / holiday cycles), spatial features (such as geographical coordinates of charging stations, regional load density), behavioral features (such as single charging duration, daily average charging frequency), and environmental features (such as the impact of temperature on battery charging efficiency, the suppression of travel demand by rainfall). The complex attributes of user behaviors are transformed 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 valley periods). Through the sliding window algorithm, the elastic correlation between the change in charging and discharging demand and the electricity price fluctuation within different periods is dynamically calculated (such as a sensitive response that when the electricity price increases by 10% in a certain period, the charging demand decreases by 15%, which can be obtained through historical data analysis). Accordingly, the electricity price interval is dynamically expanded or contracted (such as refined into multiple sub-sections during the commuting period sensitive to electricity price), and the charging and discharging feature vectors within each time section are clustered and analyzed to identify high-elastic charging mode clusters (such as user groups that only charge when the electricity price is lower than the capacitance threshold) and low-elastic charging mode clusters (such as emergency charging users insensitive to electricity price). Further, an improved long short-term memory network (LSTM) integrated with an attention mechanism is used to construct a prediction model. Taking the historical load sequence (such as the actual charging and discharging power of each period in the past week) and the charging mode cluster label as inputs, the attention mechanism automatically focuses on historical data segments highly relevant to the current section characteristics (such as identifying the suppression law of heavy rain weather on the load of suburban charging stations), outputs the predicted electricity supply and consumption considering user behavior differences, and combines the reserved safety margin required for the safe operation of the power grid to generate the predicted adjustable margin for each time section. Through the technical design route of feature vector construction, dynamic time section division, mode cluster clustering, and model prediction in this embodiment, a systematic modeling of the spatio-temporal randomness of users' charging and discharging behaviors is realized, breaking through the limitations of traditional static prediction methods based on single historical data, providing a more practical margin evaluation basis for the real-time scheduling of the power grid, and significantly improving the dynamic adaptability of charging station load prediction and the reliability of power grid operation.

[0038] S2. Determine the charging and discharging channels of each charge-discharge collaborative entity through a dual negotiation mechanism, and negotiate the disturbance amount information of the charging and discharging channels through a preset charging and discharging mode.

[0039] As an alternative embodiment, determine the charging and discharging channels of each charge-discharge collaborative entity through a dual negotiation mechanism, and negotiate the disturbance amount information of the charging and discharging channels through a preset charging and discharging mode; as Figure 2 shown, it includes 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 upper limit of the power adjustment for the charging channel; and, select 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 as the lower limit of the power adjustment for the discharging channel; S22. Use the difference between the upper limit of the power adjustment for the charging channel and the minimum safe charging power as the power adjustment range for the charging channel; and, use the difference between the upper limit of the power adjustment for the discharging channel and the minimum safe discharging power as the power adjustment range for the discharging channel; S23. Obtain the preset charging and discharging mode of the electric vehicle, determine the charging and discharging time periods and the corresponding target charging and discharging powers according to the time sensitivity and the electricity price sensitivity, and adjust the preset charging and discharging mode according to the charging and discharging time periods and the corresponding charging and discharging powers to determine the target charging and discharging mode; S24. Determine the power fluctuation range corresponding to the current time section according to the vector difference between the current charging and discharging power corresponding to the preset charging and discharging mode and the target charging and discharging power corresponding to the target charging and discharging mode; S25. Construct disturbance quantity information based on the current power consumption state of the electric vehicle, the power fluctuation range, the current charging and discharging power, the power adjustment range, the time sensitivity, and the electricity price sensitivity.

[0040] It can be understood that this embodiment adopts a dual negotiation mechanism of dynamic coupling between device power constraints and user needs. During the establishment stage of the charging and discharging channel, the hardware power limits of the charging pile and the electric vehicle are used as the safety boundaries (for example, the upper limit of the power supply of a certain home charging pile is 7 kW, and the upper limit of the power that the electric vehicle can receive is 6.6 kW. Then, the upper limit of the power adjustment of the charging channel is set to 6.6 kW). Combining with the minimum safe charging power (such as 2 kW), the power adjustment range (4.6 kW) is calculated to delimit the physically feasible range for the elastic adjustment of the charging and discharging power. During the negotiation stage of the charging and discharging mode, for different user types (such as price-sensitive household users and time-sensitive online car-hailing drivers), through the quantitative evaluation of time sensitivity (time sensitivity = the ratio of (the expected charging end start time set by the user - the current time) to the preset maximum time difference threshold) and price sensitivity (price sensitivity = the ratio of |real-time electricity price - the user's psychological expected electricity price| to the maximum electricity price deviation threshold), the preset charging and discharging mode is dynamically adjusted. For example, the preset mode for price-sensitive users during off-peak hours can be adjusted to "charge at the maximum power from 23:00 to 3:00 the next day", while the mode for emergency charging users during peak hours gives priority to the time target of "charging to 80% of the battery in 30 minutes". By calculating the power vector difference between the preset mode and the target mode (such as the increment from 3 kW to 6 kW is +3 kW), the power fluctuation range of the current section is determined, and the disturbance information is constructed by integrating the power consumption state (charging / discharging), power adjustment range, sensitivity parameters, etc. Through the technical design route of "hardware safety constraints, user demand mapping, mode dynamic calibration, and disturbance information generation", this mechanism realizes the upgrade of the charging and discharging channel from "fixed power configuration" to "tripartite coordination and adjustment of users, devices, and the power grid". It not only avoids the risk of equipment overload but also can dynamically generate accurate disturbance information according to user behavior characteristics, providing underlying data support with both security and flexibility for subsequent anti-interference communication and power grid regulation, effectively solving the industry problem that power adjustment lags behind user demand changes in the traditional single negotiation mode.

[0041] As an alternative embodiment, determining the charging and discharging time periods and the corresponding target charging and discharging power according to the time sensitivity and the price sensitivity, and adjusting the preset charging and discharging mode according to the charging and discharging time periods and the corresponding charging and discharging power to determine the target charging and discharging mode; includes the following steps: S231. Determine the time sensitivity according to the expected charging and discharging start time, charging and discharging end time set by the user, and the current time; determine the price sensitivity according to the real-time electricity price and the user's psychological expected electricity price; S232. Determine the real-time load rate of the power grid according to the rated capacity of the charging station transformer and the current load capacity; determine the load adjustment coefficient according to the preset category in which the real-time load rate of the power grid falls; determine the comprehensive sensitivity according to the time sensitivity, price sensitivity, load adjustment coefficient, and their corresponding weight coefficients; S233. Sort each time section in descending order according to the comprehensive sensitivity, and select the top n time sections with high sensitivity as the candidate charge and discharge periods; S234. Select the minimum value among the upper limit of the charging channel power regulation, 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 value among the lower limit of the discharging channel power regulation, the maximum allowable discharging power, and the maximum discharging power allowed by the remaining battery power as the target discharging power, and construct the target charge and discharge power at the current time section according to the target charging power and the target discharging power; S235. Compare the candidate charge and discharge 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 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 it exceeds the regulation range, adjust the target charge and discharge power proportionally according to the upper limit of the regulation range to determine the target charge and discharge mode, where the proportion is the ratio of the upper limit of the regulation range to the candidate target power.

[0042] It can be understood that in S231-S232 of this embodiment, the user's subjective intention is converted into a computable sensitivity index through the time difference algorithm and the electricity price deviation model - for example, if a user sets the electric car to charge at 22:00-24:00 (the expected start time is 22:00), if the current time is 21:30, the time difference of 30 minutes is normalized and mapped to high time sensitivity (approaching the upper threshold limit); the deviation between the real-time electricity price of 0.5 yuan / kWh and the user's expected 0.45 yuan / kWh is calculated by absolute value to form a quantified value of the electricity price sensitivity. At the same time, combined with the operating status of the charging station transformer (such as the rated capacity of 1250kVA, the current load is 900kVA, the load rate is 72%, which falls into the "medium load" category), the corresponding load adjustment coefficient is matched (such as the coefficient is 0.9 when the medium load is), and the comprehensive sensitivity (such as the comprehensive value of a section is 0.82) is calculated through the preset weight matrix (such as time sensitivity weight 0.4, electricity price sensitivity 0.3, load adjustment coefficient 0.3). In S233-S235, the 24-hour time sections are sorted based on comprehensive sensitivity, and the first three highly sensitive time periods (such as 22:00-23:00, 23:00-24:00, and 7:00-8:00) are selected as candidate charging and discharging time periods, and the target charging and discharging power is determined based on the equipment power limit and the user's state of charge requirements (such as the charging channel upper limit of 6.6kW, the power required for the user to charge to 80% in 1 hour is 5kW, the maximum allowed charging power is 6kW, and the minimum value is 5kW). If the power of the candidate time period (such as 5kW) is within the adjustment range of the preset mode (such as 2-6kW), the mode parameters are directly updated; if it exceeds (such as the candidate power of 7kW), it is scaled to 6kW according to the ratio of the upper limit of the caliber 6kW to the candidate power (6 / 7) to ensure that the power adjustment is within the safety range of the equipment.

[0043] This embodiment realizes the upgrade of the charging and discharging mode from "fixed preset" to "user behavior-grid status dual-driven adaptive adjustment" through the technical design route of "demand characteristics digitization, grid status indexation, and multi-objective dynamic calibration". It not only ensures the convenience of user charging, 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.

[0044] 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.

[0045] 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 a trickle transmission mode; Figure 3 As shown, the following steps are included: S31, generating a virtual identity code through the identity codes of the charging pile and the electric vehicle corresponding to the charging and discharging collaborative subject; S32. Pack the disturbance amount information into several trickle information blocks to be encrypted according to the fluctuation range of the charge and discharge power; S33. Divide the virtual identity code according to the size and quantity of the trickle information blocks to obtain virtual identity sub-codes, and configure the number information of each trickle information block for each virtual identity sub-code; S34. Encrypt the corresponding trickle information block through the virtual identity sub-code and its corresponding number information, and send it to the charging station centralized control platform through the power line carrier communication technology.

[0046] 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), realizing the decoupling of the physical device and the communication identity and blocking the direct association path of sensitive information. In S32-S33, for the charge and discharge power fluctuation range (such as the power fluctuation of ±2kW in a certain period), a transmission reliability model is constructed based on the OFDM (Orthogonal Frequency Division Multiplexing) modulation characteristics, and the optimal single-packet data volume (such as splitting the disturbance amount information into 1024 bytes / block) and the minimum transmission interval (such as 50ms) are solved through the Lagrange multiplier method to generate a sequence of trickle information blocks; at the same time, the virtual identity code is divided into 10 sub-codes (such as VF-8A, VF-3C, etc.) according to the number of information blocks (such as 10 blocks), and a unique number (1-10) is assigned to each sub-code to establish a dynamic binding relationship of "sub-code-number-information block". In S34, the AES encryption algorithm is used to encrypt each trickle information block packet by packet with the virtual identity sub-code as the key and the number as the vector parameter (such as the 3rd information block generates a dynamic key through the sub-code VF-5D and the number 3), and it is sent to the centralized control platform through the orthogonal frequency division multiplexing channel of the power line carrier communication.

[0047] In this embodiment, the designed anti-interference communication mechanism disassembles the complete data frame of the traditional centralized communication into micro data blocks adapted to the power fluctuation characteristics through the technical design route of "identity virtualization, data fragmentation, and encryption dynamicization", utilizes the inherent anti-electromagnetic interference characteristics of the power line channel, and combines the dual encryption factors of the virtual identity sub-code and the data block number to realize the secure and reliable transmission of the disturbance amount information in a strong electromagnetic noise environment, breaks through the transmission bottleneck of the fixed frame length protocol during high-load periods, and at the same time avoids the risk of sensitive information being intercepted and analyzed, providing a highly robust communication link support for the real-time regulation of the power grid.

[0048] As an alternative embodiment, the step of packing the disturbance amount information into several trickle information blocks to be encrypted according to the fluctuation range of the charge and discharge power includes the following steps: S321. Map the charging and discharging power fluctuation range to a three-dimensional feature space to construct a fluctuation feature space mapping model; construct a transmission reliability model based on the characteristics of power line carrier communication with OFDM modulation; S322. Take maximizing data transmission efficiency as the objective function, and construct the constraints of the objective function including the single-packet data volume constraint, the minimum transmission interval constraint, and the reliability constraint; solve the objective function by the Lagrange multiplier method to obtain the optimal single-packet data volume and the minimum transmission interval; S323. Pack the disturbance amount information into several trickle information blocks to be encrypted in units of the optimal single-packet data volume.

[0049] It can be understood that in S321, the charging and discharging power fluctuation range (such as the fluctuation amplitude of ±3kW during peak hours) is mapped to the three-dimensional feature space composed of "power change rate - fluctuation frequency - duration" to construct a fluctuation feature space mapping model, realizing the abstract representation of power fluctuation from the time domain to the multi-dimensional feature domain; at the same time, based on the characteristics of power line carrier communication with OFDM modulation, a transmission reliability model is established to quantify the correlation relationships of parameters such as data block size, transmission interval, and power fluctuation amount. In S322, taking maximizing data transmission efficiency as the goal, construct the constraint conditions including the upper limit of the single-packet data volume (such as 2048 bytes), the lower limit of the minimum transmission interval (such as 10ms), and the reliability threshold R (such as R≥0.9), and solve by the Lagrange multiplier method to obtain the optimal single-packet data volume (such as splitting the large-size disturbance amount information into 1500 bytes / block) and the minimum transmission interval (such as 30ms), realizing the dynamic coupling of power fluctuation characteristics and communication transmission parameters. In S323, cut the disturbance amount information into several trickle information blocks according to the optimal single-packet data volume (such as splitting the fluctuation information in a certain period into 8 data blocks) to ensure the transmission integrity of each information block in a strong electromagnetic noise environment.

[0050] 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 block division", it breaks through the adaptation limitation of the traditional fixed frame length communication protocol to power fluctuation, realizes the collaborative optimization of transmission efficiency and reliability of power line carrier communication in a dynamic power scenario, provides theoretical and algorithm support for the real-time and secure transmission of disturbance amount information in the strong electromagnetic environment of the charging station, and significantly improves the robustness of the communication link to complex working conditions.

[0051] As an alternative embodiment, the transmission reliability model has the following formula form: , where d is the data block size, t is the data transmission interval, , are respectively the power line channel attenuation coefficients, where Reflect the attenuation effect degree of the power line channel on the data block size, and reflect the attenuation influence degree of the power line channel on the data sending interval; 、 are respectively the fluctuation influence factors, used to adjust the influence degree of the charge-discharge power fluctuation amount and related factors on the transmission reliability, as a compensation item for the charge-discharge power fluctuation amount, is the OFDM sub-carrier utilization rate correction coefficient, and is the charge-discharge power fluctuation amount.

[0052] It can be understood that this transmission reliability model solves the optimal single-packet data volume and the minimum sending interval through the Lagrange multiplier method, disassembles the disturbance amount information into trickle information blocks adapted to the power fluctuation range (such as automatically optimizing the data block size to 1024 bytes and the sending interval to 50 ms in the power fluctuation ±2kW scenario), and maintains the transmission integrity in a strong electromagnetic noise environment; by dynamically adjusting the transmission parameters, it realizes the collaborative optimization of data transmission efficiency and reliability, breaks through the data congestion bottleneck of the fixed frame length protocol during high-load periods, and at the same time utilizes the inherent anti-interference characteristics of the power line channel to control the bit error rate within the industry safety threshold, providing underlying communication guarantee for the real-time and accurate transmission of charge-discharge control commands, and significantly improving the stability of the system in a complex electromagnetic environment.

[0053] S4. The charging station centralized control platform corrects the predicted adjustable margin according to the disturbance amount information at each time section to obtain the target adjustable margin.

[0054] As an optional embodiment, the charging station centralized control platform corrects the predicted adjustable margin according to the disturbance amount information at each time section to obtain the target adjustable margin; the method includes the following steps: S41. The charging station centralized control platform generates a virtual identity code according to the handshake information with the charge-discharge collaborative entity, divides the virtual identity code into several virtual identity sub-codes according to the source and quantity of the trickle information blocks, decrypts the trickle information blocks through the virtual identity sub-codes, and performs sequence recombination on them according to the number information to obtain the disturbance amount information at each time section; S42. Determine the charge-discharge correction factor according to the current charge-discharge power, time sensitivity, and electricity price sensitivity in the disturbance amount information at each time section in combination with the rated charge-discharge power; determine the real-time load rate adjustment factor according to the current charge-discharge power and the rated charge-discharge power; calculate the grid safety factor according to the real-time grid frequency deviation and voltage deviation; S43. Correct the predicted adjustable margin according to the charge-discharge correction factor, load rate adjustment factor, grid safety factor, and current charge-discharge power to obtain the target adjustable margin.

[0055] It is understandable that this embodiment adopts a prediction calibration mechanism of virtual identity dynamic decryption and multi-dimensional parameter coupling correction. In S41, the charging station centralized control platform generates a dynamic virtual identity code through the initial handshake protocol with the charge and discharge collaborative entity, based on the device unique identifier and timestamp (such as performing SHA-256 hash operation after combining the charging pile ID "CP-001" with the electric vehicle VIN code), and divides the virtual identity code into corresponding sub-codes according to the number of received trickle information blocks (such as 8 information blocks transmitted in a certain period) (each sub-code carries specific segment information of the original code), and realizes per-packet decryption and sequence recombination through the binding relationship of "sub-code - number - information block" (such as the 5th information block corresponds to the 5th sub-code), ensuring the complete restoration of the disturbance amount information received from the strong electromagnetic noise environment (such as the power fluctuation of a certain section during the evening peak period is +2kW, and the time sensitivity is 0.75). In S42, the charge and discharge correction factor is used to reflect the degree to which the actual power approaches 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 respectively, and w1 + w2 = 1), and determines the load rate adjustment factor according to the ratio of the rated capacity of the charging station transformer to the current load capacity (such as the load rate of the charging station transformer reaches 85%) falling into the preset category (such as when the load is low (such as the load rate < 50%), the adjustment factor is close to 1, allowing a higher degree of power adjustment freedom; when the load is high (such as the load rate ≥ 80%), the adjustment factor is significantly less than 1, restricting unnecessary charge and discharge power to avoid overload), and at the same time calculates the grid safety factor according to the frequency deviation (such as ±0.15Hz) and voltage deviation (such as ±3%) monitored by the power grid in real time (such as the grid safety factor = frequency deviation normalization value × h1 + voltage deviation normalization value × h2, where h1 and h2 are weight coefficients respectively, and h1 + h2 = 1); In S43, through a preset correction model (such as the target margin = prediction margin × charge and discharge correction factor × load rate adjustment factor + grid safety factor × rated capacity), the multi-dimensional parameters are dynamically coupled with the current charge and discharge power; for example, if the initial predicted adjustable margin of a certain 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 the user's real-time behavior (such as temporarily adjusting the charging period), the device operation state (such as the power utilization rate of the charging pile), and the grid working condition (such as local voltage fluctuation).

[0056] In this embodiment, through the technical design path of "communication security authentication, real-time data acquisition, and cross-domain factor calibration", the limitation of the traditional prediction model on the lag response to dynamic disturbances is broken through, and the upgrade of the adjustable margin from "static estimation driven by historical data" to "dynamic correction driven by real-time multi-source information" is realized, providing a more timely and accurate margin benchmark for power grid dispatching, and effectively improving the power regulation accuracy of the charging station in complex scenarios and the operation stability of the power grid.

[0057] S5. Determine the channel correction factor of each charge-discharge coordination entity according to the demand-supply relationship and the target adjustable margin; correct the disturbance quantity information through the channel correction factor to determine the orderly charge-discharge strategy of each charge-discharge coordination entity.

[0058] As an alternative embodiment, the step of determining the channel correction factor of each charge-discharge coordination entity according to the demand-supply relationship and the target adjustable margin; correcting the disturbance quantity information through the channel correction factor to determine the orderly charge-discharge strategy of each charge-discharge coordination entity; is as Figure 4 shown, and includes the following steps: S51. Calculate the vector value of the difference between the actual power consumption of the charge-discharge coordination entity corresponding to the time section and the power regulation caliber to determine the pre-regulation candidate entity participating in the power regulation of the charging station, where the sign of the vector value represents the power flow direction, positive for charging and negative for discharging; S52. Determine the power grid stability contribution degree based on the power grid frequency deviation and the voltage fluctuation value; determine the capacity regulation participation degree based on the historical charge-discharge regulation response rate and the regulation amplitude ratio; determine the power grid fluctuation smoothness based on the volatility and fluctuation amplitude of the power grid power during the charge-discharge period; S53. Perform weighted summation through the power grid stability contribution degree, the capacity regulation participation degree, the fluctuation smoothness, and their corresponding weight factors to determine the regulation priority weight factor of the corresponding pre-regulation candidate entity; S54. Determine the regulation quantity of each pre-regulation candidate entity according to the regulation priority weight factor, the power regulation caliber, and the actual power consumption; use the regulation quantity as the channel correction factor of the corresponding charge-discharge channel to generate the channel correction factor sequence corresponding to each charge-discharge channel; S55. Take the target adjustable margin as the constraint boundary, and sequentially extract the regulation quantity corresponding to each time period in the correction factor sequence to perform capacity expansion or reduction processing on the current charge-discharge power to guide the charge-discharge coordination entity to perform orderly charge-discharge.

[0059] It can be understood that this embodiment adopts a regulation mechanism of multi-dimensional priority evaluation and dynamic coordination of power regulation. In S51, by calculating the vector difference between the actual power consumption of the charge-discharge coordination entity (such as a charging pile and an electric vehicle) and the power regulation range (for example, the actual charging power of a certain charging pile is 4 kW, the upper limit of the regulation range is 6 kW, and the vector difference of +2 kW indicates that there is a charging expansion space of 2 kW), pre-regulation candidate entities 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 regulation direction. In S52-S53, a three-dimensional evaluation system of grid stability contribution (such as using the contribution score in the 0-1 interval of the difference between the real-time frequency deviation and the voltage fluctuation value as the grid stability contribution, for example, the contribution score is 0.9 when the frequency deviation is ±0.1 Hz), capacity regulation participation (calculating the capacity regulation participation according to the product of the historical regulation response rate and the regulation amplitude ratio), 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 respectively, and g1 + g2 + g3 = 1) is constructed, and a regulation priority weight factor (such as the comprehensive weight of a certain candidate entity is 0.85) is generated by weighting through a preset weight matrix (such as 0.4:0.3:0.3), so as to realize the cross-dimensional quantitative ranking of "grid stability contribution - user regulation ability - power fluctuation impact". In S54-S55, the regulation amount (such as 2 kW × 0.85 = 1.7 kW) is calculated according to the weight factor, the power regulation range (such as 6 kW) and the actual power (4 kW), a channel correction factor sequence (such as the regulation amounts at each time period are +1.7 kW and +1.5 kW in turn) is generated, and the current charge-discharge power is dynamically adjusted with the target adjustable margin (such as 262 kW) as the constraint boundary; for example, when a certain section needs to reduce the charging power during the peak period, the entity with a low weight factor (such as a non-emergency charging user) is preferentially reduced by the regulation amount (-1.2 kW), and the entity with a high weight (such as a bus charging station with a high grid stability contribution) maintains the original power, ensuring that the regulation process is smooth and does not exceed the margin boundary.

[0060] Through the technical design path of "potential entity screening - multi-dimensional index evaluation - weight-driven regulation - margin boundary constraint", this embodiment realizes the intelligent upgrade of the charge-discharge strategy from "disorderly and decentralized regulation" to "priority-driven and supply-demand coordination", effectively suppresses the local load peak while ensuring the grid frequency and voltage stability, solves the problem of inaccurate power regulation caused by the coupling of user behavior randomness and communication defects, and significantly improves the global optimization ability of the orderly charge-discharge of charging stations and the reliability of grid operation.

[0061] Embodiment 2: Another technical solution provided in the embodiment of the present invention is: an orderly charge-discharge system for electric vehicles, as Figure 5 shown, includes: Prediction module 101: Predict the predicted adjustable margin of the current charging station on the time section according to the spatio-temporal distribution characteristics of the user's charging and discharging behavior; Negotiation module 102: Determine the charging and discharging channels of each charging and discharging collaborative entity through a dual negotiation mechanism, and negotiate the disturbance amount information of the charging and discharging channels through a preset charging and discharging mode; Interaction module 103: Send the disturbance amount information to the charging station centralized control platform through the trickle transmission mode based on the power line carrier communication technology; Correction module 104: Correct the predicted adjustable margin according to the disturbance amount information on each time section to obtain the target adjustable margin; Execution module 105: Determine the channel correction factor of each charging and discharging collaborative entity according to the demand-supply relationship and the target adjustable margin; correct the disturbance amount information through the channel correction factor to determine the orderly charging and discharging strategy of each charging and discharging collaborative entity.

[0062] This embodiment at least has the following substantial technical effects: The prediction module accurately generates the predicted adjustable margin of the time section by integrating spatio-temporal behavior characteristics and an improved LSTM model, solving the problem of insufficient response of traditional prediction methods to the randomness of user behavior; the negotiation module dynamically matches the charging and discharging channels based on the device power limit and user sensitivity, ensuring both device safety and improving user demand adaptability; the interaction module uses the anti-interference characteristics of power line carrier communication and virtual identity encryption technology to ensure the secure and reliable transmission of disturbance amount information in a strong electromagnetic environment; the correction module enables the predicted margin to track the changes in user behavior and grid status in real time through a multi-dimensional dynamic correction mechanism, significantly improving the prediction accuracy; the execution module realizes the differential power control of the charging and discharging collaborative entities based on the priority weight factor and target margin constraint, effectively suppressing the load peak and maintaining the grid stability. Through the coordinated operation of the 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.

[0063] Embodiment 3: A technical solution provided in an embodiment of the present invention is: An electronic device includes a memory and a processor, and a computer program is stored in the memory. When the processor calls the computer program in the memory, the steps of the method for orderly charging and discharging of electric vehicles based on power line carrier communication are implemented.

[0064] Embodiment 4: A technical solution provided in an embodiment of the present invention is: A storage medium stores computer-executable instructions, and when the computer-executable instructions are loaded and executed by a processor, the steps of the method for orderly charging and discharging of electric vehicles based on power line carrier communication are implemented.

[0065] Through the description of the above embodiments, those skilled in the art can understand that, for the convenience and brevity of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of a specific device is divided into different functional modules to complete all or part of the functions described above.

[0066] In the embodiments provided in the present application, it should be understood that the disclosed structure and method can be implemented in other ways. For example, the embodiments of the structure described above are only illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of structures or units can be in an electrical, mechanical or other form.

[0067] The units described as separate components may or may not be physically separated. The components displayed as units may be one physical unit or multiple physical units, that is, they can be located in one place, or they can be distributed to multiple different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0068] In addition, each functional unit in the embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0069] 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 embodiments of the present application essentially or the part that contributes to the prior art or all or part of this technical solution can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions to enable a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods of the various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks or optical discs that can store program codes.

[0070] The above-described specific embodiments are the preferred embodiments of the method and system for the orderly charging and discharging of electric vehicles based on power line carrier communication of the present invention, and do not limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific embodiment. Any equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. An orderly charging and discharging method for electric vehicles based on power line carrier communication, characterized in that: It includes the following steps: S1. Predict the predicted adjustable margin of the current charging station on the time section according to the spatio-temporal distribution characteristics of the user's charging and discharging behavior; S2. Determine the charging and discharging channels of each charging and discharging collaborative entity through a dual negotiation mechanism, and negotiate the disturbance amount information of the charging and discharging channels through a preset charging and discharging mode; S3. Send the disturbance amount information to the charging station centralized control platform through the trickle transmission mode based on the power line carrier communication technology; S4. The charging station centralized control platform corrects the predicted adjustable margin according to the disturbance amount information on each time section to obtain the target adjustable margin; S5. Determine the channel correction factor of each charging and discharging collaborative entity according to the demand-supply relationship and the target adjustable margin; correct the disturbance amount information through the channel correction factor to determine the orderly charging and discharging strategy of each charging and discharging collaborative entity.

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 step of predicting the predicted adjustable margin of the current charging station on the time section according to the spatio-temporal distribution characteristics of the user's charging and discharging behavior; includes the following steps: S11. Obtain the spatio-temporal distribution characteristic data of the user's charging and discharging behavior to construct a charging and discharging feature vector, and the charging and discharging feature vector includes: time feature, space feature, behavior feature and environmental feature; S12. Pre-divide 24 hours into multiple electricity price intervals based on historical electricity price data, and determine the electricity price elasticity coefficient according to the change amount of charging and discharging demand and the change amount of electricity price in combination with the sliding window algorithm; S13. Expand or contract the electricity price interval according to the electricity price elasticity coefficient to obtain a time section set, and perform clustering analysis on the charging and discharging feature vectors within each time section according to the charging mode to determine the charging mode cluster; S14. Use an improved long short-term memory network to fuse the attention mechanism to construct an adjustable margin prediction model, use the historical load sequence and the charging mode cluster corresponding to the section as inputs, output the predicted power supply amount and the predicted power consumption amount, and calculate the predicted adjustable margin corresponding to each section in combination with the reserved safety margin.

3. The method for orderly charging and discharging of electric vehicles based on power line carrier communication according to claim 1, characterized in that: The step of determining the charging and discharging channels of each charging and discharging collaborative entity through a dual negotiation mechanism, and negotiating the disturbance amount information of the charging and discharging channels through a preset charging and discharging mode; includes the following steps: S21. Select the minimum value of the upper limit of the power supplyable by the charging pile and the upper limit of the power receivable by the electric vehicle as the upper limit of the power adjustment of the charging channel; And select the minimum value of the upper limit of the power receivable by the charging pile and the upper limit of the power feedable by the electric vehicle as the lower limit of the power adjustment of the discharging channel; S22. Use the difference between the upper limit of the power adjustment of the charging channel and the minimum safe charging power as the power adjustment caliber of the charging channel; and use the difference between the upper limit of the power adjustment of the discharging channel and the minimum safe discharging power as the power adjustment caliber of the discharging channel; S23. Obtain the preset charge and discharge mode of the electric vehicle, determine the charge and discharge periods and the corresponding target charge and discharge powers according to the time sensitivity and electricity price sensitivity, and adjust the preset charge and discharge mode according to the charge and discharge periods and the corresponding charge and discharge powers to determine the target charge and discharge mode; S24. Determine the power fluctuation range corresponding to the current time section according to the 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 quantity information based on the current power consumption state of the electric vehicle, the power fluctuation range, the current charge and discharge power, the power adjustment caliber, the time sensitivity, and the electricity price sensitivity.

4. The method for orderly charge and discharge of an electric vehicle based on power line carrier communication according to claim 3, wherein: The step of determining the charge and discharge periods and the corresponding target charge and discharge powers according to the time sensitivity and electricity price sensitivity, and adjusting the preset charge and discharge mode according to the charge and discharge periods and the corresponding charge and discharge powers to determine the target charge and discharge mode; includes the following steps: S231. Determine the time sensitivity according to the expected charge and discharge start time, charge and discharge end time set by the user and the current time; determine the electricity price sensitivity according to the real-time electricity price and the user's psychological expected electricity price; S232. Determine the real-time grid load rate according to the rated capacity of the charging station transformer and the current load capacity; determine the load adjustment coefficient according to the preset category in which the real-time grid load rate falls; determine the comprehensive sensitivity according to the time sensitivity, electricity price sensitivity, load adjustment coefficient and their corresponding weight coefficients; S233. Sort the time sections in descending order according to the comprehensive sensitivity, and select the first n time sections with high sensitivity as the candidate charge and discharge periods; S234. Select the minimum value among 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 value among the lower limit of the discharge channel power adjustment, the maximum allowable discharge power, and the maximum discharge power allowed by the remaining battery power as the target discharge power, and construct the target charge and discharge power under the current time section according to the target charging power and the target discharge power; S235. Compare the candidate charge and discharge 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 period is within the power adjustment caliber 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 caliber, adjust the target charge and discharge power proportionally according to the upper limit of the adjustment caliber to determine the target charge and discharge mode, and the proportion is the ratio of the upper limit of the adjustment caliber to the candidate target power.

5. The method for orderly charge and discharge of an electric vehicle based on power line carrier communication according to claim 1, wherein: The power line carrier communication technology sends disturbance quantity information to the charging station centralized control platform through the trickle transmission mode; includes the following steps: S31. Generate a virtual identity code through the identity codes of the charging pile corresponding to the charge and discharge coordination entity and the electric vehicle; S32. Pack the perturbation information into several trickle information blocks to be encrypted according to the fluctuation range of the charge-discharge power; S33. Divide the virtual identity code according to the size and quantity of the trickle information blocks to obtain virtual identity sub-codes, and configure the number information of each trickle information block for each virtual identity sub-code; S34. Encrypt the corresponding trickle information block through the virtual identity sub-code and its corresponding number information, and send it to the charging station centralized control platform through the power line carrier communication technology.

6. The method for orderly charging and discharging of electric vehicles based on power line carrier communication according to claim 5, wherein: The step of packing the perturbation information into several trickle information blocks to be encrypted according to the fluctuation range of the charge-discharge power includes the following steps: S321. Map the charge-discharge power fluctuation range to a three-dimensional feature space to construct a fluctuation feature space mapping model; construct a transmission reliability model based on the characteristics of power line carrier communication based on OFDM modulation; S322. Take maximizing the data transmission efficiency as the objective function, and construct the single-packet data volume constraint, minimum transmission interval constraint, and reliability constraint of the objective function; solve the objective function by the Lagrange multiplier method to obtain the optimal single-packet data volume and the minimum transmission interval; S323. Pack the perturbation information into several trickle information blocks to be encrypted in units of the optimal single-packet data volume.

7. The method for orderly charging and discharging of electric vehicles based on power line carrier communication according to claim 6, wherein: Transmission reliability model The formula is as follows: ， where d is the data block size and t is the data transmission interval, , are respectively the power line channel attenuation coefficients, , are respectively the fluctuation influence factors, is the OFDM subcarrier utilization rate correction coefficient, is the charge and discharge power fluctuation amount.

8. The method for orderly charging and discharging of electric vehicles based on power line carrier communication according to claim 1 or 5, wherein: The charging station centralized control platform corrects the predicted adjustable margin according to the perturbation information at each time section to obtain the target adjustable margin; the method includes the following steps: S41. The charging station centralized control platform generates a virtual identity code according to the handshake information with the charge-discharge cooperation entity, divides the virtual identity code into several virtual identity sub-codes according to the source and quantity of the trickle information blocks, decrypts the trickle information block through the virtual identity sub-code, and performs sequence recombination according to the number information to obtain the perturbation information of each time section; S42. Determine the charge-discharge correction factor according to the current charge-discharge power, time sensitivity, and electricity price sensitivity in the perturbation information of each time section in combination with the rated charge-discharge power; determine the real-time load rate adjustment factor according to the current charge-discharge power and the rated charge-discharge power; calculate the grid safety factor according to the real-time grid frequency deviation and voltage deviation; S43. Correct the predicted adjustable margin according to the charge-discharge correction factor, load rate adjustment factor, grid safety factor, and current charge-discharge power to obtain the target adjustable margin.

9. The method for orderly charging and discharging of electric vehicles based on power line carrier communication according to claim 1, wherein: Determine the channel correction factor of each charge-discharge cooperation entity according to the demand-supply relationship and the target adjustable margin; correct the perturbation information through the channel correction factor to determine the orderly charge-discharge strategy of each charge-discharge cooperation entity; the method includes the following steps: S51. Calculate the vector value of the difference between the actual power consumption of the charge-discharge coordination entity corresponding to the time section and the power regulation caliber to determine the pre-regulation candidate entities participating in the charging station power regulation, where the sign of the vector value represents the power flow direction, positive for charging and negative for discharging; S52. Determine the grid stability contribution degree based on the grid frequency deviation and voltage fluctuation value; determine the capacity regulation participation degree based on the historical charge-discharge regulation response rate and the proportion of the regulation amplitude; determine the grid fluctuation smoothness based on the volatility and fluctuation amplitude of the grid power during the charge-discharge period; S53. Perform weighted summation through the grid stability contribution degree, capacity regulation participation degree, fluctuation smoothness, and their corresponding weight factors to determine the regulation priority weight factor corresponding to the pre-regulation candidate entity; S54. Determine the regulation amount of each pre-regulation candidate entity according to the regulation priority weight factor, power regulation caliber, and actual power consumption; generate the channel correction factor sequence corresponding to each charge-discharge channel by using the regulation amount as the channel correction factor of the corresponding charge-discharge channel; S55. Take the target adjustable margin as the constraint boundary, and sequentially extract the regulation amount corresponding to each time period in the correction factor sequence to expand or reduce the current charge-discharge power to guide the charge-discharge coordination entity to perform orderly charge and discharge.

10. The electric vehicle orderly charging and discharging system is applicable to the electric vehicle orderly charging and discharging method based on power line carrier communication as described in any one of claims 1 to 9, and is characterized in that: It includes: Prediction module: Predict the predicted adjustable margin of the current charging station on the time section according to the spatio-temporal distribution characteristics of the user's charge-discharge behavior; Negotiation module: Determine the charge-discharge channels of each charge-discharge coordination entity through a dual negotiation mechanism, and negotiate the disturbance amount information of the charge-discharge channels through a preset charge-discharge mode; Interaction module: Send the disturbance amount information to the charging station centralized control platform through the trickle transmission mode based on the power line carrier communication technology; Correction module: Correct the predicted adjustable margin according to the disturbance amount information on each time section to obtain the target adjustable margin; Execution module: Determine the channel correction factor of each charge-discharge coordination entity according to the demand-supply relationship and the target adjustable margin; correct the disturbance amount information through the channel correction factor to determine the orderly charge-discharge strategy of each charge-discharge coordination entity.

11. An electronic device, characterized in that: It includes a memory and a processor. When the computer program stored in the memory is called by the processor, the steps of the method for orderly charge and discharge of electric vehicles based on power line carrier communication according to any one of claims 1 to 9 are implemented.

12. A storage medium, characterized in that: The computer-executable instructions are stored in the storage medium. When the computer-executable instructions are loaded and executed by the processor, the steps of the method for orderly charge and discharge of electric vehicles based on power line carrier communication according to any one of claims 1 to 9 are implemented.

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