Charging pile orderly charging method, device and storage medium

Abstract

The present application relates to the field of power supply for new energy vehicles and provides a method for orderly charging of charging piles, the method comprising: real-time collection of multi-source data sets; generating a dynamic scheduling strategy through a multi-objective optimization algorithm based on the multi-source data sets; generating a charging power control instruction set according to the power allocation matrix of each charging pile, and generating a charging sequence scheduling instruction based on a time window mapping table of each charging pile; issuing the charging power control instruction set and the charging sequence scheduling instruction to a target charging pile group to start a charging process; during the execution of the charging process, real-time monitoring of the actual operating parameter set of each charging pile and verifying the execution consistency of the actual operating parameters in the actual operating parameter set with the charging power control instruction set; dynamically adjusting the power allocation coefficient in the power allocation matrix based on the deviation analysis results between the operating parameter set and the dynamic scheduling strategy, triggering a rolling optimization calculation of the multi-objective optimization algorithm to update the scheduling strategy.

Description

Technical Field

[0001] The present application relates to the field of new energy vehicle power supply, and in particular to a method, device and storage medium for orderly charging of charging piles. Background Art

[0002] With the increasing prevalence of electric vehicles, a surge in charging demand has led to increased peak-to-valley variations in grid load. Disordered charging behavior can lead to transformer overloads and three-phase imbalance. Existing charging scheduling methods suffer from shortcomings such as an inability to adapt to real-time fluctuations in grid load, large deviations between actual charging power and planned values, impacts on grid safety and stability, and low charging efficiency. Summary of the Invention

[0003] The present application provides a charging pile orderly charging method, device and storage medium, which can realize a data-driven, closed-loop control and human-machine collaborative intelligent charging management system.

[0004] In one aspect, the present application provides a method for orderly charging of a charging pile, the method comprising:

[0005] Real-time collection of multi-source data sets;

[0006] Based on the multi-source data set, a dynamic scheduling strategy is generated by a multi-objective optimization algorithm, wherein the dynamic scheduling strategy includes at least a power allocation matrix and a time window mapping table for each charging pile;

[0007] generating a charging power control instruction set according to the power allocation matrix, and generating a charging sequence scheduling instruction based on the time window mapping table;

[0008] Sending the charging power control instruction set and charging sequence scheduling instruction to the target charging pile group to start the charging process;

[0009] During the execution of the charging process, real-time monitoring of the actual operating parameter set of each charging pile and verification of the execution consistency of the actual operating parameters in the actual operating parameter set and the charging power control instruction set;

[0010] Based on the deviation analysis result between the operating parameter set and the dynamic scheduling strategy, the power allocation coefficient in the power allocation matrix is dynamically adjusted, and the rolling optimization calculation of the multi-objective optimization algorithm is triggered to update the scheduling strategy.

[0011] On the other hand, the present application provides a charging pile orderly charging device, the device comprising:

[0012] Acquisition module, used to collect multi-source data sets in real time;

[0013] A first generation module is configured to generate a dynamic scheduling strategy based on the multi-source data set using a multi-objective optimization algorithm, wherein the dynamic scheduling strategy includes at least a power allocation matrix and a time window mapping table for each charging pile;

[0014] a second generating module, configured to generate a charging power control instruction set according to the power allocation matrix, and generate a charging sequence scheduling instruction based on the time window mapping table;

[0015] A sending module, configured to send the charging power control instruction set and charging sequence scheduling instruction to a target charging pile group to start the charging process;

[0016] A monitoring module, configured to monitor the actual operating parameter set of each charging pile in real time during the execution of the charging process and verify the consistency of the actual operating parameters in the actual operating parameter set with the charging power control instruction set;

[0017] A trigger module is used to dynamically adjust the power allocation coefficient in the power allocation matrix based on the deviation analysis results between the operating parameter set and the dynamic scheduling strategy, and trigger the rolling optimization calculation of the multi-objective optimization algorithm to update the scheduling strategy.

[0018] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the technical solution of the above-mentioned method for orderly charging of charging piles are implemented.

[0019] In a fourth aspect, the present application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the technical solution of the above-mentioned method for orderly charging of charging piles.

[0020] From the technical solutions provided by the present application, it can be seen that, on the one hand, by dynamically generating a dispatching strategy through a multi-objective optimization algorithm, the power allocation matrix can intelligently balance the safety of the power grid, user costs, and facility utilization, thereby reducing the peak-to-valley difference of the power grid; on the other hand, by coordinating the issuance of charging power control instructions and charging sequence dispatching instructions, combined with real-time operation monitoring and execution verification, it is ensured that the charging process strictly follows the dispatching plan, and the power deviation rate is controlled within the preset threshold, effectively preventing local overloads and timing conflicts; thirdly, the dynamic adjustment mechanism based on deviation analysis enables the system to quickly respond to sudden changes in load, and the rolling optimization calculation can complete the strategy update in a short time, which is significantly faster than the traditional static dispatch response speed. In summary, the technical solution of the present application can realize a data-driven, closed-loop controlled, and human-machine collaborative intelligent charging management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 This is a flow chart of the method for orderly charging of a charging pile provided by an embodiment of the present application;

[0023] Figure 2 This is a schematic diagram of the structure of the charging pile orderly charging device provided in an embodiment of the present application;

[0024] Figure 3 It is a schematic diagram of the structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0026] In this specification, adjectives such as first and second may be used only to distinguish one element or action from another element or action, without necessarily requiring or implying any actual such relationship or order. Where circumstances permit, reference to an element or component or step (etc.) should not be construed as being limited to only one of the elements, components, or steps, but may be one or more of the elements, components, or steps, etc.

[0027] In this specification, for the convenience of description, the sizes of various parts shown in the drawings are not drawn according to the actual proportions.

[0028] With the increasing prevalence of electric vehicles, the surge in charging demand has led to an increase in the peak-to-valley difference in grid load. Disordered charging behavior can cause transformer overloads and three-phase imbalance. Existing charging scheduling methods suffer from shortcomings such as an inability to adapt to real-time fluctuations in grid load, large deviations between actual charging power and planned values, impacts on grid security and stability, and low charging efficiency. Specifically, these shortcomings are as follows: 1) Reliance on historical data or fixed electricity price time periods makes them unable to adapt to real-time fluctuations in grid load, resulting in a disconnect between scheduling plans and actual demand; 2) A lack of an execution verification mechanism after charging instructions are issued makes it impossible to promptly correct large deviations between actual charging power and planned values; 3) Users are unable to adjust their charging plans based on their individual needs, which can easily lead to users voluntarily interrupting charging or repeatedly starting and stopping, disrupting scheduling order; and 4) Focusing on a single objective (e.g., minimizing cost) neglects the dynamic balance between grid security, charging efficiency, and user costs, making it difficult to achieve global optimization.

[0029] In view of the above problems of the prior art, this application proposes a method for orderly charging of charging piles, the flow chart of which is shown in the attached figure. Figure 1 As shown, it mainly includes steps S101 to S106, which are detailed as follows:

[0030] Step S101: Collect multi-source data sets in real time.

[0031] Considering that grid load fluctuation data is the prerequisite for power allocation, charging demand parameters are the basis for user-side optimization, and time-of-use electricity prices are a necessary condition for economic calculations, and historical data or static parameters (such as fixed electricity prices) cannot respond to sudden changes in grid load or temporary user demand, resulting in scheduling failure, a multi-source data set consisting of the above data can be collected in real time. In an embodiment of the present application, the multi-source data set collected in real time mainly includes real-time load fluctuation data of power grid nodes, charging demand characteristic parameters of each electric vehicle in the charging request queue, and time-of-use electricity price dynamic curve, etc. Among them, the real-time load fluctuation data of power grid nodes can be collected in real time at a sampling rate of about 1,000 times per second using smart monitoring terminals such as smart meters and RTU devices installed on the outlet side of the power grid transformer and the bus side of the charging station. The processed load data containing timestamps is uploaded to the edge computing node through the industrial Ethernet, and the time-of-use electricity price dynamic curve can be obtained in real time through the power trading platform API interface. Then, the electricity price data is dynamically adjusted based on the load forecast results to generate a time-of-use electricity price dynamic curve data table. Finally, the power grid time-of-use electricity price data and the generated electricity price dynamic curve data are aligned by timestamp to generate a unified time-of-use electricity price dynamic curve. It should be noted that, considering the multi-source nature of the data, the data in the above multi-source data set can be fused, that is, a time axis is established, millisecond timestamps are added to all collected data, and time deviations are eliminated through data cleaning. Then, the real-time load fluctuation data, charging demand characteristic parameters, and time-of-use electricity price dynamic curves are integrated into a multidimensional data cube according to the time window (for example, 5 minutes).

[0032] As an embodiment of the present application, collecting the charging demand characteristic parameters of each electric vehicle in the multi-source charging request queue can be: parsing the current state of charge, maximum allowable charging power curve, and expected full charge time set by the user uploaded by the on-board battery management system; obtaining the charging priority tag and electricity price sensitivity coefficient submitted by the user terminal; combining the geographical location of the charging pile with traffic data to calculate the estimated arrival time window of each electric vehicle. In the above embodiment, combining the geographical location of the charging pile with traffic data to calculate the estimated arrival time window of each electric vehicle can be achieved through steps S1011 to S1015, which are detailed as follows:

[0033] Step S1011: Real-time synchronous collection of charging pile geographic location, electric vehicle positioning data, and dynamic traffic conditions.

[0034] The aforementioned charging pile geolocation, including the access path topology from the main road to the charging pile tip, can be obtained through the charging pile management platform. Electric vehicle positioning data, including the vehicle's latitude, speed, and remaining range, can be obtained through the vehicle's onboard GPS. Dynamic traffic conditions (including real-time traffic congestion index and average speed from the vehicle's current location to the target charging pile) can be obtained by accessing a high-precision traffic condition API.

[0035] Step S1012: Based on the charging pile location and electric vehicle positioning data, a candidate path set is generated through a path planning algorithm.

[0036] The weight of each candidate path in the generated candidate path set It can be calculated based on the total distance of the route and the average speed of the road segment, for example, ,in, D is the total path distance, is the average speed of the road section. In addition, when generating the candidate path set, it is also necessary to exclude paths with a remaining range less than a certain threshold, such as 1.2 times the total path distance.

[0037] Step S1013: For each candidate path in the candidate path set, a prediction model is used to calculate a time window interval including a confidence level.

[0038] Specifically, by inputting feature data such as historical traffic flow, real-time weather, and special event data into a trained LSTM neural network, the electric vehicle speed can be predicted based on the traffic conditions for each candidate path within a certain period of time (for example, 30 minutes) in the future. Based on the predicted speed data, the estimated arrival time window for each path is calculated according to the following rules:

[0039]

[0040] in, is the driving time based on the current speed of the electric vehicle, is the standard deviation of the historical punctuality rate. Then, the route with a confidence level greater than 80% is selected as the main recommendation, where the confidence level depends on the historical punctuality rate and the real-time traffic fluctuation coefficient.

[0041] Step S1014: Filter candidate routes from the time window interval according to the route preference parameters submitted by the user, and verify the availability of the target charging pile within the time window.

[0042] Specifically, if the user terminal marks "Avoid Highways," routes that include highways are filtered from the candidate route set. The remaining available capacity of the target charging station within the estimated arrival time window is queried. If the target charging station is unavailable within the time window, a nearby charging station is selected and route replanning is triggered. That is, step S1012 is re-executed, and a new candidate route set is generated using the route planning algorithm based on the charging station location and electric vehicle positioning data.

[0043] Step S1015: When the electric vehicle deviates from the planned path in the candidate path set by more than a threshold or the traffic conditions have data updates, steps S1012 to S1014 are re-executed to generate an updated time window.

[0044] Step S102: Based on the multi-source data set, a dynamic scheduling strategy is generated through a multi-objective optimization algorithm, wherein the dynamic scheduling strategy at least includes a power allocation matrix and a time window mapping table for each charging pile.

[0045] In existing technologies, single-objective optimization (e.g., minimizing only cost) fails to balance grid security and user needs. Multi-objective optimization, however, is an effective approach to resolving conflicts between load balancing, cost control, and resource utilization. In the embodiments of this application, a multi-objective optimization algorithm is an effective model for generating dynamic scheduling strategies based on multi-source data sets. These dynamic scheduling strategies include at least a power allocation matrix and a time window mapping table for each charging station. Both are the direct basis for subsequent instruction issuance. The lack of either one can lead to an uncontrolled charging process. For example, power allocation without time planning can lead to charging period conflicts.

[0046] The multi-objective optimization algorithm in the above embodiment can be established based on a multi-source data set, namely, the real-time load fluctuation data of the power grid nodes, the charging demand characteristic parameters of each electric vehicle in the charging request queue, and the estimated arrival time window of the electric vehicle. Specifically, it includes: based on the real-time load fluctuation data of the power grid nodes, establishing a first objective function with minimizing the peak-to-valley difference of the power grid load; based on the electricity price sensitivity coefficient in the charging demand characteristic parameters, establishing a second objective function with minimizing the total charging cost of the user group; based on the estimated arrival time window, establishing a third objective function with maximizing the average utilization rate of the charging facilities; and using a dynamic weight allocation strategy to adjust the weight coefficients of the first objective function, the second objective function, and the third objective function according to the real-time load rate of the power grid. In the above embodiment, the adjustment of the weight coefficients of the first objective function, the second objective function, and the third objective function can be calculated in real time by a fuzzy logic controller. For example, when the real-time load rate of the power grid is greater than a first preset threshold (for example, 85%), the weight of the first objective function is set to , set the weight of the second objective function to , set the weight of the third objective function to ,in, and When the real-time load rate of the power grid is less than the first preset threshold (for example, 40%), the weight of the first objective function is adjusted to , adjust the second objective function to , adjust the third objective function to , compared with the case where the real-time load rate of the power grid is greater than the first preset threshold, the weights of each objective function are compared with the previous weights. , , ,and The dynamic weight mechanism of the above embodiment can solve the problem that fixed weights cannot adapt to changes in load rate, such as focusing on grid security during peak hours and focusing on user costs during off-peak hours. Compared with the fixed weight solution, it not only reduces the peak-to-valley difference in grid load, but also improves the utilization rate of facilities such as charging piles.

[0047] Furthermore, in order to prevent equipment from overloading and burning, avoid grid harmonic pollution, and ensure the basic rights and interests of charging pile users, in an embodiment of the present application, the constraints of the multi-objective optimization algorithm in the above embodiment can be set, specifically including: setting the transformer load rate to not exceed the preset safety threshold, and the three-phase imbalance calculated based on real-time load fluctuation data to be lower than the critical value; setting the charging completion time to be no later than the expected full time in the charging demand characteristic parameters; and setting the charging power adjustment rate to not exceed the step change rate in the maximum allowable charging power curve. Experiments have shown that after clarifying the constraints of the multi-objective optimization algorithm, not only the grid accident rate is significantly reduced, but also the algorithm solution speed is significantly improved because the solution space is narrowed.

[0048] After constructing a multi-objective optimization algorithm using a dynamic weight allocation strategy and clarifying its constraints, a dynamic scheduling strategy can be generated based on a rolling time window mechanism and a preset algorithm. Specifically, a preset duration (e.g., 30 minutes) can be set as a rolling optimization window, with optimization calculations triggered every several minutes. The immutability of already started charging tasks is preserved, and only tasks in unexecuted periods are optimized. A certain number (e.g., 100 groups) of candidate solutions are then randomly generated. The solution set is Pareto-front graded based on the objective function value, and offspring are generated using simulated binary crossover and polynomial mutation operations. Finally, the parent and offspring populations are merged, and the top several optimal solutions are selected to enter the next iteration, ultimately generating a dynamic scheduling strategy. As can be seen from steps S101 and S102 of the above embodiment, by utilizing the fusion analysis of the time-of-use electricity price dynamic curve and the characteristic parameters of charging demand, when generating a dynamic scheduling strategy, the peak-to-valley difference in grid load, user charging costs, and charging facility utilization are simultaneously optimized, thereby reducing the total charging cost for electric vehicle users and improving the average utilization rate of charging piles.

[0049] Step S103: Generate a charging power control instruction set according to the power allocation matrix of each charging pile, and generate a charging sequence scheduling instruction based on the time window mapping table of each charging pile.

[0050] Specifically, generating a charging power control instruction set based on the power allocation matrix of each charging pile can be done by performing cubic spline interpolation on the discrete time slice power values in the power allocation matrix (for example, one value every 5 minutes) to generate a continuous power change curve, so that the power change rate does not exceed the step change rate allowed by the battery. The charging power control instruction set is encapsulated, that is, the instruction unique identifier, time-power key-value pair, and validity check code are encapsulated, and the instruction integrity and source credibility are verified through a digital signature. As for generating charging sequence scheduling instructions based on the time window mapping table, this can be achieved through steps S1031 to S1033, as detailed below:

[0051] Step S1031: Detect time window conflict.

[0052] Specifically, it includes: traversing the time window mapping table, detecting the overlap of time windows on the same charging pile, and if a conflict is detected (for example, two electric vehicles are assigned to the same charging pile and the time overlap exceeds 5 minutes), starting the conflict resolution algorithm, that is, giving priority to retaining the time window of high-priority vehicles (such as emergency charging), adopting the time window right shift strategy for low-priority vehicles, and calculating compensation points (for example, giving 15 minutes of free charging).

[0053] Step S1032: Plan the charging pile start and stop sequence.

[0054] Specifically, it includes: waking up the charging pile and performing a self-check several minutes before charging starts; sending a charging start command and loading the power control curve when the start time arrives; and sending a gentle power reduction command (for example, linearly reducing from 50kW to 0kW) several minutes before the end time to avoid battery thermal shock.

[0055] Step S1033: embedding exception handling instructions.

[0056] Specifically, if a charging pile fails to respond to instructions for several consecutive time slices, it is marked as a fault and the backup charging pile switching process is started; if the grid load suddenly changes and exceeds the preset threshold, a global power reduction instruction is inserted and the power of all charging piles is uniformly reduced.

[0057] Furthermore, to address the inability of traditional first-come, first-served charging mechanisms to handle sudden queue-jumping of high-priority charging requests (e.g., ambulance charging) and the increased user complaint rate caused by forced scheduling, the aforementioned embodiment's generation of charging sequence scheduling instructions based on a time window mapping table may further include: dividing charging requests into an emergency charging queue and a flexible charging queue based on the time period priority tags in the time window mapping table; employing a fixed time window allocation strategy for the emergency charging queue and a dynamic time window bidding mechanism for the flexible charging queue; and, upon detecting the insertion of a high-priority charging request, initiating a queue reordering compensation algorithm and calculating a point compensation value for the affected user. The dynamic time window bidding mechanism in the aforementioned embodiment may include: pushing available flexible charging time periods and corresponding electricity price discount rates to the user terminals of electric vehicle users; receiving feedback from these users on their time period selection intentions and generating a successful bidding queue; and dynamically updating the time period allocation records in the time window mapping table based on the bidding results. This significantly reduces the average response time for high-priority charging requests, and the bidding mechanism also increases the proportion of charging during off-peak hours on the power grid.

[0058] Step S104: Send the charging power control instruction set and the charging sequence scheduling instruction to the target charging pile group to start the charging process.

[0059] Since the charging power control instruction set is generated based on the power allocation matrix of each charging pile, and the charging sequence scheduling instruction is generated based on the time window mapping table, and the power allocation matrix determines "how much to charge" and the time window mapping table determines "when to charge", the charging power control instruction set and the charging sequence scheduling instruction are sent to the target charging pile group to start the charging process. The two work together to achieve order in the time and space dimensions, preventing users or charging piles from spontaneously starting in an disorderly manner.

[0060] Step S105: During the charging process, the actual operating parameter set of each charging pile is monitored in real time and the execution consistency of the actual operating parameters in the actual operating parameter set and the charging power control instruction set is verified.

[0061] Considering that the deviation between actual operating parameters (such as instantaneous power) and command values directly affects power grid security, when there is a lack of verification, charging pile failures or user violations will cause the scheduling strategy to gradually become ineffective. However, the health indicators of charging piles (such as temperature and insulation resistance) are the prerequisite for charging safety. Traditional methods only monitor the power level and cannot prevent safety accidents caused by equipment overload. Therefore, in an embodiment of the present application, during the execution of the charging process, the actual operating parameter set of each charging pile is monitored in real time and the execution consistency of the actual operating parameters in the actual operating parameter set with the charging power control instruction set is verified, wherein the operating parameter set includes at least the instantaneous power value, the accumulated charge amount, and the equipment health status indicator, etc. Specifically, verifying the execution consistency of the actual operating parameters in the actual operating parameter set with the charging power control instruction set can be: constructing a command-response comparison model, calculating the dynamic time warping (DTW) distance between the actual power curve and the command value; when the DTW distance exceeds a preset tolerance threshold, generating an execution abnormality event log and triggering an early warning signal; and responding in a graded manner according to the abnormality type, including local power correction, charging pile switching, or global strategy recalculation. The hierarchical response in the above-mentioned embodiment includes primary, secondary, and tertiary responses. Primary response involves dynamic power compensation for instantaneous power deviations, maintaining current charging station operation. Secondary response triggers charging station switching instructions and updates the power allocation matrix in response to abnormal device health. Third-level response initiates emergency dispatch strategies for sudden grid load changes, suspending low-priority charging queues. Compared to simple threshold comparisons (e.g., generating an alarm when power deviations exceed a preset threshold), the DTW algorithm can identify anomalies in timing patterns and predict device failures in advance, thus avoiding the waste of resources caused by "blanket-size-fits-all" shutdowns. Localized corrections can significantly reduce the probability of charging interruptions.

[0062] Step S106: Based on the deviation analysis result between the operating parameter set and the dynamic scheduling strategy, the power allocation coefficient in the power allocation matrix is dynamically adjusted, and the rolling optimization calculation of the multi-objective optimization algorithm is triggered to update the scheduling strategy.

[0063] On the one hand, if only the actual operating parameter set of each charging pile is monitored without adjustment, the system will degenerate into open-loop control and will be unable to cope with continuous changes; on the other hand, the dynamic changes in grid load and user demand also have inherent requirements for real-time updates of the scheduling strategy. Therefore, in an embodiment of the present application, based on the deviation analysis results between the operating parameter set and the dynamic scheduling strategy, the power allocation coefficient in the power allocation matrix can be dynamically adjusted, and the rolling optimization calculation of the multi-objective optimization algorithm can be triggered to update the scheduling strategy. Based on the deviation analysis results between the operating parameter set and the dynamic scheduling strategy, the power allocation coefficient is dynamically adjusted and the rolling optimization calculation is triggered, so that the scheduling strategy can respond to sudden changes in grid load or charging demand insertion in real time. In the above embodiment, the rolling optimization calculation of the multi-objective optimization algorithm can be: setting the rolling time window length (the time window length can be adaptively adjusted according to the grid load fluctuation rate); retaining the immutability constraint of the executed instructions during each rolling optimization; using the improved particle swarm optimization algorithm to solve the optimal power allocation coefficient within the current time window, wherein the improved particle swarm optimization algorithm can be: introducing an inertia weight adaptive adjustment mechanism, and the weight value decays nonlinearly with the number of iterations; adding a population diversity maintenance strategy, triggering a local perturbation operation when the particle aggregation degree is too high; combining the greedy algorithm to quickly screen the Pareto optimal solution set of each iteration.

[0064] To avoid conflicting behaviors caused by forced scheduling, such as electric vehicle users pulling out their guns and switching charging piles, and to input the parameters confirmed by electric vehicle users as hard constraints into the optimization algorithm to prevent system shock caused by arbitrary modifications by electric vehicle users, the above-mentioned method embodiment also includes user collaboration and constraint feedback, that is, building a user interactive feedback loop, displaying the adjusted charging parameters in real time and receiving confirmation instructions from the electric vehicle user, and feeding the confirmed charging parameters back to the multi-objective optimization algorithm for strategy correction. Among them, building a user interactive feedback loop can be: based on the updated scheduling strategy, generating a visual suggestion interface including a comparison view of the charging period before and after adjustment and a power curve difference analysis; capturing the user's operation trajectory data on the visual suggestion interface in real time, extracting user preference features through a clustering algorithm, and dynamically updating the electricity price sensitivity coefficient and charging priority mark in the charging demand feature parameters; when it is detected that the user rejects the adjustment plan, generating at least two alternative charging plans based on the electric vehicle user's preference features, and triggering the multi-objective optimization algorithm for rapid recalculation; feeding the final charging plan confirmed by the electric vehicle user as a hard constraint condition back to the multi-objective optimization algorithm to update the power allocation matrix and time window mapping table of the charging pile.

[0065] Furthermore, the method of the above embodiment may also include a security authentication mechanism, that is, in the charging startup phase, the legitimacy of the charging pile and the electric vehicle is verified through a two-way digital certificate; in the data transmission phase, the SM4 national secret algorithm is used to perform end-to-end encryption on the control instructions; in the billing and settlement phase, blockchain technology is applied to perform distributed storage of transaction records, wherein the SM4 national secret algorithm is used to perform end-to-end encryption on the control instructions specifically as follows: a unique session key is generated for each charging session, and the key is generated based on the elliptic curve cryptography algorithm; a timestamp and device fingerprint watermark are added to the power control instruction to prevent replay attacks; key lifecycle management is implemented, and the session key is automatically destroyed after charging is completed.

[0066] Furthermore, the method of the above embodiment may also include load forecasting optimization, that is, constructing a short-term load forecasting model based on a deep belief network, with input features including historical load curves, weather data, and holiday markers; using transfer learning technology to adapt the pre-trained general model to the operating characteristics of a specific charging station; updating the prediction results once every preset time interval (for example, 15 minutes) and using them as feedforward input parameters of the multi-objective optimization algorithm.

[0067] Furthermore, in order to predict the equipment life decay trend and guide users to choose high-efficiency charging piles to reduce overall energy consumption, the method of the above embodiment may also include an energy efficiency-driven dynamic optimization link, that is, in the process of real-time monitoring of the actual operating parameter set of each charging pile, the instantaneous efficiency index of each charging pile (including unit power loss rate and energy conversion efficiency, etc.) is calculated in real time; an energy efficiency degradation model is constructed based on historical efficiency data to predict the life decay trend of the charging pile; an energy efficiency heat map is generated, which displays the life prediction results of the charging pile and pushes preferred recommendations through the user terminal; the instantaneous efficiency index and life prediction results of each charging pile are input into the multi-objective optimization algorithm as optimization variables to dynamically adjust the power allocation coefficient of the high-loss charging pile in the power allocation matrix; when it is detected that the efficiency value of a charging pile is continuously lower than the safety threshold, an equipment maintenance alarm is triggered and the charging sequence scheduling instruction is updated.

[0068] From the above attached Figure 1From the example of the orderly charging method of charging piles, it can be seen that, on the one hand, the scheduling strategy is dynamically generated through the multi-objective optimization algorithm, so that the power allocation matrix can intelligently balance the safety of the power grid, user costs and facility utilization, thereby reducing the peak-to-valley difference of the power grid; on the other hand, through the coordinated issuance of charging power control instructions and charging sequence scheduling instructions, combined with real-time operation monitoring and execution verification, it is ensured that the charging process strictly follows the scheduling plan, and the power deviation rate is controlled within the preset threshold, effectively preventing local overload and timing conflicts; thirdly, the dynamic adjustment mechanism based on deviation analysis enables the system to quickly respond to load mutations, and the rolling optimization calculation can complete the strategy update in a short time, which is significantly faster than the traditional static scheduling response speed. In summary, the technical solution of this application can realize a data-driven, closed-loop controlled, and human-machine collaborative intelligent charging management system.

[0069] Please see the attached Figure 2 , is a charging pile orderly charging device provided in an embodiment of the present application, the device may include an acquisition module 201, a first generation module 202, a second generation module 203, a sending module 204, a monitoring module 205 and a triggering module 206, which are described in detail as follows:

[0070] Acquisition module 201, for collecting multi-source data sets in real time;

[0071] A first generation module 202 is configured to generate a dynamic scheduling strategy based on a multi-source data set using a multi-objective optimization algorithm, wherein the dynamic scheduling strategy includes at least a power allocation matrix and a time window mapping table for each charging pile;

[0072] The second generation module 203 is used to generate a charging power control instruction set according to the power allocation matrix of each charging pile, and generate a charging sequence scheduling instruction based on the time window mapping table of each charging pile;

[0073] The sending module 204 is used to send the charging power control instruction set and the charging sequence scheduling instruction to the target charging pile group to start the charging process;

[0074] The monitoring module 205 is used to monitor the actual operating parameter set of each charging pile in real time during the charging process and verify the execution consistency of the actual operating parameters in the actual operating parameter set with the charging power control instruction set;

[0075] The trigger module 206 is used to dynamically adjust the power allocation coefficient in the power allocation matrix based on the deviation analysis result between the operating parameter set and the dynamic scheduling strategy, and trigger the rolling optimization calculation of the multi-objective optimization algorithm to update the scheduling strategy.

[0076] From the above attached Figure 2It can be seen from the example of the orderly charging device of the charging pile that, on the one hand, the scheduling strategy is dynamically generated through the multi-objective optimization algorithm, so that the power allocation matrix can intelligently balance the safety of the power grid, user costs and facility utilization, thereby reducing the peak-to-valley difference of the power grid; on the other hand, through the coordinated issuance of charging power control instructions and charging sequence scheduling instructions, combined with real-time operation monitoring and execution verification, it is ensured that the charging process strictly follows the scheduling plan, and the power deviation rate is controlled within the preset threshold, effectively preventing local overload and timing conflicts; thirdly, the dynamic adjustment mechanism based on deviation analysis enables the system to quickly respond to load mutations, and the rolling optimization calculation can complete the strategy update in a short time, which is significantly faster than the traditional static scheduling response speed. In summary, the technical solution of this application can realize a data-driven, closed-loop controlled, and human-machine collaborative intelligent charging management system.

[0077] Figure 3 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Figure 3 As shown, the electronic device 3 of this embodiment mainly includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and capable of running on the processor 30, such as a program for the orderly charging method of a charging pile. When the processor 30 executes the computer program 32, the steps in the embodiment of the orderly charging method of the charging pile are implemented, such as Figure 1 Alternatively, when the processor 30 executes the computer program 32, the functions of the modules / units in the above-mentioned device embodiments are realized, for example Figure 2 The functions of the acquisition module 201, the first generation module 202, the second generation module 203, the sending module 204, the monitoring module 205 and the triggering module 206 are shown.

[0078] Exemplarily, a computer program 32 for a method for orderly charging of charging piles mainly includes: real-time collection of multi-source data sets; generating a dynamic scheduling strategy based on the multi-source data sets using a multi-objective optimization algorithm, wherein the dynamic scheduling strategy includes at least a power allocation matrix and a time window mapping table for each charging pile; generating a charging power control instruction set based on the power allocation matrix of each charging pile, and generating a charging sequence scheduling instruction based on the time window mapping table of each charging pile; issuing the charging power control instruction set and the charging sequence scheduling instruction set to a target charging pile group to initiate a charging process; during the execution of the charging process, monitoring the actual operating parameter set of each charging pile in real time and verifying the execution consistency of the actual operating parameters in the actual operating parameter set with the charging power control instruction set; based on the deviation analysis results of the operating parameter set and the dynamic scheduling strategy, dynamically adjusting the power allocation coefficient in the power allocation matrix, triggering a rolling optimization calculation of the multi-objective optimization algorithm to update the scheduling strategy. The computer program 32 can be divided into one or more modules / units, one or more of which are stored in the memory 31 and executed by the processor 30 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 32 in the electronic device 3. For example, the computer program 32 may be divided into the functions of an acquisition module 201, a first generation module 202, a second generation module 203, a sending module 204, a monitoring module 205, and a trigger module 206 (modules in a virtual device). The specific functions of each module are as follows: the acquisition module 201 is used to collect multi-source data sets in real time; the first generation module 202 is used to generate a dynamic scheduling strategy based on the multi-source data sets through a multi-objective optimization algorithm, wherein the dynamic scheduling strategy includes at least a power allocation matrix and a time window mapping table for each charging pile; the second generation module 203 is used to generate a charging power control instruction set according to the power allocation matrix of each charging pile. And based on the time window mapping table of each charging pile, a charging sequence scheduling instruction is generated; a sending module 204 is used to send the charging power control instruction set and the charging sequence scheduling instruction to the target charging pile group to start the charging process; a monitoring module 205 is used to monitor the actual operating parameter set of each charging pile in real time during the execution of the charging process and verify the execution consistency of the actual operating parameters in the actual operating parameter set and the charging power control instruction set; a triggering module 206 is used to dynamically adjust the power allocation coefficient in the power allocation matrix based on the deviation analysis results between the operating parameter set and the dynamic scheduling strategy, and trigger the rolling optimization calculation of the multi-objective optimization algorithm to update the scheduling strategy.

[0079] The electronic device 3 may include but is not limited to a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3It is only an example of electronic device 3 and does not constitute a limitation of electronic device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0080] The processor 30 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0081] The memory 31 can be an internal storage unit of the electronic device 3, such as the hard drive or memory of the electronic device 3. The memory 31 can also be an external storage device of the electronic device 3, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the memory 31 can include both the internal storage unit of the electronic device 3 and an external storage device. The memory 31 is used to store computer programs and other programs and data required by the electronic device. The memory 31 can also be used to temporarily store data that has been output or is about to be output.

[0082] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0083] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0084] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0085] In the embodiments provided in this application, it should be understood that the disclosed devices / equipment and methods can be implemented in other ways. For example, the device / equipment embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0086] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0087] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0088] If the integrated module / 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 storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing related hardware through a computer program. The computer program of the orderly charging method of charging piles can be stored in a storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments, namely, real-time collection of multi-source data sets; generating a dynamic scheduling strategy based on the multi-source data sets through a multi-objective optimization algorithm, wherein the dynamic scheduling strategy at least includes a power allocation matrix and a time window mapping table for each charging pile; generating a charging power control instruction set according to the power allocation matrix of each charging pile, and generating a charging sequence scheduling instruction based on the time window mapping table of each charging pile; issuing the charging power control instruction set and the charging sequence scheduling instruction to the target charging pile group to start the charging process; during the execution of the charging process, real-time monitoring of the actual operating parameter set of each charging pile and verifying the execution consistency of the actual operating parameters in the actual operating parameter set with the charging power control instruction set; based on the deviation analysis results between the operating parameter set and the dynamic scheduling strategy, dynamically adjusting the power allocation coefficient in the power allocation matrix, triggering the rolling optimization calculation of the multi-objective optimization algorithm to update the scheduling strategy. Computer programs include computer program code, which may be in source code, object code, executable files, or some intermediate form. Storage media may include any entity or device capable of carrying computer program code, recording media, USB flash drives, removable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunications signals, and software distribution media. It should be noted that the content of storage media may be appropriately expanded or reduced based on the requirements of legislation and patent practice within a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, storage media do not include electric carrier signals or telecommunications signals.

[0089] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application. The specific implementation methods described above further explain the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only the specific implementation method of the present application and is not used to limit the protection scope of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included in the protection scope of the present invention.

Claims

1. A charging pile orderly charging method, characterized in that: The method comprises: Real-time collection of multi-source data sets, including real-time load fluctuation data of power grid nodes, charging demand characteristic parameters of each electric vehicle in the charging request queue, and time-of-use electricity price dynamic curve; Based on the multi-source data set, a dynamic scheduling strategy is generated by a multi-objective optimization algorithm, wherein the dynamic scheduling strategy includes at least a power allocation matrix and a time window mapping table for each charging pile; generating a charging power control instruction set according to the power allocation matrix, and generating a charging sequence scheduling instruction based on the time window mapping table; Sending the charging power control instruction set and charging sequence scheduling instruction to the target charging pile group to start the charging process; During the execution of the charging process, real-time monitoring of the actual operating parameter set of each charging pile and verification of the execution consistency of the actual operating parameters in the actual operating parameter set and the charging power control instruction set; Based on the deviation analysis result between the operating parameter set and the dynamic scheduling strategy, the power allocation coefficient in the power allocation matrix is dynamically adjusted, and the rolling optimization calculation of the multi-objective optimization algorithm is triggered to update the scheduling strategy.

2. The method for orderly charging a charging pile according to claim 1, wherein: The multi-source data set includes charging demand characteristic parameters of each electric vehicle in the charging request queue. Collecting the charging demand characteristic parameters of each electric vehicle in the multi-source charging request queue includes: Parse the current state of charge, maximum allowable charging power curve, and user-set expected full charge time uploaded by the vehicle battery management system; Obtaining the charging priority mark and electricity price sensitivity coefficient submitted by the user terminal; Combining the geographical location of charging piles with traffic condition data, the estimated arrival time window of each electric vehicle is calculated.

3. The method for orderly charging a charging pile according to claim 2, wherein: The construction of the multi-objective optimization algorithm includes: Based on the real-time load fluctuation data of the power grid nodes, the first objective function is established to minimize the peak-to-valley difference of the power grid load; Based on the electricity price sensitivity coefficient in the charging demand characteristic parameter, a second objective function is established with the lowest total charging cost for the user group; Establishing a third objective function based on the estimated arrival time window and maximizing the average utilization rate of the charging facilities; A dynamic weight allocation strategy is adopted to adjust the weight coefficients of the first objective function, the second objective function and the third objective function according to the real-time load rate of the power grid.

4. The method for orderly charging a charging pile according to claim 3, wherein: The constraints of the multi-objective optimization algorithm include: The transformer load factor is set to not exceed a preset safety threshold, and the three-phase unbalance calculated based on the real-time load fluctuation data is lower than a critical value; Setting the charging completion time to be no later than the expected full charge time in the charging requirement characteristic parameter; and The charging power adjustment rate is set not to exceed the step change rate in the maximum allowed charging power curve.

5. The method for orderly charging a charging pile according to claim 1, wherein: The verifying the execution consistency between the actual operating parameters in the actual operating parameter set and the charging power control instruction set includes: Build a command-response comparison model and calculate the dynamic time warping (DTW) distance between the actual power curve and the command value; When the DTW distance exceeds a preset tolerance threshold, an execution abnormality event log is generated and an early warning signal is triggered; Response is graded based on the type of anomaly, including local power correction, charging station switching, or global strategy recalculation.

6. The method for orderly charging a charging pile according to claim 1, wherein: The rolling optimization calculation of the multi-objective optimization algorithm includes: Setting the length of the rolling time window, wherein the length is adaptively adjusted according to the grid load fluctuation rate; During each rolling optimization, the immutability constraints of executed instructions are preserved; An improved particle swarm optimization algorithm is used to solve the optimal power allocation coefficient within the current time window.

7. The method for orderly charging a charging pile according to claim 1, wherein: The method further comprises: During the real-time monitoring of the actual operating parameter set of each charging pile, the instantaneous efficiency index of each charging pile is calculated in real time; Build an energy efficiency degradation model based on historical efficiency data to predict the life degradation trend of charging piles; Generate an energy efficiency heat map, the heat map showing the lifespan prediction results of each charging pile, and push a preferred recommendation through a user terminal; Input the instantaneous efficiency index and the life prediction result as optimization variables into the multi-objective optimization algorithm, and dynamically adjust the power allocation coefficient of the high-loss charging pile in the power allocation matrix; When it is detected that the efficiency value of a charging pile is continuously lower than the safety threshold, an equipment maintenance alarm is triggered and the charging sequence scheduling instruction is updated.

8. A charging pile orderly charging device, characterized in that: The device comprises: An acquisition module is used to collect multi-source data sets in real time, wherein the multi-source data sets collected in real time include real-time load fluctuation data of power grid nodes, charging demand characteristic parameters of each electric vehicle in the charging request queue, and a time-of-use electricity price dynamic curve; A first generation module is configured to generate a dynamic scheduling strategy based on the multi-source data set using a multi-objective optimization algorithm, wherein the dynamic scheduling strategy includes at least a power allocation matrix and a time window mapping table for each charging pile; a second generating module, configured to generate a charging power control instruction set according to the power allocation matrix, and generate a charging sequence scheduling instruction based on the time window mapping table; A sending module, configured to send the charging power control instruction set and charging sequence scheduling instruction to a target charging pile group to start the charging process; A monitoring module, configured to monitor the actual operating parameter set of each charging pile in real time during the execution of the charging process and verify the consistency of the actual operating parameters in the actual operating parameter set with the charging power control instruction set; A trigger module is used to dynamically adjust the power allocation coefficient in the power allocation matrix based on the deviation analysis results between the operating parameter set and the dynamic scheduling strategy, and trigger the rolling optimization calculation of the multi-objective optimization algorithm to update the scheduling strategy.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images