An intelligent charging service optimization system and method based on multi-pile cooperative scheduling

Abstract

The application provides a kind of intelligent charging service optimization system and method based on multi-pile cooperative scheduling, it is related to intelligent charging service optimization technical field, obtains the active power data of charging pile cluster;Further determine the total power allocation constraint coefficient of the charging pile cluster;According to the power fluctuation of each charging pile when executing power distribution and the power distribution feasibility of each charging pile to each charging pile total power allocation constraint coefficient Check, obtain power check sequence;Obtain the power margin of each charging pile in the charging pile cluster, determine the power distribution weight of each charging pile based on all power margins and the charging demand level of the vehicle to be charged;When the total power demand of charging pile in the same power supply transformer coverage range exceeds the upper limit of adjustable power interval, adjust the power output of each charging pile based on power check sequence and all power distribution weights, until all vehicles complete charging, can realize the coordination of charging pile power in the scene of multiple charging piles working in parallel.

Description

Technical Field

[0001] This application relates to the field of intelligent charging service optimization technology, and more specifically, to an intelligent charging service optimization system and method based on multi-charging pile collaborative scheduling. Background Technology

[0002] Intelligent charging service optimization technology aims to improve the efficiency and user experience of electric vehicle charging. Through intelligent means, it achieves rational allocation and dynamic management of charging resources. This technology combines the Internet of Things, big data analytics, and artificial intelligence algorithms to monitor the status of charging piles, grid load, and user demand in real time, dynamically adjusting charging power and time to avoid grid overload and charging wait times. By optimizing charging strategies, it not only improves the utilization rate of charging piles but also effectively reduces energy consumption and operating costs. Furthermore, intelligent charging service optimization technology supports collaborative scheduling of multiple charging piles, enhancing system reliability and flexibility, meeting charging needs in different scenarios, and promoting the popularization and sustainable development of electric vehicles.

[0003] With the popularization of new energy vehicles and the accelerated construction of charging piles, it has become common for multiple charging piles to operate in parallel in scenarios such as commercial complexes and transportation hubs. This poses a severe challenge to the power system. Technically, the power of charging piles needs to be dynamically adjusted according to battery status and user demand. However, when multiple piles are working simultaneously, power demand is prone to concentrated bursts. For example, when multiple vehicles are fast charging at the same time, the instantaneous power may far exceed the capacity of the distribution transformer, causing voltage drops, line overloads, or even power outages, affecting service continuity. At the same time, the communication protocols and adjustment logic of charging piles from different brands lack unified standards, making real-time coordination difficult. Traditional power distribution planning is based on static loads and cannot cope with the dynamic impact of charging piles. Existing technologies are mostly limited to single-pile protection and lack global optimization capabilities. In addition, factors such as peak and valley electricity prices and photovoltaic grid connection fluctuations further increase the difficulty of coordination. Therefore, how to coordinate the power of charging piles in scenarios where multiple charging piles are working in parallel has become a difficult problem for the industry. Summary of the Invention

[0004] This application provides an intelligent charging service optimization system and method based on multi-charging pile collaborative scheduling, which can coordinate the power of charging piles in scenarios where multiple charging piles work in parallel.

[0005] In a first aspect, this application provides a smart charging service optimization method based on multi-pile collaborative scheduling, comprising the following steps:

[0006] Receive multi-pile collaborative scheduling tasks issued by the intelligent charging network platform and obtain active power data of the charging pile cluster;

[0007] The total power allocation constraint coefficient of the charging pile cluster is determined based on the active power data and the power carrying capacity threshold of the power grid side in the multi-pile collaborative scheduling task.

[0008] The feasibility of power allocation for each charging pile is verified based on the power fluctuation of each charging pile during power allocation and the total power allocation constraint coefficient, resulting in a power verification sequence.

[0009] Obtain the power margin of each charging pile in the charging pile cluster, and determine the power allocation weight of each charging pile based on all the power margins and the charging demand level of the vehicle to be charged.

[0010] When the total power demand of charging piles within the coverage area of ​​the same power supply transformer exceeds the upper limit of the adjustable power range, the power output of each charging pile is adjusted based on the power verification sequence and all power allocation weights until all vehicles are fully charged.

[0011] In this embodiment, determining the total power allocation constraint coefficient of the charging pile cluster based on the active power data and the power carrying capacity threshold on the grid side in the multi-pile coordinated scheduling task specifically includes:

[0012] The power demand of the charging pile cluster is extracted from the active power data.

[0013] The available power margin on the grid side is determined based on the power carrying capacity threshold of the grid side and the power demand of the charging pile cluster in the multi-pile collaborative scheduling task.

[0014] A power trend change model for charging pile clusters is constructed to predict the short-term power demand increment of charging pile clusters.

[0015] The total power allocation constraint coefficient of the charging pile cluster is determined based on the adjustable power margin and the short-term power demand increment.

[0016] In this embodiment, the feasibility of power allocation for each charging pile is verified based on the power fluctuation during power allocation and the total power allocation constraint coefficient. The resulting power verification sequence specifically includes:

[0017] Statistical analysis of the power fluctuation amplitude of each charging pile within a preset time window;

[0018] A power fluctuation tolerance threshold is set based on the total power allocation constraint coefficient and all power fluctuation amplitudes;

[0019] For each charging station, if the power fluctuation amplitude of the charging station exceeds the power fluctuation tolerance threshold, it is determined that the charging station does not currently meet the power allocation conditions.

[0020] All charging piles that meet the power allocation conditions are selected and sorted by the power fluctuation range of the charging piles from smallest to largest to generate a power verification sequence.

[0021] In this embodiment, determining the power allocation weight of each charging station based on all power margins and the charging demand level of the vehicles to be charged specifically includes:

[0022] Analyze the charging demand level of the vehicles to be charged, where high-level demand corresponds to emergency charging tasks and low-level demand corresponds to delayed charging tasks.

[0023] For charging stations where high-demand vehicles are located, the reciprocal of the charging station's power margin is used as the power allocation weight for the charging station.

[0024] For charging stations where low-demand vehicles are located, the power allocation weight of the charging station is determined based on the ratio between the power margin of the charging station and the average power margin of the charging station cluster.

[0025] In this embodiment, adjusting the power output of each charging pile based on the power verification sequence and all power allocation weights until all vehicles have completed charging specifically includes:

[0026] Charging piles in an allocable state are selected according to the power verification sequence, and power is allocated sequentially according to priority.

[0027] For charging piles that do not meet the power allocation conditions, the requested power of the charging piles is reduced proportionally according to the power allocation weight of the charging piles until the total power demand after reduction falls within the adjustable power range.

[0028] The total power demand of charging piles within the coverage area of ​​the power supply transformer is monitored in real time. If the total power demand exceeds the limit again due to the addition of new charging tasks, the power weight re-allocation process is triggered.

[0029] When a vehicle finishes charging or actively terminates charging, the power quota it occupies is released, and the feasibility of power allocation for the remaining charging piles is re-verified. This process is repeated until all vehicles have finished charging.

[0030] In this embodiment, the multi-charging pile collaborative scheduling task refers to the optimized control command issued by the intelligent charging network platform, which includes the target charging pile cluster, power constraints, and scheduling priority strategy.

[0031] In this embodiment, the active power data represents the set of active power components of each charging pile in the charging pile cluster.

[0032] In this embodiment, the power carrying capacity threshold refers to the maximum allowable power load value that the power grid side can provide under the premise of ensuring safe operation.

[0033] In this embodiment, the upper limit of the adjustable power range refers to the maximum power limit that the power supply transformer can stably output under the current operating conditions.

[0034] Secondly, this application provides an intelligent charging service optimization system based on multi-pile collaborative scheduling, used to execute an intelligent charging service optimization method based on multi-pile collaborative scheduling, the intelligent charging service optimization system comprising:

[0035] The acquisition module is used to receive multi-pile collaborative scheduling tasks issued by the intelligent charging network platform and acquire the active power data of the charging pile cluster.

[0036] The feature processing module is used to determine the total power allocation constraint coefficient of the charging pile cluster based on the active power data and the power carrying capacity threshold of the power grid side in the multi-pile collaborative scheduling task.

[0037] The feature processing module is also used to verify the feasibility of power allocation for each charging pile based on the power fluctuation of each charging pile during power allocation and the total power allocation constraint coefficient, and to obtain a power verification sequence.

[0038] The feature processing module is also used to obtain the power margin of each charging pile in the charging pile cluster, and determine the power allocation weight of each charging pile based on all the power margins and the charging demand level of the vehicle to be charged.

[0039] The adjustment module is used to adjust the power output of each charging pile based on the power verification sequence and all power allocation weights when the total power demand of the charging piles within the coverage area of ​​the same power supply transformer exceeds the upper limit of the adjustable power range, until all vehicles are fully charged.

[0040] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0041] By receiving multi-pile collaborative scheduling tasks from the intelligent charging network platform, the active power data of the charging pile cluster is obtained; the total power allocation constraint coefficient of the charging pile cluster is determined based on the active power data and the power carrying capacity threshold of the power grid side in the multi-pile collaborative scheduling task; the feasibility of power allocation for each charging pile is verified based on the power fluctuation of each charging pile during power allocation and the total power allocation constraint coefficient, resulting in a power verification sequence; the power margin of each charging pile in the charging pile cluster is obtained, and the power allocation weight of each charging pile is determined based on all power margins and the charging demand level of the vehicles to be charged; when the total power demand of the charging piles within the coverage area of ​​the same power supply transformer exceeds the upper limit of the adjustable power range, the power output of each charging pile is adjusted based on the power verification sequence and all power allocation weights until all vehicles are fully charged.

[0042] Therefore, this application adjusts the power output of each charging pile based on the power verification sequence and all power allocation weights until all vehicles are fully charged. First, active power data captures the dynamic load characteristics of the charging pile cluster in real time, while the power carrying capacity threshold on the grid side clarifies the safe operation boundary of the power distribution system. The two are coupled to generate a total power allocation constraint coefficient for the charging pile cluster. This coefficient defines the upper limit of the total power when multiple charging piles are running in parallel at the system level, specifically addressing the problem of concentrated power surges caused by multi-vehicle fast charging and effectively avoiding risks such as voltage instability and line overload caused by exceeding the capacity of the distribution transformer. Second, by verifying the feasibility of charging pile allocation and forming a power verification sequence, unfeasible or high-risk allocation schemes can be screened out in advance, ensuring that the output of each charging pile is within the safe carrying capacity range of the power grid. This not only improves the stability and reliability of the scheduling scheme in actual execution but also lays the foundation for subsequent weight-based... Power optimization allocation provides effective boundary conditions, enabling coordinated operation among multiple charging piles and avoiding global impact caused by local failures. Next, by acquiring the power margin of each charging pile and combining it with the charging demand levels of all vehicles, a power allocation weight is constructed. This allows for differentiated allocation while ensuring grid safety: prioritizing vehicles with insufficient power and urgent needs, improving user experience and service efficiency, while fully utilizing the capacity of charging piles with larger power margins, enhancing overall energy utilization. Finally, by introducing a power verification sequence and power allocation weights, the output of each charging pile is dynamically adjusted under over-limit conditions. This ensures that the scheduling process balances grid capacity and user charging demand. The power verification sequence guarantees the feasibility of the allocation scheme, preventing global imbalance caused by unstable output from a single pile, while the power allocation weights guide limited power to be prioritized for piles with high demand levels or large power margins, improving overall allocation efficiency.

[0043] In summary, the proposed solution can coordinate the power of charging piles in scenarios where multiple charging piles operate in parallel. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart of the intelligent charging service optimization method based on multi-pile collaborative scheduling provided in this application;

[0046] Figure 2 This is an exemplary flowchart for determining the total power allocation constraint coefficient according to the present application;

[0047] Figure 3 This is an exemplary flowchart for determining power allocation weights provided in this application;

[0048] Figure 4 This is a module structure diagram of the intelligent charging service optimization system based on multi-pile collaborative scheduling provided in this application. Detailed Implementation

[0049] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0050] This application provides an intelligent charging service optimization system and method based on multi-pile collaborative scheduling. Its core is to receive multi-pile collaborative scheduling tasks from an intelligent charging network platform to obtain active power data of the charging pile cluster; determine the total power allocation constraint coefficient of the charging pile cluster based on the active power data and the power carrying capacity threshold of the power grid side in the multi-pile collaborative scheduling task; verify the feasibility of power allocation for each charging pile based on the power fluctuation of each charging pile during power allocation and the total power allocation constraint coefficient, obtaining a power verification sequence; obtain the power margin of each charging pile in the charging pile cluster, and determine the power allocation weight of each charging pile based on all power margins and the charging demand level of the vehicles to be charged; when the total power demand of charging piles within the coverage area of ​​the same power supply transformer exceeds the upper limit of the adjustable power range, adjust the power output of each charging pile based on the power verification sequence and all power allocation weights until all vehicles are fully charged. This application can coordinate the power of charging piles in scenarios where multiple charging piles operate in parallel.

[0051] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1 As shown in the figure, this is an exemplary flowchart of a smart charging service optimization method based on multi-pile collaborative scheduling according to this embodiment of the present application. The smart charging service optimization method based on multi-pile collaborative scheduling includes the following steps:

[0052] In step S1, a multi-pile collaborative scheduling task is received from the intelligent charging network platform to obtain the active power data of the charging pile cluster.

[0053] It should be noted that the multi-pile collaborative scheduling task mentioned in this application refers to the optimized control command issued by the smart charging network platform for the charging pile cluster in a specific area. This multi-pile collaborative scheduling task is issued through an encrypted communication protocol and can be triggered based on real-time grid load, changes in charging demand, or a timed mechanism. It aims to balance charging demand, grid carrying capacity, and equipment status, improve charging efficiency and optimize user experience while ensuring safety. It is the core management method for the large-scale operation of smart charging networks.

[0054] It should also be noted that the active power data mentioned in this application represents the set of active power of each charging pile in the charging pile cluster.

[0055] In step S2, the total power allocation constraint coefficient of the charging pile cluster is determined based on the active power data and the power carrying capacity threshold of the grid side in the multi-pile collaborative scheduling task.

[0056] In this embodiment, reference Figure 2 As shown in the figure, this is an exemplary flowchart for determining the total power allocation constraint coefficient in an embodiment of this application. In this embodiment, the determination of the total power allocation constraint coefficient of the charging pile cluster based on the active power data and the power carrying capacity threshold of the power grid side in the multi-pile collaborative scheduling task can be achieved by the following steps:

[0057] In step S21, the power demand of the charging pile cluster is extracted from the active power data;

[0058] In step S22, the adjustable power margin on the grid side is determined based on the power carrying capacity threshold on the grid side and the power demand of the charging pile cluster in the multi-pile collaborative scheduling task.

[0059] In step S23, a power trend change model of the charging pile cluster is constructed to predict the short-term power demand increment of the charging pile cluster.

[0060] In step S24, the total power allocation constraint coefficient of the charging pile cluster is determined based on the adjustable power margin and the short-term power demand increment.

[0061] It should be noted that the power carrying capacity threshold refers to the maximum allowable power load value that the power grid can provide under the premise of ensuring safe operation; the adjustable power margin represents the redundancy of the power grid in the current state for the additional power demand of the charging pile cluster; the short-term power demand increment represents the growth of the power demand of the charging pile cluster in the short term; and the total power allocation constraint coefficient represents the constraint parameter that limits the total power allocation of the charging pile cluster.

[0062] In specific implementation, firstly, the active power of all charging piles in the active power data is summed, and the sum is used as the power demand of the charging pile cluster. Secondly, the power demand of the charging pile cluster is subtracted from the power carrying capacity threshold, and the subtraction value is used as the adjustable power margin on the grid side. Then, the active power data (1-minute granularity) of the charging pile cluster stored in the time-series database for the past 90 days is used as the training set, and time features (peak hours, weekdays / weekends) and environmental features (temperature, precipitation) are added to construct a feature matrix. Then, an LSTM + attention mechanism model is adopted, in which the LSTM layer captures the power time-series dependence (the number of nodes in the 3 hidden layers is 256, 128, and 64), and the attention mechanism strengthens the feature weight of peak hours. After training with the Adam optimizer (learning rate 0.001, 100 iterations), the trained model is used as the power trend change model for the charging pile cluster. This model then predicts the total power of the charging pile cluster over the next 30 minutes, and this total power is used as the predicted total power of the charging pile cluster. The predicted total power is then subtracted from the current power demand of the charging pile cluster, and the resulting value is used as the short-term power demand increment of the charging pile cluster. Finally, the adjustable power margin is divided by the short-term power demand increment, and the resulting value is used as the total power allocation constraint coefficient of the charging pile cluster. The total power allocation constraint coefficient of the charging pile cluster can be calculated using the following formula: Total Power Allocation Constraint Coefficient. ,in, Indicates the first The active power of each charging station This indicates the power carrying capacity threshold on the grid side. This represents the predicted total power of the charging pile cluster. This indicates the current power demand of the charging pile cluster.

[0063] In step S3, the feasibility of power allocation for each charging pile is verified based on the power fluctuation of each charging pile during power allocation and the total power allocation constraint coefficient, resulting in a power verification sequence.

[0064] In this embodiment, the feasibility of power allocation for each charging pile is verified based on the power fluctuation during power allocation and the total power allocation constraint coefficient. The power verification sequence can be obtained by the following steps:

[0065] Statistical analysis of the power fluctuation amplitude of each charging pile within a preset time window;

[0066] A power fluctuation tolerance threshold is set based on the total power allocation constraint coefficient and all power fluctuation amplitudes;

[0067] For each charging station, if the power fluctuation amplitude of the charging station exceeds the power fluctuation tolerance threshold, it is determined that the charging station does not currently meet the power allocation conditions.

[0068] All charging piles that meet the power allocation conditions are selected and sorted by the power fluctuation range of the charging piles from smallest to largest to generate a power verification sequence.

[0069] It should be noted that the power fluctuation tolerance threshold mentioned in this application represents the maximum amplitude limit of power fluctuation of a single charging pile, which is a quantitative benchmark for determining the feasibility of power allocation; the power verification sequence represents the list of charging piles that can participate in power allocation.

[0070] In practice, firstly, relying on the high-frequency power sensor (sampling frequency 1kHz) and edge computing nodes built into the charging pile, a preset time window of 5 minutes is set. The power fluctuation amplitude of each charging pile at different sliding positions (the difference between the maximum and minimum power within the time window) is calculated using a sliding window algorithm. Secondly, the mean and standard deviation of the power fluctuation amplitude of all charging piles at different sliding positions are calculated, which can be obtained by the formula: Power fluctuation tolerance threshold = Total power allocation constraint coefficient × (mean + 0.5 standard deviation). Then, the mean of the power fluctuation amplitude of each charging pile within the preset time window is compared with the power fluctuation tolerance threshold one by one. When the mean of the power fluctuation amplitude of a charging pile within the preset time window is greater than the power fluctuation tolerance threshold, the charging pile is marked as "temporarily unable to participate in power allocation", otherwise it is marked as "can participate in power allocation". Then, the charging piles "can participate in power allocation" are processed by a sorting algorithm (such as merge sort): the power fluctuation amplitude is used as the sorting key, and the piles are arranged in ascending order to form an ordered list, which is then used as the power verification sequence.

[0071] In step S4, the power margin of each charging pile in the charging pile cluster is obtained, and the power allocation weight of each charging pile is determined based on all the power margins and the charging demand level of the vehicle to be charged.

[0072] It should be noted that the power margin mentioned in this application represents the maximum additional power output space that the charging pile can provide in the current state. It is a core indicator for measuring the power adjustment of the charging pile. The power margin of the charging pile can be obtained by subtracting the current active power of the charging pile from its rated power.

[0073] In this embodiment, reference Figure 3 As shown, this figure is an exemplary flowchart for determining power allocation weights in an embodiment of this application. In this embodiment, determining the power allocation weights of each charging pile based on all power margins and the charging demand level of the vehicle to be charged can be achieved through the following steps:

[0074] In step S41, the charging demand level of the vehicle to be charged is parsed, where a high-level demand corresponds to an emergency charging task and a low-level demand corresponds to a delayed charging task.

[0075] In step S42, for the charging pile where the high-level demand vehicle is located, the reciprocal of the power margin of the charging pile is used as the power allocation weight of the charging pile.

[0076] In step S43, for charging piles where low-level demand vehicles are located, the power allocation weight of the charging pile is determined based on the proportional relationship between the power margin of the charging pile and the average power margin of the charging pile cluster.

[0077] It should be noted that the power allocation weight mentioned in this application refers to the weighting parameter for adjusting the power allocation of the charging pile.

[0078] In practice, firstly, the current state of charge (SOC) of the vehicle's power battery is obtained through real-time communication between the charging pile and the vehicle's battery management system. This SOC is then combined with the charging mode selected by the user at the charging pile terminal (e.g., "fast charging" or "economic charging") for comprehensive judgment. When the current SOC of the vehicle's power battery is ≤20% and the user selects "fast charging," or the vehicle's battery management system sends an "emergency charging" signal, the charging demand of the vehicle to be charged is marked as a high-level demand. When the current SOC of the vehicle's power battery is ≥50% and the user selects "economic charging," or actively selects "accept off-peak scheduling," the charging demand of the vehicle to be charged is marked as a low-level demand. Secondly, for charging piles where high-level demand vehicles are located, the reciprocal of the charging pile's power margin is used as the power allocation weight for the charging pile. Then, for charging piles where low-level demand vehicles are located, the sum of the power margins of all charging piles is first calculated, and the sum is divided by the number of charging piles to obtain the average power margin. The power margin of the charging pile is then divided by the average power margin, and the result of the division is used as the power allocation weight for the charging pile.

[0079] In step S5, when the total power demand of charging piles within the coverage area of ​​the same power supply transformer exceeds the upper limit of the adjustable power range, the power output of each charging pile is adjusted based on the power verification sequence and all power allocation weights until all vehicles are fully charged.

[0080] It should be noted that the upper limit of the adjustable power range mentioned in this application refers to the maximum power limit that the power supply transformer can stably output under the current operating conditions.

[0081] In this embodiment, adjusting the power output of each charging pile based on the power verification sequence and all power allocation weights until all vehicles are fully charged can be achieved through the following steps:

[0082] Charging piles in an allocable state are selected according to the power verification sequence, and power is allocated sequentially according to priority.

[0083] For charging piles that do not meet the power allocation conditions, the requested power of the charging piles is reduced proportionally according to the power allocation weight of the charging piles until the total power demand after reduction falls within the adjustable power range.

[0084] The total power demand of charging piles within the coverage area of ​​the power supply transformer is monitored in real time. If the total power demand exceeds the limit again due to the addition of new charging tasks, the power weight re-allocation process is triggered.

[0085] When a vehicle finishes charging or actively terminates charging, the power quota it occupies is released, and the feasibility of power allocation for the remaining charging piles is re-verified. This process is repeated until all vehicles have finished charging.

[0086] It should be noted that the upper limit of the adjustable power range mentioned in this application refers to the maximum power limit that the power supply transformer can stably output under the current operating conditions.

[0087] In practice, the process begins by sequentially retrieving charging piles from the power verification sequence. Then, based on the power allocation weight of each charging pile, the power allocation is calculated according to the priority of the power verification sequence (from front to back). For example, if the total adjustable power is 200kW, and the power allocation weights of the first three charging piles in the power verification sequence are 0.3, 0.25, and 0.2 respectively, then 60kW, 50kW, and 40kW will be allocated sequentially. The allocation must simultaneously satisfy both the upper limit of the single-pile power margin (e.g., if the charging pile's power margin is 55kW, then a maximum of 55kW will be allocated) and the lower limit of vehicle demand. This process continues until the total adjustable power is allocated or the power verification sequence is fully traversed. Okay; secondly, calculate the total requested power of the charging piles that do not meet the conditions, subtract the upper limit of the adjustable power range from the total power demand, and use the difference as the power over-limit value. Then, divide the power over-limit value by the total requested power, and use the difference as the compression coefficient. Then, reduce the requested power of each charging pile that does not meet the conditions by a ratio of (1 - compression coefficient), thereby completing the reduction of the charging pile's requested power. For example, if there are 3 charging piles that do not meet the conditions, with requested power of 60kW, 45kW, and 45kW respectively, the total requested power is 150kW. The total requested power is then divided by the upper limit of the adjustable power range (e.g., 1...). Comparing the power of each charging station to the 20kW limit, the excess power (150kW - 120kW = 30kW) is calculated. Using the excess power as the numerator and the total requested power as the denominator, the required reduction ratio is obtained (30kW / 150kW = 0.2, i.e., a 20% reduction is needed). The requested power of each charging station is reduced by (1 - compression coefficient) (i.e., multiplied by 0.8) to ensure that the power of each station maintains its original weight ratio after compression (e.g., 60kW × 0.8 = 48kW, 45kW × 0.8 = 36kW, 45kW × 0.8 = 36kW). The total power is reduced to 48 + 36 + 36 = 120kW, which falls exactly within the adjustable range. The system first checks the power supply transformer's load range; then, it monitors the total power demand of charging piles within the transformer's coverage area. The total power demand is collected every 5 seconds using a transformer load monitoring device (e.g., a smart circuit breaker) and compared with the upper limit of the adjustable range. When a new vehicle connects and causes the total power demand to exceed the limit, a redistribution is immediately initiated: the power allocation weights of all charging piles are recalculated, and power is redistributed according to the new weights. Finally, upon receiving a signal that a vehicle has finished charging or has actively terminated charging, the power quota for that charging pile is released, increasing the total adjustable power. The power verification sequence and power allocation weights are then regenerated, and the preceding steps are repeated until the last vehicle finishes charging.

[0088] Therefore, this application adjusts the power output of each charging pile based on the power verification sequence and all power allocation weights until all vehicles are fully charged. First, active power data captures the dynamic load characteristics of the charging pile cluster in real time, while the power carrying capacity threshold on the grid side clarifies the safe operation boundary of the power distribution system. The two are coupled to generate a total power allocation constraint coefficient for the charging pile cluster. This coefficient defines the upper limit of the total power when multiple charging piles are running in parallel at the system level, specifically addressing the problem of concentrated power surges caused by multi-vehicle fast charging and effectively avoiding risks such as voltage instability and line overload caused by exceeding the capacity of the distribution transformer. Second, by verifying the feasibility of charging pile allocation and forming a power verification sequence, unfeasible or high-risk allocation schemes can be screened out in advance, ensuring that the output of each charging pile is within the safe carrying capacity range of the power grid. This not only improves the stability and reliability of the scheduling scheme in actual execution but also lays the foundation for subsequent weight-based... Power optimization allocation provides effective boundary conditions, enabling coordinated operation among multiple charging piles and avoiding global impact caused by local failures. Next, by acquiring the power margin of each charging pile and combining it with the charging demand levels of all vehicles, a power allocation weight is constructed. This allows for differentiated allocation while ensuring grid safety: prioritizing vehicles with insufficient power and urgent needs, improving user experience and service efficiency, while fully utilizing the capacity of charging piles with larger power margins, enhancing overall energy utilization. Finally, by introducing a power verification sequence and power allocation weights, the output of each charging pile is dynamically adjusted under over-limit conditions. This ensures that the scheduling process balances grid capacity and user charging demand. The power verification sequence guarantees the feasibility of the allocation scheme, preventing global imbalance caused by unstable output from a single pile; the power allocation weights guide limited power to be prioritized for piles with high demand levels or large power margins, improving overall allocation efficiency.

[0089] In summary, the proposed solution can coordinate the power of charging piles in scenarios where multiple charging piles operate in parallel.

[0090] Example 2: This application provides an intelligent charging service optimization system based on multi-pile collaborative scheduling, referring to... Figure 4 As shown in the figure, this is a schematic diagram of an intelligent charging service optimization system based on multi-pile collaborative scheduling according to this embodiment of the present application. The intelligent charging service optimization system based on multi-pile collaborative scheduling includes:

[0091] The acquisition module 100 is used to receive multi-pile collaborative scheduling tasks issued by the intelligent charging network platform and acquire the active power data of the charging pile cluster.

[0092] Feature processing module 200 is used to determine the total power allocation constraint coefficient of the charging pile cluster based on the active power data and the power carrying capacity threshold of the power grid side in the multi-pile collaborative scheduling task;

[0093] The feature processing module 200 is also used to verify the feasibility of power allocation for each charging pile based on the power fluctuation of each charging pile when performing power allocation and the total power allocation constraint coefficient, and to obtain a power verification sequence.

[0094] The feature processing module 200 is also used to obtain the power margin of each charging pile in the charging pile cluster, and determine the power allocation weight of each charging pile based on all the power margins and the charging demand level of the vehicle to be charged.

[0095] The adjustment module 300 is used to adjust the power output of each charging pile based on the power verification sequence and all power allocation weights when the total power demand of the charging piles within the coverage area of ​​the same power supply transformer exceeds the upper limit of the adjustable power range, until all vehicles are fully charged.

[0096] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0097] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compactdisc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0098] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A method for optimizing intelligent charging services based on multi-pile collaborative scheduling, characterized in that, Includes the following steps: Receive multi-pile collaborative scheduling tasks issued by the intelligent charging network platform and obtain active power data of the charging pile cluster; The total power allocation constraint coefficient of the charging pile cluster is determined based on the active power data and the power carrying capacity threshold of the power grid side in the multi-pile collaborative scheduling task. The feasibility of power allocation for each charging pile is verified based on the power fluctuation of each charging pile during power allocation and the total power allocation constraint coefficient, resulting in a power verification sequence. Obtain the power margin of each charging pile in the charging pile cluster, and determine the power allocation weight of each charging pile based on all the power margins and the charging demand level of the vehicle to be charged. When the total power demand of charging piles within the coverage area of ​​the same power supply transformer exceeds the upper limit of the adjustable power range, the power output of each charging pile is adjusted based on the power verification sequence and all power allocation weights until all vehicles are fully charged. Specifically, determining the total power allocation constraint coefficient of the charging pile cluster based on the active power data and the power carrying capacity threshold of the power grid side in the multi-pile collaborative scheduling task includes: The power demand of the charging pile cluster is extracted from the active power data. The available power margin on the grid side is determined based on the power carrying capacity threshold of the grid side and the power demand of the charging pile cluster in the multi-pile collaborative scheduling task. A power trend change model for charging pile clusters is constructed to predict the short-term power demand increment of charging pile clusters. The total power allocation constraint coefficient of the charging pile cluster is determined based on the adjustable power margin and the short-term power demand increment.

2. The method as described in claim 1, characterized in that, The feasibility of power allocation for each charging pile is verified based on the power fluctuations during power allocation and the total power allocation constraint coefficient. The resulting power verification sequence includes: Statistical analysis of the power fluctuation amplitude of each charging pile within a preset time window; A power fluctuation tolerance threshold is set based on the total power allocation constraint coefficient and all power fluctuation amplitudes; For each charging station, if the power fluctuation amplitude of the charging station exceeds the power fluctuation tolerance threshold, it is determined that the charging station does not currently meet the power allocation conditions. All charging piles that meet the power allocation conditions are selected and sorted by the power fluctuation range of the charging piles from smallest to largest to generate a power verification sequence.

3. The method as described in claim 1, characterized in that, The power allocation weight of each charging station is determined based on all power margins and the charging demand levels of the vehicles to be charged, specifically including: Analyze the charging demand level of the vehicles to be charged, where high-level demand corresponds to emergency charging tasks and low-level demand corresponds to delayed charging tasks. For charging stations where high-demand vehicles are located, the reciprocal of the charging station's power margin is used as the power allocation weight for the charging station. For charging stations where low-demand vehicles are located, the power allocation weight of the charging station is determined based on the ratio between the power margin of the charging station and the average power margin of the charging station cluster.

4. The method as described in claim 1, characterized in that, Adjusting the power output of each charging station based on the power verification sequence and all power allocation weights until all vehicles are fully charged specifically includes: Charging piles in an allocable state are selected according to the power verification sequence, and power is allocated sequentially according to priority. For charging piles that do not meet the power allocation conditions, the requested power of the charging piles is reduced proportionally according to the power allocation weight of the charging piles until the total power demand after reduction falls within the adjustable power range. The total power demand of charging piles within the coverage area of ​​the power supply transformer is monitored in real time. If the total power demand exceeds the limit again due to the addition of new charging tasks, the power weight re-allocation process is triggered. When a vehicle finishes charging or actively terminates charging, the power quota it occupies is released, and the feasibility of power allocation for the remaining charging piles is re-verified. This process is repeated until all vehicles have finished charging.

5. The method as described in claim 1, characterized in that, Multi-charging pile collaborative scheduling task refers to the optimized control instructions issued by the intelligent charging network platform, which include the target charging pile cluster, power constraints, and scheduling priority strategies.

6. The method as described in claim 1, characterized in that, The active power data represents the set of active power components of each charging pile within the charging pile cluster.

7. The method as described in claim 1, characterized in that, The power carrying capacity threshold refers to the maximum allowable power load that the power grid can provide under the premise of ensuring safe operation.

8. The method as described in claim 1, characterized in that, The upper limit of the adjustable power range refers to the maximum power limit that the power supply transformer can stably output under the current operating conditions.

9. A smart charging service optimization system based on multi-pile collaborative scheduling, used to execute the smart charging service optimization method based on multi-pile collaborative scheduling as described in any one of claims 1 to 8, characterized in that, The intelligent charging service optimization system includes: The acquisition module is used to receive multi-pile collaborative scheduling tasks issued by the intelligent charging network platform and acquire the active power data of the charging pile cluster. The feature processing module is used to determine the total power allocation constraint coefficient of the charging pile cluster based on the active power data and the power carrying capacity threshold of the grid side in the multi-pile coordinated scheduling task. Specifically, determining the total power allocation constraint coefficient of the charging pile cluster based on the active power data and the power carrying capacity threshold of the grid side in the multi-pile coordinated scheduling task includes: The power demand of the charging pile cluster is extracted from the active power data. The available power margin on the grid side is determined based on the power carrying capacity threshold of the grid side and the power demand of the charging pile cluster in the multi-pile collaborative scheduling task. A power trend change model for charging pile clusters is constructed to predict the short-term power demand increment of charging pile clusters. The total power allocation constraint coefficient of the charging pile cluster is determined based on the adjustable power margin and the short-term power demand increment. The feature processing module is also used to verify the feasibility of power allocation for each charging pile based on the power fluctuation of each charging pile during power allocation and the total power allocation constraint coefficient, and to obtain a power verification sequence. The feature processing module is also used to obtain the power margin of each charging pile in the charging pile cluster, and determine the power allocation weight of each charging pile based on all the power margins and the charging demand level of the vehicle to be charged. The adjustment module is used to adjust the power output of each charging pile based on the power verification sequence and all power allocation weights when the total power demand of the charging piles within the coverage area of ​​the same power supply transformer exceeds the upper limit of the adjustable power range, until all vehicles are fully charged.

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