A power distribution system and method for hierarchical and zonal charging of an electric bus fleet in response to grid demand response

Abstract

The present application relates to the technical field of electric bus fleet orderly charging and power grid demand response control, in particular to a layered and zoned charging power dynamic allocation system and method for power grid demand response, the system comprising a charging demand construction module for constructing a vehicle charging demand sequence, a density fluctuation identification module for identifying density fluctuations and dividing vehicle levels, a zoned boundary matching module for matching power supply zoned boundaries and priority level demands, a power gradient adjustment module for gradient adjustment and balanced zoned power, and a layered and zoned execution module for outputting layered and zoned charging power dynamic allocation results. The present application enhances demand dynamic response capability through time slice correlation and density fluctuation judgment, enhances supply and demand matching accuracy in combination with power supply zoned boundary sequence and section adaptation, relieves local power supply deviation and maintains power supply stability according to deviation for power transfer and cyclic adjustment, and improves allocation and on-site operation synchronization degree in combination with actual state differences for time-sharing correction.

Description

Technical Field

[0001] This invention relates to the field of orderly charging and grid demand response control technology for electric bus fleets, and particularly to a hierarchical and zoned dynamic power allocation system and method for electric bus fleets oriented towards grid demand response. Background Technology

[0002] The field of orderly charging and grid demand response control technology for electric bus fleets includes electric bus charging scheduling, grid load coordination control, time-of-use power management, charging station energy allocation, and vehicle-grid interaction. It primarily focuses on organizing the charging behavior of electric bus fleets in centralized charging scenarios and coordinating with grid load. This involves collecting bus schedule information, calculating vehicle remaining battery power, dividing charging time periods, allocating regional charging resources, receiving grid load demand response commands, and dynamically adjusting charging power. Its core components include hierarchical control and zoned management of charging power for different charging areas and vehicles based on bus arrival times, departure plans, battery state of charge, and grid load changes. Furthermore, it coordinates and allocates the overall charging process of the fleet by considering peak and off-peak electricity prices, grid load limits, and transformer capacity constraints.

[0003] Among them, the hierarchical and zoned dynamic charging power allocation system and method for electric bus fleets oriented towards grid demand response refers to the handling of issues such as load fluctuations, regional power imbalances, and demand response collaborative control during the centralized charging process of electric bus fleets. It adopts vehicle hierarchical management, charging area division, and dynamic adjustment of charging power. Its technical aspects include classifying buses according to operational priority, remaining battery power, and planned departure time; configuring regional power based on the power supply capacity of the area where the charging pile is located; adjusting the upper limit of charging power in each area according to the grid demand response period requirements; specifically, the charging station collects real-time data on vehicle battery state of charge, vehicle access time, estimated departure time, and regional total load; dynamically allocates charging power for vehicles in different areas by setting regional allocable power values, vehicle priority charging order, and time-sharing charging power adjustment rules; and redistributes the remaining power between areas based on grid load change information.

[0004] When existing technologies use a regional unified power allocation method to coordinate vehicle charging, the pace of vehicle access and the process of demand changes are subject to continuous fluctuations. There is a lack of continuous correlation between changes in vehicle demand intensity over different time periods, which can easily lead to deviations between the power allocation results within a region and the actual changes in vehicle demand. For example, during the period when high-demand vehicles are concentratedly accessing the region, the regional power supply capacity allocation results are prone to short-term deviations, resulting in load aggregation in some areas. At the same time, the load adjustment process between regions is more based on the results of each stage, lacking continuous identification of the trend and boundary changes in power supply capacity. This can easily lead to the accumulation of fluctuations in regional power supply status, affecting the charging stability and power coordination effect during the demand response process. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a hierarchical and zoned dynamic power allocation system and method for electric bus fleets oriented towards grid demand response.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a hierarchical and zoned dynamic charging power allocation system for electric bus fleets oriented towards grid demand response, the system comprising: The charging demand construction module obtains the vehicle's remaining battery power, rated battery capacity, and planned departure time, determines the energy to be replenished and available charging periods, divides continuous time slices according to the departure sequence, associates them with the voltage and current of the charging pile, forms a vehicle charging demand record, and obtains the vehicle charging demand distribution sequence. The density fluctuation identification module reads continuous time slices of each vehicle according to the vehicle charging demand distribution sequence, extracts vehicle demand descriptions and forms vehicle density sequences, identifies the direction of proportion change and direction switching, divides priority layers and regular layers according to the judgment boundary, and obtains the vehicle layer affiliation set. The partition boundary matching module, based on the vehicle hierarchical affiliation set, filters the demand descriptions of priority vehicles, determines the power supply partition, generates a description of the remaining power supply capacity of the partition, identifies transition sections and continuous sections, matches the section range of the vehicle demand description, and forms a partition power matching set. The power gradient adjustment module reads the power supply status of the section according to the partition power matching set, generates target power supply information according to the needs of priority vehicles, compares the partition deviation, divides the power output area and the receiving area, forms a transfer description according to the deviation and cyclically corrects it to obtain the partition power equalization distribution set. The hierarchical and partitioned execution module forms a vehicle charging allocation description based on the partitioned power equalization allocation set and the vehicle hierarchical results, establishes the relationship between vehicles, power supply partitions, power and time slices, obtains the voltage and current feedback at the charging pile end, locates differences and corrects the power state, and obtains the hierarchical and partitioned dynamic allocation result of charging power.

[0007] As a further embodiment of the present invention, the vehicle charging demand distribution sequence includes demand density identifier, time period occupation tag, and charging priority category; the vehicle hierarchical affiliation set specifically includes priority scheduling level, regular scheduling level, and fluctuation response category; the partition power matching set includes segment adaptation level, boundary response category, and capacity matching tag; and the partition power balance allocation set specifically includes regional balance index, power transfer category, and power supply coordination parameter.

[0008] As a further aspect of the present invention, the charging demand construction module includes: The energy determination submodule obtains the remaining power information, rated battery capacity information, and planned departure time information of electric buses in the bus charging station. It determines the energy information to be replenished for the vehicle based on the remaining power information and rated battery capacity information corresponding to the vehicle number, and associates the planned departure time information with the energy information to be replenished for the vehicle to generate the energy information to be replenished for the vehicle. The time period segmentation submodule calls the vehicle's energy replenishment information, obtains the current time and planned departure time information, determines the vehicle's available charging time period information based on the current time and planned departure time, arranges the vehicle's available charging time period information in the order of planned departure time, and divides each vehicle's available charging time period into continuous time slices according to a preset time granularity to obtain the vehicle's available charging time period information. The status association submodule, based on the vehicle's energy to be replenished information and the vehicle's available charging time period information, obtains the voltage and current information collected by the charging pile within a continuous time slice, combines them to form real-time charging status information, and associates the vehicle's energy to be replenished information, available charging time period information and real-time charging status information according to continuous time slices to form a vehicle charging demand record and obtain a vehicle charging demand distribution sequence.

[0009] As a further aspect of the present invention, the density fluctuation identification module includes: The density extraction submodule reads the continuous time slices corresponding to each vehicle in the available charging period based on the vehicle charging demand records in the vehicle charging demand distribution sequence, extracts the vehicle demand descriptions in each continuous time slice, extracts the proportions according to the time sequence, judges the number of vehicle demand descriptions and the number of segment records for the same vehicle in the continuous time slice, writes the proportions into the vehicle row and column according to the continuous time slice number, and generates a vehicle density sequence. The fluctuation judgment submodule calls the vehicle density sequence, identifies the proportion change of adjacent time slices along the vehicle density sequence, records the direction of change of proportion increase, decrease or flat, determines the change judgment range of adjacent time slices based on continuous time slices, counts the continuous occurrence of change direction and the change switching within the change judgment range, collects the number of direction changes by vehicle number, and obtains the density fluctuation judgment result. The hierarchical classification submodule, based on the density fluctuation judgment result, forms a vehicle fluctuation judgment boundary according to the change judgment range. For each vehicle, it judges the density fluctuation judgment result against the vehicle fluctuation judgment boundary, classifies vehicles that reach the vehicle fluctuation judgment boundary into the priority layer, and classifies vehicles that do not reach the vehicle fluctuation judgment boundary into the regular layer, and collects hierarchical tags according to vehicle number to obtain the vehicle hierarchical classification set.

[0010] As a further aspect of the present invention, the partition boundary matching module includes: The vehicle extraction submodule, based on the priority vehicle affiliation results in the vehicle hierarchical affiliation set, filters the vehicle demand description associated with the priority vehicles, determines the power supply zone based on the power supply range of the zone transformers in the bus charging station, and, combined with the transformer capacity information and current power supply status information of each power supply zone, judges the remaining power supply capacity of the transformer capacity information relative to the current power supply status information, and generates a description of the remaining power supply capacity of the zone. The boundary recognition submodule calls the description of the remaining power supply capacity of the partition, arranges the description of the remaining power supply capacity of the partition according to the power supply partition order to form a boundary arrangement result, identifies the change of the remaining power supply capacity of adjacent power supply partitions along the boundary arrangement result, determines whether the change direction of adjacent power supply partitions has changed, marks the position where the change direction has changed as a turning segment, and marks the position where the change direction remains continuous as a continuous segment, thus obtaining the segment boundary marking result. The segment matching submodule, based on the description of the remaining power supply capacity of the partition and the segment boundary marking results, reads the description of the remaining power supply capacity corresponding to the turning segment and the continuous segment, judges the adaptation relationship between the priority layer vehicle demand description and the remaining power supply capacity description, determines the segment range that matches the vehicle demand description, records the matching relationship according to the power supply partition number and the start and end positions of the segment, and forms a partition power matching set.

[0011] As a further aspect of the present invention, the power gradient adjustment module includes: The target generation submodule reads the current power supply status information of the power supply partitions within the matching section according to the matching section range and the corresponding remaining power supply capacity description of the partition power matching set. Combined with the priority layer vehicle demand description, it determines the correspondence between the demand that each power supply partition can undertake and the remaining power supply capacity of the section, writes the target power supply mark in the order of the power supply partitions, and generates the demand response target power supply information. The zone transfer submodule calls the demand response target power supply information, compares the current power supply status information and the demand response target power supply information according to the power supply zone order, takes the power supply zone with the current power supply status higher than the demand response target power supply information as the power output zone, and takes the remaining power supply zones as the power receiving zone. Based on the deviation correspondence between the two types of zones, a zone power transfer description is generated. The status update submodule, based on the partition power transfer description, adjusts the power distribution of the power output area and the power receiving area according to the partition power transfer description, updates the power supply status information of the power supply partition, judges the allowable deviation range between the updated power supply status information and the demand response target power supply information, and collects the records that enter the allowable deviation range according to the matching segment range to obtain the partition power balance distribution set.

[0012] As a further aspect of the present invention, the system further includes: The hierarchical and partitioned execution module forms a vehicle charging allocation description based on the partitioned power equalization allocation set and the vehicle hierarchical results, establishes the relationship between vehicles, power supply partitions, power and time slices, obtains the voltage and current feedback at the charging pile end, locates differences and corrects the power state, and obtains the hierarchical and partitioned dynamic allocation results of charging power. The hierarchical and zoned dynamic allocation results of charging power include vehicle charging status labels, zoned power allocation labels, and time period response result identifiers.

[0013] As a further aspect of the present invention, the hierarchical partitioning execution module includes: The allocation establishment submodule, based on the updated power supply status information in the partition power equalization allocation set, combined with the vehicle affiliation results of the priority layer and the regular layer, forms a vehicle charging allocation description in each power supply partition. According to the vehicle layering results, vehicle demand description and continuous time slice order, it determines the correspondence between vehicle number and power supply partition number, establishes the correspondence between vehicle, power supply partition, allocated power and continuous time slice, and generates vehicle charging allocation description. The state correction submodule calls the vehicle charging allocation description, obtains the voltage and current information fed back by the charging pile, combines them to form an actual charging state description, and determines the power supply zone, vehicle and continuous time slice corresponding to the difference based on the state difference between the actual charging state description and the vehicle charging allocation description, corrects the allocated power and charging state of the corresponding vehicle, and obtains the actual charging state description. The result processing submodule, based on the actual charging status description and the vehicle charging allocation description, organizes the vehicle charging status description according to time sequence and power supply zone number. It judges the vehicle number, power supply zone number and allocated power within the same continuous time slice, and collects the corrected charging status with the corresponding vehicle charging status description to obtain the hierarchical zone charging power dynamic allocation result.

[0014] This invention also provides a method for dynamic allocation of charging power in a hierarchical and zoned manner for electric bus fleets in response to grid demand, to implement a system for dynamic allocation of charging power in a hierarchical and zoned manner for electric bus fleets in response to grid demand. The method includes: S1: Obtain the vehicle's remaining battery power, rated battery capacity, and planned departure time; determine the energy to be replenished and available charging periods; divide the continuous time slices according to the departure sequence; associate the charging pile terminal voltage and current; form a vehicle charging demand record; and obtain the vehicle charging demand distribution sequence. S2: Based on the vehicle charging demand distribution sequence, read the continuous time slices of each vehicle, extract the vehicle demand description and form a vehicle density sequence, identify the direction of proportion change and direction switching, divide the priority layer and the regular layer according to the judgment boundary, and obtain the vehicle layer affiliation set. S3: Based on the vehicle hierarchical affiliation set, filter the demand descriptions of priority vehicles, determine the power supply zones, generate the remaining power supply capacity descriptions of the zones, identify transition sections and continuous sections, match the section ranges of the vehicle demand descriptions, and form a zone power matching set. S4: Based on the partition power matching set, read the power supply status of the section, generate target power supply information according to the needs of priority vehicles, compare the partition deviation, divide the power output area and the receiving area, form a transfer description according to the deviation and cyclically correct it to obtain the partition power equalization distribution set. S5: Based on the partitioned power equalization allocation set and vehicle hierarchical results, form a vehicle charging allocation description, establish the relationship between vehicles, power supply partitions, power and time slices, obtain the voltage and current feedback at the charging pile end, locate differences and correct the power state, and obtain the hierarchical partitioned charging power dynamic allocation results.

[0015] The working principle and advantages of this invention are as follows: In this invention, by associating the energy to be replenished by vehicles with their real-time charging status through continuous time slices, a vehicle demand distribution relationship with temporal correlation characteristics is formed. Density fluctuation judgment results are constructed based on changes in the demand ratio of adjacent time slices, enhancing the responsiveness of the vehicle demand identification process to dynamic trends. A boundary structure sequence is formed by combining changes in the remaining power supply capacity of power supply zones, and adaptation association is carried out by distinguishing between transitional and continuous sections, improving the segment matching accuracy between vehicle demand and power supply resources. Power transfer and cyclic balancing adjustments are performed between regions based on the corresponding power supply deviation, alleviating the accumulation of local power supply offsets and maintaining a continuous and stable power supply status. Time-sharing corrections are performed based on actual charging status differences, improving the synchronization between charging allocation results and on-site operating status. Attached Figure Description

[0016] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart illustrating the process of obtaining the charging demand construction module of the present invention. Figure 3 This is a flowchart illustrating the acquisition process of the density fluctuation identification module of the present invention. Figure 4This is a flowchart illustrating the acquisition process of the partition boundary matching module of the present invention. Figure 5 This is a flowchart illustrating the acquisition process of the power gradient adjustment module of the present invention. Figure 6 This is a flowchart illustrating the acquisition process of the hierarchical partitioning execution module of the present invention. Detailed Implementation

[0017] The following detailed explanation illustrates the specific implementation methods: The basic implementation examples are as follows: Figure 1 As shown: A hierarchical and zoned dynamic charging power allocation system for electric bus fleets oriented towards grid demand response, comprising: The charging demand construction module acquires information on the remaining battery power, rated battery capacity, and planned departure time of electric buses in the bus charging station. Based on the rated battery capacity and remaining battery power, it determines the energy to be replenished for each vehicle. Based on the current time and the planned departure time, it determines the available charging time slots for each vehicle. The available charging time slots for each vehicle are arranged in order of planned departure time, and each available charging time slot is divided into continuous time slices according to a preset time granularity. The module acquires the voltage and current information collected by the charging pile within the continuous time slices, combines them to form real-time charging status information, and associates the energy to be replenished, available charging time slots, and real-time charging status information according to the continuous time slices to form a vehicle charging demand record, thus obtaining a vehicle charging demand distribution sequence. The vehicle charging demand distribution sequence includes demand-intensive identifiers, time-occupancy tags, and charging priority categories.

[0018] Please see Figure 2 Specifically, the charging demand construction module includes: The energy determination submodule acquires the remaining battery power, rated battery capacity, and planned departure time information of electric buses within the bus charging station. It then determines the vehicle's energy replenishment information based on the vehicle number, matching the remaining battery power with the rated battery capacity. Finally, it associates the planned departure time information with the vehicle's energy replenishment information to generate the vehicle's energy replenishment information. The details are as follows: The system acquires information on the remaining battery power, rated battery capacity, and planned departure time of electric buses within the bus charging station. It then identifies vehicle number Bus_01 and establishes a data handshake with the vehicle's battery management system (BMS) using existing CAN bus communication technology to obtain its real-time remaining battery power status. (Unit: %) is 20%, and the rated battery capacity information in the static EEPROM memory of the on-board energy storage system is read synchronously. (Dimensions are energy in kWh) 300 kWh; Using the multiplication formula ,Right now The current remaining energy value of the vehicle is calculated. The remaining energy is 60kWh. Through the interface of the bus network cloud platform dispatch system, the planned departure time information for Bus_01 is extracted as 08:30:00 on the current day. To avoid calculation errors across days, this time string is combined with the current date and converted into a UNIX timestamp format containing the complete year, month, and day. The vehicle number Bus_01 is then associated with its calculated remaining energy value of 60kWh and rated capacity value of 300kWh, using a subtraction operation logic. ,Right now The required energy replenishment value for the vehicle was calculated. The capacity is 240kWh. If a BMS communication failure occurs... In cases where inconsistencies cannot be obtained, the vehicle's previous offline cached battery level is extracted as a replacement and marked as "accuracy degradation." The energy deficit parameter of 240kWh and the time node parameter of 08:30:00 are encapsulated into a data frame, and the data is read for the second vehicle numbered Bus_02. 35%, It is 250kWh; pass The remaining power was found to be 87.5 kWh, and then... The energy to be replenished was found to be 162.5 kWh. Based on its planned departure time of 09:15:00, the same data extraction and calculation were performed on all online vehicles in the depot to generate the vehicle energy to be replenished information.

[0019] The time-segmentation submodule retrieves vehicle energy replenishment information, obtains the current time and planned departure time, determines the available charging time slots for each vehicle based on the current time and planned departure time, arranges the available charging time slots in order of planned departure time, and divides each available charging time slot into continuous time slices according to a preset time granularity to obtain the vehicle's available charging time slot information; specifically as follows: The system retrieves the vehicle's pending energy information and uses the RTC real-time clock chip built into the main control board in conjunction with the NTP network time protocol to obtain the current instantaneous UNIX timestamp containing the specific date, which is then converted into time. At 02:00:00, the planned departure timestamp of Bus_01 is read from the vehicle's energy replenishment information and converted into... At 08:30:00, perform timestamp difference calculation. The total available charging time for the vehicle is calculated to be 6.5 hours (390 minutes). Using this time as the starting point of the time axis, the available charging period for the vehicle is determined to be [02:00:00, 08:30:00]. Similarly, the available charging time for Bus_02 is calculated as 7.25 hours (435 minutes) after subtracting 02:00:00 from 09:15:00, with a time range of [02:00:00, 09:15:00]. The time ranges for all vehicles are then calculated according to... Sort the timestamps in ascending order by size and set the time granularity parameter. For a time interval of 10 minutes, perform an integer division operation on the 390-minute time interval of Bus_01. We obtain 39 time segments, and define each 10-minute interval as a continuous time slice. ( are positive integers and ), the first time slice Corresponding to [02:00, 02:10], the last time slice For [08:20, 08:30], for Bus_02's 435 minutes, a division and round-down logic is performed to divide it into 43 standard time slices, and the remaining 5 minutes are allocated to the tail non-standard time slice. Perform independent labeling for each time slice A unique sequence index number k is assigned to form a set consisting of a series of discrete and continuous time coordinate points. The time slices of all vehicles are mapped onto a unified time axis to form a multi-dimensional time matrix. The matrix is ​​traversed to mark the validity of each time period and generate information on available charging time periods for vehicles.

[0020] The state association submodule, based on the vehicle's pending energy replenishment information and available charging time period information, acquires the voltage and current information collected by the charging pile within a continuous time slice, combines them to form real-time charging status information, and associates the vehicle's pending energy replenishment information, available charging time period information, and real-time charging status information according to continuous time slices to form a vehicle charging demand record, thus obtaining a vehicle charging demand distribution sequence; as detailed below: Based on the vehicle's energy demand information and available charging time information, the built-in DC energy meter of the charging pile numbered Pile_01 is read through the RS485 serial port in conjunction with the Modbus RTU protocol to obtain the output voltage collected in real time by the DC Hall sensor within the current continuous time slice [02:00, 02:10]. 650V, output current The current is 120A. To standardize engineering dimensions, voltage and current parameters are calculated using a combination of multiplication and division formulas. The actual charging DC power is calculated. for kW, combined with a time slice granularity of 10 minutes (i.e., 10 / 60 hours), calculate the actual DC power charged during this period. The collected voltage value of 650V, current value of 120A, power value of 78kW, and charged energy of 13kWh are packaged into a real-time charging status information packet. The initial energy to be replenished of 240kWh is read from Bus_01 in the time slice [02:00, 02:10]. At the end of this time slice, an update subtraction operation is performed. kWh, if detected or If the energy level falls below the hardware threshold or communication is interrupted, the energy input in the current time slice is determined to be 0, and an idle warning log is generated to keep the energy to be replenished unchanged. For Bus_02, the voltage of Pile_02 read in the same time slice is 600V and the current is 100A. This is determined using the formula... The calculated power is 60kW; Charged with electrical energy The vehicle's energy demand distribution sequence is generated by updating the energy to be replenished from 162.5kWh to 152.5kWh, and assembling the vehicle ID, time slice, remaining energy to be replenished, and V / I physical quantities into a linked list of tuples according to time sequence.

[0021] The density fluctuation identification module reads the continuous time slices corresponding to each vehicle within the available charging period based on the vehicle charging demand distribution sequence. It extracts the vehicle demand description within each continuous time slice, extracts the proportion according to the time sequence, and forms a vehicle density sequence. It identifies the proportion changes of adjacent time slices along the vehicle density sequence, records the direction of the proportion increase, decrease, or stagnation, and determines the change judgment range of adjacent time slices based on the continuous time slices. Within the change judgment range, it counts the continuous occurrence and direction switching of the change direction to form a density fluctuation judgment result. Based on the change judgment range, it forms a vehicle fluctuation judgment boundary. Vehicles whose density fluctuation judgment results reach the vehicle fluctuation judgment boundary are classified into the priority layer, and vehicles that do not reach the boundary are classified into the regular layer, thus obtaining the vehicle stratification set. The vehicle hierarchical affiliation set is specifically divided into priority scheduling level, regular scheduling level, and fluctuation response category.

[0022] Please see Figure 3 Specifically, the density fluctuation identification module includes: The density extraction submodule, based on the vehicle charging demand records in the vehicle charging demand distribution sequence, reads the corresponding continuous time slices for each vehicle within the available charging period, extracts the vehicle demand descriptions within each continuous time slice, extracts the proportion according to time sequence, and judges the number of vehicle demand descriptions and the number of segment records for the same vehicle within a continuous time slice. The proportions are then written into the vehicle row and column according to the continuous time slice number, generating a vehicle density sequence; specifically as follows: Based on the vehicle charging demand records in the vehicle charging demand distribution sequence, the microprocessor extracts the set of records numbered Bus_01, which contains 39 consecutive time slices. and its corresponding real-time power value The system cache is used to retrieve the planned charging power value of Bus_01, which is initially calculated based on its energy to be replenished and available charging time during the first to sixth time slices [TS_1, TS_6]. The sliding time window length is then set. (Dimensionless count value) represents 6 time slices; count the number of time slices within this window where the planned charging power value is greater than 0. ,like to If the power values ​​within are 78kW, 78kW, 0kW, 78kW, 78kW, and 78kW respectively, then a traversal counting operation is performed to obtain... The value is 5, which retrieves the total number of record segments corresponding to this window. If the value is 6, perform a division operation. The proportion of vehicle demand in the first window was calculated. The value is 0.83. The window is then moved forward by one time slice to extract the granularity of [TS_2, TS_7], and the calculation is repeated. The same logic is executed for Bus_02, extracting the power values ​​within [TS_1,TS_6] as 60kW, 0kW, 0kW, 60kW, 0kW, and 60kW respectively, and calculating its proportion. The value is 0.50. Based on the station load guidelines, the following ranges are defined: [0.70, 1.00] is the high-density range (representing strong rigid demand), [0.30, 0.70) is the medium-density range, and [0.00, 0.30) is the low-density range (representing high scheduling flexibility). Determined to be high density, Determined to be of medium density, a two-dimensional data matrix is ​​created in the cache, initialized to zero, with vehicle IDs as rows and time slice IDs as columns. Write the discrete percentage values ​​mentioned above to generate a vehicle density sequence.

[0023] The fluctuation judgment submodule calls the vehicle density sequence, identifies the proportion changes of adjacent time slices along the vehicle density sequence, records the direction of change (increase, decrease, or stagnation), determines the judgment range of changes in adjacent time slices based on continuous time slices, counts the continuous occurrence of change direction and the change switching within the judgment range, and collects the number of direction changes by vehicle number to obtain the density fluctuation judgment result; as detailed below: Call the vehicle density sequence, retrieve Bus_01 in the matrix The adjacent percentage values ​​in the first row and Perform subtraction operation The difference was found to be 0.02, and a change judgment threshold was set. The value is 0.01 (used to filter sensor measurement noise and small fluctuations), and the difference of 0.02 is compared with... To perform a size comparison, because It was determined that it had a significant growth trend, in the direction vector The corresponding position is recorded with an ascending flag "1", if the difference between adjacent percentages is within a closed interval. The internal record indicates a balance of "0". If the difference is strictly less than 0, the internal record indicates a balance of "0". Then record the decreasing flag "-1" and extract the adjacent percentage of Bus_02. and The calculated difference is -0.10, because... Record the decrease flag "-1" and set the change judgment range. For 10 consecutive time slices, the direction flag sequence of Bus_01 is extracted and identified. For example, for the sequence [1,1,0,-1,-1,1,1,1,0,-1], after ignoring the flat flag '0' to avoid invalid judgment, the valid flag sequence is [1,1,-1,-1,1,1,1,-1]. Adjacent valid flag bits are compared; three direction switches occur in bits 2-3 (1→-1), bits 4-5 (-1→1), and bits 7-8 (1→-1), and these are recorded as frequency changes. This inconsistent state indicates a sudden, abrupt change in vehicle charging intentions, which could easily cause local grid harmonic oscillations. If a continuous, consistent state with a sequence of markers always being [1,1,1…] occurs, it indicates a smooth, gradual increase in demand without causing a shock, corresponding to the number of Bus_02 direction switching times. The switching feature is accumulated according to the vehicle number to obtain the density fluctuation judgment result.

[0024] The hierarchical classification submodule, based on the density fluctuation judgment results, establishes vehicle fluctuation judgment boundaries according to the change judgment range. For each vehicle, it compares the density fluctuation judgment result with the vehicle fluctuation judgment boundaries, classifying vehicles that meet the vehicle fluctuation judgment boundaries into the priority layer and vehicles that do not meet the boundaries into the regular layer. Hierarchical labels are then collected according to vehicle numbers to obtain the vehicle hierarchical classification set; specifically as follows: Based on the density fluctuation assessment results, historical records of all vehicles in the station over the past hour were retrieved to calculate the average number of directional changes. The standard deviation, which reflects the degree of dispersion, is read 1.5 times. The value is 0.5. If the system is in a cold start phase with no historical data, then... and The built-in preset experience reference value matrix is ​​directly invoked, and the fluctuation offset coefficient is set based on statistical analysis of historical fluctuation data (e.g., using the Z value corresponding to the 95% confidence interval) or by referring to engineering experience values ​​from similar station scheduling systems. (This example takes) This means setting the boundary at a position 1.2 standard deviations above the average level, using the formula... Perform compound addition and multiplication operations, that is The calculation result 2.1 was determined as the vehicle fluctuation judgment threshold (representing the critical value for judging a drastic change in demand), and the change frequency value was extracted from the density fluctuation judgment result of Bus_01. The value is 3. The value 3 is compared with the boundary 2.1. Since... Bus_01 was determined to meet the criteria for severe fluctuations (i.e., it would cause pulse effects on power grid resources), and was forcibly assigned to the priority level requiring key protection. A "Priority" tag was added to the vehicle information table in the SQL relational database for Bus_01, and the frequency of change of Bus_02 was read. It is 0, because If Bus_02 is determined to have stable demand and does not meet the condition of drastic fluctuation, it is assigned to the regular level that can be flexibly scheduled and a "Regular" label is added. If the frequency value of a vehicle is exactly equal to 2.1, it is uniformly assigned to the priority level according to the conservative scheduling principle. The same traversal and judgment are performed on the remaining vehicles in the depot. Two sets are stored in separate contiguous address spaces in memory. The metadata such as the demand ratio of priority level Bus_01, 240kWh of energy to be replenished, and the planned departure time of 08:30:00 are packaged. The data such as the 162.5kWh of energy to be replenished of regular level Bus_02 are packaged simultaneously to generate the vehicle hierarchical assignment set.

[0025] The partition boundary matching module, based on the priority vehicle affiliation results in the vehicle hierarchical affiliation set, filters the vehicle demand descriptions associated with priority vehicles, determines the power supply partitions according to the power supply range of the partition transformers in the bus charging station, generates a partition remaining power supply capacity description by combining the transformer capacity information and current power supply status information of each power supply partition, arranges the partition remaining power supply capacity descriptions in the order of power supply partitions to form a boundary structure sequence, identifies the changes in remaining power supply capacity of adjacent power supply partitions along the boundary structure sequence, marks the position where the change direction changes as a turning segment, and marks the position where the change direction remains continuous as a continuous segment, determines the segment range of matching vehicle demand descriptions according to the adaptation relationship between priority vehicle demand descriptions and the remaining power supply capacity descriptions corresponding to turning segments and continuous segments, and forms a partition power matching set; The partitioned power matching set includes the segment adaptation level, boundary response category, and capacity matching label.

[0026] Please see Figure 4 Specifically, the partition boundary matching module includes: The vehicle extraction submodule, based on the priority vehicle attribution results in the vehicle hierarchical attribution set, filters the vehicle demand descriptions associated with priority vehicles, determines the power supply zones based on the power supply range of the zone transformers within the bus charging station, and combines the transformer capacity information and current power supply status information of each power supply zone to determine the remaining power supply capacity of the transformer capacity information relative to the current power supply status information, generating a description of the remaining power supply capacity of the zone; as detailed below: Based on the priority vehicle attribution results in the vehicle hierarchical attribution set, the vehicle number Bus_01 marked with "Priority" is extracted, and the real-time power demand parameters associated with Bus_01 are retrieved from the aforementioned vehicle charging demand distribution sequence. The power is 78kW, and its energy to be replenished (240kWh) and current time slice index are read simultaneously. The static physical topology diagram of the existing SCADA distribution network data acquisition and monitoring control system is loaded into memory, and the physical layout containing three independent transformers Trans_A, B, and C is identified. Based on the cable wiring correspondence, it is determined that Trans_A covers piles 1-10 as Zone_01, Trans_B covers piles 11-20 as Zone_02, and Trans_C covers piles 21-30 as Zone_03. The rated capacity information of the three-phase AC transformer Trans_A is then retrieved. For a capacity of 1000kVA, the phase current of the operating load on the partition bus side is read through the intelligent master meter interface based on the DL / T645 standard protocol. RMS value, line voltage RMS value and power factor The processor executes the three-phase AC active power calculation formula: Get the current active power It is 650kW. Based on the safety load factor... Calculate the safe active power limit for Zone_01. (Right now kW), perform subtraction operation ,Right now The calculated remaining active power supply capacity of Zone_01 is 200kW. The rated capacity of Trans_B is 800kVA, based on the upper limit. Calculate the remaining power supply capacity based on the current power of 720kW (kW read). for kW, retrieve Trans_C rated capacity 1200kVA; According to the upper limit kW and current power 400kW; calculate for kW, set the power redundancy assessment threshold. Greater than 300kW is judged as high redundancy range, [100kW, 300kW] is judged as medium redundancy range, and less than 100kW is judged as low redundancy (heavy load risk) range. Based on this, Zone_01 is medium redundancy, Zone_02 is low redundancy (heavy load), and Zone_03 is high redundancy. The parameters are encapsulated into a structure to generate a description of the remaining power supply capacity of the zone.

[0027] The boundary recognition submodule calls the remaining power supply capacity description of each partition, arranges the descriptions according to the power supply partition order to form a boundary arrangement result, identifies the changes in the remaining power supply capacity of adjacent power supply partitions along the boundary arrangement result, determines whether the direction of change between adjacent power supply partitions has switched, marks the position where the direction of change has switched as a turning segment, and marks the position where the direction of change remains continuous as a continuous segment, thus obtaining the segment boundary marking result; as follows: The remaining power supply capacity descriptions for each zone are retrieved. The remaining power of Zone_01 (200kW), Zone_02 (-40kW), and Zone_03 (620kW) are arranged on the numerical axis in ascending order of transformer physical electrical distance [1,2,3], forming a boundary arrangement result consisting of the numerical sequence [200,-40,620]. The difference in remaining capacity between adjacent zones is calculated by extracting the 200kW from the first zone and the -40kW from the second zone and performing a subtraction operation. The difference in kW is negative, indicating a sharp decrease in remaining power supply capacity from Zone_01 to Zone_02; subtract the -40kW from the second position and the 620kW from the third position. A positive difference in kW indicates a significant increase in remaining power supply capacity from Zone_02 to Zone_03. Comparing the characteristics of two adjacent changes, this shift from a sharp decrease to a sharp increase in remaining capacity at Zone_02 indicates a severe load imbalance at this boundary. Zone_02 and its associated bus nodes are marked as transition sections to warn the system of a sudden risk of overload exceeding limits. If a fourth zone, Zone_04, exists with a remaining power of 720kW, the calculation... Since the kW increases in a positive direction, it represents a consistent and continuous increase in the power supply margin. Zone_03 to Zone_04 are marked as continuous segments. In the system's boundary netlist, Zone_02 is marked with the "Switch_Point" feature code, and Zone_03 is marked with the "Continuous_Path" feature code, thus obtaining the segment boundary marking results.

[0028] The segment matching submodule, based on the partition's remaining power supply capacity description and segment boundary marking results, reads the remaining power supply capacity descriptions corresponding to transition segments and continuous segments. It then determines the compatibility between the priority-level vehicle demand description and the remaining power supply capacity description, identifies the segment range matching the vehicle demand description, and records the matching relationships according to the power supply partition number and the segment's start and end positions, forming a partition power matching set; specifically as follows: Based on the description of remaining power supply capacity in the partition and the segment boundary marking results, the power demand description of priority vehicle Bus_01 in the TS1 time slice is retrieved. The remaining power is 78kW. From the description of remaining power supply capacity in the zones, the remaining power of Zone_02 (marked with a sudden change risk) is read as -40kW, and the remaining power of Zone_01 (in a smooth transition) is read as 200kW, and Zone_03 (in a smooth transition) as 620kW. An adaptation coefficient α (dimensionless) reflecting load pressure is introduced, and the formula is executed. Execute on Bus_01 and Zone_02 The division yields a coefficient of -1.95. Performing 78 / 200 on Bus_01 and Zone_01 yields a coefficient of 0.39, while performing 78 / 620 on Bus_01 and Zone_03 yields a coefficient of 0.126. According to the national standard "Economic Operation of Power Transformers" (GB / T13462), the actual economic operating range needs to be calculated based on the transformer's own no-load / load losses. It was later determined that the typical reasonable range is approximately 30%-85%, with the optimal range being 40%-75%. Fit coefficient ( The load factor is considered as a 'pseudo-load rate' of the remaining capacity of the target zone. To adapt to the high-frequency dynamic load characteristics of charging stations and to reserve a certain engineering tolerance based on the above typical reasonable range, the optimal adaptation range of this system is set as [0.20, 0.85]. -1.95 is identified as a negative value, indicating that the physical capacity has been exhausted, and is directly excluded. 0.126 is less than 0.20 and belongs to the resource idle zone, while 0.39 is within the range of [0.20, 0.85], thus avoiding the transition zone Zone_02. It is determined that Bus_01 and the continuous zone Zone_01 have the best adaptation relationship. If all coefficient values ​​are equal... In cases of inconsistency outside the preferred range, a greedy algorithm is initiated. The optimal segment is selected by sorting the coefficient values ​​in ascending order of the absolute value of the difference between the coefficient value and the range boundary. The output is then forced to be reduced according to a proportional coefficient. The [Vehicle: Bus_01, Target Zone: Zone_01, Matching Power: 78kW, Duration: TS_1-TS_39] is entered into the memory matching mapping table. Simultaneously, the demand of another priority layer, Bus_03, of 150kW is extracted. The calculation of 150 / 620=0.242 matching intervals is performed, and a corresponding record between Bus_03 and Zone_03 is established. The above address guidance mapping relationship is integrated to generate a partition power matching set.

[0029] The power gradient adjustment module reads the current power supply status information of the power supply partitions within the matching section according to the matching section range and the corresponding remaining power supply capacity description of the partition power matching set. It combines the demand description of priority vehicles to form demand response target power supply information. It compares the current power supply status information and the demand response target power supply information in the order of power supply partitions to obtain the partition deviation description. The power supply partitions whose current power supply status is higher than the demand response target power supply information are designated as power output areas, and vice versa. The partition power transfer description is formed according to the deviation correspondence between the power output area and the power receiving area. The power distribution of the power output area and the power receiving area is adjusted according to the partition power transfer description. The power supply status information of the power supply partitions is updated. The adjustment is cyclically adjusted until the partition deviation description enters the allowable deviation range determined by the demand response target power supply information, thus obtaining the partition power equalization distribution set. The regional power balance allocation set specifically includes regional balance indicators, power transfer categories, and power supply coordination parameters.

[0030] Please see Figure 5 Specifically, the power gradient adjustment module includes: The target generation submodule, based on the matching segment range and corresponding remaining power supply capacity description of the partition power matching set, reads the current power supply status information of the power supply partitions within the matching segment. Combining this with the priority layer vehicle demand description, it determines the correspondence between the demand that each power supply partition can handle and the remaining power supply capacity of the segment. It then writes the target power supply flags according to the power supply partition order, generating the demand response target power supply information; specifically as follows: Based on the matching segment range and the corresponding remaining power supply capacity description of the zone in the power matching set, the real-time power demand of the priority vehicle Bus_01 associated with the first power supply zone Zone_01 is retrieved. For 78kW, the real-time power demand of priority vehicle Bus_03, which is mapped to the third power supply zone (Zone_03), is extracted synchronously. The current active power of the transformer is 150kW. The current power of Zone_01 is obtained by calling the current operating active power of the transformer in real time through the power monitoring center (PMS) of the substation. The current power of Zone_02 is 650kW. The current power of 720kW and Zone_03 The rated capacity of each transformer is 400kW. The capacities are 1000kVA, 800kVA, and 1200kVA, respectively. The safety load factor is set according to the thermal stability margin requirements of the "Distribution Network Operation Regulations". (Dimensionless) = 0.85, perform multiplication. The calculations show that the safe power supply limit for Zone_01 is 850kW, for Zone_02 it is 680kW, and for Zone_03 it is 1020kW. An addition operation is then performed on Zone_01 to receive the new demand. The expected target power is 728kW. Comparing 728 with the upper limit of 850kW, because... Determined to have secure access capabilities, execution was performed on Zone_03. kW, because Similarly, for Zone_02, which has no new additions and is marked as a transition zone, a comparison is made between the current power of 720kW and the upper limit of 680kW. Since... This indicates that the system is already in a state of heavy load and exceeding limits due to accelerated heat loss. The demand response target is forcibly reduced to a safe baseline value of 680kW to trigger the load transfer mechanism. According to the logical order of the partition number, the final target ideal value [728,680,550] calculated and verified above is sequentially filled into the target description vector to generate the demand response target power supply information.

[0031] The zone transfer submodule calls the demand response target power supply information, compares the current power supply status information with the demand response target power supply information according to the power supply zone order, designates the power supply zones with a current power supply status higher than the demand response target power supply information as power output zones, and designates the remaining power supply zones as power receiving zones, and generates a zone power transfer description based on the correlation between the two types of zones; as detailed below: The system retrieves the target power supply information in response to the demand. Following the physical space partition index [01,02,03], it extracts the ideal target power vector [728,680,550] and the actual current power supply vector [650,720,400] read from the power meter. A point-to-point subtraction difference operation is then performed based on the matrix element correspondence rule, and this is executed for Zone_01. The difference was calculated. The power is 78kW. Since the difference 78 is greater than 0, it is marked as a "power receiving area" with input absorption capacity. Execution for Zone_02 under heavy load The difference was calculated. The value is -40kW. Since this negative difference is less than 0, it is marked as a "power output zone" where pressure must be released externally. This is then executed for Zone_03. Get the difference The power output is 150kW, and it is determined that all output areas are within the power receiving area. The total overflow power is calculated by summing the absolute values ​​of all output areas. kW, the total shortfall capacity is calculated cumulatively for the receiving area. kW, the allocation weight is calculated based on the proportion of the power gap in each receiving area to the total gap. The logic is to divide the gap in a single receiving area by the total gap capacity to obtain the sharing ratio of Zone_01. ; Zone_03's contribution ratio Based on this weight matrix, the power that Zone_01 should share in receiving the transfer from Zone_02 is calculated to be... kW, Zone_03 should share the receiving load. If there is an extreme anomaly where the total overflow exceeds the total shortfall, the power will be allocated to full load according to the proportion of remaining capacity in each receiving area. The remaining untransferable overflow power will be absorbed locally by reducing the duty cycle of the bottom charging of vehicles in the regular layer. The cross-regional transfer quota instructions and flow direction flags determined above will be written into the scheduling queue to generate a partition power transfer description.

[0032] The status update submodule, based on the partitioned power transfer description, adjusts the power allocation in the power output area and the power receiving area according to the partitioned power transfer description, updates the power supply status information of the power supply partition, and judges the allowable deviation range between the updated power supply status information and the demand response target power supply information. Records that fall within the allowable deviation range are grouped according to matching segment ranges to obtain the partitioned power balance allocation set; specifically as follows: Based on the partitioned power transfer description, the specific control instructions for transferring 13.68kW from Zone_02 to Zone_01 and 26.32kW to Zone_03 are read. In the virtual register of the central controller, the original 60kW conventional vehicle power quota of Zone_02 is subject to a limit reduction (reduction of 40kW), that is, its target total power register is modified by subtraction. kW, perform superposition compensation addition operation on the current power register of Zone_01. kW; Perform a superposition addition operation on Zone_03 kW, synchronously send the adjustment value to overwrite each distribution box controller, call the final closed-loop ideal value [728,680,550] in the demand response target power supply information, and set the system allowable deviation threshold with reference to the minimum step resolution of the IGBT module inside the charging pile. It is 5.0kW; Perform absolute difference comparison operation on the updated Zone_02 kW; because It was determined that Zone_02 had been completely eliminated and entered the allowable deviation range. A comparison operation was then performed on Zone_01. kW, due to The overall planning target of 728kW has not yet been reached. Therefore, power needs to be borrowed from regular vehicles in Zone_01 to increase the power requirement. This involves retrieving Bus_02, a regular vehicle in Zone_01 with an initial power of 60kW, and sending a power increase command to it, raising its power by 64.32kW (i.e., updating the allocated power to...). (kW) to fill the space in this section, bringing the total power close to 728kW. Similarly, the same applies to Zone_03. The system compares and schedules the kW of its internal regular vehicles using an iterative adjustment algorithm: it traverses all substandard power supply zones (such as Zone_01, Zone_03), and among the regular vehicles in their respective zones, prioritizes vehicles marked as 'low' or 'medium' in 'demand density' and with a large remaining energy to be supplemented. The required power difference is then adjusted. According to the remaining energy to be replenished in each selected vehicle The allocation is based on the proportion of the total remaining energy of the selected vehicles: the allocation increment. To ensure that the power of a single vehicle after allocation does not exceed the maximum allowable power of its charging pile, the allocated power is finely adjusted (increased) until the absolute deviation between the total power of the zone and the target value is less than the dead zone condition of 5.0kW. The final steady-state results after stable convergence are collected by section and vehicle number to obtain the zone power balance allocation set.

[0033] The hierarchical and partitioned execution module, based on the updated power supply status information in the partitioned power equalization allocation set and combined with the vehicle assignment results of the priority layer and the regular layer, forms a vehicle charging allocation description within each power supply partition. According to the vehicle hierarchical results, vehicle demand description, and continuous time slice order, it establishes the correspondence between vehicles, power supply partitions, allocated power, and continuous time slices, obtains the voltage and current information fed back by the charging pile, and combines them to form an actual charging status description. Based on the status difference between the actual charging status description and the vehicle charging allocation description, it locates the corresponding power supply partition, vehicle, and continuous time slice, and corrects the allocated power and charging status of the corresponding vehicle. It organizes the vehicle charging status description according to the time sequence and power supply partition number to obtain the hierarchical and partitioned dynamic allocation result of charging power. The results of the hierarchical and zoned dynamic allocation of charging power include vehicle charging status labels, zone power allocation labels, and time period response result identifiers.

[0034] Please see Figure 6 Specifically, the hierarchical partitioning execution module includes: The allocation creation submodule, based on the updated power supply status information in the partitioned power balancing allocation set and combined with the vehicle assignment results of the priority layer and the regular layer, forms a vehicle charging allocation description within each power supply partition. According to the vehicle stratification results, vehicle demand descriptions, and continuous time slice order, it determines the correspondence between vehicle numbers and power supply partition numbers, establishes the correspondence between vehicles, power supply partitions, allocated power, and continuous time slices, and generates a vehicle charging allocation description; specifically as follows: Based on the updated power supply status information in the partitioned power balancing allocation set, the steady-state target powers of Zone_01, Zone_02, and Zone_03 after verification and convergence are read from the controller DRAM memory as 728kW, 680kW, and 550kW, respectively. Bus_01 and Bus_03 belonging to the "Priority" layer and Bus_02 belonging to the "Regular" layer are retrieved from the hierarchical affiliation set. The rigidly allocated charging power demand description of Bus_01 (78kW) is extracted from the aforementioned computational cache. The dynamic charging power description of the regular vehicle Bus_02 (124.32kW) after its power is increased due to undertaking system balancing compensation tasks is extracted. The power description of Bus_03 (150kW) is extracted. These are then combined with the current system time slice. The corresponding physical time range [02:00, 02:10] is used to retrieve the static cable connection diagram from the Distribution Network Management System (DMS). The vehicle physical location verification logic is executed, determining that the MAC addresses of the charging guns of Bus_01 and Bus_02 are both anchored to buses 1 to 10, belonging to Zone 1. Bus_03 is anchored to buses 21 to 30, belonging to Zone 3. Structured memory pointers are allocated for these binding relationships in the system configuration table, and [Vehicle:Bus_01, The target zone: Zone_01, power output: 78kW, time slice: TS_1] and the vehicle: Bus_02, zone: Zone_01, power output: 124.32kW, time slice: TS_1] are multi-dimensional mapping dictionaries. The individual transformer capacity load rate range is defined. A ratio of single-vehicle power to transformer rated capacity within (0.0, 0.2) is considered a light load condition. A division operation is performed to obtain the ratio of Bus_01's single-vehicle power to the rated capacity of the Zone_01 transformer. The Bus_02 model is classified as lightly loaded, and its single-vehicle ratio is [percentage missing]. It is a light-load system. Through strict spatial anchoring and time slice constraints, the control expectation values ​​of all micro-individuals in the entire station are transformed into the execution boundary files of the underlying charging piles, generating a vehicle charging allocation description.

[0035] The state correction submodule calls the vehicle charging allocation description, obtains the voltage and current information fed back by the charging pile, combines them to form an actual charging state description, and determines the power supply zone, vehicle, and continuous time slice corresponding to the difference based on the state difference between the actual charging state description and the vehicle charging allocation description. It then corrects the allocated power and charging state of the corresponding vehicle to obtain the actual charging state description; specifically as follows: The system invokes the vehicle charging allocation description and polls in real time via the underlying high-speed CAN bus network based on the ISO11898 standard to retrieve the output voltage value fed back by the Hall sensor inside charging pile No. 1 connected to Bus_01. 655V, output current value The voltage is 118A. The output voltage value fed back from charging pile #2 connected to Bus_02 is retrieved. 648V, output current value It is 187A; Strictly follow the dimensional formula Performing floating-point arithmetic, the actual response charging power of Bus_01 is calculated as follows: kW, the actual response charging power of Bus_02 is calculated to be The system encapsulates the vehicle identification code, the actual power output, and the NTP high-precision timestamp to generate an actual charging status description. It then extracts the calculated actual response power [77.29, 121.18] and performs a subtraction difference calculation with the theoretical command [78.0, 124.32] in the vehicle charging allocation description, calculating the execution difference for Bus_01. kW, calculate the execution difference for Bus_02 kW, combined with line loss rate and thermal efficiency of AC / DC conversion devices, sets the power correction tolerance benchmark value. The value is 2.0kW. The absolute value of the difference is compared with the reference value. Due to the difference in Bus_01... The difference was determined to be measurement noise caused by normal high-frequency ripple from the inverter. The original allocation state was maintained. Due to the difference in Bus_02... The system determines that an abnormal load drop inconsistency has occurred beyond the allowable dead zone. Based on the difference, the system locates the continuous time slice in Zone_01 where Bus_02 is located. Internally, a fine-tuning duty cycle compensation command is sent to the underlying PWM signal generator of charging pile No. 2, forcibly increasing its control power parameters by 3.14kW, thus updating the target value aimed at by the underlying execution module to [value missing]. At kW, the correction flag and feedback message are simultaneously sent back to the cloud main station to obtain a description of the actual charging status.

[0036] The results processing submodule, based on the actual charging status description and vehicle charging allocation description, organizes the vehicle charging status descriptions according to time sequence and power supply zone number. It then judges the vehicle number, power supply zone number, and allocated power within the same continuous time slice, and aggregates the corrected charging status with the corresponding vehicle charging status description to obtain the hierarchical zoned dynamic charging power allocation results; specifically as follows: Based on the actual charging status description and vehicle charging allocation description, a time-slice-based database is constructed in the time-series database (such as InfluxDB) of the depot's industrial-grade server. For root node, power supply zone For a three-layer topological graph model with branch nodes and vehicle IDs as leaf nodes, extract the current time slice based on the system running pointer. Entering the Zone_01 node tree, the underlying sampling snapshots of Bus_01 and Bus_02 after dynamic duty cycle compensation correction are called to perform consistency verification of the vehicle-pile-network three-dimensional identity. After ensuring no cross-zone crosstalk, the high-confidence power of 77.29kW and its per-unit power value actually measured by Bus_01 are serialized and aggregated with the actual steady-state power of 124.32kW after reconvergence of Bus_02 and related electrical parameters to form a dedicated time slice data block for Zone_01. Similarly, the encapsulation and writing operation is performed on Bus_03 under the leaf node of Zone_03, and boundary guards are set before storage. The constant double-check mechanism performs an addition operation to calculate the actual total power of all the latest dispatched vehicles in Zone_01 (e.g., the statistical value including Bus_01 and Bus_02 is 201.61kW), and compares it with the expected total allocated power of this zone in this dispatch cycle (e.g., 728kW) and the system macro operation panel. If the power flow of each node is verified to be normal and the deviation is within the allowable range, it confirms that there is no physical overflow or over-limit collapse in the energy conservation correspondence between the current micro-control and macro state, confirms that the structure is complete and error-free, and iteratively processes all vehicles in the circular queue. to The set of scheduling actions throughout the entire lifecycle, along with the appended encrypted hash value, is exported as a structured JSON text file set to obtain the dynamic allocation result of hierarchical and partitioned charging power.

[0037] A method for dynamic allocation of charging power in a hierarchical and zoned manner for electric bus fleets in response to grid demand includes the following steps: S1: Obtain the vehicle's remaining battery power, rated battery capacity, and planned departure time; determine the energy to be replenished and available charging periods; divide the continuous time slices according to the departure sequence; associate the charging pile terminal voltage and current; form a vehicle charging demand record; and obtain the vehicle charging demand distribution sequence. S2: Based on the vehicle charging demand distribution sequence, read the continuous time slices of each vehicle, extract the vehicle demand description and form a vehicle density sequence, identify the direction of proportion change and direction switching, divide the priority layer and the regular layer according to the judgment boundary, and obtain the vehicle layer affiliation set. S3: Based on the vehicle hierarchical affiliation set, filter the demand descriptions of priority vehicles, determine the power supply zones, generate the remaining power supply capacity descriptions of the zones, identify transitional and continuous sections, match the section ranges of the vehicle demand descriptions, and form a zone power matching set. S4: Based on the partition power matching set, read the power supply status of the section, generate target power supply information according to the needs of priority vehicles, compare the partition deviation, divide the power output area and the receiving area, form a transfer description according to the deviation and cyclically correct it to obtain the partition power equalization distribution set. S5: Based on the power equalization allocation set of the zones and the vehicle hierarchical results, form a vehicle charging allocation description, establish the relationship between vehicles, power supply zones, power and time slices, obtain the voltage and current feedback of the charging pile terminals, locate the differences and correct the power status, and obtain the dynamic allocation results of the hierarchical and zoned charging power.

[0038] It is understandable that the above method and the above system have the same execution process and the same effect, so they will not be repeated here.

[0039] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A hierarchical and zoned dynamic charging power allocation system for electric bus fleets oriented towards grid demand response, characterized in that, The system includes: The charging demand construction module obtains the vehicle's remaining battery power, rated battery capacity, and planned departure time, determines the energy to be replenished and available charging periods, divides continuous time slices according to the departure sequence, associates them with the voltage and current of the charging pile, forms a vehicle charging demand record, and obtains the vehicle charging demand distribution sequence. The density fluctuation identification module reads continuous time slices of each vehicle according to the vehicle charging demand distribution sequence, extracts vehicle demand descriptions and forms vehicle density sequences, identifies the direction of proportion change and direction switching, divides priority layers and regular layers according to the judgment boundary, and obtains the vehicle layer affiliation set. The partition boundary matching module, based on the vehicle hierarchical affiliation set, filters the demand descriptions of priority vehicles, determines the power supply partition, generates a description of the remaining power supply capacity of the partition, identifies transition sections and continuous sections, matches the section range of the vehicle demand description, and forms a partition power matching set. The power gradient adjustment module reads the power supply status of the section according to the partition power matching set, generates target power supply information according to the needs of priority vehicles, compares the partition deviation, divides the power output area and the receiving area, forms a transfer description according to the deviation and cyclically corrects it to obtain the partition power equalization distribution set. The hierarchical and partitioned execution module forms a vehicle charging allocation description based on the partitioned power equalization allocation set and the vehicle hierarchical results, establishes the relationship between vehicles, power supply partitions, power and time slices, obtains the voltage and current feedback at the charging pile end, locates differences and corrects the power state, and obtains the hierarchical and partitioned dynamic allocation result of charging power.

2. The hierarchical and zoned dynamic charging power allocation system for electric bus fleets oriented towards grid demand response as described in claim 1, characterized in that: The vehicle charging demand distribution sequence includes demand density identifiers, time period occupancy tags, and charging priority categories. The vehicle hierarchical affiliation set specifically includes priority scheduling level, regular scheduling level, and fluctuation response category. The partition power matching set includes segment adaptation level, boundary response category, and capacity matching tag. The partition power balance allocation set specifically includes regional balance index, power transfer category, and power supply coordination parameters. The hierarchical partition charging power dynamic allocation result includes vehicle charging status tag, partition power allocation tag, and time period response result identifier.

3. The hierarchical and zoned dynamic charging power allocation system for electric bus fleets oriented towards grid demand response as described in claim 1, characterized in that: The charging demand construction module includes: The energy determination submodule obtains the remaining power information, rated battery capacity information, and planned departure time information of electric buses in the bus charging station. It determines the energy information to be replenished for the vehicle based on the remaining power information and rated battery capacity information corresponding to the vehicle number, and associates the planned departure time information with the energy information to be replenished for the vehicle to generate the energy information to be replenished for the vehicle. The time period segmentation submodule calls the vehicle's energy replenishment information, obtains the current time and planned departure time information, determines the vehicle's available charging time period information based on the current time and planned departure time, arranges the vehicle's available charging time period information in the order of planned departure time, and divides each vehicle's available charging time period into continuous time slices according to a preset time granularity to obtain the vehicle's available charging time period information. The status association submodule, based on the vehicle's energy to be replenished information and the vehicle's available charging time period information, obtains the voltage and current information collected by the charging pile within a continuous time slice, combines them to form real-time charging status information, and associates the vehicle's energy to be replenished information, available charging time period information and real-time charging status information according to continuous time slices to form a vehicle charging demand record and obtain a vehicle charging demand distribution sequence.

4. The hierarchical and zoned dynamic charging power allocation system for electric bus fleets oriented towards grid demand response as described in claim 1, characterized in that: The density fluctuation identification module includes: The density extraction submodule reads the continuous time slices corresponding to each vehicle in the available charging period based on the vehicle charging demand records in the vehicle charging demand distribution sequence, extracts the vehicle demand descriptions in each continuous time slice, extracts the proportions according to the time sequence, judges the number of vehicle demand descriptions and the number of segment records for the same vehicle in the continuous time slice, writes the proportions into the vehicle row and column according to the continuous time slice number, and generates a vehicle density sequence. The fluctuation judgment submodule calls the vehicle density sequence, identifies the proportion change of adjacent time slices along the vehicle density sequence, records the direction of change of proportion increase, decrease or flat, determines the change judgment range of adjacent time slices based on continuous time slices, counts the continuous occurrence of change direction and the change switching within the change judgment range, collects the number of direction changes by vehicle number, and obtains the density fluctuation judgment result. The hierarchical classification submodule, based on the density fluctuation judgment result, forms a vehicle fluctuation judgment boundary according to the change judgment range. For each vehicle, it judges the density fluctuation judgment result against the vehicle fluctuation judgment boundary, classifies vehicles that reach the vehicle fluctuation judgment boundary into the priority layer, and classifies vehicles that do not reach the vehicle fluctuation judgment boundary into the regular layer, and collects hierarchical tags according to vehicle number to obtain the vehicle hierarchical classification set.

5. The hierarchical and zoned dynamic charging power allocation system for electric bus fleets oriented towards grid demand response as described in claim 1, characterized in that: The partition boundary matching module includes: The vehicle extraction submodule, based on the priority vehicle affiliation results in the vehicle hierarchical affiliation set, filters the vehicle demand description associated with the priority vehicles, determines the power supply zone based on the power supply range of the zone transformers in the bus charging station, and, combined with the transformer capacity information and current power supply status information of each power supply zone, judges the remaining power supply capacity of the transformer capacity information relative to the current power supply status information, and generates a description of the remaining power supply capacity of the zone. The boundary recognition submodule calls the description of the remaining power supply capacity of the partition, arranges the description of the remaining power supply capacity of the partition according to the power supply partition order to form a boundary arrangement result, identifies the change of the remaining power supply capacity of adjacent power supply partitions along the boundary arrangement result, determines whether the change direction of adjacent power supply partitions has changed, marks the position where the change direction has changed as a turning segment, and marks the position where the change direction remains continuous as a continuous segment, thus obtaining the segment boundary marking result. The segment matching submodule, based on the description of the remaining power supply capacity of the partition and the segment boundary marking results, reads the description of the remaining power supply capacity corresponding to the turning segment and the continuous segment, judges the adaptation relationship between the priority layer vehicle demand description and the remaining power supply capacity description, determines the segment range that matches the vehicle demand description, records the matching relationship according to the power supply partition number and the start and end positions of the segment, and forms a partition power matching set.

6. The hierarchical and zoned dynamic charging power allocation system for electric bus fleets oriented towards grid demand response as described in claim 1, characterized in that: The power gradient adjustment module includes: The target generation submodule reads the current power supply status information of the power supply partitions within the matching section according to the matching section range and the corresponding remaining power supply capacity description of the partition power matching set. Combined with the priority layer vehicle demand description, it determines the correspondence between the demand that each power supply partition can undertake and the remaining power supply capacity of the section, writes the target power supply mark in the order of the power supply partitions, and generates the demand response target power supply information. The zone transfer submodule calls the demand response target power supply information, compares the current power supply status information and the demand response target power supply information according to the power supply zone order, takes the power supply zone with the current power supply status higher than the demand response target power supply information as the power output zone, and takes the remaining power supply zones as the power receiving zone. Based on the deviation correspondence between the two types of zones, a zone power transfer description is generated. The status update submodule, based on the partition power transfer description, adjusts the power distribution of the power output area and the power receiving area according to the partition power transfer description, updates the power supply status information of the power supply partition, judges the allowable deviation range between the updated power supply status information and the demand response target power supply information, and collects the records that enter the allowable deviation range according to the matching segment range to obtain the partition power balance distribution set.

7. The hierarchical and zoned dynamic charging power allocation system for electric bus fleets oriented towards grid demand response as described in claim 1, characterized in that: The hierarchical partitioning execution module includes: The allocation establishment submodule, based on the updated power supply status information in the partition power equalization allocation set, combined with the vehicle affiliation results of the priority layer and the regular layer, forms a vehicle charging allocation description in each power supply partition. According to the vehicle layering results, vehicle demand description and continuous time slice order, it determines the correspondence between vehicle number and power supply partition number, establishes the correspondence between vehicle, power supply partition, allocated power and continuous time slice, and generates vehicle charging allocation description. The state correction submodule calls the vehicle charging allocation description, obtains the voltage and current information fed back by the charging pile, combines them to form an actual charging state description, and determines the power supply zone, vehicle and continuous time slice corresponding to the difference based on the state difference between the actual charging state description and the vehicle charging allocation description, corrects the allocated power and charging state of the corresponding vehicle, and obtains the actual charging state description. The result processing submodule, based on the actual charging status description and the vehicle charging allocation description, organizes the vehicle charging status description according to time sequence and power supply zone number. It judges the vehicle number, power supply zone number and allocated power within the same continuous time slice, and collects the corrected charging status with the corresponding vehicle charging status description to obtain the hierarchical zone charging power dynamic allocation result.

8. A method for dynamic allocation of charging power in a hierarchical and zoned manner for electric bus fleets in response to grid demand, characterized in that, The method is used in the hierarchical and zoned dynamic charging power allocation system for electric bus fleets oriented towards grid demand response as described in any one of claims 1-7, and includes the following steps: S1: Obtain the vehicle's remaining battery power, rated battery capacity, and planned departure time; determine the energy to be replenished and available charging periods; divide the continuous time slices according to the departure sequence; associate the charging pile terminal voltage and current; form a vehicle charging demand record; and obtain the vehicle charging demand distribution sequence. S2: Based on the vehicle charging demand distribution sequence, read the continuous time slices of each vehicle, extract the vehicle demand description and form a vehicle density sequence, identify the direction of proportion change and direction switching, divide the priority layer and the regular layer according to the judgment boundary, and obtain the vehicle layer affiliation set. S3: Based on the vehicle hierarchical affiliation set, filter the demand descriptions of priority vehicles, determine the power supply zones, generate the remaining power supply capacity descriptions of the zones, identify transition sections and continuous sections, match the section ranges of the vehicle demand descriptions, and form a zone power matching set. S4: Based on the partition power matching set, read the power supply status of the section, generate target power supply information according to the needs of priority vehicles, compare the partition deviation, divide the power output area and the receiving area, form a transfer description according to the deviation and cyclically correct it to obtain the partition power equalization distribution set. S5: Based on the partitioned power equalization allocation set and vehicle hierarchical results, form a vehicle charging allocation description, establish the relationship between vehicles, power supply partitions, power and time slices, obtain the voltage and current feedback at the charging pile end, locate differences and correct the power state, and obtain the hierarchical partitioned charging power dynamic allocation results.

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