A community charging management platform supporting reservation and off-peak charging

Abstract

The application discloses a community charging management platform supporting reservation and peak-shaving charging, comprising the following modules: a reservation management module receiving a reservation charging request; a task generation module generating a charging task and determining a charging demand duration and a latest completion time; a load analysis module calculating a reservation charging load according to the charging demand duration and determining available load margins corresponding to each scheduling period; a scheduling management module calculating a task slack according to the charging demand duration, the latest completion time and a current scheduling time, and generating a charging task scheduling sequence by using a lowest slack priority algorithm; a charging distribution module generating an initial charging distribution result according to the task slack and the available load margin; a peak-shaving scheduling module determining a community load peak-valley state according to the available load margin and performing peak clipping and valley filling scheduling to generate a charging task re-distribution result; and a charging control module performing charging control according to the charging task re-distribution result to generate a community peak-shaving charging scheduling scheme.

Description

Technical Field

[0001] This invention relates to the field of community charging management, and more particularly to a community charging management platform that supports reservation and off-peak charging. Background Technology

[0002] With the continuous growth in the number of new energy vehicles, the demand for community charging facilities is constantly increasing. To improve the utilization rate of charging resources, existing community charging systems typically support user reservations for charging and allocate charging time slots and charging equipment to vehicles based on the reservation information. Some community charging systems also schedule charging tasks based on load conditions to alleviate the pressure on community power supply resources caused by concentrated charging.

[0003] Most existing community charging scheduling solutions allocate charging tasks based solely on reservation order, fixed priority, or current load status, lacking a comprehensive analysis of the relationship between charging task completion time and charging demand duration. When the number of reserved charging tasks is large, some tasks may prematurely occupy available load resources, while time-sensitive tasks may not receive timely charging. Furthermore, existing peak-shaving scheduling methods typically only perform simple peak-valley shifting of charging tasks, lacking effective identification of the adjustability of charging tasks. This makes it difficult to achieve a reasonable redistribution of charging load while ensuring timely completion of reserved charging tasks, resulting in low utilization of community charging resources and limited peak-shaving and valley-filling effects.

[0004] Therefore, how to provide a community charging management platform that supports reservation and off-peak charging is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] One objective of this invention is to propose a community charging management platform that supports scheduled and off-peak charging. This invention fully utilizes task slack analysis, minimum slack priority scheduling, available load margin assessment, and charging task redistribution technology to prioritize, match, and schedule scheduled charging tasks. This ensures that charging tasks are completed on time, thereby achieving peak shaving and valley filling of community charging load and optimized allocation of charging resources.

[0006] A community charging management platform supporting reservation and off-peak charging according to an embodiment of the present invention includes the following steps: The reservation management module receives reservation charging requests and obtains the reservation submission time, expected completion time, target charging capacity, vehicle current battery level, and charging equipment information. The task generation module generates a charging task based on the target charging capacity, the vehicle's current battery level, and the charging equipment information, and determines the charging duration and latest completion time corresponding to the charging task. The load analysis module calculates the scheduled charging load based on the duration of charging demand, collects the operating status of charging equipment, and determines the available load margin for each scheduling period based on the scheduled charging load and the operating status of charging equipment. The scheduling management module obtains the current scheduling time generated by the platform's scheduling clock, calculates the task slack of each charging task based on the charging demand duration, the latest completion time, and the current scheduling time, and generates a charging task scheduling sequence using the lowest slack priority algorithm. The charging allocation module establishes task matching relationships based on task slack and available load margin, and generates initial charging allocation results for each charging task based on the task matching relationships and the charging task scheduling sequence. The peak-valley scheduling module determines the peak and valley status of the community load based on the available load margin, and performs peak shaving and valley filling scheduling based on the initial charging allocation results and the peak and valley status of the community load. Without changing the latest completion time, it reallocates the time period for charging tasks with a task slack greater than zero and generates charging task reallocation results. The charging control module executes charging control based on the charging task redistribution results and generates a community off-peak charging scheduling plan.

[0007] Optionally, the task generation module includes: The system parses the charging reservation request and extracts the target charging capacity, the vehicle's current battery level, the reservation submission time, the expected completion time, and the charging equipment information. The charging demand is determined based on the target charging capacity and the vehicle's current battery level, and the corresponding charging duration is generated by combining the charging equipment information. The expected completion time is associated with the charging demand duration parameter to generate the corresponding latest completion time; The charging demand, charging duration parameters, latest completion time parameters, and charging equipment information are associated and encapsulated to generate a charging task.

[0008] Optionally, the load analysis module includes: Obtain the charging demand duration and charging equipment information corresponding to each charging task, and determine the load occupancy time range corresponding to each charging task based on the charging demand duration corresponding to each charging task. Based on the load occupancy time range and charging equipment information corresponding to each charging task, the charging load corresponding to each charging task in the same time period is statistically analyzed to generate the scheduled charging load. Collect the operating status of the charging equipment and determine the charging equipment that can participate in the charging task based on the operating status of the charging equipment; Based on the scheduled charging load and the power supply capacity of the charging equipment that can participate in the charging task, the available load margin for each scheduling period is determined.

[0009] Optionally, the scheduling management module includes: Obtain the current scheduling time generated by the platform's scheduling clock, and obtain the charging requirement duration and latest completion time for each charging task; The remaining available time for each charging task is determined based on the current scheduling time and the latest completion time. Calculate the task slack for each charging task based on the remaining available time and the duration of charging demand. Associate the task slack of each charging task with the corresponding charging task to generate a set of charging tasks with task slack. Based on the set of charging tasks with task slack, the scheduling priority of each charging task is determined in ascending order of task slack. Based on the scheduling priority of each charging task, the lowest relaxation first algorithm is used to generate the charging task scheduling sequence.

[0010] Optionally, generating the charging task scheduling sequence using the lowest relaxation degree first algorithm includes: Obtain a set of charging tasks with task slack, and obtain the available load margin corresponding to each scheduling period; Based on the charging demand duration, latest completion time, and current scheduling time of each charging task in the charging task set, determine the range of filterable time periods for each charging task. Within the available time period, continuous scheduling periods that can accommodate the corresponding charging task are selected based on the available load margin, and the continuous scheduling periods are determined as the completeable load window for the corresponding charging task. Based on the available load window for each charging task, charging tasks with available load windows are assigned to the schedulable task set, while charging tasks without available load windows are marked as restricted charging tasks. Based on the task slackness of each charging task in the schedulable task set, generate the lowest slackness sorting result in ascending order of task slackness. Based on the lowest relaxation sorting results, charging tasks are selected in sequence, and the complete load window corresponding to the selected charging task is determined as the candidate execution window for the corresponding charging task. Based on the available load margin occupied by the candidate execution window during the corresponding scheduling period, an updated available load margin is generated; Based on the updated available load margin, the available load windows corresponding to the unselected charging tasks are re-filtered, and the set of schedulable tasks is updated based on the re-filtered available load windows. The minimum slack sorting is re-executed based on the updated set of schedulable tasks until all charging tasks in the charging task set have been selected. A charging task scheduling sequence is generated based on the selection order of each charging task, the candidate execution window, and the restricted charging task marker.

[0011] Optionally, the charging distribution module includes: Obtain the charging task scheduling sequence, and obtain the task slack, charging requirement duration, and latest completion time for each charging task in the charging task scheduling sequence; Obtain the available load margin corresponding to each scheduling period, and determine the allocable time period range of the corresponding charging task based on the latest completion time of each charging task. Within the allocable time period, scheduling time periods that can meet the execution requirements of the corresponding charging task are selected based on the available load margin, and a candidate allocation time period set is generated. Establish task matching relationships based on the available load margin and task slack of each scheduling period in the candidate allocation period set; Based on the task matching relationship, determine the matching order between each scheduling period and the corresponding charging task in the candidate allocation period set; Each charging task is read sequentially according to the charging task scheduling sequence, and the target allocation time period is selected from the candidate allocation time period set according to the matching order of the corresponding charging task. The target allocation time period is associated with the corresponding charging task to generate the initial charging allocation result for each charging task.

[0012] Optionally, the off-peak scheduling module includes: Obtain the available load margin corresponding to each scheduling period, and generate the community load peak and valley status based on the available load margin corresponding to each scheduling period; Determine peak and valley scheduling periods based on the community's load peak and valley status; Obtain the initial charging allocation results for each charging task, and determine the peak charging tasks allocated to the peak scheduling period based on the initial charging allocation results; Obtain the task slack corresponding to the peak charging task, and determine the adjustable capability level corresponding to the peak charging task based on the task slack. Based on the adjustable capacity level and the available load margin corresponding to the off-peak scheduling period, a redistribution relationship between peak charging tasks and off-peak scheduling periods is established. Based on the redistribution relationship, peak charging tasks with high adjustable capacity levels are allocated to valley scheduling periods with large available load margins. The initial charging allocation result is updated based on the valley scheduling period after the peak charging task allocation, and the peak shaving and valley filling scheduling result is generated.

[0013] Optionally, the step of reallocating charging tasks with a slack greater than zero to generate charging task reallocation results without changing the latest completion time includes: Without changing the latest completion time, obtain the task slack, latest completion time, original peak scheduling period and target valley scheduling period for each peak charging task in the redistribution relationship; Based on the task slackness, peak charging tasks with task slackness greater than zero are selected, and the selected peak charging tasks are identified as reassignable charging tasks. Based on the original peak scheduling period and the target valley scheduling period corresponding to the reassignable charging task, the original peak scheduling period is replaced with the target valley scheduling period to generate the adjusted execution period for the corresponding reassignable charging task; Obtain the end time corresponding to the adjusted execution period, and compare the end time with the latest completion time of the corresponding reassignable charging task to generate a time limit verification result; Based on the time limit verification results, the adjusted execution period with an end time no later than the latest completion time is determined as the reassignment period for the corresponding reassignable charging task; Update the charging allocation results of the corresponding reassignable charging tasks according to the reassignment period, and generate the time period reassignment results of the corresponding reassignable charging tasks. The time period redistribution results corresponding to each redistributable charging task are correlated and summarized to generate a charging task redistribution result. The charging task redistribution result includes the charging task identifier, the original peak scheduling time period, the redistribution time period, and the latest completion time.

[0014] Optionally, the step of executing charging control based on the charging task redistribution result to generate a community off-peak charging scheduling scheme includes: Obtain the charging task reassignment results and parse the charging task identifier, reassignment period, and latest completion time from the charging task reassignment results; The charging equipment information corresponding to the charging task is obtained based on the charging task identifier, and the charging equipment information is associated with the redistribution time period to generate charging task execution information. Based on the redistribution time period in the charging task execution information, sort the execution information of each charging task to generate the charging task execution order; The charging task execution information is read sequentially according to the order of charging task execution, and a corresponding charging control instruction is generated based on the read charging task execution information. The charging control instruction includes the charging device identifier, the charging start time, and the charging end time. According to the charging control command, the corresponding charging equipment is controlled to perform the charging task in the corresponding redistribution period, and the charging execution result is generated. Generate a charging task execution record based on the charging execution results; By associating and summarizing charging task execution records, charging task execution order, redistribution time periods, and charging equipment information, a community off-peak charging scheduling plan is generated. The community off-peak charging scheduling plan includes the charging task execution order, charging time periods, and charging equipment.

[0015] The beneficial effects of this invention are: This invention combines reservation charging management, task slack analysis, minimum slack priority scheduling, available load margin assessment, and peak shaving and valley filling scheduling to achieve continuous processing of community charging tasks from reservation reception, task generation, load analysis, priority sorting, charging allocation to off-peak redistribution. This enables the platform to dynamically schedule charging tasks based on their urgency and the available load in the community at different times, avoiding queuing congestion and task overdue issues caused by allocating charging resources solely according to reservation order.

[0016] This invention establishes a matching relationship between task slack and available load margin, so that charging tasks with tight deadlines are given priority to charging time slots that meet the time requirements, while charging tasks with adjustment margins are transferred to time slots with larger load margins. This achieves a balanced allocation of community charging resources while meeting users' scheduled completion needs.

[0017] This invention redistributes charging tasks during peak scheduling periods based on the community's load peak and valley conditions, and migrates adjustable charging tasks to valley scheduling periods without changing the latest completion time. This reduces the concentration of charging load during peak hours, improves the utilization efficiency of community charging facilities and power supply capacity, and forms a community off-peak charging scheduling scheme that includes the order of charging task execution, charging time periods, and charging equipment. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a general framework diagram of a community charging management platform that supports reservation and off-peak charging, as proposed in this invention. Figure 2 The flowchart for generating a charging task scheduling sequence using the lowest relaxation degree first algorithm proposed in this invention is shown. Detailed Implementation

[0019] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0020] refer to Figures 1-2A community charging management platform that supports reservation and off-peak charging includes the following steps: The reservation management module receives reservation charging requests and obtains the reservation submission time, expected completion time, target charging capacity, vehicle current battery level, and charging equipment information. The task generation module generates a charging task based on the target charging capacity, the vehicle's current battery level, and the charging equipment information, and determines the charging duration and latest completion time corresponding to the charging task. The load analysis module calculates the scheduled charging load based on the charging demand duration corresponding to the charging task, collects the operating status of the charging equipment, and determines the available load margin corresponding to each scheduling period based on the scheduled charging load and the operating status of the charging equipment. The scheduling management module obtains the current scheduling time generated by the platform's scheduling clock, calculates the task slack of each charging task based on the charging demand duration, the latest completion time, and the current scheduling time, and generates a charging task scheduling sequence using the lowest slack priority algorithm. The charging allocation module establishes a task matching relationship based on task slack and available load margin, and generates an initial charging allocation result for each charging task based on the task matching relationship and the charging task scheduling sequence. The initial charging allocation result includes the charging period and the charging equipment. The peak-valley scheduling module determines the peak and valley status of the community load based on the available load margin, and performs peak shaving and valley filling scheduling based on the initial charging allocation results and the peak and valley status of the community load. Without changing the latest completion time, it reallocates the time period for charging tasks with a task slack greater than zero and generates charging task reallocation results. The charging control module executes charging control based on the charging task redistribution results, generating a community off-peak charging scheduling plan that includes the charging task execution order, charging time period, and charging equipment.

[0021] In this embodiment, the task generation module includes: The system parses the charging reservation request and extracts the target charging capacity, the vehicle's current battery level, the reservation submission time, the expected completion time, and the charging equipment information. The charging demand is determined based on the target charging capacity and the vehicle's current battery level, and the corresponding charging duration is generated by combining the charging equipment information. Specifically, the charging demand is determined based on the target charging capacity and the vehicle's current battery level, and the corresponding rated charging power parameters are read from the charging equipment information. The charging demand duration corresponding to the charging task is determined based on the charging demand and the rated charging power parameters. The charging demand duration represents the estimated charging time required for the charging equipment to complete the charging demand at the rated charging power. When the charging demand is 40kWh and the rated charging power is 20kW, the determined charging demand duration is 2h. The generated charging demand duration is as follows: The expected completion time is associated with the charging demand duration parameter to generate the corresponding latest completion time; Specifically, the latest completion time corresponding to the charging task is determined based on the expected completion time in the charging reservation request, and the latest completion time is used as the time limit constraint parameter of the charging task; wherein, when the user submits the charging reservation request and fills in the expected completion time as 07:00 the next day, then 07:00 the next day is determined as the latest completion time of the corresponding charging task; the generated latest completion time and the charging demand duration are jointly associated with the corresponding charging task. The charging demand, charging duration parameters, latest completion time parameters, and charging equipment information are associated and encapsulated to generate a charging task.

[0022] In this embodiment, the load analysis module includes: Obtain the charging demand duration and charging equipment information corresponding to each charging task, and determine the load occupancy time range corresponding to each charging task based on the charging demand duration corresponding to each charging task. Specifically, the load occupancy time range is determined by the reservation submission time and the charging demand duration. The reservation submission time is used as the start time of the load occupancy time range, and the duration corresponding to the charging demand duration is used as the load occupancy time range to determine the end time of the load occupancy time range. When the reservation submission time is 18:00 and the charging demand duration is 2 hours, the corresponding load occupancy time range is from 18:00 to 20:00. For multiple charging tasks, the load occupancy time range is determined according to the corresponding reservation submission time and charging demand duration, and the determined load occupancy time range is used as the basis for statistical calculation of the reserved charging load. Based on the load occupancy time range and charging equipment information corresponding to each charging task, the charging load corresponding to each charging task in the same time period is statistically analyzed to generate the scheduled charging load. Specifically, the load occupancy time range corresponding to each charging task is mapped to the corresponding time period. The charging tasks in the load occupancy state within each time period are identified, and the rated charging power in the corresponding charging equipment information is read. If there are multiple charging tasks within the same time period, the rated charging power of the charging equipment corresponding to each charging task is accumulated to generate the reserved charging load corresponding to that time period. For example, if there are two charging tasks within the time period from 18:00 to 19:00, corresponding to a 7kW charging equipment and an 11kW charging equipment respectively, then the reserved charging load corresponding to that time period is 18kW. The generated reserved charging load... Collect the operating status of the charging equipment and determine the charging equipment that can participate in the charging task based on the operating status of the charging equipment; Based on the scheduled charging load and the power supply capacity of the charging equipment that can participate in the charging task, determine the available load margin for each scheduling period. Specifically, the available load margin is determined by the reserved charging load and the operating status of the charging equipment. The charging equipment that can participate in the charging task is identified based on the operating status of the charging equipment, and the power supply capacity corresponding to the charging equipment that can participate in the charging task is read. The power supply capacity corresponding to the charging equipment that can participate in the charging task in the same scheduling period is compared with the reserved charging load corresponding to the scheduling period to determine the remaining allocable charging load capacity for the scheduling period, and the remaining allocable charging load capacity is used as the available load margin corresponding to the scheduling period.

[0023] In this embodiment, the scheduling management module includes: Obtain the current scheduling time generated by the platform's scheduling clock, and obtain the charging requirement duration and latest completion time for each charging task; The remaining available time for each charging task is determined based on the current scheduling time and the latest completion time. Specifically, the remaining available time is determined by the latest completion time and the current scheduling time, with the current scheduling time as the calculation start point and the latest completion time as the calculation end point, to determine the remaining time length between the current scheduling time and the latest completion time, and the remaining time length is used as the remaining available time for the corresponding charging task; for example, when the current scheduling time is 18:00 and the latest completion time is 22:00, the remaining available time for the corresponding charging task is 4 hours; for multiple charging tasks, the remaining available time for each task is determined based on the corresponding latest completion time and the current scheduling time, and the determined remaining available time is used as the input parameter for calculating the task slack. Calculate the task slack for each charging task based on the remaining available time and the duration of charging demand. Specifically, the task slack is determined by the remaining available time and the charging requirement duration. The remaining available time is the length of time that the charging task can be scheduled before the latest completion time, and the charging requirement duration is the length of time required to complete the charging task. The result of subtracting the charging requirement duration from the remaining available time is used as the task slack for the corresponding charging task. For example, if a charging task has a remaining available time of 4 hours and a charging requirement duration of 2.5 hours, then the task slack for that charging task is 1.5 hours. If another charging task has a remaining available time of 2 hours and a charging requirement duration of 2.5 hours, then the task slack for that charging task is -0.5 hours. The generated task slack is associated with the corresponding charging task and is used to determine the charging task scheduling priority and generate a charging task scheduling sequence. Associate the task slack of each charging task with the corresponding charging task to generate a set of charging tasks with task slack. Based on the set of charging tasks with task slack, the scheduling priority of each charging task is determined in ascending order of task slack. Based on the scheduling priority of each charging task, the lowest relaxation first algorithm is used to generate the charging task scheduling sequence.

[0024] In this embodiment, generating the charging task scheduling sequence using the lowest relaxation degree first algorithm includes: Obtain a set of charging tasks with task slack, and obtain the available load margin corresponding to each scheduling period; Based on the charging demand duration, latest completion time, and current scheduling time of each charging task in the charging task set, determine the range of filterable time periods for each charging task. Specifically, the selectable time period range is determined by the current scheduling time, the latest completion time, and the charging demand duration. The current scheduling time serves as the earliest start boundary for scheduling a charging task, and the latest completion time serves as the mandatory end boundary for completing the charging task. The latest possible start boundary is determined backward from the latest completion time based on the charging demand duration. When the latest possible start boundary is not earlier than the current scheduling time, the time interval between the current scheduling time and the latest completion time that can accommodate the charging demand duration is determined as the selectable time period range for the corresponding charging task. For example, when the current scheduling time is 18:00, the latest completion time is 22:00, and the charging demand duration is 2 hours, the latest possible start boundary is 20:00. Therefore, any consecutive 2-hour time interval between 18:00 and 22:00 with an end time no later than 22:00 falls within the selectable time period range. This selectable time period range serves as the time boundary for selecting a complete load window. Within the available time period, continuous scheduling periods that can accommodate the corresponding charging task are selected based on the available load margin, and the continuous scheduling periods are determined as the completeable load window for the corresponding charging task. Specifically, the complete load window is determined by the range of selectable time periods, the duration of charging demand, and the available load margin. Within the range of selectable time periods, several scheduling time periods are selected consecutively in the order of scheduling time periods. It is determined whether the duration of the selected consecutive scheduling time periods meets the charging demand duration of the corresponding charging task. At the same time, it is determined whether the available load margin corresponding to each scheduling time period in the selected consecutive scheduling time periods can accommodate the charging load corresponding to the charging task. When the selected consecutive scheduling time periods meet both the charging demand duration and the available load margin requirements, the selected consecutive scheduling time periods are determined as the complete load window of the charging task. Based on the available load window for each charging task, charging tasks with available load windows are assigned to the schedulable task set, while charging tasks without available load windows are marked as restricted charging tasks. Based on the task slackness of each charging task in the schedulable task set, generate the lowest slackness sorting result in ascending order of task slackness. Based on the lowest relaxation sorting results, charging tasks are selected in sequence, and the complete load window corresponding to the selected charging task is determined as the candidate execution window for the corresponding charging task. Specifically, the candidate execution window is determined based on the lowest relaxation degree sorting result and the available load window. Charging tasks are selected sequentially according to the lowest relaxation degree sorting result, and the available load window corresponding to each selected charging task is read. When a selected charging task has only one available load window, that available load window is determined as the candidate execution window. When a selected charging task has multiple available load windows, the available load window with the larger available load margin is selected as the candidate execution window based on the available load margin of the scheduling period corresponding to each available load window. For example, if a charging task has two available load windows, 18:00 to 20:00 and 21:00 to 23:00, where the available load margin for 18:00 to 20:00 is 10kW and for 21:00 to 23:00 is 25kW, then 21:00 to 23:00 is determined as the candidate execution window for this charging task. The candidate execution window serves as the time range for subsequent available load margin occupation write-back. Based on the available load margin occupied by the candidate execution window during the corresponding scheduling period, an updated available load margin is generated; Specifically, the available load margin occupancy write-back method is as follows: After the candidate execution window is determined, the charging equipment information corresponding to the selected charging task is read, and the charging load occupied by the charging task in each scheduling period within the candidate execution window is determined; the available load margin corresponding to each scheduling period within the candidate execution window is reduced by the charging load occupied by the charging task to generate an updated available load margin; for example, if the candidate execution window for a charging task is from 19:00 to 21:00, and the charging load of the corresponding charging equipment is 7kW, if the available load margin for the scheduling period from 19:00 to 20:00 is 18kW and the available load margin for the scheduling period from 20:00 to 21:00 is 15kW, then after occupancy write-back, the available load margin for the corresponding scheduling periods is updated to 11kW and 8kW respectively; the updated available load margin is used to re-filter the completeable load windows corresponding to unselected charging tasks; Based on the updated available load margin, the available load windows corresponding to the unselected charging tasks are re-filtered, and the set of schedulable tasks is updated based on the re-filtered available load windows. The minimum slack sorting is re-executed based on the updated set of schedulable tasks until all charging tasks in the charging task set have been selected. Specifically, the method for reordering unselected charging tasks is as follows: after generating the updated available load margin, the charging tasks that have not yet been selected are obtained, and based on the updated available load margin, it is re-determined whether each unselected charging task still has a complete load window within its filterable time period; the unselected charging tasks that still have a complete load window are retained in the updated schedulable task set, and the unselected charging tasks that do not have a complete load window are marked as restricted charging tasks; the updated schedulable task set is reordered in ascending order of task slackness, and the next charging task is selected based on the reordering result; A charging task scheduling sequence is generated based on the selection order of each charging task, the candidate execution window, and the restricted charging task marker.

[0025] In this embodiment, the charging distribution module includes: Obtain the charging task scheduling sequence, and obtain the task slack, charging requirement duration, and latest completion time for each charging task in the charging task scheduling sequence; Obtain the available load margin corresponding to each scheduling period, and determine the allocable time period range of the corresponding charging task based on the latest completion time of each charging task. Specifically, the allocatable time period range is determined by the latest completion time and the charging demand duration corresponding to the charging task. The latest completion time is used as the time boundary for the corresponding charging task to complete charging, and the charging demand duration is used as the continuous charging time length required by the corresponding charging task. The scheduling time period range that can accommodate the charging demand duration is determined backward from the latest completion time, and this scheduling time period range is used as the allocatable time period range of the corresponding charging task. Within the allocable time period, scheduling time periods that can meet the execution requirements of the corresponding charging task are selected based on the available load margin, and a candidate allocation time period set is generated. Based on the available load margin corresponding to each scheduling period in the candidate allocation period set and the task slack of the corresponding charging task, a task matching relationship is established. The task matching relationship includes the charging task identifier, task slack, candidate allocation period set, and available load margin corresponding to the candidate allocation period. Specifically, the task matching relationship is established through task slack, a set of candidate allocation time periods, and the available load margin corresponding to each candidate allocation time period. For each charging task, the corresponding task slack and the available load margin corresponding to each scheduling time period in the candidate allocation time period set are obtained. Based on the correspondence between task slack and available load margin, the matching order between each scheduling time period in the candidate allocation time period set and the corresponding charging task is determined. For charging tasks with smaller task slack, candidate allocation time periods near the latest completion time that can meet the charging demand duration requirement are prioritized as high-priority matching time periods. For charging tasks with larger task slack, candidate allocation time periods with larger available load margins are prioritized as high-priority matching time periods. The task identifier, task slack, candidate allocation time period set, available load margin corresponding to each candidate allocation time period, and matching priority of each candidate allocation time period are associated and stored to generate the task matching relationship for the corresponding charging task. For example, if the task slack of charging task A is 0.5h, and the candidate allocation time periods are 18:00 to 20:00 and 20:00 to 22:00, then 20:00 to 22:00 is determined as the high-priority matching time period; if the task slack of charging task B is 3h, and the candidate allocation time periods are 18:00 to 20:00 and 22:00 to 24:00, and the available load margin corresponding to 22:00 to 24:00 is greater than that of 18:00 to 20:00, then 22:00 to 24:00 is determined as the high-priority matching time period. Based on the task matching relationship, the matching order between each scheduling period and the corresponding charging task in the candidate allocation period set is determined. Among them, the charging task with smaller task slack is given priority to be matched with the candidate allocation period within the completion time limit, and the charging task with larger task slack is given priority to be matched with the candidate allocation period with larger available load margin. Each charging task is read sequentially according to the charging task scheduling sequence, and the target allocation time period is selected from the candidate allocation time period set according to the matching order of the corresponding charging task. The target allocation time period is associated with the corresponding charging task to generate the initial charging allocation result for each charging task.

[0026] In this embodiment, the peak-shifting scheduling module includes: Obtain the available load margin corresponding to each scheduling period, and generate the community load peak and valley status based on the available load margin corresponding to each scheduling period; Specifically, the community load peak-valley status is determined by the available load margin corresponding to each scheduling period. Specifically, the available load margins corresponding to each scheduling period within the same scheduling cycle are compared. The scheduling period with the relatively lower available load margin is determined as the peak scheduling period, and the scheduling period with the relatively higher available load margin is determined as the valley scheduling period. The peak scheduling period, valley scheduling period, and their corresponding available load margins are then associated to form the community load peak-valley status. For example, if the available load margin from 18:00 to 20:00 within the same scheduling cycle is 5kW, and the available load margin from 22:00 to 24:00 is 30kW, then 18:00 to 20:00 is determined as the peak scheduling period, and 22:00 to 24:00 is determined as the valley scheduling period. The community load peak-valley status is used to determine the task migration direction in peak shaving and valley filling scheduling. Determine peak and valley scheduling periods based on the community's load peak and valley status; Obtain the initial charging allocation results for each charging task, and determine the peak charging tasks allocated to the peak scheduling period based on the initial charging allocation results; Obtain the task slack corresponding to the peak charging task, and determine the adjustable capability level corresponding to the peak charging task based on the task slack. Specifically, the adjustable capability level is determined by the task slack corresponding to the peak charging task. The task slack of the peak charging task serves as the time margin for determining whether the peak charging task can be adjusted from the peak scheduling period to the valley scheduling period. When the task slack is greater than zero, the corresponding peak charging task is determined as a charging task with time adjustment capability, and the adjustable capability level is determined according to the task slack from large to small. The larger the task slack, the higher the corresponding adjustable capability level. For example, if the task slack of peak charging task A is 3 hours and the task slack of peak charging task B is 1 hour, then the adjustable capability level of peak charging task A is higher than that of peak charging task B. The adjustable capability level is used to establish the redistribution relationship between peak charging tasks and valley scheduling periods. Based on the adjustable capacity level and the available load margin corresponding to the off-peak scheduling period, a redistribution relationship between peak charging tasks and off-peak scheduling periods is established. Specifically, the redistribution relationship is established through the adjustable capacity level and the available load margin corresponding to the valley scheduling period. This involves obtaining the adjustable capacity level corresponding to the peak charging task and obtaining the valley scheduling period and its corresponding available load margin. Peak charging tasks with higher adjustable capacity levels are preferentially associated with valley scheduling periods with larger available load margins, forming a correspondence between peak charging tasks and valley scheduling periods. The redistribution relationship includes the peak charging task, adjustable capacity level, original peak scheduling period, target valley scheduling period, and the available load margin corresponding to the target valley scheduling period. This redistribution relationship is used to update the initial charging allocation result and generate peak shaving and valley filling scheduling results. Based on the redistribution relationship, peak charging tasks with high adjustable capacity levels are allocated to valley scheduling periods with large available load margins. The initial charging allocation result is updated based on the valley scheduling period after the peak charging task allocation, and the peak shaving and valley filling scheduling result is generated.

[0027] In this embodiment, the step of reallocating charging tasks with a slack greater than zero to generate charging task reallocation results without changing the latest completion time includes: Without changing the latest completion time, obtain the task slack, latest completion time, original peak scheduling period and target valley scheduling period for each peak charging task in the redistribution relationship; Based on the task slackness, peak charging tasks with task slackness greater than zero are selected, and the selected peak charging tasks are identified as reassignable charging tasks. Based on the original peak scheduling period and the target valley scheduling period corresponding to the reassignable charging task, the original peak scheduling period is replaced with the target valley scheduling period to generate the adjusted execution period for the corresponding reassignable charging task; Specifically, the adjusted execution period is determined by the target valley scheduling period corresponding to the reassignable charging task. This involves obtaining the original peak scheduling period and the target valley scheduling period corresponding to the reassignable charging task, and using the target valley scheduling period as the adjusted execution time interval for the corresponding reassignable charging task. When the target valley scheduling period can meet the charging duration requirement of the corresponding reassignable charging task, the target valley scheduling period is determined as the adjusted execution period. For example, if a reassignable charging task was originally allocated to the peak scheduling period from 18:00 to 20:00, and the target valley scheduling period determined by the redistribution relationship is from 22:00 to 24:00, and this target valley scheduling period can meet the charging duration requirement of the corresponding charging task, then 22:00 to 24:00 is determined as the adjusted execution period for this reassignable charging task. The adjusted execution period is used to generate the time limit verification result for the corresponding reassignable charging task. Obtain the end time corresponding to the adjusted execution period, and compare the end time with the latest completion time of the corresponding reassignable charging task to generate a time limit verification result; Specifically, the time limit verification result is determined by the adjusted execution period and the latest completion time. The end time corresponding to the adjusted execution period is obtained and compared with the latest completion time of the corresponding reassignable charging task. When the end time of the adjusted execution period is not later than the latest completion time, a time limit verification result satisfying the latest completion time constraint is generated; when the end time of the adjusted execution period is later than the latest completion time, a time limit verification result not satisfying the latest completion time constraint is generated. For example, if the adjusted execution period of a reassignable charging task is from 22:00 to 24:00, and the latest completion time is 01:00 the next day, then a time limit verification result satisfying the latest completion time constraint is generated; if the latest completion time is 23:30, then a time limit verification result not satisfying the latest completion time constraint is generated. The time limit verification result is used to determine whether the adjusted execution period can be used as a reassignment period. Based on the time limit verification results, the adjusted execution period with an end time no later than the latest completion time is determined as the reassignment period for the corresponding reassignable charging task; Update the charging allocation results of the corresponding reassignable charging tasks according to the reassignment period, and generate the time period reassignment results of the corresponding reassignable charging tasks. The time period redistribution results corresponding to each redistributable charging task are correlated and summarized to generate a charging task redistribution result. The charging task redistribution result includes the charging task identifier, the original peak scheduling time period, the redistribution time period, and the latest completion time.

[0028] In this embodiment, the step of executing charging control based on the charging task redistribution result to generate a community off-peak charging scheduling scheme includes: Obtain the charging task reassignment results and parse the charging task identifier, reassignment period, and latest completion time from the charging task reassignment results; The charging equipment information corresponding to the charging task is obtained based on the charging task identifier, and the charging equipment information is associated with the redistribution time period to generate charging task execution information. Specifically, the charging task execution information is generated through the charging task redistribution result, charging device information, and redistribution period. Specifically, the charging task identifier, redistribution period, and latest completion time are obtained from the charging task redistribution result, and the charging device information corresponding to the charging task is obtained based on the charging task identifier. The charging task identifier, redistribution period, latest completion time, and charging device information are associated to generate the corresponding charging task execution information. For example, if the redistribution period for charging task A is from 22:00 to 24:00, the latest completion time is 01:00 the next day, and the corresponding charging device is charging pile P1, then charging task execution information including charging task A, 22:00 to 24:00, 01:00 the next day, and charging pile P1 is generated. The charging task execution information is used to generate the charging task execution order and the corresponding charging control instructions. Based on the redistribution time period in the charging task execution information, sort the execution information of each charging task to generate the charging task execution order; Specifically, the charging task execution order is determined by the charging task execution information. This involves reading the redistribution time periods from each charging task execution information and sorting the information from earliest to latest according to the start time of each redistribution time period. When multiple charging task execution information entries correspond to the same start time for their redistribution time periods, they are sorted from earliest to latest according to the latest completion time of the corresponding charging tasks to generate the charging task execution order. For example, if the redistribution time period for charging task A is 22:00 to 24:00 and the redistribution time period for charging task B is 20:00 to 22:00, then charging task B is placed before charging task A. If the redistribution time periods for both charging task A and charging task B are 22:00 to 24:00, and the latest completion time of charging task B is earlier than that of charging task A, then charging task B is placed before charging task A. The charging task execution order is used to sequentially read the charging task execution information and generate corresponding charging control instructions. The charging task execution information is read sequentially according to the order of charging task execution, and a corresponding charging control instruction is generated based on the read charging task execution information. The charging control instruction includes the charging device identifier, the charging start time, and the charging end time. Specifically, the charging control command is generated through charging task execution information and charging task execution order. The charging task identifier, reassignment period, latest completion time and charging device information in the charging task execution information are read sequentially according to the charging task execution order, and the charging start time and charging end time are determined according to the reassignment period. The charging task identifier, charging device information, charging start time and charging end time are associated and encapsulated to generate the charging control command for the corresponding charging task. According to the charging control command, the corresponding charging equipment is controlled to perform the charging task in the corresponding redistribution period, and the charging execution result is generated. Generate a charging task execution record based on the charging execution results; By associating and summarizing charging task execution records, charging task execution order, redistribution time periods, and charging equipment information, a community off-peak charging scheduling plan is generated. The community off-peak charging scheduling plan includes the charging task execution order, charging time periods, and charging equipment.

[0029] Example 1:

[0030] To verify the feasibility of this invention in practice, it was applied to a community charging scenario. The community had 48 AC charging devices deployed, including 40 devices with a rated power of 7kW and 8 devices with a rated power of 11kW. The community charging management platform, which supports reservation and off-peak charging as proposed in this invention, was used to uniformly manage the charging process of new energy vehicles within the community.

[0031] Within one scheduling cycle, a total of 126 charging reservation requests were received. Each request included the target charging capacity, the vehicle's current battery level, reservation submission information, expected completion time, and charging equipment information. Upon receiving a charging reservation request, the platform manages it centrally through the reservation management module and then sends it to the task generation module.

[0032] The task generation module parses the scheduled charging requests. Taking one such request as an example, the vehicle has a 70kWh battery capacity, a current battery level of 20kWh, and a target charging capacity of 60kWh. The request must be completed before the next workday. The task generation module determines the charging demand as 40kWh based on the target charging capacity and the vehicle's current battery level. It then generates the corresponding charging duration based on the power output of the charging equipment. Simultaneously, it associates the expected completion time with the charging duration parameter to generate the latest completion time. Finally, it encapsulates the charging demand, charging duration, latest completion time, and charging equipment information to generate the corresponding charging task.

[0033] Within this scheduling cycle, 126 scheduled charging requests generated 126 charging tasks. The shortest charging demand duration was 1.2 hours, the longest was 6.8 hours, and the average charging demand duration was 3.7 hours. The load analysis module obtained the charging demand duration and charging equipment information corresponding to each charging task, determined the load occupancy time range for each charging task based on the charging demand duration, and statistically analyzed the scheduled charging load for each scheduling period. The results showed that a large number of scheduled charging tasks were concentrated in some scheduling periods. During one peak scheduling period, the statistically analyzed scheduled charging load reached 286kW, while during some nighttime scheduling periods, the scheduled charging load was only 92kW. The load analysis module collected the operating status of each charging device in the community. Statistics showed that 46 out of 48 charging devices were in normal operation, and 2 were under maintenance. Based on the power supply capacity of the charging devices capable of participating in charging tasks and the scheduled charging load, the available load margin for each scheduling period was determined. The results showed that the available load margin during peak scheduling periods was the lowest at 8kW, while during some nighttime scheduling periods, the available load margin reached 135kW.

[0034] The scheduling management module obtains the current scheduling time generated by the platform's scheduling clock, and acquires the charging demand duration and latest completion time for each charging task. Based on the current scheduling time and the latest completion time, it determines the remaining available time for each charging task, and then calculates the task slack for each charging task based on the remaining available time and the charging demand duration.

[0035] Statistical results show that among the 126 charging tasks, 18 tasks have a slack time of less than 1 hour, 42 tasks have a slack time between 1 and 3 hours, and 66 tasks have a slack time greater than 3 hours. The scheduling management module associates the task slack time with the corresponding charging task, generates a set of charging tasks with task slack time, and uses the lowest slack time priority algorithm to generate a charging task scheduling sequence.

[0036] In generating the charging task scheduling sequence using the lowest slackness first algorithm, the system first sorts the tasks by slackness from smallest to largest. Then, it establishes a complete load window based on the available load margin and prioritizes allocating execution opportunities to charging tasks that meet the execution conditions. When a charging task is selected, the system writes back the available load margin for the corresponding scheduling period and recalculates the candidate execution windows for the remaining charging tasks, thereby dynamically updating the scheduling sequence. The charging allocation module obtains the charging task scheduling sequence and the available load margin corresponding to each scheduling period. It determines the allocable time period range for each charging task based on its latest completion time. Within the allocable time period range, it filters scheduling periods that meet the execution requirements based on the available load margin, generating a candidate allocation time period set. It then establishes a task matching relationship based on the available load margin and the task slackness of each scheduling period in the candidate allocation time period set. Finally, it reads each charging task sequentially according to the charging task scheduling sequence and determines the target allocation time period based on the task matching relationship, generating the initial charging allocation result.

[0037] Statistical results show that during the initial charging allocation phase, a total of 82 charging tasks were assigned to scheduling periods with relatively concentrated loads, while 44 charging tasks were assigned to scheduling periods with relatively low loads.

[0038] The off-peak scheduling module obtains the available load margin corresponding to each scheduling period and generates the community load peak and valley status based on the available load margin. Based on statistical results, scheduling periods with an available load margin below 20kW are designated as peak scheduling periods, and scheduling periods with an available load margin above 80kW are designated as valley scheduling periods. The system determines the peak charging tasks allocated to the peak scheduling periods based on the initial charging allocation results and obtains the task slack corresponding to the peak charging tasks. The adjustable capacity level corresponding to the peak charging tasks is determined based on the task slack. The larger the task slack, the higher the corresponding adjustable capacity level. A redistribution relationship is established based on the adjustable capacity level and the available load margin corresponding to the valley scheduling periods. The system prioritizes migrating peak charging tasks with higher adjustable capacity levels to valley scheduling periods with larger available load margins.

[0039] During the redistribution process, 51 peak charging tasks with a slack greater than zero were identified. The system further validated the adjusted execution periods based on the latest completion time. Ultimately, 47 charging tasks passed the time limit validation and completed the period redistribution, while 4 charging tasks retained their original allocation results because they did not meet the latest completion time constraint. After the redistribution, the reserved charging load corresponding to the original peak scheduling period decreased from 286kW to 173kW, a reduction of 39.5%; the reserved charging load corresponding to the valley scheduling period increased from 92kW to 168kW, significantly improving load utilization. The charging control module obtains the charging task redistribution results and parses the charging task identifier, redistribution period, and latest completion time. Based on the charging task identifier, it obtains the corresponding charging equipment information, generates charging task execution information, and generates the charging task execution order from earliest to latest according to the start time of the redistribution period. Based on the charging task execution order, it generates charging control instructions sequentially and controls the corresponding charging equipment to execute the charging task in the corresponding redistribution period. After execution, it generates a charging task execution record and summarizes it to generate a community off-peak charging scheduling plan.

[0040] Continuous operation statistics show that, under the same reservation scale, after adopting the solution of this invention, the average utilization rate of charging equipment increased from 61.8% to 84.7%; the average load during peak scheduling periods decreased by 37.2%; the average load during off-peak scheduling periods increased by 82.4%; the on-time completion rate of charging tasks increased from 89.6% to 99.2%; the charging waiting phenomenon caused by load concentration decreased by about 73.5%; and the overall utilization efficiency of community charging resources increased by about 35.8%.

[0041] This invention enables dynamic scheduling, load balancing, and peak shaving and valley filling management of scheduled charging tasks while meeting the requirement of the latest completion time for charging tasks. This improves the utilization rate of community charging resources and the completion rate of charging tasks, reduces the concentration of charging load during peak hours, and achieves orderly management of the community charging process.

[0042] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A community charging management platform that supports reservation and off-peak charging, characterized in that, include: The reservation management module receives reservation charging requests and obtains the reservation submission time, expected completion time, target charging capacity, vehicle current battery level, and charging equipment information. The task generation module generates a charging task based on the target charging capacity, the vehicle's current battery level, and the charging equipment information, and determines the charging duration and latest completion time corresponding to the charging task. The load analysis module calculates the scheduled charging load based on the charging demand duration, collects the operating status of the charging equipment, and determines the available load margin based on the scheduled charging load and the operating status of the charging equipment. The scheduling management module obtains the current scheduling time generated by the platform's scheduling clock, calculates the task slack of each charging task based on the charging demand duration, the latest completion time, and the current scheduling time, and generates a charging task scheduling sequence using the lowest slack priority algorithm. The charging allocation module establishes a task matching relationship based on task slack and available load margin, and generates the initial charging allocation result for each charging task based on the task matching relationship and the charging task scheduling sequence. The peak-valley scheduling module determines the peak and valley status of the community load based on the available load margin, and performs peak shaving and valley filling scheduling based on the initial charging allocation results and the peak and valley status of the community load. Without changing the latest completion time, it reallocates the time period for charging tasks with a task slack greater than zero and generates charging task reallocation results. The charging control module executes charging control based on the charging task redistribution results and generates a community off-peak charging scheduling plan.

2. A community charging management platform supporting reservation and off-peak charging as described in claim 1, characterized in that, The task generation module includes: The system parses the charging reservation request and extracts the target charging capacity, the vehicle's current battery level, the reservation submission time, the expected completion time, and the charging equipment information. The charging demand is determined based on the target charging capacity and the vehicle's current battery level, and the corresponding charging duration is generated by combining the charging equipment information. The expected completion time is associated with the charging demand duration parameter to generate the corresponding latest completion time; The charging demand, charging duration parameters, latest completion time parameters, and charging equipment information are associated and encapsulated to generate a charging task.

3. A community charging management platform supporting reservation and off-peak charging according to claim 1, characterized in that, The load analysis module includes: Obtain the charging demand duration and charging equipment information corresponding to each charging task, and determine the load occupancy time range corresponding to each charging task based on the charging demand duration corresponding to each charging task. Based on the load occupancy time range and charging equipment information corresponding to each charging task, the charging load corresponding to each charging task in the same time period is statistically analyzed to generate the scheduled charging load. Collect the operating status of the charging equipment and determine the charging equipment that can participate in the charging task based on the operating status of the charging equipment; Based on the scheduled charging load and the power supply capacity of the charging equipment that can participate in the charging task, the available load margin for each scheduling period is determined.

4. A community charging management platform supporting reservation and off-peak charging according to claim 1, characterized in that, The scheduling management module includes: Obtain the current scheduling time generated by the platform's scheduling clock, and obtain the charging requirement duration and latest completion time for each charging task; The remaining available time for each charging task is determined based on the current scheduling time and the latest completion time. Calculate the task slack for each charging task based on the remaining available time and the duration of charging demand. Associate the task slack of each charging task with the corresponding charging task to generate a set of charging tasks with task slack. Based on the set of charging tasks with task slack, the scheduling priority of each charging task is determined in ascending order of task slack. Based on the scheduling priority of each charging task, the lowest relaxation first algorithm is used to generate the charging task scheduling sequence.

5. A community charging management platform supporting reservation and off-peak charging according to claim 4, characterized in that, The process of generating the charging task scheduling sequence using the lowest relaxation degree first algorithm includes: Obtain a set of charging tasks with task slack, and obtain the available load margin corresponding to each scheduling period; Based on the charging demand duration, latest completion time, and current scheduling time of each charging task in the charging task set, determine the range of filterable time periods for each charging task. Within the available time period, continuous scheduling periods that can accommodate the corresponding charging task are selected based on the available load margin, and the continuous scheduling periods are determined as the completeable load window for the corresponding charging task. Based on the available load window for each charging task, charging tasks with available load windows are assigned to the schedulable task set, while charging tasks without available load windows are marked as restricted charging tasks. Based on the task slackness of each charging task in the schedulable task set, generate the lowest slackness sorting result in ascending order of task slackness. Based on the lowest relaxation sorting results, charging tasks are selected in sequence, and the complete load window corresponding to the selected charging task is determined as the candidate execution window for the corresponding charging task. Based on the available load margin occupied by the candidate execution window during the corresponding scheduling period, an updated available load margin is generated; Based on the updated available load margin, the available load windows corresponding to the unselected charging tasks are re-filtered, and the set of schedulable tasks is updated based on the re-filtered available load windows. The minimum slack sorting is re-executed based on the updated set of schedulable tasks until all charging tasks in the charging task set have been selected. A charging task scheduling sequence is generated based on the selection order of each charging task, the candidate execution window, and the restricted charging task marker.

6. A community charging management platform supporting reservation and off-peak charging according to claim 1, characterized in that, The charging distribution module includes: Obtain the charging task scheduling sequence, and obtain the task slack, charging requirement duration, and latest completion time for each charging task in the charging task scheduling sequence; Obtain the available load margin corresponding to each scheduling period, and determine the allocable time period range of the corresponding charging task based on the latest completion time of each charging task. Within the allocable time period, scheduling time periods that can meet the execution requirements of the corresponding charging task are selected based on the available load margin, and a candidate allocation time period set is generated. Establish task matching relationships based on the available load margin and task slack of each scheduling period in the candidate allocation period set; Based on the task matching relationship, determine the matching order between each scheduling period and the corresponding charging task in the candidate allocation period set; Each charging task is read sequentially according to the charging task scheduling sequence, and the target allocation time period is selected from the candidate allocation time period set according to the matching order of the corresponding charging task. The target allocation time period is associated with the corresponding charging task to generate the initial charging allocation result for each charging task.

7. A community charging management platform supporting reservation and off-peak charging according to claim 1, characterized in that, The off-peak scheduling module includes: Obtain the available load margin corresponding to each scheduling period, and generate the community load peak and valley status based on the available load margin corresponding to each scheduling period; Determine peak and valley scheduling periods based on the community's load peak and valley status; Obtain the initial charging allocation results for each charging task, and determine the peak charging tasks allocated to the peak scheduling period based on the initial charging allocation results; Obtain the task slack corresponding to the peak charging task, and determine the adjustable capability level corresponding to the peak charging task based on the task slack. Based on the adjustable capacity level and the available load margin corresponding to the off-peak scheduling period, a redistribution relationship between peak charging tasks and off-peak scheduling periods is established. Based on the redistribution relationship, peak charging tasks with high adjustable capacity levels are allocated to valley scheduling periods with large available load margins. The initial charging allocation result is updated based on the valley scheduling period after the peak charging task allocation, and the peak shaving and valley filling scheduling result is generated.

8. A community charging management platform supporting reservation and off-peak charging according to claim 7, characterized in that, The process of reallocating charging tasks with a slack greater than zero, without changing the latest completion time, and generating the charging task reallocation result includes: Without changing the latest completion time, obtain the task slack, latest completion time, original peak scheduling period and target valley scheduling period for each peak charging task in the redistribution relationship; Based on the task slackness, peak charging tasks with task slackness greater than zero are selected, and the selected peak charging tasks are identified as reassignable charging tasks. Based on the original peak scheduling period and the target valley scheduling period corresponding to the reassignable charging task, the original peak scheduling period is replaced with the target valley scheduling period to generate the adjusted execution period for the corresponding reassignable charging task; Obtain the end time corresponding to the adjusted execution period, and compare the end time with the latest completion time of the corresponding reassignable charging task to generate a time limit verification result; Based on the time limit verification results, the adjusted execution period with an end time no later than the latest completion time is determined as the reassignment period for the corresponding reassignable charging task; Update the charging allocation results of the corresponding reassignable charging tasks according to the reassignment period, and generate the time period reassignment results of the corresponding reassignable charging tasks. The time period redistribution results corresponding to each redistributable charging task are correlated and summarized to generate a charging task redistribution result. The charging task redistribution result includes the charging task identifier, the original peak scheduling time period, the redistribution time period, and the latest completion time.

9. A community charging management platform supporting reservation and off-peak charging according to claim 8, characterized in that, The step of executing charging control based on the charging task redistribution result and generating a community off-peak charging scheduling scheme includes: Obtain the charging task reassignment results and parse the charging task identifier, reassignment period, and latest completion time from the charging task reassignment results; The charging equipment information corresponding to the charging task is obtained based on the charging task identifier, and the charging equipment information is associated with the redistribution time period to generate charging task execution information. Based on the redistribution time period in the charging task execution information, sort the execution information of each charging task to generate the charging task execution order; The charging task execution information is read sequentially according to the order of charging task execution, and a corresponding charging control instruction is generated based on the read charging task execution information. The charging control instruction includes the charging device identifier, the charging start time, and the charging end time. According to the charging control command, control the corresponding charging equipment to perform the charging task in the corresponding redistribution period and generate the charging execution result; Generate a charging task execution record based on the charging execution results; By associating and summarizing charging task execution records, charging task execution order, redistribution time periods, and charging equipment information, a community off-peak charging scheduling plan is generated. The community off-peak charging scheduling plan includes the charging task execution order, charging time periods, and charging equipment.

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