Electric energy dispatching management method and system of charging station

Abstract

The invention relates to the technical field of charging stations, and provides an electric energy scheduling management method and system for a charging station, and the method comprises the steps: obtaining the charging demand information of an electric vehicle and a charging pile state matrix to generate a charging distribution plan, carrying out the charging task distribution through the charging distribution plan and the charging pile state matrix, and obtaining an optimized charging scheduling scheme. Power demand fitting is carried out on the charging pile working curve and the in-station load capacity period by period, and an energy storage response curve is obtained; and performing interval fitting on the energy storage response curve and an energy storage state information set of the in-station energy storage system, and generating a power distribution scheme based on a fitting result. Energy storage system simulation is carried out on the adjusted scheduling strategy, it is ensured that an energy storage system can effectively participate in electric energy scheduling in the peak period of a power grid, and the problems that the utilization rate of charging piles is low and resources are wasted in the face of the conditions that the load of a charging station is large, the charging task is complex and the charging requirement dynamically changes are solved.

Description

Technical Field

[0001] The present application relates to the technical field of charging stations, and more particularly to a method and system for managing electric energy dispatching of charging stations. Background Art

[0002] With the increasing popularity of electric vehicles (EVs), the development of EV charging infrastructure has become a key issue in energy management. As the primary location for EV charging, the dispatch and management of power at charging stations is crucial for ensuring efficient, safe, and stable charging. In recent years, with the advancement of intelligent and interconnected grid technologies, the management of charging stations not only addresses vehicle charging needs but also requires consideration of multiple factors, including grid load, power resource scheduling, and energy storage systems, striving to achieve the rational allocation and optimized dispatch of power resources.

[0003] Among the relevant technical means, the power dispatching of charging stations monitors the power output of each charging pile, the charging needs of each vehicle and the load status of the charging pile in real time, allocates charging tasks using preset dispatching rules, and imposes certain restrictions on the load of the charging pile through the power grid monitoring system, thereby preventing power grid overload, ensuring efficient use of electricity during the charging process, and meeting the charging needs of electric vehicles.

[0004] Regarding the above technical solution, although allocating charging tasks through preset scheduling rules can effectively avoid the problems of charging pile overload and excessive pressure on the power grid, when faced with situations where the charging station load is large, the charging tasks are complex, and the charging demand changes dynamically, the existing methods mostly rely on static scheduling rules, which cannot fully consider the real-time changes in charging demand, energy storage systems, and power grids, resulting in low utilization of charging piles and waste of resources. Summary of the Invention

[0005] In order to improve the problems of low utilization and waste of charging piles when the charging station is heavily loaded, the charging tasks are complex, and the charging demand changes dynamically, the present application provides a method and system for power scheduling management of a charging station.

[0006] The present invention provides an electric energy dispatching management method for a charging station, comprising: obtaining electric vehicle charging demand information and a charging pile state matrix, generating a charging allocation plan according to the electric vehicle charging demand information and the charging pile state matrix, using the charging allocation plan and the charging pile state matrix to allocate charging tasks to obtain a charging task queue; prioritizing the charging task queue to generate an optimized charging scheduling scheme, fitting the optimized charging scheduling scheme with the charging pile working curve and the load capacity in the station for power demand in each time period to obtain load forecast data; adjusting the optimized charging scheduling scheme according to the load forecast data to obtain an adjusted scheduling strategy, inputting the adjusted scheduling strategy into a preset energy storage system simulation module to obtain an energy storage response curve; performing interval fitting on the energy storage response curve and an energy storage state information set of the energy storage system in the station, and generating a power allocation scheme based on the fitting result.

[0007] As a preferred solution, the steps of obtaining electric vehicle charging demand information and a charging pile status matrix, generating a charging allocation plan based on the electric vehicle charging demand information and the charging pile status matrix, and using the charging allocation plan and the charging pile status matrix to allocate charging tasks to obtain a charging task queue include: collecting vehicle information, remaining power, and expected departure time of each electric vehicle currently connected to the charging station to construct a charging demand data set, and obtaining the current working status, current load, voltage level, and idle time prediction of all charging piles in the charging station to generate a charging pile status matrix; performing correlation analysis on the charging demand data set and the charging pile status matrix to obtain a fitness score matrix, generating a charging allocation plan based on the fitness score matrix, performing resource constraint verification and time window coordination on the charging allocation plan to generate a charging scheduling plan; combining the charging scheduling plan with the preset in-station energy strategy of the charging station to generate a charging task set, matching the charging task set with the current available time of the charging piles to generate a charging task queue.

[0008] As a preferred solution, the charging demand data set and the charging pile status matrix are correlated and analyzed to obtain a fitness score matrix, a charging allocation plan is generated based on the fitness score matrix, the charging allocation plan is checked for resource constraints and coordinated with a time window to generate a charging scheduling plan, including: standardizing the remaining power and expected departure time of each electric vehicle in the charging demand data set to generate a demand priority factor matrix, processing the current load, voltage margin and idle time of each pile position in the charging pile status matrix to construct a pile position availability factor table; cross-calculating the demand priority factor matrix and the pile position availability factor table to obtain a fitness score matrix for vehicles and pile positions, performing maximum weight matching on the fitness score matrix to generate a charging allocation plan; performing resource constraint checking and time window coordination on the pile positions in the charging allocation plan, marking out-of-limit tasks, adjusting the charging start time of the out-of-limit tasks through a preset time window sliding strategy, and generating a charging scheduling plan that meets resource constraints.

[0009] As a preferred solution, the steps of prioritizing the charging task queue, generating an optimized charging scheduling scheme, fitting the optimized charging scheduling scheme with the charging pile working curve and the in-station load capacity for power demand in each time period, and obtaining load forecast data include: calculating a scheduling urgency index based on the user departure time limit, power gap, and available power capacity of each task in the charging task queue, adjusting the scheduling urgency index by a preset load balancing factor, and obtaining a priority list; sorting the charging task queue according to the priority list, constructing an optimized charging scheduling scheme based on the sorting result, fitting the optimized charging scheduling scheme with the charging pile working curve and the in-station load capacity for power demand in each time period, and obtaining the power demand for each time period; wherein the charging pile working curve is a data set of power changes over time generated by the charging pile under different operating conditions, and the in-station load capacity refers to the maximum allowable load upper limit that the charging station can obtain directly from the power grid; fitting and matching the power demand of all time periods with the power supply capacity of the charging station to obtain load forecast data.

[0010] As a preferred solution, the steps of adjusting the optimized charging scheduling scheme according to the load forecast data to obtain an adjusted scheduling strategy, inputting the adjusted scheduling strategy into a preset energy storage system simulation module, and obtaining an energy storage response curve include: decomposing the load forecast data into a peak segment, a valley segment, and a fluctuation transition segment, adjusting the optimized charging scheduling scheme based on the peak segment, the valley segment, and the fluctuation transition segment to generate an adjusted scheduling strategy; analyzing the executable energy storage power window of the charging station according to the adjusted scheduling strategy, generating an energy storage adjustment task sequence according to the executable energy storage power window, performing a time continuity check and an energy storage power balance check on the energy storage adjustment task sequence to obtain an energy storage adjustment plan; performing a time series decomposition on the energy storage adjustment plan to generate hourly power adjustment instructions, inputting the hourly power adjustment instructions into the energy storage system simulation module to obtain an energy storage response curve.

[0011] As a preferred solution, the steps of analyzing the executable energy storage power window of the charging station according to the adjusted scheduling strategy, generating an energy storage adjustment task sequence according to the executable energy storage power window, performing a time continuity check and an energy storage power balance check on the energy storage adjustment task sequence, and obtaining an energy storage adjustment plan include: analyzing the actual load changes in each time period in the adjusted scheduling strategy, calculating the power supply capacity differences of the charging station in each time period according to the actual load changes, and analyzing the power gap of the power supply capacity in the charging station according to the power supply capacity differences; mapping the power gap with the charging and discharging efficiency and charging rate limit of the energy storage system in the charging station to obtain an executable energy storage power window; under the constraints of the executable energy storage power window, using a rolling window optimization algorithm to perform compensation power allocation for each time period in the adjusted scheduling strategy, generating an energy storage adjustment task sequence, performing a time continuity check and an energy storage power balance check on the energy storage adjustment task sequence, and obtaining an energy storage adjustment plan.

[0012] As a preferred solution, the steps of performing interval fitting on the energy storage response curve and the energy storage status information set of the on-site energy storage system, and generating a power allocation plan based on the fitting results, include: obtaining the current charging state, battery health, instantaneous power output capability, and remaining capacity of the on-site energy storage system to generate an energy storage status information set, performing interval fitting on the energy storage status information set and the power demand of each time period in the energy storage response curve to generate an adaptation scoring matrix; correcting the mismatched time periods in the energy storage response curve based on the adaptation scoring matrix to generate a dynamic compensation curve, and coordinating the dynamic compensation curve with the available power resources to generate a power allocation priority sequence; wherein the available power resources refer to the set of remaining power resources that are not locked by scheduled tasks; and fusing and matching the power allocation priority sequence with the scheduling task list to obtain a comprehensively coordinated power allocation plan; wherein the scheduling task list refers to a complete task set consisting of sorted charging tasks and their corresponding power demands, duration information, and priority.

[0013] The present application also provides an electric energy dispatching and management system for a charging station, comprising: an acquisition module for acquiring electric vehicle charging demand information and a charging pile state matrix, generating a charging allocation plan according to the electric vehicle charging demand information and the charging pile state matrix, and using the charging allocation plan and the charging pile state matrix to allocate charging tasks to obtain a charging task queue; a fitting module for prioritizing the charging task queue, generating an optimized charging scheduling scheme, fitting the optimized charging scheduling scheme with the charging pile working curve and the load capacity in the station for power demand in each time period to obtain load forecast data; an adjustment module for adjusting the optimized charging scheduling scheme according to the load forecast data to obtain an adjusted scheduling strategy, inputting the adjusted scheduling strategy into a preset energy storage system simulation module to obtain an energy storage response curve; a generation module for interval fitting the energy storage response curve with the energy storage state information set of the energy storage system in the station, and generating a power allocation scheme based on the fitting result.

[0014] Compared with the existing technology, the present application has the following beneficial effects: high utilization rate and strong flexibility. By obtaining the electric vehicle charging demand information and the charging pile status matrix, the charging task can be accurately assigned to the appropriate charging pile, avoiding the problem of charging pile overload; by prioritizing the charging task queue, it is ensured that high-priority tasks can be charged first. At the same time, taking into account the real-time changes in the grid load, the load forecast and the power demand fitting method in each period are adopted to provide accurate forecast data for the scheduling of charging tasks. Then, by simulating the energy storage system for the adjusted scheduling strategy, an energy storage response curve is generated. Combined with the charging and discharging capacity of the energy storage system, it is ensured that the energy storage system can effectively participate in the power scheduling during the peak period of the grid, balance the load demand of the charging station, and generate a power distribution plan, realizing the coordinated work of the grid, charging piles and energy storage system, improving the energy efficiency of the charging station and the completion rate of the charging task, and improving the problem of low utilization rate and waste of resources of the charging pile when the charging station load is large, the charging task is complex and the charging demand changes dynamically. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] The structures, proportions, sizes, etc. depicted in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not intended to limit the conditions under which the present invention can be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportional relationships, or adjustments in size should still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and objectives that can be achieved by the present invention.

[0017] Figure 1 1 is a flow chart of a method for managing electric energy dispatching of a charging station provided by an embodiment of the present invention; Figure 2 It is a schematic block diagram of the structure of an electric energy dispatching and management system for a charging station provided by an embodiment of the present invention.

[0018] Description of reference numerals: 10. Electric energy dispatching and management system of charging station; 11. Acquisition module; 12. Fitting module; 13. Adjustment module; 14. Generation module. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0021] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] It should be further understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0023] The technical solution of the present invention will be further described below with reference to the accompanying drawings and through specific implementation methods.

[0024] Example 1: like Figure 1 As shown, the present application provides a method for electric energy dispatching and management of a charging station, comprising steps S100 to S400.

[0025] Step S100: Obtain the electric vehicle charging demand information and the charging pile state matrix, generate a charging allocation plan based on the electric vehicle charging demand information and the charging pile state matrix, and use the charging allocation plan and the charging pile state matrix to allocate charging tasks to obtain a charging task queue.

[0026] In this step, the charging station's control system obtains vehicle charging demand information and a charging pile status matrix. This information includes the remaining charge of each EV, required charging time, target charging capacity, and estimated departure time. The charging pile status matrix includes information such as the current load, charging current, voltage level, charging power, and idle time of each charging pile. This information is collected in real time by the charging station's data acquisition system and aggregated into a single dataset. Specifically, in actual implementation, the charging station's management system regularly obtains this data from EVs and charging piles, creating a real-time database of charging demand and charging pile status. The system then allocates charging tasks based on the vehicle charging demand information and the charging pile status matrix, generating a charging task queue. This queue is sorted based on charging task priority and the idle status of the charging piles, ensuring that tasks are allocated to each charging pile based on its current load and available time, thereby preventing excessive load from being concentrated on certain charging piles.

[0027] For example, when acquiring electric vehicle charging demand information, the system calculates the required charging power for each vehicle based on its remaining charge (e.g., 20% remaining) and expected departure time (e.g., 30 minutes from now). Simultaneously, the charging station status matrix provides real-time information about charging stations, such as a charging station with a load of 50A, a voltage of 220V, and a remaining idle time of 15 minutes. Based on this information, the system then generates a charging allocation plan, matching appropriate charging stations with electric vehicles and storing these tasks in a charging task queue.

[0028] Step S200: Prioritize the charging task queue, generate an optimized charging scheduling plan, fit the optimized charging scheduling plan with the charging pile working curve and the load capacity in the station to the power demand in each time period, and obtain load forecast data.

[0029] In this step, each task in the charging task queue is prioritized based on its power requirements, the user's departure time, and the availability of charging piles. Task priority is influenced by factors such as the vehicle's remaining battery life, departure time, and the load capacity of the charging pile. By sorting the charging task queue, the system generates an optimized charging schedule to ensure that high-priority tasks receive charging first. Specifically, the optimized charging schedule uses the charging pile operating curve (i.e., the power output curve of the charging pile under different loads) and the charging station's load capacity (including the maximum power the grid can handle) to fit power demand by time period. Through this fitting process, the system can accurately predict power demand for each time period, identify peak and trough loads at the charging station, and adjust the allocation of charging tasks accordingly to ensure a balanced distribution of power resources.

[0030] For example, by prioritizing the charging task queue, an electric vehicle that needs to be charged to 80% can be assigned a high-priority task, while another electric vehicle with a smaller charging requirement can be assigned a low-priority task. The system then calculates power demand over different time periods based on the charging pile operating curve and load capacity information, generating load forecast data to guide power scheduling.

[0031] Step S300: Adjust the optimized charging scheduling plan according to the load forecast data to obtain an adjusted scheduling strategy, input the adjusted scheduling strategy into a preset energy storage system simulation module to obtain an energy storage response curve.

[0032] In this step, based on the load forecast data, the system will dynamically adjust the optimized charging scheduling plan. For example, if it is predicted that the grid load will reach a peak during a certain period of time, the system will adjust the time window of the charging task, postpone the charging time of low-priority tasks, or transfer some charging tasks to the energy storage system for charging to avoid excessive pressure on the grid. The adjusted scheduling strategy will take into account the charging and discharging capabilities of the energy storage system and ensure that the energy storage system can charge and discharge at appropriate times to balance the load. Specifically, the adjusted scheduling strategy will be input into the preset energy storage system simulation module, which simulates the charging and discharging process of the energy storage system under different load conditions. Based on the real-time status of the energy storage system, the system generates an energy storage response curve, which reflects the charging and discharging capabilities of the energy storage system during load fluctuations.

[0033] For example, if load forecast data indicates an impending peak in grid load during a certain period, the energy storage system simulation module can shift some low-priority charging tasks to the energy storage system to reduce the burden on the grid. Based on this adjustment, the energy storage system simulation module calculates the energy storage response curve to guide the energy storage system's charging and discharging strategy.

[0034] Step S400: performing interval fitting on the energy storage response curve and the energy storage status information set of the in-station energy storage system, and generating a power allocation plan based on the fitting result.

[0035] In this step, the energy storage response curve is interval-fitted with the real-time energy storage status information set of the charging station energy storage system. The energy storage status information set includes information such as the remaining capacity, charging and discharging efficiency, and health of the energy storage equipment. By fitting the energy storage response curve with the energy storage status information set, the system can determine the optimal charging and discharging strategy for the energy storage system in each time period, ensuring that the power resources in the charging station are efficiently utilized in each time period. Specifically, during the fitting process, the system will fit the energy storage response curve and the energy storage status information through an interval fitting algorithm (for example, the least squares method) to ensure that the fitting result can accurately reflect the charging and discharging capabilities of the energy storage system. After the fitting is completed, the system will generate a power allocation plan to determine the power supply source for each charging task (including the power grid, energy storage system, or charging pile) to ensure that each task can be completed in a timely manner.

[0036] For example, the system generates a charging and discharging plan based on the energy storage system's current battery capacity and charging and discharging efficiency, ensuring timely discharge of power during peak hours and charging during off-peak hours. By coordinating with load forecast data from charging stations and the grid, the system ultimately generates a power allocation plan to ensure stable operation of the grid and energy storage system.

[0037] In this embodiment, the system obtains electric vehicle charging demand information and a charging pile status matrix. The electric vehicle charging demand information includes data such as the electric vehicle's charging demand and charging duration, while the charging pile status matrix includes information such as the real-time load status, voltage level, and power output capacity of each charging pile. Next, a charging allocation plan is generated based on the electric vehicle charging demand information and the charging pile status matrix. Charging tasks are assigned using this charging allocation plan and the charging pile status matrix, resulting in a charging task queue. Subsequently, the charging task queue is prioritized to generate an optimized charging scheduling plan. Based on the optimized charging scheduling plan, the power demand for each time period is fitted based on the charging pile operating curve and the station's load capacity, thereby generating load forecast data. Based on this load forecast data, the optimized charging scheduling plan is adjusted to generate an adjusted scheduling strategy. This adjusted scheduling strategy is then input into a preset energy storage system simulation module to generate an energy storage response curve. Finally, the energy storage response curve is interval-fitted to the energy storage status information set of the station's energy storage system, and a power allocation plan is generated based on the fitting results.

[0038] By dynamically acquiring information about electric vehicle charging needs and the charging pile status matrix, charging tasks can be accurately allocated, ensuring the rational utilization of charging pile resources and avoiding resource waste. Secondly, prioritizing the charging task queue and generating an optimized charging schedule allows for dynamic adjustment of charging strategies based on actual grid load and charging pile status, thereby achieving optimal scheduling of power resources. Furthermore, combining time-period power demand fitting with load forecast data enables accurate prediction of power demand and timely adjustment of charging strategies in the face of grid load fluctuations and charging demand fluctuations. The introduction of an energy storage system, through optimization of the energy storage response curve using a simulation module, helps alleviate grid pressure during peak load periods and achieve efficient coordination between energy storage and charging tasks. Ultimately, the generation of a power allocation plan ensures the smooth operation of the grid and energy storage system, while also improving the overall charging efficiency of charging stations. This addresses the issues of low charging pile utilization and resource waste that can occur when charging stations are subject to heavy loads, complex charging tasks, and dynamically changing charging demands.

[0039] Example 2: In step S100, the vehicle information, remaining power, and expected departure time of each electric vehicle currently connected to the charging station are collected to construct a charging demand data set, and the current working status, current load, voltage level, and idle time prediction of all charging piles in the charging station are obtained to generate a charging pile status matrix.

[0040] The charging station's intelligent management system collects real-time information about each electric vehicle connected to the charging station. This information includes each electric vehicle's vehicle ID, remaining charge level, target charge level, charging time requirements, and estimated departure time. Vehicle information also includes charging port type, battery type (e.g., lithium, nickel-metal hydride, etc.), and compatibility with charging piles. Simultaneously, the charging station's control system monitors the operating status of all charging piles in real time, including each pile's load current (A), voltage level (V), charging power (kW), and idle time forecast (minutes). Specifically, the charging station's intelligent control system records and uploads information about all electric vehicles to a central data platform. Each electric vehicle's charging demand data, such as remaining charge level and expected departure time, is cross-referenced with the vehicle's real-time charging status (e.g., current charging power, charging progress), ensuring that the vehicle's charging task is assigned to the appropriate charging pile. Charging pile status data is acquired in real time by the charging pile's sensor system and control module. Information such as the pile's operating status, load, and idle time is automatically updated to facilitate dynamic scheduling. All of this information will constitute the charging demand dataset and the charging pile status matrix.

[0041] For example, suppose five electric vehicles are connected to a charging station. One of them has a remaining charge of 30% and requires charging to 80% (the target charge), with an estimated charging time of 40 minutes. Meanwhile, charging station A has a load of 15A, a voltage of 230V, and an idle time of 10 minutes. Charging station B has a load of 50A, a voltage of 220V, and an idle time of 30 minutes. The system uses this information to calculate the charging demand and time required for each vehicle. Combined with the status information of each charging station (such as idle time and load capacity), it generates a charging demand dataset and a charging station status matrix.

[0042] The charging demand dataset is correlated with the charging pile status matrix to obtain a fitness score matrix. A charging allocation plan is generated based on the fitness score matrix. The resource constraint check and time window coordination of the charging allocation plan are performed to generate a charging schedule.

[0043] By combining the charging demand data set and the charging pile status matrix, the system will calculate the compatibility between the charging pile and the electric vehicle. The charging requirements of each electric vehicle (remaining power, expected departure time, target power) are matched with the load, power output capacity, and idle time of each charging pile to generate a fitness score matrix. Each element of the fitness score matrix represents the fitness of a certain electric vehicle and a certain charging pile. The higher the fitness, the better the match between the vehicle and the charging pile. Then, based on this fitness score matrix, the system will generate a charging allocation plan to assign each electric vehicle to the most suitable charging pile. Specifically, the calculation of the fitness score matrix can adopt a weighted scoring method. For example, the charging demand of the electric vehicle, the idle time of the charging pile, the power output of the charging pile, etc. will be given different weights to calculate the matching degree of each electric vehicle and the charging pile. This score value can be calculated using the following formula: Adaptability score = w1⋅remaining power score + w2⋅charging time score + w3⋅pile load score + w3⋅idle time score; The remaining power score is determined by the ratio of the vehicle's remaining power to the charging demand; the charging time score is evaluated based on the expected charging time and the charging capacity of the charging station; The pile load score is assessed based on the load capacity of the charging pile; the idle time score reflects whether the idle time of the charging pile meets the task requirements.

[0044] For example, if EV 1 needs to charge to 80%, and charging station A has a load of 15A and an idle time of 10 minutes, it will be given a higher suitability score. On the other hand, if charging station B has a higher load and a shorter idle time, it will be given a lower suitability score. Based on this suitability score matrix, the system generates a charging allocation plan to ensure that each EV can charge to the required level without overloading the charging stations.

[0045] The steps of correlating the charging demand dataset with the charging pile status matrix to obtain a fitness score matrix, generating a charging allocation plan based on the fitness score matrix, performing resource constraint verification and time window coordination on the charging allocation plan, and generating a charging schedule include: The remaining power and expected departure time of each electric vehicle in the charging demand dataset are standardized to generate a demand priority factor matrix. The current load, voltage margin and idle time of each charging pile position in the charging pile status matrix are processed to construct a pile position availability factor table.

[0046] The charging station intelligent monitoring module receives the electric vehicle charging demand data and normalizes it to form a demand priority factor matrix. The normalization process includes normalizing the key parameters of each electric vehicle, such as the remaining power, target charging power, and expected departure time, so that they can be quantitatively compared later. At the same time, based on the data provided by the charging pile status matrix, the real-time load (current load), voltage margin (relative margin of real-time voltage), and idle time (remaining idle time) of each charging pile are standardized to generate a pile availability factor table. Specifically, each entry in the demand priority factor matrix is ​​calculated using the following formula: in, Indicates the The first electric car Priority factor; The current remaining power of the electric vehicle; Target charging capacity for electric vehicles; is the expected departure time; The maximum charging time; The urgency of the charging task, usually set according to user needs; , , It is the weight coefficient, which can be adjusted dynamically according to business needs.

[0047] At the same time, each entry in the pile availability factor table is calculated using the following formula: in, For charging piles No. Availability factor; is the current current load of the charging pile; The maximum current allowed load of the charging pile; is the voltage margin (the difference between the real-time voltage and the rated voltage); is the rated voltage; For the remaining free time; is the total task time; , , is the weight coefficient, which can be adjusted dynamically according to the actual system performance.

[0048] For example, assuming that the current remaining power of electric vehicle A is 20%, the target charging capacity is 80%, and the expected departure time is 50 minutes, while the current current load of charging pile B is 30A, the rated current is 60A, and the idle time is 15 minutes, the system will use the above formula to calculate and generate the demand priority factor matrix and the pile availability factor table, respectively recording the relevant adaptation data of the electric vehicle and the charging pile.

[0049] The demand priority factor matrix and the pile availability factor table are cross-calculated to obtain the vehicle and pile adaptability score matrix. The adaptability score matrix is ​​matched with the maximum weight to generate the charging allocation plan.

[0050] The system generates a compatibility matrix by calculating the cross-scores between the demand priority matrix and the charging station availability factor table. Each entry in the matrix represents the compatibility between an electric vehicle and a charging station. The compatibility between each electric vehicle and charging station directly determines the probability of charging task assignment. The system then optimizes the matrix matching results using a maximum weight matching algorithm (such as the Hungarian algorithm, a well-known technique for solving allocation problems) to generate a charging allocation plan.

[0051] Specifically, when calculating the suitability score, the system comprehensively considers the entries in the demand priority factor matrix and the entries in the pile location availability factor table, and provides the following suitability score formula: in, For electric vehicles With charging pile The fitness score of is the demand priority factor; is the pile location availability factor.

[0052] Then, the suitability score matrix is ​​matched with the maximum weight using the Hungarian algorithm to ensure that each electric vehicle in the charging allocation plan is assigned to the most suitable charging pile.

[0053] For example, if the system calculates that the compatibility score between vehicle A and charging pile B is 85 points, and the scores of other piles are all below 70 points, the system will prioritize assigning vehicle A to pile B, and vehicle C will choose the charging pile with the second highest score.

[0054] The charging station locations in the charging allocation plan are checked for resource constraints and coordinated with time windows. Combined with the maximum load limit set by the energy strategy within the charging station, overloaded tasks are marked. The charging start time of overloaded tasks is adjusted through the preset time window sliding strategy to generate a charging schedule that meets resource constraints.

[0055] By performing resource constraint checks on each task in the charging allocation plan (the matching result between the vehicle and the charging pile), the system ensures that the load, voltage and task allocation time of each charging pile are within the threshold range set by the system. If it is found during the verification process that some tasks exceed the limit (for example, the load limit is exceeded), the system will use a sliding strategy to adjust within the time window, including postponing the start time of low-priority tasks, scheduling some tasks to other charging piles, or using the energy storage system to share the load. Specifically, the time window sliding strategy is implemented through the following steps: calculate the load of the current charging task and check whether it exceeds the maximum load limit preset by the charging station; find the over-limit task and record the corresponding charging pile and vehicle information; slide the task time within the time window (for example, postpone the task start time by 10 minutes) until the task meets the limit; if the sliding still cannot meet the limit, transfer the task to the energy storage system or other charging piles that have not exceeded the limit.

[0056] For example, if a charging pile reaches its maximum load during peak hours, the system will choose to postpone a low-priority task for 10 minutes; if the load problem still cannot be solved, the task will be transferred to the energy storage system, and the task time will be adjusted to ensure that resource allocation requirements are met.

[0057] The charging schedule is combined with the preset energy strategy of the charging station to determine the expected start time and duration of each task, generate a charging task set, match the charging task set with the current available time of the charging pile, and generate a charging task queue.

[0058] By combining the charging schedule with the preset energy strategy of the charging station, the system first takes into account the load limits (e.g., maximum current load, maximum number of charging piles) and energy management strategies within the charging station (e.g., prioritizing charging during off-peak hours, or using the energy storage system to adjust charging tasks). Then, combining these energy strategies, the estimated start time and duration of each charging task are determined, and the time window of the charging task is matched by the current available time of the charging pile to generate a charging task queue. Specifically, according to the energy strategy within the station, the system will set it to avoid the allocation of too many charging tasks during peak hours and give priority to using the energy storage system to alleviate the load. For example, if the load on a charging pile is large, the system will adjust the charging time of the task and postpone the start time of low-priority tasks to reduce the burden on the charging pile.

[0059] For example, at a charging station, low-priority tasks are prioritized during off-peak hours (such as at night) to reduce the burden on the power grid; during peak periods, tasks are transferred to the energy storage system, or some tasks are time-adjusted to avoid grid overload and maintain a stable charging pile load.

[0060] In step S200, a scheduling urgency index is calculated based on the user's departure time limit, power gap, and available power capacity of each task in the charging task queue, and the scheduling urgency index is adjusted by a preset load balancing factor to obtain a priority list.

[0061] By analyzing the key parameters of each task in the charging task queue, the system calculates a scheduling urgency index for each task. This metric combines the user's departure time, power shortage, and the available power capacity of the charging station to quantify the urgency of the task, providing a basis for subsequent allocation and sorting. Furthermore, a preset load balancing factor is introduced to adjust scheduling urgency in the event of multiple competing tasks, thereby reducing the concentration of excessive load peaks.

[0062] Specifically, the scheduling urgency index is calculated using the following formula: in, is the scheduling urgency index of the i-th task; The target power expected by the user; The current remaining power of the vehicle; The maximum power capacity that can be provided by the charging pile associated with the target task; The time limit for users to leave the site; The urgency factor is the task, which takes into account abnormal demands or special priorities, such as urgent charging tasks.

[0063] During the adjustment process, the scheduling urgency indicators of all tasks are normalized and combined with a preset load balancing factor distribution adjustment to generate a final priority list. For example, the load balancing factor can be dynamically calculated by evaluating the location of charging stations and the load distribution, reducing the burden caused by multiple tasks competing for the same charging station.

[0064] For example, assume that the remaining power of charging task A is 10%, the target is to charge to 80%, the departure time is 30 minutes, the maximum power capacity of the associated charging pile is 50kW, and the urgency factor is set to normal priority ( =1), the system will calculate the scheduling urgency index UA of task A and compare and sort it with other tasks to form a priority list.

[0065] The charging task queue is sorted according to the priority list, and an optimized charging scheduling plan is constructed based on the sorting results. The optimized charging scheduling plan is then fitted with the charging pile working curve and the load capacity within the station to determine the power demand for each time period. The charging pile working curve is a data set of the power generated by the charging pile under different operating conditions and changes over time. The load capacity within the station refers to the maximum allowable load limit that the charging station can obtain directly from the power grid.

[0066] The charging task queue is sorted from highest to lowest urgency based on a priority list. Once sorted, an optimized charging schedule is generated, prioritizing high-urgency tasks and allocating them based on station resource constraints. The system then fits the optimized charging schedule to the charging pile operating curves and station load capacity to predict power demand fluctuations within each scheduling period.

[0067] Specifically, the power demand fitting for each period is completed according to the following logic: Obtain the charging pile working curve, which is composed of a data set of the charging pile power output changing over time under different load conditions. For example, the charging pile working curve can be expressed as: in, For charging piles At the moment Power output; is the growth rate of charging pile load power; is the initial power bias.

[0068] Based on the optimized charging schedule, the load demand for each time period is calculated and the charging pile operating curve is fitted to the load capacity within the station to ensure that the task scheduling meets resource constraints. For example, the fitting results are adjusted based on the load capacity within the station (the maximum allowable load limit that the charging station can directly obtain from the power grid) to mark overloaded or underloaded areas.

[0069] For example, assuming that the power demand of the charging pile working curve is expected to be 100kW during a certain period, and the load capacity within the station is limited to 120kW, the task distribution can meet the resource conditions; if the power demand is expected to exceed the load capacity within the station (for example, reaching 130kW), the system will delay the task or transfer the energy storage system to alleviate the burden.

[0070] The power demand in all time periods is matched with the power supply capacity of the charging station to obtain load forecast data.

[0071] By matching power demand at each time period with the charging station's power supply capacity, the system generates load forecast data. Power supply capacity includes the grid's maximum power output and the energy storage system's available charge and discharge power. The forecast is dynamically adjusted based on the mission requirements and power limits for each time period.

[0072] Specifically, the load forecast data can be calculated using the following fitting model: in, For the moment Total power demand of charging stations; For charging piles At the moment Working power (obtained from the working curve); For energy storage system at all times The charge and discharge power.

[0073] The fitting and matching process is used to verify whether the power in a period exceeds the power supply capacity, and dynamic adjustment suggestions are made to the power demand, such as peak shaving or task delay adjustment.

[0074] For example, during a specific period of time, load forecast data indicates that the grid load has reached 90% of the power supply capacity limit. The system will plan the charging and discharging tasks of the energy storage system in advance to reduce peak loads and improve overall operating efficiency, thereby stabilizing the balance between power supply capacity and demand.

[0075] In step S300, the load forecast data is decomposed into a peak section, a valley section, and a fluctuation transition section, and the optimized charging scheduling plan is adjusted based on the peak section, the valley section, and the fluctuation transition section to generate an adjusted scheduling strategy.

[0076] By analyzing the load forecast data by time period, the system divides the load forecast results into peak sections, valley sections, and fluctuation transition sections, and adopts corresponding optimization strategies for different sections. The peak section corresponds to the period when the power demand significantly exceeds the average level, the valley section is the period when the power demand is relatively small, and the fluctuation transition section refers to the transition period when the demand changes rapidly between peak and valley. The system adjusts the optimized charging scheduling plan according to the characteristics of different sections and generates an adjusted scheduling strategy to achieve reasonable allocation of resources and dynamic balance of load. Specifically, the system adjusts the optimized charging scheduling plan through the following rules: Peak periods: During peak periods, the system prioritizes reducing some low-priority charging tasks, delaying their scheduling, and utilizing the energy storage system to release power to alleviate grid load. For example, some low-priority tasks can be shifted to off-peak periods or fluctuating transition periods.

[0077] Off-peak section: In the off-peak section, the system increases the allocation of low-priority charging tasks and gives priority to charging the energy storage system to store available electricity for the peak section.

[0078] Fluctuation transition section: In the fluctuation transition section, the system prioritizes smoothing load fluctuations and schedules medium-priority tasks to be executed during the transition period. At the same time, the energy storage system dynamically responds to actual load changes to adjust the power balance.

[0079] For example, if load forecast data indicates that the peak period is from 9:00 to 11:00 a.m., the power station load demand is 150kW, but the power supply capacity is only 120kW, the system will choose to postpone non-urgent tasks A and B, releasing 20kW of energy storage power to meet high-priority charging tasks; while in the off-peak period (such as 2:00 to 4:00 a.m.), the system will arrange for tasks A and B to charge, and use the idle period to charge the energy storage system.

[0080] According to the adjusted scheduling strategy, the executable energy storage power window of the charging station is analyzed, and the energy storage adjustment task sequence is generated according to the executable energy storage power window. The time continuity check and energy storage power balance check of the energy storage adjustment task sequence are performed to obtain the energy storage adjustment plan.

[0081] By analyzing the adjusted scheduling strategy, the system calculates the charge and discharge power ranges that the energy storage system can execute during each time period based on load forecasts and charging task schedules, thus implementing the energy storage power window. The energy storage power window determines the upper and lower limits of the power that the energy storage system can adjust during each time period. Subsequently, based on the energy storage system's characteristics (charging rate, discharge rate, remaining capacity, etc.), the system generates a sequence of energy storage adjustment tasks and performs time continuity checks and energy storage power balance verification on the sequence to ensure that the energy storage plan meets actual operating conditions.

[0082] Specifically, the executable energy storage power window is calculated using the following formula: in, For the moment t Energy storage power window; is the maximum discharge power of the energy storage system; For the moment t The shortfall power required to meet load requirements; is the minimum charging power limit of the energy storage system.

[0083] When generating a sequence of energy storage regulation tasks, the tasks include charging and discharging energy storage at different time intervals. A time continuity check ensures that energy storage tasks can be started and stopped without conflict, and a storage charge balance check verifies that the charging and discharging tasks do not exceed the energy storage capacity limit.

[0084] For example, during the morning peak hours (9:00 to 11:00), load forecast data indicates a 25kW load shortfall, while the energy storage system's maximum discharge power is 30kW. Therefore, the executable energy storage power window is 25kW. During off-peak hours, the energy storage charging task is 40kW. The system continuously adjusts the energy storage system's charging and discharging tasks to generate the final energy storage regulation plan.

[0085] The energy storage regulation plan is decomposed into time series to generate hourly power regulation instructions, which are then input into the energy storage system simulation module to obtain the energy storage response curve.

[0086] By decomposing the energy storage regulation plan into a time series, the system generates hourly energy storage power regulation instructions. Specifically, based on the charge and discharge dynamics of the energy storage system, the hourly instructions accurately control the operating state of the energy storage system to achieve the power compensation target. Subsequently, the hourly power regulation instructions are input into the energy storage system simulation module. The system simulates the response curve of the energy storage system under different load conditions to provide guidance for actual operation. Specifically, the energy storage system simulation module translates the hourly instructions contained in the energy storage regulation plan into a dynamic trajectory model, for example: in, For the moment The output power of the energy storage system; For the moment Energy storage charging power; For the moment The energy storage discharge power.

[0087] The energy storage response curve output by the simulation module contains the actual trajectory of charging and discharging power changes over time, which is used to evaluate the feasibility of the regulation instructions.

[0088] For example, during peak hours, when the energy storage system is engaged and discharging, the hourly power regulation instructions are 10 kW at 9:00, 15 kW at 9:30, and 20 kW at 10:00. The energy storage simulation module calculates and outputs a response curve, demonstrating that the system can effectively deliver this power without load overshoot or overshoot.

[0089] The steps of analyzing the executable energy storage power window of the charging station based on the adjusted scheduling strategy, generating an energy storage adjustment task sequence based on the executable energy storage power window, performing a time continuity check and energy storage power balance check on the energy storage adjustment task sequence, and obtaining an energy storage adjustment plan specifically include: Analyze the actual load changes in each time period in the adjusted scheduling strategy, calculate the power supply capacity differences of the charging station in each time period based on the actual load changes, and analyze the power gap of the power supply capacity in the charging station based on the power supply capacity differences.

[0090] Through the adjusted scheduling strategy and combined with the real-time load data of the charging station, the system can calculate the actual load changes hour by hour. The main sources of load data include the power requirements of all charging tasks and the current task distribution of the charging piles. At the same time, the power supply capacity difference is calculated based on the total power available from the grid minus the power demand of the real-time load. If the power demand exceeds the power supply capacity, a power gap will be formed. Specifically, the power gap calculation formula is: in, For the moment Power gap; For the moment Total power requirements for all charging tasks; For the moment The power available to the charging station from the grid.

[0091] The power supply capacity difference analysis will evaluate the power gap in each period. >0, it means there is insufficient power supply during this period; if ≤0, it means that the power supply capacity is sufficient or excessive.

[0092] For example, during the morning peak (10:00-11:00), =150kW−120kW=30kW, indicating that the current grid power supply is less than 30kW. The system will record this power gap for reference when allocating energy storage tasks later.

[0093] The power gap is mapped to the charging and discharging efficiency and charging rate limit of the charging station's on-site energy storage system to obtain the executable energy storage power window.

[0094] The system analyzes the power gap in each time period based on the energy storage system's charge and discharge characteristics, including maximum charge and discharge power (rate) and system efficiency, and converts the power gap into a feasible range for the energy storage system's compensation capability. The size of the executable energy storage power window is determined by both the power gap and the energy storage system's performance. Specifically, the energy storage power window can be calculated using the following formula: in, For the moment Executable energy storage power window; For the moment Power gap; is the discharge efficiency of the energy storage system; The charging efficiency of the energy storage system; is the maximum discharge power of the energy storage system; is the maximum charging power of the energy storage system.

[0095] If the current energy storage task requires the energy storage system to supply energy efficiently, the energy storage response will be constrained and adjusted within the window range.

[0096] For example, if the power gap in a certain period is 20kW, the energy storage system discharge efficiency is 90% ( =0.9) and the maximum discharge power is 25kW, then the executable energy storage power window is: This means that the energy storage system can provide a maximum regulation capacity of 22.22kW during this period.

[0097] Under the constraint of the executable energy storage power window, a rolling window optimization algorithm is used to allocate compensation power to each time period in the adjusted scheduling strategy. With the optimization goal of minimizing grid power fluctuations and energy storage system losses, an energy storage regulation task sequence is generated. The energy storage regulation task sequence is checked for time continuity and energy storage power balance to obtain an energy storage regulation plan.

[0098] The energy storage system uses a rolling window optimization algorithm to allocate power time-per-hour based on the adjusted scheduling strategy. This algorithm performs calculations in stages, optimizing power allocation within a specific time window at a time, ensuring dynamic adjustment of energy storage tasks while meeting system constraints. The optimization goal is to minimize grid power fluctuations and energy storage system losses while ensuring the continuity of the energy storage system's power output.

[0099] Specifically, the objective function and constraints of the optimization problem are as follows: Objective function: in, is the energy storage system power at time t; is the energy storage loss; is the energy storage loss weight factor.

[0100] Constraints: (The energy storage power must be within the window range); (The energy storage capacity is within the allowable range).

[0101] The rolling window optimization algorithm slides according to time, processing one window at a time (for example, 1 hour), ensuring that the system's power allocation plan is dynamically adjusted without conflict.

[0102] For example, if the optimization goal for a certain time period is to reduce power fluctuations within a rolling window from 10:00 to 11:00, the system can discharge 15kW at 10:15, 20kW at 10:30, and then reduce the discharge to 10kW at 10:45 to ensure smooth power output. A time continuity check verifies that the task instructions are continuous and reasonable, and a power balance check ensures that the charging and discharging of the energy storage are within the total capacity limit.

[0103] In step S400, the current charging state, battery health, instantaneous power output capability and remaining capacity of the energy storage system in the station are obtained to generate an energy storage state information set.

[0104] Through the energy storage system's monitoring module, the system obtains multiple status data from the energy storage device in real time, including the current charging state (the battery's real-time charging completion percentage), battery health (reflecting battery degradation or usable life), instantaneous power output capability (the maximum instantaneous discharge power currently available), and remaining capacity (the total available power remaining in the energy storage device). This data is combined into a storage status information set, which is used to guide the operation and scheduling of the energy storage device.

[0105] Specifically, the entries of the energy storage status information set can be expressed as: in, is the energy storage state information set at time t; The current charging status of the energy storage system; The battery health, usually expressed as a percentage (e.g. 90% indicates good health); is the instantaneous power output capability; is the remaining capacity, which represents the total available power of the energy storage system (unit: kWh).

[0106] For example, assuming that the energy storage status at time t is: the charging state is 75%, the battery health is 85%, the instantaneous power output capacity is 50kW, and the remaining capacity is 200kWh, then the system will record the corresponding energy storage status information set ={75%,85%,50kW,200kWh}, used for subsequent scheduling analysis.

[0107] The energy storage status information set is interval-fitted with the power demand of each period in the energy storage response curve to generate an adaptation scoring matrix.

[0108] By fitting the energy storage status information set with the power demand data in the energy storage response curve on a time-period basis, the system quantitatively analyzes the dispatchability of energy storage devices and their adaptability to power demand. The fitting process calculates whether the energy storage response can effectively meet the power demand in each time period and represents the fitting results using an adaptation score matrix.

[0109] Specifically, interval fitting is performed to calculate the degree of fit between the energy storage state information set and the power demand. The scoring formula is as follows: in, For the moment Corresponding to The suitability score of each energy storage unit; For the moment Power requirements; is the remaining capacity of the energy storage unit; is the instantaneous output capacity of the energy storage unit; The percentage of the state of charge of the energy storage unit; The health of the energy storage unit; It is the fitness calculation function, which is set by the system according to the fitting model.

[0110] For example, assuming the power demand at time period t1 is 45kW, the output capacity of the energy storage unit is 50kW, the remaining capacity is 200kWh, the state of charge is 75%, and the health is 90%, then the adaptation score St1 can be calculated using the above formula as follows: The adaptation scoring matrix will record the scores of all energy storage units and time period requirements to assist in task allocation.

[0111] Based on the adaptation scoring matrix, the mismatched periods in the energy storage response curve are corrected to generate a dynamic compensation curve. The dynamic compensation curve is then coordinated with the available power resources to generate a power allocation priority sequence; the available power resources refer to the set of remaining power resources that are not locked by scheduled tasks.

[0112] By analyzing periods with low scores in the adaptation scoring matrix (i.e., periods when energy storage response cannot meet demand), the system modifies the energy storage response curve to generate a dynamic compensation curve. This compensation curve is used to adjust the energy storage system's output power and coordinate the grid's remaining power resources to prioritize power distribution when power demand is not fully matched.

[0113] Specifically, the energy storage response curve is modified: Based on the low-scoring period of the adaptive scoring matrix, the system optimizes the response curve by adjusting the energy storage charge and discharge rate or postponing the execution of some tasks. Dynamic compensation curve generation: The compensation curve maps the shortfall demand during high-load periods to the dispatchable energy storage output during other periods. The formula is as follows: in, For the moment Dynamic compensation requirements; is the power response of the energy storage system after adjustment; For the moment power requirements.

[0114] Power allocation priority sequence generation: Dynamic compensation curves are combined with the available power resources (including energy storage and surplus grid power) to form a priority allocation sequence. The priority sequence is sorted by the urgency of demand during the time period, ensuring that resources for high-priority tasks are met first.

[0115] For example, if the energy storage response does not fully meet the demand during the period of 12:00-13:00, the system will transfer the shortfall power of 5kW to the grid through the compensation curve and prioritize the remaining resources to high-urgency tasks.

[0116] The power distribution priority sequence is integrated and matched with the scheduling task list to determine the final power supply configuration for each charging task. The energy storage output, grid power supply, and load demand in each time period are closed-loop verified to eliminate power conflict sections and obtain a comprehensive and coordinated power distribution plan. Among them, the scheduling task list refers to a complete task set consisting of sorted charging tasks and their corresponding power requirements, duration information, and priority.

[0117] By integrating and matching the power allocation priority sequence with the dispatch task list, the system optimizes the resource supply configuration for each charging task. The integration and matching process involves allocating the energy storage system output and the grid's power supply capacity to the charging task, and verifying the correctness of the allocation scheme through closed-loop verification. Closed-loop verification aims to eliminate resource allocation conflicts (such as multiple tasks competing for the same energy storage unit) and ensure that all tasks are completed within the target time. Specifically, the main steps of closed-loop verification include: Task completion time verification: Check whether the task time window conflicts with the allocated resources; Verification of total resources: ensuring that the total amount of allocated resources does not exceed the system capacity; Power balance verification: Verify the balance between the energy storage system output, grid power supply, and load demand.

[0118] For example, if the scheduling task list records a charging task with the highest priority, a required power of 40kW, and a duration of 30 minutes, the system will prioritize allocating 30kW of energy storage output and 10kW of grid power supply to this task, and ensure through verification that the resources do not conflict with other tasks.

[0119] In this embodiment, by acquiring charging demand data for each electric vehicle connected to a charging station and real-time status information for charging piles, the system constructs a charging demand dataset and a charging pile status matrix. This system then generates a suitability scoring matrix through correlation analysis, effectively achieving precise allocation of charging tasks. The charging allocation plan generated using the scoring matrix undergoes resource constraint verification and time window coordination to generate a charging schedule, thereby ensuring the proper execution of charging tasks. This embodiment further integrates the station's pre-set energy strategy to determine a set of charging tasks and dynamically matches these tasks with the available time of charging piles to generate a charging task queue. Subsequently, the system generates a task priority list based on scheduling urgency indicators and load balancing factors, and accordingly sorts the charging task queue to construct an optimized charging scheduling plan, ensuring the timely completion of high-priority tasks. By fitting power demand over time periods, the system accurately predicts power demand for all time periods within the constraints of grid load capacity and generates load forecast data. Regarding energy storage scheduling, by dividing the load forecast data into peak, valley, and fluctuating transition periods, the system dynamically adjusts and optimizes the charging scheduling plan, generates an adjusted scheduling strategy, and analyzes the executable energy storage power window for the energy storage system. Using the rolling window optimization algorithm, the system generates a sequence of energy storage regulation tasks, and after time continuity checks and energy storage power balance verification, it finally generates an energy storage regulation plan. In addition, by interval fitting the energy storage state information set and the energy storage response curve, the system generates an adaptation scoring matrix, corrects the mismatched time periods, and dynamically allocates energy storage and unlocked power resources. By integrating the power distribution priority sequence with the scheduling task list, the system ensures that the power supply configuration of each charging task is optimally allocated after closed-loop verification, so that the load demand of the charging station is coordinated with the capacity of the power grid and the energy storage system. This embodiment comprehensively optimizes task allocation, power forecasting and energy storage scheduling, realizes the coordinated management of power resources, significantly improves the operating efficiency and stability of the charging station, and provides an intelligent and efficient scheduling solution for large-scale charging stations.

[0120] Example 3: like Figure 2 As shown, the present application provides an electric energy dispatching and management system 10 for a charging station, including an acquisition module 11 , a fitting module 12 , an adjustment module 13 and a generation module 14 .

[0121] The acquisition module 11 is mainly used to obtain the electric vehicle charging demand information and the charging pile state matrix, generate a charging allocation plan based on the electric vehicle charging demand information and the charging pile state matrix, and use the charging allocation plan and the charging pile state matrix to allocate charging tasks to obtain a charging task queue.

[0122] The fitting module 12 is mainly used to prioritize the charging task queue, generate an optimized charging scheduling plan, fit the optimized charging scheduling plan with the charging pile working curve and the load capacity in the station to the power demand in each time period, and obtain load forecast data.

[0123] The adjustment module 13 is mainly used to adjust the optimized charging scheduling plan according to the load forecast data to obtain an adjusted scheduling strategy, and input the adjusted scheduling strategy into a preset energy storage system simulation module to obtain an energy storage response curve.

[0124] The generation module 14 is mainly used to perform interval fitting between the energy storage response curve and the energy storage status information set of the energy storage system within the station, and to generate a power allocation plan based on the fitting result.

[0125] In this embodiment, through the collaborative work of acquisition module 11, fitting module 12, adjustment module 13, and generation module 14, the system achieves intelligent and efficient power scheduling and management for charging stations. Acquisition module 11 collects real-time electric vehicle charging demand information and the charging pile status matrix. Combining dynamic information such as the remaining power, target power, and expected departure time of the electric vehicle, as well as the current load, voltage level, and idle time forecast of the charging pile, it generates a charging allocation plan. Charging tasks are then assigned based on the allocation plan and the charging pile status matrix, resulting in a charging task queue. This process ensures the rational allocation of charging tasks and the optimal utilization of charging pile resources. Fitting module 12 further prioritizes the charging task queue and generates an optimized charging scheduling plan based on user charging urgency and grid load conditions. By fitting the scheduling plan with the charging pile operating curve and the station's load capacity to the power demand during each time period, the system can accurately predict the load demand of the charging station in different time periods, generating load forecast data to provide data guidance for subsequent adjustments. The adjustment module 13 fine-tunes the optimized charging scheduling plan based on the load forecast data, generates an adjusted scheduling strategy by dynamically analyzing the matching relationship between each task requirement and the resources within the station, and inputs the scheduling strategy into the energy storage system simulation module to obtain the energy storage response curve, thereby realizing two-way adjustment of the charging task and the energy storage resources. The generation module 14 performs interval fitting on the energy storage response curve and the energy storage status information set of the energy storage system within the station, and generates the final power allocation plan by evaluating the matching degree between the power demand of the charging task and the state of the energy storage system. The entire process effectively integrates multiple key links such as charging task allocation, load forecasting, scheduling adjustment and energy storage optimization through modular design, realizing intelligent resource scheduling of charging stations, efficient multi-task processing and optimized energy utilization. The advantage of this embodiment is that it fully combines real-time data analysis and dynamic resource allocation, significantly improves the operating efficiency of the charging station, optimizes the scheduling of electric energy resources, ensures the smooth completion of user charging tasks, and promotes the collaborative management of the energy storage system, providing important support for the realization of green energy utilization and intelligent operation of charging stations.

[0126] It should be noted that, those skilled in the art will clearly understand that, for the sake of convenience and brevity of description, the specific working processes of the above-described system and each module can refer to the corresponding processes in the aforementioned embodiment 1 and will not be repeated here.

[0127] The structures, proportions, sizes, etc. depicted in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not intended to limit the conditions under which the present invention can be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportional relationships, or adjustments in size should still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and objectives that can be achieved by the present invention.

[0128] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for managing electric energy dispatching of a charging station, characterized in that: include: Obtaining electric vehicle charging demand information and a charging pile state matrix, generating a charging allocation plan based on the electric vehicle charging demand information and the charging pile state matrix, and performing charging task allocation using the charging allocation plan and the charging pile state matrix to obtain a charging task queue; Prioritizing the charging task queue, generating an optimized charging scheduling plan, fitting the optimized charging scheduling plan with the charging pile working curve and the load capacity in the station for power demand in each time period, and obtaining load forecast data; Adjusting the optimized charging scheduling plan according to the load forecast data to obtain an adjusted scheduling strategy, and inputting the adjusted scheduling strategy into a preset energy storage system simulation module to obtain an energy storage response curve; The energy storage response curve is interval-fitted with the energy storage state information set of the energy storage system within the station, and a power allocation plan is generated based on the fitting result.

2. The method for managing electric energy dispatching of a charging station according to claim 1, characterized in that: The steps of obtaining electric vehicle charging demand information and a charging pile state matrix, generating a charging allocation plan according to the electric vehicle charging demand information and the charging pile state matrix, and performing charging task allocation using the charging allocation plan and the charging pile state matrix to obtain a charging task queue include: Collect vehicle information, remaining power, and expected departure time of each electric vehicle currently connected to the charging station to build a charging demand dataset. Also obtain the current working status, current load, voltage level, and idle time forecast of all charging piles in the charging station to generate a charging pile status matrix. Performing correlation analysis on the charging demand dataset and the charging pile status matrix to obtain a fitness score matrix, generating a charging allocation plan based on the fitness score matrix, performing resource constraint verification and time window coordination on the charging allocation plan, and generating a charging schedule; The charging schedule plan is combined with the preset energy strategy of the charging station to generate a charging task set, and the charging task set is matched with the current available time of the charging pile to generate a charging task queue.

3. The method for managing electric energy dispatching of a charging station according to claim 2, characterized in that: The steps of performing correlation analysis on the charging demand dataset and the charging pile status matrix to obtain a fitness score matrix, generating a charging allocation plan based on the fitness score matrix, performing resource constraint verification and time window coordination on the charging allocation plan, and generating a charging schedule plan include: The remaining power and expected departure time of each electric vehicle in the charging demand data set are normalized to generate a demand priority factor matrix. The current load, voltage margin and idle time of each charging pile position in the charging pile status matrix are processed to construct a pile position availability factor table; Cross-calculating the demand priority factor matrix and the charging station availability factor table to obtain a vehicle-to-charging station fitness score matrix, performing maximum weight matching on the fitness score matrix, and generating a charging allocation plan; The charging locations in the charging allocation plan are checked for resource constraints and coordinated with time windows, over-limit tasks are marked, and the charging start time of the over-limit tasks is adjusted through a preset time window sliding strategy to generate a charging schedule that meets resource constraints.

4. The method for managing electric energy dispatching of a charging station according to claim 1, characterized in that: The step of prioritizing the charging task queue, generating an optimized charging scheduling plan, fitting the optimized charging scheduling plan with the charging pile working curve and the load capacity in the station for power demand in each time period, and obtaining load forecast data includes: Calculating a scheduling urgency index based on the user's departure time limit, power shortage, and available power capacity of each task in the charging task queue, and adjusting the scheduling urgency index using a preset load balancing factor to obtain a priority list; Sorting the charging task queue according to the priority list, constructing an optimized charging scheduling plan based on the sorting result, and fitting the optimized charging scheduling plan with the charging pile operating curve and the load capacity within the station to the power demand for each time period to obtain the power demand for each time period; wherein the charging pile operating curve is a data set of power changes over time generated by the charging pile under different operating conditions, and the load capacity within the station refers to the maximum allowable load upper limit that the charging station can obtain directly from the power grid; The power demand in all time periods is fitted and matched with the power supply capacity of the charging station to obtain load prediction data.

5. The method for managing electric energy dispatching of a charging station according to claim 1, characterized in that: The step of adjusting the optimized charging scheduling scheme according to the load forecast data to obtain an adjusted scheduling strategy, and inputting the adjusted scheduling strategy into a preset energy storage system simulation module to obtain an energy storage response curve includes: Decomposing the load forecast data into a peak segment, a valley segment, and a fluctuation transition segment, adjusting the optimized charging scheduling plan based on the peak segment, the valley segment, and the fluctuation transition segment to generate an adjusted scheduling strategy; Analyzing the executable energy storage power window of the charging station according to the adjusted scheduling strategy, generating an energy storage adjustment task sequence according to the executable energy storage power window, performing a time continuity check and an energy storage power balance check on the energy storage adjustment task sequence, and obtaining an energy storage adjustment plan; The energy storage regulation plan is decomposed into a time series to generate hourly power regulation instructions, and the hourly power regulation instructions are input into an energy storage system simulation module to obtain an energy storage response curve.

6. The method for managing electric energy dispatching of a charging station according to claim 5, characterized in that: The steps of analyzing the executable energy storage power window of the charging station according to the adjusted scheduling strategy, generating an energy storage adjustment task sequence according to the executable energy storage power window, performing a time continuity check and an energy storage power balance check on the energy storage adjustment task sequence, and obtaining an energy storage adjustment plan include: Analyzing actual load changes in each time period of the adjusted scheduling strategy, calculating power supply capacity differences of the charging station in each time period based on the actual load changes, and analyzing the power gap of the power supply capacity in the charging station based on the power supply capacity differences; Mapping the power gap with the charging and discharging efficiency and charging rate limit of the energy storage system within the charging station to obtain an executable energy storage power window; Under the constraints of the executable energy storage power window, a rolling window optimization algorithm is used to perform compensation power allocation for each time period in the adjusted scheduling strategy, generate an energy storage regulation task sequence, perform time continuity check and energy storage power balance check on the energy storage regulation task sequence, and obtain an energy storage regulation plan.

7. The method for managing electric energy dispatching of a charging station according to claim 1, characterized in that: The step of performing interval fitting on the energy storage response curve and the energy storage status information set of the in-station energy storage system, and generating a power allocation plan based on the fitting result, includes: Obtain the current state of charge, battery health, instantaneous power output capability, and remaining capacity of the energy storage system within the station to generate an energy storage status information set, perform interval fitting on the energy storage status information set and the power demand for each time period in the energy storage response curve to generate an adaptation scoring matrix; Based on the adaptation scoring matrix, the mismatch period in the energy storage response curve is corrected to generate a dynamic compensation curve, and the dynamic compensation curve is coordinated with the available power resources to generate a power allocation priority sequence; wherein the available power resources are the set of remaining power resources that are not locked by the scheduled tasks; The power distribution priority sequence is integrated and matched with the scheduling task list to obtain a comprehensive and coordinated power distribution plan; wherein the scheduling task list refers to a complete task set consisting of sorted charging tasks and their corresponding power requirements, duration information and priority.

8. An electric energy dispatching and management system for a charging station, characterized in that: include: an acquisition module, configured to acquire electric vehicle charging demand information and a charging pile state matrix, generate a charging allocation plan based on the electric vehicle charging demand information and the charging pile state matrix, and allocate charging tasks using the charging allocation plan and the charging pile state matrix to obtain a charging task queue; a fitting module, configured to prioritize the charging task queue, generate an optimized charging scheduling plan, and fit the optimized charging scheduling plan with the charging pile working curve and the load capacity in the station to the power demand in each time period to obtain load forecast data; an adjustment module, configured to adjust the optimized charging scheduling scheme according to the load forecast data to obtain an adjusted scheduling strategy, and input the adjusted scheduling strategy into a preset energy storage system simulation module to obtain an energy storage response curve; A generation module is used to perform interval fitting on the energy storage response curve and the energy storage status information set of the energy storage system in the station, and generate a power allocation plan based on the fitting result.

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