Intelligent Load Balancing Energy-saving Control System and Method for Quick Charging Stations

Abstract

The present invention discloses an intelligent load balancing and energy-saving control system and method for a fast charging station, belonging to the technical field of power distribution of fast charging stations. The method includes: predicting the trend of load imbalance based on the operation data of the battery swapping cabinet and the battery data of the vehicle being charged. When a potential imbalance trend is detected in the fast charging station, a power distribution model is constructed, the power distribution of the battery swapping cabinet is adjusted, and a trigger condition for dynamically adjusting the power distribution scheme is set to dynamically adjust the power distribution of the battery swapping cabinet; according to the received power distribution instruction, the output power of the battery swapping cabinet is adjusted, and a user guidance strategy is formulated according to the current power distribution scheme, and the charging and discharging process of the energy storage battery is dynamically adjusted based on the grid load data of the fast charging station and the state of charge of the energy storage battery; aiming to solve the problems of difficult prediction of load imbalance, difficult dynamic adjustment of power distribution, and lack of user guidance strategy in fast charging stations, and realizing intelligent load balancing and energy-saving operation of fast charging stations.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution of fast charging stations, and more particularly to an intelligent load balancing energy-saving control system and method for fast charging stations. Background Art

[0002] In today's society, electric two-wheelers and electric three-wheelers have become key means of transportation for short-distance travel in cities due to their convenience and economy. However, with the rapid growth of the number of electric two-wheelers, the demand for fast charging stations has become increasingly prominent. Traditional fast charging stations often experience unbalanced loads when facing a large number of electric two-wheelers and three-wheelers charging together. Some battery swap cabinets are overloaded due to too many vehicles connected, which not only poses a safety hazard but also reduces the service life of the equipment; while some battery swap cabinets are idle, resulting in a waste of resources. This unreasonable load distribution makes the overall charging efficiency low and the power consumption increased. In addition, with the increase in electricity costs, how to achieve intelligent load balancing and energy-saving control of fast charging stations has become an important issue that needs to be solved urgently.

[0003] The patent application with publication number CN118300263A discloses an intelligent energy-saving management method, device and storage medium, which maximizes the use of solar energy to achieve a dynamic balance between the energy storage system, fast charging station and power grid system. The energy management system EMS not only meets the charging needs of electric vehicles, but also achieves smooth load balancing through calculation to achieve effective power distribution, thereby improving the utilization rate of solar energy and energy storage systems in fast charging stations. In addition, through control strategies in different time periods, the energy storage system and energy management system EMS are used to achieve real-time power dispatch, thereby further reducing the power cost of fast charging stations. However, its monitoring of abnormal conditions during the battery swap cabinet and vehicle charging process is not comprehensive and timely enough, and it is unable to accurately predict the load imbalance trend in advance, resulting in the inability to respond quickly and effectively when abnormal conditions occur, affecting the stable operation of the fast charging station; the power distribution scheme lacks a dynamic adjustment mechanism, and it is difficult to flexibly optimize according to real-time conditions such as battery swap cabinet failures, new vehicle access, and changes in grid power supply capacity, which can easily cause unreasonable power distribution of some battery swap cabinets, affecting vehicle charging efficiency and user experience; the user guidance strategy is imperfect and cannot provide comprehensive, real-time and personalized battery swap cabinet information for vehicles to be charged, resulting in a lack of effective reference for users when selecting battery swap cabinets, reducing user satisfaction; the energy storage module charging and discharging control is not precise enough, and fails to fully combine the real-time status of the grid load and the energy storage battery for dynamic adjustment, and cannot maximize the peak shaving and valley filling and load balancing functions of the energy storage system. There is still room for improvement in energy utilization efficiency. Therefore, in order to overcome these limitations, the present invention proposes an intelligent load balancing energy-saving control system and method for a fast charging station. Summary of the invention

[0004] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide an intelligent load balancing and energy-saving control system and method for a fast charging station, which solves the problems of incomplete, untimely monitoring of abnormal situations of battery swapping cabinets and vehicle charging, inability to accurately predict the trend of load imbalance, lack of dynamic adjustment of power distribution schemes, imperfect user guidance strategies, and inaccurate charge and discharge control of energy storage modules, thereby affecting the operation stability of the fast charging station, vehicle charging efficiency, user experience and energy utilization efficiency.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An intelligent load balancing and energy-saving control system for a fast charging station, which includes a monitoring module, a control module, a charging module and an energy storage module;

[0007] Based on the operation data of each battery swapping cabinet and the vehicle battery data of the vehicles being charged in the fast charging station, the monitoring module monitors abnormal situations during the charging process of the battery swapping cabinets and vehicles, and predicts the trend of load imbalance to determine whether there is a potential imbalance trend in the fast charging station;

[0008] When it is detected that there is a potential imbalance trend in the fast charging station, the control module constructs a power distribution model according to the real-time operation data of the battery swapping cabinets and the vehicle battery data, obtains the power distribution scheme of the battery swapping cabinets, adjusts the power distribution of the battery swapping cabinets, sends a power distribution instruction to the battery swapping cabinets, and sets the trigger conditions for dynamically adjusting the power distribution scheme, updates the power distribution model, and dynamically adjusts the power distribution of the battery swapping cabinets;

[0009] According to the received power distribution instruction, the charging module adjusts the output power of the battery swapping cabinets, and formulates a user guidance strategy according to the current power distribution scheme to provide real-time idle information of the battery swapping cabinets, estimated waiting time and recommended battery swapping cabinets for the vehicles to be charged;

[0010] Based on the grid load data of the fast charging station and the state of charge of the energy storage battery, the energy storage module dynamically adjusts the charge and discharge process of the energy storage battery, charges the energy storage battery during the low grid load period, and intelligently distributes the discharge power of the energy storage battery according to the power distribution scheme during the high grid load period or when the load of the fast charging station is too high.

[0011] Specifically, the specific steps for determining whether there is a potential imbalance trend in the fast charging station include:

[0012] Configure the initial load threshold of each battery swapping cabinet in the fast charging station, set the evaluation period, and update the initial load threshold of each battery swapping cabinet every time an evaluation period passes to obtain the target load threshold after the initial load threshold is updated;

[0013] Obtain the operation load data of each battery swapping cabinet in real time, compare it with the target load threshold of the current period, and calculate the load difference degree in combination with the average operation load data of the battery swapping cabinets in the same period.

[0014] Configure the load threshold. If the load difference degree of the battery swapping cabinet is greater than the load threshold, perform load anomaly monitoring, obtain the timestamp when the load difference degree of the battery swapping cabinet is greater than the load threshold, and count the duration for which the load difference degree of the battery swapping cabinet is greater than the load threshold.

[0015] Configure the load anomaly threshold. If the duration for which the load difference degree of the battery swapping cabinet is greater than the load threshold is greater than the load anomaly threshold, mark the battery swapping cabinet as an abnormal battery swapping cabinet and determine that there is a potential imbalance trend in the fast charging station.

[0016] Specifically, the steps for obtaining the target load threshold include:

[0017] Configure the evaluation threshold. According to the evaluation threshold, filter the historical operation load data of the battery swapping cabinets within the evaluation threshold of the current evaluation period.

[0018] Divide the historical operation load data by daily time periods and obtain the historical average operation load data for each time period.

[0019] Use the time series analysis method to fit the historical average operation load data for each time period within the evaluation threshold to obtain a load prediction model.

[0020] Use the fitted load prediction model to predict the average operation load data for each time period of the next evaluation period and obtain the predicted operation load data of the battery swapping cabinets.

[0021] Calculate the standard deviation of the historical average operation load data for each time period within the evaluation threshold. According to statistical principles, obtain the upper limit value of the predicted operation load data as the target load threshold.

[0022] Specifically, the specific steps for obtaining the power distribution scheme of the battery swapping cabinets include:

[0023] Obtain the real-time operation data of all the battery swapping cabinets in the fast charging station and the battery data of the vehicles being charged, construct a power distribution model, and define decision variables , which is used to represent the power output of the th battery swapping cabinet at the th moment.

[0024] Take the load of all the battery swapping cabinets in the fast charging station as the objective function, that is, the objective function is to minimize the standard deviation of the operation load data of all the battery swapping cabinets.

[0025] Set the constraint conditions of the objective function according to the rated power limit of the battery swapping cabinet, the grid power limit, the charging demand constraint of the vehicles in the battery swapping cabinet, and the integer variable constraint.

[0026] Specifically, the specific steps for obtaining the power distribution scheme of the battery swapping cabinet further include:

[0027] The rated power limit of the battery swapping cabinet is that the power output of each battery swapping cabinet does not exceed its rated power;

[0028] The grid power limit is that the sum of the power outputs of all battery swapping cabinets does not exceed the power supply capacity of the grid of the fast charging station;

[0029] According to the safe charging range of the vehicle battery, set the vehicle charging demand constraint, including the basic power constraint and the battery characteristic constraint;

[0030] The basic power constraint is that the charging power of the battery swapping cabinet needs to meet the requirement of reaching the expected power within the expected charging time of the vehicle;

[0031] The battery characteristic constraint is that the power of the battery swapping cabinet cannot exceed the maximum charging power allowed by the battery;

[0032] The integer variable constraint is that the decision variable is set as an integer variable;

[0033] Solve the power distribution model by the branch and bound method, decompose the objective function into multiple sub-problems, define the solution space of each sub-problem, gradually narrow the search range, and obtain the optimal solution of the objective function;

[0034] According to the optimal solution of the objective function, generate the power distribution scheme of each battery swapping cabinet in the fast charging station, and send the power distribution instruction to each battery swapping cabinet through the communication interface. After receiving the instruction, the battery swapping cabinet performs the charging operation according to the allocated power.

[0035] Specifically, the specific steps for dynamically adjusting the power distribution scheme include:

[0036] Set the trigger condition for dynamic adjustment. When one of the following conditions is met, trigger the adjustment of the power distribution scheme: the battery swapping cabinet fails, the power distribution scheme does not meet the charging demand of newly connected vehicles, and the power supply capacity of the grid of the fast charging station changes;

[0037] Monitor the trigger condition of the fast charging station. When it is detected that the fast charging station triggers the adjustment of the power distribution scheme, update the relevant data of the power distribution model according to the trigger condition;

[0038] Based on the updated relevant data of the power distribution model, reconstruct the power distribution model and reset the constraint conditions of the power distribution model;

[0039] Optimize and iteratively solve the reconstructed power distribution model to regenerate the power distribution plan for each battery swapping cabinet in the fast charging station.

[0040] Specifically, the specific steps for monitoring the trigger conditions of the fast charging station include:

[0041] When abnormal conditions are detected in the operating data of the battery swapping cabinet, including abnormal current, abnormal voltage, and abnormal temperature of the battery swapping cabinet, it is determined that the battery swapping cabinet has a fault, triggering an adjustment of the power distribution plan;

[0042] When it is detected that a vehicle is connected to the battery swapping cabinet, through the charging communication interface, obtain the vehicle battery information, and at the same time obtain the expected battery level and expected charging time, and calculate the charging power required for the newly connected vehicle;

[0043] Judge whether the current power distribution plan meets the charging requirements of the newly connected vehicle. If it cannot be met, it is determined that the power distribution plan does not meet the charging requirements of the newly connected vehicle, triggering an adjustment of the power distribution plan; otherwise, no operation is performed;

[0044] Obtain the working current and working voltage of the fast charging station grid in real time, set the grid working current fluctuation threshold and working voltage fluctuation threshold, configure the fluctuation monitoring period, and when each fluctuation monitoring period passes, calculate the working current fluctuation amplitude and working voltage fluctuation amplitude of the fast charging station grid within the fluctuation monitoring period;

[0045] If the working current fluctuation amplitude is greater than the grid working current fluctuation threshold or the working voltage fluctuation amplitude is greater than the working voltage fluctuation threshold, it is determined that the power supply capacity of the fast charging station grid has changed, triggering an adjustment of the power distribution plan; otherwise, no operation is performed.

[0046] Specifically, the specific steps for formulating the user guidance strategy include:

[0047] Obtain the operating data of each battery swapping cabinet and the vehicle battery data of the vehicles being charged in each battery swapping cabinet to calculate the estimated charging duration of the vehicles being charged in each battery swapping cabinet, and obtain the real-time status data of each battery swapping cabinet, including whether it is idle and whether it has a fault;

[0048] According to the rated power of each battery swapping cabinet, set its target load threshold, and calculate the difference between the operating load data of the battery swapping cabinet and its target load threshold in real time as the surplus of the battery swapping cabinet;

[0049] Sort the surpluses of the battery swapping cabinets in the fast charging station from largest to smallest to obtain the priority ranking of the recommended battery swapping cabinets for the vehicles to be charged;

[0050] For each battery swapping cabinet, calculate the estimated waiting time of the vehicle to be charged in this battery swapping cabinet in combination with the estimated charging duration of the vehicle being charged and the queuing order of the vehicles to be charged.

[0051] Push the real-time idle information of the battery swapping cabinet, the estimated waiting time, and the recommended charging location to the users of the vehicles to be charged.

[0052] Specifically, the specific steps for dynamically adjusting the charging and discharging process of the energy storage battery include:

[0053] Real-time monitor the grid load data of the fast charging station, divide the grid load stage according to the historical grid load data and the electricity consumption characteristics in different time periods;

[0054] Obtain the electricity price, divide the electricity price into time periods according to the high and low of the electricity price, and divide it into the low electricity price period and the peak electricity price period;

[0055] Obtain the real-time state of charge of the energy storage battery, and set the state of charge safety threshold, including the upper limit and the lower limit of the state of charge safety threshold;

[0056] When in the low grid load stage, judge whether the state of charge of the energy storage battery is less than the upper limit of the state of charge safety threshold. If the state of charge of the energy storage battery is less than the upper limit of the state of charge safety threshold, then judge whether it is in the low electricity price period. If it is in the low electricity price period, then charge the energy storage battery;

[0057] Otherwise, judge whether the state of charge of the energy storage battery is less than the lower limit of the state of charge safety threshold. If the state of charge of the energy storage battery is less than the lower limit of the state of charge safety threshold, then charge the energy storage battery, otherwise do not perform any operation;

[0058] When in the normal grid load stage, configure the load level threshold and the load ratio threshold. According to the power distribution plan, obtain the operation load data of each battery swapping cabinet, and calculate the ratio of the operation load data to the target load threshold. Count the number of battery swapping cabinets with a load greater than the load level threshold. If it is greater than the load ratio threshold, then determine that the load of the battery swapping cabinets in the fast charging station is too high. Judge whether the state of charge of the energy storage battery is greater than the lower limit of the state of charge safety threshold. If the state of charge of the energy storage battery is greater than the lower limit of the state of charge safety threshold, then allocate the discharge power of the energy storage battery according to the load demand of the battery swapping cabinet and the remaining capacity of the energy storage battery;

[0059] When in the high grid load stage, judge whether the state of charge of the energy storage battery is greater than the lower limit of the state of charge safety threshold. If the state of charge of the energy storage battery is greater than the lower limit of the state of charge safety threshold, then allocate the discharge power of the energy storage battery according to the grid load condition and the power distribution plan.

[0060] A smart load balancing and energy-saving control method for a fast charging station includes the following steps:

[0061] Step S1: Based on the operation data of each battery swapping cabinet and the vehicle battery data being charged in each battery swapping cabinet in the fast charging station, monitor the abnormal conditions during the battery swapping cabinet and vehicle charging processes, conduct a prediction of the load imbalance trend, and determine whether there is a potential imbalance trend in the fast charging station;

[0062] Step S2: When it is monitored that there is a potential imbalance trend in the fast charging station, construct a power distribution model according to the real-time operation data of the battery swapping cabinet and the vehicle battery data, obtain the power distribution scheme of the battery swapping cabinet, adjust the power distribution of the battery swapping cabinet, send a power distribution instruction to the battery swapping cabinet, and set the trigger condition for dynamically adjusting the power distribution scheme, update the power distribution model, and dynamically adjust the power distribution of the battery swapping cabinet;

[0063] Step S3: According to the received power distribution instruction, adjust the output power of the battery swapping cabinet, and formulate a user guidance strategy according to the current power distribution scheme to provide real-time idle information of the battery swapping cabinet, estimated waiting time, and recommended battery swapping cabinets for the vehicles to be charged;

[0064] Step S4: Based on the grid load data of the fast charging station and the state of charge of the energy storage battery, dynamically adjust the charging and discharging processes of the energy storage battery. During the low grid load period, charge the energy storage battery. During the high grid load period or when the load of the fast charging station is too high, intelligently distribute the discharging power of the energy storage battery according to the power distribution scheme.

[0065] Advantages of the present invention:

[0066] An intelligent load balancing and energy-saving control system and method for a fast charging station can, through a monitoring module, monitor the data of the battery swapping cabinet and the vehicle in real time, promptly discover abnormal conditions and potential imbalance trends, and give early warnings to prevent risks; a control module constructs and optimizes a power distribution model based on the monitoring results, solves the optimal solution in combination with various constraint conditions, realizes precise power distribution adjustment, ensures the safe operation of the battery swapping cabinet, meets the vehicle charging requirements, and can also flexibly adjust the scheme according to the trigger conditions to adapt to situations such as battery swapping cabinet failures, new vehicle access, and changes in the grid power supply capacity; a charging module adjusts the output power according to the power distribution instruction, and formulates a scientific user guidance strategy to provide real-time information for users to choose, improving the user experience; an energy storage module dynamically adjusts the charging and discharging processes according to the grid load and electricity price time period division, charges during the low period, discharges during the high period or high load, reasonably distributes the discharging power, effectively cuts peaks and fills valleys, balances the load, improves the energy utilization efficiency, reduces the operation cost, and finally realizes the safe, stable, efficient and energy-saving operation of the fast charging station. Description of the Drawings

[0067] Figure 1 It is a schematic structural diagram of the intelligent load balancing and energy-saving control system for the fast charging station of the present invention;

[0068] Figure 2 It is a flowchart of the specific steps for the present invention to judge whether there is a potential imbalance trend in a fast charging station;

[0069] Figure 3 It is a flowchart of the steps for obtaining the target load threshold of the present invention;

[0070] Figure 4 It is a flowchart of the specific steps for dynamically adjusting the power distribution scheme of the present invention;

[0071] Figure 5 It is a flowchart of the specific steps for formulating a user guidance strategy of the present invention;

[0072] Figure 6 It is a flowchart of the specific steps for dynamically adjusting the charging and discharging process of the energy storage battery of the present invention;

[0073] Figure 7 It is a flowchart of the intelligent load balancing energy-saving control method for the fast charging station of the present invention. Specific embodiments

[0074] Embodiment 1

[0075] Please refer to Figure 1 , this embodiment introduces an intelligent load balancing energy-saving control system for a fast charging station, including a monitoring module, a control module, a charging module, and an energy storage module;

[0076] Based on the operation data of each battery swapping cabinet and the vehicle battery data of each vehicle being charged in the fast charging station, the monitoring module monitors the abnormal conditions during the battery swapping cabinet and vehicle charging processes, and conducts a prediction of the load imbalance trend to judge whether there is a potential imbalance trend in the fast charging station; the operation data includes operation current data, operation voltage data, operation temperature data, and operation load data; the battery data includes battery power data, battery voltage data, and battery temperature data;

[0077] In this embodiment, the operating current data, operating voltage data, and operating load data of each battery swapping cabinet in the fast charging station are collected in real time through current sensors, voltage sensors, and load sensors. At the same time, using the vehicle battery management system communication protocol, information such as the battery power, battery voltage, and battery temperature of the vehicle being charged in each battery swapping cabinet is obtained. These data are integrated to form a unified data set, providing comprehensive data support for subsequent analysis. Continuously monitor abnormal situations during the charging process of the battery swapping cabinet and the vehicle, including abnormal current of the battery swapping cabinet, abnormal voltage of the battery swapping cabinet, abnormal temperature of the battery swapping cabinet, abnormal current of the vehicle, abnormal voltage of the vehicle, and abnormal temperature of the vehicle. Compare the data collected in real time by the sensors with the preset normal range to detect abnormal situations in a timely manner. Once an abnormal situation is detected, the warning mechanism is immediately triggered. Through various methods such as text messages, in-station alarm systems, and pop-up windows on the management platform, detailed warning information is sent to the operation and maintenance personnel. The warning information includes the type of abnormality, the location of occurrence, the degree of abnormality, etc., so that the operation and maintenance personnel can respond quickly and take corresponding measures to ensure the safe and stable operation of the fast charging station. And combining historical charging data and real-time collected data, a load prediction model for the battery swapping cabinet is established. Predict the load change situation of each battery swapping cabinet in the future for a period of time. When the prediction results show that the load of one or more battery swapping cabinets exceeds the threshold and the load difference from other battery swapping cabinets reaches a certain degree, it is judged that there is a potential load imbalance trend in the fast charging station.

[0078] Please refer to Figure 2 , preferably, the specific steps for judging whether there is a potential imbalance trend in the fast charging station include:

[0079] Configure the initial load threshold for each battery swapping cabinet in the fast charging station, set the evaluation period, and update the initial load threshold for each battery swapping cabinet every time an evaluation period passes to obtain the target load threshold after the initial load threshold is updated;

[0080] Please refer to Figure 3 , preferably, the steps for obtaining the target load threshold include:

[0081] Configure the evaluation threshold, which is used to obtain the number of evaluation periods for the historical operation data of the battery swapping cabinet. According to the evaluation threshold, screen the historical operation load data of the battery swapping cabinet within the evaluation threshold from the current evaluation period; Exemplarily, if the evaluation threshold is set to 7 evaluation periods, then obtain the historical operation load data of the battery swapping cabinet within 7 evaluation periods before the current evaluation period.

[0082] Divide the historical operation load data according to the daily time period, and obtain the historical average operation load data for each time period; Divide the historical operation load data according to the detailed daily time period, distinguishing the peak hours on weekdays (7:00 - 9:00, 17:00 - 20:00), and non-peak hours on weekdays;

[0083] Using the time series analysis method, fit the historical average running load data for each period within the evaluation threshold to obtain a load prediction model, and use the fitted load prediction model to predict the average running load data for each period in the next evaluation cycle to obtain the predicted running load data of the battery swapping cabinet; the time series analysis method includes the ARIMA model and the Prophet model.

[0084] Calculate the standard deviation of the historical average running load data for each period within the evaluation threshold. The standard deviation can reflect the degree of dispersion of the data, that is, the fluctuation of the historical average running load data. According to statistical principles, assuming that the historical average running load data follows a normal distribution, obtain the upper limit value of the predicted running load data as the target load threshold, that is:

[0085] ;

[0086] Where is the upper limit value of the predicted running load data of the th battery swapping cabinet in the th period, is the predicted running load data of the th battery swapping cabinet in the th period, is the standard deviation of the predicted running load data of the th battery swapping cabinet in the th period, ranges from {1, 2, 3,..., }, is the total number of periods per day, ranges from {1, 2, 3,..., }, is the total number of battery swapping cabinets in the fast battery swapping cabinet, is the fluctuation range coefficient, generally taking a value of 2 or 3.

[0087] Obtain the running load data of each battery swapping cabinet in real time, compare it with the target load threshold of the corresponding period, and combine the average running load data of the battery swapping cabinets in the same period to calculate the load difference degree, that is:

[0088] ;

[0089] Where is the load difference degree of the th battery swapping cabinet, is the real-time running load data of the th battery swapping cabinet, is the target load threshold of the period where the real-time running load data of the th battery swapping cabinet is located, It is the average operating load data of battery swapping cabinets in the same time period;

[0090] Configure a load threshold. If the load difference degree of the battery swapping cabinet is greater than the load threshold, perform load anomaly monitoring, obtain the timestamp when the load difference degree of the battery swapping cabinet is greater than the load threshold, and count the duration for which the load difference degree of the battery swapping cabinet is greater than the load threshold;

[0091] Configure a load anomaly threshold. If the duration for which the load difference degree of the battery swapping cabinet is greater than the load threshold is greater than the load anomaly threshold, mark the battery swapping cabinet as an abnormal battery swapping cabinet and determine that there is a potential imbalance trend in the fast charging station.

[0092] The control module is used to, when detecting a potential imbalance trend in the fast charging station, construct a power distribution model based on the real-time operating data of the battery swapping cabinets and the battery data of the vehicles, obtain the power distribution plan for the battery swapping cabinets, adjust the power distribution of the battery swapping cabinets, send a power distribution instruction to the battery swapping cabinets, and set the trigger conditions for dynamically adjusting the power distribution plan, update the power distribution model, and dynamically adjust the power distribution of the battery swapping cabinets;

[0093] Preferably, the specific steps for obtaining the power distribution plan for the battery swapping cabinets include:

[0094] Obtain the real-time operating data of all battery swapping cabinets in the fast charging station and the battery data of the vehicles being charged, construct a power distribution model, and define decision variables , which is used to represent the power output of the th battery swapping cabinet at the th moment;

[0095] Take the load of all battery swapping cabinets in the fast charging station as the objective function, that is, the objective function is to minimize the standard deviation of the operating load data of all battery swapping cabinets, that is:

[0096] ;

[0097] Among them, is the average power output of all battery swapping cabinets in the fast charging station at the th moment; By minimizing this standard deviation, the loads of each battery swapping cabinet can be distributed as evenly as possible, avoiding the situation where some battery swapping cabinets are overloaded while some are underloaded, thereby improving the operating efficiency and stability of the entire fast charging station.

[0098] Set the constraint conditions of the objective function according to the rated power limit of the battery swapping cabinet, the grid power limit, the charging demand constraint of the vehicles in the battery swapping cabinet, and the integer variable constraint, that is:

[0099] Based on the basic requirements for the safe operation of the equipment and to prevent the battery swapping cabinet from being damaged due to overload, set the rated power limit of the battery swapping cabinet, that is, the power output of each battery swapping cabinet does not exceed its rated power, that is:

[0100] ;

[0101] wherein, is the rated power of the th battery swapping cabinet; ensuring the normal operation of the battery swapping cabinet and extending its service life by limiting the rated power of the battery swapping cabinet. Any output exceeding the rated power may cause equipment failure or safety hazards.

[0102] To maintain the stable operation of the power grid of the fast charging station and avoid excessive impact on the power grid of the fast charging station, a power grid power limit is set, that is, the sum of the power outputs of all battery swapping cabinets does not exceed the power supply capacity of the power grid of the fast charging station, that is:

[0103] ;

[0104] wherein, is the power supply capacity of the power grid of the fast charging station; considering the bearing capacity of the power grid to ensure that the operation of the fast charging station will not have a negative impact on the stability and reliability of the power grid.

[0105] According to the safe charging range of the vehicle battery, protecting the vehicle battery and extending its service life, vehicle charging demand constraints are set, including basic power constraints and battery characteristic constraints. The basic power constraint is that the charging power of the battery swapping cabinet needs to meet the expected power within the expected charging time of the vehicle, that is:

[0106] ;

[0107] wherein, is the expected power of the vehicle of the th battery swapping cabinet, is the current power of the vehicle of the th battery swapping cabinet, is the expected charging time of the vehicle of the th battery swapping cabinet. The expected power and expected charging time can be set independently by the user, deduced based on vehicle parameters, or intelligently recommended based on historical data;

[0108] The battery charging efficiency and safe charging power are different in different power ranges. The battery characteristic constraint is that the power of the battery swapping cabinet cannot exceed the maximum charging power allowed by the battery, that is:

[0109] ;

[0110] wherein, is the maximum charging power allowed by the vehicle battery;

[0111] For the battery swapping cabinet with a fixed power gear output, an integer variable constraint is set, that is, the decision variable is set as an integer variable;

[0112] The power distribution model is solved by the branch and bound method. The objective function is decomposed into multiple sub-problems, and the solution space of each sub-problem is bounded. The search range is gradually narrowed to obtain the optimal solution of the objective function. Exemplarily, the upper bound is set as the total power when all battery swapping cabinets output at the rated power, which is a theoretical maximum power value. The lower bound is obtained through a heuristic algorithm. For example, the power is distributed according to the vehicle charging demand ratio, and the distribution ratio is corrected by combining the historical load fluctuation coefficient of the battery swapping cabinet, so as to obtain a relatively good initial solution as the lower bound. Iterative calculations are performed. In each iteration, a variable with a non-integer solution is selected, and two sub-problems are created respectively. In one sub-problem, the variable takes the largest integer less than its current value, and in the other sub-problem, the variable takes the smallest integer greater than its current value. Then these two sub-problems are solved respectively, and the upper bound and the lower bound are updated according to the solutions of the sub-problems. If the solution of the sub-problem is better than the current upper bound, the upper bound is updated; if the solution of the sub-problem satisfies all constraints and is better than the current lower bound, the lower bound is updated. This process is continuously repeated until the optimal solution of the objective function is obtained.

[0113] According to the optimal solution of the objective function, a power distribution plan for each battery swapping cabinet of the fast charging station is generated, and the power distribution instruction is sent to each battery swapping cabinet through the communication interface. After receiving the instruction, the battery swapping cabinet performs the charging operation according to the allocated power.

[0114] Please refer to Figure 4 , preferably, the specific steps for dynamically adjusting the power distribution plan include:

[0115] Set the trigger conditions for dynamic adjustment. When one of the following conditions is met, the adjustment of the power distribution plan is triggered: the battery swapping cabinet fails, the power distribution plan does not meet the charging requirements of newly connected vehicles, and the power supply capacity of the power grid of the fast charging station changes; so as to have a judgment basis for coping with emergencies and changes, ensure timely response at key nodes, and guarantee the stable operation of the fast charging station and the continuous quality of charging services.

[0116] Monitor the real-time trigger conditions of the fast charging station, including:

[0117] When abnormal situations are detected in the operation data of the battery swapping cabinet, including abnormal current, abnormal voltage, and abnormal temperature of the battery swapping cabinet, the number of the battery swapping cabinet with abnormal situations is obtained, the power supply to the vehicle by this battery swapping cabinet is stopped, and it is determined that the battery swapping cabinet has failed, triggering the adjustment of the power distribution plan; through the real-time monitoring of the operation data of the battery swapping cabinet, once an abnormality is found, the faulty battery swapping cabinet can be quickly located, its power supply can be stopped, the expansion of the fault can be avoided, and the safety of vehicles and equipment can be guaranteed. At the same time, the adjustment of the power distribution plan is triggered in a timely manner, so that the power distribution can be quickly re-planned to maintain the normal operation of other battery swapping cabinets and ensure that the overall charging service is not affected too much.

[0118] When it is detected that a vehicle is connected to the battery swapping cabinet, the vehicle battery information is obtained through the charging communication interface, including the battery capacity, remaining power, and maximum charging power allowed by the battery. At the same time, the desired power and desired charging time are obtained, the charging power required for the newly connected vehicle is calculated, and it is determined whether the current power distribution scheme meets the charging requirements of the newly connected vehicle. If it cannot be met, it is determined that the power distribution scheme does not meet the charging requirements of the newly connected vehicle, and the power distribution scheme adjustment is triggered; otherwise, no operation is performed. When the vehicle is connected, the vehicle battery information and the user's desired charging parameters are comprehensively obtained, the charging requirements are accurately calculated, and compared with the current power distribution scheme. If the requirements cannot be met, the adjustment is triggered to ensure that the newly connected vehicle can obtain an appropriate charging power, avoid slow charging or abnormal charging due to insufficient power, and improve the user experience. If the requirements are met, no operation is performed to maintain the stability of the existing power distribution and reduce the system fluctuations caused by unnecessary adjustments.

[0119] The working current and working voltage of the power grid of the fast charging station are obtained in real time. The fluctuation thresholds of the working current and working voltage of the power grid are set, and the fluctuation monitoring period is configured. When each fluctuation monitoring period passes, the fluctuation amplitudes of the working current and working voltage of the power grid of the fast charging station within the fluctuation monitoring period are calculated. If the fluctuation amplitude of the working current is greater than the fluctuation threshold of the working current of the power grid or the fluctuation amplitude of the working voltage is greater than the fluctuation threshold of the working voltage, it is determined that the power supply capacity of the power grid of the fast charging station has changed, and the power distribution scheme adjustment is triggered; otherwise, no operation is performed. The working current and voltage of the power grid are obtained in real time, the fluctuation thresholds are set, and the fluctuation amplitudes are monitored and calculated according to the period. When the current or voltage fluctuation exceeds the threshold, it is determined that the power supply capacity of the power grid has changed and the adjustment is triggered, which helps to respond to power grid instability factors in advance, reasonably distribute power, prevent abnormal operation of the fast charging station due to power grid problems, and ensure the coordinated and stable operation of the power grid and the fast charging station.

[0120] When it is detected that the fast charging station has triggered the power distribution scheme adjustment, according to the triggering conditions, the relevant data of the power distribution model are updated. Exemplarily, if a fault occurs in the battery swapping cabinet, the available power of the battery swapping cabinet is set to 0, its device status is updated to a fault, and the time and type of the fault occurrence are recorded. For newly connected vehicles, the relevant information of the vehicle, including vehicle ID, battery capacity, remaining power, desired charging time, maximum charging power, etc., is added to the vehicle information table of the power distribution model. If the power supply capacity of the power grid changes, the power upper limit, voltage range, frequency range, etc. of the power grid are updated.

[0121] Based on the relevant data of the updated power distribution model, reconstruct the power distribution model and reset the constraint conditions of the power distribution model; Exemplarily, if the number of available battery swapping cabinets decreases due to a battery swapping cabinet failure, correspondingly adjust the constraint conditions of the rated power limit of the battery swapping cabinet; If the power supply capacity of the power grid decreases, adjust the constraint conditions of the power grid power limit.

[0122] Optimize and iteratively solve the reconstructed power distribution model, regenerate the power distribution plan for each battery swapping cabinet in the fast charging station, and send the power distribution instruction to each battery swapping cabinet through the communication interface. After receiving the instruction, the battery swapping cabinet performs the charging operation according to the redistributed power.

[0123] The charging module adjusts the output power of the battery swapping cabinet according to the received power distribution instruction, and formulates a user guidance strategy according to the current power distribution plan, providing real-time battery swapping cabinet idle information, estimated waiting time, and recommended battery swapping cabinets for the vehicles to be charged.

[0124] Please refer to Figure 5 , preferably, the specific steps for formulating the user guidance strategy include:

[0125] Obtain the operation data of each battery swapping cabinet and the battery data of the vehicles being charged in each battery swapping cabinet to calculate the estimated charging duration of the vehicles being charged in each battery swapping cabinet, and obtain the real-time status data of each battery swapping cabinet, including whether it is idle and whether it is faulty.

[0126] According to the rated power of each battery swapping cabinet, set its target load threshold, and calculate the difference between the operation load data of the battery swapping cabinet and its target load threshold in real time as the surplus of the battery swapping cabinet; A positive surplus indicates that the battery swapping cabinet has excess power available for distribution. The larger the surplus, the more additional power can be provided.

[0127] Sort the surpluses of the battery swapping cabinets in the fast charging station from large to small to obtain the priority ranking of the recommended battery swapping cabinets for the vehicles to be charged; Arrange the battery swapping cabinet with the largest surplus at the top to obtain the priority ranking of the recommended battery swapping cabinets for the vehicles to be charged. Such a sorting method can preferentially guide the vehicles to be charged to the battery swapping cabinets with larger power surpluses, ensuring that the vehicles can be quickly charged at a higher power and improving the overall charging efficiency;

[0128] For each battery swapping cabinet, combine the estimated charging duration of the vehicles being charged and the queuing order of the vehicles to be charged to calculate the estimated waiting time of the vehicles to be charged at this battery swapping cabinet. As time goes by and the charging status changes, the estimated waiting time is updated in real time to ensure that accurate waiting time information is provided to users. For example, when a vehicle finishes charging in advance or a new vehicle joins the queue, recalculate the estimated waiting time in a timely manner.

[0129] Push information such as the idle information of the real-time battery swapping cabinet, the estimated waiting time, and the recommended charging location to the users of the vehicles to be charged. For example, set up a large display screen at the entrance of the fast charging station to display this information in the form of intuitive charts and text; at the same time, establish a real-time connection with the user's mobile phone APP to push personalized guiding information to the users.

[0130] Based on the grid load data of the fast charging station and the state of charge of the energy storage battery, the energy storage module dynamically adjusts the charging and discharging process of the energy storage battery. During the low grid load stage, the energy storage battery is charged to make full use of the low-cost electric energy. During the high grid load stage or when the load of the fast charging station is too high, according to the power distribution plan, the discharge power of the energy storage battery is intelligently distributed to achieve peak shaving and valley filling and load balancing, improve the energy utilization efficiency, and ensure the stable and efficient operation of the fast charging station.

[0131] Please refer to Figure 6 , preferably, the specific steps for dynamically adjusting the charging and discharging process of the energy storage battery include:

[0132] Real-time monitor the grid load data of the fast charging station, divide the grid load stage according to the historical grid load data and the electricity consumption characteristics at different times. The grid load stage includes the low grid load stage, the normal grid load stage, and the high grid load stage;

[0133] Obtain the electricity price, divide the electricity price into time periods according to the high and low of the electricity price, and divide it into the low electricity price period and the peak electricity price period; the low electricity price period is the period with a low electricity price set by the grid to encourage users to use electricity during the low electricity consumption period and reduce the grid load pressure; the peak electricity price period is the period when the electricity demand is strong and the electricity price is relatively high.

[0134] Obtain the real-time state of charge of the energy storage battery, and set the state of charge safety threshold, including the upper limit and the lower limit of the state of charge safety threshold;

[0135] When in the low grid load stage, judge whether the state of charge of the energy storage battery is less than the upper limit of the state of charge safety threshold. If the state of charge of the energy storage battery is less than the upper limit of the state of charge safety threshold, then judge whether it is in the low electricity price period. If it is in the low electricity price period, charge the energy storage battery, otherwise judge whether the state of charge of the energy storage battery is less than the lower limit of the state of charge safety threshold. If the state of charge of the energy storage battery is less than the lower limit of the state of charge safety threshold, charge the energy storage battery, otherwise do not perform any operation;

[0136] When in the normal stage of the grid load, configure the load level threshold and the load ratio threshold. According to the power distribution scheme, obtain the operating load data of each battery swapping cabinet, calculate the ratio of the operating load data to the target load threshold, and count the number of battery swapping cabinets with a load greater than the load level threshold. If it is greater than the load ratio threshold, it is determined that the load of the battery swapping cabinets in the fast charging station is too high. Then, determine whether the state of charge of the energy storage battery is greater than the lower limit of the state of charge safety threshold. If the state of charge of the energy storage battery is greater than the lower limit of the state of charge safety threshold, allocate the discharge power of the energy storage battery according to the load demand of the battery swapping cabinet and the remaining capacity of the energy storage battery, and provide additional power support for the battery swapping cabinets with high loads to relieve the load pressure on the grid and achieve load balancing;

[0137] When in the peak stage of the grid load, determine whether the state of charge of the energy storage battery is greater than the lower limit of the state of charge safety threshold. If the state of charge of the energy storage battery is greater than the lower limit of the state of charge safety threshold, allocate the discharge power of the energy storage battery according to the grid load condition and the power distribution scheme to relieve the peak load pressure on the grid and achieve the goal of peak shaving and valley filling.

[0138] Preferably, the specific steps for allocating the discharge power of the energy storage battery include:

[0139] Calculate the total discharge power that the energy storage battery needs to provide to relieve the grid load pressure or meet the load demand of the battery swapping cabinet according to the current total operating load data of the fast charging station and the load margin that the fast charging station grid can withstand;

[0140] Determine the discharge power that the energy storage battery can provide according to the state of charge of the energy storage battery and the maximum allowable discharge power of the energy storage battery; the lower the state of charge of the energy storage battery, the smaller the discharge power it can provide, and the discharge power that the energy storage battery can provide can be determined by the battery management system.

[0141] Determine the proportion of the discharge power to be allocated to each battery swapping cabinet according to the ratio of the operating load data of each battery swapping cabinet to the target load threshold;

[0142] Allocate the discharge power of the energy storage battery according to the discharge power ratio and the discharge power that the energy storage battery can provide. During the discharge process, continuously monitor information such as the grid load change, the actual load condition of the battery swapping cabinet, and the real-time state of charge of the energy storage battery. According to the real-time monitoring data, dynamically adjust the discharge power allocation. For example, if the load of a certain battery swapping cabinet suddenly decreases, the discharge power allocated to it can be correspondingly reduced, and this part of the power can be reallocated to other battery swapping cabinets with still large load demands; if the state of charge of the energy storage battery drops too fast and is close to the lower limit of the state of charge safety threshold, the overall discharge power can be appropriately reduced to ensure the safety of the energy storage battery and its subsequent emergency use.

[0143] Embodiment 2

[0144] Please refer toFigure 7 , this embodiment introduces an intelligent load balancing energy-saving control method for a fast charging station, including the following steps:

[0145] Step S1: Based on the operation data of each battery swapping cabinet and the vehicle battery data of the vehicles being charged in the fast charging station, monitor the abnormal situations during the battery swapping process and the vehicle charging process, and conduct a prediction of the load imbalance trend to determine whether there is a potential imbalance trend in the fast charging station;

[0146] Step S2: When it is monitored that there is a potential imbalance trend in the fast charging station, construct a power distribution model according to the real-time operation data of the battery swapping cabinet and the vehicle battery data, obtain the power distribution scheme of the battery swapping cabinet, adjust the power distribution of the battery swapping cabinet, send a power distribution instruction to the battery swapping cabinet, and set the trigger condition for dynamically adjusting the power distribution scheme, update the power distribution model, and dynamically adjust the power distribution of the battery swapping cabinet;

[0147] Step S3: Adjust the output power of the battery swapping cabinet according to the received power distribution instruction, and formulate a user guidance strategy according to the current power distribution scheme to provide real-time idle information of the battery swapping cabinet, estimated waiting time, and recommended battery swapping cabinets for the vehicles to be charged;

[0148] Step S4: Based on the grid load data of the fast charging station and the state of charge of the energy storage battery, dynamically adjust the charging and discharging process of the energy storage battery. During the low grid load period, charge the energy storage battery. During the high grid load period or when the load of the fast charging station is too high, intelligently distribute the discharge power of the energy storage battery according to the power distribution scheme.

[0149] Preferably, the specific steps for determining whether there is a potential imbalance trend in the fast charging station include:

[0150] Configure the initial load threshold of each battery swapping cabinet in the fast charging station, set the evaluation period, and update the initial load threshold of each battery swapping cabinet every time an evaluation period passes to obtain the target load threshold after the initial load threshold is updated;

[0151] Obtain the operation load data of each battery swapping cabinet in real time, compare it with the target load threshold of the corresponding time period, and calculate the load difference degree in combination with the average operation load data of the battery swapping cabinets in the same time period;

[0152] Configure the load threshold. If the load difference degree of the battery swapping cabinet is greater than the load threshold, conduct load anomaly monitoring, obtain the timestamp when the load difference degree of the battery swapping cabinet is greater than the load threshold, and count the continuous duration when the load difference degree of the battery swapping cabinet is greater than the load threshold;

[0153] Configure a load anomaly threshold. If the duration for which the load difference of the battery swapping cabinet is greater than the load threshold is greater than the load anomaly threshold, mark the battery swapping cabinet as an abnormal battery swapping cabinet, and determine that there is a potential imbalance trend in the fast charging station.

[0154] Preferably, the specific steps for obtaining the power distribution scheme of the battery swapping cabinet include:

[0155] Obtain the real-time operation data of all battery swapping cabinets in the fast charging station and the battery data of the vehicles being charged, construct a power distribution model, and define decision variables , used to represent the th battery swapping cabinet's power output at time

[0156] Take the load of all battery swapping cabinets in the fast charging station as the objective function, that is, the objective function is to minimize the standard deviation of the operation load data of all battery swapping cabinets;

[0157] According to the rated power limit of the battery swapping cabinet, the power grid power limit, the vehicle charging demand constraint of the battery swapping cabinet, and the integer variable constraint, set the constraint conditions of the objective function;

[0158] The rated power limit of the battery swapping cabinet is: the power output of each battery swapping cabinet does not exceed its rated power;

[0159] The power grid power limit is: the sum of the power outputs of all battery swapping cabinets does not exceed the power supply capacity of the power grid of the fast charging station;

[0160] According to the safe charging range of the vehicle battery, set the vehicle charging demand constraint, including the basic power constraint and the battery characteristic constraint;

[0161] The basic power constraint is to set that the charging power of the battery swapping cabinet needs to meet the vehicle's expected power within the expected charging time;

[0162] The battery characteristic constraint is to set that the power of the battery swapping cabinet cannot exceed the maximum charging power allowed by the battery;

[0163] The integer variable constraint is: set the decision variable as an integer variable;

[0164] Solve the power distribution model by the branch and bound method, decompose the objective function into multiple sub-problems, and define the solution space of each sub-problem, gradually narrowing the search range to obtain the optimal solution of the objective function;

[0165] According to the optimal solution of the objective function, generate the power distribution scheme of each battery swapping cabinet in the fast charging station, and send the power distribution instruction to each battery swapping cabinet through the communication interface. After receiving the instruction, the battery swapping cabinet performs the charging operation according to the allocated power.

[0166] Preferably, the specific steps for formulating the user guidance strategy include:

[0167] Obtain the operation data of each battery swapping cabinet and the vehicle battery data being charged in each battery swapping cabinet to calculate the estimated charging duration of the vehicles being charged in each battery swapping cabinet, and obtain the real-time status data of each battery swapping cabinet, including whether it is idle and whether it is faulty;

[0168] According to the rated power of each battery swapping cabinet, set its target load threshold, and calculate the difference between the operation load data of the battery swapping cabinet and its target load threshold in real time as the surplus of the battery swapping cabinet;

[0169] Sort the surpluses of the battery swapping cabinets in the fast charging station from large to small to obtain the priority ranking of the recommended battery swapping cabinets for the vehicles to be charged;

[0170] For each battery swapping cabinet, combine the estimated charging duration of the vehicle being charged and the queuing order of the vehicle to be charged to calculate the estimated waiting time of the vehicle to be charged in this battery swapping cabinet;

[0171] Push the real-time idle information of the battery swapping cabinet, the estimated waiting time, and the recommended charging location to the user of the vehicle to be charged.

[0172] Working principle and its effects:

[0173] The working principle and effects of an intelligent load balancing and energy-saving control system and method for a fast charging station are as follows:

[0174] The monitoring module calculates the load difference degree by collecting the operation data of the battery swapping cabinet and the vehicle battery data in real time, and configuring the initial load threshold and the evaluation period. When the load difference degree and its continuous duration exceed the established threshold, it can be determined that there is a potential imbalance trend in the fast charging station. This process can detect abnormal situations and potential risks during the charging process in advance, providing a key basis for subsequent regulation work, thus effectively ensuring the stable operation of the fast charging station.

[0175] After detecting the potential imbalance trend, the control module quickly constructs a power distribution model based on the real-time operation data of the battery swapping cabinet and the vehicle battery data. When constructing the model, various conditions such as the rated power limit of the battery swapping cabinet, the power grid power limit, and the vehicle charging demand constraint are fully considered to obtain the optimal power distribution plan. In addition, the trigger conditions for dynamic adjustment are also set. When situations such as battery swapping cabinet failure, new vehicle access, or changes in the power supply capacity of the power grid occur, the relevant data of the power distribution model are updated in a timely manner. Through this series of operations, the control module can accurately distribute power, effectively avoid the overload or idle situation of the battery swapping cabinet, and can also make timely adjustments in the face of various emergencies, significantly improving the energy utilization efficiency and extending the service life of the equipment.

[0176] After receiving the power distribution instruction sent by the control module, the charging module timely adjusts the output power of the battery swapping cabinet. At the same time, by calculating the surplus of the battery swapping cabinet and the estimated waiting time of the vehicle to be charged, and sorting according to the surplus of the battery swapping cabinet from large to small, this is used as the basis for the priority of the recommended battery swapping cabinet, and the real-time idle information of the battery swapping cabinet, the estimated waiting time, and the recommended charging location and other information are pushed to the user. This method realizes reasonable charging, effectively improves the charging efficiency, provides comprehensive and accurate guidance for users, greatly reduces the waiting time of users, and significantly improves user satisfaction.

[0177] The energy storage module real-time monitors the grid load data and electricity price of the fast charging station, divides the grid load stage and electricity price period according to historical data and electricity consumption characteristics, and at the same time sets the safety threshold of the state of charge of the energy storage battery to formulate the charge and discharge strategy, makes full use of the low valley electricity price for energy storage, and discharges during the peak period to achieve peak shaving and valley filling, which not only reduces the power cost of the fast charging station, but also ensures the stability of the power supply of the fast charging station, and further improves the energy utilization efficiency.

[0178] In summary, through the collaborative work of each module, the intelligent load balancing and energy-saving control system and method of the fast charging station realize the overall optimization of the fast charging station. From early warning of potential risks to accurate power distribution, reasonable user guidance, and efficient use of energy storage, it effectively improves the operation stability, energy utilization efficiency and user experience of the fast charging station, and provides strong support for the sustainable development of the fast charging station.

[0179] The above are only the preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions within the idea of the present invention belong to the protection scope of the present invention. It should be pointed out that for those of ordinary skill in the art in this technical field, several improvements and refinements made without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A fast charging station intelligent load balancing energy-saving control system, characterized in that: Including monitoring module, control module, charging module and energy storage module; The monitoring module monitors abnormal conditions in the battery swap cabinet and vehicle charging process based on the operating data of each battery swap cabinet in the fast charging station and the battery data of the vehicle being charged in each battery swap cabinet, and predicts the load imbalance trend to determine whether there is a potential imbalance trend in the fast charging station; The control module is used to build a power distribution model based on the real-time operation data of the battery swap cabinet and the vehicle battery data, obtain the power distribution plan of the battery swap cabinet, adjust the power distribution of the battery swap cabinet, send a power distribution instruction to the battery swap cabinet, set the trigger conditions for dynamically adjusting the power distribution plan, update the power distribution model, and dynamically adjust the power distribution of the battery swap cabinet when a potential imbalance trend is detected in the fast charging station; The charging module adjusts the output power of the battery swap cabinet according to the received power allocation instruction, and formulates a user guidance strategy according to the current power allocation plan to provide real-time battery swap cabinet idle information, estimated waiting time and recommended battery swap cabinet for the vehicle to be charged; The energy storage module dynamically adjusts the charging and discharging process of the energy storage battery based on the grid load data of the fast charging station and the charge state of the energy storage battery. The energy storage battery is charged during the low grid load period. During the peak grid period or when the load of the fast charging station is too high, the discharge power of the energy storage battery is intelligently allocated according to the power allocation plan. The specific steps of obtaining the power distribution plan of the power swap cabinet include: Obtain real-time operating data of all battery swap cabinets in the fast charging station, as well as battery data of the vehicles being charged, build a power allocation model, and define decision variables , used to indicate the The power exchange cabinet is in Power output at the moment; The load of all battery swap cabinets in the fast charging station is taken as the objective function, that is, the objective function is to minimize the standard deviation of the operating load data of all battery swap cabinets; Set the objective function constraints according to the rated power limit of the battery swap cabinet, the power limit of the power grid, the charging demand constraint of the battery swap cabinet vehicle, and the integer variable constraint; The rated power limit of the battery swap cabinet is: the power output of each battery swap cabinet does not exceed its rated power; the power limit of the power grid is: the sum of the power outputs of all battery swap cabinets does not exceed the power supply capacity of the fast charging station power grid; according to the safe charging range of the vehicle battery, the vehicle charging demand constraints are set, including basic power constraints and battery characteristic constraints; the basic power constraint is to set the charging power of the battery swap cabinet to meet the expected power within the expected charging time of the vehicle; the battery characteristic constraint is to set the power of the battery swap cabinet to not exceed the maximum charging power allowed by the battery; the integer variable constraint is: set the decision variable Set to integer variable; The power allocation model is solved by the branch and bound method, the objective function is decomposed into multiple sub-problems, and the solution space of each sub-problem is defined, the search range is gradually narrowed, and the optimal solution of the objective function is obtained; Based on the optimal solution of the objective function, a power allocation plan is generated for each battery swap cabinet in the fast charging station, and the power allocation instruction is sent to each battery swap cabinet through the communication interface. After receiving the instruction, the battery swap cabinet performs charging operations according to the allocated power.

2. The fast charging station intelligent load balancing energy-saving control system according to claim 1, characterized in that: The specific steps of determining whether there is a potential imbalance trend in the fast charging station include: Configure the initial load threshold of each battery swap cabinet in the fast charging station, set the evaluation cycle, update the initial load threshold of each battery swap cabinet after each evaluation cycle, and obtain the target load threshold after the initial load threshold is updated; Obtain the operating load data of each battery swap cabinet in real time, compare it with the target load threshold of the time period, and calculate the load difference by combining the average operating load data of the battery swap cabinets in the same period; Configure the load threshold. If the load difference of the battery swap cabinet is greater than the load threshold, perform load anomaly monitoring, obtain the timestamp when the load difference of the battery swap cabinet is greater than the load threshold, and count the duration of the load difference of the battery swap cabinet being greater than the load threshold. Configure the load abnormality threshold. If the load difference of the battery swap cabinet is greater than the load threshold and the duration is greater than the load abnormality threshold, the battery swap cabinet is marked as an abnormal battery swap cabinet, and it is determined that there is a potential imbalance trend in the fast charging station.

3. The fast charging station intelligent load balancing energy-saving control system according to claim 2, characterized in that: The step of obtaining the target load threshold comprises: Configure the evaluation threshold, and filter the historical operating load data of the power swap cabinet within the evaluation threshold of the current evaluation cycle according to the evaluation threshold; Divide the historical operating load data into daily time periods and obtain the historical average operating load data for each time period; Using the time series analysis method, the historical average operating load data of each period within the evaluation threshold is fitted to obtain the load forecasting model; The fitted load prediction model is used to predict the average operating load data of each period of the next evaluation cycle to obtain the predicted operating load data of the battery swap cabinet; Calculate the standard deviation of the historical average operating load data for each period within the evaluation threshold, and obtain the upper limit value of the predicted operating load data based on statistical principles as the target load threshold.

4. The fast charging station intelligent load balancing energy-saving control system according to claim 1, characterized in that: The specific steps of dynamically adjusting the power allocation scheme include: Set the trigger conditions for dynamic adjustment. When one of the following conditions is met, the power allocation plan will be adjusted: the battery swap cabinet fails, the power allocation plan does not meet the charging needs of the newly connected vehicles, and the power supply capacity of the fast charging station power grid changes; Monitor the trigger conditions of the fast charging station, and when it is detected that the fast charging station triggers the adjustment of the power allocation plan, update the relevant data of the power allocation model according to the trigger conditions; Based on the relevant data of the updated power allocation model, the power allocation model is reconstructed and the constraint conditions of the power allocation model are reset; The reconstructed power allocation model is optimized and iteratively solved to regenerate the power allocation plan for each battery swap cabinet in the fast charging station.

5. The fast charging station intelligent load balancing energy-saving control system according to claim 4, characterized in that: The specific steps of monitoring the trigger conditions of the fast charging station include: When abnormal conditions are detected in the operating data of the power swap cabinet, including abnormal current, voltage and temperature of the power swap cabinet, the power swap cabinet is judged to be faulty, triggering the adjustment of the power allocation plan; When a vehicle is detected to be connected to the battery swap cabinet, the vehicle battery information is obtained through the charging communication interface, and the expected power and expected charging time are obtained at the same time, and the charging power required for the newly connected vehicle is calculated; Determine whether the current power allocation plan meets the charging requirements of the newly connected vehicle. If not, it is determined that the power allocation plan does not meet the charging requirements of the newly connected vehicle, triggering the power allocation plan adjustment. Otherwise, no operation is performed; Obtain the working current and working voltage of the fast charging station power grid in real time, set the working current fluctuation threshold and working voltage fluctuation threshold of the power grid, configure the fluctuation monitoring cycle, and calculate the working current fluctuation amplitude and working voltage fluctuation amplitude of the fast charging station power grid within the fluctuation monitoring cycle after each fluctuation monitoring cycle; If the working current fluctuation amplitude is greater than the grid working current fluctuation threshold or the working voltage fluctuation amplitude is greater than the working voltage fluctuation threshold, it is determined that the power supply capacity of the fast charging station grid has changed, triggering the power allocation plan adjustment, otherwise no operation is performed.

6. The fast charging station intelligent load balancing energy-saving control system according to claim 1, characterized in that: The specific steps of formulating the user guidance strategy include: Obtain the operating data of each battery swap cabinet and the battery data of the vehicle being charged by each battery swap cabinet to calculate the expected charging time of the vehicle being charged by each battery swap cabinet, and obtain the real-time status data of each battery swap cabinet, including whether it is idle or faulty; According to the rated power of each power-swap cabinet, its target load threshold is set, and the difference between the operating load data of the power-swap cabinet and its target load threshold is calculated in real time as the surplus of the power-swap cabinet; Sort the surplus of battery swap cabinets in the fast charging station from large to small to obtain the priority ranking of recommended battery swap cabinets for vehicles to be charged; For each battery swap cabinet, the estimated waiting time of the vehicle to be charged in the battery swap cabinet is calculated based on the estimated charging time of the vehicle being charged and the queue order of the vehicles to be charged; The real-time battery swap cabinet idle information, estimated waiting time, and recommended charging locations are pushed to users of vehicles waiting to be charged.

7. The fast charging station intelligent load balancing energy-saving control system according to claim 1, characterized in that: The specific steps of dynamically adjusting the charging and discharging process of the energy storage battery include: Real-time monitoring of the grid load data of fast charging stations, and division of grid load stages based on historical grid load data and electricity consumption characteristics in different time periods; Obtain electricity prices and divide them into time periods according to the level of electricity prices, into low-price periods and peak-price periods; Obtain the real-time charge of the energy storage battery and set the charge safety threshold, including the upper and lower limits of the charge safety threshold; When the grid is in the valley stage, determine whether the charge of the energy storage battery is less than the upper limit of the charge safety threshold. If the charge of the energy storage battery is less than the upper limit of the charge safety threshold, determine whether it is in the valley period of electricity price. If it is in the valley period of electricity price, charge the energy storage battery. Otherwise, it is determined whether the charge of the energy storage battery is less than the lower limit of the charge safety threshold. If the charge of the energy storage battery is less than the lower limit of the charge safety threshold, the energy storage battery is charged, otherwise no operation is performed; When the grid load is in the normal stage, configure the load level threshold and load ratio threshold, obtain the operating load data of each battery swap cabinet according to the power allocation plan, and calculate the ratio of the operating load data to the target load threshold, count the number of battery swap cabinets that are greater than the load level threshold, if it is greater than the load ratio threshold, then determine that the load of the battery swap cabinet in the fast charging station is too high, and determine whether the charge of the energy storage battery is greater than the lower limit of the charge safety threshold. If the charge of the energy storage battery is greater than the lower limit of the charge safety threshold, allocate the discharge power of the energy storage battery according to the load demand of the battery swap cabinet and the remaining capacity of the energy storage battery; When the grid is at its peak load stage, determine whether the charge of the energy storage battery is greater than the lower limit of the charge safety threshold. If the charge of the energy storage battery is greater than the lower limit of the charge safety threshold, allocate the discharge power of the energy storage battery according to the load situation of the grid and the power allocation plan.

8. A fast charging station intelligent load balancing energy-saving control method, which is implemented based on the fast charging station intelligent load balancing energy-saving control system according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step S1: Based on the operating data of each battery swap cabinet in the fast charging station and the battery data of the vehicle being charged by each battery swap cabinet, monitor the abnormal conditions during the charging process of the battery swap cabinet and the vehicle, and predict the load imbalance trend to determine whether there is a potential imbalance trend in the fast charging station; Step S2: When a potential imbalance trend is detected in the fast charging station, a power allocation model is constructed based on the real-time operation data of the battery swap cabinet and the vehicle battery data, the power allocation plan of the battery swap cabinet is obtained, the power allocation of the battery swap cabinet is adjusted, a power allocation instruction is sent to the battery swap cabinet, and the trigger conditions for dynamically adjusting the power allocation plan are set, the power allocation model is updated, and the power allocation of the battery swap cabinet is dynamically adjusted; Step S3: According to the received power allocation instruction, the output power of the battery swap cabinet is adjusted, and according to the current power allocation plan, a user guidance strategy is formulated to provide the vehicle to be charged with real-time battery swap cabinet idle information, estimated waiting time and recommended battery swap cabinet; Step S4: Based on the grid load data of the fast charging station and the charge state of the energy storage battery, the charging and discharging process of the energy storage battery is dynamically adjusted. The energy storage battery is charged during the low grid load period. During the peak grid period or when the load of the fast charging station is too high, the discharge power of the energy storage battery is intelligently allocated according to the power allocation plan.

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