A method and system for monitoring a charging station

Abstract

The application relates to a monitoring method and system of a charging station, the method comprising the following steps: acquiring site information and historical data information of the charging station; periodically generating a charging and discharging monitoring strategy of a next operation period of the charging station according to the site information and the historical data information of the charging station through a preset strategy optimization model; pre-updating a platform recommendation strategy to a charging service platform according to the charging and discharging monitoring strategy; issuing an instruction to control the energy storage system to perform charging and discharging operation according to the charging and discharging strategy process, and generating a dynamic correction instruction to control the energy storage system to perform additional charging and discharging operation according to a dynamic regulation and control standard by monitoring the state of the energy storage system in real time; and collecting monitoring data to form a monitoring report and sending the monitoring report to a manager. On the basis of meeting the charging demand of users, the application guides users to consume clean energy of each charging station, so that the clean energy of each charging station is reasonably utilized, and a green environmental protection effect is achieved.

Description

Technical Field

[0001] This application relates to the field of charging stations, and in particular to a method and system for monitoring charging stations. Background Technology

[0002] With the increasing popularity of electric vehicles (EVs) and hybrid electric vehicles (HEVs), the development of their supporting charging stations is also accelerating. An electric vehicle charging station is a facility that provides electrical power to electric vehicles, comprising a series of charging equipment, power distribution facilities, and related monitoring and management systems. Its primary function is to safely and efficiently charge the electric vehicle's battery, enabling the vehicle to regain its driving capability.

[0003] With the deepening of energy conservation, environmental protection, and green themes, the market share of electric vehicles is increasing year by year, the number of electric vehicles is constantly increasing, and charging stations are being built continuously. While these charging stations provide convenient charging services for vehicles, their energy consumption is also constantly increasing. Therefore, many power companies often introduce clean power generation equipment, such as solar panels and wind turbines, to partially power charging stations during construction. However, different charging stations have different locations and varying passenger flow, which leads to some charging stations having insufficient clean energy to supply users' vehicles, still requiring a large amount of grid power; while other charging stations have insufficient passenger flow due to various factors, resulting in a surplus of clean energy. Existing technologies often provide charging guidance to users via the internet, indicating where they can charge. For example, Chinese patent CN106240385B discloses a method and device for monitoring charging stations.

[0004] However, how to effectively monitor the data of charging stations in order to guide users to make reasonable use of the clean energy of each charging station is an unsolved problem. Summary of the Invention

[0005] To address the aforementioned issues, this application provides a method and system for monitoring charging stations.

[0006] Firstly, this application provides a method for monitoring charging stations, employing the following technical solution:

[0007] A method for monitoring a charging station includes the following steps:

[0008] Obtain site information and historical data of charging stations;

[0009] The system periodically generates charging and discharging monitoring strategies for the next operating cycle of a charging station based on the station's information and historical data using a preset strategy optimization model. The strategy optimization model is a neural network model that is iteratively trained using historical data from multiple charging stations. The charging and discharging monitoring strategy includes a platform-recommended strategy, dynamic control standards, at least one clean charging period, at least one off-peak charging period, and charging and discharging strategy process information.

[0010] The platform's recommended strategy will be pre-updated to the charging service platform based on the charging and discharging monitoring strategy.

[0011] According to the charging and discharging monitoring strategy, commands are issued periodically to control the energy storage system to perform charging and discharging operations according to the charging and discharging strategy process. The status of the energy storage system is monitored in real time, and dynamic correction commands are generated according to the dynamic control standard to control the energy storage system to perform additional charging and discharging operations.

[0012] Regularly collect monitoring data to generate monitoring reports, which are then sent to management personnel.

[0013] Preferably, the site information includes site geographic information, site clean power generation equipment parameter information, site energy storage system parameter information, and site charging pile parameter information; the historical data information includes historical data related to passenger flow, historical data related to charging and discharging, and other operational data; the historical data related to passenger flow includes charging vehicle records and user information; the historical data related to charging and discharging includes power generation data of clean power generation equipment, energy storage system data, and charging pile data; the other operational data includes historical weather data and energy cost data.

[0014] Preferably, the site information includes site geographic information, site clean power generation equipment parameter information, site energy storage system parameter information, and site charging pile parameter information; the historical data information includes historical data related to passenger flow, historical data related to charging and discharging, and other operational data; the historical data related to passenger flow includes charging vehicle records and user information; the historical data related to charging and discharging includes power generation data of clean power generation equipment, energy storage system data, and charging pile data; the other operational data includes historical weather data and energy cost data.

[0015] Preferably, the step of periodically generating a charging and discharging monitoring strategy for the next operating cycle of the charging station based on the station information and historical data using a preset strategy optimization model specifically includes the following steps:

[0016] The system periodically generates multiple optional strategies for the next operating cycle of a charging station based on the station's site information and historical data using a preset strategy optimization model. These optional strategies include factor prediction information and platform-recommended strategies, dynamic control standards, at least one clean charging period, at least one off-peak charging period, and charging / discharging strategy process information. The factor prediction information includes passenger flow prediction information and weather prediction information.

[0017] The charging station is modeled, and simulation data is obtained by simulating multiple optional strategies.

[0018] The comprehensive score of each optional strategy is calculated based on the simulation data using a pre-set evaluation calculation formula;

[0019] The various optional strategies are compared and ranked based on their comprehensive scores, and the optional strategy with the highest comprehensive score is selected as the charging and discharging monitoring strategy for the next operating cycle of the charging station.

[0020] Preferably, the step of modeling the charging station and simulating multiple optional strategies to obtain simulation data specifically includes the following steps:

[0021] The strategy optimization model constructs a simulation model of the charging station based on the site information;

[0022] The strategy optimization model sets parameters for the simulation model of the charging station based on the station information and historical data.

[0023] The strategy optimization model loads multiple optional strategies into the simulation model of the charging station and runs the simulation for the next operating cycle to obtain simulation data.

[0024] Preferably, the strategy optimization model loads multiple optional strategies into the simulation model of the charging station and runs the simulation to obtain simulation data for the next operating cycle of the charging station, specifically including the following steps:

[0025] C1. The strategy optimization model selects one unsimulated alternative strategy from multiple alternative strategies and loads it into the simulation model of the charging station.

[0026] C2. The strategy optimization model uses factor prediction information to simulate the normal operation of the charging station in the next operating cycle through the charging station simulation model to obtain normal simulation data.

[0027] C3. The strategy optimization model, based on factor prediction information, simulates the next operating cycle of the charging station through the charging station simulation model. During the operation, a fixed number of strong sudden events are randomly generated based on the charging station's site information and historical data and inserted into the simulation to obtain sudden simulation data. The strong sudden events include severe weather events, extreme passenger flow events, and other pre-set types of events.

[0028] C4. Determine if there are any unsimulated alternative strategies;

[0029] C5. If it exists, then proceed to step C1;

[0030] C6. If not, then the normal simulation data and the sudden simulation data are packaged to generate simulation data. Preferably, the evaluation calculation formula is as follows:

[0031] Y = X1 × Z1 + X2 × Z2;

[0032] Z1=W1 +W2 +W3 +W4 ;

[0033] Z2=W1 +W2 +W3 +W4 -W5 +W6 +W7 ;

[0034] Where Y is the comprehensive score of the optional strategy, Z1 is the normal simulation operation score, X1 is the normal simulation weight coefficient, Z2 is the emergency simulation operation score, and X2 is the emergency simulation weight coefficient; X1 and X2 are both set by the administrator, and the sum of X1 and X2 is 1; e is the effective output energy, which refers to the total energy successfully output to charge vehicles through the charging pile during the simulated operation period; E is the total input energy, which refers to the sum of the energy generated by the clean power generation equipment and the energy purchased from the grid; u is the number of users whose charging needs are met during the simulated operation period, and U is the total number of vehicles that visit during the simulated operation period; d is the actual charging time of the charging pile, and D is the operating time of the charging pile in the charging station; c is the net charging revenue, and C is the total investment of the charging station; r is the duration of energy supply interruption of the energy storage system during the operation period, and R is the total operating time during the operation period; a is the number of vehicles whose emergency charging needs are met, and A is the number of vehicles whose emergency charging needs are met. 1 represents the total number of vehicles with emergency charging needs; 2 represents the average waiting time for heavy users during severe emergencies; 3 represents the preset standard waiting time; 4 represents the energy utilization efficiency rating coefficient; 5 represents the user satisfaction rating coefficient; 6 represents the user satisfaction rating coefficient; and 7 represents the user satisfaction rating under emergency conditions. Furthermore, W1, W2, W3, W4, W5, W6, and W7 are all preset by management personnel.

[0035] Preferably, the step of real-time monitoring of the energy storage system status and generating dynamic correction commands according to dynamic control standards to control the energy storage system to perform additional charging and discharging operations specifically includes:

[0036] The status of the energy storage system is monitored in real time. When the energy storage capacity of the energy storage system is lower than the low energy threshold preset in the dynamic control standard, it is determined whether it is in the clean charging period.

[0037] If not, then determine whether it is in a trough charging period;

[0038] If it is in the off-peak period, a dynamic correction command will be generated to enable the off-peak mains power to charge the energy storage system until the pre-set mains power charging threshold in the dynamic control standard is reached or the off-peak charging period ends.

[0039] If not, check whether the power output of the clean power generation equipment meets the minimum charging requirements of the energy storage system.

[0040] If the conditions are met, a dynamic correction command is generated to control the clean power generation equipment to charge the energy storage system until the stored energy reaches the safe energy threshold preset in the dynamic control standard.

[0041] If the conditions are not met, the charging power supply to the energy storage system will be cut off. Based on the real-time load of the charging station and the interaction protocol with the power grid, it will be determined whether it is permissible to purchase a small amount of electricity from the power grid at a non-off-peak price for emergency charging of the energy storage system, so as to ensure the basic operation function of the energy storage system and the emergency power supply capability of the charging station. At the same time, this emergency charging event will be recorded.

[0042] Preferably, the real-time monitoring of the energy storage system status further includes: if the energy storage capacity of the energy storage system is higher than the preset high energy threshold in the dynamic control standard, then generating a dynamic correction instruction according to the platform's recommended strategy and updating the temporary discount information on the charging service platform.

[0043] Preferably, the step of periodically collecting monitoring data to generate a monitoring report and sending it to the management personnel specifically includes: periodically collecting monitoring data and importing it into a pre-set monitoring report template, and displaying the historical power generation data, passenger flow data, and clean energy utilization rate trends of each charging station in the monitoring report using line graphs or bar graphs, with the time span being the unit operating cycle, and then sending the monitoring report to the management personnel.

[0044] Secondly, this application provides a monitoring system for charging stations, which adopts the following technical solution:

[0045] A monitoring system for a charging station, comprising:

[0046] The information acquisition module is used to acquire site information and historical data information of charging stations;

[0047] The strategy customization module is used to periodically generate charging and discharging monitoring strategies for the next operating cycle of a charging station based on the station information and historical data information of the charging station using a preset strategy optimization model. The strategy optimization model is a neural network model obtained by iterative training using historical data information from multiple charging stations. The charging and discharging monitoring strategy includes a platform-recommended strategy, dynamic control standards, at least one clean charging period, at least one off-peak charging period, and charging and discharging strategy process information.

[0048] The user guidance module is used to pre-update the platform's recommended strategy to the charging service platform based on the charging and discharging monitoring strategy.

[0049] The monitoring and control module is used to issue commands at regular intervals according to the charging and discharging monitoring strategy to control the energy storage system to perform charging and discharging operations according to the charging and discharging strategy process, and to monitor the status of the energy storage system in real time and generate dynamic correction commands according to the dynamic adjustment standard to control the energy storage system to perform additional charging and discharging operations.

[0050] The report aggregation module is used to collect monitoring data on a regular basis, generate monitoring reports, and send them to the management personnel.

[0051] In summary, this application includes at least one of the following beneficial technical effects:

[0052] By acquiring multi-dimensional site information and rich historical data, and based on the actual situation of the charging station, this study takes into account various practical factors in the operation of the charging station, such as equipment, geography, passenger flow, clean energy usage, charging and discharging, energy costs, and operation. Through strategy optimization models, data relationships are mined out, and combined with weather forecast information of the geographical location, the student flow and charging and discharging status of the charging station in the next cycle are predicted. In this way, a charging and discharging monitoring strategy for the next operating cycle of the charging station is generated intelligently and accurately, and it has strong universality.

[0053] By optimizing the recommendation information for charging stations, users are guided to consume the clean energy of each charging station while meeting their charging needs. This ensures that the clean energy of each charging station is used rationally, which is both environmentally friendly and effective in improving the profitability and utilization rate of the charging stations.

[0054] Intelligent control and monitoring of charging stations based on charging and discharging monitoring strategies facilitates the rational scheduling and use of energy storage systems. Combined with platform-recommended strategies, this maximizes the utilization rate of green energy, avoids energy spillover and loss, reduces energy costs, and improves the cost-effectiveness of charging stations. Attached Figure Description

[0055] Figure 1 This is a flowchart of a method for monitoring a charging station according to an embodiment of this application;

[0056] Figure 2This is a flowchart of the method for generating a charge / discharge monitoring strategy in the embodiments of this application;

[0057] Figure 3 This is a flowchart of a method for simulating optional strategies in an embodiment of this application;

[0058] Figure 4 This is a flowchart illustrating the method of loading optional strategies into the simulation model of the charging station to simulate the next operating cycle of the charging station, as described in this application embodiment.

[0059] Figure 5 This is a flowchart of the method for generating dynamic correction instructions to control the energy storage system to perform additional charging and discharging operations in the embodiments of this application;

[0060] Figure 6 This is a system block diagram of a monitoring system for a charging station according to an embodiment of this application.

[0061] Explanation of reference numerals in the attached diagram: 1. Information acquisition module; 2. Strategy customization module; 3. User guidance module; 4. Monitoring and control module; 5. Report summary module. Detailed Implementation

[0062] The following is in conjunction with the appendix Figure 1-6 This application will be described in further detail.

[0063] This application discloses a method for monitoring charging stations. (Refer to...) Figure 1 A method for monitoring charging stations includes the following steps:

[0064] S1. Information Acquisition: Acquire site information and historical data of the charging station; the site information includes site geographical information, site clean power generation equipment parameter information, site energy storage system parameter information, and site charging pile parameter information; the historical data includes historical data related to passenger flow, historical data related to charging and discharging, and other operational data; the historical data related to passenger flow includes charging vehicle records and user information; the historical data related to charging and discharging includes power generation data of clean power generation equipment, energy storage system data, and charging pile data; the other operational data includes historical weather data and energy cost data;

[0065] S2. Generation Processing: Periodically generate charging and discharging monitoring strategies for the next operating cycle of the charging station based on the station information and historical data information of the charging station using a preset strategy optimization model; the strategy optimization model is a neural network model obtained by iterative training using historical data information from multiple charging stations; the charging and discharging monitoring strategy includes platform recommended strategies, dynamic control standards, at least one clean charging period, at least one off-peak charging period, and charging and discharging strategy process information.

[0066] S3. Recommendation Update: The platform recommendation strategy is pre-updated to the charging service platform according to the charging and discharging monitoring strategy; the platform recommendation strategy includes information on discount intensity, discount period information, recommended resource usage requirements (APP open ads, ad pop-ups, homepage ad bar, category ad bar, etc.), temporary discount information, and other recommendation strategy information.

[0067] S4. Monitoring and Control: Based on the charging and discharging monitoring strategy, the system issues commands at regular intervals to control the energy storage system to perform charging and discharging operations according to the charging and discharging strategy process. The system also monitors the status of the energy storage system in real time and generates dynamic correction commands according to the dynamic control standard to control the energy storage system to perform additional charging and discharging operations.

[0068] S5. Report Generation: Regularly collect monitoring data to generate monitoring reports and send them to management personnel. Based on existing passenger flow prediction models, charging and discharging prediction models, and charging station promotion plan models, utilize a neural network model and iteratively train it using historical data from charging stations to obtain a strategy optimization model. By acquiring multi-dimensional site information and rich historical data, and based on the actual situation of charging stations, this system considers factors such as equipment, geography, passenger flow, clean energy usage, charging and discharging, energy costs, and operations. It fully takes into account various practical factors in charging station operation, uses a strategy optimization model to mine data relationships, and combines this with geographical location weather forecast information to predict passenger flow and charging / discharging status for the next cycle. This intelligently and accurately generates charging / discharging monitoring strategies for the next operating cycle, with strong universality. Furthermore, it optimizes charging station recommendation information, guiding users to consume clean energy at each charging station while meeting their charging needs, ensuring the rational use of clean energy and improving profitability and utilization rates. Based on the charging / discharging monitoring strategy, intelligent control and monitoring of charging stations are achieved, facilitating the rational scheduling and use of energy storage systems. Combined with the platform's recommendation strategies, this maximizes green energy utilization, avoids energy overflow and loss, reduces energy costs, and improves the cost-effectiveness of charging stations.

[0069] Reference Figure 2 The step of periodically generating a charging and discharging monitoring strategy for the next operating cycle of a charging station based on the station's information and historical data using a preset strategy optimization model specifically includes the following steps:

[0070] A1. Generate optional strategies: Periodically generate multiple optional strategies for the next operating cycle of the charging station based on the station information and historical data information through a preset strategy optimization model; the optional strategies include factor prediction information and platform recommended strategies, dynamic control standards, at least one clean charging period, at least one off-peak charging period, and charging and discharging strategy process information; the factor prediction information includes passenger flow prediction information and weather prediction information.

[0071] A2. Simulation Operation: Model the charging station and simulate multiple optional strategies to obtain simulation data;

[0072] A3. Calculate the overall score: Calculate the overall score of each optional strategy based on the simulation data using a pre-set evaluation calculation formula;

[0073] A4. Selection of Charging / Discharging Monitoring Strategy: Each optional strategy is compared and ranked based on its comprehensive score. The strategy with the highest comprehensive score is selected as the charging / discharging monitoring strategy for the next operating cycle of the charging station. Through the above steps, multiple optional strategies are intelligently decided by the strategy optimization model based on the actual situation of the charging station. The charging station is then modeled, and simulations are performed on multiple optional strategies. These simulations cover various scenarios such as power generation, energy storage, charging, and user behavior under different strategies to obtain the execution performance of different strategies. This allows for advance understanding of the strategy's performance in the face of risks and uncertainties. Furthermore, multiple optional strategies are evaluated from multiple dimensions using evaluation calculation formulas, thus selecting the more robust optional strategy as the charging / discharging monitoring strategy for the next operating cycle of the charging station. This helps improve the utilization rate of green energy, avoid energy overflow and loss, reduce energy costs, and improve the cost-effectiveness of the charging station.

[0074] Reference Figure 3 The specific steps for modeling the charging station and simulating multiple optional strategies to obtain simulation data include:

[0075] B1. Model Building: The strategy optimization model constructs a simulation model of the charging station based on the site information;

[0076] B2. Parameter settings: The strategy optimization model sets the parameters of the charging station simulation model based on the charging station's site information and historical data.

[0077] B3. Simulation: The strategy optimization model loads multiple optional strategies into the simulation model of the charging station and runs the simulation for the next operating cycle to obtain simulation data.

[0078] Reference Figure 4 The strategy optimization model loads multiple optional strategies into the simulation model of the charging station and runs the simulation for the next operating cycle of the charging station to obtain simulation data. Specifically, the steps include:

[0079] C1. Strategy Loading: The strategy optimization model selects one unsimulated alternative strategy from multiple alternative strategies and loads it into the simulation model of the charging station.

[0080] C2. Normal operation simulation: The strategy optimization model uses the charging station simulation model to simulate the normal operation of the charging station for the next operating cycle based on factor prediction information to obtain normal operation simulation data.

[0081] C3. Emergency Simulation Operation: The strategy optimization model, based on factor prediction information, again simulates the next operating cycle of the charging station through the charging station's simulation model. During the operation, a predetermined number of strong emergency events are randomly generated based on the charging station's site information and historical data and inserted into the simulation operation to obtain emergency simulation data. The strong emergency events include severe weather events, extreme passenger flow events, and other pre-set types of events.

[0082] C4. Determine if there are any unsimulated alternative strategies;

[0083] C5. If it exists, then proceed to step C1;

[0084] C6. Package and Generate Simulation Data: If not available, package the normal simulation data and the emergency simulation data to generate simulation data. During simulation, normal simulation and emergency simulation are run in parallel. Normal simulation reflects the effectiveness of different strategies for the charging station under predicted passenger flow and weather conditions, while emergency simulation, by randomly inserting strong emergencies, simulates the strategy's ability to cope with emergencies such as severe weather and extreme passenger flow. This comprehensive simulation approach ensures that the generated simulation data more realistically reflects the strategy's performance under various possible conditions, providing a more reliable basis for subsequent comprehensive evaluation.

[0085] The specific formula for the above assessment calculation is as follows:

[0086] Y = X1 × Z1 + X2 × Z2;

[0087] Z1=W1 +W2 +W3 +W4 ;

[0088] Z2=W1 +W2 +W3 +W4 -W5 +W6 +W7 ;

[0089] Where Y is the comprehensive score of the optional strategy, Z1 is the normal simulation operation score, X1 is the normal simulation weight coefficient, Z2 is the emergency simulation operation score, and X2 is the emergency simulation weight coefficient; X1 and X2 are both set by the administrator, and the sum of X1 and X2 is 1; e is the effective output energy, which refers to the total energy successfully output to charge vehicles through the charging pile during the simulated operation period; E is the total input energy, which refers to the sum of the energy generated by the clean power generation equipment and the energy purchased from the grid; u is the number of users whose charging needs are met during the simulated operation period, and U is the total number of vehicles that visit during the simulated operation period; d is the actual charging time of the charging pile, and D is the operating time of the charging pile in the charging station; c is the net charging revenue, and C is the total investment of the charging station; r is the duration of energy supply interruption of the energy storage system during the operation period, and R is the total operating time during the operation period; a is the number of vehicles whose emergency charging needs are met, and A is the number of vehicles whose emergency charging needs are met. denoted as _b_, representing the total number of vehicles with emergency charging needs; _b_, representing the average waiting time for heavy users during severe emergencies; and _B_, representing the pre-set standard waiting time. _W1_ represents the energy utilization efficiency rating coefficient, _W2_, the user satisfaction rating coefficient, _W3_, the equipment utilization rate rating coefficient, _W4_, the cost-effectiveness rating coefficient, _W5_, the risk resistance rating coefficient, _W6_, the emergency charging satisfaction rating coefficient, and _W7_, the user satisfaction rating under emergency conditions. All of these are pre-set by management personnel. By using both normal and emergency simulation data to calculate the comprehensive score, the evaluation of optional strategies becomes more comprehensive and reliable. In actual operation, charging stations face both normal operating scenarios and potential emergencies. Considering the strategy performance under both scenarios comprehensively avoids focusing solely on normal operation while neglecting risk response capabilities, thus selecting a more robust charging and discharging monitoring strategy.

[0090] Meanwhile, the strategy generation and simulation process fully considers uncertainties such as passenger flow forecasts and weather forecasts, and incorporates strong unforeseen events into the simulation to test and optimize the strategy's risk response capabilities. This forward-looking strategy design helps charging stations better cope with various emergencies in actual operation, reducing operational chaos and losses caused by unforeseen events, and improving the stability and reliability of charging stations.

[0091] Reference Figure 5 The real-time monitoring of the energy storage system's status, generating dynamic correction commands according to dynamic control standards, and controlling the energy storage system to perform additional charging and discharging operations specifically includes:

[0092] D1. Monitor the energy storage system to determine whether it is in a clean charging period: Monitor the status of the energy storage system in real time. When the energy storage capacity of the energy storage system is lower than the low energy threshold preset in the dynamic control standard, determine whether it is in a clean charging period.

[0093] D2. If not, determine whether it is in a trough charging period;

[0094] D3. Charge the energy storage system: If in the current state, a dynamic correction command will be generated to enable the off-peak mains power to charge the energy storage system until the pre-set mains power charging threshold in the dynamic control standard is reached or the off-peak charging period ends.

[0095] D4. If not, check whether the power generation of the clean power generation equipment meets the minimum charging requirements of the energy storage system.

[0096] D5. Control the clean power generation equipment to charge the energy storage system: If the condition is met, a dynamic correction command is generated to control the clean power generation equipment to charge the energy storage system until the stored energy reaches the safe energy threshold preset in the dynamic control standard.

[0097] D6. Emergency Charging of the Energy Storage System from the Grid: If this is not possible, the charging supply to the energy storage system will be cut off. Based on the real-time load of the charging station and the interaction protocol with the grid, it will be determined whether to allow the purchase of a small amount of electricity from the grid at off-peak prices for emergency charging of the energy storage system. This ensures the basic operational functions of the energy storage system and the emergency power supply capacity of the charging station. The emergency charging event will be recorded. By monitoring the energy storage system status in real time, changes in the stored capacity can be detected promptly, allowing for rapid responses based on dynamic control standards. Charging can be scheduled according to the characteristics of different time periods (clean energy charging periods, off-peak charging periods), maximizing the utilization of clean energy and off-peak electricity resources. This reduces dependence on traditional grid power and improves the utilization rate of clean energy. It also enables flexible adjustment of the energy storage system's charging and discharging operations, allowing the system to better adapt to changes in the actual operating environment.

[0098] In addition, real-time monitoring of the energy storage system's status also includes: if the energy storage capacity exceeds the pre-set high-capacity threshold in the dynamic control standard, a dynamic correction instruction is generated based on the platform's recommended strategy to update temporary discount information on the charging service platform. When the energy storage capacity is high, to avoid affecting the subsequent collection of clean energy, the platform's recommended strategy generates a dynamic correction instruction to update temporary discount information, attracting users to charge and avoiding the waste of idle clean energy. This allows for flexible adjustment of pricing strategies based on the real-time status of the energy storage system, improving the utilization rate of clean energy, creating a mutually beneficial relationship with users, and increasing user stickiness and satisfaction.

[0099] The process of regularly collecting monitoring data and generating monitoring reports for sending to management personnel includes: periodically collecting monitoring data and importing it into a pre-set monitoring report template; displaying the historical power generation data, passenger flow data, and clean energy utilization rate trends of each charging station in the monitoring report using line graphs or bar charts, with the time span being the unit of operation cycle; and then sending the monitoring report to management personnel. By acquiring multi-dimensional site information and rich historical data, and based on the actual situation of charging stations, this system considers factors such as equipment, geography, passenger flow, clean energy usage, charging and discharging, energy costs, and operations. It fully takes into account various practical factors in charging station operation, uses a strategy optimization model to mine data relationships, and combines this with geographical location weather forecast information to predict passenger flow and charging / discharging status for the next cycle. This intelligently and accurately generates charging / discharging monitoring strategies for the next operating cycle, with strong universality. Furthermore, it optimizes charging station recommendation information, guiding users to consume clean energy at each charging station while meeting their charging needs, ensuring the rational use of clean energy and improving profitability and utilization rates. Based on the charging / discharging monitoring strategy, intelligent control and monitoring of charging stations are achieved, facilitating the rational scheduling and use of energy storage systems. Combined with the platform's recommendation strategies, this maximizes green energy utilization, avoids energy overflow and loss, reduces energy costs, and improves the cost-effectiveness of charging stations.

[0100] This application also discloses a monitoring system for charging stations. (Refer to...) Figure 6 A monitoring system for a charging station, comprising:

[0101] Information acquisition module 1 is used to acquire site information and historical data information of charging stations;

[0102] The strategy customization module 2 is used to periodically generate charging and discharging monitoring strategies for the next operating cycle of the charging station based on the station information and historical data information of the charging station through a preset strategy optimization model. The strategy optimization model is a neural network model obtained by iterative training through historical data information of multiple charging stations. The charging and discharging monitoring strategy includes platform recommended strategies, dynamic control standards, at least one clean charging period, at least one off-peak charging period, and charging and discharging strategy process information.

[0103] User guidance module 3 is used to pre-update the platform's recommended strategy to the charging service platform based on the charging and discharging monitoring strategy.

[0104] The monitoring and control module 4 is used to issue commands at regular intervals according to the charging and discharging monitoring strategy to control the energy storage system to perform charging and discharging operations according to the charging and discharging strategy process, and to monitor the status of the energy storage system in real time and generate dynamic correction commands according to the dynamic control standard to control the energy storage system to perform additional charging and discharging operations.

[0105] The report summary module 5 is used to collect monitoring data on a regular basis, generate monitoring reports, and send them to the management personnel.

[0106] This application also discloses a computer-readable storage medium that stores a computer program that can be loaded by a processor and executed as described above. The computer-readable storage medium includes, for example, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0107] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on these embodiments, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still combine, add, delete, or otherwise adjust the features of the various embodiments of the present invention according to the circumstances without conflict or creative effort, thereby obtaining different technical solutions that do not fundamentally depart from the concept of the present invention. These technical solutions also fall within the scope of protection of the present invention.

Claims

1. A method of monitoring a charging station, characterized by, The method comprises the following steps: obtaining site information and historical data information of the charging station; generating a charging and discharging monitoring strategy for the next operation period of the charging station by a preset strategy optimization model according to the site information and the historical data information of the charging station; the strategy optimization model is a neural network model obtained by iterative training of historical data information of multiple charging stations; the charging and discharging monitoring strategy comprises a platform recommendation strategy, dynamic regulation standards, at least one clean charging period, at least one trough charging period, and charging and discharging strategy flow information; updating the platform recommendation strategy to the charging service platform according to the charging and discharging monitoring strategy; controlling the energy storage system to perform charging and discharging operations according to the charging and discharging strategy flow by issuing instructions according to the charging and discharging monitoring strategy, and generating dynamic correction instructions to control the energy storage system to perform additional charging and discharging operations according to the dynamic regulation standards while monitoring the state of the energy storage system in real time; collecting monitoring data regularly to form a monitoring report and sending it to the management personnel; the site information comprises site geographic information, site clean power generation equipment parameter information, site energy storage system parameter information, and site charging pile parameter information; the historical data information comprises passenger flow related historical data, charging and discharging related historical data, and other operation data; the passenger flow related historical data comprises charging vehicle records and user information; the charging and discharging related historical data comprises clean power generation equipment power generation data, energy storage system data, and charging pile data; the other operation data comprises historical weather data and energy cost data; the real-time monitoring of the state of the energy storage system according to the dynamic regulation standards to generate dynamic correction instructions to control the energy storage system to perform additional charging and discharging operations specifically comprises: monitoring the state of the energy storage system in real time, and determining whether it is in a clean charging period when the energy storage system storage capacity is lower than a pre-set low capacity threshold in the dynamic regulation standards; if not, determining whether it is in a trough charging period; if so, generating dynamic correction instructions to enable trough grid power to charge the energy storage system to a pre-set grid charging threshold in the dynamic regulation standards or the end of the trough charging period; if not, checking whether the clean power generation equipment power generation power meets the minimum charging demand of the energy storage system; if so, generating dynamic correction instructions to control the clean power generation equipment to charge the energy storage system until the energy storage system storage capacity reaches a pre-set safe capacity threshold in the dynamic regulation standards; if not, cutting off the charging power supply of the energy storage system, and determining whether it is allowed to purchase a small amount of power from the grid at a non-trough price for emergency charging of the energy storage system according to the real-time load condition of the charging station and the interaction protocol with the grid, to ensure the basic operation function of the energy storage system and the emergency power supply capacity of the charging station, while recording the emergency charging event.

2. The method of claim 1, wherein, the method of generating a charging and discharging monitoring strategy for the next operation period of the charging station by a preset strategy optimization model according to the site information and the historical data information of the charging station specifically comprises the following steps: Periodically generate a plurality of optional strategies for the next operation period of the charging station according to the site information and historical data information of the charging station through a preset strategy optimization model; the optional strategies include factor prediction information and platform recommended strategies, dynamic regulation standards, at least one clean charging period, at least one trough charging period, and charging and discharging strategy flow information; the factor prediction information includes passenger flow prediction information and weather prediction information; Model the charging station, simulate the plurality of optional strategies to obtain simulation data; Calculate the comprehensive scores of the optional strategies according to the simulation data through a preset evaluation calculation formula; Compare and sort the optional strategies based on the comprehensive scores, and select the optional strategy with the highest comprehensive score as the charging and discharging monitoring strategy for the next operation period of the charging station.

3. The method of claim 2, wherein: The step of modeling the charging station and simulating the plurality of optional strategies to obtain simulation data specifically includes the following steps: The strategy optimization model constructs a simulation model of the charging station according to the site information; The strategy optimization model sets parameters of the simulation model of the charging station according to the site information and historical data information of the charging station; The strategy optimization model loads the plurality of optional strategies into the simulation model of the charging station for simulation and simulation operation in the next operation period to obtain simulation data.

4. The method of claim 3, wherein, The step of the strategy optimization model loading the plurality of optional strategies into the simulation model of the charging station for simulation and simulation operation in the next operation period of the charging station to obtain simulation data specifically includes the following steps: C1. The strategy optimization model selects an un-simulated optional strategy from the plurality of optional strategies and loads it into the simulation model of the charging station; C2. The strategy optimization model performs normal simulation and simulation operation in the next operation period of the charging station through the simulation model of the charging station based on the factor prediction information to obtain normal simulation data; C3. The strategy optimization model performs simulation and simulation operation in the next operation period of the charging station through the simulation model of the charging station again based on the factor prediction information, and randomly generates a rated number of strong burst events in the simulation and simulation operation process based on the site information and historical data information of the charging station to insert into the simulation and simulation operation process to obtain burst simulation data; the strong burst events include severe weather events, extreme passenger flow events, and other types of events preset; C4. Determine whether there is an un-simulated optional strategy; C5. If there is, jump to step C1; C6. If there is not, package the normal simulation data and the burst simulation data to generate simulation data.

5. The method of claim 4, wherein, The evaluation calculation formula is specifically: Y = X1 × Z1 + X2 × Z2; Z1 = W1 + W2 + W3 + W4 ; Z2 = W1 + W2 + W3 + W4 - W5 + W6 + W7 ; Wherein, Y is the comprehensive score of the optional strategy, Z1 is the normal simulation running score, X1 is the normal simulation weight coefficient, Z2 is the burst simulation running score, X2 is the burst simulation weight coefficient; and X1 and X2 are set by the management personnel, and the sum of X1 and X2 is 1; e is the effective output energy, which refers to the total energy successfully output to the vehicle for charging through the charging pile in the simulation operation period; E is the total input energy, which refers to the sum of the energy generated by the clean power generation equipment and the energy purchased from the power grid; u is the number of users meeting the charging demand in the simulation operation period, U is the total number of visited vehicles in the simulation operation period; d is the actual charging time of the charging pile, D is the operable time of the charging pile in the charging station; c is the pure charging income, C is the total investment of the charging station; r is the energy supply interruption time in the operation period, R is the total operation time in the operation period; a is the number of vehicles whose emergency charging demand is met, A is the total number of vehicles with emergency charging demand; b is the average waiting time of heavy emergency users, B is the pre-set standard waiting time; W1 is the energy utilization efficiency score coefficient, W2 is the user satisfaction score coefficient, W3 is the equipment utilization rate score coefficient, W4 is the cost benefit score coefficient, W5 is the risk resistance score coefficient, W6 is the emergency charging satisfaction score coefficient, and W7 is the user satisfaction score under emergency conditions; and W1, W2, W3, W4, W5, W6 and W7 are pre-set by the management personnel.

6. The method of claim 1, wherein, The real-time monitoring of the state of the energy storage system also includes: if the energy storage amount of the energy storage system is higher than the pre-set high energy threshold in the dynamic regulation standard, a dynamic correction instruction is generated according to the platform recommendation strategy, and the temporary preferential information on the charging service platform is updated.

7. The method of claim 1, wherein, The monitoring data collected at regular intervals are imported into the pre-set monitoring report template, and the historical power generation data, passenger flow data and clean energy utilization rate trend of each charging station are displayed in the monitoring report in the form of a line chart or a column chart, the time span is a unit operation period, and the monitoring report is sent to the management personnel.

8. A monitoring system of a charging station, characterized in that, It comprises: An information acquisition module (1) for acquiring site information and historical data information of the charging station; A strategy customization module (2) for generating a charging and discharging monitoring strategy for the next operation period of the charging station by a pre-set strategy optimization model according to the site information and historical data information of the charging station at regular intervals; the strategy optimization model is a neural network model obtained by iterative training of historical data information of multiple charging stations; The charging and discharging monitoring strategy includes a platform recommendation strategy, a dynamic regulation standard, at least one clean charging period, at least one trough charging period, and charging and discharging strategy flow information; A user guidance module (3) for pre-updating the platform recommendation strategy to the charging service platform according to the charging and discharging monitoring strategy; The monitoring control module (4) is configured to send a command to control the energy storage system to perform charging and discharging operation according to a charging and discharging strategy, and to monitor the state of the energy storage system in real time to generate a dynamic correction command to control the energy storage system to perform additional charging and discharging operation according to a dynamic regulation standard; The report aggregation module (5) is configured to collect monitoring data to form a monitoring report and send the monitoring report to a manager; The site information includes site geographic information, site clean power generation equipment parameter information, site energy storage system parameter information, and site charging pile parameter information; the historical data information includes passenger flow related historical data, charging and discharging related historical data, and other operation data; the passenger flow related historical data includes charging vehicle records and user information; the charging and discharging related historical data includes clean power generation equipment power generation data, energy storage system data, and charging pile data; and the other operation data includes historical weather data and energy cost data; The real-time monitoring of the state of the energy storage system according to the dynamic regulation standard to generate a dynamic correction command to control the energy storage system to perform additional charging and discharging operation specifically includes: Real-time monitoring of the state of the energy storage system, and when the energy storage system storage capacity is lower than a pre-set low capacity threshold in the dynamic regulation standard, determining whether it is in a clean charging period; If not, determining whether it is in a valley charging period; If yes, generating a dynamic correction command to enable valley city power to charge the energy storage system to a pre-set city power charging threshold in the dynamic regulation standard or to the end of the valley charging period; If not, checking whether the clean power generation equipment power generation power meets the minimum charging demand of the energy storage system; If yes, generating a dynamic correction command to control the clean power generation equipment to charge the energy storage system until the energy storage capacity reaches a pre-set safe capacity threshold in the dynamic regulation standard; If not, cutting off the charging power supply of the energy storage system, and according to the real-time load condition of the charging station and the interaction protocol with the power grid, determining whether to allow to purchase a small amount of power from the power grid at a non-valley price to charge the energy storage system in emergency to ensure the basic operation function of the energy storage system and the emergency power supply capacity of the charging station, and recording the emergency charging event.

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