Charging station intelligent scheduling method, system and device based on energy router and medium

Abstract

The invention relates to the technical field of charging pile intelligent scheduling, and discloses a charging station intelligent scheduling method and system based on an energy router, and the method comprises the steps: periodically collecting all data of the energy router, the charging pile, energy storage and photovoltaic; performing abnormity judgment on the acquired data, and returning to continue acquisition if data abnormity exists; if the data is normal, a corresponding control strategy is selected according to the electricity price time period, the energy storage SOC, the AC side power threshold of the energy router, the required voltage and the required current of the charging pile and the photovoltaic power generation power data, and power calculation of each device is carried out; and according to the calculated power control value, the energy router issues a control instruction to each charging pile and the energy storage equipment to carry out power adjustment, and cyclically carries out periodic acquisition. According to the control strategy, economy and equipment protection are comprehensively considered, energy storage over-charging and over-discharging, user experience and green energy utilization are avoided, the operation cost is effectively reduced, the service life of the battery is prolonged, and the charging efficiency and the user satisfaction degree are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent scheduling technology for charging piles, and in particular to an intelligent scheduling method, system, device and medium for charging stations based on an energy router. Background Technology

[0002] As the electric vehicle industry enters a period of rapid development, it places higher demands on the scale and quality of supporting charging infrastructure. As a core component of electric vehicle energy replenishment, the operational efficiency and intelligent management level of charging stations have become key factors influencing user experience and industry adoption. However, the limitations of traditional charging stations in terms of scheduling mechanisms and energy management are becoming increasingly apparent. Systems often lack the ability to coordinate and control multiple charging piles, resulting in low overall charging efficiency and insufficient resource utilization.

[0003] However, most charging stations currently employ fixed-threshold scheduling strategies and simple zoned power limiting methods. These fail to dynamically adjust to real-time fluctuations in grid load, are ill-equipped to respond to power fluctuations caused by the integration of renewable energy sources like solar power, and cannot adapt to the random changes in user demand at different times. This rigid, unintelligent operating mode easily leads to insufficient regional power supply during peak hours and significant idleness of charging facilities during off-peak periods, resulting in a clear misallocation and waste of power resources. Particularly noteworthy is the highly uncertain and personalized nature of electric vehicle users' charging behavior, with significant differences in charging start times, durations, and power demands. Traditional charging operation methods based on fixed logic struggle to effectively adapt to this flexible and randomly accessing electricity demand, further hindering the overall efficiency improvement of charging facilities. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a charging station intelligent scheduling method and system based on an energy router to solve the problem that the current fixed scheduling strategy and simple power limit cannot flexibly adjust according to the grid conditions, charging demand and photovoltaic power generation in real time, resulting in insufficient power supply or waste of resources.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a smart scheduling method for charging stations based on an energy router, comprising: periodically collecting all data from the energy router, charging piles, energy storage and photovoltaics;

[0008] The system performs data anomaly detection on the collected energy router, charging pile, energy storage and photovoltaic data. If data anomalies are found, the system returns to continue collecting data.

[0009] If the data is normal, the appropriate control strategy is selected based on the electricity price period, energy storage SOC, power threshold of the AC side of the energy router, charging pile demand voltage and demand current, and photovoltaic power generation data, and the power calculation of each device is performed.

[0010] Based on the calculated power control values ​​of the charging piles and energy storage, the energy router sends control commands to each charging pile and energy storage device.

[0011] Each charging pile and energy storage device adjusts its power according to the received control commands and performs periodic data collection.

[0012] As a preferred embodiment of the intelligent scheduling method for charging stations based on an energy router described in this invention, the power threshold on the AC side of the energy router is expressed as:

[0013]

[0014] in, The rated apparent power (kVA) of the energy router. The real-time power limit (kW) of the upper-level power grid.

[0015] The capacity of an energy router is determined by its rated apparent power. Confirm the real-time power limit issued by the superior power grid. With a response time requirement, the charging station adjusts the power within the response time.

[0016] As a preferred embodiment of the intelligent scheduling method for charging stations based on an energy router described in this invention, the control strategy includes: calculating the energy storage charging and discharging control power based on the current electricity price period and the SOC of the energy storage device. Scope;

[0017] During peak electricity price periods, if energy storage The power limit range for energy storage charging and discharging is ( If the value is 0, then the energy storage power is controlled to be 0; otherwise, the energy storage power is controlled to be 0

[0018] During off-peak electricity prices, if energy storage The power limit range for energy storage charging and discharging is (0, Otherwise, control the energy storage power to be 0;

[0019] During normal electricity price periods, if energy storage The power limit range for energy storage charging and discharging is (0, Otherwise, the power limit range for energy storage charging and discharging is ( , );

[0020] in, The power (kW) for energy storage charging and discharging control. The maximum discharge power of the energy storage is (kW). The maximum charging power (kW) for energy storage. This is the energy storage protection threshold.

[0021] As a preferred embodiment of the present invention, its beneficial effects are: determining the charging and discharging power range of the energy storage system based on the electricity price period and the state of charge (SOC) of the energy storage device. It can optimize economic efficiency and energy storage battery life, and adapt to different operating scenarios.

[0022] As a preferred embodiment of the intelligent scheduling method for charging stations based on an energy router described in this invention, the control strategy further includes: calculating the required power of the charging pile based on the required voltage and required current of the unrestricted charging pile. The power limit will be set to ( ) within the first preset time period. )kW; of which, The power change value of a charging vehicle within one control cycle;

[0023] Set the power limit after the first preset time. .

[0024] As a preferred embodiment of the intelligent scheduling method for charging stations based on an energy router described in this invention, the control strategy further includes: determining whether a vehicle is charging at the intelligent current-limiting charging pile, based on whether the handshake protocol signal between the charging pile and the electric vehicle and whether the charging current is greater than 0.1A.

[0025] The power of smart current-limiting charging piles that do not have vehicles charging is limited to the minimum power. , The power is set to that of a standard slow charging station, meeting the basic power requirements of electric vehicles.

[0026] As a preferred embodiment of the intelligent scheduling method for charging stations based on energy routers described in this invention, the control strategy further includes:

[0027] Calculate the total allowable charging power of a smart current-limited charging station with vehicles charging. :

[0028] + ;

[0029] in, Photovoltaic power generation (kW);

[0030] Calculate the charging and discharging power of energy storage ;

[0031] in, The energy storage charging and discharging control power is calculated based on the current electricity price period and the SOC of the energy storage device. If the power range is not met, then... Set as a boundary value;

[0032] in, The real-time charging power demand of intelligent current-limited charging piles for charging vehicles.

[0033] As a preferred embodiment of the intelligent scheduling method for charging stations based on energy routers described in this invention, the control strategy further includes:

[0034] The control power of each smart current-limited charging pile is calculated and expressed as:

[0035] when The intelligent current-limiting charging pile controls the power to the required power. ;

[0036] when ,like The energy storage charging and discharging control power is calculated based on the current electricity price period and the SOC of the energy storage device. Within the specified power range, the intelligent current-limited charging pile controls the power to the required power; otherwise, the power of each intelligent current-limited charging pile will be adjusted accordingly. Adjustments will be made proportionally.

[0037] The proportional reduction is expressed as follows: .

[0038] Secondly, the present invention provides an intelligent scheduling system for charging stations based on an energy router, comprising: an energy router, at least one charging pile, at least one energy storage device, and at least one photovoltaic power generation device;

[0039] The energy router is connected to the 10kV power grid and connected to the DC bus; each charging pile, energy storage device and photovoltaic power generation device is connected to the DC bus.

[0040] The energy router is used for AC / DC conversion, data acquisition, and control command issuance.

[0041] The charging pile is equipped with a power adjustment unit and a communication module for power limiting function, receiving instructions from the energy router and adjusting the output power; the charging pile is divided into intelligent current-limiting charging pile and non-current-limiting charging pile according to whether power limiting is allowed.

[0042] Thirdly, the present invention provides a computer device, comprising:

[0043] Memory and processor;

[0044] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of a charging station intelligent scheduling method based on an energy router.

[0045] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the aforementioned intelligent scheduling method for charging stations based on an energy router.

[0046] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention ensures data accuracy and reliability through periodic data collection and anomaly detection; based on a dynamic control strategy considering electricity prices, energy storage SOC, grid limitations, charging demand, and photovoltaic power generation, it achieves optimized energy storage charging and discharging, precise power allocation for charging piles, and priority utilization of photovoltaic energy, overcoming the limitations of traditional fixed scheduling, achieving peak shaving and valley filling, and reducing peak-hour grid load and operating costs. Secondly, by issuing commands through an energy router and executing power adjustments and cyclic data collection, a closed-loop control is formed, ensuring real-time implementation and dynamic optimization of the strategy, enhancing system stability. The control strategy comprehensively considers economic efficiency, equipment protection, and avoids overcharging and over-discharging of energy storage, user experience, and green energy utilization, effectively reducing operating costs, extending battery life, improving charging efficiency and user satisfaction, while adhering to grid power limits and avoiding the risk of power outages due to over-limit conditions. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a schematic diagram of the overall process of an intelligent scheduling method for charging stations based on an energy router, according to an embodiment of the present invention.

[0049] Figure 2 This is a schematic diagram of the overall architecture of a charging station intelligent scheduling system based on an energy router, according to an embodiment of the present invention. Detailed Implementation

[0050] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0051] Example 1, referring to Figure 1 As an embodiment of the present invention, a smart scheduling method for charging stations based on an energy router is provided, comprising:

[0052] S100: Periodically collects all data from energy routers, charging piles, energy storage, and photovoltaics;

[0053] S200: Performs data anomaly detection on the collected energy router, charging pile, energy storage and photovoltaic data. If data anomalies are found, it returns to continue collecting data.

[0054] S300: If the data is normal, select the appropriate control strategy based on the electricity price period, energy storage SOC, power threshold of the AC side of the energy router, the required voltage and current of the charging pile, and the photovoltaic power generation data, and perform power calculation for each device.

[0055] S400: Based on the calculated power control values ​​of the charging piles and energy storage, the energy router sends control commands to each charging pile and energy storage device.

[0056] S500: Each charging pile and energy storage device adjusts its power according to the received control commands and performs periodic data collection.

[0057] It should be noted that most existing technologies adopt fixed scheduling strategies, such as first-come, first-served and simple polling, which cannot simultaneously consider multiple dynamic variables such as the economics of electricity price signals, the energy availability of energy storage SOC, the compliance of grid-side power limits, the user experience of charging demand, and the energy utilization of photovoltaic power generation. Therefore, they cannot achieve "peak shaving and valley filling". During peak grid electricity price periods, they may still operate at high power, increasing operating costs; they may also cause overcharging and over-discharging due to ignoring the energy storage SOC status, damaging battery life; and if power cannot be intelligently allocated within the grid power limit, it may trigger total power exceeding the limit, leading to a power outage for the entire station.

[0058] Therefore, through steps S100-S500, intelligent control and scheduling of charging piles are implemented to dynamically adjust the charging power based on the charging pile's power demand and grid load, achieving flexible adjustment and optimized configuration of charging power. This also responds to upper-level grid scheduling, reduces the impact on grid operation, improves the reliability and stability of the power system, and avoids the current traditional mode with fixed strategies and lack of coordination. This achieves the comprehensive goal of improving charging station operating efficiency, reducing operating costs, and improving user experience.

[0059] Example 2, refer to Figure 1 As an embodiment of the present invention, based on the above embodiment, a smart scheduling method for charging stations based on an energy router is provided.

[0060] In this embodiment of the application, in step S100, all data from the energy router, charging pile, energy storage, and photovoltaic are periodically collected;

[0061] Specifically, the collected data includes, but is not limited to, the following:

[0062] An energy router can include: AC power, DC power, voltage, current, etc.

[0063] Charging stations can include: required voltage, required current, actual charging power, charging status, etc.

[0064] Energy storage can include: state of charge (SOC), state of health (SOH), charging and discharging power, voltage, current, etc.

[0065] Photovoltaics can include: power generation, irradiance, ambient temperature, etc.

[0066] In one alternative implementation, the data collection is typically periodically conducted at fixed time intervals, which can be dynamically adjusted based on grid stability and charging demand. The default interval is 1 minute. Data from each device is periodically collected using data acquisition equipment such as a SCADA system, and can be transmitted to an energy router or central control system via communication protocols such as Modbus or IEC 61850.

[0067] In another optional implementation, the periodicity of data acquisition can be selected to be event-triggered, such as by changes in device status. The specific period is determined based on the system's real-time requirements and communication bandwidth. The acquisition frequency is dynamically adjusted by combining fixed time intervals and event-triggered mechanisms. When the system detects significant changes in key parameters, such as photovoltaic power, energy storage SOC, or grid power (e.g., a change in photovoltaic power exceeding 10%), high-frequency periodic acquisition is triggered. Under normal circumstances, low-frequency periodic timed acquisition is maintained to balance real-time performance and resource efficiency.

[0068] It should be noted that the present invention preferably collects data at fixed time intervals, such as 1 minute. This method is simple to implement, the data collection rhythm is stable, and it is convenient for system planning and resource allocation.

[0069] In this embodiment of the application, step S200 involves judging the data anomalies of the collected energy router, charging pile, energy storage and photovoltaic data. If there are data anomalies, the process returns to continue collecting data.

[0070] In one optional implementation, data anomalies in step S200 include not collecting data from all devices, invalid collected data, the number of devices not matching the actual situation, and various data such as current and voltage significantly exceeding the normal range. If the data is normal, proceed to the next step; otherwise, wait for the next round of data collection.

[0071] For example, if various data such as current and voltage significantly exceed the normal range, the following settings can be used:

[0072] Energy storage SOC: 0%≤ ≤100%;

[0073] Charging pile current: 0≤ ≤ , This is the rated current.

[0074] If the data violates any range, it is marked as abnormal, the abnormality type is recorded, and the process returns to S100 for re-collection.

[0075] In another optional implementation, the anomaly detection in S200 can be replaced by using a machine learning model to detect anomalies in the data. This model learns normal data patterns by training on historical data and automatically identifies anomalous data that deviates from the pattern. This is suitable for scenarios with complex data and where anomaly patterns are difficult to predefine. For example, an isolated forest model can be used to detect outliers in the data collected in S100. Anomaly detection is performed based on data distribution characteristics, anomaly scores are calculated, and anomaly thresholds are set. If the anomaly score is >0.9, the data is considered anomalous; if the data is marked as anomalous, it is returned to S100 for re-collection.

[0076] It should be noted that the two implementation methods of S200 described above are based on set rules and machine learning, respectively, and can be applied to different charging station scenarios. The reason for choosing to directly determine invalid data, the number of devices that does not match the site or exceeds the range is because it is simple and efficient and suitable for small or stable charging stations. Machine learning, on the other hand, is more suitable for large or dynamically changing charging stations, and both can improve detection accuracy and system robustness.

[0077] In this embodiment of the application, if the data is normal in step S300, the corresponding control strategy is selected based on the electricity price period, energy storage SOC, power threshold of the AC side of the energy router, the charging pile demand voltage and demand current, and photovoltaic power generation data, and the power calculation of each device is performed.

[0078] In this embodiment of the application, the power threshold of the energy router on the AC side in step S300 is expressed as:

[0079]

[0080] in, The rated apparent power (kVA) of the energy router. The real-time power limit (kW) of the upper-level power grid.

[0081] The capacity of an energy router is determined by its rated apparent power. Confirm the real-time power limit issued by the superior power grid. With a response time requirement, the charging station adjusts the power within the response time;

[0082] Specifically, the default response time is 30 minutes.

[0083] It should be noted that the power threshold on the AC side of the energy router... This means that the actual operating power cannot exceed the equipment's own capacity. or power grid limit The smaller value in; This is an inherent property of energy routers and depends on the hardware design; These are dynamic limits issued by the upper-level power grid to coordinate grid load, and may vary due to peak demand or stability requirements; the response time is... The execution requirements specify the timeframe within which the charging station must complete the power adjustment.

[0084] In this embodiment of the application, the control strategy described in step S300 includes the following step A1:

[0085] A1: Calculate the energy storage charging and discharging control power based on the current electricity price period and the SOC of the energy storage device. Scope;

[0086] A1-1: During peak electricity price periods, if energy storage... The power limit range for energy storage charging and discharging is ( If the value is 0, then the energy storage power is controlled to be 0; otherwise, the energy storage power is controlled to be 0

[0087] Specifically, in A1-1, when the electricity price is high and the SOC is higher than the protection threshold... , exist( Discharge is prioritized within the range of 0 to reduce the purchase of electricity from the grid and lower costs; if the SOC is too low, discharge is stopped to protect the battery.

[0088] A1-2: During off-peak electricity prices, if energy storage... The power limit range for energy storage charging and discharging is (0, Otherwise, control the energy storage power to be 0;

[0089] Specifically, in A1-2, when the electricity price is low and the SOC is not full (<100%). In (0, Prioritize charging within the specified range to make full use of low-cost electricity; if the SOC is full, stop charging to avoid overcharging.

[0090] A1-3: During normal electricity price periods, if energy storage... The power limit range for energy storage charging and discharging is (0, Otherwise, the power limit range for energy storage charging and discharging is ( , );

[0091] Specifically, adjustments should be made flexibly based on the SOC. Prioritize charging to restore capacity; otherwise, allow charging and discharging, balancing economy and flexibility.

[0092] in, The power (kW) for energy storage charging and discharging control. The maximum discharge power of the energy storage is (kW). The maximum charging power (kW) for energy storage. This is the energy storage protection threshold.

[0093] It should be noted that the main purpose of A1 is to determine the charging and discharging power range of the energy storage system based on the electricity price period (peak, flat, and valley) and the SOC of the energy storage device. To optimize economic efficiency and battery life, it can dynamically adjust charging and discharging strategies based on electricity prices and SOC to adapt to different operating scenarios.

[0094] In this embodiment of the application, the control strategy described in step S300 further includes the following step A2:

[0095] A2: Calculate the required power of the charging pile based on the required voltage and current of the unlimited current charging pile. The power limit will be set to ( ) within the first preset time period. )kW; of which, The power change value of a charging vehicle within one control cycle;

[0096] Set the power limit after the first preset time. .

[0097] For example, to avoid limiting charging power, the first preset time could be to set the power limit for the first 10 minutes. )kW, then set the power limit to ;

[0098] It should be noted that, It is mainly used to ensure that the unlimited current charging piles have enough power reserves to cope with grid fluctuations or instantaneous changes in photovoltaic power generation while meeting the charging needs of vehicles, thereby reserving a buffer for grid fluctuations or changes in photovoltaic power and reducing the risk of distribution imbalance caused by sudden power changes.

[0099] It should also be noted that the purpose of A2 is to calculate the power requirements of unlimited current charging piles, typically fast charging piles. This system is designed to meet the charging needs of high-priority users. Within a first preset time period, a power limit is set to provide a buffer for fast charging of vehicles, addressing initial charging power fluctuations or changes in grid / solar power. After the first preset time, the power limit is restored, optimizing resource allocation and preventing excessive power consumption over a long period. This ensures that fast charging stations initially meet the high power demands of vehicles, improving user charging speed and satisfaction, and enhancing overall resource utilization.

[0100] In this embodiment of the application, the control strategy described in step S300 further includes the following step A3:

[0101] A3: To determine whether a vehicle is charging at a smart current-limiting charging pile, the criteria are the handshake protocol signal between the charging pile and the electric vehicle and whether the charging current is greater than 0.1A.

[0102] The power of smart current-limiting charging piles that do not have vehicles charging is limited to the minimum power. , The power is set to that of a standard slow charging station, meeting the basic power requirements of electric vehicles.

[0103] It should be noted that the purpose of A3 is to determine whether a smart current-limiting charging pile is being used by handshake protocol signals and charging current. Typically, this involves setting a minimum power level for slow charging piles when no vehicle is charging, ensuring basic standby functionality while avoiding unnecessary power allocation. This reduces ineffective power usage, freeing up more power capacity for charging piles with vehicles and energy storage systems. Overall, by dynamically identifying the charging status and optimizing power allocation, it improves the overall operating efficiency of the charging station.

[0104] In this embodiment of the application, the control strategy described in step S300 further includes the following steps A4-A5:

[0105] A4: Calculate the total allowable charging power of smart current-limited charging piles with vehicles charging. :

[0106] ;

[0107] in, Photovoltaic power generation (kW);

[0108] It should be noted that the calculation of the total available power of intelligent current-limited charging piles takes into account grid constraints, photovoltaic power generation, and fast charging demand, rationally allocating surplus power to maximize resource utilization, and incorporating... Prioritize the use of photovoltaic power generation to reduce dependence on the power grid and decrease carbon emissions; at the same time, ensure that the total power does not exceed This avoids the risk of power outages caused by triggering grid power limits.

[0109] A5: Calculate the charging and discharging power of energy storage ;

[0110] in, The energy storage charging and discharging control power is calculated based on the current electricity price period and the SOC of the energy storage device. If the power range is not met, then... Set as a boundary value;

[0111] in, The real-time charging power demand of intelligent current-limited charging piles for charging vehicles.

[0112] It should be noted that A5 passes Fill or absorb and The difference between them is used to achieve dynamic power balance of the charging station; and combined with the range limitation of step A1, it ensures that the energy storage power complies with the electricity price optimization strategy, while avoiding overcharging and over-discharging and protecting battery life. As a regulating variable, it can compensate for the impact of photovoltaic fluctuations or grid limitations, and enhance system stability.

[0113] In this embodiment of the application, the control strategy described in step S300 further includes the following step A6:

[0114] A6: Calculate the control power of each smart current-limiting charging pile, expressed as:

[0115] when The intelligent current-limiting charging pile controls the power to the required power. ;

[0116] when ,like The energy storage charging and discharging control power is calculated based on the current electricity price period and the SOC of the energy storage device. Within the specified power range, the intelligent current-limited charging pile controls the power to the required power; otherwise, the power of each intelligent current-limited charging pile will be adjusted accordingly. Adjustments will be made proportionally.

[0117] The proportional reduction is expressed as follows: .

[0118] It should be noted that A6 is set to proportionally reduce power when it is insufficient. This ensures that all charging stations have an equal share of available power, preventing some vehicles from running out of power and achieving fairness for all users; and through dynamic adjustment Ensure total power is within Within the scope, comply with Limits are imposed to prevent power outages due to excessive limits and ensure system stability; simultaneously, according to Dynamic availability decisions, taking into account both energy storage status and charging needs, can optimize user experience and system efficiency.

[0119] Step S300's overall control strategy utilizes electricity price periods and energy storage SOC to optimize charging and discharging strategies, charging during off-peak hours and discharging during peak hours to reduce operating costs; it smooths grid load through power regulation of energy storage and smart current-limited charging piles, thereby reducing power pressure during peak periods; it prioritizes the use of photovoltaic power generation to reduce carbon footprint; it prevents overcharging and over-discharging of energy storage by limiting SOC thresholds and power ranges, thus extending battery life; and it ensures fair charging of smart current-limited charging piles through proportional allocation, while avoiding power over-limitation and enhancing the operational reliability of charging stations. This overcomes the limitations of traditional fixed scheduling strategies and achieves data-driven, multi-objective optimized intelligent scheduling.

[0120] In this embodiment of the application, in step S400, the energy router sends control commands to each charging pile and energy storage device based on the calculated power control values ​​of the charging pile and energy storage.

[0121] Specifically, the S400 converts the power control values ​​calculated by the S300 into executable control commands;

[0122] For example, obtaining the power control values ​​of each device includes: , as well as ;

[0123] The power control values ​​are converted into a device-recognizable instruction format, such as voltage, current setpoints, or power percentages. The energy router generates control instructions for each charging pile and energy storage device based on the device type and communication protocol, such as Modbus TCP, CAN, or IEC 61850. The instructions can include information such as device ID, target power value, and execution timestamp to ensure the uniqueness and traceability of the instructions.

[0124] In this embodiment of the application, in step S500, each charging pile and energy storage device adjusts its power according to the received control command and performs periodic data collection.

[0125] Specifically, each charging pile and energy storage device receives control commands from the energy router through a communication interface; the commands include the target power value, as well as the device ID, timestamp, etc.; the device parses the commands and verifies the format and legality of the commands.

[0126] Subsequently, power adjustment is performed. For example, an unlimited current charging pile sets its output power (e.g., 50kW or 60kW) according to instructions by adjusting the output voltage and current; an intelligent current-limited charging pile adjusts its power according to... or Adjust the power; if the power is insufficient, allocate it proportionally.

[0127] Energy storage devices according to The charging / discharging state is adjusted, with positive values ​​indicating charging, negative values ​​indicating discharging, and zero values ​​indicating standby. Precise power regulation is achieved by controlling the inverter or BMS (Battery Management System). When adjusting power, the device must ensure that the response time meets the requirements of the upstream power grid, such as completing the adjustment within the default 30 minutes. After executing the command, the device provides real-time feedback on the actual operating status and continues to follow the S100's periodic data acquisition mechanism.

[0128] In summary, this invention, through precise data acquisition and analysis, can dynamically adjust charging power based on the charging pile's power demand and the grid load. It enables flexible adjustment and optimized configuration of charging power even under conditions of grid load fluctuations or unstable photovoltaic power generation, thereby improving the overall operating efficiency of the charging station and effectively avoiding problems such as low charging efficiency and excessively long user waiting times caused by uneven power distribution. When the overall grid load is high, it can respond to upper-level grid dispatching, and the system can promptly limit the charging pile's charging power to ensure grid load balance, thus improving power system reliability and reducing grid faults. Overall, this solution significantly improves the intelligence level of charging stations through multi-dimensional collaborative optimization, providing an efficient and reliable solution for large-scale electric vehicle charging and renewable energy integration.

[0129] Example 3, referring to Table 1, based on the above examples, in order to verify the feasibility of the above power transmission and distribution line tower safety evaluation method, this example provides a comparative verification of a charging station intelligent scheduling method based on an energy router.

[0130] Number the unlimited current charging piles 1-5 and the intelligent current-limited charging piles 6-15. Assuming a normal period in a certain area, the photovoltaic power generation is 50kW, the energy router threshold is set to 950kW, and the power demand of all charging vehicles is 100kW. Currently, there are vehicles charging at charging piles 1-10. At this time, 5 more charging vehicles arrive. Calculate the energy storage and the power of each charging pile. The results are shown in Table 1 below.

[0131] Table 1: Comparison of Calculation Results for Energy Storage and Various Charging Piles

[0132]

[0133] As shown in Table 1, without this method, the energy router's power has already reached the threshold. Adding more charging vehicles would exceed the router's capacity, causing a power outage. Therefore, no new vehicles can be charged, and energy storage is generally not scheduled during normal periods. Using this method, energy storage discharge can be controlled, and the power of the intelligent current-limiting charging piles can be adjusted to ensure all charging vehicles are charged.

[0134] Example 4, refer to Figure 2The above is an illustrative scheme of a charging station intelligent scheduling method based on an energy router. It should be noted that the technical solution of this charging station intelligent scheduling system based on an energy router and the technical solution of the charging station intelligent scheduling method based on an energy router described above belong to the same concept. Details not described in detail in the technical solution of the charging station intelligent scheduling system based on an energy router in this embodiment can be found in the description of the technical solution of the charging station intelligent scheduling method based on an energy router described above.

[0135] This embodiment also provides another intelligent scheduling system for charging stations based on energy routers, including:

[0136] An energy router, at least one charging pile, at least one energy storage device, and at least one photovoltaic power generation device;

[0137] The energy router is connected to the 10kV power grid and connected to the DC bus; each charging pile, energy storage device and photovoltaic power generation device is connected to the DC bus.

[0138] The energy router is used for AC / DC conversion, data acquisition, and control command issuance.

[0139] The charging pile is equipped with a power adjustment unit and a communication module for power limiting function, receiving instructions from the energy router and adjusting the output power; the charging pile is divided into intelligent current-limiting charging pile and non-current-limiting charging pile according to whether power limiting is allowed.

[0140] Among them, the data acquisition function can collect the AC side voltage, current, and power of the energy router, the DC bus voltage and current, the output parameters of the photovoltaic power generation device, and the operating parameters of each charging pile and energy storage device at a preset sampling frequency.

[0141] The control command issuance function establishes a connection with each charging pile and energy storage device and transmits commands through a communication protocol.

[0142] Among them, the energy storage device can receive instructions from the energy router to perform power control and power on / off control.

[0143] For example, such as Figure 2 As shown, a new energy vehicle charging station is equipped with one 1000kVA energy router, whose AC side is connected to the municipal power grid via a 10kV cable, and whose DC side is connected to a 750V DC bus. The DC bus is connected to: 15 charging piles, supporting continuous power adjustment from 0-160kW, of which 10 are intelligent current-limiting charging piles and 5 are unlimited current charging piles; one 200kWh lithium iron phosphate battery pack with a maximum charge / discharge power of 100kW, a current SOC of 30%, and an energy storage protection threshold SOC_min = 10%; and a 100kW photovoltaic power generation device.

[0144] This embodiment also provides a computer device applicable to a charging station intelligent scheduling method based on an energy router, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the charging station intelligent scheduling method based on an energy router as proposed in the above embodiment.

[0145] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a smart scheduling method for charging stations based on an energy router as proposed in the above embodiments.

[0146] The storage medium proposed in this embodiment and the method for intelligent scheduling of charging stations based on energy routers proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0147] From the above description of the implementation methods, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0148] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A smart scheduling method for charging stations based on an energy router, characterized in that, include: Periodically collect all data from energy routers, charging piles, energy storage, and photovoltaics; The system performs data anomaly detection on the collected energy router, charging pile, energy storage and photovoltaic data. If data anomalies are found, the system returns to continue collecting data. If the data is normal, the appropriate control strategy is selected based on the electricity price period, energy storage SOC, power threshold of the AC side of the energy router, the required voltage and current of the charging pile, and the photovoltaic power generation data, and the power calculation of each device is performed. Based on the calculated power control values ​​of the charging piles and energy storage, the energy router sends control commands to each charging pile and energy storage device. Each charging pile and energy storage device adjusts its power according to the received control commands and performs periodic data collection.

2. The intelligent scheduling method for charging stations based on energy routers as described in claim 1, characterized in that, The power threshold on the AC side of the energy router is expressed as follows: in, The rated apparent power (kVA) of the energy router. The real-time power limit (kW) of the upper-level power grid. The capacity of an energy router is determined by its rated apparent power. Confirm the real-time power limit issued by the superior power grid. With a response time requirement, the charging station adjusts the power within the response time.

3. The intelligent scheduling method for charging stations based on energy routers as described in claim 2, characterized in that, The control strategy includes: calculating the energy storage charging and discharging control power based on the current electricity price period and the SOC of the energy storage device. Scope; During peak electricity price periods, if energy storage The power limit range for energy storage charging and discharging is ( If the value is 0, then the energy storage power is controlled to be 0; otherwise, the energy storage power is controlled to be 0 During off-peak electricity prices, if energy storage The power limit range for energy storage charging and discharging is (0, Otherwise, control the energy storage power to be 0; During normal electricity price periods, if energy storage The power limit range for energy storage charging and discharging is (0, Otherwise, the power limit range for energy storage charging and discharging is ( , ); in, The power (kW) for energy storage charging and discharging control. The maximum discharge power of the energy storage is (kW). The maximum charging power (kW) for energy storage. This is the energy storage protection threshold.

4. The intelligent scheduling method for charging stations based on an energy router as described in claim 3, characterized in that, The control strategy also includes: calculating the required power of the charging pile based on the required voltage and required current of the unlimited current charging pile. The power limit will be set to ( ) within the first preset time period. )kW; of which, The power change value of a charging vehicle within one control cycle; Set the power limit after the first preset time. .

5. The intelligent scheduling method for charging stations based on an energy router as described in claim 4, characterized in that, The control strategy also includes: determining whether there is a vehicle charging at the intelligent current-limiting charging pile, based on the handshake protocol signal between the charging pile and the electric vehicle and whether the charging current is greater than 0.1A; The power of smart current-limiting charging piles that are not charging vehicles is limited to the minimum power. , The power is set to that of a standard slow charging station, meeting the basic power requirements of electric vehicles.

6. The intelligent scheduling method for charging stations based on an energy router as described in claim 5, characterized in that, The control strategy also includes: Calculate the total allowable charging power of a smart current-limited charging station with vehicles charging. : + ； in, Photovoltaic power generation (kW); Calculate the charging and discharging power of energy storage ; in, The energy storage charging and discharging control power is calculated based on the current electricity price period and the SOC of the energy storage device. If the power range is not met, then... Set as a boundary value; in, The real-time charging power demand of intelligent current-limited charging piles for charging vehicles.

7. The intelligent scheduling method for charging stations based on an energy router as described in claim 6, characterized in that, The control strategy also includes: The control power of each smart current-limited charging pile is calculated and expressed as: when The intelligent current-limiting charging pile controls the power to the required power. ; when ,like The energy storage charging and discharging control power is calculated based on the current electricity price period and the SOC of the energy storage device. Within the specified power range, the intelligent current-limited charging pile controls the power to the required power; otherwise, the power of each intelligent current-limited charging pile will be adjusted accordingly. Adjustments will be made proportionally. The proportional reduction is expressed as follows: .

8. A charging station intelligent scheduling system based on an energy router, using the method described in any one of claims 1-7, characterized in that, include: An energy router, at least one charging pile, at least one energy storage device, and at least one photovoltaic power generation device; The energy router is connected to the 10kV power grid and connected to the DC bus; each charging pile, energy storage device and photovoltaic power generation device is connected to the DC bus. The energy router is used for AC / DC conversion, data acquisition, and control command issuance. The charging pile is equipped with a power adjustment unit and a communication module for power limiting function, receiving instructions from the energy router and adjusting the output power; the charging pile is divided into intelligent current-limiting charging pile and non-current-limiting charging pile according to whether power limiting is allowed.

9. A computer device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the intelligent scheduling method for charging stations based on an energy router as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-executable instructions, which, when executed by a processor, implement the steps of the intelligent scheduling method for charging stations based on an energy router as described in any one of claims 1 to 7.

Images