Micro-inverter grid-connected control method and system

Abstract

The present application relates to the technical field of micro-inverter grid-connected control, and particularly relates to a micro-inverter grid-connected control method and system, the method comprising the following steps: obtaining output power information and geographical position information of a distributed power generation unit, and charging demand information and geographical position information of an electric vehicle charging unit; dynamically matching the distributed power generation unit and the electric vehicle charging unit to form a local supply-demand group; monitoring power output of a total grid-connected point; when the power output of the total grid-connected point is greater than or equal to a preset upper limit of grid-connected power, a central controller performs multi-stage differentiated power regulation, including: reducing output power of the distributed power generation unit for the distributed power generation unit that is not matched by any electric vehicle charging unit; scheduling an electric vehicle with a vehicle-grid interaction function to absorb surplus power generated by the distributed power generation unit; and reducing output power of the distributed power generation unit. The method improves overall energy utilization efficiency and user satisfaction of the system.

Description

Technical Field

[0001] This invention relates to the technical field of grid-connected control of microinverters, and specifically to a grid-connected control method and system for microinverters. Background Technology

[0002] In modern energy systems, distributed photovoltaic (PV) power generation systems, especially those integrated with electric vehicle (EV) charging facilities, are becoming increasingly common in scenarios such as parking lots in commercial complexes. These systems typically include independent micro-inverters that convert the direct current (DC) power generated by PV into alternating current (AC) power for grid connection, while smart EV charging stations meet the vehicle charging needs.

[0003] As distributed photovoltaic (PV) systems scale up, they pose a potential threat to the stability of the power distribution network. To prevent issues such as voltage fluctuations, the power grid typically sets a low limit on reverse power transmission. In situations with strong sunlight but low electric vehicle charging demand, PV power generation generates a significant amount of surplus power. If this reverse power limit is exceeded, current technologies typically use a central controller to uniformly instruct all microinverters to proportionally reduce their output power.

[0004] However, this indiscriminate global power suppression strategy has significant limitations. Summary of the Invention

[0005] The purpose of this invention is to address the aforementioned shortcomings by proposing a micro-inverter grid-connected control method and system.

[0006] The present invention adopts the following technical solution:

[0007] A micro-inverter grid-connected control method includes the following steps: S1: acquiring the output power information and geographical location information of distributed generation units, as well as the charging demand information and geographical location information of electric vehicle charging units; S2: dynamically matching distributed generation units and electric vehicle charging units based on the geographical location information of distributed generation units and electric vehicle charging units to form a local supply and demand group; S3: monitoring the power output of the total grid connection point; S4: when the power output of the total grid connection point is greater than or equal to a preset grid-connected power limit, the central controller performs multi-level differentiated power regulation, which includes: reducing the output power of distributed generation units that are not matched with any electric vehicle charging units; scheduling electric vehicles with vehicle-to-grid interaction functions to absorb the surplus power generated by distributed generation units; reducing the output power of distributed generation units that are distributed within the local supply and demand group and have surplus power, while ensuring the charging demand of electric vehicle charging units within the local supply and demand group.

[0008] Through the above scheme, this application can achieve differentiated adjustment of the output power of distributed generation units, avoid energy waste caused by indiscriminate reduction, and prioritize the charging needs of local electric vehicles. In particular, it utilizes the vehicle-to-grid interaction function to improve the overall energy efficiency of the system and user satisfaction.

[0009] Optionally, this application also proposes a micro-inverter grid-connected control method. The steps of scheduling electric vehicles with vehicle-to-grid interaction capabilities to absorb surplus power generated by distributed generation units include: identifying electric vehicles with intermittently operating high-power on-board loads; obtaining the theoretical power absorption of the electric vehicles; setting a power margin to cover the power consumption required by the intermittently operating high-power on-board loads in the startup state; calculating the corrected absorption capacity of the electric vehicles based on the theoretical power absorption and the power margin, wherein the corrected absorption capacity of the electric vehicles is the theoretical power absorption minus the power margin; and controlling the central controller to allocate surplus power to the electric vehicles according to the corrected absorption capacity of the electric vehicles, and excluding the contribution of the power margin to the local absorption capacity in the total grid-connected power balance calculation.

[0010] Optionally, this application also proposes a micro-inverter grid-connected control method. The steps for identifying electric vehicles with intermittently operating high-power on-board loads include: when the electric vehicle is connected to the electric vehicle charging unit, starting a monitoring cycle; during the monitoring cycle, controlling the electric vehicle charging unit to maintain minimum power output or zero power output; measuring the instantaneous power consumption of the electric vehicle; analyzing the instantaneous power consumption of the electric vehicle; if the instantaneous power consumption of the electric vehicle exhibits periodic power fluctuations or sudden power fluctuations, or if the instantaneous power consumption of the electric vehicle exceeds a preset on-board load power threshold under non-charging conditions, then the electric vehicle is identified as having intermittently operating high-power on-board loads.

[0011] Optionally, this application also proposes a micro-inverter grid-connected control method. The steps for reducing the output power of distributed generation units with surplus power distributed within a local supply and demand group, while ensuring the charging needs of electric vehicle charging units within the local supply and demand group, include: acquiring charging status information and charging priority information of each electric vehicle charging unit within the local supply and demand group; determining the output power reduction order of the distributed generation units based on the charging status information and charging priority information; and, according to the output power reduction order of the distributed generation units, performing output power reduction operations on the distributed generation units with surplus power within the local supply and demand group, while ensuring the charging needs of electric vehicle charging units within the local supply and demand group.

[0012] Optionally, this application also proposes a micro-inverter grid-connected control method, wherein the steps in S2 include: acquiring the geographical location information of the distributed generation unit and the electric vehicle charging unit; parsing the geographical location information of the distributed generation unit and the electric vehicle charging unit to obtain the hierarchical location information or regional location information of the distributed generation unit and the electric vehicle charging unit; determining the electrical connection path between the distributed generation unit and the electric vehicle charging unit based on the hierarchical location information or regional location information of the distributed generation unit and the electric vehicle charging unit, combined with the internal power distribution network connection relationship of the distributed generation unit and the electric vehicle charging unit; and dynamically matching the distributed generation unit and the electric vehicle charging unit to form a local supply and demand group based on the length of the electrical connection path between the distributed generation unit and the electric vehicle charging unit or the power transmission loss between the distributed generation unit and the electric vehicle charging unit, combined with the charging demand information of the electric vehicle charging unit.

[0013] Optionally, this application also proposes a micro-inverter grid-connected control method. In step S1, the step of obtaining the charging demand information of the electric vehicle charging unit includes: obtaining the real-time total power consumption data of the electric vehicle charging unit; obtaining the real-time charging power data of the electric vehicle's battery; calculating the real-time on-board load power of the electric vehicle based on the real-time total power consumption data of the electric vehicle charging unit and the real-time charging power data of the electric vehicle's battery; determining the charging priority of the electric vehicle based on the battery status information of the electric vehicle, the user's preset charging target, and the expected dwell time; calculating the actual absorbed power of the electric vehicle based on the real-time charging power data of the electric vehicle's battery and the real-time on-board load power of the electric vehicle; and using the charging priority and the actual absorbed power of the electric vehicle as the charging demand information of the electric vehicle charging unit.

[0014] Optionally, this application also proposes a micro-inverter grid-connected control method. The steps for determining the charging priority of an electric vehicle based on its battery status information, user-preset charging target, and expected dwell time include: periodically acquiring the battery status information, user-preset charging target, and expected dwell time of the electric vehicle during the charging process; and dynamically updating the charging priority of the electric vehicle based on the periodically acquired battery status information, preset charging target, and expected dwell time.

[0015] Optionally, this application also proposes a micro-inverter grid-connected control method, wherein the step of setting the power margin includes: acquiring historical monitoring data of intermittently operating high-power vehicle loads; performing statistical analysis on the historical monitoring data of intermittently operating high-power vehicle loads to determine the range of power consumption required by the intermittently operating high-power vehicle loads in the startup state; and setting the power margin according to the range of power consumption required by the intermittently operating high-power vehicle loads in the startup state, wherein the power margin is used to cover the power consumption required by the intermittently operating high-power vehicle loads in the startup state.

[0016] Optionally, this application also proposes a micro-inverter grid-connected control method. The step of setting a power margin based on the power consumption range required by the intermittently operating high-power vehicle load in the startup state includes: obtaining statistical characteristic values ​​of the power consumption range required by the intermittently operating high-power vehicle load in the startup state; and setting a power margin based on the statistical characteristic values, wherein the power margin is used to cover the power consumption required by the intermittently operating high-power vehicle load in the startup state.

[0017] Optionally, this application also proposes a micro-inverter grid-connected control system, applied to the aforementioned micro-inverter grid-connected control method. This system includes: an information acquisition module for acquiring the output power information and geographical location information of distributed generation units, as well as the charging demand information and geographical location information of electric vehicle charging units; a supply-demand matching module for dynamically matching distributed generation units and electric vehicle charging units based on their geographical location information to form a local supply-demand group; a power monitoring module for monitoring the power output of the total grid connection point; and a power adjustment module for executing multi-level differentiated power adjustment by the central controller when the power output of the total grid connection point is greater than or equal to a preset grid-connected power limit.

[0018] The above scheme provides a system for implementing the above control method, providing hardware and software support for the practical application of the method, and enabling the entire control strategy to be effectively implemented.

[0019] As can be seen from the above, this application provides a micro-inverter grid-connected control method and system. By dynamically matching supply and demand and executing multi-level differentiated power regulation, it effectively solves the problems of energy waste and user experience degradation caused by indiscriminate power reduction in the prior art. It has the advantages of effectively solving the problems of energy waste and user experience degradation caused by indiscriminate power reduction in the prior art, realizing the local efficient consumption of clean energy, and ensuring the charging needs of electric vehicles.

[0020] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0021] Figure 1 This is a flowchart of a micro inverter grid-connected control method according to the present invention;

[0022] Figure 2 This is a schematic diagram of the structure of a micro inverter grid-connected control system according to the present invention. Detailed Implementation

[0023] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0024] This embodiment provides a micro-inverter grid-connected control method and system, combined with Figure 1 and Figure 2 As shown.

[0025] refer to Figure 1 A micro-inverter grid-connected control method includes the following steps: S1: Acquiring the output power information and geographical location information of distributed generation units, as well as the charging demand information and geographical location information of electric vehicle charging units; S2: Dynamically matching distributed generation units and electric vehicle charging units based on the geographical location information of distributed generation units and electric vehicle charging units to form a local supply and demand group; S3: Monitoring the power output of the total grid connection point; S4: When the power output of the total grid connection point is greater than or equal to the preset grid connection power limit, the central controller performs multi-level differentiated power adjustment, which includes: reducing the output power of distributed generation units that are not matched with any electric vehicle charging units; scheduling electric vehicles with vehicle-to-grid interaction functions to absorb the surplus power generated by distributed generation units; reducing the output power of distributed generation units that are distributed within the local supply and demand group and have surplus power, while ensuring the charging demand of electric vehicle charging units within the local supply and demand group.

[0026] Distributed generation units refer to devices or systems capable of independently generating electricity, specifically solar panel arrays connected to micro-inverters. These can be implemented using photovoltaic panels, small wind turbines, or micro gas turbines, primarily to provide localized clean energy supply. Electric vehicle charging units refer to devices that provide power to electric vehicles, specifically smart electric vehicle charging stations. These can be implemented using AC charging stations, DC charging stations, or wireless charging stations, primarily to meet the charging needs of electric vehicles. Charging demand information refers to the current power demand status and priority of the electric vehicle charging units. This can include battery status information, user-preset charging targets, estimated dwell time, real-time total power consumption data, real-time battery charging power data, real-time vehicle load power, charging priority, and actual absorbed power, primarily to guide power allocation and ensure that high-priority charging needs are met. Geographic location information refers to the spatial location data of the distributed generation units and electric vehicle charging units. This can be implemented using GPS coordinates, indoor positioning system data, or preset area codes, primarily to achieve nearby energy supply and demand matching, forming local supply and demand groups. Local supply and demand groups refer to a collection of distributed generation units and electric vehicle charging units that are dynamically matched based on geographical location information, are physically close to each other, and have a power supply and demand relationship. Their main purpose is to promote local power consumption and reduce dependence on the external power grid. The central controller is an intelligent control device responsible for decision-making and command issuance for the entire micro-inverter grid-connected control system. It can be implemented using industrial PCs, embedded systems, or cloud computing platforms. Its main purpose is to coordinate the operation of each unit and achieve the overall power regulation target. Multi-level differentiated power regulation refers to a refined management strategy where the central controller hierarchically and classifies the output power of distributed generation units according to different supply and demand relationships and unit characteristics. Its main purpose is to maximize local energy utilization efficiency and ensure critical charging needs while meeting the grid-connected power limit. Vehicle-to-grid (V2G) interaction refers to the ability of electric vehicles to exchange energy bidirectionally with the grid. This means that electric vehicles can not only charge from the grid but also discharge to the grid or absorb surplus power from the grid. This can be achieved using technologies such as V2G, V2L, or V2H. Its main purpose is to use electric vehicle batteries as flexible energy storage resources to improve the system's ability to absorb surplus power.

[0027] This application's solution optimizes micro-inverter grid-connected control through refined data acquisition and intelligent multi-level power regulation. First, the system continuously acquires output power and geographic location information from distributed generation units, as well as charging demand and geographic location information from electric vehicle charging units. This information forms the basis for all subsequent decisions. Output power information reflects current energy supply capacity, geographic location information provides a basis for spatial proximity matching, and charging demand information reveals the actual energy needs and priorities of electric vehicles. Based on this real-time acquired geographic location information, the system dynamically matches distributed generation units and electric vehicle charging units, forming local supply and demand groups. The formation of these local supply and demand groups is a key prerequisite for achieving refined management, decomposing complex global problems into several controllable local balancing units. Simultaneously, the system continuously monitors the power output of the total grid connection point. This monitoring is essential for ensuring the safe and stable operation of the power grid. Once the power output of the total grid connection point reaches or exceeds the preset grid-connected power limit, the central controller is triggered to execute multi-level differentiated power regulation. Multi-level differentiated power regulation is the core operating mechanism of this solution. When grid-connected power exceeds limits, the central controller first reduces the output power of distributed generation units that are not matched with any electric vehicle charging units. This is because the electricity generated by these units cannot be effectively absorbed locally, directly putting pressure on the grid, and therefore they are prioritized for suppression. Subsequently, the system schedules electric vehicles with vehicle-to-grid (V2G) interaction capabilities to absorb the surplus power generated by the distributed generation units. This scheduling fully utilizes the potential of electric vehicles as mobile energy storage units, converting potentially wasted clean energy into useful energy storage and providing the grid with flexible regulation capabilities. Finally, for distributed generation units distributed within local supply and demand groups that still have surplus power, the central controller reduces their output power, but in this process, it prioritizes ensuring the charging needs of electric vehicle charging units within the local supply and demand group.

[0028] In some preferred embodiments, this application is implemented as follows: A microinverter grid-connected control system can be deployed in the parking lot of a large commercial complex. The system includes a central controller, which can be a high-performance industrial-grade server running specialized energy management software. Each parking space in the parking lot is equipped with a distributed power generation unit, specifically a photovoltaic panel array, with each array connected to a microinverter. These microinverters interact with the central controller via power line carrier communication or a wireless communication module. Simultaneously, multiple electric vehicle charging units, specifically smart charging piles, are installed in the parking lot, and these charging piles are also connected to the central controller via a communication network. During system operation, the central controller periodically obtains the current output power information and preset geographical location information (e.g., parking space number or area coordinates) from each microinverter. Simultaneously, the central controller obtains the charging demand information of the connected electric vehicles from each smart charging pile, including the current battery level, the user-set charging target, the estimated parking duration, and the charging pile's own geographical location information. The central controller utilizes acquired geographic location information to dynamically analyze the spatial proximity and potential electrical connection paths between distributed generation units and electric vehicle charging units through internal algorithms. For example, by parsing parking space numbers or area codes, it matches distributed generation units and electric vehicle charging units located in the same area or under the same power distribution circuit, forming several local supply and demand groups. The central controller continuously monitors the total power output of the entire parking lot's grid connection point to the public grid. When the power output of the total grid connection point reaches or exceeds the preset grid connection power limit, for example, when the output power exceeds 500kW, the central controller immediately initiates a multi-level differentiated power regulation process. Specifically, the central controller first identifies those distributed generation units that are not matched with any electric vehicle charging units, such as photovoltaic arrays located in the edge area of ​​the parking lot without nearby charging piles, and sends instructions to their corresponding micro-inverters to reduce their output power, for example, by 50%. Subsequently, the central controller identifies electric vehicles with vehicle-to-grid (V2G) capabilities within the parking lot. For example, it confirms their V2G capability through vehicle identification numbers or user registration information and sends instructions to their charging stations to schedule these electric vehicles to absorb excess power generated by distributed generation units. For instance, it instructs them to charge at their maximum absorbable power. Finally, for scenarios where distributed generation units within established local supply and demand groups still have excess power, the central controller, based on the charging demand information of the electric vehicle charging units within the local supply and demand group (e.g., prioritizing vehicles with low battery levels and users urgently needing to leave), reduces the output power of the distributed generation units within the local supply and demand group while ensuring that the charging needs of local electric vehicles are met.For example, if there are three vehicles in a local supply and demand group, one of which urgently needs charging while the other two are nearly fully charged, the central controller will prioritize reducing the output power of the distributed generation unit that supplies power to the vehicle that is nearly fully charged, in order to ensure the power supply for the vehicle that urgently needs charging.

[0029] This application further proposes a method for scheduling electric vehicles with vehicle-to-grid (V2G) interaction capabilities to absorb surplus power generated by distributed generation units. This method includes: identifying electric vehicles with intermittently operating high-power on-board loads; obtaining the theoretical power absorption capacity of the electric vehicles; setting a power margin to cover the power consumption required by the intermittently operating high-power on-board loads during startup; calculating the corrected absorption capacity of the electric vehicles based on their theoretical power absorption capacity and the power margin, where the corrected absorption capacity is the theoretical power absorption capacity minus the power margin; and controlling the central controller to allocate surplus power to the electric vehicles according to their corrected absorption capacity, while excluding the contribution of the power margin to local absorption capacity in the overall grid-connected power balance calculation.

[0030] Identifying electric vehicles with intermittently operating high-power on-board loads refers to determining whether an electric vehicle carries devices that periodically or suddenly consume large amounts of electrical energy during operation, such as on-board air conditioning, on-board heaters, and on-board entertainment systems. This can be achieved by obtaining internal load information through the on-board diagnostic (OBD) system interface or by analyzing historical vehicle power consumption data to identify the operating mode of such loads. The theoretical power absorption of an electric vehicle refers to the maximum charging power allowed by its battery management system (BMS) and charging interface under ideal conditions. This can be obtained from the electric vehicle's communication interface or by querying based on the electric vehicle's model and battery specifications. Power margin refers to the capacity to handle intermittently operating high-power on-board loads within the electric vehicle. The extra power reserved for the instantaneous power consumption demand that may be generated during load startup can be set by means of historical data statistical analysis, preset fixed value, or dynamic calculation according to the type of vehicle load; the corrected absorption capacity of electric vehicles refers to the actual ability of electric vehicles to absorb external surplus power after considering their own on-board load power consumption demand, which can be calculated by subtracting the preset power margin from the theoretical absorption power of electric vehicles; the total grid-connected power balance calculation refers to the calculation process used to evaluate the relationship between the total power output of the grid connection point and the preset grid-connected power upper limit in the entire micro-inverter grid-connected control system, which can be done by aggregating data such as the output power of all distributed generation units, the power consumption of local loads, and the power absorbed by electric vehicles.

[0031] Specifically, the system first identifies electric vehicles (EVs) with intermittently operating high-power on-board loads. This is because such loads generate instantaneous high-power demands upon startup, and if not considered, the allocated surplus power may be insufficient to simultaneously meet charging and load startup needs, thus affecting the normal operation of the EV. After identifying these EVs, the system obtains their theoretical power absorption, representing their maximum charging capacity under ideal conditions. Based on this, the system sets a power margin specifically to cover the power consumption required by intermittently operating high-power on-board loads during startup, effectively reserving a safety margin for these potential instantaneous high-power demands. Because of this power margin, the system can calculate the EV's corrected power absorption capacity based on its theoretical power absorption and the power margin. The corrected absorption capacity is the EV's theoretical power absorption minus the power margin, more accurately reflecting the EV's actual ability to absorb external surplus power while ensuring its own load's normal operation. Subsequently, the central controller allocates surplus power to the EV based on this corrected absorption capacity, ensuring that the allocated power meets both charging needs and the on-board load's power consumption, avoiding disruptions to the EV's normal operation due to improper power allocation. Furthermore, in the total grid-connected power balance calculation, the system excludes the contribution of power margin to local absorption capacity. This is because power margin is reserved reserve power, not the portion actually used to absorb surplus power. Including it in local absorption capacity would lead to an overestimation of local absorption capacity, thus affecting the accuracy of the total grid-connected power balance calculation and potentially causing the grid-connected power to exceed the upper limit. By excluding this margin, the accuracy of the total grid-connected power balance calculation can be ensured, thereby maximizing the utilization of local photovoltaic energy while ensuring grid stability.

[0032] In some preferred embodiments, this application is implemented as follows. When the central controller needs to schedule electric vehicles with vehicle-to-grid (V2G) interaction capabilities to absorb surplus power generated by distributed generation units, the system first identifies electric vehicles with intermittently operating high-power on-board loads. For example, the system can obtain vehicle load information through the communication protocol between the electric vehicle charging unit and the electric vehicle (such as ISO 15118), or identify the typical operating modes and startup power consumption characteristics of high-power loads such as on-board air conditioning and on-board heaters by analyzing historical charging and power consumption data. Next, the system can obtain the theoretical power absorbed by the electric vehicle. This can be obtained from the maximum charging power in real time from the electric vehicle's battery management system (BMS), or queried from a preset database based on the vehicle model and battery capacity. Then, the system can set a power margin. For example, based on the type of identified high-power on-board load, the corresponding margin value can be found in a predefined power margin table, or based on historical monitoring data, the power consumption range required by such loads in the startup state can be statistically analyzed, and the maximum value or a certain statistical percentile can be taken as the power margin. For example, if the instantaneous power consumption of the on-board air conditioning during startup may reach 3kW, then 3kW can be set as the power margin. Based on this, the system can calculate the corrected absorption capacity of the electric vehicle (EV) based on its theoretical power absorption and the set power margin. For example, if the theoretical power absorption of an EV is 10kW and the set power margin is 3kW, then the corrected absorption capacity is 10kW minus 3kW, which is 7kW. Finally, the central controller can allocate surplus power to the EV based on the calculated corrected absorption capacity. For example, the central controller will instruct the EV charging unit to keep the surplus power allocated to the EV below 7kW to ensure that even if the vehicle's air conditioning suddenly starts, the EV still has enough power to maintain charging and load operation. Simultaneously, when performing total grid-connected power balance calculations, the system explicitly excludes the contribution of this 3kW power margin to the local absorption capacity, ensuring that the calculation results reflect the actual absorption capacity and avoiding incorrect assessments of grid-connected power.

[0033] This application further proposes a method for identifying electric vehicles with intermittently operating high-power on-board loads. The method includes the following steps: when the electric vehicle is connected to an electric vehicle charging unit, a monitoring cycle is started; during the monitoring cycle, the electric vehicle charging unit is controlled to maintain minimum power output or zero power output; the instantaneous power consumption of the electric vehicle is measured; the instantaneous power consumption of the electric vehicle is analyzed; if the instantaneous power consumption of the electric vehicle exhibits periodic power fluctuations or sudden power fluctuations, or if the instantaneous power consumption of the electric vehicle exceeds a preset on-board load power threshold under non-charging conditions, then the electric vehicle is identified as having intermittently operating high-power on-board loads.

[0034] The monitoring period refers to a specific time period set to identify whether an electric vehicle has intermittently operating high-power on-board loads. Its purpose is to provide a stable time window for continuous and undisturbed observation of the electric vehicle's power consumption. Minimum power output or zero power output refers to the extremely low power provided by the electric vehicle charging unit to maintain the basic operation of the vehicle's systems when not charging, such as maintaining vehicle communication and in-vehicle entertainment systems, but insufficient for effective charging, or the electric vehicle charging unit completely stopping power supply to the electric vehicle. Its purpose is to eliminate or significantly reduce the interference of charging current on the measurement of on-board load power consumption, thereby more accurately capturing the power characteristics of the on-board load itself. Periodic power fluctuations refer to the regular rise and fall patterns of the electric vehicle's instantaneous power consumption within a certain time interval. These fluctuations are usually caused by intermittently operating high-power loads such as on-board air conditioning and heaters. These loads start and stop periodically according to internal control logic. The purpose is to identify these regular changes to determine whether the vehicle has such intermittently operating loads. Sudden power fluctuations refer to significant and unexpected sharp increases or decreases in the instantaneous power consumption of an electric vehicle within a short period of time. These fluctuations may be caused by high-power loads such as in-vehicle refrigerators or power tools that suddenly start or stop at specific moments. The purpose is to identify whether the vehicle has non-periodic but high-power-demand intermittently operating loads by capturing these sudden, large changes. The preset on-vehicle load power threshold in non-charging states refers to the upper limit of power consumed by the on-vehicle load during normal operation when the electric vehicle is not charging. This threshold is derived from statistical analysis of a large amount of typical on-vehicle load power consumption data of electric vehicles in non-charging states. Its purpose is to provide a quantitative standard; when the instantaneous power consumption of an electric vehicle exceeds this threshold, it indicates that an abnormal or high-power non-charging load is operating, thereby helping to identify intermittently operating high-power on-vehicle loads.

[0035] The proposed solution establishes a monitoring cycle upon connection of the electric vehicle (EV) to the charging unit, thus limiting the timing of the identification process and ensuring that identification only begins after the EV is connected to the charging system. During this monitoring cycle, the charging unit maintains minimum or zero power output. This is a crucial strategy designed to eliminate interference from normal charging behavior on power consumption monitoring, allowing the monitoring results to more accurately reflect the power consumption of the on-board load. Maintaining minimum power output ensures basic power supply while reducing interference with monitoring. Subsequently, the instantaneous power consumption of the EV is measured, a fundamental step in acquiring its power consumption characteristics. Real-time power data measurement provides a data foundation for subsequent analysis. Next, the instantaneous power consumption of the EV is analyzed, which is the core of the identification process. Analysis of the power data determines whether the EV has an intermittently operating high-power on-board load. Finally, if the instantaneous power consumption of the EV exhibits periodic or sudden power fluctuations, or if the instantaneous power consumption exceeds a preset on-board load power threshold under non-charging conditions, the EV is identified as having an intermittently operating high-power on-board load. Two criteria are provided here: periodic or sudden power fluctuations reflect the characteristics of intermittently operating loads, while exceeding a power threshold indicates the presence of a high-power load. These two criteria allow for a more comprehensive and accurate identification of electric vehicles with intermittently operating high-power onboard loads. This identification method provides crucial foundational information for subsequent power scheduling strategies. When scheduling electric vehicles with vehicle-to-grid (V2G) interaction capabilities to absorb excess power, it is necessary to accurately identify whether these vehicles possess intermittently operating high-power onboard loads. This ensures that a reasonable power margin can be set to cover the power consumption required by these loads during startup when calculating their corrected absorption capacity.

[0036] In some preferred embodiments, when an electric vehicle (EV) is connected to an EV charging unit, the control module of the EV charging unit can immediately initiate a preset monitoring cycle, for example, lasting five minutes. During this monitoring cycle, the EV charging unit can be controlled to maintain zero power output, i.e., not providing charging current to the EV battery, or maintain a very small power output, such as fifty watts, to ensure the normal operation of the EV's basic communication and onboard systems, while avoiding interference with power measurement. The power measurement module integrated within the EV charging unit can measure the EV's instantaneous power consumption at a high frequency, for example, ten times per second, and transmit this data to the central controller or the EV charging unit's local processing unit. This processing unit can perform real-time analysis of the received instantaneous power consumption data. Specifically, signal processing algorithms can be used to detect power fluctuation characteristics. For example, for periodic power fluctuations, Fourier transform or wavelet analysis can be applied to identify the presence of periodic signals with specific frequencies and amplitudes, such as the regular power changes that may occur when the vehicle's air conditioning compressor starts and stops. For sudden power fluctuations, the rate of change of power data can be monitored. When the instantaneous power rises or falls by more than a preset percentage threshold within a very short time, such as one second, it can be identified as a sudden fluctuation, such as the sudden start-up of a car refrigerator or power tool. Furthermore, the processing unit can compare the measured instantaneous power consumption with a preset on-board load power threshold under non-charging conditions, for example, this threshold can be set to 500 watts. If the instantaneous power consumption continues to exceed this threshold for a period of time, such as ten seconds, it can be determined that a high-power on-board load exists. When any of the above conditions are met, the electric vehicle is identified as having an intermittently operating high-power on-board load, and this identification result is sent to the central controller for appropriate processing in subsequent power scheduling.

[0037] This application further proposes a method for reducing the output power of distributed generation units with surplus power within a local supply and demand group, while ensuring the charging needs of electric vehicle charging units within the group. This method includes: obtaining charging status information and charging priority information for each electric vehicle charging unit within the local supply and demand group; determining the order of output power reduction for distributed generation units based on the charging status and priority information; and, according to the order of output power reduction, performing output power reduction operations on distributed generation units with surplus power within the local supply and demand group, while ensuring the charging needs of electric vehicle charging units within the group.

[0038] Charging status information refers to the current charging level of the electric vehicle battery, such as the battery's state of charge, remaining charging time, and current charging power. This can be obtained by directly reading from the onboard battery management system, through communication between the charging pile and the vehicle, or by user input in the charging application. Charging priority information refers to the user's demand for charging speed and completion, such as the user-set charging completion time, the user's expected charging speed, and the vehicle type. This can be obtained by manually setting it in the charging application, automatically learning from the vehicle's historical charging behavior, or automatically allocating based on vehicle type and preset rules. Power reduction order refers to the order in which different distributed generation units (DRGs) are reduced in power when it is necessary. This can be achieved by sorting the DRGs from low to high priority, from high to low based on charging status information, or by a weighted sort considering both and other factors. Output power reduction refers to the central controller sending instructions to the DRGs to reduce their current electrical output power. This can be achieved by adjusting the inverter's maximum power point tracking algorithm, directly limiting the inverter's output current or voltage, or sending power commands to the inverter. Ensuring charging demand means ensuring that high-priority or urgently needed electric vehicle charging units receive sufficient power to meet their charging targets while reducing the output power of distributed generation units. This can be achieved by reserving specific power for high-priority charging units, dynamically adjusting the reduction ratio to avoid affecting critical charging, or continuously monitoring and fine-tuning the actual power demand of charging units during the reduction process.

[0039] The entire process forms a closed-loop control: information acquisition provides a basis for decision-making, decision-making guides the reduction sequence, and reduction operations are carried out under the premise of ensuring demand, thereby achieving refined management of local surplus power.

[0040] In some preferred embodiments, this application is implemented as follows. When the central controller needs to reduce the power output of distributed generation units with surplus power within the local supply and demand group, firstly, the central controller communicates with each electric vehicle charging unit within the local supply and demand group to obtain their charging status information and charging priority information. For example, for charging status information, the current battery state of charge of each electric vehicle can be obtained, such as the electric vehicle in parking space A having a state of charge of 20% and the electric vehicle in parking space B having a state of charge of 95%. For charging priority information, the charging target set by the user through the charging application can be obtained (e.g., the user in parking space A sets to charge to 80% within 30 minutes, and the user in parking space B sets to charge to 100% within 8 hours), or the priority automatically assigned by the system based on vehicle type (e.g., ride-hailing vehicles have higher priority than private cars). Next, the central controller calculates and determines the order of power reduction for the distributed generation units based on the obtained charging status information and charging priority information. For example, the system can set a rule: prioritize reducing the power of distributed generation units supplying electric vehicle charging units with higher battery state of charge and lower charging priority. Specifically, if the electric vehicle in parking space A has a state of charge (SOC) of 20% and a high priority, while the electric vehicle in parking space B has a SOC of 95% and a low priority, the system will prioritize reducing the output power of the distributed generation unit supplying power to the electric vehicle in parking space B. This reduction order can be determined based on a weighted scoring model that comprehensively considers factors such as SOC, charging target, and expected dwell time, assigning a comprehensive priority score to each electric vehicle charging unit, and then determining the reduction order according to the scores from lowest to highest. Finally, the central controller sends a power reduction command to the corresponding distributed generation unit according to the determined reduction order, executing the operation of reducing the output power of the distributed generation unit. For example, the central controller can send a command to the distributed generation unit supplying power to the electric vehicle in parking space B, requesting its output power to be reduced by 50%. Simultaneously, the system will continuously monitor the charging power of the electric vehicle in parking space A to ensure its charging needs are met. For example, if the charging power of the electric vehicle in parking space A decreases due to the reduction, the system can dynamically adjust the reduction ratio of other distributed generation units or obtain a small amount of supplementary power from the grid to ensure that the charging target of the electric vehicle in parking space A is not affected.

[0041] This application further proposes a step-by-step approach to dynamically match distributed generation units and electric vehicle charging units to form a local supply and demand group. This includes: obtaining the geographical location information of the distributed generation units and the electric vehicle charging units; parsing the geographical location information of the distributed generation units and the electric vehicle charging units to obtain their hierarchical or regional location information; determining the electrical connection path between the distributed generation units and the electric vehicle charging units based on their hierarchical or regional location information and the internal power distribution network connection relationship; and dynamically matching the distributed generation units and the electric vehicle charging units to form a local supply and demand group based on the length of the electrical connection path or the power transmission loss between them, combined with the charging demand information of the electric vehicle charging units.

[0042] Hierarchical location information or regional location information refers to further refining and classifying geographic location information. For example, a large parking lot can be divided into different areas, or a hierarchical structure can be formed based on floors, parking space numbers, etc. Specifically, this can be achieved through spatial analysis and aggregation of raw latitude and longitude data using a Geographic Information System (GIS), or through encoding using preset regional division rules. The purpose is to provide a structured spatial reference for subsequent analysis of power distribution network connection relationships. Internal power distribution network connection relationships refer to the physical connection topology of distributed generation units and electric vehicle charging units in the actual power system, including cable routes, transformers, switching equipment, and bus connections. Specifically, this can be obtained by reading the topology map of the power distribution network, equipment list, or real-time connection status data from the power SCADA system. The purpose is to identify the actual path of power transmission and potential transmission bottlenecks. Electrical connection paths refer to the actual physical lines through which power travels from distributed generation units to electric vehicle charging units. Specifically, this can be calculated using graph theory algorithms, such as the shortest path algorithm or minimum spanning tree algorithm, combined with internal power distribution network connection relationship data. The purpose is to provide a basis for evaluating power transmission efficiency and losses. Power transmission loss refers to the energy loss caused by factors such as resistance and inductive reactance during the transmission of electrical energy along an electrical connection path. Specifically, it can be calculated using basic principles of power systems such as Ohm's law and Kirchhoff's law, combined with parameters such as the length of the electrical connection path, the cross-sectional area of ​​the conductor, the resistivity of the material, and the transmission current. Its purpose is to quantify the efficiency of power transmission and serve as a basis for optimal matching.

[0043] This application's solution addresses the limitations of relying solely on geographic location information by refining the formation process of local supply and demand groups. First, the system acquires the geographic location information of distributed generation units and electric vehicle charging units, forming the basis for subsequent analysis. Based on this, the system further analyzes this geographic location information, converting it into more structured hierarchical or regional location information. This conversion allows the system to grasp the relative spatial relationships between units at a macro level, providing preliminary spatial constraints for subsequent distribution network analysis. Furthermore, based on this hierarchical or regional location information, and combined with the internal distribution network connections of the distributed generation units and electric vehicle charging units, the system determines the actual electrical connection paths between them. This step is the core of the solution, transcending simple spatial distance concepts and directly focusing on the physical feasibility of power transmission. By identifying the actual distribution network topology, the system can eliminate units that are geographically close but lack effective electrical connections, or identify potential connections with complex paths and high losses. Based on this, the system dynamically matches distributed generation units and electric vehicle charging units to form local supply and demand groups based on the determined electrical connection path length or the calculated power transmission losses, combined with the charging demand information of the electric vehicle charging units. Choosing shorter electrical connection paths or lower-loss power transmission means higher power transmission efficiency and reduces energy waste during transmission. Simultaneously, incorporating the charging needs of electric vehicle charging units into the matching process allows the system to prioritize charging for vehicles with urgent needs, thereby improving user experience while ensuring energy efficiency.

[0044] In some preferred embodiments, this application is implemented as follows: Assume that multiple distributed power generation units (e.g., rooftop photovoltaic arrays) and multiple electric vehicle charging units are deployed in the parking lot of a large commercial complex. First, the system obtains the precise geographical location information, such as latitude and longitude coordinates, of each distributed power generation unit and electric vehicle charging unit through a GPS module or a preset coordinate system. Subsequently, a geographic information processing module can parse this geographical location information. For example, the entire parking lot can be divided into several predefined areas, such as "East Zone," "West Zone," "South Zone," and "North Zone," or each unit can be classified into specific hierarchical or regional location information based on floor and parking area numbers. For example, a distributed power generation unit may be identified as located on "East Zone, 3rd Floor," while an electric vehicle charging unit may be located on "East Zone, 2nd Floor." Next, a network topology analysis module can determine the electrical connection path between the distributed power generation unit and the electric vehicle charging unit based on these hierarchical or regional location information and in conjunction with pre-stored internal power distribution network connection relationship data of the parking lot. This connection relationship data may include connection information of various levels of distribution cabinets, transformers, cable laying paths, and switching equipment. For example, analysis can determine that a photovoltaic unit on the third floor of the East Zone and a charging pile on the second floor of the East Zone require specific distribution cabinets and cables for electrical connection. This module can use graph theory algorithms, such as Dijkstra's algorithm, to calculate the shortest electrical connection path from each distributed generation unit to each electric vehicle charging unit. Finally, a dynamic matching module dynamically matches distributed generation units and electric vehicle charging units to form local supply-demand groups based on the length of these electrical connection paths or the power transmission loss calculated from the path parameters, combined with the real-time charging demand information of the electric vehicle charging units. For example, if the electrical connection path between distributed generation unit A and electric vehicle charging unit B is short and has low loss, while the charging demand of electric vehicle charging unit B is high, the system can prioritize matching them into a local supply-demand group.

[0045] This application further proposes that the step of obtaining the charging demand information of the electric vehicle charging unit in step S1 includes: obtaining the real-time total power consumption data of the electric vehicle charging unit; obtaining the real-time charging power data of the electric vehicle's battery; calculating the real-time on-board load power of the electric vehicle based on the real-time total power consumption data of the electric vehicle charging unit and the real-time charging power data of the electric vehicle's battery; determining the charging priority of the electric vehicle based on the battery status information of the electric vehicle, the user's preset charging target, and the expected dwell time; calculating the actual absorbed power of the electric vehicle based on the real-time charging power data of the electric vehicle's battery and the real-time on-board load power of the electric vehicle; and using the charging priority and the actual absorbed power of the electric vehicle as the charging demand information of the electric vehicle charging unit.

[0046] The real-time total power consumption data of the electric vehicle charging unit refers to the rate at which the charging unit absorbs the total electrical energy from the power grid or power supply system at a certain moment. This data can be measured in real-time using a power metering module or smart meter installed inside the charging unit, with the aim of obtaining the overall power consumption of the electric vehicle. The real-time charging power data of the electric vehicle's battery refers to the actual rate at which the battery pack receives charging energy at a certain moment. This data can be obtained using the battery management system (BMS) inside the electric vehicle or the communication interface between the charging pile and the vehicle, with the aim of distinguishing between battery charging and on-board load power consumption. The real-time on-board load power of the electric vehicle refers to the real-time power consumption of its internal electrical equipment (such as air conditioning, entertainment systems, lighting, etc.) during the charging process, excluding battery charging. This data can be calculated by subtracting the real-time battery charging power data from the real-time total power consumption data of the charging unit, with the aim of accurately identifying the power demand of non-charging components. The battery status information of the electric vehicle refers to the current health status, charge level, temperature, and other key parameters of the electric vehicle battery. This information can be obtained using data provided by the on-board battery management system (BMS), with the aim of evaluating the battery's charging efficiency. Power and safety; User-preset charging target refers to the expected charging completion rate or charging time set by the electric vehicle user through the in-vehicle system or mobile application. It can be manually entered by the user before charging begins or automatically obtained through the intelligent reservation system. Its purpose is to reflect the user's personalized charging needs; Estimated dwell time refers to the length of time the electric vehicle is expected to stay at the charging station. It can be manually entered by the user, predicted based on historical parking data, or obtained through the vehicle navigation system. Its purpose is to provide a time-dimensional reference for charging strategies; Electric vehicle charging priority refers to the level of urgency or importance of charging based on factors such as the electric vehicle's battery status information, user-preset charging target, and estimated dwell time. It can be dynamically calculated and adjusted using preset priority algorithms or rules. Its purpose is to guide power allocation to prioritize vehicles with high demand; Actual absorbed power of the electric vehicle refers to the maximum rate at which the electric vehicle's battery and on-board load can safely and effectively absorb electrical energy under the current state. It can be calculated by combining the electric vehicle's real-time battery charging power data and the electric vehicle's real-time on-board load power. Its purpose is to avoid over-allocation of power, which could lead to waste or damage to the vehicle.

[0047] This application's solution overcomes the shortcomings of traditional methods, which rely on coarse and static charging demand information, by acquiring charging demand information from electric vehicle charging units in a refined manner. For example, a vehicle with a low battery and an emergency charging target set by the user will have its charging priority set high, while a vehicle with a nearly fully charged battery and an expected extended stay will have its priority set low. This prioritization mechanism allows the system to distinguish the charging urgency of different vehicles, providing a basis for subsequent differentiated power allocation. Simultaneously, the solution calculates the actual power absorbed by the electric vehicle based on its real-time battery charging power data and the calculated real-time on-board load power. The actual power absorbed reflects the maximum power that the electric vehicle can safely and effectively absorb under current operating conditions, preventing energy waste and protecting vehicle equipment. Finally, the charging priority and actual power absorbed by the electric vehicle are used as the charging demand information for the electric vehicle charging unit and provided to the central controller. When the central controller performs multi-level differentiated power adjustment, such as when the power output at the total grid connection point is greater than or equal to the preset grid connection power limit, it no longer relies solely on a simple charging demand value, but can perform more intelligent and precise power allocation based on the charging priority and actual power absorbed by each electric vehicle.

[0048] In some preferred embodiments, obtaining the charging demand information of the electric vehicle charging unit can be implemented as follows: When an electric vehicle connects to the charging unit and begins charging, the charging unit can continuously monitor and report its real-time total power consumption data absorbed from the grid. Simultaneously, the battery management system (BMS) inside the electric vehicle can send the real-time battery charging power data of the electric vehicle to the charging unit via a communication protocol between the vehicle and the charging pile (e.g., CAN bus or PLC communication). The charging unit or a local controller connected to the charging unit can receive this data. After receiving the data, the local controller can perform calculations. For example, if the real-time total power consumption data is 10kW and the real-time battery charging power data is 8kW, then the real-time on-board load power of the electric vehicle can be calculated as 2kW (10kW - 8kW). Simultaneously, the local controller can obtain the battery status information of the electric vehicle from the BMS, such as the current battery level being 30% and the battery temperature being 25 degrees Celsius. The user can input a preset charging target (e.g., target battery level of 80%) and expected dwell time (e.g., 2 hours) through the charging pile's touchscreen interface or a companion mobile application. Based on this information, the local controller can determine the charging priority of the electric vehicle according to a preset priority algorithm. For example, if the current battery level is below 50% and the expected stay time is less than 3 hours, it can be set to high priority; if the current battery level is above 80% and the expected stay time is greater than 5 hours, it can be set to low priority. Next, the local controller can calculate the actual power absorbed by the electric vehicle based on the real-time charging power data of the electric vehicle's battery (e.g., 8kW) and the calculated real-time on-board load power (e.g., 2kW). This calculation can take into account the battery's charging curve and the instantaneous demand of the on-board load. For example, if the battery's maximum acceptable charging power at the current battery level is 15kW and the on-board load is 2kW, then the actual power absorbed can be set to 17kW (15kW + 2kW), or a more complex algorithm can be used to consider the battery's actual charging efficiency and the dynamic changes in the on-board load. Finally, the local controller packages the calculated charging priority (e.g., "high") and the actual power absorbed by the electric vehicle (e.g., 17kW) as the charging demand information for that electric vehicle charging unit and sends it to the central controller via the communication network. When making power allocation decisions, the central controller can utilize this refined information, such as prioritizing power allocation to high-priority vehicles and ensuring that the allocated power does not exceed their actual power absorption, thereby achieving smarter and more efficient energy management.

[0049] This application further proposes steps for determining the charging priority of electric vehicles, including: periodically acquiring battery status information, user-preset charging targets, and expected dwell time during the charging process of the electric vehicle; and dynamically updating the charging priority of the electric vehicle based on the periodically acquired battery status information, preset charging targets, and expected dwell time.

[0050] Periodic acquisition refers to the system automatically collecting relevant data at preset time intervals or when triggered by specific events. This can be achieved through timer triggering, event-driven methods, or data stream monitoring, aiming to ensure the system can continuously monitor real-time changes during the electric vehicle charging process. Battery status information refers to the current operating parameters of the electric vehicle battery, such as remaining battery capacity, charging current, charging voltage, battery temperature, and battery health status. This can be reported in real-time by the onboard battery management system via a communication interface, or obtained by the charging pile through a communication protocol with the vehicle. Its purpose is to reflect the battery's actual charging needs and acceptable charging conditions. User-preset charging goals refer to the user's expectations for the current charging process, such as the desired battery percentage, expected charging completion time, or desired charging amount. This can be input by the user through the charging pile interface, mobile application, or vehicle central control system, aiming to reflect the user's personalized charging needs. Estimated dwell time refers to the expected length of time the electric vehicle will be parked at the charging pile. This can be manually input by the user, predicted based on historical parking data, or obtained through the vehicle navigation system, aiming to provide a time window constraint for the charging strategy. Dynamic updates refer to the process where charging priorities are not determined all at the start of charging, but rather adjusted and corrected based on real-time data changes during the charging process. This can be achieved using rule-based logical judgments, fuzzy control algorithms, or machine learning models. The goal is to ensure that charging priorities adapt to changes in the actual charging needs of electric vehicles in real time. Charging priority refers to the importance or priority of an electric vehicle in the allocation of charging resources. It can be represented by numerical scores, grade classifications, or weighting coefficients, and its purpose is to guide the charging system in making reasonable power allocation and scheduling.

[0051] This application's solution continuously monitors and periodically acquires the electric vehicle's battery status information, user-preset charging targets, and expected dwell time during the charging process, thereby enabling real-time monitoring of changes in the electric vehicle's charging demand. Given that this information is dynamically changing, the system dynamically adjusts the charging priority of electric vehicles based on the latest periodically acquired data. For example, as battery capacity increases or the user's expected dwell time decreases, the charging priority can be raised or lowered accordingly. This dynamic adjustment mechanism ensures that the charging priority accurately reflects the actual needs of electric vehicles at different charging stages, avoiding the resource allocation problems caused by fixed priorities. Based on this, and combined with the method for acquiring charging demand information of electric vehicle charging units in this application, this dynamically updated charging priority, as part of the charging demand information, is incorporated into the entire micro-inverter grid-connected control method. When the total grid-connected power output reaches the preset upper limit, the central controller, when performing multi-level differentiated power regulation, can adjust the order of output power reduction of distributed generation units within the local supply and demand group based on this real-time updated charging priority information, ensuring the charging needs of high-priority electric vehicle charging units.

[0052] In some embodiments, this application is implemented as follows: After the electric vehicle connects to the charging pile and begins charging, the central controller or the control unit inside the charging pile can initiate a monitoring program. This program can be set to periodically send a query command to the electric vehicle's battery management system every five minutes to obtain battery status information such as the current remaining battery power, charging current, and battery temperature. Simultaneously, the system continuously monitors the charging target set by the user through the charging pile interface or mobile application, such as a target charging amount of 80% or a target charging completion time of 5 PM, as well as the user-inputted expected dwell time. Based on this periodically acquired real-time data, the system uses a preset priority evaluation model to dynamically update the electric vehicle's charging priority. For example, this model can comprehensively consider the following factors: the lower the remaining battery power, the higher the priority; the farther away from the user-set charging target, the higher the priority; the shorter the expected dwell time, the higher the priority. These factors can be assigned different weights, and a comprehensive priority score is calculated through a weighted summation. For example, if an electric vehicle's battery has less than 30% charge and the user expects to leave within the next hour, while their charging goal is to reach 90% charge, the system will immediately prioritize that vehicle to the highest level. Conversely, if another electric vehicle's battery has reached 85% charge and the user expects to remain there for several hours, its charging priority will be adjusted to a lower level. This dynamic adjustment ensures that charging resources are flexibly allocated according to the actual needs of electric vehicles and user expectations, thereby optimizing the overall charging strategy.

[0053] This application further proposes a step for setting a power margin, including: acquiring historical monitoring data of intermittently operating high-power vehicle-mounted loads; performing statistical analysis on the historical monitoring data of intermittently operating high-power vehicle-mounted loads to determine the range of power consumption required by the intermittently operating high-power vehicle-mounted loads in the startup state; and setting a power margin based on the range of power consumption required by the intermittently operating high-power vehicle-mounted loads in the startup state, wherein the power margin is used to cover the power consumption required by the intermittently operating high-power vehicle-mounted loads in the startup state.

[0054] Historical monitoring data for intermittently operating high-power on-board loads refers to non-charging-related devices inside electric vehicles with large and discontinuous power consumption fluctuations, such as air conditioning systems, heaters, in-vehicle entertainment systems, and electric seat adjustments. These devices may start or stop at any time during vehicle charging, generating high instantaneous power consumption at startup. Historical monitoring data refers to the power consumption records of these loads over a period of time under different operating conditions, including peak power at startup, duration, and startup frequency. Its purpose is to provide an objective and quantitative data basis for determining the subsequent power consumption range. Statistical analysis refers to the process of mathematically and statistically processing a large amount of historical monitoring data. Methods such as mean, variance, maximum, minimum, percentiles, and probability distribution can be used to reveal the inherent patterns and characteristics of the data. Its purpose is to extract representative power consumption characteristics from discrete historical data, providing a scientific basis for setting power margins. The power consumption range refers to the upper and lower limits of the instantaneous power consumption fluctuation of a high-power on-board load operating intermittently during startup. This range can be a fixed interval or a probability distribution interval, such as a 95% confidence interval. Its purpose is to quantify the uncertain power consumption demand during on-board load startup and ensure that the power margin can effectively cover the vast majority of startup scenarios. Power margin refers to the additional power reserve reserved based on the theoretical power absorption of an electric vehicle. It is specifically used to compensate for the additional power consumption demand that a high-power on-board load operating intermittently may generate during startup. Its purpose is to ensure that the electric vehicle can still stably absorb excess power from the grid when the on-board load starts up.

[0055] This solution lays the data foundation for setting the power margin by acquiring historical monitoring data of intermittently operating high-power vehicle-mounted loads. This historical data comprehensively records the power consumption characteristics of the vehicle-mounted load under different operating conditions, including key information such as start-up frequency, duration, and peak power. Based on this, in-depth statistical analysis of this historical monitoring data reveals the inherent distribution law of the vehicle-mounted load's power consumption, such as its maximum, minimum, average, and variance, thereby accurately determining the range of power consumption required by the intermittently operating high-power vehicle-mounted load during startup. Furthermore, based on the determined power consumption range, the power margin is dynamically and reasonably set. This power margin is precisely used to cover the power consumption required by the intermittently operating high-power vehicle-mounted load during startup. Specifically, when calculating the corrected absorption capacity of the electric vehicle, i.e., the theoretical absorption power of the electric vehicle minus the power margin, the subtracted power margin is no longer a rough estimate, but a value derived from actual historical data analysis that accurately covers the power consumption requirements of the vehicle-mounted load during startup.

[0056] In some preferred embodiments, the process of setting the power margin can be implemented as follows: First, the instantaneous power consumption data of high-power on-board loads such as the air conditioner, heater, and infotainment system operating intermittently under different operating conditions is continuously collected through the on-board diagnostic system (OBD) interface or vehicle communication bus (such as CAN bus). This data can include the current, voltage, and duration at load startup, and is stored in the vehicle's local storage unit or uploaded to a cloud server as historical monitoring data. For example, a monitoring cycle can be set, such as collecting data once per second, and recording the peak power and duration of each on-board load startup. After accumulating enough historical monitoring data, such as data from the past month or one thousand startup events, statistical analysis can be performed on this data. Specifically, data analysis software or embedded algorithms can be used to calculate the average, maximum, and minimum peak power consumption of all startup events, and further calculate their standard deviation or perform probability density distribution analysis. For example, it can be determined that 99% of the startup power consumption falls within a certain specific range. Finally, the power margin is set based on the power consumption range determined by the statistical analysis results. For example, if statistical analysis shows that 99% of intermittently operating high-power vehicle loads require power consumption between 500W and 1500W during startup, then the power margin can be set to the upper limit of this range (e.g., 1500W), or to a certain percentile of this range (e.g., the 95th percentile), to ensure that the startup power consumption of the vehicle load is covered in the vast majority of cases. This power margin will be used to correct for the electric vehicle's absorption capacity, ensuring that even if the vehicle load suddenly starts during charging, it will not cause drastic fluctuations or interruptions in charging power, thus guaranteeing the stability and efficiency of the charging process.

[0057] This application further proposes a step of setting a power margin based on the range of power consumption required by an intermittently operating high-power vehicle load in the startup state, including: obtaining statistical characteristic values ​​of the range of power consumption required by an intermittently operating high-power vehicle load in the startup state; and setting a power margin based on the statistical characteristic values, wherein the power margin is used to cover the power consumption required by the intermittently operating high-power vehicle load in the startup state.

[0058] Among them, the statistical characteristic value of the power consumption range required by intermittently operating high-power vehicle loads in the startup state refers to a quantitative indicator reflecting the overall level and fluctuation of the power consumption range. It can be obtained by mathematical statistical analysis of historical monitoring data of intermittently operating high-power vehicle loads. Its purpose is to provide a data basis for the dynamic and accurate setting of power margin. The statistical characteristic value refers to the value obtained after performing descriptive statistical analysis on a set of data. It is used to summarize the distribution, central tendency, dispersion, etc. of the data. It can include the average, maximum, minimum, variance, standard deviation, median or percentile, etc. Its purpose is to quantify the characteristics of the power consumption range so as to make scientific power margin settings. The power margin refers to the extra power reserved on the basis of the theoretical power absorption of electric vehicles. It is specifically used to cope with the extra power consumption demand that intermittently operating high-power vehicle loads may generate at the moment of startup. Its purpose is to ensure that while the electric vehicle absorbs excess power, its internal high-power load will not affect normal function or user experience due to insufficient power when starting.

[0059] Because the power consumption required by intermittently operating high-power vehicle-mounted loads during startup is not a fixed value but fluctuates, it is insufficient to determine a power consumption range solely based on historical monitoring data. Further extraction of statistical characteristic values ​​for this range is necessary. These statistical characteristic values, such as average, maximum, and variance, comprehensively reflect the central tendency and dispersion of the power consumption range. It is precisely because these statistical characteristic values ​​are obtained that the setting of the power margin is no longer a simple fixed value or a single maximum value, but can be flexibly adjusted according to the actual statistical distribution characteristics. For example, the power margin can be set based on the maximum value of the power consumption range to ensure complete coverage of startup power consumption under any circumstances, avoiding insufficient power; alternatively, it can be set based on the average value plus a safety factor calculated based on variance or standard deviation, thereby ensuring coverage while avoiding excessive power reservation and reducing waste. This dynamic and refined setting method based on statistical characteristic values ​​allows the power margin to more accurately match actual needs. When dispatching electric vehicles with vehicle-to-grid interaction capabilities to absorb surplus power generated by distributed generation units, a more accurate corrected absorption capacity of the electric vehicle can be calculated by subtracting this precisely set power margin from the theoretical absorption power of the electric vehicle.

[0060] In some preferred embodiments, this application is implemented as follows: Assume that the power consumption required by a certain model of electric vehicle's intermittently operating high-power on-board load (e.g., an air conditioning compressor) during startup, analyzed through long-term historical monitoring data, may fluctuate between 5kW and 8kW. To set a reasonable power margin, firstly, statistical characteristic values ​​of this power consumption range can be obtained. For example, instantaneous power consumption data of the air conditioning compressor during startup of this model of electric vehicle under different ambient temperatures, battery states, and usage frequencies can be collected to form a dataset. Statistical analysis of this dataset shows an average value of 6.5kW, a maximum value of 8kW, and a standard deviation of 0.8kW. Based on these statistical characteristic values, various strategies can be adopted to set the power margin. As a specific implementation method, to ensure that startup power consumption is covered in most cases, the power margin can be set to the maximum value of the power consumption range, i.e., 8kW. In this way, regardless of the power consumption level of the air conditioning compressor during startup, sufficient protection can be provided. As another specific implementation method, to ensure both reliability and energy efficiency, the power margin can be set as the average value plus a safety margin based on the standard deviation. For example, it can be set as the average value plus twice the standard deviation (6.5kW + 2 * 0.8kW = 8.1kW). This setting method can cover approximately 95% of power consumption fluctuations while avoiding excessive power reserve. In this way, the system can flexibly select the most suitable power margin setting strategy based on the actual statistical distribution characteristics, thereby better balancing the operational assurance of the electric vehicle's internal load and the absorption efficiency of external surplus power.

[0061] refer to Figure 2 This application further proposes a micro-inverter grid-connected control system, applied to a micro-inverter grid-connected control method. The system includes: an information acquisition module for acquiring the output power information and geographical location information of distributed generation units, as well as the charging demand information and geographical location information of electric vehicle charging units; a supply and demand matching module for dynamically matching distributed generation units and electric vehicle charging units according to the geographical location information of distributed generation units and electric vehicle charging units to form a local supply and demand group; a power monitoring module for monitoring the power output of the total grid connection point; and a power adjustment module for executing multi-level differentiated power adjustment by the central controller when the power output of the total grid connection point is greater than or equal to a preset grid-connected power limit.

[0062] The information acquisition module is responsible for collecting various real-time data and static configuration information required for system operation. It can be implemented using a combination of one or more sensors, data acquisition units, communication interfaces, and data preprocessing units, aiming to provide a data foundation for subsequent supply-demand matching and power regulation. The supply-demand matching module logically groups and associates distributed generation units and electric vehicle charging units according to specific conditions. It can be implemented using an algorithm processor based on a geographic information system or internal network topology analysis, combined with a database for dynamic matching, aiming to optimize local energy consumption and reduce transmission losses. The power monitoring module is a unit used to measure and report the power output at the grid connection point in real time. It can be implemented by combining one or more smart meters, current transformers, voltage transformers, and data transmission units. Its purpose is to provide real-time basis for determining whether power regulation is needed. The power regulation module is a unit that adjusts the output power of distributed generation units according to the monitored power status and preset rules. It can be implemented by a central controller or distributed controller, which sends control commands to micro-inverters and electric vehicle charging units through a communication network. Its purpose is to ensure that the grid-connected power does not exceed the upper limit and to achieve differentiated management.

[0063] In some preferred embodiments, this application is implemented as follows: The information acquisition module can consist of smart meters and GPS positioning modules deployed on each distributed generation unit and electric vehicle charging unit. These devices upload real-time output power information, charging demand information, and precise geographic location coordinates to a central data server via wireless communication networks, such as Wi-Fi or cellular networks. The supply and demand matching module can be a software service running on the central data server. This service uses geographic information system data and preset matching algorithms to dynamically identify and group distributed generation units and electric vehicle charging units that are close to each other or located in the same distribution area, thereby forming local supply and demand groups based on the received geographic location information. The power monitoring module can be a smart power quality monitor installed at the main grid connection point. This monitor can measure the total power output of the grid connection point in real time and transmit the data to the central controller via an Ethernet interface. The power regulation module can be implemented by the central controller itself, which is a high-performance industrial-grade computer running complex control logic and algorithms. When the total grid-connected power reported by the power monitoring module exceeds the preset upper limit, the power regulation module will send instructions to each micro-inverter and smart charging pile through the communication network according to the preset multi-level differentiated power regulation strategy. For example, it can control the micro-inverter to reduce the output power through the Modbus protocol, or interact with electric vehicle charging piles with vehicle-to-grid interaction function through the OCPP protocol to guide electric vehicles to absorb excess power, thereby realizing refined management of the entire grid-connected system.

[0064] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A grid-connected control method for a micro inverter, characterized in that, The method includes the following steps: S1: Obtain the output power information and geographical location information of the distributed generation unit, as well as the charging demand information and geographical location information of the electric vehicle charging unit; S2: Based on the geographical location information of distributed generation units and electric vehicle charging units, dynamically match distributed generation units and electric vehicle charging units to form local supply and demand groups; S3: Monitor the power output of the main grid connection point; S4: When the power output of the main grid connection point is greater than or equal to the preset grid connection power limit, the central controller performs multi-level differentiated power regulation, which includes: For distributed generation units that are not matched with any electric vehicle charging units, reduce the output power of the distributed generation units; Dispatch electric vehicles with vehicle-to-grid interaction capabilities to absorb the surplus power generated by distributed generation units; For distributed generation units with surplus power located within local supply and demand groups, reduce the output power of the distributed generation units and ensure the charging needs of electric vehicle charging units within the local supply and demand groups. The steps in S2 include: Obtain the geographical location information of the distributed power generation unit and the electric vehicle charging unit; The geographical location information of distributed generation units and electric vehicle charging units is analyzed to obtain hierarchical or regional location information of distributed generation units and electric vehicle charging units. Based on the hierarchical or regional location information of the distributed generation unit and the electric vehicle charging unit, and combined with the internal power distribution network connection relationship of the distributed generation unit and the electric vehicle charging unit, the electrical connection path between the distributed generation unit and the electric vehicle charging unit is determined. Based on the length of the electrical connection path between distributed generation units and electric vehicle charging units, or the power transmission loss between them, and combined with the charging demand information of electric vehicle charging units, distributed generation units and electric vehicle charging units are dynamically matched to form local supply and demand groups.

2. The micro-inverter grid-connected control method as described in claim 1, characterized in that, The steps for dispatching electric vehicles with vehicle-to-grid (V2G) interaction capabilities to absorb surplus power generated by distributed generation units include: Identify electric vehicles with high-power onboard loads that operate intermittently; Obtain the theoretical power absorbed by the electric vehicle; Set a power margin, which is used to cover the power consumption required by high-power on-board loads that operate intermittently during startup; Based on the theoretical absorbed power and power margin of electric vehicles, the corrected absorption capacity of electric vehicles is calculated. The corrected absorption capacity of electric vehicles is the theoretical absorbed power minus the power margin. Based on the corrective absorption capacity of electric vehicles, the central controller allocates surplus power to electric vehicles, and in the total grid-connected power balance calculation, the contribution of the power margin to the local absorption capacity is excluded.

3. The micro-inverter grid-connected control method as described in claim 2, characterized in that, The steps for identifying electric vehicles with high-power onboard loads that operate intermittently include: When the electric vehicle is connected to the electric vehicle charging unit, the monitoring cycle is initiated. During the monitoring period, the electric vehicle charging unit is controlled to maintain minimum power output or zero power output. Measure the instantaneous power consumption of an electric vehicle; Analyze the instantaneous power consumption of electric vehicles; If the instantaneous power consumption of an electric vehicle exhibits periodic power fluctuations or sudden power fluctuations, or if the instantaneous power consumption of an electric vehicle exceeds the preset on-board load power threshold under non-charging conditions, then the electric vehicle is identified as having an intermittently operating high-power on-board load.

4. The micro-inverter grid-connected control method as described in claim 1, characterized in that, For distributed generation units with surplus power located within local supply and demand groups, the steps to reduce the output power of these units and ensure the charging needs of electric vehicle charging units within the local supply and demand groups include: Obtain the charging status information and charging priority information of each electric vehicle charging unit within the local supply and demand group; Based on the charging status information and charging priority information of the electric vehicle charging unit, the order of reducing the output power of the distributed generation unit is determined. According to the order of output power reduction of distributed generation units, the output power reduction operation is carried out for distributed generation units with surplus power within the local supply and demand group, and the charging needs of electric vehicle charging units within the local supply and demand group are guaranteed.

5. The micro-inverter grid-connected control method as described in claim 1, characterized in that, In step S1, the step of obtaining the charging demand information of the electric vehicle charging unit includes: Obtain the real-time total power consumption data of the electric vehicle charging unit; Obtain real-time charging power data of electric vehicle batteries; The real-time on-board load power of the electric vehicle is calculated based on the real-time total power consumption data of the electric vehicle charging unit and the real-time charging power data of the electric vehicle battery. The charging priority of electric vehicles is determined based on the battery status information of the electric vehicle, the user's preset charging goal, and the expected dwell time. The actual power absorbed by the electric vehicle is calculated based on the real-time charging power data of the electric vehicle's battery and the real-time on-board load power of the electric vehicle. The charging priority of electric vehicles and the actual power absorbed by electric vehicles are used as the charging demand information of electric vehicle charging units.

6. The micro-inverter grid-connected control method as described in claim 5, characterized in that, The steps for determining the charging priority of an electric vehicle based on its battery status information, the user's preset charging goal, and the expected dwell time include: During the charging process of the electric vehicle, the battery status information, the user's preset charging target, and the expected dwell time of the electric vehicle are periodically acquired. The charging priority of electric vehicles is dynamically updated based on periodically acquired battery status information, preset charging targets, and expected dwell time.

7. The micro-inverter grid-connected control method as described in claim 2, characterized in that, The steps for setting the power margin include: Acquire historical monitoring data of intermittently operating high-power vehicle-mounted loads; Statistical analysis was performed on historical monitoring data of intermittently operating high-power vehicle-mounted loads to determine the power consumption range required by intermittently operating high-power vehicle-mounted loads in the startup state. Based on the power consumption range required by the intermittently operating high-power vehicle load during startup, a power margin is set. The power margin is used to cover the power consumption required by the intermittently operating high-power vehicle load during startup.

8. The micro-inverter grid-connected control method as described in claim 7, characterized in that, The steps for setting the power margin based on the power consumption range required by intermittently operating high-power on-board loads during startup include: Obtain statistical characteristic values ​​of the range of power consumption required by intermittently operating high-power vehicle-mounted loads in the startup state; Based on statistical characteristic values, a power margin is set to cover the power consumption required by high-power on-board loads during startup.

9. A micro-inverter grid-connected control system, applied to the micro-inverter grid-connected control method according to claim 1, characterized in that, The system includes: The information acquisition module is used to acquire the output power information and geographical location information of the distributed generation unit, as well as the charging demand information and geographical location information of the electric vehicle charging unit; The supply and demand matching module is used to dynamically match distributed generation units and electric vehicle charging units based on the geographical location information of distributed generation units and electric vehicle charging units, forming local supply and demand groups. The power monitoring module is used to monitor the power output at the main grid connection point; The power regulation module is used to enable the central controller to perform multi-level differentiated power regulation when the power output of the total grid connection point is greater than or equal to the preset grid connection power limit.

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