Miniature inverter grid-connected control method and system

Abstract

The invention relates to the technical field of micro inverter grid-connected control, in particular to a micro inverter grid-connected control method and system, and the method comprises the following steps: obtaining the output power information and geographic position information of a distributed power generation unit, and the charging demand information and geographic 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 and demand group; monitoring the power output of the total grid-connected point; when the power output of the total grid-connected point is greater than or equal to a preset grid-connected power upper limit, the central controller executes multi-stage differential power regulation, including: for distributed power generation units which are not matched with any electric vehicle charging unit, reducing the output power of the distributed power generation units; scheduling an electric vehicle with a vehicle network interaction function, and absorbing surplus power generated by the distributed power generation unit; and the output power of the distributed power generation unit is reduced. And the overall energy utilization efficiency and the user satisfaction degree of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of micro-inverter grid-connected control, and in particular to a micro-inverter grid-connected control method and system. Background Art

[0002] In the modern energy system, distributed photovoltaic power generation systems, particularly those combined with electric vehicle charging infrastructure, are becoming increasingly popular in locations such as commercial complex parking lots. These systems typically include independent microinverters that convert the DC power generated by photovoltaics into AC power for grid integration, while smart electric vehicle charging stations provide charging services.

[0003] As distributed photovoltaic systems expand in size, they pose a potential threat to the stability of distribution networks. To prevent voltage fluctuations and other issues, power grids typically set a low upper limit on reverse power transmission. During periods of high sunlight and low demand for electric vehicle charging, photovoltaic power generation can generate significant excess power. If this upper limit is exceeded, existing technologies typically address this by instructing all microinverters to proportionally reduce their output power through a centralized controller.

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

[0005] The purpose of the present invention is to address the above-mentioned deficiencies and provide a micro-inverter grid-connected control method and system.

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

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

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

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

[0010] Optionally, the present application also proposes a micro-inverter grid-connected control method, and the steps of identifying an electric vehicle with an intermittently running high-power on-board load 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 shows periodic power fluctuations or sudden power fluctuations, or the instantaneous power consumption of the electric vehicle exceeds a preset on-board load power threshold in the non-charging state, then identifying the electric vehicle as having an intermittently running high-power on-board load.

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

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

[0013] Optionally, the present 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 battery of the electric vehicle; 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 battery of the electric vehicle; 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 stay time; calculating the actual absorbed power of the electric vehicle based on the real-time charging power data of the battery of the electric vehicle and the real-time on-board load power of the electric vehicle; and using the charging priority of the electric vehicle and the actual absorbed power of the electric vehicle as the charging demand information of the electric vehicle charging unit.

[0014] Optionally, the present application also proposes a micro-inverter grid-connected control method, and the steps of determining the charging priority of the electric vehicle based on the battery status information of the electric vehicle, the charging target preset by the user, and the expected stay time include: during the charging process of the electric vehicle, periodically obtaining the battery status information of the electric vehicle, the charging target preset by the user, and the expected stay time; dynamically updating the charging priority of the electric vehicle based on the periodically obtained battery status information, the preset charging target, and the expected stay time.

[0015] Optionally, the present application also proposes a micro-inverter grid-connected control method, and the steps of setting the power margin include: obtaining historical monitoring data of intermittently running high-power vehicle loads; performing statistical analysis on the historical monitoring data of intermittently running high-power vehicle loads to determine the range of power consumption required by the intermittently running high-power vehicle loads in the startup state; setting the power margin based on the range of power consumption required by the intermittently running high-power vehicle loads in the startup state, and the power margin is used to cover the power consumption required by the intermittently running high-power vehicle loads in the startup state.

[0016] Optionally, the present application also proposes a micro-inverter grid-connected control method, and the step of setting the power margin according to the range of power consumption required by the intermittently running high-power vehicle-mounted load in the startup state includes: obtaining the statistical characteristic value of the range of power consumption required by the intermittently running high-power vehicle-mounted load in the startup state; setting the power margin according to the statistical characteristic value, and the power margin is used to cover the power consumption required by the intermittently running high-power vehicle-mounted load in the startup state.

[0017] Optionally, the present application also proposes a micro-inverter grid-connected control system, which is applied to the above-mentioned micro-inverter grid-connected control method. The system includes: an information acquisition module, which is used to obtain the output power information and geographic location information of the distributed power generation unit, as well as the charging demand information and geographic location information of the electric vehicle charging unit; a supply and demand matching module, which is used to dynamically match the distributed power generation unit and the electric vehicle charging unit according to the geographic location information of the distributed power generation unit and the geographic location information of the electric vehicle charging unit to form a local supply and demand group; a power monitoring module, which is used to monitor the power output of the total grid-connected point; and a power regulation module, which is used when the power output of the total grid-connected point is greater than or equal to the preset grid-connected power upper limit, the central controller performs multi-level differentiated power regulation.

[0018] Through the above scheme, a system for implementing the above control method is provided, which provides hardware and software support for the practical application of the method, so that the entire control strategy can be effectively implemented.

[0019] From the above, it can be seen that the present application provides a micro-inverter grid-connected control method and system, which effectively solves the problem of indiscriminate power reduction leading to energy waste and decreased user experience in the prior art by dynamically matching supply and demand and performing multi-level differentiated power regulation. It has the advantages of effectively solving the problem of indiscriminate power reduction leading to energy waste and decreased user experience in the prior art, realizing local and efficient consumption of clean energy, and ensuring the charging needs of electric vehicles.

[0020] To further understand the features and technical contents of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are only for reference and illustration and are not intended to limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of a micro-inverter grid-connected control method according to the present invention;

[0022] Figure 2 The figure is a structural diagram of a micro-inverter grid-connected control system of the present invention. DETAILED DESCRIPTION

[0023] The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the 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. In addition, the drawings of the present invention are only for simple schematic illustrations and are not depicted according to actual dimensions. It is stated in advance. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are 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 shown.

[0025] refer to Figure 1 , a micro-inverter grid-connected control method, the method comprising the following steps: S1: obtaining output power information and geographic location information of distributed power generation units, as well as charging demand information and geographic location information of electric vehicle charging units; S2: dynamically matching distributed power generation units and electric vehicle charging units according to the geographic location information of the distributed power generation units and the geographic location information of the electric vehicle charging units to form a local supply and demand group; S3: monitoring the power output of the total grid-connected point; S4: when the power output of the total grid-connected point is greater than or equal to the preset grid-connected power upper limit, the central controller performs multi-level differentiated power regulation, the multi-level differentiated power regulation including: reducing the output power of distributed power generation units that are not matched by any electric vehicle charging unit; dispatching electric vehicles with vehicle-grid interaction functions to absorb the surplus power generated by distributed power generation units; reducing the output power of distributed power generation units with surplus power distributed in the local supply and demand group, and ensuring the charging demand of the electric vehicle charging units in the local supply and demand group.

[0026] Distributed generation units (DGUs) are devices or systems capable of independently generating electricity, specifically solar panel arrays connected to microinverters. These can be implemented as photovoltaic panels, small wind turbines, or micro gas turbines, primarily to provide a localized clean energy supply. Electric vehicle charging units (EVCs) are devices that provide electrical energy to electric vehicles, specifically smart EV charging stations. These can be implemented as AC, DC, or wireless charging stations, primarily to meet the charging needs of EVs. Charging demand information refers to the current state and priority of the EV charging unit's energy demand. This information can include the EV's battery status, user-set charging goals, expected 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. This information is primarily used to guide power allocation and ensure that high-priority charging needs are met. Geographic location information refers to the spatial location data of DGUs and EVCs. This information can be implemented as GPS coordinates, indoor positioning system data, or preset area codes. This information is primarily used to match energy supply and demand locally, forming local supply and demand groups. A local supply and demand group refers to a collection of distributed generation units and electric vehicle charging units that are physically close to each other and have an energy supply and demand relationship, dynamically matched based on geographic location. This group primarily aims to promote local energy consumption and reduce dependence on the external grid. A central controller is the intelligent control device responsible for decision-making and command issuance for the entire microinverter grid-connected control system. It can be implemented as an industrial PC, embedded system, or cloud computing platform. Its primary function is to coordinate the operation of each unit and achieve overall power regulation goals. Multi-level differentiated power regulation refers to a refined management strategy in which the central controller stratifies and classifies the output power of distributed generation units based on different supply and demand relationships and unit characteristics. This strategy aims to maximize local energy efficiency and ensure critical charging needs while meeting the grid power ceiling. Vehicle-grid interaction refers to the ability of electric vehicles to engage in bidirectional energy exchange with the grid, enabling them to charge from the grid, discharge to the grid, or absorb excess power. This can be achieved through technologies such as vehicle-to-grid (V2G), vehicle-to-leverage (V2L), or vehicle-to-hybrid (V2H). It primarily utilizes electric vehicle batteries as a flexible energy storage resource, improving the system's ability to absorb excess power.

[0027] The solution of this application optimizes microinverter grid-connected control through refined data acquisition and intelligent multi-level power regulation. First, the system continuously acquires output power and geographic location information of distributed generation units, as well as charging demand and geographic location information of electric vehicle charging units. This information forms the basis for all subsequent decision-making. 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 demand and priority of electric vehicles. Based on this real-time geographic location information, the system dynamically matches distributed generation units with electric vehicle charging units to form local supply and demand groups. The formation of local supply and demand groups is a key prerequisite for achieving refined management, breaking down complex global problems into several controllable local balancing units. Simultaneously, the system continuously monitors the power output of the main grid connection point. This monitoring step is essential for ensuring the safe and stable operation of the power grid. Once the power output of the main grid connection point reaches or exceeds the preset grid power limit, it triggers the central controller to implement multi-level differentiated power regulation. Multi-level differentiated power regulation is the core operating mechanism of this solution. When grid-connected power exceeds the limit, the central controller first reduces the output power of distributed generation units (DGUs) not matched by any EV charging units. This is because the electricity generated by these units cannot be effectively consumed locally, directly straining the grid, and therefore is prioritized for suppression. Subsequently, the system dispatches electric vehicles (EVs) with vehicle-grid interaction capabilities to absorb the excess power generated by the DGUs. This scheduling fully leverages the potential of EVs as mobile energy storage units, converting otherwise wasted clean energy into useful energy storage and providing flexible regulation capabilities for the grid. Finally, for DGUs within local supply and demand groups that still have excess power, the central controller reduces their output power, prioritizing the charging needs of the EV charging units within those groups.

[0028] In some preferred embodiments, the present application is implemented as follows: a micro-inverter 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. A distributed power generation unit, specifically a photovoltaic array, is installed above each parking space in the parking lot. Each array is connected to a micro-inverter, and these micro-inverters exchange data with the central controller via power line carrier communication or wireless communication modules. 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 geographic location information, such as parking space number or area coordinates, from each micro-inverter. Simultaneously, the central controller obtains charging demand information for the connected electric vehicle from each smart charging pile, including the current battery charge level, user-set charging target, estimated parking duration, and the charging pile's own geographic location information. The central controller uses the acquired geographic location information to dynamically analyze the spatial proximity and potential electrical connection paths between distributed generation units (DGs) and EV charging units (EVCs) through an internal algorithm. For example, by parsing parking space numbers or area codes, it matches DGs and EVCs located in the same area or on the same distribution circuit, forming several local supply and demand groups. The central controller continuously monitors the total power output from the entire parking lot's grid connection points 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 DGs that are not matched to any EV charging units, such as photovoltaic arrays located at the edge of the parking lot without nearby charging stations. It then sends instructions to the corresponding microinverters to reduce their output power, for example, by 50%. The central controller then identifies electric vehicles in the parking lot that have vehicle-to-grid interaction capabilities, for example, confirming their V2G capabilities through vehicle identification codes or user registration information, and sends instructions to their charging piles, scheduling these electric vehicles to absorb the excess power generated by the distributed generation units, for example, instructing them to charge at the maximum absorbable power. Finally, for scenarios where electric vehicles are distributed within established local supply and demand groups and their internal distributed generation units still have excess power, the central controller will reduce the output power of the distributed generation units within the local supply and demand group based on the charging demand information of the electric vehicle charging units in the local supply and demand group, for example, giving priority to meeting the charging needs of vehicles with low battery power and whose users are in urgent need of leaving, while ensuring that the charging needs of local electric vehicles are met.For example, if there are three vehicles in the local supply and demand group, one of which is in urgent need of charging, and the other two are almost fully charged, the central controller will prioritize reducing the output power of the distributed generation unit that supplies power to the vehicle that is almost fully charged to ensure the power supply for the vehicle that urgently needs charging.

[0029] The present application further proposes the steps of dispatching electric vehicles with vehicle-grid interaction functions to absorb the surplus power generated by distributed power generation units, including: identifying electric vehicles with intermittently running high-power on-board loads; obtaining the theoretical absorption power of the electric vehicle; setting a power margin, which is used to cover the power consumption required by the intermittently running high-power on-board loads in the startup state; calculating the corrected absorption capacity of the electric vehicle based on the theoretical absorption power and power margin of the electric vehicle, which is the theoretical absorption power of the electric vehicle minus the power margin; controlling the central controller to allocate surplus power to the electric vehicle based on the corrected absorption capacity of the electric vehicle, and excluding the contribution of the power margin to the local absorption capacity in the total grid-connected power balance calculation.

[0030] Among them, identifying electric vehicles with intermittent high-power on-board loads means determining whether the electric vehicle carries equipment that consumes a large amount of electricity periodically or suddenly during operation, such as on-board air conditioners, on-board heaters, on-board entertainment systems, etc. It can obtain the internal load information of the vehicle through the on-board diagnostic system (OBD) interface, or identify the operating mode of such loads by analyzing the vehicle's historical power consumption data; among them, the theoretical absorbed power of an electric vehicle refers to the maximum charging power allowed by the battery management system (BMS) and charging interface of the electric vehicle under ideal conditions, which can be obtained from the communication interface of the electric vehicle, or obtained according to the model and battery specifications of the electric vehicle; among them, the power margin refers to the power required to cope with the intermittent high-power on-board loads inside the electric vehicle. The additional power reserved for the instantaneous power consumption demand that may be generated when the load is started can be set based on statistical analysis of historical data, preset fixed values ​​or dynamic calculation according to the type of on-board load; among them, the corrected absorption capacity of an electric vehicle refers to the ability of the electric vehicle to absorb external surplus power after considering its own on-board load power consumption demand, which can be calculated by subtracting the preset power margin from the theoretical absorption power of the electric vehicle; among them, the total grid-connected power balance calculation refers to the calculation process for evaluating the relationship between the total power output of the grid-connected point and the preset grid-connected power upper limit in the entire micro-inverter grid-connected control system, which can be carried out by summarizing data such as the output power of all distributed power generation units, the power consumption of local loads and the absorbed power of electric vehicles.

[0031] Specifically, the system first identifies electric vehicles with intermittent, high-power onboard loads. This is because these loads generate transient high-power demands during startup. If not accounted for, the allocated surplus power may be insufficient to simultaneously meet charging and load startup requirements, thus impacting the normal operation of the electric vehicle. After identifying these electric vehicles, the system obtains the electric vehicle's theoretical absorption power, which represents the electric vehicle's maximum charging capacity under ideal conditions. Based on this, the system sets a power margin specifically to cover the power consumption required by the intermittent, high-power onboard loads during startup, effectively reserving a safety margin for these potential transient high-power demands. This power margin allows the system to calculate the electric vehicle's corrected absorption capacity based on the electric vehicle's theoretical absorption power and the power margin. The corrected absorption capacity is the electric vehicle's theoretical absorption power minus the power margin. It more accurately reflects the electric vehicle's actual ability to absorb external surplus power while ensuring the normal operation of its own loads. The central controller then allocates surplus power to the electric vehicle based on this corrected absorption capacity, ensuring that the allocated power meets charging requirements while also balancing the power consumption of the onboard loads, thus preventing the normal operation of the electric vehicle from being impacted by improper power allocation. Furthermore, the system excludes the contribution of power margin to local absorptive capacity in the calculation of total grid-connected power balance. This is because power margin represents reserved backup power, not the portion actually used to absorb excess power. Including it in local absorptive capacity would overestimate local absorptive 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 is ensured, thereby maximizing the utilization of local photovoltaic energy while ensuring grid stability.

[0032] In some preferred embodiments, the present application is implemented as follows. When a central controller needs to dispatch electric vehicles with vehicle-grid interaction capabilities to absorb excess power generated by distributed generation units, the system can first identify electric vehicles with intermittently operating high-power onboard loads. For example, the system can obtain vehicle load information through the communication protocol (such as ISO 15118) between the electric vehicle charging unit and the electric vehicle, or analyze the vehicle's historical charging and power usage data to identify the typical operating modes and startup power consumption characteristics of high-power loads such as onboard air conditioners and heaters. Next, the system can obtain the electric vehicle's theoretical power absorption. This can be obtained from the maximum charging power obtained in real time by the electric vehicle's battery management system (BMS), or from a preset database based on the vehicle model and battery capacity. The system can then set a power margin. For example, the system can look up the corresponding margin value from a predefined power margin table based on the identified high-power onboard load type, or statistically analyze the power consumption range required by such loads during startup based on historical monitoring data and take the maximum value or a statistical percentile as the power margin. For example, if the instantaneous power consumption of the onboard air conditioner during startup is expected to reach 3kW, a power margin of 3kW can be set. On this basis, the system can calculate the EV's corrected absorption capacity based on the EV's theoretical absorption power and the set power margin. For example, if the EV's theoretical absorption power is 10kW and the set power margin is 3kW, the EV's corrected absorption capacity is 10kW minus 3kW, or 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 control the surplus power allocated to the EV to within 7kW to ensure that even if the onboard air conditioner suddenly starts, the EV still has sufficient power to maintain charging and load operation. At the same time, when calculating the total grid-connected power balance, the system will explicitly exclude 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 assessment of grid-connected power.

[0033] The present application further proposes a method for identifying an electric vehicle with an intermittently running high-power on-board load, the method comprising the following steps: when the electric vehicle is connected to an 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 presents periodic power fluctuations or sudden power fluctuations, or the instantaneous power consumption of the electric vehicle exceeds a preset on-board load power threshold in a non-charging state, then identifying the electric vehicle as having an intermittently running high-power on-board load.

[0034] The monitoring period refers to a specific time period set to identify whether an electric vehicle has intermittently operating, high-power onboard loads. Its purpose is to provide a stable time window for continuous, 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 EV's charging unit when not charging to maintain essential vehicle systems, such as vehicle communications and the in-vehicle entertainment system, but insufficient for effective charging. Alternatively, the EV charging unit may completely stop supplying power to the EV. This is intended to eliminate or significantly reduce the interference of charging current on the measurement of onboard load power consumption, thereby more accurately capturing the power characteristics of the onboard load itself. Periodic power fluctuations refer to the regular rise and fall pattern of an electric vehicle's instantaneous power consumption over a certain time interval. These fluctuations are typically caused by intermittently operating, high-power loads such as onboard air conditioners and heaters, which periodically start and stop according to internal control logic. The purpose is to identify these regular changes and determine whether the vehicle has such intermittent loads. Sudden power fluctuations refer to significant and unexpected increases or decreases in the instantaneous power consumption of electric vehicles within a short period of time. Such fluctuations may be caused by high-power loads such as on-board refrigerators and power tools that suddenly start or stop at specific moments. The purpose is to identify whether the vehicle has non-periodic but intermittent loads with high power demands by capturing such large instantaneous changes. The preset on-board load power threshold in the non-charging state refers to the upper limit of the power consumed by the normal operation of the on-board load when the electric vehicle is not charging. This threshold is derived from statistical analysis of typical on-board load power consumption data of a large number of electric vehicles in the non-charging state. 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 in operation, thereby assisting in identifying intermittent high-power on-board loads.

[0035] The solution of this application initiates a monitoring cycle when the electric vehicle is connected to the electric vehicle charging unit, thereby limiting the timing of the identification process and ensuring that identification begins only after the electric vehicle is connected to the charging system. During the monitoring cycle, the electric vehicle charging unit is controlled to maintain minimum or zero power output. This is a key strategy designed to eliminate interference from normal charging behavior on power consumption monitoring, ensuring that the monitoring results more accurately reflect the power consumption of the onboard load. Maintaining minimum power output ensures basic power supply while reducing interference with monitoring. Subsequently, the electric vehicle's instantaneous power consumption is measured, which is a fundamental step in obtaining the electric vehicle's power consumption characteristics. Real-time power measurement provides a data foundation for subsequent analysis. Next, the electric vehicle's instantaneous power consumption is analyzed, which is the core step in identification. By analyzing the power data, it can be determined whether the electric vehicle has intermittent high-power onboard loads. Finally, if the electric vehicle's instantaneous power consumption exhibits periodic or sudden power fluctuations, or if the electric vehicle's instantaneous power consumption exceeds a preset onboard load power threshold in the non-charging state, the electric vehicle is identified as having intermittent high-power onboard loads. Two criteria are provided here: periodic or sudden power fluctuations reflect the characteristics of intermittent loads, while exceeding the power threshold indicates the presence of high-power loads. These two criteria enable more comprehensive and accurate identification of electric vehicles with intermittent high-power loads. This identification method provides important foundational information for subsequent power scheduling strategies. When scheduling electric vehicles with vehicle-grid interaction to absorb excess power, it is necessary to accurately identify whether these vehicles have intermittent high-power loads. This allows for the calculation of their corrected absorption capacity to set a reasonable power margin to cover the power consumption required by these loads during startup.

[0036] In some preferred embodiments, when an electric vehicle is connected to an electric vehicle charging unit, the control module of the electric vehicle charging unit can immediately initiate a preset monitoring period, for example, lasting five minutes. During this monitoring period, the electric vehicle charging unit can be controlled to maintain zero power output (i.e., no charging current is supplied to the electric vehicle battery), or to maintain a very low power output, for example, 50 watts, to ensure the normal operation of the electric vehicle's basic communications and onboard systems while avoiding interference with power measurement. A power measurement module integrated within the electric vehicle charging unit can measure the electric vehicle's instantaneous power consumption at a high frequency, for example, ten times per second, and transmit this data to a central controller or a local processing unit within the electric vehicle charging unit. This processing unit can perform real-time analysis on 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 transforms or wavelet analysis can be applied to identify the presence of periodic signals with specific frequencies and amplitudes, such as the regular power variations that may occur when an onboard 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 in a very short time, such as one second, it can be judged as a sudden fluctuation, such as the sudden start-up of a car refrigerator or power tool. In addition, the processing unit can also compare the measured instantaneous power consumption with a preset vehicle load power threshold in a non-charging state. For example, the threshold can be set to five hundred watts. If the instantaneous power consumption continues to exceed the threshold for a period of time, such as ten seconds, it can be determined that a high-power vehicle load exists. When any of the above conditions is met, the electric vehicle is identified as a high-power vehicle load with intermittent operation, and this identification result is sent to the central controller for corresponding processing in subsequent power scheduling.

[0037] The present application further proposes steps for reducing the output power of distributed power generation units with surplus power distributed within the local supply and demand group, and ensuring the charging needs of electric vehicle charging units within the local supply and demand group, including: obtaining the charging status information and charging priority information of each electric vehicle charging unit within the local supply and demand group; determining the order of reducing the output power of the distributed power generation units based on the charging status information and charging priority information of the electric vehicle charging units; and executing the output power reduction operation of the distributed power generation units with surplus power within the local supply and demand group according to the order of reducing the output power of the distributed power generation units, and ensuring the charging needs of the electric vehicle charging units within the local supply and demand group.

[0038] Charging status information refers to the current charge level of the electric vehicle's battery, such as the battery's state of charge, remaining charging time, and current charging power. This information can be directly read through the vehicle's battery management system, obtained through communication between the charging station and the vehicle, or input by the user in the charging app. Charging priority information refers to the user's requirements for charging speed and completion, such as the user-set charging completion time, the user's desired charging speed, and the vehicle type. This information can be manually set by the user in the charging app, automatically learned by the system based on the vehicle's historical charging behavior, or automatically assigned based on vehicle type and preset rules. The curtailment order refers to the order in which different distributed generation units are curtailed when their output power needs to be curtailed. This can be achieved by sorting the EV charging units from low to high priority, sorting them from high to low based on charging status information, or a combination of both and weighted ranking incorporating other factors. Output power curtailment involves the central controller sending instructions to the distributed generation units to reduce their current power output. 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 command values ​​to the inverter. Ensuring charging demand means reducing the output power of distributed generation units while ensuring that high-priority or urgently needed EV charging units receive sufficient power to meet their charging targets. This can be achieved by reserving specific power for high-priority charging units, dynamically adjusting the reduction ratio to avoid impacting critical charging, or continuously monitoring the actual power demand of charging units during the reduction process and making fine adjustments.

[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 executed under the premise of ensuring demand, thus achieving refined management of local surplus power.

[0040] In some preferred embodiments, the present application is implemented as follows. When a central controller needs to reduce the power of distributed generation units with excess power within a local supply and demand group, the central controller first communicates with each electric vehicle charging unit within the local supply and demand group to obtain their charging status and charging priority information. For example, the charging status information may include the current battery state of charge (SOC) of each electric vehicle, e.g., the SOC of the electric vehicle in parking space A is 20% and the SOC of the electric vehicle in parking space B is 95%. The charging priority information may include the charging target set by the user through the charging app (e.g., the user in parking space A sets a target of charging to 80% within 30 minutes, and the user in parking space B sets a target of charging 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). The central controller then calculates and determines the order in which the output power of the distributed generation units should be reduced based on this obtained charging status and charging priority information. For example, the system may set a rule to prioritize reducing the power of distributed generation units that supply electric vehicle charging units with higher battery states of charge and lower charging priorities. Specifically, if the state of charge (SOC) of the electric vehicle in parking space A is 20% and has a high priority, while the state of charge (SOC) of the electric vehicle in parking space B is 95% and has a low priority, the system prioritizes reducing the output power of the distributed generation unit (DGU) serving the electric vehicle in parking space B. This reduction order can be determined based on a weighted scoring model that considers factors such as SOC, charging target, and expected dwell time. Each DGU is assigned a comprehensive priority score, and the reduction order is then determined from lowest to highest. Finally, the central controller sends power reduction instructions to the corresponding DGUs according to the determined reduction order, executing the output power reduction operation. For example, the central controller might send an instruction to the DGU serving the electric vehicle in parking space B to reduce its output power by 50%. Simultaneously, the system continuously monitors 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 a reduction, the system can dynamically adjust the reduction ratio of other DGUs or draw a small amount of supplemental power from the grid to ensure that the charging target of the electric vehicle in parking space A is not affected.

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

[0042] Hierarchical location information or regional location information refers to the further refinement and classification of 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, and so on. This can be achieved by spatially analyzing and aggregating raw latitude and longitude data using a geographic information system (GIS), or by encoding it using pre-set regional division rules. The purpose is to provide a structured spatial reference for subsequent analysis of distribution network connectivity. Internal distribution network connectivity refers to the physical connection topology between distributed generation units and electric vehicle charging units in the actual power system, including cable routing, transformers, switchgear, and busbar connections. This can be obtained by reading distribution network topology maps, equipment lists, or real-time connection status data from power SCADA systems. The purpose is to identify the actual paths and potential transmission bottlenecks of power energy. The electrical connection path refers to the actual physical route that power energy travels from distributed generation units to electric vehicle charging units. This can be calculated using graph theory algorithms, such as the shortest path algorithm or the minimum spanning tree algorithm, combined with internal distribution network connectivity 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 inductance during the transmission of power along the electrical connection path. It can be calculated based on basic power system principles 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 material resistivity, and the transmission current. Its purpose is to quantify the efficiency of power transmission as a basis for optimized matching.

[0043] The solution of this application addresses the limitations of matching based solely on geographic location by refining the process of forming local supply and demand groups. First, the system obtains the geographic location information of distributed generation units and electric vehicle charging units, which serves as the basis for subsequent analysis. Based on this information, the system further analyzes it and converts it into more structured hierarchical or regional location information. This conversion enables 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 the internal distribution network connections between 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, going beyond simple spatial distance to focus directly on the physical feasibility of power transmission. By identifying the actual distribution network topology, the system can eliminate geographically close units without effective electrical connections or identify potential connections with complex connection paths and high losses. Based on this determined electrical connection path length or the calculated power transmission losses, and combined with the charging demand information of the electric vehicle charging units, the system dynamically matches distributed generation units with electric vehicle charging units to form local supply and demand groups. Choosing shorter electrical connection paths or lower-loss power transmission means higher power transmission efficiency, reducing energy waste during transmission. At the same time, incorporating charging demand information from EV charging units into the matching process allows the system to prioritize those EVs with urgent charging needs, thereby ensuring energy efficiency while improving the user experience.

[0044] In some preferred embodiments, the present application is implemented as follows: Assume that multiple distributed generation units (DG 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 uses a GPS module or a pre-set coordinate system to obtain precise geographic location information, such as latitude and longitude coordinates, for each DG unit and electric vehicle charging unit. A geographic information processing module then parses this geographic location information. For example, the entire parking lot can be divided into several pre-defined areas, such as "East," "West," "South," and "North," or each unit can be categorized into specific hierarchical or regional location information based on floor and parking area number. For example, a DG unit might be identified as being located on the third floor of the "East Zone," while an electric vehicle charging unit is located on the second floor of the "East Zone." Next, a network topology analysis module determines the electrical connection paths between the DG units and the electric vehicle charging units based on this hierarchical or regional location information and pre-stored data on the parking lot's internal power distribution network connectivity. This connectivity data can include connection information for distribution cabinets, transformers, cable routing, and switchgear at each level. For example, analysis can determine that a photovoltaic unit on the third floor of the East District and a charging station on the second floor of the East District require a specific distribution cabinet and cable to establish an electrical connection. This module can use graph theory algorithms, such as the Dijkstra 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 with electric vehicle charging units based on the length of these electrical connection paths or the energy transmission losses calculated from the path parameters, combined with the real-time charging demand information of the electric vehicle charging units, to form local supply and demand groups. For example, if the electrical connection path between distributed generation unit A and electric vehicle charging unit B is short and has low losses, and electric vehicle charging unit B has a higher charging demand value, the system can prioritize matching them into a local supply and demand group.

[0045] The present application further proposes that 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 battery of the electric vehicle; 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 battery of the electric vehicle; 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 stay time; calculating the actual absorbed power of the electric vehicle based on the real-time charging power data of the battery of the electric vehicle and the real-time on-board load power of the electric vehicle; and using the charging priority of the electric vehicle 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 an electric vehicle charging unit refers to the total rate of electric energy absorbed by the electric vehicle charging unit from the power grid or power supply system at a certain moment. It can be measured in real time by a power metering module or smart meter installed inside the charging unit, with the purpose of obtaining the overall power consumption of the electric vehicle. The real-time charging power data of an electric vehicle battery refers to the actual charging power rate received by the electric vehicle battery pack at a certain moment. It can be obtained by using the battery management system (BMS) inside the electric vehicle or the communication interface between the charging pile and the vehicle, with the purpose of distinguishing the power consumption of battery charging from that of the on-board load. The real-time on-board load power of an electric vehicle refers to the real-time power consumption of the internal electrical equipment (such as air conditioning, entertainment system, lighting, etc.) of the electric vehicle during the charging process, in addition to battery charging. It can be calculated by subtracting the real-time charging power data of the electric vehicle battery from the real-time total power consumption data of the electric vehicle charging unit, with the purpose of accurately identifying the power demand of the non-charging part. The battery status information of an electric vehicle refers to the current health status, power level, temperature and other key parameters of the electric vehicle battery. It can use data provided by the on-board battery management system (BMS), with the purpose of evaluating the charging energy consumption of the battery. The user-preset charging target refers to the expected charging completion degree or charging time set by the electric vehicle user through the 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. The expected stay 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 dimension reference for charging strategy. The charging priority of an electric vehicle refers to the charging urgency or importance level assessed based on factors such as the electric vehicle's battery status information, the user's preset charging target, and the expected stay time. It can be dynamically calculated and adjusted using a preset priority algorithm or rule. Its purpose is to guide power allocation to prioritize high-demand vehicles. The actual absorbed power of an electric vehicle refers to the maximum rate at which the electric vehicle's battery and onboard load can safely and effectively absorb electrical energy in its current state. It can be calculated based on the electric vehicle's real-time battery charging power data and the electric vehicle's real-time onboard load power. Its purpose is to avoid excessive power allocation that leads to waste or damage to the vehicle.

[0047] The solution of the present application overcomes the shortcomings of the conventional method of crude and static charging demand information by obtaining charging demand information of the electric vehicle charging unit 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 to high, while a vehicle with a nearly full battery and an expected long-term stop can have its priority set to low. This priority mechanism enables the system to distinguish the charging urgency of different vehicles, providing a basis for subsequent differentiated power allocation. At the same time, the solution also calculates the actual absorbed power of the electric vehicle based on the real-time charging power data of the electric vehicle's battery and the calculated real-time on-board load power of the electric vehicle. The actual absorbed power reflects the maximum power that the electric vehicle can safely and effectively absorb under the current operating conditions, preventing energy waste and protecting vehicle equipment. Ultimately, the charging priority of the electric vehicle and the actual absorbed power of the electric vehicle are used as the charging demand information of the electric vehicle charging unit and provided to the central controller. When the central controller performs multi-level differentiated power regulation, for example, when the power output of the total grid-connected point is greater than or equal to the preset grid-connected power upper 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 absorbed power of each electric vehicle.

[0048] In some preferred embodiments, obtaining charging demand information from an electric vehicle charging unit can be implemented as follows. When an electric vehicle connects to a charging unit and begins charging, the charging unit can continuously monitor and report its real-time total power consumption from the grid. Simultaneously, the electric vehicle's internal battery management system (BMS) can transmit the electric vehicle's real-time battery charging power data to the charging unit via a communication protocol between the vehicle and the charging station (e.g., CAN bus or PLC communication). The charging unit or a local controller connected to the charging unit can receive this data. Upon 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, the real-time onboard load power of the electric vehicle can be calculated as 2kW (10kW - 8kW). Simultaneously, the local controller can obtain battery status information from the electric vehicle's BMS, such as a current charge level of 30% and a battery temperature of 25°C. Users can enter a preset charging target (e.g., a target charge level of 80%) and an expected dwell time (e.g., 2 hours) through the charging station's touchscreen interface or a companion mobile app. Based on this information, the local controller can determine the charging priority of the electric vehicle according to a pre-set priority algorithm. For example, if the current battery level is less than 50% and the expected dwell time is less than 3 hours, a high priority level can be assigned; if the current battery level is greater than 80% and the expected dwell time is greater than 5 hours, a low priority level can be assigned. The local controller then calculates the EV's actual absorbed power based on the EV's real-time battery charging power data (e.g., 8kW) and the calculated real-time onboard load power (e.g., 2kW). This calculation can take into account the battery's charging profile and the instantaneous demand of the onboard load. For example, if the battery's maximum acceptable charging power at its current level is 15kW and the onboard load is 2kW, the actual absorbed power can be set to 17kW (15kW + 2kW). Alternatively, a more complex algorithm can be used to account for the battery's actual charging efficiency and the dynamic changes in the onboard load. Finally, the local controller packages the calculated EV's charging priority (e.g., "high") and the EV's actual absorbed power (e.g., 17kW) as charging demand information for the EV charging unit and transmits it to the central controller via the communication network. The central controller can use this refined information when making power allocation decisions, for example, prioritizing power allocation to high-priority vehicles and ensuring that the allocated power does not exceed their actual absorbed power, thereby achieving smarter and more efficient energy management.

[0049] The present application further proposes that the steps for determining the charging priority of an electric vehicle include: during the charging process of the electric vehicle, periodically obtaining the battery status information of the electric vehicle, the user's preset charging target and the expected stay time; and dynamically updating the charging priority of the electric vehicle based on the periodically obtained battery status information, the preset charging target and the expected stay time.

[0050] Periodic acquisition refers to the system automatically collecting relevant data at preset intervals or when triggered by specific events. This can be achieved through timer triggering, event-driven methods, or data stream monitoring. The purpose is to ensure that the system can continuously monitor real-time changes in the EV charging process. Battery status information refers to the current operating parameters of the EV battery, such as the remaining battery capacity, charging current, charging voltage, battery temperature, and battery health status. This information can be reported in real time by the onboard battery management system via a communication interface, or obtained by the charging station through a communication protocol with the vehicle. The purpose is to reflect the actual charging requirements and acceptable charging conditions of the battery. The user-preset charging target refers to the user's expectations for the current charging process, such as the desired battery charge percentage, expected charging completion time, or expected charging volume. This information can be entered by the user through the charging station interface, mobile application, or vehicle central control system. The purpose is to reflect the user's personalized charging needs. The expected dwell time refers to the expected length of time the EV will be parked at the charging station. This information can be manually entered by the user, predicted based on historical parking data, or obtained through the vehicle's navigation system. The purpose is to provide time window constraints for charging strategies. Dynamic updating means that charging priority is not determined once at the start of charging, but is adjusted and revised during the charging process based on real-time data changes. This can be achieved through rule-based logical judgment, fuzzy control algorithms, or machine learning models. Its purpose is to enable charging priority to adapt in real time to the actual charging needs of electric vehicles. Charging priority refers to the importance or priority of electric vehicles in the allocation of charging resources. It can be expressed in the form of numerical scores, grade divisions, or weight coefficients. Its purpose is to guide the charging system to carry out reasonable power allocation and scheduling.

[0051] The solution of this application continuously monitors and periodically obtains the electric vehicle's battery status information, user-set charging targets, and expected duration of stay during the charging process, thereby enabling real-time monitoring of changes in the electric vehicle's charging needs. Given that this information changes dynamically, the system dynamically adjusts the electric vehicle's charging priority based on the latest periodic data. For example, as the battery charge increases or the user's expected duration of stay decreases, the charging priority can be increased or decreased accordingly. This dynamic adjustment mechanism enables the charging priority to accurately reflect the actual needs of the electric vehicle at different charging stages, avoiding the irrational resource allocation issues caused by fixed priorities. Furthermore, combined with the method for obtaining charging demand information for electric vehicle charging units in this application, this dynamically updated charging priority is incorporated into the overall microinverter grid-connected control method as part of the charging demand information. When the power output of the total grid-connected point reaches a preset upper limit, the central controller performs multi-level differentiated power regulation. Based on this real-time updated charging priority information, it can adjust the order in which distributed generation units within the local supply and demand group are reduced in output power, ensuring the charging needs of high-priority electric vehicle charging units.

[0052] In some embodiments, the present application is implemented as follows: After an electric vehicle is connected to a charging station and begins charging, a central controller or a control unit within the charging station can initiate a monitoring program. This program can be configured to periodically send query commands to the electric vehicle's battery management system every five minutes to obtain battery status information, such as the current remaining battery charge, charging current, and battery temperature. Simultaneously, the system continuously monitors the charging target set by the user through the charging station interface or mobile application, such as a target charge level of 80% or a target charging completion time of 5:00 PM, as well as the estimated duration of the vehicle's stay entered by the user. Based on this periodically acquired real-time data, the system dynamically updates the electric vehicle's charging priority using a pre-defined priority evaluation model. For example, the model can consider the following factors: lower remaining battery charge, higher priority; farther from the user-set charging target, higher priority; shorter estimated duration of stay, higher priority. These factors can be assigned different weights, and a weighted summation is used to calculate a comprehensive priority score. For example, if an electric vehicle's battery charge is less than 30% and the user plans to leave within the next hour, and their charging goal is to reach 90%, the system will immediately adjust its charging priority to the highest level. Conversely, if another electric vehicle's battery charge has reached 85% and the user plans to stay for several hours, its charging priority will be adjusted to a lower level. This dynamic adjustment ensures that charging resources can be flexibly allocated based on the actual needs of electric vehicles and user expectations, thereby optimizing the overall charging strategy.

[0053] The present application further proposes that the steps for setting the power margin include: obtaining historical monitoring data of intermittently running high-power vehicle loads; performing statistical analysis on the historical monitoring data of intermittently running high-power vehicle loads to determine the range of power consumption required by the intermittently running high-power vehicle loads in the startup state; and setting the power margin based on the range of power consumption required by the intermittently running high-power vehicle loads in the startup state, where the power margin is used to cover the power consumption required by the intermittently running high-power vehicle loads in the startup state.

[0054] Among them, historical monitoring data of intermittently operating high-power on-board loads refers to non-charging-related devices within electric vehicles with large and discontinuous power consumption fluctuations, such as air conditioning systems, heaters, in-car entertainment systems, and electric seat adjustments. These devices may start or stop at any time during the vehicle charging process and generate high instantaneous power consumption at the moment of startup. Historical monitoring data refers to the power consumption records of these loads over a period of time under different operating conditions, including information such as their peak power at startup, duration, and startup frequency. Its purpose is to provide an objective and quantitative data basis for the subsequent determination of power consumption ranges. Statistical analysis refers to the process of mathematically and statistically processing large amounts of historical monitoring data. Methods such as mean, variance, maximum, minimum, percentile, 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 and provide a scientific basis for setting power margins. The power consumption range refers to the upper and lower limits of the possible fluctuations in the instantaneous power consumption of intermittently running high-power on-board loads 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 requirements of the on-board load during startup and ensure that the power margin can effectively cover the vast majority of startup situations. The power margin refers to the additional power reserve reserved based on the theoretical power absorption of the electric vehicle. It is specifically used to compensate for the additional power consumption requirements of intermittently running high-power on-board loads 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.

[0055] This solution establishes a data foundation for power margin setting by acquiring historical monitoring data from intermittently operating high-power onboard loads. This historical data comprehensively records the power consumption characteristics of the onboard loads under different operating conditions, including key information such as startup frequency, duration, and peak power. Based on this, in-depth statistical analysis of this historical monitoring data reveals the inherent distribution patterns of onboard load power consumption, such as its maximum, minimum, average, and variance. This allows for the accurate determination of the power consumption range required by intermittently operating high-power onboard loads during startup. Based on this determined power consumption range, the power margin is dynamically and appropriately set. This power margin accurately covers the power consumption required by intermittently operating high-power onboard loads during startup. Specifically, when calculating the electric vehicle's corrected absorption capacity (i.e., the electric vehicle's theoretical absorption power 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 startup power requirements of the onboard loads.

[0056] In some preferred embodiments, the process of setting the power margin can be specifically implemented as follows: First, instantaneous power consumption data of intermittently operating high-power onboard loads such as the air conditioner, heater, and in-vehicle infotainment system in the electric vehicle under different operating conditions is continuously collected via the onboard diagnostics (OBD) interface or the vehicle communication bus (e.g., CAN bus). This data, which may include current, voltage, and duration of load startup, 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, to record the peak power consumption and duration of each onboard load startup. Once sufficient historical monitoring data has been accumulated, such as data from the past month or a 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 values ​​for all startup events, and further calculate their standard deviation or perform probability density distribution analysis. For example, this can determine that 99% of the startup power consumption falls within a 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 intermittent high-power on-board loads require a power consumption range of 500W to 1500W when starting, the power margin can be set to the upper limit of this range (for example, 1500W), or to a certain percentile of this range (for example, the 95th percentile) to ensure that the starting power consumption of the on-board loads can be covered in most cases. The power margin set in this way will be used to correct the absorption capacity of the electric vehicle, ensuring that even if the on-board load suddenly starts during the charging process of the electric vehicle, it will not cause drastic fluctuations or interruptions in the charging power, thereby ensuring the stability and efficiency of the charging process.

[0057] The present application further proposes that the steps of setting the power margin according to the range of power consumption required by the intermittently running high-power vehicle-mounted load in the startup state include: obtaining the statistical characteristic value of the range of power consumption required by the intermittently running high-power vehicle-mounted load in the startup state; setting the power margin according to the statistical characteristic value, and the power margin is used to cover the power consumption required by the intermittently running high-power vehicle-mounted load in the startup state.

[0058] Among them, the statistical characteristic value of the range of power consumption required by intermittently running high-power on-board loads in the starting state refers to a quantitative indicator reflecting the overall level and fluctuation degree of the power consumption range, which can be obtained by mathematical statistical analysis of historical monitoring data of intermittently running high-power on-board loads. Its purpose is to provide a data basis for the dynamic and accurate setting of the power margin; the statistical characteristic value refers to the numerical value obtained after descriptive statistical analysis of a set of data, which is used to summarize the distribution, central tendency, degree of dispersion, etc. of the data. It can include mean, maximum, minimum, variance, standard deviation, median or percentile, etc. Its purpose is to quantify the characteristics of the power consumption range in order to scientifically set the power margin; the power margin refers to the additional power reserved on the basis of the theoretical absorption power of the electric vehicle, which is specifically used to cope with the additional power consumption requirements that may be generated by intermittently running high-power on-board loads at the moment of starting. Its purpose is to ensure that while the electric vehicle absorbs surplus 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 onboard loads during startup is not a fixed value but rather fluctuates within a range, simply defining a power consumption range based on historical monitoring data is insufficient. Further analysis of the statistical characteristics of this range is required. These statistical characteristics, such as the mean, maximum, and variance, comprehensively reflect the concentration trend and dispersion of the power consumption range. The acquisition of these statistical characteristics allows the power margin to be set not simply as a fixed value or a single maximum value, but rather as a flexible adjustment based on 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 full coverage of startup power consumption under all circumstances and avoid power shortages. Alternatively, the power margin can be set based on the mean plus a safety factor calculated based on the variance or standard deviation, ensuring coverage while avoiding over-reserving power and reducing waste. This dynamic and refined setting approach based on statistical characteristics allows the power margin to more accurately match actual demand. When scheduling electric vehicles with vehicle-grid interaction to absorb excess power generated by distributed generation units, a more accurate corrected absorption capacity can be calculated by subtracting this precisely defined power margin from the electric vehicle's theoretical power absorption.

[0060] In some preferred embodiments, the present application is implemented as follows: Assume that the power consumption required by an intermittently running high-power onboard load (e.g., an air conditioning compressor) in a certain electric vehicle during startup may fluctuate between 5kW and 8kW, as determined by analysis of long-term historical monitoring data. To set a reasonable power margin, statistical characteristics of this power consumption range can be first obtained. For example, instantaneous power consumption data for the air conditioning compressor during startup for this electric vehicle model under different ambient temperatures, battery conditions, and usage frequencies can be collected to form a dataset. Statistical analysis of this dataset reveals an average value of 6.5kW, a maximum value of 8kW, and a standard deviation of 0.8kW. Based on these statistical characteristics, various strategies can be employed to set the power margin. As a specific implementation, 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. This ensures that the power margin is adequately covered regardless of the air conditioning compressor startup power consumption level. As another specific implementation, to ensure reliability while also taking into account energy efficiency, the power margin can be set to the average value plus a safety margin based on the standard deviation. For example, it can be set to 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 reservation. In this way, the system can flexibly select the most appropriate power margin setting strategy based on the actual statistical distribution characteristics, thereby better balancing the operational guarantee of the electric vehicle's internal load and the efficiency of absorbing external surplus power.

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

[0062] Among them, the information acquisition module refers to the unit responsible for collecting various real-time data and static configuration information required for system operation. It can be implemented by a collection of one or more sensors, data collectors, communication interfaces and data preprocessing units. Its purpose is to provide a data basis for subsequent supply and demand matching and power regulation; the supply and demand matching module refers to the unit that logically groups and associates distributed power generation units and electric vehicle charging units according to specific conditions. It can be implemented by an algorithm processor based on geographic information system or internal network topology analysis, combined with database dynamic matching, with the purpose of optimizing the local consumption of electric energy and reducing transmission losses; The power monitoring module refers to a unit used to measure and report the power output status at the total grid-connected point in real time. It can be implemented by a combination of one or more smart meters, current transformers, voltage transformers and data transmission units. Its purpose is to provide real-time basis for judging whether power regulation is needed; the power regulation module refers to a unit that adjusts the output power of the distributed power generation unit according to the monitored power status and preset rules. It can be implemented by a central controller or a distributed controller, which sends control instructions to the micro inverter and electric vehicle charging unit through the 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, the present application is implemented as follows: The information acquisition module can be composed 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 coordinate data 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 a preset matching algorithm 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 based on the received geographic location information, thereby forming local supply and demand groups. The power monitoring module can be an intelligent 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 that runs 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 will control the micro-inverter to reduce the output power through the Modbus protocol, or interact with the electric vehicle charging pile with vehicle-grid interaction function through the OCPP protocol to guide the electric vehicle to absorb surplus electricity, thereby realizing refined management of the entire grid-connected system.

[0064] The contents disclosed above are only preferred feasible embodiments of the present invention and do not limit the scope of protection of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention description and drawings are included in the scope of protection of the present invention. In addition, the elements therein can be updated as technology develops.

Claims

1. A micro-inverter grid-connected control method, characterized in that: The method comprises the following steps: S1: Obtain output power information and geographic location information of distributed generation units, as well as charging demand information and geographic location information of electric vehicle charging units; S2: Dynamically match distributed generation units and electric vehicle charging units based on their geographic location information to form a local supply and demand group; S3: monitor the power output of the total grid connection point; S4: When the power output of the total grid-connected point is greater than or equal to the preset grid-connected power upper limit, the central controller performs multi-level differentiated power regulation, which includes: For distributed generation units that are not matched by any electric vehicle charging units, the output power of the distributed generation units is reduced; Dispatching electric vehicles with vehicle-grid interaction capabilities to absorb excess power generated by distributed generation units; For distributed generation units with surplus power distributed within the local supply and demand group, the output power of the distributed generation units will be reduced, and the charging needs of the electric vehicle charging units within the local supply and demand group will be guaranteed.

2. A micro-inverter grid-connected control method according to claim 1, characterized in that: The steps for dispatching electric vehicles with vehicle-grid interaction capabilities to absorb the excess power generated by distributed generation units include: Identify electric vehicles with high-power onboard loads that operate intermittently; Get the theoretical absorbed power of the electric vehicle; Set the power margin, which is used to cover the power consumption required by intermittent high-power vehicle loads in the startup state; Based on the theoretical absorption power and power margin of the electric vehicle, the corrected absorption capacity of the electric vehicle is calculated. The corrected absorption capacity of the electric vehicle is the theoretical absorption power of the electric vehicle minus the power margin. According to the corrected absorption capacity of electric vehicles, the central controller is controlled to distribute surplus power to electric vehicles, and the contribution of power margin to local absorption capacity is excluded in the total grid-connected power balance calculation.

3. A micro-inverter grid-connected control method according to claim 2, characterized in that: The steps to identify electric vehicles with intermittent high-power onboard loads include: When the electric vehicle is connected to the electric vehicle charging unit, a monitoring cycle is initiated; During the monitoring period, the electric vehicle charging unit is controlled to maintain minimum power output or zero power output; Measuring instantaneous power consumption of electric vehicles; Analyze the instantaneous power consumption of electric vehicles; If the instantaneous power consumption of the electric vehicle presents periodic power fluctuations or sudden power fluctuations, or the instantaneous power consumption of the electric vehicle exceeds a preset on-board load power threshold in a non-charging state, the electric vehicle is identified as having an intermittently operating high-power on-board load.

4. The micro-inverter grid-connected control method according to claim 1, wherein: For distributed generation units with surplus power distributed within the local supply and demand group, the steps for reducing the output power of the distributed generation units and ensuring the charging demand of the electric vehicle charging units within the local supply and demand group include: Obtaining charging status information and charging priority information of each electric vehicle charging unit within the local supply and demand group; Determining the order in which the output power of the distributed generation units is reduced based on the charging status information and charging priority information of the electric vehicle charging units; According to the order of reducing the output power of the distributed generation units, the output power of the distributed generation units with surplus power in the local supply and demand group will be reduced, and the charging needs of the electric vehicle charging units in the local supply and demand group will be guaranteed.

5. The micro-inverter grid-connected control method according to claim 1, wherein: The steps in S2 include: Obtaining geographic location information of the distributed power generation unit and geographic location information of the electric vehicle charging unit; Parsing the geographical location information of the distributed power generation unit and the geographical location information of the electric vehicle charging unit to obtain hierarchical location information or regional location information of the distributed power generation unit and the electric vehicle charging unit; Determining an electrical connection path between the distributed generation unit and the electric vehicle charging unit based on hierarchical location information or regional location information of the distributed generation unit and the electric vehicle charging unit and in combination with an internal power distribution network connection relationship between the distributed generation unit and the electric vehicle charging unit; 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, the distributed generation unit and the electric vehicle charging unit are dynamically matched to form a local supply and demand team.

6. The micro-inverter grid-connected control method according to claim 1, wherein: In step S1, the step of obtaining charging demand information of the electric vehicle charging unit includes: Acquiring real-time total power consumption data of the electric vehicle charging unit; Obtain real-time charging power data of electric vehicle batteries; Calculate 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 battery of the electric vehicle; Determine the charging priority of electric vehicles based on the battery status information of the electric vehicles, the user's preset charging target and the expected length of stay; Calculate 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; The charging priority of the electric vehicle and the actual absorbed power of the electric vehicle are used as the charging demand information of the electric vehicle charging unit.

7. A micro-inverter grid-connected control method according to claim 6, characterized in that: The steps of 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 stay time include: During the charging process of the electric vehicle, periodically obtaining battery status information of the electric vehicle, a user-preset charging target, and an estimated stay time; The charging priority of electric vehicles is dynamically updated based on periodically acquired battery status information, preset charging targets, and expected stay duration.

8. The micro-inverter grid-connected control method according to claim 2, wherein: The steps to set the power margin include: Obtain historical monitoring data of intermittently running high-power vehicle loads; Conduct statistical analysis on historical monitoring data of intermittently running high-power vehicle loads to determine the power consumption range required by intermittently running high-power vehicle loads in the startup state; The power margin is set according to the power consumption range required by the intermittently running high-power vehicle-mounted load in the startup state. The power margin is used to cover the power consumption required by the intermittently running high-power vehicle-mounted load in the startup state.

9. A micro-inverter grid-connected control method according to claim 8, characterized in that: Based on the power consumption range required by intermittent high-power vehicle loads during startup, the steps for setting the power margin include: Obtaining statistical characteristic values ​​of the range of power consumption required by intermittently running high-power vehicle loads in a startup state; The power margin is set according to the statistical characteristic value, and the power margin is used to cover the power consumption required by the intermittently running high-power vehicle load in the startup state.

10. 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: An information acquisition module is used to obtain output power information and geographic location information of distributed power generation units, as well as charging demand information and geographic location information of electric vehicle charging units; A 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 the distributed generation units and the geographical location information of the electric vehicle charging units to form a local supply and demand group; Power monitoring module, used to monitor the power output of the main grid connection point; The power regulation module is used for the central controller to perform multi-level differentiated power regulation when the power output of the total grid-connected point is greater than or equal to the preset grid-connected power upper limit.

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