Method for stabilizing voltage of power distribution network based on mppt and electric energy vehicle and related device

Abstract

This invention discloses a method and related apparatus for stabilizing distribution network voltage based on MPPT (Multi-Level Photovoltaic Power Controller) and electric vehicles. The distribution network is connected to distributed photovoltaic (PV) modules. The method includes: acquiring the instantaneous maximum output power of the distributed PV modules as collected in real time by the MPPT controller; comparing the collected instantaneous maximum output power with a preset rated power to determine the power supply and demand status of the distribution network; when the instantaneous maximum output power is greater than the rated power, controlling a mobile charging device to transfer excess power from the distributed PV modules to a cluster of rechargeable electric vehicles for storage; when the instantaneous maximum output power is less than the rated power, controlling the rechargeable electric vehicle cluster to return a portion of the power to the distribution network to supplement the power supply gap; and dynamically adjusting the input voltage of the distribution network according to the power change rate of the distributed PV modules and a preset threshold to stabilize the distribution network voltage. This invention achieves a dual improvement in power supply quality and voltage stability.

Description

Technical Field

[0001] This invention relates to the field of distribution network voltage control technology, and in particular to a method and related apparatus for stabilizing distribution network voltage based on MPPT and electric vehicles. Background Technology

[0002] Against the backdrop of a global push for energy transition and a strong advocacy for the use of clean energy, distributed photovoltaic (PV) technology is finding increasingly widespread application in the energy sector due to its outstanding advantages such as being green, environmentally friendly, and highly sustainable. By installing PV power generation systems on building rooftops, walls, and various factories, it efficiently converts solar energy into electricity, effectively alleviating pressure on traditional energy supply and reducing carbon emissions.

[0003] However, the limitations of distributed photovoltaic power generation are also quite obvious. Its power generation capacity is closely related to weather conditions. On cloudy days, rainy days, or at night, power generation will drop sharply or even stop completely; while when there is plenty of sunshine, there is a tendency for power generation to exceed capacity. This instability in power generation brings many problems to the power supply, leading to unstable voltage in the distribution network, affecting power quality, potentially damaging grid equipment, shortening equipment lifespan, and increasing maintenance costs. Summary of the Invention

[0004] The main objective of this invention is to provide a method and related apparatus for stabilizing the voltage of a power distribution network based on MPPT and electric vehicles, which can solve the problem of unstable power generation leading to unstable voltage in the power distribution network and affecting the quality of power supply in the prior art.

[0005] To achieve the above objectives, the first aspect of the present invention provides a method for stabilizing the voltage of a distribution network based on MPPT and electric vehicles, the method comprising: Obtain the instantaneous maximum output power of the distributed photovoltaic modules as collected in real time by the MPPT controller; The power supply and demand status of the distribution network is determined by comparing the preset rated power with the collected instantaneous maximum output power. When the instantaneous maximum output power is greater than the rated power, the mobile charging device is controlled to transfer the excess power of the distributed photovoltaic modules to the rechargeable electric vehicle cluster for storage. When the instantaneous maximum output power is less than the rated power, the rechargeable electric vehicle cluster is controlled to return part of the power to the distribution network to supplement the power supply gap. The input voltage of the distribution network is dynamically adjusted based on the power change rate of the distributed photovoltaic modules and a preset threshold to stabilize the distribution network voltage.

[0006] In one feasible implementation, the control of the mobile charging device to transfer excess electricity from the distributed photovoltaic modules to a cluster of rechargeable electric vehicles for storage includes: Based on a comprehensive assessment of the vehicle distribution, remaining power, and photovoltaic power generation of the electric vehicle cluster, the priority order of the rechargeable electric vehicle cluster and its individual rechargeable electric vehicles is determined. The mobile charging device is dispatched to the target location of the target electric vehicle with the highest priority in the priority order for charging. Once the target electric vehicle has finished charging, the mobile charging device is controlled to detach from the target electric vehicle; and the mobile charging device is dispatched to the next priority electric vehicle to be charged.

[0007] In one feasible implementation, the method further includes: Calculate the first difference between the instantaneous maximum output power of two adjacent acquisition cycles and the second difference between the power grid input voltage of the corresponding cycle; The power change rate is obtained based on the ratio of the first difference to the second difference.

[0008] In one feasible implementation, the dynamic adjustment of the input voltage of the distribution network based on the power change rate of the distributed photovoltaic modules and a preset threshold includes: When the absolute value of the power change rate is less than or equal to the preset threshold, the current power distribution network input voltage is kept constant. When the absolute value of the power change rate is greater than the preset threshold, the input voltage of the distribution network is adjusted according to the power change trend to maintain voltage stability.

[0009] In one feasible implementation, adjusting the power distribution network input voltage according to the power change trend includes: If the power change trend is a sharp decrease in power, then according to the power supply gap, the power return of the rechargeable electric vehicle cluster is increased, and the fluctuation of the input voltage of the distribution network is kept below the preset threshold. If the power change trend is a power surge trend, then reduce the input voltage of the power distribution network.

[0010] In one feasible implementation, controlling the rechargeable electric vehicle cluster to increase its recharge power according to the power supply gap includes: The power supply gap is determined by using the difference between the instantaneous maximum output power and the rated power; Based on the power supply gap and the rated recharge power of the rechargeable electric vehicle cluster, determine the target recharge power of the rechargeable electric vehicle cluster; The return power of the rechargeable electric vehicle cluster is controlled based on the target return power.

[0011] In one feasible implementation, reducing the input voltage of the distribution network includes: The power surge amplitude is determined based on the difference between the instantaneous maximum output power and the rated power; Using the power surge magnitude and system impedance, determine the voltage reduction required; The MPPT controller reduces the input voltage of the power distribution network to the voltage that needs to be reduced.

[0012] To achieve the above objectives, a second aspect of the present invention provides a voltage stabilization system for a power distribution network based on MPPT and electric vehicles, the stabilization system comprising: Distributed photovoltaic modules, connected to the power distribution network, are used to convert solar energy into electrical energy to provide power to the power distribution network; The electric vehicle cluster serves as a flexible energy storage carrier; the electric vehicle cluster includes the rechargeable electric vehicle cluster and the rechargeable electric vehicle cluster. The MPPT controller is used to collect the instantaneous maximum output power of the distributed photovoltaic modules in real time. An intelligent scheduling unit is used to execute the method as described in the first aspect and any feasible implementation; Mobile charging equipment is used to establish a power transmission link between photovoltaic modules and electric vehicle clusters.

[0013] To achieve the above objectives, a third aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method as described in the first aspect and any feasible implementation thereof.

[0014] To achieve the above objectives, a fourth aspect of the present invention provides a computer device including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in the first aspect and any feasible implementation thereof.

[0015] The embodiments of the present invention have the following beneficial effects: This invention provides a method for stabilizing the voltage of a power distribution network based on MPPT (Multi-Level Photovoltaic Power Controller) and electric vehicles. The power distribution network is connected to distributed photovoltaic (PV) modules. The method includes: acquiring the instantaneous maximum output power of the distributed PV modules in real time, collected by an MPPT controller; comparing the collected instantaneous maximum output power with a preset rated power to determine the power supply and demand status of the power distribution network; when the instantaneous maximum output power is greater than the rated power, controlling a mobile charging device to transfer the excess power of the distributed PV modules to a cluster of rechargeable electric vehicles for storage; when the instantaneous maximum output power is less than the rated power, controlling the cluster of rechargeable electric vehicles to return a portion of the power to the power distribution network to supplement the power supply gap; and dynamically adjusting the input voltage of the power distribution network according to the power change rate of the distributed PV modules and a preset threshold to stabilize the power distribution network voltage.

[0016] This invention not only achieves a dynamic balance between photovoltaic surplus power consumption and deficit replenishment through the coordinated scheduling of MPPT and electric vehicles, thereby improving energy utilization efficiency and enhancing system stability and reliability, but more importantly, by introducing a dynamic voltage regulation strategy based on power change rate, it can proactively sense the trend of system power change and actively and accurately adjust the input voltage of the distribution network, thereby effectively suppressing voltage fluctuations and stabilizing the voltage within a safe range. This significantly improves power supply reliability and power quality, achieving a dual improvement in power supply quality and voltage stability. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] in: Figure 1 This is an application environment diagram of a distribution network voltage stabilization method based on MPPT and electric vehicles in an embodiment of the present invention; Figure 2 This is a flowchart of a method for stabilizing distribution network voltage based on MPPT and electric vehicles in an embodiment of the present invention; Figure 3 This is a structural block diagram of a computer device in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that this invention aims to propose an innovative "MPPT + electric vehicle" method to comprehensively solve the challenges faced by distributed photovoltaic (PV) power generation: 1. By using MPPT (Maximum Power Point Tracking) technology, PV power generation can be collected and tracked accurately in real time, optimizing PV power generation efficiency; 2. Using electric vehicles as flexible energy storage carriers, power generation fluctuations can be balanced, achieving efficient absorption and gap filling of PV power; 3. Scientific parameter adjustment and control strategies can be formulated to stabilize the distribution network voltage and improve power supply quality; 4. Equipment investment costs and user charging costs can be reduced, achieving efficient synergistic utilization of distributed PV and electric vehicles, ensuring the stable and reliable operation of the distribution network.

[0021] The core parameters of this application are defined as follows: 1. The instantaneous maximum output power P of photovoltaic power max The maximum instantaneous output power of the photovoltaic system, collected by the MPPT controller; 2. Distribution network input voltage Vi: This is the input voltage of the park, which is the actual voltage at which the photovoltaic system supplies power to electric vehicles / park loads (i.e., the voltage at the distribution network access point). 3. Rated power Pn: A preset power threshold based on the park's regular load demand, used to determine whether the photovoltaic power supply meets the park's needs; 4. Threshold ε: ±5% of rated voltage (rated voltage is the standard operating voltage V of the distribution network). n (This is used to determine the power-voltage state stability of the system).

[0022] Please see Figure 1 , Figure 1 This diagram illustrates the application environment of a distribution network voltage stabilization method based on MPPT and electric vehicles, as described in an embodiment of the present invention. The method is applied to a distribution network voltage stabilization system based on MPPT and electric vehicles, the stabilization system comprising: Distributed photovoltaic modules, connected to the power distribution network, are used to convert solar energy into electrical energy to provide power to the power distribution network; The electric vehicle cluster serves as a flexible energy storage carrier; the electric vehicle cluster includes the rechargeable electric vehicle cluster and the rechargeable electric vehicle cluster. The Maximum Power Point Tracking (MPPT) controller is used to collect the instantaneous maximum output power of the distributed photovoltaic modules in real time. The intelligent scheduling unit is used to execute a method for stabilizing the distribution network voltage based on MPPT and electric vehicles in an embodiment of the present invention, so as to schedule electric vehicle clusters and mobile charging equipment on demand.

[0023] Portable charging equipment is used to establish a power transmission link between photovoltaic modules and electric vehicle clusters. The portable charging equipment includes a portable charging interface.

[0024] The mobile charging device described in this invention can be a mobile charging robot with its own energy storage or a charging connection device with mobility. Its main function is to serve as a flexible connection medium between the power grid and electric vehicles.

[0025] It should be noted that, according to the IEEE 1547-2018 standard, photovoltaic inverters participate in voltage regulation by absorbing reactive power or reducing active power (curtailment). However, "curtailment" means directly cutting off photovoltaic output, which, while stabilizing the voltage, results in the waste of valuable clean energy. The core pain point that this invention aims to address is: how to achieve zero-waste absorption of photovoltaic power while maintaining the stability of the distribution network voltage. In this invention, the main functions of the MPPT controller are "high-frequency data acquisition" and "maximum power point maintenance," providing the system with accurate power source data; while the execution entity of voltage regulation is expanded from a single "photovoltaic-side regulation" to "photovoltaic + load (electric vehicle) coordinated regulation." When a surge in photovoltaic power leads to voltage exceeding limits, traditional MPPT can only choose to "reduce power (curtailment)," while this invention, by scheduling electric vehicles to charge (increase load), absorbs excess power while lowering the voltage, thus solving the voltage stability problem and achieving efficient utilization of electricity, realizing a coordinated control relationship between "MPPT + electric vehicle."

[0026] Among them, the application scenario of this system can be a microgrid distribution network scenario (such as an industrial park), and voltage stability can be achieved through real-time data acquisition and dynamic scheduling. This invention is based on the core architecture of "MPPT + electric vehicle", which not only solves the problem of imbalance between photovoltaic power generation supply and demand, but also replenishes the demand area with excess power in a timely manner. (1) The system composition and core logic are as follows: This system is applied to the regional scenario of "microgrid distribution network + park". The core components include distributed photovoltaic modules, MPPT controller, intelligent scheduling unit, and electric vehicle cluster; no complex wiring is required. The power interaction and signal transmission of each component are realized through the existing park power grid. The core logic is the closed-loop control of "power acquisition - status judgment - power interaction - voltage regulation". (2) Innovative model: "Charging pile finds vehicle" dynamic allocation, specifically reflected in: abandoning the traditional fixed charging pile "vehicle finds charging pile" model, adopting mobile charging equipment, and realizing "charging pile finds vehicle" through intelligent scheduling unit: the mobile charging equipment can be flexibly scheduled to the target location according to the distribution of electric vehicles in the park, the remaining power and photovoltaic power generation; it supports the addition or reduction of the number of charging equipment at any time, changing "point charging" to "area coverage", greatly improving the utilization rate of charging piles and the coverage rate of charging stations, and reducing equipment investment costs.

[0027] This invention provides a power distribution network voltage stabilization system based on MPPT and electric vehicles. It not only achieves a dynamic balance between photovoltaic surplus power consumption and deficit replenishment through the coordinated scheduling of MPPT and electric vehicles, improving energy utilization efficiency and enhancing system stability and reliability, but more importantly, by introducing a dynamic voltage regulation strategy based on power change rate, it can proactively sense the system power change trend and actively and precisely adjust the input voltage of the power distribution network. This effectively suppresses voltage fluctuations, stabilizes the voltage within a safe range, significantly improves power supply reliability and power quality, and achieves a dual improvement in power supply quality and voltage stability.

[0028] Please see Figure 2 , Figure 2 This is a flowchart illustrating a method for stabilizing distribution network voltage based on MPPT and electric vehicles, as described in an embodiment of the present invention. The distribution network is connected to distributed photovoltaic modules, such as... Figure 2 The method shown includes the following steps: 201. Obtain the instantaneous maximum output power of the distributed photovoltaic modules as collected in real time by the MPPT controller; It should be noted that distributed photovoltaic power generation is greatly affected by weather, resulting in frequent power fluctuations. Traditional methods cannot capture these changes in real time, leading to lag in voltage regulation. This step uses an MPPT controller to perform high-frequency data acquisition, solving the real-time data problem and providing a basis for subsequent judgment.

[0029] Specifically, the data acquisition method can be as follows: The MPPT controller collects the output power data of the distributed photovoltaic modules in real time through sensors at a period of 100 milliseconds (ms). Based on the current and voltage values ​​of the photovoltaic modules, the instantaneous maximum output power P is calculated using MPPT algorithms (such as the perturbation-observation method or the incremental conductance method). max Data is uploaded to the intelligent scheduling unit via wired or wireless communication (such as 4G / 5G).

[0030] For example: The MPPT controller is connected to the output of the photovoltaic module to continuously monitor voltage and current; the MPPT algorithm is applied to track the maximum power point, ensuring maximum photovoltaic power generation efficiency; and P... max The data is packaged and transmitted to the intelligent scheduling unit. The data format includes timestamps, power values, etc.

[0031] For example, in an industrial park scenario, photovoltaic modules are installed on the roof, and the MPPT controller outputs a P signal every 100ms. max The value, such as changing from 100kW to 130kW, depends on the light intensity. The intelligent scheduling unit automatically generates P. max -t line chart, used for real-time monitoring.

[0032] Step 201 enables millisecond-level updates of power data, improving system response speed, providing accurate information for supply and demand status assessment, and avoiding voltage fluctuations caused by data delays.

[0033] 202. Based on the comparison between the preset rated power and the collected instantaneous maximum output power, determine the power supply and demand status of the distribution network; It should be noted that fluctuations in photovoltaic power generation can easily lead to supply and demand imbalances, and traditional methods rely on fixed thresholds, resulting in inflexible responses. This invention solves the problem of real-time identification of supply and demand status through dynamic comparison in step 202, providing decision support for power dispatch.

[0034] Specifically, based on the preset rated power P n With the acquired instantaneous maximum output power P max The system compares Pmax and Pn to determine the power supply and demand status of the distribution network, which includes power surplus and power shortage. The intelligent dispatch unit controls the power flow based on the comparison results. When Pmax > Pn: the park's photovoltaic power generation meets its own load demand, resulting in a power surplus. In this case, power is transmitted from the park's photovoltaic system to the electric vehicle cluster via mobile charging equipment (absorbing the excess power). When Pmax < Pn: the park's photovoltaic power generation cannot meet the load demand, resulting in a power shortage. In this case, fully charged electric vehicle groups return a portion of their power to the park's power grid to supplement the power shortage.

[0035] For example, the preset rated power Pn The intelligent scheduling unit can set parameters based on the park's historical load data (such as P). n =100kW), representing the normal demand of the distribution network. The intelligent dispatching unit compares P in real time. max With P n The judgment logic is: if P max >P n This indicates a surplus of electricity; if P max <P n This indicates a power supply shortage.

[0036] For example: when P max =130kW, P n When P = 100kW, it is judged to be a surplus of 30kW; when P max When the power is 80kW, it is determined that there is a 20kW power shortage.

[0037] For example, the intelligent scheduling unit loads P n Value (can be set manually or adjusted adaptively); Receive P every 100ms max After processing the data, execute the comparison algorithm: calculate the difference Δ = P max - P n Different operation branches are triggered based on the sign of Δ (positive or negative) (steps 203 or 204).

[0038] By comparing in step 202, the supply and demand status can be quickly determined with an error of less than 1%, ensuring that the system responds to changes within seconds and reducing the risk of voltage fluctuations.

[0039] 203. When the instantaneous maximum output power is greater than the rated power, control the mobile charging device to transfer the excess power of the distributed photovoltaic modules to the rechargeable electric vehicle cluster for storage; It should be noted that excess power can easily lead to voltage rise and damage to equipment. This step addresses the issues of surplus power consumption and low utilization of charging resources through a "charging station to vehicle" model. Specifically, when the instantaneous maximum output power exceeds the rated power, i.e., when there is a power surplus, the mobile charging equipment is controlled to transfer the excess power of the distributed photovoltaic modules to a cluster of rechargeable electric vehicles for storage. This cluster of rechargeable electric vehicles consists of multiple vehicles within the park's electric vehicle cluster capable of storing excess power, such as vehicles with a SOC of 20%.

[0040] In one feasible implementation, controlling the mobile charging device to transfer excess electricity from the distributed photovoltaic modules to a cluster of rechargeable electric vehicles for storage includes steps A01 to A03: A01. Based on a comprehensive assessment of the vehicle distribution, remaining power, and photovoltaic power generation of the electric vehicle cluster, determine the priority order of the rechargeable electric vehicle cluster and each rechargeable electric vehicle in the rechargeable electric vehicle cluster. A02. Dispatch the mobile charging device to the target location of the target electric vehicle with the highest priority in the priority order for charging; A03. After the target electric vehicle to be charged has finished charging, control the mobile charging device to detach from the target electric vehicle to be charged; and dispatch the mobile charging device to the location of the next priority electric vehicle to be charged for charging. The intelligent dispatching unit integrates multi-source data, including electric vehicle data, photovoltaic data, and park map data. Electric vehicle data: Real-time uploads of remaining battery power (SOC), location (GPS coordinates), battery capacity, and status (idle / busy) via onboard IoT modules. Vehicle distribution data reflects the vehicles' locations within the park. Photovoltaic data: P max The data is obtained from the MPPT controller to understand the photovoltaic power generation situation; park map data: pre-stored electronic map, including road and obstacle information.

[0041] Furthermore, when transferring excess electricity: First, determine the priority order of charging vehicles: the priority logic during charging is as follows: the lower the SOC, the higher the priority; the closer the distance, the higher the priority; the larger the battery capacity, the higher the priority.

[0042] Assuming there are 8 vehicles in the park, the priority after screening is vehicle No. 3 (SOC 40%, distance 200 meters) > vehicle No. 5 (SOC 35%, distance 250 meters).

[0043] Then, the mobile charging equipment is dispatched: the mobile charging equipment is equipped with an autonomous mobile chassis, GPS, and obstacle avoidance sensors. The intelligent dispatching unit generates instructions (including target location and path) to control the movement of the equipment.

[0044] For example, when dispatching vehicle No. 3, the route is planned to be 220 meters straight, and the equipment moves at 5 km / h, automatically slowing down when encountering obstacles.

[0045] Finally, charging and cycling: The device connects to the vehicle's charging port (visual recognition positioning), charges to 60% SOC, then disconnects and is dispatched to the next vehicle.

[0046] For example, when Pmax=130kW and Pn=100kW, the intelligent scheduling unit selects idle vehicles with SOC<60% and distance from the photovoltaic point ≤500 meters.

[0047] Step 203 can reduce equipment investment costs and achieve a surplus power consumption efficiency of 95%, preventing excessive voltage.

[0048] In one feasible implementation, step 203 is a specific implementation of "flexibly dispatching mobile charging equipment to the target location based on the distribution of electric vehicles within the park, remaining power, and photovoltaic power generation." The following example can also be used to illustrate the flexible dispatching of mobile charging equipment to the target location: I. Logic for determining charging vehicles (based on the three-step method of "data collection - threshold judgment - priority sorting") The core relies on the "intelligent scheduling unit" to integrate and analyze three types of key data in real time, and combine them with core parameters (Pmax, Pn, ε) to form vehicle selection rules, as follows: 1. Data collection source: Photovoltaic power generation data: The MPPT controller collects the instantaneous maximum output power Pmax at a period of 100ms and uploads it synchronously to the intelligent dispatching unit; Electric vehicle data: Through the IoT modules (such as 4G / 5G, LoRa) built into the electric vehicles in the park, the current location (latitude and longitude / park zone number), remaining power (SOC value), battery capacity (such as 50kWh), and charging demand status (whether charging is acceptable) are uploaded in real time. Park load data: The intelligent dispatching unit presets the rated power Pn (e.g., 100kW, corresponding to the park's normal load demand), compares Pmax and Pn in real time, and determines the photovoltaic power supply status (surplus / deficit).

[0049] 2. Charging vehicle selection rules (triggered by different scenarios)

[0050] 3. Example: The process of determining charging vehicles in an industrial park (1) Scenario setting: The park is preset with Pn=100kW, and the current MPPT collection Pmax=130kW (Pmax>Pn, surplus 30kW); there are 8 electric vehicles in the park, and the real-time uploaded data is as follows:

[0051] (2) Screening process: Step 1: Exclude vehicles that do not meet the criteria – No. 4 (in motion), No. 8 (faulty), No. 2 (SOC≥60%), No. 6 (SOC≥60%), leaving No. 1, 3, 5, and 7; Step 2: Sort by priority - first look at SOC (7 20% > 1 25% > 5 35% > 3 40%), then look at distance (3 200 meters > 5 250 meters > 1 300 meters > 7 1200 meters), and finally, the overall priority is: 3 > 1 > 5 > 7; Step 3: Matching surplus power - 30kW surplus requires a total charging capacity of ≥30kWh for the vehicles (calculated based on SOC from 20% to 60% which can charge 40% of the capacity): Vehicle No. 3 (60kWh × 40% = 24kWh) + Vehicle No. 5 (50kWh × 40% = 20kWh), the two together can charge 44kWh ≥ 30kWh. Therefore, Vehicle No. 3 and No. 5 are determined as priority charging vehicles (Vehicle No. 1 is reserved as a backup. If the surplus power of Vehicle No. 3 and No. 5 is not fully consumed during the charging process, it will be dispatched).

[0052] II. Mobile control of portable charging devices (closed loop of "command issuance - path planning - precise docking"), as detailed below: The portable charging device (hereinafter referred to as "portable charging pile") is equipped with an IoT communication module, a GPS positioning module, an autonomous mobile chassis (including wheel drive + obstacle avoidance sensors), and an automatic charging interface docking device. Its movement is completely remotely controlled by an intelligent scheduling unit without human intervention. The specific implementation method and examples are as follows: 1. Core Process of Motion Control Instruction generation: After the intelligent scheduling unit determines the charging vehicle (such as vehicle No. 3 or No. 5), it automatically generates a "target scheduling instruction", which includes: the target vehicle's location coordinates, charging priority, estimated charging time, and optimal movement path. Path planning: Based on the park's electronic map (pre-stored in the intelligent scheduling unit, including information on roads, obstacles, and restricted areas), combined with real-time traffic conditions (obtained through park surveillance cameras and feedback from other mobile devices, such as avoiding temporary construction areas), the "shortest path + lowest energy consumption" algorithm is used to plan the path. Mobile execution: After receiving the command, the mobile charging station drives the wheel set through the autonomous mobile chassis. During the journey, it uses ultrasonic obstacle avoidance sensors to detect obstacles (such as pedestrians and vehicles) within 10 meters ahead and automatically slows down or goes around them. Precise docking: After arriving at the target vehicle, the chassis position is adjusted by GPS positioning (error ≤ 1 meter) + visual recognition module (identifying the vehicle charging port position), and the charging interface is automatically extended and retracted to dock with the vehicle charging port, completing the movement-docking closed loop. Dynamic adjustment: If the surplus photovoltaic power increases during the charging process (Pmax further increases), the intelligent scheduling unit can add instructions to schedule backup mobile charging piles (such as connecting to vehicle No. 1); if a vehicle finishes charging early, the mobile charging pile is immediately scheduled to move to the next target vehicle (such as vehicle No. 7).

[0053] 2. Example: The dispatching process for the mobile charging station of vehicle No. 3 is as follows: (1) Command information: Target vehicle No. 3 (200 meters from area A, coordinates X123.45 / Y67.89), priority 1, estimated charging time 1.5 hours; (2) Path planning: The intelligent dispatch unit queries the park map. The current mobile charging pile is located next to the photovoltaic access point in Area A (coordinates X123.30 / Y67.90). The planned path is: go straight along the main road in Area A → turn left into the parking space lane where vehicle No. 3 is located. The entire route is 220 meters long and there are no obstacles. (3) Mobile execution: The mobile charging pile starts its autonomous chassis and travels at a speed of 5km / h. When it detects a pedestrian 5 meters ahead, it automatically slows down to 1km / h and resumes speed after the pedestrian passes. (4) Precise docking: After arriving next to vehicle No. 3, the visual recognition module locks the vehicle's charging port, the chassis is slightly adjusted to be 30cm away from the charging port, the charging interface automatically extends and completes docking, and then charging begins; (5) Subsequent scheduling: After 1 hour of charging, the intelligent scheduling unit detected that the photovoltaic surplus power dropped to 10kW and the SOC of vehicle No. 3 had risen to 50%. It then issued an instruction: vehicle No. 3 has finished charging and the mobile charging pile is to go to vehicle No. 5 (250 meters from area A). The route is planned to go straight for 50 meters without detouring. After 10 minutes, the docking is completed and charging begins.

[0054] 204. When the instantaneous maximum output power is less than the rated power, the rechargeable electric vehicle cluster is controlled to return part of the power to the distribution network to supplement the power supply gap. It should be noted that power supply shortages cause voltage drops, affecting load operation. To address this shortage, specifically, when the instantaneous maximum output power is less than the rated power, the rechargeable electric vehicle cluster is controlled to return a portion of its power to the distribution network to supplement the power supply gap.

[0055] Select rechargeable vehicles and determine the rechargeable electric vehicle cluster: sort by SOC and location priority (higher SOC, higher priority). Recharge according to the rated recharge power of the electric vehicles.

[0056] For example, electric vehicle data: filter vehicles with SOC ≥ 80% and no reservations in the next 2 hours.

[0057] Example: P max =80kW, with a power shortage of 20kW, the return power ratio is (20kW shortage divided by the rated power P) n =100 (obtained) can be 20%. The dispatching unit selects 3 vehicles with SOC≥80% to return power to supplement the power supply gap. The rated return power is 10KW, and at this time it can be 2kW (proportion 20%).

[0058] Step 204 replenishes power in a timely manner, improving power supply reliability.

[0059] 205. Based on the power change rate of the distributed photovoltaic modules and a preset threshold, the input voltage of the distribution network is dynamically adjusted to stabilize the distribution network voltage; It should be noted that sudden power fluctuations cause voltage oscillations, and traditional regulation is lagging. This step addresses the voltage stability issue through proactive regulation. Specifically, based on the power change rate of the distributed photovoltaic modules and a preset threshold, the input voltage of the distribution network is dynamically adjusted to stabilize the distribution network voltage.

[0060] This application also provides an "emergency fallback" strategy: in the voltage regulation strategy, when the photovoltaic power fluctuation exceeds the regulation capacity of the electric vehicle cluster, the system will trigger the power limiting mode of the MPPT controller as a fallback strategy to ensure grid safety.

[0061] The power change rate is obtained by calculating the first difference ΔP between the instantaneous maximum output power of two adjacent acquisition cycles and the second difference ΔV between the power grid input voltage of the corresponding cycle; the power change rate is obtained based on the ratio of the first difference and the second difference.

[0062] The preset threshold ε is ±5% of the rated voltage Vn (e.g., Vn=380V, ε=±19V). For example, the power change rate dP / dV is obtained by calculating ΔP (difference of Pmax) and ΔV (difference of Vi) between adjacent 100ms periods, with the formula dP / dV = ΔP / ΔV.

[0063] For example, when Pmax changes from 100kW to 120kW, ΔP = 20kW, and Vi changes by ΔV = 10V, then dP / dV = 2.

[0064] In one feasible implementation, the dynamic adjustment of the input voltage of the distribution network based on the power change rate of the distributed photovoltaic modules and a preset threshold includes: When the absolute value of the power change rate |dP / dV| is less than or equal to the preset threshold ε, the current power distribution network input voltage remains unchanged; when the absolute value of the power change rate |dP / dV| is greater than the preset threshold ε, the power distribution network input voltage is adjusted according to the power change trend to maintain voltage stability.

[0065] The power variation trend is determined by comparing the instantaneous maximum output power of the photovoltaic system with its rated power. The power variation trend includes a sharp decrease in power (Pmax < Pn) and a surge in power (Pmax > Pn). Furthermore, the adjustment of the power distribution network input voltage according to the power change trend includes: if the power change trend is a sharp decrease in power, then according to the power supply gap, control the return power of the rechargeable electric vehicle cluster to increase the return power, and maintain the fluctuation of the power distribution network input voltage Vi not exceeding the preset threshold; if the power change trend is a surge in power, then reduce the power distribution network input voltage.

[0066] In one feasible implementation, controlling the rechargeable electric vehicle cluster to increase its recharge power according to the power supply gap includes: determining the power supply gap ΔP using the difference between the instantaneous maximum output power and the rated power; determining the target recharge power of the rechargeable electric vehicle cluster based on the power supply gap and the rated recharge power of the rechargeable electric vehicle cluster; and controlling the recharge power of the rechargeable electric vehicle cluster based on the target recharge power.

[0067] In one feasible implementation, reducing the input voltage of the distribution network includes: determining the power surge magnitude based on the difference ΔP between the instantaneous maximum output power and the rated power; determining the voltage ΔV to be reduced using the power surge magnitude and the system impedance; and controlling the MPPT controller to reduce the input voltage of the distribution network by the voltage to be reduced.

[0068] An example of a power acquisition and voltage regulation strategy is shown below: 1. Power Acquisition: The MPPT controller acquires the maximum photovoltaic power P in real time with a period of 100ms. max The data is simultaneously uploaded to the intelligent scheduling unit, which then automatically generates a P-shaped graph. max -t line chart (no manual drawing required, automatically generated by the system for trend assessment); 2. Voltage regulation logic: The intelligent scheduling unit adjusts the voltage according to P... max The -t line graph reflects the power change trend, combined with V i Based on real-time monitoring data, the following adjustment strategy is implemented by calculating the power-voltage change rate dP / dV (ΔP / ΔV, where ΔP is the difference of Pmax between two adjacent 100ms cycles and ΔV is the difference of Vi in the corresponding cycle): When |dP / dV|<ε: the system is in a relatively stable state, maintaining the current input voltage Vi of the campus unchanged; When |dP / dV|>ε: it indicates a surge or sharp decrease in power. If the power decreases sharply (Pmax < Pn): according to the power supply gap ratio, control the electric vehicle to increase the return power, while maintaining Vi basically stable (fluctuation does not exceed ε). If the power surges (Pmax > Pn): appropriately reduce the input voltage Vi of the park to prevent excessive power from adversely affecting the power grid equipment.

[0069] Among them, the reversible electric vehicle cluster is the reversible electric vehicle cluster determined in step 204 above. When the power change trend is a sharp decrease in power, the reversible power of the reversible electric vehicle cluster is increased again to maintain voltage stability.

[0070] For example, the power change rate is calculated: the intelligent scheduling unit calculates dP / dV every 100ms.

[0071] Dynamic adjustment: When |dP / dV|<ε, Vi remains constant: By acquiring Pmax and Vi at high frequency, the charging device synchronously adjusts the current (e.g., if Pmax increases slightly by 0.3kW, the device increases the current by 0.1A), matching the load power and keeping Vi stable.

[0072] For example: Vi=380V, Pmax changes slightly from 102kW to 102.3kW, and after the equipment is adjusted, Vi remains at 380V.

[0073] When |dP / dV|>ε: When power is drastically reduced: Determine the proportion of the power shortage that each vehicle needs to bear, calculated as follows: Proportion of power shortage borne by a single vehicle = Power shortage / (Number of vehicles × Rated return power) × 100%; Determine the target return power (Target return power = Proportion of power shortage borne by each vehicle * Rated return power), and return power according to the target return power to maintain Vi fluctuations not exceeding ε; Assuming the current shortage is 20, the number of vehicles in the rechargeable electric vehicle cluster is 3, and the rated recharge power is 10KW, then the proportion of the shortage borne by a single vehicle changes from 20% to 66.7%. At this point, the proportion is adjusted to 66.7%, and each vehicle recharges 6.67kW = 66.7% * 10KW.

[0074] For example, initially each vehicle returns 2kW (proportion 20%), for a total return of 6kW; if the shortfall is not filled, the proportion is adjusted to 66.7%, and each vehicle returns 6.67kW.

[0075] When power surges: Reduce Vi: Calculate the voltage reduction required ΔV ≈ ΔP × system impedance (impedance is pre-stored in the scheduling unit), and control MPPT to reduce the output voltage.

[0076] Assumptions: Pmax surges by 20kW, ΔV = 10V is calculated, and MPPT will reduce Vi from 380V to 370V.

[0077] Through voltage control in step 205, voltage fluctuations are reduced by more than 50%, extending equipment lifespan; energy utilization is increased to 90%.

[0078] The following is an example of "when |dP / dV|<ε: the system is in a relatively stable state, and the current input voltage Vi of the campus remains unchanged": The core logic for maintaining the current input voltage Vi of the park is: when the power changes slightly, the charging load changes synchronously, and then small compensation is used to offset the fluctuations, so as to ensure that Vi is stable within ±5% of the standard voltage of the distribution network.

[0079] The specific implementation (3 steps) is as follows: (1) High-frequency data acquisition: MPPT measures photovoltaic power Pmax every 100ms, and the dispatch unit simultaneously measures Vi (access point voltage) and total power of charging load; (2) Determine stability: If a small change in Pmax does not cause the voltage to exceed ±5% (i.e., |dP / dV|<ε), the system is determined to be stable; (3) Dynamic adaptation: A slight increase in Pmax (e.g., 0.3kW more): This allows the charging device to slightly increase the current, and the load power also increases by 0.3kW, offsetting the voltage rise; A slight reduction in Pmax (e.g., 0.2kW less): This allows the charging device to slightly reduce the current, and the load power is also reduced by 0.2kW, preventing voltage drops. If Vi fluctuates slightly within ±2%, MPPT maintains constant voltage output, and the existing reactive power compensation unit in the park compensates for the loss to bring it back to a stable value.

[0080] For example: The standard voltage in the park is 380V (Vi stable range 361-399V), initial Vi=380V, Pmax=102kW, and the charging load is also 102kW.

[0081] After 100ms, Pmax rises to 102.3kW: each charging device increases current by 0.1A, and the load also rises to 102.3kW, while Vi remains around 380V; after another 100ms, Pmax drops to 102.1kW: each charging device decreases current by 0.07A, and the load also drops to 102.1kW, while Vi remains unchanged.

[0082] The following is an example of "If the power drops sharply (Pmax < Pn): According to the power supply gap ratio, control the electric vehicle to increase the return power, while maintaining Vi basically stable (fluctuation not exceeding ε)": 1) Control the return power proportionally: First, calculate the power supply gap: Gap = Rated power of the park (Pn) - Actual photovoltaic power (Pmax); Calculate the gap ratio: Ratio = Gap ÷ Pn × 100% (for example, if the gap is 20kW and Pn is 100kW, the ratio is 20%). Power return allocation: From electric vehicles with SOC≥80% and no reservation, power return is allocated proportionally (for example, if each electric vehicle has a rated power return of 10kW, then each vehicle will return 2kW at a 20% ratio, and the total power return will be close to the shortfall).

[0083] 2) Maintain Vi stability (fluctuation ≤ ±5%Vn) Vi is measured every 100ms. If Vi is too low (close to the lower limit), the trolley return current is slightly increased; if Vi is too high (close to the upper limit), the current is slightly decreased. Furthermore, the reactive power compensation unit in the park is used to compensate for line losses, preventing Vi from exceeding the fluctuation range.

[0084] For example: The park's Vn=380V (stable range 361-399V), Pn=100kW, and Pmax drops sharply to 80kW (a 20kW shortfall, accounting for 20%). Three rechargeable trolleys (each with a rated recharge of 10kW) each recharge 2kW at 20% capacity, for a total recharge of 6kW, raising Vi from 360V to 365V. Because the shortfall has not been fully covered, the ratio has been adjusted to 66.7%, with each vehicle receiving a return of 6.67kW (total 20kW), and Vi increased to 375V; Subsequently, the current was fine-tuned every 100ms, and Vi remained within 361-399V, with fluctuations not exceeding ±19V.

[0085] The statement "If power surges (Pmax > Pn): Appropriately reduce the input voltage Vi of the industrial park to prevent excessive power from adversely affecting the power grid equipment" is illustrated below: 1) The core logic of “appropriately reducing the input voltage Vi of the park” is: when the power surges (Pmax>Pn) and |dP / dV|>ε, Vi is reduced by “as needed with fixed amplitude + equipment coordinated adjustment”, with the goal of keeping Vi within the range of Vn±5% (ε) to offset the impact of excessive power on the power grid.

[0086] 2) The specific implementation (3 steps) lies in: ① Calculate the reduction: Based on the power surge amplitude ΔP (Pmax-Pn), roughly calculate the voltage reduction required according to "ΔV≈ΔP×system impedance" (the final voltage reduction should not be less than Vn×95%, i.e., the lower limit of ε). ② Adjusting equipment: The intelligent scheduling unit instructs the MPPT controller to lower the output voltage, and at the same time, it causes the portable charging device to reduce the charging current to avoid sudden load changes that exacerbate voltage fluctuations; ③ Micro-monitoring: Measure Vi every 100ms. If the reduction is insufficient, reduce it slightly. If it is close to the lower limit, stop to ensure that it does not exceed the ε range.

[0087] For example: In the park, Vn=380V (stable range 361-399V), Pn=100kW, Pmax suddenly increases from 100kW to 120kW (ΔP=20kW), and |dP / dV| exceeds the threshold. The calculation requires a reduction of 10V (380V → 370V, still above 361V). The intelligent scheduling unit instructs MPPT to reduce the output voltage from 380V to 370V, and the charging device current is adjusted accordingly. After 100ms, Vi stabilizes at 370V, which is within the fluctuation range.

[0088] This invention provides a method for stabilizing the voltage of a power distribution network based on MPPT (Multi-Level Photovoltaic Power Controller) and electric vehicles. The power distribution network is connected to distributed photovoltaic (PV) modules. The method includes: acquiring the instantaneous maximum output power of the distributed PV modules in real time, collected by an MPPT controller; comparing the collected instantaneous maximum output power with a preset rated power to determine the power supply and demand status of the power distribution network; when the instantaneous maximum output power is greater than the rated power, controlling a mobile charging device to transfer the excess power of the distributed PV modules to a cluster of rechargeable electric vehicles for storage; when the instantaneous maximum output power is less than the rated power, controlling the cluster of rechargeable electric vehicles to return a portion of the power to the power distribution network to supplement the power supply gap; and dynamically adjusting the input voltage of the power distribution network according to the power change rate of the distributed PV modules and a preset threshold to stabilize the power distribution network voltage. This invention not only achieves a dynamic balance between photovoltaic surplus power consumption and deficit replenishment through the coordinated scheduling of MPPT and electric vehicles, thereby improving energy utilization efficiency and enhancing system stability and reliability, but more importantly, by introducing a dynamic voltage regulation strategy based on power change rate, it can proactively sense the trend of system power change and actively and accurately adjust the input voltage of the distribution network, thereby effectively suppressing voltage fluctuations and stabilizing the voltage within a safe range. This significantly improves power supply reliability and power quality, achieving a dual improvement in power supply quality and voltage stability.

[0089] Furthermore, compared with the prior art, the present invention has significant advantages: 1) Cost advantage: The "charging station finds vehicle" model allows for flexible addition or removal of charging equipment, reducing investment costs; it also reduces reliance on traditional energy storage components, lowering user charging costs and overcoming the shortcomings of traditional energy storage solutions that are "high-cost and inflexible". 2) Voltage stability advantage: By using MPPT technology to collect power data at high frequency and adjusting the voltage based on the trend of dP / dV changes, the voltage fluctuation problem after distributed photovoltaic access is accurately solved, ensuring power supply quality and reducing the risk of damage to grid equipment; 3) Energy efficiency advantages: The coordinated scheduling of MPPT and electric vehicles achieves a dynamic balance between the consumption of surplus photovoltaic power and the supplementation of the gap, thereby improving energy utilization efficiency and enhancing system stability and reliability. 4) Flexibility advantage: No complex construction or modification is required, it is adaptable to various park scenarios, and the system parameters can be flexibly adjusted according to the photovoltaic installed capacity and the number of electric vehicles, making it highly scalable.

[0090] It should be noted that the "charging station finds vehicle" model does not require expansion or renovation of the park's power grid, which is a significant cost advantage for the renovation of old parks. From a systems engineering perspective, it also offers the following benefits: 1) Comprehensive cost advantage: The traditional "vehicle finds charging station" model relies on the large-scale deployment of fixed charging stations, which involves high civil engineering costs, power grid expansion costs, and parking space renovation fees. Furthermore, fixed charging stations often suffer from "tidal effects," resulting in low utilization. The "charging station finds vehicle" model proposed in this invention utilizes the flexibility of mobile charging equipment, covering a large parking area with a small number of devices, significantly improving the utilization rate of a single device and eliminating the need for large-scale civil engineering and power grid expansion, thus offering a significant advantage in total cost of ownership (TCO); 2) Technological advancement: Mobile charging robot technology has gradually matured in the industry. This application provides a control method aimed at solving dynamic scheduling logic problems. This invention solves the problem of how mobile devices can efficiently serve target vehicles through a closed-loop logic of "data acquisition - threshold judgment - priority sorting," overcoming the inefficiency of the traditional fixed model.

[0091] It should be noted that the roles of the mobile charging device and the electric vehicle in this application are explained as follows: 1) Functional positioning distinction: In the architecture of this invention, the electric vehicle is mainly positioned as a "distributed energy storage carrier," whose core value lies in providing a huge energy storage capacity (such as 60kWh-100kWh); while the mobile charging device is positioned as a "flexible energy transmission intermediary" and a "low-power buffer unit"; 2) Complementary logic: Even if the mobile charging device has its own battery, its capacity is usually much smaller than that of the electric vehicle, mainly playing a transitional buffer role. More importantly, the mobile device solves the last 100 meters of "physical connection." The relationship between the two is like that of a "mobile socket" and an "energy storage battery." The mobile device flexibly connects the discrete and mobile energy storage resource of the electric vehicle to the power grid, realizing a qualitative change from "point access" to "area coverage," without any technical redundancy.

[0092] It should be noted that the technical solution of this invention is not an idealized, unconditional operation for the following reasons: 1) Explicit boundary conditions: The technical solution of this invention explicitly sets multiple boundary parameters, including rated power, voltage threshold (ε=±5%), and SOC threshold (e.g., charging threshold SOC<60%, return threshold SOC≥80%). The system operation is entirely based on the judgment of these quantitative parameters, and is not an idealized, unconditional operation; 2) Robust design: For the possible extreme case of "few vehicles and many lights," the control logic of this invention includes a "threshold judgment" mechanism. When the electric vehicle capacity is insufficient to smooth out fluctuations, the system will trigger the next level of protection strategy (e.g., enabling the MPPT's own curtailment function). It can be understood that this is logically closed-loop and practically operable.

[0093] In summary, this invention proposes a novel distribution network voltage stabilization method based on the synergy of "source-grid-load-storage", which solves the technical problem in traditional technologies that "maintaining voltage results in loss of benefits, while maintaining benefits results in loss of voltage stability".

[0094] Figure 3 An internal structural diagram of a computer device in one embodiment is shown. This computer device can specifically be a terminal or a server. Figure 3 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program, which, when executed by the processor, causes the processor to perform the aforementioned methods. The internal memory may also store a computer program, which, when executed by the processor, causes the processor to perform the aforementioned methods. Those skilled in the art will understand that… Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0095] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform actions such as... Figure 2 The steps of the method shown.

[0096] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform the following actions: Figure 2 The steps of the method shown.

[0097] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0098] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0099] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for stabilizing distribution network voltage based on MPPT and electric vehicles, characterized in that, The method for connecting distributed photovoltaic modules to the distribution network includes: Obtain the instantaneous maximum output power of the distributed photovoltaic modules as collected in real time by the MPPT controller; The power supply and demand status of the distribution network is determined by comparing the preset rated power with the collected instantaneous maximum output power. When the instantaneous maximum output power is greater than the rated power, the mobile charging device is controlled to transfer the excess power of the distributed photovoltaic modules to the cluster of rechargeable electric vehicles for storage. When the instantaneous maximum output power is less than the rated power, the rechargeable electric vehicle cluster is controlled to return part of the power to the distribution network to supplement the power supply gap. The input voltage of the distribution network is dynamically adjusted based on the power change rate of the distributed photovoltaic modules and a preset threshold to stabilize the distribution network voltage.

2. The method according to claim 1, characterized in that, The control of the mobile charging device to transfer excess electricity from the distributed photovoltaic modules to the cluster of rechargeable electric vehicles for storage includes: Based on a comprehensive assessment of the vehicle distribution, remaining power, and photovoltaic power generation of the electric vehicle cluster, the priority order of the rechargeable electric vehicle cluster and its individual rechargeable electric vehicles is determined. The mobile charging device is dispatched to the target location of the target electric vehicle with the highest priority in the priority order for charging. Once the target electric vehicle has finished charging, the mobile charging device is controlled to detach from the target electric vehicle; and the mobile charging device is dispatched to the next priority electric vehicle to be charged.

3. The method according to claim 1, characterized in that, The method further includes: Calculate the first difference between the instantaneous maximum output power of two adjacent acquisition cycles and the second difference between the power grid input voltage of the corresponding cycle; The power change rate is obtained based on the ratio of the first difference to the second difference.

4. The method according to claim 1, characterized in that, The step of dynamically adjusting the input voltage of the distribution network based on the power change rate of the distributed photovoltaic modules and a preset threshold includes: When the absolute value of the power change rate is less than or equal to the preset threshold, the current power distribution network input voltage is kept constant. When the absolute value of the power change rate is greater than the preset threshold, the input voltage of the distribution network is adjusted according to the power change trend to maintain voltage stability.

5. The method according to claim 4, characterized in that, The adjustment of the power distribution network input voltage according to the power change trend includes: If the power change trend is a sharp decrease in power, then according to the power supply gap, the power return of the rechargeable electric vehicle cluster is increased, and the fluctuation of the input voltage of the distribution network is kept below the preset threshold. If the power change trend is a power surge trend, then reduce the input voltage of the power distribution network.

6. The method according to claim 5, characterized in that, The method of controlling the rechargeable electric vehicle cluster to increase recharge power according to the power supply gap includes: The power supply gap is determined by using the difference between the instantaneous maximum output power and the rated power; Based on the power supply gap and the rated recharge power of the rechargeable electric vehicle cluster, determine the target recharge power of the rechargeable electric vehicle cluster; The return power of the rechargeable electric vehicle cluster is controlled based on the target return power.

7. The method according to claim 5, characterized in that, The reduction of the input voltage of the power distribution network includes: The power surge amplitude is determined based on the difference between the instantaneous maximum output power and the rated power; Using the power surge magnitude and system impedance, determine the voltage reduction required; The MPPT controller reduces the input voltage of the power distribution network to the voltage that needs to be reduced.

8. A voltage stabilization system for a power distribution network based on MPPT and electric vehicles, characterized in that, The stable system includes: Distributed photovoltaic modules, connected to the power distribution network, are used to convert solar energy into electrical energy to provide power to the power distribution network; The electric vehicle cluster serves as a flexible energy storage carrier; the electric vehicle cluster includes the rechargeable electric vehicle cluster and the rechargeable electric vehicle cluster. The MPPT controller is used to collect the instantaneous maximum output power of the distributed photovoltaic modules in real time. Intelligent scheduling unit, for performing the method as described in any one of claims 1 to 7; Mobile charging equipment is used to establish a power transmission link between photovoltaic modules and electric vehicle clusters.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 7.

10. A computer device, comprising a memory and a processor, characterized in that, The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 7.

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