A new energy microgrid transient stability control method, system, terminal and medium

Abstract

This application relates to a transient stability control method, system, terminal, and medium for a new energy microgrid, relating to the power grid field. The method includes: acquiring the power generation of a photovoltaic array and the grid's time-of-use pricing information; determining whether the current time-of-use pricing information falls within a high-price electricity consumption period; if so, predicting the load demand power and available photovoltaic power supply power during the high-price electricity consumption period based on historical load data and power generation, and calculating the power gap; calculating the maximum power supply power provided by the energy storage device to the electrical equipment through the distribution cabinet based on the remaining available power of the energy storage device and the expected duration of the high-price electricity consumption period; determining whether the maximum power supply power exceeds the power gap; if so, the electrical equipment is powered by the photovoltaic array and the energy storage device; if not, ensuring the basic operating power of critical loads and reducing the power of non-critical loads, with the grid providing the difference in power. This application has the effect of improving power supply reliability and reducing electricity costs.

Description

Technical Field

[0001] This application relates to the field of power grids, and in particular to a transient stability control method, system, terminal and medium for a new energy microgrid. Background Technology

[0002] With the rapid development and widespread application of new energy technologies, new energy microgrids are often presented in the form of parks integrating distributed photovoltaics in practical applications. Transient stability control technology is a key technical support for improving power supply reliability and reducing electricity costs.

[0003] In related technologies, the stability control of new energy microgrids usually adopts a passive following strategy: when the photovoltaic power is greater than the electricity load, the excess power cannot be utilized; when the photovoltaic power is less than the electricity load, it is directly supplemented by the grid, without taking into account the changes in the grid's time-of-use electricity price.

[0004] Regarding the aforementioned technologies, there is a problem in the power supply dispatching process that the regulation is not combined with time-of-use pricing and load priority, resulting in high electricity costs and insufficient protection of critical loads. Summary of the Invention

[0005] To improve power supply reliability and reduce electricity costs, this application provides a transient stability control method, system, terminal, and medium for new energy microgrids.

[0006] Firstly, this application provides a transient stability control method for a new energy microgrid, employing the following technical solution: A transient stability control method for a new energy microgrid includes: Obtain information on the power generation of the photovoltaic array and the time-of-use electricity price of the grid; Determine whether the current time-of-use electricity price information falls within a period of high electricity prices; If so, based on historical load data and power generation, predict the load demand and photovoltaic power available for the park during periods of high electricity prices, and calculate the power gap; Based on the remaining available power of the energy storage device and the expected duration of the high-price electricity period, calculate the maximum power supply that the energy storage device can provide to the electrical equipment through the distribution cabinet. The electrical equipment includes at least one of the following: charging piles, lighting systems, air conditioning systems, security equipment, and production auxiliary equipment. Determine if the maximum power supply is greater than the power deficit; If so, the electrical equipment is powered by the photovoltaic array and energy storage device; If not, ensure the basic operating power of critical loads, reduce the power of non-critical loads, and provide the difference in power from the grid.

[0007] By adopting the above technical solution, the current electricity price period and the source-load status of the park are analyzed. Power shortages are predicted by combining historical load data, and power supply capacity is assessed based on distribution capacity. Simultaneously, power-consuming equipment is categorized and controlled according to its criticality, and power supply strategy switching is implemented. This solution significantly increases the self-consumption rate of photovoltaic power during high-price periods, optimizes power resource allocation, reduces unnecessary high-price power purchases from the grid and the risk of critical load interruptions, and significantly improves the economic efficiency and reliability of power supply in the park.

[0008] Optionally, the photovoltaic array can be divided into several sub-regions; Obtain the proportion of ice-covered area and solar irradiance on the surface of photovoltaic panels in each sub-region; Determine whether the solar irradiance is lower than the preset effective ice-melting irradiance threshold; If not, then the icing coverage area ratio is predicted to decrease, and the updated icing coverage area ratio is obtained. Based on the updated icing coverage area ratio, the icing coverage level of each sub-region is determined. Based on the solar incidence angle and icing coverage level, the attenuation coefficient corresponding to each sub-region is obtained by querying the preset attenuation coefficient mapping table. The theoretical output power of each sub-region is corrected based on the attenuation coefficient, and the corrected theoretical output power of the sub-regions is aggregated to obtain the corrected photovoltaic power supply.

[0009] By employing the aforementioned technical solution, the icing coverage status of photovoltaic panels in each sub-region is analyzed. The effectiveness of de-icing is determined by combining solar irradiance and incident angle. An attenuation coefficient mapping table is consulted based on the icing coverage level. Simultaneously, the power generation capacity of each sub-region is differentiated and weighted, resulting in the corrected photovoltaic power output. This solution significantly improves the accuracy of photovoltaic power prediction in icing scenarios, reducing power overestimation or scheduling deviations caused by misjudgments of icing.

[0010] Optionally, each sub-region can be divided into multiple micro-regions; Obtain the azimuth, tilt, and edge distance of each micro-region; The instantaneous effective irradiance gain of each micro-region is calculated based on the azimuth angle and the solar incidence angle. The heat loss coefficient of each micro-region is determined based on the tilt angle and edge distance; The ablation rate of each micro-region is determined based on the instantaneous effective irradiation gain, heat loss coefficient, and ambient temperature. The icing coverage area ratio of each micro-region is predicted to decrease according to the ablation rate of each micro-region, and the updated icing coverage area ratio of the micro-region is obtained. The updated icing coverage area ratios of each micro-region within the same sub-region are aggregated to obtain the updated icing coverage area ratios of each sub-region.

[0011] By employing the above technical solution, the azimuth, tilt, and edge distance of each micro-region are analyzed. The instantaneous effective irradiance gain is calculated based on the solar incidence angle, and the ablation rate is dynamically determined based on heat dissipation characteristics and ambient temperature. Simultaneously, the icing coverage area ratio of each micro-region is independently predicted, and the results are aggregated to generate an updated icing state for the sub-region. This solution significantly improves the consistency between the predicted icing state and the actual icing distribution on the photovoltaic module surface, reducing power overestimation or scheduling errors caused by uneven icing distribution.

[0012] Optionally, the upstream adjacent microregions of each microregion can be determined based on the tilt angle and azimuth angle of each microregion; Obtain the current surface temperature of each micro-region, the current ice coverage area ratio, and the previous ice coverage area ratio. Based on the current surface temperature, the current ice coverage area ratio, and the previous ice coverage area ratio, determine whether each micro-region is located at the lower edge of the sub-region where it is located, whether the tilt angle of the sub-region is less than the preset tilt angle threshold, and whether the current surface temperature is below zero degrees. If so, then determine whether the proportion of ice-covered area in the upstream adjacent micro-region is decreasing; If so, the upstream adjacent micro-region is an effective source of meltwater, and the micro-region is determined to be in a state of refreezing risk. Set the ablation rate of micro-regions at risk of refreezing to a negative value.

[0013] By employing the aforementioned technical solution, the positional relationships of each micro-region on the photovoltaic panel are analyzed. The upstream adjacent micro-regions are determined by combining the installation tilt angle and azimuth angle. Furthermore, the meltwater flow trend is judged based on the temporal changes in surface temperature and the proportion of ice-covered area. Simultaneously, a negative melting rate is set for micro-regions at risk of re-icing to correct their ice evolution direction. This solution significantly improves the ability to identify localized re-icing phenomena in icy and snowy environments, reducing the overestimation of power generation capacity caused by neglecting edge ice growth.

[0014] Optionally, it sends discharge participation requests to each new energy vehicle connected to the V2G charging pile in the park and receives real-time status data returned by each new energy vehicle. Based on real-time status data, select dispatchable vehicles that meet the dispatchable conditions, including user authorization, state of charge not lower than the return trip safety threshold, battery temperature within the allowable range, and normal communication status. The average discharge power is calculated based on the available discharge capacity of each dispatchable vehicle and the remaining duration of the high-price electricity period. A discharge priority sequence is generated based on the state of charge, battery aging, historical participation frequency, and average discharge power of each dispatchable vehicle. The status changes of dispatchable vehicles are detected at a preset period, and the discharge priority sequence is updated accordingly. The total available discharge power is obtained by weighted summing of the average discharge power in the updated discharge priority sequence. The maximum power supply is summed with the total available discharge power to obtain the updated maximum power supply.

[0015] By adopting the above technical solution, real-time status data of new energy vehicles within the park is analyzed. Based on user authorization and battery safety conditions, dispatchable vehicles are selected. The average discharge power is calculated based on available discharge capacity and the duration of high-price periods. Simultaneously, a discharge priority sequence is generated by comprehensively considering state of charge, battery aging, and historical participation frequency. The total available discharge power is then updated and superimposed onto the park's maximum power supply capacity. This solution significantly improves the accuracy and security of V2G resource scheduling, reducing power shortages caused by blind resource allocation or misjudgment.

[0016] Optionally, an initial discharge scheduling sequence can be established based on each schedulable vehicle; Send temporary travel plan inquiry messages to user terminals in the initial discharge scheduling sequence; Based on the sent temporary travel plan inquiry message, monitor whether temporary travel information sent by any user terminal is received. The temporary travel information includes the expected departure time and destination. If so, the minimum reserve power of dispatchable vehicles is calculated based on the temporary trip information and power impact parameters, including battery aging, discharge loss and ambient temperature. Determine whether the minimum reserve power is greater than the remaining power of the dispatchable vehicles; If so, a low battery warning will be pushed to the user terminal, and the dispatchable vehicle will be removed from the initial discharge dispatch sequence; If not, recalculate the average discharge power of dispatchable vehicles based on the minimum reserved power.

[0017] By adopting the above technical solution, the initial discharge capacity of dispatchable vehicles is analyzed, an initial discharge scheduling sequence is established based on user authorization status and battery safety conditions, and temporary travel plan inquiry messages are proactively sent to user terminals. Simultaneously, the minimum reserve capacity is calculated based on the returned trip information and power impact parameters, vehicle discharge power is updated, and the scheduling sequence is adjusted. This solution significantly improves the consistency between V2G scheduling and users' actual travel needs, reducing power overdraft or scheduling failures caused by untimely trip changes.

[0018] Optionally, a trip confirmation request may be sent to the user terminal that has already received the temporary trip information; Receive trip update information returned by the user terminal in response to the trip confirmation request; If there is a discrepancy between the updated itinerary information and the temporary itinerary information, the itinerary confirmation request will be resent at a preset fixed period, and the number of changes will be accumulated. Calculate the itinerary confidence level based on the number of changes and the number of historical itinerary changes; Determine whether the trip confidence level is higher than the preset confidence threshold; If so, use the itinerary update information directly; If not, the schedulable vehicles will be temporarily marked as unschedulable.

[0019] By employing the above technical solution, the system analyzes temporary trip information returned by user terminals, combines trip validity confirmation requests with multiple feedback results to identify the reliability of travel plans, and calculates trip confidence weights based on the cumulative number of changes and historical behavior. Simultaneously, vehicles with low confidence are dynamically marked as unschedulable, and the schedulable resource pool is updated. This solution significantly improves the reliability of user trip data in V2G scheduling, reducing discharge failures and power deviations caused by frequent trip changes or inaccurate reporting.

[0020] Secondly, this application provides a transient stability control system for a new energy microgrid, which adopts the following technical solution: A transient stability control system for a new energy microgrid, comprising: The acquisition module is used to acquire power generation capacity, grid time-of-use electricity price information, remaining available electricity, and estimated duration. A memory for storing the program of the transient stability control method for the new energy microgrid; The processor and the program in the memory can be loaded and executed by the processor to implement the transient stability control method for the new energy microgrid.

[0021] By adopting the above technical solution, the acquisition module collects photovoltaic power generation, grid time-of-use electricity price, remaining available power in the distribution cabinet, and the expected duration of high-price periods in real time. The processor quickly executes power gap prediction and load dispatch control logic, and the memory stores and efficiently calls the transient stability control program. This realizes fully automated decision-making from data perception to power supply strategy generation, which significantly improves the economic efficiency of electricity consumption while ensuring the reliable operation of critical loads, and provides an efficient and reliable stability control solution for new energy microgrids.

[0022] Thirdly, this application provides a smart terminal, which adopts the following technical solution: A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described above.

[0023] Fourthly, this application provides a computer storage medium capable of storing corresponding programs, which facilitates improved power supply reliability and reduced electricity costs, and adopts the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and executed by any of the above-mentioned transient stability control methods for new energy microgrids.

[0024] In summary, this application includes at least one of the following beneficial technical effects: 1. Analyze the current electricity price period and the source-load status of the park, predict the power gap based on historical load data, assess the power supply capacity based on distribution capacity, and simultaneously control the power consumption equipment according to its criticality level, implementing power supply strategy switching. This solution significantly improves the self-consumption rate of photovoltaic power during high-price periods, optimizes the allocation of power resources, reduces unnecessary high-price power purchases from the grid and the risk of critical load interruptions, and significantly improves the economic efficiency and reliability of power supply in the park. 2. The azimuth, tilt, and edge distance of each micro-region are analyzed. The instantaneous effective irradiance gain is calculated based on the solar incidence angle, and the ablation rate is dynamically determined based on heat dissipation characteristics and ambient temperature. Simultaneously, the icing coverage area ratio of each micro-region is independently predicted for attenuation, and the results are aggregated to generate an updated icing state for the sub-region. This scheme significantly improves the consistency between the predicted icing state and the actual icing distribution on the photovoltaic module surface, reducing power overestimation or scheduling errors caused by uneven icing distribution. 3. Analyze the real-time status data of new energy vehicles within the park, combine user authorization intentions and battery safety conditions to select dispatchable vehicles, and calculate the average discharge power based on available discharge capacity and the duration of high-price periods. Simultaneously, generate a discharge priority sequence by comprehensively considering state of charge, battery aging, and historical participation frequency, update the total available discharge power, and add it to the park's maximum power supply capacity. This solution significantly improves the accuracy and security of V2G resource scheduling, reducing power shortages caused by blind resource allocation or misjudgment. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a transient stability control method for a new energy microgrid provided in an embodiment of this application.

[0026] Figure 2 This is a schematic flowchart of a photovoltaic array icing partitioning method provided in an embodiment of this application.

[0027] Figure 3 This is a schematic flowchart of a photovoltaic array icing degradation prediction method provided in an embodiment of this application.

[0028] Figure 4 This is a flowchart illustrating a method for identifying the risk of re-icing of a photovoltaic array provided in an embodiment of this application.

[0029] Figure 5 This is a flowchart illustrating a V2G power supply scheduling method for a campus provided in an embodiment of this application.

[0030] Figure 6 This is a flowchart illustrating a V2G power recalculation method based on temporary trips, provided in an embodiment of this application.

[0031] Figure 7 This is a flowchart illustrating a V2G scheduling control method based on trip confidence provided in an embodiment of this application.

[0032] Figure 8 This is a schematic diagram of the structure of a transient stability control system for a new energy microgrid provided in an embodiment of this application. Detailed Implementation

[0033] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1 to 8 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0034] This application discloses a transient stability control method for a new energy microgrid. (Refer to...) Figure 1 The method includes: Step 101: Obtain the power generation of the photovoltaic array and the grid time-of-use electricity price information.

[0035] Power generation refers to the actual electrical power output of a photovoltaic array under current sunlight and environmental conditions, reflecting the power supply capacity of the photovoltaic array. Power generation is obtained through an inverter connected to the photovoltaic array.

[0036] Time-of-use (TOU) electricity pricing information refers to differentiated electricity price data set for different time periods throughout the day. It typically includes peak, flat, and valley periods and their corresponding price standards, and is generally obtained through a locally stored preset electricity price time period table.

[0037] Step 102: Determine whether the current time-of-use electricity price information is within a high-price electricity consumption period.

[0038] Determining whether we are currently in a period of high electricity prices is to prioritize the use of photovoltaic and local energy storage power supply when electricity prices are high, thereby reducing the need to purchase electricity from the grid and lowering electricity costs.

[0039] Step 103: If so, based on historical load data and power generation, predict the load demand power and photovoltaic power supply power of the park during the high-price electricity consumption period, and calculate the power gap.

[0040] Load demand power refers to the total power that all electrical equipment in the park is expected to consume during peak electricity consumption periods.

[0041] Photovoltaic power supply capacity refers to the effective output power that a photovoltaic array is expected to continuously provide during periods of high electricity prices.

[0042] The power gap refers to the portion of the projected load demand in the industrial park during periods of high electricity prices that exceeds the power that the photovoltaic system can supply, reflecting the power that needs to be supplemented.

[0043] The model is trained using historical load data and photovoltaic power generation during the same period of the past few days as the current date, and the model is used to predict the load demand and photovoltaic power supply during periods of high electricity prices.

[0044] If the current period is not a high-price electricity period, the park will directly obtain power from the grid to supply the electrical equipment. The power generated by the photovoltaic array will be stored in the energy storage device connected to the distribution cabinet. When the energy storage device is fully charged, the excess photovoltaic power can be transferred to supply the park's power supply.

[0045] Step 104: Based on the remaining available power of the energy storage device and the expected duration of the high-price electricity period, calculate the maximum power supply that the energy storage device can provide to the electrical equipment through the distribution cabinet. The electrical equipment includes at least one of the following: charging piles, lighting systems, air conditioning systems, security equipment, and production auxiliary equipment.

[0046] Energy storage devices refer to electrochemical energy storage equipment connected to the distribution cabinet, used to store the energy generated by the photovoltaic array and release the energy to the electrical equipment when needed.

[0047] Maximum power supply refers to the maximum output power that an energy storage device can continuously provide to electrical equipment under the constraints of the remaining energy capacity of the energy storage device and the duration of the high-price electricity period. The maximum power supply is calculated by dividing the remaining available energy capacity of the energy storage device by the expected duration of the high-price electricity period. The result is the upper limit of the average power that the energy storage device can sustainably output during that period. For example, if the energy storage device currently has 60 kWh of remaining available energy, and the high-price electricity period is expected to last 3 hours, then the maximum power supply is 60 ÷ 3 = 20, meaning that the energy storage device can supply a maximum of 20 kW of power to electrical equipment during those 3 hours.

[0048] Step 105: Determine whether the maximum power supply is greater than the power gap.

[0049] Determining whether the maximum power supply exceeds the power deficit is to determine whether the combined power supply from the energy storage device and the photovoltaic array can fully meet the park's load demand, thereby deciding whether it is necessary to purchase electricity from the grid.

[0050] Step 106: If so, the electrical equipment is powered by the photovoltaic array and energy storage device.

[0051] When the maximum power supply meets the power gap, the electrical equipment in the park is powered entirely by local power sources, namely photovoltaic arrays and energy storage devices, and no longer purchases electricity from the grid.

[0052] Step 107: If not, ensure the basic operating power of critical loads, reduce the power of non-critical loads, and have the grid provide the difference in power.

[0053] Critical loads refer to electrical equipment that cannot be interrupted or must maintain basic functions during the operation of the park, such as security systems, emergency lighting, and core production equipment.

[0054] Non-critical loads refer to electrical equipment that can temporarily reduce power or be interrupted when power supply is limited without affecting the basic safety and core functions of the park, such as some air conditioners and landscape lighting.

[0055] The differential power refers to the power that still needs to be supplemented from the grid after deducting the power that the photovoltaic array and energy storage device can supply, which is the power demand of the park's load.

[0056] By adopting the above technical solution, the current electricity price period and the source-load status of the park are analyzed. Power shortages are predicted by combining historical load data, and power supply capacity is assessed based on distribution capacity. Simultaneously, power-consuming equipment is categorized and controlled according to its criticality, and power supply strategy switching is implemented. This solution significantly increases the self-consumption rate of photovoltaic power during high-price periods, optimizes power resource allocation, reduces unnecessary high-price power purchases from the grid and the risk of critical load interruptions, and significantly improves the economic efficiency and reliability of power supply in the park.

[0057] This application discloses a method for icing partitioning of a photovoltaic array. (Refer to...) Figure 2 The method includes: Step 201: Divide the photovoltaic array into several sub-regions.

[0058] Dividing the array into several sub-regions is to accurately identify the icing situation in each local area of ​​the photovoltaic array. Icing is often uneven, with some areas heavily iced while others are lightly iced or even ic-free. Dividing the array into sub-regions allows for the assessment of the degree of icing in each region and its impact on power generation, thus avoiding misjudgments of photovoltaic array power due to localized icing.

[0059] Step 202: Obtain the proportion of ice-covered area and solar irradiance on the surface of the photovoltaic panels in each sub-region.

[0060] The ice coverage ratio refers to the percentage of the photovoltaic panel surface covered by ice out of the total light-receiving area of ​​the photovoltaic panel in that sub-region, and is used to quantify the impact of ice on sunlight reception.

[0061] Solar irradiance refers to the solar radiation power received per unit area, reflecting the current light intensity and affecting the power generation of photovoltaic panels and the melting of ice.

[0062] The proportion of ice-covered area is determined by capturing images of the photovoltaic panel surface using an infrared camera, then inputting the images into a pre-trained model for pixel-level classification to distinguish between ice, snow, water stains, and clean surfaces. The proportion of pixels identified as ice out of the total effective pixels is then used to determine the proportion of ice-covered area. Solar irradiance is directly measured and output by a silicon-based irradiance sensor.

[0063] Step 203: Determine whether the solar irradiance is lower than the preset effective melting irradiance threshold.

[0064] Determining solar irradiance is to determine whether the current solar irradiance is sufficient to trigger the natural melting of ice, in order to determine whether the proportion of ice-covered area will change.

[0065] Step 204: If not, perform attenuation prediction on the icing coverage area ratio to obtain the updated icing coverage area ratio, and determine the icing coverage level of each sub-region based on the updated icing coverage area ratio.

[0066] The icing coverage level is a qualitative level based on the updated proportion of icing coverage area. It is used to indicate the severity of the impact of icing on power generation efficiency in each sub-region and is divided into three levels: mild, moderate, and severe.

[0067] By subdividing the sub-region into micro-regions, and combining the azimuth, tilt, edge distance, solar incidence angle, heat loss coefficient and ambient temperature of each micro-region, the ice melting rate is calculated for each micro-region. Based on this, the attenuation of the ice coverage area ratio is predicted, and then the data are aggregated to obtain the updated ice coverage area ratio.

[0068] If the solar irradiance is lower than the effective melting irradiance threshold, the ice layer is considered unable to melt naturally, the current proportion of ice-covered area remains unchanged, and the ice coverage level of the sub-region is determined accordingly.

[0069] Step 205: Based on the solar incidence angle and icing coverage level, query the preset attenuation coefficient mapping table to obtain the attenuation coefficient corresponding to each sub-region.

[0070] The solar incidence angle is the angle between the sunlight and the normal to the surface of the photovoltaic panel, which affects the intensity of solar radiation received per unit area.

[0071] The attenuation coefficient is a proportional factor used to correct the theoretical output power after considering the combined effects of the solar incident angle and the degree of icing on the photovoltaic panel's power generation efficiency. Its value is between 0 and 1, and the smaller the value, the more severely the actual power that can be supplied is suppressed.

[0072] Step 206: Correct the theoretical output power of each sub-region according to the attenuation coefficient, and aggregate the corrected theoretical output power of the sub-regions to obtain the corrected photovoltaic power supply.

[0073] The theoretical output power of a subregion refers to the ideal power that each subregion can output under the current solar irradiance and ambient temperature without icing.

[0074] By multiplying the theoretical output power of each sub-region by its corresponding attenuation coefficient to reflect the impact of icing and incident angle on power generation efficiency, and then summing up the corrected power of each sub-region, the photovoltaic power that can be supplied is obtained.

[0075] By employing the aforementioned technical solution, the icing coverage status of photovoltaic panels in each sub-region is analyzed. The effectiveness of de-icing is determined by combining solar irradiance and incident angle. An attenuation coefficient mapping table is consulted based on the icing coverage level. Simultaneously, the power generation capacity of each sub-region is differentiated and weighted, resulting in the corrected photovoltaic power output. This solution significantly improves the accuracy of photovoltaic power prediction in icing scenarios, reducing power overestimation or scheduling deviations caused by misjudgments of icing.

[0076] This application discloses a method for predicting icing degradation of photovoltaic arrays. (Refer to...) Figure 3 The method includes: Step 301: Divide each sub-region into multiple micro-regions.

[0077] Dividing the sub-region into multiple micro-regions is to achieve spatial refinement of the ice melting process, so as to more accurately predict the ice layer changes in each local area, thereby improving the accuracy of photovoltaic power supply correction.

[0078] Step 302: Obtain the azimuth, tilt, and edge distance of each micro-region.

[0079] Azimuth refers to the angle between the orientation of the photovoltaic panel plane where the micro-region is located and the due south direction on the horizontal plane.

[0080] Tilt angle refers to the angle between the plane of the photovoltaic panel where the micro-region is located and the horizontal plane.

[0081] Edge distance refers to the shortest straight-line distance from the center of a micro-region to the boundary of its sub-region.

[0082] The azimuth and tilt angles are predetermined using fixed design parameters during photovoltaic array installation, while the edge distance is calculated geometrically based on the position of the micro-region within the sub-region. For example, if a sub-region consists of four photovoltaic panels, and the azimuth is uniformly set to 10 degrees east of due south and the tilt angle to 25 degrees during installation, then each micro-region directly uses these design parameters. If a micro-region is located at the upper left corner of the sub-region, 0.2 meters from the left boundary and 0.15 meters from the upper boundary, then its edge distance is taken as the minimum value of 0.15 meters.

[0083] Step 303: Calculate the instantaneous effective irradiance gain of each micro-region based on the azimuth angle and the solar incidence angle.

[0084] The solar incidence angle is the angle between the sunlight and the ground plane, reflecting the sun's elevation position in the sky. In step S205, the solar incidence angle is the angle between the sunlight and the normal to the photovoltaic panel surface, reflecting the actual angle at which the light rays are incident on the panel surface.

[0085] Instantaneous effective irradiance gain refers to the ratio of the actual solar radiation received by a micro-region under the combined effect of the current solar incidence angle and its own azimuth angle to that received under ideal vertical irradiation.

[0086] The gain is determined based on the matching relationship between the solar incidence angle and the micro-region azimuth angle and the solar azimuth. When the azimuths are consistent, the sine of the solar incidence angle is taken as the gain. When the azimuth deviation exceeds a threshold, resulting in ineffective light reception, the gain is set to zero. For example, if the solar incidence angle is 50° and the micro-region azimuth angle is the same as the solar azimuth angle, the instantaneous effective irradiance gain is sin(50°)≈0.766; if the azimuths are opposite, the gain is 0.

[0087] Step 304: Determine the heat loss coefficient of each micro-region based on the tilt angle and edge distance.

[0088] The heat dissipation coefficient is a parameter that reflects the ability of a micro-region to dissipate heat to the surrounding environment. The larger the value, the faster the heat dissipation.

[0089] The heat loss coefficient is obtained by looking up the table based on the tilt angle and edge distance of the micro-region using a preset heat loss coefficient mapping table.

[0090] Step 305: Determine the ablation rate of each micro-region based on the instantaneous effective irradiation gain, heat loss coefficient, and ambient temperature.

[0091] The melting rate refers to the decrease in the proportion of a micro-region covered by ice per unit time, reflecting how fast the ice layer melts.

[0092] Using a pre-defined ablation rate mapping table, the corresponding ablation rate can be obtained by looking up the table based on the instantaneous effective irradiance gain, heat dissipation coefficient, and ambient temperature of each micro-region. For example, if the instantaneous effective irradiance gain of a micro-region is 0.75, the heat dissipation coefficient is 1.1, and the ambient temperature is −2℃, the corresponding ablation rate is found to be 0.55% / min according to the pre-calibrated ablation rate mapping table.

[0093] Step 306: The icing coverage area ratio of each micro-region is predicted to decrease according to the ablation rate of each micro-region, so as to obtain the updated icing coverage area ratio of the micro-region.

[0094] The calculation method for the ice coverage ratio of a micro-region is the same as that for the calculation of the ice coverage ratio of a sub-region in step 202.

[0095] Based on the current ice coverage area ratio and melting rate, the remaining ice coverage area ratio after melting is calculated according to a preset time step. This is achieved by subtracting the product of the melting rate and the time step from the original ratio, resulting in the updated ice coverage area ratio. For example, if the current ice coverage area ratio of a micro-region is 40%, the melting rate is 0.5% / min, and the predicted time step is 5 minutes, then the attenuated ice coverage area ratio is 40% − 0.5% × 5 = 37.5%.

[0096] Step 307: Aggregate the updated ice coverage area ratios of each micro-region within the same sub-region to obtain the updated ice coverage area ratios of each sub-region.

[0097] The arithmetically averaged proportions of the updated ice coverage areas of all micro-regions within the same sub-region are used to obtain the overall ice coverage area proportion of that sub-region.

[0098] For example, if a subregion contains 4 microregions with updated icing coverage percentages of 30%, 35%, 25%, and 40%, then the icing coverage percentage of the subregion is (30%+35%+25%+40%)÷4=32.5%.

[0099] By employing the above technical solution, the azimuth, tilt, and edge distance of each micro-region are analyzed. The instantaneous effective irradiance gain is calculated based on the solar incidence angle, and the ablation rate is dynamically determined based on heat dissipation characteristics and ambient temperature. Simultaneously, the icing coverage area ratio of each micro-region is independently predicted, and the results are aggregated to generate an updated icing state for the sub-region. This solution significantly improves the consistency between the predicted icing state and the actual icing distribution on the photovoltaic module surface, reducing power overestimation or scheduling errors caused by uneven icing distribution.

[0100] This application discloses a method for identifying the risk of re-icing in photovoltaic arrays. (Refer to...) Figure 4 The method includes: Step 401: Determine the upstream adjacent micro-regions of each micro-region based on the tilt angle and azimuth angle of each micro-region.

[0101] The upstream adjacent micro-region refers to the micro-region that is adjacent to the current micro-region and located at a higher position in the opposite direction of the water flow direction determined by the inclination angle and azimuth angle on the surface of the micro-region.

[0102] The tilt direction of the slab is determined by the dip and azimuth of the micro-regions. The micro-regions adjacent to the current micro-region on the higher side along this direction are identified as its upstream adjacent micro-regions. For example, if a micro-region has an azimuth of 90° due east and a dip angle of 15°, then the slab tilts downward from west to east. For the micro-regions located in the row to the east of this micro-region, its upstream adjacent micro-region is the micro-region immediately to its west, because meltwater flows from west to east.

[0103] Step 402: Obtain the current surface temperature of each micro-region, the current ice coverage area ratio, and the previous ice coverage area ratio.

[0104] The proportion of ice coverage area at the previous moment refers to the proportion of ice coverage area at the current moment at the previous sampling moment, serving as a reference benchmark for judging whether the ice layer is melting or growing.

[0105] The current surface temperature is obtained by a temperature sensor, and the method for obtaining the current ice coverage area ratio is the same as that for obtaining the ice coverage area ratio in step 202.

[0106] Step 403: Based on the current surface temperature, the current ice coverage area ratio, and the previous ice coverage area ratio, determine whether each micro-region is located at the lower edge of the sub-region where it is located, whether the tilt angle of the sub-region is less than the preset tilt angle threshold, and whether the current surface temperature is below zero degrees.

[0107] The identification of micro-regions prone to refreezing is based on the fact that these regions are located at the lower edge of the sub-region, where meltwater tends to accumulate, the low tilt angle leads to poor drainage, and the current surface temperature is below zero, which meets the conditions for icing. When all three conditions are met simultaneously, the risk of refreezing increases.

[0108] Step 404: If yes, then determine whether the proportion of ice-covered area in the upstream adjacent micro-region is decreasing.

[0109] To determine whether there is ice melting in the upstream adjacent micro-region, if the proportion of its ice-covered area decreases compared to the previous moment, it indicates that meltwater is being generated and may flow into the current micro-region.

[0110] If a micro-region does not meet any of the following conditions: it is located at the lower edge of a sub-region, the installation tilt angle of the sub-region is not less than a preset threshold, or the surface temperature is not lower than zero degrees, it is determined that it does not have the typical environment for re-icing to occur and will not enter the re-icing risk assessment.

[0111] Step 405: If so, the upstream adjacent micro-region is an effective source of meltwater, and the micro-region is determined to be in a state of refreezing risk.

[0112] When the proportion of ice-covered area in the upstream adjacent micro-region is decreasing, it indicates that the ice is melting and can provide meltwater. At this time, it is determined that the current micro-region is at risk of refreezing because it may receive the meltwater and is located at the edge of low temperature and low inclination angle.

[0113] If the proportion of ice-covered area in the upstream adjacent micro-region does not decrease, it is not considered an effective source of meltwater, and the current micro-region is not judged to be in a state of refreezing risk.

[0114] Step 406: Set the ablation rate of the micro-region at risk of refreezing to a negative value.

[0115] Setting the melting rate to a negative value is to reflect the risk of refreezing, indicating that the proportion of ice-covered area in the micro-region not only does not decrease due to sunshine, but may remain unchanged or increase locally due to receiving upstream meltwater, thus distinguishing it from normal melting conditions.

[0116] By employing the aforementioned technical solution, the positional relationships of each micro-region on the photovoltaic panel are analyzed. The upstream adjacent micro-regions are determined by combining the installation tilt angle and azimuth angle. Furthermore, the meltwater flow trend is judged based on the temporal changes in surface temperature and the proportion of ice-covered area. Simultaneously, a negative melting rate is set for micro-regions at risk of re-icing to correct their ice evolution direction. This solution significantly improves the ability to identify localized re-icing phenomena in icy and snowy environments, reducing the overestimation of power generation capacity caused by neglecting edge ice growth.

[0117] This application discloses a V2G power supply scheduling method for a campus. (Refer to...) Figure 5 The method includes: Step 501: Send a discharge participation request to each new energy vehicle connected to the V2G charging pile in the park, and receive real-time status data returned by each new energy vehicle.

[0118] The discharge participation request is a dispatch instruction sent to new energy vehicles that have been connected to V2G charging piles, used to inquire whether the vehicle is willing to participate in discharge during a specified period.

[0119] Real-time status data refers to the current operating information returned by a new energy vehicle after receiving a discharge participation request, including state of charge, battery temperature, user authorization status, and communication connection status.

[0120] Step 502: Based on real-time status data, filter out dispatchable vehicles that meet the dispatchable conditions. The dispatchable conditions include user authorization, state of charge not lower than the return trip safety threshold, battery temperature within the allowable range, and normal communication status.

[0121] The dispatchable conditions refer to the four basic requirements that new energy vehicles must meet simultaneously to participate in discharge dispatch, including user authorization for discharge, current state of charge not lower than the minimum charge threshold required for return trip, battery temperature within the safe discharge range, and normal communication with charging piles.

[0122] Step 503: Calculate the average discharge power based on the available discharge capacity of each dispatchable vehicle and the remaining duration of the high-price electricity period.

[0123] Available discharge capacity refers to the amount of electricity that a dispatchable vehicle can safely release for discharge while ensuring the minimum amount of electricity required for the return trip. It is the difference between the current state of charge and the safe threshold for the return trip multiplied by the total battery capacity.

[0124] Average discharge power refers to the constant discharge power obtained by evenly distributing the electricity that can be released by dispatchable vehicles during high-price electricity periods to the remaining time of that period, and is used to represent its stable power supply capability during that period.

[0125] This is calculated by dividing the available discharge capacity of each dispatchable vehicle by the remaining duration of the high-price electricity period. For example, if the total battery capacity of a new energy vehicle is 60kWh, the current state of charge is 40%, and the return trip safety threshold is 30%, then the available discharge capacity is (40%−30%)×60=6kWh; if there are 30 minutes left in the high-price electricity period, then its average discharge power is 6÷0.5=12kW.

[0126] Step 504: Generate a discharge priority sequence based on the state of charge, battery aging degree, historical participation frequency and average discharge power of each dispatchable vehicle.

[0127] The discharge priority sequence refers to the scheduling order formed by ranking vehicles according to their state of charge, battery aging, historical participation frequency, and average discharge power, based on a comprehensive score. This sequence prioritizes vehicles with better conditions and less impact during discharge. The comprehensive score assigns preset weights to each of the state of charge, battery aging, historical participation frequency, and average discharge power, and then sums the normalized values ​​to obtain a priority score for each vehicle. A higher score places the vehicle higher in the discharge priority sequence.

[0128] For example, the system sets the weight of state of charge (SBC) to 0.4, battery aging level to 0.3, historical participation frequency to -0.2 (the more participations, the lower the score), and average discharge power to 0.1. A vehicle currently has a SBC of 50% and a health status of 90%, and has participated in 4 discharges in the past 7 days with an average discharge power of 12kW. Its priority score would then be: 0.4 × 0.5 + 0.3 × 0.9 - 0.2 × 0.8 + 0.1 × 0.6 = 0.37.

[0129] Step 505: Detect changes in the status of schedulable vehicles at a preset period and update the discharge priority sequence.

[0130] Detecting vehicle status changes at preset intervals is to promptly capture changes in key parameters such as state of charge, authorized status, or communication connection, ensuring that the discharge priority sequence always reflects the true situation of currently schedulable resources and avoiding scheduling failures or over-discharge of batteries due to status lag.

[0131] Step 506: Weight the average discharge power in the updated discharge priority sequence to obtain the total available discharge power.

[0132] Total available discharge power refers to the total power value obtained by weighting and summing the average discharge power of each dispatchable vehicle in the updated discharge priority sequence according to its priority weight. It is used to represent the total discharge capacity that can be called upon by V2G in the current park.

[0133] For example, in the updated discharge priority sequence, there are three dispatchable vehicles with priority weights of 1.0, 0.8, and 0.6, respectively, and corresponding average discharge powers of 12kW, 10kW, and 8kW. The total available discharge power is: 1.0×12+0.8×10+0.6×8=24.8kW.

[0134] Step 507: Add the maximum power supply to the total available discharge power to obtain the updated maximum power supply.

[0135] The original maximum power supply in the park is added to the total available discharge power provided by V2G vehicles to obtain the updated maximum power supply, which is then used for subsequent load dispatching.

[0136] By adopting the above technical solution, real-time status data of new energy vehicles within the park is analyzed. Based on user authorization and battery safety conditions, dispatchable vehicles are selected. The average discharge power is calculated based on available discharge capacity and the duration of high-price periods. Simultaneously, a discharge priority sequence is generated by comprehensively considering state of charge, battery aging, and historical participation frequency. The total available discharge power is then updated and superimposed onto the park's maximum power supply capacity. This solution significantly improves the accuracy and security of V2G resource scheduling, reducing power shortages caused by blind resource allocation or misjudgment.

[0137] This application discloses a V2G power recalculation method based on temporary trips. (Refer to...) Figure 6 The method includes: Step 601: Establish an initial discharge scheduling sequence based on each schedulable vehicle.

[0138] The initial discharge scheduling sequence refers to the set of new energy vehicles that currently meet the scheduling conditions.

[0139] Step 602: Send a temporary travel plan inquiry message to the user terminals in the initial discharge scheduling sequence.

[0140] The temporary travel plan inquiry message is a query sent to the user's terminal to obtain information on whether the user has any additional travel information due to unforeseen circumstances.

[0141] Step 603: Based on the sent temporary travel plan inquiry message, monitor whether temporary travel information sent by any user terminal is received. The temporary travel information includes the expected departure time and destination.

[0142] Temporary trip information refers to trip data submitted by users due to sudden or undeclared travel needs, which is used to adjust the vehicle's battery reserve requirements.

[0143] Step 604: If yes, calculate the minimum reserve power of dispatchable vehicles based on the temporary trip information and power impact parameters. Power impact parameters include battery aging, discharge loss and ambient temperature.

[0144] The power impact parameter refers to the physical factors used to correct the base energy consumption when calculating the minimum reserve power.

[0145] Minimum reserve capacity refers to the minimum amount of battery power that must be retained in the battery to ensure that dispatchable vehicles can successfully complete the user's temporary trip. This is after taking into account factors such as battery aging, discharge loss, and ambient temperature.

[0146] First, based on the user's estimated departure time and destination, the navigation data is retrieved to obtain the driving mileage. Then, combined with the vehicle's energy consumption per unit mileage, the baseline energy required to complete the trip is calculated. On this basis, the current battery health status is considered, such as reserving about 10% of the energy if the battery health status is 90%. The internal resistance loss during the discharge process is also taken into account. The preset loss table is consulted according to the discharge power, and the impact of ambient temperature on the available capacity is also considered. For example, for every 5°C drop below 0°C, an additional 3% reserve is added. The compensation amounts are added one by one to finally obtain the minimum energy value that must be retained. For example, if a user submits a temporary trip to a location 20 kilometers away, and the vehicle's energy consumption is 15 kWh per 100 kilometers, then the base energy requirement is 3 kWh. The current battery health is 85%, so a 15% compensation is added based on the table, i.e., 3 × 15% ≈ 0.45 kWh. The expected discharge power is high, so a discharge loss compensation of 0.3 kWh is obtained from the table. The ambient temperature is −5℃, so a low-temperature compensation of 0.25 kWh is added based on the table. Therefore, the minimum reserve energy is 3 + 0.45 + 0.3 + 0.25 = 4.0 kWh.

[0147] If no temporary trip information is received from any user terminal, the original return trip safety power threshold for each dispatchable vehicle will be maintained, and its minimum reserved power will not be adjusted.

[0148] Step 605: Determine whether the minimum reserve power is greater than the remaining power of the dispatchable vehicles.

[0149] The purpose of this assessment is to ensure that the vehicle retains enough charge to complete the user's temporary trip after the discharge process, preventing the inability to travel due to excessive discharge.

[0150] Step 606: If so, push a low battery warning to the user terminal and remove the dispatchable vehicle from the initial discharge scheduling sequence.

[0151] When it is confirmed that the vehicle's remaining battery power is insufficient to meet the minimum reserve power required for a temporary trip, a low battery warning is issued to the user, and the vehicle is removed from the discharge scheduling sequence to avoid the user being unable to travel normally due to continued discharge.

[0152] Step 607: If not, recalculate the average discharge power of dispatchable vehicles based on the minimum reserved power.

[0153] After confirming that the remaining power of dispatchable vehicles is sufficient to meet temporary travel needs, their discharge capacity is re-determined based on the updated minimum reserve power, and the average discharge power is recalculated in conjunction with the remaining time during high-priced electricity periods to reflect the current power level available for discharge.

[0154] By adopting the above technical solution, the initial discharge capacity of dispatchable vehicles is analyzed, an initial discharge scheduling sequence is established based on user authorization status and battery safety conditions, and temporary travel plan inquiry messages are proactively sent to user terminals. Simultaneously, the minimum reserve capacity is calculated based on the returned trip information and power impact parameters, vehicle discharge power is updated, and the scheduling sequence is adjusted. This solution significantly improves the consistency between V2G scheduling and users' actual travel needs, reducing power overdraft or scheduling failures caused by untimely trip changes.

[0155] This application discloses a V2G scheduling control method based on trip confidence. (Refer to...) Figure 7 The method includes: Step 701: Send a trip confirmation request to the user terminal that has sent the temporary trip information.

[0156] A trip confirmation request is a secondary confirmation message sent to the user terminal that has submitted temporary trip information, used to verify whether the user is still traveling as originally planned or whether there are any new trip changes.

[0157] Step 702: Receive the itinerary update information returned by the user terminal in response to the itinerary confirmation request.

[0158] Trip update information refers to the latest travel data returned by the user terminal in response to the trip confirmation request, including modifications, confirmations, or cancellations of the original temporary trip.

[0159] Step 703: If there is a discrepancy between the itinerary update information and the temporary itinerary information, the itinerary confirmation request will be resent at a preset fixed period, and the number of changes will be accumulated.

[0160] The cumulative number of changes is used as a direct basis for measuring the reliability of a user's travel plan when calculating the confidence level of the trip. The more frequent the changes, the more uncertain the trip is.

[0161] Step 704: Calculate the itinerary confidence score based on the number of changes and the number of historical itinerary changes.

[0162] Trip confidence level is a value calculated based on the number of changes and the number of historical trip changes, used to indicate the likelihood that the submitted temporary trip information will actually be executed.

[0163] The confidence level is calculated by multiplying the number of changes and the number of historical trip changes by a preset attenuation coefficient, summing the results, and then subtracting the sum from the baseline value. The more changes, the lower the confidence level. For example, if the baseline value is set to 1.0, the attenuation coefficient for the number of changes is 0.15, and the attenuation coefficient for the number of historical trip changes is 0.05, then if the user has made 2 changes this time and 4 changes in the past, the trip confidence level is: 1.0 − (0.15 × 2 + 0.05 × 4) == 0.50.

[0164] Step 705: Determine whether the trip confidence level is higher than the preset confidence threshold.

[0165] Used to determine whether a user's schedule is reliable enough to decide whether to adjust the discharge schedule based on the schedule, thus avoiding scheduling failures caused by adopting highly variable schedules.

[0166] Step 706: If so, directly use the itinerary update information.

[0167] When the trip confidence level is higher than the preset confidence level threshold, the latest trip update information submitted by the user terminal is directly adopted as the basis for subsequent discharge scheduling.

[0168] Step 707: If not, temporarily mark the schedulable vehicle as unschedulable.

[0169] When the trip confidence level is lower than the preset confidence level threshold, the schedulable vehicle will be temporarily marked as unschedulable to avoid incorrect battery reservation or user travel disruptions due to trip uncertainty.

[0170] By employing the above technical solution, the system analyzes temporary trip information returned by user terminals, combines trip validity confirmation requests with multiple feedback results to identify the reliability of travel plans, and calculates trip confidence weights based on the cumulative number of changes and historical behavior. Simultaneously, vehicles with low confidence are dynamically marked as unschedulable, and the schedulable resource pool is updated. This solution significantly improves the reliability of user trip data in V2G scheduling, reducing discharge failures and power deviations caused by frequent trip changes or inaccurate reporting.

[0171] Based on the same inventive concept, embodiments of this application provide a transient stability control system for a new energy microgrid, referencing... Figure 8 The system includes: The acquisition module 801 is used to acquire power generation capacity, grid time-of-use electricity price information, remaining available electricity and estimated duration; The memory 802 is used to store the program of the transient stability control method for the new energy microgrid; The processor 803 can load and execute the program in the memory to implement the transient stability control method for the new energy microgrid.

[0172] By adopting the above technical solution, the acquisition module collects photovoltaic power generation, grid time-of-use electricity price, remaining available power in the distribution cabinet, and the expected duration of high-price periods in real time. The processor quickly executes power gap prediction and load dispatch control logic, and the memory stores and efficiently calls the transient stability control program. This realizes fully automated decision-making from data perception to power supply strategy generation, which significantly improves the economic efficiency of electricity consumption while ensuring the reliable operation of critical loads, and provides an efficient and reliable stability control solution for new energy microgrids.

[0173] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0174] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as a transient stability control method for a new energy microgrid.

[0175] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0176] Based on the same inventive concept, embodiments of this application provide a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as a transient stability control method for a new energy microgrid.

[0177] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0178] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A transient stability control method for a new energy microgrid, characterized in that, include: Obtain information on the power generation of the photovoltaic array and the time-of-use electricity price of the grid; Determine whether the current time-of-use electricity price information falls within a period of high electricity prices; If so, based on historical load data and power generation, predict the load demand and photovoltaic power available for the park during periods of high electricity prices, and calculate the power gap; Based on the remaining available power of the energy storage device and the expected duration of the high-price electricity period, calculate the maximum power supply that the energy storage device can provide to the electrical equipment through the distribution cabinet. The electrical equipment includes at least one of the following: charging piles, lighting systems, air conditioning systems, security equipment, and production auxiliary equipment. Determine if the maximum power supply is greater than the power deficit; If so, the electrical equipment is powered by the photovoltaic array and energy storage device; If not, ensure the basic operating power of critical loads, reduce the power of non-critical loads, and provide the difference in power from the grid.

2. The transient stability control method for a new energy microgrid according to claim 1, characterized in that, Before predicting the park's load demand and available photovoltaic power during peak electricity demand periods based on historical load data and power generation, the following steps are included: The photovoltaic array is divided into several sub-regions; Obtain the proportion of ice-covered area and solar irradiance on the surface of photovoltaic panels in each sub-region; Determine whether the solar irradiance is lower than the preset effective ice-melting irradiance threshold; If not, then the icing coverage area ratio is predicted to decrease, and the updated icing coverage area ratio is obtained. Based on the updated icing coverage area ratio, the icing coverage level of each sub-region is determined. Based on the solar incidence angle and icing coverage level, the attenuation coefficient corresponding to each sub-region is obtained by querying the preset attenuation coefficient mapping table. The theoretical output power of each sub-region is corrected based on the attenuation coefficient, and the corrected theoretical output power of the sub-regions is aggregated to obtain the corrected photovoltaic power supply.

3. The transient stability control method for a new energy microgrid according to claim 2, characterized in that, The step of performing attenuation prediction on the proportion of icing coverage area to obtain the updated proportion of icing coverage area also includes: Each sub-region is divided into multiple micro-regions; Obtain the azimuth, tilt, and edge distance of each micro-region; The instantaneous effective irradiance gain of each micro-region is calculated based on the azimuth angle and the solar incidence angle. The heat loss coefficient of each micro-region is determined based on the tilt angle and edge distance; The ablation rate of each micro-region is determined based on the instantaneous effective irradiation gain, heat loss coefficient, and ambient temperature. The icing coverage area ratio of each micro-region is predicted to decrease according to the ablation rate of each micro-region, and the updated icing coverage area ratio of the micro-region is obtained. The updated icing coverage area ratios of each micro-region within the same sub-region are aggregated to obtain the updated icing coverage area ratios of each sub-region.

4. The transient stability control method for a new energy microgrid according to claim 3, characterized in that, The method further includes: Based on the tilt angle and azimuth angle of each micro-region, determine the upstream adjacent micro-region of each micro-region; Obtain the current surface temperature of each micro-region, the current ice coverage area ratio, and the previous ice coverage area ratio. Based on the current surface temperature, the current ice coverage area ratio, and the previous ice coverage area ratio, determine whether each micro-region is located at the lower edge of the sub-region where it is located, whether the tilt angle of the sub-region is less than the preset tilt angle threshold, and whether the current surface temperature is below zero degrees. If so, then determine whether the proportion of ice-covered area in the upstream adjacent micro-region is decreasing; If so, the upstream adjacent micro-region is an effective source of meltwater, and the micro-region is determined to be in a state of refreezing risk. Set the ablation rate of micro-regions at risk of refreezing to a negative value.

5. The transient stability control method for a new energy microgrid according to claim 1, characterized in that, Before determining whether the maximum power supply is greater than the power shortfall, the following steps are included: Send discharge participation requests to each new energy vehicle connected to the V2G charging pile in the park, and receive real-time status data returned by each new energy vehicle. Based on real-time status data, select dispatchable vehicles that meet the dispatchable conditions, including user authorization, state of charge not lower than the return trip safety threshold, battery temperature within the allowable range, and normal communication status. The average discharge power is calculated based on the available discharge capacity of each dispatchable vehicle and the remaining duration of the high-price electricity period. A discharge priority sequence is generated based on the state of charge, battery aging, historical participation frequency, and average discharge power of each dispatchable vehicle. The status changes of dispatchable vehicles are detected at a preset period, and the discharge priority sequence is updated accordingly. The total available discharge power is obtained by weighted summing of the average discharge power in the updated discharge priority sequence. The maximum power supply is summed with the total available discharge power to obtain the updated maximum power supply.

6. The transient stability control method for a new energy microgrid according to claim 5, characterized in that, The method further includes: Based on each dispatchable vehicle, an initial discharge scheduling sequence is established; Send temporary travel plan inquiry messages to user terminals in the initial discharge scheduling sequence; Based on the sent temporary travel plan inquiry message, monitor whether temporary travel information sent by any user terminal is received. The temporary travel information includes the expected departure time and destination. If so, the minimum reserve power of dispatchable vehicles is calculated based on the temporary trip information and power impact parameters, including battery aging, discharge loss and ambient temperature. Determine whether the minimum reserve power is greater than the remaining power of the dispatchable vehicles; If so, a low battery warning will be pushed to the user terminal, and the dispatchable vehicle will be removed from the initial discharge dispatch sequence; If not, recalculate the average discharge power of dispatchable vehicles based on the minimum reserved power.

7. The transient stability control method for a new energy microgrid according to claim 6, characterized in that, The method further includes: Send a trip confirmation request to the user terminal that has already received the temporary trip information; Receive trip update information returned by the user terminal in response to the trip confirmation request; If there is a discrepancy between the updated itinerary information and the temporary itinerary information, the itinerary confirmation request will be resent at a preset fixed period, and the number of changes will be accumulated. Calculate the itinerary confidence level based on the number of changes and the number of historical itinerary changes; Determine whether the trip confidence level is higher than the preset confidence threshold; If so, use the itinerary update information directly; If not, the schedulable vehicles will be temporarily marked as unschedulable.

8. A transient stability control system for a new energy microgrid, characterized in that, The system is used to execute the transient stability control method for new energy microgrids as described in any one of claims 1 to 7, including: The acquisition module is used to acquire power generation capacity, grid time-of-use electricity price information, remaining available electricity, and estimated duration. A memory for storing the program of the transient stability control method for the new energy microgrid; The processor and the program in the memory can be loaded and executed by the processor to implement the transient stability control method for the new energy microgrid.

9. A smart terminal, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and execute the method as described in any one of claims 1 to 7.

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