Micro-grid-based vehicle-to-grid energy interaction energy mutual aid method, system, device and medium

Abstract

The application provides a micro-grid-based vehicle-grid interaction energy mutual aid method, system, device and medium, and relates to the technical field of smart grids. The method comprises the following steps: determining a target monitoring point with power lower than a preset power in a micro-grid, and calculating a power difference value between the power of the target monitoring point and the preset power; determining a mutual aid response area of the target monitoring point according to the power difference value; obtaining the area characteristics of the mutual aid response area, the position information of a plurality of electric vehicles, and the charging state and vehicle type of each electric vehicle; dividing each electric vehicle into a response group and a standby group in combination with the position information and the charging state; determining the discharging power of each electric vehicle in the response group and the standby group in combination with the area characteristics and the vehicle type; calculating the stability index of the micro-grid according to the power coupling degree between the target monitoring point and other monitoring points, and adjusting the discharging power. The application has the technical effect of solving the problem of insufficient power of the micro-grid while reducing the cost.

Description

Technical Field

[0001] This application relates to the field of smart grid technology, specifically to a method, system, device, and medium for energy exchange and interaction between vehicles and the grid based on a microgrid. Background Technology

[0002] With the rapid growth in the number of electric vehicles and the widespread application of microgrid technology, the load volatility and uncertainty of microgrid systems are increasing. When local areas of the microgrid experience power shortages, it can easily lead to an imbalance between power supply and demand, affecting the safe and stable operation of the system. Especially during peak electricity consumption periods or in emergency situations, some monitoring points in the microgrid may experience low power levels. How to allocate resources in a timely and effective manner to achieve energy mutual assistance has become a key technical problem facing the operation and management of microgrids.

[0003] Existing technologies typically employ centralized energy dispatch methods or rely on fixed energy storage devices to address the power shortage problem in microgrids. While these methods can solve the power shortage problem, they often require the construction of a large number of dedicated energy storage facilities, which are costly. Summary of the Invention

[0004] This application provides a method, system, device, and medium for energy exchange and interaction between vehicles and the grid based on microgrids, which can solve the problem of insufficient power in microgrids while reducing costs.

[0005] Firstly, this application provides a vehicle-grid interaction energy mutual assistance method based on a microgrid. The method includes: acquiring the power of multiple monitoring points in the microgrid; identifying a target monitoring point among the multiple monitoring points whose power is lower than a preset power; calculating the power difference between the power of the target monitoring point and the preset power; determining the mutual assistance response area of ​​the target monitoring point based on the power difference, wherein the mutual assistance response area is a circular area centered on the target monitoring point and with a preset length as its radius; acquiring the regional characteristics of the mutual assistance response area, the location information of multiple electric vehicles in a charging state within the mutual assistance response area, and acquiring the charging state and vehicle type of each electric vehicle within the mutual assistance response area; and combining the charging state and vehicle type of each electric vehicle within the mutual assistance response area with the charging state and vehicle type of each electric vehicle. Based on the location information of the electric vehicles and their charging status, the electric vehicles are divided into a response group and a standby group. Based on the power difference, combined with the regional characteristics and the vehicle type of each electric vehicle, the first discharge power of each electric vehicle in the response group and the second discharge power of each electric vehicle in the standby group are determined. The power coupling degree between the target monitoring point and other monitoring points in the microgrid is obtained. Based on the power coupling degree, the stability index of the microgrid is calculated. Based on the stability index, the first discharge power and the second discharge power are adjusted to generate the first target discharge power of each electric vehicle in the response group and the second target discharge power of each electric vehicle in the standby group.

[0006] By adopting the above technical solutions and establishing a mutual assistance response area determination mechanism based on power difference, precise positioning and regionalized response to local power demand in the microgrid are achieved, avoiding the inefficiency caused by excessively large resource scheduling ranges in traditional methods. A hierarchical scheduling system of response and standby groups is established by combining electric vehicle location information and charging status intelligent grouping strategies, ensuring the reliability and continuity of energy mutual assistance. By comprehensively considering differentiated power allocation algorithms based on regional characteristics and vehicle types, the optimal utilization of electric vehicle discharge capacity is achieved, improving overall mutual assistance efficiency. Through microgrid stability index calculation based on power coupling degree and a dynamic power adjustment mechanism, the safe and stable operation of the microgrid is effectively guaranteed while meeting power demand, avoiding potential system instability risks during vehicle-grid interaction. This achieves efficient, accurate, and safe vehicle-grid interaction energy mutual assistance, solving the microgrid power shortage problem while reducing costs.

[0007] Secondly, this application provides a vehicle-grid interactive energy mutual assistance system based on a microgrid. The system includes: a first acquisition module, a determination module, a second acquisition module, a first combination module, a second combination module, and a third acquisition module. The first acquisition module is used to acquire the power of multiple monitoring points in the microgrid, determine a target monitoring point among the multiple monitoring points whose power is lower than a preset power, and calculate the power difference between the power of the target monitoring point and the preset power. The determination module is used to determine the mutual assistance response area of ​​the target monitoring point based on the power difference, wherein the mutual assistance response area is a circular area centered on the target monitoring point and with a preset length as its radius. The second acquisition module is used to acquire the regional characteristics of the mutual assistance response area, the location information of multiple electric vehicles in a charging state within the mutual assistance response area, and the charging status of each electric vehicle within the mutual assistance response area. The system includes a first combining module and a second combining module. The first combining module is used to combine the location information and charging status of each electric vehicle to divide each electric vehicle into a response group and a standby group. The second combining module is used to determine the first discharge power of each electric vehicle in the response group and the second discharge power of each electric vehicle in the standby group based on the power difference, combined with the regional characteristics and the vehicle type of each electric vehicle. The third obtaining module is used to obtain the power coupling degree between the target monitoring point and other monitoring points in the microgrid, calculate the stability index of the microgrid based on the power coupling degree, adjust the first discharge power and the second discharge power based on the stability index, and generate the first target discharge power of each electric vehicle in the response group and the second target discharge power of each electric vehicle in the standby group.

[0008] Thirdly, this application provides an electronic device that adopts the following technical solution: including a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to execute a computer program of any of the above-mentioned microgrid-based vehicle-grid interaction energy sharing methods.

[0009] Fourthly, this application provides a computer-readable storage medium that employs the following technical solution: storing a computer program capable of being loaded by a processor and executing any of the above-mentioned microgrid-based vehicle-grid interaction energy exchange methods.

[0010] In summary, this application includes at least one of the following beneficial technical effects:

[0011] By establishing a mutual assistance response area determination mechanism based on power difference, precise positioning and regionalized response to local power demand in the microgrid are achieved, avoiding the inefficiency caused by excessively large resource scheduling ranges in traditional methods. A hierarchical scheduling system of response and standby groups is established by combining electric vehicle location information and charging status intelligent grouping strategies, ensuring the reliability and continuity of energy mutual assistance. By comprehensively considering differentiated power allocation algorithms based on regional characteristics and vehicle types, the optimal utilization of electric vehicle discharge capacity is achieved, improving overall mutual assistance efficiency. Through microgrid stability index calculation based on power coupling degree and a dynamic power adjustment mechanism, the safe and stable operation of the microgrid is effectively guaranteed while meeting power demand, avoiding potential system instability risks during vehicle-grid interaction. This achieves efficient, accurate, and safe vehicle-grid interaction energy mutual assistance, solving the microgrid power shortage problem while reducing costs. Attached Figure Description

[0012] Figure 1 This is an interface diagram of a vehicle-grid interactive energy exchange system based on a microgrid, provided in an embodiment of this application.

[0013] Figure 2 This is a framework diagram of a vehicle-grid interactive energy mutual assistance system based on a microgrid, provided in an embodiment of this application;

[0014] Figure 3 This is a flowchart illustrating a microgrid-based vehicle-grid interaction energy sharing method provided in an embodiment of this application.

[0015] Figure 4 This is a schematic diagram of a vehicle-grid interactive energy mutual assistance system based on a microgrid, provided in an embodiment of this application.

[0016] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0017] Explanation of reference numerals in the attached figures: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. Detailed Implementation

[0018] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0019] In the description of the embodiments in this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0020] Figure 1 This is an interface diagram of a vehicle-grid interactive energy mutual assistance system based on a microgrid, provided in an embodiment of this application. For example... Figure 1 As shown, the microgrid-based vehicle-grid interaction energy sharing method of the present invention, in practical applications, such as... Figure 1 The microgrid system shown includes multiple monitoring points, response team vehicles, standby team vehicles, and non-response vehicles. The system collects power data in real time through smart meters installed at monitoring points 1 and 3. When the power of monitoring points 1 and 3 is maintained at 85% and 90% respectively, the power of the target monitoring point at the center is only 45%, which is 15 percentage points lower than the preset 60% power threshold, immediately triggering the mutual assistance response mechanism.

[0021] The calculated power difference ΔP = preset power threshold - actual power = 60% - 45% = 15%. The system uses a linear mapping relationship to calculate the response area radius based on the power difference of 15%, with the formula being response radius = base radius × (1 + k × ΔP), where k is an adjustment coefficient of 0.5. The base radius is 200 meters, and the calculated response radius is 215 meters, forming the mutual assistance response area shown by the red dashed circle in the figure.

[0022] Within the response area, the system identified four electric vehicles in a charging state and intelligently grouped them based on a comprehensive analysis of parameters such as the State of Charge (SOC), distance from the target monitoring point, and vehicle type. EV-01, a passenger vehicle with an SOC of 80% and a distance of 120 meters from the target monitoring point, and EV-02, a commercial vehicle with an SOC of 75% and a distance of 100 meters, were assigned to the response group to undertake the main discharge task due to their proximity and sufficient battery power. EV-03 and EV-04, two passenger vehicles with SOCs of 70% and 65% respectively, and distances of 180 meters and 200 meters from the target monitoring point respectively, were assigned to the backup group to provide auxiliary support. The system uses the priority calculation formula P=α×(SOC / 100)×battery capacity×(1-β×distance / maximum distance)×vehicle type coefficient, where α=0.6, β=0.4, passenger vehicle type coefficient=1.0, and commercial vehicle type coefficient=1.5, ensuring the scientific and rational allocation of resources.

[0023] Based on the residential characteristics of the area, the importance level was determined to be medium, and the power redundancy coefficient was set to 1.2. The total discharge power was calculated to be 15% × 1.2 = 18%, equivalent to an actual power demand of 54kW. The system allocated power according to the response priority and discharge capacity of each vehicle. EV-01 in the response group was allocated 18.9kW, EV-02 was allocated 24.3kW, and EV-03 and EV-04 in the standby group were allocated 6.5kW and 4.3kW, respectively, ensuring that the power allocation matched the actual capacity of the vehicles. During the discharge process, the system continuously monitored the stability index of the microgrid. By calculating the coupling stability factor (0.85), fluctuation stability factor (0.78), and load stability factor (0.82), the comprehensive stability index S = 0.4 × 0.85 + 0.3 × 0.78 + 0.3 × 0.82 = 0.814 was obtained. Since this index was higher than the preset threshold of 0.8, the system determined that the microgrid was in a stable state and maintained the current discharge power setting unchanged.

[0024] The EV-05, located outside the response zone, was automatically excluded from the response range due to its SOC being only 40% and its distance from the target monitoring point. This prevented excessive discharge from affecting the driver's normal driving needs, demonstrating the system's protection mechanism for user interests. Through coordinated discharge from the four electric vehicles in the response and backup groups, the power at the target monitoring point rapidly increased from 45% to 63%.

[0025] Figure 2 This is a framework diagram of a vehicle-grid interactive energy mutual assistance system based on a microgrid, provided in an embodiment of this application. Figure 2 As shown in the diagram, this system architecture diagram illustrates the complete technical framework of a microgrid power regulation system based on electric vehicle mutual assistance response. The entire system adopts a layered architecture design, consisting of a data acquisition layer, a data processing layer, a decision control layer, and an execution control layer from bottom to top. Data transmission and command issuance between the layers are achieved through standardized interfaces.

[0026] The data acquisition layer includes a power monitoring module for real-time acquisition of power status data at various monitoring points of the microgrid; a vehicle information module for acquiring key information such as the location coordinates of electric vehicles, battery state of charge, charging status, and vehicle type; a grid status module for monitoring the stability parameters of the microgrid, including voltage fluctuations, frequency deviations, and load characteristics; and a communication interface module for enabling data transmission and protocol conversion between different devices to ensure accurate information transmission.

[0027] The target recognition algorithm in the data processing layer identifies monitoring points with power levels below a preset threshold by analyzing the collected data. The area delineation algorithm dynamically calculates the radius of the mutual assistance response area based on the power difference. The priority calculation algorithm prioritizes electric vehicles within the area by comprehensively considering distance weight and vehicle response capability.

[0028] The decision control layer's group decision module divides the selected electric vehicles into response groups and standby groups according to priority coefficients. The power allocation module calculates the first discharge power and the second discharge power based on the response capability of each vehicle and the grouping situation. The stability adjustment module dynamically adjusts the output power of each vehicle through real-time monitoring of the microgrid stability index to maintain system stability.

[0029] The V2G controller at the execution control layer receives instructions from the upper layer to control the specific discharge process of the electric vehicle, including precise adjustment of discharge power and reasonable arrangement of discharge time. The power grid dispatching system is responsible for coordinating the power balance of the entire microgrid to ensure that the electric vehicle provides support without affecting the normal operation of other loads. The entire system achieves rapid response and effective solution to the problem of insufficient power in the microgrid through hierarchical information processing and decision-making mechanisms.

[0030] Figure 3 This is a flowchart illustrating a vehicle-grid interaction and energy sharing method based on a microgrid, as provided in an embodiment of this application. Figure 3 As shown, the method includes S101-S106:

[0031] S101, obtain the power of multiple monitoring points in the microgrid, determine the target monitoring point whose power is lower than the preset power among the multiple monitoring points, and calculate the power difference between the target monitoring point and the preset power.

[0032] In a microgrid-vehicle-grid interactive energy exchange system, the first step is to establish a comprehensive grid monitoring system to identify power-deficient areas and quantify the degree of power shortage. This is the fundamental prerequisite for achieving precise energy dispatch. As a small power system that includes distributed power sources, energy storage devices, loads, and control systems, the power balance of a microgrid directly affects power supply quality and system stability. Therefore, real-time monitoring is essential to understand the power supply and demand situation in each area.

[0033] In practice, the system first collects power data in real time from various monitoring points in the microgrid using monitoring equipment such as smart meters and power sensors deployed at key nodes. These monitoring points are typically located at critical locations such as the outgoing line side of substations, important load connection points, and distributed power generation grid connection points. Each monitoring point is equipped with active and reactive power measurement devices, capable of uploading power data to the microgrid energy management system at a frequency of seconds. After receiving the real-time power data from each monitoring point, the system compares and analyzes it with a pre-set preset power. This preset power is a benchmark value determined based on factors such as the historical load characteristics, power supply capacity, and safety margin of the area covered by the monitoring point, representing the minimum power level required to maintain normal power supply in that area.

[0034] When the system detects that the actual power of a monitoring point is lower than its corresponding preset power, that monitoring point is identified as a target monitoring point, indicating that there is a power shortage problem in that area. The system then calculates the difference between the current power of the target monitoring point and the preset power. This power difference directly reflects the degree of power shortage in the area. For example, if the preset power of a monitoring point in an industrial park is 500kW, but the current actual power is only 350kW, then the power difference is 150kW, indicating that there is a 150kW power shortage in that area.

[0035] S102, Based on the power difference, determine the mutual assistance response area of ​​the target monitoring point. The mutual assistance response area is a circular area with the target monitoring point as the center and a preset length as the radius.

[0036] After determining the power difference at the target monitoring points, the system needs to establish a scientifically sound mutual assistance response area to optimize the allocation and scheduling efficiency of electric vehicle resources. This is a crucial spatial positioning step for achieving precise energy mutual assistance. Since electric vehicles, as mobile energy storage units, have geographically distributed characteristics, their effectiveness in participating in mutual assistance largely depends on their spatial distance from the power-deficient area. Excessive distance not only increases transmission losses but also reduces response speed and economic efficiency. Therefore, it is essential to establish an effective response range centered on the target monitoring points.

[0037] In practice, the system first dynamically determines the preset length based on the power difference calculated in the previous step. This preset length serves as the radius parameter of the mutual assistance response area. The system incorporates a positive correlation between the power difference and the preset length; that is, the larger the power gap, the more electric vehicle resources need to be mobilized, and the corresponding search range needs to be expanded. For example, when the power difference is 100kW, the preset length might be set to 2 kilometers, while when the power difference increases to 300kW, the preset length might expand to 5 kilometers. This dynamic adjustment mechanism ensures that sufficient electric vehicle resources can participate in mutual assistance under different levels of power shortage.

[0038] After determining the preset length, the system delineates a circular area within the microgrid coverage area, using the geographical coordinates of the target monitoring point as the center and the calculated preset length as the radius. This circular area is the mutual assistance response area. The mutual assistance response area represents the geographical boundary of electric vehicles that can effectively participate in the current energy mutual assistance task. The system uses Geographic Information System (GIS) technology to accurately calculate the boundary coordinates of the circular area and establishes a spatial index to quickly filter electric vehicles located within that area. The selection of the circular area is based on the physical characteristics and economic considerations of power transmission, because a circle can contain the largest area with the smallest perimeter, and can cover more potential electric vehicle resources under the same radius.

[0039] Based on the above embodiments, as an optional implementation, in S102, determining the mutual assistance response area of ​​the target monitoring point according to the power difference specifically includes S21-S22:

[0040] S21, determine the preset length based on the power difference, wherein the power difference is positively correlated with the preset length.

[0041] S22, a circular area is defined with the target monitoring point as the center and a preset length as the radius, and the circular area is used as the mutual assistance response area.

[0042] Specifically, the system analyzes the relationship between power demand and the number of successfully mobilized electric vehicles in historical mutual assistance missions, as well as the average distribution density of electric vehicles in various regions, to establish a correspondence between power difference and the required number of electric vehicles, which is then converted into a corresponding spatial search radius. For example, when the power difference is 50kW, based on the average discharge capacity and distribution density of electric vehicles in the region, the system may determine that 15-20 electric vehicles need to be mobilized, and the preset length is calculated to be 1.5 kilometers based on the average distribution density; when the power difference increases to 200kW, the number of required electric vehicles may increase to 60-80, and the corresponding preset length may be extended to 4 kilometers.

[0043] The system also incorporates a regional adjustment coefficient to adapt to the distribution characteristics of electric vehicles in different regions. In urban centers with high electric vehicle density, the regional adjustment coefficient is smaller, allowing for the search radius to find sufficient resources. In suburban or industrial areas with low electric vehicle density, the regional adjustment coefficient is larger, requiring an expanded search range to ensure resource acquisition.

[0044] S103, obtain the regional characteristics of the mutual assistance response area, the location information of multiple electric vehicles in the mutual assistance response area that are in the charging state, and obtain the charging state and vehicle type of each electric vehicle in the mutual assistance response area.

[0045] After identifying the mutual assistance response area, the system needs to comprehensively collect key information within that area to provide data support for precise energy dispatch decisions. This is a crucial information collection step for realizing intelligent vehicle-to-grid interaction. Since different areas have different importance levels and load characteristics, and electric vehicles, as mobile energy storage resources, also possess diverse technical parameters and status characteristics, a complete information profile must be established to formulate the optimal mutual assistance strategy.

[0046] The system first obtains the regional characteristics of the mutual assistance response area through a geographic information database and a power grid topology analysis module. These regional characteristics include key attributes such as the distribution of load types within the area, the number of important users, the power supply reliability requirement level, and the impact of historical power outages. For example, if the mutual assistance response area includes important loads such as hospitals and data centers, the area has a high importance level and needs to be prioritized for power supply; if the area mainly contains residential loads, the importance level is relatively low, and processing can be appropriately delayed. By analyzing these regional characteristics, the system can provide an importance weight reference for subsequent power allocation, ensuring that critical areas are prioritized for power supply.

[0047] Simultaneously, the system acquires real-time information on all electric vehicles charging within the mutual assistance response area through the vehicle-to-everything (V2X) communication platform and charging pile monitoring network. Here, "charging status" refers to an electric vehicle actively charging through a charging pile and possessing the physical connection to discharge back into the grid; only such electric vehicles can participate in V2X energy sharing. The system accurately obtains the location information of each electric vehicle, including latitude and longitude coordinates, the charging station number, and the location of the charging pile, through the fusion of GPS positioning, base station positioning, and charging pile location information. This location information is used to calculate the distance between the electric vehicle and the target monitoring point, providing a spatial basis for subsequent response priority ranking.

[0048] Based on location information, the system further collects information on the charging status and vehicle type of each electric vehicle. The charging status includes two core parameters: the current charging power and the battery state of charge (SBC). Charging power reflects the current energy transfer rate of the electric vehicle, while SBC indicates the current energy storage level of the battery, usually expressed as a percentage of battery capacity utilization. These two parameters directly determine the maximum discharge power and continuous discharge duration that the electric vehicle can provide. Vehicle type information includes technical specifications such as the electric vehicle's brand and model, battery capacity, maximum charge / discharge power, and battery technology type. Different vehicle types have different discharge capabilities and response characteristics; for example, electric buses typically have larger battery capacities and discharge power, while electric passenger vehicles may have faster response times.

[0049] S104, combining the location information and charging status of each electric vehicle, divides each electric vehicle into a response group and a standby group.

[0050] After collecting the location information and charging status of electric vehicles, the system needs to establish a scientific electric vehicle grouping mechanism to achieve hierarchical energy mutual assistance response. This is a key decision-making step to optimize resource allocation and improve system response efficiency. Since electric vehicles within the mutual assistance response area vary significantly in geographical location, battery status, and discharge capacity, a unified scheduling strategy would not only fail to fully utilize high-quality resources but could also lead to a decrease in system response speed and low energy transfer efficiency due to improper scheduling. Therefore, a grouping management system based on comprehensive evaluation must be established.

[0051] The system first uses a geographic coordinate calculation algorithm to accurately calculate the straight-line distance between each electric vehicle and the target monitoring point based on their location information. This distance calculation employs a spherical distance formula, taking into account the influence of the Earth's curvature on distance measurement, thus ensuring calculation accuracy. Distance directly affects line losses and response delays in power transmission; electric vehicles that are closer together have higher transmission efficiency and faster response speeds when participating in mutual assistance. Therefore, distance is a crucial indicator for evaluating the mutual assistance value of electric vehicles.

[0052] Next, the system performs in-depth analysis of the charging status of each electric vehicle, which includes two core parameters: charging power and battery state of charge (SOC). The system uses these two parameters to calculate the maximum discharge power and continuous discharge duration for each electric vehicle. The maximum discharge power depends on the inverter capacity, the current power output capability of the battery, and the bidirectional transmission capability of the charging station, while the continuous discharge duration is calculated based on the battery SOC, battery capacity, and discharge power. For example, an electric vehicle with an 80% SOC and a 60kWh battery capacity can theoretically discharge continuously for approximately 4.8 hours at a power output of 10kW. The system multiplies the maximum discharge power by the continuous discharge duration to generate a response capability parameter for each electric vehicle. This parameter comprehensively reflects the total energy contribution that the electric vehicle can provide throughout the entire mutual assistance process.

[0053] After obtaining the distance and response capability parameters, the system uses a weighted calculation method to generate response priority coefficients for each electric vehicle. First, the distance weight coefficient is determined based on the distance information. The system has a built-in negative correlation function between distance and weight; electric vehicles closer to the target monitoring point receive higher distance weight coefficients. For example, an electric vehicle within 1 kilometer of the target monitoring point might receive a distance weight coefficient of 0.9, while an electric vehicle 3 kilometers away might only receive a distance weight coefficient of 0.6. Then, the distance weight coefficients of each electric vehicle are weighted and summed with the response capability parameters to generate the final response priority coefficient. This coefficient considers both the advantages of spatial location and the actual contribution capability of the electric vehicle, providing a quantitative basis for scientific grouping.

[0054] Based on the calculated response priority coefficients, the system divides electric vehicles into two levels: a response group and a backup group. Electric vehicles with response priority coefficients higher than a preset priority coefficient are assigned to the response group. These electric vehicles typically have advantages such as short range, sufficient battery power, and strong discharge capacity, enabling them to respond quickly and efficiently to mutual assistance needs. Electric vehicles with response priority coefficients no higher than the preset priority coefficients are assigned to the backup group, serving as a supplement and backup force to the response group, and will participate when the response group's electric vehicles cannot meet all mutual assistance needs.

[0055] Based on the above embodiments, as an optional implementation method, in S104, the charging state includes charging power and battery state of charge. Combining the location information and charging state of each electric vehicle, the electric vehicles are divided into a response group and a standby group, specifically including S41-S44:

[0056] S41, Calculate the distance between each electric vehicle and the target monitoring point based on the location information.

[0057] S42, combining charging power and battery state of charge, calculates the maximum discharge power and continuous discharge duration of each electric vehicle, and uses the product of the maximum discharge power and continuous discharge duration as the response capability parameter of each electric vehicle.

[0058] In calculating the maximum discharge power, the system comprehensively considers multiple constraints, including the battery's power output characteristics, the inverter's power conversion capability, and the bidirectional transmission capacity of the charging pile. The minimum value among these constraints is taken as the actual maximum discharge power, ensuring the reliability and safety of the calculation results. Simultaneously, based on the battery's state of charge, total battery capacity, and expected discharge power, combined with the battery's discharge efficiency curve and temperature influence factor, the system accurately calculates the continuous discharge duration for each electric vehicle. This duration calculation also considers the requirements of battery protection strategies to ensure that the discharge process does not damage the battery; typically, 10-20% of the charge is reserved as a safety reserve.

[0059] Charging power reflects the current power handling capability of the electric vehicle and the bidirectional transmission specifications of the charging station. This parameter directly determines the upper limit of the instantaneous discharge power that the electric vehicle can provide. Battery state of charge (SOC) indicates the percentage of the battery's current energy storage level relative to its total capacity. This parameter determines the length of time the electric vehicle can continuously provide energy.

[0060] The system calculates the maximum dischargeable power and the continuous discharge duration by multiplying them mathematically to generate a response capability parameter for each electric vehicle. This response capability parameter comprehensively reflects the total energy contribution of the electric vehicle throughout the entire mutual assistance process, considering both instantaneous power output capability and the time dimension of continuous power supply, providing a unified quantitative indicator for comprehensively evaluating the mutual assistance value of electric vehicles. For example, an electric vehicle with a maximum dischargeable power of 15kW and a continuous discharge duration of 3 hours has a response capability parameter of 45kWh, indicating that the vehicle can provide a total of 45 kWh of electrical energy support during the complete mutual assistance process.

[0061] S43. Based on the distance between each electric vehicle and the target monitoring point, and the response capability parameters of each electric vehicle, calculate the response priority coefficient of each electric vehicle.

[0062] The system first normalizes the distances between each electric vehicle and the target monitoring point, unifying distances from different ranges into a standard interval. Then, it generates a distance advantage factor by taking the reciprocal of the normalized distance; electric vehicles closer to the target point receive a higher distance advantage factor. Similarly, the system normalizes the response capability parameters of each electric vehicle, generating a capability advantage factor; electric vehicles with larger response capability parameters receive a higher capability advantage factor. The system sets distance weighting coefficients and capability weighting coefficients, typically set to 0.4 and 0.6 respectively, reflecting an emphasis on the actual contribution capability of the electric vehicles. Finally, the system multiplies the distance advantage factor by the distance weighting coefficient and the capability advantage factor by the capability weighting coefficient, then adds the two results to obtain the response priority coefficient for each electric vehicle.

[0063] Based on the above embodiments, as an optional implementation, in S43, calculating the response priority coefficient of each electric vehicle by combining the distance between each electric vehicle and the target monitoring point, and the response capability parameters of each electric vehicle, specifically includes:

[0064] Based on the distance between each electric vehicle and the target monitoring point, a distance weighting coefficient for each electric vehicle is determined, where distance and distance weighting coefficient are negatively correlated. The distance weighting coefficient of each electric vehicle is then weighted and summed with the response capability parameter to generate the response priority coefficient for each electric vehicle.

[0065] Specifically, the system subtracts the actual distance of each electric vehicle from the maximum distance within the area to obtain the distance deviation value. Then, it divides the distance deviation value by the difference between the maximum and minimum distances, and multiplies it by a preset weighting coefficient range to finally generate a standardized distance weighting coefficient.

[0066] This negative correlation design ensures that electric vehicles (EVs) closer to the target monitoring point receive a higher distance weighting coefficient, fully demonstrating the crucial role of geographical location in mutual assistance efficiency. EVs participating in mutual assistance have multiple advantages, including lower power transmission losses, shorter communication delays, and faster physical response speeds. These advantages directly impact the actual effectiveness of mutual assistance and system stability. For example, an EV 500 meters from the target monitoring point might receive a distance weighting coefficient of 0.9, while an EV 2 kilometers away might only receive a distance weighting coefficient of 0.3.

[0067] Specifically, the system multiplies the distance weight coefficient of each electric vehicle with a preset distance influence factor to obtain the geographical contribution component, multiplies the response capability parameter with a preset capability influence factor to obtain the technical contribution component, and then arithmetically adds the geographical contribution component and the technical contribution component to generate the response priority coefficient of each electric vehicle.

[0068] The distance and capacity impact factors are optimized based on the system's design goals and operational experience. In application scenarios that prioritize response speed and transmission efficiency, the distance impact factor may be set relatively high, giving geographical advantages a greater weight in the overall evaluation. Conversely, in application scenarios that prioritize energy supply scale and sustainability, the capacity impact factor may be set relatively high, allowing technical capabilities to play a more significant role in the overall evaluation. The system typically employs a dynamic adjustment mechanism, automatically adjusting the values ​​of the two impact factors based on real-time conditions such as current power demand, urgency, and network status, ensuring that the overall evaluation always meets current operational requirements.

[0069] S44, electric vehicles with a response priority coefficient higher than the preset priority coefficient are assigned to the response group, and electric vehicles with a response priority coefficient not higher than the preset priority coefficient are assigned to the standby group.

[0070] The system sets a preset priority coefficient as the threshold for grouping. This threshold is typically determined based on historical experience data and system performance requirements, ensuring that the response group has sufficient high-quality resources while also ensuring that the backup group has reasonable resource reserves. Electric vehicles with a response priority coefficient higher than the preset priority coefficient are automatically assigned to the response group. These electric vehicles typically have advantages such as short range, sufficient battery power, and strong discharge capacity, enabling them to respond quickly and provide stable energy support after mutual assistance is initiated. Electric vehicles with a response priority coefficient no higher than the preset priority coefficient are assigned to the backup group. Although these electric vehicles may have some disadvantages, they still meet the basic conditions for participating in mutual assistance and can be promptly supplemented and replaced when the response group's resources are insufficient or in case of unforeseen circumstances.

[0071] S105, based on the power difference, combined with regional characteristics and the vehicle type of each electric vehicle, determines the first discharge power of each electric vehicle in the response group and the second discharge power of each electric vehicle in the standby group.

[0072] After grouping electric vehicles, the system needs to establish a precise power allocation mechanism to determine the specific discharge power of each electric vehicle. This is the core calculation step for achieving accurate energy sharing and ensuring power supply security. Since different regions have different importance levels and safety requirements, and each electric vehicle has different technical characteristics and discharge capabilities, a differentiated power allocation system that comprehensively considers regional needs, vehicle capabilities, and system safety must be established to ensure that actual power demand is met while fully leveraging the technological advantages of various electric vehicles.

[0073] The system first determines the importance level of the mutual assistance response area based on the previously acquired regional characteristic information and its built-in regional importance assessment algorithm. This importance level comprehensively considers multiple dimensions such as the number and type of important loads within the area, power supply reliability requirements, and historical power outage losses, and is typically divided into three levels: high, medium, and low. For example, areas containing hospitals and data centers have a high importance level, industrial parks and commercial areas have a medium importance level, and ordinary residential areas have a low importance level. Based on the importance level, the system determines the corresponding power redundancy coefficient. Areas with higher importance levels require more redundant power to ensure power supply security. The power redundancy coefficient for a high-importance area might be set to 1.3, meaning that 30% more power reserve is needed than the actual power difference; the power redundancy coefficient for a medium-importance area might be 1.2; and the power redundancy coefficient for a low-importance area might be 1.1.

[0074] Next, the system determines the corresponding discharge capacity parameters based on the vehicle type information of each electric vehicle. Different vehicle types have significantly different technical parameters, such as battery technology, inverter specifications, and charge / discharge characteristics. These differences directly affect the discharge capacity and applicable scenarios of electric vehicles. For example, electric buses are typically equipped with large-capacity power batteries and high-power inverters, resulting in higher discharge capacity parameters, making them suitable for high-power, long-duration discharge tasks. Electric passenger vehicles have medium discharge capacity parameters, offering good response speed and flexibility. Electric logistics vehicles have relatively lower discharge capacity parameters, but are more numerous and widely distributed. The system assigns corresponding discharge capacity parameter values ​​to each vehicle type by querying the vehicle technical specification database.

[0075] After determining the power redundancy factor, the system arithmetically multiplies the power difference at the target monitoring points with the power redundancy factor to generate a total discharge power with sufficient safety margin. This total discharge power not only compensates for the current power shortage but also provides necessary safety redundancy to ensure normal power supply during load fluctuations or when some electric vehicles are decommissioned. For example, if the power difference is 150kW and the power redundancy factor is 1.2, the total discharge power is 180kW, with an additional 30kW as a safety reserve.

[0076] The system then calculates the power allocation for electric vehicles within the response group. By comprehensively considering the response priority coefficient and discharge capacity parameters of each electric vehicle in the response group, the system uses a weighted allocation algorithm to calculate the first power allocation ratio for each electric vehicle. Electric vehicles with higher response priority coefficients and larger discharge capacity parameters will receive a higher power allocation ratio, thus making full use of high-quality resources while ensuring the rationality of power allocation. After calculating the first power allocation ratio, the system arithmetically multiplies the total discharge power by the first power allocation ratio of each electric vehicle to generate the first discharge power of each electric vehicle in the response group.

[0077] Similarly, the system calculates power allocation for electric vehicles in the standby group. Based on the response priority coefficient and discharge capacity parameters of each electric vehicle in the standby group, a second power allocation ratio is calculated, and then multiplied by the total discharge power to obtain the second discharge power of each electric vehicle. It should be noted that the power allocation of the standby group is usually a contingency plan. Under normal circumstances, the electric vehicles in the standby group will not immediately start discharging, but will only be activated when the response group cannot meet the demand or when an anomaly occurs.

[0078] Based on the above embodiments, as an optional implementation, in S105, based on the power difference, and combined with regional characteristics and the vehicle type of each electric vehicle, the determination of the first discharge power of each electric vehicle in the response group and the second discharge power of each electric vehicle in the standby group specifically includes S51-S57:

[0079] S51. Based on the regional characteristics, determine the importance level of the mutual assistance response area, and based on the importance level, determine the power redundancy coefficient of the mutual assistance response area.

[0080] The determination of importance level directly affects the setting of the power redundancy factor, which reflects the proportion of power margin reserved by the system to cope with unforeseen circumstances and ensure power supply security. Particularly important areas typically include hospitals, critical public service facilities, and other facilities with extremely high requirements for power supply continuity. Their power redundancy factor may be set to 1.5, meaning the system needs to be configured with 50% higher discharge power than the actual power demand to ensure absolute power supply security. Important areas may include large commercial centers, educational institutions, transportation hubs, and other facilities, with a power redundancy factor set to 1.3. Generally important areas are mainly ordinary residential areas and small commercial areas, with a power redundancy factor set to 1.2. Ordinary areas are industrial or agricultural areas with relatively lower requirements for power supply continuity, with a power redundancy factor set to 1.1. This differentiated redundancy configuration ensures the rational allocation of system resources and prioritizes key areas.

[0081] The system first statistically analyzes the quantity and scale of various facilities within the mutual assistance response area, and establishes comprehensive evaluation indicators to classify their importance levels. The criteria for defining an "extremely important" area are: the area contains facilities with extremely high requirements for power supply continuity, such as tertiary hospitals and critical public service facilities. These areas have extremely high requirements for power supply continuity, and power outages have a wide impact and cause significant losses; the power redundancy coefficient is set at 1.5. The criteria for defining an "important" area are: the area contains any one of the following facilities: a secondary hospital, a large commercial center (with a business area exceeding 50,000 square meters), a university, or a subway station; or simultaneously contains two or more types of facilities: a community hospital and primary and secondary schools. These areas undertake important social service functions; the power redundancy coefficient is set at 1.3. The criteria for defining a "generally important" area are: the area mainly contains ordinary residential communities (more than 1,000 households), small and medium-sized commercial areas (with a business area of ​​5,000-50,000 square meters), ordinary primary and secondary schools, community health service centers, etc. These areas involve basic public services; the power redundancy coefficient is set at 1.2. The criteria for defining a normal area are: the area mainly consists of small-scale residential areas (fewer than 1000 households), general industrial plants, agricultural facilities, warehousing and logistics facilities, etc., with relatively low requirements for power supply continuity, and the power redundancy factor is set to 1.1. When a mutual assistance response area contains facilities of multiple levels, the system classifies them according to the highest importance level to ensure that critical facilities are prioritized. The above four-level classification covers all area types and all power redundancy factor settings within the microgrid coverage area. There are no unclassified areas. The system automatically identifies the type and scale of facilities within the area through a geographic information database, realizing intelligent determination of importance levels and precise configuration of power redundancy factors.

[0082] S52, determine the discharge capacity parameters of each electric vehicle based on its vehicle type.

[0083] Meanwhile, the system accurately determines the discharge capacity parameters based on the vehicle type information of each electric vehicle. Vehicle type is a core factor affecting the discharge performance of electric vehicles; different types of electric vehicles differ significantly in battery capacity, battery technology, inverter specifications, and charging interface standards. The system maintains a detailed vehicle type database, containing the technical specifications of major electric vehicle brands and models, covering various vehicle types such as passenger cars, commercial buses, freight trucks, and special-purpose vehicles. The discharge capacity parameters comprehensively reflect key performance indicators of each electric vehicle, such as maximum discharge power, discharge efficiency, power regulation accuracy, and response speed, providing accurate technical basis for subsequent power allocation calculations. For example, the discharge capacity parameter of a large electric bus may reach 100kW, that of a passenger electric car may be 20kW, and that of an electric truck may be 50kW. These differences directly affect the power contribution potential of each vehicle in mutual aid tasks.

[0084] S53, the power difference is arithmetically multiplied with the power redundancy coefficient to generate the total discharge power.

[0085] S54. Calculate the first power allocation ratio of each electric vehicle in the response group based on the response priority coefficient and discharge capacity parameter of each electric vehicle in the response group.

[0086] S55, the total discharge power is arithmetically multiplied by the first power allocation ratio to generate the first discharge power of each electric vehicle in the response group.

[0087] S56, calculate the second power allocation ratio of each electric vehicle in the standby group based on the response priority coefficient and discharge capacity parameters of each electric vehicle in the standby group.

[0088] S57, the total discharge power is arithmetically multiplied by the second power allocation ratio to generate the second discharge power of each electric vehicle in the standby group.

[0089] After determining the total discharge power, the system performs a precise calculation of the first power allocation ratio for the electric vehicles within the response group. This calculation process comprehensively considers two key factors: the response priority coefficient and the discharge capacity parameter of each electric vehicle, and employs a weighted allocation algorithm to achieve a reasonable power distribution. The system first performs a mathematical product operation on the response priority coefficient and discharge capacity parameter of each electric vehicle within the response group to generate a comprehensive allocation weight for each vehicle. This weight reflects both the overall value ranking of the vehicles and their actual contribution capability. Then, the system calculates the sum of the comprehensive allocation weights of all electric vehicles within the response group, and divides the comprehensive allocation weight of each electric vehicle by the sum to obtain the first power allocation ratio for each electric vehicle.

[0090] The calculation of the first power allocation ratio ensures the scientific and rational nature of power allocation. Electric vehicles with high response priority coefficients and strong discharge capabilities will receive a larger power allocation ratio and assume more mutual assistance responsibilities, while electric vehicles with relatively low response priority coefficients or limited discharge capabilities will receive a correspondingly smaller power allocation ratio. This differentiated allocation strategy not only improves the overall efficiency of the system but also avoids the problem of insufficiently capable electric vehicles bearing an excessive burden, thus affecting system stability. The system generates the first discharge power of each electric vehicle within the response group by arithmetically multiplying the total discharge power by the first power allocation ratio of each electric vehicle. This power value represents the discharge power level that each electric vehicle needs to provide in normal mutual assistance mode.

[0091] For electric vehicles in the standby group, the system uses a similar algorithm to calculate the second power allocation ratio and the second discharge power. The power allocation algorithm for the standby group is consistent with that of the response group, also based on a comprehensive weight calculation and proportional allocation using response priority coefficients and discharge capacity parameters, ensuring a scientifically reasonable power configuration within the standby group. The second discharge power is typically used as a backup power reserve for the system, activated in abnormal situations such as insufficient power in the response group, partial vehicle withdrawal, or sudden load increases, providing timely power supplementation and support to the system.

[0092] For example, during the regional feature analysis phase, the system identified that the business district includes a top-tier hospital, two office buildings, a subway station, and multiple commercial facilities. The hospital houses intensive care units and operating rooms, departments with extremely high requirements for power supply continuity; the subway station serves a vital urban transportation function; and the office buildings contain data centers of several financial institutions. Based on the distribution density and power supply requirements of these critical facilities, the system classifies the area as of extremely important, setting the corresponding power redundancy factor to 1.5. Therefore, the system calculates the total discharge power to be 800kW × 1.5 = 1200kW. This means the system needs to be configured with a total discharge capacity of 1200kW to ensure that even if some vehicles are taken out of service or the load fluctuates, the basic power requirement of 800kW can still be met.

[0093] Within the response area, the system identified 15 electric vehicles capable of participating in the mutual assistance program, including 6 electric passenger cars, 4 electric taxis, 3 electric logistics vehicles, and 2 electric buses. The system retrieved the discharge capacity parameters for each vehicle from its vehicle type database: 15kW for passenger cars, 20kW for electric taxis, 35kW for electric logistics vehicles, and 80kW for electric buses. Simultaneously, the system calculated the distance between each vehicle and the fault area, and combined this with their battery state of charge and charging power to calculate the response capability parameters.

[0094] Let's take a few representative electric vehicles as examples for detailed explanation. The family electric sedan, number A01, is only 200 meters from the fault location. Its battery state of charge is 85%, its maximum discharge power is 15kW, its continuous discharge time is 4 hours, its response capability parameter is 60kWh, its distance weighting coefficient is 0.95, and its response priority coefficient is calculated as 0.95×0.4+60×0.01×0.6=0.74. The electric logistics vehicle, number B03, is 800 meters from the fault location. Its battery state of charge is 90%, its maximum discharge power is 35kW, its continuous discharge time is 6 hours, its response capability parameter is 210kWh, its distance weighting coefficient is 0.75, and its response priority coefficient is 0.75×0.4+210×0.01×0.6=1.56. The electric bus with serial number C02 is 1.2 kilometers away from the fault point. Its battery is 70% charged, with a maximum discharge power of 80kW and a continuous discharge time of 5 hours. Its response capability parameter is 400kWh, its distance weighting coefficient is 0.60, and its response priority coefficient is 0.60×0.4+400×0.01×0.6=2.64.

[0095] Based on the ranking results of the response priority coefficients, the system assigned 8 electric vehicles with a priority coefficient higher than 1.2 to the response group, including 2 electric buses, 3 electric logistics vehicles, and 3 high-performance electric sedans. The remaining 7 electric vehicles were assigned to the reserve group. For power allocation within the response group, the system calculated the comprehensive allocation weight of each vehicle. The comprehensive allocation weight of the electric bus numbered C02 was 2.64 × 80 = 211.2, and the comprehensive allocation weight of the electric logistics vehicle numbered B03 was 1.56 × 35 = 54.6. Although numbered A01 was very close, it was assigned to the reserve group due to its limited discharge capacity.

[0096] The total allocation weight of the 8 electric vehicles in the response group is 850. Therefore, the first power allocation ratio of the electric bus numbered C02 is 211.2 ÷ 850 = 0.248, and the corresponding first discharge power is 1200kW × 0.248 = 297.6kW. The first power allocation ratio of the electric logistics vehicle numbered B03 is 54.6 ÷ 850 = 0.064, and the first discharge power is 1200kW × 0.064 = 76.8kW. The allocation ratios and discharge powers of the other vehicles are calculated using the same method. The response group undertakes a total of 1200kW of main mutual assistance tasks.

[0097] The seven electric vehicles in the backup group also have their second power allocation ratios calculated based on response priority coefficients and discharge capacity parameters. The second power allocation ratio for the family electric sedan, numbered A01, in the backup group is 0.12, and its second discharge power is 1200kW × 0.12 = 144kW. The second discharge power of these backup vehicles serves as the system's reserve force, which can be quickly replenished when the response group's power is insufficient or when a vehicle withdraws.

[0098] In one specific embodiment, the activation of the second discharge power of the electric vehicles in the backup group follows a multi-level triggering mechanism, automatically activating the backup group to participate in mutual assistance when the system detects that any of the following conditions are met:

[0099] First, in the event of insufficient power in the response group, when the total actual discharge power of the electric vehicles in the response group is less than 85% of the power requirement of the target monitoring point, the system immediately starts the electric vehicle with the highest response priority coefficient in the backup group to supplement the power. For example, if the target monitoring point in an industrial park requires 500kW of power support, and the 8 electric vehicles in the response group plan to provide 480kW but can only provide 420kW in reality, the system will select 2-3 electric vehicles from the backup group to supplement the 80kW power gap.

[0100] Secondly, there is the vehicle exit compensation mechanism. When any electric vehicle in the response group exits the mutual aid due to reasons such as the battery charge dropping below 20%, the owner ending charging early, or equipment failure, the system will select a corresponding number of electric vehicles from the backup group to replace them one-to-one or many-to-one according to the response priority order, so as to ensure that the total discharge power is maintained at the design level.

[0101] The third is the load surge response strategy. When the power demand of the target monitoring point suddenly increases by more than 15% during the mutual assistance process, such as when a factory production line is started unexpectedly or a hospital uses large medical equipment, the system will start some or all of the backup electric vehicles according to the size of the increased power demand. If the increased power demand is less than 50kW, the first 30% of the backup vehicles will be started; if it is between 50-100kW, the first 60% of the vehicles will be started; if it exceeds 100kW, all backup vehicles will be started.

[0102] Fourth is the requirement for system stability assurance. When the microgrid stability index suddenly drops from the normal level of above 0.8 to the range of 0.6-0.75, the system will activate the nearest electric vehicle in the backup group to provide additional power support and voltage stabilization services. This improves the power flow distribution and voltage stability of the grid by increasing the geographical dispersion of injected power. Fifth is the emergency response mechanism. In emergency situations such as natural disasters or equipment failures, when multiple monitoring points experience power shortages simultaneously, the system will prioritize critical loads such as hospitals and data centers based on the importance level of the region. At this time, the backup groups in each mutual assistance response area will be uniformly allocated to break through the original regional restrictions and provide cross-regional support.

[0103] For example, when a hospital in Area A experiences a power outage, backup electric vehicles from Areas B and C may be called upon to support the restoration of power supply to Area A. The specific activation process for the backup group is as follows: the system first sends an activation command to the on-board terminal of the target electric vehicle. After the vehicle owner receives the economic incentive information for participating in mutual assistance and confirms their agreement, the vehicle automatically adjusts its charging mode to switch to discharging mode. The entire activation process is completed within 3-5 minutes, ensuring timely response to various power demand changes and emergency situations.

[0104] S106, obtain the power coupling degree between the target monitoring point and other monitoring points in the microgrid, calculate the stability index of the microgrid based on the power coupling degree, adjust the first discharge power and the second discharge power based on the stability index, and generate the first target discharge power of each electric vehicle in the response group and the second target discharge power of each electric vehicle in the standby group.

[0105] After initial power allocation, the system needs to establish a comprehensive stability assessment and dynamic adjustment mechanism to ensure that the vehicle-to-grid interaction process does not negatively impact the overall stability of the microgrid. This is a crucial safety control step to ensure safe system operation and optimize mutual assistance. Because a microgrid is a complex power system with close electrical coupling between monitoring points, when a large amount of electric vehicle discharge power is injected into a certain area, it will inevitably affect the power distribution and voltage levels of other areas through the grid topology. Without systematic stability analysis and power adjustment, this could trigger a chain reaction, leading to new power imbalances in other areas and even threatening the safe and stable operation of the entire microgrid.

[0106] The system first uses a power grid topology analysis module and a real-time monitoring system to obtain the electrical distance and power transfer coefficient between the target monitoring point and other monitoring points in the microgrid. Electrical distance refers to the electrical impedance between two monitoring points connected by power grid lines. Unlike geographical distance, electrical distance considers electrical characteristics such as line impedance, transformer parameters, and switch status, reflecting the ease or difficulty of transmitting electrical energy between two points. The power transfer coefficient represents the transmission efficiency and impact of power transfer from one monitoring point to another. This coefficient is affected by various factors such as line capacity, load distribution, and network topology. The system accurately calculates these electrical parameters through power flow calculations and network analysis algorithms.

[0107] After obtaining the electrical distance, the system normalizes it, unifying electrical distances of different dimensions and numerical ranges into a standard range of 0 to 1, generating a normalized electrical distance. This facilitates subsequent unified calculations and comparative analysis. Then, the reciprocal of the normalized electrical distance is taken to generate a distance influence factor. The distance influence factor is inversely proportional to the electrical distance; monitoring points with smaller electrical distances obtain larger distance influence factors, indicating a greater degree of influence from power changes at the target monitoring point. The system then arithmetically multiplies the power transmission coefficient by the distance influence factor to generate an initial coupling degree. This initial coupling degree comprehensively reflects the electrical coupling strength between the target monitoring point and other monitoring points.

[0108] To obtain a more accurate and stable power coupling degree, the system performs a weighted average of the initial coupling degrees of each monitoring point. The weighting coefficients are determined based on the importance, load capacity, and historical operating data of each monitoring point, ultimately generating the power coupling degree between the target monitoring point and other monitoring points. A higher power coupling degree indicates a greater impact of power changes at the target monitoring point on other monitoring points, requiring the system to adjust power more cautiously.

[0109] Based on the obtained power coupling degree, the system further calculates the comprehensive stability index of the microgrid. First, the system obtains the power fluctuation variance and load characteristic parameters of each monitoring point in the microgrid. The power fluctuation variance reflects the stability of power at each monitoring point; a larger value indicates more severe power fluctuations. The load characteristic parameters include characteristics such as load type, variation patterns, and adjustability. Based on the power coupling degree between monitoring points, the system calculates the coupling stability factor of the microgrid. This factor reflects the impact of the degree of mutual influence between monitoring points on system stability. Based on the power fluctuation variance of each monitoring point, the system calculates the fluctuation stability factor, reflecting the impact of power fluctuations on system stability. Based on the load characteristic parameters, the system calculates the load stability factor, reflecting the impact of load characteristics on system stability. Finally, the system performs a weighted summation of the coupling stability factor, fluctuation stability factor, and load stability factor to generate the microgrid stability index. This index comprehensively evaluates the overall stability level of the current microgrid.

[0110] Based on the calculated stability index, the system executes an intelligent power adjustment strategy. When the stability index is higher than the preset stability threshold, it indicates that the microgrid is in a stable state. The system determines that the current power allocation scheme will not adversely affect the grid stability, therefore keeping the first and second discharge powers unchanged and directly using them as the final target discharge powers. When the stability index is not higher than the preset stability threshold, it indicates that the microgrid is in a relatively unstable state. Discharging according to the original discharge power may exacerbate system instability, and the system needs to adjust the power to ensure safety.

[0111] In unstable conditions, the system calculates the difference between the stability index and the preset stability threshold to generate a stability deviation value. This deviation value reflects the degree to which the current system deviates from a stable state. The system normalizes the stability deviation value to generate a normalized stability deviation value. Then, it arithmetically multiplies the normalized stability deviation value by a preset adjustment factor to generate a power adjustment coefficient. The preset adjustment factor is an adjustment range parameter pre-set by the system based on historical operating experience and safety requirements, ensuring that power adjustment effectively improves stability without excessively reducing the mutual assistance effect. Finally, the system multiplies the first discharge power by the power adjustment coefficient to generate the first target discharge power for each electric vehicle in the response group, and multiplies the second discharge power by the power adjustment coefficient to generate the second target discharge power for each electric vehicle in the standby group.

[0112] Based on the above embodiments, as an optional implementation, in S106, obtaining the power coupling degree between the target monitoring point and other monitoring points in the microgrid, and calculating the stability index of the microgrid based on the power coupling degree, specifically includes S61-S66:

[0113] S61, obtain the electrical distance and power transmission coefficient between the target monitoring point and other monitoring points in the microgrid.

[0114] The system first acquires the electrical distance and power transfer coefficient between the target monitoring point and other monitoring points in the microgrid. Electrical distance refers to the proximity of the electrical connection between two monitoring points in the power system; it takes into account equipment parameters such as line impedance and transformers, and differs from physical distance. For example, two monitoring points may be physically close, but if they are separated by multiple transformers and long-distance lines, the electrical distance will be significant. The power transfer coefficient reflects the actual power transmission capacity between monitoring points, taking into account factors such as line capacity and transmission efficiency.

[0115] S62, normalize the electrical distance to generate a normalized electrical distance; take the reciprocal of the normalized electrical distance to generate the distance influence factor.

[0116] The system normalizes the electrical distance, mapping all electrical distance values ​​to a uniform range of 0 to 1, eliminating the influence of excessively large numerical differences and generating a normalized electrical distance. Then, the reciprocal of the normalized electrical distance is taken to generate a distance influence factor. This process aims to ensure that monitoring points with smaller electrical distances receive larger influence factor values, reflecting the physical law that closer monitoring points have stronger mutual influence.

[0117] S63, the power transmission coefficient is arithmetically multiplied by the distance influence factor to generate the initial coupling degree; the initial coupling degree is then weighted and averaged to generate the power coupling degree between the target monitoring point and other monitoring points.

[0118] The system multiplies the power transmission coefficient by the distance influence factor to obtain the initial coupling degree. This value comprehensively reflects the coupling strength between monitoring points based on distance and transmission capacity. The system further performs weighted averaging on the initial coupling degrees under different time periods and operating conditions, taking into account changes in operating conditions, and finally generates a stable and reliable power coupling degree. The higher the power coupling degree value, the stronger the mutual influence of power changes between the two monitoring points.

[0119] S64, obtain the power fluctuation variance and load characteristic parameters of each monitoring point in the microgrid.

[0120] The system simultaneously acquires the power fluctuation variance and load characteristic parameters for each monitoring point. The power fluctuation variance is calculated by analyzing historical power data at the monitoring point and reflects the severity of power changes at that point. Load characteristic parameters include attributes such as load type and regulation capacity. Different types of loads have different sensitivities to power fluctuations; industrial loads are generally more sensitive, while residential loads are relatively stable.

[0121] S65. Calculate the coupling stability factor of the microgrid based on the power coupling degree between each monitoring point; calculate the fluctuation stability factor of the microgrid based on the power fluctuation variance of each monitoring point; calculate the load stability factor of the microgrid based on the load characteristic parameters of each monitoring point.

[0122] Specifically, the system first calculates the standard deviation of the power coupling degree between all monitoring point pairs. A smaller standard deviation indicates a more uniform coupling relationship between the monitoring points, which is beneficial for stable power transmission and distribution. Then, the system calculates the average power coupling degree and uses the ratio of the standard deviation to the average value as the coupling non-uniformity coefficient. The coupling stability factor is calculated using the formula: Coupling stability factor = 1 - Coupling non-uniformity coefficient × Adjustment weight. The adjustment weight is typically set to 0.5. For example, if the average power coupling degree between monitoring points in a microgrid is 0.6 and the standard deviation is 0.15, then the coupling non-uniformity coefficient is 0.15 / 0.6 = 0.25, and the coupling stability factor is 1 - 0.25 × 0.5 = 0.875.

[0123] The system calculates the fluctuation stability factor of the microgrid based on the power fluctuation variance at each monitoring point. First, the power fluctuation variance at each monitoring point is normalized to eliminate the influence of differences in numerical magnitude. Then, a weighted average of the normalized variances is calculated, with weights determined based on the importance and load capacity of each monitoring point, with higher weights for important monitoring points. The formula for calculating the fluctuation stability factor is: Fluctuation stability factor = 1 / (1 + Weighted average normalized variance × Sensitivity coefficient), where the weighted average normalized variance is the weighted average of the normalized variances; the sensitivity coefficient is typically set to 2.0 to adjust the sensitivity of fluctuations to stability. For example, if the weighted average normalized variance is 0.3, then the fluctuation stability factor is 1 / (1 + 0.3 × 2.0) = 0.625.

[0124] The system calculates the load stability factor of the microgrid based on the load characteristic parameters of each monitoring point. The load characteristic parameters include three sub-parameters: load type coefficient, regulation capacity coefficient, and response speed coefficient. Different types of loads contribute differently to stability: the industrial load type coefficient is 0.7, commercial load is 0.8, and residential load is 0.9. The regulation capacity coefficient is determined based on the load's power regulation range; the larger the adjustable range, the higher the coefficient. The response speed coefficient is determined based on the load's response time to power changes; the faster the response, the higher the coefficient. The formula for calculating the load stability factor is: Load stability factor = Σ(Load capacity × Load type coefficient × Regulation capacity coefficient × Response speed coefficient) / Total load capacity.

[0125] For example, a microgrid contains an industrial load of 200kW (regulation capacity 0.6, response speed 0.7), a commercial load of 150kW (regulation capacity 0.8, response speed 0.8), and a residential load of 100kW (regulation capacity 0.9, response speed 0.6). Then the load stability factor is: [200×0.7×0.6×0.7+150×0.8×0.8×0.8+100×0.9×0.9×0.6] / 450=0.735.

[0126] The system generates a final stability index by weighted summation of three stability factors. The weighting is typically as follows: coupling stability factor 0.4, volatility stability factor 0.3, and load stability factor 0.3. The calculation formula is: Stability Index = 0.4 × coupling stability factor + 0.3 × volatility stability factor + 0.3 × load stability factor.

[0127] S66 performs a weighted summation of the coupling stability factor, fluctuation stability factor, and load stability factor to generate the microgrid stability index.

[0128] Based on power coupling data, the system calculates the coupling stability factor. This factor reflects the impact of the coupling relationship between monitoring points within the microgrid on stability. If the coupling degree between monitoring points is evenly and reasonably distributed, the coupling stability factor is high; if there are excessively strong or weak coupling connections, it may lead to uneven propagation of power fluctuations, resulting in a lower stability factor.

[0129] Based on the power fluctuation variance, the system calculates the fluctuation stability factor. The system analyzes the fluctuation situation of all monitoring points and calculates the statistical characteristics of the overall fluctuation. If the power changes at each monitoring point are relatively stable, the fluctuation stability factor is high; if there are monitoring points with drastic fluctuations, especially if key nodes experience large fluctuations, the stability factor will decrease.

[0130] Based on load characteristic parameters, the system calculates the load stability factor. This factor assesses the load side's ability to support system stability. If the microgrid has a high proportion of adjustable loads and a fast response speed, it can provide regulation support when power changes, resulting in a higher load stability factor; conversely, if all loads are rigid and the regulation capability is limited, the stability factor will be lower.

[0131] Finally, the system performs a weighted sum of the three stability factors to generate a stability index for the microgrid. The system dynamically adjusts the weights based on current operating conditions; for example, it increases the weight of the coupling stability factor when electric vehicles are widely integrated, and increases the weight of the fluctuation stability factor during peak load periods. A higher stability index indicates a more stable microgrid, making it more suitable for vehicle-to-grid (V2G) interaction; a lower index requires careful evaluation of mutual assistance schemes to avoid impacting grid security.

[0132] Based on the above embodiments, as an optional implementation, in S106, adjusting the first discharge power and the second discharge power according to the stability index to generate the first target discharge power of each electric vehicle in the response group and the second target discharge power of each electric vehicle in the standby group specifically includes S71-S75:

[0133] S71, when the stability index is higher than the preset stability threshold, the microgrid is determined to be in a stable state, and the first discharge power and the second discharge power are kept unchanged.

[0134] S72, when the stability index is not higher than the preset stability threshold, the microgrid is determined to be in an unstable state, the difference between the stability index and the preset stability threshold is calculated, and a stability deviation value is generated.

[0135] S73, normalize the stable deviation value to generate a normalized stable deviation value; multiply the preset adjustment factor by the normalized stable deviation value arithmetically to generate the power adjustment coefficient.

[0136] S74, the first discharge power is arithmetically multiplied by the power adjustment coefficient to generate the first target discharge power of each electric vehicle in the response group;

[0137] S75, the second discharge power is arithmetically multiplied by the power adjustment coefficient to generate the second target discharge power for each electric vehicle in the standby group.

[0138] The system first sets a preset stability threshold as the standard for judging whether the microgrid is stable. This threshold is usually set to around 0.75, but may be adjusted depending on the specific conditions of the microgrid. When the stability index calculated by the system is higher than this threshold, it indicates that the microgrid is in a stable state and can safely withstand the discharge of electric vehicles. At this time, the system maintains the first and second discharge powers unchanged, allowing all electric vehicles to discharge at the originally planned power levels.

[0139] For example, when the stability index is 0.82, which is higher than the preset stability threshold of 0.75, the system judges the grid condition to be good. The electric vehicles in the response group continue to discharge at the originally planned discharge power of 80kW, 60kW, etc., and the electric vehicles in the standby group also maintain the established standby power level. This ensures grid security while maximizing the utilization of electric vehicle resources.

[0140] When the stability index is not higher than the preset stability threshold, the system determines that the microgrid is in an unstable state. Continuing to discharge at the original power may exacerbate the grid's instability. The system calculates the difference between the stability index and the preset stability threshold to obtain the stability deviation value. This difference is a negative number; the larger the absolute value, the more severe the instability. For example, if the stability index is 0.65 and the preset stability threshold is 0.75, then the stability deviation value is -0.10.

[0141] The system normalizes the stability deviation value, converting it into a standard value between 0 and 1 for easier subsequent calculations. Then, a preset adjustment factor is multiplied by the normalized stability deviation value to generate the power adjustment coefficient. The preset adjustment factor is typically set between 0.7 and 0.8, and this value determines the intensity of the power adjustment. A larger stability deviation results in a smaller power adjustment coefficient, meaning a greater reduction in discharge power is required.

[0142] The system multiplies the initial discharge power by the power adjustment coefficient to obtain the initial target discharge power. Assuming an electric vehicle's initial discharge power is 100kW and the power adjustment coefficient is 0.8, then its initial target discharge power is 100kW × 0.8 = 80kW, which is equivalent to reducing the discharge power by 20kW to ensure grid stability.

[0143] Similarly, the system multiplies the second discharge power by the power adjustment coefficient to obtain the second target discharge power. The power adjustment of the standby group uses the same adjustment coefficient to ensure the consistency of the entire system's adjustment. In this way, when the standby group needs to be activated, the power level can be kept coordinated and unified.

[0144] Based on the above method, this application also discloses a vehicle-grid interactive energy mutual assistance system based on a microgrid, such as... Figure 4 As shown, Figure 4 This is a schematic diagram of a vehicle-grid interactive energy sharing system based on a microgrid, provided in an embodiment of this application. The system includes: a first acquisition module, a determination module, a second acquisition module, a first combination module, a second combination module, and a third acquisition module; wherein,

[0145] The first acquisition module is used to acquire the power of multiple monitoring points in the microgrid, determine the target monitoring point whose power is lower than the preset power among the multiple monitoring points, and calculate the power difference between the power of the target monitoring point and the preset power.

[0146] The determination module is used to determine the mutual assistance response area of ​​the target monitoring point based on the power difference. The mutual assistance response area is a circular area with the target monitoring point as the center and a preset length as the radius.

[0147] The second acquisition module is used to acquire the regional characteristics of the mutual assistance response area, the location information of multiple electric vehicles in the mutual assistance response area that are in the charging state, and to acquire the charging state and vehicle type of each electric vehicle in the mutual assistance response area.

[0148] The first combining module is used to combine the location information and charging status of each electric vehicle to divide each electric vehicle into a response group and a standby group.

[0149] The second combining module is used to determine the first discharge power of each electric vehicle in the response group and the second discharge power of each electric vehicle in the standby group based on the power difference, combined with regional characteristics and the vehicle type of each electric vehicle.

[0150] The third acquisition module is used to acquire the power coupling degree between the target monitoring point and other monitoring points in the microgrid, calculate the stability index of the microgrid based on the power coupling degree, adjust the first discharge power and the second discharge power based on the stability index, and generate the first target discharge power of each electric vehicle in the response group and the second target discharge power of each electric vehicle in the standby group.

[0151] It should be noted that the system provided in the above embodiments is only illustrated by the division of the above functional modules. In actual 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. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0152] Please see Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0153] The communication bus 1002 is used to realize the connection and communication between these components.

[0154] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0155] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0156] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 1001 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 1001 and may be implemented as a separate chip.

[0157] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 5 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a vehicle-to-grid energy exchange method based on a microgrid.

[0158] exist Figure 5 In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 1001 can be used to call an application program stored in the memory 1005 that is a vehicle-grid interaction energy mutual assistance method based on a microgrid. When executed by one or more processors, the electronic device performs one or more of the methods described in the above embodiments.

[0159] An electronic device readable storage medium stores instructions that, when executed by one or more processors, cause the electronic device to perform one or more of the methods described in the above embodiments.

[0160] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0161] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0162] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some service interfaces; indirect couplings or communication connections between devices or units may be electrical or other forms.

[0163] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0164] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0165] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0166] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A micro-grid-based vehicle-to-grid interaction energy mutual assistance method, characterized in that, The method comprises: acquiring power of multiple monitoring points in a micro-grid, determining a target monitoring point in which the power is lower than a preset power among the multiple monitoring points, calculating a power difference value of the power of the target monitoring point and the preset power; determining a mutual aid response area of the target monitoring point according to the power difference value, the mutual aid response area being a circular area with the target monitoring point as the center and a preset length as the radius; acquiring area features of the mutual aid response area, position information of multiple electric vehicles in a charging state in the mutual aid response area, and acquiring charging states of each electric vehicle in the mutual aid response area and vehicle types of each electric vehicle; combining the position information of each electric vehicle and the charging state of each electric vehicle, dividing each electric vehicle into a response group and a standby group; on the basis of the power difference value, combining the area features and the vehicle types of each electric vehicle, determining a first discharge power of each electric vehicle in the response group and a second discharge power of each electric vehicle in the standby group; acquiring a power coupling degree between the target monitoring point and other monitoring points in the micro-grid, calculating a stability index of the micro-grid according to the power coupling degree, comprising: acquiring an electrical distance and a power transmission coefficient between the target monitoring point and other monitoring points in the micro-grid; performing normalization processing on the electrical distance to generate a normalized electrical distance; taking the inverse of the normalized electrical distance to generate a distance influence factor; performing arithmetic multiplication of the power transmission coefficient and the distance influence factor to generate an initial coupling degree; performing weighted average processing on the initial coupling degree to generate the power coupling degree between the target monitoring point and other monitoring points; acquiring power fluctuation variances and load characteristic parameters of each monitoring point in the micro-grid; calculating a coupling stability factor of the micro-grid according to the power coupling degree between each monitoring point; calculating a fluctuation stability factor of the micro-grid according to the power fluctuation variances of each monitoring point; calculating a load stability factor of the micro-grid according to the load characteristic parameters of each monitoring point; performing weighted summation on the coupling stability factor, the fluctuation stability factor and the load stability factor to generate the stability index of the micro-grid; adjusting the first discharge power and the second discharge power according to the stability index to generate a first target discharge power of each electric vehicle in the response group and a second target discharge power of each electric vehicle in the standby group. 2.The micro-grid based vehicle-to-grid energy interaction method of claim 1, wherein, The method comprises: acquiring power of multiple monitoring points in a micro-grid, determining a target monitoring point in which the power is lower than a preset power among the multiple monitoring points, calculating a power difference value of the power of the target monitoring point and the preset power; determining a mutual aid response area of the target monitoring point according to the power difference value, the mutual aid response area being a circular area with the target monitoring point as the center and a preset length as the radius; acquiring area features of the mutual aid response area, position information of multiple electric vehicles in a charging state in the mutual aid response area, and acquiring charging states of each electric vehicle in the mutual aid response area and vehicle types of each electric vehicle; combining the position information of each electric vehicle and the charging state of each electric vehicle, dividing each electric vehicle into a response group and a standby group; on the basis of the power difference value, combining the area features and the vehicle types of each electric vehicle, determining a first discharge power of each electric vehicle in the response group and a second discharge power of each electric vehicle in the standby group; acquiring a power coupling degree between the target monitoring point and other monitoring points in the micro-grid, calculating a stability index of the micro-grid according to the power coupling degree, comprising: acquiring an electrical distance and a power transmission coefficient between the target monitoring point and other monitoring points in the micro-grid; performing normalization processing on the electrical distance to generate a normalized electrical distance; taking the inverse of the normalized electrical distance to generate a distance influence factor; performing arithmetic multiplication of the power transmission coefficient and the distance influence factor to generate an initial coupling degree; performing weighted average processing on the initial coupling degree to generate the power coupling degree between the target monitoring point and other monitoring points; acquiring power fluctuation variances and load characteristic parameters of each monitoring point in the micro-grid; calculating a coupling stability factor of the micro-grid according to the power coupling degree between each monitoring point; calculating a fluctuation stability factor of the micro-grid according to the power fluctuation variances of each monitoring point; calculating a load stability factor of the micro-grid according to the load characteristic parameters of each monitoring point; performing weighted summation on the coupling stability factor, the fluctuation stability factor and the load stability factor to generate the stability index of the micro-grid; adjusting the first discharge power and the second discharge power according to the stability index to generate a first target discharge power of each electric vehicle in the response group and a second target discharge power of each electric vehicle in the standby group. 3.The micro-grid based vehicle-to-grid energy interaction method of claim 1, wherein, The charging state includes charging power and battery state of charge, and the position information of each electric vehicle is combined with the charging state of each electric vehicle to divide each electric vehicle into a response group and a standby group, including: calculating the distance between each electric vehicle and the target monitoring point according to the position information; combining the charging power and the battery state of charge to calculate the maximum dischargeable power and the continuous discharge duration of each electric vehicle, and taking the product of the maximum dischargeable power and the continuous discharge duration as the response capability parameter of each electric vehicle; combining the distance between each electric vehicle and the target monitoring point and the response capability parameter of each electric vehicle to calculate the response priority coefficient of each electric vehicle; and dividing the electric vehicle with a response priority coefficient higher than a preset priority coefficient into the response group, and dividing the electric vehicle with a response priority coefficient not higher than the preset priority coefficient into the standby group. 4.The micro-grid based vehicle-to-grid energy interaction method of claim 3, wherein, The response priority coefficient of each electric vehicle is calculated by combining the distance between each electric vehicle and the target monitoring point and the response capability parameter of each electric vehicle, including: determining the distance weight coefficient of each electric vehicle according to the distance between each electric vehicle and the target monitoring point, wherein the distance is negatively correlated with the distance weight coefficient; and performing weighted summation on the distance weight coefficient of each electric vehicle and the response capability parameter to generate the response priority coefficient of each electric vehicle.

5. The micro-grid based vehicle-to-grid energy interaction method of claim 3, wherein, The first discharge power of each electric vehicle in the response group and the second discharge power of each electric vehicle in the standby group are determined by combining the regional characteristics and the vehicle type of each electric vehicle on the basis of the power difference value, including: determining the importance level of the mutual aid response region according to the regional characteristics, and determining the power redundancy coefficient of the mutual aid response region according to the importance level; determining the discharge capability parameter of each electric vehicle according to the vehicle type of each electric vehicle; arithmetically multiplying the power difference value and the power redundancy coefficient to generate a total discharge power; calculating the first power distribution ratio of each electric vehicle in the response group according to the response priority coefficient and the discharge capability parameter of each electric vehicle in the response group; arithmetically multiplying the total discharge power and the first power distribution ratio to generate the first discharge power of each electric vehicle in the response group; calculating the second power distribution ratio of each electric vehicle in the standby group according to the response priority coefficient and the discharge capability parameter of each electric vehicle in the standby group; and arithmetically multiplying the total discharge power and the second power distribution ratio to generate the second discharge power of each electric vehicle in the standby group. 6.The micro-grid based vehicle-to-grid energy interaction method of claim 1, wherein, The adjusting the first discharging power and the second discharging power according to the stability index, generating the first target discharging power of each electric vehicle in the response group and the second target discharging power of each electric vehicle in the standby group comprises: when the stability index is higher than a preset stability threshold, determining that the micro-grid is in a stable state, keeping the first discharging power and the second discharging power unchanged; when the stability index is not higher than the preset stability threshold, determining that the micro-grid is in an unstable state, calculating a difference value between the stability index and the preset stability threshold, generating a stability deviation value; performing normalization processing on the stability deviation value, generating a normalized stability deviation value; arithmetically multiplying a preset adjustment factor and the normalized stability deviation value, generating a power adjustment coefficient; arithmetically multiplying the first discharging power and the power adjustment coefficient, generating the first target discharging power of each electric vehicle in the response group; arithmetically multiplying the second discharging power and the power adjustment coefficient, generating the second target discharging power of each electric vehicle in the standby group.

7. A micro-grid-based vehicle-to-grid energy interaction system, characterized in that, The system comprises a first acquisition module, a determination module, a second acquisition module, a first combination module, a second combination module and a third acquisition module; the first acquisition module is configured to acquire power of multiple monitoring points in a micro-grid, determine a target monitoring point with power lower than a preset power among the multiple monitoring points, and calculate a power difference between the power of the target monitoring point and the preset power; the determination module is configured to determine a mutual aid response area of the target monitoring point according to the power difference, wherein the mutual aid response area is a circular area with the target monitoring point as the center and a preset length as the radius; the second acquisition module is configured to acquire area features of the mutual aid response area, position information of multiple electric vehicles in a charging state within the mutual aid response area, and charging states of the electric vehicles and vehicle types of the electric vehicles within the mutual aid response area; the first combination module is configured to combine the position information of the electric vehicles and the charging states of the electric vehicles, and divide the electric vehicles into a response group and a standby group; the second combination module is configured to determine a first discharge power of the electric vehicles in the response group and a second discharge power of the electric vehicles in the standby group based on the power difference, the area features and the vehicle types of the electric vehicles; the third acquisition module is configured to acquire a power coupling degree between the target monitoring point and other monitoring points in the micro-grid, calculate a stability index of the micro-grid according to the power coupling degree, and comprises the following steps: acquiring an electrical distance and a power transmission coefficient between the target monitoring point and other monitoring points in the micro-grid; performing normalization processing on the electrical distance to generate a normalized electrical distance; taking the inverse of the normalized electrical distance to generate a distance influence factor; performing arithmetic multiplication of the power transmission coefficient and the distance influence factor to generate an initial coupling degree; performing weighted average processing on the initial coupling degree to generate the power coupling degree between the target monitoring point and other monitoring points; acquiring power fluctuation variances and load characteristic parameters of the monitoring points in the micro-grid; calculating a coupling stability factor of the micro-grid according to the power coupling degrees between the monitoring points; calculating a fluctuation stability factor of the micro-grid according to the power fluctuation variances of the monitoring points; calculating a load stability factor of the micro-grid according to the load characteristic parameters of the monitoring points; performing weighted summation on the coupling stability factor, the fluctuation stability factor and the load stability factor to generate the stability index of the micro-grid; and adjusting the first discharge power and the second discharge power according to the stability index to generate a first target discharge power of the electric vehicles in the response group and a second target discharge power of the electric vehicles in the standby group.

8. An electronic device, comprising: An electronic device comprising a processor, a memory for storing instructions, a user interface and a network interface for communicating with other devices, the processor being configured to execute the instructions stored in the memory to cause the electronic device to perform the method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, A computer program stored in a memory and loadable into the working memory of a digital computer, comprising software code portions arranged to cause a processor to perform the method of any one of claims 1-6 when said computer program is carried out on the processor.

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