A power supply guaranteeing and orderly control strategy for a cluster of electric vehicles in a transformer area

Abstract

The present application belongs to the technical field of electric vehicle cluster optimization, and particularly relates to a transformer area cluster electric vehicle power supply guaranteeing orderly control strategy, electronic equipment and storage medium, the power consumption control strategy comprising: constructing a virtual electric vehicle model based on a transformer area electric vehicle cluster; constructing a power consumption target model based on the virtual electric vehicle model; and constructing a power consumption control strategy model based on the power consumption target model, priority, charging power and discharging power. The present application constructs a VEV model based on the adjustable capacity of a single electric vehicle, the VEV model simplifies the optimization scheduling process of a large-scale electric vehicle cluster by aggregating the charging and discharging capacity of single electric vehicles, can effectively manage the charging and discharging behavior of electric vehicles, optimize energy use, improve the stability and reliability of the power grid, and can effectively solve the problem of reverse power flow.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle charging facility metering technology, specifically relating to an orderly control strategy for ensuring power supply to electric vehicles in a cluster of transformer substations. Background Technology

[0002] Electric vehicles, as mobile energy storage units, not only have significant advantages in the field of green transportation, but also provide flexible regulation resources for the power system. However, how to effectively utilize the energy storage and regulation capabilities of electric vehicles in planned power outages or emergency power supply scenarios has become an important research topic for ensuring the reliability of power grid supply.

[0003] Currently, traditional electric vehicle (EV) optimization scheduling methods suffer from high computational complexity and poor scalability when dealing with large-scale EV clusters, making it difficult to meet the real-time scheduling needs of modern regional power grid outage scenarios. Furthermore, in practical engineering, regional power grids are typically disconnected from the upstream grid after a power outage, but the backflow of power flow caused by EV discharge poses a serious threat to the personal safety of maintenance personnel and increases the risk to grid operation.

[0004] In related technologies, some research has focused on scheduling methods for electric vehicles (EVs) during power outages. However, these studies mostly concentrate on the centralized optimization and control of individual EVs, failing to fully consider the computational complexity of large-scale EV clusters and effectively addressing the issue of power flow reversal. These problems limit the potential of EVs as a flexible energy storage resource in planned power outage scenarios. Summary of the Invention

[0005] In view of the above-mentioned defects or deficiencies in the prior art, the present invention proposes an orderly control strategy for power supply protection of electric vehicles in a transformer substation cluster.

[0006] Firstly, a power supply guarantee and orderly control strategy for electric vehicles in a transformer substation cluster is provided, including: constructing a virtual electric vehicle model based on the electric vehicle cluster in the transformer substation; constructing a power consumption target model based on the virtual electric vehicle model; and constructing a power consumption control strategy model based on the power consumption target model, priority, charging power, and discharging power.

[0007] In one optional implementation, a virtual electric vehicle model is constructed based on a cluster of electric vehicles in a distribution area. Specifically, this includes: determining the individual energy boundary and adjustable power boundary of each individual electric vehicle based on power consumption parameters, including the current battery state of charge (SBC), maximum allowable SBC, minimum allowable SBC, and the battery charge requirement for the next trip specified by the user; dividing the distribution area electric vehicle cluster into categories based on whether the remaining battery power and time of each individual electric vehicle upon return meet controllable conditions; constructing category-specific energy adjustable boundaries and category-specific charge / discharge power adjustable boundaries based on the individual energy boundary, the individual power adjustable boundary, and the classification model; and constructing the virtual electric vehicle model based on the category-specific energy adjustable boundaries and category-specific charge / discharge power adjustable boundaries.

[0008] In one optional implementation, determining the individual energy boundary and adjustable power boundary of a single electric vehicle based on power consumption parameters specifically includes: determining the individual energy boundary based on the power consumption parameters, wherein the individual energy boundary is characterized by the maximum rechargeable capacity of the battery and the minimum discharge capacity of the battery, and the calculation formula is as follows: In the formula, This indicates the maximum rechargeable capacity of the battery. Indicates the first The maximum permissible state of battery charge for electric vehicles in Taiwan Indicates the first The rated battery capacity of the electric vehicle; In the formula, Indicates the minimum discharge capacity. Indicates the first Minimum battery state of charge allowed for electric vehicles in Taiwan Indicates the first The remaining battery state of charge of the electric vehicle when it returns to its starting point; the adjustable power boundary of the single cell is determined based on the power consumption parameters, and the adjustable power boundary of the single cell is characterized by the user-specified power demand, calculated as follows:

[0009] In the formula, This indicates the user's specified power consumption requirement. Indicates the first The state of charge of the battery when the electric vehicle leaves for its next trip.

[0010] In one optional implementation, the electric vehicle cluster in the transformer area is divided into categories based on whether the remaining battery power and time of the individual electric vehicle upon return meet controllable conditions, specifically including:

[0011] If the remaining battery power and time are uncontrollable, the electric vehicle clusters in the distribution area are divided into three types: the first type and the third type. If the remaining battery power and time are controllable, and the remaining battery state of charge upon return is higher than the minimum allowed battery state of charge for the electric vehicle, the electric vehicle clusters in the distribution area are divided into three types. If the remaining battery power and time are controllable, and the remaining battery state of charge upon return is lower than the minimum allowed battery state of charge for the electric vehicle, the electric vehicle clusters in the distribution area are divided into three types. The classification models include the first type, the second type, and the third type.

[0012] In one optional implementation, a categorical energy adjustable boundary and a categorical charge / discharge power adjustable boundary are constructed based on the individual cell energy boundary, the individual cell power adjustable boundary, and the classification model. Specifically, this includes: constructing a first type of energy adjustable boundary and a categorical charge / discharge power adjustable boundary based on the individual cell energy boundary, the individual cell power adjustable boundary, and the first type of model, using the following calculation formula:

[0013] In the formula, Indicates the first The charging energy boundary of electric vehicles in Taiwan Indicates the first The discharge energy boundary of electric vehicles in Taiwan Indicates the current time. Indicates the first The return time of the electric vehicle from Taiwan. Indicates the first The departure time of the electric vehicle in Taiwan Indicates time At that time, electric vehicles The increase and decrease in the maximum rechargeable energy. Indicates electric vehicles The charging power; Indicates charging efficiency; The unit time interval is represented; the calculation formula for the adjustable boundary of the first type of charge / discharge power is as follows: In the formula, Indicates the first The charging power limit of electric vehicles in Taiwan Indicates the first The discharge power limit of electric vehicles in Taiwan Indicates the first The rated charging power of the electric vehicle; based on the single-unit energy boundary, the single-unit power adjustable boundary, and the second type of model, a second type of energy adjustable boundary and a classification of charge / discharge power adjustable boundaries are constructed, and the calculation formulas are as follows: In the formula, Indicates the first The maximum energy capacity of the electric vehicle; the calculation formula for the discharge energy boundary of the second type of adjustable energy boundary is as follows:

[0014] In the formula, Indicates the first The minimum energy capacity of electric vehicles in Taiwan. Indicates the first The rated discharge power of the electric vehicle Indicates the first The discharge efficiency of the electric vehicle; the calculation formula for the adjustable boundary of the second type of charging power is as follows:

[0015] The calculation formula for the adjustable boundary of the second type of discharge power is as follows: Based on the timing, the first type of adjustable energy boundary and the classified adjustable charge / discharge power boundary, the second type of adjustable energy boundary and the classified charge / discharge power, a third type of adjustable energy boundary and a classified adjustable charge / discharge power boundary are constructed.

[0016] In one optional implementation, the virtual electric vehicle model is constructed based on the classified energy adjustable boundary and the classified charge / discharge power adjustable boundary, specifically including: obtaining the total energy range by time-series summing the energy adjustable boundaries of all individual electric vehicles; obtaining the total power range by time-series summing the power adjustable boundaries of all individual electric vehicles; and constructing the virtual electric vehicle model based on the total energy range and the total power range; wherein the virtual electric vehicle model is represented as a 10-tuple vector: The vector calculation is as follows: and These represent the earliest and latest times for the virtual electric vehicle to perform charge and discharge scheduling:

[0017] In the formula, This represents the first type of model. This represents the second type of model. This represents the third type of model; and These represent the time periods. The energy adjustable upper and lower boundaries are calculated as follows: ; and These represent the virtual electric vehicles during the time period. The adjustable boundaries of charging power and discharging power are calculated as follows: ; and Let represent the charging loss coefficient and discharging loss coefficient of the virtual electric vehicle, respectively, and calculate them as follows: ; In the formula, Indicates the first Taiwanese electric vehicles during the period The connection status, Z represents the total number of electric vehicles in the regional power grid; and These represent the virtual electric vehicles during the time period. The charging power and discharging power.

[0018] In one optional implementation, constructing an electricity consumption target model based on the virtual electric vehicle model specifically includes: constructing an objective function based on the virtual electric vehicle model, and calculating it as follows: ; ; In the formula, and These represent the start and end times of the power outage in the regional power grid, respectively. Indicates the regional power grid during the time period Loss of load, Indicates the scheduling period, and ; For time period Photovoltaic power generation capacity Indicates the area of ​​the photovoltaic panel. Indicates light intensity. Indicates power generation efficiency. Indicates ambient temperature. Indicates the flexible load of the regional power grid. This represents the rigid load of the regional power grid. Based on the objective function, a constraint function is constructed, which includes: charging / discharging operation constraints, flexible compliance adjustment constraints, and power flow backflow constraints, calculated as follows: Charging / discharging operation constraints: boundary limits for charging and discharging power, expressed as follows: The mutual exclusion constraint between charging and discharging is expressed by the following formula: The constraint on the change in the cumulative energy level of the virtual electric vehicle in adjacent time periods is given by the following formula: In the formula, Indicates the virtual electric vehicle during the time period The energy accumulation level; the energy accumulation constraint of the virtual electric vehicle in the charging and discharging optimization process, the formula is: Virtual electric vehicle charge / discharge optimization and control within a time interval The controllable conditions inherent within are given by the formula: The formula for limiting the charging and discharging power of virtual electric vehicles during regional power grid outages is as follows: During non-outage periods, the power variation of the virtual electric vehicle in adjacent time periods is calculated using the following formula:

[0019] Flexible load adjustment constraints: The adjustable range of the regional power grid's flexible load in each time period, expressed by the formula: The optimized regional power grid's total electricity consumption should not exceed its expected total electricity consumption, as shown in the formula: In the formula, This represents the expected flexible electricity load of the regional power grid under uninterrupted power conditions; the load shedding limit is defined by the formula: In the formula, This represents the load shedding threshold; the backflow constraint is expressed by the following formula: .

[0020] In one optional implementation, a power consumption control strategy model is constructed based on the power consumption target model, priority, charging power, and discharging power. Specifically, this includes: if the response power of the virtual electric vehicle is the charging power, then defining a charging slack index, and calculating the charging slack index for all electric vehicles requiring charging at time [time value missing]. Charging slack: Initialize the remaining allocable power so that The charging slack indexes are sorted from smallest to largest to obtain the first priority queue; charging power is then allocated to the electric vehicles with the highest charging priority based on the first priority queue. Update the remaining available charging power: For the remaining electric vehicles, charging power is allocated to the electric vehicle with the highest charging priority based on the first priority queue, and the remaining allocable charging power is updated; until... All electric vehicles at any given time participate in the decomposition process or have remaining distributable power, making If the virtual electric vehicle's response power is equal to its discharge power, then a discharge relaxation index is defined, and the discharge relaxation of all electric vehicles requiring discharge at time t is calculated based on this index. Initialize the remaining allocable power so that The electric vehicles are sorted from largest to smallest according to their discharge relaxation index to obtain a second priority queue. Based on this second priority queue, discharge power is allocated to the electric vehicles with the highest discharge priority. Update the remaining distributable discharge power so that... For the remaining electric vehicles, discharge power is allocated to the electric vehicle with the highest discharge priority based on the second priority queue, and the remaining allocable discharge power is updated; until... All electric vehicles at any given time participate in the decomposition process or have remaining distributable power, making If the discharge power distribution process ends at that moment, then the process ends.

[0021] In a second aspect, an electronic device is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute any of the above-described orderly control strategies for ensuring power supply to electric vehicles in the distribution area cluster.

[0022] Thirdly, a medium is also provided that stores a computer program, which, when executed by a processor, implements the orderly control strategy for power supply to electric vehicles in the transformer substation cluster as described in any one of the claims.

[0023] The beneficial effects of this invention are as follows: This invention constructs a VEV model based on the adjustable capability of individual electric vehicles. By aggregating the charging and discharging capabilities of individual electric vehicles, the VEV model simplifies the optimization scheduling process for large-scale electric vehicle clusters, effectively manages the charging and discharging behavior of electric vehicles, optimizes energy use, improves the stability and reliability of the power grid, and effectively solves the problem of backflow. Attached Figure Description

[0024] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0025] Figure 1 A flowchart of an embodiment of the present invention provides a power supply order control strategy for electric vehicles in a transformer substation cluster;

[0026] Figure 2 The flexible and rigid power load curves of the regional power grid are an example of the orderly control strategy for power supply guarantee of electric vehicles in a cluster provided by an embodiment of the present invention.

[0027] Figure 3 Predicted light intensity and ambient temperature data for an example of an orderly control strategy for power supply to electric vehicles in a transformer cluster provided in an embodiment of the present invention;

[0028] Figure 4 The total power load curves of the regional power grid before and after optimization and adjustment of the orderly control strategy for power supply guarantee of electric vehicles in the distribution area provided in an embodiment of the present invention;

[0029] Figure 5 A power supply and demand comparison curve during a regional power outage, which is an example of an orderly control strategy for power supply protection of electric vehicles in a cluster provided by an embodiment of the present invention.

[0030] Figure 6 The SOC variation curves of each individual EV in the VEV are an example of the orderly control strategy for power supply guarantee of electric vehicles in a power distribution cluster provided in an embodiment of the present invention.

[0031] Figure 7 The power variation curves of each individual EV in the VEV are examples of the orderly control strategy for power supply guarantee of electric vehicles in a power distribution cluster provided in an embodiment of the present invention. Detailed Implementation

[0032] The following is in conjunction with the appendix Figures 1 to 7 The present application will be further described in detail with reference to the embodiments. It is understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0033] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0034] Please refer to Figure 1 This is a flowchart of a power supply order control strategy for electric vehicles in a transformer substation, as provided in an embodiment of the present invention. The power supply order control strategy for electric vehicles in a transformer substation includes the following steps:

[0035] Step S101: Construct a virtual electric vehicle model based on the electric vehicle cluster in the transformer area.

[0036] Step S103: Construct an electricity consumption target model based on the virtual electric vehicle model.

[0037] Step S105: Construct an electricity consumption control strategy model based on the electricity consumption target model, priority, charging power, and discharging power.

[0038] In this embodiment, step S101 involves creating a virtual electric vehicle (VEV) representing all electric vehicles in the distribution area, which simulates and predicts the charging and discharging behavior of electric vehicles. This involves aggregating all individual electric vehicle models to form a single virtual electric vehicle model, which represents the behavior of the entire electric vehicle cluster in the distribution area. The electricity consumption target model is combined with the virtual electric vehicle model to form the basis of the electricity consumption control strategy. Electric vehicles are categorized into different priority classes, and different charging and discharging strategies and time windows are set according to priority to ensure that high-priority electric vehicles receive charging resources first. The charging and discharging demand is adjusted based on priority to ensure that high-priority electric vehicles are prioritized. Considering the limitations of charging and discharging power, the charging and discharging plan is optimized to avoid overcharging or over-discharging.

[0039] Through the above steps, firstly, a VEV model is proposed based on the adjustability of individual electric vehicles. Then, the VEV model simplifies the optimization scheduling process for large-scale electric vehicle clusters by aggregating the charging and discharging capabilities of individual electric vehicles. The orderly control strategy for ensuring power supply to electric vehicles in distribution substation clusters can effectively manage the charging and discharging behavior of electric vehicles, optimize energy use, improve the stability and reliability of the power grid, and effectively solve the problem of backflow.

[0040] Step S101 involves constructing a virtual electric vehicle model based on the electric vehicle cluster in the transformer area, specifically including the following steps:

[0041] Step S1011: Determine the individual energy boundary and adjustable power boundary of the electric vehicle based on the power consumption parameters. The power consumption parameters include: the current battery state of charge of the electric vehicle, the maximum allowable battery state of charge, the minimum allowable battery state of charge, and the battery charge requirement for the next trip specified by the user.

[0042] Step S1013: Based on whether the remaining battery power and time of the individual electric vehicles when they return meet the controllable conditions, the electric vehicle cluster in the transformer area is divided to obtain a classification model.

[0043] Step S1015: Construct classified energy adjustable boundary and classified charge / discharge power adjustable boundary based on the single-cell energy boundary, single-cell power adjustable boundary and classification model.

[0044] Step S1017: Construct a virtual electric vehicle model based on the classifiable energy adjustable boundary and the classifiable charge / discharge power adjustable boundary.

[0045] In this embodiment, the power consumption parameters of each electric vehicle are collected, and the energy boundary and power boundary of each electric vehicle are calculated based on the power consumption parameters. Therefore, the adjustable energy range and adjustable power range of each electric vehicle can be determined through step S1011. By dividing the electric vehicle cluster into different categories, classification management can be facilitated. The adjustable energy and power boundaries of each category of electric vehicles can be constructed through step S1015.

[0046] The classification models are integrated into a virtual electric vehicle model, which can represent the entire electric vehicle cluster in the power distribution area. Specifically, the energy adjustable boundary and power adjustable boundary of each category are integrated into the virtual electric vehicle model to construct its mathematical expression, including the total energy adjustable boundary and the total charge / discharge power adjustable boundary. The accuracy and reliability of the model are then verified, demonstrating its ability to accurately reflect the behavior of the electric vehicle cluster in the power distribution area.

[0047] Through the above steps, a virtual electric vehicle model can be constructed. This model can accurately reflect the energy and power adjustable boundaries of the electric vehicle cluster in the transformer area, providing a basis for subsequent power control strategies.

[0048] Step S1011, determining the individual energy boundary and adjustable power boundary of a single electric vehicle based on electricity consumption parameters, specifically includes the following steps:

[0049] Step S10111: Determine the energy boundary of a single cell based on the power consumption parameters. The energy boundary of a single cell is characterized by the maximum rechargeable capacity of the battery and the minimum discharge capacity of the battery. The calculation formula is as follows:

[0050] ; (1)

[0051] In the formula, This indicates the maximum rechargeable capacity of the battery. Indicates the first The maximum permissible state of charge (SoC) for electric vehicles in Taiwan. Indicates the first The rated battery capacity of the electric vehicle.

[0052] ; (2)

[0053] In the formula, Indicates the minimum discharge capacity. Indicates the first Minimum battery state of charge allowed for electric vehicles in Taiwan Indicates the first The remaining battery charge status of the electric vehicle when it returns to its starting point.

[0054] Step S10113: Determine the adjustable boundary of individual unit power based on power consumption parameters. The adjustable boundary of individual unit power is characterized by the power demand specified by the user, and the calculation formula is as follows:

[0055] (3)

[0056] In the formula, This indicates the user's specified power consumption requirement. Indicates the first The state of charge of the battery when the electric vehicle leaves for its next trip.

[0057] Based on step S1013, the electric vehicle clusters in the transformer area are divided according to whether the remaining battery power and time of the individual electric vehicles upon return meet the controllable conditions, resulting in a classification model. The specific steps include the following:

[0058] Step S10131: If the remaining power and time are uncontrollable, divide the electric vehicle clusters in the transformer area to obtain the first type of model.

[0059] Step S10133: If the remaining power and time are controllable, and the remaining battery state of charge upon return is higher than the minimum battery state of charge allowed for electric vehicles, divide the electric vehicle cluster in the transformer area to obtain the second type of model.

[0060] Step S10135: If the remaining power and time are controllable, and the remaining battery state of charge is lower than the minimum battery state of charge allowed for electric vehicles upon return, divide the electric vehicle cluster in the transformer area to obtain the third type of model.

[0061] The classification models include the first type, the second type, and the third type.

[0062] In this embodiment, electric vehicles are divided into three categories based on controllable conditions. For each category, the energy adjustable boundary and the power adjustable boundary are calculated.

[0063] The first category consists of uncontrollable EVs that require immediate charging. These are electric vehicles that cannot participate in grid demand response due to remaining battery power or time constraints. This first category is the uncontrollable model, denoted as the set. Because their parking time is short, they must be charged immediately at rated power after connecting to the charging station to meet the energy needs of their next trip. This involves checking the remaining battery charge (SOC) and estimated parking time of each electric vehicle. For those vehicles with insufficient remaining battery charge to support their next trip (i.e., SOC below a certain threshold),... Electric vehicles that are parked for a period of time, or whose charging time is insufficient to complete the required charging, are classified as the first type of model.

[0064] The first type of electric vehicle needs to be connected to a charging station immediately and charged at the maximum permissible power to ensure that it can meet the power needs of the next trip.

[0065] The second category: controllable EVs, denoted as a set. It can immediately participate in charge and discharge regulation, and can identify electric vehicles with sufficient charge and a sufficiently long parking time. These vehicles can immediately participate in the grid's charge and discharge regulation. Specifically, for those vehicles with remaining charge higher than the maximum permissible battery state of charge... Electric vehicles with sufficient parking time are classified as the second type of model. EVs in the second type of model have sufficient parking time and a higher remaining SOC upon return. These electric vehicles can immediately participate in charge and discharge regulation to optimize grid load and improve energy efficiency. They can be dispatched to discharge to support grid demand during peak hours or to charge during off-peak hours to take advantage of cheap electricity.

[0066] The third category: controllable EVs, denoted as a set. Its remaining SOC upon return is lower than Connecting to a charging station requires accumulating at least [amount missing] energy. Afterwards, the battery needs to be charged to a certain SOC before it can participate in charge / discharge regulation. Identify electric vehicles with insufficient charge but sufficiently long parking periods; these vehicles need to be charged to a certain level before participating in grid charge / discharge regulation. For those vehicles with remaining charge below the minimum permissible battery state of charge... However, electric vehicles with sufficient parking time are classified as the third type of model. Electric vehicles in this third type need to be charged to the target SOC (State of Charge) at a charging station before they can participate in the grid's charging and discharging optimization and control. After reaching the target SOC, these electric vehicles can be scheduled for discharging to support grid demand, or continue charging during off-peak hours.

[0067] This classification method allows for more effective management and scheduling of the charging and discharging behavior of electric vehicle (EV) clusters, supporting grid demand response and optimizing energy distribution. This strategy helps balance grid load, improve grid reliability and economy, and provides EV users with more flexible charging options.

[0068] Step S1015 involves constructing a classification-based adjustable energy boundary and a classification-based adjustable charge / discharge power boundary based on the single-cell energy boundary, the single-cell power adjustable boundary, and the classification model. This includes the following steps:

[0069] Step S10151: Construct the first type of energy adjustable boundary and the classified charge / discharge power adjustable boundary based on the single-cell energy boundary, the single-cell power adjustable boundary, and the first type of model. The calculation formula for the first type of energy adjustable boundary is as follows:

[0070] (4);

[0071] In the formula, Indicates the first The charging energy boundary of electric vehicles in Taiwan Indicates the first The discharge energy boundary of electric vehicles in Taiwan Indicates the current time. Indicates the first The return time of the electric vehicle from Taiwan. Indicates the first The departure time of the electric vehicle in Taiwan Indicates time At that time, electric vehicles The increase and decrease in the maximum rechargeable energy. Indicates electric vehicles The charging power; Indicates charging efficiency; Indicates a unit time interval. Type I EV ( It lacks charging and discharging regulation capabilities and is similar to a rigid electrical load in an EV cluster.

[0072] The calculation formula for the first type of adjustable charge / discharge power boundary is as follows: (5);

[0073] In the formula, Indicates the first The charging power limit of electric vehicles in Taiwan Indicates the first The discharge power limit of electric vehicles in Taiwan Indicates the first The rated charging power of the electric vehicle.

[0074] Step S10153: Based on the single-cell energy boundary, the single-cell power adjustable boundary, and the second type of model, construct the second type of energy adjustable boundary and the classified charge / discharge power adjustable boundary. The calculation formula for the charging energy boundary of the second type of energy adjustable boundary is as follows:

[0075] (6);

[0076] In the formula, Indicates the first The maximum energy capacity of the electric vehicle in Taiwan;

[0077] The formula for calculating the discharge energy boundary of the second type of adjustable energy boundary is as follows:

[0078] (7);

[0079] In the formula, Indicates the first The minimum energy capacity of electric vehicles in Taiwan. Indicates the first The rated discharge power of the electric vehicle Indicates the first The discharge efficiency of the electric vehicle.

[0080] The calculation formula for the adjustable boundary of the second type of charging power is as follows:

[0081] (8);

[0082] The calculation formula for the adjustable boundary of the second type of discharge power is as follows:

[0083] (9);

[0084] Step S10155: Construct a third type of adjustable energy boundary and a third type of adjustable charge / discharge power boundary based on timing, the first type of adjustable energy boundary, the first type of adjustable charge / discharge power boundary, and the second type of adjustable energy boundary and the third type of adjustable charge / discharge power boundary. The third type of EV can be considered as a combination of the first and second types of EVs. Therefore, within the time range [ , Within the range, the adjustable energy and adjustable charging power boundaries are calculated using equations (4) and (5), respectively, and ;exist[ , Within the range, the adjustable boundaries of energy and charge / discharge power are calculated using equations (6), (7) and (8), (9), respectively. By timing splicing, the adjustable boundaries of energy and charge / discharge power for the third type of EV can be formed.

[0085] Further, in step S1017, a virtual electric vehicle model is constructed based on the classifiable energy adjustable boundary and the classifiable charge / discharge power adjustable boundary, specifically including the following steps:

[0086] Step S10171: The total energy range is obtained by summing the energy adjustable boundaries of all individual electric vehicles in a time sequence.

[0087] Step S10173: The total power range is obtained by summing the power adjustable boundaries of all individual electric vehicles in a time sequence.

[0088] Step S10175: Construct a virtual electric vehicle model based on the total energy range and total power range.

[0089] In this embodiment, the adjustable boundaries of the energy and charge / discharge power of each individual EV provide key parameters for VEV modeling and adjustable capability analysis. By summing the adjustable boundaries of the energy and charge / discharge power of each individual EV over time, the VEV's adjustable capability, namely the cluster energy adjustable boundary and the aggregated power adjustable boundary, can be obtained. Specifically, in step S10171, for each time point, the adjustable boundaries of the energy of all individual EVs at that time point are summed to obtain the total adjustable boundary of the EV cluster in the distribution area at that time point. This step involves accumulating the energy boundaries of all individual EVs to obtain the total energy capacity of the entire cluster at different time points. In step S10173, for each time point, the adjustable boundaries of the power of all individual EVs at that time point are summed to obtain the total adjustable boundary of the EV cluster in the distribution area at that time point. This step involves accumulating the power boundaries of all individual EVs to obtain the total power capacity of the entire cluster at different time points.

[0090] Using the total energy range and total power range obtained in steps S10171 and S10173, a virtual electric vehicle model is constructed. This model can simulate the charging and discharging behavior of the entire electric vehicle cluster in the distribution area, and can reflect the aggregation effect of the electric vehicle cluster on the power grid, including the ability to provide ancillary services such as demand response and frequency regulation during peak grid load periods, and the ability to charge during off-peak grid load periods.

[0091] This model can be used by grid operators to conduct simulations to evaluate the impact of different charging and discharging strategies on grid stability and economy, thereby formulating the optimal charging and discharging scheduling plan.

[0092] Furthermore, the virtual electric vehicle model is represented as a 10-tuple vector: The vectors in the 10-tuple vector are calculated as follows:

[0093] and These represent the earliest and latest times for the virtual electric vehicle to perform charge and discharge scheduling:

[0094] (10);

[0095] In the formula, This represents the first type of model. This represents the second type of model. This represents the third type of model. The schedulable time range ( ).

[0096] ( ) is the energy adjustable boundary. and These represent the time periods. The energy adjustable upper and lower boundaries are calculated as follows:

[0097] (11);

[0098] The adjustable power boundary is ( ), and These represent the virtual electric vehicles during the time period. The adjustable boundaries of charging power and discharging power are calculated as follows:

[0099] (12);

[0100] and Let represent the charging loss coefficient and discharging loss coefficient of the virtual electric vehicle, respectively, and calculate them as follows:

[0101] (13);

[0102] (14);

[0103] In the formula, Indicates the first Taiwanese electric vehicles during the period The connection status is given by Z, which represents the total number of electric vehicles in the regional power grid. The VEV charging / discharging energy loss coefficient is ( ), and Divided into variables that change over time, they can be used in time periods. The average charging / discharging coefficient of EVs in grid-connected state is estimated.

[0104] and These represent the virtual electric vehicles during the time period. The charging and discharging power of the VEV. This can be used as a decision variable for optimizing the power supply guarantee strategy in the future.

[0105] Step S103, constructing an electricity consumption target model based on the virtual electric vehicle model, specifically includes the following steps:

[0106] Step S1031: Construct the objective function based on the virtual electric vehicle model, and calculate it as follows:

[0107] (15); (16); (17);

[0108] In the formula, and These represent the start and end times of the power outage in the regional power grid, respectively. Indicates the regional power grid during the time period The load shedding (kWh) is calculated using (16). Indicates the scheduling period, and ; For time period The photovoltaic power generation capacity is calculated using (17). Indicates the area of ​​the photovoltaic panel. Indicates light intensity. Indicates power generation efficiency. Indicates ambient temperature. This represents the flexible load of the regional power grid, and serves as a decision variable. This indicates a rigid load in the regional power grid, which is a non-adjustable load.

[0109] Step S1033: Construct constraint functions based on the objective function. The constraint functions include: charging and discharging operation constraints, flexible compliance adjustment constraints, and power flow backflow constraints, calculated as follows:

[0110] Charge and discharge operation constraints:

[0111] The boundary limits for charging power and discharging power are defined by the following formula: (18);

[0112] The mutual exclusion constraint between charging and discharging is expressed by the following formula: (19);

[0113] The constraint on the change relationship of the cumulative energy level of the virtual electric vehicle in adjacent time periods is given by the following formula: (20);

[0114] In the formula, Indicates the virtual electric vehicle during the time period Energy accumulation level;

[0115] The energy accumulation constraint of the virtual electric vehicle in the charging and discharging optimization process is given by the following formula: (twenty one) ;

[0116] Virtual electric vehicle charge / discharge optimization and control within a time range The controllable conditions inherent within are given by the formula: (twenty two);

[0117] The charging and discharging power limits for virtual electric vehicles during regional power grid outages are defined by the following formula: (twenty three);

[0118] During non-outage periods, the power variation of the virtual electric vehicle in adjacent time periods is expressed by the following formula: (twenty four);

[0119] Flexible load adjustment constraints:

[0120] The adjustable range of the flexible load of the regional power grid in each time period is given by the following formula: (25);

[0121] The optimized regional power grid's total power consumption should not exceed its expected total power consumption, as shown by the formula:

[0122] (26);

[0123] In the formula, This represents the expected flexible power load of the regional power grid under normal operating conditions.

[0124] The load reduction limit is defined by the following formula: (27);

[0125] In the formula, Indicates the load reduction threshold;

[0126] The backflow constraint is given by the following formula:

[0127] (28).

[0128] After determining the overall scheduling power requirements of the VEV cluster, it is necessary to further allocate them to the individual EVs within it to meet actual control needs and ensure the feasibility of the scheduling strategy. To this end, a priority-based dynamic VEV power allocation strategy is adopted. This strategy measures the charging and discharging urgency of each EV by defining a charging slack index, and decomposes the charging and discharging power of each EV according to priority. This ensures that the power allocation of VEVs is completed quickly and efficiently while meeting the charging needs of the EVs.

[0129] In a planned power outage scenario, the corresponding The VEV response power at any given time can be divided into charging power. and discharge power Before a power outage, the VEV needs to enter the charging phase in advance to store electrical energy and ensure power supply during the outage. During the outage, the VEV enters the discharging phase to ensure the power supply to the regional load and maintain the stability of the power grid. After the power outage is restored, the VEV needs to enter the charging phase again to meet the user's normal travel needs and restore the EV to the level of power required for normal travel.

[0130] Step S105: Construct an electricity consumption control strategy model based on the electricity consumption target model, priority, charging power, and discharging power, specifically including the following steps:

[0131] Step S1051: If the response power of the virtual electric vehicle is the charging power, then define a charging slack index, and calculate the charging slack index for all electric vehicles that need charging at time [time value missing]. Charging slack:

[0132] (29).

[0133] Step S1052: Initialize the remaining allocable power, such that .

[0134] Step S1053: Sort the charging relaxation index from smallest to largest to obtain the first priority queue.

[0135] Step S1054: Allocate charging power to the electric vehicle with the highest charging priority based on the first priority queue:

[0136] (30).

[0137] Charging priority is the highest. Minimum.

[0138] Step S1055: Update the remaining allocable charging power:

[0139] (31);

[0140] Step S1056: Repeat the priority-based charging process for the remaining electric vehicles in descending order of charging priority.

[0141] The queue prioritizes electric vehicles for charging. Allocate charging power and update the remaining allocable charging power until... All electric vehicles at any given time participate in the decomposition process or have remaining distributable power, making If the charging power distribution process ends at that moment, then the charging power distribution process will end.

[0142] Step S1057: If the virtual electric vehicle response power is the discharge power, then define a discharge relaxation index, and calculate the discharge relaxation of all electric vehicles that need to be discharged at time t based on the discharge relaxation index:

[0143] (32).

[0144] Step S1058: Initialize the remaining allocable power, such that .

[0145] Step S1059: Sort the discharge relaxation index from largest to smallest to obtain the second priority queue.

[0146] Step S1060: Allocate discharge power to the electric vehicle with the highest discharge priority based on the second priority queue:

[0147] (33).

[0148] Among them, the highest priority is discharge. maximum.

[0149] Step S1061: Update the remaining distributable discharge power, so that (34).

[0150] Step S1062: According to the second priority queue from high to low, repeatedly allocate discharge power to the electric vehicle with the highest priority based on the second priority queue and update the remaining allocable discharge power until... All electric vehicles at any given time participate in the decomposition process or have remaining distributable power, making If the discharge power distribution process ends at that moment, then the process ends.

[0151] This invention addresses planned power outage scenarios with a VEV-based rapid charging and discharging power supply strategy, which can improve the power supply reliability of a regional power grid. This strategy achieves energy self-balancing operation of the regional power grid under planned power outage conditions through the synergistic optimization of the flexible charging and discharging capabilities of VEVs and flexible load adjustment. Considering charging and discharging operation constraints, power flow backflow constraints, and flexible load adjustment constraints, a VEV-based rapid charging and discharging power supply strategy is constructed with the goal of minimizing the amount of unserved load during power outages. This strategy considers power flow backflow constraints, ensuring that the regional power grid does not experience backflow of excess photovoltaic power generation or electric vehicle discharge power during operation, thereby protecting the safety of maintenance personnel. Verification results show that the flexible adjustability of VEVs can effectively mitigate the adverse effects of power grid outages on user electricity consumption. Furthermore, compared to traditional centralized optimization of the charging and discharging behavior of a large number of individual electric vehicles, the VEV-based optimization strategy effectively reduces computational complexity, making the model's computational burden insensitive to changes in the number of electric vehicles, thus shortening the solution time and improving its engineering application value. Meanwhile, this invention proposes a priority-based VEV response power fast allocation strategy, which determines the charging and discharging priorities of electric vehicles and dynamically allocates the remaining power to ensure fast response capability.

[0152] This invention selects a regional power grid containing 100 households for simulation verification. Based on real residential energy consumption data recorded in the PecanStreetDataset, rigid and flexible electricity load curves of the regional power grid are generated, such as... Figure 2 As shown. The simulation time is set to one day, starting at 8:00 AM. Based on weather data released by the Hawaii Energy Research Center, the solar irradiance and ambient temperature data for the regional power grid for the next day are predicted, such as... Figure 3 As shown. The photovoltaic panel area is 300m², and the power generation efficiency is 0.164. The planned power outage duration is 10 hours, ranging from 13pm to 23pm, with time intervals... Take 5 minutes.

[0153] Assume the regional power grid contains 100 EVs with V2G functionality. For simulation purposes, assume the EVs have rated charging and discharging powers of 6.6kW and -6.6kW, respectively, charging and discharging efficiencies of 0.95, and rated battery capacities of 35kWh. and We take values ​​of 0.1 and 0.9 respectively. To simulate the EV's grid connection / disconnection process, we assume the EV has remaining battery power upon return. The expected charge of the EV when it leaves follows N(0.6, 0.1), and the expected charge of the EV when it leaves follows U[0.8, 0.9].

[0154] The method of this invention, namely the use of the VEV model in the problem of ensuring power supply in regional power grids, Figure 4 The total power load curves of the regional power grid before and after optimized scheduling are presented. The total power load is the time-series algebraic sum of flexible and rigid power loads, where the gray shaded area represents the period of power grid outage (13 pm-23 pm). It can be observed that, to mitigate the impact of power supply gaps caused by power grid outages, some flexible power loads during outages are shifted to other non-outage periods, such as... Figure 4 As shown in the diagram, the electricity demand in the entire area is reduced during grid outages, and the remaining electricity load will be met by local photovoltaic power generation and VEV discharge. Figure 5 The results further provide a comparison of power supply and demand in the regional power grid during the outage. The results show that, based on local photovoltaic power generation output, the combined power output of both, through the orderly discharge of VEVs (Vehicle Electric Vehicles), can precisely meet the remaining power load demand of the regional power grid, achieving a precise balance between supply and demand. Throughout the outage, the power supplied never exceeded the actual load demand, ensuring system power balance while effectively avoiding backflow problems caused by excess photovoltaic output or EV discharge.

[0155] like Figure 6 , Figure 7 As shown, each EV begins charging before 1 PM to prepare energy storage for power supply during a power outage. From 1 PM to 11 PM, the State of Charge (SOC) decreases, reflecting the EVs' discharge phase and providing support to the grid. After 11 PM, the SOC of each EV rises rapidly, resuming the charging phase to ensure users' normal travel needs. Furthermore, the SOC of all vehicles does not exceed the upper limit or fall below the lower limit, indicating that the scheduling strategy strictly adheres to operational constraints during charging and discharging, avoiding overcharging or over-discharging. This dynamic power allocation strategy effectively ensures the safety and reliability of vehicle operation, while supporting grid operation through reasonable power allocation, demonstrating the optimized effect of VEV response scheduling.

[0156] The present invention also provides an electronic device, comprising: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute any one of the following mathematical modeling methods for orderly control strategy of power supply guarantee for electric vehicles in a power distribution area cluster.

[0157] The present invention also provides a storage medium storing a computer program, wherein the computer program, when executed by a processor, implements a mathematical modeling method for an orderly control strategy for power supply to electric vehicles in a distribution area cluster.

[0158] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Dual Data SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus Direct RAM (RDRAM), Direct Memory Bus Dynamic RAM (DRDRAM), and Memory Bus Dynamic RAM (RDRAM). The various embodiments described in this specification are presented in a progressive manner, and similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, for embodiments of apparatus, devices, and non-volatile computer storage media, since they are substantially similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments.

[0159] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A power supply guarantee and orderly control strategy for electric vehicles in a transformer substation cluster, characterized in that, include: A virtual electric vehicle model was constructed based on the electric vehicle cluster in the transformer area. Based on the virtual electric vehicle model, an electricity consumption target model is constructed, specifically including: Based on the virtual electric vehicle model, an objective function is constructed and calculated as follows: ； ； ； In the formula, and These represent the start and end times of the power outage in the regional power grid, respectively. Indicates the regional power grid during the time period Loss of load, Indicates the scheduling period, and ; For time period Photovoltaic power generation capacity Indicates the area of ​​the photovoltaic panel. Indicates light intensity. Indicates power generation efficiency. Indicates ambient temperature. Indicates the flexible load of the regional power grid. Indicates the rigid load of the regional power grid; Based on the objective function, a constraint function is constructed, which includes: charging and discharging operation constraints, flexible compliance adjustment constraints, and power flow backflow constraints, calculated as follows: Charge and discharge operation constraints: The boundary limits for charging power and discharging power are defined by the following formula: ; The mutual exclusion constraint between charging and discharging is expressed by the following formula: ; The constraint on the change relationship of the cumulative energy level of the virtual electric vehicle in adjacent time periods is given by the following formula: ; In the formula, Indicates the virtual electric vehicle during the time period Energy accumulation level; The energy accumulation constraint of the virtual electric vehicle in the charging and discharging optimization process is given by the following formula: ; Virtual electric vehicle charge / discharge optimization and control within a time range The controllable conditions inherent within are given by the formula: ; The charging and discharging power limits for virtual electric vehicles during regional power grid outages are defined by the following formula: ; During non-outage periods, the power variation of the virtual electric vehicle in adjacent time periods is calculated using the following formula: ; Flexible load adjustment constraints: The adjustable range of the flexible load in the regional power grid for each time period, expressed by the formula: ; The optimized regional power grid's total power consumption should not exceed its expected total power consumption, as shown by the formula: ； In the formula, This represents the expected flexible power load of the regional power grid under normal operating conditions. The load reduction limit is defined by the following formula: ; In the formula, Indicates the load reduction threshold; The backflow constraint is given by the following formula: ； A power consumption control strategy model is constructed based on the aforementioned power consumption target model, priority, charging power, and discharging power; in, and These represent the earliest and latest times for the virtual electric vehicle to perform charging and discharging scheduling, respectively. and These represent the time periods. The energy is adjustable at both the upper and lower boundaries. and These represent the virtual electric vehicles during the time period. The adjustable boundaries of charging power and discharging power; and These represent the charging loss coefficient and discharging loss coefficient of the virtual electric vehicle, respectively. and These represent the virtual electric vehicles during the time period. The charging power and discharging power.

2. The orderly control strategy for power supply guarantee of electric vehicles in a transformer substation cluster according to claim 1, characterized in that, A virtual electric vehicle model is constructed based on the electric vehicle cluster in the transformer area, specifically including: The individual energy boundary and adjustable power boundary of a single electric vehicle are determined based on the power consumption parameters, which include: the current battery state of charge of the electric vehicle, the maximum allowable battery state of charge, the minimum allowable battery state of charge, and the battery charge requirement for the next trip specified by the user. Based on whether the remaining battery power and time of a single electric vehicle upon return meet controllable conditions, the electric vehicle clusters in the transformer area are divided into classification models. Based on the single-unit energy boundary, the single-unit power adjustable boundary, and the classification model, construct a classification energy adjustable boundary and a classification charge / discharge power adjustable boundary. The virtual electric vehicle model is constructed based on the classified adjustable energy boundary and the classified adjustable charge / discharge power boundary.

3. The orderly control strategy for ensuring power supply to electric vehicles in a transformer substation cluster according to claim 2, characterized in that, The energy boundary and adjustable power boundary of a single electric vehicle are determined based on electricity consumption parameters, specifically including: The energy boundary of a single cell is determined based on the aforementioned power consumption parameters. This energy boundary is characterized by the battery's maximum rechargeable capacity and minimum dischargeable capacity, calculated using the following formula: ； In the formula, This indicates the maximum rechargeable capacity of the battery. Indicates the first The maximum permissible state of battery charge for electric vehicles in Taiwan Indicates the first The rated battery capacity of the electric vehicle; ； In the formula, Indicates the minimum discharge capacity. Indicates the first Minimum battery state of charge allowed for electric vehicles in Taiwan Indicates the first The remaining battery charge status of the electric vehicle when it returns to its starting point; The adjustable power boundary of the individual unit is determined based on the power consumption parameters. The adjustable power boundary of the individual unit is characterized by the power demand specified by the user, and the calculation formula is as follows: ； In the formula, This indicates the user's specified power consumption requirement. Indicates the first The state of charge of the battery when the electric vehicle leaves for its next trip.

4. The orderly control strategy for power supply guarantee of electric vehicles in the transformer substation cluster according to claim 3, characterized in that, The classification model includes a first type, a second type, and a third type. Based on whether the remaining battery power and time of a single electric vehicle upon return meet controllable conditions, the electric vehicle cluster in the distribution area is divided into classification models, specifically including: If the remaining battery power and time are uncontrollable, the electric vehicle clusters in the transformer area are divided to obtain the first type of model; If the remaining power and time are controllable, and the remaining battery state of charge upon return is higher than the minimum battery state of charge allowed for electric vehicles, the second type of model is obtained by dividing the electric vehicle clusters in the transformer area. If the remaining battery power and time are controllable, and the remaining battery state of charge upon return is lower than the minimum allowed battery state of charge for electric vehicles, the third type of model is obtained by dividing the electric vehicle clusters in the transformer area.

5. The orderly control strategy for power supply guarantee of electric vehicles in the transformer substation cluster according to claim 4, characterized in that, Based on the single-cell energy boundary, the single-cell power adjustable boundary, and the classification model, a classification energy adjustable boundary and a classification charge / discharge power adjustable boundary are constructed, specifically including: Based on the single-cell energy boundary, the single-cell power adjustable boundary, and the first type of model, a first type of energy adjustable boundary and a classification of charge / discharge power adjustable boundaries are constructed, and the calculation formulas are as follows: ； In the formula, Indicates the first The charging energy boundary of electric vehicles in Taiwan Indicates the first The discharge energy boundary of electric vehicles in Taiwan Indicates the current time. Indicates the first The return time of the electric vehicle from Taiwan. Indicates the first The departure time of the electric vehicle in Taiwan Indicates time At that time, electric vehicles The increase and decrease in the maximum rechargeable energy. Indicates electric vehicles The charging power; Indicates charging efficiency; Indicates a unit time interval; The calculation formula for the first type of adjustable charge / discharge power boundary is as follows: ; In the formula, Indicates the first The charging power limit of electric vehicles in Taiwan Indicates the first The discharge power limit of electric vehicles in Taiwan Indicates the first The rated charging power of the electric vehicle; Based on the single-cell energy boundary, the single-cell power adjustable boundary, and the second type of model, a second type of energy adjustable boundary and a classification of charge / discharge power adjustable boundaries are constructed, and the calculation formulas are as follows: ； In the formula, Indicates the first The maximum energy capacity of the electric vehicle in Taiwan; The formula for calculating the discharge energy boundary of the second type of adjustable energy boundary is as follows: ； In the formula, Indicates the first The minimum energy capacity of electric vehicles in Taiwan. Indicates the first The rated discharge power of the electric vehicle Indicates the first The discharge efficiency of the electric vehicle in Taiwan; The calculation formula for the adjustable boundary of the second type of charging power is as follows: ； The calculation formula for the adjustable boundary of the second type of discharge power is as follows: ； Based on timing, the first type of adjustable energy boundary and the classified adjustable charge / discharge power boundary, and the second type of adjustable energy boundary and the classified charge / discharge power, a third type of adjustable energy boundary and a classified adjustable charge / discharge power boundary are constructed.

6. The orderly control strategy for power supply guarantee of electric vehicles in the transformer substation cluster according to claim 4, characterized in that, The virtual electric vehicle model is constructed based on the classified adjustable energy boundary and the classified adjustable charge / discharge power boundary, specifically including: The total energy range is obtained by summing the energy adjustable boundaries of all individual electric vehicles over time. The total power range is obtained by summing the time-series adjustable power boundaries of all individual electric vehicles; The virtual electric vehicle model is constructed based on the total energy range and the total power range; The virtual electric vehicle model is represented as a 10-tuple vector: The vector calculation is as follows: and : ； In the formula, This represents the first type of model. This represents the second type of model. This represents the third type of model; and The calculation is as follows: ； and The calculation is as follows: ； and The calculation is as follows: ； ； In the formula, Indicates the first Taiwanese electric vehicles during the period The connection status is Z, which represents the total number of electric vehicles in the regional power grid.

7. The orderly control strategy for ensuring power supply to electric vehicles in a transformer substation cluster according to claim 1, characterized in that, Based on the aforementioned electricity consumption target model, priority, charging power, and discharging power, an electricity consumption control strategy model is constructed, specifically including: If the response power of the virtual electric vehicle is the charging power, then a charging slack index is defined, and the charging slack of all electric vehicles requiring charging is calculated based on the charging slack index: ； Initialize the remaining allocable power so that ; The charging relaxation indexes are sorted from smallest to largest to obtain the first priority queue; Based on the first priority queue, charging power is allocated to the electric vehicle with the highest charging priority: ； Update the remaining available charging power: ； For the remaining electric vehicles, charging power is allocated to the electric vehicle with the highest charging priority based on the first priority queue, and the remaining allocable charging power is updated. Until All electric vehicles at any given time participate in the decomposition process or have remaining distributable power, making If the charging power allocation process ends at that moment; If the virtual electric vehicle response power is equal to the discharge power, then a discharge relaxation index is defined, and the discharge relaxation of all electric vehicles requiring discharge is calculated based on the discharge relaxation index: ； Initialize the remaining allocable power so that ; The second priority queue is obtained by sorting the discharge relaxation index from largest to smallest. Discharge power is allocated to the electric vehicle with the highest priority based on the second priority queue: ； Update the remaining distributable discharge power so that ; For the remaining electric vehicles, discharge power is allocated to the electric vehicle with the highest discharge priority based on the second priority queue, and the remaining allocable discharge power is updated. Until All electric vehicles at any given time participate in the decomposition process or have remaining distributable power, making If the discharge power distribution process ends at that moment, then the process ends.

8. An electronic device, characterized in that, include: At least one processor; A memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to execute the orderly control strategy for power supply to electric vehicles in the transformer cluster as described in any one of claims 1 to 7.

9. A computer storage medium, characterized in that, The device stores a computer program, which, when executed by a processor, implements the orderly control strategy for power supply to electric vehicles in the distribution area cluster as described in any one of claims 1 to 7.

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