Station area cluster electric vehicle power supply guaranteeing ordered control strategy

Abstract

The invention belongs to the technical field of electric vehicle cluster optimization, and particularly relates to a station area cluster electric vehicle power supply guarantee ordered control strategy, electronic equipment and a storage medium, and the power supply control strategy comprises the steps: building a virtual electric vehicle model based on a station area electric vehicle cluster; constructing a power utilization target model based on the virtual electric vehicle model; and constructing a power utilization control strategy model based on the power utilization target model, the priority, the charging power and the discharging power. According to the method, the VEV model is constructed based on the adjustable capacity of the single electric vehicles, the VEV model simplifies the optimal scheduling process of a large-scale electric vehicle cluster by polymerizing the charging and discharging capacity of the single electric vehicles, the charging and discharging behaviors of the electric vehicles can be effectively managed, the energy use is optimized, the stability and reliability of a power grid are improved, and the energy consumption is reduced. And the problem of reverse power flow can be effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric vehicle charging facility metering, and in particular relates to an orderly control strategy for ensuring power supply to electric vehicles in a cluster in a substation. Background Art

[0002] As mobile energy storage units, electric vehicles not only offer significant advantages in green mobility but also provide flexible regulation resources for power systems. However, effectively utilizing the energy storage and regulation capabilities of electric vehicles in planned power outages or emergency power supply scenarios has become a key research topic for ensuring grid power reliability.

[0003] Currently, traditional electric vehicle optimization scheduling methods suffer from high computational complexity and poor scalability when dealing with large-scale swarms of electric vehicles, making them incapable of meeting the real-time scheduling requirements of modern regional power grid outage scenarios. Furthermore, in practical projects, regional power grids are typically disconnected from their parent grid after a power outage. However, the potential for backflow caused by electric vehicle discharge poses a serious threat to the safety of maintenance personnel and increases grid operation risks.

[0004] Research on scheduling electric vehicles during power outages has also focused on optimizing the control of individual electric vehicles. This approach fails to fully consider the computational complexity of large-scale clusters of electric vehicles, nor does it effectively address the issue of power flow backflow. These issues limit the potential of electric vehicles as a flexible energy storage resource during planned power outages. 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 ensuring power supply to cluster electric vehicles in a substation area.

[0006] In the first aspect, a strategy for ensuring orderly power supply for electric vehicles in a substation cluster is provided, including: constructing a virtual electric vehicle model based on the substation 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.

[0007] In an optional embodiment, a virtual electric vehicle model is constructed based on the substation electric vehicle cluster, specifically including: determining the single-cell energy boundary and single-cell power adjustable boundary of a single electric vehicle based on power consumption parameters, the power consumption parameters including: 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; dividing the substation electric vehicle cluster according to whether the remaining power and time when the single electric vehicle returns meet the controllable conditions to obtain a classification model; constructing a classification energy adjustable boundary and a classification charge / discharge power adjustable boundary based on the single-cell energy boundary, the single-cell power adjustable boundary and the classification model; and constructing the virtual electric vehicle model based on the classification energy adjustable boundary and the classification charge / discharge power adjustable boundary.

[0008] In an optional embodiment, the cell energy boundary and the cell power adjustable boundary of a single electric vehicle are determined based on the power usage parameters, specifically including: determining the cell energy boundary based on the power usage parameters, wherein the cell energy boundary is characterized by the maximum chargeable power of the battery and the minimum dischargeable power of the battery, and the calculation formula is as follows: i max =(SoC i max -SoC i end )×Q i Where, e i max Indicates the maximum chargeable capacity of the battery, SoC i max represents the maximum battery state of charge allowed for the i-th electric vehicle, Q i represents the rated battery capacity of the i-th electric vehicle; e i min =(SoC i min -SoC i end )×Q i Where, e i min Indicates the minimum discharge capacity, SoC i min The minimum battery state of charge allowed for the i-th electric vehicle, SoC i end represents the remaining battery charge state of the i-th electric vehicle when it returns to the starting point; the single-cell power adjustable boundary is determined based on the power usage parameter, and the single-cell power adjustable boundary is characterized by the user-specified power requirement. The calculation formula is as follows:

[0009] e i need =(SoCi next -SoC i end )×Q i Where, e i need Indicates user-specified power requirements, SoC i next It represents the battery state of charge of the i-th electric vehicle when it leaves the next trip.

[0010] In an optional embodiment, the electric vehicle cluster in the substation is divided according to whether the remaining power and time when a single electric vehicle returns meet controllable conditions to obtain a classification model, specifically including: if the remaining power and time are uncontrollable, the electric vehicle cluster in the substation is divided to obtain a first type of model; if the remaining power and time are controllable, and the remaining battery state of charge at the time of return is higher than the maximum battery state of charge allowed by the electric vehicle, the electric vehicle cluster in the substation is divided to obtain a second type of model; if the remaining power and time are controllable, and the remaining battery state of charge at the time of return is lower than the minimum battery state of charge allowed by the electric vehicle, the electric vehicle cluster in the substation is divided to obtain a third type of model; wherein, the classification model includes a first type of model, a second type of model and a third type of model.

[0011] In an optional embodiment, constructing a classified energy adjustable boundary and a classified charge / discharge power adjustable boundary based on the single cell energy boundary, the single cell power adjustable boundary, and the classification model specifically includes: constructing a first-class energy adjustable boundary and a classified charge / discharge power adjustable boundary based on the single cell energy boundary, the single cell power adjustable boundary, and the first-class model, and the calculation formula is as follows:

[0012] Where, represents the charging energy boundary of the i-th electric vehicle, represents the discharge energy boundary of the i-th electric vehicle, t represents the current time, t end,i represents the return time of the i-th electric vehicle, t next,i represents the departure time of the i-th electric car, e i,t-1 max+(-) represents the maximum energy increase and decrease that electric vehicle i can charge at time t-1, p i c represents the charging power of electric vehicle i; represents the charging efficiency; Δt represents the unit time interval; the calculation formula for the first type of charge / discharge power adjustable boundary is as follows: Where, represents the charging power boundary of the i-th electric vehicle, represents the discharge power boundary of the i-th electric vehicle, represents the rated charging power of the i-th electric vehicle; based on the single-cell energy boundary, the single-cell power adjustable boundary and the second-type model, the second-type energy adjustable boundary and the classified charge / discharge power adjustable boundary are constructed, and the calculation formula is as follows: Where, e i,t-1 max+ represents the maximum energy capacity of the i-th electric vehicle; the calculation formula for the discharge energy boundary of the second type of energy adjustable boundary is as follows:

[0013] Where, e i,t-1 max- represents the minimum energy capacity of the i-th electric vehicle, represents the rated discharge power of the i-th electric vehicle, represents the discharge efficiency of the i-th electric vehicle; 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: A third type of energy adjustable boundary and classified charge / discharge power adjustable boundary are constructed based on the time sequence, the first type of energy adjustable boundary and classified charge / discharge power adjustable boundary and the second type of energy adjustable boundary and classified charge / discharge power.

[0014] In an optional embodiment, constructing the virtual electric vehicle model based on the classified energy adjustable boundaries and the classified charge / discharge power adjustable boundaries specifically includes: performing a time-series summation of the energy adjustable boundaries of all individual electric vehicles to obtain a total energy range; performing a time-series summation of the power adjustable boundaries of all individual electric vehicles to obtain a total power range; 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 is calculated as follows: T end and T next They represent the earliest time and latest time for virtual electric vehicles to perform charging and discharging scheduling:

[0015] Where, Ω 1 represents the first type of model, Ω 2 represents the second type of model, Ω 3 represents the third type of model; and They represent the upper and lower bounds of the energy adjustable in time period t, respectively, and are calculated as follows: P t max+ and P t max- They represent the charging power adjustable boundary and discharging power adjustable boundary of the virtual electric vehicle in time period t, respectively, and are calculated as follows: and They represent the charging loss coefficient and discharging loss coefficient of the virtual electric vehicle, respectively, and are calculated as follows:

[0016] Where, represents the access status of the i-th electric vehicle in time period t, Z represents the total number of electric vehicles in the regional power grid; P t c and P t d They represent the charging power and discharging power of the virtual electric vehicle in time period t respectively.

[0017] In an optional embodiment, 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 as follows: P t solar =S solar ·R t ·τ·[1-0.005·(T t out -25)]; where t1 and t2 represent the start time and end time of the power outage in the regional power grid, respectively, P t lost represents the load loss of the regional power grid in time period t, T represents the dispatch period, and P t solar is the photovoltaic power generation power in period t, S solar Represents the photovoltaic panel area, R t represents the light intensity, τ represents the power generation efficiency, T t out Indicates the ambient temperature, P t com,re represents the flexible load of the regional power grid, P t com,un Represents 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 back constraints, which are calculated as follows: Charging and discharging operation constraints: boundary limits of charging power and discharging power, the formula is: Charge and discharge mutual exclusion constraint, the formula is: P t c +|P t d |≤max{P t c ,|P t d |}; The change relationship constraint of the energy accumulation level of the virtual electric vehicle in adjacent time periods is as follows: Where, represents the energy accumulation level of the virtual electric vehicle in time period t; the energy accumulation constraint of the virtual electric vehicle in the charging and discharging optimization process is as follows: Virtual electric vehicle charging and discharging optimization control in the time interval [T end ,T next ] has controllable conditions, the formula is: The charging and discharging power limits of virtual electric vehicles during regional power grid outages are calculated as follows: During the non-blackout period, the power change of the virtual electric vehicle in adjacent time periods is as follows:

[0018] Flexible load adjustment constraint: The adjustable range of the regional power grid flexible load in each period, the formula is: P t com,min ≤P t com,re ≤P t com,max The total power consumption of the optimized regional power grid does not exceed its expected total power consumption. The formula is: Where, P t com,lo It represents the expected flexible power load of the regional power grid without power outage; the load reduction limit is as follows: Where φ represents the load reduction threshold; the power flow backflow constraint is as follows: P t solar +P t d ≤P t com,re +P t com,un +P t c .

[0019] In an optional embodiment, a power consumption control strategy model is constructed based on the power consumption target model, priority, charging power, and discharging power, specifically including: if the response power of the virtual electric vehicle is the charging power, defining a charging slack index, and calculating the charging slack of all electric vehicles that need to be charged at time t based on the charging slack index: Initialize the remaining allocatable power so that P t s =P t c ; Sort by charging slack index from small to large to obtain a first priority queue; based on the first priority queue, allocate charging power to the electric vehicle with the highest charging priority: Update the remaining allocatable charging power: For the remaining electric vehicles, the 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 time t participate in the decomposition process or the remaining allocable power, so that P t s =0, then the charging power allocation process at that moment is ended; if the virtual electric vehicle response power is the discharge power, a discharge relaxation index is defined, and the discharge relaxation of all electric vehicles that need to be discharged at the moment is calculated based on the discharge relaxation index: Initialize the remaining allocatable power so that P t s =P t d ; Sort by discharge relaxation index from large to small to obtain a second priority queue; based on the second priority queue, allocate discharge power to the electric vehicle with the highest discharge priority: Update the remaining distributable discharge power so that For the remaining electric vehicles, the discharge power is allocated to the electric vehicle with the highest discharge priority based on the second priority queue, and the remaining distributable discharge power is updated; until all electric vehicles at time t participate in the decomposition process or the remaining distributable power, so that P t s =0, the discharge power distribution process at this moment ends.

[0020] In the second aspect, an electronic device is also provided, 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, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of the aforementioned orderly control strategies for power supply to electric vehicles in a substation cluster.

[0021] In a third aspect, a medium is also provided, which stores a computer program, and when the computer program is executed by a processor, it implements any of the aforementioned control strategies for ensuring orderly power supply to electric vehicles in a cluster in a substation.

[0022] The beneficial effects of the present invention are as follows: the present invention constructs a VEV model based on the adjustable capacity of individual electric vehicles. The VEV model simplifies the optimization scheduling process of large-scale electric vehicle clusters by aggregating the charging and discharging capabilities of individual 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 power flow backflow. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0024] Figure 1 This is a flow chart of an orderly control strategy for ensuring power supply to cluster electric vehicles in a substation area provided by one embodiment of the present invention;

[0025] Figure 2 Flexible and rigid power load curves of a regional power grid in an example of an orderly power supply control strategy for cluster electric vehicles in a substation area provided by an embodiment of the present invention;

[0026] Figure 3 The predicted light intensity and ambient temperature data of an example of a control strategy for ensuring orderly power supply to electric vehicles in a cluster in a substation provided by an embodiment of the present invention;

[0027] Figure 4 The total power load curve of the regional power grid before and after optimization adjustment of an example of a control strategy for ensuring orderly power supply of electric vehicles in a cluster in a substation area provided by an embodiment of the present invention;

[0028] Figure 5 This is a comparison curve of power supply and demand during a regional power grid outage in an example of a control strategy for ensuring orderly power supply to electric vehicles in a cluster area provided by an embodiment of the present invention;

[0029] Figure 6 The SOC change curve of each single EV in the VEV of an example of the orderly power supply control strategy for cluster electric vehicles in a substation provided by one embodiment of the present invention;

[0030] Figure 7 This is a power change curve of each single EV in the VEV of an example of the orderly control strategy for ensuring power supply of electric vehicles in a cluster in a substation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following is combined with Figures 1 to 7 The present invention is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the relevant invention and are not intended to limit the invention. It should also be noted that for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0032] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0033] Please refer to Figure 1 , a flow chart of an embodiment of the present invention provides a control strategy for ensuring orderly power supply to electric vehicles in a cluster in a substation area, the control strategy for ensuring orderly power supply to electric vehicles in a cluster in a substation area including the following steps:

[0034] Step S101: constructing a virtual electric vehicle model based on the electric vehicle cluster in the substation.

[0035] Step S103: constructing an electricity consumption target model based on the virtual electric vehicle model.

[0036] Step S105: Constructing a power consumption control strategy model based on the power consumption target model, priority, charging power, and discharging power.

[0037] In this embodiment, step S101 creates a virtual electric vehicle (VEV) representing all electric vehicles in the substation, so as to simulate and predict the charging and discharging behavior of electric vehicles. Among them, all individual electric vehicle models are aggregated to form a virtual electric vehicle model, which can represent the behavior of the entire substation electric vehicle cluster. The power consumption target model is combined with the virtual electric vehicle model to form the basis of the power consumption control strategy. Electric vehicles are divided into different priority categories. Different charging and discharging strategies and time windows are set according to the priority to ensure that high-priority electric vehicles can obtain charging resources first. In combination with the priority, the charging and discharging requirements are adjusted to ensure that high-priority electric vehicles are met first. Considering the limitations of charging power and discharging power, the charging and discharging plan is optimized to avoid overcharging or over-discharging.

[0038] Through the above steps, we first proposed a VEV model based on the adjustable capacity of individual EVs. Then, by aggregating the charging and discharging capabilities of individual EVs, the VEV model simplifies the optimal scheduling process for large-scale EV clusters. This orderly control strategy for ensuring power supply for clustered EVs effectively manages the charging and discharging behavior of EVs, optimizes energy use, improves grid stability and reliability, and effectively addresses the issue of power flow backflow.

[0039] Step S101, constructing a virtual electric vehicle model based on the electric vehicle cluster in the substation, specifically includes the following steps:

[0040] Step S1011: Determine the single-cell energy boundary and single-cell power adjustable boundary of a single electric vehicle based on power usage parameters, where the power usage parameters include: the electric vehicle's current battery state of charge, the maximum allowable battery state of charge, the minimum allowable battery state of charge, and the user-specified battery charge requirement for the next trip.

[0041] Step S1013: Based on whether the remaining power and time of the single electric vehicle when returning meet the controllable conditions, the electric vehicle clusters in the substation are divided to obtain a classification model.

[0042] Step S1015: constructing a classification energy adjustable boundary and a classification charge / discharge power adjustable boundary based on the single cell energy boundary, the single cell power adjustable boundary and the classification model.

[0043] Step S1017: constructing a virtual electric vehicle model based on the classified energy adjustable boundaries and the classified charge / discharge power adjustable boundaries.

[0044] In this embodiment, the electricity usage parameters of each electric vehicle are collected, and the energy and power boundaries of each electric vehicle are calculated based on the electricity usage parameters. Therefore, in step S1011, the energy and power adjustable ranges of each electric vehicle can be determined. By dividing the electric vehicle cluster into different categories, categorized management can be facilitated. In step S1015, the energy and power adjustable boundaries of each category of electric vehicles are established.

[0045] The classification models were integrated into a virtual electric vehicle model that can represent the entire substation electric vehicle cluster. The energy and power adjustable boundaries of each category were integrated into the virtual electric vehicle model, and a mathematical expression for the virtual electric vehicle model was constructed, including the total energy adjustable boundary and the total charge / discharge power adjustable boundary. The accuracy and reliability of the model were then verified to accurately reflect the behavior of the substation electric vehicle cluster.

[0046] Through the above steps, a virtual electric vehicle model can be constructed, which can accurately reflect the energy and power adjustable boundaries of the electric vehicle cluster in the substation, providing a basis for subsequent power consumption control strategies.

[0047] Step S1011, determining the single-unit energy boundary and the single-unit power adjustable boundary of the single electric vehicle based on the power consumption parameters, specifically includes the following steps:

[0048] Step S10111: Determine the cell energy boundary based on the power usage parameters. The cell energy boundary is characterized by the maximum chargeable capacity of the battery and the minimum dischargeable capacity of the battery. The calculation formula is as follows:

[0049] e i max =(SoC i max -SoC i end )×Q i ; (1)

[0050] Where, e i max Indicates the maximum chargeable capacity of the battery, SoC i max represents the maximum state of charge (SoC) allowed for the i-th electric vehicle, Qi represents the rated battery capacity of the i-th electric vehicle.

[0051] e i min =(SoC i min -SoC i end )×Q i ; (2)

[0052] Where, e i min Indicates the minimum discharge capacity, SoC i min The minimum battery state of charge allowed for the i-th electric vehicle, SoC i end represents the remaining battery state of charge of the i-th electric vehicle when it returns to the starting point.

[0053] Step S10113: Determine the power adjustment limit of a single cell based on the power consumption parameters. The power adjustment limit of a single cell is characterized by the power demand specified by the user. The calculation formula is as follows:

[0054] e i need =(SoC i next -SoC i end )×Q i ; (3)

[0055] Where, e i need Indicates user-specified power requirements, SoC i next It represents the battery state of charge of the i-th electric vehicle when it leaves the next trip.

[0056] Based on step S1013, the electric vehicle clusters in the substation area are divided according to whether the remaining power and time of the individual electric vehicles when returning meet the controllable conditions, and a classification model is obtained, which specifically includes the following steps:

[0057] Step S10131: If the remaining power and time are uncontrollable, the substation is divided into electric vehicle clusters to obtain a first type of model.

[0058] Step S10133: If the remaining power and time are controllable, and the remaining battery state of charge upon return is higher than the maximum battery state of charge allowed by the electric vehicle, the substation electric vehicle cluster is divided to obtain a second type of model.

[0059] Step S10135: If the remaining power and time are controllable, and the remaining battery state of charge upon return is lower than the minimum battery state of charge allowed for the electric vehicle, the electric vehicle cluster in the area is divided to obtain a third type model.

[0060] Among them, the classification model includes the first type model, the second type model and the third type model.

[0061] In this embodiment, electric vehicles are divided into three categories according to controllable conditions, and for each category, the energy adjustable boundary and the power adjustable boundary are calculated.

[0062] Category 1: Uncontrollable EVs, which need to be charged immediately. We can identify those electric vehicles that cannot participate in grid demand response due to remaining power or time constraints. The first category of models is an uncontrollable model, denoted by the set Ω 1 The parking time is short, and the vehicle must be charged at rated power immediately after connecting to the charging pile to meet the energy demand for the next trip. Among them, the remaining capacity (SOC) and the expected parking time of each electric vehicle are checked. For those whose remaining capacity is not enough to support the next trip demand, that is, the SOC is lower than SoC i next , or electric vehicles whose parking time is not long enough to complete the required charging, are classified as the first type of models.

[0063] Electric vehicles of the first type of model need to be connected to a charging station immediately and charged at the maximum allowed power to ensure that they can meet the power requirements of the next trip.

[0064] The second category: controllable EV, denoted as set Ω 2 , can immediately participate in charge and discharge regulation, can identify those electric vehicles with sufficient power and long enough parking time, these vehicles can immediately participate in the charge and discharge regulation of the power grid. Among them, for those electric vehicles with remaining power higher than the maximum allowable battery state of charge SoC i max The electric vehicle with sufficient parking time is classified as the second type of model. The EV of the second type of model has sufficient parking time and the remaining SOC when returning is higher than Able to immediately participate in charge and discharge regulation to optimize grid load and improve energy efficiency. These electric vehicles can be dispatched for discharge to support grid demand during peak hours, or charged during off-peak hours to take advantage of cheap electricity.

[0065] The third category: controllable EV, denoted as set Ω 3 , the remaining SOC when it returns is lower than The charging pile must have accumulated energy of at least After that, it needs to be charged to a certain SOC before it can participate in charge and discharge regulation. Identify electric vehicles that have insufficient power but have been parked for a long enough time. These vehicles need to be charged to a certain level before they can participate in the charge and discharge regulation of the power grid. For those electric vehicles whose remaining power is lower than the minimum allowable battery state of charge SoC i min However, electric vehicles with sufficient parking time are classified as the third model. Electric vehicles in the third model must first be connected to a charging station and charged to the target SOC before participating in the grid's charge and discharge optimization control. After charging to the target SOC, these electric vehicles can be scheduled for discharge to support grid demand or continue charging during off-peak hours.

[0066] This classification approach allows for more efficient management and scheduling of the charging and discharging behavior of electric vehicle fleets to support grid demand response and optimize energy distribution. This strategy helps balance grid loads, improve grid reliability and economics, while providing more flexible charging options for electric vehicle users.

[0067] Step S1015, based on the single cell energy boundary, the single cell power adjustable boundary and the classification model, constructs the classification energy adjustable boundary and the classification charge / discharge power adjustable boundary, specifically including the following steps:

[0068] Step S10151: Based on the cell energy boundary, the cell power adjustable boundary, and the first type of model, a first type of energy adjustable boundary and a classified charge / discharge power adjustable boundary are constructed. The calculation formula for the first type of energy adjustable boundary is as follows:

[0069]

[0070] Where, represents the charging energy boundary of the i-th electric vehicle, represents the discharge energy boundary of the i-th electric vehicle, t represents the current time, t end,i represents the return time of the i-th electric vehicle, t next,i represents the departure time of the i-th electric car, e i,t-1 max+ / (-) represents the maximum energy increase and decrease that electric vehicle i can charge at time t-1, p i c represents the charging power of electric vehicle i; represents the charging efficiency; Δt represents the unit time interval. 1 ) does not have the ability to regulate charge and discharge, and is similar to a rigid power load in an EV cluster.

[0071] The calculation formula for the first type of charge / discharge power adjustable boundary is as follows:

[0072] Where, represents the charging power boundary of the i-th electric vehicle, represents the discharge power boundary of the i-th electric vehicle, represents the rated charging power of the i-th electric vehicle.

[0073] Step S10153: Based on the cell energy boundary, the cell power adjustable boundary, and the second type of model, a second type of energy adjustable boundary and a classified charge / discharge power adjustable boundary are constructed. The calculation formula for the charging energy boundary of the second type of energy adjustable boundary is as follows:

[0074]

[0075] Where, e i,t-1 max+ represents the maximum energy capacity of the i-th electric vehicle;

[0076] The calculation formula for the discharge energy boundary of the second type of energy adjustable boundary is as follows:

[0077]

[0078] Where, e i,t+1 max- represents the minimum energy capacity of the i-th electric vehicle, p i d,rate represents the rated discharge power of the i-th electric vehicle, η i d represents the discharge efficiency of the i-th electric vehicle.

[0079] The calculation formula for the second type of charging power adjustable limit is as follows:

[0080]

[0081] The calculation formula for the adjustable boundary of the second type of discharge power is as follows:

[0082]

[0083] Step S10155: Based on the time sequence, the first type of energy adjustable boundary and the classified charge / discharge power adjustable boundary and the second type of energy adjustable boundary and the classified charge / discharge power, a third type of energy adjustable boundary and the classified charge / discharge power are constructed. The third type of EV can be regarded as a combination of the first and second types of EV. Therefore, in the time range [t end,i ,t c,i ], the energy adjustable and charging power adjustable boundaries are calculated using formulas (4) and (5) respectively, and In [t c,i ,t next,i], the energy and charge / discharge power adjustable boundaries are calculated using equations (6), (7) and (8), (9), respectively. By splicing the time series, the energy adjustable boundary and charge / discharge power adjustable boundary of the third type of EV can be formed.

[0084] Furthermore, step S1017, constructing a virtual electric vehicle model based on the classified energy adjustable boundaries and the classified charge / discharge power adjustable boundaries, specifically includes the following steps:

[0085] Step S10171: performing a time-series summation of the energy adjustable boundaries of all individual electric vehicles to obtain a total energy range.

[0086] Step S10173: performing a time-series summation of the power adjustable boundaries of all individual electric vehicles to obtain a total power range.

[0087] Step S10175: Construct a virtual electric vehicle model based on the total energy range and the total power range.

[0088] In this embodiment, the energy and charge / discharge power adjustable bounds of each individual EV provide key parameters for VEV modeling and adjustability analysis. By performing a time-series summation of the energy and charge / discharge power adjustable bounds of individual EVs, the VEV adjustability, namely the cluster energy adjustable bound and the aggregate power adjustable bound, can be obtained. In step S10171, at each time point, the energy adjustable bounds of all individual EVs at that time point are summed to obtain the total energy adjustable bound of the substation EV cluster at that time point. This step involves summing the energy bounds of all individual EVs to obtain the total energy capacity of the entire cluster at different time points. In step S10173, at each time point, the power adjustable bounds of all individual EVs at that time point are summed to obtain the total power adjustable bound of the substation EV cluster at that time point. This step involves summing the power bounds of all individual EVs to obtain the total power capacity of the entire cluster at different time points.

[0089] 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 electric vehicle cluster in the entire substation area and reflect the aggregation effect of the electric vehicle cluster on the power grid, including the ability to provide auxiliary services such as demand response and frequency regulation during peak load periods, and the ability to charge during low load periods.

[0090] This model can be used by grid operators for simulation to evaluate the impact of different charging and discharging strategies on grid stability and economy, thereby formulating optimal charging and discharging scheduling plans.

[0091] Furthermore, the virtual electric vehicle model is represented as a 10-tuple vector:

[0092] The vectors in a 10-tuple vector are calculated as follows:

[0093] T end and T next They represent the earliest time and latest time for virtual electric vehicles to perform charging and discharging scheduling:

[0094]

[0095] Where, Ω 1 represents the first type of model, Ω 2 represents the second type of model, Ω 3 Represents the third type of model. The schedulable time range ([T end ,T next ]).

[0096] is the energy adjustable boundary, and They represent the upper and lower bounds of the energy adjustable in time period t, respectively, and are calculated as follows:

[0097]

[0098] The power adjustable boundary is ([P t max+ ,P t max- ]), P t max+ and P t max- They represent the charging power adjustable boundary and discharging power adjustable boundary of the virtual electric vehicle in time period t, respectively, and are calculated as follows:

[0099]

[0100] and They represent the charging loss coefficient and discharging loss coefficient of the virtual electric vehicle, respectively, and are calculated as follows:

[0101]

[0102] Where, represents the access status of the i-th electric vehicle in time period t, and Z represents the total number of electric vehicles in the regional power grid. The VEV charging / discharging energy loss coefficient is and It is divided into time-varying variables and can be estimated using the mean value of the charging / discharging coefficient of EVs that are in the grid-connected state during period t.

[0103] P t c and P t d Respectively represent the charging power and discharging power of the virtual electric vehicle in time period t. The charging / discharging power of VEV ([P t c ,P t d ]) can be the decision variable for the optimization problem in the subsequent power supply guarantee strategy.

[0104] Step S103, constructing an electricity consumption target model based on the virtual electric vehicle model, specifically includes the following steps:

[0105] Step S1031: construct an objective function based on the virtual electric vehicle model, and calculate as follows:

[0106]

[0107] P t solar =S solar ·R t ·τ·[1-0.005·(T t out -25)] (17);

[0108] Where t1 and t2 represent the start time and end time of the power outage in the regional power grid, respectively. t lost represents the load loss (kWh) of the regional power grid in time period t, calculated using (16), T represents the dispatch period, and P t solar is the photovoltaic power generation power in time period t, calculated using (17), S solar Represents the photovoltaic panel area, R t represents the light intensity, τ represents the power generation efficiency, T t out Indicates the ambient temperature, P t com,re represents the flexible load of the regional power grid, is the decision variable, P t com,un It indicates the rigid load of the regional power grid, which is an unadjustable load.

[0109] Step S1033: Construct a constraint function based on the objective function. The constraint function includes: charge and discharge operation constraint, flexible compliance adjustment constraint, and power flow reverse transmission constraint. The calculation is as follows:

[0110] Charge and discharge operation constraints:

[0111] The boundary limits of charging power and discharging power are as follows:

[0112] Charge and discharge mutual exclusion constraint, the formula is: P t c +|P t d |≤max{P t c ,|P t d |} (19);

[0113] The change relationship constraint of the energy accumulation level of virtual electric vehicles in adjacent time periods is as follows:

[0114]

[0115] Where, represents the energy accumulation level of the virtual electric vehicle in time period t;

[0116] The energy accumulation constraint of the virtual electric vehicle during the charging and discharging optimization process is as follows:

[0117]

[0118] Virtual electric vehicle charging and discharging optimization control in the time interval [T end ,T next ] has controllable conditions, the formula is:

[0119] The charging and discharging power limits of virtual electric vehicles during regional power grid outages are calculated as follows:

[0120]

[0121] During the non-blackout period, the power change of the virtual electric vehicle in adjacent time periods is as follows:

[0122]

[0123] Flexible load adjustment constraints:

[0124] The adjustable range of regional power grid flexible load in each period is as follows:

[0125] P t com,min ≤P t com,re ≤P t com,max (25);

[0126] The total power consumption of the optimized regional power grid does not exceed its expected total power consumption, and the formula is:

[0127]

[0128] Where, P t com,lo It represents the expected flexible power load of the regional power grid without power outage;

[0129] Load reduction limit, the formula is:

[0130] Where, φ represents the load reduction threshold;

[0131] The power flow backflow constraint is as follows:

[0132] P t solar +P t d ≤P t com,re +P t com,un +P t c (28).

[0133] After determining the overall dispatch power demand of a VEV cluster, it must be further allocated to each individual EV within it to meet actual control needs and ensure the enforceability of the dispatch strategy. To this end, a priority-based VEV power dynamic allocation strategy measures the charging and discharging urgency of each EV by defining a charging slack index and breaking down the charging and discharging power of each EV according to priority. This ensures that VEV power allocation is completed quickly and efficiently while meeting EV charging needs.

[0134] In the planned power outage scenario, the VEV response power at time t can be divided into the charging power P t c and discharge

[0135] Power P t d Before a power outage, VEVs need to enter the charging stage in advance to reserve electricity and ensure power supply during the outage; during the power outage, VEVs enter the discharging stage to ensure regional load power supply and maintain the stability of the power grid; after the power outage is restored, VEVs need to enter the charging stage again to meet users' normal travel needs and restore the EV to the power level required for normal travel.

[0136] Step S105: Constructing a power consumption control strategy model based on the power consumption target model, priority, charging power, and discharging power, specifically including the following steps:

[0137] Step S1051: If the response power of the virtual electric vehicle is the charging power, a charging slack index is defined, and the charging slack of all electric vehicles that need to be charged at time t is calculated based on the charging slack index:

[0138]

[0139] Step S1052: Initialize the remaining allocatable power so that P t s =P t c .

[0140] Step S1053: Sort by charging slack index from small to large to obtain a first priority queue.

[0141] Step S1054: Allocate charging power to the electric vehicle with the highest charging priority based on the first priority queue:

[0142]

[0143] Charging has the highest priority, Minimum.

[0144] Step S1055: Update the remaining allocable charging power:

[0145] P t s =P t s -p i,t (31);

[0146] Step S1056: Repeat the allocation of charging power to the electric vehicle i with the highest charging priority based on the priority queue and update the remaining allocable charging power for the remaining electric vehicles in descending order of charging priority, until all electric vehicles at time t participate in the decomposition process or the remaining allocable power is updated, so that P t s =0, the charging power allocation process at this moment ends.

[0147] Step S1057: If the virtual electric vehicle response power is the discharge power, a discharge slack index is defined, and the discharge slack of all electric vehicles that need to be discharged at the time is calculated based on the discharge slack index:

[0148]

[0149] Step S1058: Initialize the remaining allocatable power so that P t s =P t d .

[0150] Step S1059: Sort by discharge relaxation index from large to small to obtain a second priority queue.

[0151] Step S1060: Allocate discharge power to the electric vehicle with the highest discharge priority based on the second priority queue:

[0152]

[0153] Among them, discharge priority is the highest, that is, maximum.

[0154] Step S1061: Update the remaining distributable discharge power so that P t s =P t s -p i,t (34).

[0155] Step S1062: Repeat the process of allocating discharge power to the electric vehicle with the highest priority according to the second priority queue and updating the remaining allocable discharge power for the remaining electric vehicles according to the second priority queue from high to low, until all electric vehicles at time t participate in the decomposition process or the remaining allocable power is sufficient, so that P t s =0, the discharge power distribution process at this moment ends.

[0156] The present invention aims at the planned power outage scenario, and the fast charging and discharging power supply guarantee strategy based on VEV can improve the power supply reliability of the regional power grid. This strategy can achieve energy self-balancing operation of the regional power grid in the case of planned power outage through the coordinated optimization of the flexible charging and discharging adjustment capability of VEV and the flexible load adjustment. Taking into account the charging and discharging operation constraints, the power flow backflow constraints and the flexible load adjustment constraints, a fast charging and discharging power supply guarantee strategy based on VEV is constructed with the goal of minimizing the amount of unserved load during the power outage. Among them, this strategy takes into account the power flow backflow constraints, which can ensure that there will be no excess photovoltaic power generation or electric vehicle discharge power backflow during the operation of the regional power grid, thereby ensuring the safety of maintenance personnel. Verification results show that the flexible and adjustable capability of VEV can effectively alleviate the adverse effects of power outages on users' electricity consumption. In addition, compared with the traditional centralized optimization of the charging and discharging behavior of a large number of single electric vehicles, the optimization strategy based on VEV can effectively reduce the computational complexity, making the computational burden of the model insensitive to changes in the number of electric vehicles, thereby shortening the solution time and improving the engineering application value. At the same time, the present invention proposes a priority-based VEV response power rapid allocation strategy, which determines the priority of electric vehicle charging and discharging and dynamically allocates the remaining power to ensure rapid response capability.

[0157] The present invention selects a regional power grid with 100 households for simulation verification. Based on the real residents' energy consumption data recorded by PecanStreetDataset, the rigid and flexible power load curves of the regional power grid are generated, such as Figure 2 The simulation time is set to one day, starting at 8am. Based on the weather data released by the Hawaii Energy Research Center, the solar intensity and ambient temperature data of the regional power grid are predicted for the next day, as shown in Figure 2. Figure 3 As shown in Figure 1, the photovoltaic panel area is 300 m2, and the power generation efficiency is 0.164. The planned power outage duration is 10 hours, with a time range of [13 pm - 23 pm] and a time interval Δt of 5 minutes.

[0158] Assume that the regional power grid contains 100 EVs with V2G capabilities. For ease of simulation, assume that the rated charging and discharging powers of the EVs are 6.6kW and -6.6kW respectively, the charging and discharging efficiencies are both 0.95, and the rated battery capacity is 35kWh. SoC i min and SoC i max To simulate the EV's on / off-grid process, assume that the remaining power SoC when the EV returns is i des Obeying N(0.6,0.1), the expected power of the EV when leaving obeys U[0.8,0.9].

[0159] The method of the present invention is to use the VEV model in the regional power grid power supply problem. Figure 4 The total power load curve of the regional power grid before and after the optimized scheduling is given. The total power load is the time series algebraic sum of the flexible and rigid power loads. The gray shaded area represents the power outage period (13pm-23pm). It can be found that in order to alleviate the impact of the power supply gap caused by the power outage, part of the flexible power load during the power outage period is shifted to other non-power outage periods, such as Figure 4 As shown in 8am-13pm and 23pm-4am, the electricity demand of the entire area during the power outage is reduced, and the remaining electricity load demand will be met by local photovoltaic power generation and VEV discharge. Figure 5 The study further presents a comparison of power supply and demand in the regional power grid during the outage. The results demonstrate that, based on the local PV generation output, by scheduling VEV discharge in an orderly manner, the combined power output of both just meets the remaining power load demand of the regional power grid, achieving a precise balance between supply and demand. Throughout the outage, the power supply never exceeded the actual load demand, ensuring system power balance while effectively avoiding power flow backflow caused by excess PV output or EV discharge.

[0160] like Figure 6、 Figure 7 As shown in the figure, each EV starts charging before 13:00 pm to prepare for energy storage during the power outage; the SOC shows a downward trend from 13:00 pm to 23:00 pm, reflecting that the EV is in the discharge phase and providing support to the grid; after 23:00 pm, the SOC of each EV rises rapidly and enters the charging phase again to ensure the normal travel needs of users. In addition, 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 the operating constraints during the charging and discharging process, avoiding overcharging or over-discharging. This dynamic power allocation strategy effectively ensures the safety and reliability of vehicle operation, while supporting grid operation by rationally allocating power, reflecting the optimization effect of VEV response scheduling.

[0161] 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, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of the mathematical modeling methods for the orderly control strategy for power supply guarantee of electric vehicles in a substation cluster.

[0162] The present invention also provides a storage medium storing a computer program, which, when executed by a processor, implements any one of the mathematical modeling methods for the orderly control strategy of power supply guarantee for cluster electric vehicles in a substation.

[0163] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may 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), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM). The various embodiments in this specification are described in a progressive manner, and similar parts between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the apparatus, device, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referenced to the partial description of the method embodiments.

[0164] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention herein is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features having similar functions disclosed in this application.

Claims

1. A control strategy for ensuring orderly power supply of electric vehicles in clusters in a substation area, characterized by: include: Construct a virtual electric vehicle model based on the electric vehicle cluster in the substation area; Building an electricity consumption target model based on the virtual electric vehicle model; A power consumption control strategy model is constructed based on the power consumption target model, priority, charging power and discharging power.

2. The orderly control strategy for ensuring power supply to cluster electric vehicles in a substation according to claim 1 is characterized in that: Constructing a virtual electric vehicle model based on the electric vehicle cluster in the substation area includes: Determining a single-cell energy boundary and a single-cell power adjustable boundary of a single electric vehicle based on power usage parameters, wherein the power usage parameters include: a current battery state of charge of the electric vehicle, a maximum allowable battery state of charge, a minimum allowable battery state of charge, and a user-specified battery charge requirement for the next trip; Based on whether the remaining power and time of a single electric vehicle when returning meet the controllable conditions, the electric vehicle clusters in the substation are divided to obtain a classification model; Constructing a classification energy adjustable boundary and a classification charge / discharge power adjustable boundary based on the single cell energy boundary, the single cell power adjustable boundary and the classification model; The virtual electric vehicle model is constructed based on the classified energy adjustable boundary and the classified charge / discharge power adjustable boundary.

3. The orderly control strategy for ensuring power supply to cluster electric vehicles in a substation according to claim 2 is characterized in that: The individual energy boundary and individual power adjustable boundary of an electric vehicle are determined based on the power consumption parameters, specifically including: The cell energy boundary is determined based on the power usage parameter. The cell energy boundary is characterized by the maximum chargeable capacity of the battery and the minimum dischargeable capacity of the battery. The calculation formula is as follows: in i max =(SoC i max -SoC i end )×Q i ; Where, e i max Indicates the maximum chargeable capacity of the battery, SoC i max represents the maximum battery state of charge allowed for the i-th electric vehicle, Q i represents the rated battery capacity of the i-th electric vehicle; in i min =(SoC i min -SoC i end )×Q i ; Where, e i min Indicates the minimum discharge capacity, SoC i min The minimum battery state of charge allowed for the i-th electric vehicle, SoC i end represents the remaining battery charge state of the i-th electric vehicle when it returns to the starting point; The power adjustment limit of the single cell is determined based on the power consumption parameter. The power adjustment limit of the single cell is characterized by the power demand specified by the user. The calculation formula is as follows: in i need =(SoC i next -SoC i end )×Q i ; Where, e i need Indicates user-specified power requirements, SoC i next It represents the battery state of charge of the i-th electric vehicle when it leaves the next trip.

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

5. The orderly control strategy for ensuring power supply to cluster electric vehicles in a substation according to claim 4 is characterized in that: Constructing a classification energy adjustable boundary and a classification charge / discharge power adjustable boundary based on the single cell energy boundary, the single cell power adjustable boundary, and the classification model specifically includes: 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 classified charge / discharge power adjustable boundary are constructed, and the calculation formula is as follows: Where, represents the charging energy boundary of the i-th electric vehicle, represents the discharge energy boundary of the i-th electric vehicle, t represents the current time, t end,i represents the return time of the i-th electric vehicle, t next,i represents the departure time of the i-th electric car, e i,t-1 max+(-) represents the maximum energy increase and decrease that electric vehicle i can charge at time t-1, p i c represents the charging power of electric vehicle i; Represents charging efficiency; Δt represents unit time interval; The calculation formula for the first type of charge / discharge power adjustable boundary is as follows: Where, represents the charging power boundary of the i-th electric vehicle, represents the discharge power boundary of the i-th electric vehicle, represents the rated charging power of the i-th 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 classified charge / discharge power adjustable boundary are constructed, and the calculation formula is as follows: Where, e i,t-1 max+ represents the maximum energy capacity of the i-th electric vehicle; The calculation formula for the discharge energy boundary of the second type of energy adjustable boundary is as follows: Where, e i,t-1 max- represents the minimum energy capacity of the i-th electric vehicle, represents the rated discharge power of the i-th electric vehicle, represents the discharge efficiency of the i-th electric vehicle; The calculation formula for the second type of charging power adjustable limit is as follows: The calculation formula for the adjustable boundary of the second type of discharge power is as follows: A third type of energy adjustable boundary and classified charge / discharge power adjustable boundary are constructed based on the time sequence, the first type of energy adjustable boundary and classified charge / discharge power adjustable boundary and the second type of energy adjustable boundary and classified charge / discharge power.

6. The orderly control strategy for ensuring power supply to cluster electric vehicles in a substation according to claim 4 is characterized in that: Constructing the virtual electric vehicle model based on the classified energy adjustable boundary and the classified charge / discharge power adjustable boundary specifically includes: The total energy range is obtained by summing the energy adjustable boundaries of all single electric vehicles in time series; The total power range is obtained by summing the power adjustable boundaries of all individual electric vehicles in time series; constructing the virtual electric vehicle model 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 is calculated as follows: T end and T next They represent the earliest time and latest time for virtual electric vehicles to perform charging and discharging scheduling: Where, Ω 1 represents the first type of model, Ω 2 represents the second type of model, Ω 3 represents the third type of model; and They represent the upper and lower bounds of the energy adjustable in time period t, respectively, and are calculated as follows: and They represent the charging power adjustable boundary and discharging power adjustable boundary of the virtual electric vehicle in time period t, respectively, and are calculated as follows: and They represent the charging loss coefficient and discharging loss coefficient of the virtual electric vehicle, respectively, and are calculated as follows: Where, represents the access status of the i-th electric vehicle in time period t, and Z represents the total number of electric vehicles in the regional power grid; P t c and P t d They represent the charging power and discharging power of the virtual electric vehicle in time period t respectively.

7. The orderly control strategy for ensuring power supply to cluster electric vehicles in a substation according to claim 6 is characterized in that: Building an electricity consumption target model based on the virtual electric vehicle model specifically includes: The objective function is constructed based on the virtual electric vehicle model and is calculated as follows: P t solar =S solar ·R t ·τ·[1-0.005·(T t out -25)]; Where t1 and t2 represent the start time and end time of the power outage in the regional power grid, respectively. t lost represents the load loss of the regional power grid in time period t, T represents the dispatch period, and P t solar is the photovoltaic power generation power in period t, S solar Represents the photovoltaic panel area, R t represents the light intensity, τ represents the power generation efficiency, T t out Indicates the ambient temperature, P t com,re represents the flexible load of the regional power grid, P t com,un Indicates the rigid load of the regional power grid; A constraint function is constructed based on the objective function. The constraint function includes: charge and discharge operation constraint, flexible compliance adjustment constraint, and power flow reverse transmission constraint. The calculation is as follows: Charge and discharge operation constraints: The boundary limits of charging power and discharging power are as follows: Charge and discharge mutual exclusion constraint, the formula is: P t c +|P t d |≤max{P t c ,|P t d |}; The change relationship constraint of the energy accumulation level of virtual electric vehicles in adjacent time periods is as follows: Where, represents the energy accumulation level of the virtual electric vehicle in time period t; The energy accumulation constraint of the virtual electric vehicle during the charging and discharging optimization process is as follows: Virtual electric vehicle charging and discharging optimization control in the time interval [T end ,T next ] has controllable conditions, the formula is: The charging and discharging power limits of virtual electric vehicles during regional power grid outages are calculated as follows: During the non-blackout period, the power change of the virtual electric vehicle in adjacent time periods is as follows: Flexible load adjustment constraint: The adjustable range of the regional power grid flexible load in each period, the formula is: P t com,min ≤P t com,re ≤P t com,max ; The total power consumption of the optimized regional power grid does not exceed its expected total power consumption, and the formula is: Where, P t com,lo It represents the expected flexible power load of the regional power grid without power outage; Load reduction limit, the formula is: Where, φ represents the load reduction threshold; The power flow backflow constraint is as follows:

8. The orderly control strategy for ensuring power supply to cluster electric vehicles in a substation according to claim 7 is characterized in that: The power consumption control strategy model is constructed based on the power consumption target model, priority, charging power and discharging power, specifically including: If the response power of the virtual electric vehicle is the charging power, a charging slack index is defined, and the charging slack of all electric vehicles that need to be charged is calculated based on the charging slack index: Initialize the remaining allocatable power so that P t s =P t c ; Sort by charging slack index from small to large to get 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 allocatable charging power: P t s =P t s -p i,t ; For the remaining electric vehicles, allocating charging power to the electric vehicle with the highest charging priority based on the first priority queue, and updating the remaining allocable charging power; All electric vehicles until time t participate in the decomposition process or have remaining distributable power, so that P t s =0, then the charging power allocation process at that moment ends; If the virtual electric vehicle response power is the discharge power, a discharge slack index is defined, and the discharge slack of all electric vehicles that need to be discharged is calculated based on the discharge slack index: Initialize the remaining allocatable power so that P t s =P t d ; Sort by discharge relaxation index from large to small to obtain the second priority queue; Based on the second priority queue, the discharge power is allocated to the electric vehicle with the highest discharge priority: Update the remaining distributable discharge power so that P t s =P t s -p i,t ; For the remaining electric vehicles, discharging power is allocated to the electric vehicle with the highest discharge priority based on the second priority queue, and the remaining discharging power that can be allocated is updated; All electric vehicles until time t participate in the decomposition process or have remaining distributable power, so that P t s =0, the discharge power distribution process at this moment ends.

9. An electronic device, characterized in that: include: at least one processor; a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the orderly power supply control strategy for cluster electric vehicles in the substation as described in any one of claims 1 to 8.

10. A medium, characterized in that A computer program is stored, and when the computer program is executed by a processor, the orderly control strategy for ensuring power supply to cluster electric vehicles in a substation according to any one of claims 1 to 8 is implemented.

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