Distribution network area emergency supply insurance method and system based on vehicle network interaction

Abstract

The invention discloses a distribution network area emergency insurance supply method and system based on vehicle network interaction, and relates to the technical field of distribution network area emergency insurance supply, and the method comprises the following steps: obtaining a distribution network area fault position and electric vehicle distribution data, and constructing a vehicle scheduling instruction set; driving the electric vehicle to move to a target area according to the scheduling instruction set, and transmitting magnetic field disturbance signals of different frequency bands through a vehicle-mounted coil array to obtain a magnetic field disturbance signal set; constructing a dynamic sensing matrix for each magnetic field disturbance signal, and performing compression reconstruction by adopting an orthogonal matching pursuit algorithm to obtain a line impedance feature vector group; constructing a security constraint set based on the line impedance feature vector group, and respectively applying the security constraint set to a multi-objective optimization algorithm to solve the optimal capacity calling proportion of the electric vehicle battery pack; and controlling the electric vehicle to output reverse power to the distribution network area based on the optimal capacity calling proportion, thereby solving the problem that power supply is difficult to optimize due to the fact that difference between multi-vehicle cooperation and a power line is not considered in a traditional method.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency power supply guarantee for distribution network substations, and more specifically, to a method and system for emergency power supply guarantee for distribution network substations based on vehicle-grid interaction. Background Art

[0002] With the rapid development of new energy and smart grid technologies, distribution networks are increasingly demanding greater reliability and flexibility in their power supply. Traditional distribution networks typically rely on distribution automation systems for isolation and restoration when faults occur. However, due to insufficient power redundancy and limited load transfer capabilities, power outages, long recovery times, and poor user experience can still occur.

[0003] In recent years, the potential of electric vehicles (EVs) to interact with the power grid as distributed mobile energy storage resources has gradually attracted attention. Vehicle-to-grid interaction technologies enable EVs to not only draw energy from the grid but also, under certain conditions, transfer stored energy back to the grid for peak shaving and valley shifting, emergency support, and other applications. However, existing V2G technologies are primarily designed for orderly charging and discharging scheduling in static environments. They lack dynamic scheduling and intelligent coordination mechanisms for emergency scenarios in local distribution substations, making them difficult to meet the practical needs of rapid response and precise control.

[0004] For example, the invention patent with announcement number CN119382132B discloses a method, system and medium for emergency power supply in distribution areas based on spatiotemporal matching of vehicle networks, which involves the field of emergency power supply technology. This solution improves the traditional emergency power supply technology for distribution areas by analyzing environmental meteorological data and electrical operation characteristics of distribution areas under different types of extreme weather conditions, and calculating the emergency power supply needs of distribution areas. It combines the trajectory, discharge and other characteristics of electric vehicles to perform multi-perspective profiling, conducts spatiotemporal matching analysis between electric vehicle discharge and emergency power supply in distribution areas, and realizes emergency power supply regulation in distribution areas under the constraints of electric vehicle discharge. At the same time, through the widely distributed vehicle-network interactive access points of electric vehicles, it realizes the power supply needs of any location in the distribution area, and realizes the rational planning and effective utilization of various electric vehicle resources.

[0005] The above disclosed technical solutions have at least the following technical problems: Traditional methods for ensuring power supply in substations fail to consider the local information of multiple electric vehicles, such as the capacity of their battery packs, when allocating power. These methods also fail to fully account for the coordination between multiple vehicles and the differentiated carrying capacity of each line in the power grid. This can lead to issues such as overloading of some vehicles, redundant power on some lines, or overload at bottlenecks during emergency power supply, making it difficult to achieve optimal operation of the overall system. This present invention proposes a solution to these problems. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a distribution network substation emergency power supply method and system based on vehicle-grid interaction. By solving the optimal capacity call ratio of electric vehicle battery packs and applying it to the substation emergency power supply, the problem that traditional methods do not consider the differences between multi-vehicle collaboration and power lines, resulting in difficulty in optimizing power supply, is solved.

[0007] To achieve the above object, the present invention provides the following technical solutions: The method for emergency power supply guarantee of distribution network substation based on vehicle-grid interaction includes the following steps: obtaining fault location and electric vehicle distribution data of distribution network substation, and constructing a vehicle dispatch instruction set based on preset grid topology constraints; driving the electric vehicle to move to the target area according to the dispatch instruction set, and transmitting magnetic field disturbance signals of different frequency bands through the vehicle-mounted coil array to obtain a magnetic field disturbance signal set; constructing a dynamic perception matrix for each magnetic field disturbance signal, and using an orthogonal matching pursuit algorithm to compress and reconstruct it to obtain a line impedance eigenvector group; constructing a safety constraint set based on the line impedance eigenvector group, and applying them to a multi-objective optimization algorithm to solve the optimal capacity call ratio of the electric vehicle battery pack; and controlling the reverse power output of the electric vehicle to the distribution network substation based on the optimal capacity call ratio.

[0008] In a preferred embodiment, the method of obtaining the fault location and electric vehicle distribution data of the distribution network substation and constructing a vehicle dispatch instruction set based on the preset grid topology constraints is as follows: collecting the voltage drop waveform of the fault area through the distribution network automation terminal, and extracting the fault phase angle to construct a fault feature vector; locating the boundary of the isolation domain upstream of the fault based on the fault feature vector, and obtaining the real-time location and remaining battery capacity data of the electric vehicles within the boundary to obtain a vehicle distribution heat map; constructing a feasible path set based on the preset grid topology constraints and according to the vehicle distribution heat map; and dynamically optimizing the feasible path set using the ant colony algorithm to output a vehicle dispatch instruction set.

[0009] In a preferred embodiment, the method of constructing a feasible path set based on the preset grid topology constraints and according to the vehicle distribution heat map is as follows: analyzing the segmented switch status and protection action sequence in the preset grid topology constraints, determining the boundary of the upstream power supply island of the fault, and calculating the first travel delay matrix from the electric vehicle to the boundary of the upstream power supply island of the fault; based on the coordinate density in the vehicle distribution heat map, obtaining the travel probability matrix according to the preset road condition prediction model; based on the travel probability matrix, extracting the predicted travel delay percentile according to the preset confidence level, and constructing the predicted travel delay interval; performing weighted fusion on the first travel delay matrix and the predicted travel delay interval to obtain the second travel delay matrix, and outputting the feasible path set in combination with the electrical access constraints of the grid.

[0010] In a preferred embodiment, the electric vehicle is driven to move to a target area according to a scheduling instruction set, and magnetic field disturbance signals of different frequency bands are emitted through an on-board coil array to obtain a magnetic field disturbance signal set. Specifically, the vehicle scheduling instruction set is sent to the on-board control system of the target electric vehicle, triggering the automatic driving module to move according to the planned path; when the vehicle arrives at the target area, the circular on-board coil array is activated and a magnetic field disturbance signal sequence is generated according to a preset distribution network line frequency response characteristic library; the electromagnetic environment noise spectrum during the signal transmission process is monitored in real time, and the driving parameters of the coil array are dynamically adjusted using an adaptive disturbance control algorithm to generate the magnetic field disturbance signal set.

[0011] In a preferred embodiment, the adaptive disturbance control algorithm is used to dynamically adjust the driving parameters of the coil array to generate a magnetic field disturbance signal set, specifically: Fourier transform is performed on the electromagnetic environment noise spectrum, the interference frequency band and amplitude are extracted and an interference spectrum feature set is constructed; the interference spectrum feature set is used as a constraint condition, and the frequency domain analysis of the magnetic field disturbance signal sequence is performed to obtain the disturbance signal spectrum structure; A disturbance signal emission model is constructed based on the spectral structure of the disturbance signal, and the minimum cross-correlation criterion is used to establish the optimization objective function. The optimal phase difference vector and drive amplitude vector of each coil in the drive coil array are iteratively solved. The solution is sent to each coil drive channel to generate a magnetic field disturbance signal set.

[0012] In a preferred embodiment, a dynamic perception matrix is ​​constructed for each magnetic field disturbance signal, and an orthogonal matching pursuit algorithm is used to perform compression and reconstruction to obtain a line impedance characteristic vector group. Specifically, for the time domain waveform of each magnetic field disturbance signal, the frequency band energy distribution is extracted by wavelet packet decomposition to construct a dynamic perception matrix; the dynamic perception matrix is ​​used as a sparse perception basis, and a first optimization model is constructed; the orthogonal matching pursuit algorithm is used to iteratively solve the first optimization model to obtain a line impedance characteristic vector group.

[0013] In a preferred embodiment, the construction of a safety constraint set based on the line impedance characteristic vector group is specifically as follows: characteristic decomposition is performed on the impedance characteristic vector to obtain a number of characteristic components, wherein the characteristic components are specifically resistance component, inductive reactance component and capacitive reactance component; the resistance component is input into a preset line temperature rise model to construct a thermally stable current vector; based on the capacitive reactance component, the resonant frequency of each line is calculated, and a frequency avoidance interval is constructed based on the resonant frequency; the thermally stable current vector is input into a preset voltage fluctuation sensitivity function to obtain a safety boundary tensor; the safety boundary tensor is deconstructed into an inequality constraint expression, and the safety constraint set is constructed in combination with the frequency avoidance interval.

[0014] In a preferred embodiment, the multi-objective optimization algorithm is applied to solve the optimal capacity call ratio of the electric vehicle battery pack, specifically: constructing a Pareto optimization model and limiting the solution space based on the safety constraint set; using the improved NSGA-III algorithm to initialize the population, and the individual genes of the population are the capacity call ratios of different electric vehicles; using the entropy weight-TOPSIS decision model to select the optimal capacity call ratio from the Pareto frontier solution set of the Pareto optimization model.

[0015] In a preferred embodiment, the reverse power output of electric vehicles to the distribution network substation is controlled based on the optimal capacity call ratio, specifically: analyzing the optimal capacity call ratio vector, and proportionally allocating the output power command value to each electric vehicle battery pack; starting the droop control module of the on-board bidirectional converter based on the output power command value, and adjusting the reactive power compensation amount in real time according to the distribution network bus voltage; continuously monitoring the line impedance changes during the power supply process, and dynamically adjusting the capacity call ratio when the safety constraint set is triggered to exceed the limit.

[0016] The technical effects and advantages of the distribution network area emergency supply guarantee method and system based on vehicle-grid interaction of the present invention are as follows: 1. This invention obtains information about fault locations, electric vehicle distribution, and grid topology, constructs a dispatching instruction set to guide electric vehicles to target areas, and uses onboard magnetic field disturbances to sense the state of grid lines, thereby reconstructing line impedance characteristics and establishing a safety constraint set for grid operation. On this basis, a multi-objective optimization algorithm is used to determine the optimal capacity utilization ratio for electric vehicle battery packs, thereby enabling coordinated reverse power output across multiple vehicles. This effectively addresses the issue of traditional emergency power supply methods that fail to consider the synergy between electric vehicles and the differences in distribution network lines, avoids local overloads or redundant power supply, improves the accuracy and efficiency of emergency response, and significantly enhances power supply reliability and the adaptive capabilities of grid operation in the event of substation faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a flow chart of the method for emergency power supply guarantee of distribution network substation based on vehicle-grid interaction of the present invention.

[0018] Figure 2 It is a structural diagram of the distribution network substation emergency supply system based on vehicle-grid interaction of the present invention. DETAILED DESCRIPTION

[0019] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] Example 1, Figure 1 The present invention provides a distribution network area emergency power supply method based on vehicle-grid interaction, which includes the following steps: S1, obtains the fault location and electric vehicle distribution data of the distribution network area, and builds a vehicle dispatch instruction set based on the preset grid topology constraints; In this example, the fault location and electric vehicle distribution data of the distribution network area are obtained, and a vehicle dispatch instruction set is constructed based on the preset grid topology constraints. Specifically, The voltage drop waveform in the fault area is collected through the distribution network automation terminal, and the fault phase angle is extracted to construct the fault feature vector; Based on the fault feature vector, the upstream isolation domain boundary of the fault is located, and the real-time location and remaining battery capacity data of electric vehicles within the boundary are obtained to obtain a vehicle distribution heat map; Construct a set of feasible paths based on preset grid topology constraints and vehicle distribution heat maps; The ant colony algorithm is used to dynamically optimize the feasible path set and output the vehicle scheduling instruction set.

[0021] It should be noted that distribution automation terminals (such as FTUs and DTUs) collect voltage drop waveforms in real time within the faulted area of ​​the distribution network. Using digital signal processing methods (such as Hilbert transforms or synchronized phasor calculations), the fault phase angle from the drop waveform is extracted to construct a fault feature vector representing the fault direction and nature. This fault feature vector is input into a pre-defined distribution network topology analysis module. Combined with real-time network status (including switch status and protection actions), the upstream isolation domain boundary of the fault point is determined, i.e., the area of ​​the power supply island where the fault could cause a power outage. This area is used to define the service boundary of dispatchable vehicles. Within this boundary area, the real-time location information (latitude and longitude) and remaining battery capacity (SOC) of electric vehicles currently in standby mode are obtained using V2G platforms, vehicle-to-vehicle systems, or third-party dispatch platforms. Spatial thermal analysis methods (such as kernel density estimation) are used on the collected data to generate a vehicle distribution heat map, which is used to quantify the available vehicle density and resource abundance in a specific area.

[0022] In this example, a set of feasible paths is constructed based on the preset grid topology constraints and the vehicle distribution heat map, specifically: Analyze the segmented switch states and protection action sequences in the preset grid topology constraints, determine the boundary of the upstream power supply island, and calculate the first pass delay matrix from the electric vehicle to the boundary of the upstream power supply island; Based on the coordinate density in the vehicle distribution heat map, the traffic probability matrix is ​​obtained according to the preset road condition prediction model; Based on the travel probability matrix, the predicted travel delay quantiles are extracted according to the preset confidence level to construct the predicted travel delay interval; The first travel delay matrix and the predicted travel delay interval are weightedly fused to obtain the second travel delay matrix, and the feasible path set is output in combination with the electrical access constraints of the power grid.

[0023] It should be noted that based on the preset distribution network topology, the connection relationships and access paths between each power supply node within the fault isolation domain are extracted. Taking into account the vehicle's current location, target node, and path accessibility, combined with the vehicle distribution heat map, a set of feasible paths for vehicle scheduling is generated. An improved ant colony optimization algorithm is introduced to dynamically optimize this set of feasible paths. Optimization objectives include, but are not limited to, shortest path time, path accessibility, maximizing battery available capacity, and swarm deployment efficiency. Through multiple rounds of iterative calculations, a set of optimal paths and corresponding vehicle scheduling instructions are output.

[0024] The resulting dispatch instruction set includes the target vehicle ID, target location (geographic coordinates), route point set, estimated arrival time window, and estimated power usage. This instruction set is sent to the target vehicle's onboard control terminal via the vehicle-network interaction platform for subsequent automated driving and power support tasks.

[0025] S2, drives the electric vehicle to move to the target area according to the scheduling instruction set, and transmits magnetic field disturbance signals of different frequency bands through the vehicle-mounted coil array to obtain a magnetic field disturbance signal set; In this example, the electric vehicle is driven to move to the target area according to the scheduling instruction set, and the on-board coil array transmits magnetic field disturbance signals of different frequency bands to obtain a magnetic field disturbance signal set, specifically: Send the vehicle dispatch instruction set to the target electric vehicle's onboard control system, triggering the autonomous driving module to move according to the planned path; When the vehicle reaches the target area, the ring-shaped on-board coil array is activated and a magnetic field disturbance signal sequence is generated based on the preset distribution network line frequency response characteristic library; The electromagnetic environment noise spectrum during signal transmission is monitored in real time, and an adaptive disturbance control algorithm is used to dynamically adjust the driving parameters of the coil array to generate a magnetic field disturbance signal set.

[0026] It should be noted that after receiving the dispatch instruction set from the vehicle dispatch module, the target electric vehicle receives the relevant information through the on-board communication system. After interpreting the instructions, the on-board control system activates the autonomous driving module, executing autonomous movement along the planned path. Path navigation combines high-precision positioning systems (such as RTK GPS and IMU inertial navigation) with a road condition perception module to achieve precise positioning and dynamic obstacle avoidance. Once the electric vehicle reaches the designated target area (such as near a fault zone or the boundary of a power island), the system automatically switches to "detection mode." At this point, the ring-shaped on-board coil array located on the underside of the vehicle is activated. This coil array generates a magnetic field disturbance signal with controllable frequency and amplitude, ranging from hundreds of Hz to tens of kHz, to adapt to the frequency response characteristics of different distribution network lines.

[0027] The system uses a preset distribution network line frequency response characteristic library, which contains frequency response models of various typical distribution network lines derived from historical measured data and simulation modeling. Based on the current location and corresponding line type, the system matches the optimal disturbance signal sequence. This signal sequence controls the coil excitation current in a time-sequential manner, achieving magnetic field emission within a specific frequency band.

[0028] In this example, an adaptive disturbance control algorithm is used to dynamically adjust the driving parameters of the coil array to generate a magnetic field disturbance signal set, specifically: Perform Fourier transform on the electromagnetic environment noise spectrum, extract the interference frequency band and amplitude, and construct the interference spectrum feature set; Taking the interference spectrum feature set as a constraint condition, the frequency domain analysis of the magnetic field disturbance signal sequence is performed to obtain the disturbance signal spectrum structure; A disturbance signal emission model is constructed based on the spectrum structure of the disturbance signal, and the optimization objective function is established using the minimum cross-correlation criterion to iteratively solve the optimal phase difference vector and driving amplitude vector of each coil in the driving coil array. The solution results are sent to each coil drive channel to generate a magnetic field disturbance signal set.

[0029] It should be noted that the on-board electromagnetic environment sensor module is activated simultaneously to collect the surrounding background electromagnetic noise spectrum in real time during signal transmission. Fourier transform analysis is used to extract the main interference frequency bands and noise intensity. The real-time noise spectrum is input into the disturbance control algorithm, which dynamically adjusts the coil array's drive parameters, including but not limited to the excitation frequency, phase, and amplitude. Based on a set signal-to-noise ratio target, the adaptive control algorithm optimizes the energy distribution within the effective frequency band during magnetic field transmission, thereby improving the accuracy of subsequent line impedance sensing. The regulated magnetic field disturbance signal is transmitted synchronously through multiple channels of the coil array, forming a set of magnetic field disturbance signals for spatial detection. This signal set can be received by detection terminals (such as current transformers or inductive receiver modules) deployed at key nodes in the distribution network and used to infer the line electrical characteristics.

[0030] S3, construct a dynamic sensing matrix for each magnetic field disturbance signal, and use the orthogonal matching pursuit algorithm to compress and reconstruct it to obtain a line impedance feature vector group; In this example, a dynamic sensing matrix is ​​constructed for each magnetic field disturbance signal, and the orthogonal matching pursuit algorithm is used for compression and reconstruction to obtain a line impedance feature vector group, specifically: For the time domain waveform of each magnetic field disturbance signal, the frequency band energy distribution is extracted through wavelet packet decomposition to construct a dynamic perception matrix; Using the dynamic perception matrix as a sparse perception basis and constructing a first optimization model; The orthogonal matching pursuit algorithm is used to iteratively solve the first optimization model to obtain a group of line impedance characteristic vectors.

[0031] It should be noted that, assuming that the magnetic field disturbance signal received in the target area is a time domain sampling vector: ,in, is the discrete sampling sequence of the disturbance signal at time t, N is the number of sampling points (for example, N=1024), is the electromagnetic disturbance intensity value at the i-th sampling point (unit: μT), Perform wavelet packet decomposition and decompose the signal into L-layer frequency bands to obtain sub-bands, assuming that the wavelet packet energy distribution is: ,in is the energy value of the jth frequency band, which is defined as the square of the second norm of the sub-signal in this frequency band, specifically:

[0032] in, is the i-th sample point in the j-th frequency band, is the number of sampling points in frequency band j.

[0033] The frequency band energy ratio is obtained by normalizing the energy, specifically:

[0034] Where P is the frequency band energy ratio, is the frequency band energy ratio of the jth frequency band.

[0035] Among them, the dynamic perception matrix is ​​specifically:

[0036] S4, constructs a safety constraint set based on the line impedance characteristic vector group, and applies it to the multi-objective optimization algorithm to solve the optimal capacity call ratio of the electric vehicle battery pack; In this example, a safety constraint set is constructed based on the line impedance characteristic vector group, specifically: Performing eigendecomposition on the impedance eigenvector to obtain a plurality of eigencomponents, wherein the eigencomponents are specifically resistance components, inductive reactance components, and capacitive reactance components; Input the resistance component into the preset line temperature rise model to construct a thermally stable current vector; Calculate the resonant frequency of each line based on the capacitive reactance component, and construct a frequency avoidance interval based on the resonant frequency; Input the thermal stability current vector into the preset voltage fluctuation sensitivity function to obtain the safety margin tensor; The safety boundary tensor is deconstructed into an inequality constraint expression, and the safety constraint set is constructed in combination with the frequency avoidance interval.

[0037] The calculation formula for the resonant frequency of each line is as follows:

[0038] in, is the resonant frequency of the mth line, The inductance is calculated from the inductive reactance of the mth circuit. The capacitance is deduced from the capacitive reactance of the mth line.

[0039] Furthermore, safety constraints are primarily based on an in-depth analysis of the line impedance characteristics perceived by electric vehicles before they are injected into the grid, constructing a comprehensive set of safety control boundaries. Specifically, the system decomposes the impedance vector into three components: resistance, inductance, and capacitance. The resistance component is used to estimate the temperature rise of the line under different loads, thereby determining the thermal stability current range that each line can withstand. The capacitance component is used to calculate possible resonant frequencies and define the frequency range that needs to be avoided when injecting energy. Ultimately, these physical constraints are combined into a set of inequality expressions to form a complete set of safety boundaries. These serve as hard constraints on the proportion of electric vehicle battery capacity used in subsequent optimized scheduling, ensuring that the power supply process meets the safety requirements of grid operation in terms of thermal stability, power quality, and frequency safety.

[0040] In this example, the multi-objective optimization algorithm is applied to solve the optimal capacity call ratio of the electric vehicle battery pack, specifically: Construct a Pareto optimization model and constrain the solution space based on a set of safety constraints; An improved NSGA-III algorithm is used to initialize the population, wherein the individual genes of the population are the capacity call ratios of different electric vehicles; The entropy weight-TOPSIS decision model is used to select the optimal capacity call ratio from the Pareto frontier solution set of the Pareto optimization model.

[0041] For example, an objective function is constructed, wherein the objective function includes a voltage stability objective , the capacity utilization maximization goal and frequency avoidance targets Each individual is a 5-dimensional vector x, representing the capacity call ratio of each vehicle. The population size is initialized to 100. Simulated binary crossover and polynomial mutation are used for 100 iterations, and the Pareto front solution set is output. The entropy-weighted TOPSIS decision model is used to extract four non-inferior solutions from the Pareto front solution set of the Pareto optimization model, as shown in the following table:

[0042] In addition, in response to the emergency power supply needs after a distribution network failure, the system first constructed a multi-objective optimization model that simultaneously considers power supply stability, capacity utilization, and frequency security based on the remaining power, charging and discharging efficiency, and output power limit of each electric vehicle. Using the improved NSGA-III algorithm, the capacity call ratios of multiple electric vehicles were evolved to generate multiple non-inferior solutions that take into account multiple objectives. Subsequently, the entropy weight-TOPSIS method was used to weight these candidate solutions, quantifying the comprehensive performance of each solution under multiple objectives, and ultimately selecting the optimal capacity call combination. This method ensures that each electric vehicle can maximize its effectiveness when participating in emergency power supply without triggering the risk of line overload or frequency resonance, thereby achieving safe, efficient, and intelligent power supply when the distribution network is disturbed.

[0043] S5, based on the optimal capacity call ratio, controls the reverse power output of electric vehicles to the distribution network area.

[0044] In this example, the reverse power output of electric vehicles to the distribution network area is controlled based on the optimal capacity call ratio, specifically: Analyze the optimal capacity call ratio vector and distribute the output power command value to each electric vehicle battery pack in proportion; The droop control module of the on-board bidirectional converter is activated based on the output power command value, and the reactive power compensation amount is adjusted in real time according to the distribution network bus voltage; During the power supply process, the line impedance changes are continuously monitored, and the capacity call ratio is dynamically adjusted when the safety constraint set exceeds the limit.

[0045] It should be noted that based on the optimal capacity call ratio obtained in the previous stage, the system first distributes the power to be output by each electric vehicle to its battery pack in proportion and generates corresponding control instructions. After receiving the power instruction, each vehicle starts its on-board bidirectional converter and injects active and reactive power into the distribution network substation through a droop control strategy. During the injection process, the system monitors the voltage changes of the distribution network bus in real time and automatically adjusts the reactive power compensation amount according to the voltage offset to maintain grid voltage stability. At the same time, the system continuously tracks the dynamic changes of the line impedance. When it detects that a certain section of the line is close to or exceeds the preset safety threshold, it will immediately trigger the safety mechanism and readjust the capacity call ratio of each electric vehicle to avoid line overload or power quality problems, thereby achieving dynamically controllable reverse power output and steady-state safety assurance.

[0046] Example 2, Figure 2 The present invention provides a distribution network area emergency supply system based on vehicle-grid interaction, including a vehicle dispatching module, a magnetic field disturbance module, an impedance reconstruction module, an optimization decision module, and a power control module: The vehicle dispatch module is used to obtain the fault location and electric vehicle distribution data of the distribution network area, and build a vehicle dispatch instruction set based on the preset grid topology constraints; The magnetic field disturbance module is used to drive the electric vehicle to move to the target area according to the scheduling instruction set, and transmit magnetic field disturbance signals of different frequency bands through the vehicle coil array to obtain a magnetic field disturbance signal set; The impedance reconstruction module is used to construct a dynamic sensing matrix for each magnetic field disturbance signal and use the orthogonal matching pursuit algorithm to perform compression reconstruction to obtain a line impedance feature vector group; An optimization decision module is used to construct a safety constraint set based on the line impedance characteristic vector group and apply it to the multi-objective optimization algorithm to solve the optimal capacity call ratio of the electric vehicle battery pack; The power control module is used to control the reverse power output of electric vehicles to the distribution network area based on the optimal capacity call ratio.

[0047] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0048] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0049] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0050] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0051] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0052] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The distribution network area emergency supply guarantee method based on vehicle-grid interaction is characterized by: The following steps are involved: Obtain distribution network fault location and electric vehicle distribution data, and build a vehicle dispatch instruction set based on preset grid topology constraints; According to the scheduling instruction set, the electric vehicle is driven to move to the target area, and magnetic field disturbance signals of different frequency bands are emitted through the vehicle-mounted coil array to obtain a magnetic field disturbance signal set; A dynamic sensing matrix is ​​constructed for each magnetic field disturbance signal, and an orthogonal matching pursuit algorithm is used to compress and reconstruct the line impedance feature vector group. A safety constraint set is constructed based on the line impedance characteristic vector group, and is applied to the multi-objective optimization algorithm to solve the optimal capacity call ratio of the electric vehicle battery pack; The reverse power output of electric vehicles to the distribution network area is controlled based on the optimal capacity call ratio.

2. The method for ensuring emergency power supply in distribution network areas based on vehicle-grid interaction according to claim 1 is characterized in that: The method of obtaining the fault location and electric vehicle distribution data of the distribution network area and constructing a vehicle dispatch instruction set based on the preset grid topology constraints is as follows: The voltage drop waveform in the fault area is collected through the distribution network automation terminal, and the fault phase angle is extracted to construct the fault feature vector; Based on the fault feature vector, the upstream isolation domain boundary of the fault is located, and the real-time location and remaining battery capacity data of electric vehicles within the boundary are obtained to obtain a vehicle distribution heat map; Construct a set of feasible paths based on preset grid topology constraints and vehicle distribution heat maps; The ant colony algorithm is used to dynamically optimize the feasible path set and output the vehicle scheduling instruction set.

3. The method for ensuring emergency power supply in distribution network areas based on vehicle-grid interaction according to claim 2 is characterized in that: The method of constructing a feasible path set based on the preset grid topology constraints and the vehicle distribution heat map is as follows: Analyze the segmented switch states and protection action sequences in the preset grid topology constraints, determine the boundary of the upstream power supply island, and calculate the first pass delay matrix from the electric vehicle to the boundary of the upstream power supply island; Based on the coordinate density in the vehicle distribution heat map, the traffic probability matrix is ​​obtained according to the preset road condition prediction model; Based on the travel probability matrix, the predicted travel delay quantiles are extracted according to the preset confidence level to construct the predicted travel delay interval; The first travel delay matrix and the predicted travel delay interval are weightedly fused to obtain the second travel delay matrix, and the feasible path set is output in combination with the electrical access constraints of the power grid.

4. The method for ensuring emergency power supply in distribution network areas based on vehicle-grid interaction according to claim 3 is characterized in that: The electric vehicle is driven to move to the target area according to the scheduling instruction set, and magnetic field disturbance signals of different frequency bands are emitted through the vehicle-mounted coil array to obtain a magnetic field disturbance signal set, specifically: Send the vehicle dispatch instruction set to the target electric vehicle's onboard control system, triggering the autonomous driving module to move according to the planned path; When the vehicle reaches the target area, the ring-shaped on-board coil array is activated and a magnetic field disturbance signal sequence is generated based on the preset distribution network line frequency response characteristic library; The electromagnetic environment noise spectrum during signal transmission is monitored in real time, and an adaptive disturbance control algorithm is used to dynamically adjust the driving parameters of the coil array to generate a magnetic field disturbance signal set.

5. The method for ensuring emergency power supply in distribution network areas based on vehicle-grid interaction according to claim 4 is characterized in that: The adaptive disturbance control algorithm is used to dynamically adjust the driving parameters of the coil array to generate a magnetic field disturbance signal set, specifically: Perform Fourier transform on the electromagnetic environment noise spectrum, extract the interference frequency band and amplitude, and construct the interference spectrum feature set; Taking the interference spectrum feature set as a constraint condition, the frequency domain analysis of the magnetic field disturbance signal sequence is performed to obtain the disturbance signal spectrum structure; A disturbance signal emission model is constructed based on the spectrum structure of the disturbance signal, and the optimization objective function is established using the minimum cross-correlation criterion to iteratively solve the optimal phase difference vector and driving amplitude vector of each coil in the driving coil array. The solution results are sent to each coil drive channel to generate a magnetic field disturbance signal set.

6. The method for ensuring emergency power supply in distribution network areas based on vehicle-grid interaction according to claim 5 is characterized in that: The dynamic sensing matrix is ​​constructed for each magnetic field disturbance signal, and the orthogonal matching pursuit algorithm is used for compression and reconstruction to obtain a line impedance feature vector group, specifically: For the time domain waveform of each magnetic field disturbance signal, the frequency band energy distribution is extracted through wavelet packet decomposition to construct a dynamic perception matrix; Using the dynamic perception matrix as a sparse perception basis and constructing a first optimization model; The orthogonal matching pursuit algorithm is used to iteratively solve the first optimization model to obtain a group of line impedance characteristic vectors.

7. The method for ensuring emergency power supply in distribution network substations based on vehicle-grid interaction according to claim 6 is characterized in that: The safety constraint set is constructed based on the line impedance characteristic vector group, specifically: Performing eigendecomposition on the impedance eigenvector to obtain a plurality of eigencomponents, wherein the eigencomponents are specifically resistance components, inductive reactance components, and capacitive reactance components; Input the resistance component into the preset line temperature rise model to construct a thermally stable current vector; Calculate the resonant frequency of each line based on the capacitive reactance component, and construct a frequency avoidance interval based on the resonant frequency; Input the thermal stability current vector into the preset voltage fluctuation sensitivity function to obtain the safety margin tensor; The safety boundary tensor is deconstructed into an inequality constraint expression, and the safety constraint set is constructed in combination with the frequency avoidance interval.

8. The method for ensuring emergency power supply in distribution network areas based on vehicle-grid interaction according to claim 7 is characterized in that: The above methods are respectively applied in the multi-objective optimization algorithm to solve the optimal capacity call ratio of the electric vehicle battery pack, specifically: Construct a Pareto optimization model and constrain the solution space based on a set of safety constraints; An improved NSGA-III algorithm is used to initialize the population, wherein the individual genes of the population are the capacity call ratios of different electric vehicle battery packs; The entropy weight-TOPSIS decision model is used to select the optimal capacity call ratio from the Pareto frontier solution set of the Pareto optimization model.

9. The method for ensuring emergency power supply in distribution network substations based on vehicle-grid interaction according to claim 8 is characterized in that: The control of the reverse power output of the electric vehicle to the distribution network area based on the optimal capacity call ratio is specifically as follows: Analyze the optimal capacity call ratio vector and distribute the output power command value to each electric vehicle battery pack in proportion; The droop control module of the on-board bidirectional converter is activated based on the output power command value, and the reactive power compensation amount is adjusted in real time according to the distribution network bus voltage; During the power supply process, the line impedance changes are continuously monitored, and the capacity call ratio is dynamically adjusted when the safety constraint set exceeds the limit.

10. A distribution network substation emergency supply guarantee system based on vehicle-grid interaction, applied to a distribution network substation emergency supply guarantee method based on vehicle-grid interaction according to any one of claims 1 to 9, characterized in that: It includes vehicle dispatch module, magnetic field disturbance module, impedance reconstruction module, optimization decision module and power control module: The vehicle dispatch module is used to obtain the fault location and electric vehicle distribution data of the distribution network area, and build a vehicle dispatch instruction set based on the preset grid topology constraints; The magnetic field disturbance module is used to drive the electric vehicle to move to the target area according to the scheduling instruction set, and transmit magnetic field disturbance signals of different frequency bands through the vehicle coil array to obtain a magnetic field disturbance signal set; The impedance reconstruction module is used to construct a dynamic sensing matrix for each magnetic field disturbance signal and use the orthogonal matching pursuit algorithm to perform compression reconstruction to obtain a line impedance feature vector group; An optimization decision module is used to construct a safety constraint set based on the line impedance characteristic vector group and apply it to the multi-objective optimization algorithm to solve the optimal capacity call ratio of the electric vehicle battery pack; The power control module is used to control the reverse power output of electric vehicles to the distribution network area based on the optimal capacity call ratio.

Images