Power distribution network disaster damage emergency resource deployment method and system based on VQNN model, and medium

Abstract

The invention discloses a power distribution network disaster damage emergency resource deployment method and system based on a VQNN model, and a medium, and relates to the technical field of emergency resource scheduling, and the method comprises the steps: determining the fault probability of each tower of a power distribution network under a natural disaster based on basic operation data and disaster environment data; simulating a power failure diffusion path after a tower broken line fault to obtain the overall fault probability of the power distribution network under natural disasters, constructing a power distribution network vulnerability curve based on the overall fault probability, and evaluating the disaster damage load loss of the power distribution network; dynamic emergency resource deployment is carried out by taking disaster loss load loss, emergency power supply resource power balance and emergency power supply resource capacity as constraint conditions and taking the minimum scheduling cost as a target; omnibearing accurate prediction from a single device to a system, from a static state to a dynamic state and from a traditional state to AI combined quantum is realized, and a reliable data basis is provided for subsequent resource scheduling.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of emergency resource scheduling, in particular to a power distribution network disaster loss emergency resource deployment method and system based on a VQNN model and a medium. BACKGROUND

[0002] With the frequent occurrence of natural disasters such as earthquakes and mudslides, regional power infrastructure is severely damaged, threatening the safe operation of power grids and the security of people's livelihood, and the vulnerability of existing power systems under natural disasters highlights the urgency of improving the emergency power supply capacity of power distribution networks. Therefore, research on the disaster loss emergency recovery method of power distribution networks under natural disasters is an urgent problem for power grid enterprises.

[0003] A power distribution network refers to a network that receives electric energy from a power transmission network or a distributed power source and distributes electric energy to end users through power distribution lines, transformers and other facilities. With the rapid development of the power industry, the penetration rate of distributed resources such as distributed photovoltaic (PV), energy storage devices and diesel generators in power distribution networks is continuously increasing. In extreme conditions such as natural disasters, how to effectively integrate these distributed resources to achieve rapid disaster recovery of power distribution networks has become a research hotspot in the current power field.

[0004] Currently, research on the rapid recovery of damaged power distribution networks under natural disaster conditions mainly focuses on two dimensions: disaster prevention and post-disaster repair. In terms of disaster prevention, existing research mainly improves the disaster resistance of the system through reinforcement of power facilities such as towers, transformers and other structural enhancements, and network topology optimization. However, in the face of the strong destructive power of extreme disasters such as earthquakes and mudslides, such traditional transformation schemes have the limitations of high engineering cost and diminishing marginal benefit. In terms of post-disaster emergency, the conventional approach relies on centralized dispatching of emergency resources such as mobile power generation vehicles and diesel generators after disaster assessment. However, practice shows that this method lacks timeliness and accuracy in disaster site assessment, and the geographical distribution of emergency power sources does not match the disaster area well, resulting in difficulty in meeting the demand for critical load supply during power supply recovery. In summary, exploring the coordinated dispatching mechanism of distributed power (DG) resources and mobile power generation equipment will become a potential breakthrough direction for improving the emergency power supply recovery capacity under natural disaster conditions. SUMMARY

[0005] The technical problems to be solved by the present application are that the disaster condition assessment is inaccurate and the recovery is delayed in the emergency recovery of the distribution network under natural disaster conditions, and the purpose of the present application is to provide a distribution network disaster loss emergency resource deployment method, system and medium based on a VQNN model, determine the failure probability of each tower of the distribution network under natural disaster based on basic operation data and disaster environment data, simulate the power outage diffusion path after the tower line failure to obtain the overall failure probability of the distribution network under natural disaster, and based on the overall failure probability, build a distribution network vulnerability curve to assess the disaster loss load loss of the distribution network, based on the disaster loss load loss and the load gap, with the balance of disaster loss load loss and emergency power supply resource power and the emergency power supply resource capacity as constraint conditions, and the minimum scheduling cost as the target, dynamically deploy emergency resources; It realizes the all-round accurate prediction from a single device to a system, from static to dynamic, from traditional to AI+ quantum, and provides a reliable data basis for subsequent resource scheduling.

[0006] The present application is realized by the following technical solutions: The present application provides a distribution network disaster loss emergency resource deployment method based on a VQNN model, which comprises: Collecting basic operation data and disaster environment data of the distribution network; Determine the failure probability of each tower of the distribution network under natural disaster based on the basic operation data and the disaster environment data; simulate the power outage diffusion path after the tower line failure to obtain the overall failure probability of the distribution network under natural disaster, and based on the overall failure probability, build a distribution network vulnerability curve; Combined with the physical propagation process under natural disaster and the distribution network vulnerability curve, the disaster loss load loss of the distribution network is evaluated; Predict the load gap of the distribution network based on the VQNN model; Based on the disaster loss load loss and the load gap, with the balance of disaster loss load loss and emergency power supply resource power and the emergency power supply resource capacity as constraint conditions, and the minimum scheduling cost as the target, dynamically deploy emergency resources.

[0007] Further optimization scheme is that the basic operation data includes power grid basic data and operation state data of the distribution network; the power grid basic data includes: distribution network topology, line profile and distribution equipment profile; the operation state data includes: distribution network operation history record, line load data, distribution transformer load data, user historical power load and historical distributed photovoltaic power generation curve; the disaster environment data includes: disaster power outage range, debris flow condition data, earthquake condition data and meteorological data.

[0008] Further optimization scheme is that the failure probability of each tower of the distribution network under natural disaster is determined based on the basic operation data and the disaster environment data; the method comprises: Based on the FEA analysis method, the inertial force converted from the earthquake acceleration time history curve is used as the earthquake load on the tower to calculate the earthquake inertial force of the tower. F eq ; and calculate the impact force of debris flow on the tower based on hydrodynamics F df ; Based on earthquake inertia force F eq and impact force F df Analyze the maximum force acting on the tower and determine the failure probability of the tower under natural disasters based on the maximum force: ; Where ∫ represents the integral; S ( F eq ) represents the inertial force of the tower under earthquake F eq Failure probability under S ( F df ) indicates the impact force of debris flow on the tower F df Failure probability under S (collapse| F eq , F df ) represents the tower conditional failure probability obtained by the engineering simulation method FEA.

[0009] A further optimization scheme is to simulate the power outage diffusion path after the tower disconnection fault to obtain the overall failure probability of the distribution network under natural disasters, and construct the distribution network vulnerability curve based on the overall failure probability; including the following methods: Based on the fault propagation algorithm and distribution network topology, the power outage diffusion path after the tower disconnection fault under natural disasters is simulated; Based on the action logic of the distribution network protection device, the overall failure probability of the distribution network under natural disasters is obtained S DN : ; in, n DN Indicates the number of towers in the distribution network; S pole.i Indicates the distribution network i Failure probability of a tower under earthquake and debris flow; gamma Indicates the first i A broken power tower caused a distribution network failure; Based on the overall probability of failure SDN Constructing power distribution network vulnerability curve eta fail : ; Wherein, Φ() represents the cumulative function of the standard normal distribution of the power distribution network natural disaster failure probability; ln represents the natural logarithm function; psi represents the natural disaster intensity, which is F eq , in the mudslide natural disaster F df ; S DN ( mu ) represents the overall mean of the overall failure probability of the power distribution network after being constructed by multiple simulations; S DN ( sigma ) represents the variance of the overall failure probability of the power distribution network after being constructed by multiple simulations.

[0010] Further optimization scheme is to evaluate the disaster loss of the power distribution network under the combination of the physical propagation process and the power distribution network vulnerability curve; including method: Disperse the earthquake and mudslide into space-time dynamic field respectively, and obtain the natural disaster intensity combined with the power distribution network vulnerability curve; the natural disaster diffusion intensity includes the earthquake diffusion impact force and psi eq Mudslide diffusion impact force psi df ; Obtain the disaster loss of the power distribution network according to the natural disaster diffusion intensity P loss : ; ; Wherein, k represents the set of load nodes in the power distribution network; P DN.j represents the j th load node in the power distribution network; psi std represents the impact force causing the tower collapse; f () represents the intermediate function.

[0011] Further optimization scheme is to predict the load gap of the power distribution network based on the VQNN model; including method: Construct the VQNN model including quantum feature mapping part, variational quantum circuit part and joint distribution modeling part; Predict the user load of the power distribution network based on the VQNN model Pld1 and distributed photovoltaic output P pv ; according to user load P ld1 and distributed photovoltaic output P pv obtain the load gap of the power distribution network P GAP : ; wherein, max() represents the maximum function; P es represents the power provided by the energy storage of the power distribution network.

[0012] Further optimization scheme is that the joint distribution modeling part outputs the conditional probability distribution of the user load and the distributed photovoltaic output of the power distribution network, and the user load and the distributed photovoltaic output satisfy a two-dimensional Gaussian distribution, and the probability density function is: ; wherein, sigma ld1 , sigma pv respectively represent the variance of the user load and the variance of the PV output; lambda ld1 , lambda pv respectively represent the mean of the user load and the mean of the PV output; represents the correlation coefficient of the user load and the PV output; p ( P ld1 , P pv ) represents the probability density function of the user load and the distributed photovoltaic; The joint distribution modeling part also obtains the user load prediction value and the PV output prediction value based on the Pauli measurement operator.

[0013] Further optimization scheme is that the disaster load loss and the load gap are used as the constraint conditions, the disaster load loss and the emergency power supply resource power balance and the emergency power supply resource capacity are used as the constraint conditions, the minimum scheduling cost is used as the target, and the emergency resource deployment is performed; including method: With the constraint conditions of load gap ≤ power provided by emergency power supply resources, power supply duration ≤ maximum power supply duration of emergency power supply resources, and distance from emergency power supply resources to fault point of the power distribution network ≤ maximum distance that emergency power supply resources can reach, and the objective function of minimum scheduling cost, in the offline planning stage, a globally optimal emergency resource deployment scheme is generated based on the NSGA-II algorithm, and in the online adjustment stage, the PPO algorithm is used to dynamically adjust the emergency resource deployment scheme according to the real-time monitoring of natural disaster evolution data.

[0014] The scheme also provides a power distribution network disaster loss emergency resource deployment system based on a VQNN model, which is used to implement the power distribution network disaster loss emergency resource deployment method based on the VQNN model. The acquisition module is configured to acquire basic operation data and disaster environment data of the power distribution network. The first calculation module is configured to determine the fault probability of each tower of the power distribution network under natural disasters based on the basic operation data and the disaster environment data, simulate the power outage diffusion path after the tower disconnection fault to obtain the overall fault probability of the power distribution network under natural disasters, and construct a power distribution network vulnerability curve based on the overall fault probability. The evaluation module is configured to evaluate the disaster loss of the power distribution network in combination with the physical propagation process under natural disasters and the power distribution network vulnerability curve. The second calculation module is configured to predict the load gap of the power distribution network based on the VQNN model. The deployment module is configured to perform dynamic emergency resource deployment based on the disaster loss of the power distribution network and the load gap, with the constraint conditions of disaster loss of the power distribution network and power of the emergency power supply resources being balanced and the capacity of the emergency power supply resources being balanced, and the objective of minimum scheduling cost.

[0015] The scheme also provides a computer readable medium having a computer program stored thereon, and the computer program is executed by a processor to implement the power distribution network disaster loss emergency resource deployment method based on the VQNN model.

[0016] Compared with the prior art, the present application has the following advantages and beneficial effects: 1. The power distribution network disaster loss emergency resource deployment method, system and medium based on the VQNN model provided by the present application introduce quantum computing and AI, optimize the prediction accuracy and speed, and improve the emergency resource deployment from a simple "post-disaster response" action to a whole process of power grid planning, early warning, response and recovery.

[0017] 2. The present invention provides a distribution network disaster emergency resource deployment method, system and medium based on the VQNN model; not only considering the disaster environment data, but also combining the basic operation data of the power grid, combining physical properties with real-time status, so that the estimation of the distribution network failure probability is far more accurate than a single method; by simulating the power outage diffusion path, the chain reaction of the failure can be dynamically evaluated, thereby obtaining the overall failure probability and drawing the vulnerability curve, which avoids viewing the system as a collection of isolated components, can capture the topological vulnerability of the power grid, and the assessed disaster load loss is more in line with reality.

[0018] 3. The present invention provides a method, system and medium for deploying emergency resources for disaster losses in distribution networks based on the VQNN model; based on the VQNN model, the load gap of the distribution network is predicted. The variational quantum neural network combines the parallel processing capability of quantum computing and the learning ability of the neural network, and can find the global optimal solution or approximate optimal solution in the huge solution space more quickly. With the minimum cost as the clear goal, on the premise of meeting the needs, the transportation cost, startup cost, operating cost, etc. of the emergency rescue resources are comprehensively considered to maximize the economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the examples. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be considered as limiting the scope. A person of ordinary skill in the art can also derive other relevant drawings based on these drawings without inventive effort. In the drawings: figure 1 This is a flow chart of the method for deploying emergency resources for distribution network disasters based on the VQNN model; figure 2 This is the structure diagram of the distribution network disaster emergency resource deployment system based on the VQNN model. DETAILED DESCRIPTION

[0020] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0021] In view of this, this solution provides the following embodiments to solve the above technical problems: Example 1: This example provides a method for deploying emergency resources for power distribution network disasters based on a VQNN model. figure 1 As shown, including: Step 1: Collect basic operation data of the distribution network and disaster environment data; Basic operation data includes basic grid data and operation status data of the distribution network; The power grid basic data includes: power distribution network topology (including the feeder branch relationship of the power distribution network, the node capacity of the power distribution network, the line switch position of the power distribution network, and the user access point distribution, etc.), line archives (including the voltage level of the power distribution network, the conductor model of the power distribution network, the tower model of the power distribution network, the windproof / waterproof level, and the anti-seismic capacity, etc.), and power distribution equipment archives (power distribution transformer capacity, distributed photovoltaic installed capacity, energy storage capacity and power, windproof or waterproof level, anti-seismic capacity); The operation state data includes: power distribution network operation history records (power distribution network equipment operation time, fault records, power distribution network structure reinforcement records), line load data (including line 96-point or daily load curves), power distribution transformer load data (including line 96-point or daily load curves), user historical electricity load (including user 96-point or daily electricity load curves), and historical distributed photovoltaic power generation curves (including distributed photovoltaic 96-point or daily power generation curves); The disaster environment data includes: disaster power failure range (including the number of power failure feeders, the number of power failure subareas, and the number of power failure users), debris flow conditions (occurrence location, impact range, flow rate (m / s), and flow volume (m³ / s)), earthquake conditions (including magnitude, epicenter location, focal depth, and intensity distribution), and meteorological data (including temperature, humidity, rainfall, solar radiation, wind speed, and wind direction); the power distribution automation system can be responsible for collecting power grid basic data such as power distribution network topology, line archives, and power distribution equipment archives, as well as power distribution transformer load and line load data; the electricity information system can collect user historical electricity load and distributed photovoltaic power generation curves; debris flow and other disaster information provided by the emergency center; earthquake data such as magnitude and epicenter location provided by the China Earthquake Network; and weather forecast information such as temperature, humidity, rainfall, and solar radiation provided by the local meteorological station.

[0022] Step two: based on the basic operation data and disaster environment data, the failure probability of each tower of the power distribution network under natural disasters is determined; the power outage diffusion path after the tower line breakage failure is simulated to obtain the overall failure probability of the power distribution network under natural disasters, and the power distribution network vulnerability curve is constructed based on the overall failure probability; In step two, the failure probability of each tower of the power distribution network under natural disasters is determined based on the basic operation data and disaster environment data; including methods: S21, based on the FEA analysis method, the inertia force converted from the earthquake acceleration time history curve is taken as the load of the tower under earthquake, and the seismic inertia force of the tower is calculated F eq ; and the impact force of the debris flow on the tower is calculated based on hydrodynamics F df ; The seismic inertia force F eq is: ; wherein: c pole represents the mass of the tower; t represents the time period of the seismic shock wave; a t represents the time history of the seismic acceleration; the impact force of the debris flow on the tower F df is: ; wherein, rho df represents the bulk density of the debris flow; v df represents the flow velocity of the debris flow; b pole represents the drag coefficient of the tower; a pole represents the projected area of the tower in the direction of the impact of the debris flow.

[0023] After the seismic inertia force and the impact force of the debris flow on the tower are determined, the failure probability of a single tower can be determined based on the maximum action force on the tower.

[0024] S22, based on the seismic inertia force F eq and the impact force F df analyzing the maximum action force on the tower, and determining the failure probability of the tower under natural disasters according to the maximum action force: ; wherein, ∫ represents integration; S F eq represents the failure probability of the tower under the seismic inertia force F eq ; S F df represents the failure probability of the tower under the impact force of the debris flow F df ; S (collapse| F eq , F df represents the conditional failure probability of the tower obtained by the engineering simulation method FEA.

[0025] ​​​Earthquake, debris flow and other natural disasters mainly affect the distribution network tower, which may lead to tower collapse, and then cause the line break between towers, and ultimately cause power outages. The impact mechanism of earthquake on the collapse of distribution network tower is that the earthquake transmits vibration energy to the tower through ground motion, causing the tower structure to vibrate. When the vibration load exceeds the structural bearing limit of the tower, the tower will collapse. The destruction of debris flow to the distribution network tower is due to the high-speed fluid carrying a large amount of sand and stones: on the one hand, the direct impact of the fluid will generate a strong force on the tower body; on the other hand, the scouring of high-speed water flow may damage the tower foundation and weaken its stability; when the combined effects of these actions break through the structural bearing limit of the tower, the tower will collapse.

[0026] FEA simulation method is an engineering simulation method with numerical analysis capability, which can be used to solve the physical field problem of seismic inertia force and debris flow impact force of complex structure; FEA simulation method discretizes the continuous physical environment into finite interconnected units, establishes the approximate equation of each unit, and solves the response of the entire distribution network to earthquake and debris flow natural disasters.

[0027] In step two, the overall failure probability of the distribution network under natural disasters is obtained by simulating the power outage diffusion path after the tower line break failure, and the vulnerability curve of the distribution network is constructed based on the overall failure probability; including methods: G21, based on the fault propagation algorithm and the topology structure of the distribution network, simulating the power outage diffusion path after the tower line break failure under natural disasters; G22, based on the action logic of the protection device of the distribution network, obtaining the overall failure probability of the distribution network under natural disasters S DN : ; Among them, n DN represents the number of towers of the distribution network; S pole.i represents the failure probability of the first tower of the distribution network under earthquake and debris flow; i represents the failure probability of the first tower of the distribution network under earthquake and debris flow; gamma represents the failure probability of the first tower of the distribution network under earthquake and debris flow; i represents the failure probability of the first tower of the distribution network under earthquake and debris flow; G23, based on the overall failure probability S DN constructing the vulnerability curve of the distribution network eta fail : ; Among them, Φ() represents the cumulative function of the standard normal distribution of the natural disaster failure probability of the distribution network; ln represents the natural logarithm function; psirepresents the intensity of natural disasters, which is F eq , in the earthquake natural disaster F df ; S DN ( mu ) represents the overall mean of the overall failure probability of the distribution network after multiple simulation constructions; S DN ( sigma ) represents the variance of the overall failure probability of the distribution network after multiple simulation constructions.

[0028] The fault propagation algorithm FPA simulates the power outage diffusion path after the tower line breakage fault based on the distribution network topology diagram, determines the island area of the distribution network according to the action logic of the pole circuit breaker, drop type fuse and other protection devices, and forms the overall failure probability of the distribution network.

[0029] Step three: evaluate the disaster load loss of the distribution network in combination with the physical propagation process under the natural disaster and the vulnerability curve of the distribution network; this step specifically includes the method: Disperse the earthquake and debris flow into a space-time dynamic field respectively, and obtain the intensity of natural disasters in combination with the vulnerability curve of the distribution network; the natural disaster diffusion intensity includes the earthquake diffusion impact force and psi eq debris flow diffusion impact force psi df ; earthquake diffusion impact force and psi eq debris flow diffusion impact force psi df , respectively: ; Among them, F eq.max represents the inertial force in the earthquake epicenter; exp() represents the exponential function; L eq.rad represents the influence radius of the earthquake; L DNeq represents the distance from the tower to the earthquake epicenter; F df.max represents the debris flow impact force of the natural disaster observation point; g df represents the gravitational acceleration of the debris flow; L DNdf represents the distance from the tower to the natural disaster observation point; omega represents the slope of the debris flow to the tower channel; delta represents the friction coefficient of the debris flow and the ground.

[0030] In the dynamic assessment of distribution network disaster risk, user load loss can be used as the basic basis for ensuring power supply when formulating distribution network disaster emergency recovery plans.

[0031] Obtain the load loss of the distribution network according to the diffusion intensity of natural disasters P loss : ; ; in, k Represents the set of load nodes in the distribution network; P DN.j Indicates the first j load nodes; psi std Indicates the impact force that causes the tower to collapse; f () indicates an intermediate function.

[0032] Step 4: Predict the load gap of the distribution network based on the VQNN model; this step specifically includes the following methods: S41, construct a VQNN model including quantum feature mapping part, variational quantum circuit part and joint distribution modeling part; specifically, S411, let the load state vector of the distribution network be P ld for: P ld =( p ld.1 , p ld.2 ,…, p ld.n ),in n Represents the number of load states, and the load state vector of the distribution network is converted into a quantum state in the Hilbert space through the quantum characteristic mapping function. phi ( P ld )>: ; in, m represents the number of quantum bits; β j Indicates the y The mapping coefficients of the qubits; Z represents the normalization coefficient; m bv Represents the calculation vector.

[0033] The quantum feature mapping function is obtained by learning the parameters of the VQNN model. The simplified mapping is obtained by angular coding. The angular coding of the VQNN model R ld for:

[0034] In the formula: theta represents the quantum rotation angle in the VQNN mapping; cos() represents the cosine function; and sin() represents the sine function.

[0035] Quantum rotation angle theta Map the power grid load state vector to the quantum rotation angle: ;

[0036] In the formula: π represents the circular constant.

[0037] S412, tensor product of user load quantum bit state phi ( P ld1 ) > is: ;

[0038] In the formula, n AE represents the dimension of the mapping in the VQNN; P ld1.i represents the i-th i dimensional user load state vector; represents the i-th n AE controlled non gate; represents n AE controlled non gate; the above process maps n AE characteristics to Hilbert space, realizing high-dimensional feature expansion.

[0039] In the variational quantum circuit part, the nonlinear characteristics are extracted by designing a parameterized quantum layer, and the parameterized quantum layer U ( theta ) is: ; In the formula, U ent represents the quantum entanglement layer of the VQNN, n L represents the number of layers of the quantum entanglement layer; n ql represents the number of quantum bits; R zd represents the rotation gate of the VQNN; theta k.j represents the quantum rotation angle of the i-th k quantum bit in the i-th j quantum entanglement layer; quantum state output by the user loadphi ( P ld1 , theta ) > is: ; Similarly, the quantum state of the PV output is calculated; S42, predicting user load of distribution network based on VQNN model P ld1 and distributed photovoltaic output P pv The VQNN model uses the Pauli measurement operator to obtain the predicted values ​​of user load and PV output. f ( P ld1 , theta ), f ( P pv , theta ) are: ; Where: O ld1 、 O pv denote the Pauli measurement operators of user load and PV output, respectively; | phi ( P pv ) > represents the tensor product of the quantum bit states of the PV output; | phi ( P pv , theta ) > represents the quantum state of PV output; P hist.ld1 、 P hist.pv Represent historical user load and PV output sample data respectively.

[0040] The joint distribution modeling of the VQNN model outputs the conditional probability distribution of the user load and distributed photovoltaic output of the distribution network, and the user load and distributed photovoltaic output satisfy the two-dimensional Gaussian distribution, and the probability density function is: ; in, sigma ld1 、 sigma pv They represent the variance of user load and PV output respectively; lambda ld1 、 lambda pv Represent the mean value of user load and the mean value of PV output respectively; Indicates the correlation coefficient between user load and PV output; p ( P ld1 , P pv ) represents the probability density function of user load and distributed photovoltaics; Minimize the negative log-likelihood loss of the joint probability density function tau for: ; Where: n T Indicates the emergency power supply period under natural disasters; P ld1.t 、 P pv.t Respectively represent t The user load and PV output values ​​at the moment. Parameters are updated through quantum classical hybrid optimization. theta value to improve the prediction accuracy of user load and PV output.

[0041] S43, based on user load P ld1 and distributed photovoltaic output P pv Get the load gap of the distribution network P GAP : ; Among them, max() represents the maximum function; P es Indicates the power provided by the energy storage in the distribution network.

[0042] Step 5: Based on the disaster load loss and load gap, dynamic emergency resource deployment is carried out with the disaster load loss, emergency power supply resource power balance and emergency power supply resource capacity as constraints, and the goal of minimizing the scheduling cost. This step specifically includes the following methods: With the constraints that load gap ≤ power provided by emergency power supply resources, available power supply duration ≤ maximum power supply duration of emergency power supply resources, and distance from emergency power supply resources to distribution network fault point ≤ maximum reachable distance of emergency power supply resources, and minimizing scheduling cost as the objective function, in the offline planning stage, the global optimal emergency resource deployment plan is generated based on the NSGA-II algorithm. In the online adjustment stage, the emergency resource deployment plan is dynamically adjusted according to the real-time monitored natural disaster evolution data based on the PPO algorithm.

[0043] Specifically, the grid company's emergency power supply resources are primarily divided into two categories: mobile generators and fixed diesel generators. Mobile generators can travel directly to the DN fault point to provide power; fixed diesel generators must be transported by truck to the distribution network fault point before providing power. This solution uses the Global Positioning System (GPS) and Beidou positioning devices to locate both types of emergency power supply resources. IoT sensors also collect information about their remaining fuel levels and real-time output power during emergency power supply. The solution then dispatches emergency power resources based on factors such as distance and fuel levels.

[0044] Duration of emergency power supply resources T SUP for: ;

[0045] in, H SUP Indicates the remaining fuel volume of the emergency power supply resource; eta SUP Indicates the fuel efficiency coefficient of emergency power supply resources; P SUP Indicates the generating power of the emergency power supply resource; T INR Indicates the maintenance interval of emergency power supply resources during the power supply process.

[0046] The emergency power supply resource state vector E is constructed as: ; in, x k 、 y k Indicates the k The horizontal and vertical coordinates of the geographic information of each emergency power supply resource; P SUP.k Indicates the k The power provided by each emergency power supply resource; T SUP.k Indicates the k The duration of power supply provided by each emergency power supply resource.

[0047] The NSGA-II algorithm is a multi-objective optimization algorithm that can simultaneously optimize multiple conflicting objectives and provide a set of non-dominated optimal solutions. Therefore, the NSGA-II algorithm is used for static scheduling of emergency power supply resources.

[0048] The optimization objective of minimizing scheduling cost calculated using the NSGA-II algorithm f FPL for: ; in, P GAP.tIndicates the t The load gap of the distribution network at the moment; P loss.t Indicates the t The load loss of the distribution network at the moment of disaster; P SUP.t Indicates the t The power provided by emergency power supply resources at all times; d SUP Indicates the distance from the emergency power supply resource to the fault point of the distribution network; w SUP Indicates the weight of the distribution network load.

[0049] The constraint expression is: ; Where: T max Indicates the maximum power supply duration of emergency resources; d SUP.max Indicates the maximum distance that emergency resources can reach.

[0050] During the offline planning phase, NSGA-II generates the globally optimal emergency power supply resource scheduling plan. During the online adjustment phase, the PPO algorithm dynamically adjusts resource allocation based on real-time monitored earthquake and debris flow disaster evolution data, and continuously optimizes the scheduling strategy.

[0051] The PPO algorithm is a policy gradient-based reinforcement learning algorithm that ensures training stability by limiting the step size of policy updates. Using an online learning mechanism, the algorithm dynamically iterates its policy based on changes in power demand caused by disasters such as earthquakes and mudslides, enabling continuous policy optimization without offline retraining. This feature makes it suitable for dynamic adjustment of emergency power supply resources.

[0052] Update strategy function of PPO algorithm ς for: ; in, s SMS Indicates the deployment path strategy of emergency resources; represents the emergency resource deployment strategy network; V t Indicates the t The value strategy of the moment, Indicates the t Strategic network at all times.

[0053] Example 2: This example provides a distribution network disaster loss emergency resource deployment system based on a VQNN model, which is used to implement the distribution network disaster loss emergency resource deployment method based on a VQNN model described in Example 1; figure 2 As shown, the system includes: The acquisition module is configured to acquire basic operation data and disaster environment data of the power distribution network. The first calculation module is configured to determine failure probabilities of each tower of the power distribution network under natural disasters based on the basic operation data and the disaster environment data, simulate power outage diffusion paths after tower disconnection failures to obtain overall failure probabilities of the power distribution network under the natural disasters, and construct a vulnerable curve of the power distribution network based on the overall failure probabilities. The evaluation module is configured to evaluate disaster loss load of the power distribution network in combination with physical propagation processes under the natural disasters and the vulnerable curve of the power distribution network. The second calculation module is configured to predict load gaps of the power distribution network based on the VQNN model. The deployment module is configured to perform dynamic emergency resource deployment based on the disaster loss load and the load gaps, with power balance between the disaster loss load and emergency power supply resources and capability of the emergency power supply resources as constraint conditions and minimum scheduling cost as an objective.

[0054] Embodiment 3: The embodiment provides a computer readable medium having a computer program stored thereon, and the computer program is executable by a processor to implement the VQNN model-based power distribution network disaster loss emergency resource deployment method of embodiment 1; and the following steps are specifically implemented: Step one: acquiring basic operation data and disaster environment data of the power distribution network; Step two: determining failure probabilities of each tower of the power distribution network under natural disasters based on the basic operation data and the disaster environment data, simulating power outage diffusion paths after tower disconnection failures to obtain overall failure probabilities of the power distribution network under the natural disasters, and constructing a vulnerable curve of the power distribution network based on the overall failure probabilities. Step three: evaluating disaster loss load of the power distribution network in combination with physical propagation processes under the natural disasters and the vulnerable curve of the power distribution network. Step four: predicting load gaps of the power distribution network based on the VQNN model. Step five: performing dynamic emergency resource deployment based on the disaster loss load and the load gaps, with power balance between the disaster loss load and emergency power supply resources and capability of the emergency power supply resources as constraint conditions and minimum scheduling cost as an objective.

[0055] The above detailed description further describes the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A distribution network disaster emergency resource deployment method based on the VQNN model, characterized by: include: Collect basic operation data of the distribution network and disaster environment data; Determine the failure probability of each tower in the distribution network under natural disasters based on basic operation data and disaster environment data; The power outage diffusion path after a tower disconnection fault is simulated to obtain the overall failure probability of the distribution network under natural disasters, and the distribution network vulnerability curve is constructed based on the overall failure probability; Combine the physical propagation process of natural disasters and the distribution network vulnerability curve to evaluate the load loss of the distribution network; Predicting the load gap of distribution network based on VQNN model; Based on the disaster load loss and load gap, dynamic emergency resource deployment is carried out with the disaster load loss, power balance of emergency power supply resources and emergency power supply resource capacity as constraints and the goal of minimizing scheduling cost.

2. The method for deploying emergency resources for distribution network disasters based on the VQNN model according to claim 1, characterized in that: The basic operation data includes basic grid data and operation status data of the distribution network; The basic data of the power grid include: distribution network topology, line files and distribution equipment files; the operating status data include: distribution network operation history records, line load data, distribution transformer load data, user historical power load and historical distributed photovoltaic power generation curves; the disaster environment data include: disaster power outage scope, mudslide status data, earthquake status data and meteorological data.

3. The method for deploying emergency resources for distribution network disasters based on the VQNN model according to claim 1, characterized in that: The method of determining the failure probability of each tower in the distribution network under natural disasters based on basic operation data and disaster environment data includes: Based on the FEA analysis method, the inertial force converted from the earthquake acceleration time history curve is used as the earthquake load on the tower to calculate the earthquake inertial force of the tower. F eq ; and calculate the impact force of debris flow on the tower based on hydrodynamics F df ; Based on earthquake inertia force F eq and impact force F df Analyze the maximum force acting on the tower and determine the failure probability of the tower under natural disasters based on the maximum force: ； Where ∫ represents the integral; S ( F eq ) represents the inertial force of the tower under earthquake F eq Failure probability under S ( F df ) indicates the impact force of debris flow on the tower F df Failure probability under S (collapse| F eq , F df ) represents the tower conditional failure probability obtained by the engineering simulation method FEA.

4. The method for deploying emergency resources for distribution network disasters based on the VQNN model according to claim 3 is characterized in that: The power outage diffusion path after the tower disconnection fault is simulated to obtain the overall failure probability of the distribution network under natural disasters, and the distribution network vulnerability curve is constructed based on the overall failure probability; Includes methods: Based on the fault propagation algorithm and distribution network topology, the power outage diffusion path after the tower disconnection fault under natural disasters is simulated; Based on the action logic of the distribution network protection device, the overall failure probability of the distribution network under natural disasters is obtained S DN : ； in, n DN Indicates the number of towers in the distribution network; S pole.i Indicates the distribution network i Failure probability of a tower under earthquake and debris flow; γ Indicates the first i A broken power tower caused a distribution network failure; Based on the overall probability of failure S DN Constructing a distribution network vulnerability curve η fail : ； Where Φ() represents the cumulative function of the standard normal distribution of the probability of natural disaster failure in the distribution network; ln represents the natural logarithm function; ψ Indicates the intensity of natural disasters. In earthquake natural disasters, F eq , in the natural disaster of debris flow F df ; S DN ( μ ) represents the overall mean value of the overall failure probability of the distribution network after multiple simulations; S DN ( σ ) represents the variance of the overall failure probability of the distribution network after multiple simulations.

5. The method for deploying emergency resources for distribution network disasters based on the VQNN model according to claim 4 is characterized in that: The method combines the physical propagation process under natural disasters and the distribution network vulnerability curve to evaluate the disaster load loss of the distribution network; including the following methods: Earthquakes and debris flows are discretized into spatiotemporal dynamic fields, and the intensity of natural disasters is obtained by combining them with the distribution network vulnerability curve. The diffusion intensity of natural disasters includes the diffusion impact of earthquakes and ψ eq Debris flow diffusion impact force ψ df ; Obtain the load loss of the distribution network according to the diffusion intensity of natural disasters P loss : ； ； in, k Represents the set of load nodes in the distribution network; P DN.j Indicates the first j load nodes; ψ std Indicates the impact force that causes the tower to collapse; f () indicates an intermediate function.

6. The method for deploying emergency resources for distribution network disasters based on the VQNN model according to claim 1, characterized in that: The load gap of the distribution network is predicted based on the VQNN model; Includes methods: Construct a VQNN model that includes a quantum feature mapping part, a variational quantum circuit part, and a joint distribution modeling part; Based on the VQNN model, the user load P of the distribution network is predicted. ld1 and distributed photovoltaic output P pv ; According to user load P ld1 and distributed photovoltaic output P pv Get the load gap of the distribution network P GAP : ； Among them, max() represents the maximum function; P es Indicates the power provided by the energy storage in the distribution network.

7. The method for deploying emergency resources for distribution network disasters based on the VQNN model according to claim 6, characterized in that: The joint distribution modeling part outputs the conditional probability distribution of the user load and distributed photovoltaic output of the distribution network, and the user load and distributed photovoltaic output satisfy the two-dimensional Gaussian distribution, and the probability density function is: ； in, σ ld1 、 σ pv They represent the variance of user load and PV output respectively; λ ld1 、 λ pv Represent the mean value of user load and the mean value of PV output respectively; Indicates the correlation coefficient between user load and PV output; p ( P ld1 , P pv ) represents the probability density function of user load and distributed photovoltaics; The joint distribution modeling part is also based on the Pauli measurement operator to obtain the user load forecast value and the PV output forecast value.

8. The method for deploying emergency resources for distribution network disasters based on the VQNN model according to claim 1, characterized in that: The method comprises the following steps: based on the disaster load loss and the load gap, taking the disaster load loss and the power balance of the emergency power supply resources and the emergency power supply resource capacity as the constraints, and minimizing the scheduling cost as the goal to deploy emergency resources; and comprising the following steps: With the constraints that load gap ≤ power provided by emergency power supply resources, available power supply duration ≤ maximum power supply duration of emergency power supply resources, and distance from emergency power supply resources to distribution network fault point ≤ maximum reachable distance of emergency power supply resources, and minimizing scheduling cost as the objective function, in the offline planning stage, the global optimal emergency resource deployment plan is generated based on the NSGA-II algorithm. In the online adjustment stage, the emergency resource deployment plan is dynamically adjusted according to the real-time monitored natural disaster evolution data based on the PPO algorithm.

9. The distribution network disaster emergency resource deployment system based on the VQNN model is characterized by: A method for deploying emergency resources for a distribution network disaster loss based on a VQNN model according to any one of claims 1 to 8; the system comprises: The acquisition module is used to collect basic operation data of the distribution network and disaster environment data; The first calculation module is used to determine the failure probability of each tower in the distribution network under natural disasters based on basic operating data and disaster environment data; simulate the power outage diffusion path after the tower line break failure to obtain the overall failure probability of the distribution network under natural disasters, and construct the distribution network vulnerability curve based on the overall failure probability; An assessment module is used to evaluate the load loss of the distribution network due to natural disasters by combining the physical propagation process of natural disasters with the distribution network vulnerability curve; The second calculation module is used to predict the load gap of the distribution network based on the VQNN model; The deployment module is used to dynamically deploy emergency resources based on disaster load loss and load gap, with the disaster load loss and emergency power supply resource power balance and emergency power supply resource capacity as constraints, and the goal of minimizing scheduling costs.

10. A computer-readable medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the distribution network disaster emergency resource deployment method based on the VQNN model as described in any one of claims 1 to 8.

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