A power distribution network mobile emergency resource pre-configuration method considering the influence of extreme weather

Abstract

The application discloses a kind of power distribution network mobile emergency resource pre-configuration method considering the influence of extreme weather, which comprises the following steps: based on power grid topology, traffic network node and power grid node are sequentially coupled corresponding, collect power grid information and traffic network information, obtain power and traffic coupling network;Mobile emergency resource driving path analysis is carried out based on Dijkstra algorithm;Based on blind number theory, construct the power distribution network fault scene under extreme weather;Construct the integrated failure rate model of line under single weather;Construct the integrated failure probability model of line under multiple weather factors;Construct the mobile emergency resource pre-configuration model considering power and traffic coupling network under extreme weather;Resource pre-configuration is carried out based on mobile emergency resource pre-configuration model.The application comprehensively considers the power distribution network fault recovery demand under extreme weather and the influence of extreme weather on traffic network traffic capacity, so that path decision is more matched actual working condition.

Description

Technical Field

[0001] This invention relates to the field of power resource allocation technology, and more specifically to a method for pre-configuring mobile emergency resources for distribution networks that takes into account the impact of extreme weather. Background Technology

[0002] With the development of global warming and the increasing frequency of extreme weather disasters, how to improve the power grid's ability to withstand and recover from similar extreme events has become a current focus. At the same time, under the background of the construction of new power systems, mobile emergency resources have developed rapidly, further expanding the solutions for fault recovery. Therefore, making full use of emerging flexible resources to carry out relevant research on power grid recovery under extreme weather conditions is of great practical significance for reducing power outage losses and improving power grid resilience.

[0003] On the one hand, extreme weather will put an extra burden on power distribution network components and increase their failure rate. For example, lightning, rainstorms, typhoons and other weather conditions can cause line galloping or even line breakage, and may also cause tower collapse, resulting in large-scale damage to the power grid. On the other hand, extreme weather will affect the transportation network, resulting in poor road visibility and slippery roads, which will reduce road traffic capacity and severely slow down vehicle speed.

[0004] Under extreme weather conditions, the risk of power outages is significant, especially affecting the power supply to critical loads. To reduce power outage losses and improve grid resilience, power grids typically deploy mobile emergency resources, such as mobile energy storage vehicles. Considering that distribution network fault risks are affected by multiple extreme weather events, and that the transportation of mobile resources will be affected by traffic flow information, it is necessary to study a mobile emergency resource pre-configuration technology that takes into account the impact of extreme weather to effectively improve the engineering applicability and feasibility of post-disaster recovery plans, providing a reference for certain engineering applications. Summary of the Invention

[0005] In order to overcome the defects and shortcomings of the existing technology, the present invention provides a method for pre-configuring mobile emergency resources of distribution networks that takes into account the impact of extreme weather. The present invention helps to reduce power outage losses and improve the resilience of the power grid.

[0006] The second objective of this invention is to provide a pre-configuration system for mobile emergency resources in power distribution networks that takes into account the impact of extreme weather.

[0007] A third objective of this invention is to provide a computer-readable storage medium.

[0008] A fourth objective of this invention is to provide a computer device.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] A method for pre-configuring mobile emergency resources for power distribution networks that takes into account the impact of extreme weather includes the following steps:

[0011] Based on the power grid topology, traffic network nodes and power grid nodes are sequentially coupled and corresponding, and power grid information and traffic network information are collected to obtain a power and transportation coupled network.

[0012] Mobile emergency resource travel path analysis based on Dijkstra's algorithm;

[0013] Based on blind number theory, a fault scenario of distribution network under extreme weather is constructed. Based on statistical data, the failure rate of power outage of the line under different levels of weather factors is obtained. A judgment matrix between different levels of factors is constructed, the eigenvector under the largest eigenvalue of the matrix is ​​obtained, and the eigenvector is normalized to obtain the confidence value under different levels. Thus, a comprehensive fault rate model of the line under a single weather condition is obtained.

[0014] Construct a judgment matrix under different weather factors, solve the proportion of different weather factors in the line failure rate, that is, the credibility under different weather factors, and obtain a comprehensive failure probability model of multiple weather factors affecting the line.

[0015] A mobile emergency resource pre-configuration model considering the coupled power and transportation networks under extreme weather conditions is constructed. The mobile emergency resource pre-configuration model includes decision variables, objective function, and constraints.

[0016] The decision variables include the location of mobile emergency resource assembly points and the quantity of different types of mobile emergency resources. The objective function includes minimizing the total cost of comprehensive load outage loss, mobile emergency resource operating fuel cost, and mobile emergency resource investment and purchase cost. The constraints include time response constraints, load restoration demand constraints, mobile emergency resource supply constraints, and mobile emergency resource allocation quantity constraints.

[0017] Resource pre-configuration is based on a mobile emergency resource pre-configuration model.

[0018] As a preferred technical solution, the method for analyzing the travel path of mobile emergency resources based on Dijkstra's algorithm includes the following steps:

[0019] A road resistance function is introduced as a standard for measuring the travel time of a road segment. Its functional form is as follows:

[0020]

[0021] t w0 =l w / v w

[0022] Among them, t w t is the time t required for a vehicle to travel through road segment w.w0 l represents the average free travel time of a vehicle through road segment w. w Indicates the basic road segment length, v w Q represents the road's design speed. w Let C be the traffic flow rate of road segment w. w Let w be the traffic capacity of road segment w, and α and β be the parameters to be calibrated.

[0023] The road network is divided into different road types, and the calibration parameters are assigned values ​​according to the different road types;

[0024] The travel time of each road is calculated using the road resistance function, and an adjacency matrix of road travel times is constructed based on the connectivity structure of the traffic network. Dijkstra's algorithm is then used to solve for the shortest travel time and corresponding travel path between any road network nodes, thus obtaining all candidate site selection schemes for the cluster point that meet the response time constraints.

[0025] As a preferred technical solution, the objective function aims to minimize the total cost, including comprehensive load outage loss costs, mobile emergency resource operating fuel costs, and mobile emergency resource investment and acquisition costs. Specifically, it is expressed as follows:

[0026] min f(x) = α1f1 + α2f2 + α3f3

[0027]

[0028]

[0029]

[0030]

[0031] Where f1 is the load outage loss cost, f2 is the fuel cost for mobile emergency resources, f3 is the investment and acquisition cost of mobile emergency resources, α1, α2, and α3 are the normalized weight coefficients of f1, f2, and f3, respectively, which can be flexibly determined according to the importance and priority of the optimization objective, N is the number of load nodes, and t i,x The shortest time from the location where mobile emergency resources are stored to node i. Y represents the power outage loss per unit time for node i's load; i The failure rate of node i's load experiencing power outage. For node i, the unit loss due to load shedding, P i load H represents the load capacity of node i, and H represents the type / quantity of mobile energy storage vehicles. The cost of moving mobile emergency resources to node i. S represents the unit fuel cost of mobile emergency resources traveling to the target node.h,i The number of h types of mobile emergency resources coupled to node i. The unit investment cost of the h-th type of mobile emergency resource is... β is the maximum output power of the h-th mobile resource. h,y,i This represents the connection status between the y-th type h mobile emergency resource and node i.

[0032] As a preferred technical solution, the time response constraint limits the longest response time from the location of the aggregation point to any faulty load node, expressed as:

[0033] t i,x ≤T res ,i∈N

[0034] Among them, T res To limit the response time of mobile emergency resources, N is the number of load nodes;

[0035] The load restoration requirement constraint is expressed as:

[0036] 0≤P i re ≤P i load

[0037]

[0038]

[0039]

[0040] In the formula, P i re , These represent the active and reactive power recovery quantities of load node i, respectively. These are the active and reactive power recovery requirements for critical loads, respectively.

[0041] The constraint on the supply of mobile emergency resources is expressed as follows:

[0042]

[0043]

[0044] In the formula, P h,i Q h,i λ represents the active and reactive power output of the h-th type of mobile emergency resource at the i-th node, respectively. h To improve the discharge efficiency of mobile emergency resources;

[0045] The constraint on the quantity of mobile emergency resources is expressed as follows:

[0046]

[0047] Among them, S H This represents the maximum number of mobile emergency resources of type h.

[0048] To achieve the second objective mentioned above, the present invention adopts the following technical solution:

[0049] A mobile emergency resource pre-configuration system for distribution networks that takes into account the impact of extreme weather includes: a power and transportation coupled network construction module, a path analysis module, a fault scenario construction module, a single weather fault rate model construction module, a multi-weather fault probability model construction module, a resource pre-configuration model construction module, and a resource pre-configuration module;

[0050] The power and transportation coupled network construction module is used to establish a power grid topology, set up transportation network nodes and power grid nodes to be coupled and correspond in sequence, collect power grid information and transportation network information, and obtain a power and transportation coupled network.

[0051] The path analysis module is used to perform mobile emergency resource travel path analysis based on Dijkstra's algorithm.

[0052] The fault scenario construction module is used to construct power distribution network fault scenarios under extreme weather conditions based on blind number theory.

[0053] The single weather failure rate model construction module is used to obtain the failure rate of power outages of the line under different levels of weather factors based on statistical data, construct a judgment matrix between different levels of factors, obtain the eigenvector under the largest eigenvalue of the matrix, normalize the eigenvector to obtain the confidence value under different levels, and obtain the comprehensive failure rate model of the line under a single weather condition.

[0054] The multi-weather fault probability model construction module is used to construct a judgment matrix under different weather factors, solve the proportion of different weather factors in the line fault rate, that is, the credibility under different weather factors, and obtain a comprehensive fault probability model of multiple weather factors affecting the line.

[0055] The resource pre-configuration model construction module is used to construct a mobile emergency resource pre-configuration model that considers the power and transportation coupled network under extreme weather conditions. The mobile emergency resource pre-configuration model includes decision variables, objective function and constraints.

[0056] The decision variables include the location of mobile emergency resource assembly points and the quantity of different types of mobile emergency resources. The objective function includes minimizing the total cost of comprehensive load outage loss, mobile emergency resource operating fuel cost, and mobile emergency resource investment and purchase cost. The constraints include time response constraints, load restoration demand constraints, mobile emergency resource supply constraints, and mobile emergency resource allocation quantity constraints.

[0057] The resource pre-configuration module is used to pre-configure resources based on the mobile emergency resource pre-configuration model.

[0058] As a preferred technical solution, the path analysis module is used for mobile emergency resource travel path analysis based on Dijkstra's algorithm, specifically including:

[0059] A road resistance function is introduced as a standard for measuring the travel time of a road segment. Its functional form is as follows:

[0060]

[0061] t w0 =l w / v w

[0062] Among them, t w t is the time t required for a vehicle to travel through road segment w. w0 l represents the average free travel time of a vehicle through road segment w. w Indicates the basic road segment length, v w Q represents the road's design speed. w Let C be the traffic flow rate of road segment w. w Let w be the traffic capacity of road segment w, and α and β be the parameters to be calibrated.

[0063] The road network is divided into different road types, and the calibration parameters are assigned values ​​according to the different road types;

[0064] The travel time of each road is calculated using the road resistance function, and an adjacency matrix of road travel times is constructed based on the connectivity structure of the traffic network. Dijkstra's algorithm is then used to solve for the shortest travel time and corresponding travel path between any road network nodes, thus obtaining all candidate site selection schemes for the cluster point that meet the response time constraints.

[0065] As a preferred technical solution, the objective function aims to minimize the total cost, including comprehensive load outage loss costs, mobile emergency resource operating fuel costs, and mobile emergency resource investment and acquisition costs. Specifically, it is expressed as follows:

[0066] min f(x) = α1f1 + α2f2 + α3f3

[0067]

[0068]

[0069]

[0070]

[0071] Where f1 is the load outage loss cost, f2 is the fuel cost for mobile emergency resources, f3 is the investment and acquisition cost of mobile emergency resources, α1, α2, and α3 are the normalized weight coefficients of f1, f2, and f3, respectively, which can be flexibly determined according to the importance and priority of the optimization objective, N is the number of load nodes, and t i,x The shortest time from the location where mobile emergency resources are stored to node i. Y represents the power outage loss per unit time for node i's load; i The failure rate of node i's load experiencing power outage. For node i, the unit loss due to load shedding, P i load H represents the load capacity of node i, and H represents the type / quantity of mobile energy storage vehicles. The cost of moving mobile emergency resources to node i. S represents the unit fuel cost of mobile emergency resources traveling to the target node. h,i The number of h types of mobile emergency resources coupled to node i. The unit investment cost of the h-th type of mobile emergency resource is... β is the maximum output power of the h-th mobile resource. h,y,i This represents the connection status between the y-th type h mobile emergency resource and node i.

[0072] As a preferred technical solution, the time response constraint limits the longest response time from the location of the aggregation point to any faulty load node, expressed as:

[0073] t i,x ≤T res ,i∈N

[0074] Among them, T res To limit the response time of mobile emergency resources, N is the number of load nodes;

[0075] The load restoration requirement constraint is expressed as:

[0076] 0≤P i re ≤P i load

[0077]

[0078]

[0079]

[0080] In the formula, P i re , These represent the active and reactive power recovery quantities of load node i, respectively. These are the active and reactive power recovery requirements for critical loads, respectively.

[0081] The constraint on the supply of mobile emergency resources is expressed as follows:

[0082]

[0083]

[0084] In the formula, P h,i Q h,i λ represents the active and reactive power output of the h-th type of mobile emergency resource at the i-th node, respectively. h To improve the discharge efficiency of mobile emergency resources;

[0085] The constraint on the quantity of mobile emergency resources is expressed as follows:

[0086]

[0087] Among them, S H This represents the maximum number of mobile emergency resources of type h.

[0088] To achieve the third objective mentioned above, the present invention adopts the following technical solution:

[0089] A computer-readable storage medium storing a program that, when executed by a processor, implements the above-described method for pre-configuring mobile emergency resources for power distribution networks, taking into account the impact of extreme weather.

[0090] To achieve the fourth objective mentioned above, the present invention adopts the following technical solution:

[0091] A computer device includes a processor and a memory for storing a processor-executable program, wherein when the processor executes the program stored in the memory, it implements the above-described method for pre-configuring mobile emergency resources for power distribution networks that takes into account the impact of extreme weather.

[0092] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0093] (1) This invention addresses the need for power distribution network fault recovery under extreme weather conditions. Based on the coupling background of power distribution network and transportation network, it considers the impact of multiple extreme weather conditions on the fault risk of power distribution network and the traffic capacity of transportation network. It also uses Dijkstra's algorithm to analyze the travel path of mobile emergency resources, making the pre-configuration method of mobile emergency resources for power distribution network in this invention more suitable for actual working conditions.

[0094] (2) This invention takes into account the impact of multiple extreme weather events and the cost of mobile emergency resources. The pre-configuration scheme can lay a good foundation for post-disaster resource scheduling, help reduce power outage losses, improve grid resilience, and provide certain engineering application references. Attached Figure Description

[0095] Figure 1 This is a flowchart illustrating the method for pre-configuring mobile emergency resources for power distribution networks that takes into account the impact of extreme weather, as described in this invention.

[0096] Figure 2 This is a diagram of the power and transportation coupling network framework of the present invention;

[0097] Figure 3 This is a topology diagram of the 33-node transportation network in this embodiment;

[0098] Figure 4 This is a distribution map showing the shortest travel time from the mobile emergency resource assembly point to each node in this embodiment. Detailed Implementation

[0099] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0100] Example 1

[0101] like Figure 1 As shown, this embodiment provides a method for pre-configuring mobile emergency resources for power distribution networks that takes into account the impact of extreme weather, including the following steps:

[0102] S1: As Figure 2 As shown, a coupled network framework for power and transportation is constructed;

[0103] In this embodiment, step S1 is as follows:

[0104] S11: The coupled network is constructed based on the power grid topology, taking into account the resource connection requirements for the recovery of each load node, and setting the traffic network nodes and power grid nodes to be coupled in sequence, so that a power grid node can only be coupled to one road network node.

[0105] S12: The information collected by the power grid mainly includes load restoration needs and resource output; the information collected by the transportation network mainly includes traffic flow. Traffic flow affects vehicle speed, so different traffic flows take different paths, which will directly affect the selection of mobile emergency resource travel routes.

[0106] like Figure 3As shown, the power grid topology and load classification are obtained. In the transportation network topology, there are a total of 33 nodes and 51 roads. Each node of the transportation network is coupled to each load node of the distribution network.

[0107] The load node parameters are shown in Table 1 below:

[0108] Table 1 Load Node Parameters

[0109]

[0110]

[0111] As shown in Table 2 below, the basic parameters of the transportation network are obtained:

[0112] Table 2 Road parameters of the transportation network

[0113]

[0114]

[0115] As shown in Table 3 below, the parameters such as the mobile emergency resource model and maximum quantity that can be selected for the pre-configuration scheme in this embodiment are obtained:

[0116] Table 3 Mobile Emergency Resource Parameters

[0117]

[0118] S2: Perform mobile emergency resource travel path analysis based on Dijkstra's algorithm;

[0119] In this embodiment, step S2 is as follows:

[0120] S21: Introduce a road resistance function as a standard for measuring the travel time of a road segment. Its functional form is:

[0121]

[0122] In the formula, t w The time (in minutes) required for a vehicle to traverse road segment w; t w0 t represents the average free travel time (in minutes) of a vehicle traversing road segment w. w0 =l w / v w Basic road section length l w Road design speed v w Q w Traffic flow (vehicles / hour) for road segment w; C w Let be the traffic capacity (vehicles / hour) of road segment w; α and β are parameters to be calibrated.

[0123] S22: Considering the impact of road type on the calibration parameters, the roads in the transportation network are divided into four types: expressways, arterial roads, secondary arterial roads, and local roads, and values ​​are assigned to the calibration parameters accordingly.

[0124] As shown in Table 4 below, the values ​​of the parameters to be calibrated are obtained for different road types:

[0125] Table 4. Parameters to be calibrated under different road types

[0126] α β expressway 1.5 5 Main road 2.5 4 Secondary arterial road 3 4 branch road 3.5 4

[0127] S23: Calculate the travel time of each road using the road resistance function, and construct the adjacency matrix of road travel time based on the connectivity structure of the traffic network. The adjacency matrix is ​​a matrix that indicates whether there is a direct connection between road network nodes. For example, if road network node i and road network node j are directly connected, then the element in the i-th row and j-th column of the matrix is ​​the travel time tij between the two nodes. If they are not connected, then it is 0.

[0128] like Figure 4 As shown, Dijkstra's algorithm is used to find the shortest travel time and corresponding travel path between any road network nodes, and then all candidate site selection schemes that meet the response time constraints are obtained.

[0129] S3: Determine the comprehensive risk of line failures under the influence of multiple weather factors;

[0130] In this embodiment, step S3 is as follows:

[0131] S31: Weather information has randomness, fuzziness, grayness and uncertainty, which meets the definition of blind information. Therefore, blind number theory can be used to process the construction of power distribution network fault scenarios under extreme weather conditions.

[0132] Blind numbers are defined as grey functions taking values ​​in the interval [0, 1]. For objects with uncertainty, their actual values ​​do not necessarily fall on a single point, but are more likely to fall within a certain interval around that point. Blind numbers involve two parts: possible values ​​and confidence level. Let g(λ) be an interval-type grey number set, consisting of a sequence of interval numbers λ with multiple possible values. u This is formed, with each interval having a confidence level of α. u The confidence sequence distributed across multiple intervals ∈[0,1] constitutes the blind number f(λ), as shown in the following equation:

[0133]

[0134] S32: First, consider the impact of a single weather factor; assume there is W u (u = 1, 2, ..., n) weather factors affect the power distribution network, and each weather factor is divided into m levels w uv(v = 1, 2, ..., m), based on statistical data, the failure rate Y of power outages under different levels of weather conditions can be obtained. uv A judgment matrix is ​​constructed for different levels of factors. Then, the eigenvectors under the largest eigenvalue of the matrix are obtained. Finally, the eigenvectors are normalized to obtain the confidence values ​​α under different levels. uv A comprehensive failure rate model for the line under a single weather condition was obtained. Specifically, it is expressed as follows:

[0135]

[0136] S33: Furthermore, considering the influence of multiple weather factors, a judgment matrix under different weather factors can be constructed to solve for the proportion of different weather factors in the line failure rate, that is, the reliability under different weather factors. The comprehensive fault probability model Y of the line affected by multiple weather factors is obtained, specifically expressed as follows:

[0137]

[0138] In this embodiment, multiple weather factors mainly consider three types of extreme weather: lightning, heavy rain, and typhoons. As shown in Table 5 below, the line failure rate under different levels of each extreme weather condition is obtained:

[0139] Table 5. Line Failure Rates under Different Extreme Weather Levels

[0140]

[0141] By establishing line failure rate models under different meteorological factors, the mean component failure rates under the influence of lightning W1, heavy rain W2, and typhoon W3 are obtained as follows:

[0142] Furthermore, a judgment matrix under different weather factors was constructed, and the weight values ​​under each weather influence were obtained. The comprehensive line fault probability under the influence of multiple weather factors was: Y = 0.1628.

[0143] S4: Construct a mobile emergency resource pre-configuration model considering the power-transportation coupled network under extreme weather conditions. The pre-configuration model includes decision variables, objective function, and constraints.

[0144] Specifically, the decision variables of the pre-configured model include the location x of the mobile emergency resource assembly point and the quantity S of different types of mobile emergency resources. h,i ;

[0145] The objective function of the pre-configured model includes minimizing the total cost of comprehensive load outage loss, mobile emergency resource operating fuel cost, and mobile emergency resource investment and acquisition cost, specifically expressed as:

[0146] minf(x) = α1f1 + α2f2 + α3f3

[0147]

[0148]

[0149]

[0150]

[0151] In the formula, f1 represents the cost of power outage losses; f2 represents the fuel cost for mobile emergency resources; f3 represents the investment and acquisition cost of mobile emergency resources; α1, α2, and α3 are the normalized weight coefficients of f1, f2, and f3, respectively, which can be flexibly determined according to the importance and priority of the optimization objective; N represents the number of load nodes; t i,x The shortest time from the location where mobile emergency resources are stored to node i; Y represents the power outage loss per unit time for node i's load; i The failure rate of node i's load experiencing a power outage; P represents the unit loss due to load loss at node i; i load H represents the load capacity of node i; H represents the type / quantity of mobile energy storage vehicles. The cost of moving mobile emergency resources to node i; The unit fuel cost for mobile emergency resources during their journey to the target node; S h,i The number of h types of mobile emergency resources coupled to node i; The unit investment cost of the h-th type of mobile emergency resource; β is the maximum output power of the h-th mobile resource; h,y,i The connection status between the y-th type h mobile emergency resource and node i;

[0152] The constraints of the pre-configured model include four categories: time response constraints, load recovery demand constraints, mobile emergency resource supply constraints, and mobile emergency resource allocation quantity constraints.

[0153] Among them, the time response constraint is as follows: When the distribution network is affected by extreme weather and a fault occurs, in order to ensure that mobile emergency resources can respond quickly, it is necessary to limit the longest response time from the location of the aggregation point to any faulty load node. The time response constraint formula is as follows:

[0154] t i,x ≤T res ,i∈N

[0155] Among them, T resThis is a time limit for mobile emergency resource response.

[0156] Load restoration requirement constraint: The restoration amount of each load node should not exceed its total load, and the total load restoration amount should be configured to support the restoration of important loads in the distribution network area. The formula for the load restoration requirement constraint is as follows:

[0157] 0≤P i re ≤P i load

[0158]

[0159]

[0160]

[0161] In the formula, P i re , These represent the active and reactive power recovery quantities of load node i, respectively. These are the active and reactive power recovery requirements for critical loads, respectively.

[0162] Mobile emergency resource supply constraint: The total power provided by mobile emergency resources connected to any load node should be greater than the recovery capacity of that load node to ensure sufficient mobile emergency resources are configured. The formula for the mobile emergency resource supply constraint is as follows:

[0163]

[0164]

[0165] In the formula, P h,i Q h,i λ represents the active and reactive power output of the h-th type of mobile emergency resource at the i-th node, respectively; h To improve the discharge efficiency of mobile emergency resources.

[0166] Mobile emergency resource allocation quantity constraints: For distribution network load nodes, the total number of various types of mobile emergency resources should not exceed their maximum allowable quantity limits. The formula for the mobile emergency resource allocation quantity constraint is as follows:

[0167]

[0168] Among them, S H This represents the maximum number of mobile emergency resources of type h.

[0169] Using the mobile emergency resource pre-configuration method of the present invention, the response time limit for mobile emergency resources is set to 20 minutes. The distribution of the shortest travel time from the mobile emergency resource assembly point to each node is obtained, as shown in Table 6 below. The shortest time from the mobile emergency resource assembly point to each node is obtained as follows:

[0170] Table 6. Shortest time from mobile emergency resource assembly point to each node

[0171]

[0172]

[0173] Based on the shortest travel time between each node considering road resistance, and taking into account factors such as load outage risk and mobile emergency resource cost, the optimal gathering point for mobile emergency power supply in the distribution network area of ​​the embodiment is node 17. Furthermore, the shortest travel time from node 17 to other nodes in the distribution network is within the response time limit, which meets the site selection requirements.

[0174] Example 2

[0175] Except for the following technical contents, the technical contents of this embodiment are the same as those of Embodiment 1;

[0176] This embodiment provides a mobile emergency resource pre-configuration system for distribution networks that takes into account the impact of extreme weather, including: a power and transportation coupled network construction module, a path analysis module, a fault scenario construction module, a single weather fault rate model construction module, a multi-weather fault probability model construction module, a resource pre-configuration model construction module, and a resource pre-configuration module.

[0177] In this embodiment, the power and transportation coupled network construction module is used to set up transportation network nodes and power grid nodes in sequence and correspond to each other based on the power grid topology, collect power grid information and transportation network information, and obtain the power and transportation coupled network.

[0178] In this embodiment, the path analysis module is used to perform mobile emergency resource travel path analysis based on Dijkstra's algorithm;

[0179] In this embodiment, the fault scenario construction module is used to construct power distribution network fault scenarios under extreme weather conditions based on blind number theory;

[0180] In this embodiment, the single weather failure rate model construction module is used to obtain the failure rate of power outage of the line under different levels of weather factors based on statistical data, construct the judgment matrix between different levels of factors, obtain the eigenvector under the largest eigenvalue of the matrix, normalize the eigenvector to obtain the confidence value under different levels, and obtain the comprehensive failure rate model of the line under a single weather condition.

[0181] In this embodiment, the multi-weather fault probability model construction module is used to construct a judgment matrix under different weather factors, solve the proportion of different weather factors in the line fault rate, that is, the credibility under different weather factors, and obtain the comprehensive fault probability model of multiple weather factors affecting the line.

[0182] In this embodiment, the resource pre-configuration model construction module is used to construct a mobile emergency resource pre-configuration model that considers the power and transportation coupled network under extreme weather conditions. The mobile emergency resource pre-configuration model includes decision variables, objective function, and constraints.

[0183] In this embodiment, the decision variables include the location of the mobile emergency resource assembly point and the quantity of different types of mobile emergency resources. The objective function includes minimizing the total cost of comprehensive load outage loss, mobile emergency resource fuel cost, and mobile emergency resource investment and purchase cost. The constraints include time response constraints, load restoration demand constraints, mobile emergency resource supply constraints, and mobile emergency resource allocation quantity constraints.

[0184] In this embodiment, the resource pre-configuration module is used to pre-configure resources based on the mobile emergency resource pre-configuration model.

[0185] In this embodiment, the path analysis module is used to perform mobile emergency resource travel path analysis based on Dijkstra's algorithm, specifically including:

[0186] A road resistance function is introduced as a standard for measuring the travel time of a road segment. Its functional form is as follows:

[0187]

[0188] t w0 =l w / v w

[0189] Among them, t w t is the time t required for a vehicle to travel through road segment w. w0 l represents the average free travel time of a vehicle through road segment w. w Indicates the basic road segment length, v w Q represents the road design speed. w Let C be the traffic flow rate of road segment w. w Let w be the traffic capacity of road segment w, and α and β be the parameters to be calibrated.

[0190] The road network is divided into different road types, and the calibration parameters are assigned values ​​according to the different road types;

[0191] The travel time of each road is calculated using the road resistance function, and an adjacency matrix of road travel times is constructed based on the connectivity structure of the traffic network. Dijkstra's algorithm is then used to solve for the shortest travel time and corresponding travel path between any road network nodes, thus obtaining all candidate site selection schemes for the cluster point that meet the response time constraints.

[0192] In this embodiment, the objective function is to minimize the total cost, including the comprehensive load outage loss cost, the fuel cost of mobile emergency resources, and the investment and acquisition cost of mobile emergency resources. Specifically, it is expressed as follows:

[0193] minf(x) = α1f1 + α2f2 + α3f3

[0194]

[0195]

[0196]

[0197]

[0198] Where f1 is the load outage loss cost, f2 is the fuel cost for mobile emergency resources, f3 is the investment and acquisition cost of mobile emergency resources, α1, α2, and α3 are the normalized weight coefficients of f1, f2, and f3, respectively, which can be flexibly determined according to the importance and priority of the optimization objective, N is the number of load nodes, and t i,x The shortest time from the location where mobile emergency resources are stored to node i. Y represents the power outage loss per unit time for node i's load; i The failure rate of node i's load experiencing power outage. For node i, the unit loss due to load shedding, P i load H represents the load capacity of node i, and H represents the type / quantity of mobile energy storage vehicles. The cost of moving mobile emergency resources to node i. S represents the unit fuel cost of mobile emergency resources traveling to the target node. h,i The number of h types of mobile emergency resources coupled to node i. The unit investment cost of the h-th type of mobile emergency resource is... β is the maximum output power of the h-th mobile resource. h,y,i This represents the connection status between the y-th type h mobile emergency resource and node i.

[0199] In this embodiment, the time response constraint limits the longest response time from the location of the aggregation point to any faulty load node, expressed as:

[0200] ti,x ≤T res ,i∈N

[0201] Among them, T res To limit the response time of mobile emergency resources, N is the number of load nodes;

[0202] The load restoration requirement constraint is expressed as:

[0203] 0≤P i re ≤P i load

[0204]

[0205]

[0206]

[0207] In the formula, P i re , These represent the active and reactive power recovery quantities of load node i, respectively. These are the active and reactive power recovery requirements for critical loads, respectively.

[0208] The constraint on the supply of mobile emergency resources is expressed as follows:

[0209]

[0210]

[0211] In the formula, P h,i Q h,i λ represents the active and reactive power output of the h-th type of mobile emergency resource at the i-th node, respectively. h To improve the discharge efficiency of mobile emergency resources;

[0212] The constraint on the quantity of mobile emergency resources is expressed as follows:

[0213]

[0214] Among them, S H This represents the maximum number of mobile emergency resources of type h.

[0215] Example 3

[0216] This embodiment provides a computer-readable storage medium, which may be a ROM, RAM, disk, optical disk, or other storage medium. The storage medium stores one or more programs. When the programs are executed by a processor, they implement the method for pre-configuring mobile emergency resources for power distribution networks that takes into account the impact of extreme weather as described in Embodiment 1.

[0217] Example 4

[0218] This embodiment provides a computing device, which may be a desktop computer, laptop computer, smartphone, PDA handheld terminal, tablet computer or other terminal device with display function. The computing device includes a processor and a memory. The memory stores one or more programs. When the processor executes the program stored in the memory, it implements the method for pre-configuring mobile emergency resources of distribution network taking into account the impact of extreme weather in Embodiment 1.

[0219] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for pre-configuring mobile emergency resources for distribution networks considering the impact of extreme weather, characterized in that, Includes the following steps: Based on the power grid topology, traffic network nodes and power grid nodes are sequentially coupled and corresponding, and power grid information and traffic network information are collected to obtain a power and transportation coupled network. Mobile emergency resource travel path analysis based on Dijkstra's algorithm; Constructing power distribution network fault scenarios under extreme weather conditions based on blind number theory; Based on statistical data, the failure rate of power outages under different levels of weather conditions is obtained. A judgment matrix between different levels of factors is constructed, the eigenvector under the largest eigenvalue of the matrix is ​​obtained, and the eigenvector is normalized to obtain the confidence value under different levels, thus obtaining a comprehensive failure rate model of the line under a single weather condition. Construct a judgment matrix under different weather factors, solve the proportion of different weather factors in the line failure rate, that is, the credibility under different weather factors, and obtain a comprehensive failure probability model of multiple weather factors affecting the line. A mobile emergency resource pre-configuration model considering the coupled power and transportation networks under extreme weather conditions is constructed. The mobile emergency resource pre-configuration model includes decision variables, objective function, and constraints. The decision variables include the location of mobile emergency resource assembly points and the quantity of different types of mobile emergency resources. The objective function includes minimizing the total cost of comprehensive load outage loss, mobile emergency resource operating fuel cost, and mobile emergency resource investment and purchase cost. The constraints include time response constraints, load restoration demand constraints, mobile emergency resource supply constraints, and mobile emergency resource allocation quantity constraints. Resource pre-configuration is based on a mobile emergency resource pre-configuration model.

2. The method for pre-configuring mobile emergency resources for distribution networks taking into account the impact of extreme weather as described in claim 1, characterized in that, The specific steps for analyzing the travel routes of mobile emergency resources based on Dijkstra's algorithm include: A road resistance function is introduced as a standard for measuring the travel time of a road segment. Its functional form is as follows: ； ； in, Let w be the time required for a vehicle to travel through road segment w. Let w be the average free travel time of a vehicle traversing road segment w. Indicates the basic road segment length. Indicates the road's design speed. Let w be the traffic flow rate. Let w be the traffic capacity of road segment w. , These are the parameters to be calibrated; The road network is divided into different road types, and the calibration parameters are assigned values ​​according to the different road types; The travel time of each road is calculated using the road resistance function, and an adjacency matrix of road travel times is constructed based on the connectivity structure of the traffic network. Dijkstra's algorithm is then used to solve for the shortest travel time and corresponding travel path between any road network nodes, thus obtaining all candidate site selection schemes for the cluster point that meet the response time constraints.

3. The method for pre-configuring mobile emergency resources for distribution networks taking into account the impact of extreme weather as described in claim 1, characterized in that, The objective function aims to minimize the total cost, including comprehensive load outage loss costs, mobile emergency resource operating fuel costs, and mobile emergency resource investment and acquisition costs. Specifically, it is expressed as follows: ； ； ； ； ； in, Costs for power outage losses, Fuel costs for mobile emergency resources. For the investment and purchase costs of mobile emergency resources, , , They are respectively , , The normalized weighting coefficients can be flexibly determined based on the importance and priority of the optimization objective, where N is the number of load nodes. The shortest time from the location where mobile emergency resources are stored to node i. The power outage loss per unit time for node i's load; The failure rate of node i's load experiencing power outage. For node i, the unit loss due to load failure. H represents the load capacity of node i, and H represents the type / quantity of mobile energy storage vehicles. The cost of moving mobile emergency resources to node i. The unit fuel cost for mobile emergency resources during their journey to the target node. The number of h types of mobile emergency resources coupled to node i. The unit investment cost of the h-th type of mobile emergency resource is... The maximum output power of the h-th mobile resource. This represents the connection status between the y-th type h mobile emergency resource and node i.

4. The method for pre-configuring mobile emergency resources for distribution networks taking into account the impact of extreme weather as described in claim 3, characterized in that, The time response constraint limits the longest response time from the location of the cluster point to any faulty load node, expressed as: ； in, To limit the response time of mobile emergency resources, N is the number of load nodes; The load restoration requirement constraint is expressed as: ； ； ； ； In the formula, , These represent the active and reactive power recovery quantities at load node i, respectively. , These are the active and reactive power recovery requirements for critical loads, respectively. The constraint on the supply of mobile emergency resources is expressed as follows: ； ； In the formula, , These represent the active and reactive power outputs of the h-th type of mobile emergency resource at the i-th node, respectively. To improve the discharge efficiency of mobile emergency resources; The constraint on the quantity of mobile emergency resources is expressed as follows: ； in, This represents the maximum number of mobile emergency resources of type h.

5. A pre-configuration system for mobile emergency resources in a power distribution network that takes into account the impact of extreme weather, characterized in that, include: The module includes a power and transportation coupled network construction module, a path analysis module, a fault scenario construction module, a single weather fault rate model construction module, a multi-weather fault probability model construction module, a resource pre-configuration model construction module, and a resource pre-configuration module. The power and transportation coupled network construction module is used to establish a power grid topology, set up transportation network nodes and power grid nodes to be coupled and correspond in sequence, collect power grid information and transportation network information, and obtain a power and transportation coupled network. The path analysis module is used to perform mobile emergency resource travel path analysis based on Dijkstra's algorithm. The fault scenario construction module is used to construct power distribution network fault scenarios under extreme weather conditions based on blind number theory. The single weather failure rate model construction module is used to obtain the failure rate of power outages of the line under different levels of weather factors based on statistical data, construct a judgment matrix between different levels of factors, obtain the eigenvector under the largest eigenvalue of the matrix, normalize the eigenvector to obtain the confidence value under different levels, and obtain the comprehensive failure rate model of the line under a single weather condition. The multi-weather fault probability model construction module is used to construct a judgment matrix under different weather factors, solve the proportion of different weather factors in the line fault rate, that is, the credibility under different weather factors, and obtain a comprehensive fault probability model of multiple weather factors affecting the line. The resource pre-configuration model construction module is used to construct a mobile emergency resource pre-configuration model that considers the power and transportation coupled network under extreme weather conditions. The mobile emergency resource pre-configuration model includes decision variables, objective function and constraints. The decision variables include the location of mobile emergency resource assembly points and the quantity of different types of mobile emergency resources. The objective function includes minimizing the total cost of comprehensive load outage loss, mobile emergency resource operating fuel cost, and mobile emergency resource investment and purchase cost. The constraints include time response constraints, load restoration demand constraints, mobile emergency resource supply constraints, and mobile emergency resource allocation quantity constraints. The resource pre-configuration module is used to pre-configure resources based on the mobile emergency resource pre-configuration model.

6. The distribution network mobile emergency resource pre-configuration system taking into account the impact of extreme weather as described in claim 5, characterized in that, The path analysis module is used to analyze the travel paths of mobile emergency resources based on Dijkstra's algorithm, specifically including: A road resistance function is introduced as a standard for measuring the travel time of a road segment. Its functional form is as follows: ； ； in, Let w be the time required for a vehicle to travel through road segment w. Let w be the average free travel time of a vehicle traversing road segment w. Indicates the basic road segment length. Indicates the road's design speed. Let w be the traffic flow rate. Let w be the traffic capacity of road segment w. , These are the parameters to be calibrated; The road network is divided into different road types, and the calibration parameters are assigned values ​​according to the different road types; The travel time of each road is calculated using the road resistance function, and an adjacency matrix of road travel times is constructed based on the connectivity structure of the traffic network. Dijkstra's algorithm is then used to solve for the shortest travel time and corresponding travel path between any road network nodes, thus obtaining all candidate site selection schemes for the cluster point that meet the response time constraints.

7. The distribution network mobile emergency resource pre-configuration system taking into account the impact of extreme weather as described in claim 5, characterized in that, The objective function aims to minimize the total cost, including comprehensive load outage loss costs, mobile emergency resource operating fuel costs, and mobile emergency resource investment and acquisition costs. Specifically, it is expressed as follows: ； ； ； ； ； in, Costs for power outage losses, Fuel costs for mobile emergency resources. For the investment and purchase costs of mobile emergency resources, , , They are respectively , , The normalized weighting coefficients can be flexibly determined based on the importance and priority of the optimization objective, where N is the number of load nodes. The shortest time from the location where mobile emergency resources are stored to node i. The power outage loss per unit time for node i's load; The failure rate of node i's load experiencing power outage. For node i, the unit loss due to load failure. H represents the load capacity of node i, and H represents the type / quantity of mobile energy storage vehicles. The cost of moving mobile emergency resources to node i. The unit fuel cost for mobile emergency resources during their journey to the target node. The number of h types of mobile emergency resources coupled to node i. The unit investment cost of the h-th type of mobile emergency resource is... The maximum output power of the h-th mobile resource. This represents the connection status between the y-th type h mobile emergency resource and node i.

8. The distribution network mobile emergency resource pre-configuration system taking into account the impact of extreme weather as described in claim 7, characterized in that, The time response constraint limits the longest response time from the location of the cluster point to any faulty load node, expressed as: ； in, To limit the response time of mobile emergency resources, N is the number of load nodes; The load restoration requirement constraint is expressed as: ； ； ； ； In the formula, , These represent the active and reactive power recovery quantities at load node i, respectively. , These are the active and reactive power recovery requirements for critical loads, respectively. The constraint on the supply of mobile emergency resources is expressed as follows: ； ； In the formula, , These represent the active and reactive power outputs of the h-th type of mobile emergency resource at the i-th node, respectively. To improve the discharge efficiency of mobile emergency resources; The constraint on the quantity of mobile emergency resources is expressed as follows: ； in, This represents the maximum number of mobile emergency resources of type h.

9. A computer-readable storage medium storing a program, characterized in that, When the program is executed by the processor, it implements the method for pre-configuring mobile emergency resources for distribution networks that takes into account the impact of extreme weather, as described in any one of claims 1-4.

10. A computer device comprising a processor and a memory for storing a processor-executable program, characterized in that, When the processor executes the program stored in the memory, it implements the method for pre-configuring mobile emergency resources for power distribution networks that takes into account the impact of extreme weather, as described in any one of claims 1-4.

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