A distribution network fault emergency restoration method considering ECV-MES active regulation

Abstract

The present invention belongs to the technical field of power grid dispatching, and discloses a distribution network fault emergency recovery method considering active regulation of ECV-MES, establishes a distribution network line failure probability model under typhoon disaster conditions; based on the power-traffic coupling network, the pre-disaster ECV-MES pre-deployment position is determined by optimizing the ECV-MES dispatching distance and response time; with the goal of minimizing the cost of distribution network communication failure and achieving the shortest repair time for emergency communication, a post-disaster communication system recovery model is constructed; a post-disaster power system recovery model is constructed considering the load importance level, and the model is solved to obtain the optimal power-communication coordination and dispatching scheme. The method described in the present invention can ensure that the distribution network can reliably and quickly restore the normal operating state, and provide a reference for improving the disaster resistance and operation efficiency of the distribution network.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid dispatching, and in particular relates to a distribution network fault emergency recovery method considering active regulation of ECV-MES. Background Art

[0002] In modern society, the power grid is the core of urban infrastructure, and its stable operation is crucial to ensuring social and economic activities and people's lives. At present, traditional distribution network restoration methods mainly rely on preset emergency response plans and the rapid response of on-site maintenance teams, which often cannot meet the needs of efficient restoration in terms of real-time, flexibility and efficiency of disaster response.

[0003] First, traditional methods lack sufficient flexibility and foresight in resource allocation and scheduling plans before disasters. When extreme disasters occur, distribution network lines may have multiple faults, and existing plans are difficult to cover all potential fault points, which prolongs the response time and increases the difficulty of recovery work; secondly, once the communication system in the distribution network is damaged, it will seriously affect fault detection, location and instant transmission of fault information, further increasing the complexity and uncertainty of recovery work. In addition, when dealing with power outages and load fluctuations caused by large-scale disasters, existing technologies often find it difficult to achieve accurate scheduling and cannot maximize the restoration of important loads, affecting the overall efficiency and effectiveness of recovery work.

[0004] In recent years, with the development of mobile energy storage technology, its application in emergency restoration of power grids has begun to attract attention. Mobile energy storage systems can provide temporary power support when the power grid is out of power or unstable, which is of great significance for improving emergency power supply and optimizing power grid operation. However, this technology still has many limitations in practical applications. For example, the deployment, scheduling and charging strategies of mobile energy storage equipment have not yet formed complete and effective standards and processes, which limits its response speed and efficiency after a disaster. In addition, although the emergency communication vehicle, as an emergency response vehicle that integrates communications and mobile energy storage, can quickly restore on-site communications when the communication network is damaged, how to efficiently integrate the role of the emergency communication vehicle in power system fault detection, positioning and restoration to achieve coordinated optimization of power and communications is still a technical problem to be solved.

[0005] Although the existing distribution network restoration technology can handle conventional faults, its ability to recover large-scale distributed energy access to the distribution network in the face of extreme disasters is still insufficient. Accelerating the rate of distribution network fault recovery under extreme climate is still an urgent problem to be solved. For example, the literature (Xu Shoudong et al. Distribution network fault recovery strategy considering MESS active regulation under extreme meteorological disasters [J]. Power System Protection and Control, 2024, 52(12): 45-57) proposed a strategy to actively participate in distribution network fault recovery by utilizing the flexibility of mobile energy storage, but did not consider how to combine mobile energy storage with emergency communication vehicles to achieve coordinated recovery of power and communication systems to improve the utilization of flexible resources. Therefore, there is currently a lack of a comprehensive solution that can break through the limitations of the single use of traditional energy storage emergency equipment to achieve multifunctional integration, and can be fast, efficient, and take into account the needs of power and communication recovery. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a distribution network fault emergency recovery method considering the active regulation of ECV-MES, which aims to accelerate the recovery rate of power and communication systems of distribution network faults under extreme climates, and at the same time break through the limitation of single use of traditional energy storage emergency equipment. The multifunctional integrated strategy of the emergency communication vehicle with mobile energy storage (ECV-MES) is used to actively participate in the emergency dispatch of the power grid, improve the utilization rate of flexible resources, reduce the impact of natural disasters and node load fluctuations caused by typhoon extreme weather, and assist in the rapid recovery of distribution network faults.

[0007] The present invention provides a distribution network fault emergency recovery method considering ECV-MES active regulation, comprising the following steps:

[0008] S1. Considering the total load on the distribution network line when encountering a typhoon, a distribution network line failure probability model is established under typhoon disaster conditions;

[0009] S2. Use geographic coupling and adjacency matrix to model road connections and distances, build a power-transportation coupling network, take the shortest dispatch distance and response time of ECV-MES in typhoon weather as the objective function, establish and solve the pre-disaster deployment model, and pre-determine the initial position of ECV-MES after the accident;

[0010] S3. To build a post-disaster communication system recovery model with the goal of minimizing the cost of distribution network communication failure after extreme disasters and the shortest repair time for the communication system to achieve emergency communication;

[0011] S4. Construct a post-disaster power system recovery model with the goal of maximizing the load recovery rate;

[0012] S5. Based on the models obtained in S3 and S4, a distribution network fault power and communication emergency recovery model considering the active regulation of ECV-MES is constructed, and the NSGA-II multi-objective optimization algorithm is used to optimize and solve the distribution network fault emergency recovery plan considering the active regulation of ECV-MES, so as to realize the coordinated optimization of the power and communication systems and obtain the optimal scheduling plan that meets the constraints.

[0013] Furthermore, S1 is specifically:

[0014] The probability of line disconnection is calculated as follows:

[0015] (1)

[0016] In the formula, P represents the probability of line disconnection; Indicates the maximum load that the line can bear; M indicates the total load that the line bears at this time, which is composed of the line's own gravity load and the wind load that it can bear; therefore, when When the total load on the line is greater than or equal to the maximum load that the line can bear, a line break will inevitably occur; when When , the total load borne by the line is less than the maximum load that the line can bear, and the probability of line disconnection is the ratio of the two. The total load borne by the line is shown in formula (2):

[0017] (2)

[0018] In the formula, G is the gravity load of the line itself; Indicates wind speed; Indicates the calculated equivalent outer diameter of the line; Indicates the angle between wind direction and conductor; is the wind pressure unevenness coefficient; is the wind pressure coefficient caused by different heights; is the body coefficient of the conductor; is the wind load adjustment factor for conductors and ground wires; The wind load increase factor caused by the increase in the load-bearing area;

[0019] (3)

[0020] In the formula, To break the tension, is the safety factor;

[0021] Consider a line of the distribution network as a series system formed by line supports between multiple towers. When any line between two towers is broken, the entire line cannot operate normally. It can only operate normally when all lines are normal and reliable. The comprehensive line failure probability of this transmission line is :

[0022] (4)

[0023] In the formula, the number of lines between adjacent towers is ,in , is the probability of line disconnection between adjacent towers.

[0024] Furthermore, S2 is specifically:

[0025] According to the actual geographical connection relationship of each node in the distribution network, an adjacency matrix D reflecting the road connection relationship and geographical distance is established, as shown in formula (5); according to the actual connection situation, if there is no road connection between the nodes or the road conditions do not allow ECV-MES to pass, the distance between the two nodes is considered to be infinite, which is expressed by formula (6):

[0026] (5)

[0027] (6)

[0028] Where N is the total number of system nodes, ; is the actual geographical connection relationship and road distance between node i and node j; is the road distance connecting node i to node j;

[0029] Since the actual road traffic conditions change in real time, the traffic congestion coefficient is introduced Modify the adjacency matrix D:

[0030] (7);

[0031] In order to achieve the goal of minimizing the ECV-MES scheduling cost in fault recovery, the Floyd algorithm is used to calculate the node distance matrix B, which is the node shortest path matrix; is the weighted adjacency matrix, whose initial matrix value is , the shortest moving path between any two nodes is obtained by interpolation iteration, as shown in formula (8):

[0032] (8)

[0033] Where A is the set of all nodes in the system; find out whether there is a node , so that node i to The path to j is shorter than the known path from node i to j and is updated;

[0034] According to the distribution network line failure probability model under typhoon disaster conditions, the shortest dispatch distance and response time of ECV-MES under various disaster weather conditions are taken as the optimization objective function. The pre-disaster deployment model is established as shown in formula (9), and the gurobi commercial solver is used to solve the model to determine the initial position of ECV-MES:

[0035] (9)

[0036] In the formula, and is the weight coefficient of the two objectives; is the shortest weighted distance for the mth group of energy storage to be dispatched from node i to node j in the distribution network fault recovery strategy under typhoon weather; is the total number of ECV-MES dispatch groups; is the comprehensive probability of line failure of transmission lines in typhoon weather; is the moving speed of ECV-MES;

[0037] Constraints:

[0038] (10)

[0039] In the formula, is the upper limit of the scheduling time of a single group of ECV-MES; The total number of ECV-MES groups that can participate in scheduling during this period; and are the maximum active power of ECV-MES involved in charging and discharging respectively; and are the maximum reactive power of ECV-MES involved in charging and discharging respectively; is the total capacity of mobile energy storage in ECV-MES; and They represent the active power and reactive power of ECV-MES in charging and discharging at time t respectively.

[0040] Furthermore, S3 is specifically:

[0041] The objective function of minimizing the cost of distribution network communication failure after extreme disasters and the shortest repair time for the distribution network communication system to achieve emergency communication is shown in formula (11):

[0042] (11)

[0043] In the formula, is the failure value of communication node n per unit time, k is the total number of failed nodes in the distribution network communication system, is the failure time of the communication node n in the distribution network, is the time consumed by the communication capability of the pth ECV-MES repair node. When the types of communication nodes in the distribution network are the same, the longer the failure time, the greater the failure cost.

[0044] Therefore, the target equivalence is the shortest repair time that satisfies the repair principle of the distribution network communication system; the shortest repair time solution is determined by the team that takes the most time to complete the assigned repair task, and p is the number of ECV-MESs for repair of communication nodes;

[0045] (12)

[0046] In the formula, For the xth vehicle The time to complete the assigned tasks, J is the total number of tasks that the x-th ECV-MES needs to complete, is the time required for ECV-MES to complete the repair of the jth communication node, The driving time for ECV-MES to perform emergency repairs.

[0047] Furthermore, S4 is specifically:

[0048] When the communication system is restored, the load equivalent recovery rate during the power failure recovery period of the distribution network is used as the indicator of the recovery plan, and the corresponding weight coefficient is set considering the importance level of the node load. Each group of ECV-MES is moved and its online power is determined, as shown in formula (13):

[0049] (13)

[0050] In the formula, To maximize the load recovery rate of the power system; R is the set of distribution network nodes; is the load weight of node i; is a binary variable, indicating the power status of node i; is the load active power of node i; Indicates the duration of the fault.

[0051] Furthermore, S5 is specifically:

[0052] S5-1. Define the goals and constraints of the optimization problem: In the disaster recovery model, two goals are defined: Minimize the time and cost of emergency communication repair by the communication system , and maximize the load recovery rate of the power system ;At the same time, consider the constraints;

[0053] S5-2. Initialize the population: The NSGA-II algorithm starts by randomly generating an initial population, where each individual (solution) represents a possible restoration scheme, which includes the configuration of the communication and power systems;

[0054] S5-3, Fitness evaluation: The fitness of each individual is evaluated according to the defined multiple objectives, and its target value under the post-disaster communication recovery and power system recovery models is calculated based on the objective function. The target value is converted into a fitness value using the normalization method to ensure comparability between different objectives;

[0055] S5-4, non-dominated sorting: NSGA-II arranges the population through non-dominated sorting, dividing the individuals in the population into different levels, and the layer with high priority (i.e., the non-dominated individual layer) has a selection advantage;

[0056] S5-5. Crowding calculation: The NSGA-II algorithm uses crowding ranking to distinguish individuals in the same dominance layer. Individuals with high crowding are more likely to be selected for the next generation of reproduction, thereby maintaining the diversity of solutions and promoting progress toward the Pareto frontier;

[0057] S5-6, Selection, Crossover and Mutation: The selection process is based on the non-dominated ordering and crowding of individuals; the selected individuals produce a new generation through genetic operations such as crossover (recombining some characteristics of individuals) and mutation (randomly modifying the characteristics of individuals);

[0058] S5-7, iterate to termination condition: repeat S5-3 to S5-6 until the termination condition is met, such as reaching a preset number of iterations or the quality of the solution reaches a certain threshold (fitness value converges);

[0059] S5-8. Result analysis and output: After the algorithm terminates, the individuals in the non-dominated layer are output as the final solution set. These solutions form the Pareto frontier in the multidimensional space of the objective function, and decision makers can choose the most suitable recovery plan according to actual needs.

[0060] Furthermore, in S5-1, the constraints set are specifically:

[0061] During the entire fault process, in order to maintain safe and reliable operation of the power grid, the following constraints should be met:

[0062] (14)

[0063] In the formula, is the maximum allowable current of the branch; and are the upper and lower limits of node i voltage respectively, and equation (14) is the branch current safety constraint and node voltage safety constraint;

[0064] (15)

[0065] In the formula, and are the maximum active and reactive power of the branch respectively; and They are the maximum active and reactive power of the power supply respectively; and Respectively represent the active power and reactive power of the branch; and They represent the active power and reactive power of the power source respectively; Formula (15) is the power constraint of the power source and the power transmission constraint of the branch;

[0066] The influence of parallel admittance is ignored in the power flow calculation model, and the power flow balance constraint is further decomposed. The specific formula is as follows:

[0067] (16)

[0068] In the formula, It is a collection of branches that can still operate normally after a fault occurs in the distribution network; and They represent the active and reactive power injected into node j respectively; and They represent the active power and reactive power of the load at node j respectively; and are the active and reactive power of the branch respectively; , , They are branch current, branch resistance and branch reactance respectively; is a binary variable, indicating the charged state of node j; , , Inject active power into each power source on node j; , , Inject reactive power into each power source on node j;

[0069] Each distribution network fault communication node is assigned to a repair vehicle, and at least one optical fiber connected to each distribution network communication node is ensured to be in normal working condition. Any repaired failed communication node can be connected to the communication node through optical fiber. The constraint formula is as follows:

[0070] (17)

[0071] In the formula, Indicates whether node j is assigned to ECV-MES for emergency repair. When the distribution network communication node j is assigned to any ECV-MES for emergency repair, The value is 1, which means it is not assigned. The value is 0; is the wth optical fiber connected to the ith communication node, the value of the normal optical fiber is 1, and the value of the broken optical fiber is 0; W is the total number of optical fibers connected to the communication node; For nodes that have restored communication, The optical fiber between any failed node to be repaired and the node with restored communication function;

[0072] The distribution network communication node where ECV-MES is located can realize the communication function, and its constraints are:

[0073] (18)

[0074] In the formula, represents the communication status of node i, A value of 1 indicates that communication node i can communicate, and a value of 0 indicates that communication node i cannot communicate; represents the distance from communication node i to the mobile base station, It is the signal coverage of the mobile base station.

[0075] The beneficial effects described in the present invention are as follows: the method described in the present invention adopts a multifunctional integrated fault recovery strategy, introduces an emergency communication vehicle (ECV-MES) loaded with mobile energy storage, significantly accelerates the recovery rate of the distribution network and communication system under extreme climatic conditions, and breaks through the limitation of the single use of traditional energy storage emergency equipment. This strategy not only optimizes resource allocation and scheduling efficiency, improves the utilization rate of flexible resources, but also realizes efficient pre-disaster pre-deployment and post-disaster rapid fault location and repair, significantly enhances the disaster resistance and operation efficiency of the distribution network, realizes the maximum recovery of important loads, and ensures that the power network can quickly return to normal operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 is a flow chart of the method of the present invention;

[0077] Figure 2 It is the flow chart of ECV-MES active regulation and control of distribution network fault recovery solution;

[0078] Figure 3 It is a schematic diagram of IEEE33-node power network and ECV-MES pre-deployment;

[0079] Figure 4 It is a schematic diagram of the important load recovery ratio curve of schemes 1 and 2;

[0080] Figure 5 This is a schematic diagram of the number of communication recovery nodes for schemes 1 and 2;

[0081] Figure 6 This is a schematic diagram of the proportion of important loads restored in each period of Scheme 1;

[0082] Figure 7 This is a schematic diagram of the recovery ratio of non-important loads in each period of Scheme 1;

[0083] Figure 8 This is a schematic diagram of load power and recovery ratio in each period of Scheme 1. DETAILED DESCRIPTION

[0084] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments in conjunction with the accompanying drawings.

[0085] Figure 1 The flowchart of the method of the present invention is as follows. The method of the present invention for emergency recovery of distribution network faults taking into account active regulation of ECV-MES comprises the following steps:

[0086] S1. Considering the total load borne by the distribution network line when encountering a typhoon, including its own gravity load and the wind load borne by the line, a distribution network line failure probability model is established under typhoon disaster conditions;

[0087] S2. Use geographic coupling and adjacency matrix to model road connections and distances, build a power-transportation coupling network, take the shortest dispatch distance and response time of ECV-MES in typhoon weather as the objective function, establish and solve the pre-disaster deployment model, and pre-determine the initial position of ECV-MES after the accident;

[0088] S3. To ensure the timeliness of repair of the communication system, the goal is set to minimize the cost of distribution network communication failure after extreme disasters and the shortest repair time for the communication system to achieve emergency communication, and a post-disaster communication system recovery model is constructed.

[0089] S4. To improve the resilience of the power grid, the goal is to maximize the load recovery rate, improve the continuous power supply capability of key loads after a fault occurs and the rapid recovery capability of power-lost loads, and build a post-disaster power system recovery model;

[0090] S5. The NSGA-II multi-objective optimization algorithm is used to optimize and solve the distribution network fault emergency recovery plan considering the active regulation of ECV-MES, realize the coordinated optimization of the power and communication systems, and obtain the optimal scheduling plan that meets the constraints.

[0091] In S1, when typhoon weather occurs, the lines of the distribution network are often disconnected. The closer to the typhoon center, the greater the impact. Line disconnection is also one of the main manifestations of power-communication failures in the distribution network caused by typhoons. The main reason is that the wind load perpendicular to the line exceeds the design standard of the line. Therefore, it is necessary to establish a distribution network line failure probability model under typhoon disaster conditions. The line disconnection probability calculation is as follows:

[0092] (1)

[0093] In the formula, P represents the probability of line disconnection; Indicates the maximum load that the line can bear; M indicates the total load that the line bears at this time, which is composed of the line's own gravity load and the wind load that it can bear; therefore, when When the total load on the line is greater than or equal to the maximum load that the line can bear, a line break will inevitably occur; when When , the total load borne by the line is less than the maximum load that the line can bear, and the probability of line disconnection is the ratio of the two. The total load borne by the line is shown in formula (2):

[0094] (2)

[0095] In the formula, G is the gravity load of the line itself; Indicates wind speed; Indicates the calculated equivalent outer diameter of the line; Indicates the angle between wind direction and conductor; is the wind pressure unevenness coefficient; is the wind pressure coefficient caused by different heights; is the body coefficient of the conductor; is the wind load adjustment factor for conductors and ground wires; The wind load increase factor caused by the increase in the load-bearing area;

[0096] (3)

[0097] In the formula, To break the tension, is the safety factor;

[0098] Consider a line of the distribution network as a series system formed by line supports between multiple towers. When any line between two towers is broken, the entire line cannot operate normally. It can only operate normally when all lines are normal and reliable. The comprehensive line failure probability of this transmission line is :

[0099] (4)

[0100] In the formula, the number of lines between adjacent towers is ,in , is the probability of line disconnection between adjacent towers.

[0101] In S2, during the distribution network fault recovery process, since the participation of ECV-MES needs to be taken into account, and the dispatching status of ECV-MES is determined by the charge state, initial position and transportation status, the road traffic nodes and the distribution network nodes are geographically coupled to build a power-transportation coupling network, and ECV-MES is used to quickly respond to distribution network interruptions under extreme weather conditions, thereby significantly improving the speed of fault recovery.

[0102] According to the actual geographical connection relationship of each node in the distribution network, an adjacency matrix D reflecting the road connection relationship and geographical distance is established, as shown in formula (5); according to the actual connection situation, if there is no road connection between the nodes or the road conditions do not allow ECV-MES to pass, the distance between the two nodes is considered to be infinite. It can be expressed by formula (6):

[0103] (5)

[0104] (6)

[0105] Where N is the total number of system nodes, ; is the actual geographical connection relationship and road distance between node i and node j; is the road distance connecting node i to node j.

[0106] Since the actual road traffic conditions change in real time, the traffic congestion coefficient is introduced Modify the adjacency matrix D:

[0107] (7);

[0108] In order to achieve the goal of minimizing the ECV-MES scheduling cost in fault recovery, the Floyd algorithm is further used to calculate the node distance matrix B, which is the node shortest path matrix; is the weighted adjacency matrix, whose initial matrix value is , the shortest moving path between any two nodes is obtained by the interpolation iteration method, as shown in formula (8). This method is simple and intuitive to implement with high computational efficiency:

[0109] (8)

[0110] Where A is the set of all nodes in the system; find out whether there is a node , so that node i to The path to j is shorter than the known path from node i to j and is updated.

[0111] By inputting the known distribution network structure parameters and coupling them with the actual traffic network connection conditions, the shortest dispatch distance between actual nodes is calculated. According to the distribution network line failure probability model under typhoon disaster conditions, the shortest dispatch distance and response time of ECV-MES under various disaster weather conditions are taken as the optimization objective function, and the pre-disaster deployment model is established as shown in formula (9). The model is solved using the gurobi commercial solver to determine the initial position of ECV-MES:

[0112] (9)

[0113] In the formula, and is the weight coefficient of the two objectives; is the shortest weighted distance for the mth group of energy storage to be dispatched from node i to node j in the distribution network fault recovery strategy under typhoon weather; is the total number of ECV-MES dispatch groups; is the comprehensive probability of line failure of transmission lines in typhoon weather; ECV-MES moving speed.

[0114] Constraints:

[0115] (10)

[0116] In the formula, is the upper limit of the scheduling time of a single group of ECV-MES; The total number of ECV-MES groups that can participate in scheduling during this period; and are the maximum active power of ECV-MES involved in charging and discharging respectively; and are the maximum reactive power of ECV-MES involved in charging and discharging respectively; is the total capacity of mobile energy storage in ECV-MES.

[0117] In S3, the optimization problem of emergency repair of multiple faults in the distribution network communication system is that the timeliness of emergency repair is the most important concern in engineering while ensuring the effectiveness of key communication nodes. The goal is set to minimize the cost of distribution network communication failure after extreme disasters and the shortest emergency repair time for the distribution network communication system to achieve emergency communication. Its objective function is shown in formula (11):

[0118] (11)

[0119] In the formula, is the failure value of communication node n per unit time, k is the total number of failed nodes in the distribution network communication system, is the failure time of the communication node n in the distribution network, is the time consumed by the communication capacity of the pth ECV-MES repair node. When the types of distribution network communication nodes are the same, the longer the failure time, the greater the failure cost; therefore, the goal can be equivalent to: shorten the repair time as much as possible under the premise of giving priority to restoring important value nodes, that is, to find the shortest repair time that meets the repair principle of the distribution network communication system. The shortest repair time plan is determined by the team that takes the most time to complete the assigned repair task, and p is the number of ECV-MES repairing the communication node;

[0120] (12)

[0121] In the formula, For the xth vehicle The time to complete the assigned tasks, J is the total number of tasks that the x-th ECV-MES needs to complete, is the time required for ECV-MES to complete the repair of the jth communication node, The driving time for ECV-MES to perform emergency repairs.

[0122] In S4, ECV-MES also has to support the restoration of power supply to some power-off nodes. ECV-MES can provide steady-state power support for some loads disconnected from the distribution network to ensure the subsequent continuous power supply to important loads. When the communication system is restored, the load equivalent recovery rate during the power failure recovery period of the distribution network is used as the indicator of the recovery plan, and the corresponding weight coefficient is set considering the importance level of the node load. Each group of ECV-MES is moved and its online power is determined, as shown in formula (13):

[0123] (13)

[0124] In the formula, To maximize the load recovery rate of the power system; R is the set of distribution network nodes; is the load weight of node i; is a binary variable, indicating the power status of node i; is the load active power of node i; Indicates the duration of the fault.

[0125] In S5, according to the determined pre-disaster deployment position of ECV-MES, the post-disaster communication system recovery model is constructed with the cost of distribution network communication failure and the time required for emergency communication repair of the communication system as the target; considering the load importance level and setting the corresponding weights, a post-disaster power system recovery model is constructed with the goal of maximizing the load recovery rate; the above two models are combined into a distribution network fault power and communication emergency recovery model considering the active regulation of ECV-MES, and finally the NSGA-II algorithm is used to solve the multi-objective optimization problem.

[0126] The NSGA-II algorithm is a second-generation non-dominated sorting genetic optimization algorithm for multi-objective optimization problems that combines the Pareto theory. The NSGA-II algorithm flow chart is shown in the attached figure. Figure 2 shown.

[0127] In order to verify the beneficial effects of the present invention, experiments are performed to demonstrate the effects.

[0128] Using the method described in the present invention, the failure probability of the distribution network line under typhoon weather is first calculated. Secondly, the known distribution network structure parameters are input and coupled with the actual traffic network connection conditions to calculate the shortest dispatching distance between actual nodes. Then, the distribution network load loss risk and fault recovery model are calculated according to the failure probability of each line, and the initial node distribution is determined by minimizing the time and space cost of ECV-MES dispatch. Taking the IEEE33-node distribution system as an example, the pre-deployed nodes of two ECV-MES are calculated, which are nodes 10 and 29, respectively. Figure 3 shown.

[0129] (1) Comparative simulation of pre-disaster deployment plans.

[0130] In order to better demonstrate the superiority of the pre-disaster deployment scheme, the following two schemes are set up for comparison as shown in Table 1. Scheme 1 considers the pre-layout of the number and position of ECV-MES, and the initial positions of the two ECV-MES are pre-positioned at nodes 10 and 29. Scheme 2 does not pre-position the ECV-MES, and the initial positions of the two ECV-MES are both at node 1.

[0131] Table 1 Comparison of pre-disaster deployment plans

[0132]

[0133] The important load recovery ratio curves of schemes 1 and 2 are given by Figure 4 As shown in the figure, at the time of the disaster, the branches disconnected due to the fault were 19-21 and 30-32, and the distribution network was divided into three isolated islands. The fault time was 8:00. Figure 4It can be seen that after the fault occurs, the recovery ratio of important loads in Scheme 1 is significantly higher than that in Scheme 2, and at 19:00 the recovery ratio is stable at more than 90%, while that in Scheme 2 is only stable at more than 50%, which proves the beneficial effect of adopting the pre-disaster deployment scheme.

[0134] (2) Simulation of post-disaster communication recovery results.

[0135] according to Figure 3 As shown in Figure 1, the faulty branches are 19-21 and 30-32, so a total of 6 nodes need to be repaired and restored for the communication system. The time required for communication restoration nodes in schemes 1 and 2 is compared as follows: Figure 5 As shown, after the disaster, Scheme 1 takes 10 hours to complete the communication recovery of all nodes, while Scheme 2 takes 20 hours to achieve the communication recovery of all nodes. This proves that the present invention can achieve the recovery of the communication system after a distribution network failure, and the use of a pre-disaster pre-deployment solution can improve the recovery efficiency of the communication system.

[0136] (3) Simulation of post-disaster power restoration results.

[0137] During the disaster recovery period, the recovery ratio of important loads, the recovery ratio of non-important loads, and the power supply load power changes of Scheme 1 (adopting the pre-deployment scheme) are as follows: Figure 6 , Figure 7 , Figure 8 As shown, the recovery rate of important loads always remains above 60%, and the recovery rate of non-important loads finally reaches 40%, with the maximum power supply load being 1900 kW. In the period of 08:00-09:00, affected by the congestion of the traffic network after the disaster, the lowest power supply load power was 520 kW. In the period of 09:00-15:00, the power supply load power showed an increasing trend. During the period of 15:00-17:00, the voltage of other nodes fluctuated slightly, but distributed power sources such as mobile energy storage had a certain voltage support capability, and the voltage of each node remained within a reasonable range. In the period of 17:00-19:00, the recovery rate of important loads reached 92%, and the system resilience was improved to a higher level. It can be proved that the present invention has a beneficial effect on realizing the recovery of the power system after a distribution network failure.

[0138] The experimental results illustrate the feasibility and practicality of the present invention. The present invention focuses on the problem of power and communication restoration after a distribution network failure containing distributed power sources under extreme climatic conditions. In particular, the application of an emergency communication vehicle (ECV-MES) with temporal and spatial flexibility and loaded with mobile energy storage in the dynamic recovery of distribution networks after disasters is discussed. The proposed ECV-MES actively participates in the distribution network fault recovery strategy, aiming to achieve rapid deployment of emergency energy storage systems under extreme meteorological conditions. This strategy not only enhances the safety and reliability of the distribution network during the fault power and communication recovery process, but also ensures that the network and equipment operate within the safe capacity, thereby enhancing the feasibility of the implementation of the distribution network fault recovery plan.

[0139] The above description is only a preferred embodiment of the present invention and is not intended to be a further limitation of the present invention. All equivalent changes made using the contents of the present specification and drawings are within the protection scope of the present invention.

Claims

1. A distribution network fault emergency recovery method considering ECV-MES active regulation, characterized in that: The following steps are involved: S1. Considering the total load on the distribution network line when encountering a typhoon, a distribution network line failure probability model is established under typhoon disaster conditions; S2. Use geographic coupling and adjacency matrix to model road connections and distances, build a power-transportation coupling network, take the shortest dispatch distance and response time of ECV-MES in typhoon weather as the objective function, establish and solve the pre-disaster deployment model, and pre-determine the initial position of ECV-MES after the accident; S3. With the goal of minimizing the cost of communication failure in the distribution network after extreme disasters and the shortest repair time for the communication system to achieve emergency communication, a post-disaster communication system recovery model is constructed; specifically: The objective function of minimizing the cost of distribution network communication failure after extreme disasters and the shortest repair time for the distribution network communication system to achieve emergency communication is shown in formula (11): (11)， In the formula, is the failure value of communication node n per unit time, k is the total number of failed nodes in the distribution network communication system, is the failure time of the communication node n in the distribution network, is the time consumed by the communication capability of the pth ECV-MES repair node. When the types of communication nodes in the distribution network are the same, the longer the failure time, the greater the failure cost. Therefore, the target equivalence is the shortest repair time that satisfies the repair principle of the distribution network communication system; the shortest repair time solution is determined by the team that takes the most time to complete the assigned repair task, and p is the number of ECV-MESs for repair of communication nodes; (12)， In the formula, For the xth vehicle The time to complete the assigned tasks, J is the total number of tasks that the x-th ECV-MES needs to complete, is the time required for ECV-MES to complete the repair of the jth communication node, The driving time for ECV-MES to perform emergency repairs; S4. Construct a post-disaster power system recovery model with the goal of maximizing the load recovery rate; S5. Based on the models obtained in S3 and S4, a distribution network fault power and communication emergency recovery model considering the active regulation of ECV-MES is constructed, and the NSGA-II multi-objective optimization algorithm is used to optimize and solve the distribution network fault emergency recovery plan considering the active regulation of ECV-MES, so as to realize the coordinated optimization of the power and communication systems and obtain the optimal scheduling plan that meets the constraints.

2. A distribution network fault emergency recovery method considering ECV-MES active regulation according to claim 1, characterized in that: S1 is specifically: The probability of line disconnection is calculated as follows: (1)， In the formula, P represents the probability of line disconnection; Indicates the maximum load that the line can bear; M indicates the total load that the line bears at this time, which is composed of the line's own gravity load and the wind load that it can bear; therefore, when When the total load on the line is greater than or equal to the maximum load that the line can bear, a line break will inevitably occur; when When , the total load borne by the line is less than the maximum load that the line can bear, and the probability of line disconnection is the ratio of the two. The total load borne by the line is shown in formula (2): (2)， In the formula, G is the gravity load of the line itself; Indicates wind speed; Indicates the calculated equivalent outer diameter of the line; Indicates the angle between wind direction and conductor; is the wind pressure unevenness coefficient; is the wind pressure coefficient caused by different heights; is the body coefficient of the conductor; is the wind load adjustment factor for conductors and ground wires; The wind load increase factor caused by the increase in the load-bearing area; (3)， In the formula, To break the tension, is the safety factor; Consider a line of the distribution network as a series system formed by line supports between multiple towers. When any line between two towers is broken, the entire line cannot operate normally. It can only operate normally when all lines are normal and reliable. The comprehensive line failure probability of this transmission line is : (4)， In the formula, the number of lines between adjacent towers is ,in , is the probability of line disconnection between adjacent towers.

3. A distribution network fault emergency recovery method considering ECV-MES active regulation according to claim 2, characterized in that: S2 is specifically: According to the actual geographical connection relationship of each node in the distribution network, an adjacency matrix D reflecting the road connection relationship and geographical distance is established, as shown in formula (5); according to the actual connection situation, if there is no road connection between the nodes or the road conditions do not allow ECV-MES to pass, the distance between the two nodes is considered to be infinite, which is expressed by formula (6): (5)， (6)， Where N is the total number of system nodes, ; is the actual geographical connection relationship and road distance between node i and node j; is the road distance connecting node i to node j; Since the actual road traffic conditions change in real time, the traffic congestion coefficient is introduced Modify the adjacency matrix D: (7)； In order to achieve the goal of minimizing the ECV-MES scheduling cost in fault recovery, the Floyd algorithm is used to calculate the node distance matrix B, which is the node shortest path matrix; is the weighted adjacency matrix, whose initial matrix value is , the shortest moving path between any two nodes is obtained by interpolation iteration, as shown in formula (8): (8)， In the formula, A is the set of all nodes in the system; Find if a node exists , so that node i to The path to j is shorter than the known path from node i to j and is updated; According to the distribution network line failure probability model under typhoon disaster conditions, the shortest dispatch distance and response time of ECV-MES under various disaster weather conditions are taken as the optimization objective function. The pre-disaster deployment model is established as shown in formula (9), and the gurobi commercial solver is used to solve the model to determine the initial position of ECV-MES: (9)， In the formula, and is the weight coefficient of the two objectives; is the shortest weighted distance for the mth group of energy storage to be dispatched from node i to node j in the distribution network fault recovery strategy under typhoon weather; is the total number of ECV-MES dispatch groups; is the comprehensive probability of line failure of transmission lines in typhoon weather; is the moving speed of ECV-MES; Constraints: (10)， In the formula, is the upper limit of the scheduling time of a single group of ECV-MES; The total number of ECV-MES groups that can participate in scheduling during this period; and are the maximum active power of ECV-MES involved in charging and discharging respectively; and are the maximum reactive power of ECV-MES involved in charging and discharging respectively; is the total capacity of mobile energy storage in ECV-MES; and They represent the active power and reactive power of ECV-MES in charging and discharging at time t respectively.

4. A distribution network fault emergency recovery method considering ECV-MES active regulation according to claim 3, characterized in that: S4 is specifically: When the communication system is restored, the load equivalent recovery rate during the power failure recovery period of the distribution network is used as the indicator of the recovery plan, and the corresponding weight coefficient is set considering the importance level of the node load. Each group of ECV-MES is moved and its online power is determined, as shown in formula (13): (13)， In the formula, To maximize the load recovery rate of the power system; R is the set of distribution network nodes; is the load weight of node i; is a binary variable, indicating the power status of node i; is the load active power of node i; Indicates the duration of the fault.

5. A distribution network fault emergency recovery method considering ECV-MES active regulation according to claim 4, characterized in that: S5 is as follows: S5-1. In the post-disaster recovery model, define the goal of the optimization problem: minimize the time and cost of emergency communication repair by the communication system. , and maximize the load recovery rate of the power system ;At the same time, consider the constraints; S5-2,NSGA-II algorithm starts by randomly generating an initial population, each individual represents a possible restoration scheme, which includes the configuration of communication and power systems; S5-3. Each individual evaluates fitness according to the defined multi-objectives, calculates its target value under the post-disaster communication recovery and power system recovery models based on the objective function, and applies the normalization method to convert the target value into a fitness value to ensure comparability between different objectives; S5-4, sort the population by non-dominated sorting, and divide the individuals in the population into different levels. The layer with high priority, i.e., the non-dominated individual layer, has the selection advantage; S5-5. Use crowding ranking to distinguish individuals in the same dominance layer; S5-6, selection, crossover and mutation are performed based on the non-dominated sorting and crowding degree of individuals, and the selected individuals produce a new generation through genetic operations; S5-7, repeat S5-3 to S5-6 until the termination condition is met, such as reaching a preset number of iterations or the quality of the solution reaches a certain threshold; S5-8. Output the individuals in the non-dominated layer as the final solution set, and the decision maker selects the most suitable recovery plan from it according to actual needs.

6. A distribution network fault emergency recovery method considering ECV-MES active regulation according to claim 5, characterized in that: In S5-1, the constraints set are specifically: During the entire fault process, in order to maintain safe and reliable operation of the power grid, the following constraints should be met: (14)， In the formula, is the maximum allowable current of the branch; and are the upper and lower limits of node i voltage respectively, and equation (14) is the branch current safety constraint and node voltage safety constraint; (15)， In the formula, and are the maximum active and reactive power of the branch respectively; and They are the maximum active and reactive power of the power supply respectively; and Respectively represent the active power and reactive power of the branch; and They represent the active power and reactive power of the power source respectively; Formula (15) is the power constraint of the power source and the power transmission constraint of the branch; The influence of parallel admittance is ignored in the power flow calculation model, and the power flow balance constraint is further decomposed. The specific formula is as follows: (16)， In the formula, It is a collection of branches that can still operate normally after a fault occurs in the distribution network; and They represent the active and reactive power injected into node j respectively; and They represent the active power and reactive power of the load at node j respectively; and are the active and reactive power of the branch respectively; , , They are branch current, branch resistance and branch reactance respectively; is a binary variable, indicating the charged state of node j; , , Inject active power into each power source on node j; , , Inject reactive power into each power source on node j; Each distribution network fault communication node is assigned to a repair vehicle, and at least one optical fiber connected to each distribution network communication node is ensured to be in normal working condition. Any repaired failed communication node can be connected to the communication node through optical fiber. The constraint formula is as follows: (17)， In the formula, Indicates whether node j is assigned to ECV-MES for emergency repair. When the distribution network communication node j is assigned to any ECV-MES for emergency repair, The value is 1, which means it is not assigned. The value is 0; is the wth optical fiber connected to the ith communication node, the value of the normal optical fiber is 1, and the value of the broken optical fiber is 0; W is the total number of optical fibers connected to the communication node; For nodes that have restored communication, The optical fiber between any failed node to be repaired and the node with restored communication function; The distribution network communication node where ECV-MES is located can realize the communication function, and its constraints are: (18)， In the formula, represents the communication status of node i, A value of 1 indicates that communication node i can communicate, and a value of 0 indicates that communication node i cannot communicate; represents the distance from communication node i to the mobile base station, It is the signal coverage of the mobile base station.

Images