Power distribution network scheduling method and device based on electric vehicle collaboration, terminal and medium

Abstract

The invention discloses a power distribution network scheduling method and device based on electric vehicle collaboration, a terminal and a medium, and relates to the technical field of power distribution networks. According to the scheme provided by the invention, firstly, extreme scene quantitative data and disaster-resistant toughness information of the power distribution network are acquired, a disaster damage simulation result of the power distribution network is obtained through a preset disaster simulation model, and the disaster damage simulation result of the power distribution network is obtained according to historical extreme scene data of the power distribution network in combination with a preset toughness entropy calculation formula; the toughness entropy of the power distribution network in the extreme scene is calculated to serve as parameters for comprehensively quantifying indexes such as the disaster occurrence probability, the recovery time and the user demand satisfaction degree, then the toughness entropy and the disaster damage simulation result are input into a preset resource scheduling optimization model, and the disaster damage simulation result is obtained through the resource scheduling optimization model. According to the method, the power distribution network resource scheduling scheme is optimized in combination with the preset optimization algorithm, and the resource scheduling of the power distribution network in the extreme scene is executed through the optimal solution scheme obtained through optimization, so that the disaster resistance toughness of the power distribution network in the extreme scene is improved.

Description

Technical Field

[0001] This application relates to the technical field of distribution networks, and particularly to a distribution network scheduling method, device, terminal, and medium based on electric vehicle collaboration. Background Art

[0002] With the frequent occurrence of global climate change and natural disasters, the resilience of distribution networks in extreme scenarios (such as typhoons, floods, and earthquakes) has become a key research direction in the power system. Traditional distribution network resource scheduling takes economy and reliability as the core objectives, but in the face of extreme disasters, problems such as insufficient recovery ability and long power outage time are likely to occur, and there are technical problems of weak disaster resistance resilience. Summary of the Invention

[0003] This application provides a distribution network scheduling method, device, terminal, and medium based on electric vehicle collaboration, which is used to solve the technical problem of weak disaster resistance resilience existing in the existing distribution network in the face of extreme disasters.

[0004] To solve the above technical problem, in the first aspect of this application, a distribution network scheduling method based on electric vehicle collaboration is provided, including:

[0005] Obtain extreme scenario quantization data and the disaster resistance resilience information of the distribution network, and through a preset disaster simulation model, obtain the simulated result of the damage of the distribution network, where the disaster simulation model includes a line damage probability function, and the line damage probability function is used to reflect the influence degree of extreme scenario intensity and line disaster resistance resilience on the line damage probability;

[0006] According to the historical extreme scenario data of the distribution network, combined with a preset resilience entropy calculation formula, calculate the resilience entropy of the distribution network in the extreme scenario;

[0007] Input the resilience entropy and the simulated result of the damage into a preset resource scheduling optimization model, so as to optimize the distribution network resource scheduling plan through the resource scheduling optimization model in combination with a preset optimization algorithm, and execute the resource scheduling of the distribution network in the extreme scenario through the optimal solution obtained by the optimization, where the constraint conditions of the resource scheduling optimization model include: electric vehicle scheduling constraints and grid operation constraint conditions.

[0008] Preferably, the resilience entropy calculation formula is specifically:

[0009]

[0010] In the formula, is the resilience entropy, is the occurrence probability of the extreme scenario s, is the average recovery time under the extreme scenario s, is the user demand satisfaction degree under the extreme scenario s, is the weight of the extreme scenario s, and S is the set of extreme scenarios.

[0011] Preferably, the objective function of the resource scheduling optimization model is specifically:

[0012]

[0013] In the formula, are the weight coefficients of each optimization objective respectively, is the resilience entropy, C is the configuration cost, S is the user demand satisfaction degree, F is the resource collaboration efficiency, and G is the electric vehicle transfer cost.

[0014] Preferably, the electric vehicle scheduling constraints include: electric vehicle charge and discharge constraint conditions, electric vehicle battery energy constraint conditions, and electric vehicle transfer constraint conditions.

[0015] Preferably, the grid operation constraint conditions specifically include:

[0016]

[0017]

[0018]

[0019]

[0020] In the formula, is the power injected by the generator at node i at time t, is the power injected by the distributed energy storage at node i at time t, is the power demand at node i at time t, is the simulated result of the damage of line li under the extreme scenario s, is the transmission power of line ij at time t, is the maximum transmission capacity of line ij, is the voltage at node i at time t, and are the lower and upper limits of the voltage at node i respectively, and are the resistance and reactance of line ij respectively, and M is the line voltage drop constraint coefficient, and are the reference voltages of node i and node j respectively, and are the active power and reactive power of line ij respectively.

[0021] Preferably, the electric vehicle transfer constraint conditions specifically include:

[0022]

[0023]

[0024] wherein, is the number of electric vehicles at node i at time t + 1, is the number of electric vehicles at node i at time t, is the number of electric vehicles transferred from node i to node j within time t, is the number of electric vehicles transferred from node j to node i within time t, is the set of time periods allowing electric vehicles to be transferred from node i to j.

[0025] Preferably, the electric vehicle battery energy constraint conditions specifically include:

[0026]

[0027]

[0028] wherein, is the electric vehicle battery energy state at node i at time t + 1, is the electric vehicle battery energy state at node i at time t, is the charging efficiency, is the discharging efficiency, is the electric vehicle charging power at node i at time t, is the electric vehicle discharging power at node i at time t, is the minimum energy capacity of the electric vehicle battery, is the maximum energy capacity of the electric vehicle battery.

[0029] The second aspect of the present application provides a distribution network scheduling device based on electric vehicle collaboration, including:

[0030] A disaster damage simulation unit, configured to obtain extreme scenario quantification data and the disaster resistance resilience information of the distribution network, and obtain the disaster-affected damage simulation result of the distribution network through a preset disaster simulation model, wherein the disaster simulation model includes a line damage probability function, and the line damage probability function is used to reflect the influence degree of extreme scenario intensity and line disaster resistance resilience on the line damage probability;

[0031] A resilience entropy calculation unit, configured to calculate the resilience entropy of the distribution network under extreme scenarios according to the historical extreme scenario data of the distribution network and in combination with a preset resilience entropy calculation formula;

[0032] A scheduling scheme optimization unit is configured to input the resilience entropy and the disaster damage simulation result into a preset resource scheduling optimization model, so as to optimize the distribution network resource scheduling scheme through the resource scheduling optimization model in combination with a preset optimization algorithm, and execute the resource scheduling of the distribution network in an extreme scenario according to the optimal solution obtained by the optimization.

[0033] The third aspect of the present application provides a distribution network scheduling terminal based on electric vehicle collaboration, including: a memory and a processor;

[0034] The memory is used to store program codes, and the program codes are used to implement the distribution network scheduling based on electric vehicle collaboration provided in the first aspect of the present application;

[0035] The processor is used to read and execute the program codes.

[0036] The fourth aspect of the present application provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores program codes, and the program codes are used to be read and executed by a processor to implement the distribution network scheduling based on electric vehicle collaboration provided in the first aspect of the present application.

[0037] From the above technical solutions, it can be seen that the present application has the following advantages:

[0038] The solution provided by the present application first obtains extreme scenario quantization data and the disaster resistance resilience information of the distribution network, and through a preset disaster simulation model, obtains the disaster damage simulation result of the distribution network. According to the historical extreme scenario data of the distribution network and in combination with a preset resilience entropy calculation formula, calculates the resilience entropy of the distribution network in an extreme scenario as a parameter that comprehensively quantifies indicators such as the probability of disaster occurrence, recovery time, and user demand satisfaction. Then, the resilience entropy and the disaster damage simulation result are input into a preset resource scheduling optimization model, so as to optimize the distribution network resource scheduling scheme through the resource scheduling optimization model in combination with a preset optimization algorithm, and execute the resource scheduling of the distribution network in an extreme scenario according to the optimal solution obtained by the optimization, realizing an equilibrium resource allocation of the distribution network with low cost and high resilience, and improving the disaster resistance resilience of the distribution network when facing extreme scenarios. Description of the Drawings

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0040] Figure 1Schematic flowchart of an embodiment of a distribution network scheduling method based on electric vehicle collaboration provided by this application.

[0041] Figure 2 Schematic flowchart of the calculation process for optimizing the distribution network resource scheduling plan in a distribution network scheduling method based on electric vehicle collaboration provided by this application.

[0042] Figure 3 Schematic structural diagram of an embodiment of a distribution network scheduling device based on electric vehicle collaboration provided by this application.

[0043] Figure 4 Schematic structural diagram of an embodiment of a distribution network scheduling terminal based on electric vehicle collaboration provided by this application. Detailed implementation manners

[0044] The embodiments of this application provide a distribution network scheduling method, device, terminal, and medium based on electric vehicle collaboration, which are used to solve the technical problem of weak disaster resistance resilience existing in the existing distribution network in the face of extreme disasters.

[0045] To make the invention purpose, features, and advantages of this application more obvious and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the embodiments described below are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

[0046] First, a detailed description of an embodiment of a distribution network scheduling method based on electric vehicle collaboration provided by this application is as follows:

[0047] Please refer to Figure 1 , a distribution network scheduling method based on electric vehicle collaboration provided by an embodiment of this application includes:

[0048] Step 101, obtain extreme scenario quantization data and the disaster resistance resilience information of the distribution network, and obtain the simulated results of the damage of the distribution network due to disasters through a preset disaster simulation model;

[0049] It should be noted that, first, obtain extreme scenario quantization data such as wind speed and rainfall, as well as the disaster resistance resilience information of the distribution network, such as the quantified data of the disaster resistance capabilities of each line and node in the distribution network. Then, input the obtained extreme scenario quantization data and the disaster resistance resilience information of the distribution network into the preset disaster simulation model to obtain the simulated results of the damage of the distribution network due to disasters.

[0050] More specifically, the disaster simulation model includes a line damage probability function, which is used to reflect the influence degrees of the extreme scenario intensity and the line disaster resistance resilience on the line damage probability. The expression of the line damage probability function is as follows:

[0051]

[0052] In the formula, is the damage probability of line ij under scenario s, is the quantization value of the disaster intensity of scenario s, is the line quantization data of the disaster resistance ability, is the damage probability function, usually a non-linear function, which is used to describe the influence of the extreme scenario intensity and the line disaster resistance ability on the damage probability, and k is a constant.

[0053] More specifically, the disaster damage simulation result mentioned in this embodiment is specifically: the line damage state quantity , is a binary variable, indicating whether line ij is damaged under the extreme scenario s. It can be randomly generated based on the damage probability . For example, a random number is generated. If , then (the line is damaged); otherwise (the line is normal); in this method, automatic disconnectors are installed at both ends of the line, and the switch automatically disconnects after a fault, so the fault will not spread along the line.

[0054] Step 102: According to the historical extreme scenario data of the distribution network, combined with the preset resilience entropy calculation formula, calculate the resilience entropy of the distribution network under the extreme scenario.

[0055] It should be noted that the resilience entropy provided in this application is specifically used to comprehensively quantify indexes such as the disaster occurrence probability, the recovery time, and the user demand satisfaction degree, so as to comprehensively quantify the resilience loss of the distribution network under the extreme scenario. Its calculation method is as follows:

[0056]

[0057] In the formula, is the resilience entropy, is the occurrence probability of extreme scenario s, is the average recovery time under extreme scenario s, is the user demand satisfaction degree under extreme scenario s, is the weight of extreme scenario s, and S is the set of extreme scenarios.

[0058] Step 103: Input the resilience entropy and the simulation results of disaster damage into a preset resource scheduling optimization model, so as to optimize the distribution network resource scheduling plan through the resource scheduling optimization model in combination with a preset optimization algorithm, and execute the resource scheduling of the distribution network in an extreme scenario according to the optimal solution obtained through the optimization.

[0059] It should be noted that, based on the resilience entropy, a multi-objective optimization model for improving the resilience of the distribution network based on the collaborative optimization of resilience entropy and electric vehicles is established in this embodiment. The total objective function is:

[0060]

[0061] In the formula, are the weight coefficients of each optimization objective respectively, is the resilience entropy, C is the configuration cost, the purpose is to reduce the economic cost of resource configuration, S is the user demand satisfaction degree, which is used to ensure the power supply reliability of critical loads, F is the resource collaboration efficiency, the purpose is to improve the collaborative utilization efficiency of multi-type resources, and G is the electric vehicle transfer cost, which aims to reduce the cost of electric vehicle transfer between different nodes.

[0062] More specifically, the calculation method of the configuration cost C can refer to the following expression:

[0063]

[0064] In the formula, i is the node number, N is the set of all nodes, t is the time number, T is the set of all time periods, is the unit cost of deploying an electric vehicle at node i at time t, is the unit cost of deploying distributed energy storage at node i, is the number of electric vehicles at node i at time t, is the distributed energy storage capacity of node i.

[0065] The calculation method of the user demand satisfaction degree S can refer to the following expression:

[0066]

[0067] In the formula, is the user demand priority of node i (the higher the value, the higher the priority), is the user demand satisfaction degree of node i in scenario s, and this satisfaction degree is determined by the difference between the load value of this node during normal operation and the actual power supply of this node during the disaster.

[0068] The calculation method of the resource collaboration efficiency F can refer to the following expression:

[0069]

[0070] In the formula, is the collaborative efficiency coefficient of the electric vehicle (depending on the charging and discharging rate), is the collaborative efficiency coefficient of the distributed energy storage (depending on the energy conversion efficiency).

[0071] The calculation method of the transfer cost G of the electric vehicle can refer to the following expression:

[0072]

[0073] In the formula, j is the target node number, is the unit cost of transferring the electric vehicle from node i to j within time t, is the number of electric vehicles transferred from node i to j within time t.

[0074] Furthermore, among the constraint conditions of the established model, there are constraints on the operation of the distribution network and related constraints on the collaborative optimization of electric vehicles. The constraint conditions of the resource scheduling optimization model mentioned in this embodiment include: electric vehicle scheduling constraints and power grid operation constraint conditions. Among them, the electric vehicle scheduling constraints include: electric vehicle charging and discharging constraint conditions, electric vehicle battery energy constraint conditions, and electric vehicle transfer constraint conditions. The specific examples are as follows:

[0075] More specifically, the electric vehicle charging and discharging constraint conditions are as follows:

[0076]

[0077] In the formula, is the charging power of the electric vehicle at node i at time t, and this power is determined by the power and quantity of the vehicles charging at node i at time t; is the maximum allowable charging power at node i, is the discharging power of the electric vehicle at node i at time t, and this power is determined by the power and quantity of the vehicles discharging at node i at time t, is the maximum allowable discharging power at node i. Among them, the meaning of the constraint formula is that electric vehicles at the same time and node cannot be charged and discharged simultaneously.

[0078] The electric vehicle battery energy constraint conditions are as follows:

[0079]

[0080]

[0081] In the formula, is the electric vehicle battery energy state at node i at time t + 1, The state of charge of the electric vehicle battery for node i at time t, is the charging efficiency (ranging from 0 to 1), is the discharging efficiency (ranging from 0 to 1), is the charging power of the electric vehicle for node i at time t, is the discharging power of the electric vehicle for node i at time t, is the minimum energy capacity of the electric vehicle battery, is the maximum energy capacity of the electric vehicle battery.

[0082] The electric vehicle transfer constraints specifically include:

[0083]

[0084]

[0085] In the formula, is the number of electric vehicles for node i at time t + 1, is the number of electric vehicles for node i at time t, is the number of electric vehicles transferred from node i to node j within time t, is the number of electric vehicles transferred from node j to node i within time t, meaning to update the number of electric vehicles at the node, is the set of time periods allowing electric vehicles to be transferred from node i to j, meaning to constrain the transfer time.

[0086] The power grid operation constraints specifically include:

[0087]

[0088]

[0089]

[0090]

[0091] In the formula, is the power injected into node i by the generator at time t, is the power injected into node i by the distributed energy storage at time t, is the power demand at node i at time t, is the simulated result of the damage of line li under the extreme scenario s, is the transmission power of line ij at time t, is the maximum transmission capacity of line ij, is the voltage at node i at time t, and are the lower and upper limits of the voltage of node i, respectively, and are the resistance and reactance of line ij, respectively, and are the reference voltages of node i and node j, respectively, and are the active power and reactive power of line ij, respectively. M is the line voltage drop constraint coefficient, which is a sufficiently large positive number. If the line is in a fault state, this constraint will be relaxed.

[0092] It can be understood that based on this embodiment, taking representing line faults as an example for illustration. Therefore, the grid operation constraint conditions are represented by the expression of : indicating that if line li is damaged, its transmission power is 0. If represents line faults, then also needs to be correspondingly adjusted to .

[0093] Based on the above resource scheduling optimization model, combined with a preset optimization algorithm, the optimization of the distribution network resource scheduling scheme is carried out. Through multi-objective optimization, by finding the optimal solution set, a series of trade-off solutions are provided instead of a single solution, enabling decision-makers to select the most suitable solution according to actual needs. Regarding the selection of the optimization algorithm, generally, the particle swarm algorithm, ant colony algorithm, or non-dominated sorting genetic algorithm, etc. can be used. The following is an example of the embodiment provided in this application using the non-dominated sorting genetic algorithm II (N-NSGA-II) improved based on neighborhood metrics to obtain the optimal solution of the distribution network resource scheduling in extreme scenarios through optimization. Among them, the algorithm pseudocode of NSGA-II improved based on neighborhood metrics provided in this embodiment is shown in Table 1:

[0094]

[0095] Among them, m represents the population size, k represents the maximum number of evolutionary generations, p c and p v represent the crossover probability and mutation probability, respectively, and K represents the neighborhood-related parameter (K-nearest neighbor method); P t is the parental population of the t-th generation, Q t is the offspring population of the t-th generation. The two are combined to obtain R t and after non-dominated sorting and crowding degree calculation, a temporary candidate set P' t is formed; if the scale exceeds m, further screening is performed according to the neighborhood metric NM(x) to obtain P (t+1) ; through selection, crossover, and mutation, P (t+1) is used to generate the offspring Q (t+1) , and the iteration count t is incremented until k is reached; finally, for Pk Perform non - dominated sorting and output the solution set with rank = 1, which is the approximate Pareto front.

[0096] In the optimization process of this embodiment, a neighborhood measure (NeighbourhoodMeasure) and a secondary screening mechanism are introduced in the re - sorting stage after merging the populations. The calculation flow chart is as Figure 2 shown.

[0097] Given a multi - objective optimization problem with N objective functions:

[0098]

[0099] where, is the decision variable, is the feasible solution space. If there exist two solutions and satisfying:

[0100]

[0101] then it is said that dominates . When a solution is not dominated by any other solution, it is called a non - dominated solution (Pareto solution). The fast non - dominated sorting of N - NSGA - II will label the individuals with ranks, providing a basis for subsequent screening.

[0102] Regarding the neighborhood measure (NeighbourhoodMeasure), in this embodiment, to characterize the local density, the distance between individuals is defined as follows (taking the objective space as an example):

[0103]

[0104] where, and are two solutions respectively, and are the values of the solution and on the n - th objective function. N represents the number of objective functions, characterizes the Euclidean distance between the solution and the solution in the objective space. This formula is used to measure the proximity between solutions and provides a basis for subsequent neighborhood determination and local density analysis in the improved algorithm.

[0105] Based on the given threshold ϵ or the k - nearest neighbor idea, define:

[0106]

[0107] Thus, the neighborhood metric can be set as:

[0108]

[0109] Here, the K-nearest neighbors are adopted, and let be the average distance of the K individuals closest to x 1 and so on.

[0110] Next is the neighborhood-based secondary screening (Elimination). When there is still redundancy after the preliminary truncation according to "rank + crowding distance" (for example, the population size needs to be strictly retained as n), then additional deletion is performed on the individuals with severe local crowding. The NM(x) can be sorted and removed from large to small until the scale meets the requirements.

[0111] Finally, after the above modeling and solution steps, an approximate Pareto front representing the optimal solution is finally obtained for the decision maker to select the most suitable energy storage deployment and electric vehicle deployment solutions.

[0112] The above is a detailed description of an embodiment of a distribution network scheduling method based on electric vehicle collaboration provided by this application. Next is a detailed description of an embodiment of a distribution network scheduling device based on electric vehicle collaboration provided by this application.

[0113] Please refer to Figure 3 , this application provides a distribution network scheduling device based on electric vehicle collaboration, including:

[0114] A disaster damage simulation unit 201, configured to obtain extreme scenario quantification data and the disaster resistance resilience information of the distribution network, and obtain the disaster-affected damage simulation result of the distribution network through a preset disaster simulation model, where the disaster simulation model includes a line damage probability function, and the line damage probability function is used to reflect the influence degree of the extreme scenario intensity and the line disaster resistance resilience on the line damage probability;

[0115] A resilience entropy calculation unit 202, configured to calculate the resilience entropy of the distribution network under extreme scenarios according to the historical extreme scenario data of the distribution network and in combination with a preset resilience entropy calculation formula;

[0116] A scheduling scheme optimization unit 203, configured to input the resilience entropy and the disaster-affected damage simulation result into a preset resource scheduling optimization model, so as to perform optimization of the distribution network resource scheduling scheme through the resource scheduling optimization model and in combination with a preset optimization algorithm, and execute the resource scheduling of the distribution network under extreme scenarios through the optimal solution obtained by the optimization.

[0117] As Figure 4As shown in the figure, the present application also provides a distribution network scheduling terminal based on electric vehicle collaboration, including: a memory 33 and a processor 31, and the memory 33 and the processor 31 can be connected through a communication bus 34;

[0118] The memory 33 is used to store program codes, and the program codes are used to implement the distribution network scheduling based on electric vehicle collaboration as described in the above embodiments;

[0119] The processor 31 is used to read and execute the program codes.

[0120] In addition, the present application also provides a computer-readable storage medium, characterized in that program codes are stored in the computer-readable storage medium, and the program codes are used to be read and executed by a processor to implement the distribution network scheduling based on electric vehicle collaboration provided in the above embodiments.

[0121] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described terminal, device, and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0122] In several embodiments provided by the present application, it should be understood that the disclosed terminal, device, and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0123] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0124] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one (one)" or its similar expression means any combination of these items, including any combination of single item (one) or plural items (ones). For example, at least one (one) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0125] The unit described as a separate component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0126] In addition, each functional unit in various embodiments of the present invention may be integrated into a processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0127] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0128] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A distribution network dispatching method based on electric vehicle collaboration, characterized in that: include: Acquire extreme scenario quantitative data and disaster resilience information of the distribution network, and obtain the disaster damage simulation results of the distribution network through a preset disaster simulation model, wherein the disaster simulation model includes a line damage probability function, and the line damage probability function is used to reflect the influence of extreme scenario intensity and line disaster resilience on line damage probability; Calculate the resilience entropy of the distribution network under extreme scenarios based on the historical extreme scenario data of the distribution network and a preset resilience entropy calculation formula; The resilience entropy and the disaster damage simulation results are input into a preset resource scheduling optimization model, so as to optimize the distribution network resource scheduling scheme through the resource scheduling optimization model in combination with a preset optimization algorithm, and execute the resource scheduling of the distribution network under extreme scenarios through the optimal solution obtained by the optimization, wherein the constraints of the resource scheduling optimization model include: electric vehicle scheduling constraints and power grid operation constraints.

2. A distribution network dispatching method based on electric vehicle collaboration according to claim 1, characterized in that: The toughness entropy calculation formula is specifically: In the formula, is the toughness entropy, is the probability of occurrence of extreme scenario s, is the average recovery time under extreme scenario s, is the user demand satisfaction in extreme scenario s, is the weight of extreme scenario s, and S is the set of extreme scenarios.

3. A distribution network dispatching method based on electric vehicle collaboration according to claim 1, characterized in that: The objective function of the resource scheduling optimization model is specifically: In the formula, are the weight coefficients of each optimization objective, is resilience entropy, C is configuration cost, S is user demand satisfaction, F is resource coordination efficiency, and G is electric vehicle transfer cost.

4. A distribution network dispatching method based on electric vehicle collaboration according to claim 1, characterized in that: The electric vehicle scheduling constraints include: electric vehicle charging and discharging constraints, electric vehicle battery energy constraints, and electric vehicle transfer constraints.

5. A distribution network dispatching method based on electric vehicle collaboration according to claim 1, characterized in that: The power grid operation constraints specifically include: In the formula, is the power injected into node i by the generator at time t, is the power injected into node i by distributed energy storage at time t, is the power demand of node i at time t, is the simulation result of the damage of line li under the extreme scenario s, is the transmission power of line ij at time t, is the maximum transmission capacity of line ij, is the voltage of node i at time t, and are the lower and upper limits of the voltage at node i, respectively. and are the resistance and reactance of line ij respectively, M is the line voltage drop constraint coefficient, and are the reference voltages of nodes i and j respectively, and are the active power and reactive power of line ij respectively.

6. A distribution network dispatching method based on electric vehicle collaboration according to claim 4, characterized in that: The electric vehicle transfer constraints specifically include: In the formula, is the number of electric vehicles at node i at time t+1, is the number of electric vehicles at node i at time t, is the number of electric vehicles transferred from node i to node j within time t, is the number of electric vehicles transferred from node j to node i within time t, is the set of time periods during which electric vehicles are allowed to transfer from node i to node j.

7. A distribution network dispatching method based on electric vehicle collaboration according to claim 4, characterized in that: The electric vehicle battery energy constraint conditions specifically include: In the formula, is the battery energy state of the electric vehicle at node i at time t+1, is the battery energy state of the electric vehicle at node i at time t, For charging efficiency, is the discharge efficiency, is the charging power of the electric vehicle at node i at time t, is the electric vehicle discharge power at node i at time t, is the minimum energy capacity of electric vehicle batteries, The maximum energy capacity of an electric vehicle battery.

8. A distribution network dispatching device based on electric vehicle collaboration, characterized in that: include: A disaster damage simulation unit is used to obtain extreme scenario quantitative data and disaster resilience information of the distribution network, and obtain the disaster damage simulation result of the distribution network through a preset disaster simulation model, wherein the disaster simulation model includes a line damage probability function, and the line damage probability function is used to reflect the influence of extreme scenario intensity and line disaster resilience on line damage probability; A resilience entropy calculation unit, used to calculate the resilience entropy of the distribution network under extreme scenarios based on historical extreme scenario data of the distribution network in combination with a preset resilience entropy calculation formula; A scheduling scheme optimization unit is used to input the resilience entropy and the disaster damage simulation results into a preset resource scheduling optimization model, so as to optimize the distribution network resource scheduling scheme through the resource scheduling optimization model in combination with a preset optimization algorithm, and execute the resource scheduling of the distribution network under extreme scenarios through the optimal solution obtained by the optimization.

9. A distribution network dispatching terminal based on electric vehicle collaboration, characterized in that: include: Memory and processor; The memory is used to store program codes, and the program codes are used to implement the distribution network scheduling method based on electric vehicle collaboration according to any one of claims 1 to 7; The processor is used for reading and executing the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, and the program code is used to be read and executed by a processor to implement the distribution network scheduling method based on electric vehicle collaboration as described in any one of claims 1 to 7.

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