Method for considering cooperative participation of multiple types of mobile emergency power supplies in power supply recovery of distribution network

Abstract

The invention discloses a method for considering collaborative participation of multiple types of mobile emergency power supplies in power supply recovery of a distribution network, and aims at a faulted power distribution network to participate in power supply recovery of the distribution network by utilizing a two-stage elastic recovery model of the multiple types of mobile emergency power supplies. Firstly, traffic route information and power distribution network load data are acquired; then, generating a potential fault scene based on a Monte Carlo simulation method; thirdly, based on the potential fault scene, an integer nonlinear programming model is used for determining the optimal number and segment positions of the charging stations and the maintenance stations; secondly, acquiring actual fault scene data, and evaluating fault damage and available resources; and finally, based on the mixed integer linear programming model, load and power distribution systems are recovered by optimizing scheduling of multiple types of mobile emergency power supplies on the transportation network. The two-stage elastic recovery model of the multi-type mobile emergency power supply participates in the power supply recovery of the distribution network, so that the fault recovery of the distribution network can be realized in the shortest time, and the fault recovery efficiency of the distribution network is improved.

Description

Technical Field

[0001] The present invention belongs to the field of power distribution network fault restoration, and more specifically, relates to a method for considering multiple types of mobile emergency power sources to collaboratively participate in power distribution network power restoration. Background Art

[0002] The frequent occurrence of extreme events in recent years and the major damage they have brought have had a great impact on the normal operation of the power system. In August 2020, California, the United States, experienced continuous high temperature weather, with the highest temperature reaching 49°C, and the load increased by 14% over the same period. At the same time, the external power supply decreased by 30%, resulting in long-term power outages and affecting a large number of power users. In February 2021, Texas, the United States, experienced extremely cold weather, with the lowest temperature reaching minus 26°C, and the load increased by 20% over the same period. Large-scale gas power plants and wind farms were forced to shut down due to lack of anti-freeze measures, and gas supply was blocked. According to statistics, about 4.5 million residents were affected by power outages. In the winter of 2020, Hunan, Jiangxi and other places in southern my country also encountered extreme cold weather, and the load increased by 20% to 30% over the same period, increasing the supply pressure of the distribution network.

[0003] In response to extreme natural disasters, improving the resilience of the power system can timely respond to and deal with the damage caused by natural disasters to the power system. Resilience refers to the ability of the system to prevent, resist, adapt to and recover from interference. The resilience of the power system is more difficult to improve than reliability, and the system is more difficult to cope with the arrival of extreme events. In previous studies, the commonly used methods to improve the resilience of the power system mainly include strengthening power system components, such as poles, lines, etc., but these measures do not have a good repair effect on the interruption of important loads caused by extreme events. Some studies have also considered mobile power sources to restore the resilience of the power system, but the impact of the transportation system on mobile power sources is not perfect. Therefore, for the recovery of distribution network faults, it is of great significance to comprehensively consider the transportation system and the distribution network system and use a two-stage resilience recovery model of multiple types of mobile emergency power sources to participate in the restoration of distribution network power supply. Summary of the invention

[0004] In view of the above defects or improvement needs of the prior art, the present invention proposes a method that considers the collaborative participation of multiple types of mobile emergency power supplies in distribution network power supply restoration. For the recovery of distribution network faults, the transportation system and the distribution network system are comprehensively considered, and a two-stage elastic recovery model of multiple types of mobile emergency power supplies is used to participate in the distribution network power supply restoration.

[0005] To achieve the above object, according to one aspect of the present invention, the present invention provides a method for considering multiple types of mobile emergency power sources to collaboratively participate in distribution network power supply restoration, comprising:

[0006] Step 1: Obtain traffic route information and distribution network load data;

[0007] Step 2: Generate potential failure scenarios based on Monte Carlo simulation method;

[0008] Step 3: Based on potential failure scenarios, an integer nonlinear programming model is used to determine the optimal number and segmentation locations of charging stations and maintenance stations.

[0009] Step 4: Obtain actual fault scenario data and assess fault damage and available resources;

[0010] Step 5: Based on the mixed integer linear programming model, the load and distribution system are restored by optimizing the dispatch of multiple types of mobile emergency power sources on the transportation network.

[0011] Preferably, the acquisition of traffic route information and distribution network load data is implemented by:

[0012] Step 1.1: Obtain traffic route information, and query the route map, vehicle information data, etc. in the area where the distribution network is located;

[0013] Step 1.2: Obtain the load data of the distribution network, and obtain the status information related to the charging facilities of each load node of the distribution network through electrical components such as sensors;

[0014] Step 1.3: Integrate traffic route information and distribution network load data to construct a dataset as input for subsequent models.

[0015] Preferably, the generation of potential failure scenarios based on the Monte Carlo simulation method is implemented by:

[0016] Step 2.1: The main challenge faced by the distribution network in short-term operation is the potential physical damage caused by high wind speeds to the distribution network. Therefore, when generating potential failure scenarios based on Monte Carlo simulation, the impact of high wind speeds on vulnerable components such as distribution poles and overhead distribution lines is evaluated. The vulnerability curve illustrates the relationship between the intensity of weather events and the probability of failure or damage of distribution network components:

[0017]

[0018] In the formula, p l (ω) represents the probability of failure of the distribution network under this wind speed; ω represents the current wind speed; ω critical represents the critical wind speed; ω collapse represents the collapse wind speed;

[0019] Step 2.2: Based on the probability function in step 3.1, use a random number generator to generate random variables with different probability distributions:

[0020] Step 2.3: Use computers to conduct random experiments to simulate the random characteristics of the distribution network system;

[0021] Step 2.4: Statistically analyze the results of random simulation to obtain a potential failure scenario based on the Monte Carlo simulation method that is close to the actual failure scenario.

[0022] Preferably, the optimal number and segmented locations of charging stations and maintenance stations are determined based on potential failure scenarios using an integer nonlinear programming model, and the implementation process is as follows:

[0023] Step 3.1: Generate potential failure scenarios based on traffic route information, distribution network load data, and Monte Carlo simulation method, and initialize the location of the maintenance team and mobile energy storage equipment;

[0024] Step 3.2: Establish an objective function with the goal of minimizing the transportation time of mobile energy. The objective function is as follows:

[0025]

[0026] Where P s represents the probability of a scenario failure; i, j represent the bus number of the distribution network; p, q represent the line number; S represents a series of failure scenarios; s represents a failure scenario; cs i is a binary variable, which is equal to 1 if bus i is equipped with a charging station; A binary variable representing the state of bus j in scenario s; k i represents the weight of bus i; SD i,j represents the shortest travel time from bus i to bus j on the transportation network; rs p Indicates a maintenance station collection; represents a binary variable, indicating the status of line l and the line from bus i to j in scenario s; K l Indicates the weight of line l; SDR p,q It represents the shortest transportation time between routes p and q in the transportation network.

[0027] The constraints are as follows:

[0028]

[0029] Where N cs Represents the bus state set; N rs Represents a line state set; represents a binary variable indicating the state of the parent bus of bus j in scenario s; represents a binary variable, indicating the state of line l and the line from bus i to j in scenario s; represents a binary variable, cs e Indicates the location status of the charging station in scene s; cs f Shows the location status of the maintenance station in scene s;

[0030] Step 3.3: Iteratively solve the model according to the objective function;

[0031] Step 3.4: After the iteration is completed, the optimal result is stored to determine the optimal number and segmentation locations of charging stations and maintenance stations.

[0032] Preferably, the actual fault scenario data is obtained, and the fault damage and available resources are evaluated, and the implementation process is as follows:

[0033] Step 4.1: Obtain actual fault scenario data through electrical components such as sensors and fault information transmitted by the platform;

[0034] Step 4.2: Analyze the actual fault scenario data, collect the traffic route information and distribution network load data of the fault area, and evaluate the number of available resources for collaborative power supply.

[0035] Preferably, the mixed integer linear programming model is based on optimizing the scheduling of multiple types of mobile emergency power supplies on the transportation network to restore the load and distribution system, and the implementation process is:

[0036] Step 5.1: Based on the actual fault scenario data, the maintenance route of the maintenance personnel is planned and scheduled, and the constraints are as follows:

[0037]

[0038] In the formula, represents a binary variable. When maintenance personnel n arrives at line p at time t, its value is 1. It represents the upper limit of maintenance personnel who can arrive at line p at the same time. L l,t is a binary variable, indicating the fault status of the line; represents the state of the damaged line d in scenario s; rt l Indicates the repair time of the damaged line;

[0039] Step 5.2: Dispatch the mobile energy storage system, and its constraints are as follows:

[0040]

[0041]

[0042] In the formula, represents a binary variable, and its value is 1 when the mobile energy storage system is connected to bus i at time t; T a Indicates different time periods; N x represents a collection of mobile energy storage systems; represents the upper limit of mobile energy storage systems that can be connected to bus i; Indicates the energy storage capacity of the mobile energy storage system; Indicates the upper and lower limits of the state of charge of the mobile energy storage system; represents the charging and discharging power of the mobile energy storage system at time t; c x,t ,d x,t Indicates whether the mobile energy storage system is in charging or discharging state;

[0043] Step 5.3: Dispatch electric vehicles with the following constraints:

[0044]

[0045]

[0046] In the formula, α i,ε,t represents a binary variable, which is 1 if the EV is connected to the candidate bus within time period t, otherwise it is 0; CS i,ε represents a binary variable; it is 1 if the EV is available for bus i during the fault period, otherwise it is 0; N ev represents the set of electric vehicles; β i,ε,t represents a binary variable; if α i,ε,t If it is 0, it is 1, otherwise it is 0; in ,t out Indicates the charge / discharge time; ROD represents the discharge power of bus i at time t; ε Indicates discharge rate; SOE i,ε,t represents the state of charge of electric vehicle ε at bus i at time i; soe ε,t represents the energy state of the electric vehicle ε at period t; Indicates the state of energy loss of electric vehicles. SOE max 、SOE min Indicates the upper and lower limits of the state of charge of an electric vehicle;

[0047] Step 5.4: The objective function of this model is to maximize the load recovery after a fault occurs. The objective function is as follows:

[0048]

[0049] In the formula, w d Parameter weights; represents the load shedding of busbar i at time t; h i,d represents a binary parameter: 1 if there is load d in bus i, 0 otherwise; l,t represents a binary variable, whose value is equal to 1 if l is connected to the distribution network;

[0050] Step 6.5: Solve this model to obtain the optimal strategy for multiple types of mobile emergency power sources to collaboratively participate in distribution network power restoration.

[0051] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method of considering multiple types of mobile emergency power sources to collaboratively participate in distribution network power supply restoration is implemented.

[0052] A computer-readable storage medium stores computer instructions, which, when executed by a processor, implement the method of considering multiple types of mobile emergency power sources to collaboratively participate in distribution network power supply restoration.

[0053] Compared with the prior art, the advantages of the present invention are as follows:

[0054] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0055] The technical solution provided by the present invention first obtains traffic route information and distribution network load data; then, generates potential fault scenarios based on the Monte Carlo simulation method; thirdly, based on the potential fault scenarios, uses an integer nonlinear programming model to determine the optimal number and segmented locations of charging stations and maintenance stations; secondly, obtains actual fault scenario data, evaluates fault damage and available resources; finally, based on a mixed integer linear programming model, restores the load and distribution system by optimizing the scheduling of multiple types of mobile emergency power supplies on the transportation network. In the existing distribution network power supply restoration method, discrete devices such as on-load tap-changing transformers and switchable capacitors with a longer time scale are often used to remove faults, and manual line inspection and maintenance are used to solve problems. This method is inefficient. In the distribution network power supply restoration method considering multiple types of mobile emergency power supplies, only one type of mobile emergency power supply, such as electric vehicles, is often focused on, and the impact of the transportation system on the mobile power supply is often not considered completely. The present invention utilizes a two-stage elastic recovery model of multiple types of mobile emergency power supplies to participate in distribution network power supply restoration, takes into account the impact of the traffic system on mobile power supplies, can formulate the optimal distribution network restoration strategy in the shortest time, realize distribution network fault recovery, restore power load to the greatest extent, reduce the impact range of the fault, and improve the efficiency of distribution network fault recovery.

[0056] In response to extreme natural disasters, improving the resilience of the power system can timely respond to and deal with the damage caused by natural disasters to the power system. Resilience refers to the ability of the system to prevent, resist, adapt to and recover from interference. The resilience of the power system is more difficult to improve than reliability, and the system is more difficult to cope with the arrival of extreme events. In previous studies, the commonly used methods to improve the resilience of the power system mainly include strengthening power system components, such as poles, lines, etc., but these measures do not have a good repair effect on the interruption of important loads caused by extreme events. Some studies have also considered mobile power sources to restore the resilience of the power system, but the impact of the transportation system on mobile power sources is not perfect. Therefore, for the recovery of distribution network faults, it is of great significance to comprehensively consider the transportation system and the distribution network system and use a two-stage resilience recovery model of multiple types of mobile emergency power sources to participate in the restoration of distribution network power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of a method provided by an embodiment of the present invention that considers the collaborative participation of multiple types of mobile emergency power supplies in distribution network power supply restoration. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0059] like Figure 1 The flowchart of a method for considering multiple types of mobile emergency power sources to collaboratively participate in power distribution network power restoration according to an embodiment of the present invention is shown, including:

[0060] Step 1: Obtain traffic route information and distribution network load data;

[0061] Step 2: Generate potential failure scenarios based on Monte Carlo simulation method;

[0062] Step 3: Based on potential failure scenarios, an integer nonlinear programming model is used to determine the optimal number and segmentation locations of charging stations and maintenance stations.

[0063] Step 4: Obtain actual fault scenario data and assess fault damage and available resources;

[0064] Step 5: Based on the mixed integer linear programming model, the load and distribution system are restored by optimizing the dispatch of multiple types of mobile emergency power sources on the transportation network.

[0065] Preferably, the acquisition of traffic route information and distribution network load data is implemented by:

[0066] Step 1.1: Obtain traffic route information, and query the route map, vehicle information data, etc. in the area where the distribution network is located;

[0067] Step 1.2: Obtain the load data of the distribution network, and obtain the status information related to the charging facilities of each load node of the distribution network through electrical components such as sensors;

[0068] Step 1.3: Integrate traffic route information and distribution network load data to construct a dataset as input for subsequent models.

[0069] Preferably, the generation of potential failure scenarios based on the Monte Carlo simulation method is implemented by:

[0070] Step 2.1: The main challenge faced by the distribution network in short-term operation is the potential physical damage caused by high wind speeds to the distribution network. Therefore, when generating potential failure scenarios based on Monte Carlo simulation, the impact of high wind speeds on vulnerable components such as distribution poles and overhead distribution lines is evaluated. The vulnerability curve illustrates the relationship between the intensity of weather events and the probability of failure or damage of distribution network components:

[0071]

[0072] In the formula, p l (ω) represents the probability of failure of the distribution network under this wind speed; ω represents the current wind speed; ω critical represents the critical wind speed; ω collapse represents the collapse wind speed;

[0073] Step 2.2: Based on the probability function in step 3.1, use a random number generator to generate random variables with different probability distributions:

[0074] Step 2.3: Use computers to conduct random experiments to simulate the random characteristics of the distribution network system;

[0075] Step 2.4: Statistically analyze the results of random simulation to obtain a potential failure scenario based on the Monte Carlo simulation method that is close to the actual failure scenario.

[0076] Preferably, the optimal number and segmented locations of charging stations and maintenance stations are determined based on potential failure scenarios using an integer nonlinear programming model, and the implementation process is as follows:

[0077] Step 3.1: Generate potential failure scenarios based on traffic route information, distribution network load data, and Monte Carlo simulation method, and initialize the location of the maintenance team and mobile energy storage equipment;

[0078] Step 3.2: Establish an objective function with the goal of minimizing the transportation time of mobile energy. The objective function is as follows:

[0079]

[0080] Where P s represents the probability of a scenario failure; i, j represent the bus number of the distribution network; p, q represent the line number; S represents a series of failure scenarios; s represents a failure scenario; cs i is a binary variable, which is equal to 1 if bus i is equipped with a charging station; A binary variable representing the state of bus j in scenario s; k i represents the weight of bus i; SD i,j represents the shortest travel time from bus i to bus j on the transportation network; rs p Indicates maintenance station assembly; L s l represents a binary variable, indicating the status of line l and the line from bus i to j in scenario s; K l Indicates the weight of line l; SDR p,q It represents the shortest transportation time between routes p and q in the transportation network.

[0081] The constraints are as follows:

[0082]

[0083] Where N cs Represents the bus state set; N rs Represents a line state set; represents a binary variable indicating the state of the parent bus of bus j in scenario s; represents a binary variable, indicating the state of line l and the line from bus i to j in scenario s; represents a binary variable, cs e Indicates the location status of the charging station in scene s; cs f Shows the location status of the maintenance station in scene s;

[0084] Step 3.3: Iteratively solve the model according to the objective function;

[0085] Step 3.4: After the iteration is completed, the optimal result is stored to determine the optimal number and segmentation locations of charging stations and maintenance stations.

[0086] Preferably, the actual fault scenario data is obtained, and the fault damage and available resources are evaluated, and the implementation process is as follows:

[0087] Step 4.1: Obtain actual fault scenario data through electrical components such as sensors and fault information transmitted by the platform;

[0088] Step 4.2: Analyze the actual fault scenario data, collect the traffic route information and distribution network load data of the fault area, and evaluate the number of available resources for collaborative power supply.

[0089] Preferably, the mixed integer linear programming model is based on optimizing the scheduling of multiple types of mobile emergency power supplies on the transportation network to restore the load and distribution system, and the implementation process is:

[0090] Step 5.1: Based on the actual fault scenario data, the maintenance route of the maintenance personnel is planned and scheduled, and the constraints are as follows:

[0091]

[0092]

[0093] In the formula, represents a binary variable. When maintenance personnel n arrives at line p at time t, its value is 1. It represents the upper limit of maintenance personnel who can arrive at line p at the same time. L l,t is a binary variable, indicating the fault status of the line; represents the state of the damaged line d in scenario s; rt l Indicates the repair time of the damaged line;

[0094] Step 5.2: Dispatch the mobile energy storage system, and its constraints are as follows:

[0095]

[0096] In the formula, represents a binary variable, and its value is 1 when the mobile energy storage system is connected to bus i at time t; T a Indicates different time periods; N x represents a collection of mobile energy storage systems; represents the upper limit of mobile energy storage systems that can be connected to bus i; Indicates the energy storage capacity of the mobile energy storage system; Indicates the upper and lower limits of the state of charge of the mobile energy storage system; represents the charging and discharging power of the mobile energy storage system at time t; c x,t ,d x,t Indicates whether the mobile energy storage system is in charging or discharging state;

[0097] Step 5.3: Dispatch electric vehicles with the following constraints:

[0098]

[0099] In the formula, αi,ε,t represents a binary variable, which is 1 if the EV is connected to the candidate bus within time period t, otherwise it is 0; CS i,ε represents a binary variable; it is 1 if the EV is available for bus i during the fault period, otherwise it is 0; N ev represents the set of electric vehicles; β i,ε,t represents a binary variable; if α i,ε,t If it is 0, it is 1, otherwise it is 0; in ,t out Indicates the charge / discharge time; ROD represents the discharge power of bus i at time t; ε Indicates discharge rate; SOE i,ε,t represents the state of charge of electric vehicle ε at bus i at time i; soe ε,t represents the energy state of the electric vehicle ε at period t; Indicates the state of energy loss of electric vehicles. SOE max 、SOE min Indicates the upper and lower limits of the state of charge of an electric vehicle;

[0100] Step 5.4: The objective function of this model is to maximize the load recovery after a fault occurs. The objective function is as follows:

[0101]

[0102] In the formula, w d Parameter weights; represents the load shedding of busbar i at time t; h i,d represents a binary parameter: 1 if there is load d in bus i, 0 otherwise; l,t represents a binary variable, whose value is equal to 1 if l is connected to the distribution network;

[0103] Step 6.5: Solve this model to obtain the optimal strategy for multiple types of mobile emergency power sources to collaboratively participate in distribution network power restoration.

[0104] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method of considering multiple types of mobile emergency power sources to collaboratively participate in distribution network power supply restoration is implemented.

[0105] A computer-readable storage medium stores computer instructions, which, when executed by a processor, implement the method of considering multiple types of mobile emergency power sources to collaboratively participate in distribution network power supply restoration.

[0106] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for considering multiple types of mobile emergency power sources to collaboratively participate in distribution network power supply restoration, characterized in that: include: Step 1: Obtain traffic route information and distribution network load data; Step 2: Generate potential failure scenarios based on Monte Carlo simulation method; Step 3: Based on potential failure scenarios, an integer nonlinear programming model is used to determine the optimal number and segmentation locations of charging stations and maintenance stations. Step 4: Obtain actual fault scenario data and assess fault damage and available resources; Step 5: Based on the mixed integer linear programming model, the load and distribution system are restored by optimizing the dispatch of multiple types of mobile emergency power sources on the transportation network.

2. The method of considering multiple types of mobile emergency power sources to collaboratively participate in distribution network power supply restoration according to claim 1 is characterized in that: Obtain traffic route information and distribution network load data: Step 1.1: Obtain traffic route information, and query the route map and vehicle information data in the area according to the area where the distribution network is located; Step 1.2: Obtain the load data of the distribution network, and obtain the status information related to the charging facilities of each load node of the distribution network through electrical components such as sensors; Step 1.3: Integrate traffic route information and distribution network load data to construct a dataset as input for subsequent models.

3. The method of considering multiple types of mobile emergency power sources to collaboratively participate in distribution network power supply restoration according to claim 1, characterized in that: Generate potential failure scenarios based on Monte Carlo simulation: Step 2.1: The main challenge facing the distribution network in short-term operation is the potential physical damage caused by high wind speeds to the distribution network. When generating potential failure scenarios based on the Monte Carlo simulation method, the impact of high wind speeds on vulnerable components such as distribution poles and overhead distribution lines is evaluated. The vulnerability curve illustrates the relationship between the intensity of weather events and the probability of failure or damage of distribution network components: In the formula, p l (ω) represents the probability of failure of the distribution network under this wind speed; ω represents the current wind speed; ω critical represents the critical wind speed; ω collapse represents the collapse wind speed; Step 2.2: Based on the probability function in step 3.1, use a random number generator to generate random variables with different probability distributions: Step 2.3: Use computers to conduct random experiments to simulate the random characteristics of the distribution network system; Step 2.4: Statistically analyze the results of random simulation to obtain a potential failure scenario based on the Monte Carlo simulation method that is close to the actual failure scenario.

4. The method of considering multiple types of mobile emergency power sources to collaboratively participate in distribution network power supply restoration according to claim 1, characterized in that: Based on potential failure scenarios, an integer nonlinear programming model is used to determine the optimal number and segmentation locations of charging stations and maintenance stations: Step 3.1: Generate potential failure scenarios based on traffic route information, distribution network load data, and Monte Carlo simulation method, and initialize the location of the maintenance team and mobile energy storage equipment; Step 3.2: Establish an objective function with the goal of minimizing the transportation time of mobile energy. The objective function is as follows: Where P s represents the probability of scenario failure; i, j represent the distribution network bus number; p, q represent the line number; S represents a series of failure scenarios; s represents a failure scenario; cs i is a binary variable, which is equal to 1 if bus i is equipped with a charging station; A binary variable representing the state of bus j in scenario s; k i represents the weight of bus i; SD i,j represents the shortest travel time from bus i to bus j on the transportation network; rs p Indicates a maintenance station collection; represents a binary variable, indicating the status of line l and the line from bus i to j in scenario s; K l represents the weight of line l; SDR p,q represents the shortest transportation time between routes p and q in the transportation network, The constraints are as follows: Where N cs Represents the bus state set; N rs Represents a line state set; represents a binary variable indicating the state of the parent bus of bus j in scenario s; represents a binary variable, indicating the status of line l and the line from bus i to j in scenario s; represents a binary variable, cs e Indicates the location status of the charging station in scene s; cs f Shows the location status of the maintenance station in scene s; Step 3.3: Iteratively solve the model according to the objective function; Step 3.4: After the iteration is completed, the optimal result is stored to determine the optimal number and segmentation locations of charging stations and maintenance stations.

5. The method of considering multiple types of mobile emergency power sources to collaboratively participate in distribution network power supply restoration according to claim 1, characterized in that: Obtain actual fault scenario data and assess fault damage and available resources: Step 4.1: Obtain actual fault scenario data through electrical components such as sensors and fault information transmitted by the platform; Step 4.2: Analyze the actual fault scenario data, collect the traffic route information and distribution network load data of the fault area, and evaluate the number of available resources for collaborative power supply.

6. The method of considering multiple types of mobile emergency power sources to collaboratively participate in distribution network power supply restoration according to claim 1, characterized in that: Based on a mixed integer linear programming model, the load and distribution system are restored by optimizing the dispatch of multiple types of mobile emergency power sources on the transportation network: Step 5.1: Based on the actual fault scenario data, the maintenance route of the maintenance personnel is planned and scheduled, and the constraints are as follows: In the formula, represents a binary variable, which takes the value of 1 when maintenance worker n arrives at line p at time t; represents the upper limit of maintenance workers who can arrive at line p at the same time; L l,t is a binary variable, indicating the fault status of the line; represents the state of the damaged line d in scenario s; rt l Indicates the repair time of the damaged line; Step 5.2: Dispatch the mobile energy storage system, and its constraints are as follows: In the formula, represents a binary variable, which takes the value of 1 when the mobile energy storage system is connected to bus i at time t; T a Indicates different time periods; N x represents a collection of mobile energy storage systems; represents the upper limit of mobile energy storage systems that can be connected to bus i; Indicates the energy storage capacity of the mobile energy storage system; Indicates the upper and lower limits of the state of charge of the mobile energy storage system; represents the charging and discharging power of the mobile energy storage system at time t; c x,t ,d x,t Indicates whether the mobile energy storage system is in charging or discharging state; Step 5.3: Dispatch electric vehicles with the following constraints: In the formula, α i,ε,t represents a binary variable, which is 1 if the EV is connected to the candidate bus within time period t, and 0 otherwise; CS i,ε represents a binary variable; If the EV is available for bus i during the fault, it is 1, otherwise 0; N ev represents a collection of electric vehicles; β i,ε,t represents a binary variable; if α i,ε,t If it is 0, it is 1, otherwise it is 0; in ,t out Indicates the charge / discharge time; ROD represents the discharge power of bus i at time t; ε Indicates discharge rate; SOE i,ε,t represents the state of charge of electric vehicle ε at bus i at time i; soe ε,t represents the energy state of the electric vehicle ε at period t; Represents the state of energy loss of electric vehicles, SOE max 、SOE min Indicates the upper and lower limits of the state of charge of an electric vehicle; Step 5.4: The objective function of this model is to maximize the load recovery after a fault occurs. The objective function is as follows: In the formula, w d Parameter weights; represents the load shedding of busbar i at time t; h i,d represents a binary parameter: 1 if there is load d in bus i, 0 otherwise; l,t represents a binary variable, whose value is equal to 1 if l is connected to the distribution network; Step 5.5: Solve this model to obtain the optimal strategy for multiple types of mobile emergency power sources to collaboratively participate in distribution network power restoration.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for considering the collaborative participation of multiple types of mobile emergency power supplies in distribution network power supply restoration as described in any one of claims 1 to 6 above is implemented.

8. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by the processor, a method for considering the collaborative participation of multiple types of mobile emergency power supplies in distribution network power supply restoration as described in any one of claims 1-6 is implemented.

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