Rural remote area low-voltage boosting method and system based on low-voltage generator car

Abstract

The invention discloses a rural remote area low-voltage boosting method and system based on low-voltage generator cars, and relates to the technical field of low-voltage boosting, and the method comprises the steps: obtaining the information data of each generator car in a current area, and determining the geographic data of each remote area; constructing a scheduling model of the generator car, and establishing a target function of the scheduling model based on the information data and the geographic data; based on the scheduling demand and the objective function of at least one remote area, a multi-objective optimization algorithm is adopted to carry out optimization solution on the scheduling model; according to a solving result, assigning a corresponding generator car to carry out low-voltage boosting on at least one remote area; the conditions of serious resource waste and potential loss caused by unreasonable distribution of the generator cars can be reduced, and a basic guarantee is provided for low-voltage boosting in remote areas.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of low-voltage promotion, more particularly, it relates to a low-voltage promotion method and system for remote rural areas based on a low-voltage power generation vehicle. BACKGROUND

[0002] With the continuous development of society in recent years, the scale of the power grid gradually expands, and the demand for user power supply is increasingly high, and the reliability of traditional power grid power supply is facing more and more challenges, and the frequency of power supply area power failure due to overload is increasing year by year; especially for remote rural areas, the permanent population is small, and the power grid can meet the load demand at ordinary times; but during the holidays, especially during the Spring Festival, a large number of migrant workers return home, which will cause the power load to surge, resulting in a decrease in power quality, causing different degrees of impact on the daily life of residential users, and even causing damage to electrical equipment or personal threats.

[0003] In view of such problems, the current low-voltage power generation vehicle can be used to input power to the local power grid to solve the problem of load surge, and the low-voltage power generation vehicle is a special vehicle equipped with a power supply device, which has the functions of power generation, equipment maintenance, and field operation, and the output of the power generation vehicle can be connected to the power grid by a cable; in actual engineering, each power supply bureau subordinate district bureau has a certain number of power generation vehicles, but due to the large distance between different district bureaus and the remote destination, the scheduling of the power generation vehicle is particularly important; in practice, due to the lack of scientific and reasonable basis for the configuration scheme of the power generation vehicle, it often causes unreasonable phenomena such as idle of power generation vehicles in many power generation vehicle configuration districts and long-term shortage of power generation vehicles in few power generation vehicle configuration districts.

[0004] Therefore, the present application is proposed. SUMMARY

[0005] The present application aims to provide a low-voltage promotion method and system for remote rural areas based on a low-voltage power generation vehicle, which can reduce the serious resource waste and potential loss caused by unreasonable allocation of power generation vehicles, and provide basic protection for low-voltage promotion in remote areas.

[0006] The above technical purpose of the present application is achieved by the following technical scheme: In a first aspect, the present application provides a low-voltage promotion method for remote rural areas based on a low-voltage power generation vehicle, comprising the following specific steps: Obtain information data of each power generation vehicle in the current area, and determine geographical data of each remote area; Construct a scheduling model of the power generation vehicle, and establish a target function of the scheduling model based on the information data and the geographical data; Based on the scheduling demand of at least one remote area and the target function, a multi-objective optimization algorithm is used to optimize and solve the scheduling model; According to the solving result, corresponding power generation vehicles are assigned to at least one remote area for low-voltage improvement.

[0007] Based on the above technical solution, the application can be further improved as follows.

[0008] Further, the information data at least includes position data and capacity data; and the geographic data at least includes address position.

[0009] Further, the objective function is established by minimizing the total scheduling cost, and the objective function is specifically: ; In the formula, is the objective function, represents the number of scheduling days, represents a capacity specification set of the power generation vehicle, is the number of positions where each power generation vehicle is located, is the number of remote areas, represents whether the power generation vehicle is in an idle and schedulable state on the day, schedulable, unschedulable; is the scheduling state, represents that the power generation vehicle is scheduled from position to position on the day, otherwise 0; represents the distance between position and position , is a demand matrix of each remote area, representing the scheduling demand of the remote area on the day .

[0010] Further, the scheduling model is optimized and solved by using a multi-objective optimization algorithm, specifically: The number of power generation vehicle scheduling is optimized and solved by using an improved genetic algorithm; According to the optimization and solving result, the configuration of the power generation vehicle is solved by using an improved binary crossover algorithm, so as to obtain the capacity configuration of each power generation vehicle in the optimization and solving.

[0011] Further, the fitness function in the improved genetic algorithm is specifically: ; In the formula, is the number of positions where each power generation vehicle is located, is the number of remote areas, represents a capacity specification set of the power generation vehicle, represents the remote area to the power generation vehicle in position . There is demand. This indicates whether the generator is in an idle and dispatchable state that day. 0 indicates that the schedulable condition is met; otherwise, it is 0. In scheduling state, The time indicated that the generator car departed from its location that day. Dispatch to location Otherwise, it is 0; Indicates position With position The distance between them.

[0012] Furthermore, in the improved binary crossover algorithm described above, the reshaping operation is implemented using the following formula: ; In the formula, In the binary crossover algorithm The new individual value after the reshaping operation This represents the inverse function of the Sigmoid function. The threshold function is used to select a random value between 0 and 1. Representing particles in the binary crossover algorithm The decimal solution of the d-th dimension after crossing.

[0013] Furthermore, the aforementioned Sigmoid function is specifically as follows: ; In the formula, This is the modified Sigmoid function.

[0014] Secondly, this application provides a low-voltage boosting system for remote rural areas based on a low-voltage generator vehicle, applicable to the low-voltage boosting method for remote rural areas based on a low-voltage generator vehicle as described in any of the first aspects, including: The data acquisition module is used to acquire information data of each generator in the current area and determine the geographical data of each remote area; The function creation module is used to build the scheduling model of the generator car, and to establish the objective function of the scheduling model based on information data and geographic data; The model solving module is used to optimize and solve the scheduling model based on the scheduling needs and objective function of at least one remote area using a multi-objective optimization algorithm. The vehicle dispatching module is used to assign the corresponding generator truck to at least one remote area to boost low voltage based on the solution results.

[0015] Thirdly, this application provides an electronic device, including: at least one processor, at least one memory, and a data bus;

[0016] The processor and the memory communicate with each other through a data bus; the memory stores program instructions executable by the processor, and the processor invokes the program instructions to execute the method of any one of the first aspect.

[0017] In a fourth aspect, the present application provides a non-transitory computer readable storage medium storing computer instructions, which cause a computer to execute the method of any one of the first aspect.

[0018] Compared with the prior art, the present application has at least the following beneficial effects: In the present application, on the one hand, the power generation vehicle can effectively solve the problem of low voltage in remote rural areas during the holiday load peak period, thereby improving the power quality, meeting the normal power demand of residents, and avoiding the impact on the daily life of residents and the damage of electrical equipment or personal threat; on the other hand, through the constructed scheduling model and established objective function, the multi-objective optimization algorithm is used to solve the problem of emergency power generation vehicle scheduling optimization, which provides better optimization suggestions for relevant departments from the perspective of power generation vehicle configuration scheme, greatly reduces the operation cost of emergency power generation vehicle, improves the work efficiency of relevant departments, and improves the power supply reliability and economy to a certain extent; at the same time, according to the characteristics of the power generation vehicle scheduling problem, the improved genetic algorithm and the improved binary vertical cross algorithm are proposed, which can handle discrete variables with pseudo-random characteristics, and provide algorithm support for solving the power generation vehicle scheduling problem. BRIEF DESCRIPTION OF DRAWINGS

[0019] The accompanying drawings, which are included to provide a further understanding of the embodiments of the application and constitute a part of this application, do not constitute a limitation to the embodiments of the application. In the drawings:

[0020] Figure 1 The method flowchart of the method for improving the embodiments of the present application;

[0021] Figure 2 The connection schematic diagram of the system for improving the embodiments of the present application;

[0022] Figure 3 The connection schematic diagram of the electronic device in the embodiments of the present application. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme of the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0024] The following detailed description of embodiments of the application in the drawings provided is not intended to limit the scope of the application as claimed, but merely represents selected embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the application.

[0025] It should be noted that similar reference numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0026] In the description of embodiments of the application, "a plurality of" represents at least 2.

[0027] Embodiment 1: In order to solve the current remote areas in the holiday due to the surge in load caused by the decline in power quality, there is a risk of damage to electrical equipment and personal safety, and the current power car configuration scheme usually lacks more scientific and reasonable basis, and often appears unreasonable phenomenon that the power car configuration is more in the district and the power car is idle, and the power car is less in the district and the power car is in short supply for a long time. The embodiment provides a low-voltage improvement method for remote rural areas based on low-voltage power car, as shown in Figure 1 The specific steps include:

[0028] S1, obtaining information data of each power car in the current area, and determining geographical data of each remote area.

[0029] Among them, the above information data at least includes position data and capacity data; the geographical data at least includes address position.

[0030] S2, constructing a scheduling model of the power car, and establishing an objective function of the scheduling model based on the information data and the geographical data.

[0031] Among them, the above objective function is established by minimizing the total scheduling cost, and the objective function is specifically: ; In the formula, is the objective function, represents the number of scheduling days, represents a capacity specification set of the power car, is the number of positions where each power car is located, is the number of remote areas, represents whether the power car is in an idle and schedulable state on the day, schedulable, non-schedulable; is the scheduling state, represents that the power car is located at position Dispatch to position , otherwise 0; Indicate the position The distance between the position , is the demand matrix of each remote area, indicating the dispatch demand of the remote area on the day.

[0032] Wherein, the above total dispatch cost is minimum, that is, the cumulative value of the dispatch distance of the power generation vehicle actually dispatched according to the dispatch demand of the power generation vehicle is minimum; the dispatch model also has the following constraint conditions, based on which the model can be optimized and solved, including variable constraint, number constraint, dispatch constraint and mileage constraint; wherein, the variable constraint is specifically as follows: , , ; Taking the capacity of the power generation vehicle as 200kw and 500kw as an example; , , Indicates the sum of the power generation vehicles with capacity specifications of 200kW and 500kW; finally, the formula of is the correspondence relationship of the optimization variable, that is, the variable to be solved, indicating the allocation situation of the power generation vehicle configuration scheme in each district.

[0033] Wherein, the number constraint can include: Indicates that the number of power generation vehicles in each position is between 2-9, of course, this can be set according to the actual situation, here is only an example; Indicates that the total number of power generation vehicles allocated to K power generation vehicle positions should be consistent with the actual total number of power generation vehicles owned by the station; Indicates that the power generation vehicle of any capacity specification placed in the position should meet the use demand of the corresponding specification power generation vehicle, otherwise it will lead to too few configurable vehicles and cause the dispatching to fail.

[0034] Wherein, the dispatch constraint can include: Indicates that the dispatch demand of the power generation vehicle in any position is the use demand of the power generation vehicle minus the power generation vehicle allocated to it, when the use demand of the power generation vehicle does not exceed the power generation vehicle allocated to it, it means that the position can meet its use demand of the power generation vehicle by itself; Indicates that the total number of power generation vehicles allocated to the position The power generation vehicle and the power generation vehicle applying for dispatching must be no less than the use demand.

[0035] The mileage constraint can include: The dispatching distance is the mileage of the power generation vehicle going to remote areas. Since whether to consider the return mileage has little effect, only one-way distance is considered here.

[0036] Specifically, in the respective expressions of the above variable constraint, number constraint, dispatching constraint and mileage constraint, The number of power generation vehicles owned by any location point, The number of power generation vehicles of the corresponding capacity specification owned by any location point, Indicates the number of power generation vehicles of the corresponding capacity specification required for use at any location point on the same day, Indicates the inventory of power generation vehicles of the corresponding capacity specification, Indicates the power generation vehicles of the corresponding capacity specification at any location point, Indicates the power generation vehicles owned by any location point, Indicates the power generation vehicle configuration scheme, The dispatching demand of the power generation vehicle of the corresponding capacity specification in any area on the nth day, The power generation vehicle of the corresponding capacity specification dispatched from the location point i to the area j on the nth day, The local use demand of the power generation vehicle of the corresponding capacity specification at any location point on the nth day, The round trip mileage between any location point and any area.

[0037] S3, based on the dispatching demand of at least one remote area and the objective function, the scheduling model is optimized and solved by using a multi-objective optimization algorithm.

[0038] Optionally, the scheduling model is optimized and solved by using a multi-objective optimization algorithm, specifically:

[0039] S31, the number of power generation vehicle dispatching is optimized and solved by using an improved genetic algorithm.

[0040] The fitness function in the improved genetic algorithm is specifically: ; In the formula, The number of locations where each power generation vehicle is located, The number of remote areas, Indicates the set of capacity specifications of the power generation vehicle, Indicates the remote area There is a demand for power generation vehicles in the location ​​This indicates whether the generator is in an idle and dispatchable state that day. 0 indicates that the schedulable condition is met; otherwise, it is 0. In scheduling state, The time indicated that the generator car departed from its location that day. Dispatch to location Otherwise, it is 0; Indicates position With position The distance between them.

[0041] S32, Based on the optimization solution results, the configuration of the generator cars is solved using an improved binary crossover algorithm to obtain the capacity configuration of each generator car in the optimization solution.

[0042] In the improved binary crossover algorithm described above, the reshaping operation is implemented using the following formula: ; In the formula, In the binary crossover algorithm The new individual value after the reshaping operation This represents the inverse function of the Sigmoid function. The threshold function is used to select a random value between 0 and 1. Representing particles in the binary crossover algorithm The decimal solution of the d-th dimension after crossing.

[0043] Specifically, the Sigmoid function mentioned above is as follows: ; In the formula, This is the modified Sigmoid function.

[0044] Specifically, the computational process of the multi-objective optimization algorithm to optimize the scheduling model mainly consists of two parts: the improved genetic algorithm and the improved binary cross-cutting algorithm. The improved genetic algorithm solves the quantity problem in the generator scheduling problem, while the improved binary cross-cutting algorithm solves the allocation problem. First, the improved genetic algorithm solves the optimal quantity problem and returns the solution to the improved binary cross-cutting algorithm to update its model. Then, the generator scheduling problem is finally solved step by step. The specific steps are as follows: 1. Initialize parameters such as population size and number of iterations; 2. Input known variables such as generator inventory at each location, regional usage demand, and dispatch distance; 3. Based on the above constraints, random generation is performed. Constituent variables 4. Generate coded individuals and form a population; 5. Combine variables The fitness of individuals is evaluated using the fitness function described above, and a number of individuals with good performance are retained, while the remaining individuals are cross-crossed. 5. Perform mutation, competition, and selection operations on the crossover individuals, and retain a good number of individuals; 6. Determine if the number of iterations is satisfied. If yes, proceed to step 7; otherwise, return to step 3. 7. Extract the best individual and update the model accordingly; 8. Randomly generate coded individuals under specific conditions (generator configuration scheme). ) constitute a population; 9. Verify the individuals within the population. If they meet the criteria, proceed to step 12; otherwise, proceed to step 10. 10. Perform the mutation operation and reset the individual code according to the constraints; 11. Perform the reshaping operation according to the reshaping calculation formula above, and pass the mutation information to the decimal individual so that the crossover operation can be performed; 12. Calculate the binary-coded individuals in step 3 ( 13. Perform lateral crossover and binary conversion operations, repeat steps 9 to 12, and replace the suboptimal solutions in Fitbest with the mediocre solutions; 14. Perform vertical crossover and binary conversion operations, repeat steps 9 to 12 again, and replace the suboptimal solution in Fitbest with the dominant solution; 15. Determine the algorithm termination condition. If it is met, proceed to step 16; otherwise, return to step 8. 16. Output the best individual in Fitbest, and the algorithm ends.

[0045] Specifically, the above-mentioned optimization algorithm is based on machine learning and combines the advantages of improved genetic algorithm and improved binary cross-multiplication algorithm. While ensuring convergence ability, it also improves the accuracy of the solution, enhances the algorithm performance, and has a better optimization ability for discrete uncertain problems. In specific implementation, the population size can be set to 50, the number of iterations of the genetic algorithm part can be set to 100, and the number of iterations of the cross-multiplication algorithm part can be set to 100 and 1000 respectively.

[0046] S4. Based on the solution results, assign the corresponding generator to provide low voltage boosting for at least one remote area.

[0047] The above solution results can be used to obtain the scheduling plan for each region. The overall scheduling plan includes information such as the number of scheduling days for each region, the capacity requirements for each region, and where to assign generators to each region.

[0048] Specifically, the solution provided in this embodiment effectively addresses the low voltage problem in remote rural areas during peak load periods of holidays by using generator trucks, thereby improving power quality, meeting residents' normal electricity needs, and avoiding disruptions to residents' daily lives, damage to electrical equipment, or threats to personal safety. Furthermore, the constructed scheduling model and established objective function, solved using a multi-objective optimization algorithm, effectively address the scheduling optimization problem of distribution network emergency generator trucks. From the perspective of generator truck configuration, it provides relevant departments with sound optimization suggestions, significantly reducing the operating costs of emergency generator trucks, improving the work efficiency of relevant departments, and to some extent enhancing power supply reliability and economic efficiency. Simultaneously, based on the characteristics of the generator truck scheduling problem, an improved genetic algorithm and an improved binary cross-multiplication algorithm are proposed, enabling them to handle discrete variables with pseudo-random characteristics, providing algorithmic support for solving the generator truck scheduling problem.

[0049] Example 2: This application provides a low-voltage boosting system for remote rural areas based on a low-voltage generator vehicle, applied to the low-voltage boosting method for remote rural areas based on a low-voltage generator vehicle in Example 1, such as... Figure 2 As shown, it includes: The data acquisition module is used to acquire information data of each generator in the current area and determine the geographical data of each remote area; The function creation module is used to build the scheduling model of the generator car, and to establish the objective function of the scheduling model based on information data and geographic data; The model solving module is used to optimize and solve the scheduling model based on the scheduling needs and objective function of at least one remote area using a multi-objective optimization algorithm. The vehicle dispatching module is used to assign the corresponding generator truck to at least one remote area to boost low voltage based on the solution results.

[0050] Example 3: This application provides an electronic device, such as... Figure 3 As shown, it includes: at least one processor, at least one memory, and a data bus; In this embodiment, the processor and the memory communicate with each other through a data bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the method as described in Embodiment 1.

[0051] Example 4: This application provides a non-transitory computer-readable storage medium that stores computer instructions that cause a computer to execute the method of Example 1.

[0052] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0054] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0055] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0056] Those skilled in the art will understand that all or part of the steps in the above facts and methods can be implemented by a program instructing related hardware. The program or the program described therein can be stored in a computer-readable storage medium. When the program is executed, it includes the following steps: at this time, the corresponding method steps are introduced. The storage medium can be ROM / RAM, magnetic disk, optical disk, etc.

[0057] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for improving low voltage in remote rural areas based on a low-voltage generator vehicle, characterized in that, The specific steps include the following: Acquire information data of each generator in the current area and determine the geographical data of each remote area; A scheduling model for generator trucks is constructed, and an objective function for the scheduling model is established based on the information data and the geographical data. Based on the scheduling needs of at least one remote area and the objective function, a multi-objective optimization algorithm is used to optimize and solve the scheduling model; Based on the solution results, the corresponding generator truck is assigned to provide low voltage boosting for at least one remote area.

2. The method for low voltage boosting in remote rural areas based on a low-voltage generator vehicle according to claim 1, characterized in that, The information data includes at least location data and capacity data; the geographic data includes at least address location.

3. The method for improving low voltage in remote rural areas based on a low-voltage generator vehicle according to claim 1, characterized in that, The objective function is established by minimizing the total scheduling cost, and the specific objective function is as follows: ； In the formula, Let be the objective function. Indicates the number of days for scheduling. This represents a set of capacity specifications for generator cars. This refers to the number of locations of each generator car. The number of remote areas, This indicates whether the generator is in an idle and dispatchable state that day. It is schedulable at that time. It is currently unschedulable; In scheduling state, The time indicated that the generator car departed from its location that day. Dispatch to location Otherwise, it is 0; Indicates position With position The distance between them Let be the demand matrix for each remote area, representing the th Dispatch needs in remote areas.

4. The method for improving low voltage in remote rural areas based on a low-voltage generator vehicle according to claim 1, characterized in that, The optimization of the scheduling model using a multi-objective optimization algorithm is as follows: An improved genetic algorithm is used to optimize the number of generator car scheduling operations. Based on the optimization solution results, the configuration of the generator cars is solved using an improved binary crossover algorithm to obtain the capacity configuration of each generator car in the optimization solution.

5. The method for low voltage boosting in remote rural areas based on a low-voltage generator vehicle according to claim 4, characterized in that, The fitness function in the improved genetic algorithm is specifically as follows: ； In the formula, This refers to the number of locations of each generator car. The number of remote areas, This represents a set of capacity specifications for generator cars. Indicates remote areas Position The generator car There is demand. This indicates whether the generator is in an idle and dispatchable state that day. 0 indicates that the schedulable condition is met; otherwise, it is 0. In scheduling state, The time indicated that the generator car departed from its location that day. Dispatch to location Otherwise, it is 0; Indicates position With position The distance between them.

6. The method for low voltage boosting in remote rural areas based on a low-voltage generator vehicle according to claim 4, characterized in that, In the improved binary crossover algorithm, the reshaping operation is implemented using the following formula: ； In the formula, In the binary crossover algorithm The new individual value after the reshaping operation This represents the inverse function of the Sigmoid function. The threshold function is used to select a random value between 0 and 1. Representing particles in the binary crossover algorithm The decimal solution of the d-th dimension after crossing.

7. The method for low voltage boosting in remote rural areas based on a low-voltage generator vehicle according to claim 6, characterized in that, The Sigmoid function is as follows: ； In the formula, This is the modified Sigmoid function.

8. A low-voltage boosting system for remote rural areas based on a low-voltage generator vehicle, characterized in that, include: The data acquisition module is used to acquire information data of each generator in the current area and determine the geographical data of each remote area; The function establishment module is used to construct a scheduling model for generator trucks, and to establish the objective function of the scheduling model based on the information data and the geographical data. The model solving module is used to optimize and solve the scheduling model based on the scheduling needs of at least one remote area and the objective function using a multi-objective optimization algorithm. The vehicle dispatching module is used to assign the corresponding generator truck to at least one remote area to perform low voltage boosting based on the solution results.

9. An electronic device, characterized in that, include: At least one processor, at least one memory, and a data bus; The processor and the memory communicate with each other via the data bus. The memory stores program instructions that can be executed by the processor, which invokes the program instructions to perform the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions that cause the computer to perform the method of any one of claims 1-7.

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