Energy management strategy generation method for flexible mobile microgrid based on intelligent optimization algorithm

Abstract

The present invention provides a method for generating a flexible mobile microgrid energy management strategy based on an intelligent optimization algorithm, comprising: initializing the parameters of each basic element in the flexible mobile microgrid energy management strategy according to the basic elements in the flexible mobile microgrid energy management strategy; determining the constraint conditions according to the technical requirements of the parameters, and constructing an objective function based on the management target of the flexible mobile microgrid energy; according to the objective function, using an improved sooty tern optimization algorithm to search for the optimal parameters of each basic element in the flexible mobile microgrid energy management strategy; and generating a flexible mobile microgrid energy management strategy based on an intelligent optimization algorithm according to the optimal parameters of each basic element. The method can form an effective energy management strategy and further improve the working efficiency of the flexible mobile microgrid.

Description

Technical Field

[0001] The present invention relates to the technical field of flexible mobile microgrid energy management, and in particular to a method for generating a flexible mobile microgrid energy management strategy based on an intelligent optimization algorithm. Background Art

[0002] Flexible mobile microgrids are mobile micro-distribution networks transformed with flexible power electronics technology. They use intelligent flexible switches to replace traditional rigid switches to achieve flexible closed-loop operation of the distribution network and improve the power quality, reliability and operating efficiency of the distribution system. Flexible mobile microgrid technology connects various units, platforms and sites in the application scenario, completes efficient energy transmission and interactive sharing between platforms, and realizes the interconnection of energy in the application scenario.

[0003] The quality of energy management strategies directly affects the working efficiency of flexible mobile microgrids. Faced with complex application scenarios, how to quickly generate energy management strategies for flexible mobile microgrids is a key issue that needs to be solved urgently. As one of the hot research topics in artificial intelligence, intelligent optimization algorithms have been widely used in the research of control methods and control strategies in various electrical fields. The Sooty tern optimization algorithm (STOA) is a new intelligent optimization algorithm that simulates the foraging behavior of sooty terns, and can also be used to generate energy management strategies for flexible mobile microgrids.

[0004] However, the Sooty Tern optimization algorithm is prone to fall into local optimal situations. When generating energy management strategies for flexible mobile microgrids, it is necessary to consider multiple optimization objectives including the working efficiency, service life, new energy utilization rate, fuel consumption rate, etc. of the flexible mobile microgrid. The existing Sooty Tern optimization algorithm lacks consideration of global optimization when performing optimization, and the management strategies obtained often fail to achieve ideal results. Summary of the invention

[0005] The present invention provides a method for generating an energy management strategy for a flexible mobile microgrid based on an intelligent optimization algorithm. The method can form an effective energy management strategy and further improve the working efficiency of the flexible mobile microgrid.

[0006] The present invention proposes a method for generating a flexible mobile microgrid energy management strategy based on an intelligent optimization algorithm, which specifically includes the following steps:

[0007] Initialize the parameters of each basic element in the flexible mobile microgrid energy management strategy according to the basic elements in the flexible mobile microgrid energy management strategy;

[0008] Determine the constraints according to the technical requirements of the parameters and construct the objective function based on the management objectives of the flexible mobile microgrid energy;

[0009] According to the objective function, the improved sooty tern optimization algorithm is used to search for the parameters of the basic elements in the optimal flexible mobile microgrid energy management strategy; wherein the improved sooty tern optimization algorithm introduces the position update mechanism of the parrot optimization algorithm and the cat swarm optimization algorithm, and comprehensively considers the update mode of switching the position according to the size of the random number, the optimal position of the sooty tern in this iteration, the average value of the sooty tern position and the selection probability of the sooty tern to update the sooty tern position;

[0010] According to the optimal parameters of each basic element, a flexible mobile microgrid energy management strategy based on the intelligent optimization algorithm is generated.

[0011] Preferably, the basic elements in the flexible mobile microgrid energy management strategy include: energy source type, energy source characteristics, load type, load characteristics, power guarantee mode and power working mode.

[0012] Preferably, the objective function is that the flexible mobile microgrid has the highest working efficiency, the longest service life, the highest energy utilization rate, or the lowest fuel consumption rate.

[0013] Preferably, the method of using the improved Sooty Tern optimization algorithm to search for the parameters of the basic elements in the optimal flexible mobile microgrid energy management strategy comprises the following steps:

[0014] The parameters of each basic element in the energy management strategy of the flexible mobile microgrid are used as sooty tern individuals, the upper and lower limits of the parameters are determined, and the position of the sooty tern population is initialized through Gaussian mapping. According to the objective function, the optimal fitness value and the optimal sooty tern position are calculated.

[0015] The position update mechanism of the parrot optimization algorithm and the cat colony optimization algorithm is introduced to improve the position update method in the original sooty tern optimization algorithm; the position is updated through the improved position update method, and the optimal fitness value and position after the current iteration are recorded;

[0016] Bidirectional analysis of the optimal sooty tern position sine Mutation, taking the black tern position with the best fitness value before and after mutation as the updated optimal black tern position;

[0017] The optimal sooty tern position is updated in sequence according to a preset maximum number of iterations to determine the optimal sooty tern position; and the parameters of the optimal flexible mobile microgrid energy management strategy are determined according to the optimal sooty tern position.

[0018] Preferably, the initializing the position of the sooty tern population by Gaussian mapping comprises the following steps:

[0019] Determining the size of a population N , Sooty Terns search for the optimal lower boundary LB and Sooty Terns Seeking the Upper Boundary UB;

[0020] Generate the next random number through Gaussian mapping x t+1 :

[0021] ;

[0022] In the formula, mod(·) is the remainder function, x t is the current random number;

[0023] Use the generated Gaussian random numbers to initialize the positions of the sooty terns:

[0024] .

[0025] Preferably, the method of performing location updating by using the improved location updating method and recording the optimal fitness value and location after the current iteration comprises the following steps:

[0026] Simulating the migration behavior of sooty terns, including conflict avoidance, aggregation, and regeneration;

[0027] Conflict avoidance: The conflict avoidance behavior process of the sooty tern is simulated and expressed as follows:

[0028] ;

[0029] Where: Indicates the current t The position of the sooty terns at the iteration; It shows the new position of the sooty tern if it does not collide with other sooty terns; Represents a variable factor to avoid collision, which is used to calculate the position after avoiding collision. Its constraint formula is as follows:

[0030] ;

[0031] Where: To adjust of control variables; t Indicates the current iteration number; As the number of iterations increases, Gradually decreases to 0; assuming is 2, Will gradually decrease from 2 to 0; Miter is the number of iterations;

[0032] Aggregation: Aggregation means that the current sooty tern moves closer to the best position among the adjacent sooty terns under the premise of avoiding conflict, that is, moves closer to the optimal position. Its mathematical expression is as follows:

[0033] ;

[0034] Where: It is t The optimal position of the Sooty Terns in the iteration; Indicated in different locations To the best position The process of moving; is a random variable that changes according to the following formula:

[0035] ;

[0036] Where: rand A random number between 0 and 1;

[0037] Update: Move the current sooty tern toward the direction of the optimal position and update the position. The mathematical expression is:

[0038] ;

[0039] Where: is the distance the sooty tern moves from its current position to the optimal position;

[0040] Attack behavior: During migration, sooty terns use their wings to increase their flight altitude, adjust their speed and attack angle, and when attacking prey, their hovering behavior in the air is defined by the following mathematical model:

[0041] ;

[0042] Where: x , y , z They are the coordinates in three directions respectively; r is the radius of each spiral; θ is a random angle value in the range [0, 2π]; u and v are the related constants that define the spiral shape, all set to 1; e is the base of natural logarithms;

[0043] The position update mechanism of the parrot optimization algorithm and the cat swarm optimization algorithm is introduced, and the position of the sooty tern is updated by considering the factors of switching the position update mode according to the size of the random number, the optimal position of the sooty tern in this iteration, the average value of the sooty tern position and the selection probability of the sooty tern:

[0044] ;

[0045] Where: For the updated t +1 iteration of Sooty Tern position; Indicates tThe best position of the iteration; is the current position of the Sooty Terns; is the distance the sooty tern moves from its current position to the optimal position; Indicates t The average of all sooty tern positions in iterations; It is t The selection probability of the current sooty tern in iteration ; and is a random number between [0,1];

[0046] ;

[0047] Where: is the current fitness value of the Sooty Tern; is the current maximum fitness value; is the current minimum fitness value;

[0048] Calculate the fitness value:

[0049] ;

[0050] In the formula, Calculate the fitness value function;

[0051] Record the best sooty tern in the iteration.

[0052] Preferably, the optimal sooty tern position is bidirectionally sine Mutation, taking the black tern position with the best fitness value before and after mutation as the updated optimal black tern position, includes the following steps:

[0053] For Dimension j , calculated according to the current number of iterations sine Chaos value, and switches between positive and negative directions with equal probability:

[0054] ;

[0055] ;

[0056] In the formula, rand A random number between 0 and 1; x 0 is the iteration sequence value;

[0057] Perform mutation perturbation on the optimal position:

[0058] ;

[0059] Where: Indicates t +1 optimal position for iteration No.j dimension; is the optimal position after mutation disturbance No. j dimension;

[0060] Greedy Update:

[0061] ;

[0062] In the formula, after each dimension is mutated, the mutation stops.

[0063] The present invention also proposes a flexible mobile microgrid energy management strategy generation system based on an intelligent optimization algorithm, the system comprising:

[0064] processor;

[0065] a memory having stored thereon a computer program executable on the processor;

[0066] Wherein, when the computer program is executed by the processor, the steps of the method for generating a flexible mobile microgrid energy management strategy based on an intelligent optimization algorithm are implemented.

[0067] The present invention also proposes a computer-readable storage medium, on which a data processing program is stored. When the data processing program is executed by a processor, the steps of the method for generating a flexible mobile microgrid energy management strategy based on an intelligent optimization algorithm are implemented.

[0068] Beneficial effects of the present invention:

[0069] The present invention proposes a method for generating a flexible mobile microgrid energy management strategy based on an intelligent optimization algorithm, and uses an improved sooty tern optimization algorithm to generate various basic parameter elements of the flexible mobile microgrid energy management strategy to form a flexible mobile microgrid energy management strategy. The present invention improves the position update method of the sooty tern, introduces the position update mechanism of the parrot optimization algorithm and the cat group optimization algorithm, and comprehensively considers the update mode of switching the position according to the size of the random number, the optimal position of the sooty tern in this iteration, the average value of the sooty tern position, the selection probability of the sooty tern and other factors to update the sooty tern position, thereby increasing the algorithm search range, enhancing the adaptability of the algorithm, and realizing global optimization considerations under multiple optimization targets, and being able to obtain better basic parameter information of the flexible mobile microgrid energy management strategy, thereby quickly generating a flexible mobile microgrid energy management strategy, and realizing stable and efficient management of the flexible mobile microgrid energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a flow chart of a method for generating a flexible mobile microgrid energy management strategy based on an intelligent optimization algorithm according to an embodiment of the present invention;

[0071] Figure 2 It is an iterative process curve of the embodiment of the present invention. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solution 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.

[0073] Example 1

[0074] The quality of energy management strategy directly affects the working efficiency of flexible mobile microgrid. Faced with complex application scenarios, how to quickly generate energy management strategy for flexible mobile microgrid is a key issue that needs to be solved urgently. To this end, the present invention combines the basic steps of STOA, and proposes an improved Sooty tern optimization algorithm (ISTOA) to generate energy management strategy for flexible mobile microgrid. The flowchart of the method for generating energy management strategy for flexible mobile microgrid is as follows: Figure 1 As shown, the specific steps are as follows:

[0075] S1: Initialize parameter information of the flexible mobile microgrid energy management strategy according to the basic elements of the flexible mobile microgrid energy management strategy to be generated, including energy source type, energy source characteristics, load type, load characteristics, power guarantee mode, working mode, etc.

[0076] S2: Establish the objective function of the flexible mobile microgrid energy management strategy based on the improved Sooty Tern optimization algorithm funtion For example, the working efficiency, service life, utilization rate of new energy, fuel consumption rate, etc. of flexible mobile microgrids.

[0077] S3: Set parameters, including: the size of the sooty tern population N , that is, the number of sooty tern individuals; the maximum number of iterations Miter , that is, the condition for stopping the iteration; Sooty Terns optimize the lower boundary LB ; Sooty Terns Seek the Upper Boundary UB .

[0078] S4: Initialize the population position of sooty terns using Gaussian mapping:

[0079] Gaussian map random number generation:

[0080] ;

[0081] Use the generated Gaussian random numbers to initialize the positions of the sooty terns:

[0082] ;

[0083] S5: Migration behavior of Sooty Terns:

[0084] The migration behavior of sooty terns, i.e. the exploration part of the algorithm, is mainly divided into three stages: conflict avoidance, aggregation and renewal.

[0085] a) Conflict avoidance.

[0086] The conflict avoidance behavior process of the sooty tern is simulated and expressed by the following formula:

[0087] ;

[0088] Where: Indicates the current t The position of the sooty terns at the iteration; It shows the new position of the sooty tern if it does not collide with other sooty terns; Represents a variable factor to avoid collision, which is used to calculate the position after avoiding collision. Its constraint formula is as follows:

[0089] ;

[0090] Where: To adjust of control variables; t Indicates the current iteration number; As the number of iterations increases, Gradually decreases to 0; if we assume is 2, It will gradually decrease from 2 to 0.

[0091] b) Gathering.

[0092] Aggregation means that the current sooty tern moves closer to the best position among the adjacent sooty terns under the premise of avoiding conflict, that is, moving closer to the optimal position. Its mathematical expression is as follows:

[0093] ;

[0094] Where: is the optimal position of the sooty tern at the tth iteration; Indicated in different locations To the best position The process of moving; is a random variable that makes exploration more comprehensive, and varies according to the following formula:

[0095] ;

[0096] Where: A random number in the range [0,1].

[0097] c) Update.

[0098] Update means that the current sooty tern moves towards the direction of the optimal position and updates the position. Its mathematical expression is:

[0099] ;

[0100] Where: is the distance the sooty tern moves from its current position toward the optimal position.

[0101] S6: Aggressive behavior:

[0102] During migration, sooty terns can use their wings to increase their flight altitude, and can also adjust their speed and attack angle. When attacking prey, their hovering behavior in the air can be defined as the following mathematical model:

[0103] ;

[0104] Where: r is the radius of each spiral; θ is a random angle value in the range [0, 2π]; u and v are related constants that define the spiral shape and can all be set to 1; e is the base of natural logarithms.

[0105] In the original Sooty Tern algorithm, only the optimal Sooty Tern position is used for guidance to update the Sooty Tern position. In order to more effectively improve the global search capability of the Sooty Tern, the position update mechanism of the Parrot Optimization Algorithm (POA) and the Cat Swarm Algorithm (CSA) is introduced to improve the Sooty Tern position update method. The Sooty Tern position is updated by comprehensively considering factors such as switching the position update mode according to the size of the random number, the optimal position of the Sooty Tern in this iteration, the average value of the Sooty Tern position, and the selection probability of the Sooty Tern, so as to avoid the local optimum in each iteration, thereby improving the global search capability of the Sooty Tern algorithm.

[0106] The position update mechanism of the parrot optimization algorithm and the cat colony optimization algorithm is introduced, and the improved sooty tern position update formula is as follows:

[0107] ;

[0108] Where: For the updated t +1 iteration of Sooty Tern position; Indicates t The best position of the iteration; is the current position of the Sooty Terns; is the distance the sooty tern moves from its current position to the optimal position; Indicates t The average of all sooty tern positions in iterations; It is t The selection probability of the current sooty tern in iteration ; and is a random number between [0,1];

[0109] ;

[0110] Where: is the current fitness value of the Sooty Tern; is the current maximum fitness value; is the current minimum fitness value.

[0111] S7: Calculate the fitness value.

[0112] ;

[0113] In the formula, is the fitness function used to calculate the fitness value.

[0114] S8: Record information. Record the best sooty tern in the iteration.

[0115] S9: For the optimal sooty tern, perform bidirectional analysis dimension by dimension sine Mutations.

[0116] For Dimension j , first calculate according to the current number of iterations sine Chaos value, and switches between positive and negative directions with equal probability.

[0117] ;

[0118] ;

[0119] Then perform mutation perturbation on the optimal position:

[0120] ;

[0121] Where: Indicates t +1 optimal position for iteration No. j dimension.

[0122] Greedy Update:

[0123] ;

[0124] After each dimension has been mutated, stop mutation.

[0125] S10: Record information. Record the best sooty tern in the iteration.

[0126] S11: Repeat steps S5 to S11. When the maximum number of iterations Miter is reached, the algorithm stops and the optimal Sooty Tern result is output, that is, the optimal flexible mobile microgrid energy management strategy based on the intelligent optimization algorithm is obtained.

[0127] In this embodiment:

[0128] Using MATLAB as the simulation platform, the Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO) and Sooty tern optimization algorithm (STOA) were selected for comparison with the ISTOA method proposed in this embodiment.

[0129] To ensure the fairness of the experiment, the population size of all algorithms is set to 30, the maximum number of iterations is set to 300, LB = 0, UB = 100, and other parameter settings are shown in Table 1

[0130] Table 1 Parameter settings of each algorithm

[0131]

[0132] Figure 2 is the iterative process curve. Figure 2 It can be intuitively found that the convergence speed of the ISTOA method is faster than that of the other three algorithms, and the convergence accuracy of the ISTOA method is better than that of the other three algorithms. The simulation results show that the ISTOA algorithm has a stronger search ability and obtains a better flexible mobile microgrid energy management strategy based on the intelligent optimization algorithm, which verifies the effectiveness of the algorithm.

[0133] The above is a flexible mobile microgrid energy management strategy generation method based on an intelligent optimization algorithm provided by an embodiment of this embodiment. Based on the same idea, this embodiment also provides a corresponding flexible mobile microgrid energy management strategy generation system based on an intelligent optimization algorithm. For the specific definition of the flexible mobile microgrid energy management strategy generation system based on the intelligent optimization algorithm, please refer to the definition of the flexible mobile microgrid energy management strategy generation method based on the intelligent optimization algorithm in the above text, which will not be repeated here. Each module in the above-mentioned flexible mobile microgrid energy management strategy generation system based on the intelligent optimization algorithm can be fully or partially implemented by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0134] This embodiment also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A flexible mobile microgrid energy management strategy generation method based on intelligent optimization algorithm is provided.

[0135] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0136] The above are only preferred embodiments of the present invention and are 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 generating a flexible mobile microgrid energy management strategy based on an intelligent optimization algorithm, characterized in that: The following steps are involved: Initialize the parameters of each basic element in the flexible mobile microgrid energy management strategy according to the basic elements in the flexible mobile microgrid energy management strategy; Determine the constraints according to the technical requirements of the parameters and construct the objective function based on the management objectives of the flexible mobile microgrid energy; According to the objective function, the improved sooty tern optimization algorithm is used to search for the parameters of the basic elements in the optimal flexible mobile microgrid energy management strategy; wherein the improved sooty tern optimization algorithm introduces the position update mechanism of the parrot optimization algorithm and the cat swarm optimization algorithm, and comprehensively considers the update mode of switching the position according to the size of the random number, the optimal position of the sooty tern in this iteration, the average value of the sooty tern position and the selection probability of the sooty tern to update the sooty tern position; According to the optimal parameters of each basic element, a flexible mobile microgrid energy management strategy based on intelligent optimization algorithm is generated; The method of using the improved Sooty Tern optimization algorithm to search for the optimal parameters of each basic element in the flexible mobile microgrid energy management strategy includes the following steps: The parameters of each basic element in the flexible mobile microgrid energy management strategy are used as sooty tern individuals, the upper and lower limits of the parameters are determined, and the position of the sooty tern population is initialized through Gaussian mapping. According to the objective function, the optimal fitness value and the optimal sooty tern position are calculated. The position update mechanism of the parrot optimization algorithm and the cat colony optimization algorithm is introduced to improve the position update method in the original sooty tern optimization algorithm; the position is updated through the improved position update method, and the optimal fitness value and position after the current iteration are recorded; Perform bidirectional sine mutation on the optimal sooty tern position, and use the sooty tern position with the best fitness value before and after mutation as the updated optimal sooty tern position; The optimal sooty tern position is updated in sequence according to a preset maximum number of iterations to determine the optimal sooty tern position; and the parameters of the optimal flexible mobile microgrid energy management strategy are determined according to the optimal sooty tern position; The method of performing position updating by using the improved position updating method and recording the optimal fitness value and position after the current iteration includes the following steps: Simulating the migration behavior of sooty terns, including conflict avoidance, aggregation, and regeneration; Conflict avoidance: The conflict avoidance behavior process of the sooty tern is simulated and expressed as follows: C s (t)=S A ×P s (t) Where: P s (t) represents the position of the sooty tern at the current iteration t; C s (t) represents the new position of the sooty tern without colliding with other sooty terns; S A Represents a variable factor to avoid collision, which is used to calculate the position after avoiding collision. Its constraint formula is as follows: S A =C f -(t×C f / Miter) Where: C f To adjust S A The control variable; t represents the current iteration number; S A As the number of iterations increases, from C f Gradually decreases to 0; assuming C f =2, S A will gradually decrease from 2 to 0; Miter is the number of iterations; Aggregation: Aggregation means that the current sooty tern moves closer to the best position among the adjacent sooty terns under the premise of avoiding conflict, that is, moves closer to the optimal position. Its mathematical expression is as follows: M s (t)=C B ×(P bs (t)-P s (t)) Where: P bs (t) is the optimal position of the sooty tern in the tth iteration; M s (t) represents the position P at different locations s (t) Towards the optimal position P bs (t) The process of moving; C B is a random variable that changes according to the following formula: C B =0.5×rand Where: rand is a random number between 0 and 1; Update: Move the current sooty tern toward the direction of the optimal position and update the position. The mathematical expression is: D s (t)=C s (t)+M s (t) Where: D s (t) is the distance the sooty tern moves from the current position to the optimal position; Attack behavior: During migration, sooty terns use their wings to increase their flight altitude, adjust their speed and attack angle, and when attacking prey, their hovering behavior in the air is defined by the following mathematical model: Where: x, y, z are the coordinates in three directions; r is the radius of each spiral; θ is a random angle value in the range [0, 2π]; u and v are related constants that define the spiral shape, both set to 1; e is the base of the natural logarithm; The position update mechanism of the parrot optimization algorithm and the cat swarm optimization algorithm is introduced, and the position of the sooty tern is updated by considering the factors of switching the position update mode according to the size of the random number, the optimal position of the sooty tern in this iteration, the average value of the sooty tern position and the selection probability of the sooty tern: Where: P s (t+1) is the updated position of the sooty tern at the t+1th iteration; P bs (t) represents the best position of the tth iteration; P s (t) is the current position of the sooty tern; D s (t) is the distance that the sooty tern moves from the current position to the optimal position; P sm (t) represents the average position of all sooty terns in the tth iteration; P s is the selection probability of the current sooty tern in the tth iteration; q1 and q2 are random numbers between [0,1]; Where: F s (t) is the current fitness value of the sooty tern; F max is the current maximum fitness value; F min is the current minimum fitness value; Calculate the fitness value: fitness(t)=F f (P s (t+1)) In the formula, F f (·) is the fitness value calculation function; Record the best sooty tern in the iteration.

2. The method for generating a flexible mobile microgrid energy management strategy based on an intelligent optimization algorithm according to claim 1, characterized in that: The basic elements of the flexible mobile microgrid energy management strategy include: energy source type, energy source characteristics, load type, load characteristics, power guarantee mode and power working mode.

3. The method for generating a flexible mobile microgrid energy management strategy based on an intelligent optimization algorithm according to claim 1, characterized in that: The objective function is that the flexible mobile microgrid has the highest working efficiency, the longest service life, the highest energy utilization rate, or the lowest fuel consumption rate.

4. The method for generating a flexible mobile microgrid energy management strategy based on an intelligent optimization algorithm according to claim 1, characterized in that: The process of initializing the position of the black tern population by Gaussian mapping comprises the following steps: Determine the population size N, the lower boundary LB and the upper boundary UB of the sooty tern; Generate the next random number x through Gaussian mapping t+1 : In the formula, mod(·) is the remainder function, x t is the current random number; Use the generated Gaussian random numbers to initialize the positions of the sooty terns: P s (t)=(UB-LB)×x t +LB。 5. The method for generating a flexible mobile microgrid energy management strategy based on an intelligent optimization algorithm according to claim 1, characterized in that: The method of performing a bidirectional sine mutation on the optimal sooty tern position and taking the sooty tern position with the best fitness value before and after the mutation as the updated optimal sooty tern position includes the following steps: For dimension j, the sine chaos value is calculated according to the current number of iterations, and the positive and negative directions are switched with equal probability: sin Value = sin(πx0) In the formula, rand is a random number between 0 and 1; x0 is the iteration sequence value; Perform mutation perturbation on the optimal position: P bs(j) (t+1)′=P bs(j) (t+1)+SinValue×P bs(j) (t+1) Where: P bs(j) (t+1) represents the optimal position P of the t+1th iteration bs The jth dimension of (t+1); P bs(j) (t+1)' is the optimal position P after the mutation disturbance bs The jth dimension of (t+1); Greedy Update: In the formula, after each dimension is mutated, the mutation stops.

6. A flexible mobile microgrid energy management strategy generation system based on intelligent optimization algorithm, characterized in that: The system comprises: processor; a memory having stored thereon a computer program executable on the processor; Wherein, when the computer program is executed by the processor, the steps of the method for generating a flexible mobile microgrid energy management strategy based on an intelligent optimization algorithm as described in any one of claims 1 to 5 are implemented.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a data processing program, and when the data processing program is executed by the processor, the steps of the method for generating a flexible mobile microgrid energy management strategy based on an intelligent optimization algorithm as described in any one of claims 1 to 5 are implemented.

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