Energy security test system design method, system and medium

Abstract

The present invention provides an energy security test system design method, system, and medium, belonging to the technical field of energy security test systems. The method comprises: determining the design parameters of the energy security test system based on its essential elements; optimizing the design parameters using an improved golden jackal optimization algorithm, using the energy transmission efficiency of the energy security test system as the objective function, to obtain the design parameters with the optimal fitness value. The improved golden jackal optimization algorithm incorporates the tuna school optimization algorithm and the mirage algorithm in the exploration and development phases to improve the position update method; and determining the energy security test system design scheme based on the optimal design parameters. This method can rapidly generate an optimal energy security test system design scheme that achieves the optimal transmission efficiency of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy security test systems, and in particular to an energy security test system design method, system and medium. Background Art

[0002] The energy security test system uses multi-energy complementarity and intelligent scheduling technology to build a dynamically optimized energy supply network, achieving an efficient, stable, and highly responsive energy security solution. Therefore, it is urgent and necessary to conduct in-depth research on the characteristics of the energy security test system and conduct research on the integrated matching design of the energy security test system.

[0003] Intelligent optimization algorithms are a hot topic in artificial intelligence research and have been widely used in optimization design in the power sector. The Golden Jackal Optimization Algorithm (GJOA), a new metaheuristic algorithm proposed in 2022, mimics the cooperative hunting behavior of golden jackals and can also be applied to the integrated matching design of energy security test systems. However, the GJOA still has some flaws, making it prone to local optimality and low convergence accuracy. This often fails to achieve ideal design results when designing integrated matching systems for energy security test systems. Summary of the Invention

[0004] The present invention provides an energy security test system design method, which can quickly generate an optimal energy security test system design scheme, and the design scheme can achieve the optimal transmission efficiency of the system.

[0005] The specific steps include:

[0006] Determine the design parameters of the energy security test system based on the basic elements of the energy security test system;

[0007] The constraints of the design parameters are determined, and the energy transmission efficiency of the energy security test system is used as the objective function. The fitness value is calculated based on the objective function, and the design parameters are optimized using an improved golden jackal optimization algorithm to obtain the design parameters with the optimal fitness value. The improved golden jackal optimization algorithm introduces the tuna school optimization algorithm and the mirage algorithm into the exploration and development phases of the original golden jackal optimization algorithm, improves the position update method, and performs the next generation position update based on the optimal position, movement trend, quality factor, and population quality of the golden jackal in the current iteration.

[0008] Determine the design scheme of the energy security test system based on the optimal design parameters.

[0009] Preferably, the position update formula in the exploration phase of the improved golden jackal optimization algorithm is:

[0010] ;

[0011] ;

[0012] Where: t is the current iteration number; For the t The location of the prey at the iteration; 、 Respectively t The positions of male and female golden jackals in the iteration; 、 Respectively t The updated positions of the male and female golden jackals corresponding to the prey at the iteration; is the quality factor of the golden jackal at the current t-th iteration;

[0013] in, Represent a random number based on the Levy distribution:

[0014] ;

[0015] Where, is the Lévy flight function;

[0016] in, Energy for the prey's escape:

[0017] ;

[0018] Where, Indicates the decline of prey energy. Represents the initial state of prey energy:

[0019] ;

[0020] ;

[0021] Where r is a random number in the range [0,1]; T is the maximum number of iterations; is a constant with a value of 1.5; during the entire iteration process, Decrease linearly from 1.5 to 0;

[0022] in:

[0023] ;

[0024] ;

[0025] ;

[0026] ;

[0027] ;

[0028] Where: 、 The weight coefficient for controlling the golden jackal's movement toward the best golden jackal and the previous golden jackal, a is a constant; b is a random number in [0,1]; and l is an intermediate parameter; is the integral area of ​​the population fitness function at the current t-th iteration, indicating the population quality; For optimal population quality; The worst population quality;

[0029] The position update formula of the golden jackal is improved as follows:

[0030] ;

[0031] Where: is the position of the golden jackal after the t+1th iteration.

[0032] Preferably, the position update formula in the development phase of the improved golden jackal optimization algorithm is:

[0033] ;

[0034] ;

[0035] Where: t is the current iteration number; For the t The location of the prey at the iteration; 、 Respectively t The positions of male and female golden jackals in the iteration; 、 Respectively t The updated positions of the male and female golden jackals corresponding to the prey at the iteration;

[0036] The formula for updating the position of the golden jackal is as follows:

[0037] ;

[0038] Where: is the position of the golden jackal after the t+1th iteration; 、 and is a random number in [0,1] that satisfies .

[0039] Preferably, the basic elements of the energy security test system include an energy input module, an energy storage module and a transmission network module;

[0040] The design parameters of the energy input module include input power range and fluctuation coefficient;

[0041] The design parameters of the energy storage module include conversion efficiency, energy storage capacity and charge and discharge rate;

[0042] The design parameters of the transmission network module include transmission line impedance, topology parameters and maximum transmission power.

[0043] Preferably, the construction of the objective function comprises the following steps:

[0044] Determine the transmission link loss model based on the design parameters of the energy input module, the energy storage module and the transmission network module;

[0045] Determine the output power according to the energy storage capacity of the energy storage module of the energy security test system;

[0046] The transmission efficiency is determined based on the actual received power, output power and transmission link loss model at the load end, and maximizing the transmission efficiency is used as the optimization goal of the structural design parameters.

[0047] The present invention also provides a design system for an energy security test system, the system comprising:

[0048] processor;

[0049] a memory having stored thereon a computer program executable on the processor;

[0050] Among them, the steps of the energy security test system design method are implemented when the computer program is executed by the processor.

[0051] The present invention also provides a computer-readable storage medium, on which a data processing program is stored. When the data processing program is executed by the processor, the steps of the energy security test system design method are implemented.

[0052] Beneficial effects of the present invention:

[0053] The present invention proposes a method for designing an energy security test system. This method uses an improved golden jackal optimization algorithm to optimize the basic parameters of the energy security test system. Based on the optimization results, an optimal energy security test system design scheme is constructed. Based on this system architecture, the best transmission efficiency can be achieved.

[0054] Among them, in the exploration stage of the improved golden jackal optimization algorithm, in order to more effectively improve the global search capability of the algorithm, the position update mechanism of the tuna school optimization algorithm and the mirage algorithm was introduced to improve the golden jackal position update method. The optimal position, movement trend, quality factor, population quality and other factors of the golden jackal in this iteration were comprehensively considered to update the golden jackal position, avoid local optimality in each iteration, and thus improve the global search capability of the algorithm, effectively covering the entire problem search space.

[0055] In addition, during the development phase of the improved golden jackal optimization algorithm, in order to more effectively improve the convergence speed of the algorithm, the position update mechanism of the tuna school optimization algorithm and the mirage algorithm was introduced to improve the golden jackal position update method to avoid the possible rapid loss of population diversity, which would cause the algorithm to converge to the local optimal solution prematurely, thereby improving the convergence speed of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a flow chart of a method for designing an energy security test system according to an embodiment of the present invention;

[0057] Figure 2 is an execution flow chart of the improved golden jackal optimization algorithm according to an embodiment of the present invention;

[0058] Figure 3 It is an iterative process curve of the embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to 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.

[0060] Example 1

[0061] The Golden Jackal Optimization Algorithm (GJOA) is a new metaheuristic algorithm proposed in 2022. This algorithm is a new intelligent optimization algorithm that imitates the cooperative hunting behavior of golden jackals. It can also be applied to the integrated matching design problem of energy security test systems. However, the Golden Jackal Optimization Algorithm still has some defects: (1) Exploration stage: The Golden Jackal Optimization Algorithm cannot effectively cover the entire problem search space, which may cause the algorithm to fall into a local optimum. (2) Development stage: The Golden Jackal Optimization Algorithm converges slowly because the local search strategy is too conservative. For example, if it relies too much on the optimal solution, the population diversity may be lost rapidly, causing the algorithm to converge to the local optimal solution too early, resulting in poor adaptability in solving complex problems. Based on the above two key deficiencies, the optimal design effect cannot be achieved when the Golden Jackal Optimization Algorithm is used for the integrated matching design of the energy security test system. To this end, the present invention proposes a method for designing an energy security test system, and its flow chart is as follows: Figure 1 The specific steps are as follows:

[0062] S1: Determine the design parameters of the energy security test system based on the basic elements of the energy security test system.

[0063] S2: Determine the constraint range of the design parameters, take the energy transmission efficiency of the energy security test system as the objective function, calculate the fitness value based on the objective function, and optimize the design parameters through the improved golden jackal optimization algorithm to obtain the design parameters with the best fitness value; among them, the improved golden jackal optimization algorithm introduces the tuna school optimization algorithm and the mirage algorithm in the exploration and development stages of the original golden jackal optimization algorithm, improves the position update method, and performs the next generation position update based on the optimal position, movement trend, quality factor and population quality of the golden jackal in this iteration.

[0064] S3: Determine the design scheme of the energy security test system based on the optimal design parameters.

[0065] Specifically, such as Figure 2 As shown in the figure, the improved golden jackal optimization algorithm includes the following steps to optimize the optimization parameters:

[0066] S2.1: Determine the basic elements of the energy security test system. The basic elements of the energy security test system include the energy input module, energy storage module, and transmission network module. The design parameters of the energy input module include the input power range and fluctuation coefficient; the design parameters of the energy storage module include conversion efficiency, energy storage capacity, and charge and discharge rate; the design parameters of the transmission network module include transmission line impedance, topology parameters, and maximum transmission power.

[0067] S2.2: Construct the objective function. Determine the transmission link loss model based on the design parameters of the energy input module, energy storage module, and transmission network module. Determine the output power based on the energy storage capacity of the energy assurance test system's energy storage module. Determine the transmission efficiency based on the actual received power and output power at the load end and the transmission link loss model. Construct the objective function with maximizing transmission efficiency as the optimization goal for the structural design parameters.

[0068] S2.3: Set parameters, including: population size (i.e., number of golden jackals) N; maximum number of iterations (i.e., conditions for stopping iterations) T ; Prey search lower boundary ; Upper bound of prey search optimization .

[0069] S2.4: Initialize the location of the golden jackal population:

[0070] (1);

[0071] Where: is the location of the initial golden jackal population; is a random number in the range [0,1]; and They are the upper and lower bounds of the problem to be solved;

[0072] In the GJOA algorithm, the prey matrix is ​​expressed as:

[0073] (2);

[0074] Where: is the prey matrix; is the j-th dimension position of the i-th prey; The first and second winners (the ones with the best and second best fitness values) are together regarded as the golden jackal pair; n is the number of prey; d is the dimension of the problem to be solved.

[0075] S2.5: Calculate the fitness value of the prey according to the objective function. The one with the best fitness value is selected as the male golden jackal, and the one with the second best fitness value is selected as the female golden jackal.

[0076] During the optimization process, the fitness (objective) function is used to estimate the fitness value of each prey. The fitness value matrix of all prey is expressed as follows:

[0077] (3);

[0078] Where: is the fitness value matrix of the prey; is the fitness function or objective function; the one with the best fitness value is the male golden jackal, and the one with the second best fitness value is the female golden jackal. The golden jackal pair obtains the location of the corresponding prey.

[0079] S2.6: Calculate the prey escape energy E. If the prey escape energy |E| ≥ 1, enter the exploration phase and calculate the prey's position; otherwise, enter the exploitation phase.

[0080] S2.6.1: Searching for prey (exploration phase)

[0081] As is the nature of golden jackals, they know how to sense and follow their prey. However, sometimes prey refuses to be caught easily and escapes. Therefore, the golden jackal waits and searches for other prey. The hunt is led by the male golden jackal, with the females following him.

[0082] In order to more effectively improve the global search capability of the algorithm, the position update mechanism of the tuna school optimization algorithm and the mirage algorithm is introduced to improve the golden jackal position update method. The golden jackal position is updated by comprehensively considering the optimal position, movement trend, quality factor, population quality and other factors of the golden jackal in this iteration to avoid local optimality in each iteration, thereby improving the global search capability of the algorithm and effectively covering the entire problem search space.

[0083] During the exploration phase of GJOA, the tuna school optimization algorithm and mirage algorithm were introduced. The improved position update formula of male and female golden jackals is:

[0084] (4);

[0085] (5);

[0086] Where: t is the current iteration number; For the t The location of the prey at the iteration; 、 Respectively t The positions of male and female golden jackals in the iteration; 、 Respectively t The updated positions of the male and female golden jackals corresponding to the prey at the iteration; is the quality factor of the golden jackal at the current t-th iteration, , is the integral area of ​​the population fitness function at the current t-th iteration, indicating the population quality; The worst population quality; For optimal population quality; 、 The weight coefficient for controlling the golden jackal's movement toward the best golden jackal and the previous golden jackal, , ; a is a constant; b is a random number in [0,1]; and l is an intermediate parameter, , .

[0087] is the escape energy of the prey, which can be calculated using the following formula:

[0088] (6);

[0089] Indicates the decline of prey energy. Represents the initial state of prey energy.

[0090] (7);

[0091] Where r is a random number in the range [0,1].

[0092] (8);

[0093] Where: T is the maximum number of iterations; is a constant with a value of 1.5; t is the current number of iterations. During the entire iteration process, Decreases linearly from 1.5 to 0.

[0094] In formulas (4) and (5), Represents a random number based on Levy distribution, which can be calculated using the following formula:

[0095] (9);

[0096] is the Lévy flight function, which is calculated as follows:

[0097] (10);

[0098] Where: and is a random number in the range of (0,1); It is a default constant with a value of 1.5.

[0099] In summary, the golden jackal's position update formula is as follows:

[0100] (11);

[0101] Where: is the position of the golden jackal after the t+1th iteration.

[0102] S2.6.2: Development phase

[0103] During the development phase of GJOA, in order to more effectively improve the convergence speed of the algorithm, the position update mechanism of the tuna school optimization algorithm and the mirage algorithm was introduced, and the golden jackal position update method was improved. The golden jackal position was updated by comprehensively considering factors such as the optimal position, movement trend, quality factor, and population quality of the golden jackal in this iteration to avoid the possible rapid loss of population diversity, which would cause the algorithm to converge to the local optimal solution prematurely, thereby improving the convergence speed of the algorithm.

[0104] During the development phase of GJOA, the tuna school optimization algorithm and the mirage algorithm were introduced. The improved position update formulas for male and female golden jackals are:

[0105] (12);

[0106] (13);

[0107] Where: t is the current iteration number; For the t The location of the prey at the iteration; 、 Respectively t The positions of male and female golden jackals in the iteration; 、 Respectively t The updated positions of the male and female golden jackals corresponding to the prey at iteration .

[0108] The formula for updating the position of the golden jackal is as follows:

[0109] (14);

[0110] Where: is the position of the golden jackal after the t+1th iteration; 、 and is a random number in [0,1] that satisfies .

[0111] S2.7: Determine whether the stopping condition is met. If not, repeat steps S2.5-S2.7. Otherwise, output the optimal prey, that is, the optimal energy security test system integrated matching design plan.

[0112] In this embodiment:

[0113] Using MATLAB as the simulation platform, the Golden Jackal Optimization Algorithm (GJOA) was selected for comparison with the proposed IGJOA method. To ensure fairness in the experiment, the population size of all algorithms was set to 30, and the maximum number of iterations was set to 300. Figure 3 is the iterative process curve. Figure 3 The results clearly show that the IGJOA method converges faster and more accurately than the GJOA algorithm. Simulation results demonstrate that the IGJOA algorithm has a stronger search capability and achieves a better energy interconnection architecture parameter matching design, validating the algorithm's effectiveness.

[0114] The above is an energy security test system design method provided in one embodiment of this embodiment. Based on the same idea, this embodiment also provides a corresponding energy security test system design system. For the specific definition of the energy security test system design system, please refer to the definition of the energy security test system design method above, and will not be repeated here. Each module in the above-mentioned energy security test system design system can be implemented in whole or in part through software, hardware, or a combination thereof. Each of 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 each of the above modules.

[0115] This embodiment also provides a computer-readable storage medium, which stores a computer program that can be used to execute the above Figure 1 Provided is the design method of energy security test system.

[0116] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0117] 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 scope of protection of the present invention.

Claims

1. A method for designing an energy security test system, characterized in that: The following steps are involved: Determine the design parameters of the energy security test system based on the basic elements of the energy security test system; The constraints of the design parameters are determined, and the energy transmission efficiency of the energy security test system is used as the objective function. The fitness value is calculated based on the objective function, and the design parameters are optimized using an improved golden jackal optimization algorithm to obtain the design parameters with the optimal fitness value. The improved golden jackal optimization algorithm introduces the tuna school optimization algorithm and the mirage algorithm into the exploration and development phases of the original golden jackal optimization algorithm, improves the position update method, and performs the next generation position update based on the optimal position, movement trend, quality factor, and population quality of the golden jackal in the current iteration. Determine the design scheme of the energy security test system based on the optimal design parameters; The basic elements of the energy security test system include energy input module, energy storage module and transmission network module; The design parameters of the energy input module include input power range and fluctuation coefficient; The design parameters of the energy storage module include conversion efficiency, energy storage capacity and charge and discharge rate; The design parameters of the transmission network module include transmission line impedance, topology parameters and maximum transmission power.

2. The energy security test system design method according to claim 1, characterized in that: The position update formula of the improved golden jackal optimization algorithm in the exploration phase is: ； ； Where: t is the current iteration number; For the t The location of the prey at the iteration; 、 Respectively t The positions of male and female golden jackals in the iteration; 、 Respectively t The updated positions of the male and female golden jackals corresponding to the prey at the iteration; is the quality factor of the golden jackal at the current t-th iteration; in, Represent a random number based on the Levy distribution: ； Where, is the Lévy flight function; in, Energy for the prey's escape: ； Where, Indicates the decline of prey energy. Represents the initial state of prey energy: ； ； Where r is a random number in the range [0,1]; T is the maximum number of iterations; is a constant with a value of 1.5; during the entire iteration process, Decrease linearly from 1.5 to 0; in: ； ； ； ； ； Where: 、 The weight coefficient for controlling the golden jackal's movement toward the best golden jackal and the previous golden jackal, a is a constant; b is a random number in [0,1]; and l is an intermediate parameter; is the integral area of ​​the population fitness function at the current t-th iteration, indicating the population quality; For optimal population quality; The worst population quality; The position update formula of the golden jackal is improved as follows: ； Where: is the position of the golden jackal after the t+1th iteration.

3. The energy security test system design method according to claim 2, characterized in that: The position update formula in the development phase of the improved golden jackal optimization algorithm is: ； ； Where: t is the current iteration number; For the t The location of the prey at the iteration; 、 Respectively t The positions of male and female golden jackals in the iteration; 、 Respectively t The updated positions of the male and female golden jackals corresponding to the prey at the iteration; The formula for updating the position of the golden jackal is as follows: ； Where: is the position of the golden jackal after the t+1th iteration; 、 and is a random number in [0,1] that satisfies .

4. The energy security test system design method according to claim 1, characterized in that: The construction of the objective function includes the following steps: Determine the transmission link loss model based on the design parameters of the energy input module, the energy storage module and the transmission network module; Determine the output power according to the energy storage capacity of the energy storage module of the energy security test system; The transmission efficiency is determined based on the actual received power, output power and transmission link loss model at the load end, and maximizing the transmission efficiency is used as the optimization goal of the structural design parameters.

5. A design system for an energy security test system, 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 energy security test system design method according to any one of claims 1 to 4 are implemented.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a data processing program, which, when executed by a processor, implements the steps of the energy security test system design method according to any one of claims 1 to 4.

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