Energy guarantee test system design method, system and medium

Abstract

The invention provides an energy guarantee test system design method and system and a medium, and belongs to the technical field of energy guarantee test systems.The method comprises the steps that design parameters of an energy guarantee test system are determined according to basic elements of the energy guarantee test system, and the energy transmission efficiency of the energy guarantee test system serves as a target function; the design parameters are optimized through an improved litsea rotundifolia optimization algorithm, and the design parameters with the optimal fitness value are obtained; wherein a tuna swarm optimization algorithm and a mirage algorithm are introduced into an exploration stage and a development stage of the improved litsea rotundifolia optimization algorithm, and a position updating mode is improved; and determining an energy guarantee test system design scheme according to the optimal design parameters. According to the method, the optimal energy guarantee test system design scheme can be quickly generated, and the optimal transmission efficiency of the system can be achieved through the design scheme.

Description

Technical Field

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

[0002] The energy security test system constructs a dynamically optimized energy supply network through multi-energy complementarity and intelligent scheduling technologies, and realizes an energy security solution with high efficiency, stability and strong emergency response capabilities. Therefore, it is urgent and necessary to deeply study the characteristics of the energy security test system and carry out research on the integrated matching design of the energy security test system.

[0003] Intelligent optimization algorithms are one of the hot topics in artificial intelligence research and have been widely used in the optimization design of the power field. The Golden jackal optimization algorithm (GJOA) is a new meta-heuristic algorithm proposed in 2022. This algorithm is a new type of intelligent optimization algorithm that mimics the cooperative hunting behavior of golden jackals and can also be applied to the integrated matching design problem of energy security test systems. However, there are still some defects in the Golden jackal optimization algorithm, which makes the algorithm prone to falling into local optima and having low convergence accuracy. When conducting the integrated matching design of the energy security test system, the ideal design effect is often not achieved. Summary of the Invention

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

[0005] Specifically, it includes the following steps: Determine the design parameters of the energy security test system according to the basic elements of the energy security test system; Determine the constraint range of the design parameters, use the energy transmission efficiency of the energy security test system as the objective function, obtain the fitness value according to the objective function, and optimize the design parameters through the improved Golden jackal optimization algorithm to obtain the design parameters with the optimal fitness value; wherein, the improved Golden jackal optimization algorithm introduces the tuna school optimization algorithm and the mirage algorithm in the exploration stage and the exploitation stage of the original Golden jackal optimization algorithm, improves the position update method, and performs the position update of the next generation according to factors such as the optimal position of the golden jackal in this iteration, the movement trend, the quality factor, and the population quality; Determine the design scheme of the energy security test system according to the optimal design parameters.

[0006] Preferably, the position update formula in the exploration stage of the improved Golden jackal optimization algorithm is: ; ; In the formula: t is the current iteration number; is the position of the prey at the t -th iteration; , are respectively the positions of the male golden jackal and the female golden jackal at the t -th iteration; , are respectively the updated positions of the male golden jackal and the female golden jackal corresponding to the prey at the t -th iteration; is the quality factor of the golden jackal at the current t-th iteration; where, represents a random number based on the Lévy distribution: ; In the formula, is the Lévy flight function; where, is the escape energy of the prey: ; In the formula, represents the energy decline process of the prey, represents the initial state of the prey energy: ; ; In the formula, r is a random number in the range of [0,1]; T is the maximum number of iterations; is a constant with a value of 1.5; during the entire iteration process, linearly decreases from 1.5 to 0; where: ; ; ; ; ; In the formula: , are the weight coefficients that control the moving trends of the golden jackal towards the best golden jackal and the previous golden jackal, a is a constant; b is a random number within [0,1]; and l are intermediate parameters; is the integral area of the population fitness function at the current t-th iteration, representing the population quality; is the best population quality; is the worst population quality; The position update formula of the golden jackal is improved as follows: ; In the formula: is the position of the golden jackal after the (t + 1)-th iteration.

[0007] Preferably, the position update formula in the development stage of the improved golden jackal optimization algorithm is: ; ; In the formula: t is the current iteration number; is the position of the prey in the t -th iteration; , are respectively the positions of the male golden jackal and the female golden jackal in the t -th iteration; , are respectively the updated positions of the male golden jackal and the female golden jackal corresponding to the prey in the t -th iteration; The position update formula of the golden jackal is as follows: ; In the formula: is the position of the golden jackal after the (t + 1)-th iteration; , and are random numbers within [0, 1] and satisfy .

[0008] Preferably, the basic elements of the energy guarantee test system include an energy input module, an energy storage module, and a transmission network module; The design parameters of the energy input module include the input power range and the fluctuation coefficient; The design parameters of the energy storage module include the conversion efficiency, the energy storage capacity, and the charge-discharge rate; The design parameters of the transmission network module include the transmission line impedance, the topological structure parameters, and the maximum transmission power.

[0009] Preferably, the construction of the objective function includes the following steps: Determine the transmission link loss model according to 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 guarantee test system; Determine the transmission efficiency based on the actual received power, output power at the load end, and the transmission link loss model, and take the maximization of the transmission efficiency as the optimization goal of the structural design parameters.

[0010] The present invention also proposes a design system for an energy security test system, and the system includes: A processor; A memory, on which a computer program that can run on the processor is stored; Wherein, the steps of the energy security test system design method implemented when the computer program is executed by the processor.

[0011] The present invention also proposes a computer-readable storage medium, on which a data processing program is stored, and the steps of the energy security test system design method are implemented when the data processing program is executed by the processor.

[0012] Advantages of the present invention: The present invention proposes an energy security test system design method, which uses an improved golden jackal optimization algorithm to optimize the basic parameters of the energy security test system, and constructs an optimal energy security test system design scheme according to the optimization results. Based on this system architecture, the best transmission efficiency can be achieved.

[0013] Among them, in the exploration stage of the improved golden jackal optimization algorithm, in order to more effectively improve the global search ability of the algorithm, the position update mechanisms of the tuna school optimization algorithm and the mirage algorithm are introduced, the golden jackal position update method is improved, and factors such as the optimal position of the golden jackal in this iteration, the movement trend, the quality factor, and the population quality are comprehensively considered to update the golden jackal position, avoiding local optima in each iteration, thereby improving the global search ability of the algorithm and effectively covering the entire problem search space.

[0014] In addition, in the exploitation stage of the improved golden jackal optimization algorithm, in order to more effectively improve the convergence speed of the algorithm, the position update mechanisms of the tuna school optimization algorithm and the mirage algorithm are introduced, the golden jackal position update method is improved, avoiding the possible rapid loss of population diversity, resulting in the algorithm converging prematurely to the local optimal solution, thereby improving the convergence speed of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flowchart of the energy security test system design method according to an embodiment of the present invention; Figure 2 is an execution flowchart of the improved golden jackal optimization algorithm according to an embodiment of the present invention; Figure 3 is an iteration process curve according to an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be 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 used to limit the present invention.

[0017] Embodiment 1 The Golden jackal optimization algorithm (GJOA) is a new meta-heuristic algorithm proposed in 2022. This algorithm is a new type of intelligent optimization algorithm that mimics the cooperative hunting behavior of golden jackals and can also be applied to the integrated matching design problem of energy security test systems. However, there are still some defects in the Golden jackal optimization algorithm: (1) Exploration stage: The Golden jackal optimization algorithm cannot effectively cover the entire problem search space, which may lead to the algorithm falling into a local optimum. (2) Exploitation stage: The Golden jackal optimization algorithm converges slowly due to its overly conservative local search strategy. For example, relying too much on the optimal solution, the population diversity may be quickly lost, resulting in the algorithm prematurely converging to a local optimal solution, thus leading to poor adaptability in solving complex problems. Based on the above two key deficiencies, when using the Golden jackal optimization algorithm for the integrated matching design of energy security test systems, the best design effect cannot be achieved. Therefore, the present invention proposes a design method for an energy security test system, and its flowchart is as Figure 1 shown, and the specific steps are as follows: S1: Determine the design parameters of the energy security test system according to the basic elements of the energy security test system.

[0018] S2: Determine the constraint range of the design parameters, use the energy transfer efficiency of the energy security test system as the objective function, obtain the fitness value according to the objective function, and optimize the design parameters through the improved Golden jackal optimization algorithm to obtain the design parameters with the optimal fitness value; among them, the improved Golden jackal optimization algorithm introduces the tuna school optimization algorithm and the mirage algorithm in the exploration stage and the exploitation stage of the original Golden jackal optimization algorithm, improves the position update method, and updates the position of the next generation according to the factors of the optimal position of the golden jackal, the movement trend, the quality factor and the population quality in this iteration.

[0019] S3: Determine the design scheme of the energy security test system according to the optimal design parameters.

[0020] Specifically, as Figure 2 shown, the steps for the improved Golden jackal optimization algorithm to optimize the parameters to be optimized are as follows: S2.1: Determine the basic elements of the energy security test system. The basic elements of the energy security test system include an energy input module, an energy storage module, and a transmission network module; the design parameters of the energy input module include the input power range and the fluctuation coefficient; the design parameters of the energy storage module include the conversion efficiency, the energy storage capacity, and the charge-discharge rate; the design parameters of the transmission network module include the transmission line impedance, the topology structure parameters, and the maximum transmission power.

[0021] S2.2: Construct the objective function. Determine the transmission link loss model according to 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; determine the transmission efficiency based on the actual received power at the load end, the output power, and the transmission link loss model, and construct the objective function with the maximization of the transmission efficiency as the optimization goal of the structural design parameters.

[0022] S2.3: Perform parameter settings, mainly including: the size of the population (i.e., the number of golden jackal individuals) N; the maximum number of iterations (i.e., the condition for stopping the iteration) T ; the lower bound of prey optimization ; the upper bound of prey optimization .

[0023] S2.4: Initialize the positions of the golden jackal population: (1); Where: is the position of the initial golden jackal population; is a random number within the range of [0, 1]; and are the upper and lower bounds of the problem to be solved respectively; In the GJOA algorithm, the prey matrix is expressed as: (2); Where: is the prey matrix; is the j-th dimension position of the i-th prey; The winners (the one with the best fitness value and the second-best) in the first and second of are used as the golden jackal pair; n is the number of preys; d is the dimension of the problem to be solved.

[0024] S2.5: Calculate the fitness value of the prey according to the objective function, and the one with the best fitness value is used as the male golden jackal, and the one with the second-best fitness value is used as the female golden jackal.

[0025] During the optimization process, the fitness function (objective function) is used to estimate the fitness value of each prey, and the fitness value matrix of all preys is expressed as follows: (3); Where: is the fitness value matrix of the prey; is the fitness function or objective function; the one with the optimal fitness value is regarded as the male golden jackal, and the one with the sub-optimal fitness value is regarded as the female golden jackal. The positions where the golden jackal pair obtains the corresponding prey.

[0026] S2.6: Calculate the prey escape energy E. If the prey escape energy |E| ≥ 1, enter the exploration stage and calculate the position of the prey; otherwise, enter the exploitation stage.

[0027] S2.6.1: Search for the prey (exploration stage) Just like the nature of golden jackals, they know how to perceive and follow the prey, but occasionally the prey cannot be easily caught and escapes. Therefore, the golden jackals will wait and look for other prey. The hunting work is led by the male golden jackal. The female golden jackal follows the male golden jackal.

[0028] To more effectively improve the global search ability of the algorithm, the position update mechanisms of the tuna school optimization algorithm and the mirage algorithm are introduced, the golden jackal position update method is improved, and factors such as the optimal position of the golden jackal, movement trend, quality factor, and population quality in this iteration are comprehensively considered to update the golden jackal position, avoiding local optimality in each iteration, thereby improving the global search ability of the algorithm and effectively covering the entire problem search space.

[0029] In the exploration stage of GJOA, the tuna school optimization algorithm and the mirage algorithm are introduced. The improved position update formulas for the male and female golden jackals are: (4); (5); In the formula: t is the current iteration number; is the t position of the prey in the , are respectively the t positions of the male and female golden jackals in the , are respectively the t updated positions of the male and female golden jackals corresponding to the prey in the is the quality factor of the golden jackal in the current t-th iteration, , is the integral area of the population fitness function in the current t-th iteration, representing the population quality; is the worst population quality; is the best population quality; , are the weight coefficients that control the movement trends of the golden jackal towards the best golden jackal and the previous golden jackal, , ; a is a constant; b is a random number within [0, 1]; and l are intermediate parameters, , .

[0030] is the escape energy of the prey and can be calculated using the following formula: (6); represents the process of the prey's energy decline, represents the initial state of the prey's energy.

[0031] (7); In the formula: r is a random number within the range of [0, 1].

[0032] (8); In the formula: T is the maximum number of iterations; is a constant with a value of 1.5; t is the current iteration number. During the entire iteration process, linearly decreases from 1.5 to 0.

[0033] In formulas (4) and (5), represents a random number based on the Lévy distribution and can be calculated using the following formula: (9); is the Lévy flight function and its calculation method is as follows: (10); In the formula: and are random numbers within the range of (0, 1); is a default constant with a value of 1.5.

[0034] In summary, the position update formula of the golden jackal is as follows: (11); In the formula: is the position of the golden jackal after the (t + 1)-th iteration.

[0035] S2.6.2: Development stage In the development stage of GJOA, in order to more effectively improve the convergence speed of the algorithm, the position update mechanisms of the tuna school optimization algorithm and the mirage algorithm are introduced, the position update method of the golden jackal is improved, and the position of the golden jackal is updated by comprehensively considering factors such as the optimal position of the golden jackal, the movement trend, the quality factor, and the population quality in this iteration, so as to avoid the possible rapid loss of population diversity, resulting in the premature convergence of the algorithm to the local optimal solution, and then improve the convergence speed of the algorithm.

[0036] In the development stage of GJOA, the tuna school optimization algorithm and the mirage algorithm are introduced. After improvement, the position update formulas for male and female golden jackals are as follows: (12); (13); In the formula: t is the current iteration number; is the position of the prey in the t -th iteration; , are respectively the positions of the male and female golden jackals in the t -th iteration; , are respectively the updated positions of the male and female golden jackals corresponding to the prey in the t -th iteration.

[0037] The position update formula of the golden jackal is as follows: (14); In the formula: is the position of the golden jackal after the (t + 1)-th iteration; , and are random numbers within [0, 1] and satisfy .

[0038] S2.7: Determine whether the stop condition is satisfied. If not, repeat steps S2.5 - S2.7. Otherwise, output the optimal prey, that is, the optimal integrated matching design scheme of the energy guarantee test system.

[0039] In this embodiment: Taking MATLAB as the simulation platform, the golden jackal optimization algorithm (GJOA) is selected for comparison with the proposed IGJOA method in this paper. To ensure the fairness of the experiment, the population size of all algorithms is set to 30, and the maximum number of iterations is set to 300. Figure 3 is the iteration process curve. From Figure 3It can be intuitively found that the convergence speed of the IGJOA method is faster than that of the GJOA algorithm, and the convergence accuracy of the IGJOA method is better than that of the GJOA algorithm. The simulation results show that the IGJOA algorithm has stronger search ability and obtains a better design scheme for the parameter matching of the energy interconnection framework, verifying the effectiveness of the algorithm.

[0040] The above is the design method of the energy guarantee test system provided by an embodiment of this embodiment. Based on the same idea, this embodiment also provides a corresponding design system of the energy guarantee test system. For the specific limitations of the design system of the energy guarantee test system, reference can be made to the limitations of the design method of the energy guarantee test system in the above text, which will not be elaborated here. Each module in the above design system of the energy guarantee test system can be implemented in whole or in part through software, hardware, and their combinations. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0041] This embodiment also provides a computer-readable storage medium, which stores a computer program, and the computer program can be used to execute the above Figure 1 provided design method of the energy guarantee test system.

[0042] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to the memory, storage, database, or other media used in the various embodiments provided by the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0043] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, 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 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; Determine the constraint range of the design parameters, take the energy transmission efficiency of the energy security test system as the objective function, obtain the fitness value according to the objective function, optimize the design parameters through the improved golden jackal optimization algorithm, and obtain the design parameters with the best fitness value; wherein, the improved golden jackal optimization algorithm introduces the tuna school optimization algorithm and the mirage algorithm in the exploration stage and the development stage of the original golden jackal optimization algorithm, improves the position update method, and performs the next generation position update according to the factors of the optimal position, movement trend, quality factor and population quality of the golden jackal in this iteration; Determine the energy security test system design plan based on the optimal design parameters.

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: ； In the formula, is the Lévy flight function; in, Energy for the prey to escape: ； In the formula, represents the decreasing process of prey energy, Represents the initial state of prey energy: ； ； In the formula, r is a random number in the range of [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 to control the golden jackal's movement trend 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 integrated area of ​​the population fitness function of the current t-th iteration, indicating the quality of the population; for optimal population quality; is 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 position update formula 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 3 is characterized in that: 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.

5. The energy security test system design method according to claim 4, characterized in that: The construction of the objective function comprises the following steps: Determine a transmission link loss model according to design parameters of an energy input module, an energy storage module, and a 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.

6. 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 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 a processor, the steps of the energy security test system design method according to any one of claims 1 to 5 are implemented.

Images