Energy interconnection system design method and system and medium

Abstract

The invention provides an energy interconnection system design method and system and a medium, and belongs to the technical field of energy interconnection systems, and the method comprises the steps: determining design parameters of an energy interconnection system; determining constraint conditions of the design parameters, taking the energy transmission efficiency of the energy interconnection system as a target function, solving a fitness value according to the target function, and optimizing the design parameters through an improved litsea rotundifolia optimization algorithm to obtain the design parameters with the optimal fitness value; according to the improved litsea rotundifolia optimization algorithm, a position updating mechanism of a dung beetle optimization algorithm and a hunger game search algorithm is introduced into an exploration stage and a development stage of an original litsea rotundifolia optimization algorithm; and determining an energy interconnection system design scheme according to the optimal design parameters. According to the method, the optimal energy interconnection 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] This invention relates to the field of energy interconnection system technology, and specifically to an energy interconnection system design method, system, and medium. Background Technology

[0002] Energy interconnection system integration and matching design is one of the key technologies for winning on land warfare platforms. Therefore, it is urgent and necessary to conduct in-depth research on the characteristics of energy interconnection systems and carry out research on energy interconnection system integration and matching design.

[0003] Intelligent optimization algorithms are a hot topic in artificial intelligence research and have been widely used in the optimization design of the power industry. For example, Xiao Wei et al. studied a composite power supply parameter optimization method based on the strength Pareto evolutionary algorithm (Xiao Wei, Du Changqing, Ren Weiqun. Composite power supply parameter optimization based on strength Pareto evolutionary algorithm [J]. Power Technology, 2022, 46(12): 1422-1427.); Li Qi et al. invented a parameter matching optimization method for fuel cell supercapacitor hybrid locomotives (Li Qi, Chen Weirong, Liu Zhixiang, Dai Chaohua, Zhang Xuexia, Guo Ai, Liu Shukui. Parameter matching optimization method for fuel cell supercapacitor hybrid locomotives [P]. Sichuan Province: CN104071033A, 2014-10-01.); Wang Wenxi et al. invented a distributed power supply planning method for active distribution networks considering source-load matching degree (Wang Wenxi, Liu Baolin, Feng Lei, Li Lingfang, Cheng Junzhao, Zhou Shaoxiong, Liao Yixu, Chen Yaosheng. A distributed power supply planning method for active distribution networks considering source-load matching degree [P]. Guangdong Province: CN107688879A, 2018-02-03.

[0004] The Golden Jackal Optimization Algorithm (GJOA), proposed in 2022, is a novel metaheuristic algorithm that mimics the cooperative hunting behavior of golden jackals. It can also be applied to the energy interconnection system integration matching design problem. However, the GJOA still has some drawbacks, making it prone to getting trapped in local optima and exhibiting low convergence accuracy. Consequently, it often fails to achieve the desired design results when performing energy interconnection system integration matching design. Summary of the Invention

[0005] This invention provides a design method for an energy interconnection system, which can quickly generate an optimal energy interconnection system design scheme that can achieve the optimal transmission efficiency of the system.

[0006] Specifically, the following steps are included: Determine the design parameters of the energy interconnection system, including the transmission capacity of the energy units, the power exchange rate between the energy units, the transmission line specifications, the maximum operating load of the energy units, and the control parameters. The constraints of the design parameters are determined, with the energy transmission efficiency of the energy interconnection system 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 position update mechanisms of the Dung Beetle optimization algorithm and the Hunger Games search algorithm into the exploration and development stages of the original Golden Jackal optimization algorithm. It improves the position update method of the Golden Jackal and comprehensively considers factors such as selecting different position update modes according to the switching probability, the optimal position of the Golden Jackal in this iteration, the deflection angle of the Golden Jackal, and the variational control parameters of the Golden Jackal for the next generation of position updates. The design scheme of the energy interconnection system is determined based on the optimal design parameters.

[0007] Preferably, the location update formula for the exploration phase of the improved golden jackal optimization algorithm is: ; ; In the formula: t is the current iteration number; Let be the position of the prey in the t-th iteration; , These represent the positions of the male and female golden jackals in the t-th iteration, respectively. , These are the updated positions of the male and female golden jackals corresponding to the prey in the t-th iteration, respectively. The deflection angle of the golden jackal; This represents a random number based on the Lévy distribution; Energy for the prey to escape; in: ; In the formula: Variational control parameters for the golden jackal; hyperbolic function ; For the golden jackal i fitness value; This represents the optimal fitness value for the golden jackal in the current t-th iteration; The formula for updating the position of the golden jackal has been improved as follows: ; In the formula: Let be the position of the golden jackal after the (t+1)th iteration; , and A random number in the range [0,1] that satisfies .

[0008] Preferably, the position update formula in the development phase of the improved golden jackal optimization algorithm is: ; ; In the formula: t is the current iteration number; Let be the position of the prey in the t-th iteration; , These represent the positions of the male and female golden jackals in the t-th iteration, respectively. , These are the updated positions of the male and female golden jackals corresponding to the prey in the t-th iteration, respectively. The deflection angle of the golden jackal; This represents a random number based on the Lévy distribution; Energy for the prey to escape; The formula for updating the golden jackal's position is as follows: ; In the formula: Let be the position of the golden jackal after the (t+1)th iteration; and It is a random number within the range [0,1].

[0009] Preferably, the constraints of the design parameters include the range of load demand variation, transmission line capacity limitations, maximum power carrying capacity of equipment, system response time requirements, and system cost.

[0010] Preferably, the construction of the objective function includes the following steps: Determine the transmission link loss model based on the design parameters of the energy interconnection system; Determine the output power based on the design parameters of the energy unit; The transmission efficiency is determined based on the actual received power and output power at the load end, as well as the transmission link loss model, with maximizing the transmission efficiency as the optimization objective of the structural design parameters.

[0011] This invention also proposes a design system for an energy interconnection system, the system comprising: processor; Memory, on which computer programs that can run on a processor are stored; Among them, the steps of the energy interconnection system design method implemented when the computer program is executed by the processor.

[0012] The present invention also proposes a computer-readable storage medium storing a data processing program, wherein the steps of an energy interconnection system design method are implemented when the data processing program is executed by a processor.

[0013] The beneficial effects of this invention are: This invention proposes a design method for an energy interconnection system. This method uses an improved Golden Jackal optimization algorithm to optimize the basic parameters of the energy interconnection system, and constructs an optimal energy interconnection system design scheme based on the optimization results. Based on this system architecture, the best transmission efficiency can be achieved.

[0014] In the exploration phase of the improved golden jackal optimization algorithm, to more effectively enhance the algorithm's global search capability, a position update mechanism combining the dung beetle optimization algorithm and the Hunger Games search algorithm is introduced. This improves the golden jackal position update method by comprehensively considering factors such as selecting different position update modes based on switching probabilities, the optimal position of the golden jackal in this iteration, the deflection angle of the golden jackal, and the variational control parameters of the golden jackal. This avoids local optima in each iteration, thereby improving the algorithm's global search capability, effectively covering the entire problem search space, and effectively obtaining better target parameters for the energy interconnection system.

[0015] Furthermore, during the development 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 dung beetle optimization algorithm and the Hunger Games search algorithm was introduced to improve the golden jackal position update method, avoid the rapid loss of population diversity, and prevent the algorithm from converging to a local optimum too early, thereby improving the convergence speed of the algorithm and quickly and efficiently locating the target parameters of the optimal energy interconnection system. Attached Figure Description

[0016] Figure 1 This is a flowchart of the energy interconnection system design method according to an embodiment of the present invention; Figure 2 This is an execution flowchart of the improved golden jackal optimization algorithm according to an embodiment of the present invention; Figure 3 This is the iterative process curve of an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

[0018] Example 1 The Golden Jackal Optimization Algorithm (GJOA) is a novel metaheuristic algorithm proposed in 2022. It is a new intelligent optimization algorithm that mimics the cooperative hunting behavior of golden jackals and can also be applied to the energy interconnection system integration matching design problem. However, the GJOA still has some shortcomings: (1) Exploration stage: The GJOA cannot effectively cover the entire problem search space, which may lead to the algorithm getting stuck in local optima. (2) Development stage: The GJOA converges slowly due to its overly conservative local search strategy. For example, it over-relies on the optimal solution, and the population diversity may be lost rapidly, causing the algorithm to converge to a local optimum too early, resulting in poor adaptability to solving complex problems. Based on these two key shortcomings, the GJOA cannot achieve the best design effect when used for energy interconnection system integration matching design. Therefore, this invention proposes an energy interconnection system design method, the flowchart of which is as follows: Figure 1 As shown, the specific steps are as follows:

[0019] S1: Determine the design parameters of the energy interconnection system, including the transmission capacity of the energy units, the power exchange rate between the energy units, the transmission line specifications, the maximum operating load of the energy units, and the control parameters.

[0020] S2: Determine the constraints of the design parameters, using the energy transmission efficiency of the energy interconnection system as the objective function. Calculate the fitness value based on the objective function, and optimize the design parameters using the improved Golden Jackal optimization algorithm to obtain the design parameters with the optimal fitness value. The improved Golden Jackal optimization algorithm introduces the position update mechanisms of the Dung Beetle optimization algorithm and the Hunger Games search algorithm into the exploration and development stages of the original Golden Jackal optimization algorithm. This improves the Golden Jackal position update method and comprehensively considers factors such as selecting different position update modes based on the switching probability, the optimal position of the Golden Jackal in this iteration, the Golden Jackal's deflection angle, and the Golden Jackal's variational control parameters for the next generation of position updates.

[0021] S3: Determine the design scheme of the energy interconnection system based on the optimal design parameters.

[0022] Specifically, such as Figure 2 As shown, the improved golden jackal optimization algorithm includes the following steps to optimize the parameters: S2.1: Determine the design parameters of the energy interconnection system, including the transmission capacity of the energy units, the power exchange rate between the energy units, the transmission line specifications, the maximum operating load of the energy units, and the control parameters.

[0023] S2.2: Determine the constraints of the design parameters, including the range of load demand variation, transmission line capacity limitations, maximum power carrying capacity of equipment, system response time requirements, and system cost.

[0024] Construct the objective function. Determine the transmission link loss model based on the design parameters of the energy interconnection system; determine the output power based on the design parameters of the energy unit; determine the transmission efficiency based on the actual received power, output power, and transmission link loss model at the load end, and use maximizing the transmission efficiency as the optimization objective of the structural design parameters.

[0025] S2.3: Configure parameters, mainly including: population size (i.e., the number of golden jackals) N; maximum number of iterations (i.e., the condition for stopping the iteration). T ; Lower boundary of prey optimization ; The upper boundary of prey optimization .

[0026] S2.4: Initialize the location of the golden jackal population: (1); In the formula: This indicates the location of the initial golden jackal population. It is a random number in the range [0,1]. and These are the upper and lower boundaries of the problem to be solved; In the GJOA algorithm, the prey matrix is ​​represented as: (2); In the formula: For the prey matrix; Let j be the position of the i-th prey; The winners of the first and second place (the one with the best and second best fitness values) are paired together as the golden jackal pair; n is the number of prey; d is the dimension of the problem solution.

[0027] S2.5: Calculate the fitness value of the prey based on the objective function. The prey with the best fitness value is designated as the male golden jackal, and the prey with the second best fitness value is designated as the female golden jackal.

[0028] During the optimization process, the fitness value of each prey is estimated using a fitness (objective) function, and the fitness value matrix of all prey is represented as follows: (3); In the formula: This is the fitness value matrix of the prey; The fitness function or objective function is used; the jackal with the best fitness value is designated as the male, and the jackal with the second best fitness value is designated as the female. The location of the prey obtained by each jackal is determined.

[0029] S2.6: Calculate the prey escape energy E. If the prey escape energy |E|≥ 1, proceed to the exploration stage and calculate the prey's location; otherwise, proceed to the development stage.

[0030] S2.6.1: Searching for Prey (Exploration Phase) Just as is their nature, golden jackals know how to sense and follow prey, but occasionally the prey escapes unnoticed. Therefore, the golden jackals wait and search for other prey. The hunting is led by the male golden jackal. The female golden jackal follows the male:

[0031] To more effectively improve the algorithm's global search capability, a position update mechanism combining the dung beetle optimization algorithm and the Hunger Games search algorithm is introduced to improve the golden jackal's position update method. This method comprehensively considers factors such as selecting different position update modes based on switching probabilities, the optimal position of the golden jackal in this iteration, the golden jackal's deflection angle, and the golden jackal's variational control parameters when updating the golden jackal's position. This avoids local optima in each iteration, thereby improving the algorithm's global search capability and effectively covering the entire problem search space.

[0032] During the exploration phase of GJOA, the dung beetle optimization algorithm and the Hunger Games search algorithm were introduced. The improved position update formulas for male and female golden jackals are as follows: (4); (5); In the formula: t is the current iteration number; Let be the position of the prey in the t-th iteration; , These represent the positions of the male and female golden jackals in the t-th iteration, respectively. , These are the updated positions of the male and female golden jackals corresponding to the prey in the t-th iteration, respectively. The deflection angle of the golden jackal; This represents a random number based on the Lévy distribution; Energy for the prey to escape; in: , Variational control parameters for the golden jackal; hyperbolic function ; Let i be the fitness value of the golden jackal. This represents the optimal fitness value for the golden jackal in the current t-th iteration; The escape energy of prey can be calculated using the following formula: (6); This indicates the process of the prey's energy decreasing. This indicates the initial energy state of the prey.

[0033] (7); In the formula: r is a random number in the range [0,1].

[0034] (8); In the formula: T is the maximum number of iterations; Let be a constant, taking the value 1.5; t is the current iteration number. Throughout the entire iteration process, It decreases linearly from 1.5 to 0.

[0035] In formulas (4) and (5), A random number based on the Lévy distribution can be calculated using the following formula: (9); This is the Lévy flight function, and its calculation method is as follows: (10); In the formula: and A random number within the range (0,1); This is a default constant with a value of 1.5.

[0036] In summary, the formula for updating the position of the golden jackal is improved as follows: (11); In the formula: Let be the position of the golden jackal after the (t+1)th iteration; , and A random number in the range [0,1] that satisfies .

[0037] S2.6.2: Development Phase During the development of GJOA, in order to more effectively improve the convergence speed of the algorithm, the position update mechanism of the dung beetle optimization algorithm and the Hunger Games search algorithm was introduced to improve the golden jackal position update method. The golden jackal position was updated by comprehensively considering factors such as selecting different position update modes according to the switching probability, the optimal position of the golden jackal in this iteration, the deflection angle of the golden jackal, and the variational control parameters of the golden jackal. This avoids the possibility of rapid loss of population diversity, which could cause the algorithm to converge to a local optimum too early, thereby improving the convergence speed of the algorithm.

[0038] During the development phase of GJOA, the dung beetle optimization algorithm and the Hunger Games search algorithm were introduced. The improved position update formula for male and female golden jackals is as follows: (12); (13); In the formula: t is the current iteration number; Let be the position of the prey in the t-th iteration; , These represent the positions of the male and female golden jackals in the t-th iteration, respectively. , These are the updated positions of the male and female golden jackals corresponding to the prey in the t-th iteration, respectively. The deflection angle of the golden jackal; This represents a random number based on the Lévy distribution; Energy for the prey to escape; In summary, the formula for updating the golden jackal's position is as follows: (14); In the formula: Let be the position of the golden jackal after the (t+1)th iteration; and It is a random number within the range [0,1].

[0039] S2.7: Determine if the stopping condition is met. If not, repeat steps S2.5-S2.7. Otherwise, output the optimal prey, i.e. the optimal energy interconnection system integration matching design scheme.

[0040] In this embodiment: Using MATLAB as the simulation platform, the Golden Jackal Optimization Algorithm (GJOA) was compared with the IGJOA method proposed in this paper. To ensure the fairness of the experiment, the population size of all algorithms was set to 30, and the maximum number of iterations was set to 300. Figure 3 The curve represents the iterative process. From... Figure 3 The results clearly show that the IGJOA method converges faster than the GJOA algorithm, and its convergence accuracy is superior. Simulation results demonstrate that the IGJOA algorithm has stronger search capabilities and obtains a better energy interconnection architecture parameter matching design scheme, thus verifying the algorithm's effectiveness.

[0041] The above is an example of an energy interconnection system design method provided in this embodiment. Based on the same idea, this embodiment also provides a corresponding energy interconnection system design system. Specific limitations of the energy interconnection system design system can be found in the limitations of the energy interconnection system design method described above, and will not be repeated here. Each module in the above-described energy interconnection system design system can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0042] This embodiment also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 The provided design methodology for energy interconnection systems.

[0043] Those skilled in the art will understand that implementing all or part of the processes in the methods of the above embodiments can be accomplished by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, 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.

[0044] The above are merely 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 within the protection scope of the present invention.

Claims

1. A design method for an energy interconnection system, characterized in that, Includes the following steps: Determine the design parameters of the energy interconnection system, including the transmission capacity of the energy units, the power exchange rate between the energy units, the transmission line specifications, the maximum operating load of the energy units, and the control parameters. The constraints of the design parameters are determined, with the energy transmission efficiency of the energy interconnection system 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 position update mechanisms of the Dung Beetle optimization algorithm and the Hunger Games search algorithm into the exploration and development stages of the original Golden Jackal optimization algorithm. It improves the position update method of the Golden Jackal and comprehensively considers factors such as selecting different position update modes according to the switching probability, the optimal position of the Golden Jackal in this iteration, the deflection angle of the Golden Jackal, and the variational control parameters of the Golden Jackal for the next generation of position updates. The design scheme of the energy interconnection system is determined based on the optimal design parameters.

2. The energy interconnection system design method according to claim 1, characterized in that, The location update formula for the exploration phase of the improved golden jackal optimization algorithm is as follows: ； ； In the formula: t is the current iteration number; Let be the position of the prey in the t-th iteration; , These represent the positions of the male and female golden jackals in the t-th iteration, respectively. , These are the updated positions of the male and female golden jackals corresponding to the prey in the t-th iteration, respectively. The deflection angle of the golden jackal; This represents a random number based on the Lévy distribution; Energy for the prey to escape; in: ； In the formula: Variational control parameters for the golden jackal; hyperbolic function ; For the golden jackal i fitness value; This represents the optimal fitness value for the golden jackal in the current t-th iteration; The formula for updating the position of the golden jackal has been improved as follows: ； In the formula: Let be the position of the golden jackal after the (t+1)th iteration; , and A random number in the range [0,1] that satisfies .

3. The energy interconnection system design method according to claim 2, characterized in that, The position update formula for the development phase of the improved golden jackal optimization algorithm is as follows: ； ； In the formula: t is the current iteration number; Let be the position of the prey in the t-th iteration; , These represent the positions of the male and female golden jackals in the t-th iteration, respectively. , These are the updated positions of the male and female golden jackals corresponding to the prey in the t-th iteration, respectively. The deflection angle of the golden jackal; This represents a random number based on the Lévy distribution; Energy for the prey to escape; The formula for updating the golden jackal's position is as follows: ； In the formula: Let be the position of the golden jackal after the (t+1)th iteration; and It is a random number within the range [0,1].

4. The energy interconnection system design method according to claim 3, characterized in that, The constraints of the design parameters include the range of load demand variation, transmission line capacity limitations, maximum power carrying capacity of equipment, system response time requirements, and system cost.

5. The energy interconnection system design method according to claim 4, 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 interconnection system; Determine the output power based on the design parameters of the energy unit; The transmission efficiency is determined based on the actual received power and output power at the load end, as well as the transmission link loss model, with maximizing the transmission efficiency as the optimization objective of the structural design parameters.

6. A design system for an energy interconnection system, characterized in that the system... include: processor; A memory on which computer programs that can run on the processor are stored; When the computer program is executed by the processor, it implements the steps of the energy interconnection system design method as described in any one of claims 1 to 5.

7. 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 interconnection system design method as described in any one of claims 1 to 5.

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