Energy interconnection framework parameter matching design method and system and medium

Abstract

The invention provides an energy interconnection framework parameter matching design method and system and a medium, and belongs to the technical field of power systems, and the method comprises the steps: determining energy interconnection framework parameters, including a framework type, a port type and a port number; determining constraint conditions of the energy interconnection framework parameters, constructing full life cycle use efficiency as a target function, calculating a fitness value according to the target function, optimizing the energy interconnection framework parameters through an improved litsea rotundifolia optimization algorithm, and obtaining the energy interconnection framework parameters with the optimal fitness value; and determining an energy interconnection framework parameter matching design scheme according to the optimal energy interconnection framework parameter. According to the method, the optimal energy interconnection framework parameter matching design scheme can be quickly generated, and the energy interconnection framework formed by the design scheme can realize the optimal use efficiency.

Description

Technical Field

[0001] This invention relates to the field of power system technology, specifically to a parameter matching design method, system, and medium for energy interconnection architecture. Background Technology

[0002] Energy interconnection architecture is an intelligent network system that integrates multiple energy forms (such as electricity, gas, heat, and cooling) based on advanced information and energy technologies. Through a highly interconnected energy network, it achieves efficient energy production, transmission, storage, and consumption, aiming to improve energy utilization efficiency, promote the absorption of renewable energy, enhance the reliability and security of energy supply, and drive the sustainable development of the energy industry. Parameter matching design for energy interconnection architecture is one of the key technologies in the power sector; therefore, in-depth research on the characteristics of energy interconnection architecture and the research on parameter matching design are urgent and necessary.

[0003] Intelligent optimization algorithms are a hot topic in artificial intelligence research and have been widely applied in the optimization design of the power industry. Among them, the Golden Jackal Optimization Algorithm (GJOA), a novel metaheuristic algorithm proposed in 2022, is a new type of intelligent optimization algorithm that mimics the cooperative hunting behavior of golden jackals and can also be applied to the power system integration matching design problem. However, the Golden Jackal Optimization Algorithm still has some drawbacks, making it prone to getting trapped in local optima and having low convergence accuracy. Therefore, it often fails to achieve ideal design results when designing parameter matching for energy interconnection architectures. Summary of the Invention

[0004] This invention provides a parameter matching design method for an energy interconnection architecture. This method can quickly generate the optimal parameter matching design scheme for the energy interconnection architecture, and the energy interconnection architecture constructed by this design scheme can achieve optimal utilization efficiency.

[0005] Specifically, the following steps are included: Determine the parameters of the energy interconnection architecture, including the architecture type, port type, and number of ports; The constraints of the energy interconnection architecture parameters are determined, and the life-cycle utilization efficiency is constructed as the objective function. The fitness value is obtained based on the objective function. The energy interconnection architecture parameters are optimized through an improved Golden Jackal optimization algorithm to obtain the energy interconnection architecture parameters with the optimal fitness value. The improved Golden Jackal optimization algorithm introduces the position update mechanism of the Dandelion optimization algorithm and the Alpha Evolution algorithm in the exploration and development stages of the original Golden Jackal optimization algorithm. It improves the position update method of Golden Jackal and comprehensively considers the selection of different position update modes based on the switching probability, the optimal position of Golden Jackal in the current iteration, the adaptive search step size of Golden Jackal, and the decay factor for the next generation of position update.

[0006] Preferably, the location update during the exploration phase of the improved golden jackal optimization algorithm includes the following steps: The formula for updating the positions of male and female golden jackals 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. This represents a random number based on the Lévy distribution; Energy for the prey to escape; Let be the position of the golden jackal after the t-th iteration; ; ; In the formula: r is a random number within [0,1]; The adaptive search step size for the golden jackal; The attenuation factor for the golden jackal; The formula for updating the position of the golden jackal has been improved as follows: ; In the formula: The location of the golden jackal after the (t+1)th iteration is the individual parameter of the energy interconnection architecture after the (t+1)th iteration in the exploration phase. , , and It is a random number within the range [0,1].

[0007] Preferably, the position update during the development phase of the improved golden jackal optimization algorithm includes the following steps: The formula for updating the positions of male and female golden jackals 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 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 is a random number within [0,1], representing an individual parameter of the energy interconnection architecture after the (t+1)th iteration in the development phase.

[0008] Preferably, the architecture type includes a radial type with single-path power supply, a ring network type with bidirectional power supply, a mesh type with multi-path interconnection, and a hierarchical partition type with a main network and sub-networks; the port type is a multi-energy port type.

[0009] Preferably, the constraints include power balance constraints and port capacity constraints; Among them, power balance constraints: ; In the formula, The generator output power is determined based on the architecture type. The charging and discharging power of energy storage devices is determined based on the port type and the number of ports. The number of ports is dynamically adjusted to meet the power requirements of the load. Line loss power is determined by the architecture type; The port capacity constraints are related to the architecture type and port type. The basic load is determined based on the architecture type, and the number of ports must meet the minimum number of ports required to meet the basic load requirements. The maximum number of ports is determined based on the port type and is limited by the physical size of the device, and the number of ports must be less than or equal to that number.

[0010] Preferably, the construction of the objective function includes the following steps: Determine the output efficiency based on the architecture type, port type, and number of ports; determine the effective energy output throughout the entire lifecycle based on the maximum load demand and output efficiency. Determine the total lifecycle cost based on the port type and number of ports; The objective function for lifecycle utilization efficiency is constructed based on the ratio of effective energy output to lifecycle cost, with maximizing this ratio as the optimization objective.

[0011] This invention also proposes a design system for a power 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 architecture parameter matching 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 architecture parameter matching 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 parameter matching design method for energy interconnection architecture. This method uses an improved Golden Jackal optimization algorithm to optimize the parameters of the energy interconnection architecture, and constructs the optimal energy interconnection architecture parameter matching design scheme based on the optimization results. Based on this system architecture, the best utilization efficiency can be achieved.

[0014] In the exploration phase of the improved Golden Jackal optimization algorithm, in order to more effectively improve the algorithm's global search capability, the position update mechanism of the Dandelion optimization algorithm and the Alpha Evolution algorithm is introduced to improve the Golden Jackal position update method. The Golden Jackal position is 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 adaptive search step size of the Golden Jackal, and the decay factor, so as to avoid local optima in each iteration, thereby improving the algorithm's global search capability and effectively covering the entire problem search space.

[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 dandelion optimization algorithm and the alpha evolution 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 adaptive search step size of the golden jackal, and the decay factor. 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. Attached Figure Description

[0016] Figure 1 This is a flowchart of the energy interconnection architecture parameter matching 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 design problem of energy interconnection architecture parameter matching. 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 prematurely, 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 architecture parameter matching design. Therefore, this invention proposes an energy interconnection architecture parameter matching design method, the flowchart of which is as follows: Figure 1 As shown, the specific steps are as follows:

[0019] S1: Determine the energy interconnection architecture parameters, including architecture type, port type, and number of ports.

[0020] S2: Determine the constraints of the energy interconnection architecture parameters and construct the full life cycle utilization efficiency as the objective function. Calculate the fitness value based on the objective function, and optimize the energy interconnection architecture parameters using an improved Golden Jackal optimization algorithm to obtain the energy interconnection architecture parameters with the optimal fitness value. The improved Golden Jackal optimization algorithm introduces the position update mechanisms of the Dandelion optimization algorithm and the Alpha Evolutionary 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 adaptive search step size of the Golden Jackal, and the decay factor for the next generation of position updates.

[0021] S3: Determine the energy interconnection architecture parameter matching design scheme based on the optimal energy interconnection architecture parameters.

[0022] Specifically, such as Figure 2 As shown, the improved Golden Jackal optimization algorithm optimizes the parameters of the energy interconnection architecture by including the following steps: S2.1: Determine the energy interconnection architecture parameters, including architecture type, port type, and number of ports. Architecture types include radial type with single-path power supply, ring network type with bidirectional power supply, mesh type with multi-path interconnection, and hierarchical and partitioned type with main network and sub-network layered; port type is multi-energy port type.

[0023] S2.2: Determine the constraints. Constraints include power balance constraints and port capacity constraints.

[0024] Among them, power balance constraints: In the formula, The generator output power is determined based on the architecture type. The charging and discharging power of energy storage devices is determined based on the port type and the number of ports. The number of ports is dynamically adjusted to meet the power requirements of the load. The line loss power is determined by the architecture type.

[0025] Port capacity constraints are related to the architecture type and port type. The basic load is determined based on the architecture type, and the number of ports must meet the minimum number of ports required to meet the basic load requirements. The maximum number of ports is determined based on the port type and is limited by the physical size of the device, and the number of ports must be less than or equal to that number.

[0026] Construct the objective function: The output efficiency is determined based on the architecture type, port type, and number of ports; the total lifecycle effective energy output is determined based on the maximum load demand and output efficiency; the total lifecycle cost is determined based on the port type and number of ports; and an objective function for the total lifecycle utilization efficiency is constructed based on the ratio of the total lifecycle effective energy output to the total lifecycle cost, with maximizing this ratio as the optimization objective.

[0027] 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 .

[0028] 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 in the j-th dimension; 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.

[0029] 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.

[0030] 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 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.

[0031] 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.

[0032] S2.6.1: Searching for Prey (Exploration Phase) 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 hunt is led by the male golden jackal. The female golden jackals follow the male.

[0033] To more effectively improve the algorithm's global search capability, the position update mechanism of the Dandelion optimization algorithm and the Alpha Evolution algorithm is introduced to improve the golden jackal position update method. The golden jackal position is updated by comprehensively considering factors such as selecting different position update modes based on the switching probability, the optimal position of the golden jackal in this iteration, the adaptive search step size of the golden jackal, and the decay factor. This avoids the occurrence of local optima in each iteration, thereby improving the algorithm's global search capability and effectively covering the entire problem search space.

[0034] Among them, the Dandelion Optimizer (DO) is a swarm intelligence optimization algorithm that simulates the propagation behavior of dandelions. Its core feature lies in achieving a dynamic balance between global search and local development by simulating the natural processes of dandelion population propagation, growth, and competition. The algorithm treats each solution as a dandelion seed and updates its position through two mechanisms: "wind propagation" and "root expansion." In the early stages of the search, long-distance jumps based on Lévy flight simulate wind propagation, allowing the population to quickly cover the solution space and avoid getting trapped in local optima. In the later stages of the search, neighborhood perturbation and elite retention strategies simulate root expansion, enabling refined development of high-quality areas. Furthermore, the algorithm introduces a dynamic weighting factor, adaptively adjusting the propagation range based on the number of iterations to balance exploration and development capabilities.

[0035] The Alpha Evolutionary Algorithm (AEA) is a novel evolutionary optimization algorithm based on natural selection and genetic mechanisms. Its core feature lies in its fusion of dynamic fitness terrain analysis and elite strategies, achieving coordinated optimization of global exploration and local development. The algorithm introduces the concept of "alpha individuals," the elite individuals with the highest fitness in each generation, which, by simulating the "leadership effect" in biological evolution, guide the population to migrate to more favorable regions. Compared to traditional genetic algorithms, AEA employs a non-uniform mutation operator, maintaining a large mutation range in the early stages of evolution to preserve population diversity, and gradually reducing the mutation range in later stages to improve convergence accuracy. Furthermore, the algorithm innovatively incorporates an "environmental feedback" mechanism, dynamically adjusting selection pressure based on the population's fitness distribution to avoid premature convergence.

[0036] Therefore, in the exploration phase of GJOA, the Dandelion Optimization Algorithm and the Alpha Evolutionary Algorithm are introduced. The adaptive search range adjustment mechanism of the Dandelion Optimization Algorithm is integrated into the algorithm, taking into account the adaptive search step size of the golden jackal to improve the global search capability. Furthermore, the adaptive step size generation mechanism of the Alpha Evolutionary Algorithm is introduced into the algorithm, that is, the decay factor of the golden jackal is introduced to regulate the search intensity, balancing the algorithm's global exploration and local development capabilities.

[0037] The improved formula for updating the positions of male and female golden jackals is 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. This represents a random number based on the Lévy distribution; Energy for the prey to escape; Let t be the position of the golden jackal after the t-th iteration.

[0038] (6); (7); In the formula: r is a random number within [0,1]; The adaptive search step size for the golden jackal; This is the attenuation factor for the golden jackal.

[0039] The escape energy of prey can be calculated using the following formula: (8); This indicates the process of the prey's energy decreasing. This indicates the initial energy state of the prey.

[0040] (9); In the formula: r is a random number in the range [0,1].

[0041] (10); 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.

[0042] In formulas (4) and (5), A random number based on the Lévy distribution can be calculated using the following formula: (11); This is the Lévy flight function, and its calculation method is as follows: (12); In the formula: and A random number within the range (0,1); This is a default constant with a value of 1.5.

[0043] The formula for updating the position of the golden jackal has been improved as follows: (13); In the formula: The location of the golden jackal after the (t+1)th iteration is the individual parameter of the energy interconnection architecture after the (t+1)th iteration in the exploration phase. , , and It is a random number within the range [0,1].

[0044] 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 dandelion optimization algorithm and the alpha evolution 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 adaptive search step size of the golden jackal, and the decay factor. 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.

[0045] During the development phase of GJOA, the mountaineering team optimization algorithm and the moss growth optimization algorithm were introduced. The update mechanism of the team collaboration rescue phase in the mountaineering team optimization algorithm was incorporated into the algorithm, taking into account the average value of all team members, that is, the average value of the golden jackal position in the t-th iteration. The core parameter of the wind-borne diffusion characteristics of moss spores in the moss growth optimization algorithm, namely the propagation distance, was also incorporated into the algorithm to balance the global exploration and local development capabilities of the algorithm.

[0046] The improved formula for updating the positions of male and female golden jackals is as follows: (14); (15); 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 formula for updating the golden jackal's position is as follows: (16); In the formula: The position of the golden jackal after the (t+1)th iteration is the individual parameter of the energy interconnection architecture after the (t+1)th iteration in the development phase. , and It is a random number within the range [0,1].

[0047] 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 architecture parameters.

[0048] 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.

[0049] The above is an embodiment of the energy interconnection architecture parameter matching design method provided in this example. Based on the same idea, this embodiment also provides a corresponding energy interconnection architecture parameter matching design system. Specific limitations of the energy interconnection architecture parameter matching design system can be found in the limitations of the energy interconnection architecture parameter matching design method described above, and will not be repeated here. Each module in the above energy interconnection architecture parameter matching 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.

[0050] 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 energy interconnection architecture parameter matching design method.

[0051] 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.

[0052] 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 parameter matching design method for an energy interconnection architecture, characterized in that, Includes the following steps: Determine the parameters of the energy interconnection architecture, including the architecture type, port type, and number of ports; The constraints of the energy interconnection architecture parameters are determined, and the life-cycle utilization efficiency is constructed as the objective function. The fitness value is obtained based on the objective function. The energy interconnection architecture parameters are optimized through an improved Golden Jackal optimization algorithm to obtain the energy interconnection architecture parameters with the optimal fitness value. The improved Golden Jackal optimization algorithm introduces the position update mechanism of the Dandelion optimization algorithm and the Alpha Evolution algorithm in the exploration and development stages of the original Golden Jackal optimization algorithm. It improves the position update method of Golden Jackal and comprehensively considers the selection of different position update modes based on the switching probability, the optimal position of Golden Jackal in the current iteration, the adaptive search step size of Golden Jackal, and the decay factor for the next generation of position update.

2. The energy interconnection architecture parameter matching design method according to claim 1, characterized in that, The location update during the exploration phase of the improved golden jackal optimization algorithm includes the following steps: The formula for updating the positions of male and female golden jackals 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. This represents a random number based on the Lévy distribution; Energy for the prey to escape; Let be the position of the golden jackal after the t-th iteration; ； ； In the formula: r is a random number within [0,1]; The adaptive search step size for the golden jackal; The attenuation factor for the golden jackal; The formula for updating the position of the golden jackal has been improved as follows: ； In the formula: The location of the golden jackal after the (t+1)th iteration is the individual parameter of the energy interconnection architecture after the (t+1)th iteration in the exploration phase. , , and It is a random number within the range [0,1].

3. The energy interconnection architecture parameter matching design method according to claim 2, characterized in that, The position update during the development phase of the improved golden jackal optimization algorithm includes the following steps: The formula for updating the positions of male and female golden jackals 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 formula for updating the golden jackal's position is as follows: ； In the formula: The position of the golden jackal after the (t+1)th iteration is the individual parameter of the energy interconnection architecture after the (t+1)th iteration in the development phase. , and It is a random number within the range [0,1].

4. The energy interconnection architecture parameter matching design method according to claim 3, characterized in that, The architecture types include radial type with single-path power supply, ring network type with bidirectional power supply, mesh type with multi-path interconnection, and hierarchical partition type with main network and sub-network layered; the port type is multi-energy port type.

5. The energy interconnection architecture parameter matching design method according to claim 4, characterized in that, The constraints include power balance constraints and port capacity constraints. Among them, power balance constraints: In the formula, The generator output power is determined based on the architecture type. The charging and discharging power of energy storage devices is determined based on the port type and the number of ports. The number of ports is dynamically adjusted to meet the power requirements of the load. Line loss power is determined by the architecture type; The port capacity constraints are related to the architecture type and port type. The basic load is determined based on the architecture type, and the number of ports must meet the minimum number of ports required to meet the basic load requirements. The maximum number of ports is determined based on the port type and is limited by the physical size of the device, and the number of ports must be less than or equal to that number.

6. The energy interconnection architecture parameter matching design method according to claim 5, characterized in that, The construction of the objective function includes the following steps: Determine the output efficiency based on the architecture type, port type, and number of ports; determine the effective energy output throughout the entire lifecycle based on the maximum load demand and output efficiency. Determine the total lifecycle cost based on the port type and number of ports; The objective function for lifecycle utilization efficiency is constructed based on the ratio of effective energy output to lifecycle cost, with maximizing this ratio as the optimization objective.

7. A design system for a power 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 architecture parameter matching design method as described in any one of claims 1 to 6.

8. 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 architecture parameter matching design method as described in any one of claims 1 to 6.

Images