Design method and system for open and highly flexible energy interconnection system

Abstract

The present invention provides a design method and system for an open, highly flexible energy interconnection system, belonging to the technical field of energy interconnection systems. The method comprises: determining the structural design parameters of the open, highly flexible energy interconnection system based on the basic elements and performance parameters of the open, highly flexible energy interconnection system; optimizing the structural design parameters using an improved snake optimization algorithm, taking transmission efficiency as the optimization objective of the open, highly flexible energy interconnection system design scheme, to obtain the optimal structural design parameters; and determining the open, highly flexible energy interconnection system design scheme based on the optimal structural design parameters. This method can rapidly generate an optimal open, highly flexible energy interconnection system design scheme that achieves optimal system transmission efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of energy interconnection systems, and in particular to a design method and system for an open and highly flexible energy interconnection system. Background Art

[0002] The integrated matching design of open and highly flexible energy interconnection systems is one of the key technologies for land warfare platforms to win. Therefore, it is urgent and necessary to conduct in-depth research on the characteristics of open and highly flexible energy interconnection systems and carry out research on the integrated matching design of open and highly flexible energy interconnection systems.

[0003] The Snake Optimization Algorithm (SOA) is a new metaheuristic algorithm proposed in 2022. It is a novel intelligent optimization algorithm that mimics the unique mating behavior of snakes and can also be applied to the integrated matching design of open, highly flexible energy interconnected systems. However, the SOA still has some flaws, making it prone to local optimality and low convergence accuracy. This often results in suboptimal design results when designing the integrated matching design of open, highly flexible energy interconnected systems. Summary of the Invention

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

[0005] The specific steps include:

[0006] Determine the structural design parameters of the open and highly flexible energy interconnection system based on its basic elements and performance parameters;

[0007] Taking the transmission efficiency between energy storage devices in an open and highly flexible energy interconnection system as the optimization goal, the improved snake optimization algorithm is used to optimize the structural design parameters to obtain the optimal structural design parameters. The improved snake optimization algorithm introduces the driving training optimization algorithm and the red-billed blue magpie optimization algorithm into the mating mode of the exploration and development stages of the original snake optimization algorithm, improves the position update method, and selects different position update modes based on the switching probability, the optimal position of the snake individual in this iteration, the driving training deceleration parameter of the snake individual, and the standard normal distribution parameter of the snake individual for the next generation of position update.

[0008] Determine the design scheme of the open and highly flexible energy interconnection system based on the optimal structural design parameters.

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

[0010] ;

[0011] ;

[0012] Where: is the male position at the t+1th iteration; is the position of the randomly selected male at iteration t; rand is a random number in the range [0,1]; the ability to find food for males; is the female position at the t+1th iteration; is the position of the randomly selected female in the tth iteration; the ability to find food for the females; is the position of the individual snake at the tth iteration; is the optimal position of the snake individual in the tth iteration, that is, the food position; 、 is a random number in the range [0,1];

[0013] ;

[0014] ;

[0015] Where: P is the deceleration parameter of driving training; CF is the standard normal distribution parameter of individual snakes.

[0016] Preferably, the position update formula in the mating mode of the development stage of the improved snake optimization algorithm is:

[0017] ;

[0018] ;

[0019] Where: For the t +1 iteration i The position of the male; For the t The first iteration i The position of the male; For the t +1 iteration i The position of the female; For the t The first iteration i The position of the female; rand is a random number in the range [0,1]; and Separate mating abilities of males and females; 、 is a random number in the range [0,1].

[0020] Preferably, the basic elements of the open and highly flexible energy interconnection system include vehicle platform parameters, power supply parameters and load parameters; the performance parameters include dynamic response speed, conversion efficiency parameters and conversion loss parameters;

[0021] The structural design parameters of the open and highly flexible energy interconnection system include energy storage device capacity and network structure parameters; the network structure parameters include the connection method between energy storage devices, line length and bandwidth of connection nodes.

[0022] Preferably, the construction of the optimization target includes the following steps:

[0023] The transmission loss model is determined based on the network structure parameters and the performance parameters of the open and highly flexible energy interconnection system. The output power is determined based on the basic elements of the open and highly flexible energy interconnection system and the capacity of the energy storage equipment. The transmission efficiency is determined based on the output power and transmission loss model, with maximizing the transmission efficiency as the optimization goal of the structural design parameters.

[0024] The present invention also proposes a design system for an open and highly flexible energy interconnection system, the system comprising:

[0025] processor;

[0026] a memory having stored thereon a computer program executable on the processor;

[0027] Among them, when the computer program is executed by the processor, the steps of the open and highly flexible energy interconnection system design method are implemented.

[0028] The present invention also proposes a computer-readable storage medium, on which a data processing program is stored. When the data processing program is executed by a processor, the steps of the open and highly flexible energy interconnection system design method are implemented.

[0029] Beneficial effects of the present invention:

[0030] The present invention proposes a design method for an open and highly flexible energy interconnection system. The method adopts an optimized snake optimization algorithm to optimize the basic structural parameters of the open and highly flexible energy interconnection system, and constructs an optimal design scheme for the open and highly flexible energy interconnection system based on the optimization results. Based on this system architecture, the optimal transmission efficiency can be achieved.

[0031] During the algorithm's exploration phase, the snake optimization algorithm updates the positions of individual male (or female) snakes solely by randomly selecting the positions of males (or females). This lacks diversity in search directions, fails to effectively cover the entire problem search space, and can cause the algorithm to become trapped in a local optimum. To more effectively improve the algorithm's performance, the present invention incorporates the position update mechanisms of the driving training optimization algorithm and the red-billed blue magpie optimization algorithm, improving the snake position update method. This method comprehensively considers factors such as the selection of different position update modes based on switching probabilities, the optimal position of the individual snake in this iteration, the individual snake's driving training deceleration parameters, and the parameters of the individual snake's standard normal distribution to update the individual snake's position. This increases the diversity of the algorithm's search directions, avoids local optima in each iteration, and thus improves the algorithm's performance.

[0032] Furthermore, the snake optimization algorithm's mating model selects parents solely based on fitness, ignoring the population's diverse distribution. This can lead to an over-concentration of high-quality individuals, reducing global search potential and making it easy to get stuck in local optima. To more effectively improve the algorithm's performance, the position update mechanism of the driving training optimization algorithm and the red-billed blue magpie optimization algorithm was introduced. This improved the way individual snake positions are updated, preventing the potential loss of population diversity that can lead to premature convergence to local optimal solutions, thereby improving the algorithm's performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a flow chart of a method for designing an open and highly flexible energy interconnection system according to an embodiment of the present invention;

[0034] Figure 2 is an execution flow chart of the improved snake optimization algorithm according to an embodiment of the present invention;

[0035] Figure 3 It is an iterative process curve of the open and highly flexible energy interconnection system design solution of the embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0037] Example 1

[0038] Snake optimization algorithm (SOA) is a new metaheuristic algorithm proposed in 2022. This algorithm is a new intelligent optimization algorithm that imitates the special mating behavior of snakes. It can also be applied to the integrated matching design problem of open and highly flexible energy interconnected systems. However, the existing snake optimization algorithm still has some defects: (1) The position update method of male (female) snake individuals in the exploration stage of the snake optimization algorithm only relies on the position of randomly selected males (males and females). The search direction lacks diversity and cannot effectively cover the entire problem search space, which may cause the algorithm to fall into local optimality. (2) In the mating mode of the snake optimization algorithm development stage, parents are selected only based on fitness values, ignoring the diversity distribution of the population, which may lead to excessive concentration of high-quality individuals, reduce the global search potential, and easily fall into local optimality. The above two key deficiencies result in the inability to achieve the best design effect when using the snake optimization algorithm for the integrated matching design of open and highly flexible energy interconnected systems. To this end, the present invention proposes a design method for an open and highly flexible energy interconnected system, and its flow chart is as follows. Figure 1 The specific steps are as follows:

[0039] S1: Determine and initialize the structural design parameters of the open and highly flexible energy interconnection system based on the basic elements and performance parameters of the open and highly flexible energy interconnection system.

[0040] S2: Taking transmission efficiency as the optimization goal of the design scheme of open and highly flexible energy interconnection system, the structural design parameters are optimized through the improved snake optimization algorithm to obtain the optimal structural design parameters; wherein, the improved snake optimization algorithm introduces the driving training optimization algorithm and the red-billed blue magpie optimization algorithm into the mating mode of the exploration stage and the development stage of the original snake optimization algorithm, improves the position update method, and selects different position update modes according to the switching probability, the optimal position of the snake individual in this iteration, the driving training deceleration parameters of the snake individual, and the standard normal distribution parameters of the snake individual to perform the next generation position update.

[0041] S3: Determine the design scheme of the open and highly flexible energy interconnection system based on the optimal structural design parameters.

[0042] Specifically, such as Figure 2 As shown in the figure, the improved snake optimization algorithm includes the following steps to optimize the optimization parameters:

[0043] S2.1: The basic elements of the open and highly flexible energy interconnection system include vehicle platform parameters, power supply parameters, and load parameters; performance parameters include dynamic response speed, conversion efficiency parameters, and conversion loss parameters.

[0044] The structural design parameters of the open and highly flexible energy interconnection system include the capacity of energy storage equipment and network structure parameters; the network structure parameters include the connection method between energy storage devices, line length and bandwidth of connection nodes.

[0045] Build optimization goals:

[0046] The transmission loss model is determined based on the network structure parameters and the performance parameters of the open and highly flexible energy interconnection system; the output power is determined based on the basic elements of the open and highly flexible energy interconnection system and the capacity of the energy storage equipment; the transmission efficiency is determined based on the output power and transmission loss model, and the maximization of the transmission efficiency is used as the optimization goal of the structural design parameters and as the objective function.

[0047] The objective function of the design scheme of open and highly flexible energy interconnection system based on the improved snake optimization algorithm is determined to be transmission efficiency.

[0048] S2.2: Set parameters, including: population size (i.e., number of snakes) N; maximum number of iterations (i.e., conditions for stopping iterations) T ; Problem optimization lower bound ; Problem optimization upper bound .

[0049] S2.3: Initialize the snake population position:

[0050] (1);

[0051] Where: is the position of the i-th snake; r is a random number in the range [0,1]; and are the upper and lower bounds of the problem to be solved.

[0052] S2.4: Divide the population into two groups: females and males

[0053] Assume that the number of males is 50% and the number of females is 50%. The population is divided into two groups: males and females. The following two formulas are used to divide the population:

[0054] (2);

[0055] (3);

[0056] Where: N The size of the snake population; is the number of males; The number of females.

[0057] S2.5: Evaluate each group and define the temperature and amount of food

[0058] According to the objective function, find the best individual in each group and get the best male and the best females and food location .

[0059] Temperature can be defined by the following formula:

[0060] (4);

[0061] Where: t is the current iteration number; T is the maximum number of iterations.

[0062] Food quantity It can be defined by the following formula:

[0063] (5);

[0064] Where: is a constant, take 0.5.

[0065] S2.6: If (Threshold ), enter the exploration phase and calculate the positions of male and female individuals respectively, otherwise go to S2.7.

[0066] S2.7: If Temp>0.6, enter the exploration phase and calculate the position of the individual snake, otherwise enter S2.8.

[0067] S2.8: If rand>0.6, enter the combat mode of the development stage and calculate the positions of male and female individuals respectively; otherwise, enter S2.9.

[0068] S2.9: Enter the mating mode of the development stage, calculate the positions of males and females respectively, and replace the positions of the worst males and the worst females.

[0069] Specifically, the exploration and development phases include the following steps:

[0070] Exploration phase (no food)

[0071] if (Threshold ), the snakes search for food by choosing any random location and update their position.

[0072] During the SOA exploration phase, the snake optimization algorithm's position update method for male (female) snakes relies solely on the positions of randomly selected males (males and females). This lacks diversity in search directions and fails to effectively cover the entire problem search space, potentially causing the algorithm to become stuck in a local optimum. To more effectively improve the algorithm's performance, the position update mechanism of the driving training optimization algorithm and the red-billed blue magpie optimization algorithm was introduced to improve the snake's position update method. This method updates the snake's position by comprehensively considering factors such as the selection of different position update modes based on switching probability, the optimal position of the snake in this iteration, the driving training deceleration parameter of the snake, and the standard normal distribution parameters of the snake. This improves the diversity of the algorithm's search directions, avoids local optimums in each iteration, and thus improves the algorithm's performance.

[0073] In the exploration phase of SOA, the driving training optimization algorithm and the red-billed blue magpie optimization algorithm were introduced. The improved position update formulas for male and female snakes are:

[0074] (6);

[0075] (7);

[0076] Where: is the male position at the t+1th iteration; is the position of the randomly selected male at iteration t; rand is a random number in the range [0,1]; the ability to find food for males; is the female position at the t+1th iteration; is the position of the randomly selected female in the tth iteration; the ability to find food for the females; is the position of the individual snake at the tth iteration; is the optimal position of the snake individual in the tth iteration, that is, the food position; 、 is a random number in the range [0,1];

[0077] (8);

[0078] (9);

[0079] Where: P is the deceleration parameter of driving training; CF is the standard normal distribution parameter of individual snakes.

[0080] Development stage (with food)

[0081] exist Under the conditions, if the temperature , then the temperature is hot. The snake will only look for food, and the position update formula is as follows:

[0082] (10);

[0083] Where: is the position of the individual snake (male or female) at the t+1th iteration; is the optimal position of the snake individual at the tth iteration, that is, the food position; rand is a random number in the range of [0,1]; is a constant, take 2.

[0084] exist Under the conditions, if the temperature , then the temperature is cold. The snake will be in fighting mode or mating mode.

[0085] (a) Combat Mode

[0086] (11);

[0087] Where: is the position of the i-th male at the t+1-th iteration; is the position of the i-th male at the t-th iteration; is the best position in the female snake group; rand is a random number in the range of [0,1]; For male fighting ability.

[0088] (12);

[0089] Where: is the position of the i-th female at the t+1-th iteration; is the position of the i-th female at the t-th iteration; is the best position in the male snake group; rand is a random number in the range of [0,1]; For female fighting ability.

[0090] and It can be calculated by the following formula:

[0091] (13);

[0092] (14);

[0093] Where: The best position for female snakes The fitness value of The best position for male snakes The fitness value of is the fitness value of the individual snake.

[0094] (b) Mating pattern

[0095] During the SOA development phase, the snake optimization algorithm's mating model selected parents solely based on fitness, ignoring the population's diverse distribution. This could lead to an over-concentration of high-quality individuals, reducing global search potential and making them prone to local optima. To more effectively improve the algorithm's performance, the position update mechanism of the driving training optimization algorithm and the red-billed blue magpie optimization algorithm was introduced to improve the snake's position update method. This method updates the snake's position based on factors such as the switching probability, the optimal position of the individual snake during the current iteration, the individual snake's driving training deceleration parameter, and the standard normal distribution parameters of the individual snake. This prevents the potential loss of population diversity, which could lead to premature convergence to a local optimum, thereby improving the algorithm's performance.

[0096] In the mating model of the development phase of SOA, the driving training optimization algorithm and the red-billed blue magpie optimization algorithm were introduced. The improved position update formula of male and female snake individuals is:

[0097] (15);

[0098] (16);

[0099] Where: is the position of the i-th male at the t+1-th iteration; is the position of the i-th male at the t-th iteration; is the position of the i-th female at the t+1-th iteration; is the position of the i-th female in the t-th iteration; rand is a random number in the range [0,1]; and Separate mating abilities of males and females; 、 is a random number in the range [0,1]; and The mating capacity of males and females can be calculated by the following formula:

[0100] (17);

[0101] (18);

[0102] Where: is the fitness value of the ith male position; is the fitness value of the ith female position.

[0103] If the eggs hatch, select the worst male and female and replace them.

[0104] (19);

[0105] (20);

[0106] Where: It is the worst position in the male snake group; This is the worst position among the female snakes.

[0107] S2.10: Determine whether the stopping condition is met. If not, repeat S2.5-S2.10. Otherwise, output the optimal snake individual, that is, the optimal open and highly flexible energy interconnection system design solution.

[0108] In this embodiment:

[0109] Using MATLAB as the simulation platform, the Snake optimization algorithm (SOA) was selected for comparison with the ISOA 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 is the iterative process curve. Figure 3 The results clearly show that the ISOA method converges faster and more accurately than the SOA algorithm. Simulation results demonstrate that the ISOA algorithm has a stronger search capability and achieves a better design for an open, highly flexible energy interconnection system, validating the algorithm's effectiveness.

[0110] The above is an embodiment of the present embodiment of the open and highly flexible energy interconnection system design method provided. Based on the same idea, the present embodiment also provides a corresponding open and highly flexible energy interconnection system design system. For the specific definition of the design system of the open and highly flexible energy interconnection system, please refer to the definition of the open and highly flexible energy interconnection system design method above, which will not be repeated here. Each module in the above-mentioned design system of the open and highly flexible energy interconnection system can be fully or partially implemented by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0111] This embodiment also provides a computer-readable storage medium, which stores a computer program that can be used to execute the above Figure 1 Provided is an open and highly flexible energy interconnection system design method.

[0112] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0113] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A design method for an open and highly flexible energy interconnection system, characterized in that: The following steps are involved: Determine the structural design parameters of the open highly flexible energy interconnection system based on the basic elements and performance parameters of the open highly flexible energy interconnection system; the basic elements of the open highly flexible energy interconnection system include vehicle platform parameters, power supply parameters, and load parameters; the performance parameters include dynamic response speed, conversion efficiency parameters, and conversion loss parameters; the structural design parameters of the open highly flexible energy interconnection system include energy storage device capacity and network structure parameters; the network structure parameters include the connection method between energy storage devices, line length, and bandwidth of connection nodes; The transmission efficiency between energy storage devices in an open, highly flexible energy interconnection system is used as an optimization target. The construction of the optimization target includes the following steps: determining a transmission loss model based on network structure parameters and performance parameters of the open, highly flexible energy interconnection system; determining output power based on basic elements of the open, highly flexible energy interconnection system and the capacity of the energy storage devices; determining transmission efficiency based on the output power and transmission loss model, and taking maximizing transmission efficiency as the optimization target of the structural design parameters; The improved snake optimization algorithm is used to optimize the structural design parameters to obtain the optimal structural design parameters. The improved snake optimization algorithm introduces the driving training optimization algorithm and the red-billed blue magpie optimization algorithm into the mating mode of the exploration and development stages of the original snake optimization algorithm, improves the position update method, and selects different position update modes based on the switching probability, the optimal position of the individual snake in this iteration, the driving training deceleration parameter of the individual snake, and the standard normal distribution parameter of the individual snake for the next generation of position update. Determine the design scheme of an open and highly flexible energy interconnection system based on the optimal structural design parameters; The position update formula of the exploration phase of the improved snake optimization algorithm is: ； ； Where: is the male position at the t+1th iteration; is the position of the randomly selected male at iteration t; rand is a random number in the range [0,1]; the ability to find food for males; is the female position at the t+1th iteration; is the position of the randomly selected female in the tth iteration; the ability to find food for the females; is the position of the individual snake at the tth iteration; is the optimal position of the snake individual in the tth iteration, that is, the food position; 、 is a random number in the range [0,1]; X min Find the lower bound for the problem; X max Find the upper bound of the problem; T is the maximum number of iterations; ； ； Where: P is the deceleration parameter of driving training; CF is the standard normal distribution parameter of individual snakes; The position update formula in the mating mode of the development stage of the improved snake optimization algorithm is: ； ； Where: For the t +1 iteration i The position of the male; For the t The first iteration i The position of the male; For the t +1 iteration i The position of the female; For the t The first iteration i The position of the female; rand is a random number in the range [0,1]; and the mating capacity of males and females, respectively; 、 is a random number in the range [0,1]; Q For the amount of food; c 3 is a constant, take 2.

2. A design system for an open and highly flexible energy interconnection 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 open and highly flexible energy interconnection system design method as claimed in claim 1 are implemented.

3. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a data processing program, and when the data processing program is executed by the processor, the steps of the open and highly flexible energy interconnection system design method as claimed in claim 1 are implemented.

Images