New energy critical permeability calculation method, system, equipment and medium

Abstract

The invention discloses a new energy critical permeability calculation method, a new energy critical permeability calculation system, new energy critical permeability calculation equipment and a medium, and relates to the technical field of new energy consumption capability assessment. Comprehensively considering operation constraints of power balance, generator output limitation, new energy output, node voltage amplitude, line power flow and system frequency and voltage regulation capability, and constructing a new energy critical permeability optimization model; a fox hunting optimization algorithm is improved by introducing a mixed disturbance mechanism and an adaptive step length strategy; and solving the new energy critical permeability optimization model by adopting an improved fox hunting optimization algorithm to obtain the new energy critical permeability under various operation constraint conditions. According to the method, the problems of low calculation efficiency, low solution precision and poor global search capability in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy absorption capacity assessment, and in particular to a new energy critical permeability calculation method, system, equipment and medium. Background Art

[0002] Renewable energy, due to its clean and renewable nature, has become a key force driving energy structural transformation. However, these sources of energy, characterized by high output uncertainty, high volatility, and poor controllability, pose significant challenges to grid dispatching, frequency and voltage stability, and safety margins.

[0003] In this context, scientifically assessing the maximum proportion of renewable energy that a power system can safely accommodate under given operating conditions (i.e., the critical penetration rate) has become a key issue in renewable energy grid integration planning and operational control. Calculating the critical penetration rate not only impacts the planning, site selection, and capacity allocation of renewable energy projects but also directly impacts the operational safety and economic efficiency of the power grid. Existing technologies include static margin methods, which assess the limits of renewable energy integration based on voltage stability margin or frequency response margin, but often fail to account for complex operational constraints and produce conservative results. Heuristic trial-and-error methods, which continuously add renewable energy output and combine it with power flow calculations to determine system defaults, offer simple but computationally inefficient methods. Traditional optimization methods, such as linear programming (LP) and mixed integer programming (MILP), while rigorous in modeling, are prone to local optima when faced with multi-constrained, nonlinear, and multi-peak problems, limiting their solution capabilities. Some researchers have employed particle swarm optimization, genetic algorithms, and gray wolf algorithms for modeling and solution, but these algorithms still suffer from issues such as premature convergence, insufficient convergence accuracy, and parameter sensitivity, making it difficult to achieve both efficient and accurate solutions.

[0004] In summary, there is an urgent need for a calculation method for the critical permeability of new energy that can take into account computational efficiency, solution accuracy, and global search capabilities, so as to improve the scientificity and practicality of new energy acceptance assessment. Summary of the Invention

[0005] The present invention provides a new energy critical permeability calculation method, system, device and medium for solving the problems of low calculation efficiency and solution accuracy and poor global search capability in the prior art.

[0006] In view of this, a first aspect of the present invention provides a method for calculating the critical permeability of a new energy source, the method comprising:

[0007] Taking the maximum renewable energy penetration rate as the objective function and comprehensively considering the operational constraints of power balance, generator output limit, renewable energy output, node voltage amplitude, line flow, and system frequency and voltage regulation capabilities, an optimization model for renewable energy critical penetration rate is constructed.

[0008] The fox hunting optimization algorithm is improved by introducing a hybrid perturbation mechanism and an adaptive step-size strategy.

[0009] The improved fox hunting optimization algorithm is used to solve the new energy critical permeability optimization model to obtain the new energy critical permeability under various operating constraints.

[0010] Optionally, the objective function is expressed as:

[0011] ;

[0012] Where, Contribute to new energy; is the total system load.

[0013] Optionally, the fox hunting optimization algorithm is improved by introducing a hybrid perturbation mechanism and an adaptive step-size strategy, including:

[0014] By combining Gaussian perturbation and Lévy flight mechanism as a hybrid perturbation mechanism, a perturbation operation is performed on each individual with a preset probability to obtain the fox's position update;

[0015] The adaptive step size strategy allows the step size to be automatically scaled according to the speed of change of the solution during the search phase.

[0016] Optionally, the improved fox hunting optimization algorithm is used to solve the new energy critical permeability optimization model to obtain the new energy critical permeability under various operating constraints, including:

[0017] S31, according to the requirements of the objective function and the operational constraints, inputting the grid parameters into the improved fox hunting optimization algorithm to initialize the fox population position;

[0018] S32, calculating the fitness value of each fox, and selecting the dominant fox according to the optimal fitness function;

[0019] S33, according to the position of the leading fox and by optimizing the position update formula through the adaptive step size strategy, the positions of other foxes are updated using the update formula;

[0020] S34. Using the mixed disturbance mechanism, perform a disturbance operation on each fox individual with the preset probability;

[0021] S35, perform constraint check on the updated fox solution to determine whether the constraints are satisfied; if not, use the penalty function to correct the objective function, if so, execute step S36;

[0022] S36: Determine whether the preset termination condition is met. If so, terminate the calculation and output the optimal critical permeability solution. Otherwise, return to S32 to continue iteration.

[0023] A second aspect of the present invention provides a new energy critical permeability calculation system, the system comprising:

[0024] A construction unit is used to construct a new energy critical penetration optimization model with the maximum new energy penetration rate as the objective function, and comprehensively consider the operational constraints of power balance, generator output limit, new energy output, node voltage amplitude, line power flow and system frequency and voltage regulation capabilities;

[0025] Improvement unit, used to improve the fox hunting optimization algorithm by introducing a hybrid perturbation mechanism and an adaptive step size strategy;

[0026] The calculation unit is used to solve the new energy critical permeability optimization model using the improved fox hunting optimization algorithm to obtain the new energy critical permeability under various operating constraint conditions.

[0027] Optionally, the objective function is expressed as:

[0028] ;

[0029] Where, Contribute to new energy; is the total system load.

[0030] Optionally, the improvement unit is specifically configured to:

[0031] By combining Gaussian perturbation and Lévy flight mechanism as a hybrid perturbation mechanism, a perturbation operation is performed on each individual with a preset probability to obtain the fox's position update;

[0032] The adaptive step size strategy allows the step size to be automatically scaled according to the speed of change of the solution during the search phase.

[0033] Optionally, the computing unit is specifically configured to:

[0034] S31, according to the requirements of the objective function and the operational constraints, inputting the grid parameters into the improved fox hunting optimization algorithm to initialize the fox population position;

[0035] S32, calculating the fitness value of each fox, and selecting the dominant fox according to the optimal fitness function;

[0036] S33, according to the position of the leading fox and by optimizing the position update formula through the adaptive step size strategy, the positions of other foxes are updated using the update formula;

[0037] S34. Using the mixed disturbance mechanism, perform a disturbance operation on each fox individual with the preset probability;

[0038] S35, perform constraint check on the updated fox solution to determine whether the constraints are satisfied; if not, use the penalty function to correct the objective function, if so, execute step S36;

[0039] S36: Determine whether the preset termination condition is met. If so, terminate the calculation and output the optimal critical permeability solution. Otherwise, return to S32 to continue iteration.

[0040] A third aspect of the present invention provides a new energy critical permeability calculation device, the device comprising a processor and a memory:

[0041] The memory is used to store program code and transmit the program code to the processor;

[0042] The processor is configured to execute the steps of the new energy critical permeability calculation method as described in the first aspect above according to the instructions in the program code.

[0043] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the new energy critical permeability calculation method described in the first aspect.

[0044] It can be seen from the above technical solutions that the present invention has the following advantages:

[0045] The present invention provides a method for calculating the critical penetration rate of renewable energy. By constructing a multi-constrained nonlinear optimization model with the goal of maximizing renewable energy penetration, it fully considers the key constraints in power system operation, effectively improving the authenticity and engineering applicability of critical penetration rate assessment. A hybrid perturbation mechanism and an adaptive step-size strategy are introduced into the fox hunting optimization algorithm. The hybrid perturbation mechanism combines Gaussian perturbation and Lévy flight to enhance search diversity and the ability to escape local optima. The adaptive step-size strategy dynamically adjusts individual search amplitudes, balancing global exploration with local fine-tuning, thereby enhancing the algorithm's global optimization capability and stability. While satisfying multiple operational constraints, the improved algorithm can quickly approach the maximum renewable energy access level allowed by the system within a relatively small number of iterations. It exhibits excellent convergence characteristics and computational efficiency, making it suitable for online evaluation and rapid response requirements in large-scale power grid systems. This method can provide technical support for renewable energy access planning and scheduling strategy formulation in power grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 A schematic flow chart of a method for calculating the critical permeability of a new energy source provided by an embodiment of the present invention;

[0048] Figure 2 The embodiment of the present invention provides a solution process for the new energy critical permeability optimization model by improving the fox hunting optimization algorithm;

[0049] Figure 3 A schematic structural diagram of a new energy critical permeability calculation system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0051] See also Figure 1 , a method for calculating the critical permeability of new energy provided in an embodiment of the present invention includes:

[0052] Step 101: Taking the maximum renewable energy penetration rate as the objective function and comprehensively considering the operational constraints of power balance, generator output limit, renewable energy output, node voltage amplitude, line flow, and system frequency and voltage regulation capabilities, a renewable energy critical penetration rate optimization model is constructed.

[0053] It should be noted that the increase in the penetration rate of new energy has a direct impact on the stability mechanism of the power grid. On the one hand, the new energy access system does not have the rotational inertia and regulation capabilities of traditional synchronous generators, resulting in a decrease in the overall inertia of the system and a deterioration in frequency stability; on the other hand, the output of new energy is greatly affected by natural resource conditions, and its power changes are sudden and unpredictable, which makes the system frequency regulation, voltage regulation, flow control and other operational adjustments significantly more difficult. In order to solve the problem of power grid stability after the access of new energy, a new energy critical penetration rate optimization model is constructed, with the maximum new energy penetration rate as the goal, and comprehensive consideration of generator output limitations, load power balance, voltage stability, line flow limitations and system frequency and voltage regulation capabilities and other operational constraints. The specific steps are as follows:

[0054] In one embodiment, step 101 includes:

[0055] 1011. Constructing the objective function:

[0056] A new energy critical permeability optimization model is constructed. The objective function of this new energy critical permeability optimization model is to maximize the new energy permeability. The expression of the objective function is:

[0057] ;

[0058] Where, Contribute to new energy; is the total system load.

[0059] 1012. Design constraints:

[0060] It should be noted that in the new energy critical penetration rate optimization model, it is assumed that the power system contains N nodes, G conventional generators, and R new energy stations, and then the following constraints are designed.

[0061] (1) Power balance constraints:

[0062] For each node , active power constraints must be met:

[0063] ;

[0064] Where, Output of the g-th conventional generator at node i; Provide power to the rth new energy station at node i; is the load at node i; 、 is the node voltage; 、 are the real and imaginary parts of the node admittance; is the phase angle difference.

[0065] (2) Conventional generator output constraints:

[0066] ;

[0067] Where, 、 This is the output limit of a conventional generator.

[0068] (3) Constraints on new energy output:

[0069] ;

[0070] Where, This is the predicted maximum output of new energy under current meteorological conditions.

[0071] (4) Node voltage amplitude constraint:

[0072] ;

[0073] Where, 、 is the voltage limit at node i.

[0074] (5) Line flow constraints:

[0075] For any transmission line ,satisfy:

[0076] ;

[0077] Where, is the complex power flow from node i to node j; The upper limit of the line transmission capacity.

[0078] (6) System frequency and voltage regulation capability constraints:

[0079] To ensure the system's ability to regulate fluctuations in new energy, the following requirements must be met:

[0080] ;

[0081] Where, Frequency regulation capacity for conventional units; It is the maximum fluctuation of new energy.

[0082] Step 102: Improve the fox hunting optimization algorithm by introducing a hybrid perturbation mechanism and an adaptive step size strategy.

[0083] It can be understood that the present invention adopts the fox hunting optimization algorithm to solve the model. In order to improve the performance of the fox hunting optimization algorithm in solving the critical permeability problem of new energy, the present invention introduces a hybrid perturbation mechanism and an adaptive step size strategy to the algorithm to enhance its global search capability and convergence accuracy in high-dimensional, multi-constraint, and nonlinear optimization problems.

[0084] It's important to note that the fox hunting optimization algorithm is a swarm intelligence optimization method based on the ability of foxes to sense prey through scent, track prey, and collaborate as a team. This algorithm simulates the behaviors of "leading the fox" and "following the fox" to find the optimal solution. It is suitable for solving multi-objective, multi-constrained nonlinear optimization problems.

[0085] Fox hunting optimization algorithm through fitness function Evaluate the prey smell strength of each fox to determine the dominant fox, which is the individual with the best current fitness: ;

[0086] In the following behavior, other foxes are updated based on the position of the leading fox:

[0087] ;

[0088] Where, 、 is a randomly selected fox; 、 A random number between 0 and 1;

[0089] In the surround behavior, the fox's position is updated as follows:

[0090] ;

[0091] ;

[0092] Where, is the enveloping coefficient.

[0093] In one embodiment, step 102 includes:

[0094] Step 1021: By fusing Gaussian perturbation and Lévy flight mechanism as a hybrid perturbation mechanism, a perturbation operation is performed on each individual with a preset probability p to obtain the updated position of the fox.

[0095] It should be noted that the hybrid perturbation mechanism and adaptive step size strategy are introduced to optimize the fox hunting optimization algorithm to enhance its global search capability and convergence accuracy.

[0096] The hybrid perturbation mechanism combines Gaussian perturbation and Lévy flight mechanism. It performs perturbation operation on each individual with a preset probability p (a certain probability, which can be set according to actual conditions and is not limited here). The updated position of the fox is:

[0097] ;

[0098] Where, is a Gaussian random variable; is the step size scaling factor; , 、 Obey normal distribution; In the mixed perturbation strategy, the probability of the two is and .

[0099] It's understandable that the hybrid perturbation mechanism combines Gaussian perturbation and the Lévy flight mechanism, effectively leveraging the strengths of both. Gaussian perturbation exhibits a certain smoothness and continuity, enabling individuals to conduct relatively stable exploration within a certain range. This helps the algorithm conduct detailed searches within local regions and find optimal local solutions. The Lévy flight mechanism, on the other hand, exhibits long jumps, allowing individuals to escape their current local optimal region with a greater probability, enhancing the algorithm's global search capabilities and allowing it to explore a wider search space. By perturbing each individual with a preset probability, the degree of effect of the two perturbation methods can be flexibly adjusted based on actual conditions. This allows the algorithm to perform both detailed local optimization and effective global exploration during the search process, thereby improving the algorithm's convergence speed and solution quality. This provides more reliable support for the calculation of the critical permeability of new energy sources and enhances the accuracy and stability of the results.

[0100] Step 1022: Adaptive step size strategy is used to automatically scale the step size according to the change rate of the solution in the search phase.

[0101] It should be noted that in order to balance global exploration and local convergence accuracy, an adaptive step size strategy is introduced to automatically scale the step size according to the "search stage + solution change speed" to avoid oscillation or premature convergence. The update formula is:

[0102] ;

[0103] Where, is the adaptive step size factor.

[0104] It is understandable that the introduction of an adaptive step size strategy can automatically scale the step size based on the "search phase + solution change rate", that is, in different search phases, the step size is flexibly adjusted according to the actual change rate of the solution. When in the global exploration phase and the solution changes rapidly, the step size is appropriately increased so that the possible optimal solution can be searched more quickly within a larger range; and when entering the local convergence phase and the solution changes more slowly, the step size is reduced, thereby improving the accuracy of local convergence and more accurately approaching the optimal solution. This method of adaptively adjusting the step size can effectively improve computational efficiency and the accuracy of calculation results, and avoid problems such as low search efficiency or falling into local optimality due to a fixed step size.

[0105] Step 103: Use the improved fox hunting optimization algorithm to solve the new energy critical penetration rate optimization model to obtain the new energy critical penetration rate under various operating constraints.

[0106] See also Figure 2 In one embodiment, step 103 includes:

[0107] S31. Inputting the grid parameters into the improved fox hunting optimization algorithm according to the requirements of the objective function and the operation constraints to initialize the fox population position;

[0108] S32, calculating the fitness value of each fox, and selecting the dominant fox according to the optimal fitness function;

[0109] It should be noted that the fitness value of each fox is calculated, that is, the value of the objective function is optimized according to the optimal fitness function. Select the dominant fox .

[0110] S33, based on the position of the dominant fox and optimizing the position update formula through the adaptive step size strategy, the positions of other foxes are updated using the update formula;

[0111] S34, through the mixed perturbation mechanism, perform a perturbation operation on each fox individual with a preset probability p (a certain probability);

[0112] S35, perform constraint check on the updated fox solution to determine whether the constraints are satisfied; if not, use the penalty function to correct the objective function, if so, execute step S36;

[0113] It should be noted that if not, the penalty function is used to correct the objective function and recalculate the fitness value. The new dominant fox is selected again according to the optimal fitness function, and the above steps of updating the fox position, performing the perturbation operation, and checking the constraints are repeated until the updated fox solution meets the constraints. The fox population position that meets the objective function and operation constraint requirements is obtained, thereby providing basic data for the subsequent calculation of the critical penetration rate of new energy.

[0114] S36: Determine whether the preset termination condition is met. If so, terminate the calculation and output the optimal critical permeability solution. Otherwise, return to S32 to continue iteration.

[0115] The embodiment of the present invention provides a method for calculating the critical penetration rate of new energy. By constructing a multi-constrained nonlinear optimization model with the goal of maximizing the penetration rate of new energy, it fully considers the key constraints in the operation of the power system, effectively improving the authenticity and engineering applicability of the critical penetration rate assessment. A hybrid perturbation mechanism and an adaptive step-size strategy are introduced into the fox hunting optimization algorithm. The hybrid perturbation mechanism integrates Gaussian perturbation and Lévy flight to enhance search diversity and the ability to escape local optimality. The adaptive step-size strategy dynamically adjusts the individual search amplitude, taking into account both global exploration and local fine-tuning, thereby improving the algorithm's global optimization capability and stability. The improved algorithm, while satisfying multiple operating constraints, can quickly approach the maximum level of new energy access allowed by the system within a relatively small number of iterations. It has good convergence characteristics and computational efficiency, making it suitable for online evaluation and rapid response needs in large-scale power grid systems. It can provide technical support for the planning and scheduling strategy formulation of new energy access to the power grid.

[0116] The above is a new energy critical permeability calculation method provided in an embodiment of the present invention. The following is a new energy critical permeability calculation system provided in an embodiment of the present invention.

[0117] See also Figure 2 , a new energy critical permeability calculation system provided in an embodiment of the present invention includes:

[0118] A construction unit 201 is used to construct a new energy critical penetration rate optimization model with the maximum new energy penetration rate as the objective function and taking into account the operation constraints of power balance, generator output limit, new energy output, node voltage amplitude, line power flow and system frequency and voltage regulation capabilities;

[0119] An improving unit 202 is used to improve the fox hunting optimization algorithm by introducing a hybrid perturbation mechanism and an adaptive step size strategy;

[0120] The calculation unit 203 is used to solve the new energy critical permeability optimization model using the improved fox hunting optimization algorithm to obtain the new energy critical permeability under various operating constraints.

[0121] The embodiment of the present invention provides a new energy critical permeability calculation system. By constructing a multi-constrained nonlinear optimization model with the goal of maximizing new energy permeability, it fully considers the key constraints in power system operation and effectively improves the authenticity and engineering applicability of critical permeability assessment. A hybrid perturbation mechanism and an adaptive step-size strategy are introduced into the fox hunting optimization algorithm. The hybrid perturbation mechanism integrates Gaussian perturbation and Lévy flight to enhance search diversity and the ability to escape local optimality. The adaptive step-size strategy dynamically adjusts the individual search amplitude, taking into account both global exploration and local fine-tuning, thereby improving the algorithm's global optimization capability and stability. The improved algorithm, while satisfying multiple operating constraints, can quickly approach the maximum allowed new energy access level of the system within a relatively small number of iterations. It has good convergence characteristics and computational efficiency, making it suitable for online evaluation and rapid response needs in large-scale power grid systems. It can provide technical support for the planning and scheduling strategy formulation of new energy access to the power grid.

[0122] Furthermore, an embodiment of the present invention also provides a new energy critical permeability calculation device, the device including a processor and a memory:

[0123] The memory is used to store program code and transmit the program code to the processor;

[0124] The processor is configured to execute the steps of the new energy critical permeability calculation method as described in the above method embodiment according to the instructions in the program code.

[0125] Furthermore, an embodiment of the present invention also provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the new energy critical permeability calculation method described in the above method embodiment.

[0126] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0127] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0128] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0129] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0130] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0131] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A new energy critical permeability calculation method, characterized in that: include: Taking the maximum renewable energy penetration rate as the objective function and comprehensively considering the operational constraints of power balance, generator output limit, renewable energy output, node voltage amplitude, line flow, and system frequency and voltage regulation capabilities, an optimization model for renewable energy critical penetration rate is constructed. The fox hunting optimization algorithm is improved by introducing a hybrid perturbation mechanism and an adaptive step-size strategy. The improved fox hunting optimization algorithm is used to solve the new energy critical permeability optimization model to obtain the new energy critical permeability under various operating constraints.

2. The new energy critical permeability calculation method according to claim 1, characterized in that: The expression of the objective function is: ； Where, Contribute to new energy; is the total system load.

3. The new energy critical permeability calculation method according to claim 1, characterized in that: The fox hunting optimization algorithm is improved by introducing a hybrid perturbation mechanism and an adaptive step-size strategy, including: By combining Gaussian perturbation and Lévy flight mechanism as a hybrid perturbation mechanism, a perturbation operation is performed on each individual with a preset probability to obtain the fox's position update; The adaptive step size strategy allows the step size to be automatically scaled according to the speed of change of the solution during the search phase.

4. The new energy critical permeability calculation method according to claim 3 is characterized in that: The improved fox hunting optimization algorithm is used to solve the new energy critical permeability optimization model to obtain the new energy critical permeability under various operating constraints, including: S31, according to the requirements of the objective function and the operational constraints, inputting the grid parameters into the improved fox hunting optimization algorithm to initialize the fox population position; S32, calculating the fitness value of each fox, and selecting the dominant fox according to the optimal fitness function; S33, according to the position of the leading fox and by optimizing the position update formula through the adaptive step size strategy, the positions of other foxes are updated using the update formula; S34. Using the mixed disturbance mechanism, perform a disturbance operation on each fox individual with the preset probability; S35, perform constraint check on the updated fox solution to determine whether the constraints are satisfied; if not, use the penalty function to correct the objective function, if so, execute step S36; S36: Determine whether the preset termination condition is met. If so, terminate the calculation and output the optimal critical permeability solution. Otherwise, return to S32 to continue iteration.

5. A new energy critical permeability calculation system, characterized in that: include: A construction unit is used to construct a new energy critical penetration optimization model with the maximum new energy penetration rate as the objective function, and comprehensively consider the operational constraints of power balance, generator output limit, new energy output, node voltage amplitude, line power flow and system frequency and voltage regulation capabilities; Improvement unit, used to improve the fox hunting optimization algorithm by introducing a hybrid perturbation mechanism and an adaptive step size strategy; The calculation unit is used to solve the new energy critical permeability optimization model using the improved fox hunting optimization algorithm to obtain the new energy critical permeability under various operating constraint conditions.

6. The new energy critical permeability calculation system according to claim 5, characterized in that: The expression of the objective function is: ； Where, Contribute to new energy; is the total system load.

7. The new energy critical permeability calculation system according to claim 5, characterized in that: The improvement unit is specifically used for: By combining Gaussian perturbation and Lévy flight mechanism as a hybrid perturbation mechanism, a perturbation operation is performed on each individual with a preset probability to obtain the fox's position update; The adaptive step size strategy allows the step size to be automatically scaled according to the speed of change of the solution during the search phase.

8. The new energy critical permeability calculation system according to claim 7, characterized in that: The computing unit is specifically configured to: S31, according to the requirements of the objective function and the operational constraints, inputting the grid parameters into the improved fox hunting optimization algorithm to initialize the fox population position; S32, calculating the fitness value of each fox, and selecting the dominant fox according to the optimal fitness function; S33, according to the position of the leading fox and by optimizing the position update formula through the adaptive step size strategy, the positions of other foxes are updated using the update formula; S34. Using the mixed disturbance mechanism, perform a disturbance operation on each fox individual with the preset probability; S35, perform constraint check on the updated fox solution to determine whether the constraints are satisfied; if not, use the penalty function to correct the objective function, if so, execute step S36; S36: Determine whether the preset termination condition is met. If so, terminate the calculation and output the optimal critical permeability solution. Otherwise, return to S32 to continue iteration.

9. A new energy critical permeability calculation device, characterized in that: The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the new energy critical permeability calculation method according to any one of claims 1 to 4 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store program code, and the program code is used to execute the new energy critical permeability calculation method according to any one of claims 1 to 4.

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