A pumped storage unit participates in power grid regulation parameter identification optimization method and system

Abstract

The application belongs to the technical field of optimizing power grid dispatching, and provides a pumped storage unit participating in power grid regulation parameter identification optimization method and system, and the technical scheme is: input excitation is input into the pumped storage unit regulation system, the obtained output is input into the fitness function, and the output of the fitness function is obtained through the identifier to obtain the identification parameter variable; wherein, the construction process of the identifier is: a double-layer structure is adopted, the top layer searches individuals by using a mycoparasitic optimization algorithm, the bottom layer searches individuals by using a swallow optimization algorithm, position updating is carried out through the swallow optimization algorithm, the obtained optimal solution is uploaded to the search individual of the mycoparasitic optimization algorithm for storage, and the top layer search individual continues to iteratively select the optimal solution; the identification parameter variable is input into the system to be identified to obtain the parameter identification result, the search efficiency and accuracy are improved, and it is of great significance to guarantee the safe and stable operation of the unit.

Description

Technical Field

[0001] This invention belongs to the field of power grid dispatching technology, and in particular relates to a method and system for identifying and optimizing parameters for pumped storage units participating in power grid regulation. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Due to the inherent uncertainty and anti-peak-shaving characteristics of new energy power generation, the available flexible regulation resources for the power grid are severely insufficient. Therefore, the requirements for the safe and stable operation of existing flexible power sources in the power grid system have increased significantly.

[0004] In power grid systems, pumped storage power stations excel in peak shaving, frequency regulation, and phase regulation, serving as reliable system regulators and backup power sources, effectively improving power quality and operational safety. Compared to conventional hydropower units, pumped storage units are characterized by frequent start-up and shutdown and operating condition transitions, as well as complex hydraulic transient processes. Therefore, their regulation systems exhibit strong nonlinearity and complex engineering application characteristics, making it challenging to extract accurate models and specific parameters for the regulation systems. Summary of the Invention

[0005] To address at least one of the technical problems mentioned above, this invention provides a method and system for identifying and optimizing parameters for pumped storage units participating in grid regulation. It proposes an improved and optimized method for identifying parameters of pumped storage units participating in grid regulation systems and constructs an improved hybrid slime mold-based Skyhawk algorithm parameter identification strategy for pumped storage unit regulation systems. This facilitates obtaining an accurate model of the pumped storage unit regulation system and is of great significance for ensuring the safe and stable operation of the units.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The first aspect of this invention provides a method for identifying and optimizing parameters for pumped storage units participating in grid regulation, comprising the following steps:

[0008] Obtain input stimulus;

[0009] The input excitation is fed into the pumped storage unit regulation system, the output is fed into the fitness function, and the output of the fitness function is passed through the identifier to obtain the identification parameter variables.

[0010] The construction process of the identifier is as follows: a two-layer structure is adopted, with the top layer searching for individuals by the slime mold optimization algorithm and the bottom layer searching for individuals by the eagle optimization algorithm. The position is updated by the eagle optimization algorithm, and the obtained optimal solution is uploaded to the search individual of the slime mold optimization algorithm for storage. The top layer search individual continues to iterate to select the optimal solution.

[0011] By inputting the identification parameter variables into the system to be identified, the result is a state variable of the unit speed, guide vane opening and pressure pipeline that is close to the actual system output.

[0012] Furthermore, the construction process of the identifier also includes: combining the characteristics of slime mold optimization algorithm and eagle optimization algorithm, selecting the search agent of eagle optimization algorithm to conduct local exploration in the early stage, and the search agent of slime mold optimization algorithm to shrink the search area in the later stage.

[0013] Furthermore, the position update formula for slime molds in the slime mold optimization algorithm is as follows:

[0014]

[0015] In the formula, X b X(t) represents the location of the individual with the highest odor concentration in the t-th iteration; vb ranges from [-a, a]; vc decreases linearly from 1 to 0; t is the iteration number; X(t) represents the location of the slime mold; X A (t) and X B (t) represents two randomly selected individuals; W represents the weight of the slime mold; LB and UB are the lower and upper limits of the search dimension, respectively; rand and r are random numbers from 0 to 1; the value range of z is [0, 0.1].

[0016] Furthermore, the formula for the top-level search individual to continue iteratively selecting the optimal solution is:

[0017]

[0018] In the formula, Z best t To obtain the optimal position, Z M t The average location of the current solution, Z R t Let be a random solution taken in the t-th iteration, x and y represent the flight methods used by the eagle when hunting prey, α and δ are invariant constants with values ​​of 0.1, QF is the mass function of the balanced search strategy, G1 is the simulated actions based on the eagle chasing prey, decaying from 2 to 0, t is the iteration number, and Z is a random solution taken in the t-th iteration. ij t Levy(D) represents the position of the i-th search agent in the top layer corresponding to the j-th search agent in the bottom layer, Levy(D) is the distribution function, and G2 is the flight slope of the Skyhawk.

[0019] Furthermore, the method also includes updating the fitness function, the updating method being:

[0020] The identification parameters are input into the system to be identified. The system to be identified obtains the output response through experimental simulation. The output response of the system to be identified is then compared with the output response of the actual system in the fitness function. The better the system to be identified matches the actual system, the smaller the value of the fitness function. If the fitness function reaches the minimum value, the actual system and the system to be identified have the highest degree of matching.

[0021] Furthermore, the input excitation includes unit speed, guide vane opening, and pressure pipeline flow rate.

[0022] Furthermore, the method also includes evaluating the parameter identification results, using the following evaluation metrics:

[0023]

[0024]

[0025] In the formula, θ i For the parameter vector of the actual system, Let m be the parameter vector of the system to be identified, and m be the number of samples of the system state variables.

[0026] A second aspect of the present invention provides a system for identifying and optimizing parameters for pumped storage units participating in grid regulation, comprising:

[0027] The stimulus acquisition module is used to acquire input stimuli.

[0028] The parameter identification module is used to input the input excitation into the pumped storage unit regulation system, input the obtained output into the fitness function, and obtain the identified parameter variables by passing the output of the fitness function through the identifier.

[0029] The construction process of the identifier is as follows: a two-layer structure is adopted, with the top layer searching for individuals by the slime mold optimization algorithm and the bottom layer searching for individuals by the eagle optimization algorithm. The position is updated by the eagle optimization algorithm, and the obtained optimal solution is uploaded to the search individual of the slime mold optimization algorithm for storage. The top layer search individual continues to iterate to select the optimal solution.

[0030] By inputting the identification parameter variables into the system to be identified, the result is a state variable of the unit speed, guide vane opening and pressure pipeline that is close to the actual system output.

[0031] A third aspect of the present invention provides a computer-readable storage medium.

[0032] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method for identifying and optimizing parameters for pumped storage units participating in grid regulation.

[0033] A fourth aspect of the present invention provides a computer device.

[0034] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the above-described method for identifying and optimizing parameters for pumped storage units participating in grid regulation.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] The improved hybrid slime mold-eagle algorithm for pumped storage unit regulation systems proposed in this invention combines the characteristics of slime mold optimization and eagle optimization. This improved algorithm successfully solves the problem of premature convergence to local solutions during the search process and achieves the goal of finding the global optimum more effectively. Through a hierarchical search strategy, it can consider both global and local information during the search process, improving search efficiency and accuracy, which is of great significance for ensuring the safe and stable operation of the unit.

[0037] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0038] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0039] Figure 1 This is a schematic diagram of the overall process of parameter identification provided in the embodiments of the present invention;

[0040] Figure 2 This is a schematic diagram illustrating the construction process of the identifier provided in an embodiment of the present invention. Detailed Implementation

[0041] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0042] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0043] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0044] As mentioned in the background section of this invention, in power grid systems, pumped storage power stations, compared with conventional hydropower units, have the characteristics of frequent start-up and shutdown and operating condition switching, and complex hydraulic transition processes. Therefore, the regulation system has strong nonlinearity and complex engineering application characteristics, making it difficult to extract an accurate model and specific parameters of the regulation system.

[0045] Therefore, this invention provides an optimized method for parameter identification of pumped-storage units participating in power grid regulation systems. It constructs an improved hybrid slime mold-eagle algorithm parameter identification strategy for pumped-storage unit regulation systems. Combining the characteristics of slime mold optimization and Eagle optimization, the improved hybrid slime mold-eagle algorithm successfully solves the defect of premature convergence to local solutions during the search process and achieves the goal of more effectively finding the global optimum. Through a hierarchical search strategy, both global and local information can be considered during the search process, improving search efficiency and accuracy. This is beneficial for obtaining an accurate model of the pumped-storage unit regulation system and is of great significance for ensuring the safe and stable operation of the unit.

[0046] Example 1

[0047] like Figure 1 As shown in the figure, this embodiment provides an optimized method for identifying parameters of pumped storage units participating in grid regulation systems, including the following steps:

[0048] Step 1: Obtain input stimulus;

[0049] Step 2: Input the excitation into the pumped storage unit regulation system, input the obtained output into the fitness function, and pass the output of the fitness function through the identifier to obtain the parameter variable to be identified;

[0050] Step 3: Input the excitation and identification parameters into the system to be identified;

[0051] Step 4: Update the fitness function by pairing the output vector of the system to be identified with the output vector of the pumped storage unit regulation system.

[0052] To more clearly illustrate the technical solution of the present invention, further explanation is provided through specific embodiments.

[0053] In step 1, in this embodiment, the input excitation includes unit speed, guide vane opening, and pressure pipeline flow rate, which are state variables of the pumped storage unit.

[0054] These three state variables are simultaneously input as stimuli into both the actual system and the system to be identified. The system to be identified consists of a controller, a servo system, a water turbine, a pressure pipeline water intake system, and a generator, simulating the actual system. However, under the same parameter changes, the output of the actual system differs significantly from the model output of the system to be identified. Therefore, a fitness function is needed to design different error output weights based on the significance of the deviation.

[0055] like Figure 2 As shown, in step 2, the process of constructing the identifier is as follows:

[0056] A two-layer structure is adopted. The top layer uses the Slime Mould Algorithm (SMA) to search for individuals, while the bottom layer uses the Aquila Optimizer (AO) to search for individuals. Combining the characteristics of SMA and AO, the AO search agent is selected to explore the local area in the early stage, and the SMA search agent narrows the search area in the later stage. The top layer contains M search agents, corresponding to M groups in the top layer, and each group in the bottom layer contains N search agents. The identification algorithm of the identifier first updates the position through the bottom-layer AO algorithm, and then selects the optimal solution obtained from each group and uploads it to the top-layer SMA search agent for storage. Then, the top-layer search agent continues to iterate to select the optimal solution. The optimal solution is the parameter in the system to be identified that is closer to the parameter in the actual system. For example, in identifying the parameters of the pumped storage unit regulation system, the parameter values ​​with the smallest error compared with the actual system are shown in Table 2.

[0057] The SMA algorithm process is as follows:

[0058] X the location of the slime mold i The definition is shown in formula (1):

[0059]

[0060] In the formula, K is the dimension of the current position of the slime mold; i is the i-th individual in the slime mold population.

[0061] Slime molds can approach food based on odors in the air, and use formula (2) to simulate their contraction pattern:

[0062]

[0063] In the formula: X b X(t) represents the location of the individual with the highest odor concentration in the t-th iteration; vb ranges from [-a, a]; vc decreases linearly from 1 to 0; t is the iteration number; X(t) represents the location of the slime mold; X A (t) and X B (t) represents two randomly selected individuals; W represents the weight of the slime mold;

[0064] a and p are calculated using the following formulas:

[0065]

[0066] p=tanh|S(i)-DF| (4)

[0067] In the formula, max_t is the maximum number of iterations; S(i) is the fitness value; and DF represents the optimal fitness value.

[0068] The update formula for the weight W of slime mold in equation (2) is shown in equation (5):

[0069]

[0070] In the formula, condition represents the case where the fitness value is in the first half; r is a random number between 0 and 1; bF represents the currently obtained best fitness value; wF represents the currently obtained worst fitness value; and SmellIndex represents the sorted sequence.

[0071] Therefore, the formula for updating the position of slime molds is as follows:

[0072]

[0073] In the formula, LB and UB are the lower and upper limits of the search dimension, respectively; rand and r are random numbers from 0 to 1; and z ranges from [0, 0.1].

[0074] The optimization formula for the Tianying optimization algorithm is as follows:

[0075]

[0076] X2(t+1)=X best (t)×Levy(D)+X R (t)+(yx)*rand (8)

[0077] X3(t+1)=[X best (t)-X M (t)]*α-rand+[(UB-LB)*rand+LB]*δ (9)

[0078] X4(t+1)=QF*X best (t)-(G1*X(t)*rand)-G2*Levy(D)+rand*G1 (10)

[0079] Formulas (7)-(10) give four different methods that eagles use when hunting prey. These methods allow them to flexibly change their hunting methods according to the different characteristics of the prey and environmental conditions. This flexibility enables eagles to capture prey more effectively, improves their hunting success rate, and demonstrates good exploration and global search capabilities.

[0080] Formula (7) is the first stage of the Skyhawk optimization. In order to expand the exploration stage, the Skyhawk expands the search range by flying vertically, selects the best hunting area, and determines the spatial area where the prey is located.

[0081] Formula (8) is the second stage in Skyhawk optimization, which is to narrow down the search stage. In this stage, Skyhawk discovers the area where the prey is located, narrowly explores the selected area of ​​the target prey, and prepares for the attack.

[0082] Formula (9) is the third stage of Skyhawk optimization, which is the expansion development stage. In this stage, Skyhawk accurately specifies the area where the prey is located, uses the selected target area to approach the prey and attack it.

[0083] Formula (10) is the fourth stage in the Skyhawk optimization. In order to narrow down the development stage, in this stage, the Skyhawk approaches the prey and launches an attack on the prey with a certain degree of randomness.

[0084] The purpose of using these four models is to flexibly change one's hunting methods according to the different characteristics of the prey and environmental conditions, with the aim of increasing the success rate of hunting.

[0085] Although swarm intelligence optimization algorithms possess excellent exploration and global search capabilities, their reliance on group behavior makes them prone to getting trapped in local optima, preventing them from finding the global optimum. This invention proposes an improved hybrid slime mold-eagle algorithm for pumped storage unit regulation systems. Combining the characteristics of slime mold optimization and eagle optimization, the improved hybrid slime mold-eagle algorithm successfully overcomes the defect of premature convergence to local solutions during the search process and achieves the goal of finding the global optimum more effectively. Through a hierarchical search strategy, it can consider both global and local information during the search process, improving search efficiency and accuracy.

[0086] By incorporating the slime mold algorithm into the Eagle algorithm and integrating the slime mold's position update into the four stages of the eagle's hunting process, the position update formula for the searched individual is obtained, as shown in equation (11):

[0087]

[0088] In the formula, Z best t To obtain the optimal position; Z M tThe average position of the current solution; Z R t Let be a random solution taken in the t-th iteration; x and y represent the flight methods used by the eagle when hunting prey; α and δ are invariant constants with values ​​of 0.1; QF is the mass function of the balanced search strategy; G1 is the various actions simulated by the eagle when chasing prey, decaying from 2 to 0; t is the number of iterations; Z ij t Levy(D) represents the position of the i-th search agent in the top layer corresponding to the j-th search agent in the bottom layer; Levy(D) is the distribution function; G2 is the flight slope of the Skyhawk.

[0089] In step 2, the fitness function maximizes the accuracy and reliability of parameter identification, thereby providing a reliable estimate of the system parameters.

[0090] The weights of different error outputs are designed based on the significance of the deviation, and the improved objective function is defined as shown in Equation (12):

[0091]

[0092] In the formula, These are the parameter variables that need to be identified; Z is the output vector of the system to be identified; j (i) = [xt, y, q] t [] represents the actual system output vector; m represents the number of samples of the system state variables; n represents the number of response outputs; and w = [w1, w2, w3] represents the weight vector.

[0093] The identification parameters are obtained through the identifier and input into the system to be identified. The system to be identified obtains the output response through experimental simulation and then compares it with the output response of the actual system in the fitness function. The better the system to be identified matches the actual system, the smaller the value of the fitness function. If the fitness function reaches the minimum value, it indicates that the actual system and the system to be identified have the highest degree of matching, and it also indicates that the HSMAAO identifier is good enough.

[0094] The evaluation indexes for the improved mixed slime mold Tianying algorithm parameter identification strategy of the pumped storage unit regulation system are shown in equations (13) and (14):

[0095]

[0096]

[0097] In the formula: θ i For the parameter vector of the actual system; Let be the parameter vector of the system to be identified.

[0098] Experimental verification

[0099] A simulation experiment was conducted using an improved hybrid slime mold eagle algorithm for the pumped storage unit regulation system to verify the effectiveness of the proposed method. The simulation experiment was performed under the no-load condition of the pumped storage unit regulation system. The parameters of the pumped storage unit regulation system under different operating conditions are shown in Table 1. The actual system and the system to be identified have the same input, and the output is used to calculate the objective function. The parameters to be identified in the simulation experiment are Kp, Ki, Ta, and Twt. Based on engineering experience, the parameter values ​​of the actual system were set to θ = [1, 0.3, 9.52, 2.33]. The simulation experiment time was 100 s, and the sampling time was 0.01 s. Each parameter identification experiment was run independently 10 times, and the average value was taken.

[0100] Table 1 Parameters of the pumped storage unit regulation system under different operating conditions

[0101]

[0102] The parameters of the pumped storage unit regulation system were identified using the improved hybrid slime mold Eagle algorithm, slime mold optimization algorithm, and Eagle optimization algorithm. The accuracy of parameter identification achieved by different algorithms was evaluated by the parameter error index. The identification results of different methods under no-load conditions are compared in Table 2. In addition, the fitness function and APE average value are shown in Table 3.

[0103] Table 2 Comparison of identification results of different methods under no-load conditions

[0104]

[0105] Table 3. Fitness functions and average APE values ​​of different methods under no-load conditions.

[0106]

[0107] As shown in Table 2, the improved hybrid slime mold-eagle algorithm exhibits superior performance under no-load conditions in the pumped storage unit's regulating system. Firstly, its identification accuracy is significantly higher than other methods, enabling it to more accurately represent the characteristics of the actual system. Secondly, regarding parameter errors, the improved hybrid slime mold-eagle algorithm demonstrates the smallest error, proving the accuracy and reliability of its identification process. Furthermore, Table 3 shows that the average fitness function and APE are also significantly better than the slime mold optimization algorithm and the eagle optimization algorithm, further confirming the superiority of the improved hybrid slime mold-eagle algorithm.

[0108] Example 2

[0109] This embodiment provides a system for identifying and optimizing parameters for pumped storage units participating in grid regulation, including:

[0110] The stimulus acquisition module is used to acquire input stimuli.

[0111] The parameter identification module is used to input the input excitation into the pumped storage unit regulation system, input the obtained output into the fitness function, and obtain the identified parameter variables by passing the output of the fitness function through the identifier.

[0112] The construction process of the identifier is as follows: a two-layer structure is adopted, with the top layer searching for individuals by the slime mold optimization algorithm and the bottom layer searching for individuals by the eagle optimization algorithm. The position is updated by the eagle optimization algorithm, and the obtained optimal solution is uploaded to the search individual of the slime mold optimization algorithm for storage. The top layer search individual continues to iterate to select the optimal solution.

[0113] By inputting the identification parameter variables into the system to be identified, the result is a state variable of the unit speed, guide vane opening and pressure pipeline that is close to the actual system output.

[0114] Example 3

[0115] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the above-described method for identifying and optimizing parameters for pumped storage units participating in grid regulation.

[0116] Example 4

[0117] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the above-described method for identifying and optimizing parameters for pumped storage units participating in grid regulation.

[0118] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0119] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0120] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0122] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0123] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying and optimizing parameters for pumped storage units participating in power grid regulation, characterized in that, Includes the following steps: Obtain input stimulus; The input excitation is fed into the pumped storage unit regulation system, the output is fed into the fitness function, and the output of the fitness function is passed through the identifier to obtain the identification parameter variables. The construction process of the identifier is as follows: a two-layer structure is adopted, with the top layer searching for individuals by the slime mold optimization algorithm and the bottom layer searching for individuals by the eagle optimization algorithm. The position is updated by the eagle optimization algorithm, and the obtained optimal solution is uploaded to the search individual of the slime mold optimization algorithm for storage. The top layer search individual continues to iterate to select the optimal solution. The process of constructing the identifier also includes: combining the characteristics of slime mold optimization algorithm and eagle optimization algorithm, selecting the search agent of eagle optimization algorithm to conduct local exploration in the early stage, and the search agent of slime mold optimization algorithm to shrink the search area in the later stage, introducing slime mold algorithm into eagle algorithm, and integrating the position update of slime mold into the four stages of eagle hunting prey to obtain the position update formula of search individual; By inputting the identification parameter variables into the system to be identified, the results obtained are the actual system output of the unit speed, guide vane opening, and pressure pipeline state variables.

2. The method for identifying and optimizing grid regulation parameters of pumped storage units as described in claim 1, characterized in that, The formula for updating the position of slime mold in the slime mold optimization algorithm is: In the formula, Indicates the first t The individual location with the highest odor concentration in the next iteration; vb The range of values ​​for is [ a , a ]; vc Linearly decreasing from 1 to 0; t This represents the number of iterations. Indicates the location of the slime mold; and They are two randomly selected individuals; W Indicates the weight of slime molds; LB and UB These are the lower and upper limits of the search dimension, respectively. rand and r A random number between 0 and 1; z The value range is [0, 0.1].

3. The method for identifying and optimizing parameters for pumped storage units participating in grid regulation as described in claim 1, characterized in that, The formula for the top-level search individual to continue iteratively selecting the optimal solution is: In the formula, To obtain the best position, The average location of the current solution For the first t A random solution is selected in the next iteration. and This refers to the flight method used by eagles when hunting prey. and It is an invariant constant. To balance the quality function of the search strategy, To simulate various movements of a hawk chasing its prey, t For the number of iterations, Representing the bottom layer j The top-level search agent corresponds to the first... i The location of the search agent, The distribution function, This represents the flight slope of the eagle.

4. The method for identifying and optimizing grid regulation parameters of pumped storage units as described in claim 1, characterized in that, The method further includes updating the fitness function, and the updating method is as follows: The identification parameters are input into the system to be identified. The system to be identified obtains the output response through experimental simulation. The output response of the system to be identified is then compared with the output response of the actual system in the fitness function. The better the system to be identified matches the actual system, the smaller the value of the fitness function. If the fitness function reaches the minimum value, the actual system and the system to be identified have the highest degree of matching.

5. The method for identifying and optimizing grid regulation parameters of pumped storage units as described in claim 1, characterized in that, The input excitations include unit speed, guide vane opening, and pressure pipeline flow rate.

6. The method for identifying and optimizing parameters for pumped storage units participating in grid regulation as described in claim 1, characterized in that, The method also includes evaluating the parameter identification results, using the following evaluation metrics: In the formula, For the parameter vector of the actual system, Let be the parameter vector of the system to be identified. m This represents the number of samples for the system state variables.

7. A system for identifying and optimizing grid regulation parameters for pumped storage units, characterized in that, include: The stimulus acquisition module is used to acquire input stimuli. The parameter identification module is used to input the input excitation into the pumped storage unit regulation system, input the obtained output into the fitness function, and obtain the identified parameter variables by passing the output of the fitness function through the identifier. The construction process of the identifier is as follows: a two-layer structure is adopted, with the top layer searching for individuals by the slime mold optimization algorithm and the bottom layer searching for individuals by the eagle optimization algorithm. The position is updated by the eagle optimization algorithm, and the obtained optimal solution is uploaded to the search individual of the slime mold optimization algorithm for storage. The top layer search individual continues to iterate to select the optimal solution. The process of constructing the identifier also includes: combining the characteristics of slime mold optimization algorithm and eagle optimization algorithm, selecting the search agent of eagle optimization algorithm to conduct local exploration in the early stage, and the search agent of slime mold optimization algorithm to shrink the search area in the later stage, introducing slime mold algorithm into eagle algorithm, and integrating the position update of slime mold into the four stages of eagle hunting prey to obtain the position update formula of search individual; By inputting the identification parameter variables into the system to be identified, the results obtained are the actual system output of the unit speed, guide vane opening, and pressure pipeline state variables.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the method for identifying and optimizing the parameters for pumped storage units participating in grid regulation as described in any one of claims 1-6.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the method for identifying and optimizing the parameters for pumped storage units participating in grid regulation as described in any one of claims 1-6.

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