Optimal power distribution method for large-scale photovoltaic power station to respond to secondary frequency modulation regulation of power grid

Abstract

The invention discloses an optimal power distribution method for a large-scale photovoltaic power station to respond to secondary frequency modulation regulation of a power grid. The method comprises the following steps: clustering inverters in the large-scale photovoltaic power station by using an FCM algorithm; decomposing a large-scale photovoltaic power station problem into top layer control and bottom layer control according to a clustering result; summarizing the adjusting capabilities of all inverters in the clustering group, and distributing power in top layer control according to the summarized adjusting capability of the whole group; optimization is carried out in top layer control by using a FATA algorithm; transmitting an optimal solution obtained in top layer control to bottom layer control, and distributing AGC signals to each inverter in a group according to a proportion; and outputting the optimal adjustment power input of each inverter, monitoring the response condition of the inverter in real time, and performing adjustment and optimization in the next control period according to feedback data. According to the method, the optimization complexity of bottom layer control can be reduced, the calculation efficiency of large-scale power station AGC scheduling is improved, and larger reactive power reserve is provided while the power deviation is minimized.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic generation control (AGC) and hierarchical power control of photovoltaic power plants, and in particular to an optimal power allocation method for large-scale photovoltaic power plants in response to secondary frequency modulation of power grids. Background Art

[0002] With the global emphasis on sustainable energy and environmental protection, countries have vigorously developed renewable energy, among which solar photovoltaic (PV) power generation has been widely used due to its advantages such as no pollution, no emissions and renewable. According to statistics, as of 2023, the global photovoltaic power generation capacity has exceeded 1,000 GW, and the average annual growth rate has exceeded 40% in the past decade. However, the randomness and volatility of photovoltaic power generation have brought great challenges to the real-time power balance of the power grid.

[0003] In order to maintain the stability and reliability of the power grid, more and more photovoltaic power plants are participating in automatic generation control (AGC) to respond to grid demand by dynamically adjusting output power. However, in large-scale photovoltaic power plants, AGC faces several major problems. First, photovoltaic power plants not only need to respond quickly to AGC signals to maintain the stability of grid frequency and voltage, but also need to consider other operating objectives, such as maintaining and optimizing reactive power regulation capabilities. This multi-objective optimization requirement makes the AGC scheduling problem more complicated. Second, traditional AGC scheduling methods mainly focus on the scheduling optimization of a single control area or a small number of power generation units, while in large-scale photovoltaic power plants, hundreds of inverters need to be adjusted simultaneously. The response characteristics and capabilities of different inverters vary greatly, and how to efficiently coordinate these inverters to achieve the best overall performance is also a difficulty. At the same time, AGC scheduling needs to be completed in a very short time (such as within a few seconds) to ensure a rapid response to grid changes. Traditional centralized control methods may not be able to meet such high real-time requirements, especially when dealing with a large number of inverters.

[0004] At present, some studies have tried to improve the AGC performance of photovoltaic power plants through control algorithms. For example, some technologies based on model predictive control are used to improve the adjustment flexibility and response speed of photovoltaic inverters; some intelligent control methods (such as neural fuzzy control and deep reinforcement learning) try to optimize the operating performance of photovoltaic power plants through more intelligent algorithms. However, these methods usually focus on the optimization of small-scale or single inverters, and do not consider the overall optimization of distributed inverters in large-scale photovoltaic power plants. Summary of the invention

[0005] The present invention proposes an optimal power allocation method for a large-scale photovoltaic power station in response to secondary frequency modulation of the power grid, and decomposes the overall power control problem of a large-scale photovoltaic power station into two levels: top-level control and bottom-level control. The top-level control is responsible for allocating AGC signals among multiple inverter groups. This level uses the fuzzy C-means (FCM) clustering algorithm to group inverters, thereby reducing the dimension and complexity of optimization, and optimizes the AGC response performance and reactive power reserve through the Fata morgana algorithm (FATA). Once the top-level control determines the power allocation of each clustering group, the bottom-level control is responsible for specific power allocation within each photovoltaic power station group. The bottom-level control, according to the instructions of the top-level control, further distributes the AGC signal allocated to each group to each inverter in the group.

[0006] The purpose of the present invention is to realize an optimal power allocation method for a large-scale photovoltaic power station in response to secondary frequency regulation of a power grid through the following technical scheme, comprising the following steps:

[0007] 1) Use the FCM algorithm to cluster inverters in all large-scale photovoltaic power plants and divide inverters with similar regulation characteristics into multiple groups;

[0008] 2) Based on the clustering results of the inverters, the large-scale photovoltaic power station problem is decomposed into top-level control and bottom-level control. The top-level control is responsible for the power allocation between multiple clustering groups, and the bottom-level control is responsible for the power allocation within each group. The top-level and bottom-level control models are established;

[0009] 3) Obtaining the AGC signal of the current photovoltaic power station, the AGC signal is provided by the power grid dispatching center and is used to indicate the required power adjustment amount;

[0010] 4) The photovoltaic power station calculates the regulation capacity of each inverter based on the current operating status and regulation capacity of the inverter;

[0011] 5) Summarize the regulation capabilities of all inverters in each cluster group, obtain the regulation capability of the entire group by summing up the regulation capabilities of each inverter, and allocate the power in the top-level control according to the regulation capability of the entire group, to ensure that the power allocation of each group is reasonable and can meet the overall regulation requirements of the system;

[0012] 6) Use the FATA algorithm to optimize in the top-level control and initialize the parameters of the FATA algorithm;

[0013] 7) The optimal solution obtained in the top-level control is transmitted to the bottom-level control, and the AGC signal is proportionally distributed to each inverter in the group;

[0014] 8) Output the optimal regulated power input of each inverter, monitor the response of the inverter in real time, and adjust and optimize in the next control cycle based on the feedback data.

[0015] Furthermore, in step 1), the FCM algorithm collects characteristic data of all inverters in the photovoltaic power station:

[0016]

[0017] Among them, U in formula (1) ij Indicates the membership degree of the i-th inverter to the j-th cluster; ||x i -v j || represents the Euclidean distance between the i-th inverter and the j-th cluster center; m is the fuzzification coefficient, which is used to control the fuzziness of the membership degree. Usually m>1. When m increases, the fuzziness of the membership degree also increases. Formula (2) is used to calculate the distance between the inverter and the cluster center v j The Euclidean distance is used to measure the similarity between the inverter and the cluster center. The smaller the distance, the more similar they are. In formula (2), D is the dimension of the feature vector; c is the number of clusters; x id represents the dth eigenvalue of the ith inverter; v jd represents the dth eigenvalue of the jth inverter; the membership matrix of the inverter is calculated by formula (1) and formula (2), so as to determine the cluster group to which each inverter belongs.

[0018] Furthermore, in step 2), the task of the top-level control is to optimize the power distribution to meet the dual-objective optimization model, i.e., minimizing the power deviation and maximizing the reactive power reserve. The optimization problem of the top-level control is expressed as:

[0019]

[0020] f=w1·f1+w2·(1-f2)(5) (5)

[0021] In formula (3), f1 represents the sum of power deviations, and the purpose is to minimize this value so that the output power of the photovoltaic power station is as close as possible to the AGC signal requirement; ΔP in (k) represents the AGC signal input to the entire PV power station at time k, which also represents the total power adjustment required by the grid dispatching center; n represents the total number of cluster groups in the PV power station; k represents the current time step; h represents the time step index, ranging from k to k+τ; in formula (4), f2 represents the sum of the maximum reactive power reserves of all cluster groups, and the goal is to maximize this value to provide sufficient reactive power regulation capability; Q max,iis the maximum reactive power that the i-th cluster group can provide, which indicates the amount of reactive power that each group can contribute under the current circumstances; in formula (5), w1 and w2 are weight factors used to adjust the importance of the two objectives. These weight factors reflect the relative attention of the PV power station to minimizing power deviation and maximizing reactive power reserve under specific operating conditions.

[0022] Furthermore, in step 3), the AGC signal is a step signal and a continuous step signal.

[0023] Furthermore, in step 5), the regulation capabilities of all inverters in each cluster group are summarized, and the regulation capability of the entire group is obtained by summing the regulation capabilities of each inverter, as shown in formula (10) and formula (11):

[0024]

[0025] Where ΔP out,i (k) represents the total power regulation capability of the i-th cluster group at time k, Q out,i (k) represents the total reactive power regulation capability of the i-th cluster group at time k, m i is the number of inverters in the i-th cluster group.

[0026] Further, step 6) specifically includes: setting the population size and the maximum number of iterations, initializing the light propagation parameters Para1 and Para2, selecting the mirage light filtering strategy to control the balance between global search and local search; using the definite integral method to calculate the integral area S of the population quality to determine whether each individual enters the next search process; executing the light propagation strategy to calculate the individual quality factor P and the individual fitness factor q, as shown in the following formula:

[0027]

[0028] Among them, P measures the quality of individuals in the light propagation group. In the power allocation optimization of large-scale photovoltaic power plants, it indicates the relative performance of a power allocation scheme in the optimization target. S is the area of ​​the fitness curve calculated based on the definite integral principle, which indicates the overall performance of the power allocation scheme. S best

[0029] and S worst Represent the integrated areas of the best and worst solutions respectively; q represents the individual fitness factor, which measures the performance of the power allocation scheme of each cluster group in responding to the grid AGC signal;

[0030] fit i represents the fitness of the i-th cluster group, reflecting the effectiveness of the group in meeting the goals of power deviation and reactive power reserve; fit best and fitworst are the best and worst fitness values ​​in the population respectively; the light propagation strategy adjusts the individual position through refraction and reflection process to optimize the power distribution; in the optimization of power distribution of large-scale photovoltaic power plants, the light propagation strategy represents the iterative adjustment process of power distribution, and ensures that the photovoltaic power plant achieves a balance between different goals by exploring a better distribution scheme, as shown in the following formula:

[0031] x next =x best +x·Para1 (14)

[0032] x next =x rand +x f ·Para2 (15)

[0033] Para1=tan(θ) (16)

[0034]

[0035] The refraction strategy is used for global search, parameter Para1 is responsible for global search, and parameter Para2 is responsible for local search; best is the current best individual; x rand represents a random individual in the population, represents the reference power allocation scheme; tan(θ) represents the refraction angle.

[0036] Furthermore, in step 7), for the bottom-level control model, the bottom-level control further distributes the AGC signal allocated to each cluster group to each inverter in the group according to the output of the top-level control. The allocation strategy is based on the regulation capability of the inverter, as shown in formula (18):

[0037]

[0038] Where ΔP in,iw (k) is the power input allocated to the wth inverter in the ith cluster group, ΔP max,iw (k) and ΔP min,iw (k) are the maximum and minimum power regulation ranges of the inverter respectively.

[0039] Furthermore, in step 8), in each control cycle, based on the power output and feedback data of each inverter, the power deviation is evaluated in real time. If the system detects that the inverter output deviates too much from the set power, the top-level control optimizes by updating the power allocation scheme for the next cycle. The specific optimization goal is to minimize the power deviation and ensure reactive power reserve, as shown in the following formula:

[0040]

[0041] Among them, ΔP in (k) represents the deviation of the input power of the photovoltaic power station at time step k; ΔP out,iw (k) represents the output power deviation of the wth inverter in the ith cluster group at time step k, and n represents the number of cluster groups. This optimization goal ensures that each inverter can achieve more accurate power regulation in the next cycle according to the feedback data.

[0042] The present invention enables the photovoltaic power station to respond quickly and accurately to changes in the AGC signal by minimizing the power deviation of the photovoltaic power station, ensuring that the output power of the photovoltaic power station can follow the AGC command as closely as possible, thereby maintaining the frequency stability of the power grid. In addition, reactive power reserves are crucial for regulating the grid voltage, especially when the grid voltage needs to be adjusted quickly to cope with instantaneous load changes. The present invention optimizes reactive power reserves through the rapid responsiveness of the FATA algorithm, which can improve the voltage regulation capability of the system and ensure the voltage stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of an optimal power allocation method for a large-scale photovoltaic power station in response to secondary frequency regulation of a power grid according to an embodiment of the present invention;

[0044] Figure 2 It is a model diagram of an optimal power allocation method for a large-scale photovoltaic power station in response to secondary frequency modulation control of a power grid in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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 described embodiments are 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 creative work are within the scope of protection of the present invention.

[0046] In this example, a large-scale photovoltaic power station including a large number of inverters is taken as an example. These inverters need to work together to quickly and accurately adjust the output power under different lighting conditions and grid demands, and respond to AGC signals to maintain the frequency stability and voltage balance of the grid. The optimal power allocation method for a large-scale photovoltaic power station in response to secondary frequency regulation of the grid proposed in the present invention can improve the AGC response performance of the photovoltaic power station, ensure that the power station can quickly track the AGC signal, optimize the reactive power reserve, and improve the voltage regulation capability of the system. Its flow chart is as follows Figure 1 As shown, the specific implementation steps are as follows:

[0047] Step 1: Use the Fuzzy C-Means (FCM) algorithm to cluster the inverters in large-scale photovoltaic power plants, and divide the inverters with similar regulation characteristics into multiple groups to simplify the overall optimization process. FCM is a clustering method based on fuzzy sets, which allows each data point to belong to multiple clusters to different degrees (fuzzy membership). FCM can handle the differences between inverter characteristics more flexibly, so that clustering results that are more suitable for actual application scenarios may be obtained. In this step, the FCM algorithm collects the characteristic data of all inverters in the photovoltaic power plant.

[0048]

[0049] Among them, U in formula (1) ij Indicates the membership degree of the i-th inverter to the j-th cluster; ||x i -v j || represents the Euclidean distance between the i-th inverter and the j-th cluster center; m is the fuzzification coefficient, which is used to control the fuzziness of the membership degree. Usually m>1. When m increases, the fuzziness of the membership degree also increases. Formula (2) is used to calculate the distance between the inverter and the cluster center v j The Euclidean distance is used to measure the similarity between the inverter and the cluster center. The smaller the distance, the more similar they are. In formula (2), D is the dimension of the feature vector; c is the number of clusters; x id represents the dth eigenvalue of the ith inverter; v jd represents the dth eigenvalue of the jth inverter. The membership matrix of the inverter is calculated by formula (1) and formula (2), so as to determine the cluster group to which each inverter belongs.

[0050] Step 2: Based on the clustering results of the inverters, the large-scale photovoltaic power station problem is decomposed into top-level control and bottom-level control. The top-level control is responsible for the power allocation between multiple clustering groups, while the bottom-level control is responsible for the power allocation within each group. The top-level and bottom-level control models are established. The control model is as follows: Figure 2 As shown. Among them, the task of top-level control is to optimize power distribution to meet the dual-objective optimization model, that is, to minimize power deviation and maximize reactive power reserve. The optimization problem of top-level control can be expressed as:

[0051]

[0052] f=w1·f1+w2·(1-f2) (5)

[0053] In formula (3), f1 represents the sum of power deviations, and the purpose is to minimize this value so that the output power of the photovoltaic power station is as close as possible to the AGC signal requirement; ΔP in(k) represents the AGC signal input to the entire PV power station at time k, which also represents the total power adjustment required by the grid dispatching center; n represents the total number of cluster groups in the PV power station; k represents the current time step;

[0054] h represents the time step index, ranging from k to k+τ. In formula (4), f2 represents the sum of the maximum reactive power reserves of all cluster groups. The goal is to maximize this value to provide sufficient reactive power regulation capability; Q max,i is the maximum reactive power that the i-th cluster group can provide, which indicates the amount of reactive power that each group can contribute under the current circumstances. In formula (5), w1 and w2 are weight factors used to adjust the importance of the two objectives. These weight factors reflect the relative attention of the PV power station to minimizing power deviation and maximizing reactive power reserve under specific operating conditions.

[0055] According to the above optimization objectives, the top-level control converts the total AGC signal ΔP in Assigned to each cluster group i, the allocation result is limited by the power regulation capability of each group, as shown in formula (6):

[0056] ΔP min,i (k)≤ΔP in,i (k)≤ΔP max,i (k), i=1,2,...,n (6)

[0057] Where ΔP min,i (k) represents the minimum adjustment power range of the i-th cluster group at time k;

[0058] ΔP max,i (k) represents the maximum adjustment power range of the i-th cluster group at time k; ΔP in,i (k)

[0059] is the AGC signal assigned to the i-th cluster group, indicating the power adjustment task assigned to this group by the top-level control.

[0060] Step 3: Get the AGC signal of the current PV power station. The AGC signal is provided by the grid dispatch center and indicates the required power adjustment amount. The AGC signal is used as input for subsequent power allocation and optimization steps. The AGC signal is used as an input parameter of the top-level control to guide the overall power regulation of the PV power station. The AGC signal can be expressed in different forms, such as step signals and continuous step signals. After receiving the AGC signal, the PV power station will calculate the regulation capacity of each cluster group based on the current operating status and regulation capacity of the inverter.

[0061] Step 4: Calculate the regulation capability of each inverter at the current moment to determine the maximum and minimum power output ranges that it can adjust in response to the AGC signal. These capabilities are limited by the inverter's current operating state and device limitations, such as maximum and minimum power output ranges.

[0062] The regulation capability of each inverter is as follows:

[0063]

[0064] ΔP in (k) = P in (k)-P in (k-1) (9)

[0065] Where P in,iw (k-1) is the current input power of the inverter at time (k-1); ΔP in (k) represents the total power regulation. By using the above formula, the regulation capacity of each inverter can be calculated and reasonable power allocation can be made accordingly. This method ensures that the photovoltaic power station can quickly and accurately adjust the output power when the grid demand changes, thereby maintaining the stability and reliability of the grid.

[0066] Step 5: Summarize the regulation capabilities of all inverters in each cluster group, and obtain the regulation capability of the entire group by summing the regulation capabilities of each inverter, as shown in formula (10) and formula (11). These summarized regulation capabilities are used for power allocation in the top-level control to ensure that the power allocation of each group is reasonable and can meet the overall regulation requirements of the system.

[0067]

[0068] Where ΔP out,i (k) represents the total power regulation capability of the i-th cluster group at time k. out,i (k) represents the total reactive power regulation capability of the i-th cluster group at time k. i is the number of inverters in the i-th cluster group.

[0069] Step 6: Use the FATA algorithm to optimize in the top-level control and initialize the algorithm parameters. This step aims to optimize the power deviation and reactive power reserve of the photovoltaic power station. Set the population size and the maximum number of iterations, initialize the light propagation parameters Para1 and Para2, and select the mirage light filtering strategy to control the balance between global search and local search. Use the definite integral method to calculate the integral area S of the population quality and decide whether each individual enters the next step.

[0070] One-step search process. Execute the light propagation strategy and calculate the individual quality factor P and individual fitness factor q, as shown in the following formula:

[0071]

[0072] Among them, P measures the quality of individuals in the light propagation group. In the power allocation optimization of large-scale photovoltaic power plants, it indicates the relative performance of a power allocation scheme in the optimization target. S is the area of ​​the fitness curve calculated based on the definite integral principle, which indicates the overall performance of the power allocation scheme. S best

[0073] and S worst Represent the integrated areas of the best and worst solutions respectively; q represents the individual fitness factor, which measures the performance of the power allocation scheme of each cluster group in responding to the grid AGC signal;

[0074] fit i represents the fitness of the i-th cluster group, reflecting the effectiveness of the group in meeting the goals of power deviation and reactive power reserve. best and fit worst are the best and worst fitness values ​​in the population, respectively.

[0075] Light propagation strategies adjust individual positions through refraction and reflection processes to optimize power distribution.

[0076] In the optimization of power allocation in large-scale photovoltaic power plants, the light propagation strategy represents the iterative adjustment process of power allocation. By exploring a better allocation scheme, the photovoltaic power plant can achieve a balance between different goals, as shown in the following formula:

[0077] x next =x best +x·Para1 (14)

[0078] x next =x rand +x f ·Para2 (15)

[0079] Para1=tan(θ) (16)

[0080]

[0081] The refraction strategy is used for global search, parameter Para1 is responsible for global search, and parameter Para2

[0082] Responsible for local search. x best is the current best individual; x randrepresents a random individual in the population, represents the reference power allocation scheme, and tan(θ) represents the refraction angle. These parameters play a key role in the power allocation optimization of photovoltaic power plants, helping to strike a balance between global and local searches, ensuring that the inverter group can effectively respond to AGC signals and optimize the power regulation of the power grid.

[0083] Step 7: The optimal solution obtained in the top-level control is transmitted to the bottom-level control, and the AGC signal is proportionally distributed to each inverter in the group. For the bottom-level control model, the bottom-level control further distributes the AGC signal allocated to each cluster group to each inverter in the group according to the output of the top-level control. The allocation strategy is based on the regulation capability of the inverter, as shown in formula (18):

[0084]

[0085] Where ΔP in,iw (k) is the power input allocated to the wth inverter in the ith cluster group, ΔP max,iw (k) and ΔP min,iw (k) are the maximum and minimum power regulation ranges of the inverter respectively.

[0086] Step 8: Output the optimal regulated power input for each inverter, monitor the response of the inverter in real time, and make necessary adjustments and optimizations in the next control cycle based on the feedback data. In each control cycle, the power deviation is evaluated in real time based on the power output and feedback data of each inverter. If the system detects that the inverter output deviates too much from the set power, the top-level control will optimize by updating the power allocation plan for the next cycle. The specific optimization goal is to minimize the power deviation and ensure reactive power reserve, as shown in the following formula

[0087]

[0088] Among them, ΔP in (k) represents the deviation of the input power of the photovoltaic power station at time step k; ΔP out,iw (k) represents the output power deviation of the wth inverter in the i-th cluster group at time step k. n represents the number of cluster groups. This optimization goal ensures that each inverter can achieve more accurate power regulation in the next cycle based on the feedback data.

[0089] The specific model diagram of the above steps is as follows Figure 2 shown.

[0090] Compared with the prior art, the present invention has the following advantages and effects:

[0091] (1) The present invention proposes a clustering hierarchical power control method based on the mirage algorithm, which can simultaneously optimize the AGC response performance and reactive power reserve. Compared with the traditional method, this method has better optimization effect and provides greater adjustment flexibility for the dynamic operation of the power station.

[0092] (2) Compared with the existing optimization scheduling method of large-scale photovoltaic power stations, by introducing the FCM clustering algorithm based on adjusting performance parameters, the present invention reduces the optimization complexity of the underlying control and significantly improves the computational efficiency of AGC scheduling of large-scale power stations.

[0093] (3) The optimal power allocation method for a large-scale photovoltaic power station in response to secondary frequency regulation of the power grid proposed in the present invention finds a balance between two optimization objectives, thereby minimizing power deviation and providing a larger reactive power reserve.

[0094] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An optimal power allocation method for a large-scale photovoltaic power station in response to secondary frequency regulation of a power grid, characterized in that: The following steps are involved: 1) Use the FCM algorithm to cluster inverters in all large-scale photovoltaic power plants and divide inverters with similar regulation characteristics into multiple groups; 2) Based on the clustering results of the inverters, the large-scale photovoltaic power station problem is decomposed into top-level control and bottom-level control. The top-level control is responsible for the power allocation between multiple clustering groups, and the bottom-level control is responsible for the power allocation within each group. The top-level and bottom-level control models are established; 3) Obtaining the AGC signal of the current photovoltaic power station, the AGC signal is provided by the power grid dispatching center and is used to indicate the required power adjustment amount; 4) The photovoltaic power station calculates the regulation capacity of each inverter based on the current operating status and regulation capacity of the inverter; 5) Summarize the regulation capabilities of all inverters in each cluster group, obtain the regulation capability of the entire group by summing up the regulation capabilities of each inverter, and allocate the power in the top-level control according to the regulation capability of the entire group, to ensure that the power allocation of each group is reasonable and can meet the overall regulation requirements of the system; 6) Use the FATA algorithm to optimize in the top-level control and initialize the parameters of the FATA algorithm; 7) The optimal solution obtained in the top-level control is transmitted to the bottom-level control, and the AGC signal is proportionally distributed to each inverter in the group; 8) Output the optimal regulated power input of each inverter, monitor the response of the inverter in real time, and adjust and optimize in the next control cycle based on the feedback data.

2. The optimal power allocation method for a large-scale photovoltaic power station in response to secondary frequency modulation of a power grid according to claim 1, characterized in that: In step 1), the FCM algorithm collects characteristic data of all inverters in the PV power station: Among them, U in formula (1) ij Indicates the membership degree of the i-th inverter to the j-th cluster; ||x i -v j || represents the Euclidean distance between the i-th inverter and the j-th cluster center; m is the fuzzification coefficient, which is used to control the fuzziness of the membership degree. Usually m>1. When m increases, the fuzziness of the membership degree also increases. Formula (2) is used to calculate the distance between the inverter and the cluster center v j The Euclidean distance is used to measure the similarity between the inverter and the cluster center. The smaller the distance, the more similar they are. In formula (2), D is the dimension of the feature vector; c is the number of clusters; x id represents the dth eigenvalue of the ith inverter; v jd represents the d-th eigenvalue of the j-th inverter; The membership matrix of the inverter is calculated by formula (1) and formula (2), so as to determine the cluster group to which each inverter belongs.

3. The optimal power allocation method for a large-scale photovoltaic power station in response to secondary frequency modulation of a power grid according to claim 1, characterized in that: In step 2), the task of the top-level control is to optimize the power distribution to meet the dual-objective optimization model, i.e., minimizing the power deviation and maximizing the reactive power reserve. The optimization problem of the top-level control is expressed as: f=w1·f1+w2·(1-f2)(5) (5) In formula (3), f1 represents the sum of power deviations, and the purpose is to minimize this value so that the output power of the photovoltaic power station is as close as possible to the AGC signal requirement; ΔP in (k) represents the AGC signal input to the entire PV power station at time k, which also represents the total power adjustment required by the grid dispatching center; n represents the total number of cluster groups in the PV power station; k represents the current time step; h represents the time step index, ranging from k to k+τ; in formula (4), f2 represents the sum of the maximum reactive power reserves of all cluster groups, and the goal is to maximize this value to provide sufficient reactive power regulation capability; Q max,i is the maximum reactive power that the i-th cluster group can provide, which indicates the amount of reactive power that each group can contribute under the current circumstances; in formula (5), w1 and w2 are weight factors used to adjust the importance of the two objectives. These weight factors reflect the relative attention of the PV power station to minimizing power deviation and maximizing reactive power reserve under specific operating conditions.

4. The optimal power allocation method for a large-scale photovoltaic power station in response to secondary frequency modulation of a power grid according to claim 1, characterized in that: In step 3), the AGC signal is a step signal and a continuous step signal.

5. The optimal power allocation method for a large-scale photovoltaic power station in response to secondary frequency modulation of a power grid according to claim 1, characterized in that: In step 5), the regulation capabilities of all inverters in each cluster group are summarized, and the regulation capability of the entire group is obtained by summing the regulation capabilities of each inverter, as shown in formula (10) and formula (11): Where ΔP out,i (k) represents the total power regulation capability of the i-th cluster group at time k, Q out,i (k) represents the total reactive power regulation capability of the i-th cluster group at time k, m i is the number of inverters in the i-th cluster group.

6. The optimal power allocation method for a large-scale photovoltaic power station in response to secondary frequency modulation of a power grid according to claim 1, characterized in that: Step 6) specifically includes: setting the population size and the maximum number of iterations, initializing the light propagation parameters Para1 and Para2, selecting the mirage light filtering strategy to control the balance between global search and local search; using the definite integral method to calculate the integral area S of the population quality to determine whether each individual enters the next search process; executing the light propagation strategy to calculate the individual quality factor P and the individual fitness factor q, as shown in the following formula: Among them, P measures the quality of individuals in the light propagation group. In the power allocation optimization of large-scale photovoltaic power plants, it indicates the relative performance of a power allocation scheme in the optimization target. S is the area of ​​the fitness curve calculated based on the definite integral principle, which indicates the overall performance of the power allocation scheme. S best and S worst Represent the integrated areas of the best and worst solutions respectively; q represents the individual fitness factor, which measures the performance of the power allocation scheme of each cluster group in responding to the grid AGC signal; fit i represents the fitness of the i-th cluster group, reflecting the effectiveness of the group in meeting the goals of power deviation and reactive power reserve; fit best and fit worst are the best and worst fitness values ​​in the population respectively; the light propagation strategy adjusts the individual position through refraction and reflection process to optimize the power distribution; in the optimization of power distribution of large-scale photovoltaic power plants, the light propagation strategy represents the iterative adjustment process of power distribution, and ensures that the photovoltaic power plant achieves a balance between different goals by exploring a better distribution scheme, as shown in the following formula: x next =x best +x·For1 (14) x next =x rand +x f ·Para2 (15) Para1=tan(θ) (16) The refraction strategy is used for global search, parameter Para1 is responsible for global search, and parameter Para2 Responsible for local search; x best is the current best individual; x rand represents a random individual in the population, represents the reference power allocation scheme; tan(θ) represents the refraction angle.

7. The optimal power allocation method for a large-scale photovoltaic power station in response to secondary frequency modulation of a power grid according to claim 1, characterized in that: In step 7), for the bottom-level control model, the bottom-level control further distributes the AGC signal allocated to each cluster group to each inverter in the group according to the output of the top-level control. The allocation strategy is based on the regulation capability of the inverter, as shown in formula (18): Where ΔP in,iw (k) is the power input allocated to the wth inverter in the ith cluster group, ΔP max,iw (k) and ΔP min,iw (k) are the maximum and minimum power regulation ranges of the inverter respectively.

8. The optimal power allocation method for a large-scale photovoltaic power station in response to secondary frequency modulation of a power grid according to claim 1, characterized in that: In step 8), in each control cycle, based on the power output and feedback data of each inverter, the power deviation is evaluated in real time. If the system detects that the inverter output deviates too much from the set power, the top-level control optimizes by updating the power allocation scheme for the next cycle. The specific optimization goal is to minimize the power deviation and ensure reactive power reserve, as shown in the following formula: Among them, ΔP in (k) represents the deviation of the input power of the photovoltaic power station at time step k; ΔP out,iw (k) represents the output power deviation of the wth inverter in the ith cluster group at time step k, and n represents the number of cluster groups. This optimization goal ensures that each inverter can achieve more accurate power regulation in the next cycle according to the feedback data.

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