A multi-type inverter interface resource frequency parameter coordination method and device

Abstract

The application provides a multi-type inverter interface resource frequency parameter coordination method and device, which comprises the following steps: S1, according to the frequency response characteristic difference of each type of inverter interface resource, a system frequency response model and a frequency parameter optimization model distinguishing each type of inverter interface resource are built; S2, a non-constrained fitness function is constructed based on the frequency parameter optimization model, the non-constrained fitness function is put into the Nelder-Mead algorithm, the system frequency response model is iteratively optimized, and the optimal frequency parameter of each type of inverter after aggregation, including virtual inertia and droop coefficient, is solved; and S3, according to the rated power capacity of each type of inverter interface resource, the optimized and aggregated frequency parameter is distributed to each type of inverter interface resource station. The application can improve the frequency active support level of the inverter interface resource, simultaneously consider the operation economy of the inverter interface resource, and improve the system frequency stability.

Description

Technical Field

[0001] This invention relates to the field of power system operation control technology, and in particular to a method and apparatus for coordinating and setting frequency regulation parameters of interface resources for multiple types of inverters. Background Technology

[0002] The proportion of asynchronous power sources in the current power grid has increased significantly, and small-scale grid operation has become the new normal. Furthermore, inter-regional power support capabilities have decreased, and the system's inertial response and frequency regulation capabilities have weakened considerably. The instability modes and weak points differ significantly from those of the current power grid. When large disturbances such as large-capacity DC bipolar blocking occur, the maximum frequency change rate and maximum frequency deviation of the power grid become difficult to accurately determine, leading to a sharp increase in the risk of low-frequency load shedding.

[0003] Existing control strategies for various resource types mainly employ fixed parameters to provide the system with inertia support capabilities. However, in practical applications, different types of resources have their own unique dynamic characteristics (e.g., in photovoltaic power generation, changes in light intensity can lead to unstable output power of the photovoltaic power station; moreover, photovoltaic power generation is greatly affected by weather conditions, and its output power will decrease significantly on cloudy days, rainy days, or at night). If fixed reserve ratios and frequency regulation parameters are used to uniformly regulate the frequency of various resource types, it will result in poor frequency recovery speed of the entire power system, long frequency regulation time, and a tendency for the frequency response process to become unstable.

[0004] Therefore, there is still a lack of clear guidance on how to effectively coordinate important frequency regulation parameters such as virtual inertia and primary frequency regulation coefficient of multiple types of resources. At present, it is impossible to guide the access system review opinions to put forward requirements on the support capabilities of inverter-type interface resources. In order to meet the current operation and control needs and improve the frequency stability of the system, the industry still needs to conduct in-depth research on frequency control technology and parameter optimization configuration strategies for coordinating multiple types of resources. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method and device for coordinating and setting frequency regulation parameters of inverter interface resources of multiple types. The method accurately optimizes the power frequency parameters of inverter interface resource frequency control in regional power grid, which can effectively improve the active frequency support level of inverter interface resources, while taking into account the operating economy of inverter interface resources and improving system frequency stability.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical method: a method for coordinating and tuning frequency regulation parameters of interface resources for multiple types of inverters, comprising:

[0007] Step S1: Based on the differences in frequency response characteristics of various types of inverter interface resources, build a system frequency response model and frequency regulation parameter optimization model to distinguish between various types of inverter interface resources.

[0008] Step S2: Construct an unconstrained fitness function based on the frequency regulation parameter optimization model, incorporate the unconstrained fitness function into the Nelder-Mead algorithm, iteratively optimize the frequency response model of the drive system, and solve for the optimal frequency regulation parameters after aggregation of various types of inverters, including the virtual inertia coefficient and droop coefficient.

[0009] Step S3: Based on the rated power capacity of each type of inverter interface resource, allocate the optimized and aggregated frequency regulation parameters to each type of inverter interface resource site.

[0010] Furthermore, in the system frequency response model built in step S1, the various types of inverters include distributed photovoltaic (PV), energy storage, DC, and flexible DC inverters. Distributed PV participates in the frequency response through virtual inertial control and droop control, and its maximum power increment for frequency regulation meets the load shedding constraints. DC frequency limiting control adopts PI control including dead zone and limiting links, and its output power meets the limiting constraints. The flexible DC transmission system participates in the frequency regulation of the AC grid by adding a frequency control link in the active power control loop. The energy storage system participates in the frequency response through droop control and virtual inertial control, and its operation meets the state of charge constraints and power constraints.

[0011] Furthermore, the frequency regulation parameter optimization model takes minimizing the total reserve capacity requirement of multi-type inverter interface resources as the objective function, and uses unit reserve capacity constraints, system frequency security constraints, and physical limits of frequency regulation parameters as constraints.

[0012] Furthermore, step S2, which uses the Nelder-Mead algorithm to solve for the optimal frequency modulation parameters, is as follows:

[0013] S201, based on the requirements of power system frequency regulation control, sets the reserve rates for distributed photovoltaic, energy storage, DC, and flexible DC power. Virtual inertia coefficient With droop coefficient Define the upper and lower limits of the range and specify the precision threshold; initialize the decision variables within the feasible region, and... Generated in Euclidean space The initial simplex is formed by the vertices. Boundary constraint correction and special coupling constraint verification are performed on each vertex, and the objective function value of each vertex is calculated.

[0014] S202, call the Nelder-Mead algorithm to perform geometric transformation operations on the simplex based on hierarchical decision logic. Based on the objective function value of the trial point, select to perform reflection, expansion, external contraction, internal contraction or compression operations to update the simplex. In each iteration, first calculate the objective function value corresponding to each vertex.

[0015] S203, based on the convergence determination of multi-criteria fusion, determines whether the iteration meets the termination condition. If it does, the algorithm stops; if not, it continues to iterate until the simplex gradually shrinks to the optimal region of the objective function.

[0016] Furthermore, in step S201: determine the optimization parameter vector, let the parameter vector to be optimized be... , Indicates the first One parameter to be optimized. The number of parameters to be optimized. express A 3D real vector space; define variable boundary constraints and set lower bound vectors for each optimization parameter. and upper bound vector , and They are respectively The lower and upper bound vectors constitute the feasible region. ;exist In Euclidean space, the simplex is formed by... The simplest convex polyhedron composed of affine-independent vertices is used to generate the first vertex in the feasible region using a Latin hypercube sampling or uniform random sampling strategy. The first vertex's... Each component satisfy:

[0017] (1)

[0018] In the formula, express Uniform distribution over the interval; These are independent and identically distributed random numbers;

[0019] Perform special coupling constraint verification: Based on the physical characteristics of the inverter interface resource frequency modulation parameters, under specific conditions, the relevant parameters are forcibly set to zero, and the remaining parameters are generated based on the coordinate direction perturbation method. For each vertex, using the first vertex as a reference, a step-size perturbation is applied along the positive direction of each coordinate axis to construct the remaining vertices, as shown in the following formula:

[0020] (2)

[0021] (3)

[0022] (4)

[0023] In the formula, , The first vertex and the second vertex respectively One vertex; For the first The perturbation step size in each direction; For the first Standard unit vectors in each coordinate direction; Step size factor;

[0024] Perform boundary constraint correction: Perform a boundary projection operation on each generated vertex, as follows:

[0025] (5)

[0026] In the formula, For any vertex, the first One variable to be optimized;

[0027] Calculate the objective function value for each vertex and record the number of function evaluations. ;

[0028] For the simplex The vertices are sorted in ascending order according to the objective function value, and then renumbered so that:

[0029] (6)

[0030] In the formula, The vector of the current optimal vertex. The vector of the second most inferior vertex. The vector of the worst vertex; Indicates ranking in The objective function value of the vertex;

[0031] Calculate the remaining vertices except the worst vertex. The geometric centroids of the vertices are given by the following formula:

[0032] (7)

[0033] In the formula, The geometric center of mass is the current point of view. The average position of the better vertices reflects the concentration trend of the region with lower objective function values ​​and serves as a reference point for subsequent reflection, expansion, and contraction operations.

[0034] Furthermore, in step S202:

[0035] Reflection operation: Mirror the current worst vertex along its centroid, and reflect the point. The calculation formula is:

[0036] (8)

[0037] In the formula, The reflection coefficient controls the distance of the reflection point relative to the center of gravity. When the reflection point and the worst vertex are symmetrical about the centroid, when At that time, the reflection point moves further away from the worst vertex, when At that time, the reflection point is close to the center of gravity;

[0038] Based on the comparison between the objective function value at the reflection point and the objective function value at the current simplex vertex, different decision branches are entered:

[0039] Case A: If equation (9) is satisfied, the reflection operation is moderately successful, and the worst vertex is replaced by the reflection point;

[0040] (9)

[0041] Case B: If equation (10) is satisfied, it means that the reflection direction is a favorable descent direction, and the extension operation is performed;

[0042] (10)

[0043] Case C: If equation (11) is satisfied, perform the external contraction operation;

[0044] (11)

[0045] Case D: If equation (12) is satisfied, perform the internal contraction operation;

[0046] (12).

[0047] Furthermore, in the expansion operation, the expansion point... The calculation formula is:

[0048] (13)

[0049] In the formula, The expansion coefficient is used to determine the expansion result: if ,accept replace ;like ,accept replace ;

[0050] External contraction point during external contraction operation The calculation formula is:

[0051] (14)

[0052] In the formula, The contraction coefficient is used to determine the external contraction result: if The external contraction operation was successful; external contraction point accepted. Replace the worst vertex Proceed to step S203; if Perform the compression operation;

[0053] Internal contraction point during internal contraction operation The calculation formula is:

[0054] (15)

[0055] In the formula, the result of internal contraction is determined as follows: if The internal contraction operation was successful; internal contraction point accepted. Replace the worst vertex Proceed to step S203; if The internal contraction operation failed, so a compression operation is performed.

[0056] In the compression operation, the entire simplex is shrunk towards the optimal vertex, as shown in the following equation:

[0057] (16)

[0058] In the formula, Let be the compressibility factor. After compression, the volume of the simplex is reduced to 1 / 3 of its original volume. times.

[0059] Furthermore, in step S203, a convergence determination strategy based on multi-criteria fusion is adopted. When any one of the following conditions is met, the algorithm stops and outputs the optimal solution sought at the current iteration number. ;

[0060] 1) Maximum number of iterations criterion: If the current iteration number is... The algorithm terminates when the preset maximum value is reached.

[0061] 2) Function evaluation frequency criterion: If the cumulative function evaluation frequency... The algorithm terminates when the preset upper limit is reached.

[0062] 3) Convergence criterion for objective function value:

[0063] (17)

[0064] (18)

[0065] In the formula, To represent all The average of the objective function values ​​of each vertex; if the standard deviation of the objective function values ​​of all vertices... or range Less than the function value tolerance threshold This indicates that the simplex has shrunk to a region where the objective function values ​​are nearly equal, the algorithm has converged, and the iteration terminates.

[0066] 4) Vertex position convergence criterion:

[0067] (19)

[0068] In the formula, The maximum Euclidean distance is defined as follows: if it is less than the position tolerance threshold... This indicates that the simplex has shrunk to a very small spatial region, and the algorithm has converged.

[0069] 5) Continuous No Improvement Criterion: Record the improvement amount of the optimal objective function value. As shown in the following formula:

[0070] (20)

[0071] In the formula, Indicates the first The currently found global optimal objective function value after the iteration.

[0072] If the improvement amount in M ​​consecutive iterations is less than the preset improvement amount threshold If none of the above termination conditions are met, return to simplex vertex sorting and centroid calculation to continue iterating.

[0073] Preferably, in step S3, the optimized and aggregated frequency regulation parameters are allocated to the various types of inverter interface resource sites according to the rated power capacity of each type of inverter interface resource, as shown in the following formula:

[0074] (twenty one)

[0075] In the formula, This refers to the collection of all inverters, including distributed photovoltaic, energy storage, DC, and flexible DC. Indicates assignment to the first Frequency regulation parameters of the inverter; The type is The optimal frequency modulation parameters; For the first The rated power capacity of each inverter interface resource station.

[0076] The present invention also provides a device for coordinating and tuning frequency regulation parameters of interface resources of multiple types of inverters. The device includes a microprocessor and a memory connected to each other. The memory stores a computer program. The microprocessor executes the computer program to implement the aforementioned method for coordinating and tuning frequency regulation parameters of interface resources of multiple types of inverters.

[0077] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a microprocessor, implements the aforementioned method for coordinating and tuning frequency modulation parameters of interface resources for multiple types of inverters.

[0078] The present invention also provides a computer program product, which includes a computer program that, when executed by a microprocessor, implements the aforementioned method for coordinating and adjusting the frequency modulation parameters of the interface resources of multiple types of inverters.

[0079] Compared to traditional fixed-parameter methods, the frequency regulation parameter coordination and tuning method for multi-type inverter interface resources provided in this invention not only constructs a system frequency response model that distinguishes between different types of inverter interface resources, but also combines system frequency response simulation with Nelder-Mead optimization. This allows for the simultaneous determination of the optimal virtual inertia and droop parameters, overcoming the limitations of traditional fixed-parameter methods. This adaptive exploration and development strategy can effectively navigate complex parameter spaces while maintaining solution quality, and provides better frequency support, effectively ensuring system stability under various emergency scenarios. Furthermore, this invention provides system operators with a practical frequency regulation parameter coordination and tuning method, enhancing grid stability as renewable energy integration increases, and can be extended to more complex system configurations. Attached Figure Description

[0080] Figure 1 This is a flowchart of the method for coordinating and adjusting the frequency regulation parameters of interface resources for multiple types of inverters proposed in this invention;

[0081] Figure 2 This is a flowchart illustrating the process of solving the optimal frequency regulation parameters for interface resources of multiple types of inverters based on the Nelder-Mead algorithm in this invention.

[0082] Figure 3 This is a schematic diagram of the power system in an embodiment of the present invention;

[0083] Figure 4 The following are comparison charts of frequency curves before and after parameter optimization under the expected fault in the embodiments of the present invention (in the figure, (a) is a comparison chart of frequency curves before and after parameter optimization under AC tie line trip fault in scenario 1; (b) is a comparison chart of frequency change rate curves before and after parameter optimization under AC tie line trip fault in scenario 1; (c) is a comparison chart of frequency curves before and after parameter optimization under AC tie line trip fault in scenario 2; (d) is a comparison chart of frequency change rate curves before and after parameter optimization under AC tie line trip fault in scenario 2). Detailed Implementation

[0084] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to embodiments and accompanying drawings. The content mentioned in the embodiments is not intended to limit the present invention.

[0085] like Figure 1 As shown, a method for coordinating and tuning frequency regulation parameters of interface resources for multiple types of inverters includes:

[0086] Step S1: Based on the differences in frequency response characteristics of various types of inverter interface resources, build a system frequency response model and frequency regulation parameter optimization model to distinguish between various types of inverter interface resources.

[0087] S101, analyze the differences in frequency response characteristics of inverter interface resources due to the heterogeneity of control strategies, build a refined system frequency response model that distinguishes between multiple types of inverter interface resources such as distributed photovoltaic, energy storage, flexible DC and conventional DC, and incorporate traditional system frequency response models to construct a system frequency response model that considers multiple types of inverter interface resources.

[0088] 1) Distributed photovoltaic

[0089] Considering the participation of distributed photovoltaic (PV) power sources with PD-VIC in the frequency response, when the system experiences an active power surge, the system frequency deviates. Distributed PV inverters participate in the frequency response through virtual inertial control and droop control loops. These two control strategies can be used individually or in combination to achieve better frequency regulation. Proportional and derivative elements of the frequency deviation are introduced to simulate the primary frequency regulation and inertial response characteristics of a synchronous generator. Considering the response delay characteristics of the PV inverter, as shown in the following equation:

[0090] (twenty two)

[0091] In the formula, This refers to the output power modulation amount of distributed photovoltaic systems. The droop factor for distributed photovoltaic power generation; The virtual inertia coefficient for distributed photovoltaic systems; This refers to the deviation of the system's inertial center frequency. This is the response time constant of the photovoltaic inverter; For the Laplace operator.

[0092] When photovoltaic (PV) systems are operating at reduced load, a certain power margin must be reserved as a frequency regulation reserve. That is, the maximum power increment of PV systems participating in frequency regulation must meet the constraints of the reduced load operation, as shown in the following formula:

[0093] (twenty three)

[0094] (twenty four)

[0095] In the formula, For frequency regulation reserve power, its practical meaning is the difference between the maximum power point tracking power and the initial operating power of the photovoltaic system, and it can also be defined by the load shedding ratio; For distributed photovoltaic (PV) systems, the load shedding reserve rate is required. For maximum power point tracking power; This represents the initial operating power of the photovoltaic system.

[0096] 2) DC (i.e., conventional DC)

[0097] DC Frequency Limiting Control (FLC) is an additional control function in high-voltage direct current (HVDC) transmission systems used to support AC grid frequency stability. The control strategy of DC FLC is similar to the primary frequency regulation of a synchronous machine. FLC control employs a PI control structure with dead-time and limiting elements. When the system frequency deviation exceeds the set dead-time, the controller participates in system frequency regulation by adjusting the DC transmission power. The power modulation amount is shown in the following formula:

[0098] (25)

[0099] In the formula, The power modulation amount of the FLC control output; For FLC integral control coefficients, also known as FLC virtual inertia coefficients; This is the FLC proportional control factor, also known as the FLC droop factor; For FLC control, the delay factor is used. This is the dead-time function, defined as follows:

[0100] (26)

[0101] In the formula, This is the frequency dead zone threshold.

[0102] At the same time, the FLC output power must meet the limiting constraint, as shown in the following formula:

[0103] (27)

[0104] In the formula, and These represent the upper and lower limits of the FLC output power, respectively.

[0105] 3) Flexible DC

[0106] Consider a flexible DC transmission system that participates in AC grid frequency regulation by adding a frequency control loop to the active power control loop. When the AC system frequency deviates, the frequency deviation generates an additional power command via a droop control loop. This command, superimposed on the active power reference value, regulates the converter output power. Considering the converter response delay, the power modulation amount is shown in the following formula: (28)

[0107] In the formula, Additional power modulation amount for flexible DC; This refers to the flexible DC frequency-power droop control coefficient. is the response time constant of the flexible DC converter.

[0108] The flexible DC power control inner loop uses a PI controller, which The shaft current reference value is:

[0109] (29)

[0110] In the formula, For the flexible DC power control inner loop output Shaft current reference value; , These are the proportional control coefficient and integral control coefficient of the inner loop of the flexible DC power control, which are also the flexible DC droop coefficient and virtual inertia coefficient, respectively. This is a reference value for the active power of a flexible DC converter; This represents the actual output active power of the flexible DC converter.

[0111] 4) Energy storage

[0112] Energy storage systems, due to their rapid power response and flexible bidirectional regulation characteristics, can quickly respond to frequency deviations. Energy storage systems participate in frequency response through droop control and virtual inertia control; the relationship between power modulation and frequency deviation is shown in the following equation:

[0113] (30)

[0114] In the formula, The output power modulation amount for energy storage; This represents the droop factor for energy storage; The virtual inertia coefficient for energy storage; This is the energy storage response time constant.

[0115] Energy storage systems must meet the state of charge (SOC) requirements to operate. The constraints and power constraints are as follows:

[0116] (31)

[0117] (32)

[0118] In the formula, State of charge of the energy storage system; and They are respectively The lower and upper limits of allowed operation; This refers to the rated power of the energy storage system. This represents the upper limit of the energy storage system's capacity.

[0119] S102, Construct a frequency modulation parameter optimization model.

[0120] With the goal of improving operational economy and frequency security, a frequency regulation parameter optimization model is constructed by comprehensively considering unit reserve capacity constraints, system frequency security constraints, and physical limits of frequency regulation parameters. Specifically:

[0121] The objective function of this frequency regulation parameter optimization model aims to minimize the total reserve capacity requirement of interface resources for multiple types of inverters, as shown in the following equation:

[0122] (33)

[0123] In the formula, , , , These are the standby rates for photovoltaic, DC, flexible DC, and energy storage, respectively. , , , These are the rated capacities for photovoltaic, DC, flexible DC, and energy storage, respectively.

[0124] The operational safety constraints of this frequency regulation parameter optimization model include unit reserve capacity constraints, system frequency safety constraints, and physical limits of frequency regulation parameters, as shown in the following formula:

[0125] 1) Physical limits of frequency modulation parameters

[0126] (34)

[0127] (35)

[0128] In the formula, These are the lower limits of the virtual inertia coefficients for distributed photovoltaic, DC, flexible DC, and energy storage, respectively. These are the upper limits of the virtual inertia coefficients for distributed photovoltaic, DC, flexible DC, and energy storage, respectively. These are the lower limits of the droop coefficients for distributed photovoltaic, DC, flexible DC, and energy storage, respectively. These are the upper limits of the droop coefficients for distributed photovoltaic, DC, flexible DC, and energy storage, respectively.

[0129] 2) Unit standby capacity constraints

[0130] (36)

[0131] In the formula, These represent the maximum load shedding reserve rates for distributed photovoltaic, DC, flexible DC, and energy storage, respectively.

[0132] By constraining the above equations (34)-(36), the physical characteristics and operating conditions of each inverter interface resource can be ensured, and the maximum reduction in active power output can be limited to the allowable threshold.

[0133] 3) System frequency security constraints

[0134] To prevent the frequency protection relay from being triggered after a disturbance, a frequency index constraint is introduced into the frequency modulation parameter optimization model to ensure the transient frequency safety of the system under anticipated faults, as shown in the following equation:

[0135] (37)

[0136] (38)

[0137] In the formula, It represents the rate of frequency change within the measurement time window after a fault, which mainly depends on the system's inertia level and the scale of the fault; This indicates the maximum frequency deviation after the fault. and These represent the system's tolerance. The frequency deviation limit between the upper and lower limits; To anticipate the amount of system failures; The virtual inertia coefficient for distributed photovoltaic, DC, flexible DC, and energy storage; For the reserve rate of distributed photovoltaic, DC, flexible DC, and energy storage; The droop factor is for distributed photovoltaic, DC, flexible DC, and energy storage.

[0138] Step S2: Construct an unconstrained fitness function based on the frequency regulation parameter optimization model, incorporate the unconstrained fitness function into the Nelder-Mead algorithm, iteratively optimize the frequency response model of the drive system, and solve for the optimal frequency regulation parameters after aggregation of various types of inverters.

[0139] To address the system-level transient frequency security issue under anticipated unforeseen events, a dynamic penalty function is introduced to transform the transient frequency security index into a weighted term of the objective function. This involves introducing penalty weights to incorporate system frequency security constraints into the objective function, thereby generating an unconstrained fitness function compatible with Nelder-Mead optimization. As shown in the following formula:

[0140] (39)

[0141] In the formula, , , , , For the penalty weight, where They are applied to conventional DC, flexible DC and energy storage respectively to prevent excessive frequency regulation and ensure steady-state transmission power.

[0142] To assess post-fault frequency security, system frequency response simulation is used. The aforementioned unconstrained fitness function is iteratively optimized using the Nelder-Mead algorithm to drive SFT (System Frequency Transient) simulations. The optimal virtual inertia and droop coefficients for each type of inverter are tuned with the goal of minimizing total reserve capacity requirements. Figure 2 The specific solution steps are as follows:

[0143] S201, based on the requirements of power system frequency regulation control, sets the reserve rates for distributed photovoltaic, energy storage, DC, and flexible DC power. Virtual inertia coefficient With droop coefficient Define the upper and lower limits of the range and specify the precision threshold; initialize the decision variables within the feasible region, and... Generated in Euclidean space The initial simplex is formed by vertices. Boundary constraint corrections and special coupling constraint checks are performed on each vertex, and the objective function value for each vertex is calculated. This process ensures that the optimization search has sufficient exploration space while satisfying the power system operation constraints.

[0144] S202, the Nelder-Mead algorithm is invoked to perform geometric transformations on the simplex based on hierarchical decision logic. Based on the objective function values ​​at the trial points, reflection, expansion, external contraction, internal contraction, or compression operations are selected to update the simplex. In each iteration, the objective function value corresponding to each vertex is first calculated, where the objective function comprehensively considers... and It meets frequency safety standards and reflects the trade-off between frequency modulation performance and economy.

[0145] S203, based on a convergence criterion fusion, determines whether the iteration meets the termination condition. If it does, the algorithm stops; otherwise, it continues iterating. As the iteration progresses, the simplex gradually shrinks to the optimal region of the objective function. The resulting optimal frequency regulation parameters provide optimized frequency control parameters for diverse and heterogeneous resources in the power system, improving frequency support capabilities in actual operation while also considering operational economy.

[0146] Preferably, in step S201, the optimization parameter vector is determined, and the parameter vector to be optimized is denoted as . , Indicates the first One parameter to be optimized. The number of parameters to be optimized. express A 3D real vector space; define variable boundary constraints and set lower bound vectors for each optimization parameter. and upper bound vector , and They are respectively The lower and upper bound vectors constitute the feasible region. ;exist In Euclidean space, the simplex is formed by... The simplest convex polyhedron is formed by affine-independent vertices. For two-dimensional problems, the simplex is a triangle; for three-dimensional problems, the simplex is a tetrahedron; this generalizes to... Dimension, simplex is a form with A hypertetrahedron with n vertices. The first vertex is generated within the feasible region using either Latin hypercube sampling or uniform random sampling strategies. The nth vertex of this first vertex... Each component satisfy:

[0147] (1)

[0148] In the formula, express Uniform distribution over the interval; These are independent and identically distributed random numbers.

[0149] Perform special coupling constraint verification: Based on the physical characteristics of the inverter interface resource frequency modulation parameters, under specific conditions, the relevant parameters are forcibly set to zero, and the remaining parameters are generated based on the coordinate direction perturbation method. For each vertex, using the first vertex as a reference, a step-size perturbation is applied along the positive direction of each coordinate axis to construct the remaining vertices, as shown in the following formula:

[0150] (2)

[0151] (3)

[0152] (4)

[0153] In the formula, , The first vertex and the second vertex respectively One vertex; For the first The perturbation step size in each direction; For the first Standard unit vectors in each coordinate direction; This is the step size factor, with a default value of 0.05;

[0154] Perform boundary constraint correction and feasibility assurance: Perform boundary projection operation on each generated vertex, as follows:

[0155] (5)

[0156] In the formula, For any vertex, the first One variable to be optimized.

[0157] Calculate the objective function value of each vertex according to formula (39), and record the number of function evaluations. ;

[0158] For the simplex The vertices are sorted in ascending order according to the objective function value, and then renumbered so that:

[0159] (6)

[0160] In the formula, The vector of the current optimal vertex. The vector of the second most inferior vertex. The vector of the worst vertex; Indicates ranking in The objective function value of the vertex;

[0161] Calculate the remaining vertices except the worst vertex. The geometric centroids of the vertices are given by the following formula:

[0162] (7)

[0163] In the formula, The geometric center of mass is the current point of view. The average position of the better vertices reflects the concentration trend in the region with lower objective function values ​​and serves as a reference point for subsequent reflection, expansion, and contraction operations. The calculation of the centroid excludes the worst vertices, reflecting the algorithm's basic search strategy of avoiding the worst solution.

[0164] After obtaining the geometric centroid, this embodiment uses the Nelder-Mead algorithm to achieve adaptive movement and deformation of the simplex in the parameter space through a series of carefully designed geometric transformation operations. These operations include reflection, expansion, external contraction, internal contraction, and compression, and each operation follows a strict hierarchical decision-making logic.

[0165] Reflection operation: Mirror the current worst vertex along its centroid, and reflect the point. The calculation formula is:

[0166] (8)

[0167] In the formula, The reflection coefficient controls the distance of the reflection point relative to the center of gravity. When the reflection point and the worst vertex are symmetrical about the centroid, when At that time, the reflection point moves further away from the worst vertex, when At that time, the reflection point is close to the center of gravity;

[0168] Based on the comparison between the objective function value at the reflection point and the objective function value at the current simplex vertex, different decision branches are entered:

[0169] Case A: If equation (9) is satisfied, the reflection operation is moderately successful, and the worst vertex is replaced by the reflection point;

[0170] (9)

[0171] Case B: If equation (10) is satisfied, it means that the reflection direction is a favorable descent direction, and the extension operation is performed;

[0172] (10)

[0173] Case C: If equation (11) is satisfied, perform the external contraction operation;

[0174] (11)

[0175] Case D: If equation (12) is satisfied, perform the internal contraction operation;

[0176] (12).

[0177] In this embodiment, when the reflection operation (Case B) is successful, an expansion operation is introduced to increase the search step size. Expansion point The calculation formula is:

[0178] (13)

[0179] In the formula, The expansion coefficient is used to determine the expansion result: if ,accept replace ;like ,accept replace That is, when the reflection point is significantly better than the current optimal point, an expansion operation is introduced to accelerate the search. If the result at the expanded point is better, it indicates that the algorithm may be moving towards a better solution. Therefore, a further expansion operation is performed to increase the step size in that direction. If the result at the expanded point is better, the reflection point is directly replaced. Through expansion, the algorithm can speed up the convergence and avoid getting trapped in local optima.

[0180] In this embodiment, when the reflection point is between the second-worst and worst vertices (case C), an outward contraction operation is performed. Outward contraction point The calculation formula is:

[0181] (14)

[0182] In the formula, The contraction coefficient is used to determine the external contraction result: if The external contraction operation was successful; external contraction point accepted. Replace the worst vertex Proceed to step S203; if Perform the compression operation;

[0183] In this embodiment, when the reflection operation completely fails (Case D), an inward contraction operation is performed. Inward contraction point. The calculation formula is:

[0184] (15)

[0185] In the formula, the result of internal contraction is determined as follows: if The internal contraction operation was successful; internal contraction point accepted. Replace the worst vertex Proceed to step S203; if The internal contraction operation failed, so a compression operation is performed instead.

[0186] The difference between inward contraction and outward contraction: Outward contraction searches for candidate points on the reflection side of the center of gravity (away from the worst vertex), while inward contraction searches for candidate points on the worst vertex side of the center of gravity (closer to the worst vertex).

[0187] When the shrinking operation fails to improve the objective function value, the compression operation is performed to shrink the entire simplex towards the optimal vertex:

[0188] (16)

[0189] In the formula, Let be the compressibility factor. After compression, the volume of the simplex is reduced to 1 / 3 of its original volume. times.

[0190] Preferably, in step S203, a convergence determination strategy based on multi-criteria fusion is adopted. The algorithm stops and outputs the optimal solution when any of the following conditions are met. .

[0191] 1) Maximum number of iterations criterion: If the current iteration number is... The algorithm terminates when the preset maximum value is reached.

[0192] 2) Function evaluation frequency criterion: If the cumulative function evaluation frequency... The algorithm terminates when the preset upper limit is reached.

[0193] 3) Convergence criterion for objective function value:

[0194] (17)

[0195] (18)

[0196] In the formula, To represent all The average of the objective function values ​​of each vertex; if the standard deviation of the objective function values ​​of all vertices... or range Less than the function value tolerance threshold This indicates that the simplex has shrunk to a region where the objective function values ​​are nearly equal, the algorithm has converged, and the iteration terminates.

[0197] 4) Vertex position convergence criterion:

[0198] (19)

[0199] In the formula, The maximum Euclidean distance is defined as follows: if it is less than the position tolerance threshold... This indicates that the simplex has shrunk to a very small spatial region, and the algorithm has converged.

[0200] 5) Continuous No Improvement Criterion: Record the improvement amount of the optimal objective function value. As shown in the following formula:

[0201] (20)

[0202] In the formula, Indicates the first The currently found global optimal objective function value after the iteration.

[0203] If the improvement amount in M ​​consecutive iterations is less than the preset improvement amount threshold If the condition is not met, the iteration will terminate prematurely; if none of the above termination conditions are met, the iteration will return to simplex vertex sorting and centroid calculation to continue.

[0204] Step S3: After the algorithm converges, this embodiment allocates the optimized and aggregated frequency regulation parameters to each type of inverter interface resource site according to the rated power capacity of each type of inverter interface resource, as shown in the following formula:

[0205] (twenty one)

[0206] In the formula, This refers to the collection of all inverters, including distributed photovoltaic, energy storage, DC, and flexible DC. Indicates assignment to the first Frequency regulation parameters of the inverter; The type is The optimal frequency modulation parameters; For the first The rated power capacity of each inverter interface resource site. This allocation method ensures that each inverter site receives a proportional share of optimized parameters based on its capacity.

[0207] Based on the above implementation steps, this example applies the proposed method for coordinating and setting the frequency regulation parameters of multi-type inverter interface resources to a real power system to set the frequency regulation parameters of the inverter units in that power system. A schematic diagram of the power system is shown below. Figure 3 As shown, this system is a receiving-end grid connected by two AC tie lines, each with a transmission capacity of 2000MW. It incorporates 18 distributed photovoltaic units, one flexible DC transmission line, two conventional DC transmission lines, and energy storage frequency regulation resources for frequency regulation, with rated capacities of 18*67.5MW, 1500MW, 8000MW (5000MW+3000MW), and 500MW, respectively. Test scenarios 1 and 2 respectively consider the failure of the two AC tie lines to break, with frequency safety limits set at... , The virtual inertia coefficient and droop coefficient of the inverter are set to range from 3-12s and 20-50 MW / Hz, respectively.

[0208] Table 1 Comparison of Distributed Photovoltaic Parameters

[0209] ;

[0210] Table 2 Comparison of Conventional DC Parameters

[0211] ;

[0212] Table 3 Comparison of Flexible DC Parameters

[0213] ;

[0214] Table 4 Comparison of Energy Storage Parameters

[0215] ;

[0216] The results in Tables 1-4 show that, under traditional fixed parameter operation, the systems in both scenarios encountered frequency criticality safety challenges during sudden events. This demonstrates the frequency regulation parameter optimization results for distributed photovoltaic, conventional DC, flexible DC, and energy storage systems. The data in the tables show that, compared to traditional empirical fixed parameters, the optimization strategy proposed in this invention provides better frequency regulation parameters for each resource. , All have undergone meticulous adjustments and significant improvements. For example, the virtual inertia coefficient of distributed photovoltaic (PV) was optimized from 3.00s to 8.26s in Scenario 1 and to 9.22s in Scenario 2. The droop coefficient (i.e., primary frequency regulation coefficient) of distributed PV was significantly improved from 20.00MW / Hz to 42.06MW / Hz in Scenario 1 and to 39.05MW / Hz in Scenario 2. Similarly, conventional DC, flexible DC, and energy storage... and All parameters have been optimized and adjusted to varying degrees. These optimized parameters are no longer single empirical values, but rather optimal combinations found using the Nelder-Mead simplex method based on different system operating conditions and fault characteristics, aiming to maximize frequency support capabilities. It is worth emphasizing that the parameter optimization was carried out while meeting frequency safety limits and taking into account the operational economy of all resources.

[0217] like Figure 4 As shown, (a) and (b) illustrate Scenario 1: Under anticipated failure, the system frequency after using traditional fixed parameters and the optimization strategy proposed in this invention (…). ) and rate of change of frequency ( ) Comparison of response curves. As can be seen from the frequency curve in (a), after adopting the optimization strategy proposed in this invention, the lowest point of the system frequency is significantly raised, and the frequency recovery speed is faster, resulting in a more stable overall frequency response. This directly reflects the improved performance after optimization. The increased value results in a stronger primary frequency modulation capability. As can be seen from the frequency change rate curve in (b), the optimization strategy of this invention... The peak value is significantly lower than that of traditional fixed parameters, indicating that the drastic frequency drop has been effectively suppressed. This is precisely the result of optimization. The increased value directly reflects the stronger virtual inertia support, effectively mitigating the initial inertial response to frequency drops.

[0218] like Figure 4 As shown, (c) and (d) illustrate scenario 2: under the anticipated fault, the system frequency after using traditional fixed parameters and the optimization strategy proposed in this invention ( ) and rate of change of frequency ( Response curve comparison. Similarly, the optimization strategy of this invention makes the frequency response curve perform excellently in both key indicators.

[0219] Table 5 Comparison of Frequency Security Indicators

[0220] ;

[0221] Table 5 further quantifies the improvement in frequency safety indicators under two fault scenarios: Compared to the traditional fixed-parameter strategy, the optimization strategy of this invention significantly improves the frequency dynamic response in both scenarios: the absolute value of the maximum frequency deviation (the reference value is 50Hz, calculated by rounding) decreased from 0.28Hz and 0.32Hz to 0.25Hz and 0.25Hz, respectively, with reductions of 10.7% and 22.3%; the maximum frequency change rate ( The inertia was improved from -0.32Hz / s and -0.22Hz / s to -0.21Hz / s and -0.15Hz / s, respectively, with absolute reductions of 33.4% and 31.3%. These results demonstrate that the optimized control parameters effectively improve the system's equivalent inertia level, enhance inertia support and primary frequency modulation capability in the early stages of a fault, and show a more significant improvement in the Maohu scenario where inertia loss is more pronounced. This verifies the adaptability and robustness of the proposed method under different fault modes.

[0222] In summary, compared to traditional methods (with fixed reserve ratio and frequency regulation parameters), the method proposed in this invention can coordinate and adjust the reserve ratio and frequency regulation parameters of various inverter interface resources for different system operating conditions. Compared to strategies that fix initial reserve power and control parameters, it increases the minimum frequency point while reducing inverter resource reserve power. The method proposed in this invention has wider applicability, and can accurately optimize different initial reserve power and corresponding control parameters according to different scenarios, maintaining frequency safety while considering control costs. Furthermore, the parameter optimization and tuning method proposed in this invention can provide clear guidance on important parameters such as virtual inertia and primary frequency regulation coefficient, and set requirements for the support capabilities of inverter-type interface resources.

[0223] The present invention also provides a device for coordinating and tuning frequency regulation parameters of interface resources of multiple types of inverters. The device includes a microprocessor and a memory connected to each other. The memory stores a computer program. The microprocessor executes the computer program to implement the aforementioned method for coordinating and tuning frequency regulation parameters of interface resources of multiple types of inverters.

[0224] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a microprocessor, implements the aforementioned method for coordinating and adjusting the frequency modulation parameters of interface resources for multiple types of inverters.

[0225] The present invention also provides a computer program product, which includes a computer program that, when executed by a microprocessor, implements the aforementioned method for coordinating and adjusting the frequency modulation parameters of the interface resources of multiple types of inverters.

[0226] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, computer-readable storage media, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, computer-readable storage media, and computer program products according to embodiments of the present 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, produce an implementation of the flowchart. Figure 1 One or more processes and / or boxes Figure 1 The 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 functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus 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.

[0227] The above embodiments are preferred implementations of the present invention. In addition, the present invention can be implemented in other ways. Any obvious substitutions without departing from the concept of the present technical solution are within the protection scope of the present invention.

[0228] To facilitate understanding by those skilled in the art of the improvements of this invention over the prior art, some of the accompanying drawings and descriptions have been simplified, and for clarity, some other elements have been omitted. Those skilled in the art should realize that these omitted elements also constitute the content of this invention.

Claims

1. A method for coordinating and tuning frequency regulation parameters of interface resources for multiple types of inverters, characterized in that, include: Step S1: Based on the differences in frequency response characteristics of various types of inverter interface resources, build a system frequency response model and frequency regulation parameter optimization model to distinguish between various types of inverter interface resources. The system frequency response model includes multiple types of inverters, including distributed photovoltaic (PV), energy storage, DC, and flexible DC inverters. Distributed PV participates in the frequency response through virtual inertial control and droop control, and its maximum power increment for frequency regulation meets the load shedding constraints. DC frequency limiting control uses PI control with dead zone and limiting elements, and its output power meets the limiting constraints. The flexible DC transmission system participates in the frequency regulation of the AC grid by adding a frequency control element to the active power control loop. The energy storage system participates in the frequency response through droop control and virtual inertial control, and its operation meets the state of charge constraints and power constraints. The frequency regulation parameter optimization model takes minimizing the total reserve capacity requirement of multi-type inverter interface resources as the objective function, and uses unit reserve capacity constraints, system frequency security constraints, and physical limits of frequency regulation parameters as constraints. Step S2: Construct an unconstrained fitness function based on the frequency regulation parameter optimization model, incorporate the unconstrained fitness function into the Nelder-Mead algorithm, iteratively optimize the frequency response model of the drive system, and solve for the optimal frequency regulation parameters after aggregation of various types of inverters, including the virtual inertia coefficient and droop coefficient. The steps for solving the optimal frequency modulation parameters using the Nelder–Mead algorithm are as follows: S201, based on the requirements of power system frequency regulation control, sets the reserve rates for distributed photovoltaic, energy storage, DC, and flexible DC power. Virtual inertia coefficient With droop coefficient Define the upper and lower limits of the range and specify the precision threshold; initialize the decision variables within the feasible region, and... Generated in Euclidean space The initial simplex is formed by the vertices. Boundary constraint correction and special coupling constraint verification are performed on each vertex, and the objective function value of each vertex is calculated. S202, call the Nelder-Mead algorithm to perform geometric transformation operations on the simplex based on hierarchical decision logic. Based on the objective function value of the trial point, select to perform reflection, expansion, external contraction, internal contraction or compression operations to update the simplex. In each iteration, first calculate the objective function value corresponding to each vertex. S203, based on the convergence determination of multi-criteria fusion, determines whether the iteration meets the termination condition. If it does, the algorithm stops; if it does not, the iteration continues until the simplex gradually shrinks to the optimal region of the objective function. Step S3: Based on the rated power capacity of each type of inverter interface resource, allocate the optimized and aggregated frequency regulation parameters to each type of inverter interface resource site.

2. The method for coordinating and tuning frequency regulation parameters of multi-type inverter interface resources according to claim 1, characterized in that: In step S201: Determine the optimization parameter vector, let the parameter vector to be optimized be... , Indicates the first One parameter to be optimized. The number of parameters to be optimized. express A 3D real vector space; define variable boundary constraints and set lower bound vectors for each optimization parameter. and upper bound vector , and They are respectively The lower and upper bound vectors constitute the feasible region. ;exist In Euclidean space, the simplex is formed by... The simplest convex polyhedron composed of affine-independent vertices is used to generate the first vertex in the feasible region using a Latin hypercube sampling or uniform random sampling strategy. The first vertex's... Each component satisfy: （1） In the formula, express Uniform distribution over the interval; These are independent and identically distributed random numbers; Perform special coupling constraint verification: Based on the physical characteristics of the inverter interface resource frequency regulation parameters, under specific conditions, the relevant parameters are forcibly set to zero, and the remaining parameters are generated based on the coordinate direction perturbation method. For each vertex, using the first vertex as a reference, a step-size perturbation is applied along the positive direction of each coordinate axis to construct the remaining vertices, as shown in the following formula: （2） （3） （4） In the formula, , The first vertex and the second vertex respectively One vertex; For the first The perturbation step size in each direction; For the first Standard unit vectors in each coordinate direction; Step size factor; Perform boundary constraint correction: Perform boundary projection operation on each generated vertex, and simultaneously perform special coupling constraint checks on each vertex to ensure that the parameter configuration meets the physical constraints, as shown in the following equation: （5） In the formula, For any vertex, the first One variable to be optimized; Calculate the objective function value for each vertex and record the number of function evaluations. ; For the simplex The vertices are sorted in ascending order according to the objective function value, and then renumbered so that: （6） In the formula, The vector of the current optimal vertex. The vector of the second most inferior vertex. The vector of the worst vertex; Indicates ranking in The objective function value of the vertex; Calculate the remaining vertices except the worst vertex. The geometric centroids of the vertices are given by the following formula: （7） In the formula, The geometric center of mass is the current point of view. The average position of the better vertices reflects the concentration trend of the region with lower objective function values ​​and serves as a reference point for subsequent reflection, expansion, and contraction operations.

3. The method for coordinating and tuning frequency regulation parameters of multi-type inverter interface resources according to claim 2, characterized in that: In step S202: Reflection operation: Mirror the current worst vertex along its centroid, and reflect the point. The calculation formula is: （8） In the formula, The reflection coefficient controls the distance of the reflection point relative to the center of gravity. When the reflection point and the worst vertex are symmetrical about the centroid, when At that time, the reflection point moves further away from the worst vertex, when At that time, the reflection point is close to the center of gravity; Based on the comparison between the objective function value at the reflection point and the objective function value at the current simplex vertex, different decision branches are entered: Case A: If equation (9) is satisfied, the reflection operation is moderately successful, and the worst vertex is replaced by the reflection point; （9） Case B: If equation (10) is satisfied, it means that the reflection direction is a favorable descent direction, and the extension operation is performed; （10） Case C: If equation (11) is satisfied, perform the external contraction operation; （11） Case D: If equation (12) is satisfied, perform the internal contraction operation; （12） Extension point in extension operation The calculation formula is: （13） In the formula, For the expansion coefficient, the expansion result is determined as follows: If ,accept replace ;like ,accept replace ; External contraction point during external contraction operation The calculation formula is: （14） In the formula, The contraction coefficient is used to determine the external contraction result: if The external contraction operation was successful; external contraction point accepted. Replace the worst vertex Proceed to step S203; if Perform the compression operation; Internal contraction point during internal contraction operation The calculation formula is: （15） In the formula, the result of internal contraction is determined as follows: if The internal contraction operation was successful; internal contraction point accepted. Replace the worst vertex Proceed to step S203; if The internal contraction operation failed, so a compression operation is performed. In the compression operation, the entire simplex is shrunk towards the optimal vertex, as shown in the following equation: （16） In the formula, Let be the compressibility factor. After compression, the volume of the simplex is reduced to 1 / 3 of its original volume. times.

4. The method for coordinating and tuning frequency regulation parameters of multi-type inverter interface resources according to claim 3, characterized in that: In step S203, a convergence determination strategy based on multi-criteria fusion is adopted. When any of the following conditions are met, the algorithm stops and outputs the optimal solution sought at the current iteration number. ; 1) Maximum number of iterations criterion: If the current iteration number is... The algorithm terminates when the preset maximum value is reached. 2) Function evaluation frequency criterion: If the cumulative function evaluation frequency... The algorithm terminates when the preset upper limit is reached. 3) Convergence criterion for objective function value: （17） （18） In the formula, To represent all The average of the objective function values ​​of each vertex; if the standard deviation of the objective function values ​​of all vertices... or range Less than the function value tolerance threshold This indicates that the simplex has shrunk to a region where the objective function values ​​are nearly equal, the algorithm has converged, and the iteration terminates. 4) Vertex position convergence criterion: （19） In the formula, The maximum Euclidean distance is defined as follows: if it is less than the position tolerance threshold... This indicates that the simplex has shrunk to a very small spatial region, and the algorithm has converged. 5) Continuous No Improvement Criterion: Record the improvement amount of the optimal objective function value. As shown in the following formula: （20） In the formula, Indicates the first After the current iteration, the globally optimal objective function value is found. If the improvement amount in M ​​consecutive iterations is less than the preset improvement amount threshold If the condition is not met, the iteration will terminate prematurely; if none of the above termination conditions are met, the iteration will return to the simplex vertex sorting and geometric centroid calculation to continue.

5. The method for coordinating and tuning frequency regulation parameters of multi-type inverter interface resources according to claim 4, characterized in that: Step S3: Based on the rated power capacity of each type of inverter interface resource, allocate the optimized and aggregated frequency regulation parameters to each type of inverter interface resource site, as follows: （21） In the formula, This refers to the collection of all inverters, including distributed photovoltaic, energy storage, DC, and flexible DC. Indicates assignment to the first Frequency regulation parameters of the inverter; The type is The optimal frequency modulation parameters; For the first The rated power capacity of each inverter interface resource station.

6. A multi-type inverter interface resource frequency regulation parameter coordination and tuning device, the device comprising a microprocessor and a memory interconnected thereon, the memory storing a computer program, characterized in that: The microprocessor executes the computer program to implement the multi-type inverter interface resource frequency regulation parameter coordination tuning method according to any one of claims 1-5.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the microprocessor, it implements the method for coordinating and adjusting the frequency regulation parameters of the interface resources of multiple types of inverters as described in any one of claims 1-5.

8. A computer program product comprising a computer program, characterized in that: When the computer program is executed by the microprocessor, it implements the method for coordinating and adjusting the frequency regulation parameters of the interface resources of multiple types of inverters as described in any one of claims 1-5.

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