A network-constructing new energy power backup and droop coefficient optimization method, system, device and storage medium

Abstract

The application discloses a network-constructed new energy power backup and droop coefficient optimization method, system, device and storage medium, and the method comprises the following steps: collecting basic data of a power system to be optimized and short-time power disturbance characteristics under a certain working condition; constructing a system frequency response model considering frequency emergency control and network-constructed new energy, and obtaining a time domain expression of system frequency by using Laplace inverse transform; determining initial values of network-constructed new energy power backup and droop coefficient, and calculating upper and lower boundaries of emergency control effectiveness at this time; calculating an initial value of an emergency control effective boundary area; determining an iteration step; iteratively calculating the upper and lower boundaries of emergency control and the emergency control effective boundary area when the network-constructed new energy power backup and the droop coefficient are new values, and obtaining optimized new energy power backup and droop coefficient; and the application aims at the problem that frequency emergency control has poor adaptability under short-time power disturbance, optimizes each parameter of the network-constructed new energy, and thus improves the system frequency safety level.

Description

Technical Field

[0001] This invention relates to a method, system, device, and storage medium for optimizing power reserve and droop coefficient, and particularly to a method, system, device, and storage medium for optimizing power reserve and droop coefficient in grid-connected new energy sources, belonging to the field of power systems and their automation technology. Background Technology

[0002] With the continuous expansion of new energy installed capacity, the equivalent inertia level and disturbance rejection capability of the power system will significantly decrease, leading to a series of frequency security incidents. For example, from 2021 to 2022, three grid accidents occurred in the Texas power system in the United States, with new energy sources continuously entering low-voltage ride-through states, ultimately resulting in a large number of grid disconnections. Similarly, during periods of high wind power generation, the Hebei North Power Grid in China also experienced repeated low-voltage ride-throughs of new energy power plants, causing continuous power fluctuations. It is evident that in low-inertia systems, the short-term power surges caused by low-voltage ride-throughs of new energy sources due to AC / DC faults have become a new type of transient problem threatening system safety and stability.

[0003] Compared to the permanent power disturbance caused by a single fault, large-scale low-voltage ride-through of renewable energy sources causes the system to experience a large power surge in a short period, resulting in a significant and faster frequency drop. Traditional frequency regulation methods such as primary and secondary frequency regulation of synchronous generators are insufficient to offset the power shortage in a short time due to response delays. Therefore, frequency emergency control—a frequency control measure to address anticipated grid faults—needs to be considered. However, improper settings of frequency emergency control measures may lead to high-frequency or low-frequency problems in the system. Furthermore, unreasonable settings of grid-connected renewable energy power reserves and droop coefficients can also worsen the frequency emergency control boundary. All of these factors pose new challenges to the effectiveness of frequency emergency control under short-term power disturbances, necessitating the research of new methods for optimizing grid-connected renewable energy power reserves and droop coefficients to address the adaptability issues of frequency emergency control under short-term power disturbances.

[0004] Existing literature on optimizing the power reserve and droop coefficient of grid-connected renewable energy mainly uses frequency indicators in the frequency response model as constraints, such as frequency extrema. However, most current frequency response models fail to comprehensively consider the impact of emergency frequency control and the limiting effect of synchronous motor governors on system frequency indicators. Furthermore, as the frequency change rate increases and the disturbance forms become more complex in low-inertia systems, the frequency regulation effect of emergency frequency control after disturbances has a significant impact on frequency indicators. Existing optimization methods fail to combine the active support capability of renewable energy with emergency frequency control, and only optimize the frequency regulation parameters of renewable energy themselves. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to provide a more accurate and comprehensive method, system, device, and storage medium for optimizing grid-type renewable energy power reserve and droop coefficient using iterative models.

[0006] Technical solution: The present invention provides a method for optimizing grid-connected renewable energy power reserve and droop coefficient, comprising:

[0007] Collect basic data of the power system to be optimized under a specific operating condition;

[0008] The characteristics of short-term power disturbances caused by a large number of new energy sources entering the low-voltage ride-through when the power system to be optimized experiences an N-1 fault are determined.

[0009] Based on basic data and short-term power disturbance characteristics, a system frequency response model considering emergency frequency control and grid-connected new energy sources is constructed, and the time-domain expression of the system frequency is obtained by using the inverse Laplace transform.

[0010] Determine the initial value ΔP for grid-connected renewable energy power reserves. max (0) Initial value K of the droop coefficient w (0), and calculate the upper and lower boundaries of the emergency control effectiveness at this time;

[0011] Based on the upper and lower boundaries of emergency control effectiveness, the curves of the upper and lower boundaries of emergency control are fitted using the least squares method, and the initial value of the area of ​​the effective boundary of emergency control is calculated.

[0012] The iteration step size of the grid-type renewable energy power reserve and droop coefficient is determined according to the trapezoidal rule;

[0013] The area of ​​the upper and lower boundaries of emergency control and the area of ​​the effective boundary of emergency control are iteratively calculated when the grid-type renewable energy power reserve and droop coefficient are new values, until the area of ​​the effective boundary of emergency control is maximized, thus obtaining the optimized renewable energy power reserve and droop coefficient.

[0014] Furthermore, the collection of basic data of the power system to be optimized under a certain operating condition includes:

[0015] The equivalent inertia M of the generator, equivalent damping D, system equivalent turbine characteristic coefficient α, turbine equivalent time constant T, and droop rate R of the synchronous machine governor of the power system to be optimized are determined by the weighted average method.

[0016] The determination of the short-term power disturbance characteristics caused by a large number of new energy sources entering the low-voltage ride-through when the system experiences an N-1 fault includes: determining the active power deficit ΔP of new energy sources entering the low-voltage ride-through. fc Low voltage ride-through time t1 and low voltage ride-through recovery time t2.

[0017] Furthermore, the construction of a system frequency response model considering emergency frequency control and grid-connected new energy sources, based on basic data and short-term power disturbance characteristics, includes:

[0018] The system frequency is a piecewise function. When the speed controller has not reached saturation, the dynamic s-domain expression of the system frequency is:

[0019]

[0020] in:

[0021]

[0022] When both the speed governor and the grid-connected renewable energy backup reach saturation, the dynamic s-domain expression of the system frequency is:

[0023]

[0024] Where K ec K is the emergency control quantity proportional coefficient, 0≤K ec ≤1, τ0 is the emergency control delay, α is the system equivalent turbine characteristic coefficient, T is the turbine equivalent time constant, R is the droop rate of the synchronous governor, x sg The capacity percentage of the synchronous machine in the system is given by x = 1 - x. sg M is the equivalent inertia of the system generator, D is the equivalent damping, and T is the equivalent inertia of the system generator. j For the virtual inertia coefficient of grid-type new energy, K w ΔP is the droop control coefficient for grid-type new energy sources. fc For the active power deficit of new energy sources entering low voltage ride-through, t1 is the low voltage ride-through time, t2 is the low voltage ride-through recovery time, and ΔP is the active power deficit. max1 For speed governor limiting, ΔP max2 For grid-connected renewable energy reserves, Δω0 and Δω0′ are non-zero initial state values;

[0025] The time-domain expression for the system frequency obtained using the inverse Laplace transform includes:

[0026] When the speed controller has not reached saturation, the time-domain expression of the system frequency obtained by inverse Laplace transform is as follows:

[0027]

[0028] in:

[0029]

[0030] Where u() is the step function and t is the current time; when the speed governor and the grid-type new energy source are simultaneously saturated, the time-domain expression of the system frequency is obtained through the inverse Laplace transform as follows:

[0031]

[0032] in:

[0033]

[0034] Furthermore, the initial value ΔP for determining the grid-connected renewable energy power reserve is... max (0) Initial value K of the droop coefficient w (0), and calculate the upper and lower boundaries of the emergency control effectiveness at this time, including:

[0035] Set the initial value of the synchronizer percentage to x. sg (0) = 0, initial value of emergency control quantity proportional coefficient K ec (0) = 0. With the proportion of the synchronizer fixed, the emergency control quantity ratio coefficient is increased by a preset fixed step size until the following iterative objective is met:

[0036]

[0037] The output at this point is the initial value x of the synchronizer. sg (0) corresponds to the two emergency control quantity boundary values ​​K ec1 and K ec2 , The proportion of the synchronous machine is the initial value x. sg (0) corresponds to the effective range of emergency control actions, where ε is the accuracy value and Δω is the system frequency time domain after the inverse Laplace transform. After the first iteration ends, the proportion of the synchronizer continues to increase with a fixed step size, and the boundary values ​​K of the two emergency control quantities at this time are iteratively calculated. ec1 and K ec2 ;

[0038] The minimum emergency control ratio coefficient is required when the system frequency is at its lowest point of 49Hz. This minimum value K corresponds to a series of values ​​for different proportions of renewable energy sources. ec1 The curve formed represents the lower boundary of emergency control; the required emergency control proportional coefficient is at its maximum value when the system frequency reaches its highest point of 50.5Hz. This series of maximum values ​​K corresponds to different proportions of renewable energy sources. ec2 The resulting curve represents the upper boundary of emergency control.

[0039] Furthermore, the upper and lower boundaries based on the effectiveness of emergency control are fitted using the least squares method to obtain the upper and lower boundary curves of emergency control, and the initial value of the area of ​​the effective boundary of emergency control is calculated, including:

[0040] The intersection point of the two curves is determined based on the curve fitted by the least squares method. The step size interval [x] for each synchronizer percentage after the curve intersection point is then determined. sg (i),x sg[i+1], the area between each cell is calculated using the trapezoidal rule:

[0041]

[0042] Where A(0) is the initial value of the emergency control effectiveness boundary, A i (0) represents the boundary area for each small step size, i is the iteration number of the synchronization machine, and K is the boundary area for each small step size. ec1 and K ec2 x is the boundary value for emergency control quantities. sg This represents the capacity percentage of the synchronous machine in the system.

[0043] Furthermore, the iterative step size for determining the grid-type renewable energy power reserve and droop coefficient according to the trapezoidal rule includes:

[0044] Determine the grid-type renewable energy power reserve ΔP using the gradient descent method. max droop coefficient K w The iteration value,

[0045]

[0046] Where η1 and η2 are the learning rates, k is the number of iterations, and Δω is the system frequency in the time domain after the inverse Laplace transform, controlling the step size of parameter updates in each iteration.

[0047] Furthermore, the iterative calculation of the emergency control upper and lower boundaries and the emergency control effectiveness boundary area when the grid-type renewable energy power reserve and droop coefficient are new values ​​is continued until the area of ​​the emergency control effectiveness boundary is maximized, thereby obtaining the optimized renewable energy power reserve and droop coefficient, including:

[0048] Based on the new grid-type renewable energy power reserve and droop coefficient parameter values, a series of emergency control quantity boundary data under different proportions are re-iterated. The upper and lower boundary curves of emergency control are fitted by the least squares method. If the curves have no intersection, the emergency control under the grid-type power reserve and droop coefficient has no effective boundary, and this parameter is not the optimal parameter. If the curves have intersection, the effective boundary area of ​​emergency control is recalculated until A(k+1)≥A(k) is no longer satisfied, and then the optimal parameter value at this time is output.

[0049] Based on the same inventive concept, this invention also provides a grid-type renewable energy power reserve and droop coefficient optimization system, comprising:

[0050] The data processing module is used to collect basic data of the power system to be optimized under a certain operating condition; and to determine the short-term power disturbance characteristics caused by a large number of new energy sources entering the low voltage ride-through when the power system to be optimized experiences an N-1 fault.

[0051] The frequency model construction module is used to construct a system frequency response model that considers emergency frequency control and grid-connected new energy sources based on basic data and short-term power disturbance characteristics, and to obtain the time-domain expression of the system frequency using the inverse Laplace transform.

[0052] The parameter optimization module is used to determine the initial value ΔP of the grid-connected renewable energy power reserve. max (0) Initial value K of the droop coefficient w (0), and calculate the upper and lower boundaries of the emergency control effectiveness at this time; fit the upper and lower boundary curves of the emergency control according to the least squares method, and calculate the initial value of the area of ​​the effective boundary of the emergency control; determine the iteration step size of the grid-type new energy power reserve and droop coefficient according to the trapezoidal rule; iteratively calculate the upper and lower boundaries of the emergency control and the area of ​​the effective boundary of the emergency control when the grid-type new energy power reserve and droop coefficient are new values, until the area of ​​the effective boundary of the emergency control is maximized, and obtain the optimized new energy power reserve and droop coefficient.

[0053] Based on the same inventive concept, the present invention also provides a computing device, comprising: one or more processors, one or more memories, and one or more programs, wherein the programs are stored in the memory and configured to be executed by the processor, and when the programs are loaded onto the processor, they implement the steps of the grid-type renewable energy power reserve and droop coefficient optimization method according to any of the preceding claims.

[0054] Based on the same inventive concept, the present invention also provides a storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the steps of the grid-type new energy power reserve and droop coefficient optimization method according to any one of the preceding claims.

[0055] Beneficial effects: Compared with existing technologies, this invention addresses the short-term power disturbance problem caused by low-voltage ride-through of renewable energy sources by constructing an extended frequency response model that considers emergency frequency control and grid-connected renewable energy, making the model more accurate and comprehensive, and providing a more realistic iterative model for the parameters to be optimized; by comparing the effective boundary area values ​​of emergency frequency control under different parameters, this invention effectively characterizes the advantages and disadvantages of power reserve and droop coefficient of grid-connected renewable energy; through continuous iterative calculation, this invention determines the optimal values ​​of power reserve and droop coefficient of grid-connected renewable energy, effectively solving the problem that emergency frequency control is not suitable or has poor adaptability under short-term power disturbances. Attached Figure Description

[0056] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0057] Figure 2 This is a schematic diagram of the method and iterative process according to an embodiment of the present invention;

[0058] Figure 3 This is a schematic diagram of the area formed by the effective boundary of frequency emergency control in an embodiment of the present invention. Detailed Implementation

[0059] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0060] As attached Figure 1 As shown, the grid-connected renewable energy power reserve and droop coefficient optimization method of this embodiment includes:

[0061] Step 1: Collect basic data of the power system to be optimized under a specific operating condition;

[0062] Step 2: Determine the characteristics of the short-term power disturbance caused by a large number of new energy sources entering the low-voltage ride-through when the power system to be optimized experiences an N-1 fault;

[0063] Step 3: Based on basic data and short-term power disturbance characteristics, construct a system frequency response model that considers emergency frequency control and grid-connected new energy sources, and use the inverse Laplace transform to obtain the time-domain expression of the system frequency;

[0064] Step 4: Determine the initial value ΔP for grid-connected renewable energy power reserve. max (0) Initial value K of the droop coefficient w (0), and calculate the upper and lower boundaries of the emergency control effectiveness at this time;

[0065] Step 5: Based on the upper and lower boundaries of emergency control effectiveness, fit the upper and lower boundary curves of emergency control using the least squares method, and calculate the initial value of the area of ​​the effective boundary of emergency control.

[0066] Step 6: Determine the iteration step size of the grid-type renewable energy power reserve and droop coefficient according to the trapezoidal rule;

[0067] Step 7: Iteratively calculate the areas of the upper and lower boundaries of emergency control and the effective boundary of emergency control when the grid-type renewable energy power reserve and droop coefficient are at new values, until the area of ​​the effective boundary of emergency control is maximized, and obtain the optimized renewable energy power reserve and droop coefficient.

[0068] Specifically, in step one, basic data of the power system to be optimized under a certain operating condition are collected to provide the necessary initial values ​​for the system frequency response model in step three.

[0069] The equivalent inertia M of the generator, the equivalent damping D, the equivalent turbine characteristic coefficient α, the equivalent time constant T of the turbine, and the droop rate R of the synchronous speed governor of the system are obtained by weighted average method.

[0070] Step 2: Determine the characteristics of short-term power disturbances caused by a large number of new energy sources entering the low-voltage ride-through when an N-1 fault occurs in the power system to be optimized;

[0071] Determine the active power deficit ΔP of new energy sources during low voltage ride-through. fc Low voltage ride-through time t1, low voltage ride-through recovery time t2.

[0072] Step 3: Construct a system frequency response model that considers emergency frequency control and grid-connected new energy sources, and use the inverse Laplace transform to obtain the time-domain expression of the system frequency;

[0073] The system frequency is a piecewise function with multiple variables. When the speed controller has not reached saturation, the dynamic s-domain expression of the frequency can be obtained from the system transfer function as follows:

[0074]

[0075] The frequency-time domain expression obtained from the inverse Laplace transform is:

[0076]

[0077] in:

[0078]

[0079] When both the speed governor and the grid-connected renewable energy backup reach saturation, the system frequency dynamic S-domain expression is:

[0080] Similarly, the system frequency time-domain expression obtained from the inverse Laplace transform is:

[0081]

[0082] in:

[0083]

[0084] Where K ec The proportional coefficient for emergency control is between 0 and 1; τ0 is the emergency control delay; α is the equivalent turbine characteristic coefficient; T is the turbine equivalent time constant; R is the droop rate of the synchronous governor; and x... sg The capacity percentage of the synchronous machine in the system is given by x = 1 - x. sg M is the equivalent inertia of the system generator, D is the equivalent damping of the system, and T is the equivalent inertia of the system generator. j For the virtual inertia coefficient of grid-type new energy, K w For grid-type new energy droop control coefficient, ΔP max1 For speed governor limiting, ΔP max2The maximum value of the grid-connected renewable energy reserve is given by Δω0 and Δω0′, which are non-zero initial state values. s is the complex frequency domain. The signal is represented by the complex frequency domain of the system block diagram and then transformed into the time domain form according to the Laplace transform.

[0085] Step 4: Determine the initial value ΔP for grid-connected renewable energy power reserves. max (0) Initial value K of the droop coefficient w (0), and calculate the upper and lower boundaries of the emergency control effectiveness at this time;

[0086] The proportion of the synchronizer is the initial value x. sg In the case of (0), the emergency control quantity proportional coefficient is increased by a fixed step size of 0.001 until the following iteration objective is met:

[0087]

[0088] The output at this point is the initial value x of the synchronizer. sg (0) corresponds to the two emergency control quantity boundary values ​​K ec1 K ec2 , The proportion of the synchronous machine is the initial value x. sg (0) corresponds to the effective emergency control action range, where ε is the accuracy value, and the first iteration ends. Continue to increase the proportion of the synchronizer by a fixed step size of 0.001, and recalculate the boundary values ​​K of the two emergency control quantities at this time. ec1 K ec2 .

[0089] Step 5: Fit the upper and lower boundary curves of the emergency control system using the least squares method, and calculate the initial area formed by the effective boundary of the emergency control system, such as... Figure 3 As shown;

[0090] Each iteration of the synchronization machine's proportion in step four corresponds to two emergency control quantity boundary values ​​K. ec1 K ec2 The proportion of the synchronizer is x sg With K ec1 Each iteration of the data is fitted using the least squares method to obtain the lower boundary curve for emergency control; similarly, the proportion of the synchronizer is x sg With K ec2 Each iteration of the data is fitted using the least squares method to obtain the upper boundary curve of the emergency control.

[0091] Method for calculating the initial area formed by the effective boundary of emergency control:

[0092] The intersection points of the two upper and lower boundary curves are determined based on the curve fitted by the least squares method. For each synchronizer percentage step size interval after the curve intersection point [x], the... sg (i),x sg[i+1], the area between each cell is calculated using the trapezoidal rule:

[0093]

[0094] Where A(0) is the initial value of the emergency control effectiveness boundary, A i (0) represents the boundary area for each small step size, and i represents the number of iterations.

[0095] Step 6: Determine the iteration step size of the grid-type renewable energy power reserve and droop coefficient according to the trapezoidal rule;

[0096] The iterative values ​​of the power reserve and droop coefficient of the grid-type renewable energy are determined using the gradient descent method.

[0097]

[0098] Where η1 and η2 are the learning rates, which control the step size of parameter updates in each iteration.

[0099] Step 7: Iteratively calculate the areas of the upper and lower boundaries and the effective boundary of emergency control when the grid-type renewable energy power reserve and droop coefficient are new values, until the area of ​​the effective boundary of emergency control is maximized, and obtain the optimized renewable energy power reserve and droop coefficient.

[0100] Based on the new parameter values ​​in step six, repeat steps four and five: iteratively determine the upper and lower boundaries of emergency control, and fit the upper and lower boundary curves of emergency control using the least squares method. If the curves have no intersection, it indicates that the emergency control under the power reserve and droop coefficient of this network configuration has no effective boundary, and this parameter is not optimal. If the curves have intersection, recalculate the effective boundary area of ​​emergency control until A(k+1)≥A(k) is no longer satisfied, and then output the optimal parameter value at this time. The method and iterative process in this embodiment are as follows. Figure 2 As shown.

[0101] Based on the same inventive concept, this embodiment also provides a grid-type renewable energy power reserve and droop coefficient optimization system, comprising:

[0102] The data processing module is used to collect basic data of the power system to be optimized under a certain operating condition; and to determine the characteristics of short-term power disturbances caused by a large number of new energy sources entering the low voltage ride-through when the system experiences an N-1 fault.

[0103] The frequency model construction module is used to construct a system frequency response model that considers emergency frequency control and grid-connected new energy sources based on basic data and short-term power disturbance characteristics, and to obtain the time-domain expression of the system frequency using the inverse Laplace transform.

[0104] The parameter optimization module is used to determine the initial value ΔP of the grid-connected renewable energy power reserve. max(0) Initial value K of the droop coefficient w (0), and calculate the upper and lower boundaries of the emergency control effectiveness at this time; fit the upper and lower boundary curves of the emergency control according to the least squares method, and calculate the initial value of the area of ​​the effective boundary of the emergency control; determine the iteration step size of the grid-type new energy power reserve and droop coefficient according to the trapezoidal rule; iteratively calculate the upper and lower boundaries of the emergency control and the area of ​​the effective boundary of the emergency control when the grid-type new energy power reserve and droop coefficient are new values, until the area of ​​the effective boundary of the emergency control is maximized, and obtain the optimized new energy power reserve and droop coefficient.

[0105] In the data processing module, the equivalent inertia M of the generator, the equivalent damping D, the equivalent turbine characteristic coefficient α, the equivalent time constant T of the turbine, and the droop rate R of the synchronous speed governor are determined by the weighted average method.

[0106] In the data processing module, the active power deficit ΔP of new energy sources during low-voltage ride-through is determined. fc Low voltage ride-through time t1, low voltage ride-through recovery time t2.

[0107] In the frequency model construction module, the system frequency is a piecewise function of multiple variables. When the speed governor has not reached saturation, the dynamic s-domain expression of the frequency can be obtained from the system transfer function as follows:

[0108]

[0109] The frequency-time domain expression obtained from the inverse Laplace transform is:

[0110]

[0111] in:

[0112]

[0113] When both the speed governor and the grid-connected renewable energy backup reach saturation, the system frequency dynamic S-domain expression is:

[0114] Similarly, the system frequency time-domain expression obtained from the inverse Laplace transform is:

[0115]

[0116] in:

[0117]

[0118] Where K ecThe proportional coefficient for emergency control is between 0 and 1; τ0 is the emergency control delay; α is the turbine characteristic coefficient; T is the turbine equivalent time constant; R is the droop rate of the synchronous governor; and x... sg The capacity percentage of the synchronous machine in the system is given by x = 1 - x. sg M is the system's equivalent inertial time constant, D is the system's equivalent damping, and T is the system's equivalent inertial time constant. j For the virtual inertia coefficient of grid-type new energy, K w ΔP is the droop control coefficient for grid-type new energy sources. max1 For speed governor limiting, ΔP max2 Δω0 and Δω0′ represent the maximum reserve value for grid-connected renewable energy, and are non-zero initial state values.

[0119] In the parameter optimization module, based on the initial value x of the synchronous machine proportion... sg (0) Initial value of emergency control quantity proportional coefficient K ec (0), with the proportion of the synchronizing machine fixed, the emergency control quantity ratio coefficient is increased by a fixed step size of 0.001 until the following iteration objective is met:

[0120]

[0121] The output at this point is the initial value x of the synchronizer. sg (0) corresponds to the two emergency control quantity boundary values ​​K ec1 K ec2 , The proportion of the synchronous machine is the initial value x. sg (0) corresponds to the effective emergency control action range, where ε is the accuracy value, and the first iteration ends. Continue to increase the proportion of the synchronizer by a fixed step size of 0.001, and recalculate the boundary values ​​K of the two emergency control quantities at this time. ec1 K ec2 .

[0122] In the parameter optimization module, the minimum emergency control proportional coefficient is required when the system frequency is at its lowest point of 49Hz. This minimum coefficient K is applied to a series of values ​​corresponding to different proportions of renewable energy sources. ec1 The curve formed represents the lower boundary of emergency control; the required emergency control proportional coefficient is at its maximum value when the system frequency reaches its highest point of 50.5Hz. This represents the series of maximum values ​​K corresponding to different proportions of renewable energy sources. ec2 The resulting curve represents the upper boundary of emergency control.

[0123] In the parameter optimization module, the intersection point of two curves is determined based on the curve fitted by the least squares method, and the step size interval [x] for each synchronizer percentage after the curve intersection point is determined. sg (i),x sg [i+1], the area between each cell is calculated using the trapezoidal rule:

[0124]

[0125] Where A(0) is the initial value of the emergency control effectiveness boundary, A i (0) represents the boundary area for each small step size, and i represents the number of iterations for the synchronous machine.

[0126] In the parameter optimization module, the iterative values ​​of the grid-type renewable energy power reserve and droop coefficient are determined according to the gradient descent method.

[0127]

[0128] Where η1 and η2 are the learning rates, which control the step size of parameter updates in each iteration.

[0129] In the parameter optimization module, based on the new grid-type renewable energy power reserve and droop coefficient parameter values, a series of emergency control quantity boundary data under different proportions are iterated again, and the upper and lower boundary curves of emergency control are fitted by the least squares method. If the curves have no intersection, it means that the emergency control under the grid-type power reserve and droop coefficient has no effective boundary, and this parameter is not the optimal parameter. If the curves have intersection, the area of ​​the effective boundary of emergency control is recalculated until A(k+1)≥A(k) is no longer satisfied, and then the optimal parameter value at this time is output.

[0130] Based on the same inventive concept, this embodiment also provides a computing device, including: one or more processors, one or more memories, and one or more programs, wherein the programs are stored in the memory and configured to be executed by the processor, and when the programs are loaded onto the processor, they implement the steps of the grid-type new energy power reserve and droop coefficient optimization method according to any one of the above.

[0131] Based on the same inventive concept, this embodiment also provides a storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the steps of the grid-type new energy power reserve and droop coefficient optimization method according to any one of the above claims.

[0132] The storage medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage devices, magnetic disk storage devices or other magnetic storage devices, flash memory, or any other media that can be used to store desired program code in the form of instructions or data structures and is accessible by a computer.

[0133] The processor is used to execute a computer program stored in memory to implement the various steps in the methods described in the above embodiments.

Claims

1. A method for network-constructed new energy power backup and droop coefficient optimization, characterized in that, The method comprises the following steps: collecting basic data of a power system to be optimized under a certain working condition; determining a short-time power disturbance feature caused by a large amount of new energy entering low-voltage ride-through when the power system to be optimized has an N-1 fault; based on the basic data and the short-time power disturbance feature, constructing a system frequency response model considering frequency emergency control and grid-forming new energy, and obtaining a time-domain expression of system frequency by using Laplace inverse transform; the step of constructing the system frequency response model considering frequency emergency control and grid-forming new energy based on the basic data and the short-time power disturbance feature comprises the following steps: The system frequency is a piecewise function, and the system frequency is dynamic The domain expression is: ； wherein: ； When the governor and the grid-forming new energy backup are both saturated, the system frequency dynamics The domain expression is: ； wherein is the emergency control quantity proportionality coefficient, , is the emergency control time delay, is the system equivalent turbine characteristic coefficient, is the turbine equivalent time constant, is the governor droop of the synchronous machine, is the capacity proportion of the synchronous machine in the system, , is the equivalent inertia of the system generator, is the equivalent damping, is the virtual inertia coefficient of the grid-forming new energy, is the droop control coefficient of the grid-forming new energy, is the active power shortage of the new energy entering low voltage ride through, is the low voltage ride through time, is the low voltage ride through recovery time, is the governor limit, is the maximum value of the grid-forming new energy reserve, and is a non-zero initial state value; Determining initial value of network-forming new energy power backup , initial value of droop coefficient , and calculating upper and lower boundaries of emergency control effectiveness at this time based on the upper and lower boundaries of the effectiveness of emergency control, fitting an emergency control upper and lower boundary curve according to the least square method, and calculating an initial value of an emergency control effective boundary area; determining an iteration step length of the power reserve and droop coefficient of the grid-forming new energy according to the trapezoidal rule; iteratively calculating the emergency control upper and lower boundary and the emergency control effective boundary area when the power reserve and droop coefficient of the grid-forming new energy are new values, until the area of the emergency control effective boundary is maximum, and obtaining the optimized power reserve and droop coefficient of the new energy.

2. The network-constructing new energy power backup and droop coefficient optimization method according to claim 1, characterized in that, The step of collecting the basic data of the power system to be optimized under the certain working condition comprises the following steps: Determination of the equivalent inertia of the generators of a power system to be optimized by the weighted average method , equivalent damping , equivalent turbine characteristic coefficients of the system , equivalent time constant of the turbine and the droop of the synchronous machine governor ; The determination of the characteristics of the short-term power disturbance caused by a large amount of new energy entering low-voltage ride-through when the power system to be optimized has an N-1 fault includes: determining the active power shortage of new energy entering low-voltage ride-through , low-voltage ride-through time and low-voltage ride-through recovery time .

3. The method for optimizing the power reserve and droop coefficient of the grid-forming new energy according to claim 1, characterized in that, the step of obtaining the time-domain expression of system frequency by using Laplace inverse transform comprises the following steps: when the governor has not reached saturation, the time-domain expression of system frequency obtained by using Laplace inverse transform is: ； wherein: ； wherein, is a step function, is the current time; when the governor and the grid-forming new energy are saturated at the same time, the time-domain expression of the system frequency is obtained by Laplace inverse transform as follows: ； wherein: 。 4. The network-constructing new energy power backup and droop coefficient optimization method according to claim 1, characterized in that, The initial value of the determined network type new energy power backup , the initial value of the droop coefficient And calculate the upper and lower boundaries of the effectiveness of the emergency control at this time, including: Setting the initial value of the synchronous machine proportion , the initial value of the emergency control amount proportionality coefficient , increasing the emergency control amount proportionality coefficient at a preset fixed step under the condition that the synchronous machine proportion is fixed, until the following iteration target is met: ； The output of the synchronizer at this time is the initial value The two emergency control amount boundary values corresponding to the initial value of the synchronizer And , The initial value of the synchronizer The emergency control effective action amount interval corresponding to the initial value of the synchronizer, The precision value, The system frequency time domain after Laplace inverse transformation, the first iteration is completed, and the synchronizer is increased by a fixed step. The two emergency control amount boundary values corresponding to the initial value of the synchronizer are iteratively calculated And ; The minimum value of the emergency control amount proportional coefficient required when the system frequency is at the minimum point of 49Hz, and a series of minimum values corresponding to different new energy proportions The curve formed by the minimum values is the lower boundary of the emergency control; the maximum value of the emergency control amount proportional coefficient required when the system frequency is at the maximum point of 50.5Hz, and a series of maximum values corresponding to different new energy proportions The curve formed by the maximum values is the upper boundary of the emergency control.

5. The network-constructing new energy power backup and droop coefficient optimization method according to claim 1, characterized in that, the step of fitting the emergency control upper and lower boundary curve according to the least square method and calculating the initial value of the emergency control effective boundary area based on the upper and lower boundaries of the effectiveness of emergency control comprises the following steps: The intersection of the two curves is determined according to a curve fitted by the least square method, and each step interval of the synchronous machine proportion after the curve intersection The area of each small interval is calculated by using the trapezoidal rule. ； wherein is the initial value of the emergency control effectiveness boundary, is the boundary area of each small step, is the iteration number of the capacity ratio of the synchronous machine, and is the boundary value of the emergency control amount, is the capacity ratio of the synchronous machine in the system.

6. The network-constructing new energy power backup and droop coefficient optimization method according to claim 1, characterized in that, the step of determining the iteration step length of the power reserve and droop coefficient of the grid-forming new energy according to the trapezoidal rule comprises the following steps: Determining network-forming new energy power reserve according to gradient descent method , droop coefficient of iteration value, ； wherein and is the learning rate, is the number of iterations, is the system frequency time domain after Laplace inverse transform, controls the step size of parameter update in each iteration.

7. The network-constructing new energy power backup and droop coefficient optimization method according to claim 1, characterized in that, the step of iteratively calculating the emergency control upper and lower boundary and the emergency control effective boundary area when the power reserve and droop coefficient of the grid-forming new energy are new values, until the area of the emergency control effective boundary is maximum, and obtaining the optimized power reserve and droop coefficient of the new energy comprises the following steps: According to the new network type new energy power reserve, droop coefficient parameter value, a series of emergency control quantity boundary data under different proportion are re-iterated, and the emergency control upper and lower boundary curves are fitted through the least square method. If the curves have no intersection point, the emergency control under the power reserve and droop coefficient of the network type has no effective boundary, the parameter is not the optimal parameter. If the curves have intersection points, the emergency control effective boundary area is re-calculated until the condition is not met The optimal parameter value at this time is output.

8. A network-constructed new energy power backup and droop coefficient optimization system using the network-constructed new energy power backup and droop coefficient optimization method of claim 1, characterized in that, comprising: a data processing module configured to collect basic data of a power system to be optimized under a certain working condition; determining a short-time power disturbance feature caused by a large amount of new energy entering low-voltage ride-through when the power system to be optimized has an N-1 fault; a frequency model construction module configured to, based on the basic data and the short-time power disturbance feature, construct a system frequency response model considering frequency emergency control and grid-forming new energy, and obtain a time-domain expression of system frequency by using Laplace inverse transform; A parameter optimization module is configured to determine initial values of the grid-forming new energy power reserve and the droop coefficient , initial values of the droop coefficient , and calculate upper and lower boundaries of the emergency control effectiveness at this time; fit an emergency control upper and lower boundary curve according to the least square method, and calculate initial values of an effective boundary area of the emergency control; determine iteration steps of the grid-forming new energy power reserve and the droop coefficient according to the trapezoidal rule; iteratively calculate the emergency control upper and lower boundaries and the effective boundary area of the emergency control when the grid-forming new energy power reserve and the droop coefficient are new values, until the area of the effective boundary of the emergency control is maximum, and the optimized new energy power reserve and the droop coefficient are obtained.

9. A computing device, comprising: comprising: one or more processors, one or more memories, and one or more programs stored in the memories and configured to be executed by the processors, the programs, when loaded into the processors, implement the steps of the method for optimizing the power reserve and droop coefficient of the grid-forming new energy according to any one of claims 1 to 7.

10. A storage medium, characterized by The storage medium stores a computer program, the computer program comprising program instructions, the program instructions causing the processor to execute the steps of the network construction type new energy power backup and droop coefficient optimization method according to any one of claims 1 to 7 when executed by the processor.

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