Photovoltaic and wind generator group collaborative control method, device, equipment and medium

Abstract

The present application belongs to the field of new energy grid-connected power fluctuation control technology, and discloses a photovoltaic and wind turbine group cooperative control method, device, equipment and medium; the method comprises the following steps: establishing a regression equation of a photovoltaic output control signal according to observation data; establishing a controlled target function of photovoltaic power station group and wind turbine group cooperative control; jointly solving to obtain a target of photovoltaic power station group and wind turbine group cooperative control at time t; and outputting the target of photovoltaic power station group and wind turbine group cooperative control at time t. The present application can realize the simultaneous cooperative control and joint grid connection of wind power and photovoltaic renewable energy generation, can inhibit the power voltage fluctuation of renewable energy on the electrical transient characteristic, can reduce the output randomness, can improve the controllability and schedulability of the wind and light combination to the overall power grid, can effectively reduce the impact challenge of renewable energy to the power system, and can improve the cooperation performance of new energy cluster power generation in tracking the power output of power grid scheduling plan.

Description

Technical Field

[0001] This invention belongs to the field of power fluctuation control technology for new energy grid connection, and specifically relates to a method, device, equipment and medium for coordinated control of photovoltaic and wind turbine generator groups. Background Technology

[0002] The grid connection of large-scale renewable energy sources such as wind and solar power will significantly increase the risk of power imbalance in the power grid system, posing a great challenge to the power transmission and safe and stable operation of the grid. Large-scale clustered photovoltaic power plants, as an important dispatchable power generation resource for grid stability, have promising application prospects in the future. To address these challenges, one feasible technical approach is to research large-scale photovoltaic and wind power coordinated control technologies to improve the grid connection efficiency of new energy power generation. However, no mature large-scale photovoltaic and wind power coordinated control technologies have been publicly disclosed in the current technology field. Summary of the Invention

[0003] The purpose of this invention is to provide a method, device, equipment and medium for coordinated control of photovoltaic and wind turbine generator groups, so as to improve the grid connection effect of new energy power generation.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] In a first aspect, the present invention provides a method for coordinated control of photovoltaic and wind turbine generator groups, comprising:

[0006] Get several groups The observation data; The goal is to achieve coordinated control of photovoltaic power plant clusters and wind turbine clusters. For photovoltaic output control signals; according to the aforementioned groups The observation data were used to establish a regression equation for the photovoltaic output control signal;

[0007] Establish the controlled objective function for the coordinated control of photovoltaic power plant clusters and wind turbine clusters;

[0008] By jointly solving the regression equation of the photovoltaic output control signal and the controlled objective function of the coordinated control of the photovoltaic power plant group and the wind turbine group, the objective function of the coordinated control of the photovoltaic power plant group and the wind turbine group at time t is obtained.

[0009] Output the target of the coordinated control of the photovoltaic power station group and the wind turbine group at time t.

[0010] A further improvement of the present invention is that: the acquisition of several groups The observation data; The goal is to achieve coordinated control of photovoltaic power plant clusters and wind turbine clusters. For photovoltaic output control signals; according to the aforementioned groups The steps for establishing a regression equation for the photovoltaic output control signal based on observational data specifically include:

[0011] Establish photovoltaic output control signal Dynamic nonlinear time-varying parameter regression function:

[0012]

[0013] In the formula: To observe noise; These are the parameters of the mathematical model, where t is the current time. Let be the photovoltaic output control signal at time t; The objective of coordinated control of the photovoltaic power station group and the wind turbine group at time t;

[0014] According to known Observational data T p (i), Establish the regression equation for the photovoltaic output control signal:

[0015]

[0016] In the formula, For parameters The time-varying estimate, Photovoltaic output control signal The estimated value.

[0017] A further improvement of the present invention is that it also includes the following steps:

[0018] Define error:

[0019]

[0020] In the formula, The observed data for the i-th group of photovoltaic output control signals; T p (i) represents the observed power data of the nodes in the i-th group of wind turbines; For the parameters of the i-th group of wind turbines The time-varying estimate;

[0021] The norm of the error is:

[0022]

[0023] Where TL represents the current time step; as the criterion function, it is optimized according to the principles of mathematical analysis. Minimize ν;

[0024]

[0025]

[0026] Solving for:

[0027]

[0028]

[0029] A further improvement of this invention is that the step of establishing the controlled objective function for the coordinated control of the photovoltaic power plant group and the wind turbine group specifically includes:

[0030] Based on the inverse function of equation (1), let x i (t) is the input quantity, u o (t) is the output quantity, u o (t-1) is the output at the previous time step (t-1). The objective function for the coordinated control of the photovoltaic power station group and the wind turbine group is derived as follows:

[0031]

[0032] ε represents the error coefficient;

[0033] Substituting equations (7) and (8) into equation (9), we get:

[0034]

[0035] After Laplace transform, the transfer function of the controlled system is derived as follows:

[0036]

[0037] Among them, U o (S) represents u o (t) The variable after the Laplace transform.

[0038] A further improvement of the present invention is that the target of the coordinated control of the photovoltaic power station group and the wind turbine group is an electrical control quantity; the electrical control quantity is power, voltage or frequency.

[0039] Secondly, the present invention provides a photovoltaic and wind turbine generator group coordinated control device, comprising:

[0040] The first modeling module is used to obtain several sets of... The observation data; The goal is to achieve coordinated control of photovoltaic power plant clusters and wind turbine clusters. For photovoltaic output control signals; according to the aforementioned groups The observation data were used to establish a regression equation for the photovoltaic output control signal;

[0041] The second modeling module is used to establish the controlled objective function for the coordinated control of photovoltaic power plant groups and wind turbine groups;

[0042] The solver module is used to jointly solve the regression equation of the photovoltaic output control signal and the controlled objective function of the coordinated control of the photovoltaic power plant group and the wind turbine group, so as to obtain the objective function of the coordinated control of the photovoltaic power plant group and the wind turbine group at time t.

[0043] The output module is used to output the target of the coordinated control of the photovoltaic power station group and the wind turbine group at time t.

[0044] A further improvement of the present invention is that: the acquisition of several groups The observation data; The goal is to achieve coordinated control of photovoltaic power plant clusters and wind turbine clusters. For photovoltaic output control signals; according to the aforementioned groups The steps for establishing a regression equation for the photovoltaic output control signal based on observational data specifically include:

[0045] Establish photovoltaic output control signal Dynamic nonlinear time-varying parameter regression function:

[0046]

[0047] In the formula: To observe noise; These are the parameters of the mathematical model, where t is the current time. Let be the photovoltaic output control signal at time t; The objective of coordinated control of the photovoltaic power station group and the wind turbine group at time t;

[0048] According to known Observational data T p (i), Establish the regression equation for the photovoltaic output control signal:

[0049]

[0050] In the formula, For parameters The time-varying estimate, Photovoltaic output control signal The estimated value;

[0051] Define error:

[0052]

[0053] In the formula, The observed data for the i-th group of photovoltaic output control signals; Tp (i) represents the observed power data of the nodes in the i-th group of wind turbines; For the parameters of the i-th group of wind turbines The time-varying estimate;

[0054] The norm of the error is:

[0055]

[0056] Where TL represents the current time step; as the criterion function, it is used to find the optimal value based on the principles of mathematical analysis. Minimize ν;

[0057]

[0058]

[0059] Solving for:

[0060]

[0061]

[0062] A further improvement of this invention is that the step of establishing the controlled objective function for the coordinated control of the photovoltaic power plant group and the wind turbine group specifically includes:

[0063] Based on the inverse function of equation (1), let x i (t) is the input quantity, u o (t) is the output quantity, u o (t-1) is the output at the previous time step (t-1). The objective function for the coordinated control of the photovoltaic power station group and the wind turbine group is derived as follows:

[0064]

[0065] ε represents the error coefficient;

[0066] Substituting equations (7) and (8) into equation (9), we get:

[0067]

[0068] After Laplace transform, the transfer function of the controlled system is derived as follows:

[0069]

[0070] Among them, U o (S) represents u o (t) The variable after the Laplace transform.

[0071] Thirdly, the present invention provides an electronic device comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the aforementioned photovoltaic and wind turbine cluster coordinated control method.

[0072] Fourthly, the present invention provides a computer-readable storage medium storing at least one instruction, which, when executed by a processor, implements the photovoltaic and wind turbine cluster coordinated control method.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] This invention provides a method, apparatus, equipment, and medium for the coordinated control of photovoltaic and wind turbine generator clusters. It enables coordinated control of photovoltaic and wind turbine generator clusters, and joint coordinated control and grid connection of wind power and photovoltaic renewable energy generation. This suppresses power and voltage fluctuations of renewable energy in terms of electrical transient characteristics, reduces output randomness, and improves the controllability and dispatchability of the wind-solar complex on the overall power grid. It effectively reduces the impact of renewable energy on the power system and improves the coordination performance of new energy cluster power generation in tracking the power output of the grid dispatch plan. The invention's control method can analytically calculate the step size of each step, making it suitable for use in parallel hardware development such as FPGAs and improving development efficiency.

[0075] This invention provides a method for coordinated control of photovoltaic and wind turbine generator groups, which can enhance the peak-shaving and frequency regulation stability of the power grid. It dynamically adjusts the power output level of the combined system in real time according to grid dispatch requirements. The seamless coordination of wind and photovoltaic power can reduce the overall social cost of traditional spinning reserve and pumped storage power sources. Furthermore, it can be linked with receiving-end load regulation to improve the real-time dynamic power supply reliability and high-quality power quality of the power source and grid to load users. In today's era of global warming and frequent natural disasters, wars, and other emergencies, the joint control of wind and photovoltaic power and other new energy sources is an important and long-term sustainable energy lifeline for emergency power, effectively improving the emergency response capabilities of the entire society. It is the best foreseeable clean energy combination under current technological conditions, and its application scenarios will be further deepened and refined, with a broader research space. Attached Figure Description

[0076] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0077] Figure 1 The simulation example diagram shows the wind turbine and BESS collaborative control system.

[0078] Figure 2This is a photovoltaic collaborative control response signal;

[0079] Figure 3 Output active power to the photovoltaic cluster control system;

[0080] Figure 4 To improve the voltage disturbance control effect of photovoltaic control systems;

[0081] Figure 5 The waveforms show the system current characteristics with and without coordinated control; the waveform without coordinated control goes offline, while the waveform with coordinated control returns to stability.

[0082] Figure 6 A comparison chart of active power disturbance characteristics of grid-connected systems; waveforms with coordinated control can recover to stability, while waveforms without coordinated control experience offline power loss;

[0083] Figure 7 A comparison of reactive power disturbance characteristics of grid-connected systems;

[0084] Figure 8 This is a diagram illustrating the effect of coordinated control; the frequency can recover stability after a fault disturbance.

[0085] Figure 9 This is a diagram showing the effect without coordinated control; the grid connection frequency of the wind turbine experiences continuous oscillation and instability after a fault disturbance.

[0086] Figure 10 This is a flowchart illustrating a method for coordinated control of photovoltaic and wind turbine generator groups according to the present invention.

[0087] Figure 11 This is a structural block diagram of a photovoltaic and wind turbine generator group coordinated control device according to the present invention;

[0088] Figure 12 This is a schematic diagram of the structure of an electronic device according to the present invention. Detailed Implementation

[0089] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0090] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0091] Example 1

[0092] The timescale of grid faults differs significantly from that of the rotational inertia of the wind turbine rotor's mechanical components. Therefore, in cascading faults, the electromagnetic transient characteristics of the wind turbine far outweigh the impact of changes in wind speed and rotational speed. Consequently, the influence of wind speed on the turbine can be considered relatively small during cascading faults. In the context of microsecond-level transients caused by the severe fluctuations in large-scale wind power grid connection, this invention addresses how to utilize photovoltaic clusters for effective power-friendly control and grid fluctuation suppression, representing a highly valuable technology.

[0093] The objective of coordinated control of photovoltaic power plant clusters and wind turbine clusters is to This invention models photovoltaic output control signals. The dynamic nonlinear time-varying parameter regression function is:

[0094]

[0095] In the formula: To observe noise; These are the parameters of the mathematical model, where t is the current time. Let be the photovoltaic output control signal at time t; The objective is to coordinate the control of the photovoltaic power station group and the wind turbine group at time t.

[0096] According to known N sets of observation data T p (i), (i = t - ΔL + 1, where t is the current time and ΔL is the width of the dynamically identified data set), let its regression equation be:

[0097]

[0098] In the formula, For parameters The time-varying estimate, Photovoltaic output control signal The estimated value. Error is defined as:

[0099]

[0100] In the formula, The observed data for the i-th group of photovoltaic output control signals; T p (i) represents the observed power data of the nodes in the i-th group of wind turbines; For the parameters of the i-th group of wind turbines The time-varying estimate of .

[0101] Then the norm of the error:

[0102]

[0103] Where TL represents the current time step; as the criterion function, it is used to find the optimal value based on the principles of mathematical analysis. Minimize ν.

[0104]

[0105]

[0106] Solving for:

[0107]

[0108]

[0109] Based on the inverse function of equation (1) above, let x i (t) is the input quantity, u o (t) is the output quantity, u o (t-1) is the output at the previous time step (t-1). The objective function for the coordinated control of the photovoltaic power station group and the wind turbine group is derived as follows:

[0110]

[0111] ε represents the error coefficient;

[0112] Substituting (7) and (8) into (9), we get:

[0113]

[0114] After Laplace transform, the transfer function of the controlled system can be derived as follows:

[0115]

[0116] Among them, U o (S) represents u o (t) Variables after Laplace transformation; According to the above formulas (1)-(8) and (9)-(11), the analytical equations of the control system model and the analytical equations of the controlled system can be established respectively. By jointly solving formulas (1)-(11), the target of the coordinated control of the photovoltaic power station group and the wind turbine group at time t can be directly obtained. The tracking control method is applicable to a variety of electrical control quantities, including power, voltage, frequency, etc.

[0117] To verify the regulating effect of the BESS transient model after grid disturbance, a simulation analysis was conducted on the active power output changes of the large power grid system before and after the application of a cluster photovoltaic power station during a wind farm cascade grounding short-circuit fault. Based on the aforementioned control model, a simulation algorithm was built. Figure 1 As shown.

[0118] according to Figure 1The example shown calculates and verifies the simulation analysis effect of the clustered photovoltaic power station on power fluctuation suppression under cascading failures in wind farms. Specifically, as follows... Figure 2 The photovoltaic collaborative control response signal is shown below; Figure 2 It can be seen that the cooperative control system can respond with control based on the control objective; Figure 2 The vertical axis represents the control system signals, with 0 indicating stop and 1 indicating start-up power output.

[0119] Figure 3 To output active power to the photovoltaic cluster control system, Figure 4 To improve the voltage disturbance control effect of photovoltaic control systems; active power output and voltage disturbance control can be performed according to control needs.

[0120] Figure 5 The diagram shows a comparison of system current characteristics; a comparison of waveforms with and without coordinated control. The waveform without coordinated control goes offline, while the waveform with coordinated control recovers stability.

[0121] Figure 6 The diagram shows a comparison of the active power disturbance characteristics of the grid-connected system. It can be seen that the waveform with coordinated control can recover to stability, while the waveform without coordinated control experiences offline power loss.

[0122] Figure 7 This is a comparison chart of reactive power disturbance characteristics of grid-connected systems, reflecting the effect curve and response of reactive power control.

[0123] Figure 8 This is a schematic diagram illustrating the effect of coordinated control, showing that the frequency can recover and stabilize after a fault disturbance. Figure 9 Without coordinated control, the grid connection frequency of the wind turbines experienced continuous oscillations and instability after a fault disturbance.

[0124] This invention primarily focuses on grid-connected simulation analysis of the established large-scale cluster photovoltaic system model and cooperative control algorithm. The transient characteristics of the photovoltaic model's frequency and active power under fault-free conditions are analyzed and simulated. The photovoltaic control effect under cascading short-circuit faults and turbine tripping conditions in both large power grids and large-scale wind power systems is simulated. Simulations show that the transient stability characteristics of the system are significantly improved after the photovoltaic model is integrated into the grid-connected system. The proposed cooperative control algorithm is highly effective in mitigating fluctuations in large-scale cluster renewable energy, resisting disturbances during extreme cascading fault transient processes, and maintaining system power stability under fault power tripping conditions. Simulation analysis demonstrates that the control model established in this invention is effective in maintaining voltage and power stability in large power grid systems under wind and photovoltaic combined grid-connected scenarios, and can significantly improve the energy impact and power quality of large-scale wind power grid connection. The control method of this invention can analytically calculate the step size for each step, making it suitable for use in parallel hardware development such as FPGAs and improving development efficiency.

[0125] Example 2

[0126] Please see Figure 10 As shown, the present invention provides a method for coordinated control of photovoltaic and wind turbine generator groups, including:

[0127] Get several groups The observation data; The goal is to achieve coordinated control of photovoltaic power plant clusters and wind turbine clusters. For photovoltaic output control signals; according to the aforementioned groups The observation data were used to establish a regression equation for the photovoltaic output control signal;

[0128] Establish the controlled objective function for the coordinated control of photovoltaic power plant clusters and wind turbine clusters;

[0129] By jointly solving the regression equation of the photovoltaic output control signal and the controlled objective function of the coordinated control of the photovoltaic power plant group and the wind turbine group, the objective function of the coordinated control of the photovoltaic power plant group and the wind turbine group at time t is obtained.

[0130] Output the target of coordinated control of the photovoltaic power station group and the wind turbine group at time t.

[0131] In one specific implementation, the acquisition of several groups The observation data; The goal is to achieve coordinated control of photovoltaic power plant clusters and wind turbine clusters. For photovoltaic output control signals; according to the aforementioned groups The steps for establishing a regression equation for the photovoltaic output control signal based on observational data specifically include:

[0132] Establish photovoltaic output control signal Dynamic nonlinear time-varying parameter regression function:

[0133]

[0134] In the formula: To observe noise; These are the parameters of the mathematical model, where t is the current time. Let be the photovoltaic output control signal at time t; The objective of coordinated control of the photovoltaic power station group and the wind turbine group at time t;

[0135] According to known Observational data T p (i), Establish the regression equation for the photovoltaic output control signal:

[0136]

[0137] In the formula, For parameters The time-varying estimate, Photovoltaic output control signal The estimated value.

[0138] In one specific implementation, the following steps are also included:

[0139] Define error:

[0140]

[0141] In the formula, The observed data for the i-th group of photovoltaic output control signals; T p (i) represents the observed power data of the nodes in the i-th group of wind turbines; For the parameters of the i-th group of wind turbines The time-varying estimate;

[0142] The norm of the error is:

[0143]

[0144] Where TL represents the current time step; as the criterion function, it is used to find the optimal value based on the principles of mathematical analysis. Minimize ν;

[0145]

[0146]

[0147] Solving for:

[0148]

[0149]

[0150] In one specific embodiment, the step of establishing the controlled objective function for the coordinated control of the photovoltaic power plant group and the wind turbine group specifically includes:

[0151] Based on the inverse function of equation (1), let x i (t) is the input quantity, u o (t) is the output quantity, u o (t-1) is the output at the previous time step (t-1). The objective function for the coordinated control of the photovoltaic power station group and the wind turbine group is derived as follows:

[0152]

[0153] ε represents the error coefficient;

[0154] Substituting equations (7) and (8) into equation (9), we get:

[0155]

[0156] After Laplace transform, the transfer function of the controlled system is derived as follows:

[0157]

[0158] Among them, U o (S) represents u o (t) The variable after the Laplace transform.

[0159] In one specific implementation, the objective of the coordinated control of the photovoltaic power station group and the wind turbine group is an electrical control quantity; the electrical control quantity is power, voltage, or frequency.

[0160] Example 3

[0161] Please see Figure 11 As shown, the present invention also provides a photovoltaic and wind turbine generator group coordinated control device, comprising:

[0162] The first modeling module is used to obtain several sets of... The observation data; The goal is to achieve coordinated control of photovoltaic power plant clusters and wind turbine clusters. For photovoltaic output control signals; according to the aforementioned groups The observation data were used to establish a regression equation for the photovoltaic output control signal;

[0163] The second modeling module is used to establish the controlled objective function for the coordinated control of photovoltaic power plant groups and wind turbine groups;

[0164] The solver module is used to jointly solve the regression equation of the photovoltaic output control signal and the controlled objective function of the coordinated control of the photovoltaic power plant group and the wind turbine group, so as to obtain the objective function of the coordinated control of the photovoltaic power plant group and the wind turbine group at time t.

[0165] The output module is used to output the target of the coordinated control of the photovoltaic power station group and the wind turbine group at time t.

[0166] In one specific implementation, the acquisition of several groups The observation data; The goal is to achieve coordinated control of photovoltaic power plant clusters and wind turbine clusters. For photovoltaic output control signals; according to the aforementioned groups The steps for establishing a regression equation for the photovoltaic output control signal based on observational data specifically include:

[0167] Establish photovoltaic output control signal Dynamic nonlinear time-varying parameter regression function:

[0168]

[0169] In the formula: To observe noise; These are the parameters of the mathematical model, where t is the current time. Let be the photovoltaic output control signal at time t; The objective of coordinated control of the photovoltaic power station group and the wind turbine group at time t;

[0170] According to known Observational data T p (i), Establish the regression equation for the photovoltaic output control signal:

[0171]

[0172] In the formula, For parameters The time-varying estimate, Photovoltaic output control signal The estimated value;

[0173] Define error:

[0174]

[0175] In the formula, The observed data for the i-th group of photovoltaic output control signals; T p (i) represents the observed power data of the nodes in the i-th group of wind turbines; For the parameters of the i-th group of wind turbines The time-varying estimate;

[0176] The norm of the error is:

[0177]

[0178] Where TL represents the current time step; as the criterion function, it is used to find the optimal value based on the principles of mathematical analysis. Minimize ν;

[0179]

[0180]

[0181] Solving for:

[0182]

[0183]

[0184] In one specific embodiment, the step of establishing the controlled objective function for the coordinated control of the photovoltaic power plant group and the wind turbine group specifically includes:

[0185] Based on the inverse function of equation (1), let x i (t) is the input quantity, u o (t) is the output quantity, u o (t-1) is the output at the previous time step (t-1). The objective function for the coordinated control of the photovoltaic power station group and the wind turbine group is derived as follows:

[0186]

[0187] ε represents the error coefficient;

[0188] Substituting equations (7) and (8) into equation (9), we get:

[0189]

[0190] After Laplace transform, the transfer function of the controlled system is derived as follows:

[0191]

[0192] Among them, U o (S) represents u o (t) The variable after the Laplace transform.

[0193] Example 4

[0194] Please see Figure 12 As shown, the present invention also provides an electronic device 100 for realizing a method for coordinated control of photovoltaic and wind turbine generator groups; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0195] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the photovoltaic and wind turbine cluster coordinated control method described in Embodiment 1 or 2 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0196] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0197] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for coordinated control of photovoltaic and wind turbine generator groups, and the processor 102 can execute the multiple instructions to achieve the following:

[0198] Get several groups The observation data; The goal is to achieve coordinated control of photovoltaic power plant clusters and wind turbine clusters. For photovoltaic output control signals; according to the aforementioned groups The observation data were used to establish a regression equation for the photovoltaic output control signal;

[0199] Establish the controlled objective function for the coordinated control of photovoltaic power plant clusters and wind turbine clusters;

[0200] By jointly solving the regression equation of the photovoltaic output control signal and the controlled objective function of the coordinated control of the photovoltaic power plant group and the wind turbine group, the objective function of the coordinated control of the photovoltaic power plant group and the wind turbine group at time t is obtained.

[0201] Output the target of coordinated control of the photovoltaic power station group and the wind turbine group at time t.

[0202] Example 5

[0203] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0204] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of 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-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0205] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0206] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0207] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0208] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for coordinated control of photovoltaic and wind turbine generator groups, characterized in that, include: Get several groups , The observation data; The goal is to achieve coordinated control of photovoltaic power plant clusters and wind turbine clusters. For photovoltaic output control signals; according to the aforementioned groups , The observation data were used to establish a regression equation for the photovoltaic output control signal; Establish the controlled objective function for the coordinated control of photovoltaic power plant clusters and wind turbine clusters; By jointly solving the regression equation of the photovoltaic output control signal and the controlled objective function of the coordinated control of the photovoltaic power plant group and the wind turbine group, the objective function of the coordinated control of the photovoltaic power plant group and the wind turbine group at time t is obtained. ; Output the target of coordinated control of the photovoltaic power station group and the wind turbine group at time t. ; The acquisition of several groups , The observation data; The goal is to achieve coordinated control of photovoltaic power plant clusters and wind turbine clusters. For photovoltaic output control signals; according to the aforementioned groups , The steps for establishing a regression equation for the photovoltaic output control signal based on observational data specifically include: Establish photovoltaic output control signal Dynamic nonlinear time-varying parameter regression function: (1) In the formula: To observe noise; , These are the parameters of the mathematical model, where t is the current time. Let be the photovoltaic output control signal at time t; The objective of coordinated control of the photovoltaic power station group and the wind turbine group at time t; According to known , Observational data , Establish the regression equation for the photovoltaic output control signal: (2) In the formula, , For parameters , The time-varying estimate, Photovoltaic output control signal The estimated value; It also includes the following steps: Define error: (3) In the formula, The observed data for the i-th group of photovoltaic output control signals; The observed power data for the nodes of the i-th group of wind turbines; , For the parameters of the i-th group of wind turbines , The time-varying estimate; The norm of the error is: (4) Where TL represents the current time step; as the criterion function, it is used to find the optimal value based on the principles of mathematical analysis. , ,make Minimum; (5) (6) Solving for: (7) (8)； The steps for establishing the controlled objective function for the coordinated control of photovoltaic power plant clusters and wind turbine clusters specifically include: Based on the inverse function of equation (1), let It is the input quantity. It is the output quantity. It was the previous step Based on the output, the controlled objective function for the coordinated control of the photovoltaic power plant group and the wind turbine group is derived as follows: （9） Indicates the error coefficient; Substituting equations (7) and (8) into equation (9), we get: （10） After Laplace transform, the transfer function of the controlled system is derived as follows: （11） in, express The variable after Laplace transformation.

2. The method for coordinated control of photovoltaic and wind turbine generator groups according to claim 1, characterized in that, The objective of the coordinated control of the photovoltaic power station group and the wind turbine group is the electrical control quantity; the electrical control quantity is power, voltage or frequency.

3. A photovoltaic and wind turbine generator group coordinated control device, characterized in that, include: The first modeling module is used to obtain several sets of... , The observation data; The goal is to achieve coordinated control of photovoltaic power plant clusters and wind turbine clusters. For photovoltaic output control signals; according to the aforementioned groups , The observation data were used to establish a regression equation for the photovoltaic output control signal; The second modeling module is used to establish the controlled objective function for the coordinated control of photovoltaic power plant groups and wind turbine groups; The solver module is used to jointly solve the regression equation of the photovoltaic output control signal and the controlled objective function of the coordinated control of the photovoltaic power plant group and the wind turbine group, so as to obtain the objective function of the coordinated control of the photovoltaic power plant group and the wind turbine group at time t. ; The output module is used to output the target of the coordinated control of the photovoltaic power station group and the wind turbine group at time t. ; The acquisition of several groups , The observation data; The goal is to achieve coordinated control of photovoltaic power plant clusters and wind turbine clusters. For photovoltaic output control signals; according to the aforementioned groups , The steps for establishing a regression equation for the photovoltaic output control signal based on observational data specifically include: Establish photovoltaic output control signal Dynamic nonlinear time-varying parameter regression function: (1) In the formula: To observe noise; , These are the parameters of the mathematical model, where t is the current time. Let be the photovoltaic output control signal at time t; The objective of coordinated control of the photovoltaic power station group and the wind turbine group at time t; According to known , Observational data , Establish the regression equation for the photovoltaic output control signal: (2) In the formula, , For parameters , The time-varying estimate, Photovoltaic output control signal The estimated value; Define error: (3) In the formula, The observed data for the i-th group of photovoltaic output control signals; The observed data are for the node power of the i-th group of wind turbines. , For the parameters of the i-th group of wind turbines , The time-varying estimate; The norm of the error is: (4) Where TL represents the current time step; as the criterion function, it is used to find the optimal value based on the principles of mathematical analysis. , ,make Minimum; (5) (6) Solving for: (7) (8)； The steps for establishing the controlled objective function for the coordinated control of photovoltaic power plant clusters and wind turbine clusters specifically include: Based on the inverse function of equation (1), let It is the input quantity. It is the output quantity. It was the previous step Based on the output, the controlled objective function for the coordinated control of the photovoltaic power plant group and the wind turbine group is derived as follows: （9） Indicates the error coefficient; Substituting equations (7) and (8) into equation (9), we get: （10） After Laplace transform, the transfer function of the controlled system is derived as follows: （11） in, express The variable after Laplace transformation.

4. An electronic device, characterized in that, The electronic device includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the photovoltaic and wind turbine cluster coordinated control method as described in any one of claims 1 to 2.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the photovoltaic and wind turbine cluster coordinated control method as described in any one of claims 1 to 2.

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