Self-synchronizing voltage source photovoltaic unit voltage out-of-limit optimization method under static stability constraint

Abstract

The invention provides a voltage out-of-limit optimization method for a self-synchronizing voltage source photovoltaic unit under static stability constraint, and the method comprises the steps: building an electrical constraint model for the grid-connected operation of the photovoltaic unit, and enabling the electrical constraints to at least comprise a grid-connected point voltage amplitude constraint, an inverter output current constraint, and an apparent power transmission capability constraint; establishing a control constraint model, and performing unified modeling on control constraints under the conditions of pure inductive, pure resistive and resistive-inductive hybrid power grids based on a line impedance angle by introducing a virtual power rotation matrix; constructing a multi-objective optimization problem according to the electrical constraint model and the control constraint model; and solving the optimization problem through a numerical optimization method, and calculating to obtain the maximum active power transmission limit of the photovoltaic unit meeting all constraints under the current power grid condition. According to the technical scheme, optimization control over the voltage out-of-limit problem of the self-synchronizing voltage source type photovoltaic unit is achieved, and the operation stability and the power transmission capacity of the system under the complex power grid condition are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy power generation devices, in particular to a self-synchronous voltage source photovoltaic unit voltage out-of-limit optimization method under static stability constraints, equipment and storage medium. BACKGROUND

[0002] With the continuous improvement of renewable energy penetration, grid-connected converters have become a research hotspot due to their ability to simulate the behavior of synchronous generators. One of the main difficulties is how to ensure maximum active power transmission and voltage not out-of-limit under complex grid conditions. Existing technologies mainly analyze maximum active power transmission and grid-connected point voltage out-of-limit from two dimensions of electrical constraints and control constraints, and are mostly based on the assumption of purely inductive power grids. In terms of electrical constraint modeling, the main considerations are power flow constraints such as grid-connected point voltage amplitude limit and power device output current limit under inductive power grids; in terms of control constraint modeling, based on the active-reactive decoupling characteristics under inductive power grids, the static constraints of “active-frequency” (such as power synchronization ring) and “reactive-voltage” (such as reactive-voltage droop) control laws are considered.

[0003] In the prior art, a maximum power supply capacity calculation method of a power grid considering dynamic voltage stability constraints is disclosed in CN114899818A. Step 1: establish a maximum power supply capacity calculation model of a partition interconnected power grid considering dynamic voltage stability constraints, including: establishing a target function of maximum power supply capacity; establishing constraint conditions of maximum power supply capacity, including: partition interconnected device constraints, AC power grid equality constraints, node voltage amplitude constraints and dynamic voltage stability constraints; wherein, in the dynamic voltage stability constraints, an improved small disturbance analysis method is used, and the voltage of the load node is used instead of the equivalent potential in the electromagnetic power; Step 2: use a deep deterministic policy gradient algorithm to solve the maximum power supply capacity calculation model of the partition interconnected power grid considering dynamic voltage stability constraints, and obtain the maximum power supply capacity value.

[0004] However, the above method is mainly for purely inductive power grids and is difficult to apply to resistance-inductive hybrid power grids with large resistance. The main problems are as follows: 1) active power and reactive power are difficult to decouple under resistance-inductive power grids, how to establish control static constraints considering active-reactive coupling characteristics; 2) for purely inductive, purely resistive and resistance-inductive hybrid power grids, how to establish a unified calculation framework for maximum transmission active power under electrical constraints and control constraints. SUMMARY

[0005] In view of the defects in the prior art, the purpose of the present application is to provide a self-synchronous voltage source photovoltaic unit voltage out-of-limit optimization method under static stability constraints.

[0006] In a first aspect of the present application, a static stability constraint self-synchronization voltage source photovoltaic unit voltage out-of-limit optimization method is provided, comprising: An electrical constraint model of grid-connected operation of a photovoltaic unit is established, and the electrical constraint at least includes: a grid-connected point voltage amplitude constraint, an inverter output current constraint, and an apparent power transmission capability constraint; A control constraint model is established, and by introducing a virtual power rotation matrix, the control constraint under conditions of a pure inductive, pure resistive, and resistive-inductive hybrid power grid is uniformly modeled based on a line impedance angle; According to the electrical constraint model and the control constraint model, a multi-objective optimization problem is constructed; The optimization problem is solved by a numerical optimization method, and the maximum active power transmission limit of the photovoltaic unit that meets all constraints under the current power grid condition is calculated.

[0007] Optionally, the condition expression of the grid-connected point voltage amplitude constraint is: ; In the formula, is the grid-connected point voltage amplitude, and are the upper and lower voltage limits; The condition expression of the output current constraint is: ; In the formula, is the current component in the control coordinate system, is the maximum current limit of the converter; The apparent power transmission capability constraint is equivalent to the output current constraint.

[0008] Optionally, the establishment of the control constraint model, the introduction of the virtual power rotation matrix related to the grid impedance angle, the coordinate transformation of the active power and the reactive power by the virtual power rotation matrix to obtain the virtual active power and the virtual reactive power, the introduction of the virtual active power and the virtual reactive power into the active-frequency droop control and the reactive-voltage droop control link to form the control constraint, and the unified control constraint modeling under the conditions of the pure inductive, pure resistive, and resistive-inductive hybrid power grid to obtain the pure inductive control constraint modeling, the pure resistive control constraint modeling, and the resistive-inductive control constraint modeling are included. The virtual power rotation matrix related to the grid impedance angle is introduced; The active power and the reactive power are coordinate-transformed by the virtual power rotation matrix to obtain the virtual active power and the virtual reactive power; The virtual active power and the virtual reactive power are introduced into the active-frequency droop control and the reactive-voltage droop control link to form the control constraint; The unified control constraint modeling under the conditions of the pure inductive, pure resistive, and resistive-inductive hybrid power grid is obtained according to the control constraint, and the pure inductive control constraint modeling, the pure resistive control constraint modeling, and the resistive-inductive control constraint modeling are obtained.

[0009] Optionally, the coordinate transformation of the active power and the reactive power by the virtual power rotation matrix to obtain the virtual active power and the virtual reactive power includes: Obtain the virtual power rotation matrix related to the grid impedance angle; Based on the power flow calculation formula, the active power and reactive power are obtained; The active power and reactive power are rotated in coordinates using the virtual power rotation matrix to obtain virtual active power and reactive power. The expression for the virtual power rotation matrix is ​​as follows: ; In the formula, For virtual power rotation matrix; The line impedance angle; The line impedance angle Defined as: ; In the formula, For line inductance component, For line impedance components; The expressions for the active power and the reactive power are: ; In the formula, Active power; Reactive power; This refers to the voltage amplitude at the grid connection point. δ represents the amplitude of the AC grid voltage; δ represents the phase of the voltage at the grid connection point relative to the AC grid voltage.

[0010] The expressions for the virtual active power and reactive power are: ; In the formula, Line impedance; This is a virtual power rotation matrix.

[0011] Optionally, the unified control constraint modeling under pure inductive, pure resistive, and mixed resistive-inductive power grid conditions yields pure inductive control constraint modeling, pure resistive control constraint modeling, and resistive-inductive control constraint modeling, wherein: Under purely inductive power grid conditions, the control constraint expression for the purely inductive control constraint modeling is as follows: ; In the formula, , These are reference values ​​for active and reactive power. This is the voltage reference value. This is the voltage droop factor. This is the reactive power droop coefficient; Under purely resistive power grid conditions, the control constraint expression for the purely resistive control constraint modeling is: ; wherein, is a virtual active power reference value; is a virtual reactive power reference value; is an active power; is a reactive power; is a grid-connected point voltage amplitude; In the condition of a resistive-inductive hybrid power grid, the control constraint expression of the resistive-inductive control constraint modeling is: ; wherein, is a virtual active power; is a virtual reactive power.

[0012] Optionally, in the multi-objective optimization problem constructed according to the electrical constraint model and the control constraint model, the optimization objectives include: controlling the grid-connected point voltage to be within an allowable range, minimizing the risk of voltage out-of-limit and maximizing the active power output under the premise of static stability, and the expression is: ; wherein, is a grid-connected point voltage amplitude, and are upper and lower voltage limits; is an optimization objective, i.e., maximizing the actual active power.

[0013] Optionally, the multi-objective optimization problem constructed according to the electrical constraint model and the control constraint model includes: obtaining the electrical constraint model and the control constraint model; defining the coordinates of the grid-connected point voltage Us as (x, y) and inputting into the electrical constraint model and the control constraint model to redefine the constraint conditions of the electrical constraint model and the control constraint model under the coordinates; according to the redefined constraint conditions, constructing the constraint conditions of the optimization problem to obtain a commonly defined feasible region; wherein, the expression of the constraint conditions is: ; wherein, is a voltage amplitude constraint; is a current amplitude constraint; is a virtual active power control constraint; is a virtual reactive power control constraint; wherein, and are upper and lower voltage limits; is a maximum current amplitude; is the AC grid voltage amplitude; is the virtual active power reference value; is the virtual reactive power reference value; is the line impedance; is the proportion of voltage-reactive power control constraint in the reactive power control constraint.

[0014] Optionally, the optimization problem is solved by a numerical optimization method to calculate the maximum active power transmission limit of the photovoltaic unit under the current grid condition to meet all constraints, comprising: determining various constraint conditions in the constraint space, and determining the feasible solution region by drawing and numerical calculation operation; finding the optimal solution in the feasible region by iterative search or gradient descent algorithm, wherein the optimal solution needs to meet the electrical constraint and the control constraint at the same time, and output the maximum active power; outputting the active reference value and the reactive reference value corresponding to the optimal solution as the control instruction of the photovoltaic unit.

[0015] In the second aspect of the application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the static stability constraint self-synchronous voltage source photovoltaic unit voltage out-of-limit optimization method when executing the program.

[0016] In the third aspect of the application, a computer readable storage medium is provided, which stores a computer program, and the program is executed by a processor to implement the steps of the static stability constraint self-synchronous voltage source photovoltaic unit voltage out-of-limit optimization method.

[0017] The static stability constraint self-synchronous voltage source photovoltaic unit voltage out-of-limit optimization method provided by the application adopts the technical means of establishing electrical constraint conditions and control constraint conditions, and combining a numerical optimization method, and can establish a maximum transmission active power unified optimization framework under the electrical constraint and the control constraint by accurately modeling three grid working conditions including pure inductance, pure resistance and resistance-inductance hybrid; under the premise of no voltage out-of-limit, the method provides a quantitative reference for setting the maximum active reference value of the grid-connected photovoltaic inverter under complex resistance-inductance grid and different grid intensity, and prevents the static instability problems such as loss of operating point; the method can realize the safe and stable operation and maximum power transmission of the grid-connected photovoltaic unit under complex grid conditions, and successfully overcomes the systematic defects of the prior art in grid adaptability, control accuracy and operation stability, and provides an innovative solution for the safe and stable operation of the new power system.

[0018] Other technical effects brought by the additional features will be further described in the corresponding embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0019] Other features, objects, and advantages of the application will become more apparent from the following detailed description when read in conjunction with the accompanying drawings: Figure 1 A flow chart of a method for voltage ride-through optimization of a self-synchronized voltage source photovoltaic unit under static stability constraints according to an example embodiment; Figure 2 A schematic diagram of a self-synchronized voltage source photovoltaic inverter topology and control structure according to an example embodiment; Figure 3 A flow chart of a method for voltage ride-through optimization of a self-synchronized voltage source photovoltaic unit under static stability constraints according to an example embodiment; Figure 4 A flow chart of a method for voltage ride-through optimization of a self-synchronized voltage source photovoltaic unit under static stability constraints according to an example embodiment; Figure 5 A schematic diagram of a HIL test system structure according to an example embodiment; Figure 6 A schematic diagram of a HIL test system structure according to an example embodiment; A MATLAB / Simulink simulation result chart according to an example embodiment; Figure 7 A MATLAB / Simulink simulation result chart according to an example embodiment; A HIL test result chart according to an example embodiment; Figure 8 A MATLAB / Simulink simulation result chart according to an example embodiment; A MATLAB / Simulink simulation result chart according to an example embodiment Figure 9 A MATLAB / Simulink simulation result chart according to an example embodiment; A MATLAB / Simulink simulation result chart according to an example embodiment. DETAILED DESCRIPTION

[0020] The application will be described in detail below with reference to specific embodiments. The following examples are helpful for those skilled in the art to further understand the application, but do not limit the application in any form. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the application, and these all belong to the protection scope of the application. The parts not described in detail in the following examples can be implemented by using existing technology.

[0021] In the prior art, it is difficult to apply to a resistance-inductive hybrid power grid with large resistance, and the following problems mainly exist: 1) active power and reactive power are difficult to decouple under the resistance-inductive hybrid power grid, how to establish control static constraints considering the active-reactive coupling characteristics; 2) how to establish a unified calculation framework of maximum transmission active power under the electrical constraints and control constraints for purely inductive, purely resistive and resistance-inductive hybrid power grids. Based on the above problems, an embodiment of the present application provides a static stability constraint self-synchronous voltage source photovoltaic unit voltage out-of-limit optimization method to solve the above problems.

[0022] Referring to Figure 1 As shown in the figure, in an embodiment of the present application, a static stability constraint self-synchronous voltage source photovoltaic unit voltage out-of-limit optimization method includes: S1, an electrical constraint model of the photovoltaic unit operating in parallel with the grid is established, and the electrical constraint at least includes: a grid-connected point voltage amplitude constraint, an inverter output current constraint and an apparent power transmission capability constraint; S2, a control constraint model is established, and the control constraint under the conditions of purely inductive, purely resistive and resistance-inductive hybrid power grids is uniformly modeled based on the line impedance angle by introducing a virtual power rotation matrix; S3, a multi-objective optimization problem is constructed according to the electrical constraint model and the control constraint model; S4, the optimization problem is solved by a numerical optimization method, and the maximum active power transmission limit of the photovoltaic unit under the current grid condition satisfying all constraints is calculated.

[0023] Specifically, first, an electrical constraint model of the photovoltaic unit operating in parallel with the grid is established, and this model covers constraint conditions such as grid-connected point voltage amplitude, inverter output current and apparent power transmission capability; then a control constraint model is established, and a virtual power rotation matrix is introduced, and the control constraint under the conditions of purely inductive, purely resistive and resistance-inductive hybrid power grids is uniformly modeled according to the line impedance angle; on this basis, the electrical constraint and the control constraint are coupled, a multi-objective optimization problem with the goal of "maximizing active power transmission" while meeting all static stability requirements is constructed; finally, a numerical optimization algorithm is used to solve the problem, and the maximum active power limit that the photovoltaic unit can output under the current grid condition is calculated, avoiding voltage out-of-limit and other unstable phenomena.

[0024] The above embodiments of the present application solve the strong dependence of the traditional control strategy on the type of power grid (inductive / resistive / mixed) through unified modeling, improving the universality of control constraints; combined with the multi-objective optimization framework of electrical constraints and control constraints, the maximum active output capacity of the photovoltaic unit can be accurately calculated under the premise of ensuring grid-connected point voltage stability, inverter current not exceeding the limit, and sufficient transmission capacity, effectively improving the power generation efficiency; the introduction of numerical optimization method ensures the accuracy and efficiency of the solution, providing reliable technical support for the stable operation and power optimization of photovoltaic units in actual power grids, which helps to improve the new energy consumption capacity and ensure the static stability of the power grid.

[0025] It should be noted that the apparent power transmission capacity constraint and the inverter output current constraint can be equivalent.

[0026] In some specific embodiments, the condition expression for the grid-connected point voltage amplitude constraint is: ; In the formula, is the grid-connected point voltage amplitude, and are the upper and lower limits of the voltage; The condition expression for the output current constraint is: ; In the formula, is the current component in the control coordinate system, is the maximum current limit of the converter.

[0027] Wherein, the apparent power transmission capacity constraint is equivalent to the output current constraint.

[0028] A typical self-synchronous voltage source converter grid-connected topology is shown in Figure 2 . In the grid-connected control mode, in order to control the voltage overrun, a complete electrical constraint model needs to be established, focusing on various electrical limit conditions when the photovoltaic unit is connected to the grid. Mainly including the allowable fluctuation range of the grid-connected point voltage, ensuring that the voltage amplitude is always maintained within the safe operation interval; the output current of the inverter should not exceed the rated capacity limit to protect the safety of power devices; and the apparent power transmission capacity constraint is considered to ensure that the system does not overload.

[0029] Specifically, the DC side: is the DC input voltage, is the DC filter capacitor, used to stabilize the DC voltage and filter out the DC side harmonics; the converter: through PWM modulation to realize "DC-AC" conversion, output AC voltage ; filter and line impedance: is the filter inductance on the converter side, used to filter out high-order harmonics of the converter output; , Lgrid is the equivalent inductance and resistance of the grid side circuit; Vgrid is the AC grid voltage amplitude; grid represents the AC grid; current and voltage detection: Igrid is the converter side output current, Vgrid is the grid point voltage amplitude, used for subsequent power calculation and control feedback.

[0030] By collecting and , the active power; reactive power; then based on the rotation matrix coordinate transformation, the active power, reactive power is converted into the virtual rotating coordinate system of P' and Q'; the , active and reactive reference values are converted into virtual active power reference value; virtual reactive power reference value as the control target.

[0031] Then, the deviation of the actual active P' from the reference active is calculated through the transfer function ;( virtual moment of inertia, virtual damping coefficient) to obtain the frequency deviation ; and then superimposed with the rated angular frequency to obtain the angular frequency of the virtual synchronous machine, which is integrated 1 / s to obtain the angle , realizing the "active - frequency" droop characteristic.

[0032] At the same time, the deviation of the actual voltage from the reference voltage , the deviation of the actual reactive Q' from the reference reactive , are calculated through the proportional coefficient Kv (virtual regulation coefficient) to obtain the voltage regulation amount Δ and the reactive regulation amount Δ ; and then superimposed with the rated voltage Ut0 to obtain the output voltage reference Ut of the virtual synchronous machine, finally generating , realizing the "reactive - voltage" droop characteristic.

[0033] Set i.e. the q-axis voltage reference to 0, introduce a virtual impedance outer loop and current inner loop processing; convert the voltage reference in the dq rotating coordinate system into a three-phase voltage reference in the abc stationary coordinate system, and perform PWM modulation, generate switching signals according to the three-phase voltage reference, drive the power devices (such as IGBT) of the converter, and finally realize accurate control of the AC output.

[0034] In some specific embodiments of the present application, for establishing a control constraint model, the control constraints under the conditions of purely inductive, purely resistive and resistive-inductive mixed power grids are uniformly modeled based on the line impedance angle by introducing a virtual power rotation matrix, including: Introducing a virtual power rotation matrix related to the grid impedance angle; through the virtual power rotation matrix, the active power and the reactive power are coordinate-transformed to obtain virtual active power and virtual reactive power; the virtual active power and the virtual reactive power are introduced into the active-frequency droop control and the reactive-voltage droop control link to form control constraints; according to the control constraints, the control constraints under the conditions of purely inductive, purely resistive and resistive-inductive mixed power grids are uniformly modeled to obtain purely inductive control constraint modeling, purely resistive control constraint modeling and resistive-inductive control constraint modeling.

[0035] Specifically, a virtual power rotation matrix closely related to the impedance angle of the grid line is constructed, the actual active power and the reactive power are coordinate-transformed by using the introduced virtual power rotation matrix, the original active power and the reactive power are converted into virtual active power and virtual reactive power through specific mathematical operations, and the power is expressed in a new coordinate system; the virtual active power and the virtual reactive power obtained by transformation are introduced into the active-frequency droop control and the reactive-voltage droop control link respectively, and combined with the characteristics of droop control, the control constraint conditions based on virtual power are formed; finally, control constraint modeling is carried out for the three different grid conditions of purely inductive, purely resistive and resistive-inductive mixed. Through analysis and deduction of the parameters and constraint relationships under different grid conditions, the results of purely inductive control constraint modeling, purely resistive control constraint modeling and resistive-inductive control constraint modeling are finally obtained.

[0036] Among them, in terms of control constraint modeling, the mathematical model of the virtual synchronous machine control architecture is mainly constructed. It mainly includes the modeling of the active-frequency droop characteristic and the reactive-voltage droop characteristic of the two core control links. At the same time, special attention should be paid to the power coupling effect brought by the resistive-inductive line working condition, and the corresponding coupling relationship matrix is established to accurately describe the power interaction characteristics in the dq coordinate system; in order to quantify the coupling strength under different grid conditions, a rotation matrix T PQ is introduced, which is related to the grid impedance ratio and can effectively reflect the influence of grid impedance characteristics on control performance.

[0037] The above embodiments of the present application uniformly model by introducing a virtual power rotation matrix related to the grid impedance angle, so that the control method can flexibly adapt to power grid environments with different characteristics such as purely inductive, purely resistive and resistive-inductive mixed, and is no longer limited to a single grid condition, greatly improving the universality and adaptability of the photovoltaic unit control strategy.

[0038] It should be noted that the static stability constraint boundary considering the resistance and inductance characteristics is established by electrical and control constraints, and the premise of applying the voltage out-of-limit optimization method of network-type photovoltaic unit is to determine the constraint boundary, therefore, the electrical constraints need to be modeled first: the electrical constraints not only cover the limitation of grid-connected point voltage amplitude, but also include the output current limitation of power devices and other constraints. In addition to the electrical constraints, the control constraints under different grid conditions also need to be uniformly modeled: in the traditional grid model, it is usually assumed that the grid is a pure inductive system, ignoring the influence of resistance in the grid, and the control constraint modeling is relatively simple; but in the resistance and inductance hybrid grid, the resistance and inductance interact, resulting in the coupling characteristics of active and reactive power becoming complex, at this time, the virtual power rotation matrix needs to be introduced in the control constraint modeling, so as to realize the unified modeling of control constraints under complex grid conditions, and ensure the stability and power transmission capacity of the grid under complex conditions.

[0039] In some specific embodiments of the present application, for the active power and the reactive power are transformed by the virtual power rotation matrix, the virtual active power and the virtual reactive power are obtained, including: Obtaining a virtual power rotation matrix related to a grid impedance angle; obtaining active power and reactive power according to a power flow calculation formula; performing coordinate rotation on the active power and the reactive power by the virtual power rotation matrix to obtain virtual active power and virtual reactive power.

[0040] Wherein, the expression of the virtual power rotation matrix is: ; In the formula, is the virtual power rotation matrix; is a line impedance angle.

[0041] The line impedance angle is defined as: ; In the formula, is a line inductive component, is a line impedance component.

[0042] The expression of the active power and the reactive power is: ; In the formula, is the active power; is the reactive power; is the grid-connected point voltage amplitude; is the AC grid voltage amplitude; and δ is the phase of the grid-connected point voltage relative to the phase of the AC grid voltage.

[0043] The expression of the virtual active power and the virtual reactive power is: ; wherein Zgis the line impedance; is the virtual power rotation matrix.

[0044] In the active-frequency droop control and the reactive-voltage droop control, the virtual active power and the virtual reactive power are introduced to realize power decoupling.

[0045] Meanwhile, the control constraints are divided into active power difference-free control constraints and voltage-reactive power control constraints, and the analysis steps under different working conditions are as follows: The unified control constraint modeling is carried out under the conditions of purely inductive, purely resistive and resistive-inductive hybrid power grids to obtain the purely inductive control constraint modeling, the purely resistive control constraint modeling and the resistive-inductive control constraint modeling, wherein: Under the condition of purely inductive power grid, the control constraint modeling mainly considers the influence of the inductance characteristics of the power grid on the stability of the system. First, a virtual synchronous machine control model based on the assumption of inductive power grid is established, and the relationship between the power angle stability and the frequency regulation characteristics is analyzed. At this time, the virtual active power and the virtual reactive power are the actual active power and the actual reactive power, and the control constraint expression of the purely inductive control constraint modeling is: ; wherein, , is the active power reference value, is the voltage reference value, is the voltage droop coefficient, is the reactive power droop coefficient.

[0046] Under the condition of purely resistive power grid, the control constraint modeling needs to mainly consider the influence of the resistance characteristics of the power grid on the operation of the system. Unlike inductive power grid, at this time the stability of the system mainly embodies the problem of voltage stability. First, a virtual synchronous machine control model based on the assumption of resistive power grid is established, and the dynamic relationship between the voltage regulation characteristics and the reactive power output is analyzed. Under this working condition, the stable control of the voltage amplitude becomes the key constraint of the system operation, and the control constraint expression of the purely resistive control constraint modeling is: ; wherein, is the virtual active power reference value; is the virtual reactive power reference value; is the active power; is the reactive power; is the voltage amplitude of the grid-connected point.

[0047] Under the condition of resistive-inductive hybrid power grid, the control constraint modeling needs to comprehensively consider the combined influence of resistance and inductance components. First, a hybrid power grid control model based on impedance ratio is established, which can smoothly transition between the purely inductive and purely resistive models according to the actual grid parameters. Under this working condition, system stability involves both power angle stability and voltage stability, and the complexity of control constraints increases significantly. The control constraint expression of resistive-inductive control constraint modeling is: ; In the formula, is a virtual active power; is a virtual reactive power.

[0048] In some specific embodiments of the present application, for constructing a multi-objective optimization problem according to the electrical constraint model and the control constraint model, the optimization objectives include: under the premise of static stability, controlling the grid point voltage within the allowable range, minimizing the risk of voltage out-of-limit, and maximizing the active power output, the expression is: ; In the formula, is the grid point voltage amplitude, and are the upper and lower voltage limits; is the optimization objective, i.e., the maximum actual active power output.

[0049] In some specific embodiments of the present application, for constructing a multi-objective optimization problem according to the electrical constraint model and the control constraint model, including: obtaining the electrical constraint model and the control constraint model; defining the coordinates of the grid point voltage Us as (x, y) and inputting into the electrical constraint model and the control constraint model, redefining the constraint conditions of the electrical constraint model and the control constraint model under the coordinates; according to the redefined constraint conditions, constructing the constraint conditions of the optimization problem, obtaining the commonly defined feasible region.

[0050] Among them, the expression of the constraint condition is: ; Among them, is the voltage amplitude constraint; is the current amplitude constraint; is the virtual active power control constraint; is the virtual reactive power control constraint; In the formula, and are the upper and lower voltage limits; is the maximum current limit; is the AC grid voltage amplitude; is the virtual active power reference value; This is a virtual reactive power reference value; Line impedance; This represents the proportion of voltage-reactive power control constraints in reactive power control constraints.

[0051] Specifically, refer to Figure 4 As shown, after inputting the initial active power reference, grid voltage, and phase angle, the feasible region and operating point of the system are first determined by constraints such as voltage amplitude, current amplitude, and virtual active and reactive power control. Then, parameters are searched with maximizing active power as the objective function. After iterative iteration, the optimal operating parameters (active power reference, grid voltage, and phase angle) and maximum active power that satisfy all constraints are finally obtained, realizing the optimal output of active power of the system within the safety constraints.

[0052] In the embodiments described above, a virtual power rotation matrix is ​​introduced into the control constraint modeling to quantitatively analyze the impact of coordinate transformation on droop control characteristics, optimize the control strategy, and improve the system stability under complex power grid conditions. Traditional virtual synchronous machine control strategies employ decoupling assumptions and do not fully consider the impact of resistive-inductive lines on active-reactive power coupling, leading to steady-state operating point deviation. This application comprehensively considers electrical and control constraints, incorporating power transmission limit analysis and voltage over-limit control into a unified framework. This provides a quantitative reference for setting the maximum active power reference value under complex resistive-inductive power grids and different grid strengths, ensuring that photovoltaic units achieve maximum power output while meeting static stability constraints.

[0053] In some specific embodiments of this application, the calculation of the maximum active power transmission limit of the photovoltaic unit satisfying all constraints under current grid conditions by solving the optimization problem through numerical optimization methods includes: Various constraints are determined in the constraint space. The feasible solution region is determined through plotting and numerical calculation. The optimal solution is found within the feasible region through iterative search or gradient descent algorithm. The optimal solution must simultaneously satisfy electrical constraints and control constraints and output the maximum active power. The active power reference value and reactive power reference value corresponding to the optimal solution are output as control commands for the photovoltaic unit.

[0054] Specifically, firstly, various constraints are determined in the constraint space, and the feasible solution region is determined through plotting and numerical calculations. Then, the optimal solution is searched within the feasible region. This solution must simultaneously satisfy all constraints and achieve the optimization objective, ensuring that the grid-connected voltage of the photovoltaic converter does not exceed the limit, while simultaneously outputting the theoretical maximum active power. Based on the above analysis, the overall algorithm flow for satisfying the constraints and achieving the optimization objective is as follows: Figure 3As shown. For this optimization question, first define the optimization objective as maximizing the active power output, the optimization parameters include the grid-connected point voltage Us and the active power reference value Pref, determine the feasible region and static operating point by judging whether the constraint condition is met, analyze the stability of the grid-connected type converter under various operating conditions, and at the same time enable the grid-connected type converter to output the theoretically maximum active power.

[0055] The above-mentioned embodiments of the present application comprehensively consider various constraints such as electricity and control, enhance the adaptability of photovoltaic units to different grid conditions, help maintain the voltage and frequency stability of the grid, accurately determine the feasible solution region in the constraint space, and use an efficient algorithm to search for the optimal solution, accurately calculate the maximum active power transmission limit of the photovoltaic unit under the current grid condition that meets all constraints, improve energy utilization efficiency, and avoid energy waste. At the same time, output the active and reactive power reference values corresponding to the optimal solution as control instructions to ensure that the photovoltaic power generation system realizes optimal power output under the premise of meeting the grid constraints, and improves the power quality.

[0056] Based on the same technical concept, in another embodiment of the present application, an electronic device is also provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to realize the steps of a static stability constraint-based self-synchronous voltage source photovoltaic unit voltage out-of-limit optimization method.

[0057] Based on the same technical concept, in another embodiment of the present application, a computer readable storage medium is also provided, which stores a computer program, and the program is executed by a processor to realize the steps of a static stability constraint-based self-synchronous voltage source photovoltaic unit voltage out-of-limit optimization method.

[0058] Optionally, a memory is configured to store a program; the memory can include volatile memory (e.g., random-access memory (RAM), such as static random-access memory (SRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDR SDRAM), etc.), and / or non-volatile memory (e.g., flash memory). The memory is configured to store computer programs (e.g., application programs, functional modules, etc. for implementing the above-described methods), computer instructions, etc. The computer programs, computer instructions, etc. described above can be stored in one or more memories in a partitioned manner. Moreover, the computer programs, computer instructions, data, etc. described above can be invoked by the processor.

[0059] The computer programs, computer instructions, etc. described above can be stored in one or more memories in a partitioned manner. Moreover, the computer programs, computer instructions, data, etc. described above can be invoked by the processor.

[0060] The processor is configured to execute the computer programs stored in the memory to implement each step in the methods described above in relation to the embodiments. Details can be found in the related descriptions in the method embodiments above.

[0061] The processor and the memory can be independent structures, or can be integrated into an integrated structure. When the processor and the memory are independent structures, the memory and the processor can be coupled and connected through a bus.

[0062] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage media, etc.) containing computer-usable program code.

[0063] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart or flows and / or block or blocks. Figure 1 one or more flows and / or blocks. Figure 1 one or more blocks.

[0064] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device that implements the function specified in the flowchart or flows and / or block or blocks. Figure 1 one or more flows and / or blocks. Figure 1 one or more blocks.

[0065] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart or flows and / or block or blocks. Figure 1 one or more flows and / or blocks. Figure 1 one or more blocks.

[0066] The present application will be further described below in conjunction with specific application examples / Comparative Examples, so as to better understand the above technical solutions of the present application. It should be understood that the following are only some examples and do not limit the present application.

[0067] The above method is verified by MATLAB / Simulink simulation, and the topology structure adopted is as shown in the following figure. Figure 2 In addition, the method is also tested under actual power grid conditions through a StarSim hardware-in-loop (HIL) platform, and the test system configuration is as shown in the following figure. Figure 5 The specific parameters of the system are shown in Table 1.

[0068] Table 1:

[0069] In order to fully verify the voltage out-of-limit optimization method of the network type photovoltaic unit considering static stability constraints proposed in the present application, different active reference values, reactive reference values, line impedance ratios and other working conditions are set.

[0070] Application Example 1: With different active power reference values ​​set, the MATLAB / Simulink simulation results are as follows: Figure 6 As shown, the HIL results are as follows: Figure 7 As shown, when the value is small, the system remains stable but does not reach its maximum effective power output. When a critical value is exceeded, the system experiences power collapse and loses stability. Through optimization algorithms, a critical value can be determined that maintains system stability while achieving the theoretical maximum power output, and simultaneously keeps the system voltage within its static stability range.

[0071] Application Example 2: With different reactive power reference values ​​set, the MATLAB / Simulink simulation results are as follows: Figure 8 As shown, when the system short-circuit ratio (SCR) remains constant, the maximum active power output will decrease as the SCR decreases. Similarly, under fixed conditions, the maximum active power exhibits a trend of first decreasing and then increasing, eventually stabilizing as the SCR increases, while maintaining the system grid connection voltage within the system's static stability range.

[0072] Application Example 3: Set different line resistance ratios The MATLAB / Simulink simulation results are as follows: Figure 9 As shown. When SCR remains constant, as A decrease in the ratio will reduce the maximum active power output. Conversely, when... When the ratio is fixed, as the SCR increases, the maximum active power will gradually increase and eventually reach saturation. At the same time, under stable system conditions, the voltage will still be kept within limits, thus achieving optimized control of photovoltaic unit voltage exceeding limits.

[0073] Through simulation verification of the above three embodiments, it can be clearly seen that the grid-connected photovoltaic unit voltage over-limit optimization method considering static stability constraints proposed in this application can ensure that the photovoltaic unit outputs the theoretically maximum active power while simultaneously achieving grid connection point voltage without exceeding the limit under different operating conditions. Regardless of the active power reference value... Changes, reactive power reference value Change or line resistance-to-inductance ratio Even under changing conditions, the method proposed in this application ensures stable system operation and prevents the photovoltaic grid connection voltage from exceeding limits. This method significantly improves the stability, reliability, and flexibility of photovoltaic power generation systems, providing crucial technical support for future applications of photovoltaic power generation systems. The above describes some specific embodiments of the present application. It needs to be understood that the present application is not limited to the above specific embodiments, and various modifications or changes can be made by those skilled in the art within the scope of the claims, which does not affect the essential content of the present application. The above preferred features can be combined in any manner without conflict.

Claims

1. A method for optimizing the voltage limit of a self-synchronized voltage source photovoltaic unit under static stability constraints, characterized in that, The application relates to a photovoltaic unit maximum active power transmission limit calculation method. The application comprises the following steps: An electrical constraint model of grid-connected operation of a photovoltaic unit is established, and the electrical constraint at least comprises a grid-connected point voltage amplitude constraint, an inverter output current constraint and an apparent power transmission capability constraint; A control constraint model is established, and by introducing a virtual power rotation matrix, the control constraint under conditions of a pure inductive, pure resistive and resistive-inductive mixed power grid is uniformly modeled based on a line impedance angle; According to the electrical constraint model and the control constraint model, a multi-objective optimization problem is constructed; 2. The method of claim 1, wherein the method further comprises: So the condition expression of the grid-connected point voltage amplitude constraint is: ; wherein is the grid point voltage amplitude, and are the voltage upper and lower limits; The expression of the condition of the output current constraint is: ; wherein is the current component in the coordinate system, is the maximum current limit of the converter; By solving the optimization problem through a numerical optimization method, the maximum active power transmission limit of the photovoltaic unit under the current power grid condition and meeting all the constraints is calculated.

3. The method of claim 1, wherein the method further comprises: The apparent power transmission capability constraint is equivalent to the output current constraint. The control constraint model is established by introducing a virtual power rotation matrix, and the control constraint under conditions of a pure inductive, pure resistive and resistive-inductive mixed power grid is uniformly modeled based on a line impedance angle, and the method comprises the following steps: A virtual power rotation matrix related to a power grid impedance angle is introduced; By using the virtual power rotation matrix, active power and reactive power are subjected to coordinate transformation to obtain virtual active power and virtual reactive power; The virtual active power and the virtual reactive power are introduced into active-frequency droop control and reactive-voltage droop control links to form control constraints; 4. The method of claim 3, wherein the method further comprises: According to the control constraints, the control constraint modeling under conditions of a pure inductive, pure resistive and resistive-inductive mixed power grid is uniformly modeled to obtain pure inductive control constraint modeling, pure resistive control constraint modeling and resistive-inductive control constraint modeling. The virtual active power and the virtual reactive power are obtained by using the virtual power rotation matrix to perform coordinate transformation on the active power and the reactive power, and the method comprises the following steps: The virtual power rotation matrix related to the power grid impedance angle is obtained; Active power and reactive power are obtained according to a power flow calculation formula; The virtual active power and the virtual reactive power are obtained by performing coordinate rotation on the active power and the reactive power through the virtual power rotation matrix. ； wherein is a virtual power rotation matrix; is a line impedance angle; The line impedance angle is defined as: ; wherein is the line inductive component, is the line resistive component; The expression of the virtual power rotation matrix is as follows: ； wherein P is active power; Q is reactive power; V is grid point voltage amplitude; V is AC grid voltage amplitude; δ is grid point voltage phase relative to AC grid voltage phase; The expression of the active power and the reactive power is as follows: ； wherein is the line impedance; is the virtual power rotation matrix.

5. The method of claim 3, wherein the method further comprises: The expression of the virtual active power and the virtual reactive power is as follows: The control constraint modeling under conditions of a pure inductive, pure resistive and resistive-inductive mixed power grid is uniformly modeled to obtain pure inductive control constraint modeling, pure resistive control constraint modeling and resistive-inductive control constraint modeling, and the method comprises the following steps: ； wherein, , is the active, reactive reference value, is the voltage reference value, is the voltage droop coefficient, is the reactive droop coefficient; Under the condition of a pure inductive power grid, the control constraint expression of the pure inductive control constraint modeling is as follows: ； wherein is a virtual active power reference value; is a virtual reactive power reference value; is an active power; is a reactive power; is a grid point voltage amplitude; Under the condition of a pure resistive power grid, the control constraint expression of the pure resistive control constraint modeling is as follows: ； wherein is the virtual active power; is the virtual reactive power.

6. The method of claim 1, wherein the method further comprises: Under the condition of a resistive-inductive mixed power grid, the control constraint expression of the resistive-inductive control constraint modeling is as follows: ； wherein is the grid point voltage amplitude, and are the voltage upper and lower limits; is the optimization objective, i.e. maximizing the actual active power.

7. The method of claim 6, wherein the method further comprises: In the step of constructing a multi-objective optimization problem according to the electrical constraint model and the control constraint model, the optimization objective comprises controlling the grid-connected point voltage within an allowable range, minimizing voltage out-of-limit risk and maximizing active power output under the premise of static stability, and the expression is as follows: The step of constructing a multi-objective optimization problem according to the electrical constraint model and the control constraint model comprises the following steps: The electrical constraint model and the control constraint model are obtained; The coordinates of the grid-connected point voltage Us are defined as (x, y) and input into the electrical constraint model and the control constraint model, and the constraint conditions of the electrical constraint model and the control constraint model under the redefined coordinates are redefined; According to the redefined constraint conditions, the constraint conditions of the optimization problem are constructed, and a common feasible region is obtained; The expression of the constraint condition is: ； wherein, is a voltage magnitude constraint; is a current magnitude constraint; is a virtual active power control constraint; is a virtual reactive power control constraint; wherein and are voltage upper and lower limits; is the maximum current limit; is the AC grid voltage magnitude; is the virtual active power reference; is the virtual reactive power reference; is the line impedance; is the proportion of the voltage-reactive power control constraint in the reactive power control constraints.

8. The method of claim 1, wherein the method further comprises: The optimization problem is solved by the numerical optimization method, and the maximum active power transmission limit of the photovoltaic unit under the current grid condition is calculated, including: In the constraint space, various constraint conditions are determined, and the feasible solution region is determined by drawing and numerical calculation operation; An optimal solution is searched for in the feasible region by an iterative search or a gradient descent algorithm, wherein the optimal solution needs to satisfy the electrical constraint and the control constraint at the same time, and the maximum active power is output; The active reference value and the reactive reference value corresponding to the optimal solution are output as the control instruction of the photovoltaic unit.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the steps of the method in any one of claims 1 to 8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps of the method in any one of claims 1 to 8.

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