Grid-forming hybrid system feasible region analysis, converter capacity optimization method and device

Abstract

The application discloses a follow-up network hybrid system feasible region analysis and converter capacity optimization method and device, and belongs to the technical field of power system stability analysis. The method comprises the following steps: constructing an impedance model of a GFL-GFM coupled system to obtain a closed-loop admittance matrix of the system; performing rational approximation on the closed-loop admittance matrix through vector fitting to extract system eigenvalues; obtaining the action direction and action degree of power of any converter on the dominant eigenvalue of the system according to the relationship between the system eigenvalues and different converter output powers; combining capacity constraints and power constraints, discretizing the feasible region, and solving the system feasible region; on the basis of the obtained feasible region, quantifying the grid strength of the system, combining the grid strength of the system and the change of the converter output power, and analyzing the change of the system feasible region, so that the converter capacity is optimized. The application can reduce the calculation burden of the feasible region solving, and accurately guide the capacity configuration and power distribution of the converter.

Description

Technical Field

[0001] This invention belongs to the field of power system stability analysis technology, and more specifically, relates to a feasible domain analysis, converter capacity optimization method and device for parallel grid-connected (GFL) and grid-connected (GFM) converter systems based on impedance method. Background Technology

[0002] With the rapid development of renewable energy and smart grids, the penetration rate of power electronic converters in power systems has increased significantly. This has led to a shift in power systems from being dominated by traditional synchronous generators to increasingly relying on power electronic devices, posing challenges to the stable operation of power systems. Since grid-connected inverters, widely used in power grids, lack the ability to actively support the grid, while grid-connected inverters exhibit superior stability under weak grid conditions due to their voltage source characteristics, they are gradually being applied in power systems.

[0003] However, at the same time, inverter control has gradually evolved from being dominated by phase-locked loops (PLLs) to a coexistence of PLL and virtual synchronous motor (VSG) control. The complexity of control and the multi-timescale interactions between inverters and between inverters and the grid have brought new challenges to the operation, configuration, and stability analysis of power systems.

[0004] To address these issues, impedance analysis has been widely applied in small-signal analysis of inverter-dominated systems. However, without a suitable analysis method, this can lead to increased computational burden and low efficiency. Traditional impedance analysis divides the system into source-side and grid-side subsystems and uses Nyquist curves to determine system stability, but it does not consider the influence of right-half-plane (RHP) poles. For complex multi-inverter systems, selecting appropriate analysis points and determining the impedance slopes at both ends of those points is challenging. Analyzing the impedance characteristics of inverters at different frequencies requires a large amount of impedance data to fit the closed-loop transfer function, further increasing data demands. Furthermore, converters exhibit different stability at different operating points. Summary of the Invention

[0005] To address the shortcomings and improvement needs of existing technologies, this invention provides a feasible domain analysis and converter capacity optimization method and system for a grid-connected hybrid system. The purpose is to use the closed-loop transfer function analysis method to deeply study the voltage and current interaction between the source and grid sides of the system, obtain the relationship between the system characteristic roots and each impedance, and then solve the system stability boundary under complex operating conditions by integrating capacity constraints and power constraints, thereby guiding the power allocation and capacity optimization of the converter in actual operation.

[0006] To achieve the above objectives, this invention provides a feasible domain analysis and converter capacity optimization method for a hybrid grid system, comprising the following steps:

[0007] An impedance model of the GFL-GFM parallel system is constructed. The impedance model includes a source-side impedance matrix and a grid-side admittance matrix, which are used to characterize the interaction characteristics between the converter and the power grid.

[0008] By combining the source-side impedance matrix and the network-side admittance matrix in the impedance model, the closed-loop admittance matrix of the GFL-GFM parallel system is derived.

[0009] The closed-loop admittance matrix is ​​rationally approximated using a vector fitting method to extract system eigenvalues.

[0010] An analysis method based on the closed-loop admittance matrix is ​​used to establish the correlation between system eigenvalues ​​and each impedance parameter;

[0011] Based on the correlation between the system characteristic value and each impedance parameter, the participation factor of each converter on the system characteristic value is calculated. The participation factor is used to quantify the degree of influence of the converter on the system characteristic value.

[0012] By taking the partial derivatives of the participation factors of different converters with respect to their output power, we can obtain the direction and degree of influence of the power of any converter on the dominant characteristic value of the system under specific operating conditions. By quantifying this direction and degree of influence, we can determine the power allocation strategy to improve system stability.

[0013] Based on the power allocation strategy, and combined with the capacity and power constraints of the converter, the feasible region of the system is discretized, and the stability boundary of the system is obtained by solving for all discrete points in the feasible region.

[0014] The grid strength of the system is quantified, and the changes in the system stability boundary are analyzed based on the changes in the converter output power under different grid strengths, thereby optimizing the converter capacity.

[0015] Furthermore, the GFL-GFM parallel impedance model is as follows:

[0016] ;

[0017] Z gfl With Z gfm Represent the impedances of GFL and GFM in the dq coordinate system, respectively; matrix T is the coordinate transformation matrix; θ gfls With θ gfms These represent the phase difference between GFL and GFM in their respective local coordinate systems and the global coordinate system.

[0018] Furthermore, the closed-loop admittance matrix of the GFL-GFM parallel system is as follows:

[0019] ;

[0020] Where Z is the source-side impedance matrix composed of all converters, which includes the impedances of GFL and GFM, as well as the grid impedance; Y net The network-side admittance matrix;

[0021] The matrix is ​​a 2n-order matrix, where n is the number of impedances considered as power sources. The physical meaning is:

[0022] ;

[0023] Among them, Z k Z is the source-side impedance of node k. gk It is the equivalent impedance of all remaining systems as seen from node k.

[0024] Furthermore, the correlation between the system characteristic values ​​and each impedance is as follows:

[0025] ;

[0026] in, These are the system's eigenvalues; It is the Frobenius inner product of two matrices; express exist Residue at the location; Δ(Z) represents the conjugate transpose of the residue matrix; k +Z gk fi means in The corresponding frequency f i Impedance disturbance at the location.

[0027] By keeping the impedance constant at other points in the system, and performing eigenvalue analysis on the impedance change at a specific point in the system, we can obtain the contribution factor of the converter to the system eigenvalues ​​at that point:

[0028] ;

[0029] Among them, Z gk Z represents the impedance at other locations in the system. k The impedance at point k; Let be the participation factor of the converter at point k.

[0030] Furthermore, the partial derivatives of the participation factors of different converters with respect to their output power are calculated using the following formula:

[0031] ;

[0032] in, It is the partial derivative of the converter participation factor with respect to the output power; S iThis represents the apparent power of the power supply at the i-th port; S represents i The impact on the principal eigenvalue.

[0033] Furthermore, the capacity constraints of the converter are as follows:

[0034] ;

[0035] Among them, P gfl Q represents the active power of the GFL. gfl P represents the reactive power of the GFL. gfm Q represents the active power of the GFM. gfm This represents the reactive power of the GFM.

[0036] Furthermore, based on the power allocation strategy and considering the capacity and power constraints of the converter, the feasible region of the system is discretized, including:

[0037] ;

[0038] Where R represents the value range of all running points;

[0039] Discretizing R yields the following feasible region:

[0040]

[0041] in, Indicates the step size of the change in active power; This indicates the step size of the reactive power change.

[0042] Furthermore, the process of solving for all discrete points in the feasible region to obtain the stability boundary of the system includes:

[0043] First, substitute the active power of the operating point GFL and the reactive power of GFM into the system to perform power flow calculation and obtain the output power of each power source in the system; if the capacity constraint is satisfied, then substitute the power flow calculation results into the impedance mathematical model.

[0044] Next, the discretized impedance value is substituted into the closed-loop admittance matrix to obtain the values ​​of all elements in the matrix. The d-axis closed-loop transfer admittance of the energy storage port is selected for study. The poles of the element are obtained by rational fitting. After solving for all points in the discretized feasible region of the system, the feasible region of the system is obtained.

[0045] Furthermore, the power grid strength of the quantification system is specifically as follows:

[0046] ;

[0047] in, This is the system's rated voltage; The power grid frequency; For grid impedance; The sum of the GFL capacity and the GFM capacity, i.e., S sum = S gfl + S gfm .

[0048] A device for feasible domain analysis and converter capacity optimization of a hybrid grid system includes:

[0049] The impedance model construction module is used to quantify the grid strength of the system. Under different grid strengths, it analyzes the changes in the system stability boundary based on the changes in the converter output power, thereby optimizing the converter capacity.

[0050] The closed-loop admittance matrix derivation module is used to combine the source-side impedance matrix and the network-side admittance matrix in the impedance model to derive the closed-loop admittance matrix of the GFL-GFM parallel system.

[0051] The system feature value extraction module is used to rationally approximate the closed-loop admittance matrix using a vector fitting method to extract system feature values.

[0052] The eigenvalue and impedance correlation module is used to establish the correlation between system eigenvalues ​​and each impedance parameter based on the closed-loop admittance matrix analysis method.

[0053] The converter participation factor calculation module is used to calculate the participation factor of each converter on the system characteristic value based on the correlation between the system characteristic value and each impedance parameter. The participation factor is used to quantify the degree of influence of the converter on the system characteristic value.

[0054] The power allocation strategy determination module is used to calculate the partial derivative of the participation factors of different converters with respect to their output power, so as to obtain the direction and degree of the effect of the power of any converter on the dominant characteristic value of the system under specific operating conditions. By quantifying the direction and degree of the effect, the power allocation strategy to improve the stability of the system is determined.

[0055] The feasible region analysis module is used to discretize the feasible region of the system based on the power allocation strategy and the capacity and power constraints of the converter, and solve for all discrete points in the feasible region to obtain the stability boundary of the system.

[0056] The grid strength quantification module is used to quantify the grid strength of the system. Under different grid strengths, it analyzes the changes in the system stability boundary based on the changes in the converter output power, thereby optimizing the converter capacity.

[0057] In summary, the above-described technical solutions conceived in this invention can achieve the following beneficial effects:

[0058] To address the challenges of complex grid stability analysis, converter instability, poor compatibility with weak grids, and blind capacity allocation in power grids with high renewable energy penetration, this invention proposes a feasible region analysis method and converter capacity optimization method for parallel GFL and GFM systems based on the discrete impedance method. This invention analyzes system impedance from the perspective of the closed-loop transfer function, avoiding the influence of right-half-plane poles on system stability analysis in traditional impedance analysis methods, thus improving the accuracy of stability analysis. By combining power constraints and converter capacity constraints, a feasible region graph is generated, intuitively demonstrating the impact of different operating points on system stability. Finally, by comprehensively considering the influence of grid strength and converter output power on the feasible region, this invention provides a reference for optimal converter capacity allocation. Attached Figure Description

[0059] Figure 1 This is the topology and control block diagram of the power system tested in the embodiments of the present invention;

[0060] Figure 2 This is a schematic diagram of the closed-loop admittance matrix generation in an embodiment of the present invention, where (a) is a system decomposed into source side and grid side, and (b) is the closed-loop transfer function of the system current response after introducing voltage disturbance;

[0061] Figure 3 This is a flowchart of the system feasible region solution based on the discrete impedance method in an embodiment of the present invention;

[0062] Figure 4 This is the impedance frequency sweep result of the system in the embodiment of the present invention;

[0063] Figure 5 These are the results of solving the feasible region of the system under different power grid intensities according to the embodiments of the present invention, where (a) is the feasible region of the system under the weak power grid condition with SCR'=2, and (b) is the feasible region of the system under the medium strong power grid condition with SCR'=4.

[0064] Figure 6 The range of the dominant pole of the system with different operating point under different power grid intensities is given in the embodiments of the present invention. Among them, (a) is the range of the dominant pole of the system under the weak grid condition with SCR'=2, and (b) is the range of the dominant pole of the system under the medium strong grid condition with SCR'=4.

[0065] Figure 7 This refers to the participation factor of each converter in the dominant oscillation when SCR'=4 in the embodiment of the present invention;

[0066] Figure 8 These are the stability margins of the variable operating point system under different power grid intensities according to embodiments of the present invention, where (a) is the stability margin of the system under the weak power grid condition with SCR'=2, and (b) is the stability margin of the system under the medium-strong power grid condition with SCR'=4. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0068] This invention provides a feasible domain analysis and converter capacity optimization method for a hybrid grid system, comprising the following steps:

[0069] An impedance model of the GFL-GFM parallel system is constructed. The impedance model includes a source-side impedance matrix and a grid-side admittance matrix, which are used to characterize the interaction characteristics between the converter and the power grid.

[0070] By combining the source-side impedance matrix and the network-side admittance matrix in the impedance model, the closed-loop admittance matrix of the GFL-GFM parallel system is derived.

[0071] The closed-loop admittance matrix is ​​rationally approximated using a vector fitting method to extract system eigenvalues.

[0072] An analysis method based on the closed-loop admittance matrix is ​​used to establish the correlation between system eigenvalues ​​and each impedance parameter;

[0073] Based on the correlation between the system characteristic value and each impedance parameter, the participation factor of each converter on the system characteristic value is calculated. The participation factor is used to quantify the degree of influence of the converter on the system characteristic value.

[0074] By taking the partial derivatives of the participation factors of different converters with respect to their output power, we can obtain the direction and degree of influence of the power of any converter on the dominant characteristic value of the system under specific operating conditions. By quantifying this direction and degree of influence, we can determine the power allocation strategy to improve system stability.

[0075] Based on the power allocation strategy, and combined with the capacity and power constraints of the converter, the feasible region of the system is discretized, and the stability boundary of the system is obtained by solving for all discrete points in the feasible region.

[0076] Figure 1 A parallel GFL-GFM system is used as a specific example.

[0077] like Figure 1As shown, the GFL synchronizes with the grid voltage through a phase-locked loop and uses a power-current dual PI loop to precisely control the output power and output current; the GFM uses virtual synchronization control, achieving synchronization with the grid power by simulating the equations of a synchronous generator. In terms of reactive power control, it uses a dual voltage-current PI loop as the inner loop control, while the outer loop employs reactive power integral control.

[0078] like Figure 2 As shown in Figure (a), the closed-loop transfer function analysis method is used to decompose the system into two parts: the source side and the grid side, to study the voltage and current interaction between these two parts in detail. In the proposed model, the power grid is regarded as a power source to better analyze the interaction between different converters. The GFL-GFM parallel impedance model is as follows:

[0079]

[0080] Z gfl With Z gfm Represent the impedances of GFL and GFM in the dq coordinate system, respectively; matrix T is the coordinate transformation matrix; θ gfls With θ gfms These represent the phase difference between GFL and GFM in their respective local coordinate systems and the global coordinate system.

[0081] A virtual voltage disturbance is introduced between the two systems. The voltage and current responses of the two systems are observed (e.g., ...). Figure 2 The characteristics of the system are analyzed using the diagram shown in (a). According to circuit theory, the closed-loop transfer function from voltage disturbance to final current response can be expressed as: Figure 2 The form of (b). According to Figure 2 As shown in (b), the expression for the closed-loop transfer function (closed-loop admittance matrix) of the system is derived as follows:

[0082]

[0083] Where Z is the impedance matrix composed of all converters, which includes the impedances of GFL and GFM, as well as the grid impedance; Y is the grid-side admittance matrix.

[0084] The matrix is ​​a 2n-order matrix, where n is the number of impedances considered as power sources. Its diagonal submatrices are 2n-order submatrices. It has a clear physical meaning, which is:

[0085]

[0086] Among them, Z k Z is the source-side impedance of node k. gkIt is the equivalent impedance of all remaining systems as seen from node k.

[0087] Based on the closed-loop analysis method, the system characteristic root λ i The relationship between each impedance can be further obtained through the following expression:

[0088]

[0089] in, These are the system's eigenvalues; It is the Frobenius inner product of two matrices; express exist Residue at the location; Δ(Z) represents the conjugate transpose of the residue matrix; k +Z gk fi means in The corresponding frequency f i Impedance disturbance at the location.

[0090] When the impedance Z at other locations in the system gk With the system characteristics remaining constant, all changes in the system's eigenvalues ​​are due to the impedance Z at point k. k Therefore, the participation factor of the converter at point k can be calculated:

[0091]

[0092] Among them, Z gk Z represents the impedance at other locations in the system. k The impedance at point k; Let be the participation factor of the converter at point k.

[0093] By further substituting the partial derivatives of different converters with respect to power, we can obtain the direction and degree of influence of the power of any converter on the dominant eigenvalue under a specific operating condition. By quantizing this vector, we can obtain a power allocation strategy to improve system stability.

[0094]

[0095] in, It is the partial derivative of the converter participation factor with respect to the output power; S i This represents the apparent power of the power supply at the i-th port; S represents i The impact on the principal eigenvalue.

[0096] Due to capacity limitations and stability requirements, GFL and GFM need to operate within specific ranges. Therefore, verifying the system's operational boundaries is crucial. However, the system's complexity lies in having four variables (P... gfl Qgfl P gfm and Q gfm (Q) can change. To simplify the analysis, assume the voltage at PCC is constant. gflo and Q gfmo The sum of these becomes a fixed value. By setting the active power output of the GFM to 0 and changing the active power of the GFL and the reactive power of the GFM, the system variables can be simplified to two (the reactive power of the GFL changes according to the reactive power of the GFM). Considering that overload may cause equipment damage, the capacity constraints of the GFL and GFM can be expressed as follows:

[0097]

[0098] Among them, P gfl Q represents the active power of the GFL. gfl P represents the reactive power of the GFL. gfm Q represents the active power of the GFM. gfm This represents the reactive power of the GFM.

[0099] Combining power constraints, the feasible region of the system can be obtained:

[0100]

[0101] Where R represents the value range of all running points.

[0102] Discretizing R yields the following feasible region:

[0103]

[0104] in, Indicates the step size of the change in active power; This indicates the step size of the reactive power change.

[0105] like Figure 3 As shown, the process for solving the feasible region of the system is described below:

[0106] 1) Substitute the active power of the operating point GFL and the reactive power of GFM into the system to perform power flow calculations and obtain the output power of each power source in the system.

[0107] 2) If the solution result in step (1) satisfies the capacity constraint, then substitute the power flow calculation result into the impedance mathematical model. Next,

[0108] 3) Substitute the discretized impedance values ​​into the closed-loop admittance matrix to obtain the values ​​of all elements in the matrix.

[0109] 4) Select the d-axis closed-loop transfer admittance of the energy storage port for study, and find the poles of the element by rational fitting.

[0110] 5) After solving for all points in the discretized feasible region of the system, the feasible region of the system can be obtained.

[0111] This embodiment verifies the correctness of the system modeling by first detecting the voltage and current response and calculating the small-signal impedance of the system; then, by solving the feasible region of the system under different short-circuit ratios, it demonstrates the superiority of the feasible region solution method proposed in this invention.

[0112] A disturbance of 1-500Hz is introduced in the dq coordinate system, the voltage and current response of the system is detected, and the small-signal impedance of the system is calculated. Figure 4 The results of the system frequency sweep and the mathematical model are shown to be consistent, which verifies the correctness of the system modeling of the present invention.

[0113] P gfl and Q gfm As a variable traversal system, iterates through the possible operating points of the system, according to... Figure 3 The process described above solves for the feasible region, and the results are obtained. Figure 5 The results of the stability region. Figure 5 Figure (a) shows the system's stability under weak network conditions with SCR'=2. The operable region of the system can be obtained by finding the intersection of the GFL capacity limit, GFM capacity limit, and stability limit; regions not meeting any of these conditions are considered inoperable. Figure 5 As shown in (a), the stable region of the system is concentrated in the region where the GFM energy storage outputs reactive power. When the GFM energy storage absorbs reactive power (i.e., the GFL renewable energy outputs reactive power), the system will become inoperable due to stability issues. Under medium-strong grid conditions with SCR'=4, as follows: Figure 5 As shown in (b), the system's stability region is divided into two halves around the point where reactive power is zero. More reactive power output or absorption is more beneficial to system stability. When SCR' increases, the reactive power required to deliver the same amount of active power decreases. Therefore, the stability boundary when SCR'=2 shifts positively, becoming the stability boundary of the positive reactive power region. The negative reactive power boundary is due to the decrease in the GFM port voltage and the weakening of the equivalent grid strength when absorbing reactive power, thus improving system stability. The solution results demonstrate the superiority of the feasible region solution method proposed in this invention.

[0114] This invention also provides a converter capacity optimization method for a GFL-GFM system. After obtaining the system's stability boundary, the following steps are performed:

[0115] The grid strength of the system is quantified, and the changes in the system stability boundary are analyzed based on the changes in the converter output power under different grid strengths, thereby optimizing the converter capacity.

[0116] Still with Figure 1 The GFL-GFM parallel system shown is a specific embodiment.

[0117] In the converter capacity optimization method for the GFL-GFM system proposed in this invention, the impedance modeling method and feasible region solution process of the GFL-GFM system are the same as the feasible region analysis method of the GFL-GFM system proposed in this invention. The impedance modeling method of the GFL-GFM system is as follows: Figure 2 As shown, the feasible region solution process of the GFL-GFM system is as follows: Figure 3 As shown. It is necessary to further explain the system's grid strength definition method:

[0118]

[0119] in, This is the system's rated voltage; The power grid frequency; For grid impedance; The sum of the GFL capacity and the GFM capacity, i.e., S sum = S gfl + S gfm .

[0120] This embodiment first... Figure 4 Based on the original frequency sweep results, different GFM capacities were set to analyze the changes in system impedance under different GFM capacities. Secondly, by setting different operating points of the system, the influence of different operating points of different capacities on the changes in the dominant characteristic value of the system under different short-circuit ratios was analyzed. Subsequently, based on the participation factors of each converter and grid impedance, the causes of system oscillations were analyzed. Finally, the impact of converter capacity changes on system stability margin was analyzed, and the feasible region results were verified, demonstrating the superiority of the converter capacity optimization method proposed in this invention.

[0121] To investigate the effect of GFM capacitance on system impedance, capacitance configurations of 5%, 10%, 20%, and 30% were set for the GFM, and the dd and qq impedances of the GFM were obtained as follows: Figure 4 As shown, when the capacity of the GFM decreases, its impedance amplitude increases significantly, far exceeding that of the GFL. Conversely, when the capacity of the GFM increases, the impedance amplitude decreases. However, the capacity change has little effect on the impedance phase; the phase angle remains almost constant when outputting the same power. At 10% capacity, the impedance amplitude of the GFM is closest to that of the GFL, indicating a significant mutual influence between the two at this capacity energy storage configuration.

[0122] Figure 6Figures (a) and (b) describe the range of dominant eigenvalues ​​at different operating points and capacities of the system under SCR'=4 and SCR'=2 grid conditions, respectively. As can be seen from the figures, regardless of grid strength, the range of variation of the system's dominant eigenvalues ​​decreases with increasing GFM capacity. Conversely, the range of variation of the system's dominant eigenvalues ​​is larger when the GFM capacity decreases.

[0123] To analyze the causes of system oscillations, this embodiment determines the participation factors of each converter and grid impedance under the condition of SCR'=4, such as... Figure 7 As shown. The GFL is set to output active power of 9000W and reactive power of 45Var; the GFM outputs active power of 0W and reactive power of 400Var. From... Figure 7 It can be seen that the participation factors of GFM and grid impedance are significantly higher than those of GFL. This apparent oscillation is due to the limited capacity of GFM and its close electrical distance from the grid. The reactive power of GFM is the key factor determining system stability.

[0124] The stability margin is defined as the distance between the real part of the dominant eigenvalue and the imaginary axis, where M = -real( The larger the value of M, the stronger the system stability. When M > 0, the system is stable; when M < 0, the system becomes unstable under small disturbances. This embodiment... Figure 5 Based on the solution results of the feasible region of the system under different grid strengths, the stability margin of the system at different operating points under the conditions of SCR'=2 and SCR'=4 is analyzed, such as... Figure 8 As shown in the diagram. The x and y axes represent the active power of new energy sources and the reactive power of energy storage, while the z-axis represents the system's stability margin M. Consistent with the analysis above, in a weak grid, when the GFM outputs more reactive power and the GFL outputs more active power, the system's stability margin is higher. In a strong grid, when the GFM outputs more reactive power or absorbs more reactive power, the system's stability margin is higher.

[0125] This invention illustrates the impact of GFM capacity on the stability of GFL-GFM systems through specific embodiments. The conclusions show that as GFM capacity increases, the impact of the system operating point on stability weakens, and the range of system stability margin variation decreases. In medium-intensity power grids, when the GFL outputs or absorbs more active power, or the GFM outputs or absorbs more reactive power, it is beneficial to maintain stable system operation. In weak power grids, when the GFL outputs more active power and the GFM outputs more reactive power, the system tends to stabilize. These conclusions provide a reference for converter capacity planning in GFL-GFM systems, demonstrating the superiority of the converter capacity optimization method proposed in this invention.

[0126] This invention also provides a device for feasible domain analysis and converter capacity optimization of a hybrid grid system, comprising:

[0127] The impedance model construction module is used to quantify the grid strength of the system. Under different grid strengths, it analyzes the changes in the system stability boundary based on the changes in the converter output power, thereby optimizing the converter capacity.

[0128] The closed-loop admittance matrix derivation module is used to combine the source-side impedance matrix and the network-side admittance matrix in the impedance model to derive the closed-loop admittance matrix of the GFL-GFM parallel system.

[0129] The system feature value extraction module is used to rationally approximate the closed-loop admittance matrix using a vector fitting method to extract system feature values.

[0130] The eigenvalue and impedance correlation module is used to establish the correlation between system eigenvalues ​​and each impedance parameter based on the closed-loop admittance matrix analysis method.

[0131] The converter participation factor calculation module is used to calculate the participation factor of each converter on the system characteristic value based on the correlation between the system characteristic value and each impedance parameter. The participation factor is used to quantify the degree of influence of the converter on the system characteristic value.

[0132] The power allocation strategy determination module is used to calculate the partial derivative of the participation factors of different converters with respect to their output power, so as to obtain the direction and degree of the effect of the power of any converter on the dominant characteristic value of the system under specific operating conditions. By quantifying the direction and degree of the effect, the power allocation strategy to improve the stability of the system is determined.

[0133] The feasible region analysis module is used to discretize the feasible region of the system based on the power allocation strategy and the capacity and power constraints of the converter, and solve for all discrete points in the feasible region to obtain the stability boundary of the system.

[0134] The grid strength quantification module is used to quantify the grid strength of the system. Under different grid strengths, it analyzes the changes in the system stability boundary based on the changes in the converter output power, thereby optimizing the converter capacity.

[0135] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this 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-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0136] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0137] 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.

[0138] 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.

[0139] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0140] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A feasible domain analysis and converter capacity optimization method for a hybrid grid system, characterized in that, include: An impedance model of the GFL-GFM parallel system is constructed. The impedance model includes a source-side impedance matrix and a grid-side admittance matrix, which are used to characterize the interaction characteristics between the converter and the power grid. By combining the source-side impedance matrix and the network-side admittance matrix in the impedance model, the closed-loop admittance matrix of the GFL-GFM parallel system is derived. The closed-loop admittance matrix is ​​rationally approximated using a vector fitting method to extract system eigenvalues. An analysis method based on the closed-loop admittance matrix is ​​used to establish the correlation between system eigenvalues ​​and each impedance parameter; Based on the correlation between the system characteristic value and each impedance parameter, the participation factor of each converter on the system characteristic value is calculated. The participation factor is used to quantify the degree of influence of the converter on the system characteristic value. By taking the partial derivative of the participation factors of different converters with respect to their output power, we can obtain the direction and degree of the effect of the power of any converter on the dominant characteristic value of the system under specific operating conditions. By quantifying the direction and degree of the effect, we can determine the power allocation strategy to improve the stability of the system. Based on the power allocation strategy, and combined with the capacity and power constraints of the converter, the feasible region of the system is discretized, and the stability boundary of the system is obtained by solving for all discrete points in the feasible region. The grid strength of the system is quantified, and the changes in the system stability boundary are analyzed based on the changes in the converter output power under different grid strengths, thereby optimizing the converter capacity.

2. The feasible domain analysis and converter capacity optimization method for a hybrid network system as described in claim 1, characterized in that, The parallel impedance model of GFL-GFM is as follows: ； Z gfl With Z gfm θ represents the impedances of GFL and GFM in the dq coordinate system, respectively; gfls With θ gfms These represent the phase difference between GFL and GFM in their respective local coordinate systems and the global coordinate system.

3. The feasible domain analysis and converter capacity optimization method for a hybrid grid system as described in claim 1, characterized in that, The closed-loop admittance matrix of the GFL-GFM parallel system is as follows: ； Where Z is the source-side impedance matrix composed of all converters, which includes the impedances of GFL and GFM, as well as the grid impedance; Y net The network-side admittance matrix; The matrix is ​​a 2n-order matrix, where n is the number of impedances considered as power sources. The physical meaning is: ； Among them, Z k Z is the source-side impedance of node k. gk It is the equivalent impedance of all remaining systems as seen from node k.

4. The feasible domain analysis and converter capacity optimization method for a hybrid grid system as described in claim 3, characterized in that, The relationship between the system characteristic values ​​and each impedance is as follows: ； in, These are the system's eigenvalues; It is the Frobenius inner product of two matrices; express exist Residue at the location; Δ(Z) represents the conjugate transpose of the residue matrix; k +Z gk ) fi Indicates in The corresponding frequency f i Impedance disturbance at the point; By keeping the impedance constant at other points in the system, and performing eigenvalue analysis on the impedance change at a specific point in the system, we can obtain the contribution factor of the converter to the system eigenvalues ​​at that point: ； in, Let be the participation factor of the converter at point k.

5. The feasible domain analysis and converter capacity optimization method for a hybrid grid system as described in claim 4, characterized in that, The partial derivative of the participation factors of different converters with respect to their output power is calculated using the following formula: ； in, It is the partial derivative of the converter participation factor with respect to the output power; S i This represents the apparent power of the power supply at the i-th port; S represents i The impact on the principal eigenvalue.

6. The feasible domain analysis and converter capacity optimization method for a hybrid grid system as described in claim 1, characterized in that, The capacity constraints of the converter are as follows: ； Among them, P gfl Q represents the active power of the GFL. gfl P represents the reactive power of the GFL. gfm Q represents the active power of the GFM. gfm This represents the reactive power of the GFM.

7. The feasible domain analysis and converter capacity optimization method for a hybrid grid system as described in claim 6, characterized in that, The feasible region of the system is discretized based on the power allocation strategy, combined with the capacity and power constraints of the converter, including: ； Where R represents the value range of all running points; Discretizing R yields the following feasible region: ； in, Indicates the step size of the change in active power; This indicates the step size of the reactive power change.

8. The feasible domain analysis and converter capacity optimization method for a hybrid grid system as described in claim 7, characterized in that, The process of solving for all discrete points in the feasible region to obtain the stability boundary of the system includes: First, substitute the active power of the operating point GFL and the reactive power of GFM into the system to perform power flow calculation and obtain the output power of each power source in the system; if the capacity constraint is satisfied, then substitute the power flow calculation results into the impedance mathematical model. Next, the discretized impedance value is substituted into the closed-loop admittance matrix to obtain the values ​​of all elements in the matrix. The d-axis closed-loop transfer admittance of the energy storage port is selected for study. The poles of the element are obtained by rational fitting. After solving for all points in the discretized feasible region of the system, the feasible region of the system is obtained.

9. The feasible domain analysis and converter capacity optimization method for a hybrid grid system as described in claim 7, characterized in that, The power grid strength of the quantification system is specifically as follows: ； in, This is the system's rated voltage; The power grid frequency; The impedance of the power grid; The sum of the GFL capacity and the GFM capacity, i.e., S sum = S gfl + S gfm .

10. A device for feasible domain analysis and converter capacity optimization of a hybrid grid system, characterized in that, include: The impedance model construction module is used to quantify the grid strength of the system. Under different grid strengths, it analyzes the changes in the system stability boundary based on the changes in the converter output power, thereby optimizing the converter capacity. The closed-loop admittance matrix derivation module is used to combine the source-side impedance matrix and the network-side admittance matrix in the impedance model to derive the closed-loop admittance matrix of the GFL-GFM parallel system. The system feature value extraction module is used to rationally approximate the closed-loop admittance matrix using a vector fitting method to extract system feature values. The eigenvalue and impedance correlation module is used to establish the correlation between system eigenvalues ​​and each impedance parameter based on the closed-loop admittance matrix analysis method. The converter participation factor calculation module is used to calculate the participation factor of each converter on the system characteristic value based on the correlation between the system characteristic value and each impedance parameter. The participation factor is used to quantify the degree of influence of the converter on the system characteristic value. The power allocation strategy determination module is used to calculate the partial derivative of the participation factors of different converters with respect to their output power, so as to obtain the direction and degree of the effect of the power of any converter on the dominant characteristic value of the system under specific operating conditions. By quantifying the direction and degree of the effect, the power allocation strategy to improve the stability of the system is determined. The feasible region analysis module is used to discretize the feasible region of the system based on the power allocation strategy and the capacity and power constraints of the converter, and solve for all discrete points in the feasible region to obtain the stability boundary of the system. The grid strength quantification module is used to quantify the grid strength of the system. Under different grid strengths, it analyzes the changes in the system stability boundary based on the changes in the converter output power, thereby optimizing the converter capacity.

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