New energy base maximum bearing degree evaluation method considering system stability margin

Abstract

The invention discloses a new energy base maximum bearing degree evaluation method considering a system stability margin. The method comprises the following steps: firstly, carrying out admittance modeling on the new energy multi-infeed system, and deducing a calculation method of a power grid strength index in a large-scale new energy access scene and a linear equation of a critical stability index of the system about a steady-state equilibrium point; then, taking maximization of the sum of active output of the new energy equipment as a target function, and establishing a new energy base maximum bearing degree evaluation optimization model considering the power grid intensity; and finally, obtaining a final new energy base maximum bearing degree evaluation result based on semi-definite programming and a sub-optimization problem iteration method. The method can accurately analyze the optimal configuration problem of the active power output of the new energy equipment under various complex operation conditions such as an unrated condition and a non-unity power factor condition, and has practical engineering significance.

Description

Technical Field

[0001] The present invention relates to the field of new energy operation planning, and in particular to a method for evaluating the maximum carrying capacity of a new energy base taking into account system stability margin. Background Art

[0002] With the massive access of power electronic devices such as new energy penetration and the corresponding withdrawal of traditional synchronous generators, the grid strength of new power systems dominated by inverters is declining significantly. Especially in the scenario of weak power grid (low short-circuit ratio), it usually faces small disturbance stability problems caused by control links such as the phase-locked loop of the grid-following converter. Reasonable planning of the capacity configuration of new energy equipment is considered to be an effective means to improve the operating stability margin of new power systems. Therefore, it has become a key research issue to quantitatively analyze the impact mechanism of the capacity configuration of new energy equipment on the stability margin of the power system and design a maximum transmission capacity scheme for new energy bases under the condition of meeting the stability margin.

[0003] Although a large number of scholars have conducted in-depth research on the capacity configuration of renewable energy equipment, they usually focus on the scenarios of single equipment grid connection or simple operating conditions. There is still a lack of research on how to configure equipment capacity under complex operating conditions of renewable energy multi-feed systems. Hui Yuan et al. derived the grid strength index considering the actual operating conditions and used it as a theoretical basis to linearly reconstruct the system model, and proposed a method for rapid adjustment of the active output of equipment that meets the small disturbance stability constraints of renewable energy multi-feed systems. However, this method does not clearly explain how to evaluate the load capacity of renewable energy bases under complex operating conditions, and the strength index used cannot be applied to complex operating conditions such as non-unit power factor, which has limitations (H. Yuan, H. Xin, Z. Li, S. Li, J. Dai and H. Gao, "Enhancing the Small-Signal Stability of Power Grids via Optimally Coordinating Inverter-Based Resources," IEEE Trans. Sustain. Energy., 2024, early access).

[0004] Therefore, there is an urgent need for a method to analyze the impact of new energy equipment capacity configuration on system stability margin and provide the maximum load-bearing capacity assessment results of new energy bases. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and propose a maximum load-bearing capacity assessment method for a new energy base taking into account the system stability margin. First, the new energy multi-feedback system is modeled by admittance, and the calculation method of the power grid strength index (generalized short-circuit ratio) in the scenario of large-scale new energy access and the linear equation of the system's critical stability index (generalized critical short-circuit ratio) about the steady-state equilibrium point are derived; next, taking the maximization of the total active output of new energy equipment as the objective function, an optimization model for the maximum load-bearing capacity assessment of a new energy base taking into account the power grid strength is established; finally, the final maximum load-bearing capacity assessment result of the new energy base is obtained based on the semi-positive definite programming and the iterative method of the sub-optimization problem.

[0006] The purpose of the present invention is achieved by at least one of the following technical solutions.

[0007] A method for evaluating the maximum carrying capacity of a new energy base taking into account system stability margin comprises the following steps:

[0008] S1. Establish an admittance model of a renewable energy multi-feed system taking into account the system steady-state balance parameters, including the admittance model of the grid-following converter and the admittance model of the power network;

[0009] S2. Establish a closed-loop dynamic model of the renewable energy multi-feed system, calculate the grid strength index (generalized short-circuit ratio), and derive the linear equation of the critical stability index (critical generalized short-circuit ratio) of the renewable energy multi-feed system about the steady-state equilibrium point;

[0010] S3. Establish stability margin constraints for renewable energy multi-feedback systems;

[0011] S4. Establish steady-state parameter constraints for renewable energy multi-feedback systems;

[0012] S5. According to the stability margin constraints and steady-state parameter constraints, a new energy base maximum load assessment model taking into account the system stability margin is constructed, and the optimal active output configuration of new energy equipment is obtained through sub-optimization problem iteration and semi-positive definite programming method.

[0013] Furthermore, in step S1, the admittance model of the i-th new energy device in the new energy multi-feed system, namely the grid-following converter (GFL), is specifically as follows:

[0014] ΔI i =-S Bi Y PEDi (s)ΔU i ,i=1,2,…,n

[0015]

[0016] Among them, ΔI i and ΔU iare the injected current and the voltage at the feeding point of the i-th grid-following converter respectively; S Bi is the capacity of the ith grid-connected converter; s represents the frequency domain Laplace operator; V di0 is the steady-state voltage of the ith grid-connected converter, I di0 ,I qi0 are the dq axis components of the steady-state current balance value of the i-th grid-following converter; Y 11 (I di0 ,I qi0 ,V di0 ,s),Y 12 (I di0 ,I qi0 ,V di0 ,s),Y 21 (I di0 ,I qi0 ,V di0 ,s) and Y 22 (I di0 ,I qi0 ,V di0 ,s) are obtained according to the specific control parameters of the power / DC voltage outer loop, the current inner loop, and the voltage feedforward (L. Huang, H. Xin, W. Dong, and F. "Impacts of grid structure on PLL-synchronization stability ofconverter-integrated power systems," 2019, arXiv:1903.05489.[Online].Available:https: / / arxiv.org / abs / 1903.05489);

[0017] Then, the overall grid-following converter admittance model of the renewable energy multi-feedback system is:

[0018] ΔI=-SY PED (s)ΔU

[0019]

[0020]

[0021] Wherein, ΔI and ΔU are the injection current and feed-in point voltage matrices of all grid-following converters sorted from 1 to n, respectively; I2 is a 2×2 unit matrix; Y PED (s) is the admittance matrix of all grid-type converters sorted from 1 to n, which is a diagonal matrix; S B1 ~S Bn and Y PED1(s)~Y PEDn (s) are the capacity and admittance models of grid-type converters No. 1 to n respectively.

[0022] Furthermore, in step S1, the admittance model of the power network is as follows:

[0023]

[0024] Among them, Y net (s) is the admittance matrix of the power network; nodes connected to the grid-type converter are active nodes, and nodes not connected to the grid-type converter are passive nodes. ΔI and are the injected currents of n active nodes and m passive nodes in the renewable energy multi-feed system respectively; ΔU and are the voltages at the feed-in points of n active nodes and m passive nodes in the renewable energy multi-feed system respectively; is the line susceptance matrix of the renewable energy multi-feed system, and and They are all block matrices of matrix B; is the transmission line admittance matrix, s represents the frequency domain Laplace operator; τ = R / X represents the line impedance ratio, R and X correspond to the line resistance and inductance under the per-unit value respectively; ω0 is the synchronous stable angular frequency.

[0025] Furthermore, in step S2, a closed-loop dynamic model of the renewable energy multi-feed system is established, as follows:

[0026] Based on Kron-reduction, the m passive nodes in the power network are eliminated and the following is obtained:

[0027]

[0028] Therefore, after elimination and simplification, the closed-loop characteristic equation of the renewable energy multi-feed system is:

[0029]

[0030] in, It represents the admittance model of the power network after the Kron-reduction simplification and elimination process;

[0031] Furthermore, in step S2, the generalized short-circuit ratio is used as a grid strength indicator, as follows:

[0032] Considering the closed-loop characteristic equation of the renewable energy multi-feed system is diagonalized and its eigenvalues ​​are all positive and satisfy 0<λ1<λ2<…<λ n ,matrix is broken down into:

[0033]

[0034] Where V = [v1, v2, …, v n ] represents the right eigenvector matrix corresponding to the matrix eigenvalue;

[0035] Based on the above formula and matrix decomposition theory, the closed-loop characteristic equation of the renewable energy multi-feed system is transformed into:

[0036]

[0037] Among them, λ i The corresponding matrix is ​​the i-th eigenvalue;

[0038] For renewable energy multi-feed power systems, the grid strength can be measured by the minimum short-circuit ratio in a series of single-feed systems, namely the generalized short-circuit ratio:

[0039]

[0040] in, is the minimum eigenvalue of the matrix, and gSCR is the generalized short-circuit ratio.

[0041] Furthermore, in step S2, the critical generalized short-circuit ratio is used as the critical stability index of the renewable energy multi-feed system, and a linear equation of the critical stability index of the renewable energy multi-feed system with respect to the steady-state equilibrium point is derived, which is specifically as follows:

[0042] The critical generalized short-circuit ratio, CgSCR, is the value of the short-circuit ratio SCR corresponding to the critical stability of the weakest single-feed system, and its expression is:

[0043]

[0044] in, is the eigenvalue solution of the equation; s c =jω c denotes the imaginary axis where the dominant eigenvalue lies on the complex plane, ω c represents the angular frequency of critical oscillation;

[0045] Through the data-driven fitting method, the critical short-circuit ratio CSCR of each grid-following converter can be calculated. i Expressed as δV i ,δI di and δI qi The linear equation is:

[0046]

[0047] Among them, δVi represents the deviation between the actual voltage and the rated voltage of the ith grid-type converter, δI di and δI qi They represent the deviation between the actual dq axis current and the rated current of the ith grid-type converter; a i , b i 、c i and d i are the coefficients of the linear equation, which change with the specific control parameters of each grid-type converter. When the control parameters of all devices in the renewable energy multi-feed system are consistent, a i , b i 、c i and d i The same for every grid-type converter;

[0048] Similarly, CgSCR can be expressed as δI d_equ ,δI q_equ and δV equ The linear equation is:

[0049] CgSCR(δV equ ,δI d_equ ,δI q_equ )=aδV equ +bδI d_equ +cδI q_equ +d

[0050]

[0051] Among them, p i represents the participation factor of the ith grid-following converter; a, b, c and d are the coefficients of the linear equation, δV equ Indicates the deviation between the actual voltage and the rated voltage after weighting, δI d_equ and δI q_equ They respectively represent the deviation between the actual dq-axis current and the rated current after weighting.

[0052] Furthermore, in step S3, establishing the stability margin constraint of the renewable energy multi-feed system includes:

[0053] gSCR-CgSCR=Δ≥δ0

[0054] Among them, Δ is used to characterize the stability margin of the renewable energy multi-infeed system; δ0 is the expected stability margin of the system; gSCR and CgSCR are the generalized short-circuit ratio and the critical generalized short-circuit ratio, respectively. This constraint can ensure the small-disturbance stability of the renewable energy multi-infeed system.

[0055] Furthermore, in step S4, steady-state parameter constraints of the renewable energy multi-feed system are established, including AC power flow constraints, power constraints and voltage amplitude constraints;

[0056] The AC power flow constraints are as follows:

[0057]

[0058] The power constraints are as follows:

[0059] 0≤P i ≤P imax ,i=1,2,…,n

[0060] 0≤Q i ≤Q imax ,i=1,2,…,n

[0061] The voltage amplitude constraints are as follows:

[0062] 0.9≤V i ≤1.1,i=1,2,…,n

[0063] From top to bottom, they are AC power flow constraint, power constraint and voltage amplitude constraint; among them, B ij Corresponding line admittance parameter; V i and V j is the voltage of the i-th and j-th grid-type converters; θ ij =θ i -θ j is the phase angle difference between the i-th and j-th converters, θ i The phase angle of the i-th grid-type converter, θ j is the phase angle of the jth grid-type converter; P i , Q i is the active and reactive output of the i-th grid-connected converter; P imax and Q imax is the upper limit of active and reactive output of the ith grid-type converter, and Q imax Subject to the power factor of the grid-following converter.

[0064] Furthermore, in step S5, constructing a new energy base maximum carrying capacity assessment model taking into account the system stability margin includes:

[0065]

[0066] Among them, P i , Q i is the active and reactive output of the i-th grid-type converter.

[0067] Furthermore, in step S5, the maximum load assessment model of the renewable energy multi-feed system is linearly reconstructed, decoupled into a series of sub-optimization problems, and the reconstructed model is solved based on semi-positive definite programming and sub-optimization problem iterative method, as follows:

[0068]

[0069] The reconstruction model is the kth sub-optimization problem after the linear reconstruction of the maximum load-bearing capacity assessment model of the renewable energy multi-feed system, that is, the superscript k represents the kth iteration; where, and represents the first-order derivative of the k-th iteration system stability margin with respect to the active and reactive outputs of the grid-following converter; is the stability margin that the system expects to increase in the kth iteration; D k is the interval of increase of active and reactive power output in the kth iteration; and is the initial active output value of the i-th and j-th grid-connected converters at the k-th iteration, and is the initial reactive power output value of the i-th and j-th grid-connected converters at the k-th iteration;

[0070] The maximum load assessment model of the renewable energy multi-feed system has the characteristics of non-convex nonlinearity. By linearly reconstructing it, the original problem is decoupled into a series of sub-optimization problems. The local optimal solution is solved each time, and the global optimal solution is finally approached.

[0071] When k = 0, that is, when the initial point calculation is performed, the original problem can be transformed and solved by the semi-positive definite programming method:

[0072]

[0073] Among them, P imax is the upper limit of active output of the i-th grid-type converter;

[0074] When k>0, if the maximum carrying capacity evaluation results of the new energy base twice before and after satisfy the following formula, the model converges to the global optimal solution:

[0075]

[0076] Where ε is the convergence error.

[0077] Compared with the prior art, the maximum load capacity assessment method of a new energy base taking into account the system stability margin of the present invention has the following beneficial effects:

[0078] When evaluating the maximum load capacity of a new energy base, the present invention can take into account complex conditions such as non-rated operating conditions and non-unit power factor operating conditions, and the evaluation result is more accurate and practical.

[0079] The present invention has good scalability and can adapt to the capacity configuration problems of new energy bases in other different scenarios.

[0080] The present invention can not only ensure the stability of new energy equipment and the entire new energy multi-feedback system under different grid strengths, especially weak grid strength scenarios, but also accurately analyze the optimal configuration of the active output of new energy equipment under various complex operating conditions such as non-rated conditions and non-unit power factor conditions, and has practical engineering significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a flow chart of a method for evaluating the maximum carrying capacity of a new energy base taking into account the system stability margin in an embodiment of the present invention;

[0082] Figure 2 This is a control structure diagram of a new energy base and network equipment in an embodiment of the present invention;

[0083] Figure 3 The figure is a schematic diagram of a test network structure for simulating a new energy base example in an embodiment of the present invention. DETAILED DESCRIPTION

[0084] In order to make the objectives, technical solutions and advantages of the present invention more clear and explicit, the present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0085] Embodiment 1:

[0086] In this embodiment, a two-region interconnected four-machine new energy base is tested to specifically illustrate the maximum load evaluation method of the new energy base taking into account the system stability margin provided by the present invention. The grid-connected converter control structure and the power network topology are as follows: Figure 2 and Figure 3 As shown;

[0087] A method for evaluating the maximum carrying capacity of a new energy base taking into account the system stability margin is proposed. Figure 1 As shown, the following steps are included:

[0088] In this embodiment, the control parameters of each link of the grid-connected converter and the power network line parameters in the two-region interconnected four-machine new energy base are obtained, and their specific values ​​are shown in Table 1.

[0089] Table 1. Control parameters and power network parameters of the grid-connected converter of the two-region interconnected four-machine new energy base

[0090]

[0091]

[0092] It should be noted that in this example, the control outer loop of the grid-following converter adopts PQ power control, and the control structure and control strategy of each converter are the same.

[0093] S1, such as Figure 3 As shown, an admittance model of a renewable energy multi-feed system considering the steady-state balance parameters of the system is established, including an admittance model of a grid-following converter and an admittance model of a power network;

[0094] The admittance model of the i-th renewable energy device in the renewable energy multi-feed system, namely the grid-following converter (GFL), is as follows:

[0095] ΔI i =-S Bi Y PEDi (s)ΔU i ,i=1,2,…,n

[0096]

[0097] Among them, ΔI i and ΔU i are the injected current and the voltage at the feeding point of the i-th grid-following converter respectively; S Bi is the capacity of the ith grid-connected converter; s represents the frequency domain Laplace operator; V di0 is the steady-state voltage of the ith grid-connected converter, I di0 ,I qi0 are the dq axis components of the steady-state current balance value of the i-th grid-following converter respectively; in this embodiment, based on the prior art (L. Huang, H. Xin, W. Dong, and F. "Impacts of grid structure on PLL-synchronization stability of converter-integrated power systems," 2019, arXiv:1903.05489.[Online].Available: https: / / arxiv.org / abs / 1903.05489), Y 11 (I di0 ,I qi0 ,V di0 ,s),Y 12 (I di0 ,I qi0 ,V di0 ,s),Y 21(I di0 ,I qi0 ,V di0 ,s) and Y 22 (I di0 ,I qi0 ,V di0 ,s) are obtained according to the specific control parameters of the power / DC voltage outer loop, the current inner loop, and the voltage feedforward;

[0098] Then, the overall grid-following converter admittance model of the renewable energy multi-feedback system is:

[0099] ΔI=-SY PED (s)ΔU

[0100]

[0101]

[0102] Wherein, ΔI and ΔU are the injection current and feed-in point voltage matrices of all grid-following converters sorted from 1 to n, respectively; I2 is a 2×2 unit matrix; Y PED (s) is the admittance matrix of all grid-type converters sorted from 1 to n, which is a diagonal matrix; S B1 ~S Bn and Y PED1 (s)~Y PEDn (s) are the capacity and admittance models of grid-connected converters No. 1 to No. n respectively. Further, in step S1, the admittance model of the power network is as follows:

[0103]

[0104] Among them, Y net (s) is the admittance matrix of the power network; nodes connected to the grid-type converter are active nodes, and nodes not connected to the grid-type converter are passive nodes. ΔI and are the injected currents of n active nodes and m passive nodes in the renewable energy multi-feed system respectively; ΔU and are the voltages at the feed-in points of n active nodes and m passive nodes in the renewable energy multi-feed system respectively; is the line susceptance matrix of the renewable energy multi-feed system, and and They are all block matrices of matrix B; is the transmission line admittance matrix, s represents the frequency domain Laplace operator; τ = R / X represents the line impedance ratio, R and X correspond to the line resistance and inductance under the per-unit value respectively; ω0 is the synchronous stable angular frequency.

[0105] S2. Establish a closed-loop dynamic model of the renewable energy multi-feedback system, calculate the grid strength index (generalized short-circuit ratio), and derive the linear equation of the system's critical stability index (critical generalized short-circuit ratio) about the steady-state equilibrium point;

[0106] A closed-loop dynamic model of a renewable energy multi-feed system is established as follows:

[0107] Based on Kron-reduction, the m passive nodes in the power network are eliminated and the following is obtained:

[0108]

[0109]

[0110] Therefore, after elimination and simplification, the closed-loop characteristic equation of the renewable energy multi-feed system is:

[0111]

[0112] in, Represents the power network admittance model after Kron-reduction simplification and elimination

[0113] The generalized short-circuit ratio is used as a grid strength indicator, as follows:

[0114] Considering the closed-loop characteristic equation of the renewable energy multi-feed system is diagonalized and its eigenvalues ​​are all positive and satisfy 0<λ1<λ2<…<λ n ,matrix is broken down into:

[0115]

[0116] Where V = [v1, v2, …, v n ] represents the right eigenvector matrix corresponding to the matrix eigenvalue;

[0117] Based on the above formula and matrix decomposition theory, the closed-loop characteristic equation of the system is transformed into:

[0118]

[0119] For renewable energy multi-feed power systems, the grid strength can be measured by the minimum short-circuit ratio in a series of single-feed systems, namely the generalized short-circuit ratio:

[0120]

[0121] in, is the minimum eigenvalue of the matrix, and gSCR is the generalized short-circuit ratio;

[0122] Taking the critical generalized short-circuit ratio as the critical stability index of the renewable energy multi-feed system, the linear equation of the critical stability index of the renewable energy multi-feed system with respect to the steady-state equilibrium point is derived, as follows:

[0123] The critical generalized short-circuit ratio, CgSCR, is the value of the short-circuit ratio SCR corresponding to the critical stability of the weakest single-feed system, and its expression is:

[0124]

[0125] in, is the eigenvalue solution of the equation; s c =jω c denotes the imaginary axis where the dominant eigenvalue lies on the complex plane, ω c Represents the angular frequency of critical oscillation.

[0126] Through the data-driven fitting method, the critical short-circuit ratio CSCR of each grid-following converter can be calculated. i Expressed as δV i ,δI di and δI qi The linear equation is:

[0127]

[0128] Among them, δV i represents the deviation between the actual voltage and the rated voltage of the ith grid-type converter, δI di and δI qi They represent the deviation between the actual dq axis current and the rated current of the ith grid-type converter; a i , b i 、c i and d i are the coefficients of the linear equation, which change with the specific control parameters of each grid-type converter. When the control parameters of all devices in the renewable energy multi-feed system are consistent, a i , b i 、c i and d i The same for every grid-type converter;

[0129] Similarly, CgSCR can be expressed as δI d_equ ,δI q_equ and δV equ The linear equation is:

[0130] CgSCR(δV equ ,δI d_equ ,δI q_equ )=aδV equ+bδI d_equ +cδI q_equ +d

[0131]

[0132] where p i represents the participation factor of the ith grid-following converter; a, b, c and d are the coefficients of the linear equation, δV equ Indicates the deviation between the actual voltage and the rated voltage after weighting, δI d_equ and δI q_equ They respectively represent the deviation between the actual dq-axis current and the rated current after weighting.

[0133] S3. Establishment of stability margin constraints for renewable energy multi-feedback systems includes:

[0134] gSCR-CgSCR=Δ≥δ0

[0135] Among them, Δ is used to characterize the stability margin of the renewable energy multi-infeed system; δ0 is the expected stability margin of the system; gSCR and CgSCR are the generalized short-circuit ratio and the critical generalized short-circuit ratio, respectively. This constraint can ensure the small-disturbance stability of the renewable energy multi-infeed system.

[0136] S4. Establish steady-state parameter constraints for renewable energy multi-feed systems, including AC power flow constraints, power constraints, and voltage amplitude constraints;

[0137] The AC power flow constraints are as follows:

[0138]

[0139] The power constraints are as follows:

[0140] 0≤P i ≤P imax ,i=1,2,…,n

[0141] 0≤Q i ≤Q imax ,i=1,2,…,n

[0142] The voltage amplitude constraints are as follows:

[0143] 0.9≤V i ≤1.1,i=1,2,…,n

[0144] From top to bottom, they are AC power flow constraint, power constraint and voltage amplitude constraint; among them, B ij Corresponding line admittance parameter; V i and V j is the voltage of the i-th and j-th grid-type converters; θ ij =θi -θ j is the phase angle difference between the i-th and j-th converters, θ i The phase angle of the i-th grid-type converter, θ j is the phase angle of the jth grid-type converter; P i , Q i is the active and reactive output of the i-th grid-connected converter; P imax and Q imax is the upper limit of active and reactive output of the ith grid-type converter, and Q imax Subject to the power factor of the grid-following converter.

[0145] S5. According to the stability margin constraints and steady-state parameter constraints, a new energy base maximum load assessment model taking into account the system stability margin is constructed, and the optimal active output configuration of new energy equipment is obtained through sub-optimization problem iteration and semi-positive definite programming method.

[0146] The construction of a new energy base maximum load assessment model taking into account system stability margin includes:

[0147]

[0148] Among them, P i , Q i is the active and reactive output of the i-th grid-type converter.

[0149] Since the above-mentioned maximum load assessment model of the renewable energy multi-feed system has the characteristics of non-convex nonlinearity and is difficult to solve, it is necessary to linearly reconstruct it and decouple it into a series of sub-optimization problems. The reconstructed model is solved based on semi-positive definite programming and sub-optimization problem iterative method as follows:

[0150]

[0151] The reconstruction model is the kth sub-optimization problem after the linear reconstruction of the maximum load-bearing capacity assessment model of the renewable energy multi-feed system, that is, the superscript k represents the kth iteration; where, and represents the first-order derivative of the k-th iteration system stability margin with respect to the active and reactive outputs of the grid-following converter; is the stability margin that the system expects to increase in the kth iteration; D k is the interval of increase of active and reactive power output in the kth iteration; and is the initial active output value of the i-th and j-th grid-connected converters at the k-th iteration, and is the initial reactive power output value of the i-th and j-th grid-connected converters at the k-th iteration;

[0152] The maximum load assessment model of the renewable energy multi-feed system has the characteristics of non-convex nonlinearity. By linearly reconstructing it, the original problem is decoupled into a series of sub-optimization problems. The local optimal solution is solved each time, and the global optimal solution is finally approached.

[0153] When k = 0, that is, when the initial point calculation is performed, the original problem can be transformed and solved by the semi-positive definite programming method:

[0154]

[0155] Among them, P imax is the upper limit of active output of the i-th grid-type converter.

[0156] When k>0, if the maximum carrying capacity evaluation results of the new energy base twice before and after satisfy the following formula, the model converges to the global optimal solution:

[0157]

[0158] Where ε is the convergence error.

[0159] In this embodiment, the convergence error ε is set to 10 -3 , and obtain the converged solution P k , which includes the corresponding capacity of each grid-connected converter in the new energy base and the total maximum load evaluation results, as shown in Table 2.

[0160] Table 2 Evaluation results of the maximum carrying capacity of the new energy base in Example 1

[0161]

[0162] For the results in Table 2, the convergence error is satisfied and the stability margin is greater than 0 (indicating that the system meets the requirements of small disturbance stability). Since certain errors will be generated when performing data-driven fitting of the system critical stability index and linear reconstruction of the system model, it is necessary to leave a certain margin (about 0.1) for the stability margin to meet the stability requirements.

[0163] Embodiment 2:

[0164] In Example 2, compared with Example 1, the line parameters of the power network of the new energy base remain unchanged; the outer loop of each grid-type converter adopts PQ power control, the PI parameter of the phase-locked loop is changed to 9,8700, and the control structure and control strategy of each converter are the same.

[0165] The evaluation results of the maximum carrying capacity of the new energy base in Example 2 are shown in Table 3.

[0166] Table 3 Evaluation results of the maximum carrying capacity of the new energy base in Example 2

[0167]

[0168] For the results in Table 3, the convergence error is satisfied and the stability margin is greater than 0 (indicating that the system meets the requirements of small disturbance stability). Since the stability problem of the grid-following converter is usually caused by the phase-locked loop parameters, from the results in Table 3, different grid-following converter phase-locked loop parameters will affect the maximum load evaluation results of the new energy base.

[0169] Embodiment 3:

[0170] In Example 3, compared with Example 1, the line parameters of the power network of the new energy base remain unchanged; the outer loop of each grid-connected transformer adopts DC voltage control, the PI parameter of the phase-locked loop is changed to 9,8700, and the control structure and control strategy of each transformer are the same.

[0171] The evaluation results of the maximum carrying capacity of the new energy base in Example 3 are shown in Table 4.

[0172] Table 4 Evaluation results of the maximum carrying capacity of the new energy base in Example 3

[0173]

[0174] For the results in Table 4, the convergence error is satisfied and the stability margin is greater than 0 (indicating that the system meets the requirements of small disturbance stability). From the results in Table 4, different grid-following converter phase-locked loop parameters and different control strategies will affect the maximum load evaluation results of the new energy base.

[0175] Obviously, the above-mentioned embodiments only express several implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A method for evaluating the maximum carrying capacity of a new energy base taking into account the system stability margin, characterized in that: The following steps are involved: S1. Establish an admittance model of a renewable energy multi-feed system taking into account the system steady-state balance parameters, including the admittance model of the grid-following converter and the admittance model of the power network; S2. Establish a closed-loop dynamic model of the renewable energy multi-feed system, calculate the grid strength index, and derive the linear equation of the critical stability index of the renewable energy multi-feed system with respect to the steady-state equilibrium point; S3. Establish stability margin constraints for renewable energy multi-feedback systems; S4. Establish steady-state parameter constraints for renewable energy multi-feedback systems; S5. According to the stability margin constraints and steady-state parameter constraints, a new energy base maximum load assessment model taking into account the system stability margin is constructed, and the optimal active output configuration of new energy equipment is obtained through sub-optimization problem iteration and semi-positive definite programming method.

2. A method for evaluating the maximum carrying capacity of a new energy base taking into account the system stability margin according to claim 1, characterized in that: In step S1, the admittance model of the i-th new energy device in the new energy multi-feed system, namely the grid-following converter (GFL), is specifically as follows: ΔI i =-S Bi Y PEDi (s)ΔU i ,i=1,2,…,n Among them, ΔI i and ΔU i are the injected current and the voltage at the feeding point of the i-th grid-following converter respectively; S Bi is the capacity of the ith grid-connected converter; s represents the frequency domain Laplace operator; V di0 is the steady-state voltage of the ith grid-connected converter, I di0 ,I qi0 are the dq axis components of the steady-state current balance value of the i-th grid-following converter; Y 11 (I di0 ,I qi0 ,V di0 ,s),Y 12 (I di0 ,I qi0 ,V di0 ,s),Y 21 (I di0 ,I qi0 ,V di0 ,s) and Y 22 (I di0 ,I qi0 ,V di0 ,s) are obtained according to the specific control parameters of the power / DC voltage outer loop, the current inner loop, and the voltage feedforward; Then, the overall grid-following converter admittance model of the renewable energy multi-feedback system is: ΔI=-SY PED (s)ΔU Wherein, ΔI and ΔU are the injection current and feed-in point voltage matrices of all grid-following converters sorted from 1 to n, respectively; I2 is a 2×2 unit matrix; Y PED (s) is the admittance matrix of all grid-type converters sorted from 1 to n, which is a diagonal matrix; S B1 ~S Bn and Y PED1 (s)~Y PEDn (s) are the capacity and admittance models of grid-type converters No. 1 to n respectively.

3. The maximum carrying capacity evaluation method of a new energy base taking into account system stability margin according to claim 2 is characterized in that: In step S1, the admittance model of the power network is as follows: Among them, Y net (s) is the admittance matrix of the power network; nodes connected to the grid-type converter are active nodes, and nodes not connected to the grid-type converter are passive nodes. ΔI and are the injected currents of n active nodes and m passive nodes in the renewable energy multi-feed system respectively; ΔU and are the voltages at the feed-in points of n active nodes and m passive nodes in the renewable energy multi-feed system respectively; is the line susceptance matrix of the renewable energy multi-feed system, and and They are all block matrices of matrix B; is the transmission line admittance matrix, s represents the frequency domain Laplace operator; τ = R / X represents the line impedance ratio, R and X correspond to the line resistance and inductance under the per-unit value respectively; ω0 is the synchronous stable angular frequency.

4. The maximum carrying capacity evaluation method of a new energy base taking into account system stability margin according to claim 3 is characterized in that: In step S2, a closed-loop dynamic model of the renewable energy multi-feed system is established, as follows: Based on Kron-reduction, the m passive nodes in the power network are eliminated and the following is obtained: Therefore, after elimination and simplification, the closed-loop characteristic equation of the renewable energy multi-feed system is: in, It represents the admittance model of the power network after the Kron-reduction simplification and elimination process; 5. The maximum carrying capacity evaluation method of a new energy base taking into account system stability margin according to claim 4 is characterized in that: In step S2, the generalized short-circuit ratio is used as a grid strength indicator, as follows: Considering the closed-loop characteristic equation of the renewable energy multi-feed system is diagonalized and its eigenvalues ​​are all positive and satisfy 0<λ1<λ2<…<λ n ,matrix is broken down into: Where V = [v1, v2, …, v n ] represents the right eigenvector matrix corresponding to the matrix eigenvalue; Based on the above formula and matrix decomposition theory, the closed-loop characteristic equation of the renewable energy multi-feed system is transformed into: Among them, λ i The corresponding matrix is ​​the i-th eigenvalue; For renewable energy multi-feed power systems, the grid strength is measured by the minimum short-circuit ratio in a series of single-feed systems, namely the generalized short-circuit ratio: in, is the minimum eigenvalue of the matrix, and gSCR is the generalized short-circuit ratio.

6. A method for evaluating the maximum carrying capacity of a new energy base taking into account the system stability margin according to claim 5, characterized in that: In step S2, the critical generalized short-circuit ratio is used as the critical stability index of the renewable energy multi-feed system, and a linear equation of the critical stability index of the renewable energy multi-feed system with respect to the steady-state equilibrium point is derived, which is specifically as follows: The critical generalized short-circuit ratio, CgSCR, is the value of the short-circuit ratio SCR corresponding to the critical stability of the weakest single-feed system, and its expression is: in, is the eigenvalue solution of the equation; s c =jω c denotes the imaginary axis where the dominant eigenvalue lies on the complex plane, ω c represents the angular frequency of critical oscillation; Through the data-driven fitting method, the critical short-circuit ratio CSCR of each grid-connected converter is calculated. i Expressed as δV i ,δI di and δI qi The linear equation is: Among them, δV i represents the deviation between the actual voltage and the rated voltage of the ith grid-type converter, δI di and δI qi They represent the deviation between the actual dq axis current and the rated current of the ith grid-type converter; a i , b i 、c i and d i are the coefficients of the linear equation, which change with the specific control parameters of each grid-type converter. When the control parameters of all devices in the renewable energy multi-feed system are consistent, a i , b i 、c i and d i The same for every grid-type converter; CgSCR is expressed as δI d_equ ,δI q_equ and δV equ The linear equation is: CgSCR(δV equ ,δI d_equ ,δI q_equ )=aδV equ +bδI d_equ +cδI q_equ +d Among them, p i represents the participation factor of the ith grid-following converter; a, b, c and d are the coefficients of the linear equation, δV equ Indicates the deviation between the actual voltage and the rated voltage after weighting, δI d_equ and δI q_equ They respectively represent the deviation between the actual dq-axis current and the rated current after weighting.

7. A method for evaluating the maximum carrying capacity of a new energy base taking into account the system stability margin according to claim 6, characterized in that: In step S3, establishing the stability margin constraint of the renewable energy multi-feed system includes: gSCR-CgSCR=Δ≥δ0 Among them, Δ is used to characterize the stability margin of the renewable energy multi-feed system; δ0 is the expected stability margin of the system; gSCR and CgSCR are the generalized short-circuit ratio and critical generalized short-circuit ratio, respectively.

8. A method for evaluating the maximum carrying capacity of a new energy base taking into account the system stability margin according to claim 7, characterized in that: In step S4, steady-state parameter constraints of the renewable energy multi-feed system are established, including AC power flow constraints, power constraints and voltage amplitude constraints; The AC power flow constraints are as follows: The power constraints are as follows: 0≤P i ≤P imax ,i=1,2,…,n 0≤Q i ≤Q imax ,i=1,2,…,n The voltage amplitude constraints are as follows: 0.9≤V i ≤1.1,i=1,2,…,n From top to bottom, they are AC power flow constraint, power constraint and voltage amplitude constraint; among them, B ij Corresponding line admittance parameter; V i and V j is the voltage of the i-th and j-th grid-type converters; θ ij =θ i -θ j is the phase angle difference between the i-th and j-th converters, θ i The phase angle of the i-th grid-type converter, θ j is the phase angle of the jth grid-type converter; P i , Q i is the active and reactive output of the i-th grid-connected converter; P imax and Q imax is the upper limit of active and reactive output of the ith grid-type converter, and Q imax Subject to the power factor of the grid-following converter.

9. A method for evaluating the maximum carrying capacity of a new energy base taking into account the system stability margin according to any one of claims 1 to 8, characterized in that: In step S5, constructing a new energy base maximum carrying capacity assessment model taking into account system stability margin includes: Among them, P i , Q i is the active and reactive output of the i-th grid-type converter.

10. A method for evaluating the maximum carrying capacity of a new energy base taking into account the system stability margin according to claim 9, characterized in that: In step S5, the maximum load assessment model of the renewable energy multi-feed system is linearly reconstructed, decoupled into a series of sub-optimization problems, and the reconstructed model is solved based on semi-positive definite programming and sub-optimization problem iterative method, as follows: The reconstruction model is the kth sub-optimization problem after the linear reconstruction of the maximum load-bearing capacity assessment model of the renewable energy multi-feed system, that is, the superscript k represents the kth iteration; where, and represents the first-order derivative of the k-th iteration system stability margin with respect to the active and reactive outputs of the grid-following converter; is the stability margin that the system expects to increase in the kth iteration; D k is the interval of increase of active and reactive power output in the kth iteration; and is the initial active output value of the i-th and j-th grid-connected converters at the k-th iteration, and is the initial reactive power output value of the i-th and j-th grid-connected converters at the k-th iteration; The maximum load assessment model of the renewable energy multi-feed system has the characteristics of non-convex nonlinearity. By linearly reconstructing it, the original problem is decoupled into a series of sub-optimization problems. The local optimal solution is solved each time, and the global optimal solution is finally approached. When k = 0, that is, when the initial point calculation is performed, the original problem is transformed and solved by the semi-positive definite programming method: Among them, P imax is the upper limit of active output of the i-th grid-type converter; When k>0, if the maximum carrying capacity evaluation results of the new energy base twice before and after satisfy the following formula, the model converges to the global optimal solution: Where ε is the convergence error.

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