Optimal occupation ratio configuration method of follow-up / network type parallel system based on GRA-VIKOR method

Abstract

The application discloses a follow / grid network type parallel system optimal proportion configuration method based on a GRA-VIKOR method, and the follow / grid network type parallel system optimal proportion configuration method comprises the following steps: through standardization processing of original data of a new energy field station under different grid type converter capacity proportions, a normalized data matrix is obtained; subjective weights and objective weights of evaluation indexes are determined, and a final combination weight is determined according to a game combination weighting method; a weighted evaluation matrix is determined, and positive and negative ideal solutions of each evaluation index of a grid type control field station grid connection performance are obtained; through a GRA idea, grey correlation degrees between each evaluation index value and the positive and negative ideal solutions are obtained; group utility values and individual regret values are obtained to determine final group utility values and individual regret values, finally, comprehensive evaluation indexes of the new energy field station under different grid type converter capacity proportions are obtained; and finally, the best grid type converter capacity proportion is determined. The application can accurately measure the proportion problem between the follow network type converter and the grid network type converter in the new energy field station.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power grid control, and particularly relates to a follow / grid-forming grid-connected system optimal proportion configuration method based on a GRA-VIKOR method. BACKGROUND

[0002] With the gradual increase of the proportion of new energy access to the power grid, power electronic converters are increasingly widely used in power systems, gradually replacing a large number of traditional synchronous generators. However, this change has brought significant challenges to the stability of the power system. The reduction in the number of traditional generators results in their inability to continue to maintain the stable operation of the power system, bringing great challenges to the stable operation of the system.

[0003] At present, most new energy power generation units adopt a grid following (GFL) control strategy, which follows the frequency and phase angle of the alternating current grid voltage through a phase-locked loop (PLL) to achieve synchronization with the grid. However, GFL control is unstable in a weak grid environment and does not have the ability to actively support the grid. In contrast, new energy power generation units based on grid forming (GFM) control can achieve autonomous synchronization of the grid without relying on PLL, which can solve the problem of stable operation of traditional power generation units in a weak grid environment, and has the ability to support grid inertia, frequency and voltage. However, grid-forming converters have poor stability in a strong grid and are prone to low-frequency oscillation problems similar to traditional synchronous generator groups.

[0004] Although new energy converters in the current power system mainly adopt grid following control, as the number of grid forming converters increases, it becomes increasingly important to analyze the interaction between grid forming converters and PLL-based grid following converters. Current domestic and foreign scholars have carried out a lot of research on determining the proportion of grid following and grid forming converters. Some scholars have analyzed the mechanism of GFM control to improve subsynchronous oscillation, pointing out that GFM control becomes a "positive resistance" under reactive power loop control, which can offset part of the negative damping effect caused by GFL control. Some scholars have started from the short circuit ratio and established the relationship between the number of GFM devices and the short circuit ratio, but have not solved the problem of the optimal proportion of GFM devices in the system. Some scholars have studied the influence of the proportion of GFM devices on the voltage and frequency of the power system, as well as the influence on the power exchange between the distribution network and the transmission network, but only gave the range of the proportion of GFM devices, without accurately determining the specific value.

[0005] In summary, although the number of grid forming converters in the system is gradually increasing, the proportion of GFL and grid forming converter devices in new energy stations still needs further research. SUMMARY

[0006] To address the shortcomings of existing research, the present invention aims to provide an optimal ratio configuration method for grid-connected / grid-connected parallel systems based on the GRA-VIKOR method. By using transient stability analysis of grid-connected / grid-connected systems and the GRA-VIKOR method, and selecting stability and support capability as test and evaluation indicators, the invention accurately measures the ratio between grid-connected and grid-connected converters in renewable energy power plants.

[0007] To achieve the above technical objectives, the present invention adopts the following technical solution: an optimal proportion configuration method for a parallel system based on the GRA-VIKOR method, comprising the following steps:

[0008] Step 1: The total capacity of the inverters in the hybrid new energy power station is P. W If the capacities of the grid-type and network-type converters are P1 and P2 respectively, then P W =P1+P2; The output power P of the grid-type converter ref Less than its maximum output active power limit P max Under this premise, the capacity of grid-type converters is continuously increased; the original data of new energy power plants under different grid-type converter capacity ratios are standardized to eliminate the influence of dimensions and obtain a standardized data matrix Y. m×n Where m is the evaluation object and n is the evaluation index;

[0009] Step 2: Combine the Bayesian BWM method to obtain the subjective weight w1 of the evaluation index, and the critical method (CRITIC method) to determine the objective weight w2 of the evaluation index. Then, determine the final combined weight w3 based on the game combination weighting method.

[0010] Step 3: Determine the weighted evaluation matrix to obtain the positive and negative ideal solutions for each evaluation index of the grid-connected performance of the grid-type control station; use the GRA (Grey Relationship Assessment) concept to obtain the grey relational degree between each evaluation index value and the positive and negative ideal solutions; obtain the group utility value and individual regret value to determine the final group utility value Q. i And individual regret value R i Finally, the comprehensive evaluation index T for new energy power plants under different grid-type converter capacity ratios was obtained. i ;

[0011] Step 4: Determine the optimal capacity ratio of grid-type converters: From Step 3, T can be obtained for the capacity ratio of each type of grid-type converter. i Values, assuming the optimal proportion is P1 and the second-best proportion is P2, and based on the maximum group utility value Q. i The capacity ratios of different grid-type converters are ranked, and the optimal solution is the one that satisfies two conditions.

[0012] Further preferably, the evaluation indicators include:

[0013] Stability evaluation metrics: rate of change of angular frequency, transient angular frequency deviation

[0014] Support capacity assessment indicators: damping active power and inertia active power.

[0015] Further preferably, step one includes the following sub-steps:

[0016] Step S101: Conduct simulation experiments on parallel systems with different grid-type converter capacity ratios and collect data:

[0017] Step S102: Standardize and align the grid connection performance evaluation indicators of grid-connected power stations.

[0018] Further preferably, step two includes the following sub-steps:

[0019] Step S201: Conduct simulation experiments on grid-connected / parallel grid systems with different grid-type converter capacity ratios and collect data:

[0020] Step S202: Calculate the evaluation indicators for stability and support capability under experimental scenarios with different grid-type converter proportions, and then calculate the subjective weights of the proposed evaluation indicators using the Bayesian method.

[0021] Step S203: Calculate the objective weights of the proposed evaluation indicators using the CRITIC method:

[0022] Step S204: Use the game theory weighting method to determine the overall weight.

[0023] Further preferably, step three includes the following sub-steps:

[0024] Step S301: Construct a weighted evaluation matrix and determine the positive and negative ideal solutions under the experimental scenario with different grid-type converter proportions;

[0025] Step S302: Determine the grey relational degree between each evaluation index value and the positive and negative ideal solutions, and the final group utility value Q. i And individual regret value R i ;

[0026] Step S303: Obtain the comprehensive evaluation index (VIKOR value) T of new energy power plants under different grid-type converter capacity ratios. i .

[0027] The present invention has the following advantages:

[0028] 1) The evaluation method for determining the proportion of different grid-type converters based on the GRA-VIKOR method proposed in this invention quantifies the stability and dynamic support capability of grid-type control stations to grid-following control stations. The method selects two aspects of test evaluation indicators, namely stability and support capability, to accurately measure the ratio between grid-following and grid-type converters in new energy power stations.

[0029] 2) This invention is based on constructing an electromagnetic transient model of a grid-connected parallel system, analyzing the influence mechanism of adding a grid-connected converter on the transient stability of the grid-connected converter, and deriving the following for the grid-connected converter (GFL) under pure active current ω: GFM It is a key variable affecting its transient stability.

[0030] 3) This method can be used to conveniently determine the proportion of grid-type converters.

[0031] 4) This invention tests and evaluates converters with different grid configurations under experimental scenarios, and conducts in-depth research on their characteristic evaluation indicators and performance test schemes. Attached Figure Description

[0032] Figure 1 It is a topology diagram of a parallel network / networked system.

[0033] Figure 2 It is the equivalent circuit of a parallel system.

[0034] Figure 3 It is a transient analysis model for parallel network systems.

[0035] Figure 4 These are GFL phase plane diagrams under different GFM inertia J.

[0036] Figure 5 These are phase plane diagrams of GFL under different GFM inertia D.

[0037] Figure 6 It is a grid-connected converter system.

[0038] Figure 7 This is a flowchart of the optimal proportion configuration method for a parallel system based on the GRA-VIKOR method. Detailed Implementation

[0039] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0040] In this embodiment, firstly, an electromagnetic transient model of a parallel system with a root / grid configuration is constructed, and the influence mechanism of the active power loop control parameters (J, D) of the grid configuration on the transient stability of the parallel system with a root / grid configuration is analyzed, including the following steps:

[0041] Step 1-1: Topology and mathematical model of parallel network / construction network system.

[0042] Parallel network system topology such as Figure 1 As shown, the two converters are connected to the Point of Common Coupling (PCC) via LC filters and transmission line impedance, and then connected to the power grid via transmission lines. Figure 1 Medium: i abc1,2 Let u be the current in the filter inductor of the grid-connected converter and the network-connected converter, respectively; abc1,2 These are the output voltages of the grid-connected converter and the network-connected converter, respectively; L f1 C f1 L f2 C f2 These are the filter inductance and filter capacitor for the grid-connected converter and the network-connected converter, respectively; L1, R1, L2, and R2 are the equivalent line inductance and equivalent line resistance of the grid-connected converter and the network-connected converter connected to the PCC, respectively; U dc A constant voltage source on the DC side; U pcc R is the voltage at point PCC; g and L g These are the resistor and inductor connected to the power grid, respectively.

[0043] Because a grid-connected converter behaves as a parallel high-impedance controllable current source, while a grid-connected converter behaves as a series low-impedance controllable voltage source, it can be considered as... Figure 1 The topological structure diagram is equivalent to Figure 2 The equivalent circuit model is shown. Figure 2 In the middle, the grid voltage vector U g The phase angle is used as the reference phase angle, I GFL and I GFM These are the output current vectors for the grid-connected converter and the network-connected converter, respectively; U GFL and U GFM The output voltage vectors δ and δ of the grid-connected converter are respectively. GFL and δ GFM These are the output voltage vectors U of the grid-type converter. GFL The output voltage vector U of the grid-type converter GFM With grid voltage vector V g The phase angle difference. δ pcc For the voltage vector U at PCC point PCC With grid voltage vector V g The phase angle difference; For the output voltage vector U of the grid-type converter GFL With output current vector I GFL The phase angle difference, i.e. Due to the large inductance in the circuit, the resistive components are neglected. It is worth noting that neglecting resistance has no substantial impact on the analysis results. Therefore, the following analysis will focus on the transient stability analysis of a parallel network system with a purely inductive network.

[0044] X2 and X g X1 represents the line impedance between nodes 2 and 1, and between nodes 1 and 3, respectively. X2 is the impedance from the grid converter to node 1. Based on the node voltage equations, the circuit equations for the grid-connected parallel system are as follows:

[0045]

[0046] In the formula, This refers to the output current vector of the grid-type converter; This refers to the output current vector of the grid-type converter; The grid current vector; The grid voltage vector; PCC voltage vector; For the output voltage vector of the grid-type converter, the injected current at node 1 Injected current at node 2 j is the imaginary unit.

[0047] According to equation (1), U can be obtained pcc ∠δ pcc satisfy:

[0048]

[0049] Where, ω N The rated angular frequency, For the output voltage vector U of the grid-type converter GFL With output current vector I GFL The phase angle difference, δ GFL and δ GFM These are the output voltage vectors U of the grid-type converter. GFL The output voltage vector U of the grid-type converter GFM With grid voltage vector V g The phase angle difference;

[0050] Therefore, the expressions for the terminal voltage amplitude and phase angle of the grid-connected converter can be obtained as follows:

[0051]

[0052] To simplify the analysis, the following definitions were made:

[0053]

[0054] Among them, k0, k1, k2, and k3 are all definitions made for simplified analysis, and are still impedances.

[0055] Combining equation (4), equation (3) can be rewritten as:

[0056]

[0057] Using the PLL phase angle as the reference coordinate system, coordinate transformation is performed. From equation (5), the q-axis component of the grid-connected converter output voltage can be obtained as follows:

[0058] U GFLq =k1i GFLd -k3U g sinδ GFL +k2U GFM sin(δ GFM -δ GFL (6)

[0059] In the formula, i GFLd To determine the d-axis component of the output current of a grid-connected converter, typically δ GFM ,δ GFL ∈[0,π / 2]. According to equation (6), the last term of this equation can be understood as the coupling term of the grid-type converter connected to the new energy power station in the GFL transient model, defined as the coupling impedance voltage drop term. This coupling term is related to k2 and U GFM δ GFM and δ GFL related.

[0060] Substituting equation (6) into the phase-locked loop expression of the grid converter, we can obtain the synchronous machine-like expression as follows:

[0061]

[0062] J eq Defined as the equivalent inertia coefficient; δ GFL To match the equivalent power angle of the grid converter, dt 2 For the second derivative with respect to time, Δω GFL To measure the difference between the output angular frequency and the rated angular frequency of the grid converter, δ GFM For the output phase angle of the grid-type converter, ω GFL To match the output angular frequency of the grid converter, ω GFM For the output angular frequency of the grid-type converter, i GFLd To represent the d-axis component of the output current of the grid-connected converter, k i_pll k represents the integral coefficient of the phase-locked loop. p_pll D is the proportional gain of the phase-locked loop; eq1 Defined as the equivalent damping coefficient; D eq2 It is related to the deviation between the angular frequencies of the grid-type converter and is defined as the equivalent mutual damping coefficient; Pref_eq and P GFL_eq These are defined as the equivalent mechanical power and equivalent output power of the grid converter, respectively, but in essence they are still the line impedance voltage drop and the q-axis component of the grid voltage.

[0063] The output power of the grid converter is:

[0064]

[0065] Substituting equation (2) into equation (8), we can obtain the output active power and reactive power of the grid-type converter in the parallel system as follows:

[0066]

[0067] According to equation (9), the output active power and reactive power of the grid-type converter in the parallel system of the grid-type converter have an additional expression related to the output current and equivalent power angle of the grid-type converter, which are defined as active power coupling term and reactive power coupling term, respectively.

[0068] Therefore, the transient analysis model of the parallel system with a grid structure can be obtained as follows: Figure 3 As shown.

[0069] Step 1-2: Analysis of the influence of network structure parameters on transient stability.

[0070] The control parameters of the active power loop in a grid-type converter include inertia and damping. For example... Figure 4 As shown, increasing the inertia of the grid converter can simultaneously reduce ω. GFM and ω GFL The acceleration process during the fault, and the larger the inertia ω GFM and ω GFL The smaller the acceleration, the better the transient stability of both grid-type and grid-connected converters; the same applies to damping. Therefore, it can be concluded that regardless of increasing the active power command value or the J and D values ​​of the grid-type converter, the effect of the grid-type converter on the grid-connected converter is through ω. GFM This is caused by the following: When injecting pure active current into a grid-connected converter, reducing the active power command value of the grid-connected converter and increasing the J and D values ​​leads to ω... GFM Deceleration, δ GFM The value decreases. The value will gradually become negative, with a range of [-π / 2, 0]. Within this range, its sine function value is negative, while its cosine function value is positive. At this time, the addition of the grid-type converter reduces the acceleration area of ​​the grid-type converter during faults and increases the deceleration area after fault clearance, thus improving the transient stability of the grid-type converter. If pure reactive current is injected into the grid-type converter, The value of will gradually become positive, with a range of [0, π / 2]. Within this range, its sine function value is positive, while its cosine function value is negative. At this time, the addition of the grid-type converter increases the acceleration area of ​​the GFL during faults and reduces the deceleration area after fault clearance, leading to a deterioration in the transient stability of the grid-type converter. Therefore, we only discuss the optimal proportion of the grid-type converter when injecting pure active current into the grid-type converter.

[0071] In parallel systems with grids, this application assumes that, under the condition that the system is not unstable, the output power of the grid converter is generally equal to its active power command value.

[0072] The relationship between the capacity and control parameters (J, D) of grid-type converters is analyzed, and an evaluation index system is constructed, including the following steps:

[0073] Step 2-1: Relationship between the capacity of the grid-type converter and the control parameters (J, D).

[0074] Will Figure 2 Removing I1 and X1 from the code yields a parallel system with a grid structure, such as... Figure 6 As shown.

[0075] The active and reactive power at the internal potential are:

[0076]

[0077] In the formula δ e Let f be the phase of the internal electric potential in the common coordinate system, E be the amplitude of the internal electric potential, and f be the phase of the internal electric potential. P For E and δ e The relevant active function, f Q For E and δ e Related reactive power functions.

[0078] The expression for a combined GFM-type synchronous machine can be obtained.

[0079]

[0080] Among them, P ref This is the active power command value. For δ GFM The differential, For δ GFM The second differential, J GFM For the moment of inertia of the grid-type converter, D GFM The damping coefficient of the grid-type converter;

[0081] Then, in the above formula, δ VSG Replace with δ e We can obtain:

[0082]

[0083] in

[0084]

[0085] Among them, f t (δ e ) for δ e The function, δ e The phase of the internal electric potential in the common coordinate system. For δ e The differential, For δ e The second differential;

[0086] Within the normal network parameter range (except for extremely weak power grids), the region where the system operating point is located under shallow disturbances satisfies the following:

[0087]

[0088] That is, the output power and the internal potential exhibit a monotonically increasing phase relationship.

[0089] For f in equation (13) t (δ e Taking the derivative, we get:

[0090]

[0091] f t (δ e The value of f increases with the increase of the internal potential phase. Therefore, f t (δ e It increases with increasing power level.

[0092] Based on the above theoretical analysis, since the GFM capacity is directly proportional to its output active power, f t (δ e The value increases with increasing power level, and the parameters J, D and f... t (δ e ω is directly proportional to J and D, therefore the GFM capacity is positively correlated with J and D. GFM These are key variables for measuring the transient stability of a grid-connected converter (GFL). When the active power P increases, the output angular frequency of the GFL increases; when the damping coefficient D and inertia J increase, the output angular frequency decreases. The capacity of the GFL can be changed by adjusting the active power, inertia, and damping parameters. When connecting to renewable energy power plants, it is necessary to determine an appropriate capacity ratio to ensure stable operation in weak grid environments.

[0093] Step 2-2: Construction of evaluation indicators.

[0094] While increasing the capacity of a grid-connected converter may worsen the transient stability of the gas-cooled power line (GFL), it also enhances its support capability for the GFL. Therefore, to comprehensively consider these two aspects, this application is based on ω GFM A quantitative evaluation index characterizing the transient stability of GFL is proposed to determine the optimal proportion of GFL converters.

[0095] Stability assessment indicators

[0096] 1) Rate of change of angular frequency RoCoW

[0097] The rate of change of angular frequency (RoCoW) is defined as an evaluation index of inertia. The larger the RoCoW value, the faster the frequency of the power grid at the station changes and the worse the frequency stability.

[0098]

[0099] In the formula: ω max The maximum output angular frequency of the internal grid converter during the first oscillation cycle; ω0 is the initial angular frequency value; T C For ω max The rise time.

[0100] 2) Transient angular frequency deviation K ω

[0101] Define transient angular frequency deviation K ω It is an evaluation index for transient frequency stability, reflecting the evaluation index of the system maintaining stability under the condition of major load shock or other abnormal conditions.

[0102] K ω =ω max -ω0 (17)

[0103] Support Capability Assessment Indicators

[0104] 1) Damped active power

[0105] Define damped active power It is an evaluation metric that characterizes the ability of a converter to suppress output power oscillations.

[0106]

[0107] In the formula: P is the average value of the damped active power. D0 For the initial damped active power, t D1 , t D2 The active power of the converter damping is greater than P. D0 The initial and end times, i.e., the time window value, are set to 50ms.

[0108] 2) Inertia and active power

[0109] Define inertia and active power It is an evaluation index that characterizes the short-term support capability of a converter. The larger the value, the faster the station can respond to sudden changes in grid frequency.

[0110]

[0111] In the formula: P is the average value of active power due to inertia. H0 The initial inertia is the active power; t H1 , t H2 The active power operating value of the converter at the frequency is greater than P. H0 The initial and final times of the inertial active power, i.e., the time window value, are set to 500ms.

[0112] In summary, this invention proposes quantitative evaluation indicators to characterize the transient stability of GFL from two aspects: stability and support capability, which facilitates the analysis in the following text.

[0113] Based on the above analysis, this embodiment proposes an optimal proportion configuration method for a parallel system with a tail-to-tail network based on the GRA-VIKOR method, such as... Figure 7 As shown, it includes the following steps:

[0114] Step 1: Assume the total capacity of the inverters in the hybrid renewable energy power station is P. W (This capacity is known in advance), where the capacities of the grid-type and network-type converters are P1 and P2 respectively, then P W =P1+P2; The output power P of the grid-type converter ref Less than its maximum output active power limit P max Under this premise, the capacity of grid-type converters is continuously increased; the original data of new energy power plants under different grid-type converter capacity ratios are standardized to eliminate the influence of dimensions and obtain a standardized data matrix Y. m×n Where m is the evaluation object and n is the evaluation index;

[0115] Step 2: Combine the Bayesian BWM method to obtain the subjective weight w1 of the evaluation index, and the critical method (CRITIC method) to determine the objective weight w2 of the evaluation index. Then, determine the final combined weight w3 based on the game combination weighting method.

[0116] Step 3: Determine the weighted evaluation matrix based on equation (34) to obtain the positive and negative ideal solutions for each evaluation index of the grid-connected performance of the grid-type control station; obtain the grey relational degree between each evaluation index value and the positive and negative ideal solutions through the GRA concept; combine equations (36, 37) to obtain the group utility value and individual regret value to determine the final group utility value Q. i And individual regret value R i Finally, based on equation (40), the comprehensive evaluation index (VIKOR value) T of the new energy power station under the capacity ratio of different grid-type converters is obtained. i ;

[0117] Step 4: Determine the optimal GFM capacity ratio; T for each GFM ratio can be obtained from Step 3. i Values, assuming the optimal proportion is P1 and the second-best proportion is P2, and based on the maximum group utility value Q. i The different GFM capacity proportions are ranked, and the optimal solution is the one that satisfies two conditions. Step one includes the following sub-steps:

[0118] Step S101: Conduct simulation experiments on parallel systems with different GFM capacity ratios and collect data:

[0119] Step S102: Standardize and align the above-mentioned grid-connected performance evaluation indicators for grid-type power stations.

[0120] When processing evaluation indicator data, issues arise regarding the different positive / negative attributes and dimensions of the various indicators. Therefore, standardization and normalization are necessary before calculation. The method for converting negative evaluation indicators into positive ones is as follows:

[0121]

[0122] In the formula: μ is the compatibility coefficient, which is generally taken as 0.1; x ij x' represents the corresponding evaluation indicator value. ij X represents the value of the evaluation index after normalization. i For the i-th row of matrix X; max|X i | represents the maximum value in the i-th row of matrix X.

[0123] The standardization method for handling dimensionless evaluation indicators is as follows:

[0124]

[0125] In the formula, x i " j represents the standardized evaluation index value, and m represents the number of evaluation indexes in the i-th row of matrix X.

[0126] The standard matrix Y can be obtained from the above formula.m×n .

[0127] Step two includes the following sub-steps:

[0128] Step S201: Conduct simulation experiments on parallel systems connected to the grid / network under different GFM capacity ratios and collect data:

[0129] Step S202: Calculate the evaluation indicators for stability and support capability under different GFM capacity ratio experimental scenarios, and then calculate the subjective weights of the proposed evaluation indicators using the Bayesian method.

[0130] First, we invite k experts to evaluate the system of indicators (A = {A1, A2, ..., A...}). n Determine the optimal evaluation index A in}) B And worst-case assessment indicator A W Using the 1-9 scale, A B and A W As a reference comparison object, construct the best and worst comparison matrix C. B ={a B1 ,a B2 ,…,a Bn}, C W ={a 1W ,a 2W ,…,a nW},A1,A2,…,A n These are the evaluation indicators, a B1 ,a B2 ,…,a Bn These represent the optimal evaluation index values ​​for the 1st, ..., nth experts, respectively, and a 1W ,a 2W ,…,a nW These are the worst evaluation index values ​​for the 1st, ..., nth experts, respectively.

[0131] Secondly, a multinomial distribution is used for the input A. B A W Modeling. The target layer is treated as a random event, and the weights of the evaluation index layer are treated as the probability of the random event occurring. The worst-case evaluation index A is... W The polynomial probability distribution is as follows:

[0132]

[0133] In the formula: w is the probability distribution (weights), a jw Let w be the j-th worst-case evaluation metric. j Total number of random events If they are directly proportional, then the relationship between the weights and the expert scores can be obtained:

[0134]

[0135] Among them, w w The weight of the worst-case assessment indicator;

[0136] Similarly, input A B The polynomial probability distribution is:

[0137]

[0138] Among them, a Bj For the j-th optimal evaluation index, w B The optimal evaluation index weights are determined by these weights.

[0139] The prior distribution corresponding to the output w is introduced by the Dirichlet distribution:

[0140]

[0141] In the formula: B(x) is a multivariate Beta function, x is a dimensionless Dirichlet prior distribution parameter, and x j Let be the parameters of the j-th dimensionless Dirichlet prior distribution. By the conjugate property of the Dirichlet distribution, the posterior distribution of the corresponding output w can be obtained:

[0142]

[0143] Among them, C jw Let w be the number of observations for category j and output w.

[0144] Finally, based on the model's input and output, the subjective weight vector W1 for different GFM proportion experimental scenarios can be obtained by formulating a Bayesian hierarchical model. Understandably, the calculation process for the subjective weights of the evaluation indicators using the Bayesian hierarchical model can be obtained using existing technologies, and this is not limited here.

[0145] Step S203: Calculate the objective weights of the proposed evaluation indicators using the CRITIC method:

[0146] The CRITIC method uses standard deviation to reflect the variability of evaluation indicators. That is, the difference in the values ​​of the same evaluation indicator under different scenarios indicates the strength of the evaluation indicator. This is expressed by Y. m×n The coefficient of variation for each evaluation indicator can be obtained:

[0147]

[0148] In the formula: s i Let be the standard deviation of the i-th evaluation indicator; V represents the mean of the i-th evaluation indicator. i V is the coefficient of variation; i is the coefficient of variation.

[0149] 1) Determining the conflict of evaluation indicators

[0150] The correlation coefficient can reflect the conflict between evaluation indicators. Based on the correlation coefficient, an expression can be constructed to characterize the conflict between evaluation indicators:

[0151]

[0152] In the formula: S i This indicates the conflict between the i-th evaluation indicator and other evaluation indicators, r. ij Let x be the correlation coefficient between evaluation index i and evaluation index j. i Let i be the evaluation index. Let y be the mean of the i-th evaluation indicator. j Let j be the evaluation indicator. Let be the mean of the j-th evaluation indicator.

[0153] 2) Calculate the objective weights of the evaluation indicators

[0154] V i With S i Multiplying them together yields the information content G of the i-th evaluation indicator. i G i The larger the value, the more information the evaluation indicator contains, and the greater the weight it should be. Therefore, the normalized weight of the i-th evaluation indicator is:

[0155]

[0156] Based on the model's input and output, the objective weight values ​​w corresponding to each performance evaluation index can be obtained using the above calculation formula. i By aggregating the objective weights corresponding to each evaluation indicator, we can obtain the objective weight vector W2 corresponding to different GFM proportion experimental scenarios.

[0157] Step S204: Use the game theory weighting method to determine the overall weights, the steps are as follows:

[0158] 1) Construct the combined weight vector

[0159]

[0160] In the formula: For subjective weight vectors, Objective weight vector, The optimal combination weights are A1 and A2, which are the combination coefficients corresponding to each weight.

[0161] A1 and A2 are obtained using game theory formulas:

[0162] 2) Introducing game theory concepts

[0163]

[0164] Among them, A V Let V be the coefficient variable for combination, where V = 1, 2, A1 is the coefficient of W1, and A2 is the coefficient of W2. The corresponding subjective or objective weights are defined as follows: W1 is the subjective weight, and W2 is the objective weight.

[0165] Solving the above equation yields the final combination coefficient A. h (h=1,2)

[0166] 3) Normalize the coefficients:

[0167]

[0168] 4) Obtain the optimal combination weights:

[0169]

[0170] in, The combination coefficients of subjective weights, The combination coefficients of objective weights, For combined weights.

[0171] Step three includes the following sub-steps:

[0172] Step S301: Construct a weighted evaluation matrix and determine the positive and negative ideal solutions under the experimental scenario with different grid-type converter proportions.

[0173] 1) Construct a weighted evaluation matrix

[0174] Select m evaluation objects and n evaluation indicators. Based on the calculated evaluation indicator data, determine the original standard matrix Y = (x ij ) m×n By normalizing Y and multiplying it by the combined weights of the evaluation indicators, we can obtain the constructed weighted evaluation matrix Z:

[0175] Z = (z ij ) m×n =(y ij ω i ) m×n (34)

[0176] 2) Determine the positive and negative ideal solutions

[0177] The improved TOPSIS method constructs a sequence of optimal and worst-case values ​​using the optimal and worst-case limit values ​​from the actual data of each evaluation index, and then normalizes this sequence to serve as the positive and negative ideal solutions Z for each evaluation index. + Z -.

[0178] Step S302: Determine the grey relational degree between each evaluation index value and the positive and negative ideal solutions, and the final group utility value Q. i And individual regret value R i .

[0179] 1) Calculate the grey relational coefficient

[0180] The grey relational coefficient between the j-th evaluation metric and the positive and negative ideal solutions in the i-th GFM proportion experimental scenario is:

[0181]

[0182] Where ρ is the resolution coefficient, ρ∈[0,1]; Let be the grey relational coefficient between the j-th evaluation index and the positive ideal solution for the i-th type of grid converter. Let z be the grey relational coefficient between the j-th evaluation index and the negative ideal solution for the i-th type of grid converter. ij Z represents the j-th weighted evaluation index for the proportion of cases with the i-th type of grid-connected converter. + Z represents the positive ideal solution. - Indicates the negative ideal solution;

[0183] 2) Determine the final group utility value Q i And individual regret value R i numerical values

[0184] First, calculate the group utility value Q in experimental scenarios where different GFM proportions of GRA are not considered. i And individual regret value R i numerical values

[0185]

[0186]

[0187] In the formula, ω j For the evaluation index z j Weight, For each of the j-th evaluation indicators, the positive ideal solution is... For each of the j-th evaluation indicators, the negative ideal solution is given; when the group utility value Q... i And individual regret value R i The smaller the value, the better the GFM proportion scheme.

[0188] The final group utility value Q i And individual regret value R i The numerical value is obtained using the following formula.

[0189]

[0190]

[0191] Step S303: According to equation (40), the comprehensive evaluation index (VIKOR value) T of the new energy power station under the different grid-type converter capacity ratios can be obtained. i

[0192]

[0193] In the formula, Q + =min(Q) i ), Q - =max(Q i ), R + =min(R) i ), R - =max(R) i ), where θ is the decision mechanism coefficient, θ∈[0,1]; when θ∈[0,0.5], it indicates a greater emphasis on pursuing individual regret value; when θ∈[0.5,1], it indicates a greater emphasis on group utility value; since this embodiment does not conduct in-depth research on θ, a compromise coefficient of 0.5 is taken, which means that it is necessary to maximize group utility value while minimizing individual regret value.

[0194] In step four, T is obtained from step three under the capacity ratio of each GFM. i The optimal GFM capacity allocation can be determined by the following rules. Assume the optimal allocation is P1, the second-best is P2, and the maximum group utility value Q is used. i The different GFM capacity proportions are ranked. The optimal solution is selected when the following two conditions are met:

[0195] Condition 1: T(P1) - T(P2) ≥ 1 / n - 1; T(P1) is the T of the optimal proportion scheme. i The value, T(P2), is the T value of the second-best proportion scheme. i value;

[0196] Condition 2: The optimal proportion scheme P1 has a group utility value Q i and individual regrets R i It appears first in the order of the middle.

[0197] The criteria for the optimal proportion scheme based on the above two conditions are as follows:

[0198] 1. When conditions 1 and 2 are met, then P1 is the optimal proportion scheme;

[0199] 2. When condition 1 is met, P1 and P2 are the optimal proportion schemes.

[0200] 3. When condition 2 is met, any pre-selected percentage scheme that does not meet condition 1 is the optimal percentage scheme.

[0201] The foregoing has shown and described the basic principles, main methods, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above examples; the examples and descriptions in the specification are merely illustrative of the principles and solutions of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimal proportion configuration of parallel systems with a tracking / network structure based on the GRA-VIKOR method, characterized in that, Includes the following steps: Step 1: The total capacity of the inverters in the hybrid new energy power station is P. W If the capacities of the grid-type and network-type converters are P1 and P2 respectively, then P W =P1+P2; The output power P of the grid-type converter is... ref Less than its maximum output active power limit P max Under this premise, the capacity of grid-type converters is continuously increased; the original data of new energy power plants under different grid-type converter capacity ratios are standardized to eliminate the influence of dimensions and obtain a standardized data matrix Y. m×n Where m is the evaluation object and n is the evaluation index; Step 2: Combine the Bayesian BWM method to obtain the subjective weight w1 of the evaluation index, and the CRITIC method to determine the objective weight w2 of the evaluation index. Then, determine the final combined weight w3 based on the game combination weighting method. Step 3: Determine the weighted evaluation matrix to obtain the positive and negative ideal solutions for each evaluation index of the grid-connected performance of the grid-type control station; use the GRA (Grey Relationship Assessment) concept to obtain the grey relational degree between each evaluation index value and the positive and negative ideal solutions; obtain the group utility value and individual regret value to determine the final group utility value Q. i And individual regret value R i Finally, the comprehensive evaluation index T for new energy power plants under different grid-type converter capacity ratios was obtained. i ; Step 4: Determine the optimal grid-type converter capacity ratio: T is obtained from Step 3 under the capacity ratio of each grid-type converter. i Values, assuming the optimal proportion is P1 and the second-best proportion is P2, and based on the maximum group utility value Q. i The optimal solution is determined by ranking the capacity proportions of different grid-type converters and selecting the optimal grid-type converter capacity proportion scheme that satisfies the following two conditions: Condition 1: ; T is the optimal proportion scheme i value, T is the second best proportion scheme i value; Condition 2: The optimal proportion scheme P1 has a group utility value Q i and individual regrets R i It appears first in the order of the middle.

2. The optimal proportion configuration method for a parallel system based on the GRA-VIKOR method according to claim 1, characterized in that, The evaluation indicators include: Stability evaluation metrics: rate of change of angular frequency, transient angular frequency deviation Support capacity assessment indicators: damping active power and inertia active power.

3. The optimal proportion configuration method for a parallel system based on the GRA-VIKOR method according to claim 1, characterized in that, Step one includes the following sub-steps: Step S101: Conduct simulation experiments on parallel systems with different grid-type converter capacity ratios and collect data: Step S102: Standardize and align the grid connection performance evaluation indicators of grid-connected power stations.

4. The optimal proportion configuration method for a parallel system based on the GRA-VIKOR method according to claim 1, characterized in that, Step two includes the following sub-steps: Step S201: Conduct simulation experiments on grid-connected / parallel grid systems with different grid-type converter capacity ratios and collect data: Step S202: Calculate the evaluation indicators for stability and support capability under experimental scenarios with different grid-type converter proportions, and then calculate the subjective weights of the proposed evaluation indicators using the Bayesian method. Step S203: Calculate the objective weights of the proposed evaluation indicators using the CRITIC method: Step S204: Use the game theory weighting method to determine the overall weight.

5. The optimal proportion configuration method for a parallel system based on the GRA-VIKOR method according to claim 1, characterized in that, Step three includes the following sub-steps: Step S301: Construct a weighted evaluation matrix and determine the positive and negative ideal solutions under the experimental scenario with different grid-type converter proportions; Step S302: Determine the grey relational degree between each evaluation index value and the positive and negative ideal solutions, and the final group utility value Q. i And individual regret value R i ; Step S303: Calculate the comprehensive evaluation index T of the new energy power plant under the different grid-type converter capacity ratios. i .

6. The optimal proportion configuration method for a parallel system based on the GRA-VIKOR method according to claim 4, characterized in that, In step S202, the subjective weights of the proposed evaluation indicators calculated using the Bayesian method are as follows: First, we invite k experts to evaluate the system of indicators A={A1,A2,…,A…}. n Determine the optimal evaluation index A in} B And worst-case assessment indicator A W Using the 1-9 scale, A B and A W As a reference comparison object, construct the best and worst comparison matrix C. B ={a B1 ,a B2 ,…,a Bn }, C W ={a 1W ,a 2W ,…,a nW },A1,A2,…,A n These are the evaluation indicators, a B1 ,a B2 ,…,a Bn These represent the optimal evaluation index values ​​for the 1st, ..., nth experts, respectively, and a 1W ,a 2W ,…,a nW These are the worst evaluation index values ​​for the 1st, ..., nth experts, respectively. Secondly, a multinomial distribution is used for the input A. B、 A W Modeling; treating the target layer as random events, and the weights of the evaluation index layer as the probability of random events occurring, with the worst-case evaluation index A. W The polynomial probability distribution is as follows: ； In the formula: w is the weight, a jw Let w be the j-th worst-case evaluation indicator; j a, the total number of random events jw / If they are directly proportional, then the relationship between the weights and the expert scores can be obtained: ； Among them, w w The weight of the worst-case assessment indicator; Similarly, input A B The polynomial probability distribution is: ； in, For the j-th optimal evaluation index, The optimal evaluation index weight; The prior distribution corresponding to the output w is introduced by the Dirichlet distribution: ； In the formula: B(x) is a multivariate Beta function, x is a dimensionless Dirichlet prior distribution parameter, and x j Let be the parameters of the j-th dimensionless Dirichlet prior distribution; by the conjugate of the Dirichlet distribution, the posterior distribution of the corresponding output w is obtained: ； Among them, C jw The number of observations for category j and output w; Finally, based on the model's input and output, a subjective weight vector W1 is obtained under experimental scenarios of different grid-type converter capacity ratios by formulating a Bayesian hierarchical model.

7. The optimal proportion configuration method for a parallel system based on the GRA-VIKOR method according to claim 6, characterized in that, In step S203, the process of calculating the objective weights of the proposed evaluation indicators according to the CRITIC method is as follows: The strength of an evaluation indicator is illustrated by the difference in its value under different schemes. m×n The coefficient of variation for each evaluation indicator was obtained: ； In the formula: s i Let be the standard deviation of the i-th evaluation indicator; Let be the mean of the i-th evaluation indicator. V is the coefficient of variation; i The coefficient of variation; Conflict in evaluation indicators: ； In the formula: S i This indicates the conflict between the i-th evaluation indicator and other evaluation indicators. Let i be the correlation coefficient between evaluation index i and evaluation index j. Let i be the evaluation index. Let be the mean of the i-th evaluation indicator. Let j be the evaluation indicator. Let be the mean of the j-th evaluation indicator; Calculate the objective weights of the evaluation indicators: V i With S i Multiplying them together yields the information content G of the i-th evaluation index. i The normalized weight of the i-th evaluation indicator is: ； Based on the model's input and output, the objective weight values ​​w corresponding to each performance evaluation index are obtained through the above calculation formula. i After aggregating the objective weights corresponding to each evaluation indicator, we obtain the objective weight vector W2 corresponding to the capacity ratio of different grid-type converters in the experimental scenario.

8. The optimal proportion configuration method for a parallel system based on the GRA-VIKOR method according to claim 4, characterized in that, Step S204: Use the game theory weighting method to determine the overall weights, the steps are as follows: Construct the combined weight vector: ； In the formula: For subjective weight vectors, For objective weight vectors, The optimal combination of weights is defined by A1 and A2, which are the combination coefficients corresponding to each weight. A1 and A2 are obtained using game theory formulas: Introducing game theory concepts ； Among them, A V Let V be the coefficient variable for combination, where V = 1, 2, A1 is the coefficient of W1, and A2 is the coefficient of W2. , The corresponding subjective or objective weights are defined, with W1 being the subjective weight and W2 being the objective weight. Solving the above equation yields the final combination coefficients. , ; Normalize the coefficients: ； To obtain the optimal combination weights: ； in, The combination coefficients of subjective weights, The combination coefficients of objective weights, For combined weights.

9. The optimal proportion configuration method for a parallel system based on the GRA-VIKOR method according to claim 5, characterized in that, Step S301 includes: Constructing a weighted evaluation matrix: Select m evaluation objects and n evaluation indicators. Based on the calculated evaluation indicator data, determine the original standard matrix Y=(x ij ) m×n Normalize Y and multiply it by the combined weights of the evaluation indicators to obtain the constructed weighted evaluation matrix Z. Determining the positive and negative ideal solutions: Constructing optimal and worst-case value sequences using the optimal and worst-case limit values ​​from the actual data of each evaluation index, and then normalizing them, these sequences serve as the positive and negative ideal solutions Z for each evaluation index. + Z - ; In step S302, the final group utility value Q is determined. i And individual regret value R i The numerical process is as follows: First, calculate the group utility value Q in the experimental scenario where the capacity ratio of converters with different grid configurations in GRA is not considered. i And individual regret value R i Value: ； ； In the formula, ω j For the evaluation index z j Weight, For each of the j-th evaluation indicators, the positive ideal solution is... z is the negative ideal solution for each of the j-th evaluation indicators; ij The j-th weighted evaluation index represents the proportion of cases with the i-th type of grid-connected converter. The final group utility value Q i And individual regret value R i The value is obtained by the following formula: ； ； in, Let be the grey relational coefficient between the j-th evaluation index and the positive ideal solution for the i-th type of grid converter proportion case. The grey relational coefficient between the j-th evaluation index and the negative ideal solution for the i-th type of grid converter proportion case; In step S303, the comprehensive evaluation index T of the new energy power station under the capacity ratio of different grid-type converters is obtained according to the following formula. i : ； In the formula, , , , , For decision-making mechanism coefficient, .

10. The optimal proportion configuration method for a parallel system based on the GRA-VIKOR method according to claim 1, characterized in that, In step four, the criteria for the optimal proportion scheme based on the two conditions are as follows: When conditions 1 and 2 are met, then P1 is the optimal proportion scheme; When condition 1 is met, P1 and P2 are the optimal proportion schemes; When condition 2 is met, any pre-selected percentage scheme that does not meet condition 1 is the optimal percentage scheme.

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