Reliability assessment method of k-factor for AC / DC power grid based on second-order cone approximation of converter station

Abstract

The present application discloses a k-factor reliability assessment method for an AC / DC power grid based on second-order cone approximation of a converter station, and relates to the field of AC / DC hybrid power grid planning. The method specifically includes: firstly determining the upper limit of the k-factor based on the total number of components in the system and the forced outage rate corresponding to each component, and using the upper limit of the k-factor to reduce the fault scenario; then, the reliability indicators of the system under each k-factor less than the upper limit are evaluated in parallel, and the optimal load shedding model based on the second-order cone approximation of the converter station is used to calculate the optimal load reduction amount under the current system state during the evaluation; finally, the overall reliability indicator of the system is determined based on the results of the parallel evaluation. The present application is used to improve the reliability assessment calculation efficiency of AC / DC hybrid systems.

Description

Technical Field

[0001] The present application relates to the field of AC / DC hybrid power grid planning, and in particular to a k-factor reliability assessment method for AC / DC power grids based on second-order cone approximation of converter stations. Background Art

[0002] With the application of voltage source converter based high voltage direct current (VSC-HVDC) transmission technology and the access of large-scale renewable energy, there are more and more power electronic interfaces in the power system, which makes the power system develop rapidly towards a high proportion of renewable energy and a high proportion of power electronic equipment. The "double high" power system has new and more complex characteristics, such as significant differences in primary energy characteristics, number of components and types of components compared with traditional power systems. Comprehensive and rapid evaluation of the reliability of the "double high" power system containing VSC-HVDC AC / DC hybrid under the access of new energy is a major and urgent national need.

[0003] However, in reliability assessment, since the optimal load shedding of the system is mostly a complex non-convex optimization problem, the solver requirements are high, the calculation speed is slow, and even a feasible solution may not be found. As the scale of the power grid increases, the number of calculation variables increases, the complexity of risk assessment increases significantly, and it is difficult to adapt to the calculation needs of actual large-scale hybrid power grids. Summary of the invention

[0004] The present application provides an AC / DC power grid k-factor reliability assessment method based on second-order cone approximation of a converter station. The method first reduces the fault scenarios and calculates the optimal load using an optimal load shedding model based on second-order cone approximation of a converter station during the assessment process, thereby improving the reliability assessment calculation efficiency of the AC / DC hybrid system.

[0005] To achieve the above object, the present application provides a k-reduction reliability assessment method for AC / DC power grids based on second-order cone approximation of converter stations, comprising the following steps:

[0006] S1: Obtain the system topology, the forced outage rate of each component in the system, the new energy fluctuation model and the load fluctuation model;

[0007] S2: Determine the upper limit of the k factor according to the total number of components in the system and the forced outage rate corresponding to each component; wherein the k factor is the number of faulty components in the system;

[0008] S3: performing parallel evaluation on the reliability index of the system under each k coefficient less than the upper limit value; wherein the evaluation steps of the reliability index of the system under each k coefficient less than the upper limit value specifically include:

[0009] S31: according to the value of the current k coefficient, enumerate the fault scenarios included in the current k coefficient, and determine the system state corresponding to each fault scenario and the total number of system states;

[0010] S32: Initialize the system state and set the initial system state code m=1;

[0011] S33: Determine the status of the AC part, DC part, converter station part, generator part of the current system, the actual output of the load, and the output upper limit of the new energy unit;

[0012] S34: According to the states of the AC part, DC part, converter station part, generator part, actual output of the load and output upper limit of the new energy unit of the current system obtained in step S33, the topology of the system is corrected, and the power flow of the system is analyzed according to the topology;

[0013] S35: Determine whether the system power flow is converged or whether the power flow exceeds the limit;

[0014] If the power flow does not converge or exceeds the limit, go to step S36; otherwise, set the system state coding fault scenario number m=m+1, and go to step S33;

[0015] S36: Calculate the optimal load reduction amount under the current system state using the optimal load shedding model based on the second-order cone approximation of the converter station;

[0016] S37: Determine whether the number of iterations reaches the set value, if so, proceed to step S38; otherwise, the system state number m=m+1, proceed to step S33;

[0017] S38: Calculate the reliability index value of the system under the current value of the k coefficient;

[0018] S4: Determine the overall reliability index of the system based on the results of the parallel evaluation.

[0019] Furthermore, step S2 specifically includes:

[0020] S21: setting a k coefficient according to the total number of components in the system and presetting an increment threshold, wherein the k coefficient is the number of faulty components in the system;

[0021] S22: Assume that v components in the system fail. Calculate the maximum probability that v components in the system fail at the same time based on the forced outage rate of each component in the system.

[0022] S23: Calculate the maximum increment of the expected load shedding power of the reliability indicator according to the maximum probability and the total load of the system;

[0023] S24: Set the maximum increment to be smaller than the increment threshold, and calculate the upper limit value of the k coefficient.

[0024] Furthermore, the step S33 includes:

[0025] S331: Determine the states of the AC part, DC part, converter station part, and generator part of the system according to the current system state;

[0026] S332: Determine the simulation operation time of the current system, and determine the actual output of the load and the output upper limit of the new energy unit based on the simulation operation time of the current system according to the new energy output model and the load fluctuation model.

[0027] Furthermore, the power flow of the system analyzed in step S34 adopts an alternating iterative calculation algorithm of the power flow of the AC / DC hybrid power grid.

[0028] Furthermore, the optimal load shedding model based on the converter station second-order cone approximation in step S36 includes an objective function and constraints after second-order cone relaxation, wherein

[0029] The objective function is expressed as:

[0030]

[0031] Where P Di , P Li are the active load before and after load node reduction, S L is the set of system load nodes, G n represents the collection of new energy generators, G h Represents a collection of thermal power units.

[0032] Furthermore, the constraints include equality constraints and inequality constraints;

[0033] The equality constraints include AC branch constraints after second-order cone relaxation, DC network constraints, converter station power flow constraints, pure AC node constraints, and AC-DC coupling node AC side constraints; the inequality constraints include pure AC system constraints, coupling node inequality constraints, and generator climbing constraints.

[0034] Furthermore, the AC branch constraint after second-order cone relaxation is expressed as:

[0035]

[0036]

[0037] The DC network constraints after second-order cone relaxation are expressed as:

[0038]

[0039] The power flow constraint of the converter station after second-order cone relaxation is expressed as:

[0040]

[0041]

[0042]

[0043]

[0044] P i C +P i DC =0,i∈S c .

[0045] Furthermore, the new energy fluctuation model and the load fluctuation model are constructed according to the typical days of new energy output and load fluctuation selected in each month respectively;

[0046] The specific steps of selecting a typical day include: normalizing the new energy output data and load data according to the historical output data of the system, and selecting the day with the largest peak-to-valley difference between new energy and the day with the largest load fluctuation in each month as the typical day for new energy output and the typical day for load fluctuation respectively.

[0047] Compared with the prior art, this application has the following beneficial effects:

[0048] 1) This application adopts the optimal load shedding model based on the second-order cone approximation of the converter station to calculate the optimal load reduction under the current system state, which greatly improves the calculation speed and reduces the calculation difficulty while ensuring a certain accuracy of the reliability assessment results. It adapts to the actual planning and calculation needs of large-scale AC / DC hybrid power grids.

[0049] 2) During the evaluation process, an AC / DC power flow alternating iterative algorithm is embedded to realize the analysis and calculation of the system power flow.

[0050] 3) Consider the impact of the volatility of renewable energy on the reliability of AC / DC hybrid power grids. Model the time-series output of renewable energy based on historical data, and retain the daily and monthly variation patterns of renewable energy. Combining this renewable energy time-series output model with the enumeration method, the sequential method is used to effectively evaluate the operating risks of the AC / DC hybrid power grid with renewable energy as the main body. The impact of the volatility of renewable energy on the peak-shaving capacity of the power grid is fully considered, which can provide quantitative reliability indicators for power system design and planning personnel, and provide guidance for the safety assessment of new power systems with renewable energy as the main body.

[0051] 4) The parallel evaluation method of multiple fault states is adopted to effectively reduce the fault scenarios and retain the fault scenarios with greater impact on reliability; and parallel computing can be used to further improve the computing efficiency and adapt to the computing needs of large-scale power grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 It is the equivalent diagram of voltage source converter station;

[0054] Figure 2 To establish the new energy output timing model and load fluctuation model diagram;

[0055] Figure 3 This is the parallel evaluation framework diagram after k reduction;

[0056] Figure 4 Provide a reliability assessment flow chart for each layer;

[0057] Figure 5 Wiring diagram for improved IEEE-4 node system. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0059] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0060] The present application provides a method for evaluating the k-factor reliability of an AC / DC power grid based on a second-order cone approximation of a converter station. The systems in the method all refer to an AC / DC hybrid power grid system. The method includes the following steps:

[0061] S1: Obtain the system topology, parameters of each component in the system, new energy fluctuation model and load fluctuation model. The parameters include the forced outage rate ρ of each component. 1 ,ρ 2 ,…,ρ n , load fluctuation rate, total load a MW.

[0062] S2: Determine the upper limit of the k factor based on the total number of components in the system and the forced outage rate corresponding to each component; wherein the k factor is the number of faulty components in the system. The number of k factors selected is the same as the number of components in the AC / DC hybrid system.

[0063] S21: Set the k factor k according to the total number of components in the system n And preset the increment threshold, where k coefficient k n is the number of faulty components in the system.

[0064] S22: Assume that there are v components in the system that fail. Calculate the maximum probability that v components in the system fail at the same time based on the forced outage rate of each component in the system.

[0065] S23: Calculate the maximum increment of the expected load shedding power of the reliability index according to the maximum probability and the total load of the system

[0066] S24: Make the maximum increment smaller than the increment threshold, and calculate the upper limit value of the k coefficient.

[0067] S3: See Figure 3 , 4 , the reliability index of the system under each k coefficient less than the upper limit value is evaluated in parallel; wherein the evaluation steps of the reliability index of each k coefficient less than the upper limit value specifically include:

[0068] S31: according to the value of the current k coefficient, enumerate the fault scenarios included in the current k coefficient, determine the system state corresponding to each fault scenario and the total number of system states, and encode each system state in turn according to the total number of system states;

[0069] S32: Initialize the system state, set the initial system state code m=1, and set the number of iterations;

[0070] S33: Determine the status of the AC part, the DC part, the converter station part, the generator part, the actual output of the load, and the output upper limit of the new energy unit under the current system status;

[0071] S34: Correct the system topology according to the states of the AC part, DC part, converter station part, generator part, actual output of the load and output upper limit of the new energy unit obtained in step S33, and analyze the system power flow according to the topology;

[0072] S35: judging whether the power flow of the AC / DC hybrid system is converged or whether the power flow exceeds the limit;

[0073] If the power flow does not converge or exceeds the limit, it means that the power grid cannot meet the load demand at this time and load reduction is required, and the process goes to step S36; otherwise, the AC / DC hybrid system state coding fault scenario number m=m+1, and the process goes to step S33;

[0074] S36: Calculate the optimal load reduction amount under the current system state using the optimal load shedding model based on the second-order cone approximation of the converter station;

[0075] S37: Determine whether the number of iterations reaches a set value, which is the total number of system states; if so, proceed to step S36; otherwise, the system state number m=m+1, proceed to step S33;

[0076] S38: Calculate the reliability index value of the system under the current value of the k coefficient.

[0077] S4: Calculate the reliability index of the AC / DC hybrid system based on the results of the parallel evaluation.

[0078] As a preferred embodiment of the present invention: the power flow of the system analyzed in step S34 adopts an alternating iterative algorithm of the power flow of the AC / DC hybrid power grid.

[0079] As a preferred embodiment of the present invention: step S33 specifically includes:

[0080] S331: Determine the states of the AC part, the DC part, the converter station part, and the generator part of the system according to the system state;

[0081] S332: Determine the simulation operation time of the current system, and determine the actual output of the load and the output upper limit of the new energy unit based on the new energy output model and the load fluctuation model from the current simulation operation time.

[0082] As a preferred embodiment of the present invention: the new energy fluctuation model and the load fluctuation model are respectively constructed according to the typical days of new energy output and load fluctuation selected in each month; the specific steps of selecting typical days include: standardizing the new energy output data and load data according to the historical output data of the system, and selecting the day with the largest new energy peak-to-valley difference and the day with the largest load fluctuation in each month as the typical day of new energy output and the typical day of load fluctuation respectively.

[0083] For example, the historical data of wind power generation, photovoltaic power generation and load in Shaanxi Power Grid in 2018 is used as the original sequence. After standardizing the data, the day with the largest peak-to-valley difference is selected as the typical day in each of the 12 months of the year. The output of this typical day represents the wind power and photovoltaic output of this month. Similarly, the typical load day of each month is selected. Therefore, in a year, the power system has 12 operating scenarios, corresponding to different wind power, photovoltaic output and load fluctuations in each month. The selected wind power, photovoltaic output and load fluctuations in each month are shown in the attached figure. Figure 2 shown.

[0084] As a preferred embodiment of the present invention: the optimal load shedding model based on the second-order cone approximation of the converter station in step S36 includes an objective function and constraints, wherein

[0085] The objective function is expressed as:

[0086]

[0087] Where P Di , P Li are the active load before and after load node reduction, S L is the set of system load nodes. n represents the collection of new energy generators, G h Represents a collection of thermal power units.

[0088] Constraints include equality constraints and inequality constraints.

[0089] 1. Equality constraints

[0090] 1) AC branch constraints

[0091] P ij =V i 2 g ij -V i V j (g ij cosθ ij +b ij sinθ ij ),i,j∈(S a ∪S c )

[0092] Q ij =-V i 2 b ij -V i V j (g ij sinθ ij -b ij cosθ ij),i,j∈(S a ∪S c )

[0093] The above formulas are respectively the active and reactive power constraints of each branch. ij is the equivalent conductance of the branch connecting the ij node, b ij is the equivalent susceptance of the branch connecting the ij node. i 、V j ,θ ij are the node voltages at both ends of the branch and the phase angle difference of the node voltages respectively.

[0094] Due to the following characteristics of actual power grid operation: the absolute value of the phase angle difference between the two ends of the branch rarely exceeds 30°, and most of them are within 10°. The voltage amplitude of each node is mostly around 1p.u. After the second-order cone relaxation of the above formula combined with the above conditions, the active power constraint formula of the AC branch can be simplified to:

[0095]

[0096]

[0097] Where: S a , S c They are the node set of the pure AC part of the system and the node set of the AC / DC coupling part.

[0098] Then transform the active power constraint and reactive power constraint into a second-order cone form:

[0099]

[0100]

[0101] 2) Pure AC node constraints

[0102]

[0103] P Gi -P Li -P i =0,i∈S a

[0104]

[0105] Q Gi -Q Li -Q i =0,i∈S a

[0106] The equality constraints for pure AC nodes include the active and reactive power constraints injected by each node into the connected AC branch and the power balance constraints of each node.Gi , Q Gi , P Li , Q Li They represent the active and reactive power of the generator and the active and reactive power of the load at node i respectively.

[0107] 3) AC-side constraints of AC-DC coupling nodes

[0108]

[0109] P Gi -P Li -P i +P i AC =0,i∈S c

[0110]

[0111]

[0112] The above equation is the equality constraint on the AC side of the AC / DC coupling nodes, which contains active and reactive power constraints on these nodes. They respectively represent the active and reactive power injected into the AC system by the converter station.

[0113] 4) Converter station constraints

[0114] The loss of the VSC converter valve is equivalent to a loss resistor. Therefore, the converter station can be further equivalent to an ideal converter valve, an equivalent resistor Z ci The ideal converter valve can be equivalent to a controllable voltage source on the AC side and a controllable current source on the DC side, as shown in the attached figure. Figure 1 shown.

[0115]

[0116]

[0117]

[0118]

[0119] P i C +P i DC =0,i∈S c

[0120] The above formula is the converter station constraint after simplification by second-order cone relaxation. The constraints on the converter station include active power constraint, reactive power constraint and active power conservation constraint before and after rectification of the ideal converter.

[0121] 5) DC network constraints

[0122]

[0123]

[0124] Since the DC voltage at the AC / DC coupling node is also around 1.pu, the DC branch power constraint is simplified. The above formulas are the simplified DC branch power constraint and the power constraint injected from the coupling node to the DC system.

[0125] 2. Inequality constraints

[0126] 1) Pure AC system constraints

[0127] V i min ≤V i ≤V i max ,i∈S a

[0128]

[0129]

[0130] 0≤P Li ≤P Di ,i∈S L

[0131]

[0132]

[0133] The above formulas are: node voltage amplitude constraint, generator output constraint, load capacity constraint and branch capacity constraint.

[0134] 2) Coupling node inequality constraints

[0135] V i min ≤V i ≤V i max ,i∈S c

[0136]

[0137]

[0138]

[0139] The above formulas are voltage constraints, converter station constraints and DC branch power constraints respectively.

[0140] 3) Generator climbing constraints

[0141]

[0142]

[0143] In the formula, S G represents the set of generator nodes, and t represents the time.

[0144] Example 2: Authentication of evaluation method

[0145] In order to verify the effectiveness of the reliability assessment method for the AC / DC hybrid power grid under the access of new energy designed in the present invention, this paper conducts program testing through an improved IEEE-4 system, and the calculated reliability indicators are shown as follows.

[0146] 1) Load loss probability L OLP (loss of load probability), that is, the probability of a power outage:

[0147]

[0148] In the above formula, X off A state collection indicating a load shedding event in the power system; Indicates system status X j The corresponding probability.

[0149] 2) Expected power shortage time L OLE (loss of load expectation). It indicates the expected value of the time that the load demand cannot be met within a given time. Unit: h

[0150] L OLE =T×L OLP

[0151] Where T represents the given simulation time.

[0152] 3) Insufficient power supply expectation E DNS (Expected demand not supply). Indicates the expected power value that the system cannot meet the load demand within a given time. Unit: MW

[0153]

[0154] In the above formula, Indicates that the system is in the Xth j Active load removal power in state.

[0155] 4) Insufficient power supply expectation EENS (Expected energy not supply). It indicates the expected value of power loss due to load removal in a given period of time. Unit: MW·h / a

[0156] E ENS =E DNS ×T

[0157] 1. Improved IEEE-4 node system

[0158] Based on the IEEE-4 node system, a DC line was added between nodes 3 and 4, new energy sources were connected, and some thermal power units were replaced. The system wiring is shown in the attached figure. Figure 5 shown.

[0159] The generator parameters of the system are as follows:

[0160] Table 1 Improved IEEE-4 system generator parameters

[0161]

[0162] Among them, the upper limit of active output of wind power and photovoltaic power is only their installed capacity, and the actual maximum active output needs to change according to the new energy time-series output model.

[0163] Except for the generator, the reliability parameters of the remaining components of the system are as follows:

[0164] The forced outage rates of the AC lines connecting nodes 1 and 2, 1 and 3, 2 and 4, and 3 and 4 are 4.38×10 -4 , 2.40×10 -4 , 2.63×10 -4 , 1.80×10 -3 The forced outage rate of the tributary line connecting 3 and 4 is 4.38×10 -4 The forced outage rates of the voltage source converter stations at nodes 3 and 4 are both 0.01.

[0165] Set the threshold of k coefficient stratification λ = 10 -7 , k=1 or k=2 can be selected. That is, according to the k-factor stratification method proposed by the present invention, the fault scenarios of the system are reduced and stratified into N-1 and N-2 faults.

[0166] After testing, the reliability indicators LOLP and EDNS of this system in each month are shown in the following table:

[0167] Table 2 Improved reliability indicators of IEEE-4 system in each month

[0168]

[0169] Using the reliability index formula, the reliability index of this system throughout the year is as shown in the following table:

[0170] Table 3 Improved IEEE-4 system reliability indicators throughout the year

[0171]

[0172] The total computing time for the reliability assessment throughout the year was 7495 seconds, with an average computing time of 625 seconds per month.

[0173] Taking March as an example, if the load shedding model is not subjected to second-order cone relaxation but the original non-convex model is used, the reliability indicators LOLP and EDNS calculation results for March are 0.0346 and 5.05MW respectively, and the operation time is 1356 seconds. Based on the evaluation results corresponding to this non-convex model, the errors of the evaluation results for March in Table 2 are 0.87% and 2.18% respectively, and the calculation time is shortened to half of the original, and the calculation efficiency is improved.

[0174] In actual large-scale power grids, when the scale of the power grid becomes larger, the number of components increases, and the total simulation time increases, the computational efficiency of the AC / DC hybrid power grid risk assessment algorithm based on the second-order cone form optimal load shedding model will be more significantly improved.

[0175] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. The k-factor reliability assessment method of AC / DC power grid based on second-order cone approximation of converter station is characterized by: The following steps are involved: S1: Obtain the system topology, the forced outage rate of each component in the system, the new energy fluctuation model and the load fluctuation model; S2: Determine the upper limit of the k factor based on the total number of components in the system and the forced outage rate corresponding to each component, including: S21: setting a k coefficient according to the total number of components in the system and presetting an increment threshold, wherein the k coefficient is the number of faulty components in the system; S22: Assume that v components in the system fail. Calculate the maximum probability that v components in the system fail at the same time based on the forced outage rate of each component in the system. S23: Calculate the maximum increment of the expected load shedding power of the reliability indicator according to the maximum probability and the total load of the system; S24: setting the maximum increment to be smaller than the increment threshold, and calculating the upper limit value of the k coefficient; S3: performing parallel evaluation on the reliability index of the system under each k coefficient less than the upper limit value; wherein the evaluation step of the reliability index of the system under each k coefficient less than the upper limit value specifically includes: S31: according to the value of the current k coefficient, enumerate the fault scenarios included in the current k coefficient, and determine the system state corresponding to each fault scenario and the total number of system states; S32: Initialize the system state and set the initial system state code m=1; S33: Determine the status of the AC part, DC part, converter station part, generator part of the current system, the actual output of the load, and the output upper limit of the new energy unit; S34: According to the states of the AC part, DC part, converter station part, generator part, actual output of the load and output upper limit of the new energy unit of the system obtained in step S33, the topology of the system is corrected, and the power flow of the system is analyzed according to the topology. The power flow of the system is analyzed by an alternating iterative calculation algorithm of the power flow of the AC / DC hybrid power grid; S35: Determine whether the system power flow is converged or whether the power flow exceeds the limit; If the power flow does not converge or exceeds the limit, go to step S36; otherwise, set the system state coding fault scenario number m=m+1, and go to step S33; S36: Calculate the optimal load reduction amount under the current system state using the optimal load shedding model based on the second-order cone approximation of the converter station, wherein the optimal load shedding model based on the second-order cone approximation of the converter station includes an objective function and constraints after second-order cone relaxation, wherein The objective function is expressed as: Where P Di , P Li are the active load before and after load node reduction, S L is the set of system load nodes, G n represents the collection of new energy generators, G h Represents a collection of thermal power units; The constraints include equality constraints and inequality constraints; The equality constraints include AC branch constraints, DC network constraints, converter station power flow constraints, pure AC node constraints, and AC-DC coupling node AC side constraints after second-order cone relaxation; the inequality constraints include pure AC system constraints, coupling node inequality constraints, and generator ramp constraints; S37: Determine whether the number of iterations reaches the set value, if so, proceed to step S38; otherwise, the system state number m=m+1, proceed to step S33; S38: Calculate the reliability index value of the system under the current value of the k coefficient; S4: Determine the overall reliability index of the system based on the results of the parallel evaluation.

2. The AC / DC power grid k-factor reliability assessment method based on second-order cone approximation of converter stations according to claim 1 is characterized in that: The step S33 comprises: S331: Determine the states of the AC part, DC part, converter station part, and generator part of the system according to the current system state; S332: Determine the simulation operation time of the current system, and determine the actual output of the load and the output upper limit of the new energy unit based on the simulation operation time of the current system according to the new energy output model and the load fluctuation model.

3. The AC / DC power grid k-factor reliability assessment method based on second-order cone approximation of converter stations according to claim 1 is characterized in that: The AC branch constraint after second-order cone relaxation is expressed as: The DC network constraints after second-order cone relaxation are expressed as: The power flow constraint of the converter station after second-order cone relaxation is expressed as: P i C +P i DC =0,i∈S c ; Where: The converter station constraints include active power constraints, reactive power constraints and active power conservation constraints before and after rectification of the ideal converter, g ij is the equivalent conductance of the branch connecting the ij node, b ij is the equivalent susceptance of the branch connecting the ij node, V i 、V j ,θ ij are the phase angle difference of the node voltage at both ends of the branch and the node voltage, S a , S c They are the node set of the pure AC part of the system and the node set of the AC / DC coupling part. They respectively represent the active and reactive power injected into the AC system by the converter station.

4. The AC / DC power grid k-factor reliability assessment method based on second-order cone approximation of converter stations according to claim 2 is characterized in that: The new energy fluctuation model and the load fluctuation model are constructed according to the new energy output typical days and load fluctuation typical days selected in each month respectively; The specific steps of selecting a typical day include: normalizing the new energy output data and load data according to the historical output data of the system, and selecting the day with the largest peak-to-valley difference between new energy and the day with the largest load fluctuation in each month as the typical day for new energy output and the typical day for load fluctuation respectively.

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