Optical storage power station fault support capability layered evaluation method, system, equipment and medium

Abstract

The invention relates to the field of optical storage power station performance evaluation, in particular to an optical storage power station fault support capability layered evaluation method, system and device and a medium. Dividing strength grades based on the power grid strength index, and generating a customized fault scene; calculating the support contribution degree of each power generation unit in the optical storage power station in the customized fault scene based on the calibrated network construction type unit model and the calibrated network following type unit model of the optical storage power station; performing weighted aggregation on the support contribution degree and the support strength of the whole station to generate a comprehensive evaluation value; and evaluating the fault supporting capability of the optical storage power station by adopting the comprehensive evaluation value. According to the invention, dynamic quantitative evaluation can be carried out on the fault supporting capability of the optical storage power station based on power grid fluctuation.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of performance evaluation of optical storage power stations, in particular to a layered evaluation method, system, device and medium for fault support capacity of optical storage power stations. BACKGROUND

[0002] In recent years, the proportion of new energy represented by photovoltaic in the power system continues to rise, and the capacity of distributed photovoltaic access to distribution transformers increases year by year. This process not only promotes the green transformation of energy structure, but also brings serious challenges to the safe and stable operation of the power grid. As a key link for new energy consumption, the receiving end of the power grid is facing the systematic risk of "weakening of the power grid strength". In the scenario of high proportion of new energy, optical storage power stations combine photovoltaic and energy storage to absorb electricity during load valleys and support electricity during load peaks, thereby balancing the load peak-valley difference of the power station and supplementing it with photovoltaic power generation system to achieve effective operation and optimize the current receiving end of the power grid.

[0003] In order to ensure the stability of power supply, new energy power generation units should be able to run continuously at a short-circuit ratio of 1.5 and above, but for optical storage power stations in actual power systems, including network-type units and network-following-type units, how to test the transient stability characteristics in different intensity power grids is not clear, and how to effectively verify and evaluate the adaptive operation capability and active support capability of optical storage power stations in different intensity power grids still needs to be studied.

[0004] There are essential differences between network-type units and network-following-type units in fault support characteristics in optical storage power stations: network-type units have the ability to actively adjust voltage and frequency, and have significant support in weak power grids (SCR<2); network-following-type units rely on grid voltage signals for response and perform better in strong power grids. The existing evaluation methods have the following problems: 1. The influence of dynamic changes in power grid intensity on support capability is not considered, and the use of a unified standard for evaluation leads to underestimation of the advantages of network-type units in weak power grids; 2. The characteristics of power generation units are not distinguished, and the evaluation results are simply added, which cannot reflect the true support effect of multiple unit coupling; 3. Limited by the capacity of test equipment, full-condition field testing is difficult to achieve, and pure simulation evaluation has large errors due to insufficient model accuracy. SUMMARY

[0005] In order to solve the problem that the existing technology cannot realize the adaptive operation capability and active support capability of optical storage power stations in different intensity power grids, the present application proposes a layered evaluation method for fault support capacity of optical storage power stations, which comprises:

[0006] Obtaining a grid intensity index based on the grid connection point parameters of the optical storage power station;

[0007] Dividing the intensity level based on the grid intensity index and generating a customized fault scenario;

[0008] Based on the network-constructing unit model and the network-following unit model of the calibrated optical storage power station, the support contribution of each power generation unit in the optical storage power station under the customized fault scenario is calculated;

[0009] The support contribution is weighted and aggregated with the station support strength to generate a comprehensive evaluation value;

[0010] The comprehensive evaluation value is used to evaluate the fault support capability of the optical storage power station.

[0011] Optionally, the grid strength index based on the grid-connection point parameters of the optical storage power station comprises:

[0012] The grid-connection point parameters of the optical storage power station are obtained, including short-circuit ratio, fluctuation matrix, and voltage fluctuation rate;

[0013] The short-circuit ratio, fluctuation matrix, and voltage fluctuation rate are normalized and weighted to obtain the grid strength index.

[0014] Optionally, the calculation formula of the grid strength index GSI is:

[0015]

[0016] Wherein, k1 is the weight of the short-circuit ratio term, k2 is the weight of the fluctuation matrix term, k3 is the weight of the voltage fluctuation rate term, SCR min is the lower limit of the short-circuit ratio, SCR max is the upper limit of the short-circuit ratio, |Z eq | min is the lower limit of the fluctuation matrix, |Z eq | max is the upper limit of the fluctuation matrix, ΔU min is the lower limit of the voltage fluctuation rate, ΔU max is the upper limit of the voltage fluctuation rate, SCR is the short-circuit ratio, |Z eq | is the fluctuation matrix, and ΔU is the voltage fluctuation rate.

[0017] Optionally, the grid strength index is used to divide the strength level and generate a customized fault scenario, comprising:

[0018] According to the grid strength index, a multi-level grid strength level is divided;

[0019] Different working conditions are matched for the multi-level grid strength level to generate a customized fault scenario; wherein, the different working conditions include at least two of different voltage drop depths, different low-frequency oscillations, different harmonic distortions, or different frequency fluctuations.

[0020] Optionally, the multi-level power grid strength level includes weak, medium and strong three-level power grid strength levels, and the matching of the multi-level power grid strength level to different working conditions to generate customized fault scenarios includes:

[0021] For the weak power grid strength level, a fault scenario with voltage drop depth in a first preset range is generated and superimposed with low-frequency oscillation;

[0022] For the medium power grid strength level, a fault scenario with voltage drop depth in a second preset range is generated and superimposed with harmonic distortion;

[0023] For the strong power grid strength level, a transient fault scenario with voltage drop depth in a third preset range is generated and superimposed with frequency fluctuation;

[0024] Among them, the average value of the first preset range is greater than the average value of the second preset range, and the average value of the second preset range is greater than the average value of the third preset range.

[0025] Optionally, the first preset range is 40% to 70%; the second preset range is 20% to 40%; the third preset range is 10% to 20%; the frequency of the low-frequency oscillation is 2 to 5 Hz, the degree of the harmonic distortion is 5% to 10%, and the frequency fluctuation is ±0.5 Hz.

[0026] Optionally, the process of obtaining the grid-connected unit model and the grid-following unit model of the calibrated optical storage power station is:

[0027] An electromechanical or electromagnetic simulation software is used to construct the grid-connected unit model and the grid-following unit model;

[0028] The grid-connected unit model is calibrated by using a virtual synchronous machine control model, and the grid-following unit model is calibrated by using a PQ control model, so that the transient error and the steady-state error do not exceed a preset value.

[0029] Optionally, based on the grid-connected unit model and the grid-following unit model of the calibrated optical storage power station, the support contribution of each power generation unit in the optical storage power station under the customized fault scenario is calculated, including:

[0030] Based on the historical fault database, the analytic hierarchy process is used to assign each scene occurrence probability weight to the customized fault scenario;

[0031] According to the scene occurrence probability weight, a working condition combination is extracted;

[0032] The working condition combination is calculated by using the calibrated grid-connected unit model or grid-following unit model of the optical storage power station to obtain a fault ride-through test result;

[0033] Based on the fault ride-through test result, each unit support strength index is calculated;

[0034] obtaining a support contribution degree based on the support strength index.

[0035] Optionally, the support strength index comprises reactive support strength S Iq , active support strength S p and time support strength S t , and the calculation formula of the support contribution degree is:

[0036] When the power generation unit is a network construction type, the support contribution degree C i is:

[0037]

[0038] When the power generation unit is a network following type, the support contribution degree C i is:

[0039]

[0040] Wherein, GSI is a grid strength index, SCR max is an upper limit of short circuit ratio, C0 is a basic support contribution degree, C0 = αS Iq + βS p + γS t , and α, β and γ are weight coefficients.

[0041] Optionally, the calculation formula of the overall support strength F s is:

[0042] F s = F s0 × (1-0.1×|Z eq |)

[0043] Wherein, F s0 is the support strength under ideal working conditions, and |Z eq | is a fluctuation matrix.

[0044] Optionally, the calculation formula of the comprehensive evaluation value SEV is:

[0045] SEV = (k4×∑C i +k5×F s +k6×D s )*100

[0046] Wherein, k4, k5 and k6 are weight coefficients, D s is the support persistence of the whole station, and C i is the support contribution degree of the i-th power generation unit.

[0047] The second aspect of the present application provides a photovoltaic storage power station fault support capacity hierarchical evaluation system, comprising:

[0048] The acquisition module is configured to acquire a grid strength index based on the grid-connected point parameter of the optical storage power station;

[0049] The first generation module is configured to divide a strength level based on the grid strength index and generate a customized fault scenario;

[0050] The calculation module is configured to calculate a support contribution degree of each power generation unit in the optical storage power station under the customized fault scenario based on the network-constructing unit model and the network-following unit model of the calibrated optical storage power station;

[0051] The second generation module is configured to generate a comprehensive evaluation value by weighting and aggregating the support contribution degree and the overall station support strength;

[0052] The evaluation module is configured to evaluate the fault support capability of the optical storage power station by using the comprehensive evaluation value.

[0053] Optionally, the acquisition module acquires the grid strength index based on the grid-connected point parameter of the optical storage power station, and the method comprises the following steps:

[0054] Acquiring the grid-connected point parameter of the optical storage power station, including short-circuit ratio, fluctuation matrix, and voltage fluctuation rate;

[0055] The short-circuit ratio, the fluctuation matrix, and the voltage fluctuation rate are normalized and weighted to obtain the grid strength index.

[0056] Optionally, the calculation formula of the grid strength index GSI in the acquisition module is:

[0057]

[0058] wherein k1 is the weight of the short-circuit ratio term, k2 is the weight of the fluctuation matrix term, k3 is the weight of the voltage fluctuation rate term, SCR min is the lower limit of the short-circuit ratio, SCR max is the upper limit of the short-circuit ratio, |Z eq | min is the lower limit of the fluctuation matrix, |Z eq | max is the upper limit of the fluctuation matrix, ΔU min is the lower limit of the voltage fluctuation rate, ΔU max is the upper limit of the voltage fluctuation rate, SCR is the short-circuit ratio, |Z eq is the fluctuation matrix, and ΔU is the voltage fluctuation rate.

[0059] Optionally, the first generation module divides the strength level based on the grid strength index and generates the customized fault scenario, and the method comprises the following steps:

[0060] Dividing a multi-level grid strength level according to the grid strength index;

[0061] The different working conditions include at least two of different voltage drop depth, different low-frequency oscillation, different harmonic distortion or different frequency fluctuation. Optionally, the multi-level grid strength level in the first generation module includes weak, medium and strong three-level grid strength levels, and the matching of the multi-level grid strength level to the different working conditions to generate the customized fault scenario includes:

[0062] For the weak grid strength level, a fault scenario with voltage drop depth in a first preset range is generated and superimposed with low-frequency oscillation;

[0063] For the medium grid strength level, a fault scenario with voltage drop depth in a second preset range is generated and superimposed with harmonic distortion;

[0064] For the strong grid strength level, a transient fault scenario with voltage drop depth in a third preset range is generated and superimposed with frequency fluctuation;

[0065] The average value of the first preset range is greater than the average value of the second preset range, and the average value of the second preset range is greater than the average value of the third preset range.

[0066] Optionally, the first preset range in the first generation module is 40% to 70%; the second preset range is 20% to 40%; the third preset range is 10% to 20%; the frequency of the low-frequency oscillation is 2 to 5 Hz, the degree of the harmonic distortion is 5% to 10%, and the frequency fluctuation is ±0.5 Hz.

[0067] Optionally, the acquisition process of the grid-connected type unit model and the grid-following type unit model of the calibrated optical storage power station in the calculation module is:

[0068] An electromechanical or electromagnetic simulation software is used to construct the grid-connected type unit model and the grid-following type unit model;

[0069] The grid-connected type unit model is calibrated by a virtual synchronous machine control model, and the grid-following type unit model is calibrated by a PQ control model, so that the transient error and the steady-state error do not exceed a preset value.

[0070] Optionally, the calculation module calculates the support contribution of each power generation unit in the optical storage power station under the customized fault scenario based on the calibrated grid-connected type unit model and the grid-following type unit model of the optical storage power station, including:

[0071] Based on the historical fault database, the analytic hierarchy process is used to assign each scenario occurrence probability weight to the customized fault scenario;

[0072] According to the scenario occurrence probability weight, a working condition combination is extracted;

[0073] The working condition combination is calculated by using the network-constructing unit model or network-following unit model of the calibrated optical storage power station to obtain a fault ride-through test result;

[0074] A unit support strength index is calculated based on the fault ride-through test result;

[0075] A support contribution degree is obtained based on the support strength index.

[0076] Optionally, the support strength index in the calculation module comprises reactive power support strength S Iq , active power support strength S p and time support strength S t , and the calculation formula of the support contribution degree is:

[0077] When the power generation unit is of the network-constructing type, the support contribution degree C i is:

[0078]

[0079] When the power generation unit is of the network-following type, the support contribution degree C i is:

[0080]

[0081] wherein, GSI is a grid strength index, SCR max is an upper limit of a short-circuit ratio, C0 is a basic support contribution degree, C0 = αS Iq + βS p + γS t , and α, β and γ are weight coefficients.

[0082] Optionally, the calculation formula of the overall support strength F s in the second generation module is:

[0083] F s = F s0 × (1-0.1×|Z eq |)

[0084] wherein, F s0 is the support strength under an ideal working condition, and |Z eq | is a fluctuation matrix.

[0085] Optionally, the calculation formula of the comprehensive evaluation value SEV in the second generation module is:

[0086] SEV = (k4×∑C i +k5×F s +k6×D s )*100

[0087] wherein k4, k5, k6 are weight coefficients, D s is the support persistence of the whole station, C i is the support contribution degree of the i-th generating unit.

[0088] In still another aspect, the present application also provides a computing device, comprising: at least one processor and a memory;

[0089] The memory is configured to store one or more programs;

[0090] When the one or more programs are executed by the one or more processors, a photovoltaic storage power station fault support capability hierarchical evaluation method as described above is implemented.

[0091] In still another aspect, the present application also provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed to implement a photovoltaic storage power station fault support capability hierarchical evaluation method as described above.

[0092] Compared with the prior art, the present application has the following beneficial effects:

[0093] The present application provides a photovoltaic storage power station fault support capability hierarchical evaluation method, system, device and medium, the method comprising: obtaining a grid strength index based on photovoltaic storage power station grid connection point parameters; dividing the strength level based on the grid strength index, and generating a customized fault scenario; calculating the support contribution degree of each generating unit in the photovoltaic storage power station under the customized fault scenario based on the calibrated network-forming unit model and network-following unit model of the photovoltaic storage power station; weighting and aggregating the support contribution degree and the whole station support intensity to generate a comprehensive evaluation value; and evaluating the fault support capability of the photovoltaic storage power station using the comprehensive evaluation value.

[0094] 1. The present application can obtain the grid strength index in real time based on the photovoltaic storage power station grid connection point parameters, and can realize continuous quantification and dynamic adaptation of the grid intensity, solve the limitations of traditional fixed SCR-based evaluation, and more accurately reflect the influence of grid intensity changes on the fault support capability of the photovoltaic storage power station; the customized fault scenario is constructed based on the dynamic grid strength index, and the calibrated network-forming unit and network-following unit are used to run the customized fault scenario, so that the support effect of different types of generating units in the network-forming unit and network-following unit under different grid strengths can be obtained, solving the problem of distorted support capability evaluation under the coupling effect of multiple generating units, and using the comprehensive evaluation value obtained by weighting the support contribution degree of different fault conditions and the whole station support intensity to represent the fault support capability of the photovoltaic storage power station, strengthening the influence of fault conditions and improving the reliability and rationality of the evaluation.

[0095] 2、The power grid strength index of the present application integrates multiple index dynamic factors such as short-circuit ratio, fluctuation matrix and power grid fluctuation rate, and can more comprehensively and reasonably represent the real-time changes of the power grid.

[0096] 3、The present application filters out key influencing factors under different power grid strengths based on the influence of different influencing factors on power grid strength, formulates weak power grid strength level fault scenarios, medium power grid strength level fault scenarios and strong power grid strength level fault scenarios, which not only ensures the rationality of scenario construction but also avoids data redundancy.

[0097] 4、The constructed grid-constructing unit model and grid-following generating unit model are corrected to ensure the simulation accuracy of the grid-constructing unit model and the grid-following generating unit model. BRIEF DESCRIPTION OF DRAWINGS

[0098] Figure 1 The flow chart of the fault support capacity hierarchical evaluation method of the optical storage power station proposed by the present application;

[0099] Figure 2 The flow chart of the fault support capacity hierarchical evaluation method of the optical storage power station proposed by the present application; Figure 1 The detailed flow chart of step S1 in the method;

[0100] Figure 3 The detailed flow chart of step S2 in the method; Figure 1 The detailed flow chart of step S2 in the method;

[0101] Figure 4 The detailed flow chart of step S3 in the method; Figure 1 The detailed flow chart of step S3 in the method;

[0102] Figure 5 The structure diagram of the fault support capacity hierarchical evaluation system of the optical storage power station proposed by the present application;

[0103] Figure 6 The structure diagram of the electronic device proposed by the present application. DETAILED DESCRIPTION

[0104] The present application proposes a fault support capacity hierarchical evaluation method and system of an optical storage power station, which comprises a grid-constructing unit and a grid-following unit, wherein the grid-following unit comprises a grid-following photovoltaic generating unit and a grid-following energy storage unit, and the grid-constructing unit comprises a grid-constructing energy storage unit. The present application firstly quantifies the power grid strength dynamically, and then constructs a grid-constructing unit model and a grid-following unit model to distinguish the fault support characteristics of each generating unit of the grid-constructing unit and the grid-following unit, so as to evaluate the fault support capacity of the optical storage power station from the level of each generating unit, solve the distortion problem of support capacity evaluation under the coupling effect of multiple generating units, especially highlight the advantage of the grid-constructing unit in a weak power grid, and provide a technical basis for the control strategy optimization and power grid planning of the optical storage power station.

[0105] Embodiment 1

[0106] A method for hierarchical assessment of fault support capability of a photovoltaic energy storage power station, as shown in the figure, comprises the following steps S1-S5. Figure 1

[0107] S1: Obtain a grid strength index based on parameters of a grid connection point of the photovoltaic energy storage power station.

[0108] In a further preferred scheme, as shown in the figure, step S1 comprises the following steps S11-S12: Figure 2

[0109] S11: Obtain parameters of the grid connection point of the photovoltaic energy storage power station, including short-circuit ratio, fluctuation matrix, and voltage fluctuation rate;

[0110] S12: Normalize and weight the short-circuit ratio, fluctuation matrix, and voltage fluctuation rate to obtain the grid strength index.

[0111] In this embodiment, the parameters of the grid connection point of the photovoltaic energy storage power station include short-circuit ratio SCR, fluctuation matrix Zeq, and voltage fluctuation rate ΔU, which can be obtained in the following manner.

[0112] A broadband impedance tester (0.1 Hz-1 kHz), a high-precision vector analyzer (preferably with an error of ≤0.1%), and a PMU with a 2 kHz sampling rate are installed at the grid connection point of the photovoltaic energy storage power station to form a three-in-one data acquisition network, thereby obtaining the short-circuit ratio SCR, the equivalent impedance modulus |Zeq| and phase angle, and the voltage fluctuation rate ΔU.

[0113] (1) Calculate the short-circuit ratio SCR by the broadband impedance tester, collect data every 10 ms, and take a 3 s sliding average after ±5% pulse filtering;

[0114] (2) Real-time monitor the modulus and phase angle of the equivalent impedance by the high-precision vector analyzer, calculate the daily fluctuation coefficient according to the peak and valley periods, and establish the fluctuation matrix Zeq;

[0115] (3) Obtain the ratio of the standard deviation of the voltage deviation from the rated value to the rated value within 1 minute by the PMU, and calculate the voltage fluctuation rate ΔU.

[0116] Map SCR, 1 / |Zeq|, and 1 / ΔU to the interval [0, 10] respectively by using a normalization and weighting algorithm, and calculate the grid strength index (GSI) according to the preset weight:

[0117]

[0118] ​​wherein, k1 is the weight of short-circuit ratio item (e.g. 0.6), k2 is the weight of fluctuation matrix item (e.g. 0.3), k3 is the weight of voltage fluctuation rate item (e.g. 0.1), k1+k2+k3=1, SCR min is the lower limit of short-circuit ratio, SCR max is the upper limit of short-circuit ratio, |Z eq | min is the lower limit of fluctuation matrix, |Z eq | max is the upper limit of fluctuation matrix, ΔU min is the lower limit of voltage fluctuation rate, ΔU max is the upper limit of voltage fluctuation rate, e.g. SCR min =1.0, SCR max =510.0; |Zeq| min =0.5Ω, |Zeq| max =10Ω; ΔU min =2%, ΔU max =20%, SCR is short-circuit ratio, |Z eq is fluctuation matrix, ΔU is voltage fluctuation rate.

[0119] S2: dividing intensity levels based on grid intensity index, and generating customized fault scenarios.

[0120] In a further preferred scheme, as shown in FIG. 2, step S2 comprises: Figure 3

[0121] S21: dividing multi-level grid intensity levels according to grid intensity index;

[0122] S22: matching different working conditions for the multi-level grid intensity levels to generate customized fault scenarios; wherein the different working conditions include at least two of different voltage drop depth, different low-frequency oscillation, different harmonic distortion or different frequency fluctuation.

[0123] In this embodiment, the multi-level grid intensity levels include weak, medium and strong three-level grid intensity levels, for example, GSI values less than 3 are divided into weak grids, GSI values in the interval [3, 6] are divided into medium-intensity grids, and GSI values greater than 6 are divided into strong grids, and a hysteresis comparator is used to avoid boundary jitter, and a grid intensity level signal with a time scale is output.

[0124] In a further preferred scheme, the matching of different working conditions for the multi-level grid intensity levels to generate customized fault scenarios comprises:

[0125] For weak grid intensity levels, generating a fault scenario with voltage drop depth in a first preset range and superimposing low-frequency oscillation;

[0126] ​For the medium grid strength level, a fault scenario with voltage drop depth in a second preset range is generated and superimposed with harmonic distortion;

[0127] For the strong grid strength level, a transient fault scenario with voltage drop depth in a third preset range is generated and superimposed with frequency fluctuation;

[0128] The average value of the first preset range is greater than the average value of the second preset range, and the average value of the second preset range is greater than the average value of the third preset range.

[0129] For example, the weak grid scenario considers a voltage drop depth of 40%-70% superimposed with 2-5 Hz low-frequency oscillation; the medium strength grid voltage drop depth is 20%-40% superimposed with 5%-10% harmonic distortion; and the strong grid is mainly 10%-20% transient fault superimposed with ±0.5 Hz frequency fluctuation.

[0130] S3: Based on the calibrated grid-forming unit model and grid-following unit model of the optical storage power station, the support contribution of each power generation unit in the optical storage power station under the customized fault scenario is calculated.

[0131] In this embodiment, the power generation unit includes a photovoltaic power generation unit and an energy storage unit.

[0132] In a further preferred scheme, the process of obtaining the calibrated grid-forming unit model and grid-following unit model of the optical storage power station is:

[0133] An electromechanical or electromagnetic simulation software is used to construct the grid-forming unit model and the grid-following unit model.

[0134] The grid-forming unit model is calibrated using a virtual synchronous machine control model, and the grid-following unit model is calibrated using a PQ control model, so that the transient error and steady-state error do not exceed a preset value, for example, the transient error of simulation data and test data does not exceed 15%, and the steady-state error does not exceed 10%, and the test working condition is fault ride-through.

[0135] In a further preferred scheme, as shown in Figure 4 The step S3 specifically includes

[0136] S31: Based on the historical fault database, the analytic hierarchy process is used to assign each scene occurrence probability weight to the customized fault scenario; for example, the weight of the weak three-phase short circuit is 0.3.

[0137] S32: According to the scene occurrence probability weight, a working condition combination is extracted; a single evaluation covers ≥80% of high probability scenes to ensure the representativeness of the evaluation.

[0138] S33: The working condition combination is calculated using the calibrated grid-forming unit model or grid-following unit model of the optical storage power station to obtain the fault ride-through test result;

[0139] S34: calculating a unit support strength index based on the fault ride-through test result;

[0140] S35: obtaining a support contribution degree based on the support strength index.

[0141] The support strength index comprises reactive power support strength S Iq , active power support strength S p and time support strength S t .

[0142] The reactive power support strength S Iq is the ratio of the reactive power current ΔIq during the fault to the rated current In of the generating unit; the active power support strength S p is the ratio of the active power ΔP during the fault to the rated power Pn of the generating unit; and the time support strength S t is the ratio of the fault duration T sup to the standard specified fault duration T ref .

[0143] In a further preferred scheme, the calculation formula of the support contribution degree is as follows:

[0144] When the generating unit is of the grid-forming type, the support contribution degree C i of the i-th generating unit is as follows:

[0145]

[0146] When the generating unit is of the grid-following type, the support contribution degree C i of the i-th generating unit is as follows:

[0147]

[0148] wherein GSI is the grid strength index, SCR max is the upper limit of the short-circuit ratio, C0 is the basic support contribution degree, C0 = αS Iq + βS p + γS t , α, β, γ are weight coefficients, and usually α > 0.75.

[0149] S4: weighting and aggregating the support contribution degree and the overall station support strength to generate a comprehensive evaluation value.

[0150] The evaluation index of the overall station comprises the support strength and the support persistence.

[0151] The overall station support strength F s is the ratio of the steady-state value of the reactive power current at the grid connection point of the light storage power station during the fault to the rated current, and the correction formula is as follows:

[0152] F s =F s0 *(1-0.1*|Z eq |)

[0153] Wherein, F s0 is the support strength under ideal working condition, |Z eq | is the fluctuation matrix.

[0154] The support persistence Ds is the active power steady-state support time / fault duration.

[0155] The calculation formula of the comprehensive evaluation value SEV is:

[0156] SEV=(k4*∑C i +k5*F s +k6*D s )*100

[0157] Wherein, k4, k5, k6 are weight coefficients, D s is the support persistence of the whole station, and C i is the support contribution degree of the i-th generating unit.

[0158] In the embodiment, SEV∈[0,100], SEV≥80 is excellent, 60≤SEV<80 is qualified, and SEV<60 is unqualified.

[0159] S5: the comprehensive evaluation value is used to evaluate the fault support capability of the light storage power station.

[0160] A 200MW light storage power station (network type energy storage accounts for 40%), when GSI=2.5 (weak power grid), the network type energy storage Ci weight is increased to 52%, and the system layer Fs reaches 0.92 after impedance correction (1.05 without correction); under the same fault, the traditional method SEV=65, and the SEV of the application is 83, which can better reflect the support capability of the network type energy storage to the power grid in the weak power grid scenario.

[0161] In the application, the proposed comprehensive grid strength index GSI realizes continuous quantization and dynamic adaptation of the grid strength, solves the limitations of the traditional fixed SCR-based evaluation, and can more accurately reflect the influence of the change of the grid strength on the fault support capability of the light storage power station; the customized scene is used for the calculation of the support contribution degree of each generating unit according to the differences between the network type unit and the network following type unit, which can reflect the support effect of different types of generating units under different grid strengths; the "unit layer-integral station layer" hierarchical evaluation is adopted, combined with "actual measurement-simulation" verification, which reduces the cost of field test, and improves the reliability and accuracy of the evaluation.

[0162] Embodiment 2:

[0163] The application also provides a photovoltaic and energy storage power station fault support capacity hierarchical evaluation system based on the same inventive concept, as shown in the accompanying drawings, comprising: Figure 5

[0164] An acquisition module is configured to acquire a grid strength index based on photovoltaic and energy storage power station grid connection point parameters;

[0165] A first generation module is configured to divide a strength level based on the grid strength index and generate a customized fault scenario;

[0166] A calculation module is configured to calculate support contribution degrees of each power generation unit in the photovoltaic and energy storage power station under the customized fault scenario based on the calibrated network-constructing unit model and network-following unit model of the photovoltaic and energy storage power station;

[0167] A second generation module is configured to aggregate the support contribution degrees and the overall station support strength to generate a comprehensive evaluation value;

[0168] An evaluation module is configured to evaluate the fault support capacity of the photovoltaic and energy storage power station by using the comprehensive evaluation value.

[0169] In a further preferred scheme, the acquisition module acquires the grid strength index based on the photovoltaic and energy storage power station grid connection point parameters, and comprises:

[0170] Acquiring photovoltaic and energy storage power station grid connection point parameters, including short-circuit ratio, fluctuation matrix, and voltage fluctuation rate;

[0171] The short-circuit ratio, fluctuation matrix, and voltage fluctuation rate are normalized and weighted to obtain the grid strength index.

[0172] In a further preferred scheme, the calculation formula of the grid strength index GSI in the acquisition module is:

[0173]

[0174] wherein k1 is the weight of the short-circuit ratio term, k2 is the weight of the fluctuation matrix term, k3 is the weight of the voltage fluctuation rate term, SCR min is the lower limit of the short-circuit ratio, SCR max is the upper limit of the short-circuit ratio, |Z eq | min is the lower limit of the fluctuation matrix, |Z eq | max is the upper limit of the fluctuation matrix, ΔU min is the lower limit of the voltage fluctuation rate, ΔU max is the upper limit of the voltage fluctuation rate, SCR is the short-circuit ratio, |Z eq | is the fluctuation matrix, and ΔU is the voltage fluctuation rate.

[0175] ​In a further preferred implementation, the first generation module divides the grid strength index into strength levels and generates customized fault scenarios, including:

[0176] dividing the grid strength index into multiple levels of grid strength levels;

[0177] matching different operating conditions for the multiple levels of grid strength levels to generate customized fault scenarios; wherein the different operating conditions include at least two of different voltage drop depths, different low-frequency oscillations, different harmonic distortions, or different frequency fluctuations. In a further preferred implementation, the multiple levels of grid strength levels in the first generation module include weak, medium, and strong three levels of grid strength levels, and the matching different operating conditions for the multiple levels of grid strength levels to generate customized fault scenarios includes:

[0178] for the weak grid strength level, generating a fault scenario with a voltage drop depth in a first preset range and superimposing a low-frequency oscillation;

[0179] for the medium grid strength level, generating a fault scenario with a voltage drop depth in a second preset range and superimposing a harmonic distortion;

[0180] for the strong grid strength level, generating a transient fault scenario with a voltage drop depth in a third preset range and superimposing a frequency fluctuation;

[0181] wherein the average value of the first preset range is greater than the average value of the second preset range, and the average value of the second preset range is greater than the average value of the third preset range.

[0182] In a further preferred implementation, the first preset range in the first generation module is 40% to 70%; the second preset range is 20% to 40%; the third preset range is 10% to 20%; the frequency of the low-frequency oscillation is 2 to 5 Hz, the degree of the harmonic distortion is 5% to 10%, and the frequency fluctuation is ±0.5 Hz.

[0183] In a further preferred implementation, the process of obtaining the grid-connected unit model and the grid-following unit model of the calibrated optical storage power station of the calculation module is:

[0184] constructing a grid-connected unit model and a grid-following unit model using mechanical or electromagnetic simulation software;

[0185] calibrating the grid-connected unit model using a virtual synchronous machine control model and calibrating the grid-following unit model using a PQ control model, so that the transient error and the steady-state error do not exceed a preset value.

[0186] In a further preferred solution, the calculation module calculates the support contribution degree of each power generation unit in the photovoltaic storage power station under the customized fault scenario based on the network-constructing unit model and the network-following unit model of the calibrated photovoltaic storage power station, including:

[0187] Based on the historical fault database, the analytic hierarchy process is used to assign each scenario occurrence probability weight to the customized fault scenario;

[0188] According to the scenario occurrence probability weight, the working condition combination is extracted;

[0189] The working condition combination is calculated using the network-constructing unit model or the network-following unit model of the calibrated photovoltaic storage power station to obtain the fault ride-through test result;

[0190] Based on the fault ride-through test result, the support strength index of each unit is calculated;

[0191] Based on the support strength index, the support contribution degree is obtained.

[0192] In a further preferred solution, the support strength index in the calculation module includes reactive power support strength S Iq , active power support strength S p and time support strength S t , and the calculation formula of the support contribution degree is:

[0193] When the power generation unit is a network-constructing type, the support contribution degree C i is:

[0194]

[0195] When the power generation unit is a network-following type, the support contribution degree C i is:

[0196]

[0197] Wherein, GSI is the grid strength index, SCR max is the upper limit of short-circuit ratio, C0 is the basic support contribution degree, C0 = αS Iq + βS p + γS t , and α, β and γ are weight coefficients.

[0198] In a further preferred solution, the calculation formula of the overall support strength F s in the second generation module is:

[0199] F s = F s0 × (1-0.1×|Z eq |)

[0200] Wherein, Fs0 For the support strength under ideal working condition, |Z eq | is the fluctuation matrix.

[0201] In a further preferred scheme, the calculation formula of the comprehensive evaluation value SEV in the second generation module is:

[0202] SEV = (k4 x∑C i +k5 x F s +k6 x D s )*100

[0203] Wherein, k4, k5, k6 are weight coefficients, D s is the support persistence of the whole station, C i is the support contribution degree of the i-th power generation unit.

[0204] Embodiment 3

[0205] As Figure 6 shown, the present application also provides an electronic device, which can be a computer device, a single-chip microcomputer device, a smart mobile device, etc. The electronic device in the embodiment can include a processor, a memory, a transceiver component, etc. The memory, the processor and the transceiver component are connected through a bus; the memory can be used to store an execution program, and the exemplary execution program can include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be called and / or modified when the instructions are executed.

[0206] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), ready programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in the storage medium to implement a corresponding method flow or a corresponding function, so as to implement the steps of the optical storage power station fault support capacity hierarchical evaluation method in the above embodiment.

[0207] Embodiment 4

[0208] Based on the same inventive concept, the application further provides a readable storage medium, specifically, an electronic device readable storage medium (Memory). The electronic device readable storage medium is a memory device in the electronic device, and is used for storing programs and data. It can be understood that the storage medium herein can include a built-in storage medium in the electronic device, and of course can also include an extended storage medium supported by the electronic device. The storage medium provides a storage space, and the storage space stores an operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more execution programs (including program codes). It should be noted that the storage medium herein can be a high-speed RAM memory or a non-volatile memory such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, so as to realize the steps of the optical storage power station fault support capacity hierarchical evaluation method in the above embodiment.

[0209] Those skilled in the art will appreciate that embodiments of the application can be supplied as a method, a system, or a computer program product. Therefore, the application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage media, etc.) containing computer-usable program code.

[0210] The application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device that implements the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The functions specified in one or more flows and / or blocks

[0211] These computer program instructions can also be stored in a computer-readable memory capable of guiding the computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including instruction devices, which implement the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The functions specified in one or more flows and / or blocks

[0212] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, so that the instructions executed on the computer or other programmable devices provide processes for implementing the flow Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0213] The above merely illustrates the embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.

Claims

1. A method for hierarchical assessment of fault support capability of a light storage power station, characterized in that, The method comprises the following steps: obtaining a grid strength index based on grid-connected point parameters of the optical storage power station; dividing a strength level based on the grid strength index and generating a customized fault scenario; calculating support contribution of each power generation unit in the optical storage power station under the customized fault scenario based on the calibrated network-constructing unit model and network-following unit model of the optical storage power station; aggregating the support contribution and the overall station support strength to generate a comprehensive evaluation value; evaluating the fault support capability of the optical storage power station by using the comprehensive evaluation value.

2. The method of claim 1, wherein, The step of obtaining the grid strength index based on the grid-connected point parameters of the optical storage power station comprises the following steps: obtaining the grid-connected point parameters of the optical storage power station, including short-circuit ratio, fluctuation matrix and voltage fluctuation rate; normalizing and weighting the short-circuit ratio, fluctuation matrix and voltage fluctuation rate to obtain the grid strength index.

3. The method of claim 2, wherein, The calculation formula of the grid strength index GSI is: where k1 is the weight of the short circuit ratio term, k2 is the weight of the fluctuation matrix term, k3 is the weight of the voltage fluctuation rate term, SCR min is the lower limit of the short circuit ratio, SCR max is the upper limit of the short circuit ratio, |Z eq | min is the lower limit of the fluctuation matrix, |Z eq | max is the upper limit of the fluctuation matrix, ΔU min is the lower limit of the voltage fluctuation rate, ΔU max is the upper limit of the voltage fluctuation rate, SCR is the short circuit ratio, |Z eq is the fluctuation matrix, and ΔU is the voltage fluctuation rate.

4. The method of claim 1, wherein, The step of dividing the strength level based on the grid strength index and generating the customized fault scenario comprises the following steps: dividing a multi-level grid strength level according to the grid strength index; matching different working conditions for the multi-level grid strength level to generate a customized fault scenario; wherein the different working conditions include at least two of different voltage drop depths, different low-frequency oscillations, different harmonic distortions or different frequency fluctuations.

5. The method of claim 4, wherein, The multi-level grid strength level includes weak, medium and strong three-level grid strength levels, and the step of matching different working conditions for the multi-level grid strength level to generate a customized fault scenario comprises the following steps: for the weak grid strength level, generating a fault scenario with a voltage drop depth in a first preset range and superimposing a low-frequency oscillation; for the medium grid strength level, generating a fault scenario with a voltage drop depth in a second preset range and superimposing a harmonic distortion; for the strong grid strength level, generating an instantaneous fault scenario with a voltage drop depth in a third preset range and superimposing a frequency fluctuation; wherein the average value of the first preset range is greater than the average value of the second preset range, and the average value of the second preset range is greater than the average value of the third preset range.

6. The method of claim 5, wherein, The first preset range is 40% to 70%, the second preset range is 20% to 40%, the third preset range is 10% to 20%, the frequency of the low-frequency oscillation is 2 to 5 Hz, the degree of the harmonic distortion is 5% to 10%, and the frequency fluctuation is ±0.5 Hz.

7. The method of claim 1, wherein, The process of obtaining the calibrated network-constructing unit model and network-following unit model of the optical storage power station comprises the following steps: constructing the network-constructing unit model and the network-following unit model by using electromechanical or electromagnetic simulation software; calibrating the network-constructing unit model by using a virtual synchronous machine control model and calibrating the network-following unit model by using a PQ control model, so that the transient error and the steady-state error do not exceed a preset value.

8. The method of claim 1 or 7, wherein, The step of calculating the support contribution of each power generation unit in the optical storage power station under the customized fault scenario based on the calibrated network-constructing unit model and network-following unit model of the optical storage power station comprises the following steps: assigning each scene occurrence probability weight to the customized fault scenario based on a historical fault database by using an analytic hierarchy process; extracting a working condition combination according to the scene occurrence probability; The working condition combination is calculated by using the network-constructed unit model or the network-following unit model of the calibrated optical storage power station to obtain a fault ride-through test result; A unit support strength index is calculated based on the fault ride-through test result; A support contribution degree is obtained based on the support strength index.

9. The method of claim 8, wherein, The support strength index includes reactive power support strength S Iq , active power support strength S p , and time support strength S t The calculation formula of the support contribution degree is: When the power generation unit is of the grid-connected type, the support contribution C i is: When the power generation unit is of the grid-following type, the support contribution C i is: wherein GSI is the grid strength index, SCR max is the upper limit of the short circuit ratio, C0 is the basic support contribution, C0 = αS Iq + βS p + γS t , α, β, γ are weight coefficients.

10. The method of claim 1, wherein, The overall support strength F s The calculation formula is: F s = F s0 × (1 - 0.1 × |Z eq |) where F s0 is the support strength under ideal working conditions, |Z eq is the fluctuation matrix.

11. The method of claim 10, wherein, The calculation formula of the comprehensive evaluation value SEV is: SEV = (k4 x ∑C i + k5 x F s + k6 x D s ) * 100 wherein k4, k5, k6 are weight coefficients, D s is the support persistence of the entire station, C i is the support contribution of the i-th generating unit.

12. A hierarchical assessment system for failure support capability of a photovoltaic power storage station, characterized in that, The method comprises the following steps: An acquisition module is configured to acquire a grid strength index based on grid-connected point parameters of the optical storage power station; A first generation module is configured to divide a strength level based on the grid strength index and generate a customized fault scene; A calculation module is configured to calculate a support contribution degree of each power generation unit in the optical storage power station under the customized fault scene based on the network-constructed unit model and the network-following unit model of the calibrated optical storage power station; A second generation module is configured to aggregate the support contribution degree and the overall station support strength to generate a comprehensive evaluation value; An evaluation module is configured to evaluate the fault support capability of the optical storage power station by using the comprehensive evaluation value.

13. The photovoltaic power station failure support capacity hierarchical evaluation system according to claim 12, characterized in that, The acquisition module acquires the grid strength index based on the grid-connected point parameters of the optical storage power station, which comprises the following steps: Grid-connected point parameters of the optical storage power station are acquired, including a short-circuit ratio, a fluctuation matrix, and a voltage fluctuation rate; The short-circuit ratio, the fluctuation matrix, and the voltage fluctuation rate are normalized and weighted to obtain the grid strength index.

14. The photovoltaic power station failure support capacity hierarchical evaluation system according to claim 13, characterized in that, The calculation formula of the grid strength index GSI in the acquisition module is: where k1 is the weight of the short circuit ratio term, k2 is the weight of the fluctuation matrix term, k3 is the weight of the voltage fluctuation rate term, SCR min is the lower limit of the short circuit ratio, SCR max is the upper limit of the short circuit ratio, |Z eq | min is the lower limit of the fluctuation matrix, |Z eq | max is the upper limit of the fluctuation matrix, ΔU min is the lower limit of the voltage fluctuation rate, ΔU max is the upper limit of the voltage fluctuation rate, SCR is the short circuit ratio, |Z eq is the fluctuation matrix, and ΔU is the voltage fluctuation rate.

15. The photovoltaic power station failure support capacity hierarchical evaluation system according to claim 12, characterized in that, The first generation module divides the strength level based on the grid strength index and generates the customized fault scene, which comprises the following steps: A multi-level grid strength level is divided according to the grid strength index; Different working conditions are matched for the multi-level grid strength level to generate the customized fault scene; wherein the different working conditions include at least two of different voltage drop depths, different low-frequency oscillations, different harmonic distortions, or different frequency fluctuations.

16. The photovoltaic power station failure support capacity hierarchical evaluation system according to claim 15, characterized in that, The different working conditions are matched for the multi-level grid strength level to generate the customized fault scene, which comprises the following steps: For a weak grid strength level, a fault scene with a voltage drop depth in a first preset range is generated and superimposed with a low-frequency oscillation; For a medium grid strength level, a fault scene with a voltage drop depth in a second preset range is generated and superimposed with a harmonic distortion; For a strong grid strength level, a transient fault scene with a voltage drop depth in a third preset range is generated and superimposed with a frequency fluctuation; The average value of the first preset range is greater than the average value of the second preset range, and the average value of the second preset range is greater than the average value of the third preset range.

17. The photovoltaic power station failure support capacity hierarchical evaluation system according to claim 16, characterized in that, In the first generation module, the first preset range is 40% to 70%, the second preset range is 20% to 40%, the third preset range is 10% to 20%, the frequency of the low-frequency oscillation is 2 to 5 Hz, the degree of the harmonic distortion is 5% to 10%, and the frequency fluctuation is ±0.5 Hz.

18. The photovoltaic power plant failure support capability stratified assessment system of claim 12, wherein, The acquisition process of the network-constructed unit model and the network-following unit model of the calibrated optical storage power station in the calculation module is as follows: An electromechanical or electromagnetic simulation software is used to construct the network-constructed unit model and the network-following unit model; A virtual synchronous machine control model is used to calibrate the network-constructed unit model, and a PQ control model is used to calibrate the network-following unit model, so that the transient error and the steady-state error do not exceed a preset value.

19. The photovoltaic power station failure support capacity hierarchical evaluation system according to claim 12 or 18, characterized in that, The computing module calculates the support contribution degree of each power generation unit in the photovoltaic storage power station under the customized fault scene based on the network construction type unit model and the network following type unit model of the calibrated photovoltaic storage power station, including: Based on the historical fault database, the analytic hierarchy process is used to assign each scene occurrence probability weight to the customized fault scene; According to the scene occurrence probability weight, the working condition combination is extracted; The working condition combination is calculated by using the network construction type unit model or the network following type unit model of the calibrated photovoltaic storage power station to obtain the fault ride-through test result; Based on the fault ride-through test result, the support strength index of each unit is calculated; Based on the support strength index, the support contribution degree is obtained.

20. The photovoltaic power station failure support capacity hierarchical evaluation system according to claim 19, characterized in that, The support strength index in the calculation module includes reactive support strength S Iq , active support strength S p , and time support strength S t , and the calculation formula of the support contribution degree is: When the power generation unit is of the network-forming type, the support contribution C i is: When the power generation unit is of the grid-following type, the support contribution C i is: Wherein, GSI is grid strength index, SCR max is upper limit of short circuit ratio, C0 is basic support contribution, C0=αS Iq +βS p +γS t , α, β, γ are weight coefficients.

21. The photovoltaic power plant failure support capacity stratified assessment system of claim 12, wherein, The second generation module in the overall support strength F s The formula is: F s = F s0 × (1 - 0.1 × |Z eq |) where F s0 is the support strength under ideal working conditions, |Z eq is the fluctuation matrix.

22. The photovoltaic power station failure support capacity hierarchical evaluation system according to claim 21, characterized in that, The calculation formula of the comprehensive evaluation value SEV in the second generation module is: SEV = (k4 x ∑C i + k5 x F s + k6 x D s )* 100 wherein k4, k5, k6 are weight coefficients, D s is the support persistence of the entire station, C i is the support contribution of the i-th generating unit.

23. A computer device, comprising: including: At least one processor and a memory; The memory and the processor are connected through a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the photovoltaic storage power station fault support ability hierarchical evaluation method in any one of claims 1 to 11 is implemented.

24. A computer-readable storage medium, characterized in that, An execution program is stored thereon, and when the execution program is executed, the photovoltaic storage power station fault support ability hierarchical evaluation method in any one of claims 1 to 11 is implemented.

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