Construction Method of Aggregation Model for Household Distributed Photovoltaic Power Generation System

Abstract

The present invention discloses a method for constructing an aggregation model of a household distributed photovoltaic power generation system, including: statistically calculating the total installed capacity of the household distributed photovoltaic power generation system and the installed capacity ratio of household inverters of each brand in the modeling target area; conducting a voltage sag experiment on the dynamic characteristics of the inverter and recording data; constructing the active power and reactive power response data of a virtual household distributed photovoltaic power generation system cluster; identifying the control parameters of the aggregation model SM1 of the virtual household distributed photovoltaic power generation system cluster; then establishing a detailed simulation model of the modeling target area; establishing a model between the amplitude of the head-end voltage of the modeling target area and the off-grid ratio of the household distributed photovoltaic power generation system through simulation; and finally constructing an aggregation model of the household distributed photovoltaic power generation system with partial off-grid function. The present invention can solve the problem that it is difficult to determine the aggregation model parameters due to the different types of household photovoltaic inverters and the partial off-grid function, and improve the simulation accuracy of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, especially distributed photovoltaic power generation systems, and particularly to a method and system for constructing an aggregation model of a household distributed photovoltaic power generation system. Background Art

[0002] The access voltage levels of rooftop distributed photovoltaic power generation systems (including those installed on rural open spaces) are generally single-phase 220V and three-phase 380V. The installed capacity of each power generation system is generally between several kilowatts and dozens of kilowatts, and the grid connection method of "spontaneous self-use and surplus electricity grid connection" is adopted by users, which is usually called a "household distributed photovoltaic power generation system".

[0003] Currently, in the simulation analysis of the power system, the power supply area containing a large number of household distributed photovoltaic power generation systems is still equivalent to a load model. The specific method is to simply deduct the total power generation of household distributed photovoltaics from the load power. To improve the simulation analysis accuracy of the entire power system, the load model must accurately reflect the actual dynamic characteristics of the power supply area containing a high proportion of household distributed photovoltaic power generation systems, which involves the aggregation modeling problem of household distributed photovoltaic power generation system clusters.

[0004] The existing power load modeling technology is mainly for electrical equipment. The existing technology obtains the aggregation model of electrical equipment clusters through statistical synthesis or overall measurement and identification methods, and there is no clear technical solution for the aggregation modeling of a large number of household distributed photovoltaic power generation systems. The aggregation modeling of photovoltaic power stations is relatively close to the aggregation modeling of a large number of household distributed photovoltaic power generation systems. However, the models of each photovoltaic inverter inside a photovoltaic power station are unified (i.e., the model parameters are unified), the port voltages are similar, and they all have the ability to ride through low voltages. The existing aggregation modeling technology can be used to obtain the equivalent aggregation model of the photovoltaic power station; while the models of household distributed photovoltaic power generation systems are different (i.e., the model parameters are different), the access points are scattered, and they do not have the ability to ride through low voltages. The voltage change degrees at the access points are also different during grid faults, which will cause some household distributed photovoltaic power generation systems to be disconnected from the grid while others remain grid-connected. However, the aggregation model of household distributed photovoltaic power generation systems constructed by the existing aggregation modeling method cannot represent the phenomenon of "partial disconnection", and can only be all disconnected or all remain grid-connected. Summary of the Invention

[0005] In view of the technical problems existing in the aggregation simulation of a large number of household distributed photovoltaic power generation systems, the present invention proposes a method for constructing an aggregation model of a household distributed photovoltaic power generation system. First, it is necessary to solve the problem that it is difficult to determine the parameters of the aggregation model due to the various models of household distributed photovoltaic power generation systems. Second, it is necessary to add a function to the aggregation model to characterize the "partial grid disconnection" of the household distributed photovoltaic power generation system, so as to help construct an accurate equivalent load model of the power supply area containing a large number of household distributed photovoltaic power generation systems, and further provide support for constructing an equivalent load model of the power supply area containing a large number of household distributed photovoltaic power generation systems.

[0006] To achieve the above object, the present invention mentions a method for constructing an aggregation model of a household distributed photovoltaic power generation system, including the following steps:

[0007] Step 1: Statistically calculate the total installed capacity S of the household distributed photovoltaic power generation systems in the modeling target area Σ , and the proportion α of the installed capacity of household distributed photovoltaic inverters of each brand i (the subscript i is the serial number of the inverter brand, the same below);

[0008] Step 2: Conduct voltage drop experiments on the dynamic characteristics of household distributed photovoltaic inverters of each brand and record the data;

[0009] Step 3: According to the results of Step 1 and Step 2, construct the active power and reactive power response data of the virtual household distributed photovoltaic power generation system cluster;

[0010] Step 4: Identify the control parameters of the aggregation model (SM1) of the virtual household distributed photovoltaic power generation system cluster according to the results of Step 3;

[0011] Step 5: Establish a detailed simulation model of the modeling target area;

[0012] Step 6: Based on the model established in Step 5, establish a model between the amplitude of the voltage at the head end of the modeling target area and the grid disconnection ratio of the household distributed photovoltaic power generation system, that is, the grid disconnection ratio model (MC);

[0013] Step 7: Obtain the aggregation model (SM2) of the household distributed photovoltaic power generation system with the "partial grid disconnection" function based on the grid disconnection ratio model constructed in Step 6, and combine the aggregation model (SM2) of the household distributed photovoltaic power generation system with the "partial grid disconnection" function and the equivalent impedance (Z) of the distribution network to construct the aggregation model (MA) of the household distributed photovoltaic power generation system with the "partial grid disconnection" function.

[0014] Among them, in step 2, the steps of conducting the voltage dip experiment are as follows: For each residential distributed PV inverter brand, only one model is selected for the experiment, that is, it is considered that the technologies adopted by residential PV inverters of the same brand are the same, so their dynamic characteristics are consistent; a PV array simulation power supply or a DC regulated power supply is used to replace the actual PV panel array; based on the rated capacity of each brand of inverter, active power with the same per-unit value is output; the same steady-state voltage amplitude is adopted, and based on the rated voltage of each brand of inverter, the same per-unit value of voltage dip degree is adopted, where the voltage dip degree satisfies: not triggering the tripping of the equipment under test.

[0015] Furthermore, the data to be recorded in the voltage dip experiment are the variation processes of the positive-sequence voltage amplitude U i (t), the variation process of the positive-sequence current amplitude I i (t), and the variation process of the power factor angle U i (t) and I i (t) are per-unit values based on the rated capacity and rated voltage of each brand of inverter; the recorded U i (t), I i (t) and have the same length, and the moments of voltage dip in the data are the same.

[0016] Furthermore, for single-phase PV inverters, the single-phase voltage and single-phase current obtained from the experiment are regarded as the positive-sequence voltage and positive-sequence current of a three-phase PV inverter.

[0017] Furthermore, in step 3, the specific method for constructing the active power and reactive power response data of the virtual residential distributed PV power generation system cluster is as follows: First, taking the installed capacity ratio α i of each brand of inverter in the modeling target area as the weight coefficient, the input voltage amplitude U(t) of the virtual residential distributed PV power generation system cluster is obtained according to formula (1); then, the active power component I P (t) and reactive power component I Q (t) of the output current of the virtual residential distributed PV power generation system cluster are obtained according to formula (2); finally, the active power response P(t) and reactive power response Q(t) of the virtual residential distributed PV power generation system cluster are calculated according to formula (3).

[0018]

[0019]

[0020]

[0021] In the formula, N is the number of inverter brands.

[0022] Further, in step 4, the specific method for identifying the control parameters in the aggregation model SM1 is as follows: First, select an existing simplified model of a household distributed photovoltaic power generation system (including a low-voltage disconnection judgment module); then, using the input voltage U(t) (with the phase fixed at zero degree) obtained in step 3 as the input, and the active power response P(t) and reactive power response Q(t) as the optimization objectives, use existing parameter identification techniques to identify the two sets of PI controller parameters for the active power and reactive power control loops; by setting different rated capacity values for this simplified model, the aggregation model SM1 representing different scales of household distributed photovoltaic power generation system clusters can be obtained, where the two sets of PI controller parameters do not change with the change of the rated capacity.

[0023] Further, in step 5, the specific method for establishing the detailed simulation model M1 of the modeling target area is as follows, which should include distribution lines, distribution transformers, dispersed access power loads, and dispersed access household distributed photovoltaic power generation systems; for different household distributed photovoltaic power generation systems with the grid connection point on the distribution network side at the same location, their rated capacities can be accumulated and set into the model SM1, so as to obtain their aggregation model, and the line impedance connected to the distribution network can be ignored.

[0024] Further, the specific method for establishing the disconnection ratio model MC in step 6 is as follows: Based on the model M1 established in step 5, through simulation, obtain the critical amplitude U of the voltage at the head end of the modeling target area that triggers the disconnection of the household distributed photovoltaic power generation system C0 , and the voltage amplitude U that can cause a certain proportion β1 of the household distributed photovoltaic power generation systems to disconnect C1 ; then, according to the points (U C0 , 0) and (U C1 , β1), construct an equation as shown in Equation (4), which is the disconnection ratio model MC;

[0025]

[0026] When the voltage U is higher than U C0 , the disconnection ratio is zero; when the voltage U is lower than U C0 , the disconnection ratio β is determined by the model MC, and the maximum value of β is limited to 1.

[0027] Further, the aggregation model MA of the household distributed photovoltaic power generation system with the "partial disconnection" function is composed of the aggregation model SM2 of the household distributed photovoltaic power generation system with the "partial disconnection" function and the equivalent impedance Z of the distribution network connected in series.

[0028] Further, the specific method for constructing the aggregated model SM2 of the household distributed photovoltaic power generation system with the "partial islanding" function is as follows: Integrate the islanding ratio model MC with the aggregated model SM1 established in Step 4, that is, the islanding signal is still sent by the model SM1, and the islanding ratio β is obtained from the model MC; when an islanding event is triggered, the ratio of non-islanded household distributed photovoltaics (1-β) will be multiplied by the active power reference value P ref , reactive power reference value Q ref , and steady-state current value respectively, so as to achieve the simulation effect of "partial islanding" of the household distributed photovoltaics; the rated capacity, steady-state active power value, and steady-state reactive power value of SM2 are the total installed capacity S Σ , total steady-state active power P Σ , and total steady-state reactive power Q Σ of all household distributed photovoltaic power generation systems in the modeling area respectively, and the numerical values of the two sets of PI controller parameters in SM2 are kept the same as those in SM1.

[0029] Further, the specific method for identifying the equivalent impedance Z of the distribution network in the aggregated model MA of the household distributed photovoltaic power generation system with the "partial islanding" function is as follows: First, remove the models of all household distributed photovoltaic systems from the detailed simulation model M1 of the modeling target area established in Step 5, and connect the aggregated model MA of the household distributed photovoltaic power generation system to the head end of M1, so as to obtain the simulation model M2 of the modeling target area; then, under the same transmission network fault, with the principle of minimizing the active power and reactive power errors of the models M1 and M2 during the voltage dip duration, use the existing parameter identification technology to identify the numerical value of the equivalent impedance Z of the distribution network.

[0030] From the technical solutions of the present invention above, its remarkable beneficial effects are as follows:

[0031] The construction method of the aggregated model of the household distributed photovoltaic power generation system proposed by the present invention solves, on the one hand, the problem that it is difficult to determine the parameters of the aggregated model due to the various types of household distributed photovoltaic power generation systems, and on the other hand, realizes the "partial islanding" function in the aggregated model of the household distributed photovoltaics. Compared with the existing method of simply deducting the power generation power of the household distributed photovoltaics from the load power, the technical solution based on the present invention helps to construct a more accurate equivalent model of the power supply area with a large number of household distributed photovoltaics, and can significantly improve the simulation accuracy of the entire power system; the dynamic response of the model constructed by the present invention is basically close to that of the detailed model, and can accurately characterize the dynamic characteristics of the power supply area with a large number of household distributed photovoltaic power generation systems. Description of the Drawings

[0032] Figure 1 is the flowchart of constructing the aggregated model MA of the household distributed photovoltaic power generation system of the present invention.

[0033] Figure 2 These are the results of the voltage sag experiments of residential distributed PV inverters of three brands.

[0034] Figure 3 These are the dynamic responses of the virtual residential distributed PV power generation system cluster and the fitting effect of simulating SM1.

[0035] Figure 4 These are the structure diagrams of the aggregation model SM1 of the virtual residential distributed PV power generation system cluster.

[0036] Figure 5 These are the structure diagrams of the IEEE-33 node distribution network system with multiple residential distributed PV power generation systems added.

[0037] Figure 6 These are the composition diagrams of the detailed simulation model M1 of the modeling target area built in the simulation software Matlab.

[0038] Figure 7 These are the schematic diagrams of the composition of the aggregation model MA of the residential distributed PV power generation system.

[0039] Figure 8 These are the integrated model diagrams of the off-grid ratio model MC and the low-voltage off-grid module in the PV aggregation model SM1.

[0040] Figure 9 These are the schematic diagrams of the access positions of the non-off-grid PV ratio (1-β) in the residential PV aggregation model.

[0041] Figure 10 These are the composition diagrams of the simulation model M2 of the modeling target area after the aggregation of the residential distributed PV power generation system.

[0042] Figure 11 These are the response curves of the aggregation model MA of the residential distributed PV power generation system before the identification of the equivalent impedance of the distribution network.

[0043] Figure 12 These are the response curves of the aggregation model MA of the residential distributed PV power generation system after the identification of the equivalent impedance of the distribution network. Specific implementation manners

[0044] For a better understanding of the technical content of the present invention, specific embodiments are hereby given and described in conjunction with the accompanying drawings as follows.

[0045] Aspects of the present invention are described with reference to the accompanying drawings in this disclosure, in which many illustrative embodiments are shown. The embodiments of this disclosure are not necessarily intended to cover all aspects of the present invention. It should be understood that the various concepts and embodiments introduced above, as well as those described in more detail below, can be implemented in any of many ways, because the concepts and embodiments disclosed in the present invention are not limited to any implementation. In addition, some aspects of the present invention can be used alone or in any suitable combination with other aspects disclosed in the present invention.

[0046] Combined with Figure 1 Shown in the exemplary flowchart, the present invention generally proposes a method for constructing an aggregation model of a household distributed photovoltaic power generation system, including the following steps: Step 1, statistically model the total installed capacity of the household distributed photovoltaic power generation system in the target area, and the proportion of the installed capacity of household distributed photovoltaic inverters of each brand; Step 2, conduct a voltage dip experiment on the dynamic characteristics of household distributed photovoltaic inverters of each brand and record the data; Step 3, construct the active power and reactive power response data of the virtual household distributed photovoltaic power generation system cluster according to the results of Step 1 and Step 2; Step 4, identify the control parameters in the aggregation model (SM1) of the virtual household distributed photovoltaic power generation system cluster according to the results of Step 3; Step 5, establish a detailed simulation model of the target area for modeling; Step 6, based on the model established in Step 5, establish a model between the amplitude of the voltage at the head end of the target area for modeling and the disconnection ratio of the household distributed photovoltaic power generation system, that is, the disconnection ratio model (MC); Step 7, obtain the aggregation model (SM2) of the household distributed photovoltaic power generation system with the "partial disconnection" function based on the disconnection ratio model constructed in Step 6, and combine the aggregation model (SM2) of the household distributed photovoltaic power generation system with the "partial disconnection" function and the equivalent impedance (Z) of the distribution network to construct the aggregation model (MA) of the household distributed photovoltaic power generation system with the "partial disconnection" function.

[0047] Among them, in the said Step 2, the process of conducting the voltage dip experiment includes:

[0048] Only select one model for each household photovoltaic inverter brand for the experiment, that is, it is considered that the same brand of household photovoltaic inverters adopt the same technology, so the dynamic characteristics are the same;

[0049] Use a photovoltaic array simulation power supply or a DC regulated power supply to replace the actual photovoltaic panel array;

[0050] Based on the rated capacity of each photovoltaic inverter, output the active power with the same per-unit value;

[0051] Adopt the same steady-state voltage amplitude, and take the rated voltage of each brand's inverter as the reference, and adopt the same per-unit value of voltage sag degree, where the voltage sag degree meets the requirement: not triggering the experimental equipment to trip off the grid.

[0052] In the step 2, the data to be recorded in the voltage sag experiment include:

[0053] The change process of the positive-sequence voltage amplitude U i (t), the change process of the positive-sequence current amplitude I i (t) and the change process of the power factor angle The subscript i is the serial number of the brand;

[0054] U i (t) and I i (t) are per-unit values based on the rated capacity and rated voltage of each brand's photovoltaic inverter respectively; and the U i (t), I i (t) and Have the same length, and the moments of voltage sag in the data are the same.

[0055] Among them, in the step 2, for a single-phase photovoltaic inverter, the single-phase voltage and single-phase current obtained from the voltage sag experiment are regarded as the positive-sequence voltage and positive-sequence current of a three-phase photovoltaic inverter.

[0056] In the step 3, construct the active power and reactive power response data of the virtual residential distributed photovoltaic power generation system cluster, including:

[0057] First, take the installed capacity ratio α i Of each brand's photovoltaic inverter in the modeling target area as the weight coefficient, and obtain the input voltage amplitude U(t) of the virtual residential distributed photovoltaic power generation system cluster according to the following formula (1);

[0058]

[0059] Then, obtain the active power component I P (t) and reactive power component I Q (t) of the output current of the virtual residential distributed photovoltaic power generation system cluster according to formula (2);

[0060]

[0061] Finally, obtain the active power response P(t) and reactive power response Q(t) of the virtual residential distributed photovoltaic power generation system cluster according to formula (3), that is, construct the aggregation model (SM1) of the virtual residential distributed photovoltaic power generation system cluster:

[0062]

[0063] Among them, in the above formula (1), N represents the number of PV inverter brands.

[0064] In the step 4, a simplified model of the existing distributed photovoltaic power generation system, such as Simplified_Model_of_Distributed_PV_Generation_and_Influence_Analysis_on_Load_Characteristics published in the IEEE conference, the model is as Figure 4 shown, is used as the aggregation model (SM1) of the virtual household distributed photovoltaic power generation system cluster, and the control parameters therein are identified according to the result of step 3. The process includes:

[0065] First, select an existing simplified model of the household distributed photovoltaic power generation system;

[0066] Then, with the input voltage U(t) obtained in step 3 as the input, and the active power response P(t) and the reactive power response Q(t) as the optimization objectives, use parameter identification technology to identify two sets of PI controller parameters for the active power and reactive power control loops; by setting different rated capacity values, aggregation models representing different scales of household distributed photovoltaic power generation system clusters can be obtained, and the two sets of PI controller parameters do not change with the change of the rated capacity.

[0067] In another embodiment, the aggregation model (SM1) can also adopt other existing simplified models of the household distributed photovoltaic power generation system to identify its control parameters and participate in the construction of the subsequent detailed simulation model M1.

[0068] In the step 5, the process of establishing the detailed simulation model (M1) of the modeling target area includes the following:

[0069] The detailed simulation model (M1) of the modeling target area includes distribution lines, distribution transformers, dispersed access power loads, and dispersed access household distributed photovoltaic power generation systems; for different household distributed photovoltaic power generation systems with the grid connection point on the distribution network side at the same location, their rated capacities are accumulated and set into the aggregation model (SM1) of the virtual household distributed photovoltaic power generation system cluster, so as to obtain their aggregation model.

[0070] In the step 6, the process of establishing the islanding ratio model (MC) includes:

[0071] First, based on the detailed simulation model (M1) of the modeling target area established in step 5, through simulation, obtain the critical amplitude U of the head-end voltage of the modeling target area that triggers the islanding of the household distributed photovoltaic power generation system C0, and the voltage amplitude U that can cause a certain proportion β1 of household distributed photovoltaic power generation systems to lose grid connection C1 ;

[0072] Then, based on the points (U C0 , 0) and (U C1 , β1), an equation as shown in Equation (4) is constructed, which is the grid disconnection proportion model (MC):

[0073]

[0074] Where:

[0075] When the voltage U is higher than U C0 , the grid disconnection proportion β is zero;

[0076] When the voltage U is lower than U C0 , the grid disconnection proportion β is determined by the grid disconnection proportion model (MC), and the maximum value of β is limited to 1.

[0077] In the said Step 7, the aggregated model (MA) of household distributed photovoltaic power generation systems with the "partial grid disconnection" function is constructed by connecting in series the aggregated model (SM2) of household distributed photovoltaic power generation systems with the "partial grid disconnection" function and the equivalent impedance (Z) of the distribution network.

[0078] In Step 7, the process of constructing the aggregated model (SM2) of household distributed photovoltaic power generation systems with the "partial grid disconnection" function includes:

[0079] Integrate the grid disconnection proportion model (MC) with the aggregated model (SM1) of the virtual household distributed photovoltaic power generation system cluster, that is, the grid disconnection signal is still sent by the aggregated model (SM1) of the virtual household distributed photovoltaic power generation system cluster, and the grid disconnection proportion β is obtained from the grid disconnection proportion model (MC):

[0080] When a grid disconnection event is triggered, the proportion of non-grid-disconnected household distributed photovoltaics (1-β) will be multiplied by the active power reference value P ref , reactive power reference value Q ref , and steady-state current value in the aggregated model (SM1) of the virtual household distributed photovoltaic power generation system cluster, so as to achieve the simulation effect of "partial grid disconnection" of household distributed photovoltaics;

[0081] The rated capacity, steady-state active power value, and steady-state reactive power value of the aggregated model (SM2) of household distributed photovoltaic power generation systems with the "partial grid disconnection" function are the total installed capacity S Σ , total steady-state active power P Σ , and total steady-state reactive power Q Σ of all household distributed photovoltaic power generation systems in the modeling area, respectively., in the aggregated model (SM2) of the residential distributed photovoltaic power generation system with the "partial off-grid" function, the numerical values of the two groups of PI controller parameters remain the same as those in the aggregated model (SM1) of the virtual residential distributed photovoltaic power generation system cluster.

[0082] In step 7, the specific process of identifying the equivalent distribution network impedance Z in the aggregated model MA of the residential distributed photovoltaic power generation system with the "partial off-grid" function is as follows:

[0083] First, remove the models of all residential distributed photovoltaic systems in the detailed simulation model (M1) of the established modeling target area in step 5, and connect the aggregated model (MA) of the residential distributed photovoltaic power generation system with the "partial off-grid" function to the head end of the detailed simulation model (M1) of the modeling target area, so as to obtain the simulation model (M2) of the modeling target area; then, under the same transmission grid fault, with the principle of minimizing the active power and reactive power errors during the voltage dip duration between the detailed simulation model (M1) and the simulation model (M2) of the modeling target area, use the parameter identification technology to identify the numerical value of the equivalent distribution network impedance (Z).

[0084] Next, according to Figure 1 and Figures 2 - 12 for illustration, the implementation process of constructing the aggregated model of the residential distributed photovoltaic power generation system in the embodiments of the present invention will be described more specifically.

[0085] Step 1: Statistically calculate the total installed capacity S of the residential distributed photovoltaic power generation systems in the modeling target area Σ , and the proportion α of the installed capacity of residential distributed photovoltaic inverters of each brand i , where the subscript i is the serial number of the inverter brand.

[0086] Take the total installed capacity of the residential distributed photovoltaic power generation systems in a certain area as 5 MW; there are three brands used, namely "Brand 1", "Brand 2" and "Brand 3"; the installed capacity and proportion of the residential distributed photovoltaic inverters of these three brands are statistically obtained as shown in Table 1.

[0087] Table 1 Installed capacity and proportion of photovoltaic inverters of three brands in this example

[0088]

[0089] Step 2: Conduct voltage dip experiments on the dynamic characteristics of residential photovoltaic inverters of each brand and record the data.

[0090] As described above, the steps for conducting the voltage sag experiment are as follows: For each residential distributed PV inverter brand, only one model is selected for the experiment. That is, it is considered that the technologies adopted by residential PV inverters of the same brand are the same, so their dynamic characteristics are consistent. A photovoltaic array simulation power supply (or DC regulated power supply) is used to replace the actual photovoltaic panel array. Based on the rated capacity of each brand of inverter, active power with the same per-unit value is output. The same steady-state voltage amplitude is adopted, and based on the rated voltage of each brand of inverter, the voltage sag degree with the same per-unit value is adopted (the voltage sag degree should not trigger the tripping of the equipment under test).

[0091] For three brands of PV inverters, one model is selected for each brand for the experiment, as specifically listed in Table 2. The inverters of the three brands are all single-phase grid-connected, with a rated voltage of 220V and a rated capacity of 5kW each. During the experiment, the per-unit value of the output power of each inverter is 1.0 p.u., the steady-state voltage value is set to 1.0 p.u., and the per-unit value of the voltage sag amplitude manufactured by the voltage sag device (which is a prior art and will not be elaborated here) is 0.15 p.u.

[0092] Table 2 Three brands of PV inverters for the voltage sag experiment

[0093] Inverter Brand Inverter Model Rated Voltage and Power Brand 1 SG5K - D Single - phase 220V, 5kW Brand 2 5KTL - CN - WIFI Single - phase 220V, 5kW Brand 3 GCI - 1P5K - 4G Single - phase 220V, 5kW

[0094] Furthermore, the data to be recorded in the voltage sag experiment are the variation process of the positive-sequence voltage amplitude U i (t), the variation process of the positive-sequence current amplitude I i (t), and the variation process of the power factor angle U i (t) and I i (t) are per-unit values based on the rated capacity and rated voltage of each brand of inverter respectively; the recorded U i (t), I i (t), and have the same length, and the voltage sag moments in the data are the same.

[0095] The U i (t), I i (t), and curves of the experimental data recorded during the experiment process are as Figure 2 shown.

[0096] Furthermore, for single-phase PV inverters, the single-phase voltage and single-phase current obtained from the experiment are regarded as the positive-sequence voltage and positive-sequence current of a three-phase PV inverter.

[0097] In this example, single-phase grid-connected inverters are used for all three brands. Therefore, the measured single-phase voltage and single-phase current are regarded as the positive-sequence voltage and positive-sequence current of a three-phase photovoltaic inverter.

[0098] Step 3: According to the results of Step 1 and Step 2, construct the active power and reactive power response data of the virtual residential distributed photovoltaic power generation system cluster.

[0099] As described above, the specific method for constructing the active power and reactive power response data of the virtual residential distributed photovoltaic power generation system cluster is as follows: First, taking the installed capacity ratio α i of each brand of inverter in the modeling target area as the weighting coefficient, calculate the input voltage amplitude U(t) of the virtual residential distributed photovoltaic power generation system cluster according to Equation (1); then, obtain the active power component I P (t) and reactive power component I Q (t) of the output current of the virtual residential distributed photovoltaic power generation system cluster according to Equation (2); finally, calculate the active power response P(t) and reactive power response Q(t) of the virtual residential distributed photovoltaic power generation system cluster according to Equation (3).

[0100]

[0101]

[0102]

[0103] Where N is the number of inverter brands.

[0104] The voltage sag curve, active power response curve, and reactive power response curve of the virtual residential distributed photovoltaic power generation system cluster constructed by combining the above formulas (1)-(3) are as shown Figure 3 by the thick gray line in.

[0105] Step 4: Identify the control parameters in the aggregated model SM1 of the virtual residential distributed photovoltaic power generation system cluster according to the results of Step 3.

[0106] Furthermore, the specific method for identifying the control parameters in the aggregated model SM1 is as follows: First, select the existing simplified model of the residential distributed photovoltaic power generation system (including the low-voltage disconnection judgment module), and the model structure is as shown in the appendix Figure 4As shown; then, using the input voltage U(t) obtained in step 3 (fixing the phase at zero degree) as the input, and the active power response P(t) and reactive power response Q(t) as the optimization objectives, use the existing parameter identification technology to identify the two sets of PI controller parameters of the active power and reactive power control loops; by setting different rated capacity values for this simplified model, the aggregated model SM1 representing the aggregated model of different-scale household distributed photovoltaic power generation system clusters can be obtained, where the two sets of PI controller parameters do not change with the change of the rated capacity.

[0107] The structure of the aggregated model SM1 of the virtual household distributed photovoltaic power generation system cluster is as Figure 4 shown, where there are two PI controllers in the control loops of active power and reactive power respectively. The proportional and integral parameters of the active PI controller are K PP and K IP , and the proportional and integral parameters of the reactive PI controller are K PQ and K IQ . In the Figure 4 shown model, the thresholds of the low-voltage disconnection module are listed in Table 3, which is taken from the industry standard "Technical Regulations for Distributed Power Sources Connected to the Grid Q / GDW 480-2010".

[0108] Table 3 Thresholds of the low-voltage disconnection module in the aggregated model

[0109] Grid - connection Point Voltage Requirements <![CDATA[U < 50%U N > The maximum opening time does not exceed 0.2s <![CDATA[50% U N ≤ U < 80% U N > The maximum opening time does not exceed 2s <![CDATA[85% U N ≤ U < 110% U N > Continuous operation <![CDATA[110% U N ≤ U < 135% U N > The maximum opening time does not exceed 2s <![CDATA[135% U N ≤ U]]> The maximum opening time does not exceed 0.2s

[0110] The identification results of the four parameters in the aggregated model SM1 are listed in Table 4, and the fitting effect on the experimental data is shown in the Figure 3 black thin line. The fitting errors of active power and reactive power are shown in the last two columns of Table 4. The results show that the aggregated model SM1 can better represent the dynamic characteristics of the virtual household distributed photovoltaic power generation system cluster.

[0111] Table 4 Parameter identification results of the virtual household distributed photovoltaic power generation system cluster model

[0112]

[0113] Step 5: Establish a detailed simulation model M1 of the modeling target area.

[0114] Furthermore, the specific method for establishing the detailed simulation model M1 of the modeling target area is that it should include distribution lines, distribution transformers, dispersed access power loads, and dispersed access household distributed photovoltaic power generation systems; for different household distributed photovoltaic power generation systems with the grid connection point on the distribution network side at the same location, their rated capacities can be accumulated and set into the model SM1, so as to obtain their aggregated model, and the line impedance connected to the distribution network can be ignored.

[0115] In this example, the IEEE-33 node standard distribution network system (rated voltage 10 kV) is adopted as the structure of the modeling target area, as Figure 5 shown. Figure 5 Each black dot in represents a node in the modeling target area, and there is a grid-connected load model (not detailed in the figure) on each node; clusters of household distributed photovoltaic power generation systems of different scales are Figure 5 represented by diamonds in, and a total of ten places are connected. Figure 5 The parameters and output powers of the loads on the nodes in are different, and the rated powers and output powers of different clusters of household distributed photovoltaic power generation systems are different, but the two sets of PI control parameters obtained in step 4 are the same. In this embodiment, an infinite system of 220 kV is first stepped down to 110 kV, and then the double-circuit line is sent to the 110 / 10 kV substation and then connected to the Figure 5 IEEE-33 node system shown. The detailed simulation model M1 of the modeling target area built under the Matlab / Simulink simulation software is as Figure 6 shown.

[0116] Step 6: Based on the model built in step 5, establish a model MC (hereinafter referred to as the "disconnection ratio model") between the amplitude of the voltage at the head end of the modeling target area and the disconnection ratio of the household distributed photovoltaic power generation system.

[0117] Furthermore, the specific method for establishing the disconnection ratio model MC is as follows: Based on the model M1 established in step 5, the critical amplitude U of the voltage at the head end of the modeling target area that triggers the disconnection of the household distributed photovoltaic power generation system is obtained through simulation C0 , and the voltage amplitude U that can cause a certain proportion β1 of the household distributed photovoltaic power generation system to be disconnected C1 ; then, according to the points (U C0 , 0) and (U C1 , β1), an equation as shown in Equation (4) is constructed, which is the disconnection ratio model MC; when the voltage U is higher than U C0 , the disconnection ratio is zero; when the voltage U is lower than U C0 , the disconnection ratio β is determined by the model MC, and the maximum value of β is limited to 1.

[0118]

[0119] Based on Figure 6 the simulation model M1 shown, the critical voltage amplitude U for triggering the disconnection of the household distributed photovoltaic power generation system obtained through simulation C0 is 0.5 p.u., and the voltage amplitude U that can cause 72% of the household distributed photovoltaic power generation system with capacity to be disconnected C1It is 0.3 p.u. Therefore, the off-grid ratio model MC established based on the points (0.5, 0) and (0.3, 0.72) is as shown below:

[0120]

[0121] Step 7: Construct the aggregated model MA of the household distributed photovoltaic power generation system with the "partial off-grid" function.

[0122] Furthermore, the aggregated model MA of the household distributed photovoltaic power generation system with the "partial off-grid" function is formed by connecting the aggregated model SM2 of the household distributed photovoltaic power generation system with the "partial off-grid" function in series with the equivalent impedance Z of the distribution network.

[0123] The composition of the aggregated model MA of the household distributed photovoltaic power generation system with the "partial off-grid" function is as Figure 7 indicated by the dashed box in.

[0124] Furthermore, the specific method for constructing the aggregated model SM2 of the household distributed photovoltaic power generation system with the "partial off-grid" function is: integrating the off-grid ratio model MC with the aggregated model SM1 established in Step 4, that is, the off-grid signal is still sent by the model SM1, and the off-grid ratio β is obtained from the model MC; when an off-grid event is triggered, the proportion of non-off-grid household distributed photovoltaics (1 - β) will be multiplied by the active power reference value P ref and the reactive power reference value Q ref and the steady-state current value in SM1 respectively, so as to achieve the simulation effect of "partial off-grid" of the household distributed photovoltaics; the rated capacity, steady-state active power value, and steady-state reactive power value of SM2 are the total installed capacity S Σ of all household distributed photovoltaic power generation systems in the modeling area, the total steady-state active power P Σ and the total steady-state reactive power Q Σ respectively, and the numerical values of the two sets of PI controller parameters in SM2 are kept the same as those in SM1.

[0125] Figure 8 shows the integrated model diagram of the off-grid ratio model MC and the low-voltage off-grid module in the photovoltaic aggregation model SM1. Those skilled in the art can easily reproduce this function according to Figure 8 and thus it will not be elaborated here. Figure 9 is the schematic diagram of multiplying the proportion of non-off-grid household distributed photovoltaics (1 - β) by the active power reference value P ref , the reactive power reference value Q ref and the steady-state current value. Those skilled in the art can easily reproduce this function according to Figure 8 and thus it will not be elaborated here.

[0126] Furthermore, the specific method for identifying the equivalent distribution network impedance Z in the aggregated model MA of the household distributed photovoltaic power generation system with the "partial off-grid" function is as follows: First, in the detailed simulation model M1 of the modeling target area established in step 5, remove the models of all household distributed photovoltaic systems, and connect the aggregated model MA of the household distributed photovoltaic power generation system to the head end of M1, so as to obtain the simulation model M2 of the modeling target area; Then, under the same transmission network fault, based on the principle of minimizing the active power and reactive power errors of models M1 and M2 during the voltage dip duration, use the existing parameter identification technology to identify the value of the equivalent distribution network impedance Z.

[0127] Figure 10 It is a schematic diagram of the simulation model M2 of the modeling target area obtained after removing the models of all household distributed photovoltaic systems in the detailed simulation model M1 of the modeling target area and connecting the aggregated model MA of the household distributed photovoltaic power generation system to the head end of M1. Before identifying the equivalent distribution network impedance Z, the active power and reactive power responses of the simulation models M1 and M2 are as Figure 11 shown. It can be seen that during and after the fault duration, there are obvious errors in the outputs of the two models, especially the active power error is more obvious. The equivalent distribution network impedance Z is identified based on the principle of minimizing the error within the range of the dashed box in Figure 11 . In this embodiment, the identified value of Z is (1.1 + 0.7i) Ω.

[0128] So far, the construction of the aggregated model of the household distributed photovoltaic power generation system is completed.

[0129] Figure 12 The dynamic response comparison diagram of the detailed simulation model M1 of the modeling target area and the model constructed by the present invention is given. It can be seen from it that the dynamic response of the model constructed by the present invention is very close to that of the detailed model, that is, it can accurately characterize the dynamic characteristics of the power supply area containing a large number of household distributed photovoltaic power generation systems. This is the beneficial effect of the present invention.

[0130] Although the present invention has been disclosed above with preferred embodiments, it is not intended to limit the present invention. Those with ordinary knowledge in the technical field to which the present invention pertains can make various modifications and refinements without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be subject to what is defined by the claims.

Claims

1. A method for constructing an aggregation model of a household distributed photovoltaic power generation system, characterized in that, It includes the following steps: Step 1: Statistically model the total installed capacity of the residential distributed photovoltaic power generation systems within the target area, and the proportion of the installed capacity of residential distributed photovoltaic inverters of each brand; Step 2: Conduct voltage sag experiments on the dynamic characteristics of the residential distributed photovoltaic inverters of each brand and record the data; Step 3: Based on the results of Step 1 and Step 2, construct the active power and reactive power response data of the virtual residential distributed photovoltaic power generation system cluster; Step 4: Identify the control parameters of the aggregated model of the virtual residential distributed photovoltaic power generation system cluster according to the results of Step 3; Step 5: Establish a detailed simulation model of the target area for modeling; Step 6: Based on the model established in Step 5, establish a model between the amplitude of the voltage at the head end of the target area for modeling and the grid disconnection ratio of the residential distributed photovoltaic power generation system, that is, the grid disconnection ratio model; Step 7: Obtain the aggregated model of the residential distributed photovoltaic power generation system with the "partial grid disconnection" function based on the grid disconnection ratio model constructed in Step 6, and combine the aggregated model of the residential distributed photovoltaic power generation system with the "partial grid disconnection" function and the equivalent impedance of the distribution network to construct the aggregated model of the residential distributed photovoltaic power generation system with the "partial grid disconnection" function; Among them, in Step 5, when establishing the detailed simulation model of the target area for modeling, it includes the following process: The detailed simulation model of the target area for modeling includes distribution lines, distribution transformers, dispersed power loads, and dispersed residential distributed photovoltaic power generation systems; for different residential distributed photovoltaic power generation systems with grid connection points on the distribution network side at the same location, their rated capacities are accumulated and set into the aggregated model of the virtual residential distributed photovoltaic power generation system cluster, so as to obtain their aggregated model; In Step 6, the process of establishing the grid disconnection ratio model includes: First, based on the detailed simulation model of the modeling target area established in Step 5, the critical amplitude U of the first-end voltage of the modeling target area that triggers the grid disconnection of the household distributed photovoltaic power generation system is obtained through simulation. C0 , and the voltage amplitude U that can cause a certain proportion β1 of the household distributed photovoltaic power generation system to be disconnected from the grid. C1 ; Then, based on the points (U C0 , 0) and (U C1 , β1), an equation as shown in Equation (4) is constructed, which is the off-grid ratio model: Wherein: When the voltage U is higher than U C0 , the disconnection ratio β is zero; When the voltage U is lower than U C0 , the disconnection ratio β is determined by the disconnection ratio model, and the maximum value β max of β is limited to 1; In Step 7, the aggregated model of the residential distributed photovoltaic power generation system with the "partial grid disconnection" function is constructed by connecting in series the aggregated model of the residential distributed photovoltaic power generation system with the "partial grid disconnection" function and the equivalent impedance of the distribution network. Specifically, the process of constructing the aggregated model of the residential distributed photovoltaic power generation system with the "partial grid disconnection" function includes: Integrate the grid disconnection ratio model with the aggregated model of the virtual residential distributed photovoltaic power generation system cluster, that is, the grid disconnection signal is still sent by the aggregated model of the virtual residential distributed photovoltaic power generation system cluster, and the grid disconnection ratio β is obtained from the grid disconnection ratio model: When the off-grid event is triggered, the proportion of residential distributed photovoltaics that are not off-grid (1-β) will be multiplied by the active power reference value P ref , the reactive power reference value Q ref , and the steady-state current value respectively, so as to achieve the simulation effect of "partial off-grid" of residential distributed photovoltaics; The rated capacity, steady-state active power value, and steady-state reactive power value of the aggregated model of a household distributed photovoltaic power generation system with the "partial off-grid" function are the total installed capacity S of all household distributed photovoltaic power generation systems within the modeling area Σ , the total steady-state active power P Σ , and the total steady-state reactive power Q Σ . The numerical values of the two sets of PI controller parameters in the aggregated model of the household distributed photovoltaic power generation system with the "partial off-grid" function are kept consistent with those in the aggregated model of the virtual household distributed photovoltaic power generation system cluster 2. The method for constructing an aggregation model of a household distributed photovoltaic power generation system according to claim 1, wherein In Step 2, the process of conducting the voltage sag experiment includes: Only select one model for each brand of residential photovoltaic inverter for the experiment, that is, it is considered that the technologies adopted by the residential photovoltaic inverters of the same brand are the same, so their dynamic characteristics are consistent; Use a photovoltaic array simulation power supply or a DC regulated power supply to replace the actual photovoltaic panel array; Based on the rated capacity of each photovoltaic inverter respectively, output the active power with the same per-unit value; Adopt the same steady-state voltage amplitude, and based on the rated voltage of each brand of inverter respectively, adopt the same per-unit value of voltage sag degree, where the voltage sag degree satisfies: not triggering the grid disconnection of the equipment under test.

3. The method for constructing an aggregation model of a household distributed photovoltaic power generation system according to claim 2, wherein, In Step 2, the data that needs to be recorded in the voltage sag experiment includes: The variation process of the positive-sequence voltage amplitude U i (t), the variation process of the positive-sequence current amplitude I i (t) and the variation process of the power factor angle The subscript i is the serial number of the brand; U i (t) and I i (t) are per-unit values based on the rated capacity and rated voltage of each brand of PV inverter; and the recorded U i (t), I i (t) and have the same length, and the moments of voltage dips in the data are the same.

4. The method for constructing an aggregation model of a household distributed photovoltaic power generation system according to claim 2, wherein In step 2, for a single-phase PV inverter, the single-phase voltage and single-phase current obtained from the voltage dip test are regarded as the positive-sequence voltage and positive-sequence current of a three-phase PV inverter.

5. The method for constructing an aggregation model of a household distributed photovoltaic power generation system according to claim 1, wherein In step 3, the active power and reactive power response data of the virtual residential distributed PV power generation system cluster are constructed, including: First, taking the installed capacity ratio α of each brand of photovoltaic inverter in the modeling target area i as the weight coefficient, the input voltage amplitude U(t) of the virtual household distributed photovoltaic power generation system cluster is obtained according to the following formula (1); Then, obtain the active power component I P (t) and reactive power component I Q (t) of the virtual household distributed photovoltaic power generation system cluster according to Equation (2); Finally, according to Equation (3), the active power response P(t) and reactive power response Q(t) of the virtual residential distributed PV power generation system cluster are obtained, that is, the aggregated model of the virtual residential distributed PV power generation system cluster is constructed: Among them, N in Equation (1) above represents the number of PV inverter brands.

6. The method for constructing an aggregation model of a household distributed photovoltaic power generation system according to claim 1, wherein In step 4, the identification of the control parameters in the aggregated model of the virtual residential distributed PV power generation system cluster includes the following process: First, a simplified model of the existing residential distributed PV power generation system is selected; Then, with the input voltage U(t) obtained in step 3 as the input and the active power response P(t) and reactive power response Q(t) as the optimization objectives, the parameter identification technology is used to identify the two sets of PI controller parameters of the active power and reactive power control loops; by setting different rated capacity values, the aggregated models representing different scales of the residential distributed PV power generation system cluster can be obtained, and the two sets of PI controller parameters do not change with the change of the rated capacity.

7. The method for constructing an aggregation model of a household distributed photovoltaic power generation system according to claim 1, wherein In step 7, the specific process of identifying the equivalent distribution network impedance Z in the aggregated model MA of the residential distributed PV power generation system with the "partial islanding" function includes: First, in the detailed simulation model of the modeling target area established in step 5, the models of all residential distributed PV systems are removed, and the aggregated model of the residential distributed PV power generation system with the "partial islanding" function of the residential distributed PV power generation system is connected to the head end of the detailed simulation model of the modeling target area, so as to obtain the simulation model of the modeling target area; Then, under the same transmission network fault, with the principle of minimizing the active power and reactive power errors during the voltage dip duration of the detailed simulation model and the simulation model of the modeling target area, the parameter identification technology is used to identify the value of the equivalent distribution network impedance.

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